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 there is potential for broadly applicable efficiency 

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

efficiencies would result in corresponding reductions in greenhouse gas emissions. 

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

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

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

the estimated capital costs of those improvements. 

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 coal-fired EGUs are mines in regions along the 

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

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

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

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

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

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

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

coal or coal refuse is burned in limited quantities. 

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

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

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

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

combustion configurations. 

Comment [A1]: This section read quite weakly as

initially written.   

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 

SO2 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 900o

C). 

Bubbling 

fluidized bed 

(BFB) 

Operates at relatively low gas stream 

velocities and with coarse-bed size 

particles. Air in excess of that 

required to fluidize the bed passes 

through the bed in form of bubbles. 

Circulating 

fluidized bed 

(CFB) 

Operates at higher gas stream 

velocities and with finer-bed size 

particles. No defined bed surface. 

Must use high-volume, hot cyclone 

separators to recirculate entrained 

solid particles in flue gas to maintain 

the bed and achieve high combustion 

efficiency. 

Integrated Coal 

Gasification 

Combined 

Cycle (IGCC) 

Synthetic combustible gas (“syngas”) 

derived from an on-site coal 

gasification process is burned in a 

combustion turbine. The hot exhaust 

gases from the combustion turbine 

pass through a heat recovery steam 

generator to produce steam for 

driving a steam turbine/generator 

unit. 

Coal gasification units are unique among coal-firing 

configurations because a gaseous fuel (synfuel or syngas) 

is burned instead of solid coal because the combustion 

and power generation process and combines the Rankine 

and Brayton thermodynamic cycles as is the case for a 

combined cycle power plant. 

Cyclone 

Furnace 

Combustion 

Coal is crushed into small pieces and 

fed through a burner into the cyclone 

furnace. A portion of the combustion 

air enters the burner tangentially 

creating a whirling motion to the 

incoming coal. 

Designed to burn coals with low-ash fusion temperatures 

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

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

Stoker-fired 

Coal 

Combustion 

Coal is crushed into large lumps and 

burned in a fuel bed on a moving, 

vibrating, or stationary grate. Coal is 

fed to the grate by a mechanical 

device called a “stoker.” 

One of three types of stoker mechanisms can be used that 

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

unto the grate. 

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

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

coal combustion 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 selected 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 cycle electric generation process. The efficiency of 

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

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

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

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

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

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

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

processes.. . 

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

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

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

the U.S. electric utility market. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

heat and power, or CHP, facilities. 

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 efficiency of a given coal-fired EGU. The actual 

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

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

factors affecting EGU efficiency at a given facility include: 

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

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

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

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

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

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

efficiencies are lower than design efficiencies. 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 burning 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 cooling systems (e.g., cooling towers). However, 

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

recirculating cooling systems. 

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

temperatures at the facility site potentially may have 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 drives, 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 potential efficiency improvement techniques applicable 

to existing coal-fired EGUs.1

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

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

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

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

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

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

technical feasibility of using a given technology. 

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

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

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

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

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 more consistent size and finer coal particles. 

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

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

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

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

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

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

requirements are about 0.1% 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 many aspects of the plant’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. Additionally, improvements to seals on the air heaters also reduce 

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

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

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

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

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

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

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 outlet results in 1% efficiency improvement. These 

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

needs a complete replacement. 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 ranges 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 exchange surfaces to the existing heaters or adding 

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

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

repair. 

Condenser 

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

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

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

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

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

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

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

$80,000. 

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 offers, the plant is able to reduce the auxiliary 

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

reductions between 10-150 Btu/kWh. 

Emission Control Technologies 

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

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

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

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

emissions reduction. The three technologies discussed are the flue gas desulfurization (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 further in this section. Older units typically 

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

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

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

around 13 Btu/kWh. 

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

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

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

ID fan. Turning vanes and a perforated gas distribution plate improve gas distribution. The cost 

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

In a wet FGD multiple spray levels are installed to deliver the limestone slurry. If a power plant 

is operating with SO2 levels below its permit limit, turning off one spray level will reduce the 

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

available. 

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 technology 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 NOX emissions from 

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

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

as dilution for the ammonia vaporizer can reduce the auxiliary power necessary. The heat rate 

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

to $100,000 each. 

Water Treatment System 

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

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

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

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

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

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

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

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

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

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

aspects already mentioned. 
DRAFT April 4, 2014 

11 

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

Efficiency 

Improvement 

Technology

Description 

Reported 

Efficiency 

Increasea

Combustion 

Control 

Optimization 

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

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

0.15 to 0.84% 

Cooling System 

Heat Loss 

Recovery 

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

0.2 to 1% 

Flue Gas Heat 

Recovery 

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

0.3 to 1.5% 

Low-rank Coal 

Drying 

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

0.1 to 1.7% 

Sootblower 

Optimization 

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

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

0.1 to 0.65% 

Steam Turbine 

Design 

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

0.84 to 2.6 

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

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

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

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

potential efficiency improvements and associated resulting CO2 emission reductions. These 

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

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

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

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 different in the level of detail and assumptions, the 

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

ranging from 9% to 15% is theoretically possible. 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 CO2 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 EGUs and assumes a target heat rate in order 

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

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

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

has not been determined with regards to how the CO2 emissions limit will be averaged, the paper 

discusses many potential options that coal-fired EGUs may have to meet new standards. These 

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

sources. 

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

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

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

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

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

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

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

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

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

on multiyear historical hourly data. While we understanoodunderstood that engineering 

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

intendeds that this assessment be a more substantial basis for estimating the fleet-wide HRI 

potential for coal-fired EGUs. 

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

Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping

Key Study 

Assumptions

Study 

Results

1 “Reducing CO2 

Emissions by 

Improving the 

Efficiency of the 

Existing Coal-fired 

Power Plant Fleet” 

NETL 

• Plant design: age and 

steam cycle 

• Category improvement is 

the bottom 90% of EGUs in 

each group improving their 

heat rate to the average 

performance of top 10% in 

that category 

• Units with capacity factors 

under 50% were removed 

from dataset 

• 15% reduction in 

overall heat rate of 

coal-fired EGUs 

2 “Improving the 

Efficiency of CoalFired Power Plants 

for Near Term 

Greenhouse Gas 

Emissions 

Reductions” 

NETL 

• Plant design: coal type, 

steam cycle, and size 

• Category improvement is 

the bottom 90% of EGUs in 

each group improving their 

heat rate to the average 

performance of top 10% in 

that category • Units with 

anomalous data, capacity 

factors under 10%, using 

less than 97% coal, and 

gasification plants were 

removed from dataset 

• Low pressure subcritical 

units and 0-200 MW 

subbituminous units 

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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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 CO2

emissions of about 

$1.64 per ton, 

improving heat rates 

by 0.3 to 0.9 % 

5 “Closing the Power 

Plant Carbon 

Pollution Loophole: 

Smart Ways the 

Clean Air Act Can 

Clean Up 

America’s Biggest 

Climate Polluters” 

NRDC 

• Plant design: coal type 

and steam cycle 

Improvements are broken into 

3 group 

• Top 10% for each 

subcategory, no change 

• Top 11% to 49%, improve 

heat rate by 50% of the 

difference between facility 

heat rate and performance of 

top 10% in class or 600 

Btu/kWh, whichever is less 

• Bottom 50%, improve heat 

rate by 100% of the difference 

between facility heat rate and 

performance of top 10% in 

class or 600 Btu/kWh, 

whichever is less 

EPA’s Heat Rate Improvement Assessment 

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

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

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

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

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

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

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 significant heterogeneity of coal-fired EGUs; even 

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

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

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

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

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

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

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

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

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

or replacing existing components with improved versions. 

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

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

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

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

research EGUs where such methods were applied. 

Study Population and Data 

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

reported both heat input2

 and electrical output to the EPA’s Clean Air Markets Division in 2012.3

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

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

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

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

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 CO2 flow and concentration. 3

 Information on the Clean Air Markets Division data is available at http://ampd.epa.gov/ampd/
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study period is representative of a wide range of conditions, including growth and recessionary 

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

different regulatory constraints. 

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

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

complete and accurate emission measurement and reporting to support emission trading 

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

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

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

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

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

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

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

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

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

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

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

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

We also 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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Table 2-4. Study Population Unit Count by Year4

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 sensitive to air temperature and 

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

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

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

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

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

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

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

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

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

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

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

population of EGUs (see Table 2-4). 

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

EGUs shown in Table 2-4. These units emitted 1,606 million tons of CO2 in 2012. In contrast, 

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

                                                            

4 The study population for each year includes those EGUs that reported both heat input and electric generation.
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1,669 million tons according to the EIA.6 Since the study population of EGUs accounted for over 

96% of CO2 emissions from the fleet of U.S. coal-fired EGUs, the EPA considers the results of 

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

Subcategorization 

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

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

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

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

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

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

heat rates for the broad categories has significant overlap (see 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 thermodynamic 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 Subcategory7

                                                            

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 CO2 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/UNDER 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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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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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 are known to operate at higher heat rates. 

Therefore, we evaluated the potential impact of such 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 events is responsible for less than 4% of total 

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

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

to be marginal. 

9,500

9,550

9,600

9,650

9,700

9,750

9,800

9,850

9,900

9,950

10,000

20022003200420052006200720082009201020112012

Heat Rate (btu/kwh)

Annual Heat Rate

11-yr Avg Heat Rate

Comment [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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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 cycle8

 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 have 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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electricity demand capacity factor follows. Figure 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 Analysis 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, these outliers tend to occur during low load 

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

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

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

capacities all results are generation-weighted.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 measure) over a prescribed 30-year interval and location. 

The chart shows the 1981-2010 climate normal monthly 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

100

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Electric generation monthly 2012 capacity 

factor and normal temperature

Capacity Factor

(Monthly %, 2012)

Climate Normal

(BWI, MD)

Comment [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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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 single variable analysis of capacity factor is 

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

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

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

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

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

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

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

30% of the change in heat rate. These are typically, 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, which compares the study population duty cycles in 

2008 to 2012. A significant share of 2008 generation 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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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 data reported under Part 75. Sources and vendors 

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

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

indicator that defines the quality of the electric generation process.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 across 11 years is 5.4%. Table 2-8 summarizes 

eleven years of results for the study population by quartiles ordered by the 11-year generationweighted RSD (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 generation – the most of any segment. The results 

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

potential for heat rate improvement. 

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

Quartile RSD 

Average 

RSD 

Minimum 

RSD 

Maximum 

Share of 

Generation 

1 3.5 1.6 4.2 33

2 4.7 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 essential 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 higher if system-wide real-time optimization is included. 14 Nine EGU RSD values exceeded 2.6 standard deviations above the mean and were removed from the results in the 

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

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

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

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

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

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

temperature. 

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

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

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

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

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

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

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, these units appear to have low variability. 

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

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

operation to reduce variability and heat rate. 

                                                            

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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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 three categories: EGUs with at least one year-toyear decrease in heat rate > 8.5%; units with at least one year-to-year increase in heat rate > 

8.5%; and, units with both. We examined whether the large heat rate changes were due to 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 factor 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 9950

RSD heat rate % 

(generation weighted)

Heat Rate
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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. This does not imply that the changes associated 

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

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

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

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

decrease’ EGUs (generation weighted) 

Quartile RSD 

Average 

RSD 

Minimum 

RSD 

Maximum 

1 4.0 2.3 4.8 

2 5.3 4.8 5.9 

3 6.6 5.9 7.5 

4 10.5 7.6 26.5 

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 for measuring stack gas volumetric flow rate during a 

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

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

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

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

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

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

effect of changes in flow reporting methods on fleet heat rate performance.
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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 variation 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 and 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-year average heat rate for each unit. The percent 

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

to reduced heat inputs is the potential heat rate improvement for that unit.23 Using this approach, 

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

take proportionally smaller reductions. 

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

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

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

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

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

                                                            

20 The 26% result is the generation weighted average of r-squared values from the multivariate 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 wide 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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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 heat 

rate (Btu/kWhgross) 

Study population 

heat rate 

improvement (%)

10 

9,755 

9,625 1.3 

20 9,495 2.7 

30 9,365 4.0 

40 9,235 5.3 

50 9,105 6.7 

Assessment of potential heat rate improvement via equipment upgrades 

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

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

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

that reported 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 believe 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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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 trend 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 due to operating conditions (capacity factor, 

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

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

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

maintain the status quo. 

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 the study population average. Reducing heat rate 

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

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

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

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

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

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

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

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

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

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

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

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

The equipment upgrades results are supported by numerous studies25 and by the EPA’s 

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

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

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

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

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

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

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

electric sector CO2 emissions from coal. 

Sensitivity Analysis Removing Planned 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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The EPA’s research found 230 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 CO2 emissions. For the purpose of this TSD analysis, it is 

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

emission cost savings minus capital cost) is positive: 

Annual Net HRI Savings = Coal Cost Savings + CO2 Emission Cost Savings – Capital Cost27

Heat Rate Improvement Capital Cost Assumption 

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

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

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

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

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

Btu reductions and associated range of capital costs are obtained: 

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

Combined Min-Max HRI Capital Cost: $40-150/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 specific EGU designs, fuel types, age, size, ambient 

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

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

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

                                                            

26The 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 sufficiently small relative to the associated annualized 

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

therefore does not consider the small economic impact of HRI-related O&M costs. 

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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Heat Rate Reduction Assumption 

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

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

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

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

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

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

has already applied some or many of the available HRI methods.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 historical heat rate data and the literature on 

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

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

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

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

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

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

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

6%. 

Heat Rate Improvement Breakeven Economic Analysis 

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

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

reduced via HRI. 

                                                            

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

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

mentioned above. 

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

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

economic fleet wide, 

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

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

 If there were additional HRI savings due to avoided future CO2 emission costs, HRI 

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

rate reduction percentages. 

Estimated Fleet-wide Coal EGU CO2 Reduction and Cost via HRI 

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

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

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

 Average CO2 emission rate = 0.976 tonne/MWh net 

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

 Average capacity factor = 78% 

 Pre-HRI CO2 emissions = 1.62 billion tonne/yr (calculated) 

 HRI Btu and CO2 reduction = 6% 

 HRI capital cost = $100/kW 

 Annual capital charge rate = 14.3% 

 Average coal cost = $2.62/MMBtu 

Estimated Fleet-wide Results: 

 Fleet-wide CO2 reduction via HRI = 97 million tonne/yr 

 Total HRI capital cost = $24 billion 

 Annualized HRI capital cost = $3.5 billion 

 Annual coal cost savings (cost) = $2.7 billion 

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

 Annual net savings (cost) of CO2 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 today’s coal prices if the associated average 

capital cost is about $75/kW or less. 

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

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

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

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

$2.62/MMBtu in 2020). 

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

$2.62/MMBtu, the fleet-wide cost of CO2 reduction via 6% HRI would be a relatively 

low $7.7/tonne. 

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

particular fleet-wide amount of HRI, it is clear that HRI is an available low-cost approach to CO2

reduction for existing coal-fired EGUs 

Definitions/Abbreviations

Btu British thermal unit 

capacity factor 

electricity generation expressed as a percentage of maximum 

electricity generation (i.e., actual generation / maximum potential 

generation) 

CO2 carbon dioxide 

EGU electric generating unit 

EIA Energy Information Administration 

EPA Environmental Protection Agency 

heat input amount of energy consumed in a combustion unit (e.g., boiler) 

expressed in Btu 

heat rate improvement decrease in the amount of heat input required to generate 1 kWh of 

electricity 

heat rate gross heat input required to generate 1 kWh of electricity, expressed 

in gross Btu/kWh. 

MMBtu million Btu 
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MW megawatt 

PC pulverized coal (boiler) 

RSD Relative standard deviation 

S & L report 

Sargent & Lundy engineering study on the potential heat rate 

improvement from equipment upgrades (EPA 2009 version) 

[Available at: 

http://www.epa.gov/airmarket/resource/docs/coalfired.pdf] 

start 

a startup event in which an EGU begins combusting fossil fuel and 

generates some measurable amount of electricity before ceasing fossil 

fuel combustion 

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

Docket Datasets 

Name Description 

hour_QA_data.txt 2002-2012 hourly dataset 

hour_QA_regression_data.txt 2002-2012 hourly dataset for regression analysis 

units_885.txt List of study units and characteristics 

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

options 
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HRI Partial Bibliography 

PowerEng 2002, “Heat Rate Optimization Pays Dividends,” Power Engineering, January 1, 

2002, available at http://www.power-eng.com/articles/print/volume-106/issue-1/features/heatrate-optimization-pays-dividends.html

NETL 2008, Reducing CO2 Emissions by Improving the Efficiency of the Existing Coal-fired 

Power Plant Fleet, DOE/NETL-2008/1329, July 2008 

NETL 2009, Opportunities to Improve the Efficiency of Existing Coal-fired Power Plants, 

Workshop Report, NETL July 2009, available at http://www.netl.doe.gov/energyanalyses/pubs/NETL%20Power%20Plant%20Efficiency%20Workshop%20Report%20Final.pdf

NETL 2010, Improving the Thermal Efficiency of Coal-Fired Power Plants in the United States, 

DOE/NETL Technical Workshop Report, February 2010, available at 

http://www.netl.doe.gov/File%20Library/Research/Energy%20Analysis/Publications/ThermalEf

ficCoalFiredPowerPlants-TechWorkshopRpt.pdf

NETL 2010a, Improving the Efficiency of Coal-Fired Power Plants for Near Term GHG 

Emissions Reductions, DOE/NETL-2010/1411, April 2010, available at 

http://www.alrc.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=307

Sargent & Lundy 2009, Coal-Fired Power Plant Heat Rate Reductions, SL-009597, Final Report, 

January 2009, available at http://www.epa.gov/airmarkets/resource/docs/coalfired.pdf

Storm 2009, “Applying the Fundamentals for Best Heat Rate Performance of Pulverized Coal 

Fueled Boilers, Storm Technologies, Inc, EPRI 2009 Heat Rate Conference, available at 

http://www.stormeng.com/pdf/EPRI2009HeatRateConference%20FINAL.pdf

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

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

Lehigh 2009, Reducing Heat Rates Of Coal-Fired Power Plants, Lehigh Energy Update, January 

2009, available at http://www.lehigh.edu/~inenr/leu/leu_61.pdf

ESC/OnLocation, Efficient Heat Rate Benchmarks for Coal-Fired Generating Units, draft, B. 

Roberts, Economic Sciences Corporation, L Goudarzi, OnLocation, Inc., 1998, available at 

http://www.onlocationinc.com/heatratepaper.pdf

CRS 2013, Increasing the Efficiency of Existing Coal-Fired Power Plants, Congressional 

Research Service, December 2013, available at http://www.fas.org/sgp/crs/misc/R43343.pdf

Evonik/VGB 2008, “Power Plant Performance Reporting and Improvement under the Provision 

of the Indian Energy Conservation Act – Output 1.1”, Evonik/VGB 2008, available at 

http://www.emt-india.net/PowerPlantComponent/Output1.1/Output1.1.pdf

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

CO2 Emissions, OECD/IEA-CIAB 2010, available at 

http://www.iea.org/ciab/papers/power_generation_from_coal.pdf

EPRI 2009, Renewed Interest in Reducing Heat Rate, EPRI Journal, Spring 2009, available at 

http://mydocs.epri.com/docs/CorporateDocuments/EPRI_Journal/2009-

Spring/1019287_Heat%20Rate.pdf
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EPRI/Korellis 2011, Identifying Ways to Improve Plant Heat Rate, Energy-Tech Magazine, 

March 2011, available at http://www.energy-tech.com/article.cfm?id=30528

EPRI 2011a , Opportunities to Enhance Electric Energy Efficiency in the Production and 

Delivery of Electricity, EPRI Technical Report 1024651, November 2011, available at 

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=1&ved=0CCsQF

jAA&url=http%3A%2F%2Fwww.pserc.wisc.edu%2Fdocuments%2Fpublications%2Fspecial_in

terest_publications%2FEPRI_Electricity_Use_Report_Final_1024651.pdf&ei=Qo9yUvmwMY

Xb4AOZz4GYAw&usg=AFQjCNElzekbtoSNCR5SKFwkfbKx83p0Uw&bvm=bv.55819444,d.

dmg

RFF 2013, Regulating Greenhouse Gases from Coal Power Plants under the Clean Air Act, RFF 

DP 13-05, February 2013, available at http://www.rff.org/RFF/documents/RFF-DP-13-05.pdf
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GHG Abatement Measures Technical Support Document 

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

Table of Contents

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

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

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

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

Technical Considerations .......................................................................................................... 1312

Analysis of Cost-Effectiveness ................................................................................................. 1817

 
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Overview 

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

lowering the carbon dioxide (CO2) emissions intensity of the power sector by 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 operation, and the economics of electricity production 

and delivery in the context of cost-effective CO2 emission reduction opportunities. 

Introduction

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

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

of generation and transmission facilities.”1

 Electricity demand varies across geography and time 

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

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

manner possible. 

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

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

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

the least expensive power source first.2

 

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

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

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

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

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

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

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

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

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

                                                            

1

 Energy Policy Act of 2005 

2

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

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

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

Comment [A1]: General Comments:

1)The section on electric markets contains a

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

the section is necessary.

2)It should be made clear in introductory

sections (and throughout TSD) that the analysis

of building block 2/re‐dispatch is based on

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

units to natural gas combined cycle (NGCC) units. 

Power Sector Background

 Electric Dispatch 

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

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

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

 The 

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

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

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

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

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

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

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

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

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

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

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

associated with running pollution controls.4

 Other generating technologies, like renewables, 

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

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

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

sequentially, such that total 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 infrastructure, the utilities themselves often form the 

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

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

buy and sell power with other balancing authorities. 

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

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

efficient and least-cost manner possible.6

 They coordinate, control, and monitor electricity 

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

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

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

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

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

North America (including Canada). 

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

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

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

power through these wholesale electricity markets. 

State Public Utility Commissions (PUCs)7

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

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

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

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

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

utilities, and arbitrate disputes among relevant parties and stakeholders. In restructured markets, 

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

                                                            

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 real-time (hourly, or shorter time periods). These 

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

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

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

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

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

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

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

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

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

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

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

protocols and varies considerably from one utility to the next. 

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

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

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

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

is taken when needed. 

Reliability 

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

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

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

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

to ensure that scheduled generation dispatch can meet load without adverse impacts. If the 

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

adjusted by the system operator. The North American Electric Reliability Corporation (NERC) 
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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 CO2 emission rates9

 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 considerably higher-emitting sources than NGCCs, on 

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

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

CO2 emission reductions. 

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

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

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

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

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

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

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

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

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

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

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

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

                                                            

8

 http://www.nerc.com/ 

9

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

“2012 eGrid Data” file provided in the docket 

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

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

Comment [A3]: This is incorrect. Other forms of

generation are dispatchable. Please clarify.
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evaluate re-dispatch opportunities to unused NGCC generation potential in the system, the EPA 

reviewed recent NGCC fleet operating data to determine capacity factors. Redispatch for GHG 

abatement purposes would require one net MWh of a lower emitting technology displacing one 

net MWh of generation from higher emitting technology. Therefore, when the EPA was 

assessing capacity factor it used the net generation of a given NGCC unit as the numerator. The 

EPA was interested in the relationship of a unit’s total net generation relative to its net generating 

capacity (i.e., capacity factor). Net generating capacity is a function of weather/temperature 

conditions at the site, which varies throughout the year. While some units may model actual 

weather adjusted capacity by the hour/minute, 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. Whereas availability refers to the 

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

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

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

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

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

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

generation. 

Availability for NGCC fleet................................87% to 92% (84% to 89%)13 

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

To quantify the GHG reduction potential from re-dispatch, the EPA considered 

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

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

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

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

                                                            

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 generation/name plate value to a net 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 projections describing NGCC characteristics and 

operating behavior. 

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

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

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

Table 3-1: Existing NGCC Capacity, by Age15

Online Year Capacity 

(Name Plate Capacity – MW) 

Percentage of 

Total Existing NGCC 

Fleet 

Pre 1950 103 0% 

1950-1959 1,769 0.7% 

1960-1969 3,087 1.3% 

1970-1979 6,909 2.8% 

1980-1989 7,658 3.1% 

1990-1999 28,467 11.7% 

2000-2009 174,947 71.7% 

2010+ 21,068 8.6% 

Total 244,008 100% 

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 greater than or equal to its nameplate capacity 

x 8784 hours * 70%. That is, a capacity factor that was 70% or greater (see Figures 3-1 and 3-

2).16 

                                                            

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%

Capacity Factor

% 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%

Capacity Factor

% 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,

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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

# of Units

Unit Distribution by Capacity Factor

0

20

40

60

80

100

120

140

160

180

200

# of Units

Unit Distribution by Capacity Factor

Formatted: Font: (Default) Times New Roman,

12 pt, Complex Script Font: Times New Roman,

12 pt
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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 and 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 adverse effects on the electric system. While many 

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

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

state. 

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

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

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

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

981 TWh, up from 796 TWh in 2011 (22% growth in one year). The extent of this capability 

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

NGCC capacity. 

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

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

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

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

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

CO2. 

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

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

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

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

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

                                                            

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 includes 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 the quick re-dispatch in response to market or 

economic drivers. 

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

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

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

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

advantage is similar to the incentive that a $15/metric ton of CO2 price signal would create.20 

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

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

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

expected to grow more than 20% by 2020 relative to its 2012 levels, creating more fuel resources 

to foster the potential continued and increasing redispatch to NGCC generating technology.21

Table 3-2: 2011 and 2012 Gas and Coal Generation22

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 consistently operate at levels greater than 

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

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

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

                                                            

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 largely determined by the ability to change 

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

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

timing, and cost. 

NGCC Availability 

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

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

national average annual capacity factors in the range of 40-50%.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 historical data for particular units and their design 

and engineering specifications. 

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

part, by statistics on the average availability factor of NGCCs. 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 reported availability factors of 90 to 92 percent to NERC (compared 

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

                                                            

23 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayinenergy/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/issue-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/issue-2/features/higher-availability-of-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 use, the rule allows for emission rate averaging 

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

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

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

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

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

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

use of natural gas use in 2012 increased by 20% over 201130 and resulted in a national average 

capacity factor for NGCC of 49% on average, 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 constraints on the pipeline or electricity transmission 

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

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

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

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

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

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

supplies or electricity transmission. 

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

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

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

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

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

infrastructure when needed to allow the retirement of uneconomic coal plants.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 generally be less extensive than upgrades of that 

infrastructure potentially needed for siting of new 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 deliverability to the natural gas pipeline network to 

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

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

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

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

be possible by the pipeline industry.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 increases in pipeline deliverability, the EPA analyzed the 

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

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

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

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

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

                                                                                                                                                                                               

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

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

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 Foundation, 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 

Area39

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 Administration, 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 compliance 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 CO2

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

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

broader economic and emissions implications of shifting generation from coal-fired steam EGUs 
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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 restrictive 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 incentivizing re-dispatch from more carbon-intensive to less carbonintensive EGUs, the EPA introduced two additional elements to the IPM framework: 

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

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

emissions rate greater than 1,100 lbs/MWh.42

2. Generation constraints that maintain the sum of state-level generation in the Base Case43

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

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

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

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

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

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

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

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

                                                            

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

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

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

of the IPM model can be found at http://www.epa.gov/powersectormodeling 41 In practice, unit dispatch does not respect state boundaries because least-cost supply must be balanced with 

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

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

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

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

different states; as a result, costs and prices in this analysis are overstated. 

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

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

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

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

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

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

Cost and Availability of Economic Re-Dispatch Opportunities 

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

relationship between the amount and cost of re-dispatch. Figure 3-5 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 CO2 reduced by comparing emissions and costs 

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

power sector emissions). 

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 60

Average Capacity Factor

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 CO2 cost imposed 

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

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

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

comprehensive perspective regarding cost-effectiveness. 

 

58%

62%

65%

68% 71%

74% 75%

0%

10%

20%

30%

40%

50%

60%

70%

80%

 ‐  10 20 30 40 50 60

Average Capacity Factor

Average $/tonne

Projected Capacity Factor for Existing NGCCs (2020) 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 

CO2 Cost Level 

Imposed 

National Average 

NGCC 

Capacity Factor 

Average Cost 

($/tonne) 

2020 Power Sector CO2

Emissions 

(MMT / % Change from 

2020) 

Base Case 52% N/A 2,161 

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

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

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

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

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

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

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

 

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

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

lower for several reasons: 

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

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

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

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

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

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

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

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

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

clearly illustrated in the declining slope of the best-fit line in Figure 3-5. A uniform 

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

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

Comment [A7]: Can this be quantified?
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steam are not able to produce incremental economic re-dispatch at units within that state; 

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

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

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

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

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

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

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

demonstrating that the main analysis’s imposition of artificial re-dispatch boundaries on 

state borders overstates the cost-effectiveness of re-dispatch potential. 

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

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

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

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

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

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

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

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

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

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

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

prohibiting interstate re-dispatch opportunities, comparison to the business-as-usual case 

indicates that the average cost of the CO2 reductions achieved ranged from [$xx] to [$xx] per 

metric ton of CO2. 

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

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

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

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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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 greater re-dispatch flexibility, the cost of achieving 

the quantity of CO2 reductions corresponding to a nationwide average NGCC unit utilization of 

70% was notably lower. 

 

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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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 (CO2) emissions from 

fossil fuel-fired electric generating units (EGUs) through 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 technologies 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 other resources regularly replenished by physical and 

biological cycles. To integrate the rapidly increasing and 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 taking into account renewable potential in particular 

regions of the country.

Additionally, the EPA believes that the planned expansion of new nuclear generating capacity 

and the preservation of existing nuclear generating capacity represent a cost-effective means to reduce 

CO2 emissions 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 a technically viable and economically efficient approach for reducing CO2

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 extensively 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 addressed in TSDs on Survey of Existing State Actions, 

State Plan Considerations, Projecting EGU CO2 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. Every state in the union is producing electricity from 

renewable resources, and some states have achieved significant 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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a quarter of in-state generation. State-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 and 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 requirements established through Renewable Portfolio 

Standards (RPS), which provide specific quantifiable RE generation requirements over time. The EPA 

used these RPS-mandated quantities as the basis for deriving regional targets that could be applied to 

states as part of BSER, using the RPS-based targets as a reasonable benchmark of regionally costeffective RE generation which states could grow towards 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 approximately 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 increase 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 compliance 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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Lawrence Berkeley National Laboratory (LBNL) RPS Status Update found, in the period 1998-2012, 

67% of all non-hydro U.S. RE capacity additions, totaling 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-usual 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 CO2 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 from 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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state policies that affect renewable sources specifically 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 performance 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, existing 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 source 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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 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 Columbia8 - 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 generation, but the District does have distributed RE resources 

contributing to the electrical grid. It can also be noted that the non-RE generation reported closed in 2012, so the 

District of Columbia no longer has any utility-scale generation. 
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 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 continually improving strategies for RE development are 

increasing the extent of economically utilized renewable 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 potential 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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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 targets. 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 quantitative 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/PVPS_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/docs/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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requirements from the Database for State Incentives for Renewables and Efficiency (DSIRE).15 This 

information enabled the EPA to determine the effective 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 percentage 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 primary RPS requirements to simplify the analysis of 

primary and secondary RPS requirements in determining 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 the United States. DSIRE is 

currently operated by the N.C. Solar Center at N.C. State 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 version. 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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Figure 4.2 RPS Data Structure19

 

The RPS compliance schedules in six states implement 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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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 their 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 resources. The regional similarities can be inferred from the 

 

20 For more information on the structure of these regions, please refer to the Regulatory Impact Analysis Chapter 3. 
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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 unique 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 generation 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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Table 4.3. List of States Included in Regions for Development of RE Best Practices Scenario22 

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, Louisiana, 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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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 RPS requirement. This average regional RE generation 

target offers a basis for the determination of state-level 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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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% 

Southeast 10% 

West 21% 

4.2.3 Determine start year for state efforts 

The best practice scenario assumes that RE generation will begin increasing in 2017, the year 

following the initial state plan submission deadline23, 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 generation 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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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 generation 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, calculating an annual growth factor based on the 

difference between both states’ historical 2005 and 2012 RE generation. Growth factors for each region 

are provided in Table 4.5. 

Table 4.5. Regional Annual 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 region’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 calculation 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 remainder 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 period. 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
INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

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16 

starting with a lower absolute amount of RE generation 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 within the relevant state. However, the EPA notes that 

state-level RPS policies are often established with the 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 satisfying 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. Beyond 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. 
INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

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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 

 
INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

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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 
INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

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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 Generation 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. 
INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

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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 targets, expressed in absolute (megawatt-hour) terms 

and as a percentage level of each state’s RE generation as a share of its total generation, along with 2012 

RE levels for each state, are provided in Table 4.8. For 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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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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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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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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 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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 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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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 generating more RE have changed significantly in the 

past decade, particularly for wind and solar. According 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 purchase 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 levelized 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 Solar 2012: An Empirical Analysis of Project Cost, 

Performance, and Pricing Trends in the United States. Lawrence Berkeley National Laboratory. LBNL-6408E. 

September 2013. p. 19. 

28 Bolinger, Mark, and Samantha Weaver. Utility-Scale Solar 2012: An Empirical Analysis of Project Cost, 

Performance, and Pricing Trends in the United States. 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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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 integration 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 report 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 needs 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 median change in retail electricity price to be $0.0004 

per kilowatt-hour (only a 0.7 percent increase), the median monthly bill impact to be between $0.13 and 

$0.82, and the median CO2 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. Beyond 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 Efficiency Alliance, December 2010. See also Brown, Marilyn 

A., Etan Gumerman, Xiaojing Sun, Kenneth Sercy, and 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 Berkeley National Laboratory, March 2007, available at 
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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 CO2 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 emissions 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 relatively 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 this proposal, it is reasonable to view the incremental 

cost associated with the CO2 emission reductions available from completion of these units as zero for 

purposes of setting states’ CO2 reduction goals. Completion of these units therefore represents a highly 

cost-effective opportunity to reduce CO2 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 any individual EGU will retire, we view this eight 

 http://emp.lbl.gov/publications/weighing-costs-and-benefits-state-renewables-portfolio-standards-comparativeanalysis-s. 

38 Galen Barbose, “Renewables Portfolio Standards in the 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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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, additional 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 shortfall 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 Rule. 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 state level data, and calculations for Option 1 

 Appendix 2 - State level goals, underlying state level data, and calculations 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 assumptions 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 proposal: (1) Option 1 with state emission rate goals 

based on reasonable assumptions related 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 determining 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-effectiveness 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 construction (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 savings by start of 2025 and thereafter 

*Note – The above values for non-hydro RE and EE saving rates are nationwide 

averages. Each state’s CO2 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 determine 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 technology 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 and 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 vary 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 data compiled using the same methodology as in 

EPA’s Emissions and Generation Resource Integrated Database (eGRID) to determine the 

following 2012 values at the state level, which become 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” emissions2

 “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 emissions 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, including, 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 allows 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 purpose 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 the 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 ultimate 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 represent 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 BSER 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 under 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 forms 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 definition of “existing” unit. 

These were the Dominion Brunswick plant in Virginia (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 appropriateness of the state goals estimated using this 

approach. 

 
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3. Emission Rate Calculation EPA’s methodology for calculating state goals is described in the steps noted below. The implementation of each step is illustrated in the table below its description, using the state of Ohio as an example. Step 1: Calculation of unadjusted 2012 state fossil emission rate for covered sources As noted above, the EPA begins the state goal emission rate calculation by starting with actual historic data at the state level. Unit-level data areissummed to state-level values describing: coal generation, coal emission rate, NGCC generation, NGCC emission rate, OG steam generation, OG steam emission rate, “other” emissions, “other” generation, and NGCC capacity. These categories are identified as they represent the historical generation that is subject to building block application and/or would likely be subject to the state goal. For combined heat and power (CHP) units that are covered under the rule, the entire emissions and generation (including steam-based) from the facility are counted when determining emission rates because such facilities’ entire emissions and generation would be considered when determining compliance with rule.5 However, only the electricity generation portion of CHP is available for redispatch purposes, and therefore only this portion of the cogeneration operation is reflected in the generation totals. There are four coal facilities that co-fired biomass in 2012 that did not report emissions under Part 75, in these cases an emission rate factor is used to estimate stack CO2 emissions attributable to the type of biomass reported.6 These technology specific values become the basis for calculating the state’s emission rate. All emission rates provided in the state goal determination are on a net basis. Therefore, they will appear slightly higher than the emission rates viewed on a gross generation basis. 

2012 Rate (lbs/MWh) 2012 mass (lbs) 2012 Generation (MWh) 2012 Capacity 

Starting covered 

source rate (lbs/MWh) 

A B C D E F G H I J 

Coal Rate 

OG Steam 

Rate 

NGCC 

Rate Other Emissions Coal Gen OG Steam gen NGCC Gen Other Gen NGCC MW 

Ohio 2,187 2,196 963 0 84,726,936 343,627 20,907,183 0 4,343 1,946 

5 A separate state goal rate is calculated for IPM power sector modeling that reflects total emissions from CHP units, but just the electricity provided to grid 

portion of their generation. This was necessary as IPM reflects total emissions associated with cogeneration operation, but only the generation associated with meeting net electricity demand. 

6 http://www.theclimateregistry.org/downloads/2013/04/2013-Climate-Registry-Default-Emissions-Factors.pdf 
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Note, when the values in the above cells shaded green and blue are applied to the equation below, the state’s 2012 “unadjusted fossil” emission rate (or historical fossil rate) for likely covered sources is equal 1,946 lb/MWh. 

Historical emission rate = (coal gen. x coal emission rate)+ (OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) + “Other” emissions 

 Coal gen. + OG gen. + NGCC gen. + “Other” gen. 

Step 2: BSER Block one - Calculation of state fossil emission rate goal for covered sources resulting from 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 element 

of BSER. The EPA assumes that a 6% heat rate improvement at the facility will directly translate to a 6% reduction in the net CO2 emission rate. 

Therefore, in conjunction with determining state goals under option 1 in this proposal, the EPA adjusts the state-level coal emission rate (column A) 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 1.7

2012 Rate (lbs/MWh) 2012 mass (lbs) 2012 Generation (MWh) 2012 Capacity 

State Goal Post Block 1 

(lbs/MWh) 

A B C D E F G H I J 

Coal Rate 

OG Steam 

Rate 

NGCC 

Rate Other Emissions Coal Gen OG Steam gen NGCC Gen Other Gen NGCC MW 

Ohio 2,056 2,196 963 0 84,726,936 343,627 20,907,183 0 4,343 1,841 

Note that when the total emission rate is recalculated with heat rate improvement reflected in the adjustment to the state’s coal emission rate, the 

state emission rate drops from 1,946 lb/MWh to 1,841 lbs/MWh. This is not the final state emission rate goal rate, rather an emission rate reflecting building block 1 application before moving onto the remaining blocks. The bold areas in the equation below reflect the values that are adjusted from their historical level at this stage. 

7 EPA makes a similar adjustment for option 2, but uses a 4% HRI improvement assumption when reducing the coal rate 
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State Emission Rate Post Block 1 = (coal gen. x coal emission rate)+ (OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) + “Other” Emissions  Coal 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 to existing NGCC capacityIn the GHG Abatement Measures TSD, the EPA described how historical 2012 data illustrated that a significant number of NGCC units had a net 

generation that was greater than or equal to its nameplate capacity x 8784 * 0.7 – in other words a 70% or greater net capacity factor. It also 

provided analysis supporting a 70% capacity factor as cost-effective and historical data that illustrated the ability to increase utilization at 

combined cycle units under favorable market conditions. That portion of the TSD also describes the engineering analysis that suggests the average 

availability of a NGCC is greater than 70%. Building block 2 was intended to reflect the potential redispatch to the state’s existing NGCC fleet up 

to 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 multiplied by 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 sources 

support such a level.8 Therefore, if this ceiling value (MWh) is less than the 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 coal and OG steam generation are ramped down by an equivalent amount of generation. If the generation of existing NGCC at a 70% capacity factor is greater than the sum of the state’s 2012 coal, OG steam, and NGCC net generation, the historical NGCC generation is only adjusted to equal the total of the 2012 net fossil generation from these sources. In summary, adjusted NGCC net generation is equal to the lesser of existing NGCC fleet at 70% capacity factor or 2012 total fossil generation from BSER subjected sources. This preserves the historical total covered fossil generation in the state for BSER subjected sources, but reapportions it assuming a 70% state-average net capacity factor ceiling for the NGCC 

fleet. The increase in NGCC generation is subtracted from the coal and OG steam generation in proportion to the state’s historic generation. For 

example, if coal historically accounted for 90% of the total coal and OG steam generation in the state, then its historic generation would be 

8 Fossil sources here refers to those units subject to BSER redispatch building block treatment (i.e., coal, o/g steam, and NGCC)
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reduced by 90% of the amount that NGCC generation increases. Likewise, the OG steam generation would be reduced by 10% of the amount by which NGCC generation is increased. 

2012 Rate (lbs/MWh) 2012 mass (lbs) 2012 Generation (MWh) 2012 Capacity 

State Goal Post Block 

2.1 (lbs/MWh) 

A B C D 

E 

F G H I J 

Coal Rate 

OG Steam 

Rate 

NGCC 

Rate Other Emissions Coal Gen OG Steam gen NGCC Gen Other Gen NGCC MW 

Ohio 2,056 2,196 963 0 78,956,358 320,223 26,701,164 0 4,343 1,781 

Note – in the above example, the total covered fossil generation has not increased in the state from the historic totals shown in step 1. It has only 

been reapportioned assuming a 70% capacity factor for NGCC. NGCC generation is adjusted upwards by approximately 6,000,000 MWh, and 

coal and O/G are adjusted downward by the same amount. This increase in NGCC generation reflects the existing fleet increasing from a 

historical level of approximately 55% CF up to the ceiling of 70% CF. When these updated generation values are folded into the state goal calculation, the emission rate drops from 1,841 lb/MWh in Step 2 to 1,781 lb/MWh. This completes building block two application. The values in bold below reflect the historic data points that have been adjusted to reflect building block implementation at this point in the process.910

State 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” emissions 

Coal 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 existing and “en route” NGCC capacity9 While 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 this 

level in EPA’s projected compliance scenarios.

10 In option 2 state goal calculations, the EPA makes similar adjustments to the historic data but utilize a 65% capacity factor ceiling in place of the 70% CF shown above 

4,343 MW x 8784 hours x 70% CF = 26,701,164
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Step 3 is repeated for “en route” NGCC capacity that would also be considered an existing covered fossil source. For this step, “en route” is defined as anything that came online in 2013 or that was under construction, site prep, or testing by January 8, 2014. The EPA looked at reported data for 2012 and calculated the average performance of NGCCs that came online in the past 5 years and observed that 55% was the average 

capacity factor for these units under a business as usual scenario.11 Therefore, the EPA assumed that a 55% capacity factor would be a reasonable 

representation for the expected generation of “en route” NGCCs capacity under a business as usual scenario. The EPA conservatively designated 

the generation associated with this 55% capacity factor 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 the state 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 at 

55% 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%) is 

assumed to be available for redispatch purposes. The MWh associated with this 15% additional CF displaces coal and OG steam generation in the same 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 first 55%) is captured 

in the “other” category. The generation and emissions from these sources are still included in the ultimate emission rate quantified as the state 

goal by averaging in their unadjusted emissions and generation. For “en route” NGCC units that have no 2012 historical data, the emissions and 

generation associated with the 55% baseline capacity factor is obtained by using the “en route capacity” and historical average emission rate for NGCCs in the state.12 The emissions total for these units is based on multiplying the generation at a 55% CF by the average emission rate for 

existing NGCCs in the state. 

11 http://ampd.epa.gov/ampd/12 Note, both Wyoming and Kentucky had “en route” NGCC capacity, but did not have existing covered NGCC in the state from which a proxy emission rate could be derived for the “en route” capacity. Therefore, the nationwide average NGCC emissions rate of 919 lb/MWh (net basis) became the assumed emission rate for the “en route” NGCC in these two states. 
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2012 Rate (lbs/MWh) 2012 mass (lbs) 2012 Generation (MWh) 

2012 Capacity 

"En Route" 

NGCC 

State Goal Post 

Block 2.2 

(lbs/MWh) 

A B C 

D 

E F G H I J K 

Coal Rate 

OG 

Steam 

Rate 

NGCC 

Rate Other Emissions Coal Gen 

OG Steam 

gen NGCC Gen Other Gen NGCC MW NGCC MW 

Ohio 2,056 2,196 963 2,506,741,578 78,249,041 317,354 27,411,350 2,604,017 4,343 539 1,754 

Note that when the total emission rate is recalculated to reflect the “en route” NGCC capacity, the state’s emission rate at this step drops from 1,781lb/MWh to 1,754 lbs/MWh. This is not the final state emission rate goal rate, rather an interim rate reflecting building blocks one and two application before moving onto the remaining blocks. The bold areas in the equation below reflect the values that are adjusted from their historical level 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” emissions

Coal gen. + OG gen. + NGCC gen. + “Other” gen. 

Step 5: BSER Block Three - Calculation of state fossil emission rate goal resulting from heat rate improvement, redispatch to existing and en route NGCC capacity, and new and preserved nuclear In Step 5, the total under construction nuclear capacity is determined.13 Additionally, an amount of existing nuclear capacity that is “at risk” of 

being retired is identified as approximately 8% of the current nuclear fleet (see GHG Abatement Measures TSD for further discussion on how the 

percent assumption was derived). This new and preserved nuclear capacity was incorporated into the state goals as zero emitting generation. This 13 Watts Bar in TN, Vogtle in GA, and Summer in SC 

4,343 MW x 8784 hours x 70% CF = 26,701,164

539 x 8784 x 15% CF = 710,186

Total = 27,411,350
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was achieved by applying a 90% capacity factor to the nuclear capacity identified as under construction or “at risk” and then applying the resulting MWh figure into the denominator of the state goal calculation. Nuclear is not considered a “dispatchable” resource as is NGCC capacity. 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 fossil sources. The differing dispatch economics for NGCC and nuclear leads to a slightly different treatment in the goal setting. Existing nuclear is not 

assumed to increase generation in the same manner as the NGCC fleet is assumed capable of in the above steps. Instead of redispatching from 

coal to nuclear in the same fashion as the previous steps, the expected generation for new and preserved nuclear is simply added to the state goal 

denominator – resulting in a lovwerlover state goal emission rate. However, it is important to note that this “new and preserved” nuclear capacity is 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 averaged in 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 fossil sources do not necessarily have to lower their own emission rate to help the state achieve the lower state goal reflected subsequent to adding in this nuclear technology provided that the nuclear capacity continues to operate at a 90% capacity factor. 

2012 Rate (lbs/MWh) 2012 mass (lbs) 2012 Generation (MWh) 

2012 Capacity 

"En Route" 

NGCC 

New and 

Preserved 

Nuclear 

State Goal 

Post Block 3.1 

(lbs/MWh) 

A B C D E F G H I J K L 

Coal 

Rate 

OG 

Steam 

Rate 

NGCC 

Rate Other Emissions Coal Gen 

OG Steam 

gen NGCC Gen Other Gen 

NGCC 

MW NGCC MW MWh 

Ohio 2,056 2,196 963 2,506,741,578 78,249,041 317,354 27,411,350 2,604,017 4,343 

539 

 1,351,239 1,733 

Ohio has 0 MW of under construction nuclear and 171 MW of “at risk” nuclear capacity share as described in the “GHG Abatement Measures 

TSD”. This results in an expected nuclear generation amount of 1,351,239 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/MWh to 1,733 lb/MWh

State 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” emissions 

 Coal gen. + OG gen. + NGCC gen. + “Other” gen. + Nuclear gen. 
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Step 6: BSER Block Three, cont. - Calculation of state fossil emission rate goal resulting from heat rate improvement, redispatch to existing and en 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 has low variable operating cost and utilization that is dictated by the renewable resources available. Like nuclear generating capacity, its utilization is not expected to vary significantly between a business-as-usual scenario and a policy scenario. Therefore, the generation from these sources is added into the denominator in a similar fashion. Consequently, all non-hydro RE generation (existing and new build) would be eligible for demonstrating compliance with the state goal. The GHG Abatement Measures TSD describes how the RE generation total for each state was derived from a combination of existing renewable 

generation in the state and target RE levels informed by existing Renewable Portfolio Standards (RPS) in the region. Although the amount of 

assumed RE generation varies by state to reflect the different existing portfolios of renewable resources and the amount of renewable fuels available in different regions of the country, the nationwide average assumed under Option 1 is approximately 13% non-hydro RE by the end of 2029.14 A potential non-hydro RE generation value is calculated for each state annually for all years between 2020-2029 to reflect the annual 

growth as states move toward a particular regional non-hydro RE benchmark. The state-specific non-hydro RE generation values that relate to this goal nationwide average are available in the Excel attachment to this TSD and the GHG Abatement Measures TSD. These non-hydro RE MWh totals include both existing non-hydro renewable resources in the state and new non-hydro renewable generation. 

2012 Rate (lbs/MWh) 

2012 mass (lbs) 2012 Generation (MWh) 

2012 Capacity 

"En 

Route" 

NGCC 

New and 

Preserved 

Nuclear 

Existing and 

New NonHydro RE 

State Goal 

Post Block 3.2 

(lbs/MWh) 

A B C D E F G H I J K L M 

Coal 

Rate 

OG 

Steam 

Rate 

NGCC 

Rate 

Other 

Emissions Coal Gen 

OG 

Steam 

gen 

NGCC 

Gen 

Other 

Gen 

NGCC 

MW 

NGCC 

MW MWh MWh 

Ohio 2,056 2,196 963 2,506,741,578 78,249,041 317,354 27,411,350 2,604,017 4,343 539 1,351,239 13,775,594 1,540 

14 See “GHG Abatement Measures TSD” 
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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 generation 

would be added into the state goal by adding it in the denominator. The RE value and resulting state goal shown above reflect the 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” emissions 

 Coal 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, “en route” NGCC, under construction and preserved nuclear, RE, and demand-side EEA state-specific percent value is determined for each state reflecting the amount of total MWh sales that could potentially be avoided through demand-side energy efficiency measures (see GHG Abatement Measures TSD). Annual percentage savings rates for MWh sales are derived for each state for all years between 2020 and 2029 in Option 1. A different set of annual percent savings are derived for 2020-2024 under 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 only BSER components that increase each year. The GHG Abatement Measures TSD explains the rationale and derivation of these annual savings rates. Once these state-specific percent savings rates for MWh sales are derived, they are multiplied by the state’s 2012 historical retail sales and 

scaled by a factor of 1.0751 to obtain an avoided generation value in MWhs.16 The 7.51% scaling factor effectively convertscoverts the retailssales figure into a corresponding total net generation value that accounts for transmission and distribution losses. 17 The lesser of 1) this avoided net generation value, or 2) the avoided net generation value multiplied by the state’s generation share of its sales is then applied to the denominator. This last step helps assure that building block four 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 its state goal. Therefore, if a state achieves the assumed avoided generation based on EE, it does not have to further lower its emission rate from covered fossil15 http://www.eia.gov/electricity/data/state/16 http://www.eia.gov/electricity/data/state/ “Retail Sales of Electricity by State and Sector” 17 The 7.51% scaler represents the historical difference between total net generation of electricity (4,036 TWh) and retail sales of electricity (3,754 TWh). http://www.eia.gov/electricity/state/pdf/sep2010.pdf
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fuel fired generators determined in the previous steps. Instead, adding the avoided generation to the denominator (i.e., averaging in the avoided generation 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) 

2012 Capacity 

"En 

Route" 

NGCC 

New and 

Preserved 

Nuclear 

Existing and 

New NonHydro RE 

Avoided 

Sales via 

demandside EE 

Post Block 

4 

(lbs/MWh

A B C D E F G H I J K L M N 

Coal 

Rate 

OG 

Steam 

Rate 

NGCC 

Rate 

Other 

Emissions Coal Gen 

OG 

Steam 

gen 

NGCC 

Gen 

Other 

Gen 

NGCC 

MW 

NGCC 

MW MWh MWh MWh 

Ohio 2,056 2,196 963 2,506,741,578 78,249,041 317,354 27,411,350 2,604,017 4,343 539 1,351,239 13,775,594 17,507,556 1,349 

As described in the DdemandDemand-side energy efficiency section of the GHG Abatement Measures TSD, the 2029 percent of generation savings due to energy efficiency is calculatedassumed to be 11.56% in Ohio. This value is then multiplied by the retail electricity sales in the state (152,456,864 MWh)18 and multiplied by 1.0751 to reflect the net generation commensurate with these sales (i.e., inclusive of transmission and 

distribution losses). This results in 18,942,382 MWh of avoided generation. This value is then multiplied by 92.43% (Ohio’s share of sales 

coming from in state power generation) to reflect the amount of avoided 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 added 

into 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 all four building blocks for the year 2029, and the 1,349 lb/MWh rate reflects Ohio’s Adjusted emission 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” emissions 

 Coal gen. + OG gen. + NGCC gen. + “Other” gen. + Nuclear gen. + RE gen. + EE gen. 

Step 8: Interim and Final State Goals The completion of the previous seven steps results in a 2029 emission rate for the state. However, as noted above, the RE and EE assumptions change for each year from 2020 through 2029. Thus this procedure is repeated for each of those years using the corresponding RE and EE assumption for that year. This results in a set of annual adjusted emission rates for the years 2020-2029. However, this rulemaking only issues 18 http://www.eia.gov/electricity/data/state/ 
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two state goals – an interim and final state goal. Thus, goals are derived by averaging or taken directly from and the annual adjusted emission rate values.  For Option 1:  Interim State Goal – Average of the adjusted yearly emission rates for the period 2020-2029  Final State Goal – The 2029 emission rate (as calculated above) becomes the state goal for 2030 and each year thereafter  For Option 2:  Interim State Goal – Average of the adjusted yearly rates for the period 2020-2029  Final State Goal – The 2024 emission rate (as calculated above) becomes the state goal for 2030 and each year thereafter The EPA recognizes that the emission rate goals calculated here include measures that may yield reductions in mass emissions but may not yield emission rate reductions in practice when observing monitored and reported emission and generation data from affected units. As such, it is not fully informative to compare the state goals calculated here to observable emission rates across affected units either historically or in future years. For more discussion about how states can demonstrate compliance with these emission rate goals, please see the “State Plans Considerations TSD”.Finally, the assumptions used to arrive at each state’s goal are not prescriptive of necessary actions that a state must take. As described in the preamble, these values are used only for calculating a state goal. A state may chose to comply with that state goal through any combination of abatement 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 Interim and Final State Goals for Options 1 and 2. This workbook shows all the calculations used to transform the historical emission rate (column B) to the Interim and Final State Goal (columns BB and BC) for option 1, and (columns AM and AN) for option 2. 
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APPENDIX 

Appendix 1 - State level goals, underlying state 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 state 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 data to the EPA under 40 CFR Part 75, and Energy 

Information Administration data from forms EIA-860 and EIA-923. The data were assembled at the unit 

level and were aggregated to the state-level generation 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 certain 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 Part 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 unit 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 

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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 

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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.