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

Sent: Monday, May 05, 2014 5:04 PM

To: Higgins, Cortney

Cc: Elman, Barry
Subject: RE: Comments on TSDs for RIN 2060-AR33

Thank you Cortney, I'll get these out to the team asap.

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, North Carolina 27709

Office: (919) 541-1045 Cell: (919) 306-2290 Fax: (919) 541-5450

Email: wayland.robertj@epa.gov

**************************************
DRAFT/Deliberative Document - FOIA EXEMPT

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

From: Higgins, Cortney <Cortney_Higgins@omb.eop.gov>

Sent: Monday, May 5, 2014 5:02 PM

To: Wayland, Robertj

Cc: Elman, Barry
Subject: Comments on TSDs for RIN 2060-AR33

Hi Bob,

Attached are interagency comments on the GHG Abatement TSD, Chapters 2-4 and the Goal Setting TSD. More documents will be coming your way tonight.

Please let me know if you have any questions.

Regards, Cortney

# ________________________________________________ Cortney Higgins, PhD Policy Analyst
OMB/OIRA/NRE

202.395.7856 chiggins@omb.eop.gov

<< GHG Abatement Measures - Chapter 2 04-04-2014_InteragencyCommentsunderEO12866

.docx >> << GHG Abatement Measures TSD-Chapter 3_04-14-14_InteragencyCommentsUnderEO12866

.docx >> << GHG Abatement Measures TSD-Chapter 4_04-14-14_InteragencyCommentsUnderEO12866

.docx >> << TSD for goal setting_InteragencyCommentsUnderEO12866.docx >>

## DRAFT April 4, 2014

Draft Deliberative 

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

Introduction

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

units (EGUs), it is evident can be presumed that EGUs are generally less efficient at converting

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

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

in individual EGU efficiency indicates that ther e is potential for broadly applicable efficiency

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

efficiencies would result in corresponding reductions in greenhouse gas emissions.

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

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

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

the estimated capital costs of those improvements.

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

Coal in the United States and is predominately used for electric power generation. Most

coal-fired EGUs in the United States burn either bituminous or subbituminous coals. The largest

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

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

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

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

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

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

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

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

coal or coal refuse is burned in limited quantities.

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

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

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

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

combustion configurations. Comment [A1]: This section read quite weakly as initially written.

Comment [A2]: MATS Compliance. MATS compliance will lead to significant installations of
pollution control equipment (PCE) over the 2013 to

2016 period on capacity that owners intend to keep

in service beyond the MATS compliance deadline

but would not be MATS

‐ compliant without
augmenting the PCE that was in place during 2012.

The PCE systems likely to be installed on such units

include FGD systems that may consume significant

power and therefore, all else equal, increase energy input per net kilowatt

‐ hour of generation. It would

be helpful to understand if or how additions of PCE

for MATS compliance are reflected in the analysis of

HRI in the draft TSD.

## DRAFT April 4, 2014

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

Coal-firing Configuration Coal Combustion Process Description Distinctive Design/Operating Characteristics

Pulverized

Coal

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 can be designed for combustion within the bed to occur at atmospheric or elevated pressures. FBC operating temperatures are in the range of
1,500 to 1,650  F (800 to 900 o 

### C). Bubbling

fluidized bed (BFB) Operates at relatively low gas stream

velocities and with coarse-bed size particles. Air in excess of that required to fluidize the bed passes through the bed in form of bubbles.

Circulating

fluidized bed (CFB) Operates at higher gas stream

velocities and with finer-bed size particles. No defined bed surface. Must use high-volume, hot cyclone separators to recirculate entrained solid particles in flue gas to maintain the bed and achieve high combustion efficiency.

Integrated Coal

Gasification

Combined Cycle (IGCC) Synthetic combustible gas (“syngas”)

derived from an on-site coal gasification process is burned in a

combustion turbine. The hot exhaust gases from the combustion turbine pass through a heat recovery steam generator to produce steam for driving a steam turbine/generator

unit. Coal gasification units are unique among coal-firing configurations because a gaseous fuel (synfuel or syngas)

is burned instead of solid coal because the combustion and power generation process and combines the Rankine and Brayton thermodynamic cycles as is the case for a combined cycle power plant.

Cyclone

Furnace

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

Stoker-fired

Coal

Combustion Coal is crushed into large lumps and burned in a fuel bed on a moving, vibrating, or stationary grate. Coal is fed to the grate by a mechanical

device called a “stoker.” One of three types of stoker mechanisms can be used that ether feed the coal by pushing, dropping, or flipping coal unto the grate.



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

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

coal combustion involves pulverizing the coal into a fine powder, which allows the coal to be

burned more easily and efficiently. This is the coal-firing configuration predominately used at Comment [A3]: We would suggest reducing the

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

Comment [A4]: It might be helpful to indicate

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

## DRAFT April 4, 2014

3 existing EGUs, and is the most frequently select ed technology for new coal-fired EGU projects.

However, FBC EGUs are becoming more prevalent since they have a greater fuel flexibility. The

most recent coal technology combustion development involves the integration of coal

gasification technologies with the combined cycl e electric generation process. The efficiency of

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

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

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

PM, SO 2 

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

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

prior to combustion), generation of less solid waste, and 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 efficiency and other advantages. However, a few remaining old stokerfired EGUs and cyclone furnaces still remain in service for a small number of existing EGUs in

the U.S. electric utility market.

Influence of Heat Rate on Coal-Fired EGU CO 2 

## Emission Rate

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

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

a corresponding decrease in CO 2 

## and other air emissions. Heat rate is expressed as the number of

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

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

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

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

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

auxiliary (or parasitic) electricity used to op erate the EGU (e.g., elect ricity to power fuel

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

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

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

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

heat and power, or CHP, facilities. Formatted: Complex Script Font: 11 pt, Strikethrough Comment [A5]: No one is building coal plants
currently in the U.S., so the future tense is not
appropriate. Who is building coal plants using FBC?

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

Formatted: Subscript

Comment [A6]: One of the primary benefits of

IGCC is the concentrated CO2 stream and ability to

use in CCS. Needs to be highlighted.

## DRAFT April 4, 2014

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

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

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

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

factors affecting EGU efficiency at a given facility include:

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

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

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

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

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

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

efficiencies are lower than design efficiencies . The majority of existing PC-fired EGUs in

the U.S. electric fleet use subcritical boilers

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

to be more efficient than EGUs burn ing lower quality coals (e.g., lignite).

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

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

However, as equipment size increases the differences in these losses start to taper off.

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

equipment reduces the overall efficiency of the EGU.

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

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

affect EGU efficiency.

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

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

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

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

recirculating cooling systems.

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

temperatures at the facility site potentially may have a measureable impact on EGU

efficiency. At higher elevations, air pressure is lower and less oxygen is available for

combustion per unit volume of ambient air than at lower elevations. Cooler ambient Comment [A7]: Could indicate the approximate

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

as the influence of moisture.

Comment [A8]: : Could indicate approximate

turbine size / size class here.

## DRAFT April 4, 2014

5 temperatures theoretically could increase the overall EGU efficiency by increasing the

draft pressure of the boiler flue gases and the condenser vacuum, and by increasing the

efficiency of a condenser recirculating cooling system.

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

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

customer electricity demand.

 EGU equipment manufacturers – The efficiency specifications of major EGU

components such as boilers, turbines, and electrical generators provided by equipment

manufacturers can affect EGU efficiency.

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

tend to be more efficient.

Technologies to Improve Existing Coal-Fired EGU Heat Rate

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

articles and technical papers identifying potentia l efficiency improvement techniques applicable

to existing coal-fired EGUs. 1 

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

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

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

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

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

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

technical feasibility of using a given technology.

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

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

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

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

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

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

1 

### See HRI Partial Bibliography at the end of this chapter. Comment [A9]: Geographic location could also

influence cooling water issues, mentioned earlier
(i.e. coal plants in the arid west).

Comment [A10]: This seems less about the

manufacturers and more about the specific
technology choice. Suggest describing this as “EGU
technology choice” and, if necessary, note that
efficiency can vary by manufacturer or by product.

## DRAFT April 4, 2014

6 Boiler

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

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

Materials Handling

The coal handling portion of materials handling typically requires about 0.07% of the

gross electrical output of a power plant. Depending on the state of the motors and drives,

replacing them with energy-efficient motors and variable frequency drives (VFDs) can reduce

the auxiliary power requirements. The VFDs also limit the stress and strain on the other

equipment.

Coal pulverizers can be upgraded to provide mo re consistent size and finer coal particles.

The fine particles improve combustion efficiency, consequently reducing fuel cost and heat rate.

The costs for changes to the pulverizer system ar e significant, and historically, the projects have

improved the heat rate justifiably only when the existing equipment has degraded.

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

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

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

requirements are about 0.1% of the plant’s gross output.

Economizer

The replacement of the economizer can lead substantial heat rate improvements around

50-100 Btu/kWh, but is large capital investment (~$2-8M). Due to this high cost, economizers

upgrades are not generally performed unless the existing equipment has degraded or a

replacement is necessary due to the installation of new control equipment.

Boiler Control System

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

control capabilities can control and evaluate ma ny aspects of the plan t’s operations. Commonly

referred to as the Neural Network (NN), computer models are able to control the plant’s

processes by predicting performance during static and dynamic changes. Many vendors offer NN

systems to improve the overall efficiency. NN systems are typically around $550,000-$750,000

and offer heat rate reductions up to 150 Btu/kWh.

Sootblowers

Intelligent sootblowers (ISB) may be installed to improve system efficiency. The ISB

system monitors the furnace exhaust gas temperatures and steam temperatures. Other readings Comment [A11]: Did you want to include an

estimate of pulverizer electric demand?

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

some efficiency gains are given in percent, and

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

either one common unit (suggest Btu/kWh) or provide both metrics.

Comment [A13]: Please add a sentence on what

an economizer is and does.

## DRAFT April 4, 2014

7 may be incorporated into the ISB system which also communicates with the boiler control

system. This system uses real-time data to identify which areas need sootblowing. Boiler

efficiency improvements have ranges from 0.3%-1.5% with capital costs around $300,000-

$500,000 and $50,000/year for fixed operating and maintenance costs.

Air Heaters

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

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

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

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

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

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

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

power to pump more flue gas. Ad ditionally, improvements to seal s on the air heaters also reduce

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

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

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

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

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

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

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

Generally, a 40°F reduction in the air heater outl et results in 1% efficiency improvement. These

systems are costly and depend on whether the air heater is adequate, needs modifications or

needs a complete replacement. The capital costs can range from $1.5-18M for heat rate

reductions of 50-120 Btu/kWh.

Turbine

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

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

pumps.

Turbine

The performance and longevity of turbines is constantly increasing with advances in

technology. Most turbines in the industry are maintained so that the efficiency improvements are

1-3%; however, some extremely degraded turbines may benefit up to 10% with a turbine

## DRAFT April 4, 2014

8 overhaul. The capital costs for these projects range s from $2-25M with heat rate reductions of

100-300 Btu/kWh.

Feedwater Heaters

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

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

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

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

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

repair.

Condenser

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

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

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

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

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

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

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

$80,000.

Boiler Feed Pumps

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

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

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

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

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

Flue Gas System

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

efficiency are to improve the forced draft (FD) and induced draft (ID) fan efficiencies and

implementation of VFDs.

ID Fans

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

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

methods, such as variable inlet vanes (VIV), VFD, and variable pitch blades (VPB), available to Comment [A14]: Not clear whether the 1

‐ 3% efficiency improvement refers to improvement resulting from an overhaul. It might be helpful to

indicate what size of turbines correspond to these

costs.

Comment [A15]: Please spell out acronym

–

even if it has been spelled out earlier, I have
forgotten and couldn’t find the location on looking

back.

Comment [A16]: For all these subtitles, it

wouldn’t hurt to spell out the acronym in the

section title.

## DRAFT April 4, 2014

9 control the flue gas flow allowing highly efficient fan performance. These upgrades or

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

VFD

It is apparent with the control that a VFD offe rs, the plant is able to reduce the auxiliary

load immensely. Considering upgrading all drives, the capital costs are $6-16M with heat rate

reductions between 10-150 Btu/kWh.

Emission Control Technologies

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

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

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

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

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

electrostatic precipitator (ESP), and the selective catalytic reduction (SCR) systems.

FGD

Coal-fired power plants contain many variances between their FGD systems. A few

common technology improvements are discussed fu rther in this section. Older units typically

contained a venturi throat that increased the velocity of the fluid, but resulted in a large pressure

drop and greater power to operate the ID fans. To improve this operation, a co-current spray

tower quencher may replace the unit. The capital co st is about $2.5M with heat rate reductions

around 13 Btu/kWh.

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

improvement of the gas flow coming into contact with the absorber sorbent increases SO 2


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

ID fan. Turning vanes and a perforated gas dist ribution 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 FGD multiple spray levels are installed to deliver the limestone slurry. If a power plant

is operating with SO 2 

## levels below its permit limit, turn ing off one spray level will reduce the

auxiliary power necessary. If this is possible, a unit heat rate reduction of 16 Btu/kWh may be

available.

ESP

The best operation for an ESP involves maintaining the maximum applied voltage, but

below the level in which spark-over occurs. ESP energy management system (EMS) upgrades

## DRAFT April 4, 2014

10 often help improve the ESP performance by maintaining the optimal performance and lowering

power consumption. The installation for this t echnology can be from minimal to $0.8M and can

lower heat rate by 5 Btu/kWh.

SCR

For the last 15 years, SCR systems have been in use to reduce NO X 

## emissions from

power plants. Extensive modeling was performed to achieve the necessary reduction with

minimum ammonia slip. The results showed that reducing pressure drop and using secondary air

as dilution for the ammonia vaporizer can redu ce the auxiliary power ne cessary. The heat rate

reduction is 0-10 Btu/kWh and capital costs between $0.5-$2M with fixed and variable costs up

to $100,000 each.

Water Treatment System

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

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

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

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

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

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

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

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

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

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

aspects already mentioned.DRAFT April 4, 201411Table 2-2. Existing coal-fired EGU efficiency improvementsreported for actual efficiency improvement projectsEfficiencyImprovementTechnologyDescriptionReportedEfficiencyIncreaseaCombustionControlOptimizationCombustion controls adjust coal and air flow to optimize steam production for the steam turbine/generator set.However, combustion control for a coal-fired EGU is complex and impacts a number of important operatingparameters including combustion efficiency, steam temperature, furnace slagging and fouling, and NOXformation. The technologies include instruments that measure carbon levels in ash, coal flow rates, air flow rates,CO levels, oxygen levels, slag deposits, and burner metricsas well as advanced coal nozzles and plasma assistedcoal combustion.0.15 to 0.84%Cooling SystemHeat LossRecoveryRecover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circulationthorough a cooling tower or discharge to a water body. The identified technologies include replacing the coolingtower fill (heat transfer surface) and tuning the cooling tower and condenser.0.2 to 1%Flue Gas HeatRecoveryFlue gas exit temperature from the air preheater can range from 250 to 350°F depending on the acid dew pointtemperature of the flue gas, which is dependent on the concentration of vapor phase sulfuric acid and moisture.For power plants equipped with wet FGD systems, the flue gas is further cooled to approximately 125°F as it issprayed with the FGD reagent slurry. However, it may be possible to recover some of this lost energy in the fluegas to preheat boiler feedwater via use of a condensing heat exchanger.0.3 to 1.5%Low-rank CoalDryingSubbituminous and lignite coals contain relatively large amounts of moisture (15 to 40%) compared to bituminouscoal (less than 10%). A significant amount of the heatreleased during combustion of low-rank coals is used toevaporate this moisture, rather than generate steam for the turbine. As a result, boiler efficiency is typically lowerfor plants burning low-rank coal. The technologies include using waste heat from the flue gas and/or cooling watersystems to dry low-rank coal prior to combustion.0.1 to 1.7%SootblowerOptimizationSootblowers intermittently inject high velocity jets of steam or air to clean coal ash deposits from boiler tubesurfaces in order to maintain adequate heat transfer. Proper control of the timing and intensity of individualsootblowers is important to maintain steam temperature and boiler efficiency.The identified technologies includeintelligent or neural-network sootblowing (i.e., sootblowing in response to real-time conditions in the boiler) anddetonation sootblowing.0.1 to 0.65%Steam TurbineDesignThere are recoverable energy losses that result from the mechanical design or physical condition of the steamturbine. For example, steam turbine manufacturers haveimproved the design of turbine blades and steam sealswhich can increase both efficiency and output (i.e., steam turbine dense pack technology).0.84 to 2.6Source: National Energy Technology Laboratory (NETL), 2008.Reducing CO2Emissions by Improving the Efficiency of the Existing Coal-fired Power PlantFleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 2008. Available at:<http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>.aReported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual component efficiencies for a referenceplant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assumptions, the referencepower plant has an overall efficiency of 32% and a net heat rate of 10,600 Btu/kWh. As a result, if a particular efficiency improvement method was reportedto achieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewise, a reported 100 Btu/kWhdecrease in net heat rate would be converted to a 0.30% point increase in overall efficiency.Comment [A17]:Themathinthisfootnoteisuntraceable.Suggestaddingalittlemoreexplanation–anequationexample‐‐ofhowthismathleadstothatpercentageorheatrate.

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12 Previous Studies on Heat Rate Improvements

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

potential efficiency improvements and associated resulting CO 2 

## emission reductions. These

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

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

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

Studies 1 and 2 used a benchmarking approach that evaluated the design factors that are

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

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

Potential efficiency improvements were calculated based on an assumption that the lowerperforming EGUs in each group should to be able to do as well as the better performing EGUs in

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

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

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

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

ranging from 9% to 15% is theoretically possible. Study 3 used a less detailed approach and

evaluated technologies applicable to bituminous and subbituminous coals to estimate potential

fleet wide reductions.

An alternative approach to evaluate heat rate improvement is used for Study 4. This study

focused on the operating efficiency (synonymous with heat rate) of the entire existing U.S. coalfired EGU fleet. The authors evaluated decades of data from industrial responses to economic

factors such as demand, coal price and energy policies. This approach determined that given

specific changes in the coal price and a carbon tax, the industry will make heat rate

improvements on their own. In this specific example, the coal price was assumed to be a 10%

increase and the CO 2 

## emissions tax at $1.64 per ton for heat rate reductions of 0.3 to 0.9%.

Study 5 considered the fleet of coal-fired EG Us and assumes a target heat rate in order

for the EGUs to comply with an inferred standard. As opposed to the above studies that

determined by how much the efficiencies can improve, this approach estimated how the industry

will meet any imposed standards and calculated the heat rates necessary to meet a standard. As it

has not been determined with regard s to how the CO 2 

## emissions limit will be averaged, the paper

discusses many potential options that coal-fired EGUs may have to meet new standards. These Comment [A18]: To avoid ambiguity it might be
helpful to cite (footnote) studies when referring to

them.

Comment [A19]: Suggest an alternate phrasing

such as: This approach sought to estimate overall
changes in industry fleet efficiency in response to changes in fuel prices or carbon prices.

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13 options include additional options such as adding renewable, natural gas, and combined-cycle

sources.

The EPA observed that most of the existin g studies develop fleet-wide average HRI

potentials/goals that are higher than values c onsidered feasible and sustainable by the power

sector engineers with whom the EPA has informal ly discussed this subject. The EPA also noted

that all existing HRI studies have relied on a si ngle year annual average heat rate data, whereas

many of the operational impacts on heat rate (diurnal temperatures, load following, etc) become

more apparent in the variability of hourly performance data. We further recognized that an

examination of heat rate data over a multiple year period, perhaps a decadal time frame might

reveal patterns of performance that should also inform estimates of HRI potential. For these

reasons, the EPA has developed in this TSD an additional assessment of HRI potential that draws

on multiyear historical hourly data. While we understanoodunderstood that engineering

judgment would remainsstill be essential to a proper interpre tation of the results, the EPA

intend eds that this assessment be a more substan tial 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

Industr y Grou p in g Key Study

Assum p tions Study

Results

1 “Reducing CO 2 

### Emissions by Improving the Efficiency of the Existing Coal-fired

Power Plant Fleet”

NETL • Plant design: age and steam cycle • Category improvement is

the bottom 90% of EGUs in each group improving their heat rate to the average performance of top 10% in that category • Units with capacity factors

under 50% were removed

from dataset • 15% reduction in

overall heat rate of

coal-fired EGUs

2 “Improving theEfficiency of CoalFired Power Plants

for Near Term

Greenhouse Gas

Emissions

Reductions”

NETL • Plant design: coal type, steam cycle, and size • Category improvement is

the bottom 90% of EGUs in each group improving their heat rate to the average performance of top 10% in that category • Units with anomalous data, capacity factors under 10%, using less than 97% coal, and gasification plants were

removed from dataset • Low pressure subcritical

units and 0-200 MW

subbituminous units assumed retired for goal • Lost generation made up by • 8.7% reduction in

overall heat rate of

coal-fired EGUs Comment [A20]: The reasons for considering

the existing studies unrealistic could be discussed

more explicitly. Perhaps, for example, there is a

more explicit argument to be made about specific

interventions and timescales.

Comment [A21]: Not certain this is correct ‐ the

RFF study abstract states: “This paper examines the
operation of coal

‐ fired generating units over 25

years to estimate the marginal costs and potential
magnitude of emissions reductions from improving

their efficiency.” Please correct if appropriate.

Comment [A22]: Please check on the tense

– past or present?

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14 Study

ID Study Title

Author Factors Used for Industry Grouping Key Study Assumptions Study

Results

more efficient coal-fired

EGUs

3 “Reducing Heat

Rates of Coal-Fired

Power Plants” Lehigh Energy Update All possible heat rate improvements are made including drying of high

moisture coal: • 10% improvement

for bituminous coalfired EGU • 15% improvement

for subbituminous

coal-fired EGU

4 “Regulating

Greenhouse Gases

from Coal Power

Plants under the

Clean Air Act”

Resources for the

Future • Analyze the actual operating efficiency of the

entire fleet of U.S. coalfired EGUs

• Assess abatement

opportunities and costs by observing how coal plants respond to market and regulatory incentives to improve energy efficiency Overall efficiency improvements of 2 to 5%,

from other literature studies 10% coal price increase, corresponding to a

tax on 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



## EPA’s Heat Rate Improvement Assessment

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

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

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

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

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

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

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

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

heat rates. Potential changes in net heat rates are then addressed later in this section. Unit-level Comment [A23]: Please include text regarding

the peer review to which all these reports have

been subject. If not, please consider subjecting this

concept to technical peer review in the period
following proposal release. Comment [A24]: Is there a reason there is no

comment in this box?

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15 evaluations allowed the EPA to recognize the sign ificant heterogeneity of coal-fired EGUs; even

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

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

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

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

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

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

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

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

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

or replacing existing components with improved versions.

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

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

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

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

research EGUs where such methods were applied.

Study Population and Data

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

reported both heat input 2 

## and electrical output to the EPA’s Clean Air Markets Division in 2012. 3



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

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

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

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

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

comparison of these units’ heat rates.

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

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

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

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

2 

### Sources calculate heat input using an ‘F factor’ for the carbon content of the fuel being combusted and the average hourly measurements of CO 2 

### flow and concentration.

3 

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

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

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

different regulatory constraints.

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

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

complete and accurate emission measurement and reporting to support emission trading

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

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

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

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

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

designed to overestimate emissions. This conservative bias is intended to create an incentive for

better emission measurement – the overestimate incurs an economic penalty because, at the end

of the compliance period, an EGU must surrender allowances equal to total reported emissions.

Because of this conservative bias and the impact it would have on the results of this study, the

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

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

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

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

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

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

unit counts by year for the study population used in this work. This population corresponds to

9,350 unit-years of data at 884 distinct EGUs.

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17 Table 2-4. Study Population Unit Count by Year 4



### Year Unit Count

2002 

### 833

2003 

### 828

2004 

### 833

2005 

### 836

2006 

### 845

2007 

### 844

2008 

### 851

2009 

### 860

2010 

### 865

2011 

### 876

2012 

### 879



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

20,000 weather stations worldwide. 5 

## Since EGU performance is se nsitive to air temperature and

barometric pressure, which vary with elevation, we use meteorological data from stations that are

reasonably close to the EGU's location and elevation to account for the impact of ambient

conditions. For each of the plants in this study’s da tabase, we identified the nearest stations that

reported a minimum of 8,400 hourly observations in the calendar year (i.e., greater than 95% of

the hours in a year). Generally, each plant was associated with meteorological data from the two

closest stations over the eleven-year study pe riod. The average distance between the plant and

station was 22 miles and the average difference in elevation was 366 feet. At nine plants we used

data with as few as 7,000 observations in or der to keep the maximum difference in elevation

under 10001,000 feet. The EPA believes that nearby w eather station measurements are a good

approximation of ambient meteorological conditions at each facility. Joining the trimmed heat

rate and hourly temperature datasets resulted in 61,848,580 hourly records for the study

population of EGUs (see Table 2-4).

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

EGUs shown in Table 2-4. These units emitted 1,606 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

4 

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

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18 1,669 million tons according to the EIA. 6 

## Since the study population of EGUs accounted for over

96% of CO 2 

## emissions from the fleet of U.S. coal-fired EGUs, the EPA considers the results of

this study to be applicable to the coal-fired fleet at large.

Subcategorization

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

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

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

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

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

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

heat rates for the broad categories has significant overlap (see Figure 2-1) and therefore makes it

challenging to develop appropriate categorization. The figure below displays available

information on coal-fired EGUs considered in this work for the years 2009-2011 in typical

subcategories of capacity, fuel rank, and thermody namic cycle. As the figure reflects, the means

are clustered and the ranges of heat rates overlap.

Figure 2-1 . . Three3 -Year Average Heat Rates by Subcategory 7

6 

### Preliminary 2012 results from http://www.eia.gov/tools/faqs/faq.cfm?id=77&t=3 . Revised 2012 results will be available in March, 2014. The somewhat lower 2012 CO 2 

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

of substitute data

. 7 

### Abbreviations in the figure: BIT means bituminous, SUB means subbituminous, LIG means lignite, SUPER means supercritical, SUB means subcritical OVER/UNDE R means greater/less than indicated MW capacity. 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 BIT SUPER

SUB SUPER

BIT OVER 200

PC

BIT 100 to 200

SUB OVER 100

LIG OVER 100

BIT UNDER 100

SUB UNDER 100

LIG UNDER 100

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

Comment [A25]: Suggest including on the figure

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

Comment [A26]: The footnote uses SUB twice

for different terms. Please clarify/fix.

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19 Observed Trends in the Period 2002-2012

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

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

electric generation, capacity factor, and gross heat rate declined by 9%, 21%, and 2%,

respectively. The decrease in coal-fired generation and capacity factor may be because of

reduced demand for electricity resulting from the recession starting in late 2008 and greater use

of natural gas and renewables to generate electricity.

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

(see Table 2-4) was 9,763 (Btu/kWh). The decrease in study population annual heat rate between

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

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

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

rate (9,650 (Btu/kWh)) occurred in 2008, near peak generation for the years under consideration,

and was approximately 1% below the average. Table 2-5 displays the annual heat input,

electricity generation, capacity factor, and heat rate for the study population of EGUs. Figure 2-2

displays the average annual gross heat rate by year and the 11-year average.

Table 2-5. Trends 2002-2102

Year Heat Input (million Btu) Electric

Generation (MWh-gross) Operating

Hours Capacity

Factor

(%) Heat Rate(Btu/kWhgross)

2002 18,661,321,692 1,877,860,222 6,130,609 64 9,938

2003 18,490,002,370 1,868,191,406 6,044,559 65 9,897

2004 18,488,140,910 1,880,068,323 6,048,381 65 9,834

2005 18,754,700,745 1,916,068,402 6,141,697 66 9,788

2006 18,746,450,250 1,922,588,408 6,155,013 65 9,751

2007 18,787,425,977 1,927,419,559 6,138,888 66 9,748

2008 18,545,285,797 1,921,820,484 6,088,562 64 9,650

2009 16,670,621,043 1,726,641,303 5,448,438 54 9,655

2010 17,771,232,591 1,838,417,252 5,754,116 58 9,667

2011 17,006,597,908 1,750,427,841 5,507,574 53 9,716

2012 15,010,214,648 1,541,305,063 4,816,547 44 9,739 Comment [A27]: Suggest making the Table title

more descriptive.

Also could present fewer signficant digits in table

...i.e. heat input ‐ MM BTU x 106 ‐‐

Comment [A28]: This capacity factor decline in

2012 is very different from the CF used later, see

comment on page p. 35, i.e., 78% in 2020. Which is

correct? And, did coal CF at 44% factor into the EPA modeling?

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20 Figure 2-2. Study population Average Gross Heat Rate by Year

Startups and Shutdowns - Impact On the Results of This Study

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

Therefore, we evaluated the potential impact of su ch events on our evaluation. A startup event,

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

measurable amount of electricity. Table 2-6 summarizes the study population average,

maximum and total starts by year. On average, coal-fired EGUs start combusting fuel and

generating electricity 11 times per year. The total number (approximately 9,000) of starts for the

study population of EGUs has remained stable over the study period. Our data reflects that some

coal-fired units operate in a load following capacity and may report upwards of 200 starts in a

single year, but these units tend to have low annual capacity factors. The subset of EGUs with

more than 20 annual startup and shutdown even ts is responsible for less than 4% of total

generation in any study year. Therefore while the number of starts is an important variable at a

small number of EGUs, its impact on heat rate performance evaluated in this study is considered

to be marginal. 9,500 9,550 9,600 9,650 9,700 9,750 9,800 9,850 9,900 9,950 10,00020022003200420052006200720082009201020112012Heat Rate (btu/kwh)



### Annual Heat Rate

11-yr Avg Heat Rate

Comment [A29]: At about this point, the
chapter transitions into the quantitative analysis.

Suggest highlighting that in some way. There seem

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

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21 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



## Impact of capacity factor and ambient temperature

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

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

of the EGUs in the study population.

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

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

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

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

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

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

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

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

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

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

cost, and transmission constraints.

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

thermodynamic steam cycle 8 

## and, 2) in many regions of the country, as temperatures increase

8 

### The availability of a cold heat sink in the condenser is a key factor in that cycle. The design of the heat exchanger, type of cooling system and availability of water all ha ve an impact on performance. An increase in ambient air Comment [A30]: After this sentence, suggest adding a sentence stating why this is important to do, and a sentence summarizing the approach.

Comment [A31]: Suggest discussing explicitly

the choice of CF as an independent variable

– why is

it reasonable to think of it as an independent

element? Are there better factors that EPA could

have used but was unable to because of information
availability?

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22 electricity demand capacity factor follows. Figu re 2-3 shows the average monthly capacity factor

in 2012 alongside the climate normal monthly temperature. 9 

## The lines intersect in the spring as

temperatures begin to rise and the need for cooling drives electricity demand. Peak capacity

factor and generation in most parts of the U.S. occur on the hottest days of the year.

Figure 2-3. Monthly Capacity Factor, 2012.



## Regression Analysi s to assess impact of CF and Ambient Temperature on heat rate

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

capacity factor and ambient temperature. The single variable analyses help to examine the

relative influence of capacity factor and ambient temperature on heat rate. Since this analysis

seeks to evaluate heat rate under normal operating conditions, we removed records with hourly

heat rate values outside of +/- 2.6 standard deviations (1.9% of records) before performing the

regressions. 10 

## Similarly to partial operating hours, th ese outliers tend to occur during low load

conditions. The records trimmed amount to one-four th of a percent of the total study population

generation. Regression results describe the goodness of fit for the model and are expressed as the

coefficient of determination or ‘r-squared’. To re present the relative contribution of varying unit

capacities all results are generation-weighted. 11 

## The average study population r-squared for the

multivariate regression is 26%. This means that hourly ambient temperature and capacity factor

temperature, and consequent increase in water temperature, typically lower the effectiveness of the cooling system, the condenser, and, therefore, overall plant efficiency.

9 

### Climate normal is the average of temperature (or other me asure) over a prescribed 30-y ear interval and location.

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

10 

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

11 

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

## Electric generation monthly 2012 capacity factor and normal temperature

Capacity Factor (Monthly %, 2012)

Climate Normal (BWI, MD)

Comment [A32]: Could more granularity on unit characteristic/type of fuel/design be included as
part of the regression analysis?

Comment [A33]: It is sometimes hard to follow

the outline of the chapter

– currently uses bold,

bold italic, underline. Suggest using headings like

1.1, 1.1.2, etc. or other indicators.

Comment [A34]: Capacity factor is also

influenced by temperature

– any considerations for
collinearity effects in the regression?

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23 together explain 26% of the change in heat rate for the study population over the study period.

The average study population r-squared from the singl e variable analysis of capacity factor is

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

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

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

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

capacity factor is a key variable influencing changes in heat rate.

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

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

30% of the change in heat rate. These are typical ly, but not exclusively, units with once-through,

fresh water, cooling systems. Identifying temperature-responsive EGUs allows us to understand

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

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

rates may increase by 2-4% compared to winter months, but at a temperature-responsive EGU

that figure may be as high as 10%.

Our results analysis found indicates that as EGUs cyclecycled more, i.e., movemoved

from base load to load following, capacity factor will tendtended to have a larger effect on heat

rate. Since 2008, the study population capacity factors moved from the top load bin into lower

load bins. This can be seen from Figure 2-4, wh ich compares the study population duty cycles in

2008 to 2012. A significant share of 2008 genera tion occurred at EGUs running at greater than

85% annual capacity factor . In 2012, little generation took place in that bin, and generation was

reduced by about half in the next lower bin (79-84% capacity factor).

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

Heat Rate Variability and Indication of Improvement Potential

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

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

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

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

affect emission measurements. Heat rate lends itse lf to process control si nce it is the principal

indicator that defines the quality of the electric generation process. 12,13 

## Therefore, in general,

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

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

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

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

eleven years of results for the study population by quartiles ordered by the 11-year generationweighted RSD (ascending). 14 

## The RSD of the top quartile (3.5%) is significantly lower than the

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

are not outliers; they report a third of all ge neration – the most of any segment. The results

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

potential for heat rate improvement.

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

Quartile RSD Average RSD

Minimum RSD

Maximum Share of

Generation

1 3.5 1.6 4.2 

### 33

2 4.7 4.2 5.3 

### 26

3 6.1 5.3 7.0 

### 24

4 9.8 7.0 25.2 

### 16

12 

### “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)

13 

### Since accurate measurement is essentia l to process control the introduction of increasingly sophisticated digital control systems (DCS) presents new opportunities for finding inefficiencies. Vendors (ABB, SeimensSeiemens

, 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 highe r if system-wide real-time optimization is included.

14 

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

table.

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

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

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

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

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

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

temperature.

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

generation-weighted average of up to eleven annual values. The study population generationweighted mean RSD over the study period is 4.5%. This percentage represents the total

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

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

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

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

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

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

Table 2-9. RSD of residual heat rate (generation weighted) 15



### Quartile RSD

average RSD

Minimum RSD

Maximum

1 2.7 0.0 3.2

2 3.6 3.2 4.1

3 4.7 4.1 5.3

4 6.9 5.3 10.4



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

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

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

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

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

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

operation to reduce variability and heat rate.

15 

### This table excludes nine units where RSD exceeded 2.6 standard deviations from the mean. Comment [A35]: This Table (and others like it)
might be more clearly presented as a simple line
graph.

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26 Heat rate variability and performance

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

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

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

Figure 2-5 below summarizes the results of regressing RSD of heat rate onto annual heat rate. 16



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

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

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



## Units with large heat rate changes

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

change in heat rate greater than +/- 8.5%. 17 

## We consider this magnitude to be in the upper range

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

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

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

heat rate and capacity factor for any of the three groups in Table 2-10. 18 

## This would indicate that

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

16 

### This table excludes nine units where RSD exceeded 2.6 standard deviations from the mean. dThe regression analysis was performed on 9,348 unit-years of study data which were trimmed to remove values

outside 2.6 standard deviations.

17 

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

18 

### The correlation remains weak even when limited to cases where capacity fact or changed more than 30 percent. 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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27 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. 19 

## The large changes

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

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

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

Description Count Correlation of

heat rate to capacity factor

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

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

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



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

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

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

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

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

Table 2-11. RSD in reported heat rate of 166 ‘large decrease’ EGUs (generation weighted)

Quartile RSD Average RSD

Minimum RSD

Maximum

1 4.0 2.3 4.8

2 5.3 4.8 5.9

3 6.6 5.9 7.5

4 10.5 7.6 26.5



## Assessment of heat rate improvement potential via best practices

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

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

19 

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

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28 population heat rate. 20 

## This means that on average 74% of the change remains unexplained after

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

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

rate, other factors held equal, the range of varia tion indicates that significant potential heat rate

improvement is available through the application of best practices.

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

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

matrix of 168 bins. 21 

## For a given EGU, a temperature and capacity factor bin will have all the

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

model finds the reported hourly heat input value corresponding to the 10th percentile(p10). 22



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

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

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

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

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

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

to reduced heat inputs is the potential heat rate improvement for that unit. 23 

## Using this approach,

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

take proportionally smaller reductions.

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

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

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

wide improvement in heat rate. In effect, the m odel proportionately reduces heat rate variability

and improves performance for each unit while controlling for temperature and capacity factor.

20 

### The 26% result is the generation weighted average of r-squa red values from the multivar iate regression analysis.

21 

### 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.

22 

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

23 

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

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29 The heat rate improvement for the study population is derived from the performance of each

individual EGU as compared to its own record.

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

% Reduction from reported heat input to p10 Study population

heat rate (Btu(kWh-gross) 24 

### Reduced study population heatrate (Btu/kWhgross) Study population

heat rate improvement (%)

10

9,755 9,625 1.3

20 9,495 2.7

30 9,365 4.0

40 9,235 5.3

50 9,105 6.7



## Assessment of potential heat rate improvement via equipment upgrades

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

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

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

that reported one or more single year-to-year heat rate improvements of 3-8%. In two of these

cases we were able to identify equipment upgrades responsible for 2-3% heat rate improvement

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

cases, while our research was unable to confirm specific equipment upgrades, based on the

elimination of other possible explanations we be lieve that equipment upgrades were the most

likely cause of some of the observed heat rate improvements.

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

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

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

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

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

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

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

24 

### Fleet heat rate for entire study population is 9,763; the 9, 755 value is derived from the dataset that includes hourly temperature values. Comment [A36]: This technical analysis of the
possibility for heat rate improvements should be

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

EPA intend to do so after release of the proposal?

Comment [A37]: Do you really mean in a single

year to year change, as an indicator of a functional
change in the equipment? Otherwise, 8% annual
improvement year after year is immense. Suggested edit, but change at EPA’s discretion.

Comment [A38]: Is it possible to give any more
information about this or provide further support?

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

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

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

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

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

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

already made these upgrades.

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

No-Cost and Low-Cost Options Condenser Cleaning Intelligent Soot Blowers

ESP Modification Boiler Feed Pump Rebuild Air Heater and Duct Leakage Control

Neural Network SCR System Modification FGD System Modification Cooling Tower Advanced Packing

Higher Cost Options Economizer Replacement

Acid Dew Point Control

Combined VFD and Fan

Turbine Overhaul

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

method of least squares. Using the slope of the tre nd line, 2002-2012, as an indicator of heat rate

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

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

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

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

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

maintain the status quo.

Combined study population results

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

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

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

by 2%.

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

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

significantly lower heat rate variability than th e study population average. Reducing heat rate

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

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

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

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

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

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

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

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

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

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

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

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

The equipment upgrades results are supported by numerous studies 25 

## and by the EPA’s

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

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

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

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

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

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

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

electric sector CO 2 

## emissions from coal.

Sensitivity Analysis Removing Plan ned or Announced EGU Retirements

25 

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

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32 The EPA’s research found 23 0 EGUs that have announced they will retire before 2016. 26



## A sensitivity analysis was applied to the EGUs in the study population that plan to operate

through 2015. The results are identical to the full population – both achieve a heat rate reduction

of 4% under the 30 percent difference option described in best practices.

Heat Rate Improvement – Economics

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

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

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

in coal fuel cost and any cost related to CO 2 

## emissions. For the purpose of this TSD analysis, it is

assumed that HRI can be economic if the annua lized net savings (coal cost savings plus CO 2


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

Annual Net HRI Savings = Coal Cost Savings + 

## CO 2 

### Emission Cost Savings – Capital Cost 27



## Heat Rate Improvement Capital Cost Assumption

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

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

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

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

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

Btu reductions and associated range of capital costs are obtained:

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

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

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 capital cost to ach ieve whatever HRI Btu reduction is possible at

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

26 

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

27 

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

therefore does not consider the small econo mic impact of HRI-related O&M costs. Comment [A39]: This is an important statement,

that is accorded little explanation. I presume these

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

How did these anticipated requirements factor into

the state goal setting, and then into the IPM modeling?

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33 Heat Rate Reduction Assumption

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

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

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

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

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

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

has already applied some or many of the available HRI methods. 28



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

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

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

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

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

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

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

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

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

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

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

6%.

Heat Rate Improvement Breakeven Economic Analysis

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

using the assumptions described above, also assuming there is no CO 2 

## emission cost that is

reduced via HRI.

28 

### 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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34 Figure 2-6. HRI Breakeven Economics

Notes:

1. Capital cost S/MWh assumes the following: HRI capital cost = $100/kW; capital charge rate = 14.3%; IPM projected 2020 annual capacity factor = 78%

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

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

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

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

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

directionally as follows:

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

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

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

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

$2.38/MMBtu for all coal ranks and $2.89/MMBtu for bituminous coals in 2012. 29 

## This

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

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

29 

### EIA, Electricity DataTable 7.4, Average Weighted Cost of Fossil Fuels for the Electric Power Industry 2002- 2012, http://www.eia.gov/electricity/annual/html/epa_07_04.html 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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35  At an EGU net heat rate that is higher than the IPM projected 2020 average value of

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

mentioned above.

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

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

economic fleet wide,

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

economic at an average coal cost of about $2.50/MMBtu.

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

## emission costs, HRI

could become economic at lower coal costs, or at higher capital costs, or at lower heat

rate reduction percentages.

Estimated Fleet-wide Coal EGU CO 2 

## Reduction and Cost via HRI

It is possible to make an order-of-magnitude estimate of the fleet-wide extent and costeffectiveness of HRI using reasonable assumptions as in the following example:

Fleet-wide 2020 Assumptions (basis: similar to IPMv5.13 Base Case):

 Coal fleet capacity applying combined HRI methods = 244,000 MW

 Average CO 2 

## emission rate = 0.976 tonne/MWh net

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

 Average capacity factor = 78%

 Pre-HRI CO 2 

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

 HRI Btu and CO 2 

## reduction = 6%

 HRI capital cost = $100/kW

 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 Comment [A40]: Throughout the paper, it might
help to indicate the “fleet
‐ wide” data refer to the

national fleet (i.e. all the coal facilities in the U.S.) if

this is the case.

Comment [A41]: It was ostensibly 44% in 2012

–

see Table 2

‐ 5, page 19 above

– or am I missing something? Please clarify. Is this assuming a lot of

coal plant closures to get the capacity factor up? If

so, please clarify the discrepancy/change over time

in the text and how this might affect state goal
setting, especially building block 2?

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36  Annual net savings (cost) = ($0.8 billion)

 Annual net savings (cost) of CO 2 

## reduction = ($7.7/tonne)

Conclusion - HRI Economics

This necessarily simplified HRI economic analysis supports the following summary conclusions:

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

 If a fleet-wide average 6% HRI is technically feasible, it would also be economic on the

basis of fuel savings alone, before consideration of the value of the associated CO2

emission reductions, on a fleet-wide basis at toda y’s coal prices if the associated average

capital cost is abou t $75/kW or less.

 If a fleet-wide average 6% HRI is technically feasible, and the associated average capital

cost is as much as $100/kW, 6% HRI coul d become economic on the basis of fuel

savings alone, before consideration of the value of the associated CO2 emission

reductions, if/when average coal prices rise to about $3.25/MMBtu (IPM projects coal at

$2.62/MMBtu in 2020).

 Even at a capital cost of $100/kW and an IPM projected 2020 coal price of

$2.62/MMBtu, the fleet-wide cost of CO 2 

## reduction via 6% HRI would be a relatively

low $7.7/tonne.

Thus, although there is currently some uncertainty associated with the costs of achieving a

particular fleet-wide amount of HRI, it is clear that HRI is an available low-cost approach to CO 2


## reduction for existing coal-fired EGUs

Definitions/Abbreviations

Btu British thermal unit

capacity factor electricity generation expressed as a percentage of maximum 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

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37 MW megawatt

PC pulverized coal (boiler)

RSD Relative standard deviation

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

start a startup event in which an EGU begins combusting fossil fuel and generates some measurable amount of electricity before ceasing fossil

fuel combustion
unit-year data for one EGU over a one year period



### Docket Datasets

Name Description

hour_QA_data.txt 2002-2012 hourly dataset

hour_QA_regression_data.txt 2002-2012 hourly dataset for regression analysis

units_885.txt List of study units and characteristics

year_bin_10_50_data.txt 2002-2012 unit-year binned results at 10 – 50% difference options

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38 HRI Partial Bibliography

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

NETL 2008, Reducing CO 2 

## Emissions by Improving the Efficiency of the Existing Coal-fired Power Plant Fleet, DOE/NETL-2008/1329, July 2008

NETL 2009, Opportunities to Improve the Effi ciency of Existing Coal-fired Power Plants, Workshop Report, NETL July 2009, available at http://www.netl.doe.gov/energyanalyses/pubs/NETL%20Power%20Plant%20E fficiency%20Workshop%20Report%20Final.pdf

NETL 2010, Improving the Thermal Efficiency of Coal-Fired Power Plants in the United States, DOE/NETL Technical Workshop Report, February 2010, available at http://www.netl.doe.gov/File%20Library/Resear ch/Energy%20Analysis/Publications/ThermalEf ficCoalFiredPowerPlants-TechWorkshopRpt.pdf

NETL 2010a, Improving the Efficiency of Coal-Fired Power Plants for Near Term GHG Emissions Reductions, DOE/NETL-2010/ 1411, April 2010, available at http://www.alrc.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=307

Sargent & Lundy 2009, Coal-Fired Power Plant Heat Rate Reductions, SL-009597, Final Report, January 2009, available at http://www.epa.gov/airmarket s/resource/docs/coalfired.pdf

Storm 2009, “Applying the Fundamentals for Best Heat Rate Performance of Pulverized Coal Fueled Boilers, Storm Technologies, Inc, EPRI 2009 Heat Rate Conference, available at http://www.stormeng.com/pdf/EPRI2009HeatRateConference%20FINAL.pdf

NRDC 2013, Closing the Power Plant Carbon Pollution Loophole, NRDC Report R:12-11-A,

March 2013 available at http://www.nrdc.org/air/pollution-st andards/files/pollution-standardsreport.pdf

Lehigh 2009, Reducing Heat Rates Of Coal-Fired Power Plants, Lehigh Energy Update, January 2009, available at http://www.lehigh.edu/~inenr/leu/leu_61.pdf

ESC/OnLocation, Efficient Heat Rate Benchmarks for Coal-Fired Generating Units, draft, B. Roberts, Economic Sciences Corporation, L Goudarzi, OnLocation, Inc., 1998, available at http://www.onlocationinc .com/heatratepaper.pdf

CRS 2013, Increasing the Efficiency of Exis ting Coal-Fired Power Plants, Congressional Research Service, December 2013, available at http://www.fas.org/s gp/crs/misc/R43343.pdf

Evonik/VGB 2008, “Power Plant Performance Reporting and Improvement under the Provision of the Indian Energy Conservation Act – Output 1.1”, Evonik/VGB 2008, available at http://www.emt-india.net/PowerPlan tComponent/Output1.1/Output1.1.pdf

IEA 2010, Power Generation from Coal - Measuring and Reporting Efficiency Performance and

CO 2 

## Emissions, OECD/IEA-CIAB 2010, available at http://www.iea.org/ciab/papers/ power_generation_from_coal.pdf

EPRI 2009, Renewed Interest in Reducing Heat Rate, EPRI Journal, Spring 2009, available at http://mydocs.epri.com/docs/CorporateDocuments/EPRI_Journal/2009- Spring/1019287_Heat%20Rate.pdf

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39 EPRI/Korellis 2011, Identifying Ways to Improve Plant Heat Rate, Energy-Tech Magazine, March 2011, available at http://www.energy-tech.com/article.cfm?id=30528

EPRI 2011a , Opportunities to Enhance Electri c Energy Efficiency in the Production and Delivery of Electricity, EPRI Technical Re port 1024651, November 2011, available at

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=1&ved=0CCsQF jAA&url=http%3A%2F%2Fwww.pserc.wisc.edu% 2Fdocuments%2Fpublications%2Fspecial_in terest_publications%2FEPRI_Electricity_Us e_Report_Final_1024651.pdf&ei=Qo9yUvmwMY Xb4AOZz4GYAw&usg=AFQjCNElzekbtoSNCR5SKFwkfbKx83p0Uw&bvm=bv.55819444,d. dmg

RFF 2013, Regulating Greenhouse Gases from Coal Power Plants under the Clean Air Act, RFF DP 13-05, February 2013, available at http://www.rff.org/RFF/documents/RFF-DP-13-05.pdf

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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

Technical Considerations ......................................................................................................

.... 1312

Analysis of Cost-Effectiveness ................................................................................................

. 1817

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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 reducing

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

possible, of less carbon-intensive existing fossil fuel-fired EGUs. The TSD provides background

on existing power plants, power system operati on, 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

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

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.

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3 

## 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 ma rginal 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 in to electricity markets such that their variable

costs are covered. For fossil fuel-fired electric ge nerating units, variable costs are dominated by

the cost of the fuel, although coal-fired units often also have considerable variable costs

associated with running pollution controls. 4 

## Other generating technologies, like renewables,

hydroelectric, and nuclear, have little or no vari able costs and are generally dispatched to the

extent possible. In order to maintain least-cost dispatch, the units with the lowest variable costs

will be called upon first, then other units (with higher variable costs) will be called upon

sequentially, such that total 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

3 

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

4 

### In addition to fuel costs, variable costs also include costs associated operating and maintenance, and costs of operating a pollution control and/or emission allowance charges.

5 

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

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## 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

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

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

. Comment [A2]: It’s probably more appropriate

to say that vertically

‐ integrated utilities seek the

lowest total cost, not the lowest marginal cost.

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## 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 the

scheduled dispatch is not feasible within the li mits of the transmission system, it must be

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

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## 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 - 2,354 lbs/MWh

 Oil/Gas (O/G) Steam - 1,624 lbs/MWh

 NGCC – 926 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 technology

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

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

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

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

8 

### http://www.nerc.com/

9 

### Emission rates in this document are shown on a net gene ration basis and reflect Hawaii and Alaska sources. See “2012 eGrid Data” file provided in the docket

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 Comment [A3]: This is incorrect. Other forms of
generation are dispatchable. Please clarify.

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7 

## evaluate re-dispatch opportunities to unused NG CC generation potential in the system, the EPA

reviewed recent NGCC fleet operating data to de termine capacity factors. Redispatch for GHG

abatement purposes would require one net MWh of a lower emitting technology displacing one

net MWh of generation from higher emitting technology. Therefore, when the EPA was

assessing capacity factor it used the net generation of a given NGCC unit as the numerator. The

EPA was interested in the relation ship of a unit’s total net generatio n relative to its net generating

capacity (i.e., capacity factor). Net generating capacity is a function of weather/temperature

conditions at the site, which varies throughout the year. While some units may model actual

weather adjusted capacity by the hour/minute, theisethis data iaresis not reported for the fleet.

Therefore, the EPA used the reported 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. Wher eas 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% (84% to 89%) 13



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

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 state’s NGCC fl eet as part of BSER, the EPA

12 

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

13 

### Because the 2012 capacity factor used in this exercise focuses on net generation as a percent of name plate capacity, the availability is converted from a gross genera tion/name plate value to a ne t generation/name plate value using a .97net/gross conversion resulting in a range is 84% to 89%.

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## 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%

Of 1,812 NGCC units operating in 2012, the EPA observed that 179 units (10% of

NGCC units) had a net generation value that was gr eater than or equal to its nameplate capacity

x 8784 hours * 70%. That is, a capacity factor th at was 70% or greater (see Figures 3-1 and 3-

2). 16

14 

### See the National Electricity Energy Data Systems (NEEDS) file at http://www.epa.gov/powersectormodeling/BaseCasev513.html

15 

### See “Operable” worksheet in “GeneratorY2012” Workbook in 2012 Zip file at http://www.eia.gov/electricity/data/eia860/

16 

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

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

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

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 Formatted: Font: (Default) Times New Roman, 12 pt, Complex Script Font: Times New Roman, 12 pt

Formatted: Font: (Default) Times New Roman, 12 pt, Complex Script Font: Times New Roman, 12 pt

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## Figure 3-2: Frequency of NGCC Capacity Factors

In 2012, roughly 10% of NGCC units operated at an annual capacity factor of 70% or

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

10% of units 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, 17 

## and looking more closely at data during the

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

17 

### Air Markets Program Data (at http://ampd.epa.gov/ampd/). 0 20 40 60 80 100 120 140 160 180 200#ofUnits 

# Unit Distribution by Capacity Factor

0 20 40 60 80 100 120 140 160 180 200#ofUnits 

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## were operated at a capacity factor greater than 70%. 18 

## 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. 19 

## 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.

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 generation from coal units to

NGCCs, absent any federal CO 2 

## requirements. 2012 net generation from NGCC units grew to

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 to

gauge 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

18 

### Summer defined as June, July and August. Winter defined as December, January, and February. Estimates are for units for which data was provided to EPA.

19 

### 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. Comment [A4]: 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.

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## 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. 20



## 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

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. 21



## Table 3-2: 2011 and 2012 Gas and Coal Generation 22



## Year NGCC

Name Plate

Capacity

(GW) NGCC Heat

Input for

Electricity

(TBtu) NGCC Net

Generation

(TWh) NGCC

Capacity

Factor HH Natural

Gas Price

($/mmBtu Coal Net

Generation

(TWh)

2011 235 5,912 796 39% $4.00 1,719

2012 242 7,224 981 46% $2.76 1,503

The demonstrated ability of the NGCC units to co nsistently operate at levels greater than

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

## percentile unit),

the historic evidence supporting quick and sign ificant 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.

20 

### 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”)

21 

### http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2014ER&subject=0-AEO2014ER&table=13- AEO2014ER®ion=0-0&cases=ref2014er-d102413a

22 

### EIA form 860 and EIA form 923

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## Technical Considerations
Emission reductions through re-dispatch are la rgely 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

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%. 23,24 

## 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. 25 

## 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. 26,27



## 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). 28



## 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

23 

### EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayine nergy/detail.cfm?id=14611 (for recent data)

24 

### EIA, Electric Power Annual 2009, http://www.eia.gov/electricity/annual/archive/03482009.pdf (Table 5-2 for 2009 and earlier data)

25 

### NERC, 2008-2012 Generating Unit Statistical Brochure – All Units Reporting, http://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx

26 

### 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

27 

### 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

28 

### http://site.ge-energy.com/corporate/network/downloads/7FA_Evolution.pdf

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## from General Electric Power Systems). 29 

## 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

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 30 

## and resulted in a national average

capacity factor for NGCC of 49% on aver age, and higher in some states. 31



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

factors are typically well above average capacity factors. 32 

## The pattern of capacity utilization by

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

approximately 50% from the Hour 11 to Hour 21. 33 

## 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% 34 

## , this indicates that the current system can

29 

### 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.

30 

### Energy Information Administration, U.S. Natural Gas Consumption by End Use, http://www.eia.gov/dnav/ng/ng_cons_sum_dcu_nus_a.htm.

31 

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

32 

### 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#

33 

### In this figure, hour 11 is the hour ending at 11 AM, and similarly for other hours.

34 

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

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

period when there are most likely to be constrai nts 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

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 3-3. Average Utilization of Natu ral 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. 35 

## This rule

35 

### 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

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## 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 ge nerally be less extensive than upgrades of that

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

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. 36 

## 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. 37 

## 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. 38



## 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

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

.

36 

### Energy Information Administration, Today in Energy , Natural Gas Pipeline Additions in 2011. Additions averaged around 20Bcf per day from 2008 to 2011.

37 

### 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.

38 

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

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## 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 3-4. 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

Northeast, Southeast, Midwest and Western regions in Figure 3-4. The net pipeline capacity for

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

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

Area 39



## 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, pipeline

requirements for increased use of natural gas in existing facilities can be expected to be largely

on existing pipeline facilities and corridors, so that the types of capacity expansion required will

39 

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

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## 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 complian ce changes in re-dispatch of natural gas power

plants.

Figure 3-4. Energy Information Administration Natural Gas Pipeline Regions

Source: Energy Information Administration.

Analysis of Cost-Effectiveness
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

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## to NGCC units within defined areas. 40 

## 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), 41 

## 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

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. 42



## 2. Generation constraints that maintain the sum of state-level generation in the Base Case 43



## 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,

40 

### 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

41 

### 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.

42 

### 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.

43 

### http://epa.gov/powersectormodeling/BaseCasev513.html Comment [A5]: 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.

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## 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 3-5 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).

Figure 3-5: NGCC National Capacity Factor, 2020 Initial Analyses

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) Comment [A6]: $/tonne costs seem high

– would be helpful to have more detail on methodology/analysis used to derive these costs?

Formatted: Font: (Default) Times New Roman, 12 pt, Complex Script Font: Times New Roman, 12 pt

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## The EPA believes average $/tonne – which is distinct from the $/tonne CO 2 

## cost imposed

in these analytic scenarios on affected coal st eam, 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) Formatted: Font: (Default) Times New Roman, 12 pt, Complex Script Font: Times New Roman, 12 pt

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## Table 3-4: 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 impl ementing this requirement in a comp liance 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 1,100

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

clearly illustrated in the declining slope of th e best-fit line in Figure 3-5. 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 Comment [A7]: Can this be quantified?

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23 

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

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

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

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

generation among EGUs was not limited by state boundaries. In one set of analyses, redispatch was allowed to occur across 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

, , demonstrating that the main analysis’s imposition of artificial re-dispatch boundaries on

state borders overstates the cost-effectiveness of re-dispatch potential.

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

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

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

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

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

the lowest total cost, subject to all other 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 scenario reflecting a 70 percent NGCC utilization rate and a constraint

prohibiting interstate re-dispatch opportuniti es, 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



## .

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

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

electricity routinely change in response to economic conditions. As a result, this case assumes no Comment [A8]: Can you clarify if this 8% greater

emission reduction is relative to base case or
relative to reductions achieved from redispatch

without regional scenario?

Comment [A9]: Also, can this be stated
alternately as a drop in cost for a given CO2

reduction level?

Comment [A10]: It would be helpful

to have the results – can EPA provide this? Will it be in the

docket?

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24 

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

impacts of re-dispatch.

In the 70 percent NGCC unit utilization rate scenario with restrictions on interstate redispatch, natural gas prices were projected to increase by a range of from [x] percent to [x]

percent, which is within the range of historic natural gas price variability. Projected wholesale

electricity price increases ranged from [x] to [x] percent, which similarly is within the range of

historic electric price variability.

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

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

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

regional transmission organizations (RTOs), while in another set of analyses, national redispatch was simulated (subject to other constraints specified in the model, including

transmission limits). In these scenarios with grea ter re-dispatch flexibility, the cost of achieving

the quantity of CO 2 

## reductions corresponding to a nationwide average NGCC unit utilization of

70% was notably lower. Comment [A11]: 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 [A12]: Can the actual numbers on

costs be presented in a table or other format, other

than just stating “notably lower”?

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1 GHG Abatement Measures Technical Support Document Chapter 4: Cleaner Generation Sources

4.1. Introduction

Renewable energy is a cost-effective approach for reducing carbon dioxide (CO 2 

### ) emissions from

fossil fuel-fired electric generating units (EGUs) thro ugh the substitution of electricity generated from

renewable resources, referred to in this document as renewable energy (RE). The portfolio of available

RE sources encompasses a wide variety of technologi es from utility-scale RE plants to smaller-scale

distributed generation sited at residential, commercial , or industrial facilities. RE technologies are fueled

by the sun, wind, water, organic matter, and othe r resources regularly replenished by physical and

biological cycles. To integrate the rapidly increasing an d evolving portfolio of RE into the Best System of

Emission Reductions (BSER), the EPA has developed a best practices scenario that builds upon current

state policy encouraging increased production of RE taki ng into account renewable potential in particular

regions of the country.

Additionally, the EPA believes that the planne d expansion of new nuclear generating capacity

and the preservation of existing nuclear generating capacity represent a cost-effective means to reduce

CO 2 

### emissions at fossil fuel-fired EGUs by providing carbon-free generation that can replace generation

at those EGUs. Increasing the amount of nuclear capac ity relative to the amount that would otherwise be

available to operate is a technically viable and economically efficient approach for reducing CO 2



### emissions from affected fossil fuel-fired EGUs.

This TSD is intended to support discussion of cleaner generation sources (RE and nuclear) as a

component of BSER in the preamble (most extensivel y in these sections: Building Blocks for Setting

State Goals and Considerations, State Goals, State Plans, and Impacts of the Proposed Rule) and its

representation within the RIA. Results from this chapter feed into the technical support document (TSD)

on state goal setting. Cleaner generation is also addre ssed in TSDs on Survey of Existing State Actions,

State Plan Considerations, Projecting EGU CO 2 

### Emission Performance, and Legal Memorandum.

4.2. “Best Practices” RE Targets

To estimate the potential RE available for inclusion as part of BSER the EPA developed a “best

practices” RE generation scenario. The best practices scenario is derived from state experience with

policies that drive investment in RE and the generation that results from those efforts. The EPA focused

on state-level RE policy for several reasons. Ev ery state in the union is producing electricity from

renewable resources, and some states have achieved sign ificant levels of renewable generation, surpassing Comment [A1]: EPA's methodology to

define renewable energy goals is very difficult

to follow. Additional explanation or a specific

state example would be helpful.

Comment [A2]: Hydro Comment -- Document expresses support for existing nuclear generating capacity, as it represents a

cost-effective means to reduce emissions – why then not express support for existing RE generating capacity (e.g. historic hydro)?

We understand that existing nuclear capacity may be more vulnerable to decommissioning – but existing RE generating capacity also represents a cost-effective means to reduce emissions. Can we get a similar statement of support for existing hydro generating capacity?

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2 a quarter of in-state generation. St ate-level RE requirements have been implemented in 29 states plus

Washington, DC, representing all regions of the country. Eight states have voluntary goals. 1 

### These statelevel goals and requirements have been developed an d implemented with technical assistance from statelevel regulatory agencies and utility commissions such that they reflect expert assessments of RE

technical and economic potential that can be cost-effectively developed for that state’s electricity

consumers.

The best practices scenario focuses on RE requi rements established through Renewable Portfolio

Standards (RPS), which provide specific quantifia ble RE generation requirements over time. The EPA

used these RPS-mandated quantities as the basis for de riving regional targets that could be applied to

states as part of BSER, using the RPS-based targ ets as a reasonable benchmark of regionally costeffective RE generation which states could grow towa rds over time. While EPA’s best practices scenario

is derived from RPS data, states may also consider a broad variety of other RE policies to increase

generation, such as performance-based incentives, financial assistance programs, regulatory changes to

facilitate the development of renewable sources and their interconnection to the grid and “lead by

example” strategies integrating RE generation into state properties. 2 

### Because the EPA did not quantify

potential that could be tapped through any of those policy approaches, the agency believes that the RE

targets derived from RPS mandates represent a conservative estimate of cost-effective generation that

could actually be developed by states.

While future RPS requirements will necessitate more RE generation and capacity beyond current

levels, the EPA does not expect the anticipated rate of growth required to meet those requirements to

exceed the historical rate of RE deployment. Full compliance with current RPS requirements through

2035 would necessitate the deployment of approximat ely 3 to 5 GW of new renewable capacity per year

through 2020 and 2 to 3 GW through 2035. Average deployment of RPS-supported renewable capacity

from 2007-2012 has exceeded 6 GW per year. 3 

### In addition, recent improvements in RPS compliance rates

indicate to the EPA the reasonableness of current RPS growth trajectories. Weighted average compliance

rates among all states have improved in each of the past three reported years (2008 - 2011) from 92.1

percent to 95.2 percent despite a 40 percent in crease in RPS obligations during this period. 4 

### As the

1 

### Map: Renewable Portfolio Standard Policies, www.dsireusa.org, March 2013, can be located at: http://www.dsireusa.org/summarymaps/.

2 

### See State Plan Considerations TSD for a discussion of how states can incorporate such RE policies into their state plans for this rule.

3 

### Barbose, Galen, “Renewables Portfolio Standards in the United States: A Status Update,” Lawrence Berkeley National Lab, November 2013.

4 

### http://emp.lbl.gov/rps, retrieved March 2014. The RPS comp liance measure cited is inclusive of credit multipliers and banked RECs utilized for compliance, but excludes alternative compliance payments, borrowed RECs, deferred

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3 Lawrence Berkeley National Laboratory (LBNL) RPS Status Update found, in the period 1998-2012,

67% of all non-hydro U.S. RE capacity additions, to taling roughly 46,000 MW, was built in states with

RPS requirements. 5



### This scenario provides an estimate of an achievable level of total RE generation within states. It

does not represent an EPA forecast of business-as-us ual impacts of state policies or an EPA estimate of

the full potential of RE available to the power system; rather, it is intended to represent a feasible

development scenario that enables reductions of CO 2 

### emissions from fossil fuel-fired EGUs in all states

and that is generally consistent with ongoing trends in RE development. The scenario uses a level of

performance that has already been demonstrated or required by policies of leading states, while

considering each state’s unique existing level of RE performance and allowing appropriate time for each

state to increase from their current level of performance to the identified best practice level. In the context

of this rulemaking, RE “performance” and RE targets are measured as the share of total generation

represented by non-hydro renewables, 

## renewables as explained further below.

The following steps were taken to establish the inputs for development of the best practice scenario

for each state:

4.2.1 Determine current level of performance

4.2.2 Determine best practice level of performance

4.2.3 Determine start year for state efforts

4.2.4 Determine pace at which states improve fr om start year to best practice level of

performance

4.2.5 Calculate RE targets for interim and final state goals

4.2.1 Determine current level of performance

The type and extent of current RE capacity varies significantly across states, and is influenced by

the renewable resources available, the economics of the power sector to date in different regions, and the

obligations, and excess compliance. This estimate does not represent official compliance statistics, which vary in methodology by state.

5 

### Barbose, Galen, “Renewables Portfolio Standards in the United States: A Status Update,” Lawrence Berkeley National Lab, November 2013. Slide 8. Comment [A3]: Why focus on non-hydro in this recent period? If the cited study excluded hydro, is there a different one that did not?

Comment [A4]: Why exclude hydro? In almost all RPS, hydro is an eligible resource. Policy should be to encourage new-hydro, especially where it is counted in state RPS

calculations. If the BSER is built on state RPS (that consider hydro an eligible generation source) – hydro should be an eligible generation source.

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4 state policies that affect renewable sources specifica lly and energy production generally. The extent of

that generation has also changed rapidly in the past few years, and states with RE policies have

significantly increased their renewable capacity. To characterize the current level of RE use, we have

used the most current state-level data on generation: 2012 net generation data by state. 6



### Definitions of qualifying RE vary across state RPS requirements

. For the sake of comparing RPS

requirements in order to derive RE targets for each region, the EPA adopted a broad interpretation of RE

generation to include any non-fossil renewable fuel type, with the exception of generation from existing

hydroelectric power facilities

. . Large existing hydroelectric facilities provide a large percentage of RE

generation in a few states , (hydropower is America’s largest source of RE), and inclusion of this

generation in current and projected levels of perfor mance would distort the best practices scenario by

presuming future development potential of large hydroelectric capacity in other states. These types of

facilities have not been the focus of most RPS policiesBecause RPS policies were implemented to

stimulate the development of new RE generation, exis ting hydroelectric facilities are often excluded from

RPS accounting., and many RPS policies specifically exclude their use in compliance . No states are

expected to significantly increase generation from existing large hydropower facilities nor develop any

new large facilities. 7 

### The analysis of potential informing regional RE targets does not explicitly account

for the potential of building new smaller hydroelectric facilities as a so urce under RPS policies; however,

generation from such facilities would not be excluded from compliance with a state’s goal under this rule

. Can we have a more affirmative statement to recognize that such development can be included and

is encouraged?

.

The most recent 2012 performance data is shown in Table 4-1. Consistent with the design of a

number RPS policies, RE “performance” is measured here 

## as the share of total generation represented

by non-hydro RE

..

Table 4-1. 2012 RE Performance by State

State Non-Hydro RE

Generation Total

Generation Performance

Level

Alabama 2,776,554 152,878,688 2%

Alaska 39,958 6,946,419 1%

6 

### U.S. EIA state level data available at http://www.eia.gov/electricity/data/state/.

7 

### U.S. EIA, Annual Energy Outlook 2014, p. 121, available at http://www.eia.gov/forecasts/archive/aeo13/index.cfm.. http://www.eia.gov/forecasts/archive/aeo13/index.cfm

. Check this – do we agree. I thought we do have examples of significant new generation via efficiency improvements or expansions at existing facilities. Comment [A5]: True, but almost all RPSs identify hydropower is an eligible resource.

Hydropower rules related to RPS differ state by state, but generally restrict hydropower by capacity/size, vintage, or technology. The predominant limiting factor to hydropower RPS inclusion is age.

Comment [A7]: Suggest including this clarification in the preamble. What about EPA’s interpretation of RE that can be included as part of a state’s compliance? Comment [A6]: This would seem to indicate

that biomass would count in states where it’s

an eligible RE source. Is this right?

Comment [A8]: Because – the intent of

RPSs is to stimulate new renewable development – that is why old, existing development is excluded.

Comment [A9]: Reclamation does not agree with this statement. What does “significantly increase” mean? How much generation is significant? The agency is currently installing optimization systems at existing, large facilities. Optimization systems will allow facilities to generate an additional 1-3% annually. Once all facilities are optimized, we expect to see an additional 410,000 MWh – 1,230,000 MWh annually. This is a significant

increase. Also – an additional generating unit is scheduled to be constructed at Black Canyon (an existing hydropower facility) to capture

additional flows. I am not certain what the expected increase in annual generation is – but it could be “significant.”

Comment [A10]: What is meant by this? Is future hydro implicitly included? It should be counted and encouraged.

Comment [A11]: Suggest including this clarification in the preamble.

Comment [A12]: Along with ineligible, “old” hydro – some RPS eligible hydro (e.g. new, small, development) would certainly have been thrown out in this data set. Why throw out eligible hydro?

Comment [A13]: Same comment -- Aren’t we excluding some eligible hydro here?

Comment [A14]: Might be helpful to include

units: MWh.

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5 Arizona 1,697,652 110,904,994 2%

Arkansas 1,660,370 65,005,678 3%

California 29,966,846 199,518,567 15%

Colorado 6,192,082 52,556,701 12%

Connecticut 666,525 36,117,544 2%

Delaware 131,051 8,633,694 2%

District of Columbia 8 

### - 71,787 0%

Florida 4,523,798 221,096,136 2%

Georgia 3,278,536 122,306,364 3%

Hawaii 924,815 10,469,269 9%

Idaho 2,514,502 15,499,089 16%

Illinois 8,372,660 197,565,363 4%

Indiana 3,546,367 114,695,729 3%

Iowa 14,183,424 56,675,404 25%

Kansas 5,252,653 44,424,691 12%

Kentucky 332,879 89,949,689 0%

Louisiana 2,430,042 103,407,706 2%

Maine 4,098,795 14,428,596 28%

Maryland 898,152 37,809,744 2%

Massachusetts 1,843,419 36,198,121 5%

Michigan 3,785,439 108,166,078 3%

Minnesota 9,453,871 52,193,624 18%

Mississippi 1,509,190 54,584,295 3%

Missouri 1,298,579 91,804,321 1%

Montana 1,261,752 27,804,784 5%

Nebraska 1,346,762 34,217,293 4%

Nevada 2,968,630 35,173,263 8%

New Hampshire 1,381,285 19,264,435 7%

New Jersey 1,280,715 65,263,408 2%

New Mexico 2,573,851 36,635,909 7%

New York 5,192,427 135,768,251 4%

North Carolina 2,703,919 116,681,763 2%

North Dakota 5,280,052 36,125,159 15%

Ohio 1,738,622 129,745,731 1%

Oklahoma 8,520,724 77,896,588 11%

Oregon 7,207,229 60,932,715 12%

Pennsylvania 4,459,118 223,419,715 2%

8 

### The District of Columbia has no utility-scale RE generati on, but the District does have distributed RE resources contributing to the electrical grid. It can also be noted th at the non-RE generation reported closed in 2012, so the District of Columbia no longer has any utility-scale generation.

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6 Rhode Island 101,895 8,309,036 1%

South Carolina 2,143,473 96,755,682 2%

South Dakota 2,914,666 12,034,206 24%

Tennessee 836,458 77,724,264 1%

Texas 34,016,697 429,812,510 8%

Utah 1,099,724 39,402,961 3%

Vermont 465,169 6,569,670 7%

Virginia 2,358,444 70,739,235 3%

Washington 8,214,350 116,835,474 7%

West Virginia 1,296,563 73,413,405 2%

Wisconsin 3,223,178 63,742,910 5%

Wyoming 4,369,107 49,588,606 9%

4.2.2 Determine best practice level of performance

Achievable RE potential exists at significant and comparable levels in all regions of the country.

While varied regional characteristics (e.g., the extent of renewable resources available, cost of competing

sources of power, and level of past RE development) affect estimates of achievable potential, ongoing

improvements in technologies and practices, and conti nually improving strategies for RE development are

increasing the extent of economically utilized renewabl e resources across all regions of the United States.

RE has been capturing a growing percentage of new capacity additions in the past few years. In 2012, RE

accounted for more than 56% of all new electrical capacity installations in the U.S. – a major increase

from 2004 when renewable installations captured only 2% of new capacity additions. 9 

### The economics of

the fastest growing RE technologies – on-shore wind and solar photovoltaics (PV) – are improving . and

are competitive in many regions. In 2012, cumulative installed wind capacity increased by nearly 28%

and cumulative installed solar PV capacity grew more than 83% from the previous year. 10 

### In the United

States, installed wind electricity capacity increased more than 23 fold between 2000 and 2012. 11 

### Solar

electricity generating capacity grew by a factor of over 21 between 2000 and 2012 and currently accounts

for 0.3% of annual U.S. electricity generation. 12 

### The National Renewable Energy Laboratory (NREL) has

also found that the continental U.S. has solar pote ntial that exceeds high solar generating countries like

Germany, which now has the capacity to generate over 6% of their electricity from solar. 13 

### Looking

9 

### U.S. Department of Energy. 2012 Renewable Energy Data Book. DOE/GO-102013-4291.October 2013. p. 3.

10 

### U.S. Department of Energy. 2012 Renewable Energy Data Book. DOE/GO-102013-4291.October 2013. p. 18.

11 

### Ibid. p. 53.

12 

### Ibid. p. 63.

13 

### NREL. Photovoltaic Solar Resource: The United States, Spain and Germany. 2009. Available at:
http://www.nrel.gov/gis/images/us_germany_spain/pvmap _usgermanyspain%20poster-01.jpg. Also IEA. PVPS Comment [A15]: A significant amount of

solar was installed in 2013, namely 4.8 GW, bringing the total amount of installed solar to

12.1 GW (SEIA Solar Market Insight Report

2013 Year in Review). The annual generation

of the current solar fleet will be significantly
larger than 0.3%.

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7 forward, the U.S. Department of Energy has found that 46 states would have substantial wind

development by 2030 under a scenario in which 20% of national generation is provided by wind. The

distribution of that deployment is shown in Figure 4.1. 14



### The best practices scenario is also based upon an analysis of renewable portfolio standards, a

policy that facilitates the quantification of RE targ ets. By only examining the impact of one type of

policy, the analysis is inherently conservative, as many other policy options are also available to states in

addition to RPS.

Figure 4.1. 

## DOE Projected Installed Wind Capacity by State under 20% National Generation

Scenario

4.2.2.1. Quantification of Effective RE Levels from State-Level RPS Requirements

In order to apply the various RPS policies to the development of a best practice level of

performance, the EPA used publicly-available quant itative information about mandatory state RPS

Snapshot of Global PV 1992-2013. Report IEA- PVPS T1-24:2014, March 31, 2014. http://www.ieapvps.org/fileadmin/dam/public/report/statistics/PV PS_report_-_A_Snapshot_of_Global_PV_-_1992-2013_-
_final_3.pdf.

14 

### U.S. Department of Energy. 20% Wind Energy by 2030: Increasing Wind Energy’s Contribution to U.S. Electricity Supply – Executive Summary. December 2008. http://www.nrel.gov/doc s/fy09osti/42864.pdf. Comment [A16]: The TSD could do with

more information on actual state resources for wind, solar and geothermal, to support the current regional averaging procedure for eliciting future RE rates.

Also, the text at the bottom of this figure is

unreadable.

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8 requirements from the Database for State Incentives for Renewables and Efficiency (DSIRE). 15 

### This

information enabled the EPA to determine the effectiv e RE levels in 2020 for states with mandatory RPS

requirements. 16



### DSIRE provides regularly-updated RPS Data Spreadsheets that detail state RPS requirements by

year, resources, and other key component parts. 17 

### The RPS compliance schedules are broken down into

the specific annual percentages requirements for the years 2000 to 2030. Many states have multiple

compliance requirements, including the main per centage requirements for eligible resources and

additional resource-specific percentage requirements that states are increasingly using to promote the

development of a particular set of resources or technologies (e.g., solar PV). DSIRE called each of these

sets of resource requirements “tiers” and applied a standardized approach to them, “in order to compare

RPS policies on equal footing.” 18 

### The benefit of this approach is that state resource requirements become

additive and facilitate a process of selection and exclusion. The EPA added together each state’s tiers, as

standardized by DSIRE, to determine states’ effective RE levels for 2020, but excluded tiers, other than

main tiers, that include energy efficiency or any fossil fuel.

In addition, six states have established more than one set of RPS requirements for in-state

utilities, including “secondary” and “tertiary” RPS requirements for smaller utilities, municipal utilities,

or cooperative utilities. The EPA only included the prim ary RPS requirements to simplify the analysis of

primary and secondary RPS requirements in determin ing states’ effective RE levels for 2020. By only

considering primary requirements, there is additional inherent conservatism in the RPS estimates, as

additional state-level RPS obligations are not included in the calculated targets.

15 

### Database of State Incentives for Renewables & Efficiency (DSIRE) is a very comprehensive source of information on incentives and policies that support renewables and energy efficiency in th e United States. DSIRE is currently operated by the N.C. Solar Center at N.C. Stat e University, with support from the Interstate Renewable Energy Council, Inc. DSIRE is funded by the U.S. Department of Energy. http://www.dsireusa.org/.

16 

### EPA did not include targets that were capacity-based.

17 

### DSIRE. RPS Data Spreadsheet. April 2013 versi on. http://www.dsireusa.org/rpsdata/index.cfm.

18 

### DSIRE. DSIRE RPS Field Definitions. April 2011. http://www.dsireusa.org/rpsdata/RPSFieldDefinitionsApril2011.pdf. p. 1.

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9 Figure 4.2 RPS Data Structure 19



### The RPS compliance schedules in six states im plement maximum requirements prior to 2020;

their effective RE levels for 2020 are set to those maximum levels. In fact, most states maintain their

percentage requirements indefinitely.

TABLE 4.2 . Effective RE Levels Derived from RPS Requirements

RPS

States Primary Target Target

Year 2020

Effective

RE

Levels Exclusions

AZ 15% 2025 10%

CA 33% 2020 33%

CO 30% 2020 30% Secondary RPS requirement

CT 23% 2020 23% Class 3 includes non-RE

DC 20% 2023 20%

DE 25% 2027 19%

HI 40% 2030 25%

IL 25% 2025 16% Secondary RPS requirement

KS 20% 2020 20%

19 DSIRE. RPS Data Spreadsheet. April 2013 version. Comment [A17]: Question for clarification – does this column include existing hydro in the number, thereby reporting a total state RE rate of both hydro and non-hydro, or is it limited to the states’ non-hydro rate?

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10 MA 33% 2030 22%

MD 20% 2022 18%

ME 40% 2017 40%

MI 10% 2015 10%

MN 30% 2020 30% Secondary RPS requirement

MO 15% 2021 10%

MT 15% 2015 15%

NC 13% 2021 10% Secondary RPS requirement

NH 25% 2025 20%

NJ 24% 2021 22%

NM 20% 2020 20% Secondary RPS requirement

NV 25% 2025 22%

NY 29% 2015 29%

OH 13% 2024 9%

OR 25% 2025 20% Secondary & tertiary RPS requirements

PA 8% 2021 8% Class 2 includes non-RE

RI 16% 2019 16%

WA 15% 2020 15%

WI 10% 2015 10%

4.2.2.2. Development of Regional RE Generation Targets from State-level Effective RE Levels

To take into account the varied availability of different renewable resources across regions of the

United States, the EPA uses the state-level effective RE levels derived from RPS requirements to quantify

regional RE targets consistent with states’ best practice in RE development. This methodology helps us to

quantify RE potential in states which do not have an RPS policy from which the renewable resource

potential can be inferred. Specifically, the scenario estimates each region’s RE potential by assuming all

states in each region can achieve by 2030 the average of the 2020 requirements of RPS states in that

region.

The regions assigned to states to quantify th eir RE generation target are based upon North

American Electric Reliability Corporation (NERC) regions and Regional Transmission Organizations

(RTOs) and are the same as the regions used in the modeling of the “regional” compliance scenarios as

outlined in the proposal RIA (see Figure 4-3). 20 

### States within each region exhibit similar profiles of RE

potential or have similar levels of renewable resource s. The regional similarities can be inferred from the

20 

### For more information on the structure of these regions, pl ease refer to the Regulatory Impact Analysis Chapter 3.

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11 state-level technical potential reported in an NREL GIS-based analysis. 21 

### The results show clear trends for

the regions used to create the best practices scenario , with portfolios of particular technologies showing

clear dominance in specific regions. North Central and South Central regions have strong on-shore wind

resource potential. The East Central and Southeast regions show moderate to strong resources in both

biopower and rooftop PV potential. The West has uni que potential in geothermal (hydrothermal) power

and concentrating solar power. The Northeast has strong resources in off-shore wind and moderate

biopower and solar resources available. It should be noted that high technical potential in a particular

renewable resource is not necessarily needed to reach the generation levels called for in this proposal. For

example, Maine produced 28% of its electricity ge neration in 2012 from biopower and onshore wind,

while it is estimated in this report to have moderate technical potential for biopower and low levels of

onshore wind capacity.

Figure 4.3. Regions for Development of Best Practices RPS Scenario

21 

### NREL. U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. NREL/TP-A20-51946. July 2012.
http://www.nrel.gov/docs/fy12osti/51946.pdf. Comment [A18]: Can we note hydro potential in pacific northwest?

Formatted: Font: 11 pt

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12 Table 4.3. List of States Included in Regions for Development of RE Best Practices Scenario 22



### Region States

East Central Delaware, District of Columbia, Maryland, New Jersey, Ohio, Pennsylvania,

Virginia, West Virginia

North Central Illinois, Indiana, Iowa, Michigan, Minnesota , Missouri, North Dakota, South

Dakota, Wisconsin

Northeast Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode

Island, Vermont

South Central Arkansas, Kansas, Loui siana, Nebraska, Oklahoma, Texas

Southeast Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South

Carolina, Tennessee

22 

### Alaska and Hawaii are not grouped with other states into regions. Formatted: Font: 11 pt, Complex Script
Font: 11 pt

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13 West Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico,

Oregon, Utah, Washington, Wyoming

The EPA compiled the state-level effective RE levels derived above, organized them by region,

and determined an average percent generation target for that region. This average did not include any

values for states in which there is no binding RP S requirement. This average regional RE generation

target offers a basis for the determination of state-le vel RE targets for informing state goals, as described

below. Given their unique locations, Alaska and Hawaii are not grouped with other states into these

regions. As a conservative approach to estimating cost-effective RE generation potential in Alaska and

Hawaii, the EPA developed RE generation targets for each of those states based on the lowest values for

the six regions evaluated here, equivalent to the regional target for the Southeast region. Comment [A19]: Is there a reason why EPA did not weigh the RPS targets by current generation before averaging across the region? As currently defined, RPS targets for smaller states with lower total generation receive the same weight as RPS targets for large states. Can EPA explain its rationale?

Comment [A20]: May be better to verify with some independent estimate since these regions are geographically quite different.

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14 Table 4.4. Regional RE Generation Targets

Region Regional RE Generation Targets

Alaska 10%

East Central 16%

Hawaii 10%

Northeast 25%



## North Central 15%



## South Central 20%

South 

## east 10%

West 21%

4.2.3 Determine start year for state efforts

The best practice scenario assumes that RE gene ration will begin increasing in 2017, the year

following the initial state plan submission deadline 23 

### , and continues through 2029, by which time the EPA

assumes the regions can achieve the identified regional RE target level of performance. The EPA has set

each state’s level of performance prior to the start year of the scenario (2017) to be equal to its current

level of performance (as shown above using 2012 ge neration data). This approach assumes neither

improvement nor decline in performance between 2012 and 2017.

4.2.4 Determine pace at which states improve from start year to best practice level of performance

In order to account for the time needed to plan and construct the required additional amounts of

renewable capacity, the best practices scenario assumes an increasing trend over time of annual levels of

RE generation that can carry the start year performance level of each region (in 2017, assumed to be

equivalent to its 2012 observed performance level) to that region’s RE generation target by 2029. This

2017-2029 trend yields an annual growth factor that is unique to each region and based upon each

region’s current renewable generation level and its RE target level identified above.

23 

### See Preamble Section 8.E – Process for State Plan Submittal and Review for further discussion of timing requirements for state plan submittals. Comment [A21]: 2013 has been a strong

year for solar, and we expect continued

acceleration in deployment, especially with

the ITC continuing to 2016 and rapidly
declining technical costs. We also expect 2014

to be a strong year for wind, with many projects starting construction prior to expiration of the PTC. Keeping renewable
growth flat from 2012 to 2017 will grossly

underestimate the renewable deployment in

the 2017 start year. For states with existing

RPSs, DOE recommends assuming they

continue to meet their RPS targets through

2017.

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15 To derive the annual growth factor, the EPA determined the amount of additional renewable

generation (in megawatt-hours) that would be required beyond each region’s historic (2012) generation to

reach that region’s RE target. The EPA then determined the constant rate at which each region would

need to increase its generation each year to reach the regional RPS target, if these rates are applied in the

period 2017-2029. The constant rate of annual RE gene ration increase calculated from this approach is

called the growth factor in this context. Due to their unique location, the EPA used a different method to

calculate growth factors for Alaska and Hawaii, cal culating an annual growth factor based on the

difference between both states’ historical 2005 and 2 012 RE generation. Growth factors for each region

are provided in Table 4.5.

Table 4.5. Regional Annua l RE Growth Factors

Region Growth Factor



## Alaska 8%

East Central 17%

Hawaii 8%

North Central 6%

Northeast 10%

South Central 8%

Southeast 13%

West 6%

Then, for all states in a given region, that regi on’s annual growth factor was applied to each

state’s historic (2012) RE generation level to calculate a new level of RE generation for that state in the

initial year (2017). This cal culation is then repeated for each year in the 2017-2029 time period. If, as the

growth factor is applied annually, a state reaches an RE generation level that equals or exceeds the

regional RE percent generation target, their RE generation target is made equal to the RE percent

generation target and is no longer grown over the rema inder of the time period. If a state’s RE generation

in 2012 has already exceeded the regional RE target, their annual RE generation levels are held to the

regional RE target for all years in the 2017-2029 time pe riod. For all other states, the annual growth factor

is applied through 2029. These annual RE generation estimates represent the realization of the best

practice scenario for each state. These RE generation levels are provided in absolute and percentage

(share of total generation) terms in Table 4.6 and Table 4.7.

This approach imposes the same regional RE target in percentage (share of total generation) terms

to all states in a given region; therefore, the absolute megawatt-hour target will be smaller for states Comment [A22]: Is this explained in greater detail anywhere else?

Comment [A23]: Please check that the

value for the Northeast is correct.

Comment [A24]: States exceeding the
regional RPS average in 2012 have cost

‐ effective renewable resources and would
easily meet CO2 guidelines. Thus, EPA is reducing the stringency of the proposed rule

due to lack of historical policy action in laggard states rather than providing a level of
stringency consistent across states

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16 starting with a lower absolute amount of RE genera tion and larger for a state starting with a higher

absolute amount of RE generation.

This approach applies the calculated growth factors and regional RE targets to state-level

generation, whereas the state-level RPS requirements upon which they are based are not necessarily

applied in practice to generation that is produced with in the relevant state. However, the EPA notes that

state-level RPS policies are often established with th e aim of developing in-state renewables generation. 24



### This intention is evident in RPS policies that include minimum requirements for specific types of

renewable resources whose development is desired in that state. Regional analysis by NREL has also

shown that many states in the west are satisf ying RPS requirements with in-state generation. 25



### Furthermore, the regional RE target is not applied directly as an immediate requirement of each state but

is instead used to calculate a regional growth factor that is then applied to each state’s pre-existing RE

generation, such that historic RE performance acts as a limiting factor on the extent to which a state is

assumed to reach the regional target. Over the program period, several states do not reach the RE

percentage target in the best practices scenario, such as Kentucky in the Southeast and Nevada in the

West. Thus, this approach is designed to respect each state’s ability to improve toward the RE targets

developed above.

Table 4.6. State Best Practice RE Generation Levels (Thousand Megawatt-hours)

State Historic

RE Best Practice RE Generation Levels (thousand MWh)

2012 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029

AK 40 43 47 50 54 58 63 68 73 79 85 92 100 107

AL 2,777 3,150 3,573 4,053 4,597 5,214 5,915 6,709 7,611 8,633 9,793 11,108 12,600 14,293

AR 1,660 1,799 1,949 2,112 2,288 2,479 2,686 2,911 3,154 3,417 3,702 4,011 4,346 4,709

AZ 1,698 1,807 1,924 2,048 2,181 2,322 2,472 2,632 2,802 2,983 3,175 3,381 3,599 3,832

CA 29,967 31,903 33,965 36,160 38,497 40,984 41,151 41,151 41,151 41,151 41,151 41,151 41,151 41,151

CO 6,192 6,592 7,018 7,472 7,955 8,469 9,016 9,598 10,219 10,840 10,840 10,840 10,840 10,840

CT 667 750 845 951 1,071 1,206 1,358 1,529 1,721 1,938 2,182 2,457 2,766 3,114

DE 131 154 180 211 248 291 341 399 468 549 644 755 886 1,038

FL

24 

### Wiser, Ryan H., and Galen L. Barbose. 2008. Renewable Portfolio Standards in the United States: A Status Report with Data Through 2007 . LBNL-154E. Berkeley, CA: Lawrence Berkeley National Laboratory, p. 7.

25 

### Hurlbut, David, Joyce McLaren, and Rachel Gelman. Be yond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West. NREL/TP-6A20- 57830. Golden, CO: NREL, August 2013.

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17 4,524 5,131 5,821 6,603 7,490 8,496 9,637 10, 931 12,400 14,066 15,955 18,098 20,529 22,110

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18 GA 3,279 3,719 4,219 4,785 5,428 6,157 6,984 7,922 8,987 10,194 11,563 12,231 12,231 12,231

HI 925 998 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047

IA 14,183 8,566 8,566 8,566 8,566 8,566 8,566 8,566 8,566 8,566 8,566 8,566 8,566 8,566

ID 2,515 2,677 2,850 3,034 3,197 3,197 3,197 3,197 3,197 3,197 3,197 3,197 3,197 3,197

IL 8,373 8,873 9,404 9,967 10,563 11,195 11,864 12,574 13,326 14,123 14,968 15,863 16,812 17,818

IN 3,546 3,758 3,983 4,222 4,474 4,742 5,025 5,326 5,645 5,982 6,340 6,719 7,121 7,547

KS 5,253 5,691 6,166 6,681 7,239 7,843 8,498 8,885 8,885 8,885 8,885 8,885 8,885 8,885

KY 333 378 428 486 551 625 709 804 912 1,035 1,174 1,332 1,511 1,714

LA 2,430 2,633 2,853 3,091 3,349 3,629 3,931 4,260 4,615 5,001 5,418 5,870 6,361 6,892

MA 1,843 2,076 2,337 2,631 2,962 3,335 3,755 4,228 4,761 5,360 6,035 6,795 7,650 8,613

MD 898 1,053 1,235 1,448 1,698 1,991 2,335 2,738 3,210 3,764 4,414 5,176 5,982 5,982

ME 4,099 3,612 3,612 3,612 3,612 3,612 3,612 3,612 3,612 3,612 3,612 3,612 3,612 3,612

MI 3,785 4,012 4,252 4,506 4,776 5,061 5,364 5,685 6,025 6,385 6,767 7,172 7,601 8,056

MN 9,454 7,889 7,889 7,889 7,889 7,889 7,889 7,889 7,889 7,889 7,889 7,889 7,889 7,889

MO 1,299 1,376 1,459 1,546 1,638 1,736 1,840 1,950 2,067 2,190 2,322 2,460 2,608 2,764

MS 1,509 1,712 1,942 2,203 2,499 2,834 3,215 3,647 4,137 4,692 5,323 5,458 5,458 5,458

MT 1,262 1,343 1,430 1,523 1,621 1,726 1,837 1,956 2,082 2,217 2,360 2,513 2,675 2,848

NC 2,704 3,067 3,479 3,946 4,477 5,078 5,760 6,534 7,412 8,407 9,536 10,817 11,668 11,668

ND 5,280 5,460 5,460 5,460 5,460 5,460 5,460 5,460 5,460 5,460 5,460 5,460 5,460 5,460

NE 1,347 1,459 1,581 1,713 1,856 2,011 2,179 2,361 2,558 2,771 3,003 3,254 3,525 3,819

NH 1,381 1,555 1,751 1,971 2,220 2,499 2,814 3,168 3,567 4,016 4,522 4,822 4,822 4,822

NJ 1,281 1,502 1,761 2,065 2,421 2,839 3,329 3,904 4,577 5,367 6,294 7,380 8,654 10,147

NM 2,574 2,740 2,917 3,106 3,306 3,520 3,748 3,990 4,248 4,522 4,814 5,125 5,457 5,809

NV 2,969 3,160 3,365 3,582 3,814 4,060 4,322 4,602 4,899 5,216 5,553 5,912 6,294 6,700

NY 5,192 5,846 6,582 7,411 8,344 9,395 10,578 11,910 13,409 15,098 16,999 19,139 21,549 24,262

OH 1,739 2,039 2,391 2,803 3,287 3,854 4,519 5,299 6,214 7,287 8,544 10,019 11,748 13,776

OK 8,521 9,232 10,003 10,838 11,743 12,723 13,785 14,936 15,579 15,579 15,579 15,579 15,579 15,579

OR 7,207 7,673 8,169 8,697 9,259 9,857 10,494 11,172 11,894 12,567 12,567 12,567 12,567 12,567

PA 4,459 5,229 6,131 7,189 8,430 9,885 11,591 13,592 15,938 18,688 21,914 25,696 30,131 35,331

RI 102 115 129 145 164 184 208 234 263 296 334 376 423 476

SC 2,143 2,431 2,758 3,128 3,549 4,025 4,566 5,180 5,875 6,665 7,560 8,575 9,676 9,676

SD 2,915 1,819 1,819 1,819 1,819 1,819 1,819 1,819 1,819 1,819 1,819 1,819 1,819 1,819

TN 836 949 1,076 1,221 1,385 1,571 1,782 2,021 2,293 2,601 2,950 3,346 3,796 4,306

TX 34,017 36,857 39,934 43,268 46,880 50,794 55,034 59,629 64,607 70,001 75,845 82,177 85,963 85,963

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19 UT 1,100 1,171 1,246 1,327 1,413 1,504 1,601 1,705 1,815 1,932 2,057 2,190 2,331 2,482

VA 2,358 2,765 3,243 3,802 4,459 5,228 6,131 7,189 8,429 9,884 11,192 11,192 11,192 11,192

VT 465 524 590 664 748 842 948 1,067 1,201 1,353 1,523 1,645 1,645 1,645

WA 8,214 8,745 9,310 9,912 10,552 11,234 11,960 12,733 13,556 14,432 15,365 16,358 17,415 18,540

WI 3,223 3,416 3,620 3,837 4,066 4,310 4,567 4,841 5,130 5,437 5,762 6,107 6,472 6,859

WV 1,297 1,520 1,783 2,090 2,451 2,874 3,370 3,952 4,634 5,434 6,372 7,471 8,761 10,273

WY 4,369 4,651 4,952 5,272 5,613 5,975 6,362 6,773 7,210 7,676 8,172 8,700 9,263 9,861



### Table 4.7. State Best Practice RE Genera tion Levels (% of Total Generation)

State Historic

RE Best Practice RE Generation Levels

2012 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029

AK 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 0.9% 1.0% 1.1% 1.1% 1.2% 1.3% 1.4% 1.5%

AL 2% 2.1% 2.3% 2.7% 3.0% 3.4% 3.9% 4.4% 5.0% 5.6% 6.4% 7.3% 8.2% 9.3%

AR 3% 2.8% 3.0% 3.2% 3.5% 3.8% 4.1% 4.5% 4.9% 5.3% 5.7% 6.2% 6.7% 7.2%

AZ 2% 1.6% 1.7% 1.8% 2.0% 2.1% 2.2% 2.4% 2.5% 2.7% 2.9% 3.0% 3.2% 3.5%

CA 15% 16.0% 17.0% 18.1% 19.3% 20.5% 20.6% 20. 6% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6%

CO 12% 12.5% 13.4% 14.2% 15.1% 16.1% 17.2% 18. 3% 19.4% 20.6% 20.6% 20.6% 20.6% 20.6%

CT 2% 2.1% 2.3% 2.6% 3.0% 3.3% 3.8% 4.2% 4.8% 5.4% 6.0% 6.8% 7.7% 8.6%

DE 2% 1.8% 2.1% 2.4% 2.9% 3.4% 3.9% 4. 6% 5.4% 6.4% 7.5% 8.7% 10.3% 12.0%

FL 2% 2.3% 2.6% 3.0% 3.4% 3.8% 4.4% 4. 9% 5.6% 6.4% 7.2% 8.2% 9.3% 10.0%

GA 3% 3.0% 3.4% 3.9% 4.4% 5.0% 5.7% 6. 5% 7.3% 8.3% 9.5% 10.0% 10.0% 10.0%

HI 9% 9.5% 10.0% 10.0% 10.0% 10.0% 10.0% 10. 0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0%

IA 25% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15. 1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1%

ID 16% 17.3% 18.4% 19.6% 20.6% 20.6% 20.6% 20. 6% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6%

IL 4% 4.5% 4.8% 5.0% 5.3% 5.7% 6.0% 6.4% 6.7% 7.1% 7.6% 8.0% 8.5% 9.0%

IN 3% 3.3% 3.5% 3.7% 3.9% 4.1% 4.4% 4.6% 4.9% 5.2% 5.5% 5.9% 6.2% 6.6%

KS 12% 12.8% 13.9% 15.0% 16.3% 17.7% 19.1% 20. 0% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0%

KY 0% 0.4% 0.5% 0.5% 0.6% 0.7% 0.8% 0.9% 1.0% 1.2% 1.3% 1.5% 1.7% 1.9%

LA 2% 2.5% 2.8% 3.0% 3.2% 3.5% 3.8% 4.1% 4.5% 4.8% 5.2% 5.7% 6.2% 6.7%

MA 5% 5.7% 6.5% 7.3% 8.2% 9.2% 10.4% 11. 7% 13.2% 14.8% 16.7% 18.8% 21.1% 23.8%

MD 2% 2.8% 3.3% 3.8% 4.5% 5.3% 6.2% 7. 2% 8.5% 10.0% 11.7% 13.7% 15.8% 15.8%

ME 28% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0 % 25.0% 25.0% 25.0% 25.0% 25.0%

MI 3% 3.7% 3.9% 4.2% 4.4% 4.7% 5.0% 5.3% 5.6% 5.9% 6.3% 6.6% 7.0% 7.4%

MN 18% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15. 1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1%

MO 1% 1.5% 1.6% 1.7% 1.8% 1.9% 2.0% 2.1% 2.3% 2.4% 2.5% 2.7% 2.8% 3.0%

MS 3% 3.1% 3.6% 4.0% 4.6% 5.2% 5.9% 6. 7% 7.6% 8.6% 9.8% 10.0% 10.0% 10.0%

MT 5% 4.8% 5.1% 5.5% 5.8% 6.2% 6.6% 7. 0% 7.5% 8.0% 8.5% 9.0% 9.6% 10.2%

NC 2% 2.6% 3.0% 3.4% 3.8% 4.4% 4.9% 5. 6% 6.4% 7.2% 8.2% 9.3% 10.0% 10.0% Comment [A25]: Such a low commitment, a combination of low starting point and low annual rate of improvement.

Comment [A26]: EPA should reconsider the policy of reducing a small number of state mandates below what they are already doing or have committed to doing. This would not be hard to do, just average the regions then for the small number of states with higher levels, accept the higher. EPA’s previous response to this question was that “states may obtain renewables from externally, hence the need for restraint to allow flexibility” does not obviate this opportunity to maintain state

commitments.

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20 ND 15% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15. 1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1%

NE 4% 4.3% 4.6% 5.0% 5.4% 5.9% 6.4% 6. 9% 7.5% 8.1% 8.8% 9.5% 10.3% 11.2%

NH 7% 8.1% 9.1% 10.2% 11.5% 13.0% 14.6% 16. 4% 18.5% 20.8% 23.5% 25.0% 25.0% 25.0%

NJ 2% 2.3% 2.7% 3.2% 3.7% 4.4% 5.1% 6. 0% 7.0% 8.2% 9.6% 11.3% 13.3% 15.5%

NM 7% 7.5% 8.0% 8.5% 9.0% 9.6% 10.2% 10. 9% 11.6% 12.3% 13.1% 14.0% 14.9% 15.9%

NV 8% 9.0% 9.6% 10.2% 10.8% 11.5% 12.3% 13. 1% 13.9% 14.8% 15.8% 16.8% 17.9% 19.0%

NY 4% 4.3% 4.8% 5.5% 6.1% 6.9% 7.8% 8. 8% 9.9% 11.1% 12.5% 14.1% 15.9% 17.9%

OH 1% 1.6% 1.8% 2.2% 2.5% 3.0% 3.5% 4. 1% 4.8% 5.6% 6.6% 7.7% 9.1% 10.6%

OK 11% 11.9% 12.8% 13.9% 15.1% 16.3% 17.7% 19. 2% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0%

OR 12% 12.6% 13.4% 14.3% 15.2% 16.2% 17.2% 18. 3% 19.5% 20.6% 20.6% 20.6% 20.6% 20.6%

PA 2% 2.3% 2.7% 3.2% 3.8% 4.4% 5.2% 6. 1% 7.1% 8.4% 9.8% 11.5% 13.5% 15.8%

RI 1% 1.4% 1.6% 1.8% 2.0% 2.2% 2.5% 2.8% 3.2% 3.6% 4.0% 4.5% 5.1% 5.7%

SC 2% 2.5% 2.9% 3.2% 3.7% 4.2% 4.7% 5. 4% 6.1% 6.9% 7.8% 8.9% 10.0% 10.0%

SD 24% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15. 1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1%

TN 1% 1.2% 1.4% 1.6% 1.8% 2.0% 2.3% 2.6% 2.9% 3.3% 3.8% 4.3% 4.9% 5.5%

TX 8% 8.6% 9.3% 10.1% 10.9% 11.8% 12.8% 13. 9% 15.0% 16.3% 17.6% 19.1% 20.0% 20.0%

UT 3% 3.0% 3.2% 3.4% 3.6% 3.8% 4.1% 4.3% 4.6% 4.9% 5.2% 5.6% 5.9% 6.3%

VA 3% 3.9% 4.6% 5.4% 6.3% 7.4% 8.7% 10. 2% 11.9% 14.0% 15.8% 15.8% 15.8% 15.8%

VT 7% 8.0% 9.0% 10.1% 11.4% 12.8% 14.4% 16. 2% 18.3% 20.6% 23.2% 25.0% 25.0% 25.0%

WA 7% 7.5% 8.0% 8.5% 9.0% 9.6% 10.2% 10. 9% 11.6% 12.4% 13.2% 14.0% 14.9% 15.9%

WI 5% 5.4% 5.7% 6.0% 6.4% 6.8% 7.2% 7. 6% 8.0% 8.5% 9.0% 9.6% 10.2% 10.8%

WV 2% 2.1% 2.4% 2.8% 3.3% 3.9% 4.6% 5. 4% 6.3% 7.4% 8.7% 10.2% 11.9% 14.0%

WY 9% 9.4% 10.0% 10.6% 11.3% 12.0% 12.8% 13. 7% 14.5% 15.5% 16.5% 17.5% 18.7% 19.9%



### 4.2.5. Calculate RE targets for interim and final state goals

The agency then translated the annual RE best practice performance levels for each state into

state-level interim and final RE targets for informing this rule’s quantification of state goals. Separate

interim and final RE targets were calculated for the proposed state goals and the alternate state goals in

this proposed rulemaking. For the proposed state goals, the interim RE target for each state was calculated

as the average of that state’s RE best practice performance level from 2020-2029, and the final target is

equivalent to that state’s RE best practice performance level in the year 2029. For the alternate state goals,

the interim RE target for each state was calculated as the average of that state’s RE best practice

performance level from 2020-2024, and the final target is equivalent to that state’s RE best practice

performance level in the year 2024. State-level RE ta rgets, expressed in absolute (megawatt-hour) terms

and as a percentage level of each state’s RE generati on as a share of its total generation, along with 2012

RE levels for each state, are provided in Table 4.8. Fo r an explanation of how these state-level RE targets

informed the calculations of state goals in this rule, please refer to the Goal Computation TSD. Comment [A27]: This explanation falls short of the necessary level of detail that is laid out in the Appendix, and the effect of the mathematical equation. There are two aspects

that need additional discussion:



### 1. The formula inconsistency in how EPA proposes to incorporate existing and new RE into the denominator, as compared to new

and at risk Nuclear.



### 2. The effect of using a state’s total 2012 power purchase to generate the outyear RE increments, and the effect this has on states with very high percentage renewable levels

in 2012.

Additonal discussion is need with those intimately familiar with the Appendix and Excel spreadsheet.

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21 Table 4.8. Proposed and Alternate State Goals for RE Generation as a Percentage of Total Generation,

with 2012 Historical RE Generation

State 2012 Proposed Goals Alternate Goals

Interim

Level Final Level Interim

Level Final Level

Alabama 2% 6% 9% 4% 5%

Alaska 1% 1% 2% 1% 1%



### Arizona 2% 3% 3% 2% 3%

Arkansas 3% 5% 7% 4% 5%

California 15% 20% 21% 20% 21%

Colorado 12% 19% 21% 17% 19%

Connecticut 2% 5% 9% 4% 5%

Delaware 2% 7% 12% 4% 5%

Florida 2% 6% 10% 4% 6%

Georgia 3% 8% 10% 6% 7%

Hawaii 9% 10% 10% 10% 10%



### Idaho 16% 21% 21% 21% 21%

Illinois 4% 7% 9% 6% 7%

Indiana 3% 5% 7% 4% 5%

Iowa 25% 15% 15% 15% 15%

Kansas 12% 19% 20% 19% 20%

Kentucky 0% 1% 2% 1% 1%

Louisiana 2% 5% 7% 4% 4%

Maine 28% 25% 25% 25% 25%

Maryland 2% 10% 16% 6% 8%

Massachusetts 5% 15% 24% 11% 13%

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22 State 2012 Proposed Goals Alternate Goals

Interim

Level Final Level Interim

Level Final Level

Michigan 3% 6% 7% 5% 6%

Minnesota 18% 15% 15% 15% 15%

Mississippi 3% 8% 10% 6% 8%

Missouri 1% 2% 3% 2% 2%

Montana 5% 8% 10% 7% 7%

Nebraska 4% 8% 11% 6% 7%

Nevada 8% 15% 19% 12% 14%

New Hampshire 7% 19% 25% 15% 19%

New Jersey 2% 8% 16% 5% 7%

New Mexico 7% 12% 16% 10% 12%

New York 4% 11% 18% 8% 10%

North Carolina 2% 7% 10% 5% 6%

North Dakota 15% 15% 15% 15% 15%

Ohio 1% 6% 11% 4% 5%

Oklahoma 11% 19% 20% 18% 20%

Oregon 12% 19% 21% 17% 20%

Pennsylvania 2% 9% 16% 5% 7%

Rhode Island 1% 4% 6% 3% 3%

South Carolina 2% 7% 10% 5% 6%

South Dakota 24% 15% 15% 15% 15%

Tennessee 1% 3% 6% 2% 3%

Texas 8% 16% 20% 13% 15%

Utah 3% 5% 6% 4% 5%

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23 State 2012 Proposed Goals Alternate Goals

Interim

Level Final Level Interim

Level Final Level

Virginia 3% 12% 16% 9% 12%

Washington 7% 12% 16% 10% 12%

West Virginia 2% 8% 14% 5% 6%

Wisconsin 5% 8% 11% 7% 8%

Wyoming 9% 15% 20% 13% 15%

Table 4.9. Proposed and Alternate State Goals for RE Generation in Megawatt-hours, with 2012

Historical RE Generation

State 2012 Proposed Goals Alternate Goals

Interim

Level Final Level Interim

Level Final Level

Alabama 2,776,554 8,647,278 14,292,801 6,009,218 7,610,632

Alaska 39,958 78,090 107,370 63,422 73,413

Arizona 1,697,652 2,937,707 3,831,642 2,481,537 2,801,621

Arkansas 1,660,370 3,370,253 4,708,823 2,703,555 3,153,509

California 29,966,846 40,868,634 41,150,704 40,586,564 41,150,704

Colorado 6,192,082 9,945,536 10,839,820 9,051,252 10,218,740

Connecticut 666,525 1,934,220 3,114,375 1,376,991 1,721,274

Delaware 131,051 561,909 1,038,351 349,356 468,394

Florida 4,523,798 13,971,137 22,109,614 9,790,728 12,399,889

Georgia 3,278,536 9,392,695 12,230,636 7,095,644 8,986,583

Hawaii 924,815 1,046,927 1,046,927 1,046,927 1,046,927

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24 Idaho 2,514,502 3,196,687 3,196,687 3,196,687 3,196,687

Illinois 8,372,660 13,910,775 17,818,004 11,904,488 13,326,217

Indiana 3,546,367 5,892,120 7,547,086 5,042,327 5,644,522

Iowa 14,183,424 8,565,921 8,565,921 8,565,921 8,565,921

Kansas 5,252,653 8,577,482 8,884,938 8,270,026 8,884,938

Kentucky 332,879 1,036,717 1,713,556 720,442 912,434

Louisiana 2,430,042 4,932,549 6,891,619 3,956,800 4,615,333

Maine 4,098,795 3,611,728 3,611,728 3,611,728 3,611,728

Maryland 898,152 3,728,926 5,982,069 2,394,301 3,210,129

Massachusetts 1,843,419 5,349,504 8,613,477 3,808,366 4,760,555

Michigan 3,785,439 6,289,326 8,055,859 5,382,246 6,025,037

Minnesota 9,453,871 7,888,544 7,888,544 7,888,544 7,888,544

Mississippi 1,509,190 4,272,197 5,458,430 3,266,297 4,136,743

Missouri 1,298,579 2,157,527 2,763,528 1,846,357 2,066,863

Montana 1,261,752 2,183,403 2,847,804 1,844,362 2,082,259

Nebraska 1,346,762 2,733,684 3,819,427 2,192,912 2,557,879

Nevada 2,968,630 5,137,075 6,700,268 4,339,384 4,899,105

New Hampshire 1,381,285 3,727,303 4,822,223 2,853,632 3,567,113

New Jersey 1,280,715 5,491,354 10,147,466 3,414,138 4,577,463

New Mexico 2,573,851 4,453,928 5,809,243 3,762,317 4,247,605

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25 New York 5,192,427 15,068,148 24,261,905 10,727,168 13,409,233

North

Carolina 2,703,919 8,135,750 11,668,176 5,852,016 7,411,538

North Dakota 5,280,052 5,459,957 5,459,957 5,459,957 5,459,957

Ohio 1,738,622 7,454,735 13,775,594 4,634,830 6,214,090

Oklahoma 8,520,724 14,666,348 15,579,318 13,753,378 15,579,318

Oregon 7,207,229 11,551,256 12,567,372 10,535,140 11,894,029

Pennsylvania 4,459,118 19,119,477 35,330,855 11,887,147 15,937,543

Rhode Island 101,895 295,694 476,110 210,507 263,140

South

Carolina 2,143,473 6,534,613 9,675,568 4,639,057 5,875,334

South Dakota 2,914,666 1,818,850 1,818,850 1,818,850 1,818,850

Tennessee 836,458 2,605,058 4,305,814 1,810,322 2,292,760

Texas 34,016,697 67,689,311 85,962,502 55,388,864 64,607,260

Utah 1,099,724 1,903,020 2,482,103 1,607,517 1,814,865

Virginia 2,358,444 8,608,808 11,192,008 6,287,155 8,429,425

Washington 8,214,350 14,214,545 18,539,981 12,007,295 13,556,072

West Virginia 1,296,563 5,559,307 10,273,036 3,456,386 4,634,107

Wisconsin 3,223,178 5,355,156 6,859,301 4,582,807 5,130,122

Wyoming 4,369,107 7,560,533 9,861,177 6,386,526 7,210,300

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26 The annual rates used to set state RE goals under this best practices scenario are comparable to

rates that leader states have been able to approach in the past. Eleven states across four regions have

already achieved over 10% of total generation from RE , surpassing the lowest regional target applied in

the Southeast. Two states, Maine and Iowa, have already equaled or surpassed the highest regional target

of 25% of generation, with South Dakota close behind at 24%. Finally, five states have already reached

their region’s required target.

4.3 Cost Effectiveness of RE

The costs of building new RE capacity and generati ng more RE have changed significantly in the

past decade, particularly for wind and solar. Accordi ng to an EPA analysis based on EIA levelized costs,

costs of new RE, particularly on-shore wind, are becoming comparable to the cost-effectiveness of other

re-dispatch technologies such as new NGCC plants. 26



### The economics of the fastest growing RE technologies – on-shore wind and solar PV – are

improving. According to recent analyses of wind and solar project costs and pricing trends by U.S.

Department of Energy, levelized long-term power purch ase agreement (PPA) prices have been declining.

PPA prices, in general, reflect actual agreements to pay for power from wind or solar projects over a longterm and cover the cost of installing, operating, and maintaining a wind or solar project, along with a

profit margin. 27 

### For utility-scale solar PV, those levelized PPA prices have fallen by more than two-thirds

in the past five years “driven primarily by lower installed PV project prices (which, in turn, have been

driven primarily by declining module prices), as well as expectations for further cost reductions in future

years.” 28 

### More recent PPAs in the West are reporting le velized PPA prices in the range of $50-60/MWh

(in 2012 dollars). 29 

### For wind, PPA prices have fallen since 2009 despite a trend within the wind industry

to build projects at lower-quality wind resource sites. 30 

### “The average levelized long-term price from wind

26 

### EPA calculations of estimated dollar per tonne costs, based on EIA AEO 2013 levelized costs of electricity.

27 

### Bolinger, Mark, and Samantha Weaver. Utility-Scale So lar 2012: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United Stat es. Lawrence Berkeley National Laboratory. LBNL-6408E. September 2013. p. 19.

28 

### Bolinger, Mark, and Samantha Weaver. Utility-Scale So lar 2012: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United Stat es. Lawrence Berkeley National Laboratory. LBNL-6408E. September 2013. p. ii.

29 

### Ibid.

30 

### Wiser, Ryan H., and Mark Bolinger. 2012 Wind Technologies Market Report. Lawrence Berkeley National Laboratory. August 2013. p. viii. Comment [A28]:  the AEO2013 levelized cost report shows NGCC costs ranging from 60.0 – 76.1 with an average of 65.6. Wind ranges from 73.5 – 99.8 with an average of 86.6. In the commenter’s view, the average levelized costs are not properly characterized as being “comparable.” As discussed in a report posted on EIA’s AEO website, levelized costs are not necessarily a very good measure of the cost-effectiveness of re-dispatch -- see http://www.eia.gov/forecasts/aeo/electricit y_generation.cfm

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27 PAs signed in 2011/2012—many of which were for projects built in 2012—fell to around $40/MWh

nationwide.” 31



### Examining RE resource availability regionally, several recent studies have found cost-effective or

economic RE resources are available to serve future needs. The National Renewable Energy Laboratory

(NREL) examined the future availability of RE in the West after Western state RPS requirements level off

in 2025. 32 

### The study compares the cost of RE generation from the West's most productive RE resource

areas—including any needed transmission and integra tion costs—with the cost of energy from a new

natural gas-fired generator built near the customers it serves. The report indicates that by 2025 wind and

solar PV generation could become cost-competitive, if new RE development occurs in the most

productive locations. 33 

### It also has shown that a cost decrease of 10% in 2025 would bring solar power to

cost parity with NGCC in the West, with similar possibilities for utility scale geothermal. In 2010, the

Southeast Energy Efficiency Alliance published a re port by Marilyn Brown et al. titled Renewable

Energy in the South. 34 

### In addition to highlighting significant RE resources in different parts of the region,

it stated, “Under realistic renewable expansion and policy scenarios, the region could economically

supply a large proportion of its future electricity n eeds from both utility-scale and customer-owned RE

sources.” 35 

### This study suggested that increased RE utilization should not necessarily lead to significant

rate increases in part because RE resources may moderate forecasted rate increases in the next decade or

two. 36



### Several studies have found the cost of RPS-driven RE deployment to be modest. One

comparative analysis that "synthesize[d] and analyze[d] the results and methodologies of 28 distinct state

or utility-level RPS cost impact analyses" found the me dian change in retail electricity price to be $0.0004

per kilowatt-hour (only a 0.7 percent increase), the me dian monthly bill impact to be between $0.13 and

$0.82, and the median CO 2 

### reduction cost to be $3 per metric ton. 37 

### This finding has been confirmed with

31 

### Ibid.

32 

### Hurlbut, David, Joyce McLaren, and Rachel Gelman. Be yond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West. NREL/TP-6A20- 57830. Golden, CO: NREL, August 2013.

33 

### Ibid. p. xvi.

34 

### Brown, Marilyn A., Etan Gumerman, Youngsun Baek, Joy Wang, Cullen Morris, and Yu Wang. 2010. Renewable Energy in the South. Atlanta, GA: Southeast Energy Effi ciency Alliance, December 2010. See also Brown, Marilyn A., Etan Gumerman, Xiaojing Sun, Kenneth Sercy, a nd Gyungwon Kim. 2012. “Myths and Facts about Clean Electricity in the U.S. South,” Energy Policy, 40: 231-241.

35 

### Ibid. p. xxii.

36 

### Ibid., p. 109.

37 

### Chen et al., "Weighing the Costs and Benefits of State Renewable Portfolio Standards: A Comparative Analysis of State-Level Policy Impact Projections," Lawrence Berk eley National Laboratory, March 2007, available at

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28 more recent RPS cost data, including a report that determined 2010-2012 retail electricity price impacts

due to state RPS policies to be less than two percent, with only two states experiencing price impacts of

greater than three percent. 38



### 4.4. Cost Effectiveness of Nuclear Energy

Nuclear generating capacity facilitates CO 2 

### emission reductions at fossil fuel-fired EGUs by

providing carbon-free generation that can replace generation at those EGUs. Increasing the amount of

nuclear capacity relative to the amount that would otherwise be available to operate is therefore a

technically viable approach for reducing CO2 em issions from affected fossil fuel-fired EGUs.

One way to increase the amount of available nuclear capacity is to build new nuclear EGUs.

However, nuclear generating capacity is relativel y expensive to build compared to other types of

generating capacity, and little new nuclear capacity has been constructed in the U.S. in recent years.

Nevertheless, five new nuclear EGUs at three plants are currently under construction: Watts Bar 2 in

Tennessee, Vogtle 3-4 in Georgia, and Summer 2-3 in South Carolina. The EPA believes that since the

decisions to construct these units were made prior to th is proposal, it is reasonable to view the incremental

cost associated with the CO 2 

### emission reductions available from completion of these units as zero for

purposes of setting states’ CO 2 

### reduction goals. Completion of these units therefore represents a highly

cost-effective opportunity to reduce CO 2 

### emissions from affected fossil fuel-fired EGUs. For this reason,

we are proposing that the emission reductions achievable at affected sources due to the generation

provided at the identified new nuclear units should be factored into the state goals for the respective states

where these new units are located.

Another way to increase the amount of available nuclear capacity is to preserve existing nuclear

EGUs that would otherwise be retired. While each retirement decision is based on the unique

circumstances of that individual unit, the EPA believes that increased fixed costs associated with unit age

play a role, particularly as the units approach the point of deciding whether to apply for renewal of their

initial operating licenses (which have 40-year terms). Of the existing fleet of nuclear EGUs, units with 7.8

GW of capacity, representing approximately eight percent of total existing U.S. nuclear generating

capacity, have not yet submitted applications for license renewal to the Nuclear Regulatory Commission.

Without making any judgment about the likelihood that an y individual EGU will retire, we view this eight

http://emp.lbl.gov/publications/weighing-costs-and-benefi ts-state-renewables-portfolio-standards-comparativeanalysis-s.

38 

### Galen Barbose, “Renewables Portfolio Standards in th e United States: A Status Update,” Lawrence Berkeley National Lab, November 2013. Comment [A29]: General Comment: The
framing of the discussion is that EPA finds new
renewables to be a cost

‐ effective means of
reducing CO2 but it does not find new nuclear

to be cost

‐ effective. This language should be

clarified. At a minimum, it must be clear that

those determinations are part of how EPA

decided to include or exclude technology
deployments in setting the goals. The way it is

written seems like it is telling states not to

pursue new nuclear.

This also seems to be at odds with earlier statements (p. 1):

Comment [A30]: Please consider providing

more information on the choice of 8%.

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29 percent share of nuclear capacity as a reasonable proxy for the amount of nuclear capacity at risk of

retirement for reasons including, but not limited to, a dditional fixed costs related to age and relicensing.

We believe that, based on available information regarding the cost and performance of the

nuclear fleet, preserving the operation of this existing, at-risk nuclear capacity is likely to be a relatively

cost-effective approach to achieving CO2 reductions from affected EGUs. According to a recent report,

nuclear units may be experiencing up to a $6/MWh sh ortfall in covering their operating costs with

electricity sales. 39 

### Assuming that such a revenue shortfall is representative of the incentive to retire the

estimated eight percent of at-risk nuclear capacity, one can estimate the value of offsetting the revenue

loss at these at-risk nuclear units to be about $12 to $17 per metric ton. The EPA views this cost as

reasonable. We therefore propose that the emission reductions achievable by retaining in operation eight

percent of each state’s existing nuclear capacity should be factored into the state goals for the respective

states.

39 

### “Nuclear... The Middle Age Dilemma?” Eggers, et al., Credit Suisse, February 2013 Comment [A31]: Including additional references would be helpful.

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Technical Support Document (TSD) for the CAA Section 111(d) Emission Guidelines for Existing Power

Plants

Docket ID No. EPA-HQ-OAR-2014-XXXX



# Goal Computation Technical Support

Document

U.S Environmental Protection Agency

Office of Air and Radiation

June 2014

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State Emission Rate Goal Setting under 111(d)

This Technical Support Document (TSD) provides information that supports EPA’s determination of
state-level emission rate goals under the proposed Ru le. Section VII of the preamble discusses state
emission rate goals more broadly, and the GHG Abatement Measures TSD explains the technical basis for
the development of the Best System of Emission Reductions (BSER). This TSD provides detailed
explanation of the data, the data underlying the state goal calculation, the BSER-based calculations used
to determine the state goals, and the state goals. The TSD is organized as follows:

1. BSER Factors Informing State Emission Rate Goals

a. Heat rate improvement

b. Coal to gas redispatch

c. Renewable and nuclear

d. End-use energy efficiency

2. Data Used

a. Rationale for historic data based targets

b. Source of data

i. Emissions & Generation Integrated Resource Database (eGRID)

ii. Data sources for under construction units

c. State-level data rationale and significance

3. Example of State Goal Calculation and Discussion

4. Appendix (attached Excel Workbook)

 Appendix 1 - State level goals, underlying stat e level data, and calculations for Option 1

 Appendix 2 - State level goals, underlying stat e level data, and calcul ations for Option 2

 Appendix 3 – Summary table of state goals

 Appendix 4 - Details of state-level data development

Note – This TSD also contains an excel attachment that contains the aggregate state level data,
calculations, and proposed state emission rate goals. The underlying unit-level data is also available in
the Docket for this rulemaking. Comment [A1]: A number of the comments in

this TSD mirror comments made in the preamble

and RIA, with a request to sit with EPA to run through the math details in the Appendix Excel file.

Comment [A2]: In the TSD and the preamble

discussion (ref. preamble, pg. 41

‐ 42), EPA should
clarify the purpose and role of the 1990 and 2005

data discussed in the preamble in terms of the state goal calculations

Within the TSD, there is no reference to this data,
simply a sentence on pg. 4 of the TSD which

indicates “The EPA used 2012 state level data to

determine each state’s emission rate goal.”

Comment [A3]: Appendix 1

– Option 1, Column

V sums to 282 TWh for the year 2020.

If biomass is not included, then attaining the

additional ~70 TWh in the 2015

‐ 2020 timeframe

appears of moderate stringency. If biomass is
included, then states need only meet an additional

~10 TWh in that timeframe, which appears very

conservative. Therefore, if biomass isn’t included in

the RE targets, then EPA’s ruling excludes a major potential GHG abatement tool; if biomass is

included in the RE targets, then the proposed
rulemaking should include a higher deployment

number in 2020 and for subsequent years.

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In EPA’s technical evaluation, reasonable assumpti ons regarding application of each GHG reducing
technology were identified (see GHG Abatement Measures TSD). The EPA used these assumptions to
develop the two regulatory options provided in this pr oposal: (1) Option 1 with state emission rate goals
based on reasonable assumptions rela ted to BSER, and (2) Option 2 with less ambitiousmore moderate
assumptions relative to those used for Option 1.

1. BSER Factors Informing State Emission Rate Goals

The GHG Abatement Measures TSD describes the four categories of emission reduction
measures (building blocks) used in determini ng the state emission rate goals. That document
describes EPA’s historic data review and analysis underlying each technology and informing
EPA’s assessment of its feasibility and cost-eff ectiveness as part of a BSER. The technology
estimates determined through EPA’s analysis and documented in the GHG Abatement Measures

TSD are summarized below. These estimates wearewere used in EPA’s calculation of state
emission rate goals, as described in this TSD.

a. Heat Rate Improvement
Option 1 – A 6% heat rate improvement in the state’s coal fleet
Option 2 – A 4% heat rate improvement in the states coal fleet

b. Redispatch to Existing Natural Gas Combined Cycle Plants (NGCC)
Option 1 – a 70% capacity factor (CF) ceiling for the state’s NGCC fleet
Option 2 – a 65% capacity factor ceiling for the state’s NGCC fleet

Note – These capacity factor values represent ceilings for NGCC utilization. The EPA
used these ceilings while calculating state goal adjustments related to redispatching coal
and/or oil and gas (og) steam generation in a state to the state’s NGCC capacity.

c. Renewable and Nuclear
Both option 1 and 2 include under constructi on (5.5 GW) and preserved nuclear capacity
(~8% of existing nuclear capacity)
Option 1 – Non-Hydro RE at 13% by start of 2030 and thereafter*
Option 2 – Non-Hydro RE at 9.4% by start of 2025 and thereafter*

d. End-use Energy Efficiency (EE)*

Option 1 – 10.65% cumulative savings by start of 2030 and each year thereafter
Option 2 – 5.17% cumulative saving s by start of 2025 and thereafter

*Note – The above values for non-hydro RE and EE saving rates are nationwide
averages. Each state’s CO 2 

### emission rate goal is informed by state-specific non-hydro RE
and EE values that relate to its pre-existing non-hydro RE generation and EE savings
rates respectively as described in the GHG Abatement Measures TSD. Comment [A4]: Initial text implies that option 1

was “immoderate.”

Comment [A5]: Need to pick a single font,

suggest Times New Roman.

Comment [A6]: The discussion on the limits on
increased dispatch to NGCC suggests the possibility

of evaluating similar constraints to redispatch away
from coal plants

– e.g., idling coal and an option for

a floor level ‐‐ and the effects on coal generation in

states with mixed coal and NG fossil generation, i.e.,
zeroing out coal generation in about 10 states.

These states are evident in the Excel appendix as
the zero coal generation post BB2 redispatch. This

would be a valuable discussion with EPA with access

to and familiarity with the Excel file and calculation

math.

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The technology assumptions described above are used to determine an emission rate (lb/MWh)
for each state that constitutes the state goal. As these building blocks reflect both fossil and nonfossil measures, the corresponding state goal reflects a composite emission rate including fossil
and zero emitting non-fossil technologies. The EPA proposed to use an emission rate metric for
state goals, rather than a mass-based metric to promote state flexibility. See preamble section VII
for more description on the rate based standard.

2. Data Used

A. Rationale for Historic Data Basis for Target Setting

The EPA used 2012 state level data to determin e each state’s emission rate goal. The EPA
carefully considered using a historic year data set, a projected year data set, or a hybrid of the two
as a starting point for applying the above t echnology assumptions and calculating the state’s
emission rate goals. 1 

### Ultimately the EPA chose the historic data approach as it reflected actual
historic performance at the state level. EPA chose the year 2012 as it represented the most recent
year for which complete data were available at the time of the analysis. The EPA also considered
the possibility of using average fossil generation an d emission rate values over a baseline period
(e.g., 2009 – 2012), but determined that there would be little variation in results compared to a
2012 base year data set results due to the rate-based nature of the goal. The form of the goal is an
emission rate representing the deployment of BSER measures, and that deployment level is
largely a function of technology specific reduction capability and total capacity currently on the
ground. Because these two critical variables do not va ry significantly (the latter not at all) when
looking at a larger baseline period, the benefits of an expanded baseline period are dampened, and
overshadowed by the complexities introduced through creating a “hypothetical” year.
Consequently, the EPA decided that using data from an actual year (for which facility
representatives had reported the values as complete and accurate to the best of their knowledge)
was preferable to using calculated values that did not reflect any real year supply/demand

balance. For this work, the EPA used 2012 da ta compiled using the same methodology as in

EPA’s Emissions and Generation Resource Inte grated Database (eGRID) to determine the
following 2012 values at the state level, which be come the basis from which state emission rate
goals are calculated.

 State level coal generation

 State level coal emission rate

 State level oil/gas steam generation

 State level oil/gas steam emission rate

 State level NGCC generation

 State level NGCC emission rate

 State level NGCC capacity

1 

### Data sources for future year or hybrid approach would be EPA Base Case Power Sector modeling. Comment [A7]: Does this mean to say that not

one new power plant

– fossil or wind

– came online

between 2009

– 2012. Please consider whether this
parenthetical needs to be tempered. Suggest deleting the parenthetical.

Formatted: Font: (Default) Times New Roman

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 “other” emissions 2



###  “other” generation

All generation values are expressed as net generation . Emission rate values are net emission rates
and expressed as lbs/MWh. The capacity expressed is nameplate capacity in Megawatts.

B. Source of Data

i. Emissions & Generation Integrated Resource Database (eGRID)

eGRID is a comprehensive inventory of environmental attributes of the U.S. electric power

system. It is the preeminent source of air emi ssions data for the electric power sector, based on

available plant-specific data for all U.S. electricity generating plants that provide power to the

electric grid and report data to the U.S. government. eGRID integrates many different data

sources on power plants and power companies, in cluding, but not limited to: the EPA, the Energy

Information Administration (EIA), the North American Electric Reliability Corporation (NERC),

and the Federal Energy Regulatory Commission (FERC). Emissions data from the EPA are

carefully integrated with generation data from EIA to produce useful values like pounds per

megawatt-hour (lb/MWh) of emissions, which al lows direct comparison of the environmental

attributes of electricity generation. Although 2012 eGRID data had not yet been released, the

EPA applied its eGRID methodology for matching the publically available and reported 2012

emissions and generation data. The EPA relies on this most recent data to calculate state goals.

The state level totals for each technology category described in the above bullets are intended to

reflect the electric generating units (EGUs) meeting the following criteria: 3



### An EGU is a boiler or turbine that is: (1) capable of combusting more than 250 MMBtu/h heat

input of fossil fuel, (2) constructed for the pur pose of supplying more than one-third of its

potential net- electric output capacity . . . to any utility power distribution system for sale (that is,

to the grid), and (3) constructed for the purpose of supplying . . . more than 25 MW net-electric

output.

Integrated gasification combined cycles (IGCCs) and combustion turbines (CTs) operating at a

capacity factor greater than 33% are not part of th e inventory of units subjected to building blocks

one and two. Although they are covered units under the rule, they are excluded from the

2 

### “Other” includes fossil sources that are likely subject to 111(d) rulemaking, but not subject to building block

abatement measures.

3 

### The set of sources used to compile state level totals and eventual state goal rates does not constitute an applicability determination for any particular EGU. The u ltimate universe of EGUs subject to this rulemaking may vary from that used in this goal setting exercise and will be determined by the rule’s applicability language and actual operating conditions at that plant.

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technology that are considered eligible for blocks one and two, and instead designated as the

“other” category. IGCCs and high capacity factor CTs present unique circumstances and are not

subjected to building block application. IGCCs re present a very small sample size of 3 plants and

have a different utilization pattern and different capital cost profile than NGCCs that result in a

different set of redispatch economics. Likewise , high capacity factors CTs that may be covered

by the rule are generally less efficient and have higher emission rates than NGCCs, and are

therefore generally less cost effective for redispatch purposes. Therefore, IGCCs and CTs were

not considered for cost-effective deployment of BS ER building blocks one and two (i.e., there are

no redispatch

, or heat rate improvements assumed to occur at these units). The historical

generation and emissions from IGCCs and high capacity factor CTs are still averaged into the

state goal rate to account for the notion that these units are affected sources and their generation

and emissions would be averaged into the state emission rate for compliance purposes. This

category of affected units that are covered unde r the 111(d) applicability language, but not

subjected to BSER technologies

, is accounted for in an “other” category. This represents a small

portion (i.e., less than 1%) of total generation and emissions relative to the other affected source

categories

, and has a small impact on the state goal rates.

Appendix A shows the state level aggregate data and corresponding calculations applied to

achieve the state goal rate.

ii. Data sources for under construction units

Although the 2012 eGRID data described above form s the starting point for the goal calculation,
the EPA’s BSER methodology also included under construction nuclear and NGCC capacity that
was not existing in 2012. For this small subset of units, EPA relied on its National Electric
Energy Data System (NEEDS). NEEDS includes basic geographic, operating, air emissions, and
other data on existing or under construction generating units. NEEDS was completely updated for
EPA's new power sector modeling platform v.5. 13. For a description of the sources used in
preparing NEEDS v.5.13, see Documentation, Chapter 4: Generating Resources. 4 

### In addition to

those under construction NGCC units in NEEDS, the EPA identified three other NGCC plants
that were under construction and would likely fit the rulemaking’s definiti on of “existing” unit.
These were the Dominion Brunswick plant in Vi rginia (1,358 MW), the Cheyenne Generating
Station in Wyoming (220 MW), and the Cane Run plant in Kentucky (640 MW).

C. State-Level Data Rationale and Significance

The EPA aggregated EGU-level data to the state level for purposes of state-specific emission rate

goal calculation. Then, for each state, the EPA made BSER-related adjustments to the historical

4 

### http://www.epa.gov/powersectormodeling/BaseCasev513.html#needs Comment [A8]: Perhaps outside of the scope of

this proposal, but shouldn’t there be considerations

for emissions from reciprocating natural gas engines

used for power generation? One engine generates

<20 MW, but these engines are typically stacked at

one plant to yield >100 MW.

Comment [A9]: Suggesting being very consistent

with the applicable terminology here and
elsewhere, because the issue of existing, under

construction (as of Jan 8, 2014, and hence legally
“existing” for the purposes of this rule), at risk, and

new (i.e., not existing or under construction now)

are important concepts, in order to maintain logically consistency between RE and Nuclear in this

BSER. See comment below.

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state-level data to derive a new emission rate that constitutes the state goal. In making

adjustments to aggregate state-level data, the EPA is simply suggesting that BSER assumptions

(e.g., 6% heat rate improvement) can be achieved on average at the state level. The EPA is not

making any assertions about specific units or plants. The EPA recognizes the uniqueness and

complexity of individual power plants, and is aware that there are site-specific factors that may

prevent some EGUs from achieving performance equal to state-level assumptions. Likewise, the

EPA also recognizes that some EGUs are capable of, and regularly do, achieve performance

levels that surpass the BSER values assumed. In any case, the EPA is not making those unit-level

evaluations in this exercise. The EPA is instead attempting to quantify what is feasible at the

state level based on application of the BSER values to historic state-level data. Therefore, the

ability or inability of a specific EGU to under/ overachieve the assumed technology value cannot

be taken, on its own, as an indication of the appr opriateness of the state goals estimated using this

approach.INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.3.Emission Rate CalculationEPA’s methodology for calculating state goals is described in the steps noted below. The implementation of each step is illustrated in the tablebelow its description, using the state of Ohio as an example.Step 1:Calculation of unadjusted 2012 state fossil emission rate for covered sourcesAs noted above, the EPA begins the state goal emission rate calculation by starting with actual historic data at the state level. Unit-level dataareissummed to state-level values describing:coal generation, coal emission rate, NGCC generation, NGCC emission rate, OG steam generation, OGsteam emission rate, “other” emissions, “other” generation, andNGCC capacity. These categories are identified as they represent the historicalgeneration that is subject to building block application and/or would likely be subject to the state goal. For combined heat and power (CHP) unitsthat are covered under the rule, the entire emissions and generation (including steam-based) from the facility are counted whendeterminingemission rates because such facilities’ entire emissions and generation would be considered when determining compliance with rule.5However,only the electricity generation portion of CHP is available for redispatch purposes, and therefore only this portion of the cogeneration operation isreflected in the generation totals. There are four coal facilities that co-fired biomass in 2012 that did not report emissionsunder Part 75, in thesecases an emission rate factor is used to estimate stack CO2emissions attributable to the type of biomass reported.6These technology specificvalues become the basis for calculating the state’s emission rate. All emission rates provided in the state goal determinationare on a net basis.Therefore, they will appear slightlyhigher than the emission rates viewed on a gross generation basis.2012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012 CapacityStarting coveredsource rate (lbs/MWh)ABCDEFGHIJCoal RateOG SteamRateNGCCRateOther EmissionsCoal GenOG Steam genNGCC GenOther GenNGCC MWOhio2,1872,196963084,726,936343,62720,907,18304,3431,9465A separate state goal rate is calculated for IPM power sector modeling that reflects total emissions from CHP units, but justthe electricity provided to gridportion of their generation. This was necessary as IPM reflects total emissions associated with cogeneration operation, but only the generation associated withmeeting net electricity demand.6http://www.theclimateregistry.org/downloads/2013/04/2013-Climate-Registry-Default-Emissions-Factors.pdfComment [A10]:Youmightwanttoaddasectionbeforethisbeingexplicitregardingthefundamentalimportanceofthemathematicalformulatothesecalculations.Comment [A11]:Thisapproachseemslikeitmightconflictwiththeapproachdiscussedinthepreambleonpages103‐104,inthediscussionofBuildingBlock1,whichindicatesthatimprovementtousefulthermalenergywouldalsobecredited:“HeatrateimprovementsarechangesthatincreasetheefficiencywithwhichanEGUconvertsfuelenergytoelectricenergy(andusefulthermalenergyinthecaseofunitsthatcogeneratesteamforprocessuseaswellaselectricity),therebyreducingtheamountoffuelneededtoproducethesameamountofelectricityandloweringtheamountofCO2producedasabyproductoffuelcombustion.”&“TheheatrateofanEGUistheamountoffuelenergyinputneeded(Btu,higherheatingvaluebasis)toproduce1kWhofnetelectricalenergyoutput(andusefulthermalenergyinthecaseofcogenerationunits).”Commentersincludingusefulthermalenergywhenconsideringcogenerationunits.Comment [A12]:Forpurposesofcalculatingstategoals,co‐firedbiomassunitsarepresumedtoemitCO2atalevelbasedonthetypeofbiomass(TSD,p.8).Itshouldbenotedthatthecalculationsofbiomasscontributionsmaybeimpactedbyfuturebiomassrulemakings.Further,thesefuturebiomassrulemakingscouldcontributetofuturecoalunitretirementdecisions.Aclarifyingstatementaboutfuturecountingofbiomassemissionswouldbehelpfulinthiscontext.INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.Note, when the values in the above cells shaded green and blue are applied to the equation below, the state’s 2012 “unadjustedfossil” emissionrate (or historical fossil rate) for likely covered sources is equal 1,946 lb/MWh.Historicalemission rate = (coal gen. x coal emission rate)+ (OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) + “Other” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen.Step 2: BSER Block one- Calculation of state fossil emission rate goal for covered sources resultingfrom heat rate improvement (HRI)After this historical data is collected for each state, the EPA begins to adjust particular variables to the data to reflect each building block elementof BSER. The EPA assumes that a 6% heat rate improvement atthe facility will directly translate to a 6% reduction in the netCO2emission rate.Therefore, in conjunction with determining state goals under option 1 in this proposal, the EPA adjusts the state-level coal emission rate (columnA) downwards by 6% to reflect the 6% heat rate improvement. In the case of Ohio, this results in a drop from 2,187 lb/MWh to 2,056 lb/MWh.This completes the building block 1 treatment for option 1.72012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012 CapacityState Goal Post Block 1(lbs/MWh)ABCDEFGHIJCoal RateOG SteamRateNGCCRateOther EmissionsCoal GenOG Steam genNGCC GenOther GenNGCC MWOhio2,0562,196963084,726,936343,62720,907,18304,3431,841Note that when the total emission rate is recalculated with heat rate improvement reflected in the adjustment to the state’s coal emission rate, thestate emission rate drops from 1,946 lb/MWh to 1,841 lbs/MWh. Thisis not the final state emission rate goal rate, rather an emission ratereflecting building block 1 application before moving onto the remaining blocks. The bold areas in the equation below reflectthe values that areadjusted from their historical level at this stage.7EPA makes a similar adjustment for option 2, but uses a 4% HRI improvement assumption when reducing the coal rateINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.State Emission Rate Post Block 1= (coal gen.xcoal emission rate)+ (OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) + “Other” EmissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen.Step 3: BSER Block Two -Calculation of state fossil emission rate goal resulting from heat rate improvement and redispatch toexisting NGCCcapacityIn the GHG Abatement Measures TSD, the EPA described how historical 2012 data illustrated that a significant number of NGCC units had a netgeneration that was greater than or equal to its nameplate capacity x 8784 *0.7 – in other words a 70% or greaternet capacity factor. It alsoprovided analysis supporting a 70% capacity factor as cost-effective and historical data that illustrated the ability to increase utilization atcombined cycle units under favorable market conditions. That portion of the TSD also describes the engineering analysis that suggests the averageavailability of a NGCC is greater than 70%. Building block 2 was intended to reflect the potential redispatch to the state’s existing NGCC fleet upto a 70% capacity factor level.To operationalize the 70% CF ceiling in the state goal setting, the state’s 2012 existing NGCC nameplate capacity is multipliedby 8,784 hours(the number of hours in the 2012 year) and then by 70% to get total potential net NGCC generation at a 70% capacity factor. However, this 70%capacity factor represents a redispatch ceiling, and the state’s NGCC generation is only adjusted up to this ceiling if its historic fossil sourcessupport such a level.8Therefore, if this ceiling value (MWh) isless thanthe sum of the state’s 2012 coal, OG steam, and NGCC net generation,the historic NGCC generation is increased to this calculated ceiling value representing the NGCC fleet at a 70% capacity factor, and historic coaland OG steam generation are ramped down by an equivalent amountof generation. If the generation of existing NGCC at a 70% capacity factor isgreater thanthe sum of the state’s 2012 coal, OG steam, and NGCC net generation, the historical NGCC generation is only adjusted to equalthetotal of the 2012 net fossil generation from these sources. In summary, adjusted NGCC net generation is equal to the lesser ofexisting NGCCfleet at 70% capacity factor or 2012 total fossil generation from BSER subjected sources. This preserves the historical total covered fossilgeneration in the state for BSER subjected sources, but reapportions it assuming a 70% state-average net capacity factor ceiling for the NGCCfleet. The increase in NGCC generation is subtracted from the coal and OG steam generation in proportion to the state’s historic generation. Forexample, if coal historically accounted for 90% of the total coal and OG steam generation in the state, then its historic generation would be8Fossil sources here refers to those units subject to BSER redispatch building block treatment (i.e., coal, o/g steam, and NGCC)Comment [A13]:Asnotedabove,wewouldbeinterestedindiscussingtheprosandconsofacoal“floor”thatmightbebasedonhistoricalempiricaldataregardinglowlevelsofcoaldispatchandidling.Comment [A14]:Thismightnotcapturefullpotentialofredispatch–ifregionalscenarios/modelingisdone,existingNGCCinthesestatescouldpotentiallyincreaseto70%capacityfactorbyexportingtheadditionalgas/re‐dispatchingcoalinothercoal‐heavystates.IsitpossibleforEPAtoprovideitsregionalmodelingofpotentialredispatchforBSER?INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.reduced by 90% of the amount that NGCC generation increases. Likewise, the OG steam generation would be reduced by 10% of theamount bywhich NGCC generation is increased.2012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012 CapacityState Goal Post Block2.1 (lbs/MWh)ABCDEFGHIJCoal RateOG SteamRateNGCCRateOther EmissionsCoal GenOG Steam genNGCC GenOther GenNGCC MWOhio2,0562,196963078,956,358320,22326,701,16404,3431,781Note – in the above example, the total covered fossil generationhas not increased in the state from the historic totals shownin step 1. It has onlybeen reapportioned assuming a 70% capacity factor for NGCC.NGCC generation is adjusted upwards by approximately 6,000,000 MWh, andcoal and O/G are adjusted downward by the same amount. This increase in NGCC generation reflects the existing fleet increasingfrom ahistorical level of approximately 55% CF up to the ceiling of 70% CF. When these updated generation values are folded into thestate goalcalculation, the emission rate drops from 1,841 lb/MWh in Step2 to 1,781 lb/MWh. This completes building block two application. The values inbold below reflect the historic data points that have been adjusted to reflect building block implementation at this point in the process.910State Emission Rate Post Block 2.1= (coal gen. x coal emission rate)+ (OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) + “Other” emissionsCoal gen. + OG gen. + NGCC gen. +“Other” gen.Step 4:BSER Block Two, cont. -Calculation of state fossil emission rate goal resulting from heat rate improvement and redispatch to existingand“en route” NGCC capacity9While a 70% capacity factor ceiling is assumed in the state goal setting exercise, the majority of states are not expected to operate their existing fleet at thislevel in EPA’s projected compliance scenarios.10In option 2 state goal calculations, the EPA makes similar adjustments to the historic data but utilize a 65% capacity factorceiling in place of the 70% CFshown above4,343MWx8784hoursx70%CF=26,701,164Formatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.Step 3 is repeated for “en route” NGCC capacity that would also be considered an existing covered fossil source. For this step, “en route” isdefined as anything that came online in 2013 or that was under construction, site prep, or testing by January 8, 2014. The EPAlooked at reporteddata for 2012 and calculated the average performance of NGCCs thatcame online in the past 5 years and observed that 55% was the averagecapacity factor for these units under a business as usual scenario.11Therefore, the EPA assumed that a 55% capacity factor would be a reasonablerepresentation for the expected generation of “en route” NGCCs capacity under a business as usual scenario. The EPA conservatively designatedthe generation associated with this 55% capacityfactor as unavailable for redispatch to reduce CO2(i.e., not qualifying for building block 2),instead, reserving that amount of generation potential to meet other system needs presumed to have motivated the construction of the “en route”NGCCs. Because these sources are nevertheless covered under thestate emission rate goal, the emissions and generation from this 55%generation are added to the “other” category and averaged into the state goal calculation. The EPA assumes that while these units would operate at55% under a business as usual scenario, the average availability for these units is greater than 55%, and they too could ramp up to 70% CF ceiling,on average, under a BSER framework and displace relatively higher CO2-emitting generation. Thus, 15% of their ultimate CF (70% - 55%) isassumed to be available for redispatch purposes. The MWh associated with this 15% additional CF displaces coal and OG steam generation in thesame manner as in step 3.The portion of the “en route” NGCC unit’s generation that is not considered subject to BSER abatement measures (i.e., the first55%) is capturedin the “other” category. The generation and emissions from these sources are still included in the ultimate emission rate quantified as the stategoal by averaging in their unadjusted emissions and generation. For “en route” NGCC units that have no 2012 historical data, the emissions andgeneration associated with the 55% baseline capacity factor isobtained by using the “en route capacity” and historical averageemission rate forNGCCs in the state.12The emissions total for these units is based on multiplying the generation at a 55% CF by the average emission rate forexisting NGCCs in the state.11http://ampd.epa.gov/ampd/12Note, both Wyoming and Kentucky had “en route” NGCC capacity, but did not have existing covered NGCC in the state from which aproxy emission ratecould be derived for the “en route” capacity. Therefore, the nationwide average NGCC emissions rate of 919 lb/MWh (net basis)became the assumed emissionrate for the “en route” NGCC in these two states.Comment [A15]:Isthisdifferentfrom“existing”underthelegaldefinitionforhowweseparate“new”from“existing”undertheNSPS?Asnotedabove,keepingtheterminologysimpleandconsistentisimportant,unlesstherearesomefundamentaltimingissuesthatweneedtoaddressbetweenthevarioussourcesofMWhrsandhowtheyarecomingonlinearoundtheJan8,2014period.Formatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.2012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012Capacity"En Route"NGCCState Goal PostBlock 2.2(lbs/MWh)ABCDEFGHIJKCoal RateOGSteamRateNGCCRateOther EmissionsCoal GenOG SteamgenNGCC GenOther GenNGCC MWNGCC MWOhio2,0562,1969632,506,741,57878,249,041317,35427,411,3502,604,0174,3435391,754Note that when the total emission rate is recalculated to reflect the “en route” NGCC capacity, the state’s emission rate at this step drops from1,781lb/MWh to 1,754 lbs/MWh. This is not the final state emission rate goal rate, rather an interim rate reflecting buildingblocks one and twoapplication before moving onto the remaining blocks. The bold areas in the equation below reflect the values that are adjustedfrom their historicallevel at this stage.State Emission Rate post block 2.2= (coal gen. x coal emission rate)+ (OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) +“Other” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen.Step 5: BSER Block Three -Calculation of state fossil emission rate goal resulting fromheat rate improvement, redispatch to existing and enroute NGCC capacity, andnew and preserved nuclearIn Step 5, the total under construction nuclear capacity is determined.13Additionally, an amount of existing nuclear capacity that is “at risk” ofbeing retired is identified as approximately 8% of the currentnuclear fleet (see GHG Abatement Measures TSD for further discussion on how thepercent assumption was derived). This new and preserved nuclearcapacity was incorporated into the state goals as zero emitting generation. This13Watts Bar in TN, Vogtle in GA, and Summer in SC4,343MWx8784hoursx70%CF=26,701,164539x8784x15%CF=710,186Total=27,411,350Comment [A16]:Terminologyissue–isthisNuclearatthefivereactorsactually“new”underthelegalterminology,orisitunderconstructionasofJan82014andhence“existing”inthekindoflegallanguageintheNSPS?Or,dowehaveamixofunderconstructionandnotquiteunderconstructionnuclear?Thisisnotaimedatbeingpedantic,becauseasyouwillseeundertheREcommentsbelowthereappearstobealogicalinconsistencyinthewaythatthisproposaldealswithRE(allexistingandnew)andnuclear(notexisting,buttheunderconstruction,atrisk,andaka“new”.)INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.was achieved by applying a 90% capacity factor to the nuclear capacity identified as under construction or “at risk” and then applying the resultingMWh figure into the denominator of the state goal calculation. Nuclear is not considered a “dispatchable” resource as is NGCCcapacity. That is,its utilization will remain fairly constant in both a business-as-usual and policy scenario due to its low variable operating cost relative to fossilsources. The differing dispatch economics for NGCC and nuclear leads to a slightly different treatment in the goal setting. Existing nuclear is notassumed to increase generation in the same manner as the NGCC fleetis assumed capable of in the above steps. Instead of redispatching fromcoal to nuclear in the same fashion as the previous steps, the expected generation for new and preserved nuclear is simply added to the state goaldenominator – resulting in alovwerloverstate goal emission rate. However, it is important tonote that this “new and preserved” nuclear capacityis also part of the inventory that the state can count towards compliance with its state goal (i.e., the zero emitting generation that can be averagedin with that of the covered fossil sources in order to obtain a rate that is equal to or less than the state emission rate goal). Therefore, the fossilsources do not necessarily have to lower their own emission rate to help the state achieve the lower state goal reflected subsequent to adding in thisnuclear technology provided that the nuclear capacity continues to operate at a 90% capacity factor.2012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012Capacity"En Route"NGCCNew andPreservedNuclearState GoalPost Block 3.1(lbs/MWh)ABCDEFGHIJKLCoalRateOGSteamRateNGCCRateOther EmissionsCoal GenOG SteamgenNGCC GenOther GenNGCCMWNGCC MWMWhOhio2,0562,1969632,506,741,57878,249,041317,35427,411,3502,604,0174,3435391,351,2391,733Ohio has 0 MW of under construction nuclear and 171 MW of “at risk” nuclear capacity share as described in the “GHG Abatement MeasuresTSD”. This results in an expected nuclear generation amount of1,351,239 MWh that is incorporated into the state goal calculation (based on 90%capacity factor). When this value is added into the state goal calculation, the state’s emission rate drops from 1,754 lb/MWhto 1,733 lb/MWhState Emission Rate Post Block 3.1= (coal gen. x coal emission rate)+) +(OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) +“Other” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen. + Nuclear gen.Comment [A17]:Theapproachforpreservednuclearplantsassumesthatthoseat‐riskplantsthatwillbekeptactivewilloperateatacapacityfactorof90%.Giventheadvancedageoftheseunits,thatmaybeanaggressiveassumption.Acapacityfactorof85%maybemoreprudent.Comment [A18]:?Comment [A19]:Alittlemisleading,asthe“newandpreserved”nuclearisactuallyaddedtoEPA’sgenerationofthestategoal,andhencethestateisgoingtohavetobuildandkeepthenuclearorelsefindanothersourceofzeroemissionMWhr.Comment [A20]:Shouldthisnumberbe1,351,858?(171MW*0.9*8784)?Pleasecheck.INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.Step 6: BSER Block Three, cont. -Calculation of state fossil emission rate goal resulting from heat rate improvement, redispatch to existing anden route NGCC capacity, new and preserved nuclear, and Renewable (RE)Existing and new renewable generation is incorporated into the state goal in a similar manner as the nuclear generation. Renewable generation haslow variable operating cost and utilization that is dictated bythe renewable resources available. Like nuclear generating capacity, its utilization isnot expected to vary significantly betweena business-as-usual scenario and a policyscenario. Therefore, the generation fromthese sources isadded into the denominator in a similar fashion. Consequently, all non-hydro RE generation (existing and new build) would be eligible fordemonstrating compliance with the state goal.The GHG Abatement Measures TSD describes how the RE generationtotal for each state was derived from a combination of existingrenewablegeneration in the state and target RE levels informed by existing Renewable Portfolio Standards (RPS) in the region. Althoughthe amount ofassumed RE generation varies by state to reflect the different existing portfolios of renewable resources and the amount of renewable fuelsavailable in different regions of the country, the nationwide average assumed under Option 1 is approximately 13% non-hydro REby the end of2029.14A potential non-hydro RE generation value is calculated foreach state annually for all years between 2020-2029 to reflect the annualgrowth as states move toward a particular regional non-hydro REbenchmark. The state-specific non-hydro RE generation values that relate to thisgoal nationwide average are available in the Excel attachment to this TSD and the GHG Abatement Measures TSD. These non-hydroRE MWhtotals include both existing non-hydro renewable resourcesin the state and new non-hydro renewable generation.2012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012Capacity"EnRoute"NGCCNew andPreservedNuclearExisting andNew NonHydro REState GoalPost Block 3.2(lbs/MWh)ABCDEFGHIJKLMCoalRateOGSteamRateNGCCRateOtherEmissionsCoal GenOGSteamgenNGCCGenOtherGenNGCCMWNGCCMWMWhMWhOhio2,0562,1969632,506,741,57878,249,041317,35427,411,3502,604,0174,3435391,351,23913,775,5941,54014See “GHG Abatement Measures TSD”Comment [A21]:It’snotimmediatelyclearwhetherbiomassisincludedinbuildingblock3.TotheextentbiomassisincludedinstateRPSs(e.g.,NCincludesswineandpoultrywasteinitsRPS),commentersexpectsthatitis,butinthatcasethelevelofREdeploymentanticipatedbyEPAinthisTSDseemsquitelow.In2012,non‐hydroREexcludingbiomassaccountedfor170TWhofgeneration,andbiomassaccountedforanadditional58TWh.Weexpectthatcurrentin‐serviceandin‐constructionwind,solar,andgeothermalwouldaddanadditional~40TWh/year,leadingtoatotalof~270TWh.Comment [A22]:ThisistheapparentlogicalinconsistencybetweennuclearandRE.Itwouldlikelybemathematicallydistortingtoaddallexistingnuclearunderthedenominator.Theconversedoesnotovertlyappeartobeproblematic,i.e.,toremovethesmallamountsofexistingnon‐hydroREfromthedenominator,anda)subtractthisexistingREfromtheultimatestategoalandb)givethestatekudosinthetext/tableforhavingthislevelofexistingnon‐hydroRE.Thebenefitofsubtractingexistingnon‐hydroREfromthespreadsheetgoalswouldbetomaintainlogicalconsistencywiththewaywearetreating“new”andatrisknuclear,ascomparedtohowwearetreatingexistingnuclear.... [1]Comment [A23]:Yes–welookedatthiscomparisonoutto2030ofbasev.policyprojectionsintheRIA,anditisquitedisconcertingthatallthisefforttoempowerREhasverylittleimpactin2030accordingtothemodeling.CanEPAexplainthis–shouldwebelookingformoreRE?Comment [A24]:ThisismoreacommentfortheGoalsTSD,buttherearethreeconstraintsongeneratingastate’sREgoal:regionalaverage,stateREstartingpoint,andstatehistoricrateofprogress.Theendresultsaredisconcertingdifferencesbetweenstategoals–suchasMAat24%in2029,andKYat1.9%in2029.Also,asintheMAandMEexamples,onewonderswhywearereducingasmallnumberofstateREgoalsbelowwhatthestatehasitselfset.INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.Ohio had 1,738,622 MWh of non-hydro RE in 201215(approximately 1% of total net generation) and would grow to 13,775,594 MWh by 2029(approximately 10.6% of current net generation) under the building block three assumptions. This 13,775,594 MWh of non-hydro RE generationwould be added into the state goal by adding it in the denominator. The RE value and resulting state goal shown above reflectthe 2029 year.When this is added into the state goal calculation, the state’s emission rate drops from 1,733 lb/MWh to 1,540 lb/MWh.State Emission Rate Post Block 3.2= (coal gen. x coal emission rate)+) +(OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) +“Other” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen. + Nuclear gen. + RE gen.Step 7: BSER Block 4 -Calculation of state fossil emission rate goal resulting from heat rate improvement, redispatch to existing NGCC, “enroute”NGCC, under construction and preserved nuclear, RE,anddemand-side EEA state-specific percent value is determined for each state reflecting the amount of total MWh sales that could potentially beavoided throughdemand-side energy efficiency measures (see GHG Abatement MeasuresTSD). Annual percentage savings rates for MWh sales are derived foreach state for all years between 2020 and 2029 in Option 1. A different set of annual percent savings are derived for 2020-2024under Option 2.Because of the temporal nature of demand-side energy efficiency, this building block, along with RE portion of building block three, are the onlyBSER components that increase each year. The GHG Abatement Measures TSD explains the rationale and derivation of these annualsavingsrates. Once these state-specific percent savings rates forMWh sales are derived, they are multiplied by the state’s 2012 historical retail sales andscaled by a factor of 1.0751 to obtain an avoided generation value in MWhs.16The 7.51% scaling factor effectivelyconvertscovertsthe retailssales figure into a corresponding total net generation value that accounts for transmission and distribution losses.17The lesser of 1) this avoidednet generation value, or 2) the avoided net generation value multiplied by the state’s generation share of its sales is then applied to thedenominator. This last step helps assure that building blockfour reductions for net importer states are linked to their in-state generation.This step further reduces the state goal. However, the state may also count any future EE measures towards compliance with itsstate goal.Therefore, if a state achieves the assumed avoided generation based on EE, it does not have to further lower its emission ratefrom covered fossil15http://www.eia.gov/electricity/data/state/16http://www.eia.gov/electricity/data/state/“Retail Sales of Electricity by State and Sector”17The 7.51% scaler represents the historical difference betweentotal net generation of electricity (4,036 TWh) and retail salesof electricity (3,754 TWh).http://www.eia.gov/electricity/state/pdf/sep2010.pdfComment [A25]:ThisTSDdoesnotexplainanothermathcalculationthatsetsthedenominatorrequirementforREMWhr,namelymultiplyingthestateREgoalbythetotalstateMWhrpurchasesfor2012,whichincludesexistinghydropower,nuclear,andREthatexistsin2012.Thisstronglydifferentiallyimpactsasmallnumberofstatesthathavelargehydropowerin2012,e.g.,IdahoandWashington,becausetherelativelysubstantialnewnon‐hydroREgoalrequirement(generatedbecauseoflargeexistingzeroemissionhydro)isassignedtoarelativelysmallfossilenergyMWhrratebase.Thisisbestdiscussedthroughlookingatdetails(appendix,graphs)onindividualstateratereductionsresultingspecificallyfromBB3.Comment [A26]:T&Dlossesvaryregionally.Itmaybehelpfultoincorporatethisvariation.Comment [A27]:Theremaybeotheroptionstoconsiderthatwouldbemoreappropriate.Commenterslookforwardtodiscussingthis.Formatted:Font: (Default) Times New RomanFormatted:Default Paragraph Font, Font:(Default) Times New RomanFormatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.fuel fired generators determined in the previous steps. Instead, adding the avoided generation to the denominator (i.e., averaging in the avoidedgeneration at a 0 lbs/MWh emission rate) will lower the state’s emission rate used for compliance with the state goal.2012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012Capacity"EnRoute"NGCCNew andPreservedNuclearExisting andNew NonHydro REAvoidedSales viademandside EEPost Block4(lbs/MWh)ABCDEFGHIJKLMNCoalRateOGSteamRateNGCCRateOtherEmissionsCoal GenOGSteamgenNGCCGenOtherGenNGCCMWNGCCMWMWhMWhMWhOhio2,0562,1969632,506,741,57878,249,041317,35427,411,3502,604,0174,3435391,351,23913,775,59417,507,5561,349As described in theDdemandDemand-side energy efficiency section of theGHG Abatement Measures TSD, the 2029 percent of generationsavings due to energy efficiency iscalculatedassumedto be 11.56% in Ohio. This value is then multiplied by the retail electricity sales in the state(152,456,864 MWh)18and multiplied by 1.0751 to reflect the net generation commensurate with these sales (i.e., inclusive of transmission anddistribution losses). This results in 18,942,382 MWh of avoided generation. This value is then multiplied by 92.43% (Ohio’s share of salescoming from in state power generation) to reflect the amount ofavoided in-state generation that results from the energy efficiency investments.This final adjustment results in 17,507,556 MWh of avoided generation for Ohio that is assumed for 2029. This avoided generation value is addedinto the denominator and results in the state goal being decreased from 1,540 lbs/MWh to 1,349 lbs/MWh. This concludes the application of allfour building blocks for the year 2029, and the 1,349 lb/MWh rate reflects Ohio’sAdjustedemission rate for the year 2029.State Emission Rate Post Block 4= (coal gen. x coal emission rate)+) +(OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) +“Other” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen. + Nuclear gen. + RE gen. + EE gen.Step 8: Interim and Final State GoalsThe completion of the previous seven steps results in a 2029 emission rate for the state. However, as noted above, the RE andEE assumptionschange for each year from 2020 through 2029. Thus this procedure is repeated for each of those years using the corresponding RE and EEassumption for that year. This results in a set of annual adjusted emission rates for the years 2020-2029. However, this rulemaking only issues18http://www.eia.gov/electricity/data/state/Formatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.two state goals – an interim and final state goal. Thus,goals are derived by averaging or taken directly from and the annual adjusted emission ratevalues.For Option 1:Interim State Goal – Average of the adjusted yearly emission rates for the period 2020-2029Final State Goal – The 2029 emission rate (as calculated above) becomes the state goal for 2030 and each year thereafterFor Option 2:Interim State Goal – Average of the adjusted yearly rates for the period 2020-2029Final State Goal – The 2024 emission rate (as calculated above) becomes the state goal for 2030 and each year thereafterThe EPA recognizes that the emission rate goals calculated hereinclude measures that may yield reductions in mass emissions but may not yieldemission rate reductions in practice when observing monitored andreported emission and generation data from affected units. As such, it is notfully informative to compare the state goals calculated here toobservable emission rates across affected units either historically or in futureyears. For more discussion about how states can demonstrate compliance with these emission rate goals, please see the “State PlansConsiderations TSD”.Finally, the assumptions used to arriveat each state’s goal are not prescriptive ofnecessary actions that a state must take.As described in thepreamble, these values are used only for calculating a state goal.A state may chose to complywith that state goal through any combination ofabatement measures that differ in class and magnitude from those assumed in the state goal calculation.Example of Final State Goal Calculation for Ohio:Final State Goal Rate for Ohio, Option 1– ((2,056 x 78,249,041) + (2,196 x 317,354) + (963 x 27,411,350)+ 2,506,741,578)= 1,349 lb/MWh((78,249,041 + 317,354 + 27,411,350 + 2,604,017 + 1,351,239 +13,755,594 + 17,507,556)See the accompanying Excel Workbook for all state level data, calculations, and final state level emission rate goals used to calculate to Interimand Final State Goals for Options 1 and 2. This workbook shows all the calculations used to transform the historical emissionrate (column B) tothe Interim and Final State Goal (columns BB and BC) for option 1, and (columns AM and AN) for option 2.Formatted:Font: (Default) Times New RomanComment [A28]:Andviceversa–maybethiswouldbeagoodspottomentiontheIPMmodelingintheRIA,whichprovidesanoverallestimateoftheactualcumulativeCO2reductionsnationwideby2030.Formatted:Font: (Default) Times New RomanFormatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.

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APPENDIX

Appendix 1 - State level goals, underlying stat e level data, and calculations for Option 1
See “Appendix 1” worksheet in the Excel Attachment titled “State Goal Data and
Computation”

Appendix 2 - State level goals, underlying stat e level data, and calculations for Option 2
See “Appendix 2” worksheet in the Excel Attachment titled “State Goal Data and
Computation”

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Appendix 3

– Summary of state goals under the proposed Existing Source Performance Standard

Option 1 State Goals

State 2020 State

Goal Interim Goal

(2020

‐ 2029) Final Goal

(2030 and

after) State 2020

State

Goal Interim Goal

(2020

‐ 2029) 

### Final Goal

(2030 and

after)

Alabama 1,196 1,113 1,024 Montana 2,034 1,900 

### 1,781

Alaska 1,185 1,039 878 Nebraska 1,713 1,587 

### 1,471

Arizona 767 724 691 Nevada 757 697 

### 644

Arkansas 1,077 1,012 950 New Hampshire 630 542 

### 485

California 618 580 558 New Jersey 783 657 

### 531

Colorado 1,254 1,164 1,112 New Mexico 1,188 1,088 

### 1,000

Connecticut 651 588 533 New York 749 647 

### 557

Delaware 985 922 848 North Carolina 1,172 1,064 

### 977

Florida 811 750 692 North Dakota 1,857 1,821 

### 1,788

Georgia 962 885 826 Ohio 1,594 1,468 

### 1,349

Hawaii 1,481 1,388 1,304 Oklahoma 984 916 

### 879

Idaho 264 241 225 Oregon 472 408 

### 375

Illinois 1,512 1,391 1,293 Pennsylvania 1,323 1,200 

### 1,067

Indiana 1,723 1,627 1,549 Rhode Island 866 816 

### 773

Iowa 1,409 1,351 1,310 South Carolina 880 802 

### 736

Kansas 1,728 1,597 1,516 South Dakota 868 794 

### 731

Kentucky 1,955 1,861 1,778 Tennessee 1,336 1,234 

### 1,142

Louisiana 1,170 1,080 993 Texas 1,029 932 

### 855

Maine 445 423 407 Utah 1,446 1,375 

### 1,318

Maryland 1,521 1,325 1,166 Virginia 978 869 

### 793

Massachusetts 732 646 567 Washington 376 294 

### 235

Michigan 1,433 1,337 1,261 West Virginia 1,872 1,766 

### 1,636

Minnesota 948 891 851 Wisconsin 1,363 1,265 

### 1,185

Mississippi 775 717 673 Wyoming 1,897 1,801 

### 1,701

Missouri 1,705 1,621 1,544 Comment [A29]: The proposed rule only relates

to the existing fleet of fossil power plants and the

state

‐ based BSER to implement CO2 rate (and
ultimately mass) reductions. This is not a CO2

emission rate for EGUs in any state in 2030. For
instance, if this were the state rate for all EGUs in

2030, then we would be legitimating a whole bunch

of states at 1700

– 1900 lbs/MWhr. This rule only

relates to reductions from the fossil EGU sources
that exist today, and we have another rule (the
NSPS) for new fossil EGUs.

At the other end of the spectrum, if this were the

state EGU rate in 2030, then a whole bunch of

states would be extremely hard pressed to build a

new NGCC (even at the NSPS proposed rate of 1000 lb/MWhr), because there state based emission rates

are so low, such as Washington at 235 lb/MWhr.

And, this is very good reason not to include any new
post 2014 NGCC or even Coal

‐ CCS in this

denominator.

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Option 2 State Goals

State 2020 State

Goal Interim Goal

(2020

‐ 2024) Final Goal

(2025 and

after) State 2020

State

Goal Interim Goal

(2020

‐ 2024) 

### Final Goal

(2025 and

after)

Alabama 1,274 1,243 1,208 Montana 2,084 2,031 

### 1,980

Alaska 1,192 1,142 1,086 Nebraska 1,760 1,710 

### 1,661

Arizona 785 767 751 Nevada 760 736 

### 714

Arkansas 1,153 1,128 1,101 New Hampshire 632 593 

### 553

California 630 611 599 New Jersey 788 740 

### 688

Colorado 1,317 1,274 1,232 New Mexico 1,244 1,203 

### 1,163

Connecticut 664 640 617 New York 799 756 

### 715

Delaware 1,058 1,036 1,010 North Carolina 1,233 1,189 

### 1,145

Florida 818 795 771 North Dakota 1,899 1,887 

### 1,874

Georgia 1,025 992 958 Ohio 1,657 1,611 

### 1,566

Hawaii 1,497 1,464 1,429 Oklahoma 1,061 1,022 

### 986

Idaho 268 259 252 Oregon 483 449 

### 418

Illinois 1,577 1,529 1,484 Pennsylvania 1,389 1,342 

### 1,293

Indiana 1,774 1,738 1,704 Rhode Island 871 854 

### 837

Iowa 1,468 1,447 1,428 South Carolina 921 889 

### 858

Kansas 1,769 1,698 1,644 South Dakota 913 885 

### 856

Kentucky 2,006 1,971 1,937 Tennessee 1,384 1,345 

### 1,307

Louisiana 1,242 1,208 1,171 Texas 1,101 1,059 

### 1,016

Maine 456 448 440 Utah 1,502 1,476 

### 1,451

Maryland 1,573 1,495 1,417 Virginia 1,061 1,011 

### 955

Massachusetts 741 708 675 Washington 389 350 

### 316

Michigan 1,508 1,470 1,434 West Virginia 1,915 1,877 

### 1,835

Minnesota 1,023 1,001 981 Wisconsin 1,444 1,404 

### 1,366

Mississippi 777 754 729 Wyoming 1,941 1,902 

### 1,862

Missouri 1,757 1,726 1,694

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Appendix 4 - Details of state-level data development

The unit-level emissions and generation data derived using the eGRID methodology was relied upon for
determining state level totals ultimately used in the goal calculation. One notable difference is that
published editions of eGRID match emissions and generation at the plant level, while the development of
the state-level data matches emissions and generation at the unit level. Also, the data for this state-level
data goal setting were limited to the following elements at the unit level: CO2 emissions, nameplate
capacity, net generation, state location, and information used to place the unit in each category. The data
sources are from reports from units that submit da ta to the EPA under 40 CFR Part 75, and Energy

Information Administration data from forms EIA-860 a nd EIA-923. The data were assembled at the unit
level and were aggregated to the state-level generati on totals, capacity totals, and emission rates for the
following categories: coal steam, Oil Gas Steam (O/G steam), Natural Gas Combined Cycle (NGCC) and
Simply Cycle Combustion Turbines (CTs), and Integrated Gasification Combined Cycle (IGCC). The
year 2012 historical values for these categories were then modified for each state to derive the state goal
emission rates as described in the main body of the TSD.

Industrial units that are not grid connected are excluded. Units for which fossil fuel was less than 10% of
the heat input in year 2012 were also excluded.

For units that report to the EPA under 40 CFR Part 75, reported CO2 emissions were used. These

emissions are either determined from continuous emissions monitors that measure CO2 concentration and
stack gas volumetric flow or, for units that combust ce rtain gaseous and liquid fuels, fuel flow meters and
fuel testing as required under appendix D and E of 40 CFR Part 75.

For units that do not report to the EPA under 40 CFR Pa rt 75, CO2 emissions are calculated from fuel use
reported in the EIA-923 and emission factors under EPA’s Inventory of U.S. Greenhouse Gas Emissions
and Sinks or The Climate Registry Default Emission Factors. If no unit level fuel use exists in the EIA923 dataset and prime mover fuel data exists, and there are more than one unit at a plant that have the
same prime mover (e.g. steam turbine, combustion turbine, etc.), then prime mover fuel level emissions
are distributed to each generator in the prime mover by proportionately by nameplate capacity. For most
cases (where all units in the prime mover would likely be covered by the rule), this apportionment does

not matter because emissions are summed to the state level. However, if there is a smaller unit in the
prime mover that would not likely be covered by the rule, then this apportionment may not exactly match
the amount of fuel actually burned and the associated emissions for each unit.

Net generation is taken from EIA-923 data. If no uni t level net generation exists in the EIA-923 dataset
and prime mover fuel data exists, and there are more than one unit at a plant that have the same prime
mover (e.g. steam turbine, combustion turbine, etc. ), then prime mover fuel level net generation is
distributed to each generator in the prime mover by proportionately by nameplate capacity.

Logic for determination of source CATEGORY:

CATEGORY Category Name Includes Formatted: Font: (Default) Times New Roman

### I NTERAGENCY 

### W ORKING 

### C OMMENTS ON 

### D RAFT 

### L ANGUAGE UNDER 

### EO12866 I NTERAGENCY 

### R EVIEW



### . S UBJECT

TO 

### F URTHER 

### P OLICY 

### R EVIEW



### .

COALST Coal Steam - likely covered by rule Coal is designated as primary fuel. Nameplate capacity 25 MW or greater or if heat input capacity is 250MMBtu/hr

or greater

OGST Oil Gas Steam - likely covered by rule All steam units not in “Coal Steam” category. Nameplate capacity 25 MW or greater.

NGCC Natural Gas Combined Cycle Units - likely covered by rule. Duct burners and heat recovery steam generators are included with

combustion turbines that are 25 MW. NG is primary fuel or if actual fuel use is >90% NG. Combustion turbine parts having nameplate capacity 25 MW or greater. Any associated duct burners and heat recovery steam generators are included.

SST Simple Cycle Combustion Turbines – 25 MW & likely covered by rule Nameplate capacity 25 MW or greater & 33% capacity factor & 219,000MWh

IGCC IGCC IGCCs at: Wabash, Polk, Edwardsport Formatted: Font: (Default) Times New Roman, Complex Script Font: +Body CS (Arial), 11 pt

Formatted: Font: (Default) Times New Roman, Complex Script Font: +Body CS (Arial), 11 pt

Formatted: Font: (Default) Times New Roman, Complex Script Font: +Body CS (Arial), 11 pt

Formatted: Font: (Default) Times New Roman, Complex Script Font: +Body CS (Arial), 11 pt Formatted: Font: (Default) Times New Roman, Complex Script Font: +Body CS (Arial), 11 pt

Formatted: Font: (Default) Times New Roman, Complex Script Font: +Body CS (Arial), 11 pt

### Page 15: [1] Comment [A22] Author



## This is the apparent logical inconsistency between nuclear and RE. It would likely be mathematically distorting to

add all existing nuclear under the denominator. The converse does not overtly appear to be problematic, i.e., to

remove the small amounts of existing non

‐ hydro RE from the denominator, and a) subtract this existing RE from

the ultimate state goal and b) give the state kudos in the text/table for having this level of existing non

‐ hydro RE.

The benefit of subtracting existing non

‐ hydro RE from the spreadsheet goals would be to maintain logical
consistency with the way we are treating “new” and at risk nuclear, as compared to how we are treating existing

nuclear.

It is possible that subtracting the existing RE from the ultimate state goals may lead to an inconsistency in the way
the EPA calculated goal appears when place side

‐ by

‐ side with specific state mandates in out years. We would be
happy to discuss this balancing of logical consistency with optics.