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

Sent: Sunday, May 25, 2014 11:16:34 PM 

To: Wayland, Robertj 

Cc: Culligan, Kevin; Elman, Barry 

Subject: 111(d) rule -- Interagency Comments on GHG Abatement Chapters and TSDs 

Hi Bob, 

Attached are redline documents on a number of the GHG Abatement chapters and some of the 

TSDs. I’ve also attached an RFF paper on heat rate improvements that you may want to include 

and reference for the rule. 

Regards, 

Cortney 

<< GHG Abatement_Ch2_InteragencyCommentsunderEO12866_052252014.docx >> 

<< GHG Abatement_Ch3_InteragencyCommentsunderEO12866_052252014.docx >> 

<< GHG Abatement_Ch5_InteragencyCommentsunderEO12866_05252014.docx >> 

<< GHG Abatement_Ch6_InteragencyCommentsunderEO12866_05252014.docx >> 

<< GHG Abatement_Ch7_InteragencyCommentsunderEO12866_05252014.docx >> 

<< Projecting Emission Performance TSD_InteragencyCommentsUnderEO12866_5252014.docx >> 

<< TSD for goal setting_InteragencyCommentsUnderEO12866+05252014.docx >> 

<< Memo BB 1+2_InteragencyCommentsUnderEO12866_05252014.docx >> 

<< Aggregate emissions reduction opportunities from HR improvements_May 20 ....pdf >> 
DRAFT May 15, 2014 

Draft Deliberative 

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

2.0 Introduction 

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

units (EGUs), it is evident that EGUs are generally less efficient at converting fuel into 

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

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

individual EGU efficiency indicates that there is potential for broadly applicable efficiency 

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

efficiencies would result in corresponding reductions in greenhouse gas emissions. 

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

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

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

the estimated capital costs of those improvements. 

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

Coal in the United States is predominately used for electric power generation. Most coalfired EGUs in the United States burn either bituminous or subbituminous coals. The largest 

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

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

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

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

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

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

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

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

coal or coal refuse is burned in limited quantities. 

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

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

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

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

combustion configurations. 
DRAFT May 15, 2014 

2 

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

Coal-firing 

Configuration 

Coal Combustion Process 

Description Distinctive Design/Operating Characteristics 

Pulverized 

Coal (PC) 

Combustion 

Coal is ground to a fine powder that 

is pneumatically fed to a burner 

where it is mixed with combustion 

air and then blown into the furnace. 

The pulverized-coal particles burn in 

suspension in the furnace. Unburned 

and partially burned coal particles 

are carried off with the flue gas. 

Wall-fired 

An array of burners fire into the 

furnace horizontally, and can be 

positioned on one wall or opposing 

walls depending on furnace design. 

Tangential-fired 

(Corner-fired) 

Multiple burners are positioned in 

opposite corners of the furnace 

producing a fireball that moves in a 

cyclonic motion and expands to fill 

the furnace. 

Fluidized-bed 

Combustion 

(FBC) 

Coal is crushed into fine particles. 

The coal particles are suspended in a 

fluidized bed by upward-blowing jets 

of air creating a turbulent mixing of 

combustion air with the coal 

particles. Typically, the coal is mixed 

with a sorbent such as limestone (for 

SO2 emission control). FBC have a 

greater fuel flexibility than PC EGUs 

and can be designed for combustion 

within the bed to occur at 

atmospheric or elevated pressures. 

FBC operating temperatures are in 

the range of 1,500 to 1,650F (800 to 

900o

C). 

Bubbling 

fluidized bed 

(BFB) 

Operates at relatively low gas stream 

velocities and with coarse-bed size 

particles. Air in excess of that 

required to fluidize the bed passes 

through the bed in form of bubbles. 

Circulating 

fluidized bed 

(CFB) 

Operates at higher gas stream 

velocities and with finer-bed size 

particles. No defined bed surface. 

Must use high-volume, hot cyclone 

separators to recirculate entrained 

solid particles in flue gas to maintain 

the bed and achieve high combustion 

efficiency. 

Integrated Coal 

Gasification 

Combined 

Cycle (IGCC) 

Synthetic combustible gas (“syngas”) 

derived from an on-site coal 

gasification process is burned in a 

combustion turbine. The hot exhaust 

gases from the combustion turbine 

pass through a heat recovery steam 

generator to produce steam for 

driving a steam turbine/generator 

unit. 

Coal gasification units are unique among coal-firing 

configurations because a gaseous fuel (synfuel or syngas) 

is burned instead of solid coal because the combustion 

and power generation process and combines the Rankine 

and Brayton thermodynamic cycles as is the case for a 

combined cycle power plant. 

Cyclone 

Furnace 

Combustion 

Coal is crushed into small pieces and 

fed through a burner into the cyclone 

furnace. A portion of the combustion 

air enters the burner tangentially 

creating a whirling motion to the 

incoming coal. 

Designed to burn coals with low-ash fusion temperatures 

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

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

Stoker-fired 

Coal 

Combustion 

Coal is crushed into large lumps and 

burned in a fuel bed on a moving, 

vibrating, or stationary grate. Coal is 

fed to the grate by a mechanical 

device called a “stoker.” 

One of three types of stoker mechanisms can be used that 

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

unto the grate. 

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

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

coal combustion is the coal-firing configuration predominately used at existing EGUs. The most 
DRAFT May 15, 2014 

3 

recent coal technology combustion development involves the integration of coal gasification 

technologies with the combined cycle electric generation process. The efficiency of an IGCC 

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

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

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

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

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

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

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

processes. 

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

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

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

the U.S. electric utility market. 

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

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

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

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

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

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

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

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

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

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

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

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. 
DRAFT May 15, 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. 

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

to be more efficient than EGUs burning lower quality coals with a greater moisture 

content (e.g., lignite). Bituminous coals have higher heating values of greater than 10,500 

British thermal units per pound and lignite coals have higher heating values of less than 

8,300 British thermal units per pound. 

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

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

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

incorporating improvements from advanced technologies, and c3) the economy of scale 

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

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

equipment reduces the overall efficiency of the EGU.

 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, 
DRAFT May 15, 2014 

5 

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

recirculating cooling systems. 

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

temperatures at the facility site potentially may have an impact on EGU efficiency. At 

higher elevations, air pressure is lower and less oxygen is available for combustion per 

unit volume of ambient air than at lower elevations. Cooler ambient temperatures 

theoretically could increase the overall EGU efficiency by increasing the draft pressure of 

the boiler flue gases and the condenser vacuum, and by increasing the efficiency of the 

cooling system. Also, geographic location influences the type of cooling system that can 

be used (e.g., EGUs located in arid locations often cannot use once through cooling) 

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

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

in customer electricity demand. 

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

tend to be more efficient. 

2.3 Technologies to Improve Existing Coal-Fired EGU Heat Rate 

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

articles and technical papers identifying potential efficiency improvement techniques applicable 

to existing coal-fired EGUs.1

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

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

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 

                                                            

1

 See HRI Partial Bibliography at the end of this chapter. 
DRAFT May 15, 2014 

6 

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

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

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

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

2.3.1 Boiler 

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

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

2.3.1.1 Materials Handling2

The coal handling portion of materials handling typically requires about 0.07% (7 

Btu/kWh) of the gross electrical output of a power plant. Depending on the state of the motors 

and drives, replacing them with energy-efficient motors and variable frequency drives can 

reduce the auxiliary power requirements. The variable frequency drives also limit the stress and 

strain on the other equipment. 

Coal pulverizers typically require about 0.6% (60 Btu/kWh) of the gross electrical output 

and can be upgraded to provide more consistent size and finer coal particles. The fine particles 

improve combustion efficiency, consequently reducing fuel cost and heat rate. The costs for 

changes to the pulverizer system are significant, and historically, the projects have improved the 

heat rate justifiably only when the existing equipment has degraded. 

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% (10 Btu/kWh) of the plant’s gross output. 

2.3.1.2 Economizer 

An economizer is a heat exchanger that improves the efficiency of an EGU by recovering 

energy from the exhaust gases to preheat the boiler feedwater. The replacement of the 

economizer can lead 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 

                                                            

2 The Sargent and Lundy report did not provide potential savings for material handling operations. Energy use in 

Btu/kWh has been provided to compare the energy use of materials handling relative to the potential energy savings 

from other efficiency activities.
DRAFT May 15, 2014 

7 

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

installation of new control equipment. 

2.3.1.3 Boiler Control System 

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

control capabilities can control and evaluate many aspects of the plant’s operations. Commonly 

referred to as the Neural Network, computer models are able to control the plant’s processes by 

predicting performance during static and dynamic changes. Many vendors offer Neural Network 

systems to improve the overall efficiency. Neural network systems are typically around 

$550,000-$750,000 and offer heat rate reductions up to 150 Btu/kWh. 

2.3.1.4 Sootblowers 

Intelligent sootblowers may be installed to improve system efficiency. The intelligent 

sootblowers system monitors the furnace exhaust gas temperatures and steam temperatures. 

Other readings may be incorporated into the intelligent sootblowers system which also 

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

areas need sootblowing. Boiler efficiency improvements have ranges from 30-(150 Btu/kWh) 

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

maintenance costs. 

2.3.1.5 Air Heaters 

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

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

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

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

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

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

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

power to pump more flue gas. 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 
DRAFT May 15, 2014 

8 

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. 

The capital costs can range from $1.5-18M for heat rate reductions of 50-120 Btu/kWh. 

2.3.2 Turbine 

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

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

pumps. 

2.3.2.1 Turbine 

Replacements or overhauls of existing steam turbines with advanced turbine designs 

improves the efficiency of converting the energy in the steam to electrical energy. The capital 

costs for these projects ranges from $2-25M with heat rate reductions of 100-300 Btu/kWh.

2.3.2.2 Feedwater Heaters 

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

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

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

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

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

repair. 

2.3.2.3 Condenser 

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

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

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

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

water quality. To improve water quality, closed cooling water systems 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. 
DRAFT May 15, 2014 

9 

2.3.2.4 Boiler Feed Pumps 

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

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

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

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

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

2.3.3 Flue Gas System 

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

efficiency are to improve the forced draft and induced draft fan efficiencies and implementation 

of variable frequency drives. 

2.3.3.1 Induced Draft Fans 

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

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

methods, such as variable inlet vanes , variable frequency drives, and variable pitch blades, 

available to control the flue gas flow allowing highly efficient fan performance. These upgrades 

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

2.3.3.2 Variable Frequency Drives 

It is apparent with the control that a variable frequency drives offers, the plant is able to 

reduce the auxiliary load immensely. Considering upgrading all drives, the capital costs are $6-

16M with heat rate reductions between 10-150 Btu/kWh. 

2.3.4 Emission Control Technologies 

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

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

of auxiliary power with their benefit being 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, the 

electrostatic precipitator, and the selective catalytic reduction systems. 
DRAFT May 15, 2014 

10 

2.3.4.1 Flue Gas Desulfurization 

Coal-fired power plants contain many variances between their flue gas desulfurization 

systems. A few common technology improvements are discussed further in this section. Older 

units typically contained a venturi throat that increased the velocity of the fluid, but resulted in a 

large pressure drop and greater power to operate the induced draft fans. To improve this 

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 

induced draft fan. Turning vanes and a perforated gas distribution plate improve gas distribution. 

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

In a wet flue gas desulfurization 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. 

2.3.4.2 Electrostatic Precipitator 

The best operation for an electrostatic precipitator involves maintaining the maximum 

applied voltage, but below the level in which spark-over occurs. Electrostatic precipitator energy 

management system upgrades often help improve the electrostatic precipitator performance by 

maintaining the optimal performance and lowering power consumption. The installation for this 

technology can be from minimal to $0.8M and can lower heat rate by 5 Btu/kWh. 

2.3.4.3 Selective Catalytic Reduction 

For the last 15 years, selective catalytic reduction systems have been in use to reduce 

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. 
DRAFT May 15, 2014 

11 

2.3.5 Water Treatment System 

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

treatment and the cooling tower improvements. 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 May 15, 2014 

12 

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. 
DRAFT May 15, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

13 

2.4 Previous Studies on Heat Rate Improvements 

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

potential efficiency improvements and associated resulting CO2 emission reductions. These 

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

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

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

The NETL studies used a benchmarking approach that evaluated the design factors that 

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

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

boilers. Potential efficiency improvements were calculated based on an assumption that the 

lower-performing EGUs in each group should to be able to do as well as the better performing 

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

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

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

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

ranging from 9% to 15% is theoretically possible. The Lehigh study used a less detailed 

approach and evaluated technologies applicable to bituminous and subbituminous coals to 

estimate potential fleet wide reductions. 

An alternative approach to evaluate heat rate improvement is used by Resources for the 

Future. This study focused on the operating efficiency (synonymous with heat rate) of the entire 

existing U.S. coal-fired EGU fleet. The authors evaluated decades of data from industrial 

responses to economic factors such as demand, coal price and energy policies. This approach 

sought to estimate overall changes in industry fleet efficiency in response to changes in fuel 

prices or carbon prices. In 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%. 

The National Resources Defense Council approach considered the fleet of coal-fired 

EGUs and assumes a target heat rate in order for the EGUs to comply with an inferred standard. 

As opposed to the above studies that determined by how much the efficiencies can improve, this 

approach estimated how the industry will meet any imposed standards and calculated the heat 
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rates necessary to meet a standard. As it has not been determined with regard to how the CO2

emissions limit will be averaged, the paper discusses many potential options that coal-fired 

EGUs may have to meet new standards. These options include additional options such as adding 

renewable, natural gas, and combined-cycle sources. 

The EPA observed that existing HRI studies using a benchmarking approach have relied 

on a single year annual average heat rate data, whereas many of the operational impacts on heat 

rate (diurnal temperatures, load following, etc) become more apparent in the variability of hourly 

performance data. We further recognized that an examination of heat rate data over a multiple 

year period, perhaps a decadal time frame might reveal patterns of performance that should also 

inform estimates of HRI potential. For these reasons, the EPA has developed in this TSD an 

additional assessment of HRI potential that draws on multiyear historical hourly data. While we 

understand that engineering judgment remains essential to a proper interpretation of the results, 

the EPA intends that this assessment be a more substantial basis for estimating the fleet-wide 

HRI potential for coal-fired EGUs. 

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

Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping 

Key Study 

Assumptions 

Relevant HRI 

Results 

1 “Reducing CO2 

Emissions by 

Improving the 

Efficiency of the 

Existing Coal-fired 

Power Plant Fleet” 

NETL 

• Plant design: age and 

steam cycle 

• Category improvement is 

the bottom 90% of EGUs in 

each group improving their 

heat rate to the average 

performance of top 10% in 

that category 

• Units with capacity factors 

under 50% were removed 

from dataset 

• 15% reduction in 

overall heat rate of 

coal-fired EGUs 

2 “Improving the 

Efficiency of CoalFired Power Plants 

for Near Term 

Greenhouse Gas 

Emissions 

Reductions” 

NETL 

• Plant design: coal type, 

steam cycle, and size 

• Category improvement is 

the bottom 90% of EGUs in 

each group improving their 

heat rate to the average 

performance of top 10% in 

that category • Units with 

anomalous data, capacity 

factors under 10%, using 

less than 97% coal, and 

gasification plants were 

removed from dataset 

• Low pressure subcritical 

units and 0-200 MW 

subbituminous units 

• 8.7% reduction in 

overall heat rate of 

coal-fired EGUs 
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Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping 

Key Study 

Assumptions 

Relevant HRI 

Results 

assumed retired for goal 

• Lost generation made up by 

more efficient coal-fired 

EGUs 

3 “Reducing Heat 

Rates of Coal-Fired 

Power Plants” 

Lehigh Energy 

Update 

 All possible heat rate 

improvements are made 

including drying of high 

moisture coal: 

• 10% improvement 

for bituminous coalfired EGU 

• 15% improvement 

for subbituminous 

coal-fired EGU 

4 “Regulating 

Greenhouse Gases 

from Coal Power 

Plants under the 

Clean Air Act” 

Resources for the 

Future 

• Analyze the actual 

operating efficiency of the 

entire fleet of U.S. coalfired EGUs 

• Assess abatement 

opportunities and costs by 

observing how coal plants 

respond to market and 

regulatory incentives to 

improve energy efficiency 

Overall efficiency 

improvements of 2 to 5%, 

from other literature studies 

10% coal price 

increase, 

corresponding to a 

tax on CO2

emissions of about 

$1.64 per ton, 

improving heat rates 

by 0.3 to 0.9 % 

5 “Closing the Power 

Plant Carbon 

Pollution Loophole: 

Smart Ways the 

Clean Air Act Can 

Clean Up 

America’s Biggest 

Climate Polluters” 

NRDC 

• Plant design: coal type 

and steam cycle 

Improvements are broken into 

3 group 

• Top 10% for each 

subcategory, no change 

• Top 11% to 49%, improve 

heat rate by 50% of the 

difference between facility 

heat rate and performance of 

top 10% in class or 600 

Btu/kWh, whichever is less 

• Bottom 50%, improve heat 

rate by 100% of the difference 

between facility heat rate and 

performance of top 10% in 

class or 600 Btu/kWh, 

whichever is less 

 Broader analysis 

not strictly focused 

on heat rate 

improvements. 11% 

reduction on overall 

fossil fuel-fired EGU 

emission rates; 

includes switching 

from coal to natural 

gas. 

2.5 EPA’s Heat Rate Improvement Assessment 

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

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

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

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

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

improvement that, on average, could be achieved by the fleet. 
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This data analysis portion of the study relies on unit-level heat input and gross generation 

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

heat rates. Potential changes in net heat rates are then addressed later in this section. Unit-level 

evaluations allowed the EPA to recognize the significant heterogeneity of coal-fired EGUs; even 

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

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

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

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

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

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

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

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

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

or replacing existing components with improved versions. 

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

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

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

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

research EGUs where such methods were applied. 

2.5.1 Study Population and Data 

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

reported both heat input3

 and electrical output to the EPA’s Clean Air Markets Division in 2012.4

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

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

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

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

                                                            

3 Sources calculate heat input using an ‘F factor’ for the carbon content of the fuel being combusted and the average 

hourly measurements of CO2 flow and concentration. 4

 Information on the Clean Air Markets Division data is available at http://ampd.epa.gov/ampd/
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heat. Therefore, it is difficult for the EPA, using available data, to make a meaningful 

comparison of these units’ heat rates. 

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

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

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

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

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

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

different regulatory constraints. 

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

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

complete and accurate emission measurement and reporting to support emission trading 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

heat input, electric generation, heat rate and unit counts by year for the study population used in 

this work. This population corresponds to 9,388 unit-years of data at 884 distinct EGUs. Figure 

2-1 displays the study population average gross heat rate by year and the 11-year average. 
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Table 2-4. Study Population Annual Heat Input, Generation, Heat Rate and Unit Count 

2002 - 2012 5

Year 

Heat Input 

(million 

MMBtu) 

Electric 

Generation 

(million MWhgross) 

Heat Rate 

(Btu/kWhgross) Unit Count 

2002 18,601 1,874 9,924 839 

2003 18,428 1,864 9,886 834 

2004 18,405 1,875 9,819 836 

2005 18,665 1,910 9,774 838 

2006 18,644 1,914 9,743 848 

2007 18,704 1,920 9,740 846 

2008 18,459 1,914 9,643 852 

2009 16,588 1,719 9,649 864 

2010 17,693 1,831 9,662 869 

2011 16,934 1,744 9,708 878 

2012 14,947 1,536 9,732 884 

                                                            

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

9,500

9,550

9,600

9,650

9,700

9,750

9,800

9,850

9,900

9,950

10,000

20022003200420052006200720082009201020112012

Heat Rate (btu/kwh)

Annual Heat Rate

11-yr Avg Heat Rate
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Figure 2-1. Study Population Average Gross Heat Rate by Year 

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

20,000 weather stations worldwide.6

 Since EGU heat rate performance is sensitive to air 

temperature and barometric pressure, which vary with elevation, we use meteorological data 

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

impact of ambient conditions. For each of the plants in this study’s database, we identified the 

nearest stations that reported a minimum of 8,400 hourly observations in the calendar year (i.e., 

greater than 95% of the hours in a year). Generally, each plant was associated with 

meteorological data from the two closest stations over the eleven-year study period. The average 

distance between the plant and the nearest station was 22 miles and the average difference in 

elevation was 366 feet. At nine plants we used data with as few as 7,000 observations in order to 

keep the maximum difference in elevation under 1,000 feet. The EPA believes that nearby 

weather station measurements are a good approximation of ambient meteorological conditions at 

each facility. Joining the trimmed heat rate and hourly temperature datasets resulted in 

61,848,580 hourly records for the study population of EGUs (see Table 2-4). 

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

EGUs shown in Table 2-4. These units emitted 1,605 million tons of CO2 in 2012. In contrast, 

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

                                                            

6 Temperature data is from NOAA’s Integrated Surface Data at http://www.ncdc.noaa.gov/data-access/land-basedstation-data/land-based-datasets/integrated-surface-database-isd.

9,500

9,550

9,600

9,650

9,700

9,750

9,800

9,850

9,900

9,950

10,000

20022003200420052006200720082009201020112012

Heat Rate (btu/kwh)

Annual Heat Rate

11-yr Avg Heat Rate

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1,669 million tons according preliminary data from the EIA.7

 Since the study population of 

EGUs accounted for over 96% of CO2 emissions from the fleet of U.S. coal-fired EGUs, the 

EPA considers the results of this study to be applicable to the coal-fired fleet at large. 

2.5.2 Subcategorization 

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

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

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

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

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

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

heat rates for the broad categories has significant overlap (see box and whisker chart in Figure 2-

2) and therefore makes it challenging to develop appropriate categorization. The figure below 

displays available information on coal-fired EGUs considered in this work for the years 2009-

2011 in typical subcategories of capacity, fuel rank, and thermodynamic cycle. As the figure 

reflects, the means are clustered and the ranges of heat rates overlap. 

                                                            

7

 Preliminary 2012 results from http://www.eia.gov/tools/faqs/faq.cfm?id=77&t=3.    
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Figure 2-2. Three-Year Average Heat Rates by Subcategory8

2.5.3 Observed Trends in the Period 2002-2012 

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

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

electric generation and gross heat rate declined by 9% and 2%, respectively. Capacity factor for 

                                                            

8 Abbreviations in the figure: BIT means bituminous, SUB means subbituminous, PC means petroleum coke, LIG 

means lignite, SUPER means supercritical, OVER/UNDER means greater/less than indicated MW capacity. Unit 

counts (n) by category: BIT SUPER, n=80; SUB SUPER, n=30; BIT OVER, 200 n=196; PC, n=2; BIT 100 to 200 

n=140; SUB OVER 100, n=299; LIG OVER 100, n=20; BIT UNDER 100, n=68; SUB UNDER 100, n=56; LIG 

UNDER 100, n=2. Total unit count is 893.

6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000

BIT SUPER

SUB SUPER

BIT OVER 200

PC

BIT 100 to 200

SUB OVER 100

LIG OVER 100

BIT UNDER 100

SUB UNDER 100

LIG UNDER 100

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

Subcategory

6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000

BIT SUPER

SUB SUPER

BIT OVER 200

PC

BIT 100 to 200

SUB OVER 100

LIG OVER 100

BIT UNDER 100

SUB UNDER 100

LIG UNDER 100

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

Subcategory

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the study population fell by 14% comparing the same time periods (see Table 2-5). The decrease 

in coal-fired generation and capacity factor may be because of reduced demand for electricity 

resulting from the recession starting in late 2008 and greater use of natural gas and renewables to 

generate electricity. 

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

(see Table 2-4) was 9,754 (Btu/kWh). The decrease in study population annual heat rate between 

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

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

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

rate (9,643 (Btu/kWh)) occurred in 2008 and was approximately 1% below the 11-year average. 

Table 2-5. Reported Annual Capacity Factor 2002-20129

Year Capacity 

Factor (%) 

2002 68 

2003 69 

2004 70 

2005 71

2006 70 

2007 71 

2008 70 

2009 62 

2010 65 

2011 61 

2012 53 

2.5.4 Startups and Shutdowns - Impact On the Results of This Study 

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

Therefore, we evaluated the potential impact of such events in our study. A startup event, as 

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

amount of electricity. Table 2-6 summarizes the study population average, maximum and total 

                                                            

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

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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. 
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Table 2-6. EGU Start Count by Year 

Year Average Maximum (at 

any single EGU) Total 

2002 11.1 209 9,363 

2003 10.6 194 8,936

2004 10.7 134 9,081 

2005 11.0 183 9,265 

2006 10.5 139 8,859 

2007 10.5 164 8,908 

2008 10.4 134 8,880

2009 10.3 178 8,902 

2010 10.5 211 9,110 

2011 10.6 206 9,295 

2012 9.9 119 8,805 

To understand the potential for heat rate improvement available with existing coal-fired 

steam EGUs, the EPA conducted a number of quantitative analyses. These included: (1) 

regression analyses to understand the impact of capacity factor and ambient temperature; (2) 

using a bin model to determine the potential from best practices; and, (3) evaluating available 

data and information to assess the potential from equipment upgrades. These analyses are 

described in the following sections. 

2.5.5 Impact of capacity factor and ambient temperature 

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

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

of the EGUs in the study population. Power plant operators today typically use digital control 

systems to capture hundreds of data points in near real-time that are summarized in the unit heat 

rate statistic. EPA has access to a small fraction of that information. A key reason this study used 

capacity factor and ambient temperature as independent variables is that both were available as 

hourly data. Preliminary analyses of heat rate at higher time increments, such as month, were 

useful to describe aspects such as seasonality but we determined hourly data was necessary to 

understand how heat rate was responding to constantly changing operating conditions. We tested 

for collinearity between capacity factor and ambient temperature using a zero-order correlation 
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matrix on the entire hourly data set. The correlation between the independent variables was -.048 

– well below an indication of collinearity. 

We also considered fixed unit characteristics such as unit type, fuel rank and age as 

independent variables. As noted above in the discussion on subcategorization, these factors can 

be helpful to understanding the heat rate performance of EGUs. The purpose of this study, 

however, is to find the potential for heat rate improvement across the fleet. We use heat rate 

variability as a key statistic to measure this potential. The correlation between the potential for 

heat rate improvement and fixed characteristics is typically low.10 

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

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

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

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

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

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

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

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

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

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

cost, and transmission constraints. 

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

thermodynamic steam cycle11 and, 2) in many regions of the country, as temperatures increase 

electricity demand and capacity factor follow. Figure 2-3 shows the average monthly capacity 

factor in 2012 alongside the climate normal monthly temperature.12 The lines intersect in the 

spring as temperatures begin to rise and the need for cooling drives electricity demand. 

                                                            

10 For example, the correlation between annual unit heat rate variability (discussed below as relative standard 

deviation) and unit nameplate capacity (MW) is in the -0.1 range. 11 The availability of a cold heat sink in the condenser is a key factor in that cycle. The design of the heat exchanger, 

type of cooling system and availability of water all have an impact on performance. An increase in ambient air 

temperature, and consequent increase in water temperature, typically lower the effectiveness of the cooling system, 

the condenser, and, therefore, overall plant efficiency.

12 Climate normal is the average of temperature (or other measure) over a prescribed 30-year interval and location. 

The chart shows the 1981-2010 climate normal monthly temperature at Baltimore-Washington Airport, MD.
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Generally, peak capacity factor and generation in most parts of the U.S. occur on the hottest days 

of the year. Yet, the relationship between ambient temperature and capacity factor is complex. 

Each plant responds differently depending on design, meteorological conditions and electricity 

demand. For example, a base load plant may operate at a high capacity factor seven days a week 

regardless of temperature. As noted above, the collinearity between these two variables is low. 

Figure 2-3. Monthly Capacity Factor, 2012. 

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

Heat rate

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

capacity factor and ambient temperature. Since this analysis seeks to evaluate heat rate under 

normal operating conditions, we removed records with hourly heat rate values outside of +/- 2.6 

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)

0

20

40

60

80

100

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Electric generation monthly 2012 capacity 

factor and normal temperature

Capacity Factor

(Monthly %, 2012)

Climate Normal

(BWI, MD)

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standard deviations (1.9% of records) before performing the regressions.13 Similarly to partial 

operating hours, these outliers tend to occur during low load conditions. The records trimmed 

amount to one-fourth of a percent of the total study population generation. Regression results 

describe the goodness of fit for the model and are expressed as the coefficient of determination 

or ‘r-squared’. To represent the relative contribution of varying unit capacities all results are 

generation-weighted.14 The average study population r-squared for the multivariate regression is 

26%. This means that hourly ambient temperature and capacity factor together explain 26% of 

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

population r-squared from the single variable analysis of capacity factor is 16%; the 

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

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

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

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

factor is a key variable influencing changes in heat rate. 

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

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

up to 30% of the change in heat rate. These are typically, but not exclusively, units with oncethrough, fresh water, cooling systems. Identifying temperature-responsive EGUs allows us to 

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

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

month heat rates may increase by 2-4% compared to winter months, but at a temperatureresponsive EGU that figure may be as high as 10%. 

Our analysis indicates that as EGUs moved from base load to load following, capacity 

factor tended to have a larger effect on heat rate. Since 2008, the study population capacity 

factors moved from the top load bin into lower load bins. This can be seen from Figure 2-4, 

which compares the study population duty cycles in 2008 to 2012. A significant share of 2008 

generation occurred at EGUs running at greater than 84% annual capacity factor. In 2012, little 

                                                            

13 The 2.6 standard deviation bound is used in other EPA regulatory analyses. 14 In a weighted average, each component is multiplied by a factor reflecting its importance. In this case, generationweighted r-squared is the sum of r-squared for each unit multiplied by its annual generation divided by the sum of 

generation for all units.
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generation took place in that bin or above, and generation was reduced by about half in the next 

lower bin (78-83% capacity factor). 

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

2.5.6 Heat Rate Variability and Indication of Improvement Potential 

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

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

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

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

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

indicator that defines the quality of the electric generation process.15,16 Therefore, in general, 

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

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

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

                                                            

15 “The principal indicator that defines the quality of the process is heat rate.” (Fredrick & Todd, 1993. Statistical 

Process Control Methods in Performance Monitoring. Available at 

famos.scientech.us/Papers/1993/1993section11.pdf) 

16 Since accurate measurement is essential to process control the introduction of increasingly sophisticated digital 

control systems (DCS) presents new opportunities for finding inefficiencies. Vendors (ABB, Siemens, Emerson) 

claim heat rate improvements of 2-5 percent can result from upgrading to a modern DCS and advanced control 

technologies. The improvement can be even higher if system-wide real-time optimization is included. 
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eleven 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 average (ascending).17 The RSD of the top quartile (3.5%) is significantly lower 

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

quartile are not outliers; they report a third of all generation – the most of any segment. The 

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

the potential for heat rate improvement. 

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

Quartile RSD 

Average 

RSD 

Minimum 

RSD 

Maximum 

Share of 

Generation 

1 3.5 1.6 4.2 33

2 4.8 4.2 5.3 26

3 6.1 5.3 7.0 24

4 9.8 7.1 25.2 16

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

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

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

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

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

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

temperature. 

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

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

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

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

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

                                                            

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

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

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

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

Table 2-9. RSD of residual heat rate (generation weighted)18

Quartile RSD 

average 

RSD 

Minimum 

RSD 

Maximum 

1 2.7 0.0 3.2 

2 3.6 3.2 4.1 

3 4.7 4.1 5.3 

4 6.9 5.3 10.4 

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

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

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

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

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

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

operation to reduce variability and heat rate. 

2.5.6.1 Heat rate variability and performance 

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

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

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

Figure 2-5 below summarizes the results of regressing RSD of heat rate onto annual heat rate.19

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

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

                                                            

18 This table excludes nine units where RSD exceeded 2.6 standard deviations from the mean. 19 The regression analysis was performed on 9,388 unit-years of study data which were trimmed to remove values 

outside 2.6 standard deviations.
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Figure 2-5. Regression analysis of reported heat rate RSD onto annual heat rate 

2.5.7 Units with large heat rate changes 

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

change in heat rate greater than +/- 8.5%.20 We consider this magnitude to be in the upper range 

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

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

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

heat rate and capacity factor for any of the three groups in Table 2-10.21 This would indicate that 

                                                            

20 After removing unit-years where annual capacity factor fell under 50 percent the count is 313 EGUs. 21 The correlation remains weak even when limited to cases where capacity factor changed more than 30 percent. 

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

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

approximately two-thirds of the large decreases in heat rate can be associated with changes in 

reporting method implemented to provide more accurate heat input data.22 The large changes 

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 

                                                            

22 EPA Reference method 2 specifies the normal procedure for measuring stack gas volumetric flow rate during a 

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

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

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

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

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

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

effect of changes in flow reporting methods on fleet heat rate performance.
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2.5.8 Assessment of heat rate improvement potential via best practices 

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

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

population heat rate.23 This means that on average 74% of the change remains unexplained after 

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

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

rate, other factors held equal, the range of variation indicates that significant potential for heat 

rate improvement is available through the application of best practices. 

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

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

matrix of 168 bins.24 For a given EGU, a temperature and capacity factor bin will have all the 

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

model finds the reported hourly heat input value corresponding to the 10th percentile (p10).25

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

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

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

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

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

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

to reduced heat inputs is the potential heat rate improvement for that unit.26 Using this approach, 

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

take proportionally smaller reductions. 

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

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

                                                            

23 The 26% result is the generation weighted average of r-squared values from the multivariate regression analysis. 24 The matrix provides up to 168 bins but only 164 contained hourly values. Temperature bins ranged from -20 to 

greater than 110 with 10 degrees F in each. Capacity factor bins ranged from 0% to greater than 110% with 10% in 

each. 

25 Performing the calculation with a minimum of 30 values in each bin has a modest effect on the results in Table 2-

12. For example, a 30% reduction obtains a 3.9% fleet wide improvement in heat rate (rather than 4.0%). 26 Heat rate is calculated as the sum of heat input (Btu, reported or reduced) divided by the sum of generation (kWh) 

for the given population and time period.  
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study population wide reduction. Alternatively, a 50% reduction will result in a 6.7% study 

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

variability and improves performance for each unit while controlling for temperature and 

capacity factor. The heat rate improvement for the study population is derived from the 

performance of each individual EGU as compared to its own record. 

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

% Reduction from 

reported heat input 

to p10 

Study population 

heat rate 

(Btu(kWh-gross)27

Reduced study 

population heat 

rate (Btu/kWhgross) 

Study population 

heat rate 

improvement (%)

10 

9,755 

9,625 1.3 

20 9,495 2.7 

30 9,365 4.0 

40 9,235 5.3 

50 9,105 6.7 

2.5.9 Assessment of potential heat rate improvement via equipment upgrades 

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

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

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

that reported a single year-to-year heat rate improvement of 3-8%. In two of these cases we were 

able to identify equipment upgrades responsible for 2-3% heat rate improvement using the 

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

our research was unable to confirm specific equipment upgrades, based on the elimination of 

other possible explanations we believe that equipment upgrades were the most likely cause of 

some of the observed heat rate improvements. 

Two other sources provide information about heat rate improvements after equipment 

upgrades at existing plants. EPA Region 7 provided data for seven coal-fired units at three 

anonymous plants with details on specific equipment modifications. These included turbine 

efficiency and condenser performance upgrades, installation of variable frequency drive fans, 

                                                            

27 Fleet heat rate for study population as described in Table 2-4 is 9,754; the 9,755 value is derived from the dataset 

that includes hourly temperature values.
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reducing boiler air in-leakage and others. Together, these measures achieved from 0.25% to 

3.5% heat rate improvement at the seven EGUs. 

An EPA study (SRA, 2001) describes WEPCO’s two-phase efficiency program at four 

coal-fired plants over a ten-year period. In the first phase, 1990 – 1994, WEPCO installed 

equipment upgrades that included retractable turbine packing, variable speed drives on the forced 

and induced draft fans, feed water heater replacements and new performance monitoring 

instrumentation. The four units reported heat rate improvements ranging from 2.3% – 4.1% as a 

result of the equipment upgrades. In the second phase, 1995-2000, WEPCO implemented 

changes that would generally fall into the best practices category: equipment control and 

metering upgrades, boiler cleaning, feed water heater improvements and reduced condenser air 

in-leakage and thermal losses. These gained an additional 0.5% per year heat rate improvement 

(for a total of 2.5%). 

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

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

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

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

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

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

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

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

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

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

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

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

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

already made these upgrades. 

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

No-Cost and Low-Cost Options 

Condenser Cleaning 

Intelligent Soot Blowers 
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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 the heat 

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

The annual trend line incorporates performance due to operating conditions (capacity factor, 

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

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

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

maintain the status quo. 

2.5.10 Combined study population results 

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

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

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

by 2%. 

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

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

significantly lower heat rate variability than the study population average. Reducing heat rate 

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

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

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

temperature bins. The 30% approach is in the middle of the range of options shown in Table 2-12 
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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 studies28 and by the EPA’s 

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

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

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

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

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

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

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

electric sector CO2 emissions from coal. 

2.5.11 Sensitivity Analysis Removing Planned or Announced EGU Retirements 

The EPA’s research found 233 coal-fired, non-cogeneration EGUs that have announced 

they will retire before 2016.29 A sensitivity analysis was applied to the EGUs in the study 

population that plan to operate through 2015. The results are identical to the full population – 

both achieve a heat rate reduction of 4% under the 30 percent difference option described in best 

practices. 

2.6 Heat Rate Improvement – Economics 

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

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

                                                            

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

29 IPM documentation includes a list of the announced retirements. See Table 4-36 of IPM Documentation: 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/Chapter_4.pdf and 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/NEEDS_v513.xlsx Formatted: Complex Script Font: +Body CS

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

in coal fuel cost and any cost related to CO2 emissions. For the purpose of this TSD analysis, it is 

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

emission cost savings minus capital cost) is positive: 

Annual Net HRI Savings = Coal Cost Savings + CO2 Emission Cost Savings – Capital Cost30

2.6.1 Heat Rate Improvement Capital Cost Assumption 

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

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

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

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

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

Btu reductions and associated range of capital costs are obtained: 

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

Combined Min-Max HRI Capital Cost: $40-150/kW31

EPA Assumed Combined HRI Capital Cost: $100/kW 

The wide ranges of estimated HRI Btu and costs are indicative of the wide range of real 

differences in the many details of site specific EGU designs, fuel types, age, size, ambient 

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

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

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

2.6.2 Heat Rate Reduction Assumption 

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

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

                                                            

30 This TSD analysis assesses a broadly combined application of multiple HRI methods. As estimated in the 2009 

Sargent & Lundy study most HRI-related O&M costs are sufficiently small relative to the associated annualized 

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

therefore does not consider the small economic impact of HRI-related O&M costs. 31 Note that highest cost does not necessarily align with greatest heat rate improvement. A low cost HRI method can 

have a large HRI potential (e.g., upgraded digital control system, neural network). Also, economy of scale causes 

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

Formatted: Font: (Default) Times New Roman
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Sargent & Lundy estimates given above, HRI methods could possibly reduce this average coal 

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

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

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

has already applied some or many of the available HRI methods.32 

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

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

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

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

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

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

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

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

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

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

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

6%. 

2.6.3 Heat Rate Improvement Breakeven Economic Analysis 

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

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

reduced via HRI. 

                                                            

32 Based on the EPA informal discussions with Sargent & Lundy and other power sector engineering firms. The 

EPA has found no comprehensive data set on the extent to which specific HRI methods have already been applied at 

individual EGUs. The EPA believes that many EGU owners consider such information to be confidential. 
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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 2012.33 This 

                                                            

33 EIA, Electricity DataTable 7.4, Average Weighted Cost of Fossil Fuels for the Electric Power Industry 2002-

2012, http://www.eia.gov/electricity/annual/html/epa_07_04.html 

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

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

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

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

mentioned above. 

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

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

economic fleet wide, 

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

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

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

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

rate reduction percentages. 

2.6.4 U.S. Coal-steam EGUs – Estimated Fleet-wide CO2 Reduction and Cost via HRI 

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

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

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

 Average CO2 emission rate = 0.976 tonne/MWh net 

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

 Average capacity factor = 78% 

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

 HRI Btu and CO2 reduction = 6% 

 HRI capital cost = $100/kW 

 Annual capital charge rate = 14.3% 

 Average coal cost = $2.62/MMBtu 

Estimated Fleet-wide Results: 

 Fleet-wide CO2 reduction via HRI = 97 million tonne/yr 
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 Total HRI capital cost = $24 billion 

 Annualized HRI capital cost = $3.5 billion 

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

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

 Annual net savings (cost) of CO2 reduction = ($7.7/tonne) 

2.6.5 Conclusion - HRI Economics 

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

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

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

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

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

capital cost is about $75/kW or less. 

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

expressed in Btu 

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

electricity 

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

in gross Btu/kWh. 

MMBtu million Btu 

MW megawatt 

PC pulverized coal (boiler) 

RSD Relative standard deviation 

S & L report 

Sargent & Lundy engineering study on the potential heat rate 

improvement from equipment upgrades (EPA 2009 version) 

[Available at: 

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

start 

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

generates some measurable amount of electricity before ceasing fossil 

fuel combustion 

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

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..........................................................................................................................................7

Natural Gas Supply......................................................................................................................................15

Technical Considerations............................................................................................................................ 17

Analysis of Cost‐Effectiveness..................................................................................................................... 23

Natural Gas Price Impacts........................................................................................................................... 31

 

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Overview 

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

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

generationreducing utilization of from the most carbon-intensive existing EGUs and increasing 

utilization, to the extent possible, of less carbon-intensive existing fossil fuel-fired EGUs. More 

specifically, the examination focuses on opportunities to improve emissions intensity by 

increasing the utilization of existing natural gas combined cycle units. The TSD provides 

background on existing power plants, power system operation, and the economics of electricity 

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

Introduction

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

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

of generation and transmission facilities.”1

 Electricity demand varies across geography and time 

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

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

manner possible. 

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

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

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

the least expensive power source first.2

 

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

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

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

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

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

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

                                                            

1

 Energy Policy Act of 2005 

2

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

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

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

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

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

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

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

units to natural gas combined cycle (NGCC) units. 

Power Sector Background

 Electric Dispatch 

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

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

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

 The 

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

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

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

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

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

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

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

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

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

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

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

associated with running pollution controls.4

 Other generating technologies, like renewables, 

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

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

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

sequentially, such that total system demand is met. The economic order in which units are 

                                                            

3

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

4

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

operating a pollution control and/or emission allowance charges. 

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dispatched to meet demand, at any particular point in time, is commonly called a dispatch 

“curve.”5

 

                                                            

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.

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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) 

Comment [A1]: Balancing authorities are not

reliability coordinators.  Reliability coordinators

monitor and direct balancing authorities to

ensure reliable operation (through a variety of

activities).  Suggest revising this text to say

“Reliability coordinators, who monitor and direct

balancing authorities, are responsible for the

reliable operation of the bulk electric system.”
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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 - 2,220354 lbs/MWh 

 Oil/Gas (O/G) Steam - 1,463624 lbs/MWh 

 NGCC – 90726 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 

                                                            

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 

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

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

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

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

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

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

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

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

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

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

may model actual weather adjusted capacity by the hour/minute, 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 

                                                            

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%. 

Comment [A2]: Is EPA saying that the units

with the high capacity factors are unavailable for

redispatch? If so, DOE recommends making that

clearer as the sentence currently reads as if no

units other than NGCCs are available for

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

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

considered historical data and modeling projections describing NGCC characteristics and 

operating behavior. 

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

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

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

Table 3-1: Existing NGCC Capacity, by Age15

Online Year Capacity 

(Name Plate Capacity – MW) 

Percentage of 

Total Existing NGCC 

Fleet 

Pre 1950 103 0% 

1950-1959 1,769 0.7% 

1960-1969 3,087 1.3% 

1970-1979 6,909 2.8% 

1980-1989 7,658 3.1% 

1990-1999 28,467 11.7% 

2000-2009 174,947 71.7% 

2010+ 21,068 8.6% 

Total 244,008 100% 

Of 4641,812 NGCC plantsNGCC units operating generating in 2012 and greater than 25 

MW, the EPA observed that 50179 plants units (more than 1010% of NGCC plantsunits) had a 

                                                            

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

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

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

http://www.eia.gov/electricity/data/eia860/ 
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net generation value that was greater than or equal to its nameplate capacity x 8784 hours * 70%. 

That is, a capacity factor that was 70% or greater (see Figures 3-1 and 3-2).16 

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

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

                                                            

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

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

Comment [A3]: 

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12 pt

Comment [A4]: For this figure and the one

below, I would not normally call this a “cumulative

CF”. I believe it is a cumulative density function.

Cumulative CF doesn’t really make sense, as you

wouldn’t add up individual CFs to get anything

unless you were trying to generation weight and

take a fleet average (which is not the case here). So I

would suggest finding a different title such as

“Distribution of CF across NGCC fleet” or similar.

Also why are there two here in 3‐1 and three below

in Fig 3‐2? Each should be clearly described.

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17

                                                            

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

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

Comment [A5]: Unclear what this is plotting and

why it is different from data in figure immediately

above, and in table immediately below

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

Capacity Factor # of NGCC 

plants 

% of NGCC 

Plants 

Less than 5% 40 8.62% 

5%-9% 26 5.60% 

10%-14% 23 4.96% 

15%-19% 25 5.39% 

20%-24% 16 3.45% 

25%-29% 18 3.88% 

30%-34% 27 5.82% 

35%-39% 38 8.19% 

40%-44% 30 6.47% 

45%-49% 36 7.76% 

50%-54% 33 7.11% 

55%-59% 39 8.41% 

60%-64% 36 7.76% 

65%-69% 27 5.82% 

70%-74% 20 4.31% 

75%-79% 13 2.80% 

80-84% 10 2.16% 

85-89% 4 0.86% 

Greater than 90% 3 0.65% 

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

basis, the fleet of NGCC units was relied upon heavily during certain periods of time, in response 

to higher demand. On a seasonal basis, a significant number of units achieved capacity factors 

greater than 50%, and even up to 80%. Using data reported to the EPA,18 and looking more 

closely at data during the summer and winter peak electricity demand timeframes nationwide, 

more than 10% of NGCCs were operated at a capacity factor greater than 70%.19 In fact, 19% of 

NGCCs achieved 70% capacity factor during the winter of 2011/2012 and 20% hit that level or 

                                                            

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

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

for units for which data was provided to EPA. 

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higher during the summer.20 During periods where demand levels are typically lower, some 

NGCCs were idled or operated at lower capacity factors. Nonetheless, a notable number of 

existing NGCCs have demonstrated the ability to achieve a 70% capacity factor for extended 

periods of time. These units achieved high capacity factors without adverse effects on the electric 

system. While many units demonstrated an ability to deliver net generation that was more than 

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

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

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

higher.

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

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

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

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

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 

                                                            

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

February of 2012. Summer includes June, July, and August. 
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a one year period demonstrates the ability for the quick re-dispatch in response to market or 

economic drivers. 

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

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

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

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

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

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

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

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

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

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

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

Year 

NGCC 

Name Plate 

Capacity 

(GW) 

NGCC Heat 

Input for 

Electricity 

(TBtu) 

NGCC Net 

Generation 

(TWh) 

NGCC 

Capacity 

Factor 

Henry Hub

Natural Gas 

Price 

($/mmBtu 

Coal Net 

Generation 

(TWh) 

2011 2395 5,912 796 389% $4.00 1,719 

2012 2442 7,224 981 46% $2.76 1,503 

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

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

percentile plantunit), the historic evidence supporting quick and significant redispatch to NGCC, 

and the cost-effectiveness of high NGCC utilization demonstrated later in this TSD all supported 

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

approach. 

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

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

                                                            

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

for NGCC (based of “2012 eGrid file”) 

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

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

23 EIA form 860 and EIA form 923 

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

applicability criteria was 95932 TWh.24 After the application of NGCC re-dispatch to the 70% 

level,25 these same existing sources were calculated to collectively generate 1,39076 TWh. 

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

total NGCC generation calculated in the goal setting to 1,44427 TWh. 

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

measures, the EPA believes that upwards oft roughly 1,400 TWh from existing and under 

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

approximately 430 TWh (an 8180% increase) between 2005 and 2012. EPA is calculating that 

NGCC generation in 2020 could increase by approximately 5-45% form today’s levels. This 

reflects a smaller ramp rate in NGCC generation than has been observed from 2005 to 2012. 

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

 

NGCC 

Name Plate 

Capacity 

(GW) 

NGCC Net 

Generation 

(TWh) 

Growth in 

NGCC 

Generation 

from 2005 to 

2012 

Growth in 

NGCC 

Generation 

from 2012 to 

2020 

Nationwide 

NGCC 

Capacity 

Factor 

2005 199 551 NA NA 32% 

2012 2442 981 8178% NA 46% 

2020 State Goal Calculation 256 1,44427 45% 47% 643% 

Natural Gas Supply 

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

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

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

                                                            

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

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

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

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

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

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

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

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

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

consumption of roughly 3.54.4 TCF for the electricity sector.28 

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

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

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

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

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

                                                            

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

Natural Gas and Oil Resources. http://www.npc.org/reports/rd.html (see Figure 1.2 on p. 47). 27 http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=3&pid=3&aid=6 28 Assuming 1,024 Btu/cubic foot and 10,000 mmBtu/MWh 29 National Petroleum Council, 2012 (http://www.npc.org/PD_update‐80112.pdf) 30 http://www.npc.org/reports/rd.html

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number/units in footnote: 10,000 mmBtu/MWh?

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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%.31,32 However, NGCCs are 

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

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

and engineering specifications. 

                                                            

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

2009 and earlier data) 

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

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

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

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

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

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

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

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

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

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

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

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

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

percent). 

Natural Gas Pipeline and Electricity Transmission 

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 

                                                            

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

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

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

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

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

30. 
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will constrain only a limited percentage of the state-wide NGCC fleet. The ability for the current 

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

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

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

capacity factor for NGCC of 45.89% on average, and higher in some states.39

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

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

hour for 2005 to 2010 is shown in Figure 3-43. In this figure, capacity factors in 2010 are 

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

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

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

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

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

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

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

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

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

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 3-43. Average Utilization of Natural Gas Combined Cycle Power Plant Fleet 

                                                            

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

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

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

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

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

42 Air Markets Program Data (AMPD), ampd.epa.gov, EPA, 2014
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Although there can be site-specific constraints on utilization at some NGCC facilities, 

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

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

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

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

provides for flexible implementation that will permit efficient scheduling of infrastructure 

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

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

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

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. 

                                                            

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

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

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

http://www.pjm.com/~/media/committees-groups/committees/teac/20131211/20131211-december-2013-pjm-boardapproval-of-rtep-whitepaper.ashx. 
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In addition, natural gas pipeline capacity is regularly added in response to increased gas 

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

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

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

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

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

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

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

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

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

techniques.46 

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

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

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

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

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

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

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

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

shown in Figure 3-54. 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 3-54. The net pipeline capacity for 

these regions from 2005 to 2011 is shown in Table 3-43 below. 

Table 3-43. Natural Gas Pipeline Net Capacity by Region, 2005 - 2011 by Gas Consuming 

Area47

                                                            

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

averaged around 20Bcf per day from 2008 to 2011. 

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

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

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

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

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 3-54. Energy Information Administration Natural Gas Pipeline Regions

Comment [A9]: largely ‘dependent’?
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Source: Energy Information Administration. 

Analysis of Cost-Effectiveness 

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

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

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

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

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

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

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

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

dispatch, and reliability constraints. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

to NGCC units within defined areas.48 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),49 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: 

                                                            

48 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 49 In practice, unit dispatch does not respect state boundaries because least-cost supply must be balanced with 

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

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

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

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

different states; as a result, costs and prices in this analysis are overstated. 
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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.50

2. Generation constraints that maintain the sum of state-level generation in the Base Case51

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

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

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

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

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

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

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

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

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

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

Cost and Availability of Economic Re-Dispatch Opportunities

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

relationship between the amount and cost of re-dispatch. Figure 3-65 below presents the 

projected national average capacity factor for NGCCs in 2020 (the first year of the assumed redispatch incentive) and the associated average $/tonne of CO2 reduced by comparing emissions 

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

difference in power sector emissions). While the charge is applied exogenously in IPM, the 

average $/tonne shown below is calculated from the modeling results and projections by dividing 

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

emissions (compared to the base case). 

                                                            

50 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. 

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

Comment [A10]: If EPA wishes to keep this

analysis, it would be helpful to explain the

tension between the costs it generates and those

of the subsequent analysis of redispatch on p. 29.

For example, the reader might infer from Table 3‐

5 that the cost of doing a 75% redispatch

scenario for building block 2 is $50/tonne.

However, the later text indicates that a 75%

redispatch nationwide scenario has a cost of

$40/tonne as shown in Table 3‐6. Further

confusing this issue is that the actual achieved

nationwide capacity factor from a building block

2 scenario will be less than the target percentage.

As stated in the text, the 70% target corresponds

to an achieved 63% capacity factor. To better

clarify the issues for the reader, DOE

recommends that EPA specify the achieved NGCC

capacity factor from a range of targets for

building block 2. In addition, EPA should explain

how to interpret the differences between the

numbers in tables 3‐5 and 3‐6 and indicate to the

reader which is a better guide to understanding

the cost associated with the building block.

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Figure 3-65: NGCC National Capacity Factor, 2020 Initial Analyses 

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

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

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

58%

62%

65%

68% 71%

74% 75%

0%

10%

20%

30%

40%

50%

60%

70%

80%

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Average Capacity Factor

Average $/tonne

Projected Capacity Factor for Existing NGCCs (2020)

58%

62%

65%

68% 71%

74% 75%

0%

10%

20%

30%

40%

50%

60%

70%

80%

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Average Capacity Factor

Average $/tonne

Projected Capacity Factor for Existing NGCCs (2020)

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

comprehensive perspective regarding cost-effectiveness. 

 
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Table 3-54: 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 compliance52 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 

                                                            

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

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

manner.

Comment [A11]: Can this be quantified?

Comment [A12]: EPA Response:

Footnote added.  This is shown, in an illustrative

manner, in the RIA.
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clearly illustrated in the declining slope of the best-fit line in Figure 3-65. A uniform 

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

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

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

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

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

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

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

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

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

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

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

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

cost-effectiveness of re-dispatch potential. 

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

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

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

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

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

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

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

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

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

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

the total costs and emissions from each scenario to the costs and emissions from a business-asusual scenario. For the 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.

Comment [A14]: EPA Response:

Clarification added.

Comment [A13]: 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 [A15]: Also, can this be stated

alternately as a drop in cost for a given CO2

reduction level?

Comment [A16]: EPA Response:

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

have analytic scenarios that can quantify this

concept because we did not model “a given CO2

reduction level” between state and regional re‐

dispatch scenarios.

Comment [A17]: It would be helpful 

to have the results – can EPA 

provide this? Will it be in the 

docket?

Comment [A18]: EPA Response:  EPA will

provide this modeling in the docket for the

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

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

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

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

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

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

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

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

national capacity factor of 63 percent. 

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

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

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

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

impacts of re-dispatch. 

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

interstate re-dispatch, natural gas prices were projected to increase by a range of from [x] percent 

to [x] percent, which is within the range of historic natural gas price variability. Projected 

wholesale electricity price increases ranged from [x] to [x] 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. 

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

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

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

unit utilization of 70% was $33 per ton. 

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

Existing NGCC 

Average 

National 

Capacity 

Factor 

Re-Dispatch 

Constraint 

Average Cost 

($/tonne, 2020-

2029) 

Average CO2

Emissions 

(MMT, 2020-

2029) 

Reductions 

from Base Case 

(%, 2020-2029) 

Base Case NA NA 2,215 NA 

65% Regional $21 2,022 9% 

70% Regional $30 1,969 11% 

75% Regional $40 1,915 14% 

65% State $22 2,024 9% 

70% State $33 1,971 11% 

Natural Gas Price Impacts 

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

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

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

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

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

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

averaged 18.5% over the period from 1981 to 2012.53 Projected wholesale electricity price 

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

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

                                                            

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

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

in the East, the variation is virtually unchanged from the PJM example (at 19.6%).54 

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

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

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

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

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

Power Sector (Average 2020-2029) 

Existing NGCC 

Average National 

Capacity Factor 

Re-Dispatch 

Constraint Price ($/mmBtu) % Change 

Base Case NA $5.94 

65% Regional $6.36 7% 

70% Regional $6.53 10% 

75% Regional $6.69 13% 

65% State $6.37 7% 

70% State $6.52 10% 

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

 Power Sector (Average 2020-2029) 

Price ($/mmBtu) % Change 

Base Case $6.05 

$10/tonne $6.21 3% 

$15/tonne $6.33 5% 

$20/tonne $6.42 6% 

$25/tonne $6.57 9% 

$30/tonne $6.61 9% 

                                                            

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

Midcontinent ISO).

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$40/tonne $6.78 12% 

$50/tonne $6.90 14% 

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Chapter 5: Demand-side Energy Efficiency (EE) 

Introduction 

This chapter provides information on demand-side energy efficiency (EE) as an 

abatement measure for reducing carbon dioxide (CO2) emissions from fossil fuel-fired electric 

generating units (EGUs). Specifically, this chapter addresses EE as a component of both the 

“best system of emission reduction” (BSER) and state goals, and the inclusion of EE within the 

impacts assessment. Support is provided in this chapter for the discussion of the EE abatement 

measure throughout 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 Goal Computation. EE is also addressed in TSDs on state plan 

considerations and projecting emissions performance. 

This chapter is organized as follows: 

 Background 

– EE Technologies and Practices 

– Barriers to EE Investment 

– EE Policies 

– EE Programs 

 The EE Opportunity 

– Rapid Growth in EE 

– EE Program Impacts 

– EE Potential 

– Costs and Cost-Effectiveness of State EE Policies 

– EE as an Abatement Measure 

 State Goal Setting 

– Approach 

– Inputs 

– Calculations 

– Results 
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 Impacts Assessment 

– Approach 

– Inputs 

– Calculations 

– Results 

 Analysis Considerations 

 Appendices 

 References 

Background 

As discussed in the State Plan Considerations TSD (Appendix: “Survey of Existing State 

Policies and Programs that Reduce Power Sector CO2 Emissions”), demand-side energy 

efficiency policies and programmatic efforts have existed for decades and are now used in all 50 

states. These strategies are intended to help states achieve energy savings goals, reduce the 

environmental impacts (including carbon dioxide (CO2) emissions) of meeting energy service 

needs, save energy and money for consumers, and provide a significant resource for meeting 

power system capacity requirements. EE policies currently in place are considered by states to be 

cost-effective strategies for contributing to these policy objectives1

. Moreover, states – through 

their utilities, primarily – have been rapidly increasing their funding of EE programs in recent 

years, more than tripling budgets in the five years from 2006 to 2011, from $1.6 billion to $5.9 

billioni

. In 2012, the cumulative impacts of these programs represented a 3.7% reduction in 

national electricity demandii. And, EE spending is projected to continue to grow at a substantial 

rate. A recent study by Lawrence Berkeley National Laboratory (LBNL) projects EE program 

spending to reach $8.1 billion to $12.2 billion (“Medium Case” and “High Case,” respectively) 

in 2025 even “without considering possible major new policy developments,” such as 

requirements under Clean Air Act, Section 111(d)2iii. 

                                                            

1

 See below for discussion of cost-effectiveness and related cost tests used by states to evaluate EE programs. 2 Specifically, the LBNL study states: “By virtue of limiting the analysis to current energy efficiency policies, we do 

not consider the potential impact of major new federal (or state) policy initiatives (e.g., a national energy efficiency 

resource standard, clean energy standard, or carbon policy) that could result in customer-funded energy efficiency 

program spending and savings that exceed the values in our High Case.” 

Comment [A1]: Suggest including citation.

Comment [A2]: EPA RESPONSE: Citation is at

end of the sentence (iii).

Comment [A3]: Calls into question the need for

BB 4??

Comment [A4]: EPA RESPONSE: This building

block will provide incentive to reach these forecast

levels and beyond. State EE policies are being

reconsidered in many states and sometimes rolled

back (e.g., OH). At the same time, we clearly are

building upon the ongoing work of leading states,

integrating their best ideas and successes into the

basis of this building block.
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This section provides relevant background for the subsequent sections that address the EE 

opportunity, EE as a component of BSER, EE within state goal setting, and the integration of EE 

within the benefit, cost, and impacts assessments as reported in the RIA and elsewhere. This 

section begins with a discussion of EE technologies and practices, and then describes the market 

failures that limit cost-effective EE investments. We then summarize EE policy objectives and 

discuss policy types, their relative impacts, and discuss in more detail the key strategy of 

employing EE programs. 

EE Technologies and Practices 

Energy efficiency is using less energy to provide the same or greater level of service. 

Demand-side energy efficiency refers to an extensive array of technologies, practices and 

measures that are applied throughout all sectors of the economy to reduce energy demand while 

providing the same, and sometimes better, level and quality of service. Utilities’ employ a large 

array of strategies in implementing energy efficiency programs, these include financial 

incentives such as rebates and loans, technical services such as audits and retrofits, and 

educational campaigns about the benefits of energy efficiency improvements. The purpose of 

these EE programs is to induce EE investments and practices that would not otherwise occur in 

the presence of market failures and behavioral impediments. In the residential sector, examples 

of EE activities include the purchase of more efficient products and equipment (e.g., ENERGY 

STAR labeled), the upgrading of insulation in attics and walls, sealing of air leaks, and 

undertaking home energy audits leading to customized whole home retrofits. Opportunities for 

cost-effective EE in commercial buildings include optimization of heating, ventilation, and air 

conditioning (HVAC) and heat pump systems, upgrades of windows, and use of more efficient 

office equipment at replacement. In the industrial sector key EE strategies include motor 

upgrades and maintenance programs, recovery of waste heat streams, and optimization of 

processes through modern instrumentation and controls systems. 

The opportunity presented for economic investment in EE is dynamic, growing over time 

as technologies and practices advance, as populations grow, and as investment occurs in the 

construction of new homes, buildings, and industrial facilities. As new policies are enacted, 

leading to the acceleration of investment in EE, an additional portion of the expanding 

Comment [A5]: Can EPA explain why the EE

intervention is needed?

Comment [A6]: EPA RESPONSE: As discussed in

the following section “Barriers to EE Investment,” a

number of market failures and behavioral

impediments hinder the realization of energy

efficiency level that is socially optimal. The diversity

of EE programs is designed and devised to address

these market failures and behavioral impediments.

Also, made edits to the text to clarify.      

Comment [A7]: Suggesting this as a means to

clarify if, indeed, heat pumps are considered EE, as

these would indeed be a substantial EE contribution

for both residential and business use.

Comment [A8]: Although Figure 5.1 some pages

ahead does some of this, this would be a good

opportunity to bulletize real world examples of EE

actions, like windows, light bulbs, etc., with their

cost and efficiency and gains, even maybe a graph

showing how EE are the lowest cost GHG actions.  

Otherwise, the next few pages read like a

philosophical discussion of an abstract concept.  

Comment [A9]: EPA REPONSE: Thanks for this

observation. Real world examples are provided in

this paragraph. As you point out, Fig. 5.1 also

provides a sense of the varied nature of the

opportunities and strategies.  
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opportunity is realized. After decades of experience implementing policies to accelerate 

investment in cost-effective energy efficiency, states are finding renewed opportunities as they 

develop more sophisticated and effective strategies, evolving from a focus on individual enduses and products to whole-building and systems-based strategies that account for the 

interactions between the many energy end-uses in buildings and industryiv. As will be discussed, 

the experience in the U.S. has been that on balance, a persistent and large potential for 

achievable and cost-effective EE has remained even as the impact of past and ongoing efforts 

have accumulated. 

Barriers to EE Investment 

Despite the persistent and large potential for electricity savings through investment in EE 

technologies and practices, market failures, as well as non-market failures, limit the realization of 

the many benefits of these investments. Several market failures that lead to inefficiencies in 

energy use are well recognized by analysts and practitioners, and are discussed extensively in the 

economic literature.v

 Some of the most common examples of these market failures include: 

 Pollution externalities. Energy consumption is associated with negative externalities, such as 

emissions of CO2, SO2, and NOx that cause human health and environmental damages. 

Energy prices that do not correctly reflect these externalities lead to investments in energy 

efficiency below the socially optimal levels. 

 Imperfect information. Energy users often lack accurate information about energy savings 

and other attributes of energy efficient products or practices to understand the costs and 

benefits of EE investments. Market failure due to information imperfection leads to 

underinvestment in energy efficiency by consumers. 

 Split incentives (or the “principal-agent problem”). Incentives of individuals who make EE 

investment decisions are not always aligned with incentives of those who use and pay for 

energy. Examples include misalignment between landlords and tenants, and between builders 

and homeowners. Split incentives also persist within organizations and institutions that lead 

to underinvestment in EE in both public and the private entities.vi
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 Credit constraints. Limited access to credit may prevent some consumers, especially lowincome consumers, from making cost-effective EE improvement decisions due to the higher 

upfront cost of energy efficient products or practices. 

 Under-provision of research and development (R&D). Because of the public good nature of 

knowledge, technology innovation invested by one firm likely spills over to other firms. As a 

result, firms involved in technology development may be less willing to invest in R&D, 

leading to sub-optimal levels of EE investments from a social perspective.vii 

 Supply market imperfections. Market for energy efficient products is incomplete. 

Manufacturers do not have perfect information about consumer preferences and may supply 

limited menu of products to consumers. High start-up costs and the existence of patents may 

create barriers to entry in markets and result in oligopolistic or monopolistic behavior. 

Supply chains of EE products is fragmented, leading to underinvestment in innovation and 

energy efficiency by suppliers. In addition, supply chain fragmentation may also add 

complexity to the purchase and installation of otherwise economically rational investments, 

thereby slowing the adoption of EE technologies. 

 Behavioral impediments. Behavioral economics and psychology have identified potential 

behavioral phenomena that lead to consumers to deviate from the standard theory of welfare 

maximizing in consumption and other decisions, including energy efficiency investments. 

Behavioral economics posits possible explanations, including bounded rationality, heuristic 

decision-making, and non-standard preference and belief.viii 

In the presence of market failures, users of electricity, or those making energy efficiency 

investments, face prices or incentives that prevent them from weighing the social benefits and 

costs of their investments and thus under-invest in approaches to reduce electricity consumption. 

The behavioral impediments discussed above explain why individuals do not always make 

energy efficiency investments that are seemingly in their own best interest to reduce their total 

expenditure, given prevailing electricity prices. 

In addition to market failures and behavioral impediments, other factors, such as hidden 

costs, risk and uncertainty experienced by both consumers and suppliers of energy efficient 
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products, and heterogeneity among consumers, producers and markets, also influence EE 

investment decisions3

. Examples of such factors include: 

 Risk and uncertainty. Adopting an unfamiliar, typically more expensive EE technology can 

be an uncertain undertaking given the lack of credible information on product performance 

and future energy prices, and the irreversibility of the investment. Imperfect or asymmetric 

information can exacerbate the perceived risk of energy efficiency investments and help 

explain why consumers and firms do not always invest in EE measures. Suppliers also face 

risk and uncertainty, without perfect information of consumer preferences for energy 

efficiency. In the presence of risk and uncertainties, consumers and suppliers alike will 

underinvest in EE. 

 Transaction costs. Consumers face transaction costs in searching, assessing and acquiring 

energy efficient technologies and services. It can be time-consuming and difficult for 

consumers to estimate lifetime operating costs of a product. The complexity of the search 

process puts many efficient products at a disadvantage relative to less-efficient products with 

lower upfront costs. 

 Capital market barriers. Consumers sometimes face higher interest rates to finance EE 

investments compared to other investments. Lenders can be reluctant to invest in EE loan 

portfolios in part because energy efficiency loans may lack standardization and financial 

markets have difficulty ascertaining the likely payoff from such investments. 

EE policies and programs can play an important role in correcting market failures and 

addressing the barriers to the investment and adoption of socially beneficial energy efficiency 

opportunities. Examples of effective EE policies and programs include public funding of R&D, 

information programs (such as energy labeling, the voluntary ENERGY STAR Program, and 

                                                            

3 It has been recognized that there is a difference between cost-effective energy efficiency investment levels, based 

on cost-minimizing consideration, and observed levels of energy efficiency. This phenomenon, also termed ‘energy 

paradox,’ or ‘energy efficiency gap,’ has been studied extensively in the literature. See, for example, Jaffe, AB, and 

RN Stavins. 1994. “The Energy Paradox and the Diffusion of Conservation Technology.” Resource and Energy 

Economics 16(2): 91–122; Sanstad, A. H. and R. B. Howarth. 1994. ‘Normal’ markets, market imperfections and 

energy efficiency, Energy Policy, 22: 811-818; DeCanio 1998; DeCanio, SJ and WE Watkins. 2008. Investment in 

Energy Efficiency: Do the Characteristics of Firms Matter? The Review of Economics and Statistics, 80: 95-107;

Allcott, H, and M. Greenstone. 2012. Is There an Energy Efficiency Gap? Journal of Economic Perspectives 26 

(1):3-28. 
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consumer education), rebates for high-efficiency products, product energy performance 

standards, financing and loan programs, and technical assistance. 

EE Policies4

Objectives and Role in Reducing CO2 Emissions from the Power Sector 

EE policies are implemented by states to meet a number of closely related policy goalsix

including: 

- Reducing costs to electricity customers,

- Providing a significant resource for meeting power system capacity needs,

- Meeting energy savings goals,

- Stimulating local economic development and new jobs, and

- Reducing the environmental impacts of meeting electricity service needs.

EE policies currently in place are considered by states to be cost-effective strategies for 

contributing to each of these policy objectivesx

. While each of these objectives, and others, 

contribute to the motivation of state policymakers to pursue EE policies, reducing energy costs 

over the long term is the leading objective in pursuing these policies. In addition, EE policies are 

central to meeting state objectives for reducing CO2 emissions from the power sector. As noted 

in the State Plan Considerations TSD, EE policies are a leading tool for achieving CO2

reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in 

ten states5

 with statutory requirements for greenhouse gas reductions. 

Economy-wide studies of climate mitigation scenarios confirm that energy efficiency plays a 

critical role in reducing the costs and enhancing the flexibility of meeting long-term climate 

stabilization targetsxi. Analysis by the International Energy Agency (IEA) suggested that in order 

to stabilize carbon concentration in the atmosphere at 450 ppm, as much as 44% of the estimated 

global abatement potential in 2035 derives from greater energy efficiency in the world 

economyxii. Several recent Energy Modeling Forum (EMF) studies have investigated the role of 

technology in achieving climate policy objectives in the U.S. (“EMF 24” and “EMF 25” studies) 

                                                            

4

 Existing state EE policies are described extensively in the State Plan Considerations TSD. 

5 States with GHG reduction laws include: California, Connecticut, Hawaii, Maine, Maryland, Massachusetts, 

Minnesota, New Jersey, Oregon, and Washington. 

Comment [A11]: EPA RESPONSE: The rebound

effect is referenced in “Cost of Saved Energy”

section, below and a fuller discussion has been

added to that section.

Comment [A10]: Rebound effect??

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and globally (“EMF 27” study).6

 These studies concluded that compared to business-as-usual 

energy efficiency, improvements in energy efficiency in various economic sectors would slow 

the increases of GHG emissions in the short run, substantially reduce the costs of GHG 

mitigation (on average, by about 50%7

), and ease the technology transformation pathways to 

achieve long-term carbon reduction goalsxiii,xiv. 

Several economic studies (including EMF25 studies) examined the role of energy efficiency 

policies (such as energy efficiency standards and subsidies) in relation to other climate policy 

instruments (such as carbon taxes). These studies found that when energy efficiency policies 

address market failures, they are welfare improving and can complement climate policy.xv In 

addition, EE policies are recognized to be an appropriate response to demonstrated market 

failures and behavioral impediments, particularly in contexts where these failures have broader 

societal implications such as environmental externalities.xvi

In addition to providing cost-effective opportunities for reducing GHG emissions, energy 

efficiency is recognized to provide other co-benefits, including air quality and public health 

benefits, waste reduction from energy generation, energy security, energy system reliability, 

community economic and social development, and consumer amenities.xvii Energy efficiency 

investments and policies are also found to spur productivity growth, technology learning and 

innovation.xviiixix Recently, more attention has been paid to developing methods for recognizing 

these co-benefits and integrating them into the cost-benefit analysis framework used by state 

utility commissions and administrators of EE programsxx. These co-benefits have not been fully 

accounted for in the EPA analysis. 

Policy Types 

                                                            

6

 Energy Modeling Forum (EMF) is a consortium of energy economists and energy economic modeling teams that 

was established in 1976. Through ad hoc working groups, the EMF has focused on a series of energy and 

environmental topics that are of interest to policy decisions. In recent years, the EMF is recognized for its 

contribution to the advancement of economics of climate change and the reports of the Intergovernmental Panel on 

Climate Change (IPCC).   7 It should be noted that these energy-economy modeling studies do not typically include the costs of implementing 

energy efficiency measures or would treat such costs as exogenous.  

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EE policies come in many forms. The most prominent and impactful EE policies in most 

states include those that drive development and funding of EE programs8

, and building energy 

codes. Other policies that are leading to significant impacts include state appliance and 

equipment standards, building energy disclosure requirements, innovative financing strategies 

(e.g., Property Assessed Clean Energy or “PACE”), state tax policies, and “lead by example” 

strategies targeting energy use in state operations. Comparing the relative impact (achieved or 

potential) of the different policy types is challenging, particularly to do so comprehensively, 

across all states, and at the national level. EE programs are the only state EE approach that has 

comprehensive and detailed reporting of impacts, costs, and other characteristics from all 50 

states9

. This information is generally based upon measurement and verification studies submitted 

annually, most commonly to state utility commissions, and reported to the Energy Information 

Administration (EIA) for all program administrator types (all utility types, third-parties, and 

government agencies). EE program data reported to EIA includes incremental and cumulative 

energy and peak demand savings, program costs broken down by component, and composition 

by end-use sector (residential, commercial, industrial). In 2012, utilities and other program 

administrators in 48 states reported savings from EE programs to EIA through form EIA-861. At 

a national level, the EPA is not aware of a comprehensive dataset reported by states of the 

achieved impacts of strategies other than those that lead to investment in EE programs. However, 

state and regional-level information does exist. For example, the Northwest Power and 

Conservation Council (NPCC) has been compiling the impacts of EE policies (including utility 

and third-party EE programs, state building energy codes, and federal appliance standards) 

across their member states (ID, MT, OR, WA) for more than three decades. For the past decade, 

EE programs have accounted for more than 75% of the cumulative energy savings from state EE 

policies for NPCC, with building energy codes accounting for the remaining savingsxxi. 

                                                            

8

 EE programs are described in more detail in the following section of this chapter and in the State Plan 

Considerations TSD. 

9

 This is a reference to data collected by the Energy Information Administration (EIA) through Form EIA-861. In 

2011, EIA began collecting data from third-party administrators of programs. Prior to 2011, this was a significant

shortcoming in the breadth of the data collected. The breadth and quality of information collected through Form 

EIA-861 has improved over time, however, outside entities (e.g., ACEEE) have found that the data can be improved 

through expert review and supplementation with other data sources. While now fairly comprehensive, the EIA data 

can be improved further with regards to data quality and consistency. See “Analysis Limitations” section for further 

discussion.
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Another representation of the relative opportunity provided by different state EE 

strategies is presented by evaluations of EE achievable potential or projections of the impacts of 

EE policies. The results from two recent evaluations at a national level are presented in Table 5-

1. EE programs account for 77% and 82% of achievable savings in ACEEExxii and Georgia 

Techxxiii studies, respectively. These studies indicate that the substantial majority of potential 

savings from state EE efforts are available through EE programs, and that state and local 

building energy codes can make a significant additional contribution. Massachusetts provides a 

state example of the impacts of EE programs relative to other state EE policies. The 

Massachusetts Global Warming Solutions Act of 2008 established statewide limits on 

greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. To achieve this 

target, Massachusetts is relying upon an integrated portfolio of clean energy policies. State EE 

policies are expected to provide the largest contribution to meeting the 25 percent target with 

utility sponsored EE programs and state building energy codes accounting for 76% and 17%, 

respectively, of those policiesxxiv. In their 2013 progress report, Massachusetts indicates that they 

are generally on track for meeting or exceeding these projections.xxv
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TABLE 5-1 

Relative Opportunities Provided by Key EE Programs and Building Codes 

Study Year EE Programs Building Codes Other 

ACEEE 2030 77% 13% 10% 

Georgia Tech 2035 82% 18% 0% 

The full range of EE policies are addressed in greater detail (including designs, authority, 

obligated parties, measurement and verification (M&V), penalties for non-compliance, and 

implementation status) in the State Plan Considerations TSD. Because EE programs have 

provided the majority of state EE-policy electricity savings to-date and offer the majority of 

potential savings going forward, we next summarize key characteristics of this strategy. 

EE Programs 

EE programs (actually portfolios of programs) are comprised of numerous measures and 

measure types that are applied across all sectors of electricity end-users. Figure 5-1xxvi illustrates 

the multi-level composition and breadth of EE program portfolios. The diversity represented by a 

typical portfolio of EE programs implemented by a utility (or other program administrator) is an 

important characteristic relevant to analysis of EE policies. Every detailed program type (as 

illustrated in the lower half of the figure) represents a unique set of characteristics including 

costs of energy saved, ratio of program to participant costs, investment life, scale, M&V 

approach, etc.10

 

                                                            

10 See following sections for discussion of these factors. 

Formatted: Not Highlight
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FIGURE 5-111

Energy Efficiency Program Portfolio 

Administrators 

EE programs are administered by a variety of entities (“program administrators”) 

including utilities of all ownership types (investor-owned, municipals, and cooperatives), nonprofit and for-profit third-parties (e.g., Vermont Energy Investment Corporation), and state and 

local government agencies (e.g., NYSERDA). Most EE programs (including all investor-owned 

utilities which account for more than 75% of reported savingsxxvii) are overseen by state utility 

commissions, which review and approve program plans, projected impacts, and associated 

budgets; and establish annual reporting and M&V requirements. 

 

                                                            

11 The “EM&V” box is not comparable to the other program types and is not relevant to this discussion.  It was

included in the referenced source to indicate that EM&V is a key activity within a program portfolio.

Comment [A12]: The placement of EM&V in

Figure 5‐1 as separate from the actual programs is

misleading. Each program has EM&V associated

with it, so showing it as a separate activity is

inaccurate and possibly confusing.

Comment [A13]: EPA RESPONSE: See added

footnote 11. We cannot easily edit this graphic.
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Policy Drivers 

EE programs result from a number of different policy approaches or “drivers.”12 These 

include energy efficiency resource standards (EERS) (26 states)xxviii, system benefit charges (14 

states), integrated resource planning (IRP) requirements (34 states), demand-side management 

plan or multi-year energy efficiency budget (28 states), and statutory requirement to acquire “allcost-effective EE” (6 states)xxix13. EERS is a more recently developed strategy and has quickly 

become the leading driver of the rapid growth in EE programs due to their clear goals and proven 

success as a policy toolxxx. These policy drivers lead to the evaluation, planning, and adoption of 

EE programs and associated budgets, which are supported through different funding 

mechanisms. 

Funding Sources 

Funding sources for EE programs are varied but for most states are dominated by revenues 

collected from ratepayers through electricity surcharges, typically ranging from $1 to $4 per 

megawatt-hourxxxi. More recently adopted funding sources include proceeds from the auction of 

allowances in the Regional Greenhouse Gas Initiative (RGGI) states and from EE resources bid 

into the forward capacity market operated by the New England Independent System Operator 

(NE-ISO). Ratepayer-funding accounts for more than 90% of total EE program support 

nationally. 

The EE Opportunity 

As discussed, states are employing a number of EE strategies with EE programs yielding the 

most significant impacts both historically as well as in terms of future potential. Furthermore, EE 

programs are unique among state EE strategies in the comprehensiveness and transparency of 

their reported impacts, funding, and other characteristics. In this section we address the rapid 

                                                            

12 These policies are discussed in depth in State Plan Considerations TSD. 

13 The number of EERS states is from ACEEE (see endnote) and includes states with explicit EERS, those with 

long-term energy savings targets for individual program administrators, and those with EE incorporated as an 

eligible resource in a renewable portfolio standard. The numbers for the other policy approaches are from LBNL 

(see endnote). 
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growth in EE programs, estimated impacts of EE programs to-date and projections of the impacts 

of existing EE programs and trends, and the electricity savings potential achievable through 

expanded use of EE policies and programs. Finally, we will discuss the costs and costeffectiveness of EE programs, specifically. 

Rapid Growth in EE 

Funding for EE programs has increased rapidly in recent years driven by recent policy 

innovations and increasing evidence of the effectiveness of these new strategies. Table 5-2 

presents levels of EE program funding in the U.S. since 2006xxxii. In the previous five years, 

funding increased by more than 200%, from $1.6 billion in 2006 to $5.9 billion in 2011. 

TABLE 5-2 

U.S. Electric Utility EE Program Funding (2006-2011) 

Year 

2006 2007 2008 2009 2010 2011 2012

Electric Efficiency 

Program Budgets 

(billions of $s, nominal)

1.6 

1.6

2.2 2.6 3.4 4.6 5.9 5.9

 Key new state policies that have helped to drive these rapid increases in EE program 

funding include EERS, electricity savings goals, and “all cost-effective energy efficiency” 

requirements. The adoption of EERS, in particular, increased through this period and clearly has 

been the primary driving force behind the increasing success of and investment in EE programs. 

Table 5-3 shows the number of states adopting EERS by yearxxxiii. 

TABLE 5-3 

U.S. State Adoption of Energy Efficiency Resource Standards 

Year States Adopting an EERS Total 

1997-2004 California, Hawaii, Texas, Vermont 4 

Comment [A14]: Please update this table to

include the most recent numbers

Comment [A15]: EPA RESPONSE: Done.

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2005 Nevada, Pennsylvania 2 

2006 Rhode Island, Washington 2 

2007 Colorado, Connecticut, Illinois, Minnesota, North Carolina 5 

2008 Maryland, Michigan, New Mexico, New Year, Ohio 5 

2009 Arizona, Indiana, Iowa, Maine, Massachusetts 5 

2010 Arkansas, Oregon 2 

2011 Wisconsin 1 

1997-2011 26 

EE Program Impacts 

Impacts to-date 

The primary sources for EE program information (including costs and impacts) are 

annual EE program reports required by utility commissions, or cooperative or municipal utility 

boards of directors. These reports are based on M&V studies of individual EE programs within 

the program portfolio. The Energy Information Administration (EIA) has been collecting data on 

EE programs through Form 861, “Annual Electric Power Industry Report,” for more than three 

decades.

14 with The data collection reflects an increasing degree of breadth and detail over time. 

For example, third-party-administered programs were not initially required to report but were 

added beginning in 2011. Data fields have been added over the years to reflect industry trends 

(e.g., EE programs are now reported separately from load management programs). Outside 

organizations have taken the EIA data, supplemented it with additional sources including surveys 

of utility commissions and program administrators, and published their own annual reports that 

capture EE program impactsxxxiv,xxxv. 

The EPA has relied on the EIA Form 861 dataset for identifying historic impacts of EE 

programs by state. Specifically, the reported sales data, and incremental and cumulative 

electricity nergy savings in the 2012 EIA 861 dataset are used to estimate electricity EE impacts 

                                                            

14 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/. 

Formatted: Superscript

Comment [A16]: As noted elsewhere in the

comments, this dataset can be problematic.

Comment [A17]: EPA RESPONSE: EPA is aware

of issues associated with consistency and quality of

reported DSM program data in Form EIA‐861. Our

goal setting analysis utilizes two variables in the EIA‐

861 data. Specifically, we use the 2012 electricity

sales data and reported incremental annual

electricity savings of demand‐side management

programs to estimate electric EE program impacts

for each state. We recognize that different utilities

and states may have different approaches to

estimate incremental savings, but over time there

has been increased standardization in data

reporting. The instructions for the form direct the

filer to report net energy savings and most

states/utilities estimate net savings. We rely on this

dataset because it remains the most comprehensive

one publicly available that we are aware of. In the

preamble, we are soliciting comments on better or

more appropriate dataset that can be used for our

analysis. In addition, a FN is added to discuss the

data and caveats and the dataset is addressed

further in Analysis Limitations section. We would

welcome support from DOE on improving this

dataset prior to Final.
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by state.15 EIA data is reported by program administrator (e.g., utility, third-party, or state 

agency) and requires the disaggregation of reported data by state for administrators with 

programs in multiple states (e.g., multi-state investor-owned utilities). Program administrators in 

48 states reported savings in 2012. The EPA has compiled this information and aggregated key 

data to the state level. Table 5-4 provides a summary of this data by state for the 2012 reporting 

year, the most recent available. At the national level, incremental electricity savings16 in 2012 

was 0.58% of retail sales with individual state values ranging from 0.00% to 2.19%. Cumulative 

electricity savings17 (representing the remaining impacts of programs from all prior years) 

reported at the national level for 2012 represent 3.74% of retail sales with individual state values 

ranging from 0.0% to 15.44%. 

TABLE 5-4 

 2012 Reported Electricity Savings by State 

State 

Incremental Savings as a % 

of Retail Sales (2012) 

Cumulative Savings as a % 

of Retail Sales (2012) 

Alabama 0.07% 0.78% 

Arizona 1.61% 5.39% 

Arkansas 0.11% 0.39% 

California 1.24% 13.67% 

Colorado 0.84% 4.67% 

Connecticut 1.05% 13.37% 

Delaware 0.00% 0.00% 

                                                            

15 EPA recognizes concerns issues associated with consistency and quality of 861 data that different reporting 

entities utilities and states may have used different methodologies approaches to estimate incremental savings and 

the EIA 861 data are self-reported. Over time, however, there has beenis increased standardization in data reporting. 

We recognize that different utilities and states may have different approaches to estimate incremental savings, but 

over time there is increased standardization in data reporting. We believe rely on this dataset because it remains to 

be the most comprehensive publically available dataset. See “Analysis Limitations” section below for further 

discussion.

16 Incremental savings (also known as first-year savings) represent the reduction in electricity use in a given year 

associated with new EE activities in that same year, either new participants in DSM programs that already existed in 

the previous years, or new DSM programs that existed for the first time in the current year. 17 Cumulative savings (also known as annual savings) represent the reduction in electricity use in a given year from 

EE activities in that year and all preceding years, taking into account the lifetimes of installed measures. 

Comment [A18]: EPA needs to provide much

more detail about how this data was derived.

Comment [A19]: EPA RESPONSE: See response

to immediately prior and following comments. The

data is from 861 as is now noted. And discussion has

been added addressing limitations of the data.

Comment [A20]: EPA used EIA 861 data

Comment [A21]: EPA RESPONSE: That is correct.

The estimates are based on the 2012 EIA 861 data.

A note is added at the end of the table. The text in

the previous paragraph is also modified to provide

more explanation of the use of EIA data.

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District of Columbia 0.00% 0.57% 

Florida 0.27% 3.60% 

Georgia 0.18% 0.67% 

Idaho 0.79% 6.20% 

Iowa 1.05% 7.80% 

Illinois 0.93% 2.15% 

Indiana 0.58% 1.72% 

Kansas 0.02% 0.24% 

Kentucky 0.23% 1.04% 

Louisiana 0.00% 0.00% 

Maine 1.96% 5.42% 

Maryland 0.89% 2.47% 

Massachusetts 0.94% 6.27% 

Michigan 1.01% 2.77% 

Minnesota 1.12% 13.10% 

Mississippi 0.08% 0.50% 

Missouri 0.12% 0.55% 

Montana 0.66% 5.85% 

Nebraska 0.30% 0.99% 

Nevada 0.54% 6.19% 

New Hampshire 0.48% 4.90% 

New Jersey 0.03% 1.04% 

New Mexico 0.60% 1.86% 

New York 0.93% 6.89% 

North Carolina 0.37% 1.26% 

North Dakota 0.07% 0.22% 

Ohio 0.87% 3.20% 

Oklahoma 0.21% 0.70% 

Oregon 1.09% 7.72% 
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Pennsylvania 1.06% 3.08% 

Rhode Island 0.78% 11.22% 

South Carolina 0.35% 1.12% 

South Dakota 0.13% 0.33% 

Tennessee 0.31% 1.76% 

Texas 0.19% 1.54% 

Utah 0.74% 6.59% 

Vermont 2.19% 15.44% 

Virginia 0.03% 0.30% 

Washington 0.93% 7.37% 

West Virginia 0.18% 0.20% 

Wisconsin 1.05% 6.61% 

Wyoming 0.14% 0.71% 

Continental U.S. Total 0.58% 3.75% 

Alaska 0.02% 0.10% 

Hawaii 0.04% 0.25% 

U.S. Total 0.58% 3.74% 

Source: EPA calculation based on 2012 EIA fForm 861 data.

Projected Spending and Savings from EE Programs 

In 2013, Lawrence Berkeley National Laboratory (LBNL) published an update to a 2009 

analysis and projected future spending levels and savings through 2025 from energy efficiency 

programs funded by electric and gas utility customers in the United States under three scenarios 

(high, medium, and low cases)xxxvi. The scenarios represent “a range of potential outcomes under 

the current policy environment” and were based on detailed, bottom-up analysis of existing state 

energy efficiency policies. Significantly, the study presumes no new major policy developments 

such as a “national energy efficiency standard, clean energy standard, or carbon policy” and 

specifies that such policy changes could “result in customer-funded energy efficiency program 

spending and savings that exceed the values in our High Case.” 

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The study concludes that efficiency programs are “poised for dramatic growth over the 

course of the next 10 to 15 years” with the most significant increases occurring in regions with 

lower levels of program spending, historically, including the Midwest and South. For example, 

under the medium scenario total U.S. spending on electric efficiency programs increase by 40% 

to $8.1 billion in 2025 from 2012 levels. Under the high scenario, spending more than doubles 

from 2012 levels to $12.2 billion in 2025. Incremental savings levels grow commensurately, to 

0.8% and 1.1% of sales under the medium and high scenarios, respectively. The study results 

indicate that under the high scenario 20 states would be achieving 1.5% or higher levels of 

incremental savings, with 11 of those reaching or exceeding 2.0%18. Table 5-5 summarizes the 

results of the LBNL analysis. 

 

                                                            

18 LBNL provided these unpublished results from their analysis.

Comment [A22]: So what does this imply about

BB #4? Why does EPA put into the baseline?

Comment [A23]: EPA RESPONSE: This building

block will provide incentive to reach these forecast

levels and beyond. State EE policies are being

reconsidered in many states and sometimes rolled

back (e.g., OH). At the same time, EPA is clearly

building upon the ongoing work of leading states,

integrating their best ideas and successes into the

basis of this building block.
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Table 5-5 

Summary of Impacts: 

Scenarios of Future Utility Customer-Funded Energy Efficiency Programs 

Case

2025 

Incremental Savings 

(% of Sales) 

Program Costs 

(billions of $, nominal) 

Programs Costs 

(% of Revenues) 

Low 0.5% 5.5 1.1% 

Medium 0.8% 8.1 1.7% 

High 1.1% 12.2 2.7% 

EE Potential

Evaluations of EE Potential 

Energy efficiency potential studies are a common tool for informing the development of EE 

program plans and budgets, as well as supporting the development of electricity savings targets, 

required savings levels under an EERS, or “all cost-effective” EE requirement. In conducting 

these studies, states and utilities have developed a standardized methodology that is often 

described as a “bottom-up, engineering-based” approachxxxvii. EE potential studies are conducted 

at various geographic scopes (national, regional, state, and utility service territory level) and at 

different degrees of aggregation (e.g., economy-wide, sectoral, and program), and can be broadly 

grouped into a few types: technical, economic, market, and program.19

 Technical potential represents the theoretical maximum amount of energy use that could 

be displaced by efficiency, without regard to non-engineering constraints such as costs 

and the willingness of energy consumers to adopt the efficiency measures. It often 

assumes immediate implementation of all technologically feasible energy saving 

measures, with additional efficiency opportunities assumed as they arise. 

                                                            

19 The definitions discussed below largely follow that outlined in the Guide for Conducting Energy Efficiency 

Potential Studies (NAPEE 2007) but the variations in definition are also discussed (e.g., Sathaye and Murtishaw 

2004; Huntington 2011). 
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 Economic potential refers to the subset of the technical potential that is economically 

cost-effective. Definition of “economic potential” can vary to some degree by study. 

Some estimate economic potential by evaluating technology upfront cost, operating costs 

that considers energy prices, product lifetime and discount rate, compared to a 

conventional alternative or the supply-side energy resources. Others incorporate 

consideration of consumer preferences in addition to consumers’ out-of-pocket 

expenditure when evaluating the economic potential. Both technical and economic 

potential estimates assume immediate implementation of efficiency measures without 

regard to technology adoption process or real-life program implementation. In addition, 

these estimates do not always reflect market failures or barriers that impede energy 

efficiency and often fail to capture transaction costs (e.g., administration, marketing, 

analysis, etc.) beyond the costs of efficiency measures. 

 Market potential (or “achievable” potential) refers to the subset of economic potential 

that reflects the estimated amount of energy savings that can realistically be achieved, 

taking into account factors such as technology adoption process, market failures or 

barriers that inhibit technology adoption, transaction costs, consumer preferences, social 

and institutional constraints, and possibly the capability of programs and administrators 

to ramp up program activity over time. 

 Program potential refers to the subset of market potential that can be realized given 

specific program funding levels and designs. Program potential studies can consider 

scenarios ranging from a single program to a full portfolio of programs20. 

As mentioned, the EE industry standard for potential studies is the bottom-up, 

engineering evaluation of energy efficiency potential of individual end-use technologies and 

measuresxxxviii. Bottom-up analyses all employ a similar methodology but can vary significantly 

in key assumptions (e.g., breadth of sectors and end-uses considered, study period, discount rate, 

pattern of technology penetration, whether economically justified early replacement of 

                                                            

20 Each subsequent potential estimate described above is a subset of the previous potential estimate, e.g., the market 

potential is a subset of the economic potential, and the economic potential is a subset of the technical potential. 
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technologies is allowed for, and whether continued improvement in efficiency of technology is 

provided for). As a result, estimated efficiency potential can vary significantly among studies21. 

Overview of Results 

Studies of energy efficiency potential are numerous. In recent years, dozens of studies 

have been conducted at regional, state, and utility levels. This section reviews recent studies and 

presents a summary of findings. We first address meta-analyses that summarize results from 

multiple utility, state, and regional studies, and then we address the few national studies that have 

been conducted. To normalize results of analyses addressing different study periods, we present 

average annual achievable potential by dividing cumulative percentage savings in the last year of 

the study by the duration (in years) of the study period. This is a common method of 

normalization for energy efficiency potential studies. 

At the regional and state level, two meta-analyses, Sreedharan (2013)xxxix and Eldridge et 

al. (2008)xl, captured numerous studies conducted between 2001 and 2009. The meta-analysis 

conducted by Sreedharan (2013) presents average annual values of 4.1% per year in technical 

potential, 2.7% per year in economic potential, and 1.2% per year in maximum achievable 

potential. In comparison, Eldridge et al. (2008) estimated average annual values of 2.3% per year 

in technical potential, 1.8% per year in economic potential, and 1.5% per year in achievable 

potential. To supplement these studies with more recent data, the EPA has conducted a metaanalysis of twelve studies conducted between 2010 and 2014 at the utility, state or regional level 

(see Appendix 5-1). The EPA review indicates an average annual achievable potential of 1.5% 

per year across the reviewed studies. See Appendix 5-2 (Summary of Recent (2010-2014) 

Electric Energy Efficiency Potential Studies) for complete results from the EPA research. 

 

                                                            

21 Because of the complex consumer behavior, energy market and macroeconomic drivers of energy use and energy 

efficiency, and in some cases due to the lack of consistent data, quantifying energy efficiency potential and energy 

savings from policies and programs remains a challenging analytical task. Assumptions about consumer technology 

adoption behavior, market barriers and failures, and how technology diffusion occurs can also affect estimated 

potential. 

Comment [A24]: Font is different in footnote

Comment [A25]: EPA RESPONSE: FN font

corrected.

Comment [A26]: Assuming these studies are

correct, what, if any, policies are needed to realized

the achievable potential of 1.2%‐1.5%?

Comment [A27]: EPA RESPONSE: See p. 49,

section “Results in Context,” and Table 5‐22. We

compare our best practice scenario and alternative

(options 1 & 2) results with results from these

studies of achievable potential and find that our

scenarios are either within or below ranges that

result from these scenarios (below the

utility/state/regional results and within the national

results). As discussed, EERS is one policy type that

could be used to realize these levels of impacts,

however, other policies could be used instead of or

in addition to EERS.

Comment [A28]: Make sure to check this

carefully!!

Comment [A29]: EPA RESPONSE: We have re‐

checked.

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Table 5-6 presents a summary of these three meta-analyses of EE potential. 

TABLE 5-6 

Summary of Meta-Analyses of EE Potential at Utility, State, and Regional Levels 

Study Dates of Studies Number of 

Studies 

Average Annual Achievable 

Potential 

Sreedharan (2013) 2001-2009 10 1.2%/year 

Eldridge (2008) 2001-2007 20 1.5%/year 

EPA (2014) 20010-2014 12 1.5%/year 

In addition to the numerous studies conducted at the utility, state, or regional levels since 

2001, a number of studies have evaluated efficiency potential at the national level, applying a 

generally consistent methodology and employing a common data set, across all regions of the 

country. Sreedharan (2013) evaluated four major energy efficiency potential studies at the 

national level, namely, McKinsey and Co. (2007), McKinsey and Co. (2009), EPRI (2009), and 

AEO (2008) Energy Efficiency Side Case. All four studies used the AEO 2008 reference case as 

the baseline but differed in other key respects (e.g., breadth of end-uses, assumed technology 

improvement over time, and definition of cost test for economic potential screening). These 

studies suggest technical electricity savings potential in the range of 25-40% and economic 

potential in the range of 10-25%, as a percentage of total demand in 2020. Of these studies, only 

EPRI provided an estimate of achievable potential. On a normalized basis, the EPRI 2009 study 

provides an achievable annualized potential range of 0.2-0.4% per year (realistically achievable 

and maximum achievable potential, respectively) through 2030 at the national level. 

Two more recent studies also provide national estimates of achievable EE potential: 

EPRI (2014)xli updates their 2009 analysis, using a conventional bottom-up engineering 

approach, and ACEEE (2014)xlii, using a top-down, policy-based approach derived from state 

experience and their evaluated results. EPRI (2014) results show an average annual achievable 

potential range of 0.5% to 0.6% per year (achievable and high achievable potential, 

respectively). ACEEE found average annual achievable potential of 1.5% per year. The results of 

the EPRI and ACEEE studies are summarized in Table 5-7. 

Comment [A30]: Seems like too few studies to

do a valid meta‐analysis.

Comment [A31]: EPA RESPONSE: EPA is

interested in any supplementary references that are

available and relevant to the evaluation of EE

potential at a regional or sub‐regional level.  
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TABLE 5-7 

Summary of National EE Potential Studies 

Study Study Type Average Annual Achievable 

Potential 

EPRI (2009) Bottom-up, engineering 0.2%-0.4%/year 

 (realistic to maximum achievable) 

EPRI (2014) Bottom-up, engineering 0.5%-0.6%/year 

(achievable to high achievable) 

ACEEE (2014) Top-down, policy-based 1.6%/year 

 Notably, each of these national potential studies show significant potential in every 

region of the country including regions with lower electricity prices like the southeast, regions 

with historically high levels of EE program budgets like the northeast and west coast, and across 

regions with varied sectoral composition (e.g., higher manufacturing regions like the midwest 

and south, as well as higher service industry regions like the northeast and California). Both 

EPRI studies illustrate the substantial and similar scale opportunity across all regions. For 

instance, EPRI (2014) shows achievable potential ranging from 8% to 14% relative to baseline in 

2035 across the thirteen regions of their analysis as well as significant opportunity in the 

residential, commercial and industrial sectors in every region. The ACEEE (2014) study also 

shows consistently large potential across all states and regions through 2030, with an average 

potential of 24% and a range of 20% to 36% across 50 states. 

Costs and Cost-Effectiveness of State EE Policies

EE Cost-Effectiveness 

States enact EE policies to meet multiple policy objectives including reduction of customer 

electricity bills, lower costs of meeting electricity supply needs, energy reduction, environment 

Comment [A32]: This raises the question of peer

review.  Have these been independently peer

reviewed? If so, state it.  If not, is EPA planning to do

so?   

Comment [A35]: EPA RESPONSE: Note that this

metric is not the same as the 1.5% incremental

savings as a % of sales that is used as a basis for the

best practices scenario.  As presented on p. 49,

section “Results in Context,” and Table 5‐22, the

best practices scenarios result in average annual

pace of <1%, significantly lower than this value from

one study. Also, note that this particular study is

developed based upon the use of multiple EE policy

types rather than the single type (EE programs) that

serves as the basis for EPA’s BB4 and, thus, results in

higher levels of savings. If EPA had included, for

example, the potential additional impacts of

building codes, appliance standards, and other state

policies, our BB4 savings levels would be higher.

ACEEE presents a credible case indicating that these

levels are achievable.

Comment [A34]: If these are the average

achievables, is it really feasible to go for 1.5% for all

states in each year?

Comment [A33]: EPA RESPONSE: EPA is not

aware of a formal peer review of these studies. EPA

is not planning to conduct a peer review of these

studies prior to proposal.
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and health benefits, and local economic development benefitsxliii. Most states evaluate their EE 

policy options through the application of cost tests, weighing the projected benefits with the 

costs of the energy efficiency technologies and practicesxliv xlv. Each state determines their own 

policies for the specific costs and benefits to include in these tests. The costs and benefits are 

compared on an equal footing by using present value analysis. This is necessary because EE 

typically requires primarily upfront expenditures (e.g., a whole home retrofit) while the 

economic benefits (e.g., electricity bill savings) accrue over the life of the investment (“measure 

life”) which can range from a few to twenty or more years. As such, the choice of discount rate 

and the estimation of measure life are significant determinants of the cost-effectiveness results. 

Most states employ multiple tests, adjusting cost and benefit categories depending upon the 

economic perspective of interest (e.g., utility, ratepayer, program participant, society), and 

consider the results from each one, usually with an identified primary test typexlvi. Policies that 

are selected are those that are found to be cost-effective, with benefits greater than costs, as 

determined by the utility applying methods defined by their state utility commission. 

There are five primary cost-effective tests used in the U.S.: 

(1) Participant cost test from the perspective of the customer installing the measure. Costs 

may include incremental equipment and installation costs; benefits include incentive payments, 

bill savings, and applicable tax credits or incentives. 

(2) Utility/program administrator cost test from the perspective of utility, government 

agency or third-party implementing the program. Costs may include program incentive, 

installation, and overhead costs; benefits may include avoided energy and capacity costs - 

including generation, transmission and distribution - by the utility. 

(3) Ratepayer impact measure test from the perspective of utility ratepayers not participating 

in available energy efficiency programs. This text includes the costs and benefits that will affect 

utility rates, including program and administration costs, as well as “lost revenues” to the utility; 

benefits include avoided energy and capacity costs, and additional resource savings. 

(4) Total resource cost test from the perspective of all utility customers in the service area. 

Costs may include the full incremental cost of the measure, program installation and overhead 

costs; benefits may include avoided energy and capacity costs, and additional resource savings. 
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(5) Societal cost test from the social perspective. In addition to benefits considered in total 

resource cost test, may also include non-monetized benefits such as environmental and health 

benefits. 

While many states consider more than one cost test in evaluating EE programs, the most 

commonly used (29 states) primary test is the total resources cost test. This test is considered to 

be the best measure of the interests of all utility customers. The utility and societal cost tests are 

the next most commonly used primary tests, used by five states each. The utility cost test is 

considered to be the most comparable metric to compare with supply-side resource investments 

from a utility resource planning perspective. 

Economic and modeling analyses of climate change policy suggests that energy 

efficiency presents a large potential in reducing greenhouse gas emissions and plays a critical 

role in offsetting the costs and enhancing the flexibility to achieve long-term GHG reduction 

targetsxlvii. Consistently, evaluations of the economic potential for carbon dioxide reductions 

from the United States’ power sector identify demand-side energy efficiency as the lowest cost 

strategy (typically, as noted above, with positive net present value) as well as the strategy having 

the greatest reduction potentialxlviii. For example, McKinsey (2007)xlix found that EE accounted 

for more than 60% of their mid-range potential for greenhouse gas reductions from the U.S. 

power sector and that it was available at positive net present value if “persistent barriers to 

market efficiency” could be addressed. 

Costs of Saved Energy 

A common metric for comparing alternative electricity resource options within utility 

resource plans is the levelized cost of energy (LCOE) or, for EE resources, the levelized cost of 

saved energy (LCSE)l

. LCSE EE is often compared favorably with LCOE of alternative new 

generation sources such as fossil-fueled or nuclear power plants, or renewable energy resources 

like wind or solar-power generation. In these comparisons, typically only utility (or program) 

costs are considered, not the total costs of saved energy that are discussed later in this chapter. 

The energy efficiency analysis literature reports average LCSE in the range of 1-6 cents/kWh 

Comment [A36]: These LCSE numbers by most

metrics would be considered very competitive and

sometimes even lowest‐cost.  The methodology

presented later, based on program costs, ends up

providing numbers that seem high. Please consider

revisiting the methodology if appropriate.

Comment [A37]: EPA RESPONSE: Comment is

unclear to EPA. The LCSE values here do represent

program/utility costs. The later cost discussion

considers both program/utility and participant

costs. The later values are estimates of the total

costs of realizing EE savings at BB4 levels and are

include several conservative judgments leading to

relatively high values.
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based on program administrator cost.22 A recent review by ACEEE (2014) examined studies 

across 20 states between 2009 and 2012, and estimated LCSE for electricity energy efficiency 

programs in the range of 1.3-5.6 cents/kWh, with a mean value of 2.8 cents/kWh.li Earlier 

reviews of utility EE programs identified a similar range of LCSE. Friedrich et al. (2009) 

reviewed 14 utility studies of LCSE and found a range from 1.6 to 3.3 cents/kWh, with a mean 

value of 2.5 cents/kWh.lii An earlier ACEEE study (2004) reviewed cost-effectiveness analysis 

results in nine states and suggested that reported utility LCSE ranged between 2.3-4.4 

cents/kWh, with a mean value of 3 cents/kWh.liii

The economic literature also evaluates the LCSE from EE measures using othervarious

techniques (e.g., empirical econometricsal, top-down modeling), although this body of studies is 

much smaller compared to the bottom-up, engineering-based analysis. The economic literature 

has varying treatment of the free ridership, EE program endogeneity, and the rebound effect., 

The different assumptions used in these analyses make direct comparison challenging, but 

overall and these empirical analyses presents a wider range of estimates of cost of saved energy. 

For example, a recent study by Auffhammer et al. (2008) examining utility DSM programs 

founestimated the average utility cost of saved energy in the range of $5.1 to $14.6 cents per 

kWh.23 Some other studies in the economic literature recent analyses suggest estimated LCSE in 

a similar range as in from the bottom-up energy efficiency analysies literature. Gillingham et al. 

(2004) estimated an average cost of 3.4 cents per kWh saved from utility EE programs.liv Using 

a top-down approach that evaluates the savings potential of EE investments using state- and 

region-specific price elasticity, Paul et al. (2011) estimated that electricity savings were available 

at an average utility LCSE of 2.5-3.5 cents/kWh.lv In an recent econometric analysis of utility 

rate-payer funded demand-side management and energy efficiency programs between 1992 and 

2006, Arimura et al. (2009) found that the estimated energy savings in electricity consumption 

were achieved at an expected average cost to utilities of approximately 5 cents/kWh, using a 5% 

                                                            

22 Unless otherwise noted, estimates of LCSE discussed in this section refer to program administrator cost (also 

known as utility cost). The discount rates, average measure lives, and other assumptions affecting the calculation of 

LCSE were not always consistent or not reported in all studies. 23 Auffhammer M., C. Blumstein, M. Fowlie. 2008. Demand Side Management and Energy Efficiency Revisited. 

Energy Journal 29(3): 91-104. It is noted that the econometric analyses in the literature addresses 

Comment [A38]: “Under the scenarios modeled, 

electricity savings of between 1 and 3 percent are 

achievable at a marginal cost of $50 per megawatt 

hour (MWh) and a corresponding average cost of 

$25–$35/MWh.”

  

Comment [A39]: EPA RESPONSE: See revised

text below including  

The marginal cost estimate from the paper.

Comment [A41]: EPA RESPONSE: Your

calculation omits the large and critical cost savings

from avoided power system operations, etc. (fuel,

variable O&M, and avoided/delayed new generation

and T&D, etc.).  EPA believes that those savings

need to be included in estimating the net cost per

ton CO2 reduced. See added discussion later in this

chapter with results of EPAs EE costs per ton CO2

reduced showing a conservative range of $16‐

$24/ton.

Comment [A40]: Implies $50/MWh and coal

emits appox. 1 metric ton/MWh which is valued at

an SCC of approximately $40. Am I missing

something? I guess the co‐benefits make it

economically viable?   

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discount rate.

lvi Using a top-down approach that evaluates the savings potential of EE 

investments using state- and region-specific price elasticity, Paul et al. (2011) estimated that 

electricity savings of 1 to 3 percent were available at a marginal cost of 5 cents/kWh and a 

corresponding an average utility LCSEcost of 2.5-3.5 cents/kWh.lvii 

A number of analytical and data considerations related to LCSE estimation are also 

discussed in the literature, including the issue of “free riders” in EE programslviii, and the 

accuracy of utility reported costs and energy savings.

lix. Energy efficiency practitioners also 

recognize the need to consider “free rider” and “spillover” effects in program evaluation. A 

slight majority of states adjust for free ridership in energy savings estimates, leading to higher 

LCSE values than otherwise would be the case. A smaller number of states adjust for spillover 

effects which reduce LCSE values when addressed.lx

Another consideration related to LCSE estimation is the rebound effect. The economic 

literature has extensive discussion of the potential rebound effect, market interactions and 

economy-wide response of energy efficiency policies and investments. An improvement in 

energy efficiency would effectively reduce the cost of a service or production input, potentially 

boosting its demand or production output thus increasing energy use (“direct” rebound). In 

addition, money saved from energy efficiency can be used for consumption or investment that 

can increase energy consumption in other markets of the economy and ; lower energy prices as a 

result of energy efficiency improvement may increase energy consumption (two forms of 

“indirect” rebound). Reviews suggest that both direct and indirect rebound effects exist and the 

size of such effects varies among different studies, technologies, sectors and income groups.lxi 

Overall, however, rebound effects are found to be relatively modest compared to the importance 

of energy efficiency as an effective way of reducing energy consumption and carbon emissions 

(Greening et al. 2000lxii; Sorrell 2007lxiii; Davis 2008lxiv; Gillingham et al. 2013lxv). 

EE as an Abatement Measure 

Demand-side energy efficiency is a technically viable and, broadly applicable, and 

generally cost-effective measure for achieving significant reductions in the amount of generation 

Comment [A42]: “Under the scenarios modeled, 

electricity savings of between 1 and 3 percent are 

achievable at a marginal cost of $50 per megawatt 

hour (MWh) and a corresponding average cost of 

$25–$35/MWh.”  

Comment [A43]: EPA RESPONSE: See revised

text including  

The marginal cost estimate from the paper.

Formatted: Indent: First line: 0.5"

Comment [A44]: Suggest making a table

comparing the estimated LCSE for these studies.

Comment [A45]: EPA RESPONSE: EPA believes

that the discussion presents this information well.

We will consider alternative presentation

approaches for Final.
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required and associated emissions from utilization of affected EGUs. Moreover, this measure has 

been adopted by nearly every state and most utilities across the country, typically through 

multiple policy approaches. Increased use of, and impacts from, state energy efficiency policies 

is a leading industry trend over recent years and the trend of increasing investment in EE 

programs is projected to continue for the next decade, at least. These findings support the 

inclusion of demand-side energy efficiency as an abatement measure for reducing carbon dioxide 

(CO2) emissions from fossil fuel-fired electric generating units (EGUs). In the next section, we 

address the setting of state-specific goals for electricity savings levels resulting from state 

demand-side energy efficiency efforts. In the final section, the integration of these goals into the 

impacts assessment is presented and we consider the costreasonableness-effectiveness of the 

costs of this building block within the context of the broader proposal.

State Goal Setting 

Approach 

To estimate the potential CO2 reductions at affected EGUs that could be achieved 

through implementation of demand-side energy efficiency policies as a part of state goals, the 

EPA developed a “best practices” demand-side energy efficiency scenario. This scenario 

provides an estimate of the potential for states to implement policies that increase investment in 

cost-effective demand-side energy efficiency technologies and practices, and projects the annual 

impacts of the scenario for each state. The scenario does not distinguish between policies that are 

currently in place and additional policies that in most states would be required to be implemented 

to realize the goals established. It does not represent an EPA forecast of business-as-usual 

impacts of state energy efficiency policies or an EPA estimate of the full potential of end-use 

energy efficiency available to the power system, but rather is intended to represent a feasible 

policy scenario showing the reductions of CO2 emissions from fossil fuel-fired EGUs resulting 

from accelerated use of energy efficiency policies in all states, generally consistent with ongoing 

industry trends. The scenario uses: 1) a level of performance that has already been demonstrated 

or required by policies (e.g., energy efficiency resource standards) of many leading states,; 2) 

considers each state’s unique existing level of performance; , and 3) allows appropriate time for 

Comment [A46]: EPA needs to provide data to

show how DSM is cost‐effective relative to other

abatement techniques (e.g. increased NGCC

capacity and generation)

Comment [A47]: EPA RESPONSE: This section

summarizes the appropriateness of EE as an

abatement measure. In later sections we discuss

cost analysis of the EE best practices scenario,

specifically, and the reasonableness of those costs

including a conservative estimation of costs per ton

of CO2 reduced.

Comment [A48]: 1) If it is projected to

increase then why is it not in the baseline? And 2)

If it does increase then the cost of achieving

incremental energy savings will be increasing.

Comment [A49]: EPA RESPONSE: The issue of

baseline is discussed in Analysis Limitations section

below. In short, the EE‐based state goals are not

additive to existing EE policies, but inclusive of those

policies. The EE goals generally represent both

continuation and expansion of existing policies. The

full costs and benefits of the goals are accounted for

without attempting to forecast BAU EE

policies/impacts through 2030.

Comment [A50]: Does it include, though, BAU

for states that should be in the baseline?   

Comment [A51]: EPA RESPONSE: See added

sentence.  Also see Analysis Limitations section

below for additional discussion.
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each state to increase from their current level of performance to the identified best practices 

level. 

The best practices scenario is derived from state experience with, and reliance on, 

policies that drive investment in energy efficiency programs, and the energy savings that result 

from those efforts. We focus on energy efficiency programs for several reasons: 

 EE programs have achieved significant levels of savings and are being used in almost 

every state, 

 EE program spending and savings levels are reported by utility or other program 

administrator, by state, and compiled nationally, using standardized elements and 

definitions, and 

 EE program savings are projected and evaluated under requirements established and 

overseen by state utility commissions, and by municipal and cooperative utility boards of 

directors. 

While the approach is derived from information about energy efficiency programs 

overseen by state utility commissions, other state energy efficiency policies are available to 

realize a state’s goals24 such as building energy codes, appliance standards, and building energy 

benchmarking requirements. All policies included in a state plan will need to meet established 

requirements or guidance for EM&V25. 

The following steps were taken to establish the inputs for development of the best 

practices scenario for each state: 

 Step 1: Determine current level of performance 

 Step 2: Determine best practices level of performance 

 Step 3: Determine start year for state efforts 

 Step 4: Determine start year level of performance 

 Step 5: Determine pace at which states improve from start year to best practices level of 

performance 

 Step 6: Determine average portfolio measure life and distribution of measure lives 

                                                            

24 See State Plan Considerations TSD. 

25 See State Plan Considerations TSD. 
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 Step 7: Determine sustainability of best practices level of performance 

Inputs 

Step 1: Determine Current Level of Performance 

A fundamental indicator of the level of energy efficiency program performance is 

incremental annual savings as a percent of retail sales. This is a common metric defining savings 

levels for energy efficiency resource standards and is readily calculated from EIA Form 861 data 

for each state. Incremental annual savings are also more directly estimated and evaluated than 

are cumulative savings26. For the best practices scenario, we aggregated the most recent year of 

EIA Form 861 data to the state level to establish each state’s current level of performance. These 

results were presented previously in Table 5-4. 

 

                                                            

26 Estimates of cumulative savings impacts in a given year are derived from incremental savings values and 

information on measure lives. Information on measure lives is less consistently gathered than is information on 

incremental savings values. 

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single
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Step 2: Determine Best Practices Level of Performance

As discussed previously, achievable demand-side energy efficiency potential exists at 

significant and comparable levels (on the basis of total cumulative potential over a period of ten 

to twenty years) in all regions of the country. While varied regional characteristics (e.g., avoided 

power system costs, economic growth, sectoral mix, climate, and level of past energy efficiency 

efforts) affect estimates of achievable potential, ongoing improvements in energy-efficient 

technologies and practices, economic growth, population increases, and continually improving 

strategies for program delivery have resulted in persistent and substantial levels of achievable 

potential regardless of specific regional characteristics. 

A direct indicator of the achievable incremental levels of energy savings performance is 

provided by past performance at the state and utility levels, and by requirements states have put 

in place for levels of savings to be achieved by 2020. As discussed, these requirements are 

typically in the form of energy efficiency resource standards or similar savings goals that are 

applied to utilities in the state27. 

Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA 

Form 861 (2012) datalxvi, aggregated to the state level, and categorized into four ranges of 

savings levels (>< 0.5%, 0.5% to 0.99%, 1.0% to 1.49%, and >= 1.5%). As shown, three states 

achieved the highest level of performance (> 1.5%) and an additional eight states achieved the 

second highest level of performance (1.0% to 1.49%). 

TABLE 5-8 

2012 Reported State Levels of Incremental Annual Savings 

Incremental Savings as % of 

Retail Sales 

# of States States 

>= 1.5% 3 AZ, ME, VT 

1.0% to 1.49% 8 CA, CT, IA, MI, MN, OR, PA, WI 

0.5% to 0.99% 14 

< 0.5% 25 

                                                            

27 See State Plan Considerations TSD for more information. 

Formatted: Space After: 0 pt, Line spacing: 

single

Comment [A52]: What and where is this

superscript?  

Comment [A53]: EPA RESPONSE: This is an

endnote that provides reference to the EIA 861

data. It is deleted to avoid confusion and the data is

referenced elsewhere in the chapter.

Comment [A54]: Should this be < 0.5?  

Comment [A55]: EPA RESPONSE: Thanks for

pointing out the typo. It is corrected in the text.  

Comment [A56]: What evidence does EPA have

that 1) these states achieved this level of EE due to

the DSM policies they have in place; 2)  these

policies will continue to have this level of effect in

the future; and 3) that demonstrates that these

DSM will be able to push the remaining states to

achieve the same level of success with these

policies?  

Comment [A57]: EPA RESPONSE: 1)  This is

discussed throughout this TSD. These are the values

that administrators of EE programs report to EIA as

representing the impacts due to their EE programs.

The large majority of savings reported are also

reported to state utility commissions using

methodologies required by the commissions. The

commissions statutory role is to ensure that rates

are “just and reasonable” and EE program costs are

a component of rates. 2) In this section we are not

addressing are asserting this point. 3) the ability of

other states to meet these levels is supported in

several sections of this chapter including the

following table, the discussion of studies of EE

achievable potential, and the results in context

section below.
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Table 5-9 summarizes incremental savings levels required by state policy on or before 

2020 and categorized into the same four rangeslxvii. Eleven states are required to achieve the 

highest level of performance (> 1.5%) and an additional five states are required to achieve the 

next highest level of performance (1.0% to 1.49%). 

TABLE 5-9 

Levels of Incremental Savings Required by State Policy on or before 2020 

Incremental Savings as % 

of Retail Sales 

# of States States 

>= 1.5% 11 AZ, CO, IL, IN, 

 MA, MN, NY, OH, RI, VT, WA 

1.0% to 1.49% 5 HI, IA, ME, MI, OR 

0.5% to 0.99% 3 AR, CA, WI 

< 0.5% 1 TX 

 For the best practices level of performance for Option 128, the EPA has chosen 1.5% 

incremental savings as a percentage of retail sales. This level was achieved by three states (AZ, 

ME, and VT) in 2012 and an additional nine states (CO, IL, IN, MA, MN, NY, OH, RI, and 

WA), accounting for overlap, are expected to achieve this level by 2020. Thus, twelve states 

have either achieved or are required to achieve this level of performance by 2020. 

For Option 229, the EPA has chosen 1.0% incremental savings as a percentage of retail 

sales as the best practices level of performance for this alternate approach. This level was 

achieved by eleven states in 2012 and an additional twelve states are expected to achieve this 

level by 2020. In total, twenty states (accounting for duplication between the two sets of states) 

have either achieved or are required to achieve this level of performance by 2020. 

Step 3: Determine Start Year for State Efforts 

                                                            

28 See Preamble and Goal Computation TSD for description of Option 1. 

29 See Preamble and Goal Computation TSD for description of Option 2. 

Comment [A58]: Indicates that all these states

should be in the baseline and not the policy

scenariors.

Comment [A59]: EPA RESPONSE: See sentence

added above in Approach section of this, the Goal

setting section. Also see discussion below in Analysis

Limitations. EPA establishes these goals to be

inclusive of, not in addition to, existing state efforts

including statutory/regulatory requirements for

future years.
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For construction of the best practices scenario, the EPA has used 2017, the year following 

the required state plan submittal30, as the first year for state efforts. 

Step 4: Determine Start Year Level of Performance 

For construction of the best practices scenario, the EPA has set each state’s level of 

performance (incremental savings) in the start year (2017) to its current level of performance 

(aggregated to the state-level from reported EIA Form 861 data). This approach reflects neither 

improvement nor decline in performance between 2012 and 2017. Any improvement in EE 

savings performance between 2012 and 2017 will benefit a state in meeting its state EE goals for 

the 2020-2029 interim compliance period31. 

Step 5: Determine Pace at Which States Improve from Start Year to Best Practices Level of 

Performance 

To determine a trajectory of incremental savings levels from the 2017 level to the best 

practices level, the EPA considered past performance of individual program administrators32 as 

well as requirements of existing state energy efficiency resource standards. For the past 

performance of individual program administrators, we first screened the data and divided them 

into moderate and high performing sub-groups. The moderate group (47 entities) was defined as 

programs that achieved from 0.8% to 1.5% maximum incremental savings levels and the high 

group (26 entities) was defined as programs that achieved greater than 1.5% maximum 

incremental savings levels. We then calculated the rate at which each entity had increased 

savings over time and calculated average values for each sub-group. For the moderate group, the 

average rate of improvement of incremental annual savings rate was 0.30% per year. For the 

high group, the average rate of improvement of incremental annual savings rate was 0.38% per 

year. See Appendix 5-3 for supporting data and analysis. 

 The EPA also considered requirements of existing state EERS and evaluated the rate at 

which their incremental savings levels increase over time. For several EERS, we were unable to 

                                                            

30 See Preamble and State Plan Considerations TSD for descriptions of the schedule for state plan submittals. 

31 See Preamble for description of interim and final compliance periods. 

32 EIA 861 was the primary data source; however, we supplemented EIA 861 data with data for third-party program 

administrators because prior to 2011 EIA did not collect data from third-party program administrators. 

Comment [A60]: In the preamble, it is noted

that states can have an additional year to submit a

final state plan, or two additional years if submitting

a multi‐state plan.  How is this reflected in terms of

determining the start year for state efforts?

Comment [A61]: EPA RESPONSE: The start year

used for this analysis is 2017. This is the year

following state plan submittal if not requesting

delay for one or two years. However, that does not

preclude a state from taking steps on EE programs

consistent with this goal setting approach and they

will benefit in EE contributing to their compliance by

doing so.
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clearly identify ramp-up schedules. We identified ten states with clear schedules and calculated 

the average rate of improvement for each. The average rate of improvement of incremental 

annual savings rate required for these ten states is 0.21% per years. See Appendix 5-3 for 

supporting data and analysis. 

Based on these results, for the best practices rate of improvement the EPA has chosen 

0.2% per year and 0.15% per year for Options 1 and 2, respectively. These values are 

conservative by comparison with our analysis of past state performance and future state 

requirements. 

Step 6: Determine Average Portfolio Measure Life and Distribution of Measure Lives 

The next step in defining the best practices scenario requires projecting the cumulative 

future impacts of the annual incremental savings levels for each state. The incremental savings 

impacts reflect the savings from EE measures put in place in that year, driven by EE program 

activities in that year. The cumulative annual savings represent the total impacts of all EE 

measures put in place in that year and all prior years, due to EE program activities. The 

cumulative savings account for the continuing impacts of energy efficiency measures that remain 

in place for a period of time (the “measure life”) before being replaced. For example, the 

purchase of a high-efficiency refrigerator may lead to savings for twelve years, before being 

replaced with a new model. To estimate cumulative impacts of a series of years of incremental 

savings, the industry uses the concept of an average measure life for the entire portfolio of EE 

programs. Rather than use a single, average measure life to represent a diverse portfolio of 

programs, that range in measure lives from as little as a few years (e.g., certain lighting 

technologies and applications) to as long as fifteen or twenty years (e.g., adding insulation to an 

attic), the EPA is assuming a distribution of measure lives around the average to account for 

future impacts of incremental savings levelslxviii. 

 In 2014, ACEEE updated their 2004 and 2009 national reviews of EE program costs and 

related program characteristics, including measure lives. They reviewed electricity EE program 

data from 20 states and summarized average measure lifetimes by state and customer class. 

Table 5-10 summarizes the results from the ACEEE study and shows an average across all 

sectors for these states of 10.6 years.

Comment [A62]: Again if the improvements are

not driven by 111(d) then they should be in the

baseline.

Comment [A63]: EPA RESPONSE: See other

responses to this comment, above. This sentence is

simply defining the term incremental savings.

Comment [A64]: What are the costs?

Comment [A65]: EPA RESPONSE: EE program

costs include costs associated with all activities of

operating a comprehensive EE program portfolio.

These include costs such as: customer incentives for

investing in more efficient technologies and

practices, development and implementation of

information programs, subsidized energy audits,

etc., as well as the administrative costs associated

with planning, managing and implementing the full

portfolio.
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TABLE 5-10 

Average Electricity Measure lifetimes by state and customer class 

Sector 

Residential Commercial/Business Industrial All Sectors 

Average 8.1 12.5 9.5 10.6 

Other studies have found slightly higher values for average measure life for EE portfolios, 

ranging from 10 to 13 yearslxix. Our assumption of 10 years is conservative by comparison and 

leads to lower cumulative impacts over time and correspondingly lower state goals. 

To approximate a distribution of measure lives across an EE portfolio, consistent with an 

average measure life of ten years, we have assumed an even distribution from one year in length 

to two times the average measure life (twenty years) in length. Our approach is generally 

supported by the substantial range in measure lives reviewed and summarized in a 2014 study by 

LBNL which shows an interquartile range from five to 25 years across twelve program 

categories (e.g., low income, residential new construction, commercial/industrial custom, etc.). 

Our approach represents a first-order approximation of the distribution of measure lives across a 

diverse portfolio of programs. The more common approach in other studies is to assume a 

portfolio with no diversity of measure lives whatsoever, with the entirety of incremental savings 

being realized in each year from the first through the full average measure life and then dropping 

to zero in the following year. Our approach is a conservative one, leading to the same quantity of 

total energy savings, but with a greater portion of the savings occurring in later years than occurs 

with the more common, and simpler, approach. This results in lower cumulative impacts in 

earlier years and correspondingly lower state goals through 2030. 

 

Step 7: Determine Sustainability of Best Practices Level of Performance 

For construction of the best practices scenario, once a state achieves the best practices 

level of performance, the EPA has kept the level of performance constant through 2030. For 

states with lower levels of current performance (and, hence, later achievement of the best 

practices level of performance – as late as 2025 in some instances), this requires sustaining the 

target level for as little as five years. For states currently at or above the best practices level of 

Comment [A66]: Consider referencing other

sources in addition to ACEEE data, including a

forthcoming DOE technical memo and Northwest

Regional Forum documents.

Comment [A67]: EPA RESPONSE: EPA is

interested in reviewing the DOE technical memo

cited. We do believe the values cited in this table

are a reasonable basis for the measure life

assumption for this analysis, particularly since we

are erring on the conservative side of lower average

measure life which leads to lower EE goals and

higher LCSE.

Comment [A68]: The distribution of measure

lives is not uniform, but if you do want to use a

uniform distribution, it should be longer, since LBNL

has shown that some measures last 25 years. (Based

on data, the best estimate of the top end of a

uniform distribution is the maximum of the

observations.) If you use a uniform distribution from

0 to 25 years, and take the midpoint as your

estimate, moving from 10 to 12.5 years would have

a significant impact.   

Comment [A69]: EPA RESPONSE: EPA recognizes

that there are measures that have longer lives than

20 years and we are familiar with the LBNL analysis

referred to. We believe our approach is a

reasonable first order approximation that is an

improvement over the industry standard approach

of assuming no variation whatsoever across the

portfolio. If we took the approach suggested, we

would increase our average measure life to 12.5

solely on the basis that some measures have a life

longer than 20 years and we do not agree with this

approach as the 12.5 avg measure life is not

supported by known data sources.

Comment [A70]: Does this mean that they no

longer make any improvements after the reach the

best practices level of performance  

Comment [A71]: EPA RESPONSE: This means

that a state continues to achieve 1.5% (or 1.0%)

incremental savings each year. This requires

continuing implementation of effective EE programs

(or other policies). Without this ongoing effort, the

effects of EE programs wane over time. This ongoing

effort is a significant aspect of the state EE goal

setting approach.
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performance, this reflects an ability to sustain the target level for thirteen years (2017 through 

2030). 

 Limited empirical data suggests the reasonableness of this approach; however, 

comprehensive data, across all regions and states, does not exist because these levels of 

performance have not been achieved and sustained nationwide previously. The Northwest Power 

Conservation Council (NPCC) provides one such example. NPCC has been conducting the most 

consistent and long-running series of evaluations of achievable cost-effective potential in the 

country, updated every five years, as part of their five-state33 regional energy resource planslxx. 

These analyses have become more detailed, reliable, and purposeful over time. Since 1998, 

NPCC’s estimates of achievable potential have more than tripled even as evaluated electricity 

savings from energy efficiency programs have increased rapidly, more than quadrupling between 

1998 and 2010 (while levelized costs of saved energy achieved have remained flat), and 

exceeding plan targets every year since 2005. A study of the NPCC results concludes: “our 

research shows that when programs invest in higher levels of efficiency, this helps drive 

measurement improvements and technical innovation, resulting in larger and more reliable 

conservation supply estimates.”lxxi Table 5-11 summarizes the NPCC’s achievable potential 

estimates and evaluated savings since 1998lxxii. 

TABLE 5-11 

NPCC Achievable EE Potential and Achieved Incremental Savings (1998-2010) 

Year 

1998 2005 2010 

Achievable Potential over 20 Years 

(GWh) 

13,447 24,651 51,684 

Achieved Incremental Savings from 

EE Programs (GWh) 

547 1,184 2,248 

                                                            

33 NPCC’s resource plans cover Idaho, Oregon, and Washington in their entirety, and western regions of Montana 

and Wyoming. 

Comment [A72]: EPA could reference the 12‐13

year average measure life. This matches very well

with the sustained period from 2017 to 2030.

Comment [A73]: EPA RESPONSE: The measure

life represents the ongoing impact of a single year’s

portfolio of EE investments. It does not support the

ability of a state to sustain a best practice level of

incremental/first‐year performance for 10 or more

years.

Comment [A74]: Not peer‐reviewed. It would

be good for EPA to provide peer‐reviewed evidence

on the efficacy of these types of DSM programs.

Comment [A75]: EPA Response: EPA is

interested in reviewing any relevant peer‐reviewed

analysis on this issue that we have not referenced.

EPA believes that NPCC’s experience and analysis is

very significant and relevant. They are one of a small

number of leading entities that has been developing

resource plans that include EE policies, tracking the

impacts, and revising plans for several decades.

Their work is reviewed through extensive public

processes and provides a critical basis for energy

planning in the northwest. As such, their study has

significant relevance. In the next paragraph we

point to further support for the assumption being

addressed in this section.  

Formatted: Not Highlight
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 Additional substantiation of this approach is provided by average annual achievable rates 

from reviewed studies, as discussed previously, and comparison of those with the rates resulting 

from the best practices scenario. We address this in a later section, Results in Context, after 

presenting those results. 

Summary of Best Practices Scenarios Construction 

 Table 5-12 provides a summary of inputs for the best practices scenarios for Options 1 

and 2. The pace of improvements, average measure life, and distribution of measure lives are 

each conservative and, therefore, contribute lower state goals than would otherwise result. 

Similarly, the best practices level of performance, being based solely on results from and 

requirements of EE programs, is less stringent than a level would be that accounted for potential 

impacts of other state EE policies such as building energy codes, building energy benchmarking 

requirements, and state appliance standards. The use of 2012 level of performance for the 2017 

start year, allows states that increase their use of effective EE policies prior to submitting their 

implementation plan to benefit. 

Table 5-12 

Summary of EE Best Practices Scenario Inputs 

Input Option 1 Option 2 

Current Level of Performance 

(incremental savings as % of 

retail sales) 

Data from 2012 EIA 861 

(2012) 

Data from 2012 EIA 861 

(2012) 

Best Practices Level of 

Performance 

(incremental savings as % of 

retails sales) 

1.5% 1.0% 

Start Year 2017 2017 

Start Year Level of 

Performance 

Data from 2012 EIA 861 

(2012) 

Data from 2012 EIA 861 

(2012) 

Pace of Improvement 0.20% per year 0.15% per year 
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(increase in incremental 

savings rate per year) 

Average Measure Life and 

Distribution of Measure Lives 

10 years; evenly distributed 

across 20 years 

10 years; evenly distributed 

across 20 years 

Continued Performance 

Once achieved, best 

practices level sustained 

through 2030 

Once achieved, best practices 

level sustained through 2025 

Calculations 

This section addresses the calculations for determining cumulative savings levels 

(cumulative savings as a percentage of baseline sales) for each state, for each year of the interim 

and final compliance periods for Options 1 and 2. The cumulative savings levels are derived 

based upon the key inputs summarized in Table 5-12. These levels represent the demand-side EE 

component of the state goals for each state. See the Goal Computation TSD for a detailed 

description of how the demand-side EE component is used as one of several inputs to the 

calculation of interim and final state emission rate goals. 

Calculating the net cumulative savings as a percent of electricity sales for each state involves 

six steps. For each state, for each year (2017-2030 for Option 1 and 2017-2025 for Option 2) the 

following steps are taken: 

1. Determine annual business as usual (BAU) sales 

2. Determine annual incremental EE savings as a percentage of sales 

3. Determine annual incremental EE savings (GWh) and sales after net EE (GWh) 

4. Determine annual expiring EE savings (GWh) 

5. Determine net cumulative EE savings (GWh) 

6. Determine net cumulative EE savings as a percentage of BAU sales 

To illustrate these calculations, each step is described and results provided for one 

state (using South Carolina as an example) for 2017 through 2025 for Option 1. We 
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truncate the results at 2025 for simplicity, but full results are presented for all states in the 

section. 

 

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Step 1: Determine the Annual Business as Usual (BAU) Sales 

BAU sales are derived by taking 2012 sales from EIA Form 861 data for the state 

and increasing them for each subsequent year by the average annual growth rate from the 

AEO 2013 Reference Case for the region corresponding to the state. For South Carolina 

the corresponding region is SERC and the average annual growth rate from 2012 to 2040 

is 1.10% per year. The resulting values are summarized in Table 5-13 for South Carolina. 

TABLE 5-13 

BAU Sales for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

BAU Sales 

(GWh) 82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005 

Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales

As discussed, the 2017 value for annual incremental EE savings as a percentage 

of sales is set at the 2012 value based upon EIA-861 reported data. For South Carolina 

that value is 0.34%. This value is then increased by the pace of improvement of 0.2% per 

year (for Option 1) until the goal level of 1.50% (for Option 1) is reached and then held 

constant. The resulting values are summarized in Table 5-14 for South Carolina. 

TABLE 5-14 

Annual Incremental EE Savings as a Percentage of Sales for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Annual 

Incremental 

EE Savings 

(% sales) 

0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50% 

Step 3: Determine Annual Incremental EE Savings (GWh) and Sales after net EE

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Annual incremental EE savings are calculated by multiplying the annual 

incremental savings as a percentage of sales times the prior year sales after net EE. Sales 

after net EE are calculated by subtracting net cumulative savings from BAU sales. The 

resulting values are summarized in Table 5-15 for South Carolina. 

TABLE 5-15 

Annual Incremental EE Savings and Sales after Net EE Savings for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Annual 

Incremental 

EE Savings 

(GWh) 

274 440 608 777 945 1,113 1,250 1,249 1,249 

Sales after 

Net EE 

(GWh) 

82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346 

Step 4: Determine Annual Expiring EE Savings (GWh)

Expiring EE savings are calculated as the sum of all expired savings in a given 

year from all prior years’ incremental (first-year) savings based upon an average measure 

life of 10 years and a linear decline in first-year savings over twenty years. As an 

example, Figure 2 illustrates the decline in first-year savings from EE measures installed 

in 2017. The resulting values for expiring EE savings are summarized in Table 5-16 for 

South Carolina. 
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FIGURE 1 

Generalized Distribution of First-Year Savings over Time. 

TABLE 5-16 

Expiring EE Savings for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Expiring 

EE Savings 

(GWh) 

0 14 38 70 110 160 219 285 350 

Step 5: Determine the Net Cumulative EE Savings (GWh)

Net cumulative EE savings in a given year are equal to annual incremental 

savings for that year minus total expiring savings for that year plus net cumulative 

savings for the prior year. The resulting values are summarized for South Carolina in 

Table 5-17.

 

Formatted: Indent: First line: 0.5"
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TABLE 5-17 

Net Cumulative EE Savings for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Net 

Cumulative 

EE Savings 

(GWh) 

274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659 

Step 6: Determine the Net Cumulative EE Savings as a Percentage of BAU Sales

Net cumulative EE savings as a percentage of BAU sales are equal to net 

cumulative savings divided by BAU sales. The resulting values are summarized for South 

Carolina in Table 5-18. 

TABLE 5-18 

Net Cumulative EE Savings as a Percentage of BAU Sales for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Net 

Cumulative 

EE Savings 

as % of 

BAU Sales 

0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40% 

Summary of General Formulas and Results by Step for South Carolina 

 Tables 5-19 and 5-20 provide summaries of the generic formulas and results for 

South Carolina for each step. 
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TABLE 5-19 

Summary of Calculation Formulas by Step 

Step Result Formula 

1 BAU Sales (GWh) BAU Sales year i = BAU Sale year i-1 * Annual Average Sales Growth Rate 

2 

Annual Incremental 

EE Savings (% of 

Sales) 

Annual Incremental EE Savings 2017 = Annual Incremental EE Savings 2012; 

Annual Incremental EE Savings year i = Annual Incremental EE Savings year 

i-1 + annual pace of improvement (until goal level is reached) 

3 

Annual Incremental 

EE Savings (GWh) 

Annual Incremental Savings year i = Annual Incremental Savings as a 

Percent of Sales year i * Sales After Net EE year i-1

3 

Sales after Net EE 

(GWh) 

Sales After Net EE year i = BAU Sales year i – Net Cumulative Savings year i

4 Expiring EE 

Savings (GWh) 

Expiring Savings year i = Σ Expiring measures from all prior program years 

(10-year average measure life with linearly decline over 20 years) 

5 

Net Cumulative 

Savings (GWh) 

Net Cumulative Savings year i = Σ Annual Incremental Savings YTD – 

Expiring Savings year i

6 

Net Cumulative 

Savings (% of Sales) 

Net Cumulative Savings year i = Net Cumulative Savings year i / BAU Sales

year i

TABLE 5-20 

Summary of Results by Step for South Carolina. 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

BAU Sales 

(GWh) 

82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005 

Annual 

Incremental 

0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50% 
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EE Savings 

(% sales) 

Annual 

Incremental 

EE Savings 

(GWh) 

274 440 608 777 945 1,113 1,250 1,249 1,249 

Sales after 

Net EE 

(GWh) 

82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346 

Expiring EE 

Savings 

(GWh) 

0 14 38 70 110 160 219 285 350 

Net 

Cumulative 

EE Savings 

(GWh) 

274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659 

Net 

Cumulative 

EE Savings 

as % of BAU 

Sales 

0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40% 

Results 

Summary of Results 

As discussed, the EE goals for each state are represented as cumulative savings as a 

percentage of retail sales by year for each option. Table 5-21 summarizes these values for the 

first and last year of the interim compliance period for Options 1 and 2. See Appendix 5-4 for 

comprehensive results by state, for years 2017 through 2030, including both annual incremental 

and cumulative savings as a percentage of retail sales, for each options. 
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TABLE 5-21 

Summary of State EE Goals for Options 1 and 2 

State 

EE State Goal 

Cumulative Savings as a % of Retail Sales 

Option 1 Option 2 

2020 2029 2020 2024 

Alabama 1.36% 9.48% 1.07% 3.86%

Arizona 1.52% 9.71% 1.24% 4.10%

Arkansas 5.24% 11.42% 3.52% 5.98%

California 4.95% 11.56% 3.55% 6.08%

Colorado 3.92% 11.01% 3.32% 5.87%

Connecticut 4.71% 11.88% 3.61% 6.25%

Delaware 1.14% 9.47% 0.86% 3.59%

District of Columbia 1.14% 9.47% 0.86% 3.59%

Florida 2.03% 9.98% 1.75% 4.65%

Georgia 1.76% 9.83% 1.48% 4.36%

Idaho 4.36% 11.63% 3.52% 6.15%

Iowa 3.80% 11.10% 3.28% 5.88%

Illinois 3.20% 11.11% 2.89% 5.70%

Indiana 4.65% 11.66% 3.58% 6.17%

Kansas 1.22% 9.52% 0.94% 3.70%

Kentucky 1.91% 10.02% 1.63% 4.55%

Louisiana 1.14% 9.33% 0.85% 3.56%

Maine 4.43% 11.77% 3.55% 6.21%

Maryland 4.21% 11.51% 3.47% 6.10%

Massachusetts 5.37% 12.13% 3.61% 6.25%

Michigan 4.59% 11.77% 3.59% 6.22%
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Minnesota 4.80% 11.72% 3.58% 6.17%

Mississippi 1.58% 9.92% 1.29% 4.20%

Missouri 1.40% 9.59% 1.12% 3.93%

Montana 3.36% 10.90% 3.01% 5.69%

Nebraska 2.84% 11.00% 2.56% 5.49%

Nevada 2.37% 10.26% 2.09% 4.98%

New Hampshire 1.25% 9.58% 0.96% 3.74%

New Jersey 3.10% 10.60% 2.81% 5.50%

New Mexico 4.42% 11.76% 3.54% 6.20%

New York 1.39% 9.71% 1.11% 3.95%

North Carolina 2.20% 10.40% 1.91% 4.89%

North Dakota 2.95% 10.69% 2.67% 5.45%

Ohio 4.17% 11.56% 3.47% 6.12%

Oklahoma 1.86% 9.97% 1.57% 4.49%

Oregon 4.66% 11.41% 3.55% 6.06%

Pennsylvania 4.67% 11.69% 3.58% 6.18%

Rhode Island 3.90% 11.56% 3.35% 6.06%

South Carolina 2.32% 10.23% 2.04% 4.94%

South Dakota 1.60% 9.91% 1.32% 4.22%

Tennessee 2.21% 10.26% 1.93% 4.86%

Texas 1.78% 9.91% 1.50% 4.40%

Utah 3.62% 11.03% 3.19% 5.82%

Vermont 1.23% 9.33% 0.95% 3.67%

Virginia 5.37% 12.13% 3.61% 6.25%

Washington 4.24% 11.26% 3.45% 6.00%

West Virginia 4.68% 11.79% 3.60% 6.22%

Wisconsin 1.77% 10.11% 1.49% 4.44%

Wyoming 1.61% 9.73% 1.33% 4.19%

Continental U.S. 3.05% 10.66% 2.44% 5.18%
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Alaska 1.22% 9.45% 0.94% 3.69%

Hawaii 1.29% 9.52% 1.01% 3.79%

U.S. Total 3.04% 10.65% 2.43% 5.17%

Results in Context 

 To provide context for state cumulative savings results presented in Table 5-21 and 

Appendix 5-4, the average annual savings were calculated for each state through 2025 and 2030, 

starting from 2017. Table 5-22 summarizes the results. 

TABLE 5-22 

Summary of Average Annual Savings Rates of Best Practices Scenario 

Option Years 

Number 

of Years 

Range of Cumulative 

Savings (% of Sales) 

across States in Last 

Year (2025/2030) 

Range of Average 

Annual Savings 

Rates across States 

National 

Average Annual 

Savings Rate 

1 

2017-

2030 

13 9.9% to 12.5% 0.76%/year to 

0.96%/year 0.86% per year 

2 

2017-

2025 

8 4.3% to 6.8% 0.54%/year to 

0.85%/year 0.72% per year 

 The state range and national values for the average annual savings rate represented in the 

EE best practices scenario are below the range of values found in recent utility, state, and 

regional studies (1.2% to 1.5% per year) as summarized in Table 5-6, and within the range of 

values found in the 2014 national studies from EPRI and ACEEE (0.5%/0.6% to 1.6% per year) 

as summarized in Table 5-7. These results provide additional support for the feasibility of the EE 

best practices scenario and associated state-specific EE goals. 

Impacts Assessment 

Approach 
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In the Goal Computation TSD, state-specific EE goals from the previous section are 

integrated with the other building blocks and used to set state-specific emission rate goals for the 

interim and final compliance periods. These state emission rate goals are then represented as 

requirements within the power sector modeling for the Regulatory Impacts Analysis (RIA). In 

addition, the EE state goals, resulting from the EE best practices scenario, are used to adjust 

electricity demand levels used as exogenous inputs to power sector modeling for the illustrative 

compliance scenarios. In other words, the degree to which EE is employed as an abatement 

resource is not determined endogenously within the power sector modeling based upon 

optimization of costs but, rather, “hard wired” into the illustrative compliance scenarios. This 

approach is taken because the EPA has determined, as discussed previously, that EE is costeffective at the established EE goal levels. The EE goal levels were constrained by practical 

considerations of state EE policy implementation, specifically, the current levels of EE 

performance and the pace at which states can feasibly improve their levels of performance over 

time. 

The EE goals represented in the illustrative compliance scenarios lead to substantial 

reductions in power system costs due to the reductions in specified electricity demand. Since EE 

is not represented endogenously as an abatement measure within the power sector modeling, the 

costs associated with the EE best practice scenario must be estimated outside of the power sector 

modeling and integrated with the results from that modeling. These EE cost estimates, their 

basis, and calculations are addressed in the following sections. 

Inputs 

The following steps were taken to establish the inputs for development of the EE cost 

estimates for each state. 

 Step 1: Determine state-specific electricity savings by year 

 Step 2: Determine first-year program costs of saved energy 

 Step 3: Determine the ratio of program to participant costs 

 Step 4: Determine the escalation rate of EE costs 

Step 1: Determine State-Specific Electricity Savings by Year

Comment [A76]: This section creates inputs that

feed directly through to end costs. These costs are

external to the IPM modeling and are simply added

to the “rest of system” costs produced by IPM. The

ultimate program costs are therefore sensitive to

these input parameters. Highlighted below are

several places where it seems EPA may have been

overly conservative in its choice of parameters.

Using other numbers that are cited in this report it

is possible to deduce input costs that are only half

as high as what EPA uses. Even a more moderate

calculation reduces input costs by roughly 20‐30%.

Since this rule BSER relies on high EE, even a few

percentage points of reduction in these EE inputs

may lead to significant changes in the RIA cost

calculations. Please consider revisiting EE cost

inputs.

Comment [A77]: EPA RESPONSE: EPA

acknowledges that our determination of these

inputs have generally resulted in conservative inputs

(leading to higher costs) and, in combination, these

have a cumulative impact leading to costs that are

higher than would have resulted from less

conservative determinations.
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 Results from the previous section, State Goal Setting, provide the starting point for 

estimation of EE costs. From those results, state-specific annual incremental savings (MWh) and 

yearly distribution of associated continuing savings (MWh) in future years are used as inputs to 

the cost estimation calculations. 

Step 2: Determine First-Year Program Costs of Saved Energy

 First-year program costs refer to the full costs (e.g., administration, incentive payments, 

marketing, information to consumers, etc.) incurred by a utility or other administrator of EE 

programs in a given year that lead to EE measures (technologies and practices) put in place in 

that year and resulting in reductions in electricity demand in that and future years (driven by the 

mix of measure lives across the portfolio of EE programs employed). Unlike participant costs, 

program costs are readily known by the administrator of EE programs and are, therefore, an 

appropriate starting point for EE program cost analysis. In 2009, ACEEE conducted a national 

review of data on EE program costs from program annual reports, evaluation reports, and 

information compiled from contacts at program administers in 14 states. Compiled data was 

sourced from multiple EE program administrators in each state and over multiple years of data 

for each administrator. ACEEE found average first-year net34 costs of $275/MWh (2011$). The 

EPA has used this value for our analysis. 

 Two recent national analyses have found slightly substantially (16-36%) lower program 

costs than the 2009 ACEEE study. In 2014, ACEEE updated their analysis from 2009, 

expanding the number of states to 20, and including a greater number of program administrators 

and years. In this analysis ACEEE found average first-year net costs of $230/MWh (2011$). In 

2014, an LBNL study presented results from a uniquely comprehensive study of EE program 

costs. The LBNL analysis reviewed program-level data from over 100 program administrators in 

31 states. Data wasere collected from over 1,700 individual programs for up to three years 

(2009-2011), covering more than 4,000 individual program years of data points. Because of the 

broad scope of their study and the lack of net savings information for many programs, LBNL 

                                                            

34 “Net costs” refers to costs per electricity saved after accounting for effects of free-ridership on those savings. 

Depending upon the state, spillover effects may also be accounted for in net costs. 

Comment [A78]: Some commenters

recommend using either the most recent ACEEE

analysis, with a first‐year net cost of $230/MWh or

the recent LBNL study, with a calculated first‐year

net cost of $175/MWh.  If used, these numbers

would lead to a cost of saved energy more in line

with the available literature than what EPA is

currently proposing.

Comment [A79]: EPA Response: EPA agrees that

use of those sources would lower our cost values.

The LBNL value cited, however, is not directly

applicable as it represents costs per GROSS MWh

saved and we are seeking to represent costs per

NET MWh saved. LBNL study does not offer any data

to suggest how to derive costs per NET Mwh from

their study. EPA believes that our inputs are in line

with reasonable ranges from available literature but

our conservatism does lead to cost estimates

towards the higher end of available bottom‐up

engineering based estimates.

Comment [A83]: EPA RESPONSE: Already

responded to. See above.

Comment [A82]: Suggest EPA analysis use these

more recent and more robust numbers.  

Comment [A80]: The RFF discussion paper

“Supply Curves for Conserved Electricity” at

http://www.rff.org/documents/RFF‐DP‐11‐11.pdf

has cost estimates that could be included: “Under

the scenarios modeled, electricity savings of

between 1 and 3 percent are achievable at a

marginal cost of $50 per megawatt hour (MWh) and

a corresponding average cost of $25–$35/MWh.”  

Comment [A81]: EPA RESPONSE: This RFF paper

is referenced in the earlier discussion on “costs of

saved energy,” where estimates of levelized cost of

saved energy in the literature are discussed. This

section discusses the first‐year program costs, thus

is not the most appropriate place to reference this

paper
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focused on gross35, rather than net, savings values. LBNL found national average first-year cost 

of gross savings of $162/MWh (2012$). Applying an average net-to-gross ratio of 0.9 and 

deflating costs at 3%, results in an estimated national average first-year cost of net savings of 

$175 (2011$). The up-to-date, more comprehensive results from the ACEEE and LBNL studies, 

indicate that the value of $275/MWh used for this analysis is conservative, resulting in 

comparatively higher total costs than would be the case based upon the newer studies. 

Step 3: Determine the Ratio of Program to Participant Costs

 As noted above, while program costs are readily known and consistently reported by the 

program administrator, participant costs require significant effort to estimate, and are less 

consistently estimated and reported. The ratio between program and participant costs will vary 

significantly from one program to the next within a utility’s portfolio. The EPA has used a 

generic approach to estimate the ratio of program to participant costs across an entire portfolio, 

thus providing for the estimation of total costs once program costs are determined. To derive the 

ratio, the EPA reviewed 2012 EE annual reports from program administrators in 22 states 

identified as leaders in EE programs36 based upon their magnitude of savings or their savings as 

a percentage of retail sales. Complete information on full portfolio participant costs were 

available for nine of the 22 states. Across the nine states, the average program and participant 

costs as a percentage of total costs were 53% and 47%, respectively. See Appendix 5-3 for the 

data and analysis documenting this review. Based on this review, the EPA has taken a slightly 

conservative approach37 and used a ratio of 1:1 between program and participant costs. We use 

this ratio to derive participant and total costs based upon program costs. Starting from program 

CSE of $275/MWh and applying the 1:1 ratio, we estimate participant CSE of $275/MWh and 

total CSE of $550/MWh (all values 2011$). 

Step 4: Determine the escalation rate of EE costs

                                                            

35 “Gross savings” refers to electricity savings before any accounting for effects of free-ridership or spillover. 36 Leaders were identified using results from the 2013 ACEEE State Energy Efficiency Scorecard based on energy 

savings as a percentage of retail sales or total savings. 

37 If we had used the 53% and 47% values, starting from program costs, total costs would have been slightly lower 

than calculated with the 1:1 split used. 

Comment [A84]: It may not be necessary to

take the most conservative assumption particularly

when there have been subsequent refinements to

the study that may create more reliable numbers. In

addition, it may not be appropriate to use this old

number as a single point estimate to calculate EE

program costs for this rule. Please consider

revisiting if appropriate.

Comment [A85]: EPA RESPONSE: EPA

acknowledges repeatedly the conservatism of the

cost estimates but also believe they are supported

by the sources provided with appropriate expert

judgement and recognition of context in which

estimates are being made.

Comment [A86]: A commenters suggest DOE

may have updated participant cost data.

Comment [A87]: EPA RESPONSE: EPA is

interested in reviewing any data or analysis DOE has

to provide.

Comment [A88]: If possible, please provide

further support for using a number different than

EPA’s calculations. If the derived number is used

(53/47), the total program costs would drop by

about 6%. This again inflates EE costs relative to

what the empirical evidence suggests they would

be.

Comment [A89]: EPA RESPONSE: Given the

survey on which these values are based, EPA finds it

reasonable to err towards the higher value for our

analysis. At higher levels of savings, national

breadth, etc., it is reasonable to anticipate the

historic results might underestimate portion of costs

borne by participants in future expanded program

efforts.

Comment [A90]: EPA seems to have taken a

conservative approach to costs that may not be

justified by the empirical literature. Please consider

revisiting if appropriate.

Comment [A91]: EPA RESPONSE:  EPA

acknowledges repeatedly the conservatism of the

cost estimates but also believe they are supported

by the sources provided with appropriate expert

judgement and recognition of context in which

estimates are being made.
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 The level of EE program impacts represented in the state EE goals are substantial and 

represent a scenario that has not previously been achieved and sustained at a national level in the 

U.S. Thus, even though the EPA has taken a conservative approach (i.e., leading to higher 

estimates of costs) to the development of the EE state goals as well as to other factors that affect 

the EE cost estimates, we have also chosen to take a cautious approach to the escalation of EE 

costs at higher levels of performance (i.e., as states improve from their historic levels of 

incremental savings to the best practices level of 1.5% of retails sales). Economic theory 

suggests two mechanisms that would change EE costs as higher levels of performance are 

achieved. Economies of scale in the operation of larger EE programs and larger portfolios of EE 

programs, and learning and expertise gained over time from the continued implementation of 

programs, are two factors that would lower costs as programs scale up and expand to realize 

higher levels of performance. However, the limited supply of EE abatement measures and the 

need to employ higher cost measures, over time, to reach higher levels of performance, and to 

sustain high levels of performance, are factors that would increase costs as higher levels of 

performance are achieved. Analysis based upon limited empirical data does provide support for 

significant economies of scale and/or cost reductions over time as learning and expertise are 

gainedlxxiii. “Supply curves” of EE as an energy resource, as well as EE as a measure represented 

within a GHG abatement curve, provide support for escalating costs as higher levels of savings 

are realizedlxxiv. In a recent analysis, Lawrence Berkeley National Laboratory (LBNL) adopted 

an approach that generically represented both effects discussed abovelxxv. LBNL changed EE 

costs (first-year program costs) as a function of EE savings levels, decreasing costs at savings 

levels up to 0.5%, leaving costs constant at the base level at savings levels from 0.5% to 1.5%, 

and increasing costs at savings levels above 1.5%. Another recent analysis, by ACEEE, provides 

weak statistical support for a cost increase of 20% when going from 0.5% to 1.0% savings rate 

and an additional cost increase of 20% when going from 1.0% to 1.5% savings ratelxxvi. 

In consideration of the above discussion, the EPA has chosen to escalate EE costs over 

three steps as a function of incremental savings (as a percentage of electricity sales) at the state 

level. Until a state reaches a 0.5% savings level, their costs are set at the base level; for savings 

levels between 0.5% and 1.0%, state costs are escalated to 120% of the base level; and for 

savings levels over 1.0%, state costs are escalated to 140% of the base level. This approach leads 

Comment [A92]: On the timescales of this rule

(16 years) we can expect a reasonable amount of

technological change to be occurring over that

period as well, which would offset some of the cost

escalation from extracting new supply. Please

consider incorporating or accounting for this.

Comment [A93]: EPA RESPONSE: EPA is

interested in reviewing any data or analysis on this

topic that may be relevant to this discussion.

Comment [A94]: Please consider incorporating

literature on learning and cost reductions over time

across many technologies, and particularly in many

energy technologies.   

Comment [A95]: EPA RESPONSE: EPA is

interested in reviewing any data or analysis on this

topic that may be relevant to this discussion.

Comment [A96]: EPA methodology seems to

select only the supply curve dimension and not the

cost improvement dimension. DOE now uses cost

improvements as part of their efficiency rulemaking

so it seems appropriate to apply that principle here.

Please consider revisiting if appropriate.

Comment [A97]: EPA RESPONSE: EPA is

interested in reviewing any relevant data or analysis

on cost improvements and would like to incorporate

them into the final rule.

Comment [A98]: This is quite different from the

approach EPA chose. The LBNL study takes into

consideration more programs and more driving

factors and seems to be a robust study. Please

consider revisiting EPA’s approach if appropriate.

Comment [A99]: EPA RESPONSE: EPA agrees

this is a significantly different approach, however,

LBNL was applying expert judgement in a context of

EE investments and savings at a substantially lower

level than EPA’s Best Practices scenario.  

Comment [A100]: Also came in lower than the

approach EPA chose.  Please consider revisiting

approach.

Comment [A101]: EPA RESPONSE: EPA agrees

this is a conservative assumption but we believe it is

reasonable and appropriate.

Comment [A102]: Here is a third example of

possibly overly conservative assumptions.

Reasonable alternates are to use 100% to base level

and then an adder, say 120%, above 1.5. LBNL

actually discounts from 0 to 0.5 level. If a cost

escalation to 140% is justified, please consider doing

it after 1.25 of base.

Comment [A103]: EPA RESPONSE: EPA agrees

this is a conservative assumption but we believe it is

reasonable and appropriate.
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to higher costs relative to the one used by LBNL when applied to EPA’s EE best practices 

scenario. 

Summary of Inputs for EE Cost Analysis 

 Table 5-23 provides a summary of inputs for the EE cost analysis including first-year 

costs of saved energy, ratio of program to participant costs, and escalation of costs as a function 

of the rate of incremental savings. Each of these factors incorporates some level of conservatism, 

leading to higher costs than would otherwise result. 

Table 5-23 

Summary of EE Cost Analysis Inputs 

Input Source or Assumption 

State-Specific Electricity Savings by Year Results from state goal setting 

First-Year Program Cost of Saved Energy $275/MWh (2011$) 

Ratio of Program to Participant Costs 1:1 

First-Year Participant Cost of Saved Energy $275/MWh (2011$) 

First-Year Total Cost of Saved Energy $550/MWh (2011$) 

Escalation of Costs 

Incremental savings rate 

0.5% - 1.0% > 1.0% 

120% of base costs: 

$660/MWh (2011$) 

140% of base costs: 

$770/MWh (2011$) 

Summary of Alternate EE Cost Analysis Inputs based on ACEEE 2014 

Input Source or Assumption 

State-Specific Electricity Savings by Year Results from state goal setting 

First-Year Program Cost of Saved Energy $230/MWh (2011$) 

Ratio of Program to Participant Costs 53:47 

First-Year Participant Cost of Saved Energy $204/MWh (2011$) 

First-Year Total Cost of Saved Energy $434/MWh (2011$) 

Escalation of Costs Incremental savings rate 

Comment [A104]: Shown below that the EE cost

inputs can, under defensible and reasonable

alternate assumptions, range from  34% to 50%

lower; and are very likely to be at least 10‐15%

lower, than the numbers in this original Table 5‐23.

Suggest that EPA revisit the set of assumptions and

modify accordingly.

Comment [A105]: EPA RESPONSE: EPA

understands your alternative approach and

justification. We believe our approach is defensible,

reasonable, and more conservative. Given the

unique nature of the attaining the EE Best Practices

Scenario at a national level and the aggregate

impacts, EPA believes that this conservatism is

appropriate.

Comment [A114]: To clarify, we are not

suggesting these are redline additions, but instead

are providing the information for illustrative

purposes.

Comment [A107]: EPA RESPONSE: OK

Comment [A106]: Using only information

provided in the preceding paragraphs about cost

inputs, derived below are two alternate price inputs,

one based on the LBNL study and one based on the

updated ACEEE numbers.  
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0.5% - 1.0% > 1.0% 

100% of base costs: 

$434/MWh (2011$) 

120% of base costs: 

$520/MWh (2011$) 

Summary of EE Cost Analysis Inputs based on LBNL 

Input Source or Assumption 

State-Specific Electricity Savings by Year Results from state goal setting 

First-Year Program Cost of Saved Energy $175/MWh (2011$) 

Ratio of Program to Participant Costs 53:47 

First-Year Participant Cost of Saved Energy $155/MWh (2011$) 

First-Year Total Cost of Saved Energy $330/MWh (2011$) 

Escalation of Costs 

Incremental savings rate 

0.5% - 1.0% > 1.0% 

100% of base costs: 

$330/MWh (2011$) 

120% of base costs: 

$396/MWh (2011$) 

Calculations 

This section addresses the calculations for estimating the costs associated with the statespecific EE goals discussed above. The results of these calculations are then used within the RIA 

and preamble. Specifically, three values are calculated (annual first-year costs, levelized cost of 

saved energy (LCSE), and annualized costs); for each, program and participant components are 

then calculated using the 1:1 ratio (i.e., 50% of total for each) derived above. Specific results 

from prior sections on state goal setting and impacts assessment inputs are used as inputs for 

these calculations. For each state, the following steps are taken for each year (2017-2030) and for 

each option. Calculations for steps 2 and 3 are done using real discount rates of 3% and 7%. 

 The steps are: 

1. Calculate annual first-year costs 

Comment [A108]: As stated above this

escalation can be treated in a couple of ways based

on the cited literature. Using the following:

0 To 1 = 100%

1 To 1.5 = 120%

Alternate suggestion (or some variation thereon):

0 to 0.5 = 80%

0.5 to 1 = 100%

1 to 1.25 = 120%

1.25to 1.5 = 140%

Comment [A109]: EPA RESPONSE: OK

Comment [A110]: This uses LBNL seed data plus

100/120 escalation.

Comment [A111]: EPA RESPONSE: OK

Comment [A112]: Please compare these final

numbers to the original calculation of 660/770

$/MWh and consider revisiting approach.

Comment [A116]: 

Comment [A115]: EPA RESPONSE: OK
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 57 

2. Calculate levelized cost of saved energy (LCSE) 

3. Calculate annualized costs 

To illustrate these calculations, each step is described and results are provided for one 

state (using South Carolina as an example) for 2017 through 2025 for Option 1. The 

results are truncated at 2025 for simplicity, but full national results (through 2030) are 

presented below. 

 

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Step 1: Calculate Annual First-Year Costs

Annual total first-year costs are calculated by multiplying annual total incremental 

savings (MWh) (from Table 5-15) by the first-year total CSE (from Table 5-23 with 

escalation based upon results from Table 5-14). Program and participant portions of the 

first-year costs are then calculated as 50% of total first-year costs for each per Table 5-23. 

The resulting values are summarized for South Carolina in Table 5-24. 

TABLE 5-24 

Calculation of Annual First-Year Costs for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Annual 

Incremental 

Savings 

(GWh) 

274 440 608 777 945 1,113 1,250 1,249 1,249 

First-Year 

Total Cost of 

Saved Energy 

(2011$/MWh) 

$550 $660 $660 $660 $770 $770 $770 $770 $770 

First-Year 

Total Cost 

(millions 

2011$) 

151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

First-Year 

Program 

(millions of 

2011$) 

75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9 

First-Year 

Participant 

(millions of 

75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9 

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2011$) 

Step 2: Calculate Levelized Cost of Saved Energy

Levelized costs of saved energy (LCSE) are based on levelization of all savings 

(first and future years) resulting from EE activities in a given year. The levelization 

algorithm is based on the 2002 California Standard Practice Manuallxxvii. The net present 

value of all savings from a single year’s EE activities (i.e., over the entire distribution of 

program lifetimes) is calculated using the real discount rate. The levelized cost of saved 

energy is then calculated by dividing the annual first-year costs (from Table 5-24) by the 

levelized savings. The resulting values are summarized for South Carolina in Table 5-25. 

TABLE 5-25 

Levelized Cost of Saved Energy for South Carolina (at 3% discount rate) 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Levelized 

Savings 

(GWh) 

2,313 3,720 5,139 6,563 7,987 9,405 10,562 10,553 10,554 

First-Year 

Total Cost 

(millions 

2011$) 

151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

Total LCSE

(cents/kWh)

6.51 7.81 7.81 7.81 9.11 9.11 9.11 9.11 9.11 

Program 

LCSE

(cents/kWh)

3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56 

Participant 

LCSE

(cents/kWh)

3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56 

Comment [A117]: There seems to be an error in

the calculation of LCSE in Table 5‐25 below (factor

of 10 difference). Please check.

Comment [A118]: EPA RESPONSE: Calculation

has been re‐checked.

Formatted Table

Comment [A119]: Might want to check

calculation – seems there is a factor of 10

difference?

Comment [A120]: EPA RESPONSE: Calculation

has been checked again.

Comment [A122]: EPA RESPONSE: Thanks.

Cents/kWh, added to the table.

Comment [A121]: Unit?.
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Step 3: Calculate Annualized Costs

The costs of the EE program can also be represented as annualized costs in a 

given year. Annualized costs are calculated by multiplying the LCSE for each year by the 

estimated remaining savings in each year through the full distribution of measure 

lifetimes. For each year in the analysis, the annualized costs resulting from all current and 

past investments are summed to calculate the total annualized costs in that year. The 

resulting values are summarized for South Carolina in Table 5-26. 

TABLE 5-26 

Annualized Costs for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Annualized 

Total Costs 

(millions 

2011$) 

17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2 

Annualized 

Program 

Costs 

(millions 

2011$) 

8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1 

Annualized 

Participant 

Costs 

(millions of 

2011$) 

8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1 

Summary of General Formulas and Results by Step for South Carolina 

 Tables 5-27 and 5-28 provide summaries of the generic formulas and results for 

South Carolina for each step. 
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TABLE 5-27 

Summary of Calculation Formulas by Step 

Step Result Formula 

1 

Annual FirstYear Costs 

(2011$) 

Annual First-Year Costs year i = Annual Incremental Savings year i x First-Year CSE year i 

First-Year CSE year i = f(incremental savings rate) per Table 5-23 

2 

Levelized 

Savings 

(GWh) 

Levelized Savings year i = ∑ ஺௡௡௨௔௟ ூ௡௖௥௘௠௘௡௧௔௟ ௌ௔௩௜௡௚௦ ೤೐ೌೝ	೔

ሺଵା௥ሻ೔

்

௜ୀ଴ , 

where T = measure life, r = discount rate.Levelized Savings i = NPV [Lifetime 

Savings from EE Investments year i at Discount Rate]

2 

LCSE 

(2011$/MWh) 

Levelized Cost of Saved Energy year i = Annual First-Year Incremental Cost year i / 

Levelized Savings year i

3 

Annualized 

Costs (2011$) 

Annualized Cost of Saved Energy year i = 

௡ ௧ሻି௘௔௥	௜௬ ݅ ݎ݁ܽݕ ݅݊ ݏ݅݊݃ݒܽݏ ݈ܽݐ݁݉݁݊ݎ݅݊ܿ ݈ܽݑܽ݊݊ ൈ ௧ି௘௔௥	௜௬ܧܵܥܮሺ∑ 

௧ୀ଴ , 

where LCSE year i-t is the LCSE of EE programs implemented in year i-t, 

 estimated represents ௧ି௘௔௥	௜௬	݅	ݎ݁ܽݕ	݅݊	ݏ݅݊݃ݒܽݏ	݈ܽݐ݁݉݁݊ݎ݅݊ܿ	݈ܽݑܽ݊݊ 

incremental savings in year i from EE programs implemented in year i-t. Annualized 

Cost of Saved Energy year i = (LCSE year i x Remaining Savings year i) 

+ (Levelized Cost of Saved Energy year i-1 x Remaining Savings year i-1) + ... 

+ (Levelized Cost of Saved Energy year i-n x Remaining Savings year i-n) 

Annualized Cost of Saved Energy year i = ∑

TABLE 5-28 

Summary of Results by Step for South Carolina. 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Total FirstYear Costs 

(millions 2011$) 

151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

Formatted Table

Formatted: Subscript
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Total LCSE 

(2011$/MWh) 

65.1 78.1 78.1 78.1 91.1 91.1 91.1 91.1 91.1 

Annualized 

Total Costs 

(millions 2011$) 

17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2 

 
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Results 

Summary of National Results 

Tables 5-29 and 5-31 summarize the national first-year and annualized EE costs for 

Option 1 for 2018, 2020, 2025, and 2030. Table 5-30 summarizes national LCSE for Option 1 

for the same years. Each of the three tables includes values for program, participant, and total 

costs. 

TABLE 5-29 

First-Year EE Costs (3% discount rate, billions 2011$) 

(Continental U.S.) 

Year 

2018 2020 2025 2030 

Program 10.2 15.4 21.8 21.8 

Participant 10.2 15.4 21.8 21.8 

Total 20.5 30.7 43.6 43.5 

TABLE 5-30 

Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh) 

(Continental U.S.) 

Year 

2018 2020 2025 2030 

Program 42 43 45 45 

Participant 42 43 45 45 

Total 83 85 89 90 

 

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TABLE 5-31 

Annualized EE Costs (3% discount rate, billions 2011$) 

(Continental U.S.) 

Year 

2018 2020 2025 2030 

Program 2.0 5.1 14.4 21.4 

Participant 2.0 5.1 14.4 21.4 

Total 4.1 10.2 28.9 42.7 

See Appendix 5-4 for comprehensive data sheets of EE cost results at the national level 

by year for Options 1 and 2, and at discount rates of 3% and 7%. These data sheets provide 

results of LCSE (total, program and participant), first-year costs (total, program and participant), 

and annualized costs (total, program and participant). 

Results in Context

 To provide context for the pace of increase in EE program spending levels represented by 

Option 1, we consider the compound annual growth rate (CAGR) of the recent rapid increase in 

historic investment (2006 to 2011) and the CAGR from 2011 through 2018, 2020, and 2025 

represented by Option 1 program costs. Historic data is from Table 5-2 and Option 1 data is from 

Table 5-29. Table 5-32 provides a summary of the results. The CAGRs represented by Option 1 

through 2018, 2020, and 2025 vary from 8% to 11%. The historic growth rate reflecting the rapid 

recent growth in EE program spending is 30%, roughly three times the Option 1 values. 

TABLE 5-32 

Comparison of Historic and Projected (Option 1) Annual Growth Rates 

Time Period (Years) Compound Average Growth Rate 

Historic (2006-2011) 29.8% 

Option 1 (2011-2018) 8.1% 

Option 1 (2011-2020) 11.3% 

Option 1 (2011-2025) 9.8% 
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Costs per Tonne CO2 Reduced

 To estimate the reductions in power system costs and CO2 emissions associated with this 

building block, EPA analyzed a scenario incorporating the resulting reduction in electricity 

demand (the “energy efficiency scenario”) and compared the results with the base case scenario. 

Both analyses were conducted using the Integrated Planning Model (IPM) described in 

earlier chapters. Combining the resulting power system cost reductions with the energy 

efficiency cost estimates associated with the energy efficiency scenario, EPA derived net cost 

impacts for 2020, 2025, and 2030. Dividing these net cost impacts by the associated CO2

reductions for each year, EPA found that the average cost of the CO2 reductions achieved ranged 

from $16 to $24 per metric tonne of CO2. Although EPA considers this estimated range of 

average $/tonne to be reasonable, we expect the $/tonne would be lower in combination with the 

other building blocks because, in that context, power system costs would be somewhat higher 

and, thus, avoided power system costs due to this building block would be higher as well, 

leading to lower $/tonne CO2 avoided. 

Analysis Considerations 

 Two considerations are worth noting in regards to the analysis described in the previous 

two sections, “Goal Setting” and “Impacts Assessment:” 1) state energy efficiency policies 

implicitly represented in the baseline electricity demand and 2) Form EIA-861 as a data source. 

State Energy Efficiency Policies in the Baseline Electricity Demand 

 The baseline electricity demand forecast used for the state goal setting 

methodologyapproach represented in this chapter, as well as for the power sector modeling 

discussed in the Regulatory Impacts Assessment, is , is calibratedbased upon to the AEO 2013X 

reference case scenario. AEO 2013 does not explicitly represent existing utility energy efficiency 

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programs or future requirements (e.g., EERS) to achieve savings goals through such programs. 

For example, existing state EERS are not evaluated and represented in the AEO 2013 reference 

case. However, to some degree, AEO 2013 does implicitly reflect a continuation of the effects of 

existing state energy efficiency programs in the electricity demand projections represented in the 

reference case. This implicit representation is captured in part through a calibration process that 

is affected by several historic factors including reported electricity sales and sectoral energy 

consumption surveys. 

 As noted, EPA’s state goal setting approach for demand-side energy efficiency is built 

upon the AEO 2013 forecast of electricity demand. However, because the goal setting approach 

uses percentage incremental savings by year to derive percentage cumulative savings by year 

(for each state), the resulting state goals (expressed in percentage cumulative savings by year, by 

state) are not affected by the underlying electricity demand forecast. The impacts assessment of 

the demand-side energy efficiency building block is affected, to some degree, by the implicit 

representation of a continuation of existing energy efficiency programs because the assessment is 

built partly from absolute energy savings values that are partly derived from the business-asusual (BAU) demand forecast. If the BAU forecast did not implicitly represent a continuation of 

existing energy efficiency programs, the forecast would indicate higher electricity demand, at 

least in the near term. However, the direction (higher or lower) of the net cost impacts (energy 

efficiency program costs as well as power system cost reductions) is not clear as it is possible 

that program costs could increase while avoided power system costs also increase. 

Energy Information Administration Form EIA-861 as Data Source 

Comprehensive data on energy efficiency programs’ spending and energy savings are 

limited for evaluating and comparing energy efficiency programs and their effectiveness at the 

utility, state, and national scale. Issues related to the lack of standardized definitions and 

reporting, and data quality are noted to limit evaluation of energy efficiency programs.lxxviii The 

Energy Information Administration (EIA) Form 861, “Annual Electric Power Industry Report,” 

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remains the most comprehensive effort that collects data annually on utility demand-side 

management (DSM) programs, including their spending and energy savings impacts, 

nationally.lxxix The form is requested for electric utilities, electric power producers, energy 

service providers, wholesale power marketers, and all DSM program managers and entities 

responsible to estimate the DSM activity for the reporting year using their best available data, 

including costs and incremental and cumulative energy savings from energy efficiency programs 

and load management programs. 

This analysis ustilizes only two EIA-861 data variables. Specifically, we use the 2012 

sales data and reported incremental annual energy savings of energy efficiency programs to 

estimate the current performance of energy efficiency programs to inform setting best practices 

performance level for the state EE goal setting. 

EPA notes potential concerns associated with consistency and quality of reported DSM 

program data in Form EIA-861. Specifically, the data are self-reported by utilities and DSM 

program administrators. The definition and data categories may not be consistently applied 

across utility, state, and data year. Over time, however, the data quality has improved 

significantly and there is increased standardization in data reporting and more detailed data 

categories are being reported. For instance, in 2011, EIA began collecting data from third-party 

administrators of programs. While now fairly comprehensive, outside entities have found that the 

EIA-861 data can be improved through supplementation with publicly available annual energy 

efficiency program reports.lxxx

 

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Appendices 

Appendix 5-1: Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies 

Appendix 5-2: Incremental Electricity Savings Pace of Improvement Analysis 

Appendix 5-3: Review of the Ratio of Program to Participant Costs 

Appendix 5-4: Comprehensive Results: State Goal Setting and Impacts Assessment 

 

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Appendix 5-1

Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies 

The following table summarizes estimates of economic and achievable energy efficiency 

potential from a number of recent studies (2010-2014) for states, utilities, and other agencies 

across the U.S. Study periods ranged from five to twenty-one years in length. As Table 1 shows, 

across the eleven studies that reported achievable potential, results for average annual achievable 

potential range from 0.8% per year to 2.9% per year (of baseline sales) with an average of 1.5% 

per year. 

TABLE 1 

Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies 

State Client Analyst 

Study 

Year 

Study 

Period 

End-year Projected 

Potential as % of 

Baseline Sales 

Average Annual Projected 

Potential as % of Baseline 

Sales 

Economic Achievable Economic Achievable 

Arizona 

Salt River 

Project Cadmus Group 2010 

2012-

2020 

29% 20% 3.2% 2.2% 

California 

California 

Energy 

Commission 

California 

Energy 

Commission 

2013 

2014-

2024 

Not 

reported 

9.6% N/A 0.9% 

Colorado 

Xcel Energy Kema, Inc. 2010 

2010-

2020 

20% 15% 1.8% 1.4% 

Delaware 

Delaware 

DNR/DEC 

Optimal Energy, 

Inc. 

2013 

2014-

2025 

26.3% Not reported 2.2% N/A 

Illinois 

ComEd ICF International 2013 

2013-

2018 

32% 10% 5.3% 1.7% 

Michigan 

Michigan PSC GDS Associates 2013 

2013-

2023 

33.8% 15% 3.1% 1.4% 
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New Jersey 

Rutgers 

University 

EnerNOC Utility 

Solutions 

2012 

2010-

2016 

12.8% 5.90% 1.8% 0.8% 

New Mexico 

State of New 

Mexico 

Global Energy 

Partners 

2011 

2012-

2025 

14.7% 11.1% 1.1% 0.8% 

New York 

ConEd Global Energy 

Partners 

2010 

2010-

2018 

26% 15% 2.9% 1.7% 

Pacific 

Northwest 

(Idaho, 

Montana, 

Oregon, 

Washington) 

US 

Department of 

Energy 

Lawrence 

Berkeley 

National 

Laboratory 

2014 

2011-

2021 

11% Not reported 1.9% Not reported 

Pennsylvania 

Pennsylvania 

PUC 

GDS Associates 

and Nexant 

2012 

2013-

2018 

27.2% 17.3% 4.5% 2.9% 

Tennessee 

Tennessee 

Valley 

Authority 

Global Energy 

Partners 

2011 

2009-

2030 

24.8% 19.8% 1.1% 0.9% 

Range 

0.8% - 2.9% 

per year 

Average 

1.5% 

Per year 

References 

Arizona: The Cadmus Group, Inc. 2010. Salt River Project 2012-2017 Energy Efficiency Plan – 

Final Report. Prepared for Salt River Project, April 16. 

Colorado: Kema, Inc. 2010. Colorado DSM Market Potential Assessment – Final Report. 

Prepared for Xcel Energy, March 12. 
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 73 

Delaware: Optimal Energy, Inc. 2013. Delaware Economic Energy Efficiency Potential. 

Prepared for the Delaware Department of Natural Resources and Environmental Control, May 

24. 

Illinois: ICF International. 2013. ComEd Energy Efficiency Potential Study Report, 2013-2018. 

August 20. 

Michigan: GDS Associates, Inc. 2013. Michigan Electric and Natural Gas Energy Efficiency 

Potential Study – Final Report. Prepared for the Michigan Public Service Commission, 

November 1. 

New Jersey: EnerNOC Utility Solutions Consulting. 2012. New Jersey Energy Efficiency 

Market Potential Assessment, Volume 1: Executive Summary. Report Number 1401, Prepared 

for Rutgers, The State University of New Jersey, October 17, 2012. 

New Mexico: Global Energy Partners. 2011. Energy Efficiency Potential Study for the State of 

New Mexico, Volume 2: Electric Energy Efficiency Analysis. June 30. 

New York: Global Energy Partners, LLC. 2010. Energy Efficiency Potential Study for 

Consolidated Edison Company of New York, Inc., Volume 2: Electric Potential Report. March. 

Pacific Northwest states (combined): Barbose, Galen, and Alan Sanstad, Charles Goldman, 

Stuart McMenamin, Andy Sukenik. 2014. Incorporating Energy Efficiency into Western 

Interconnection Transmission Planning. Draft report, Lawrence Berkeley National Laboratory, 

January. 

Pennsylvania: GDS Associates, Inc., and Nexant. 2012. Electric Energy Efficiency Potential for 

Pennsylvania – Final Report. Prepared for the Pennsylvania Public Utilities Commission, May 

10. 
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Tennessee: Global Energy Partners. 2011. Tennessee Valley Authority Potential Study, Final 

Report, Volume 1: Executive Summary. Report Number 1360, December 21.
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Appendix 5-2 

Incremental Electricity Savings Pace of Improvement Analysis 

This appendix summarizes and analyzes data to characterize the pace of improvement of 

incremental (or first-year) savings as a percentage of retail sales for electricity energy efficiency 

(EE) programs. We considered two different perspectives: 1) historical data reflecting achieved 

savings of EE programs and 2) requirements of existing state energy efficiency resource 

standards (EERS). For the historical perspective, we reviewed data from the Energy Information 

Administration’s Form EIA-861 on EE program electricity savings (supplemented as needed 

with program administrator reports) and identified the pace at which entities reaching higher 

savings levels have historically increased energy savings over time.38 Specifically, we reviewed 

the historical savings data in the following two groups of energy efficiency program 

administrators. 

1. Top saver 1% - a group with 47 entities that achieved a maximum first-year savings level 

of 0.8% to 1.5%. 

2. Top saver 2% - a group with 26 entities that achieved a maximum first-year savings level 

of 1.5% to 3.0%.39

For the existing state requirements perspective, we reviewed energy savings ramp-up 

schedules established under EERS for states that provide clear ramp-up schedules. According to 

ACEEE’s 2013 State Energy Efficiency Scorecardlxxxi, there are a total of 26 states that have 

                                                            

38 The EIA 861 was the main data source. However, we have supplemented the EIA 861 with third-party program 

administrator data because the EIA 861 just started to collect third-party administrator data in 2011. The third-party 

entities included in our analysis are Efficiency Vermont, Energy Trust of Oregon, Efficiency Maine Trust, and Cape 

Light Compact. In addition, we supplemented the EIA 861 database with additional data for two utilities that we 

found achieved high energy savings, but did not report savings data in the EIA 861 data for one or two years. These 

entities are Burlington Electric and Massachusetts Electric Company (now part of National Grid). 

39 In addition to these maximum first-year savings thresholds, we screened program administrators for the following 

conditions: (a) the maximum savings levels occurs after the minimum savings levels; (b) sufficient amounts of 

increase in first-year savings are provided to evaluate reasonable ramp-up schedules to gain an incremental 1% firstyear savings; and (c) savings data series are continuous between the years for the minimum and maximum savings 

levels.

Formatted: Not Highlight

Comment [A123]: Some commenters

recommend referencing DSIRE as the data source on

which states have EERSs rather than ACEEE

scorecard.

Comment [A124]: EPA RESPONSE: ACEEE is the

best quality and up‐to‐date source EPA has found.  

All summary sources like ACEEE or DSIRE are based

upon state‐by‐state research. We are soliciting

comment in the preamble on all aspects of the

method. EPA will consider switching to DSIRE as a

source (or EPA or DOE research/analysis) for final

rule.
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mandatory EERS policies.40 Our analysis contains 10 states for which clear ramp-up schedules 

were identifiable. 

Our research findings on historical savings performance are: 

 The “Top Saver 1%” group (savings between 0.8% and 1.5%) exhibits a trend that these 

entities took or would take about 3.4 years on average to increase first-year savings by 

1% (with a range of 1.6 years to 10 years) (see Table 1Table 1). The entities in this 

group have increased the level of first-year savings by 0.30% per year on average from 

their minimum to their maximum first-year savings levels (with a range of 0.10% per 

year to 0.63% per year).41 

 The “Top Saver 2%” group (savings between 1.5% and 3%) exhibits a trend that took or 

would take about 2.6 years on average to increase savings by 1% (with a range of 0.8 

years to 7.3 years) (see Table 1Table 1). The entities in this group have increased the 

level of first-year savings by 0.38% per year on average from the minimum to the 

maximum first-year savings levels (with a range of 0.14% per year to 1.28% per year).42 

Table 1. Energy savings ramp-up trends in first-year savings for “Top Saver 1%” and 

“Top Saver 2%” groups43

 Top Saver 1% Top Saver 2% 

                                                            

40 ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, 41 This is a simple average estimate of the annual average increase in first-year savings from each entity in this 

group. 

42 This is the simple average estimate of the annual average increase in first-year savings from each entity in this 

group. 

43 Data sources: EIA-861; ISO New England, “Compilation of raw PA data for the 2013 EE Forecast,” Jun 21, 2013; 

energy efficiency program administrators’ 2012 energy efficiency annual reports for the following entities: 

Burlington Electric Department, Efficiency Vermont, Efficiency Maine, Energy Trust of Oregon, Cape light 

Compact, and Wisconsin Focus on Energy. 

Formatted: Font:

Formatted: Font:
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Average 

Annual 

First-Year 

Savings 

Increase 

Estimated 

Years to 

Gain 

Incremental 

1% 

Average 

Annual 

First-Year 

Savings 

Increase 

Estimated 

Years to 

Gain 

Incremental 

1% 

Average 0.30% 3.4 0.38% 2.6 

Median 0.29% 3.4 0.34% 3.0 

Max 0.63% 1.6 1.28% 0.8 

Min 0.10% 10 0.14% 7.3 

# of sample entities 47 26 

Our research findings on incremental electricity savings ramp-up based on existing state 

EERS policies are: 

 The states with EERS policies which exhibit savings ramp-up schedules are requiring 

increases in first-year energy savings at a pace that ranges from 0.11% (Colorado and 

Oregon) to 0.40% (Rhode Island) as shown in Table 2. 

 The first-year savings pace of increase averages 0.21% per year across the 10 states. This 

savings level translates to about 4.7 years to achieve an incremental 1% first-year savings 

increase. 

Table 2. First-Year Energy Savings Ramp-up Review of State EERS Policies44

State 

Minimum 

Target 

Maximum 

Target 

Climb 

Time 

(years) 

Annual 

Average % 

Increase 

Years to 

Achieve 1% 

Min Year Max Year Increase 

 a b c d e=d-b f=(c-a)/e g=1/f 

Arizona 1.25% 2011 2.5% 2016 5 0.25% 4.0 

                                                            

44 Data sources: ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, Arkansas Public 

Service Commission, Docket Nos. 13-002-U. Order No. 7, September 9, 2013. 
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Arkansas 0.25% 2011 0.9% 2015 4 0.16% 6.2 

Colorado 0.80% 2011 1.7% 2019 8 0.11% 9.3 

Illinois 0.20% 2008 2.0% 2015 7 0.26% 3.9 

Indiana 0.30% 2010 2.0% 2019 9 0.19% 5.3 

Massachusetts 1.4% 2010 2.6% 2015 5 0.24% 4.2 

Michigan 0.3% 2009 1.0% 2012 3 0.23% 4.3 

Ohio 0.3% 2009 1.2% 2019 10 0.17% 5.9 

Oregon 0.8% 2010 1.0% 2013 3 0.07% 15.0 

Rhode Island 1.7% 2011 2.5% 2013 2 0.40% 2.5 

Average 0.21% 4.8 

References 

MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual 

Report to the Iowa Utilities Board," May 1, 2013, available at: 

http://www.state.ia.us/government/com/util/energy/energy_efficiency/ee_plans_reports.html

Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 

2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012-

Annual-Report.pdf

National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas 

Energy Efficiency Plan," Docket No. 12-109, November 2, 2012, available at: 

http://www.env.state.ma.us/dpu/docs/electric/13-120/8113ngptapb.xlsx

PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission, 

for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215, 

November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf

Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at: 

http://www.efficiencyvermont.com/docs/about_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf 

 
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Appendix 5-3 

Review of the Ratio of Program to Participant Costs 

Introduction and Summary 

This appendix summarizes and analyzes data on EE costs (program and participant) to 

develop a ratio to enable the estimation of participant costs from known program costs. We 

reviewed cost data from leading EE program administrators in 22 states. Our research findings 

are as follows: 

 A 1:1 ratio between program and participant costs is a reasonable and slightly 

conservative (i.e., slightly higher total costs) basis for estimating participant costs from 

known program costs. 

 Reported data was reviewed from 22 states; however, program administrator reports from 

only nine states contained sufficient information (participant costs across entire portfolio 

of EE programs) to inform the analysis. 

 Participant cost data from ten program administrators in nine states indicate that the 

weighted average and simple average participant costs were 47 percent of total costs. 

Participant Cost Analysis 

We first identified states having high incremental electricity savings rates or high 

absolute savings levels based upon 2013 ACEEE State Energy Efficiency Scorecard.45 These 

states represent a large portion of total EE savings in the U.S. We identified 22 states meeting 

these criteria and collected publicly available EE program reports for major program 

administrators within each state. From these program reports we identified 10 program 

administrators across nine states where we were able to identify both program administrator and 

participant costs across their full portfolio of EE programs. The table below provides the 2012 

portfolio-level program administrator and participant costs for the nine states. Program 

                                                            

45 For the purpose of this research, we have defined leading or high impact states as the top 15 states in the 2013 

ACEEE State Energy Efficiency Scorecard in terms of incremental savings as a percentage of retail sales or absolute 

annual energy savings in terms of total annual MWh savings. These criteria resulted in a total of 22 states which 

include Arizona, California, Connecticut, Florida, Hawaii, Illinois, Indiana, Iowa, Maine, Massachusetts, Michigan, 

Minnesota, New Jersey, New York, North Carolina, Ohio, Oregon, Pennsylvania, Rhode Island, Texas, Vermont, 

and Washington. 
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administrator costs represent the program administrator’s program development and 

implementation costs, and the participant costs represent the customer costs to partake in the 

program. Total program costs are the sum of both costs. Each state’s program administrator and 

participant costs are presented as a percentage of total program costs in Table 3. The weighted 

and simple average program and participant costs across all nine states are presented as a 

percentage of total program costs. The weighted average cost shares were based on each 

program’s spending by administrator and participants. 

Table 1 

2012 Participant and Program Cost Information from Reported Entities 

2012 Portfolio Costs

State

Program

Administrator

Program  

Costs

(Million $s)

Participant

Costs

(Million $s)

Total Costs

(Million $s)

Program

Costs as

Percent of

Total Cost

(%)

Participant

Costs as

Percent of

Total Cost

(%)

A b c = a + b a / c b / c

California

Southern

California

Edison $         316 $    269 $        585 54.0% 46.0%

Hawaii Hawaii Energy $           31 $      37 $          68 45.6% 54.4%

Iowa

MidAmerican

Energy

Company $           50 $      70 $        120 41.5% 58.5%

Maine Efficiency

Maine Trust $           24 $      36 $          60 39.8% 60.2%

Massachusetts National Grid $         173 $      54 $        227 76.3% 23.7%

Minnesota Xcel Energy $           53 $      98 $        151 35.1% 64.9%

Pennsylvania PECO $           68 $    109 $        178 38.5% 61.5%

Rhode Island National Grid $           63 $      13 $          75 83.2% 16.8%

Vermont

Efficiency

Vermont;

Burlington

Electric

Department $           34 $      23 $          57 59.3% 40.7%

Weighted Average 53.4% 46.6%

Simple Average 52.6% 47.4%
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In our analysis, the weighted average program and participant costs are 53.4% and 

46.6%, respectively, of total costs. On a simple average basis, program and participant costs are 

52.6% and 47.4%, respectively, of total costs. Participant cost results range from a low of 17% of 

total costs (National Grid in Rhode Island) to a high of 65% (Xcel Energy in Minnesota). When 

deriving participant costs from program costs, using a ratio of 1:1 is consistent with these results 

and slightly conservative, leading to slightly higher total costs than the precise average values 

would provide. 

References 

MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual 

Report to the Iowa Utilities Board," May 1, 2013, available at: 

http://www.state.ia.us/government/com/util/energy/energy_efficiency/ee_plans_reports.html

Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 

2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012-

Annual-Report.pdf

National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas 

Energy Efficiency Plan," Docket No. 12-109, November 2, 2012, available at: 

http://www.env.state.ma.us/dpu/docs/electric/13-120/8113ngptapb.xlsx

PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission, 

for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215, 

November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf

Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at: 

http://www.efficiencyvermont.com/docs/about_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf 

Burlington Electric Department, "2012 Energy Efficiency Annual Report," 2012, available at: 

https://www.burlingtonelectric.com/ELBO/assets/2012%20DSM%20Annual%20Report%20Mas

ter.pdf

Hawaii Energy, “Annual Report Program Year 2012 July 1, 2012 - June 30, 2013,” 2013, 

available at http://www.hawaiienergy.com/information-reports 
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Xcel Energy, "2012 Status Report & Associated Compliance Filings Minnesota Electric and 

Natural Gas Conservation Improvement Program Docket No. E,G002/CIP-09-198, " April 1, 

2013, available at 

http://www.xcelenergy.com/staticfiles/xe/Regulatory/Regulatory%20PDFs/MN-DSM-CIP-2012-

Status-Report.pdf 

National Grid, "2012 Energy Efficiency Year-End Report," May 31, 2013, available at 

http://www.nationalgridus.com/non_html/eer/ri/4295-YearEnd%20Rept%20(PUC%205-31-

13).pdf 

Southern California Edison, "2013 Energy Efficiency Annual Report," June 2013, available at 

http://eega.cpuc.ca.gov/Documents.aspx http://eega.cpuc.ca.gov/Documents.aspx 

 
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Appendix 5-4 

Comprehensive Results: State Goal Setting and Impacts Assessment 

See attached file, “Abatement Measures TSD Appendix 5-5.xlsx,” containing the 

following: 

Goal Setting Sheets 

 Option 1 – Incremental Savings as % of Sales by State (2017-2030) 

 Option 1 – Cumulative Savings as % of Sales by State (2017-2030) 

 Option 2 – Incremental Savings as % of Sales by State (2017-2030) 

 Option 2 – Cumulative Savings as % of Sales by State (2017-2030) 

Impacts Assessment Sheets 

 Option 1 – National Costs at 3% Discount Rate (2017-2030) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 

 Option 1 – National Costs at 7% Discount Rate (2017-2030) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 

 Option 2 – National Costs at 3% Discount Rate (2017-2030) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 

 Option 2 – National Costs at 7% Discount Rate (2017-2030) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 

Formatted: Not Highlight
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                                                                                                                                                                                                lx U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs 

(Best Practices, Technical Methods, and Emerging Issues for Policy-Makers): a Resource of the National Action 

Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/cost-effectiveness.pdf. lxi Sorrell, S. 2007. “The Rebound Effect: an assessment of the evidence for economy‐wide energy savings from

improved energy efficiency.” A report produced by the Sussex Energy Group for the Technology and Policy

Assessment function of the UK Energy Research Centre. ISBN 1‐903144‐0‐35. lxii Greening, LA, DL Greene, and C Difiglio. 2000. Energy efficiency and consumption — the rebound effect — a

survey, Energy Policy, 28: 389‐401. lxiii Sorrell, S. 2007. “The Rebound Effect: an assessment of the evidence for economy‐wide energy savings from

improved energy efficiency.” A report produced by the Sussex Energy Group for the Technology and Policy

Assessment function of the UK Energy Research Centre. ISBN 1‐903144‐0‐35 lxiv Davis, LW 2008. Durable goods and residential demand for energy and water: evidence from a field trial. The

RAND Journal of Economics, 39: 530–546. lxv Gillingham K, MJ Kotchen, DS Rapson, G Wagner. 2013. Energy policy: the rebound effect is overplayed. Nature

493: 475‐476. lxvi U.S. Energy Information Administration Form EIA-861 data files (2012). Available at 

http://www.eia.gov/electricity/data/eia861/. 

lxvii American Council Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency Resource 

Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/policy-brief/eers-02-2014.pdf. lxviii Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence 

Berkeley National Laboratory. March 2014. The Program Administrator Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf. lxix Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence 

Berkeley National Laboratory. March 2014. The Program Administrator Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf. 

lxx Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and 

Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/. lxxi Gordon, Fred, Lakin Garth, Tom Eckman, and Charles Grist, “Beyond Supply Curves,” Proceedings of 2008 

ACEEE Summer Study on Energy Efficiency in Buildings, August 17, 2008 available at: 

http://aceee.org/files/proceedings/2008/data/papers/8_419.pdf. lxxii Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and 

Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/. lxxiii Kenji Takahashi and David Nichols, Synapse Energy Economics, Inc. 2008. The Sustainability and Costs of 

Increasing Efficiency Impacts: Evidence from Experience to Date. ACEEE Summer Study on Energy Efficiency in 

Buildings. http://www.aceee.org/files/proceedings/2008/data/papers/8_434.pdf. lxxiv For example: Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest 

Conservation and Electric Power Plan,” Council Document 2010-09. Available at 

www.nwcouncil.org/energy/powerplan/6/plan/; and McKinsey & Company. December 2007. Reducing U.S. 

Greenhouse Gas Emissions: How Much at What Cost? Available at 

http://www.mckinsey.com/client_service/sustainability/latest_thinking/reducing_us_greenhouse_gas_emissions. lxxv Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend. lxxvi Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility 

Energy Efficiency Programs. ACEEE Report No. U1402. Available at http://www.aceee.org/research-report/u1402. lxxvii State of California Governor’s Office of Planning and Research. July 2002. California Standard Practice 

Manual: Economic Analysis of Demand-Side Programs and Projects. Available at 

http://www.calmac.org/events/SPM_9_20_02.pdf. lxxviii MJ Bradley & Associates, LLC. 2011. Benchmarking Electric Utility Energy Efficiency Portfolios in the U.S.
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                                                                                                                                                                                                lxxix More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/ lxxx Add citation lxxxi American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. 
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Chapter 6: Fuel Switching 

Coal-to-Natural Gas Switching 

Introduction 

Firing natural gas in a boiler designed for coal-fired generation is one approach to reducing the 

output-based CO2 emissions rate (lbs/MWh) in these boilers. The CO2 emission rate is reduced 

when natural gas is substituted for coal because the gas has a much higher percentage of 

hydrogen and a lower percentage of carbon than the coal it replaces. When quantities of gas and 

coal are burned with oxygen from air to produce the same amounts of heat, the higher hydrogen 

content of natural gas produces more water vapor (H2O) than coal, but far less CO2. 

The discussion below focuses solely on the conversion of an existing coal-fired boiler to burn 

natural gas instead of, or along with, coal. There are other technical options for gas substitution 

in an existing coal-steam EGU that are not examined in any detail here. They include: 

 Repowering an existing coal EGU by providing heat input to the boiler from the exhaust 

of a newly installed gas turbine generator; and, 

 Gasification of coal, producing substitute natural gas (SNG) that provides heat input to 

the existing coal-fired boiler. 

These other options have higher capital cost and thus would not be as economic as the direct 

substitution of natural gas in an existing coal boiler, for reasons that will become apparent in this 

analysis. 

This chapter evaluates the cost-effectiveness of widespread adoption of coal-to-gas switching at 

a national level for the purpose of setting CO2 emissions goals consistently in each state. 

Description of Technology 

 Engineering Considerations 

Most existing coal-fired EGU boilers can be modified to switch to 100% gas input, or to co-fire 

gas with coal in any desired proportion. This transition typically requires at least some plant 

modifications and might have some negative impact on the efficiency of the unit as described 

later in this chapter. 
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A conversion from coal to gas firing first requires either an existing natural gas delivery system 

to the boiler or the installation of a new gas pipeline to serve the boiler. While it is sometimes 

assumed that a need to install a new gas pipeline would render the conversion uneconomic, this 

analysis will show that the cost of a new gas pipeline is not likely to be the determining factor for 

the project’s economic merit, given the significance of the change to the cost of fuel for 

generation from the converted boiler. 

Conversion to natural gas firing in a coal-fired boiler typically involves installation of new gas 

burners and supply piping, modifications to combustion air ducts and control dampers, and 

possibly modifications to the boiler’s steam superheater, reheater, and economizer heating 

surfaces that transfer heat from the hot flue gas exiting the boiler furnace. The conversion may 

also involve some modification and possible deactivation of some downstream air pollution 

emission control equipment. Engineering studies are performed to assess changes in furnace heat 

absorption and exit gas temperature; material changes affecting heat transfer surfaces; the need 

for sizing of flue gas recirculation fans; and operational changes to sootblowers, spray flows, air 

heaters, and emission controls. 

Whether co-firing with coal or switching completely to natural gas, boilers will become less 

efficient due to the high hydrogen content of natural gas. When combusted, the additional 

hydrogen yields increased moisture content (water vapor) in the flue gas. The increased 

moisture content, in turn, results in additional heat lost up the stack instead of being directed 

towards electricity generation. Additionally, depending on the design of the boiler and extent of 

modifications, some boilers may incur some derate (reduction in generating capacity) in order to 

maintain steam temperatures at or within design limits, or for other technical reasons. 

Even with a decrease in boiler efficiency, the overall net output efficiency of a coal-steam boiler 

EGU that switches from coal to natural gas firing may change only slightly, depending on how 

much auxiliary load is converted to net output by avoiding the need to run coal pulverizers, 

conveyors, ash sluice pumps, and relevant air pollution control equipment (e.g., PM and SO2

controls). 

Fuel Considerations 

Delivery of natural gas via pipeline is critical for conversion of a coal-fired boiler to a gas-fired 

boiler. Some coal boilers are connected to the natural gas pipeline network for purposes of using 

Comment [A1]: Would there also be a reduction

in emissions equipment (and possible removal of

baghouses, electrostatic precipitators, and

scrubbers.).If so, consider making this point.

EPA:  Clarification added.
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gas as a startup fuel, or are located at facilities with onsite gas-fired generators. These boilers are 

likely able to co-fire to some degree with gas (at least 10% total output1

) without constructing 

additional gas pipeline capacity. For purposes of this analysis, the EPA conservatively assumed 

that gas use of 10% or greater at these boilers, or any gas use at boilers without an existing gas 

pipeline, would require construction of additional pipeline capacity. 

Unlike coal, natural gas cannot be stored in quantities sufficient for sustained utilization on site. 

To the extent that firm (uninterruptible) gas supply is contractually unavailable or costprohibitive, any potential interruption in gas supply could impact the ability of the unit to 

continue operating without increasing its CO2 emissions rate (since it would likely need to 

substitute more CO2-intensive fuel for the unavailable natural gas). Additionally, for boilers that 

switch to 100% gas, interruption in gas impacts the ability of the unit to continue generating at 

all if gas is unavailable. For these reasons, an EGU switching to a large percentage of gas use 

may elect to install more than one new gas supply pipeline from separate sources. Although the 

EPA assumes the addition of one gas pipeline in the simplified cost analysis presented below, it 

will be seen that pipeline cost will generally not be the main driver of economic feasibility. 

Cost and Performance Impacts of Coal-to-Gas Switching 

The analysis described in this section presents a hypothetical conversion of a boiler from burning 

100% coal to burning varying proportions of gas (10%, 50%, and 100%). The capital cost of 

modifying a coal boiler to switch to natural gas includes the new gas burners and piping, 

combustion air ductwork and control damper modifications, air heater upgrades, gas 

recirculating fans, control systems modifications, and other site-specific modifications, as well as 

any pipeline installation costs that would be necessary to supply the unit’s assumed level of gas 

combustion following the conversion. 

For this analysis, the EPA assumes capital costs for pulverized coal (PC) and cyclone boiler 

modifications are as follows2

: 

$/kW = 267*(75/MW)0.35 (pulverized coal) 

                                                            

1 Based on assumed use of Class 1 igniters (10% of burner capacity) as defined in NFPA 85 Boiler and Combustion

Systems Hazards Code. 2 EPA assumptions on costing and performance associated with coal‐to‐gas conversion and pipeline additions in

this analysis are generally consistent with assumptions presented and discussed in EPA’s power sector modeling

documentation, Chapter 5.7, at: http://www.epa.gov/airmarkets/progsregs/epa‐ipm/docs/v513/Chapter_5.pdf
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$/kW = 374*(75/MW)0.35 (cyclone) 

Based on the above formula, a 500 MW pulverized coal unit would have a capital cost of 

$137/kW to convert the boiler such that it could burn any proportion of natural gas. For this 

illustrative example, to support 100% gas combustion we assume that a 50-mile gas pipeline3

 at 

$50 million, 4 or $100/kW for a 500 MW unit, is also required, which raises the unit’s total 

capital cost for conversion to $237/kW. Black & Veatch also used a similar cost level in a recent 

case study.5

 At a 14.3% capital charge rate6 and 75% annual capacity factor, the total capital cost 

in this example equates to an annualized capital cost of about $5/MWh. This $/MWh capital cost 

is relatively insignificant compared to the increase in fuel cost discussed later. 

Due to a reduced need for operators, maintenance materials, and maintenance staff, EPA 

engineering staff assumed that fixed O&M costs are reduced by 33% as a result of switching 

from coal to 100% gas. Similarly, variable O&M costs are assumed to be reduced by 25% due to 

reduced waste disposal, reduced auxiliary power requirement, and miscellaneous other costs. 

EPA engineering staff also assumed for this analysis that there would be no derate in the net 

EGU output, and estimated that the impact on net heat rate for an average unit would be a 3% 

increase for a switch from coal to 100% natural gas firing. The assumed 3% increase in net heat 

rate is conservative compared to the 2% assumption used by Black & Veatch in their previously 

mentioned case study. 

Cost of Fuel 

For this analysis, the EPA uses base case projections for delivered gas prices that are about 

double projected delivered coal prices on average ($2.62/MMBTU for coal, $5.36/MMBTU for 

                                                            

3 Based on EPA analysis, the majority of existing coal units would require less than 50 miles of new gas pipelines to

switch fuels from coal to 100% natural gas.  See Chapter 5 and Table 5‐22 of Documentation for EPA Base Case

v.5.13 Using the Integrated Planning Model, available at: http://www.epa.gov/airmarkets/progsregs/epa‐

ipm/BaseCasev513.html

4 For those plants that require additional pipeline capacity, the average capital cost of constructing new pipelines is

assumed to be approximately $1 million per mile of pipeline built, which is consistent with assumptions used in

EPA’s IPM modeling. 5 A Case Study on Coal to Natural Gas Fuel Switch, Black & Veatch, Power‐Gen International, December 2012,

available at http://bv.com/redirects . 6 Capital charge rate at 14.3% is the average of the regulated utility and unregulated merchant rates as used in

IPMv5.13 for environmental retrofits having a 15 year book life.  

Comment [A2]: Are there any references to cite

for fixed O&M and variable O&M cost reductions?

EPA: EPA included these assumptions only for 

completeness: annualized non-fuel O&M costs are 

small compared to the sum of annualized capital and 

fuel costs and do not materially affect the economics.

Comment [A3]: Suggest providing further

support for this choice. What is the sensitivity to

conclusions from this decision?

EPA:  This conservative assumption is not

controversial and has little effect on the economic

outcome.  
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gas).7 As a result, the fuel cost for a typical converted boiler burning 100% gas is expected to be 

at least double its prior fuel cost on an output basis as well ($27/MWh for coal, $57/MWh for 

gas).8,9 Compared to the estimated $5/MWh capital cost impact presented above, a $30/MWh 

increase in fuel cost would make the difference in fuel costs the most significant driver of project 

economics when switching from coal to gas in a coal boiler.10 This difference would increase 

with higher gas prices, which would be projected to result from an increase in overall gas 

demand caused by widespread adoption of gas co-firing. 

Emission Reduction Potential 

The CO2 reduction potential is directly related to the amount of gas co-fired, and is due largely to 

the different carbon intensities of each fuel. More reductions in CO2 rate are achieved at higher 

levels of gas co-firing as shown in Table 6-1. At 10% gas co-firing, the net emissions rate 

(lbs/MWh net) of a typical unit would decrease by approximately 4%. At 100% gas co-firing, 

the net emissions rate (lbs/MWh net) of a typical unit would decrease by approximately 40%. 

Table 6-1. CO2 Rates at Various Levels of Natural Gas Co-Firing 

Case Heat Rate

(Btu/kWh)

CO2 Rate

(lbs/MWh net)

Reduction in CO2 Rate from 100% Coal

(lbs/MWh net)

100% Coal 10,340 2,108 N/A

10% Gas 10,370 2,021 4%

50% Gas 10,490 1,673 21%

100% Gas 10,640 1,239 41%

In addition to reducing CO2 emissions, natural gas co-firing at a coal-fired steam EGU will 

generally also reduce criteria air pollution. Reducing CO2 and criteria air pollution will result in 

                                                            

7 EPA Base Case 5.13, projections for 2020    8 This estimated fuel cost also accounts for the decrease in efficiency that results from switching from coal to gas in

a boiler, as well as the decrease in parasitic power consumption. 9 Combusting natural gas using combined‐cycle turbine technology can remain economically attractive

notwithstanding these types of fuel price differentials because combined cycle turbine technology converts a

substantially higher share of the fuel’s heat input into electricity output as compared to boiler technology.  The

$/MWh impact of a higher gas price in that instance is significantly mitigated by higher MWh output produced for

a given amount of heat input from the fuel purchased. 10 This demonstration assumes that the converting boiler in question remains a “price taker” in the fuel

marketplace, such that the projected gas and coal prices would be unaffected by this hypothetical unit’s potential

decision to convert.  However, if enough other units might be expected to make similar conversions, the aggregate

increased demand for natural gas would be likely to further increase the price differential between coal and gas,

making fuel costs an even more influential factor in the evaluation of such a project’s economic merit.

Comment [A4]: Would it be worth mentioning

the potential volatility of natural gas prices (vs. coal

prices)?  

1.http://www.eia.gov/naturalgas/

http://www.eia.gov/coal/

Comment [A5]: EPA: We believe that annual

average fuel cost is a more appropriate metric for

making this assessment.

Comment [A6]: Suggest inclusion of heat rate

units.

Comment [A7]: EPA: Thank you.  See revised

table.
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climate benefits and human health co-benefits. The impacts of these pollutants on the 

environment and health are discussed in detail in Chapter 5 of the RIA for this proposed rule. 

For this analysis, EPA estimated the PM2.5-related human health co-benefits of SO2, NOX, and 

direct-PM2.5 emission reductions attributable to a range of natural gas co-firing levels at an 

illustrative coal steam unit burning bituminous coal in 2020.11 The estimated monetized cobenefits do not include climate benefits or health effects from direct exposure to NO2, SO2, and 

HAP; ecosystem effects; or visibility impairment. Only the unit-level emissions of SO2, NOX and 

direct-PM2.5 are considered in this illustrative exercise. Additionally, emissions from the 

extraction and transport of the fuels used by these technologies are not considered. Furthermore, 

there may be differences in upstream greenhouse gas emissions (in particular, methane) from 

different technologies but those were not quantified for this assessment. The estimated avoided 

emissions under 10% gas co-firing and a 100% switch to gas are presented Table 6-2. 

Table 6-2. Avoided Emissions at Various Levels of Co-Firing, based on Illustrative Unit 

(lbs/MWh net) 

 

10% 

Gas 

100% 

Gas 

SO2 0.3 3.1 

NOX 0.2 2.04 

PM2.5 0.02 0.2 

To estimate human health co-benefits for this illustrative coal steam unit, the EPA used PM2.5-

related benefit-per-ton estimates for SO2, NOX, and direct-PM2.5 emission reductions described 

in detail in Chapter 5 of the RIA for this proposal. To estimate the benefits associated with cofiring, we determine the emission reductions for co-firing in Table 6-2 and apply the 2020 social 

benefit values discussed in Chapter 5 of the RIA for this proposal. Specifically, we multiply the 

reduction in SO2, NOX, and direct-PM2.5 emissions by the PM2.5-related benefit per-ton estimates, 

and add those values to get a measure of 2020 benefits. Table 6-3 shows the PM2.5-related 

benefits expected based on the estimated emission reductions that would occur in this illustrative 

example. These estimates are purely illustrative as the EPA does not assert a specific location for 

                                                            

11 The illustrative unit in this analysis was assumed to be a 500 MW coal‐steam unit burning bituminous coal with a

heat rate of 10,339 btu/kWh (net) operating at 75% capacity factor.  Furthermore, this unit was assumed to

operate a wet scrubber, cold‐side ESP, and SNCR.
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the illustrative electricity generation technologies and is therefore unable to specifically 

determine the population that would be affected by their emissions. Therefore, the benefits for 

any specific unit can be different than the estimates shown here. 

Table 6-3. Rounded PM2.5-related Co-benefits ($/MWh net) of Gas Co-firing (2011$) 

Health Co‐benefit Discount Rate

   3% Discount Rate 7% Discount Rate

Gas Co‐firing 10% $6.5 to $15 $5.9 to $13

Gas Co‐firing 100% $67 to $150 $61 to $140

Note: All estimates are rounded to two significant figures. Co‐benefits are based on national benefit‐per‐ton estimates for directly emitted

PM2.5 and PM2.5 precursors, SO2 and NOX. It is important to note that the monetized health co‐benefits do not include reduced health effects

from ozone or direct exposure to NO2, SO2, and HAP; ecosystem effects; or visibility impairment. Emissions of directly emitted particles are

disaggregated into EC+OC or crustal components using the method discussed in Appendix.12 5A of the RIA for this proposal. The health co‐

benefits reflect the sum of the PM2.5 co‐benefits and reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) to

Lepeule et al. (2012)).

The precise incremental health co-benefits associated with lower emissions would depend 

primarily on the location of the co-firing unit, the specific types of coals that natural gas would 

replace, and the pollution controls installed on that unit. This illustrative assessment is unable to 

account for these characteristics. However, these factors will not change the qualitative 

conclusion. There will always be incremental human health co-benefits associated with co-firing 

natural gas in an existing coal steam boiler, independent of the location, coal type, and operating 

pollution controls. 

A related beneficial use of natural gas in existing coal boilers can be via gas reburning, a NOx 

reduction technology.13 Gas reburning involves firing natural gas (between 10 and 25% of total 

heat input) above the primary combustion zone in the boiler furnace. This upper-level firing 

creates a slightly fuel-rich zone. NOx produced in the primary zone of the furnace is "reburned" 

in this zone and converted to molecular nitrogen and other reduced nitrogenous species. Overfire 

                                                            

12 Krewski D.; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes; Y. Shi, et al. 2009. Extended Follow‐Up and Spatial Analysis

of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140,

Health Effects Institute, Boston, MA.

Lepeule, J.; F. Laden; D. Dockery; J. Schwartz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended

Follow‐Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives, July, 120(7):965‐

70.

13 DOE/NETL 2001, Evaluation of Gas Reburning and Low‐NOx Burners on a Wall‐Fired Boiler, DOE/NETL‐

2001/1143, February 2001, available at

http://www.netl.doe.gov/File%20Library/Research/Coal/major%20demonstrations/cctdp/Round3/GRLNBPPA.pdf
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air is injected downstream of the reburn zone to burn out the remaining combustibles and convert 

the reduced nitrogenous species to molecular nitrogen. The heat input from gas would 

approximately substitute for a similar heat input from coal, thus reducing CO2 and other coal 

emissions in a manner similar to gas co-firing as discussed above. 

Cost of Reductions and Cost Effectiveness 

This analysis examines the average $/tonne14 cost of avoided CO2 that results from applying a 

range of natural gas co-firing levels to a typical baseload coal boiler. We capture the capital 

costs of boiler modifications and new pipeline construction (assuming 50 miles of new 

pipeline),15 decreased FOM and VOM costs, and incremental fuel costs (based on IPMv5.13 

Base Case average delivered fuel price projections for coal and gas in 2020). For a typical coal 

boiler at current base case fuel prices, the average cost of avoided CO2 ranges from $83/tonne for 

100% gas switch to $150/tonne for co-firing at 10% (see Table 6-4). 

Table 6-4. Average Cost of Avoided CO2 and CO2 Emission Rate Reductions from 100% Coal at 

Various Levels of Natural Gas Co-Firing at Base Case Projected Gas Price ($5.36/MMBtu) 

Case Average Cost of Avoided CO2

($/tonne)

Change in CO2 Rate from 100% Coal

(lbs/MWh net)

100% Coal N/A N/A

10% Gas 150 4%

50% Gas 91 21%

100% Gas 83 41%

Note: Based on a typical 500 MW bituminous coal steam unit operating at 75% capacity factor.  Assumes

construction of new 50‐mile pipeline. EPA estimated reduced total capital costs for the 50% and 10% gas cases; for

example, total capital cost for 10% gas was estimated to be about one‐half of the capital costs for the 100% gas

case.

However, widespread adoption of gas co-firing would increase overall gas demand and place 

upward pressure on the natural gas price, which would consequently increase the average cost of 

avoided CO2 of a potential boiler conversion. 

   

                                                            

14 This document uses “tonne” to refer to a metric tonne.  All control costs in this analysis are presented in dollars

per metric tonne, or “$/tonne.” 15 Based on EPA analysis, the majority of existing coal units would require less than 50 miles of new gas pipelines to

switch fuels from coal to 100% natural gas.  See Chapter 5 and Table 5‐22 of Documentation for EPA Base Case

v.5.13 Using the Integrated Planning Model, available at: http://www.epa.gov/airmarkets/progsregs/epa‐

ipm/BaseCasev513.html

Comment [A8]: What is the sensitivity to your

gas price assumptions?  

Comment [A9]: EPA: These estimates are based

on projected base case fuel prices.  The following

paragraph explains that these estimates would

increase with widespread deployment of gas co‐

firing.  Since these estimates are at the lower end of

projected gas prices under a scenario that would in

practice increase gas use, we believe that our

conclusion is robust against other potential future

gas prices with or without increased gas co‐firing.  
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Conclusion 

Switching from coal to gas is a relatively costly approach to CO2 reductions at existing coal 

steam boilers when compared to other measures such as heat rate improvements and re-dispatch 

of generation supply to other existing capacity with lower CO2 emission rates. Moreover, this 

analysis shows that coal-to-gas conversion of an existing boiler is less efficient than constructing 

a new natural gas combined cycle (NGCC) turbine in its place. For example, EPA analysis 

indicates that replacing the coal steam plant discussed above with a new NGCC facility would 

reduce the net CO2 emission rate of the generating capacity by 62% at a cost of about $50/tonne 

of avoided CO2 under the base case projected gas price and about $81/tonne of avoided CO2 at a 

future gas price 50% higher than the base case projection. [INSERT CROSS-REFERENCE TO 

LOCATION IN RULE MATERIALS WHERE WE EXPLAIN WHY WE ARE NOT 

CONSIDERING NEW NGCC POTENTIAL TO REPLACE EXISTING COAL AS A 

REDUCTION MEASURE FOR CRAFTING STATE GOALS.] See preamble section VI.C.3.c.

The EPA is considering cost-effectiveness at a national level for the purpose of setting emissions 

goals consistently in each state. While this analysis suggests that cost-effective reductions of 

CO2 are not available on a national basis from widespread adoption of natural gas co-firing, it 

does not preclude the potential for individual EGUs to utilize co-firing as a way to reduce CO2

and other emissions, nor does it preclude states from factoring in that unit-level potential into the 

design of state plans for compliance with the 111(d) standard. EPA notes that there are utilities 

that see merit in converting some existing coal units to burn 100% gas, and several are currently 

doing so.16,17

   

                                                            

16 Reuters 2014, “Southern to repower three Alabama coal power plants with natgas,” Reuters U.S. Edition,

January 16, 2014  , available at http://www.reuters.com/article/2014/01/16/utilities‐southern‐alabama‐

idUSL2N0KP1WA20140116

17 Dominion 2012, “Dominion Virginia Power Proposes To Convert Bremo Power Station From Coal To Natural

Gas,” Dominion News, September 5, 2012, available at http://dom.mediaroom.com/2012‐09‐05‐Dominion‐

Virginia‐Power‐Proposes‐To‐Convert‐Bremo‐Power‐Station‐From‐Coal‐To‐Natural‐Gas

Comment [A10]: Is there any reference to cite

for these numbers?

EPA:  Added clarification of source.

Comment [A11]: EPA should discuss the

tension between these numbers and modeling

analyses that shows significant deployment of

new NGCC capacity at low carbon prices. For

example, the EIA $10 Carbon Price side case from

AEO2014 builds an additional 20 GW of new

NGCC capacity over reference by 2020.  

Comment [A12]: please fix.

EPA: Citation inserted.
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Biomass Co-firing 

Introduction 

Co-firing biomass in existing boilers designed for coal-fired generation, or converting those 

boilers to consume entirely biomass, is another approach to potentially reduce the output-based 

CO2 emissions rate (lbs/MWh) of these boilers. In the analysis presented in this technical 

support document, the physical CO2 emissions rate at the boiler stack could increase or decrease, 

depending on the amount of coal energy replaced by biomass energy and differences in the 

properties of a selected biomass and the coal it replaces.18 

There are many possible combinations of coals and biomass types that could be co-fired. Sitespecific economics and accessibility would determine which combinations might actually be 

feasible. This TSD analysis does not attempt to estimate an economically feasible national 

average increase or decrease in CO2 emission rate via biomass co-firing. Instead, this analysis 

simply employs one reasonably representative case to evaluate the cost effectiveness of biomass 

energy substitution in reducing the physical CO2 emission rate based only on the CO2 coming 

from coal. This analysis indicates that while the co-firing of biomass with coal is technically 

feasible as a means of reducing the coal-based CO2 emission rate due to the substitution of 

biomass for coal, it generally has limited economic feasibility due to the generally higher cost of 

energy from biomass as compared to coal. This general finding largely explains the very limited 

amount of biomass co-firing currently practiced in the U.S. It is also consistent with recent 

findings by others19 and others, including an earlier study by the State of Maryland20 that 

concluded as follows: 

“Due to the higher cost of biomass fuels when compared to coal, cofiring with biomass 

will lead to an increase in fuel costs. Without consideration for any environmental 

benefits, it is unlikely that any Maryland coal-fired facility would make the investments 

required to cofire with a more expensive and less efficient fuel.” 

                                                            

18 Fuel properties particularly affecting relative CO2 emission rates are: higher heating value, carbon and hydrogen

contents, and as‐fired moisture content.    19 Nowling, Una, Black & Veatch, “Utility Biomass Use: Turning Over a New Leaf?, Power, May 2014, available at

http://accessintelligence.imirus.com/Mpowered/book/vpow14/i5/p52

20 The Potential for Biomass Cofiring in Maryland, Maryland Department of Natural Resources, March 2006, (pg 53)

http://esm.versar.com/pprp/bibliography/PPES_06_02/PPES_06_02.pdf

Comment [A13]: You might also mention coal

plants that have been retired (i.e. decommissioned)

or smaller facilities that have been converted

entirely to biomass (i.e. Schiller, NH and others).

EPA:  

We appreciate the commenter’s perspective

regarding biomass use in EGUs.  

This TSD analysis evaluates the co‐firing of about

10% biomass with coal in many existing large boilers

because we believe that scenario supports a

consideration of the theoretical impacts of generally

increasing biomass combustion.  We do not believe

that the potential conversion of individual boilers to

100% biomass (in limited instances where the

economics might be favorable) would materially

affect the evaluation we present here.  We have

selectively adopted the suggested edits in

accordance with this view.

Comment [A14]: Maryland is not representative

of biomass co‐firing across the United States,

neither is a 2006 study. For example, limiting to this

case ignores coal plants that have been retired or

facilities that have been converted entirely to

biomass (i.e. Schiller, NH and others).

EPA: Additional clarification added, with an

additional recent citation.  

Comment [A15]: : Biomass will be more

economically feasible with an implicit carbon tax on

fossil emissions, as well as increased recognition of

the other air quality benefits (e.g. lowered toxic

emissions, etc.) that could be realized by cofiring.

Indeed the co‐firing of biomass is much more

prevalent than is CCS, which EPA has described in

more generous terms in the 111(b) rulemaking. It

also ignores one of the major economic barriers to

biomass co‐firing which has been the difficulty/lack

of desire/cost to open up Clean Air Act permits to

allow biomass co‐firing where that was not part of

the original permitting proposal.  

EPA:  This chapter assesses the cost effectiveness of

fuel switching relative to other CO2 reduction

technologies from a technical perspective.  The

chapter does conclude that “[while] cost‐effective

reductions of CO2 are not available on a national

basis from widespread adoption of biomass co‐

firing, [this rule] does not preclude the potential for

individual EGUs to utilize co‐firing as a way to

reduce overall CO2 emissions, nor does it preclude

states from factoring in that unit‐level potential into

the design of state plans for compliance with the

111(d) standard.”

Formatted: Font: Italic
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Based on the basic analysis of the cost effectiveness of biomass energy substitution in reducing 

the physical CO2 emission rate based only on the CO2 coming from coal presented below, the 

EPA concludes in this TSD that biomass co-firing would not be a cost-effective measure on 

which to base state goals with two caveats.

 21

First, EPA notes that this analysis does not include evaluation of stack biogenic CO2

emissions relative to the net landscape and process-related carbon fluxes associated with the 

production and use of the biogenic feedstocks combusted. Issues related to methods for assessing 

biogenic CO 2 emissions from stationary sources are currently being evaluated by the EPA. In 

general, the overall net atmospheric contribution of CO 2 resulting from the use of a biogenic 

feedstock by a stationary source, such as an EGU, will ultimately depend on the stationary 

source process and the type of feedstock used, as well as the conditions under which that 

feedstock is grown and harvested. In September 2011, the EPA submitted a draft Accounting 

Framework to the Science Advisory Board (SAB) Biogenic Carbon Emissions (BCE) Panel for 

peer review. The SAB BCE Panel delivered its Peer Review Advisory to the EPA on September 

28, 2012. In its Advisory, the SAB recommended revisions to the EPA's proposed accounting 

approach, and also noted that biomass cannot be considered carbon neutral a priori, without an 

evaluation of the carbon cycle effects related to the use of the type of biomass being considered. 

The cost effectiveness of biomass co-firing for CO2 emissions reductions is improved the more 

that carbon cycle is accounted for when calculating the reduction in GHGs afforded by switching 

from coal to biomass fuels. 

Second the analysis in this TSD chapter does not attempt to account for conditions that 

would effectively make burning coal to generate electricity more expensive and consequently 

would make burning biomass more cost competitive. For example, state plans such as that 

                                                            

21 This analysis does not include evaluation of stack biogenic CO2 emissions relative to the net landscape and

process‐related carbon fluxes associated with the production and use of the biogenic feedstocks combusted. Issues

related to methods for assessing biogenic CO 2 emissions from stationary sources are currently being evaluated by

the EPA. In general, the overall net atmospheric contribution of CO 2 resulting from the use of a biogenic feedstock

by a stationary source, such as an EGU, will ultimately depend on the stationary source process and the type of

feedstock used, as well as the conditions under which that feedstock is grown and harvested. In September 2011,

the EPA submitted a draft Accounting Framework to the Science Advisory Board (SAB) Biogenic Carbon Emissions

(BCE) Panel for peer review. The SAB BCE Panel delivered its Peer Review Advisory to the EPA on September 28,

2012. In its Advisory, the SAB recommended revisions to the EPA's proposed accounting approach, and also noted

that biomass cannot be considered carbon neutral a priori, without an evaluation of the carbon cycle effects

related to the use of the type of biomass being considered.  
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currently in the RGGI states that limits ghg emissions from EGUs would make coal-fired 

electricity more expensive vis-à-vis alternative renewable sources of electricity generation such 

as biomass. Under those conditions, burning biomass to generate electricity would also become 

more cost competitive relative to the analysis in this chapter. For example, Europe is currently 

co-firing substantial amounts of biomass to help lower carbon emissions of their fossil fuel fired 

electricity sector (Ehrig and Behrendt 2013). That study notes that co-firing biomass is one of the 

lower cost options to reach the EU-2020 targets. 

This TSD’s analysis indicates that while the co-firing of biomass with coal is technically 

feasible, and in some cases it can yield increases in the stack emission rate of CO2 (lb/MWh). In 

the absence of this rulemaking or a program that puts an implicit price on fossil carbon biomass 

co-firing ) and has limited economic feasibility given the delivered cost of available biomass 

versus the cost of coal. However, the use of co-firing biomass to help meet the 111(d) state goals 

may be economically feasible if the cost of reducing fossil carbon emissions is high. For 

example, Europe is currently co-firing substantial amounts of biomass to help lower carbon 

emissions of their fossil fuel fired electricity sector (Ehrig and Behrendt 2013). That study notes 

that co-firing biomass is one of the lower cost options to reach the EU-2020 targets. 

Description of Technology 

Engineering/Economic Considerations 

The technical feasibility of biomass co-firing in existing coal-fired boilers has been thoroughly 

investigated in many research and engineering studies, as well as in test burns at coal power 

plants in the U.S. and globally.22 It has been demonstrated that the boiler and related systems of 

almost any existing coal-fired EGU can accept or be modified to support co-firing of at least 

some small percentage of biomass. In some cases, major modifications can be made to support a 

switch to 100% biomass.23 

                                                            

22 See Partial Bibliography – Biomass Co‐firing at end of this section. 23 For example, one unit at Schiller Station (NH) was converted in 2006 to burn biomass exclusively.  See:

https://www.psnh.com/PlantsTerritory/Schiller‐Station.aspx.

Comment [A16]: See added text.   

Comment [A17]: EPA: Thank you for the

comment and suggested edit.  See conclusion for

similar language regarding the potential for

individual EGUs to utilize co‐firing as a way to

reduce overall CO2 emissions.

Comment [A18]: For example, Schiller, NH

facility.

EPA:  Thank you for the comment. Partial

Bibliography, includes references to extensive tests.  

Also, see new footnote 23.
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A decision to actually modify an existing coal-fired boiler for biomass co-firing at any 

percentage level depends on numerous technical and economic factors, including reliable 

availability of suitable biomass at an economic cost; adequate onsite space for biomass receiving, 

storage, preparation, and handling systems; potential corrosive effects of biomass ash in the 

boiler furnace; potential impacts of co-firing on boiler efficiency even at low biomass 

percentages, and the likely reduction (derate) in unit generating output at very high biomass 

percentages. 

There are considerable physical differences between coal and biomass that will generally limit 

the extent to which biomass can be reasonably used to replace coal in a boiler. For example, 

compared to most coals, many solid biomass fuels have both a significantly higher as-fired 

moisture content and a significantly lower heating value per unit of weight. Most solid biomass 

fuels are also significantly less dense than most coals. For example, a typical biomass might have 

twice the moisture, half the heating value, and less than half the density of coal.24 Important 

consequences of these physical differences are that the weight of biomass needed to provide a 

given amount of heat energy could be twice the weight of the coal it replaces, and the volume 

(cubic feet) of biomass needed could be four-to-eight times the volume of coal replaced. 

Biomass requires space for storage after delivery to a facility, and the length of time that the 

biogenic material would remain on site prior to use can differ. For example, wood chips could 

be delivered year-round while crop residue delivery would follow specific seasons in which the 

crop was grown. As noted above, the four-fold or greater increase in volume occupied by 

biomass relative to coal means that the necessary additional storage space could be large.

However, if pre-prepared or condensed biomass fuels such as pelletized or torrefied biomass is 

used, some of these concerns may not applybe lessened, recognizing that such pre-prepaerations 

of the feedstock couldwill entail additional costs. Stored biomass can be at even greater risk of 

spontaneous combustion than stored coal; this may limit the safe height of biomass piles and 

further increase storage area requirements.25

                                                            

24 Biomass Energy Data Book‐ Edition 4, October 2012, DOE‐EERE‐ORNL,  http://cta.ornl.gov/bedb/index.shtml 25 Properties of Wood Waste Stored for Energy Production, Purdue University, 2011,

http://www.extension.purdue.edu/extmedia/ID/ID‐421‐W.pdf  

Comment [A19]: A commenter suggest that EPA

could do the following here:

elaborate on the challenges of preparing biomass

fuel for cofiring in pulverized systems (i.e. in

reducing biomass fuel particle size to close to

pulverized coal size).  

consider the influence of biomass on the potential

markets for flyash in concrete.  

mention the need for fuel drying systems for many

types of biomass.  

EPA:  Thank you for the comment. EPA believes that

the current description of factors to consider in

biomass combustion is sufficiently detailed to

support the conclusions presented herein.

Comment [A20]: There is extensive effort and

additional research being done to mitigate these

concerns through pre‐preparation of the feedstock.

Wood pellets and low rank coal may have

approximately the same energy density, and

depending on the cost structure of the feedstock

supply chain, the reduced transportation and

storage benefits could outweigh the preparation

costs, particularly for cases like torrefaction that

may produce economic co‐products.  

EPA:  Thank you for the comment and the edit. We

accept the edit, with the slight modifications shown.
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The volumetric differences alone can have other unexpected consequences. For this analysis, 

experienced EPA engineering staff estimated that a 500 MW baseload coal plant co-firing 10% 

biomass and receiving biomass deliveries 10 hours per day and 5 days per week would require a 

20-ton truck delivery to the plant every 10 minutes, in addition to the ongoing coal deliveries. 

Limiting traffic issues may arise in some situations. Also, because of the low energy density of 

biomass and its relatively higher transportation cost per unit of delivered energy, it may only be 

economically viable to transport biomass a limited distance from where it is grown. This could 

limit the both the percentage of biomass co-firing in a single boiler and the maximum MW 

output from biomass at a single site. New technologies under development, such as torrefaction 

of biomass, but could mitigate some of these transportation, storage and energy content concerns, 

but are not yet commercially available.

The relatively higher moisture content and lower heat content of biomass reduces boiler 

efficiency, and typically requires a derating in unit generation at very high co-firing percentages 

as furnace volume and boiler fan capacities become inadequate. 

For all of the above reasons, the EPA assumed for this analysis that coal-steam EGU boilers will 

generally only co-fire with biomass to a limited degree. While the actual level at which any 

plant can co-fire with biomass is highly site-specific, this analysis adopts the assumption used in 

EPA’s fleet wide IPM modeling of the electric power sector: a reasonable average limit on 

biomass co-firing is up to 10% on any single boiler, not to exceed 50 MW total biomass powered 

output at an individual plant site (which aligns with the magnitude of some of the larger such 

entities currently in the U.S.). This amount of co-firing has been used as representative practical 

limit in other studies as well.26 But EPA notes that higher percentages of co-firing and at higher 

levels may be available in other cases as have been noted in many other studies. 

For example, Europe’s 2nd biggest coal-fired power plant is being converted to burn wood 

pellets imported from the United States and Canada to power half of the plants 4 gigawatt 

capacity. The 700 million Euro project will “transform Drax from the largest carbon emitter by 

site in UK to one of the largest clean-energy plants in the world.” (Financial Times, August 14, 

2013). Similarly, in Canada the potential to reduce coal generation through co-firing is assumed 

                                                            

26 The Potential for Biomass Cofiring in Maryland, Maryland Department of Natural Resources, March 2006,

http://esm.versar.com/pprp/bibliography/PPES_06_02/PPES_06_02.pdf

Comment [A21]: The use of sawmill residues,

when avaialabe, could provide a high quality

feedstock at potentially lower moisture content (i.e.

planer shavings). A high quality feedstock could

justify greater transportation distances (perhaps

75+ miles)

EPA:  Thank you for the comment.

Comment [A22]: EPA:  Thank you for the edit.

We will include it as slightly modified.

Comment [A23]: : Might mention, for

perspective, that 50MW is in line with some of the

largest standalone biomass powered electrical

facilities in the US. Agreed that 50 MW upper limit is

realistic. However if EU cofiring practices were

adopted in the US, this could exceed 100MW at an

individual site.

EPA:  Thank you for the comment.
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to be 20 to 30%, much higher than IPM’s assumptions (See Canadian Clean Power Coalition 

http://www.canadiancleanpowercoalition.com/files/7813/2621/8340/Append ix%20C.pdf). As 

such EPA specifically requests for comment on data to support higher levels of biomass co-firing 

in the EPA’s IPM model and for this rulemaking. 

Costs and Performance Impacts of Retrofitted Biomass Co-firing 

For this analysis the EPA adopted capital and O&M costs, and performance impacts for 

retrofitted biomass co-firing capability that are approximately representative of EPA assumptions 

used in its IPM modeling and discussed in the documentation for IPM v.5.13.27

EPA estimated that the capital cost to install 50 MW of biomass co-firing capability would be at 

least $10 million.28 As applied to a 500 MW coal unit, the minimum cost of this 10% co-firing 

capability would then be $20/kW. Fixed O&M cost was estimated by EPA engineering staff to 

be 10% greater than with coal alone, and variable O&M cost was estimated to remain 

unchanged. 

The heat rate impact (Btu/kWh) of 10% biomass co-firing as estimated by EPA engineering staff 

for this analysis was an increase of slightly more than 1% compared to coal alone. At low 

biomass cofiring rates, this factor slightly affects calculated biomass fuel consumption and any 

associated CO2 emission from biomass. 

Cost of Fuel 

For this analysis, the EPA uses a delivered biomass cost of $4/MMBtu, representative of 

delivered woody crops grown specifically for energy-generating combustion,29 and roughly 50% 

greater than IPM projected 2020 average delivered coal costs.30 This analysis also considers a 

sensitivity scenario assuming a higher $6/MMBtu biomass price. 

The EPA recognizes that the cost of biomass is highly site-specific, and in some cases could be 

largely comprised of collection and transportation cost (as is the case for opportunity fuels with 

                                                            

27 See Sec 5.3, pg 5‐19 at: http://www.epa.gov/airmarkets/progsregs/epa‐ipm/docs/v513/Chapter_5.pdf 28 Generally consistent with EPA assumptions in IPM modeling; also see the following source using the same

retrofit capital cost assumption: Cofiring Biomass and Coal for Fossil Fuel Reduction and Other Benefits – Status of

North American Facilities in 2010, USDA, August 2012, http://www.fs.fed.us/pnw/pubs/pnw_gtr867.pdf 29 Average biomass price as projected by EPA modeling in IPMv5.13 Base Case 30 EIA, Electric Power Annual 2012 – Electricity (Table 7.4)

http://www.eia.gov/electricity/annual/html/epa_07_04.html

Comment [A24]: EPA:  We respectfully decline

the suggested edits with regard to the Drax facility

and the Canadian business coalition’s assumptions

of biomass co‐firing potential with respect to power

sector modeling.  First of all, EPA believes that

making reference to the Drax facility would require

a fuller discussion of differences in regulatory and

economic variables affecting US and European

facilities that would unnecessarily complicate the

evaluation at hand.  Secondly, while this evaluation

“borrows” a technical framework for analysis from

IPM assumptions about the potential extent of

biomass co‐firing, the analysis is focused on the cost

and emission impacts of generally increasing

biomass combustion.  The drivers of that evaluation

and its conclusions described in this document are

not sensitive to the level of co‐firing potential

assumed; furthermore, this document only cites

IPM assumptions as a reference and starting point

for this technical evaluation, not in order to open a

discussion of power sector modeling assumptions

(which are not germane to this document’s

conclusions given that EPA is not relying on

modeling projections to make this evaluation or

reach these conclusions).

Comment [A25]: Should this number be

$200/kW? ($200/kW) x (1,000 kW/MW) x (50 MW)

=  $10 million

EPA:  At $10 million, $200/kW would be right for the

50,000 kW of biomass capacity. The $20/kW value is

applied to the entire 500 MW of unit capacity. They

are equivalent in this case.

Comment [A26]: There are some assumptions in

this section that state “estimated by EPA staff”.

Suggest providing further support if possible.

EPA:  Thank you for the comment.  This TSD includes

discussion of many routine power plant design,

operation, and engineering economics issues that

are generally neither speculative nor controversial

among those familiar with the subject matter. EPA

believes these estimates are adequately supported

by the document as written.   

Comment [A27]: This is intriguing, and it might

be worthwhile providing more detail about this

(perhaps in a separate Table)‐ especially given the

emphasis on heat rates in chapter 2.

EPA:  Thank you for the comment.  We believe the

current level of detail suffices.
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little to no other market value). The transportation component depends primarily on the distance 

that biomass needs to be transported. For example, the EPA engineering staff estimate that for a 

one-way distance of 50 miles with a 20-ton semi-trailer truck, transportation costs could be $10-

20/ton. For biomass at a total delivered price of $4/MMBtu with an indicative heating value of 

5,000 Btu/lb (higher heating value (HHV) basis), transportation cost in this example case could 

account for 25-50 percent of the total delivered biomass cost. In any case, it is the total delivered 

price of biomass on a $/MMBtu basis that will primarily determine the economic feasibility of 

biomass co-firing. 

Emission Reduction Potential 

The CO2 reduction potential of biomass co-firing is directly related to the amount and type of 

biomass co-fired and is due to the difference in heating value, moisture content and 

hydrogen/carbon ratios31 for a selected biomass fuel compared to the particular coal it replaces. 

The types of biomass typically available to EGUs in the United States include woody-based 

feedstocks such as wood chips, forest industry byproducts, and to a lesser degree agricultural 

crop residues, as well as emerging dedicated energy crops such as switchgrass and short-rotation 

woody crops.32 In general, when comparing coal-only versus co-firing coal with biomass, cofiring may result in either an increase or decrease in the stack CO2 emission rate. The extent to 

which the use of biomass in lieu of fossil fuels to biogenic emissions can reduce fossil 

contributes to net emissions to the atmosphere is being considered in EPA’s current study on the 

framework for accounting for biogenic emissions accounting. See preamble Section VIII.G. 

European studies show that biomass can significantly displace fossil emissions in a co-fired 

plant. EPA specifically requests for comment on the degree to which biomass can be co-fired to 

reduce fossil emissions in the United States.

Cost of Reductions and Cost Effectiveness 

In order to evaluate cost-effectiveness of potential reductions, the EPA first estimated the cost of 

avoided coal CO2 emissions in a hypothetical scenario where biomass CO2 emissions are not 

                                                            

31 IFRF Combustion Handbook, Combustion File No. 23, What is Biomass? (Van Krevelen Diagram),

http://www.handbook.ifrf.net/handbook/cf.html?id=23 32 Biomass Combined Heat and Power Catalog of Technologies, U.S. EPA, September 2007,  

http://www.epa.gov/chp/documents/biomass_chp_catalog.pdf

Comment [A28]: It might be worth

distinguishing transportation costs for plantation

grown biomass vs. forest harvesting residues vs.

wood products manufacturing residues.

EPA:  Thank you for the comment.  We believe the

current level of detail suffices.

Comment [A30]: Shouldn’t this at least include a

discussion of the potential net/lifecycle emissions

benefits of utilizing biomass.

EPA:  This discussion is not necessary since this TSD

concludes that, even if CO2 emissions from biomass

are not counted, cost‐effective reductions of CO2

would not be available on a U.S. national basis from

widespread adoption of biomass co‐firing.

Comment [A29]: However, isn’t it safe to say

that most studies indicate a decrease in CO2

emissions with increasing biomass amounts.  

Example‐ Mann and Spath 2001.

http://link.springer.com/article/10.1007%2Fs10098

0100109

EPA:  EPA is adding language which directs the

reader to the preamble discussion of the biogenic

accounting framework, where comment solicitation

is more appropriate than in a TSD.  This document is

not intended to include any evaluation of biomass

accounting studies
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included in total stack CO2 emissions (in effect, biogenic CO2 emissions are subtracted from 

total CO2 emissions measured at the stack). The estimated results presented below are based on 

a reasonably representative case using a baseload bituminous coal-fired boiler with a net heat 

rate of 10,340 btu/kWh that shifts from 100% bituminous coal to 90% coal and 10% biomass 

(assuming fuel prices of $2.62/MMBtu for coal in 2020 as projected in IPMv5.13 Base Case and 

$4/MMBtu for biomass as explained above). When biogenic stack emissions are not counted as 

part of total emissions, the cost of avoided CO2 for a “typical” baseload coal boiler co-firing 10% 

biomass is $30/tonne. This estimated cost of avoided coal CO2 emissions would increase if some 

portion of the biogenic CO2 emissions from the co-fired biomass were included. At higher 

delivered fuel price differentials, the cost of avoided coal CO2 emissions would increase (for 

example, at a biomass price of $6/MMBtu, cost of avoided CO2 is $80/tonne if CO2 emissions 

from biomass are not counted). Similarly, the costs of avoided coal CO2 emissions would 

decrease at lower fuel price differentials. delivered biomass prices associated with waste and 

residue materials, at higher coal prices than assumed here, or if other means of GHG reductions 

were less available. 

Conclusion

Replacing some coal with low levels of biomass co-firing may result in stack CO2

emission increases. However, the net impact to atmospheric emissions from the operation of the 

biomass EGU depends on the biogenic feedstock used and whether or not an assessment system 

is applied that evaluates biogenic CO2 emissions from the stack in relation to the terrestrial 

carbon cycling associated with the production and use of that biogenic feedstock. If biogenic 

CO2 emissions are not counted as part of stack emissions in favor of accounting for such 

emissions in the terrestrial carbon cycle, biomass co-firing will be a less costly approach to CO2

reductions at existing coal steam boilers compared to the case when lifecycle emissions are not 

considered. The cost of reducing aggregate emissions through the use of biomass co-firing is 

likely to become more costly as the percentage of fossil fuel feedstock is replaced with biomass. 

As with all abatement technologies, such as heat rate improvements and re-dispatch of 

generation supply to other existing capacity with lower CO2 emission rates, the amount of cofiring that would be efficient will depend on facility specific factors such as available feedstock 

supplies and ability to retrofit the facility. For example, Europe is currently co-firing substantial 

amounts of biomass to help lower carbon emissions of their fossil fuel fired electricity sector 

Comment [A31]: This is a cost/ton that is seems

centrally in line with other measures that are

included in BSER. If that is indeed true, EPA should

probably be more explicit in addressing:  

(a) why, given the costs, biomass cofiring is not

in BSER

(b) How (or whether) biomass cofiring could be

included for compliance in state plans

Please note that this reviewer is not requesting that

biomass cofiring be included in BSER, just suggesting

the clarifications above.

Formatted: Subscript

Comment [A32]: EPA: Thank you for the

suggested edit, which we have adopted with slight

revisions.
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(Ehrig and Behrendt 2013). That study notes that co-firing biomass is one of the lower cost 

options to reach the EU-2020 targets. 

Replacing some coal with low levels of biomass co-firing may result in stack CO2 emission 

increases.33 Even if biogenic CO2 emissions are not counted as part of stack emissions, biomass 

co-firing is a relatively costly approach to CO2 reductions at existing coal steam boilers when 

compared to other measures such as heat rate improvements and re-dispatch of generation supply 

to other existing capacity with lower CO2 emission rates. 

The EPA is considering cost-effectiveness at a national level for the purpose of setting emissions 

goals consistently in each state. While this analysis concludes that cost-effective reductions of 

CO2 are not available on a national basis from widespread adoption of biomass co-firing, it does 

not preclude the potential for individual EGUs to utilize co-firing as a way to reduce overall CO2

emissions, nor does it preclude states from factoring in that unit-level potential into the design of 

state plans for compliance with the 111(d) standard.34 

Partial Bibliography for Technical Feasibility of Biomass Co-firing 

(a) Briggs, J. and J. M. Adams, Biomass Combustion Options for Steam Generation, Presented at 

Power-Gen 97, Dallas, TX, December 9 – 11, 1997. 

(b) Grusha, J and S. Woldehanna, K. McCarthy, and G. Heinz, Long Term Results from the First 

US Low NOx Conversion of a Tangential Lignite Fired Unit, presented at 24th International 

Technical Conference on Coal & Fuel Systems, Clearwater, FL., March 8 – 11, 1999. 

(c) EPRI, Biomass Co-firing: Field Test Results: Summary of Results of the Bailly and Seward 

Demonstrations, Palo Alto, CA, supported by U.S. Department of Energy Division of Energy 

Efficiency and Renewable Energy, Washington D.C.; U.S. Department of Energy Division 

                                                            

33 Depending on biogenic feedstocks used and whether or not an assessment system is applied that evaluates

biogenic CO2 emissions from the stack in relation to the terrestrial carbon cycling associated with the production

and use of that biogenic feedstock. 34 EPA notes that states will need to consider any future EPA finding regarding an assessment framework that

considers carbon fluxes on the biogenic feedstock production landscape applied when evaluating net stack CO2

emissions from biomass co‐firing.

Comment [A33]: Suggest changing this section

to be accurate – this paragraph instead of what was

in the prior draft.   

Comment [A34]: It appears that $30/ton is in

line with these other measures. Is that

interpretation incorrect?

Comment [A35]: EPA:  Thank you for the

suggested edits.  The purpose of this chapter is to

assess the cost‐effectiveness of fuel switching at a

national level for the purpose of setting emissions

goals consistently in each state, and this paragraph

as originally written appropriately summarizes EPA’s

finding.  EPA directs the commenter to the following

paragraph, which highlights the ability of some units

in some states to cost‐effectively co‐fire with

biomass.

Comment [A36]: Suggest the following 2

references: Spath, P.L.; Mann, M.K.  

Life Cycle Assessment of a Natural Gas Combined‐

Cycle Power Generation System  

http://www.nrel.gov/docs/fy00osti/27715.pdf

Mann, M.K.; Spath, P.L.  

A Life Cycle Assessment of biomass cofiring in a coal

fired power plant.  

https://www.bioenergykdf.net/sites/default/files/z

pdfzholderz/NREL_Dat a/KC_091102094512.pdf  

EPA:  we do not believe "lifecycle" assessments are

necessary to include here because the rule is

addressing EGU emissions, not lifecycle emissions.
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Federal Energy Technology Center, Pittsburgh PA; Northern Indiana Public Service Company, 

Merrillville, IN; and GPU Generation, Inc., Johnstown, PA: 1999. TR-113903. 

(d) Laux S., J. Grusha, and D. Tillman, Co-firing of Biomass and Opportunity Fuels in Low NOx 

Burners, PowerGen 2000 - Orlando, FL 

(e) Tillman, D. A., Co-firing Biomass for Greenhouse Gas Mitigation, presented at Power-Gen 

99, New Orleans, LA, November 30 – December 1, 1999. 

(f) Tillman, D. A. and P. Hus, Blending Opportunity Fuels with Coal for Efficiency and 

Environmental Benefit, presented at 25th International Technical Conference on Coal Utilization 

& Fuel Systems, Clearwater, FL., March 6 – 9, 2000 

(g) Tillman D A, Harding N S (2004) Fuels of opportunity: characteristics and uses in 

combustion systems. Elsevier, Oxford, UK 

(k) http://esm.versar.com/pprp/bibliography/PPES_06_02/PPES_06_02.pdf 

(h) Tillman D, Conn R, Duong D (2010) Coal characteristics and biomass cofiring in pulverized 

coal boilers. In: Electric Power, Baltimore, MD, USA, 18 - 20 May, 2010 

(i) Renewable and Alternative Energy Fact Sheet – Co-firing Biomass with Coal, Pennsylvania 

State University, 2010, http://pubs.cas.psu.edu/FreePubs/PDFs/ub044.pdf 

(j) Fernando R, Cofiring High Ratios of Biomass with Coal, IEA Clean Coal Centre (CCC/194), 

January 2012 

(k) DOE-EERE-ORNL, Biomass Energy Data Book- Edition 4, October 2012, 

http://cta.ornl.gov/bedb/index.shtml 
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Chapter 7: Carbon Capture & Storage 

Introduction 

Another possible approach for reducing CO2 emissions from existing fossil fuel-fired 

EGUs is through the application of carbon capture and storage technology (CCS; sometimes also 

referred to as carbon capture and sequestration). In the recently proposed standards of 

performance for new fossil fuel-fired EGUs (79 FR 1430), the EPA proposed to find that the best 

system of emission reduction for new fossil fuel-fired boilers and IGCC units is partial 

application of CCS. In that proposal, the EPA found that, for new units, partial CCS has been 

adequately demonstrated; it is technically feasible; it can be implemented at reasonable costs; it 

provides meaningful emission reductions; and its implementation will serve to promote further 

development and deployment of the technology. This chapter examines the potential for 

implementation of CCS technology at existing fossil fuel-fired utility boilers and IGCC units. 

Carbon Capture Options for Existing Fossil Fuel-fired EGUs 

In general, CO2 capture technologies applicable to existing fossil fuel-fired power 

generation can be categorized into three approaches – (1) post-combustion capture; (2) precombustion capture; and (3) oxy-combustion. Each of these is described and discussed in more 

detail below. 

Post-combustion Capture 

Post-combustion CO2 capture refers to removal of CO2 from a combustion flue gas prior 

to discharging to the atmosphere. Separating CO2 from such a gas stream can be challenging for 

a number of reasons. Because CO2 is a dilute fraction of the combustion flue gas – typically 13-

15 % in coal-fired systems and 3-4 % in natural gas-fired systems – a large volume of flue gas 

must be treated. The flue gas from typical combustion systems is usually at near atmospheric 

pressure. Therefore, most of the available capture systems rely on chemical absorption 

(chemisorption) options (e.g., amines) that require added energy to release the captured CO2 and 

regenerate the solvent. Many of the chemical solvents require a flue gas stream that is free of or 

has very low quantities of components – such as SO2, NOX, and HCl – that can degrade the 

solvent. The captured CO2 must then be compressed from near atmospheric pressure to much 

higher pipeline pressures (about 2,000 psia). 

 
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Pre-combustion Capture 

Pre-combustion capture systems are applicable to fossil fuel gasification power plants 

(i.e., IGCC units) where coal or other solid fossil fuel (e.g., pet coke) is converted into a 

synthesis gas (or “syngas”) by applying heat under pressure in the presence of steam and limited 

O2. The product syngas contains primarily H2 and CO – and, depending on the fuel and 

gasification system – some lesser amount of CO2. The amount of CO2 in the resulting syngas 

stream can be increased by “shifting” the composition via the catalytic water-gas shift (WGS) 

reaction. This process involves the catalytic reaction of steam (“water”) with CO (“gas”) to form 

H2 and CO2. The resulting CO2 contained in the syngas is then captured before combustion of 

the H2-enriched syngas for power generation in a combined cycle system. Contrary to the postcombustion capture flue gas, the IGCC syngas can contain a high volume of CO2 and is 

pressurized. This allows the use of physical absorbents (e.g., SelexolTM, Rectisol®) that require 

much less added energy to release the captured CO2 and require less compression to get to 

pipeline standards. 

Oxy-combustion 

Oxy-combustion systems for CO2 capture rely on combusting coal or other fuels with 

relatively pure O2 diluted with recycled CO2 or CO2/steam mixtures. Under these conditions, the 

primary products of combustion are water and CO2, with the CO2 purified by condensing the 

water. Challenges associated with oxy-combustion include the capital cost and energy 

consumption for a cryogenic air separation unit (ASU) to produce oxygen, introduction of N2 via 

boiler air infiltration, and excess O2 in the CO2 product stream. 

CO2 Transportation and Storage 

CO2 Pipeline Infrastructure 

Carbon dioxide has been transported via pipelines in the U.S. for nearly 40 years. 

Approximately 50 million metric tons of CO2 are transported each year through 3,600 miles of 

pipelines. Moreover, a review of the 500 largest CO2 point sources in the U.S. shows that 95 

percent are within 50 miles of a possible geologic sequestration site, which would lower 

transportation costs. There are multiple factors that contribute to the cost of CO2 transportation 

via pipelines including but not limited to: availability and acquisition of rights-of-way for new 

pipelines, capital costs, operating costs, length and diameter of pipeline, terrain, flow rate of 

CO2, and the number of sources utilizing the pipeline. 
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Geologic Storage 

 Existing project and regulatory experience, research, and analogs (e.g. naturally existing 

CO2 sinks, natural gas storage, and acid gas injection), indicate that geologic sequestration is a 

viable long term CO2 storage option. The viability of geologic sequestration of CO2 is based on a 

demonstrated understanding of the fate of CO2 in the subsurface. Geologic storage potential for 

CO2 is widespread and available throughout the U.S. and Canada. Nearly every state in the U.S. 

has or is in close proximity to formations with carbon storage potential including vast areas 

offshore. Estimates based on DOE studies indicate that areas of the U.S. with appropriate 

geology have a storage potential of 2,300 billion to more than 20,000 billion metric tons of CO2

in deep saline formations, oil and gas reservoirs and un-mineable coal seams.1

 Other types of 

geologic formations such as organic rich shale and basalt may also have the ability to store CO2; 

and the DOE is currently evaluating their potential storage capacity. 

 Further evidence of the widespread availability of CO2 storage reserves in the U.S. 

comes from the Department of Interior’s U.S. Geological Survey (USGS) which has recently 

completed a comprehensive evaluation of the technically accessible storage resource for carbon 

storage for 36 sedimentary basins in the onshore areas and State waters of the United 

States.2

 The USGS assessment estimates a mean of 3,000 billion metric tons of subsurface CO2

storage potential across the United States. For comparison, this amount is 500 times the 2011 

annual U.S. energy-related CO2 emissions of 5.5 Gigatons (Gt).3

Enhanced Oil Recovery (EOR) 

Geologic storage options also include use of CO2 in EOR, which is the injection of fluids 

into a reservoir to increase oil production efficiency. EOR is typically conducted at a reservoir 

after production yields have decreased from primary production. EOR using CO2, sometimes 

referred to as ’CO2 flooding’ or CO2-EOR, involves injecting CO2 into an oil reservoir to help 

mobilize the remaining oil and make it available for recovery. The crude oil and CO2 mixture is 

                                                            

1

 The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition, U.S Department of Energy, Office of 

Fossil Energy, National Energy Technology Laboratory (NETL). 2

 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National 

assessment of geologic carbon dioxide storage resources – Results: U.S. Geological Survey Circular 1386, 41 p., 

http://pubs.usgs.gov/fs/2013/1386/. 3

 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National 

assessment of geologic carbon dioxide storage resources – Summary: U.S. Geological Survey Factsheet 2013-3020, 

6p.http://pubs.usgs.gov/fs/2013/3020/.
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produced, and sent to a separator where the crude oil is separated from the gaseous hydrocarbons 

and CO2. The gaseous CO2-rich stream then is typically dehydrated, purified to remove 

hydrocarbons, recompressed, and re-injected into the oil or natural gas reservoir to further 

enhance recovery. 

CO2-EOR has been successfully used at many production fields throughout the U.S. to 

increase oil recovery. The oil and natural gas industry in the United States has over 40 years of 

experience of injection and monitoring of CO2 in the deep subsurface for the purposes of 

enhancing oil and natural gas production. This experience provides a strong foundation for the 

injection and monitoring technologies that will be needed for successful deployment of CCS.

Evaluation of Retrofit CCS as BSER for Existing Fossil Fuel-fired EGUs 

Technical Feasibility 

In evaluating partial CCS as the BSER for new fossil fuel-fired boilers and IGCC units, 

the EPA determined that the technology is feasible and adequately demonstrated for new units 

because the major components of CCS – the capture, the transportation, and the storage – are all 

proven technologies that have been demonstrated at large scale. While the EPA found that partial 

CCS is technically feasible for new fossil fuel-fired boilers and IGCC units, it is much more 

difficult to make that determination for the entire fleet of existing fossil fuel-fired EGUs. 

Developers of new generating facilities can select a physical location that is more amenable to 

CCS – such as a site that is near an existing CO2 pipeline or an existing oil field. Existing sources 

do not have the advantage of pre-selecting an appropriate location. Some existing facilities are 

located in areas where CO2 storage is not geologically favorable and are not near an existing CO2

pipeline. Developers of new facilities also have the advantage of integrating the partial CCS 

system into the original design of the new facility. Integrating a retrofit CCS system into an 

existing facility is much more challenging. Some existing sources have a limited footprint and 

may not have the land available to add partial CCS system. Integration of the existing steam 

system with a retrofit CCS system can be particularly challenging. 

Partial CCS has been demonstrated at existing EGUs. It has been demonstrated at a pilotscale at Southern Company’s Plant Barry; it is being installed for large-scale demonstration at 

NRG’s WA Parish facility; and it will very soon be applied at commercial-scale as a retrofit at 

SaskPower’s Boundary Dam coal-fired EGU in Canada. However, all of these facilities are 

located in areas that are either near an existing oil field or in an area that is geologically 
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favorable for CO2 storage. Thus, at some existing facilities, the implementation of partial CCS 

may be a viable GHG mitigation option and some utilities may choose to pursue that option. 

However, the EPA does not believe that it can serve as the best system of emission reduction for 

a broadly applicable GHG mitigation program. Therefore, the EPA does not propose to find that 

CCS is a component of the best system of emission reduction for CO2 emissions from existing 

fossil fuel-fired EGUs.

Reasonableness of Cost 

In the proposed standard of performance for new fossil fuel-fired EGUs (79 FR 1430), 

the EPA found that the costs to implement partial CCS (to a level to meet the proposed emission 

standard of 1,100 lb/MWh-gross) were consistent with costs for other non-natural gas-fired 

generating technologies – such as nuclear, biomass and geothermal – that utilities are considering 

for new intermediate and base load generating capacity. The EPA also noted in the proposal, 

that most of the relatively few new projects that are in the development phase are already 

planning to implement CCS; and, as a result, the standard would not have a significant impact on 

nationwide energy prices. 

In contrast, the EPA did not identify full or partial CCS noted that identifying CCS as 

BSER for new natural gas-fired stationary combustion turbines, noting technical challenges to 

implementation of CCS at NGCC units as compared to implementation at new solid fossil fuelfired sources. The EPA also noted that, because virtually all new fossil fuel-fired power is 

projected to use NGCC technology, requiring full or partial CCS would have more of an impact 

on the price of electricity than the few projected coal plants with CCS and the number of projects 

would make it difficult to implement in the short term. 

would likely result in higher nationwide electricity prices and could adversely affect the 

supply of electricity since the majority of new fossil-fuel fired power generation is projected to 

be natural gas combined cycle (NGCC). An emission standard for existing units based on CCS 

(or even partial CCS) would most certainly have an even more significant effect on nationwide 

electricity prices and could affect the reliability of the supply of electricity. Therefore, we do not 

find that the cost to implement existing source emission standards to be reasonable, which 

further supports the determination that CCS is not an appropriate component of the best system 

of emission reduction for CO2 emissions from existing fossil fuel-fired EGUs. 

Emission Reductions and Promotion of Advanced Technology 
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An emission standard for existing units based on CCS (or even partial CCS) would 

clearly result in significant emission reductions and would certainly serve to promote further 

deployment, development and improvement in the most advanced technology. However, the 

EPA has determined that such an emission standard is not technically feasible in all cases and 

cannot be done at a reasonable cost at this time. 

Comment [A1]: Is EPA implicitly saying that a

standard has to be technically feasible in all cases in

order to be BSER? You might walk this statement

back a little bit.
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Technical Support Document (TSD) for 

Carbon Pollution Guidelines for Existing Power Plants: 

Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric 

Utility Generating Units 

Docket ID No. EPA-HQ-OAR-2013-0602 

Projecting EGU CO2 Emission Performance in State Plans 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

June 2014 

 
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Table of Contents 

I. Introduction ........................................................................................................................3 

II. Analytic Approaches for Projecting CO2 Emissions from Affected EGUs ..................5 

A. Electricity System Modeling Approaches for Projecting EGU CO2 Emissions .......5 

B. Growth Tools for Projecting Future EGU Utilization and CO2 Emissions...............9 

III. Translating from a Rate-Based Emission Performance Goal to a Mass-Based 

Emission Performance Goal ............................................................................................11 

A. Concept ...................................................................................................................11 

B. Projection Scenarios ................................................................................................13 

IV. Projecting CO2 Emission Performance under a State Plan .........................................17 

V. Applied Considerations for Different Types of State Plans .........................................18 

A. End-Use Energy Efficiency and Renewable Energy .............................................18 

B. Emission Budget Trading Programs ......................................................................32 

VI. Process Considerations ....................................................................................................38 

A. Provision of the EPA Guidance .............................................................................38 

B. Party that Translates the Rate-Based Goal to a Mass-Based Goal ........................40 

 
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I. Introduction 

As discussed in the preamble, in section VIII.F.7, all state plans will need to include a 

projection of the CO2 emission performance by affected EGUs that will be achieved under a state 

plan (inclusive of plan measures that avoid CO2 emissions from affected EGUs, such as end-use 

energy efficiency and renewable energy). Depending on the type of plan approach, this will 

include either a projection of the average CO2 emission rate achieved by affected EGUs or total 

CO2 emissions from affected EGUs. 

The EPA is also proposing that a state may translate the state rate-based CO2 emission 

performance goal for affected EGUs to an equivalent mass-based CO2 emission performance 

goal. If this translation option is used, a state plan must also include a projection used to derive 

the mass-based CO2 emission performance goal. This translation will involve a projection of CO2

emissions from affected EGUs during the plan period, under a scenario that assumes the ratebased goal in the emission guidelines is met. 

As discussed in the preamble, the EPA is striving to find a balance between providing 

state implementation flexibility and ensuring that the emission performance required by CAA 

section 111(d) is properly defined in state plans and that plan performance projections have 

technical integrity. The credibility of state plans under section 111(d) will depend in large part on 

ensuring credible and consistent emission performance projections in state plans. Therefore, the 

use of appropriate methods, tools, and assumptions for such projections is critical. 

The preamble, at section VIII.F.7, seeks comment on options presented for how CO2

emission projections might be conducted in an approvable state plan, and how different types of 

state plan approaches are represented in these projections. These options include the use of 

historical data and parameters for estimating the future impact of individual state programs and 

measures. Alternatively, a projection could be based on modeling, such as use of a capacity 

expansion and dispatch planning model, or a dispatch simulation model.1

 This latter approach 

would be able to capture dynamic interactions within the electricity sector, based on system 

                                                            

1

 In many cases, this approach will also require the development of parameters for estimating the effect of individual 

state programs and measures, for use as input assumptions for modeling. 
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operation and market forces, including interactions among state programs and measures and the 

dynamics of market-based measures. 

In the preamble, the EPA further seeks comment on whether the EPA should develop 

guidance that describes acceptable projection approaches, tools, and methods for use in an 

approvable plan, as well as whether the EPA should provide technical resources for conducting 

projections. 

This technical support document (TSD) elaborates these options and considerations. The 

TSD discusses possible analytic approaches for translating from a rate-based CO2 emission 

performance goal to a mass-based goal, and projecting the CO2 emission performance that will 

be achieved through a state plan. It discusses both modeling and non-modeling approaches, such 

as electricity sector capacity expansion and dispatch planning models, dispatch simulation 

models, and growth tools that base projections on historical data and algorithms. The TSD also 

discusses possible approaches for developing inputs that are used for emission projections and 

applied considerations for different types of state plans. Topics addressed include: 

 Section II discusses analytic approaches for projecting CO2 emissions from affected 

EGUs 

 Section III discusses the concept and practice of translating from a rate-based CO2

emission performance goal to a mass-based CO2 emission performance goal 

 Section IV discusses the concept and practice of projecting EGU CO2 emission 

performance under a state plan 

 Section V discusses applied considerations for projecting EGU CO2 emission 

performance under different types of state plans, including: 

o Considerations that should be addressed in conducting projections of emission 

performance for different types of plans 

o Data needs and methods for developing inputs to EGU CO2 emission projections, for 

different types of state plans 

 Section VI discusses process considerations for conducting EGU CO2 emission 

projections for state plans, including: 

o Regional coordination among states in conducting projections 
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o Whether the EPA should provide guidance and other analytic support for conducting 

projections in state plans and translation of rate-based CO2 emission performance 

goals to mass-based goals 

II. Analytic Approaches for Projecting CO2 Emissions from Affected EGUs 

This section surveys different types of methods and tools for projecting CO2 emissions 

from affected EGUs, including modeling tools and other tools that base projections on historical 

data and use algorithms to extrapolate future CO2 emissions performance based on past 

performance. 

A. Electricity System Modeling Approaches for Projecting EGU CO2 Emissions 

1. National-scale capacity expansion and dispatch planning models 

National-scale electricity capacity expansion and dispatch planning models are typically 

used for fundamentals-based projections of the power sector (i.e., projections of the expected 

response of the sector to factors such as electricity demand, fuel prices, and emission constraints) 

that may extend over a period of several decades. These models are built to evaluate the impacts 

of market, technical, and regulatory factors on the electric power sector and related markets. 

Typical outputs of such models include EGU dispatch, fuel consumption, fuel prices, wholesale 

electricity prices, emissions, EGU retirements, and infrastructure expenditures (e.g., addition of 

new EGU capacity and installation of retrofit pollution control technologies). 

 

National-scale electricity capacity expansion and dispatch planning models have 

moderate spatial detail with broad scope, generally encompassing the entire country or 

transmission system interconnects (i.e. Eastern, Western, and ERCOT), which are subdivided 

into smaller areas, such as balancing authorities or control areas.2

 For computational efficiency, 

these models generally model several representative hours of the year, or aggregate hours into 

representative bundles with similar electricity demand profiles (i.e. peak, shoulder, off-peak). In 

these models, to reduce model size, existing EGUs may be aggregated into “model plants” to the 

extent that such EGUs share key unit characteristics, such as location, size, efficiency, operating 

                                                            

2

 A “balancing area” or “control area” refers to a specified portion of the transmission system where electricity 

demand and generation are balanced in real time by a system administrator to maintain grid reliability. 
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costs, pollution retrofit control status, age, and fuel type use/availability. These types of models 

are well suited to project dynamic entry and exit (capacity expansion and unit retirement) to meet 

energy and capacity requirements while minimizing system costs, maintaining reliability criteria, 

and following other constraints, such as minimum build times, transmission constraints, 

renewable energy availability, or emission limitations. 

2. Utility-Scale Capacity Expansion and Dispatch Planning Models 

Utility-scale capacity expansion and dispatch planning models are similar in concept to 

related national-scale models. However, utility-scale capacity expansion and dispatch planning 

models are typically used for evaluating narrower utility planning and investment decisions, such 

as procurement of a specific new electric generating facility and retirement or retrofit decisions 

for existing capacity within a specific utility’s territory. Utility planning models typically project 

outcomes for periods of up to two decades. Utility-scale capacity expansion and dispatch 

planning models are an industry -standard, used regularly in state electricity regulatory 

proceedings. Within the electricity sector there is broad familiarity with these models at state 

PUCs. Multiple vertically integrated utilities use capacity expansion and dispatch planning 

models to conduct forward planning and review the economics of specific EGU retrofit 

decisions. Utilities that submit integrated resource plans (IRP) typically use a utility-scale 

capacity expansion and dispatch planning model to examine long-term strategies and develop 

short-term action plans.3

 Utilities have experience using these models to examine CO2 emission 

reduction strategies or CO2 emission constraints, as IRP scenarios may include greater 

penetration of end-use energy efficiency or renewable energy, proxy CO2 emission prices, 

emission trading, and limits on CO2 mass emissions. 

                                                            

3

 See, for example: The Duke Energy Carolinas IRP (Annual Report), September 2012, available 

athttp://www.energy.sc.gov/files/view/Duke_IRP 2012.pdf; PacifiCorp 2013 IRP (Vol 1), April 2013, available 

athttp://www.pacificorp.com/es/irp.html; TVA IRP, March 2011, available at 

http://www.tva.gov/environment/reports/irp/archive/index.htm; Georgia Power Company 2013 IRP, January 31, 

2013, available at http://facts.psc.state.ga.us/Public/GetDocument.aspx?ID=145981; 2011 Joint IRP of Louisville 

Gas and Electric and Kentucky Utilities Company, April 2011, available at 

http://psc.ky.gov/PSCSCF/2011%20cases/2011-00140/20110421_LG%26E-KU_IRP_Volume%20I.pdf; Great 

River Energy IRP, November 2012, available at 

http://www.greatriverenergy.com/makingelectricity/resourceplan/pdoc295631.pdf; Public Service Company of 

Oklahoma IRP, October 2012, available at http://occeweb.com/pu/PSO%202012%20IRP.pdf. 
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Utility-scale capacity expansion and dispatch planning models tend to have relatively 

high spatial detail with limited geographic scope, generally encompassing a utility service 

territory or a sub-regional scale. These models generally have better temporal resolution than the 

national-scale capacity expansion and dispatch planning models, with each model year typically 

dispatched based on an annual hourly load duration curve.4

 Utility-scale capacity expansion and 

dispatch planning models typically represent individual EGUs, where each EGU has specific 

operational characteristics. 

Due to the additional spatial and temporal resolution of these models as compared to the 

national-scale models, the number of technology options for capacity expansion is generally 

limited to reduce the runtime of the model. This can be done through an outside-the-model 

screening analysis to pre-select the resources most likely to be economic in a planner’s area of 

interest, or by running the model iteratively to eliminate rarely chosen technology alternatives. 

3. Electricity System Dispatch Simulation Models 

Dispatch simulation models are regularly used by utilities, grid operators, and 

independent power producers (IPP) for short-term planning, ratemaking, dispatch decisions, and 

market intelligence. Dispatch simulation models are typically driven by near-term economics, 

system restrictions and market constraints, including a typically more detailed representation of 

an EGU’s operational constraints (e.g., ramp rates, heat input curves, and unit downtime for 

maintenance). These models typically do not add or retire generating capacity on an economic 

basis, although EGU additions and retirements may be exogenously input to these models. As a 

result, projections from these models tend to be considered more robust in the shorter term.5

 Grid 

operators, including utilities, and independent system operators (ISO) use dispatch simulation 

models in near real-time to match demand with electric generation from available generating 

units and dispatch EGUs on a least-cost basis. EGU owners and operators run dispatch 

                                                            

4

 Load duration curves typically represent electricity load during a typical week for each month. 5

 Some planners use dispatch simulation models in conjunction with national or utility-scale capacity expansion and 

dispatch planning models, where the capacity expansion and dispatch planning model indicates the disposition of 

new and existing generating resources, and the dispatch simulation model is used to simulate operation of individual 

EGUs. In this case, these models can be used in the same time horizon as a capacity expansion and dispatch 

planning model (i.e., decades). 
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simulation models to assist in fuel procurement, forecast revenues and costs, and calculate the 

avoided generation supply costs related to procurement of end-use energy efficiency and 

renewable energy resources.6

. Other utilities use dispatch simulation models to forecast retail 

rates for ratemaking proceedings and other planning purposes. 

Electricity system dispatch simulation models (also called production cost models) utilize 

security constrained economic dispatch (SCED) algorithms to determine which EGUs operate on 

an hourly (or shorter) basis to meet electricity demand.7

 These models typically have a very 

broad spatial scope, generally covering multiple Regional Transmission Organization (RTO) 

regions, and often modeling entire interconnects (i.e. Western, Eastern, and ERCOT). While 

individual EGUs are modeled in detail, including fuel and variable costs and operational 

constraints, transmission is simplified to characterize thermal constraints between zones, which 

typically represent control areas or balancing authorities. Zones contain both load (electricity 

demand) and EGUs; EGU dispatch and electricity demand are balanced to maintain transmission 

system reliability while providing least-cost service on a variable cost basis. Some versions of 

these models operate at a “nodal” level, where transmission constraints between individual 

EGUs and load are modeled as well.8

 Dispatch simulation models typically operate 

chronologically, modeling either all 8760 hours of the year, or typical weeks of the year. These 

models contain substantially more detail about individual EGUs than regional or national 

capacity expansion and dispatch planning models, including EGU ramp rates, minimum outages, 

maintenance schedules, emission rates, fuel use constraints, and heat rate curves depicting 

expected efficiency changes at various levels of output. 

                                                            

6

 For example, see Hornby, R., P. Chernick, D. White, et al., 2013. Avoided Energy Supply Costs in New England: 

2013 Report. http://www.rieermc.ri.gov/documents/2013%20Evaluation%20Studies/AESC%20Report%20-

%20With%20Appendices%20Attached.pdf 7

 The Federal Energy Regulatory Commission (FERC) defines security constrained economic dispatch as “the level 

at which each available resource should be operated, given actual load and grid conditions, such that reliability is 

maintained and overall production costs are minimized”. SCED optimizes dispatch based not only on the marginal 

costs of all available generating resources, but also constraints on transmission availability and ancillary services. 

See FERC, “Security Constrained Economic Dispatch: Definition, Practices, Issues, and Recommendations” (2006), 

available at http://www.ferc.gov/industries/electric/indus-act/joint-boards/final-cong-rpt.pdf. 8

 “A “nodal” model represents the entire transmission network in a given area, without making the simplifying 

assumptions that load is served on a more aggregate “zonal” level.” A caveat is that while a nodal model has more 

“links” and places where supply must meet demand, it is still an approximation in terms of the electrical operation of 

the transmission network (the network is represented as a DC flow network, not an AC flow network).
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4. Multi-Sector Models 

Multi-sector energy models are typically used to examine the effect of energy and 

environmental policies that affected multiple economic sectors, such as multi-sector emission 

trading systems. Multi-sector models are used to review trends in emissions, expected broadscale resource use, and energy sector impacts under changing regulatory and economic 

conditions, and often review changes over a period of decades. 

Multi-sector models cover a broad range of energy sectors beyond the electricity sector, 

and can better reflect energy demand and technology choices by energy end-users. Such models 

typically have relatively limited spatial detail with broad scope, generally encompassing the 

entire country subdivided into from one to 30 regions. Such models tend to have much more 

limited temporal resolution and treatment of EGU dispatch. EGUs are typically aggregated to a 

few broad technology types. A key strength of multi-sector energy models is the ability to 

provide multi-sectoral feedback between energy resource use and price (i.e., tracking national 

fuel supplies and adjusting price to account for demand). 

The range of national- and utility scale capacity expansion and dispatch planning models, 

and dispatch simulation models identified above all tend to focus on properly characterizing the 

electricity sector in order to answer sector-specific questions. Multi-sector energy models 

attempt to include many other energy-using sectors of the economy in order to better capture 

interactions between these sectors. The more aggregated representation of the electricity sector in 

multi-sector models may limit their use to providing input data for more specific analysis in an 

electricity sector model, and to better understanding variations in electricity load that may result 

from changes outside the electricity sector. 

B. Growth Tools for Projecting EGU Utilization and CO2 Emissions 

Organizations use growth tools for a variety of reasons;, including to estimate future 

emissions inventories for state and regional air quality modeling,,9 and to estimate the impact of 

load-reduction measures such as end-use energy efficiency and distributed renewable energy on 

                                                            

9

 See, for example, the ERTAC Load Growth Model, available at http://www.ertac.us/index_egu.html. 
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individual EGU emissions, and county, state,10 and regional emissions rates.11 Because these 

algorithms are generally based on publicly available data12 and do not rely on economic data or 

proprietary information regarding individual EGUs, they provide a low-cost, simple, and often 

transparent framework for estimating how EGUs will respond to changing system conditions. 

Non-modeling approaches, such as growth tools, approximate future emissions and 

generation from existing and new fossil fuel-fired EGUs under different assumed growth, 

retrofit, and load-reduction scenarios. These forecast tools do not simulate economic EGU 

dispatch, but could use demand growth rates and electricity production trends from other energy 

modeling forecasts as an input assumption.13 The algorithms in these forecast tools assume that 

EGU dispatch behavior generally follows simple rules based on past operation. In the absence of 

significant shifts in fuel prices and electricity demand, EGUs may be expected to behave 

similarly in the future as they did in the past. Several common features of these forecast tools are 

that they (a) generally build on historical generation and emissions output from individual EGUs, 

(b) are insensitive to fuel and emission price forecasts, (c) do not solve for optimal economic 

EGU dispatch or EGU capacity expansion, and (d) do not capture transmission constraints or 

limits. The algorithms in these forecast tools generally divide the contiguous US14 into regional 

power markets, following ISO boundaries, eGRID boundaries, NERC regional boundaries, or 

similar designations. These algorithms generally seek to examine how operation and emissions 

from individual EGUs could be expected to change with changes in environmental regulations or 

installed pollution control retrofits and additional or reduced hourly electricity demand. Some 

algorithms use the observed historical behavior of individual EGUs to approximate future 

behavior, while others add additional steps of differentiating EGUs into fuel groups and unit 

                                                            

10 See EPA’s AVERT (Avoided Emissions and Generation Tool), available at 

http://epa.gov/statelocalclimate/resources/avert/index.html. 11 See Flexibility Weighted Hourly Average Emissions Rate (FW-HAER) tool, available at 

http://nepis.epa.gov/Adobe/PDF/P1002UQO.pdf; Time Matched Marginal (TMM) emissions tool, a proprietary 

algorithm used to estimate avoided emissions; D. Jacobson and C. High, “U.S. Policy Action Necessary to Ensure 

Accurate Assessment of the Air Emission Reduction Benefits of Increased Use of Energy Efficiency and Renewable 

Energy Technologies,” Journal of Energy & Environmental Law (2010). 12 Hourly emissions and generation data for all fossil fuel-fired EGUs greater than 25 MW are available from EPA’s 

Clean Air Markets Division (CAMD) through its Air Markets Program Data (AMPD). 

13 For example, EIA’s AEO 2013 electricity load growth rates and EGU production trends are available at 

http://www.eia.gov/forecasts/aeo/assumptions/pdf/0554(2013).pdf.

14 Hawaii and Alaska do not report hourly generation and emission data from individual EGUs to EPA, and are 

therefore generally excluded from these forecast tools. 
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types, with implicit differentiation of economic outcomes for these different groups. Some of 

these algorithms may contain subroutines to add new generating capacity automatically to meet 

load requirements. 

III. Translating from a Rate-Based Emission Performance Goal to a Mass-Based Emission 

 Performance Goal 

As discussed in the preamble, in section VIII.C.2, the EPA is proposing that the projected 

CO2 emission performance by affected EGUs (taking into account the qualifying impacts of plan 

measures that avoid CO2 emissions from affected EGUs) must be equivalent to, or better than, 

the required CO2 emission performance level in the state plan. This required level of emission 

performance in the state plan will reflect the rate-based goal for the state in the emission 

guidelines, or a translated mass-based goal if the state chooses to use this approach. State plans 

that are projected to achieve an average CO2 emission rate (CO2 lb/MWh) or tonnage CO2

emission outcome by all affected EGUs equal to, or lower than, the required level of CO2

emission performance in the state plan would be considered to meet this plan approvability 

criterion. States may demonstrate such emission performance by affected EGUs either by state or 

jointly on a multi-state basis. 

This section of the TSD discusses the concept of a mass-based CO2 emission 

performance goal, considerations for constructing projection scenarios for such a translation, 

methods for conducting such a translation, and key input assumptions for such a translation. 

A. Concept 

A mass-based CO2 emission performance goal translates the application of a rate-based 

emission performance goal to an expected CO2 emissions outcome in tons during a plan 

performance period. A mass-based CO2 emission performance goal is calculated by projecting 

the tons of CO2 that would be emitted during a state plan performance period (e.g., 2020-2029, 

2030-2032) by affected EGUs in the state if they hypothetically were meeting the state ratebased CO2 emission performance goal for affected EGUs established in the emission guidelines. 

The translation of a rate-based goal (expressed in lb CO2/MWh of useful energy output from 

affected EGUs) to tons (expressed as total tons of CO2 emissions from affected EGUs over a 

Comment [A1]: Is it possible to provide an 

example or equation as to how this is done? Would 

be helpful in clarifying the approach. 

EPA response: 

Translating a rate-based goal to a mass-based 

goal is based on an analytical process, as 

explained in this section, rather than a specific 

equation. We have, however, added a practical 

example later in this section in response to this 

comment. 
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specified time period) is based on a projection of affected EGU utilization and dispatch mix. 

Importantly, this projection is conducted assuming the absence of qualifying state programs and 

measures contained in a state plan, and applying the rate-based goal in the emission guidelines as 

a proxy emission limit for affected EGUs. State programs and measures in the state plan are not 

included in this projection, because the purpose of this analysis is to determine the tonnage CO2

emissions that corresponds to the state-specific rate-based CO2 emission performance goal for 

affected EGUs in the emission guidelines, without the eligible state programs and measures that 

are included in the state plan. 

Translation of a rate goal to a mass goal seeks to answer the question, “What would 

happen to EGU CO2 emissions if one applied the rate goals in the emissions guidelines instead of 

the measures in the state plan?” Because the emission guidelines do not specify the emissions 

reduction measures that a state must use in its plan, only the level of emission performance that 

must be achieved through a plan, there is no specified “policy” or set of emission reduction 

measures in the emission guidelines to apply when conducting this projection. However, a proxy 

policy can be applied to project the emission performance in tons that would be achieved if the 

state plan were to include suitable measures to achieve the required level of emission 

performance established through the state rate-based CO2 emission performance goals in the 

emission guidelines. This proxy involves applying the rate-based emission goal for a state as a 

rate-based average CO2 emission limit for affected EGUs in a state and then projecting the CO2

emissions that would occur if such a limit were applied. 

When demonstrating projected emission performance under a mass-based plan, a state 

would project the CO2 emissions outcome that would be achieved under the suite of 

requirements, programs, and measures in its plan. The state plan requirements, programs, and 

measures substitute for EPA’s application of the best system, which is represented by the ratebased goal in the emission guidelines. If the CO2 emissions outcome, in total tons of CO2

emissions over each plan performance period, is equal to or less than what would be emitted by 

affected EGUs through the application of the rate-based goal in the emission guidelines (i.e., 

equal to or less than the translated mass-based goal), the plan would be deemed to achieve the 

required emission performance criterion. 
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B. Projection Scenarios 

As described above, the projection scenario for translating from a rate-based CO2

emission performance goal to a mass-based CO2 emission performance goal does not include 

requirements, programs, and measures included in a state plan. Construction of this scenario 

must therefore carefully consider treatment of eligible “on-the-books” state requirements, 

programs and measures included in the state plan.15

Projection scenarios for translating from a rate-based CO2 emission performance goal to a 

mass-based CO2 emission performance goal, and comparing projected emission performance 

under a state plan to this goal, include the following: 

1. A Reference Case Scenario. This scenario projects the average CO2 emission rate and 

CO2 emissions from affected EGUs in the absence of the EPA emission guidelines or any 

enforceable requirements, programs, and measures included in a state plan. This scenario 

does, however, include all current on-the-books state requirements, programs, and 

measures that are not included as enforceable measures in a state plan. These measures 

are complementary to the state plan. Because these measures will influence EGU CO2

emissions, they should, however, be included in the reference case projection scenario. 

2. A Mass-Based CO2 Emission Goal Policy Scenario. This projection scenario is used to 

translate a rate-based goal to a mass-based goal. The scenario applies a rate-based CO2

emission limit to affected EGUs that is equivalent to the state-specific rate-based lb 

CO2/MWh emission goal in the EPA emission guidelines.16 The CO2 emissions from 

affected EGUs projected during the specified plan performance period in this scenario 

represents the translated mass-based CO2 emission performance goal for the state plan. 

                                                            

15 An “existing measure” refers to a state or utility requirement, program, or measure that is currently “on the 

books.” For the purposes of this discussion, this may include a legal requirement that includes current and future 

obligations or current programs and measures that are in place and are anticipated to be continued or expanded in the 

future in accordance with established plans. Existing measures may have past, current, and future impacts on EGU 

CO2 emissions. 16 The proxy emission limit applied includes crediting for end-use energy efficiency, renewable energy, and nuclear 

generation included in building blocks three and four, which were used by EPA when calculating the state-specific 

CO2 emission performance goals for affected EGUs in the proposed emission guidelines. In essence, these measures 

can be used by affected EGUs as “compliance” flexibility mechanisms when “complying” with the proxy emission 

rate limit in this projection scenario. As a result, the proxy rate-based emission limit is able to capture all four 

building blocks included by EPA when calculating these rate-based CO2 emission performance goals. 

Comment [A2]: Would this be an average 

emission rate based on total MWh (entire generation) 

or only fossil fleet generation? Would be helpful to 

clarify. 

EPA response: 

As stated here, this is the average CO2 emission 

rate and CO2 mass emissions for affected EGUs. 

As used in the preamble and TSDs, “affected 

EGUs” refers to fossil fuel-fired EGUs subject to 

a state plan under CAA section 111(d). 

Comment [A3]: This seems to imply that the 

state-specific rate would be multiplied by the state’s 

generation (including existing fossil fleet, 8% 

existing nuclear + under construction nuclear, 

existing renewables, and MWh energy savings) to 

reach a mass-based target above the reference 

scenario? Would be helpful to clarify the approach 

using an example/equation. 

EPA response: 

As stated here, this projection scenario applies a 

proxy rate-based emission limit to affected EGUs 

in a state that equals the state-specific CO2 goal 

for affected EGUs in that state. The scenario then 

projects the CO2 mass emissions from those 

affected EGUs. The proxy emission rate limit that 

is applied includes the ability to credit for end-use 

energy efficiency, renewable energy, and nuclear 

generation as a compliance flexibility mechanism. 

In this way, the scenario includes end-use energy 

efficiency, renewable energy, and nuclear energy 

generation consistent with measures in the 

proposed BSER building blocks three and four. 

We have added clarifying text to the footnote in 

response to this comment.
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To construct this scenario, this emission limit is added to the underlying reference case 

scenario described above. 

3. A State Plan Policy Scenario. This projection scenario includes the enforceable 

requirements, programs, and measures included in the state plan, and is used to project 

CO2 emission performance by affected EGUs under the state plan. To construct this 

scenario, the enforceable requirements, programs, and measures included in the state plan 

are added to the underlying reference case scenario described above. 

An applied example is provided below in Box 1. 

Box 1. Example Mass-Based Goal Translation and Projection of Plan Performance 

Example State Rate-Based CO2 Emission Performance Goal Assumptions: 

 State interim rate-based CO2 emission performance goal for affected EGUs: 1,100 lb CO2/MWh 

 State final rate-based CO2 emission performance goal for affected EGUs: 1,000 lb CO2/MWh 

Reference Case Scenario: 

 Under this scenario, the projected average CO2 emission rate for affected EGUs is 1,500 lb CO2/MWh during 

the 2020-2029 interim performance period and 1,500 lb CO2/MWh in 2030. 

Mass-Based CO2 Emission Goal Policy Scenario: 

 A proxy emission rate limit is applied to affected EGUs (1,100 lb CO2/MWh on average during 2020-2029; 

1,000 lb CO2/MWh in 2030 and subsequent years during the projection period). 

 Under this scenario, the projected CO2 mass emissions from affected EGUs during the 2020-2029 interim 

performance period are 100,000,000 tons of CO2 (an average of 10,000,000 tons per year during this period) 

and 9,000,000 tons of CO2 in 2030. 

 The mass-based interim emission performance goal for affected EGUs is 100,000,000 tons of cumulative 

CO2 emissions during the 2020-2029 interim plan performance period. 

 The mass-based final emission performance goal for affected EGUs is 9,000,000 tons of CO2 per year, in 

2030 and subsequent years. During plan implementation, this final goal can be met on a three-year rolling 

average basis, beginning with the period 2030-2032, as discussed in the preamble at section VIII.B.2.c. 

State Plan Policy Scenario: 

 This scenario includes the suite of requirements, programs, and measures included in the state plan. 

 Under this scenario, the projected CO2 mass emissions from affected EGUs during the 2020-2029 interim 

plan performance period are 98,000,000 tons of CO2 and 8,800,000 tons of CO2 in 2030. 

 Based on this analysis, the state plan is projected to achieve both the translated interim and final massbased CO2 emission performance goals for affected EGUs. 

1. Constructing the Reference Case 

A key consideration for translating from a rate-based CO2 emission performance goal to a 

mass-based CO2 emission performance goal is proper construction of a reference case scenario. 
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This includes assumptions for key variables that may drive EGU CO2 emission projections,17

such as: 

 Electricity load growth projections (energy and peak demand) 

 Fuel supply, delivery, and pricing assumptions 

 Cost and performance of electric generating technologies 

 EGU firm builds and retirements (e.g., those scheduled with a regional transmission 

organization or independent system operator (RTO/ISO))

18

 Transmission capability and ISO/RTO transmission expansion plans 

 Applicable federal regulations (other than the EPA emission guidelines) 

 Applicable state regulations and programs (other than those that are included in the state 

plan) 

It may be necessary in many instances to include assumptions about other state programs 

implemented by neighboring states in the same region. This would be especially relevant for 

states that are located within the same electric power pool (or adjoining power pools) that are 

administered by a common RTO/ISO. 

 

                                                            

17 Process considerations related to constructing reference case scenarios are addressed in section VI of this TSD. 18 ISOs and RTOs are independent organizations that administer a regional electric power pool (EGU dispatch and 

electricity transmission systems), and often also administer related wholesale electricity markets for electric energy 

and capacity.
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2. Treatment of Existing State Regulations and Programs in the Reference Case 

A key aspect of the reference case is proper treatment of existing state regulations, 

programs, and measures. As discussed previously, existing state regulations, programs, and 

measures that are not enforceable measures in a state plan are complementary to the state plan – 

as a result, they should be included in the reference case scenario rather than the state plan 

scenario. These state regulations, programs, and measures should not, however, be ignored 

altogether, as they influence EGU CO2 emissions. Instead, these state actions are occurring in the 

background – either when a proxy rate-based CO2 emission limit is applied in the Mass-Based 

CO2 Emission Goal Policy Scenario; or when the state plan requirements, programs, and 

measures are applied in the State Plan Policy Scenario. As a result, these state actions are 

properly addressed in the reference case scenario. 

In effect, when evaluating whether a state plan will meet the mass-based CO2 emission 

performance goal, the two policy scenarios described above, each of which involve adding a 

different CO2 emission reduction policy on top of the reference case scenario, are compared to 

one another. If the State Plan Policy Scenario achieves projected tonnage CO2 emissions equal 

to or lower than the projected CO2 emissions in the Mass-Based CO2 Emission Goal Policy 

Scenario, then the state plan can be deemed to meet the translated mass-based CO2 emission 

performance goal. 

As discussed in the preamble, the EPA is proposing that a state may apply toward its 

required emission performance level the emission reductions that are achieved by existing state 

requirements, programs, and measures during the plan period, due to actions19 taken after the 

date of the proposal of the emission guidelines.20, 21 In practice, this means that emission 

                                                            

19 An “action” as used here refers to an action taken pursuant to a state requirement, program, or measure. For 

example, the installation of an end-use energy efficiency measure, such as an energy-efficient refrigerator installed 

through a utility energy efficiency program to meet the utility’s obligations under a state energy efficiency resource 

standard (EERS), would constitute an “action” taken pursuant to an existing state requirement. 20 The preamble also takes comment on alternative dates for eligible actions, including: the start date of the plan 

period, the date of promulgation of emission guidelines, the end date of the base period for the EPA’s BSER-based 

goals analysis (e.g., beginning of 2013 for blocks 1-3 and beginning of 2017 for block 4, end-use energy efficiency), 

the end of 2005, or another date. 

21 As discussed in the preamble, at section VIII.F.2.b, the EPA is also proposing that this proposed limitation would 

not apply to existing renewable energy requirements, programs, and measures because existing renewable energy 

Comment [A4]: EPA: Edits in this section are 

conforming edits given changes made to the 

preamble at section VIII.F.2.b. 
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reductions that occur in 2020 and later due to actions taken pursuant to an existing state 

requirement, program, or measure could be applied toward meeting the required level of 

emission performance in a state plan if these actions occur after proposal of the emission 

guidelines (e.g., as of June 2014 and in subsequent years). For example, emission reductions 

during the plan period that result from end-use energy efficiency or renewable energy

technologies and measures installed beginning in June 2014 could be applied toward meeting the 

required level of emission performance during the plan period. These investments might be made 

to meet requirements under an existing end-use energy efficiency resource standard (EERS) or 

renewable portfolio standard (RPS). 

Under a rate-based plan, the approach described above would address the eligibility date 

for qualifying renewable energy and demand-side energy efficiency measures that, through 

MWh generation or MWh savings, avoid CO2 emissions from affected EGUs. Measures installed 

after the eligibility date could generate MWh generation or MWh savings during the plan period, 

and related avoided CO2 emissions, that could be applied toward meeting a required rate-based 

emission performance level. Under the proposed option, the eligibility date would be the date of 

proposal of the emission guidelines.22 For example, under this approach, new renewable energy 

generating capacity installed in June 2014 or later to meet an existing, on-the-books RPS would 

be a qualifying measure in a state plan. However, only MWh generation beginning in 2020 and 

related avoided CO2 emissions could be applied toward meeting a required emission rate 

performance level.

                                                                                                                                                                                               

generation prior to the date of proposal of the emission guidelines was factored into the state-specific CO2 goals as 

part of BSER building block three.

22 Although such a limitation is not proposed for RE measures, we also describe here how such a limitation could be 

applied to RE measures under such an approach. For example, under this approach in the context of a rate-based 

plan, new renewable energy generating capacity installed in June 2014 or later to meet an existing, on-the-books 

RPS would be a qualifying measure in a state plan. However, only MWh generation beginning in 2020 and related 

avoided CO2 emissions could be applied toward meeting a required emission rate performance level in a state plan.

Similarly, under this approach in the context of a mass-based plan, the reference case might include the required 

MWh of renewable energy generation necessary to meet a state RPS as of the date of proposal of the emission 

guidelines (e.g., as of June 2014). Renewable energy generation that is used to meet a state RPS, but that is 

incremental to the amount of generation used to meet the existing RPS obligation included in the reference case as 

of June 2014, could be used toward demonstration of CO2 emission performance under a state plan. This renewable 

energy generation would be incremental to the renewable energy generation already assumed in the reference case 

scenario. 
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Under a mass-based plan, the approach described above would be applied when 

establishing a reference case scenario projection that is used to translate a rate-based goal to a 

mass-based goal. For example, qualifying renewable energy and the assumed amount of energy 

savings from demand-side energy efficiency measures installed subsequent to the eligibility date 

that are used to meet an existing EERS after a respective eligibility date would not be included in 

the electricity load forecast used in the reference case scenario. Energy efficiency measures 

installed prior to the eligibility date to meet an existing EERS would be factored into the 

electricity load forecast used in the reference case scenario. This could be achieved by not 

including an EERS or RPS requirement in a reference case projection, beginning at a specified 

date. The reference case would, however, include the level of such requirements up to the date at 

which such measures would become eligible for inclusion in a state plan. This treatment would 

represent in the reference case past actions taken under existing state programs and measures that 

are not eligible for inclusion in a state plan. 

For example, the reference case might include the required MWh of renewable energy 

generation necessary to meet a state RPS as of the date of proposal of the emission guidelines 

(e.g., as of June 2014). Renewable energy generation that is used to meet a state RPS, but that is 

incremental to the amount of generation used to meet the existing RPS obligation included in the 

reference case as of June 2014, could be used toward demonstration of CO2 emission 

performance under a state plan. This renewable energy generation would be incremental to the 

renewable energy generation already assumed in the reference case scenario. 

IV. Projecting CO2 Emission Performance under a State Plan 

 As discussed in the preamble, in section VIII.D.4, one of the required components of a 

state plan is a projection that a plan will achieve the required level of CO2 emission performance 

by affected EGUs that is specified in the plan. (This identified level of performance must be 

consistent with either the state-specific rate-based CO2 emission performance goals for affected 

EGUs identified in the emission guidelines, or an equivalent translated mass-based CO2 emission 

performance goal.) 
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This emission projection is based on a scenario that includes the suite of requirements, 

programs, and measures in the state plan – the State Plan Policy Scenario described above. 

Depending on the plan approach, construction of this state plan scenario may be straightforward. 

For example, if the state plan consists solely of an emission limit, the state plan scenario would 

apply this emission limit to the underlying reference case scenario. Other types of state plan 

approaches are more involved, in particular when the use of end-use energy efficiency and 

renewable energy regulations, programs, or measures are included in a state plan. This could 

include a state plan that applies a rate-based CO2 emission limit that provides credit for end-use 

energy efficiency and renewable energy measures that avoided CO2 emissions, or the inclusion 

of such measures through a portfolio approach. These types of plans will require input 

assumptions for the amount of end-use energy efficiency and renewable energy resource (in 

MWh over the plan period) that will be realized through implementation of the plan. Utilitydriven portfolio approaches may also include a diverse set of actions taken directly at affected 

EGUs that will need to be properly represented in the state plan scenario. 

Considerations for developing estimates of energy savings and energy generation that 

will be achieved through end-use energy efficiency and renewable energy regulations, programs, 

and measures included in a state plan are discussed below in section VI. 

V. Applied Considerations for Different Types of State Plans23

There are a number of considerations for properly representing different state plan 

approaches in CO2 emission projection scenarios. This includes both accurate representation of 

the attributes of state regulations, programs, and measures in the scenario, as well as the methods 

and data sources used to derive certain input assumptions that are used in CO2 emission 

projections. This includes: 

 Estimating the future effects of end-use energy efficiency and renewable energy 

requirements, programs, and measures 

 Properly characterizing flexibilities included in emission limits, such as emission budget 

trading programs 

                                                            

23 Different potential state plan approaches are described in detail in the accompanying State Plan Considerations 

TSD. 
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 Properly addressing characteristics of multi-state regulations and programs 

A. End-Use Energy Efficiency and Renewable Energy 

Evaluating the impact of a state end-use energy efficiency requirement or program as part 

of a projection of CO2 emissions from affected EGUs under a state plan involves projections of 

the impact of the requirement or program on energy and capacity savings, and the impact of this 

reduction in electricity load and peak demand on EGU dispatch and CO2 emissions from affected 

EGUs.24 This involves projections of the level of current and future energy and capacity savings 

achieved through program investment or activities in each year, the distribution of those savings 

on a daily and seasonal basis (e.g., the load shape of the energy and capacity savings), and the 

effective useful life of those energy and capacity savings (e.g., the life of installed measures 

representing the persistence of energy savings). Such projections may also need to address net 

program energy savings after accounting for potential “free-ridership,” which involves energy 

savings that were likely to have occurred in the absence of program incentives, and “spillover,” 

which involves broader market transformation impacts that result from the program. These 

energy efficiency program projections would then be used to adjust electricity load forecasts, as 

necessary, for modeling runs using an electricity sector dispatch and capacity expansion planning 

model. This modeling would evaluate the impact of the energy and capacity savings on the 

dispatch of EGUs, construction and retirement of EGUs, and related CO2 emissions from 

affected EGUs. 

Similar considerations apply for projecting the impact of renewable energy requirements 

and programs included in a state plan on CO2 emissions from affected EGUs. The renewable 

energy generation and generating capacity in a state or region that results from renewable energy 

requirements, programs, and measures in a state plan will affect EGU dispatch and CO2

emissions from affected EGUs. This section discusses these considerations in more depth, 

including approaches and data sources for estimating the impact of end-use energy efficiency 

                                                            

24 The evaluation discussed in this section is relevant for state plans that implement a rate-based CO2 emission limit 

applicable to affected EGUs that provides for adjustment or crediting of the CO2 emission rate of an affected EGU 

based on the effects of end-use energy efficiency and renewable energy. It is also relevant for state plans that take 

either a rate-based or mass-based portfolio approach. Descriptions of these plan approaches are provided in the 

accompanying State Plan Considerations TSD. 
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and renewable energy requirements and programs as part of state plan projections of CO2

emission performance by affected EGUs. 

1. Potential Approaches for Estimating the Future Effects of State Energy 

 Efficiency Requirements, Programs, and Measures 

There are two basic approaches for developing a ten-year or longer forecast of end-use 

energy efficiency resources that will result from state energy efficiency requirements and 

programs: a bottom-up or a top-down approach. These forecasts would contain data that could be 

used to develop inputs to EGU CO2 emission projections in a state plan, using modeling or an 

EGU growth tool, both of which are summarized above. 

A bottom-up approach is based on annual evaluated and reported data from state and 

utility energy efficiency programs, and program-level utility compliance reports under state 

energy efficiency requirements, such as an end-use energy efficiency resource standard (EERS). 

One example of a bottom-up approach is the state-by-state end-use energy efficiency projection 

developed by ISO New England for use in its system planning process.25 In simple terms, ISO 

New England develops production cost curves for energy efficiency measures based on historical 

performance of energy efficiency as documented in evaluation, measurement, and verification 

(EM&V) studies. ISO New England then applies those production cost curves (with adjustments 

for inflation and other factors) to future state or utility energy efficiency program budgets for 

each of the six New England states over a ten-year horizon. The ISO New England energy 

efficiency forecast includes both reductions in annual energy use (MWh) and peak demand 

(MW). ISO New England has produced an energy efficiency forecast for the last three Regional 

System Plans (2011, 2012, and 2013), and the near-term estimates in these forecasts have been 

validated by the actual quantity of end-use energy efficiency that has qualified for the annual 

capacity auctions conducted by ISO New England as part of its forward capacity market. Other 

                                                            

25 Each year, ISO New England produces a Regional System Plan (RSP) that provides a comprehensive assessment 

of the New England bulk power system that is used as input data for evaluating the future reliability of the grid. One 

component of the annual RSP is a future estimate of peak loads and annual energy use that is modified (reduced) by 

the energy efficiency forecast. Information about the ISO New England energy efficiency forecasting method and 

assumptions is included in, ISO-New England (ISO-NE) Energy-Efficiency Forecast Working Group, Draft Final 

Energy-Efficiency Forecast 2018-2023 (March 2014), available at: http://www.isone.com/committees/comm_wkgrps/othr/enrgy_effncy_frcst/ 
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approaches to projecting energy efficiency impacts, such as those used by New York ISO 

(NYISO), PJM Interconnection, and the California Public Utilities Commission (CPUC), 

estimate the effects of energy efficiency requirements and programs over a shorter time frame. In 

these cases, energy efficiency projections are typically consistent with the periods for which 

energy efficiency programs or program budgets have been approved by state PUCs, or consistent 

with the period for which energy and demand savings are acquired in ISO and RTO forward 

capacity markets. 

Top-down projection methods analyze aggregate energy use changes resulting from enduse energy efficiency requirements or programs, often for a geographic region, entire industry, or 

economic sector. A top-down approach is determined on the basis of state energy efficiency 

policy requirements, and assumes that utilities and other parties implement efficiency programs 

necessary to achieve required energy and demand savings. In this way, a top-down approach is 

typically based on an estimated annual percentage reduction in energy use that results from state 

energy efficiency requirements and programs. One example of a top-down approach is the EPA’s 

state-level projection of the impacts of “on-the-books”26 energy requirements and programs. 

EPA’s approach uses these state requirements and program commitments as the basis for 

adjusting national electricity load forecasts for use in electricity system modeling.27 In this case, 

EIA Annual Energy Outlook (AEO) projections of future electricity use (MWh) and peak loads 

(MW) are modified to account for the reduction in energy sales due to state and utility programs 

and associated energy savings requirements.28 These energy savings requirements are examined 

on a state-by-state basis and adjusted, as necessary, to reflect known increases or decreases in 

energy efficiency program budgets and other factors likely to affect whether state energy 

efficiency requirements are achieved. In addition to EPA’s projections, a top-down approach is 

                                                            

26 As used here, “on the books” refers to state energy-efficiency requirements and programs currently in place and 

specified in state legislation or administrative order. These requirements include energy efficiency resource 

standards (EERS) and dedicated sources of energy efficiency program funding.   27 While EPA’s approach utilizes AEO forecasts of electricity use and peak loads, other credible and long-term 

forecasts could be used in a similar manner. U.S. EPA, Background and Draft Methodology for Estimating Energy 

Impacts of EE/RE Policies (March 2014), available at: http://epa.gov/statelocalclimate/state/statepolicies.html. 28 For example, annual incremental and cumulative energy savings requirements for utilities included in a state 

EERS. 
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used to develop EE forecasts by LBNL, NREL29, and some state energy offices and 

commissions. 

The LBNL30 and the EPA forecast models are useful for comparison with the bottom-up 

ISO/RTO approaches. Where discrepancies between the national top-down analyses of end-use 

energy efficiency savings and the more granular bottom-up energy efficiency forecasts 

conducted by ISOs and RTOs exist, review of key assumptions regarding state policies for the 

different approaches may be warranted. Ideally, using the two approaches as complementary 

analyses may provide for helpful comparison and reconciliation of energy savings estimates used 

in state plan projections of EGU CO2 emission performance. 

The electricity load forecasts developed by ISOs and RTOs are important because they 

are often the base case from which adjusted load forecasts that incorporate projected energy 

efficiency program savings are developed. For reasons described above, ISOs and RTOs are 

beginning to include some adjustments to their base case load forecasts to account for energy 

efficiency program impacts. In most cases, these efforts are focused on including the future 

anticipated impacts of existing “on the books” energy efficiency requirements and programs and 

adjusting load forecasts accordingly. However, in some cases there is also an effort to anticipate 

new incremental energy efficiency investments in the future that are not tied to existing energy 

efficiency requirements and programs.31 When conducting a projection of EGU CO2 emission 

performance as part of a state plan that includes energy efficiency requirements and programs, it 

will be important to know how the base electricity load forecast for the state or region was 

developed, whether it already includes the effect of state and utility energy efficiency 

requirements and programs, and whether and how it should be adjusted to account for the future 

                                                            

29 [Forthcoming April 2014 – Citation TBD] 

30 Lawrence Berkeley National Laboratory (LBNL), The Future of Utility Customer-Funded Energy Efficiency 

Programs in the United States: Projected Spending and Savings to 2015 (January 2013), available at: 

http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programs-united-states-projectedspend. 

31 In New England states that have policies to pursue “all cost-effective energy efficiency,” the ISO-NE energy 

efficiency forecast assumes that energy efficiency program investments will continue beyond the approved program 

funding period (typically documented three-year plans), either at a constant level or some discounted level based on 

assumptions about an increase in the cost of saved energy over time. 
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effects of existing on-the-books and incremental (i.e., new) energy efficiency requirements and 

programs that are included in the state plan. 

1.1 Potential Uses of Energy Efficiency Projections for State Plan Emission 

 Projections 

 Whether developed through a bottom-up or top-down approach, projections of annual 

energy and peak load reductions that result from state energy efficiency requirements and 

programs included in a state plan are necessary inputs to projections of EGU CO2 emission 

performance under the state plan. Energy and peak demand savings estimates included in energy 

efficiency projections are used as a decrement to future electricity load forecasts that are used in 

conducting EGU CO2 emission performance projections. The energy efficiency projection is 

used as an input, or modifier, to various models or other tools used to project the impact of 

energy efficiency resources on state or regional CO2 emissions. For example, capacity expansion 

and dispatch planning models are capable of incorporating energy efficiency program impact 

data, such as annual energy and peak load reductions, when projecting EGU dispatch and 

capacity additions, and the avoided CO2 emissions that are projected to result from these 

requirements and programs. 

1.2 Data Issues and Considerations for Using and Developing Energy Efficiency 

 Projections 

 Depending on what data sources and approaches are used to project the future energy and 

demand savings from energy efficiency requirements and programs, there are certain strengths 

and limitations regarding the approach and methodology used. Generally, these strengths and 

limitations relate to data availability and energy efficiency forecast assumptions, such as: 

 Energy efficiency requirements and programs included 

 Jurisdictions covered 

 Planning timeframe 

 Embedded energy and demand savings from energy efficiency requirements and 

programs in base electricity load forecasts 

 Cost of saved energy for different energy efficiency measures 
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 Energy efficiency measure life and measure energy performance decay 

 Time of energy and demand savings for energy efficiency measures (availability of 

hourly data necessary to derive a load reduction profile for an energy efficiency measure) 

Bottom-up versus top-down forecasting approaches have different strengths and 

limitations related to the data and assumptions described above. In the case of bottom-up 

approaches, state energy efficiency requirements and programs can be fully addressed in the 

analysis. In top-down approaches, such as EIA AEO analysis, greater focus is placed on federal 

policies (e.g., codes and standards) but less data is incorporated to reflect state energy efficiency 

requirements and programs. Bottom-up approaches typically include state-specific data that are 

rolled up to a regional level, while top-down national electricity load forecasts do not have a 

comparable level of granular state data detail. ISO New England provides energy and demand 

savings forecasts for state energy efficiency programs (average annual and peak demand 

coincident with the ISO defined peak load periods). The ISO NE energy efficiency forecast is 

informed by data collected at the energy efficiency measure, individual program, program 

portfolio, sector, and state levels. 

An important consideration with both top-down and bottom-up energy efficiency 

forecasts is the extent to which energy efficiency program effects have become embedded into 

the long-range economic forecasts used as an input in developing electricity load forecasts. In 

some cases the economic forecast includes the effect of historical energy efficiency policies, 

either implicitly or explicitly, while in other cases the economic forecast may also anticipate 

some limited quantity of future energy efficiency investment that is incremental to the 

anticipated effect of existing energy efficiency requirements and programs. An additional 

consideration is whether and how building energy codes and appliance standards (both current 

and future) are accounted for in the base electricity load forecast. 

 

Another consideration, for all energy efficiency projections, is energy efficiency program 

spending. While policies “on the books” energy efficiency requirements and programs may 

include multi-year approved energy efficiency program budgets, actual program expenditures 

may differ. This has been the case in some of the New England states, for example, where ISO 
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New England has made adjustments to “discount” the spending levels to reflect actual spending 

trends, and not used the state approved budgets identified through stakeholder discussions. 

Additionally, assumptions are made about how the overall cost of saved energy for a portfolio of 

energy efficiency programs will change over time. It is generally assumed in most energy 

efficiency projections that the cost of installing energy efficiency measures will become more 

expensive into the future as state programs move beyond “low-hanging fruit” and increasingly 

focus on achieving deeper and broader energy savings through whole-building, multi-fuel 

programs addressing new buildings and building retrofits).32 

While many existing sources of energy efficiency projection data do not account for 

hourly savings, it is becoming increasingly possible for states to examine and incorporate 

information about the time -dimension of energy efficiency impacts. Smart meters combined 

with data-sharing and analysis technologies are making it easier for utilities and other energy 

efficiency program administrators to more accurately determine how the total energy savings 

achieved in a calendar year are spread out across the hours of that year. These data can be 

applied to energy efficiency projections to provide a better estimate of the timing of energy and 

demand savings. Such time-differentiated data is valuable for identifying the marginal EGU or 

cohort of marginal EGUs that are affected by energy efficiency requirements and programs, 

which can be used to provide more refined estimates of avoided CO2 emissions due to changes in 

EGU dispatch and the addition of new generating capacity. 

2. Potential Approaches for Estimating the Future Effects of State Renewable 

 Energy Requirements, Programs, and Measures 

This section describes data and analytic considerations for the representation of 

renewable energy requirements and programs included in state plans, when conducting 

projections of EGU CO2 emission performance that will be achieved under a plan. A range of 

data and analytic considerations are examined that may be relevant when using different 

                                                            

32 Evidence to date is mixed as to a relationship of larger scale energy efficiency programs and broader energy 

efficiency measure portfolios, and the associated deeper levels of energy savings, to increasing cost of saved energy. 

Economies of scale, and expertise gained by program administrators from managing larger programs for multiple 

years, can lead to cost reductions. However, at some point, as program administrators move away from initial lowcost strategies and end-use energy efficiency measures, it is assumed that the cost of saved energy will increase. 
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modeling approaches, including use of capacity expansion and dispatch planning models, and 

less sophisticated statistical or top-down projection approaches. Examples of specific 

considerations are described for three renewable energy policies that may be used as enforceable 

measures in a state plan— renewable portfolio standards (RPS), feed-in tariffs (FIT), and 

performance-based tax incentives. 

2.1 Renewable Portfolio Standards 

RPS requirements are typically set at the state level with an increasing percentage of total 

retail sales over a set schedule. The RPS requirement defines the eligible resource types (e.g., 

wind, solar, etc.) and in some cases, may specify resource-specific requirements, such as a 

percentage of the overall RPS target that is set-aside for a specific resource. It also defines the 

allowable geographic boundary for obtaining renewable energy or RECs. With this context, the 

following issues may be important to consider when projecting the impact of state RPS on EGU 

CO2 emissions. 

2.1.1 RPS as an Input to Electricity Sector Modeling 

Electricity sector modeling includes the use of dispatch simulation models and capacity 

expansion and dispatch planning models that simulate the operation of individual EGUs (or 

aggregations of EGUs) onin the electric system over time based on a detailed characterization of 

those EGUs, engineering and market operating constraints, other market factors (e.g., fuel prices, 

transmission constraints), emission constraints, and the requirement to meet a certain level 

energy and peak demand. These models may be based on a small control area, but more likely 

are regional or national in scope. Some, such as capacity expansion and dispatch planning 

models, simulate EGU dispatch and also consider the impact of long-term generating capacity 

investment decisions when optimizing system operation and buildout over a long-term planning 

horizon. These are typically optimization frameworks that have as their objective meeting 

electricity demand subject to a broad range of operating, environmental and market constraints, 

including meeting RPS requirements. 
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When using these types of analytic tools to project EGU CO2 emissions, a number of 

analytic and data considerations are relevant for analysis of RPS impacts on EGU CO2

emissions. These analytic and data considerations are summarized below. 

Translation of RPS Requirements to Renewable Energy Generation Requirements

Most RPS are retail sales-based requirements, while most electricity sector analytic tools 

are generation-based models. When projecting CO2 emission performance by affected EGUs 

using a generation-based analysis tool, it is necessary to translate the RPS sales-based 

requirement to a generation requirement using a transmission and distribution loss rate to “gross 

up” the sales estimate. For example, if an RPS requires 100 MWh of sales be met with eligible 

renewable energy resources, then the required renewable energy generation necessary to meet 

the RPS is 100 MWh / (1 + T&D Lloss rate).33

RPS-Eligible Resources vs. State Plan-Eligible Resources 

Some RPS rules define a broad range of eligible renewable energy generating resources. 

These could include renewable energy EGUs that emit CO2, and EGUs that were constructed and 

began operation recently or many years ago. Some of these specific EGUs may not be eligible 

for use in a plan, in particular units that began operation prior to eligibility dates for actions that 

may be included in a state plan (if such limitations were applied to renewable energy measures in 

state plans).

34 Thus, the RPS requirement would need to be adjusted to reflect the use of only 

those resources eligible for use in state plans. This would be addressed through the treatment of 

existing RPS in the analysis base case, as described above in section III.B.2. 

RPS Vintage Rules

                                                            

33 According to EIA data, nationally, annual electricity transmission and distribution losses are equivalent to about 

seven percent of the electricity that is input to the transmission system in the United States. 34 As discussed in the preamble, and explained in section III.B.2 of this TSD, EPA is proposing that emission 

reductions that occur in 2020 and later due to actions taken pursuant to an existing state requirement, program, or 

measure could be applied toward meeting the required level of emission performance in a state plan if these actions 

occur after proposal of the emission guidelines (e.g., as of June 2014 and in subsequent years). As discussed in the 

preamble, at section VIII.F.2.b, the EPA is also proposing that this proposed limitation would not apply to existing 

renewable energy requirements, programs and measures because existing renewable energy generation prior to the 

date of proposal of the emission guidelines was factored into the state-specific CO2 goals as part of BSER building 

block three.

Comment [A5]: EPA: Edits in this section are 

conforming edits given changes made to the 

preamble at section VIII.F.2.b. 
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Some RPS requirements provide that only renewable energy EGUs that began operation 

after a certain date are eligible. To the extent these renewable energy EGUs are eligible under a 

state RPS but not under the state plan, the analysis should explicitly address these resources. This 

could be done either through an adjustment of the MWh needed to meet the RPS, to account for 

renewable energy resources that are not eligible for use in a state plan, or through the use of two 

modeled RPS requirements for each class of renewable energy resource, with the RPS that 

addresses resources not eligible in a state plan addressed in the modeling reference case. 

In/Out-of-State Contribution

Some RPS allow some of the requirement to be met by out-of-state resources. Some 

modeling frameworks may allow the user to explicitly specify the source region of eligible 

renewables. From a modeling perspective, this means a state would allow a broader supply area 

and model a broader area generally. If the renewable energy resources have different 

characteristics across the relevant geographic area, then data is needed on the characteristics of 

these resources by region (e.g., resource availability, performance, and capital and production 

costs). EIA (for NEMS), EPA (for IPM), and NREL (ReEDS) provide information on the 

characteristics and performance of renewable energy resources by region.35 

Characteristics of Renewable Resources 

In a dispatch modeling framework, better emission projections will result when the 

source of renewable energy is known and the energy profile is modeled. Renewable energy 

resources, such as wind or solar PV, may differ substantially in generation profile, as the supply 

of electricity to the grid from the EGU is based on availability of the renewable energy resource. 

The time during which electricity generation is supplied will influence the marginal fossil fuelfired EGU (or cohort of EGUs) that is displaced by renewable energy generation. Understanding 

the renewable energy resource base, including the energy profile of the relevant renewable 

energy resource types will improve the modeling of EGU dispatch and projections of avoided 

CO2 emissions that result from renewable energy generation. Data may include a detailed 8760 

energy output profile for renewable energy resources, or at a minimum, a seasonal diurnal profile 

                                                            

35 EPA’s Integrated Planning Model generation profiles for wind and solar in the EPA IPM v.5.13 base case are 

available at http://www.epa.gov/powersectormodeling/BaseCasev513.html. 
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for such resources. If no state-specific data is available, generic output profiles for different 

renewable energy resources isare available from NREL.36 

Renewable Energy Resource Availability and Economics

Emission projections will be enhanced by a representation of the new renewable energy 

EGUs that are likely to be brought online as a result of a state RPS, based on an analysis of 

available renewable energy resources and economics. This requires understanding the relative 

economics (e.g., capital costs, fixed operation and maintenance (FOM) costs, variable operation 

and maintenance (VO&M) costs, and fuel costs) and operating conditions for different types of 

renewable energy generators, as well as renewable energy resource availability (e.g., MW 

capacity available at different wind classes, solar insolation levels, and the characteristics of 

biomass). Having this information allows for better projections of the level and quality of 

renewable resources likely to be brought forward as the result of a state RPS. This will result in 

better emissions projections. 

NREL has a suite of tools that can be used to estimate the potential costs of renewable 

energy under various assumptions.37 For example, the CREST model is a cost-of-energy analysis 

tool intended to assist policy makers evaluating the appropriate payment rate for a cost-based 

renewable energy incentive policy. The model aims to determine the cost-of-energy, or minimum 

                                                            

36 NREL’s datasets are intended for use by energy professionals, such as transmission planners, utility planners, 

project developers, and university researchers who perform wind and solar integration studies and need to estimate 

power production from hypothetical wind and solar plants. The Eastern Wind Dataset 

(http://www.nrel.gov/electricity/transmission/eastern_wind_methodology.html) contains modeled wind farm data 

points for the eastern United States for 2004, 2005, and 2006. The Western Wind Dataset 

(http://www.nrel.gov/electricity/transmission/western_wind_methodology.html) includes information about the 

methodology used to develop the dataset, the accuracy of the data, site selection, and power output. The Solar 

Integration Datasets (http://www.nrel.gov/electricity/transmission/solar_integration_methodology.html) are solar 

photovoltaic (PV) power plant five-minute and hourly day-ahead forecasts of generation output for approximately 

6,000 simulated PV plants in the United States for the year 2006. 37 NREL's Energy Analysis web site (http://www.nrel.gov/analysis/models_tools.html) has links to tools such as the 

Cost of Renewable Energy Spreadsheet Tool (CREST) and the System Advisor Model (SAM). CREST 

(https://financere.nrel.gov/finance/content/crest-cost-energy-models) makes performance predictions and cost of 

energy estimates for grid-connected power projects based on system design parameters that the user specifies. The 

SAM cash flow model (http://sam.nrel.gov/) helps assess solar, wind, or geothermal projects, design cost-based 

incentives, and evaluate the impact of tax incentives or other support structures on renewable energy projects. 
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revenue per unit of production, needed for a sample (modeled) renewable energy project to meet 

its investors' assumed minimum required after-tax rate of return. 38 

Retail Rate Impacts

Many state RPSs have caps on the allowable impact on retail rates or on customer bills 

that can result from implementation of an RPS. If these caps are triggered, it will delay further 

increases in the RPS targets, depending on RPS implementation rules. This would lower 

estimates of renewable energy generation needed to satisfy the RPS requirements. 

Market Impacts – Multiple State Players

One important issue is to account for actions of others. Modeling results that consider the 

RPS-related actions of other states could differ substantially from those that consider a state’s 

actions in isolation. This requires having at least a regional, if not national, modeling framework. 

A broader framework captures competition for renewables (and increased prices, greater reliance 

on poorer resources), as well as market-wide impacts on EGU dispatch and capacity expansion. 

2.1.2 Data Needs for Estimating Generation Resulting from State RPS 

Projections of renewable energy generation will require assembling the following data: 

 Resource availability, resource performance, and resource costs will be necessary for the 

more sophisticated modeling approaches described above. 

 Projections of retail sales, by year, adjusted to account for any exclusions from the RPS 

obligation (e.g. small utilities, cooperatives, municipalities, or perhaps certain large 

industrial loads). These covered sales must be calculated and projected using forecasts of 

annual retail demand. 

 RPS requirements by year, so the annual forecast of covered retail sales can be multiplied 

by these percentages. 

                                                            

38 The CREST model is a product of a 2009-2010 partnership between the National Renewable Energy Laboratory 

(NREL), the U.S. Department of Energy (DOE) Solar Energy Technologies Program (SETP), and the National 

Association of Regulatory Utility Commissioners (NARUC). The model was developed by Sustainable Energy 

Advantage (SEA) under the direction of NREL. The report, user manual, and CREST models are free and available 

for download at: https://financere.nrel.gov/finance/content/crest-cost-energy-models. 
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 The extent to which credit multipliers are used for compliance. Credit multipliers reduce 

the number of MWh needed for RPS compliance, and a downward adjustment should be 

made based on the percentage of compliance achieved by credit multipliers. 

 Estimates of transmission and distribution (T&D) losses, so that retail sales can be 

“grossed up” as described above. T&D losses estimates are available at the national level 

and can also be obtained for various grid regions from the appropriate regional grid 

operator. 

 If an electricity sector capacity expansion and dispatch planning model is not used, 

estimates of the geographic origin, by state and grid-region, of generation used to satisfy 

the RPS will be necessary. If generation to satisfy the RPS is expected to come from out 

of state, the basis for these assumptions should be documented. 

 CO2 emission rate associated with renewable energy generation, by resource type (if 

applicable). 

 Generation profile, by renewable energy resource type 

2.2 Feed-in Tariff (FIT) and Other Performance-Based Incentives 

A feed-in-tariff (FIT) is a performance-based incentive (similar to production tax credits) 

that typically guarantees utility customers who own a eligible renewable electricity generation 

system (e.g., roof-top solar PV system) will be paid a specific amount by their utility for the 

electricity the system generates and provides to the grid over a fixed period of time. Such tariffs 

usually offer a fixed payment amount per kWh, but variations may require competitive bids. In 

some cases, FIT payments may be in addition to other incentives (such as a production tax 

credit). With a FIT, the amount of renewable energy that will be generated is not specified in the 

tariff schedule. A similar uncertainty about the amount of renewable energy generation that will 

occur exists for production-based tax credits. In the case of an RPS, the amount of renewable 

energy generation necessary to meet the RPS is known given the required RPS level and a utility 

sales projection.39 In the case of FITs the response to the program may be more uncertain. The 

remainder of the discussion focuses on estimating these impacts. 

                                                            

39 Most state RPS schedules are established as a percentage of utility sales in a given year, although some include 

specified MWh amounts or are based on MW of generating capacity. 
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The key information required to estimate the generation that will occur as a result of a 

FIT includes: 

 An estimate of the renewable energy generation that will be provided by year for the FIT, 

along with estimates of payments to be made. These should capture the impacts of 

interactions with other policies, including RPS requirements, net metering, and federal 

investment and production tax credits. If generators receiving FIT payments are also 

eligible to satisfy an RPS, states should ensure their estimates of renewable electricity 

generation are not double-counted. For analysis using a detailed capacity expansion and 

dispatch planning model, market penetration of renewable energy generation to meet an 

RPS may be an output of the model. In this case, information is required on the capital 

costs, FOM costs, and VOM costs of the renewable energy generating technology. This is 

the same information required for modeling an RPS; however, the modeling or analysis 

mechanism may differ. For example, these costs may be reduced directly in a modeling 

framework to reflect the FIT payment (e.g., reducing VOM to reflect the FIT in the years 

it is available). 

 Information on the types of renewable energy resources, including their location, output 

levels, energy output profiles (across all 8,760 hours of the year or seasonally), and 

operating life of the EGU (by technology, so that they can be included in an electric 

dispatch model or mapped to an appropriate load shape or marginal avoided emission rate 

if using simpler analytical methods. 

 An estimate of whether the projected retail price impacts of the FIT will lead to lowering 

the amount paid to eligible renewable energy generators, if a FIT includes caps on 

generation subject to the tariff based on rate impacts. If retail electricity rate impacts or 

budgetary impacts become unsustainable, the projected generation resulting from the FIT 

will be overstated. 

2.2.1 Data Needs for Estimating Generation Resulting from Feed-In Tariff and 

 Performance-Based Tax Incentives 
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Projections of renewable generation that will occur as the result of FITs and other 

performance-based incentives will require assembling the following data about the specific 

incentive programs that will support renewable energy generation: 

 Caps on generating capacity, if any, by year, because such caps will limit the amount of 

renewable energy generation resulting from the tariff or tax incentive. 

 Capacity amounts by renewable energy resource type that will be subject to a FIT or 

other performance-based incentives, and the capacity factors for these resource types, so 

that total generation in MWh can be calculated. 

 Payment levels per kWh or MWh for each year of the FIT or other production-based 

incentive. 

 Budget amounts, if known, by year, which may be expended to support renewable 

generation. When divided by the incentive payment levels, on a per-kWh or per-MWh 

basis, these budget amounts will yield an estimate of the total electric generation that will 

be supported by the FIT or other production-based incentive. 

 State experience and trends with FITs and other production-based incentives. Lacking 

budget or capacity limits, prior experience in the state, or the experience of other states 

and utilities, normalized for state or utility size, could be the basis for estimated 

renewable energy generation at a given incentive level. 

 Because performance is metered at the busbar (i.e., the point of interconnection to the 

electricity transmission or distribution system), no adjustments would be needed for T&D 

losses. 

 Estimates of the geographic origin, by state, of generation that receives support from 

these programs. In most cases, eligible generation will be in-state, but if generation is 

expected to come from out of state, the basis for these assumptions should be 

documented. 

 CO2 emission of renewable energy generation, by resource type (if applicable). 

B. Emission Budget Trading Programs 

Emission budget trading programs establish an emission limit for a group of emission 

sources and establish a budget of tradable emission allowances equal to the emission constraint 

Comment [A6]: Here and throughout this TSD, 

has EPA considered using “system” instead of 

“program(s)”? 

EPA response: 

“Program” has typically been used to refer to 

various emission budget trading programs, 

including RGGI, the Acid Rain Program, and the 

NOX Budget Trading Program.
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for the group of sources. An emission allowance typically represents a limited authorization to 

emit one ton of a regulated pollutant. These programs also typically include a number of 

additional flexibility mechanisms beyond the ability to trade allowances. These include multiyear compliance periods, the ability to bank allowances issued in a previous compliance period 

for use in a subsequent compliance period, the use of out-of-sector project-based emission 

offsets, and cost-containment allowance reserves that make additional allowances available to 

the market if pre-established allowance price thresholds are achieved.40 As a result, annual 

emissions from affected sources subject to an emission budget trading program often differ from 

the established annual emission budget for affected sources. In addition, these programs may be 

multi-sector in nature, regulating emissions for source categories in addition to EGUs. As a 

result, state plan emission projections will need to accurately account for and represent these 

compliance flexibilities, as well as the scope of affected sources if they are broader than EGUs 

affected under CAA section 111(d).41 In general, most electricity sector capacity expansion and 

dispatch planning models can be configured to evaluate these program flexibilities and project 

CO2 emissions from affected sources, considering these compliance flexibilities. 

1. Addressing the Sectoral Scope of Emission Budget Trading Programs 

Some existing state emission budget trading programs addressing GHG emissions 

regulate emission sources in addition to EGUs, such as industrial sources.42 We refer to these 

here as multi-sector emission budget trading programs. All existing state emission budget trading 

programs addressing GHG emissions include out-of-sector project- based emission offsets, 

which may be used to cover a portion of the compliance obligation of affected sources. 

For multi-sector emissions budget trading programs, state plan emission projections 

would need to evaluate projected CO2 emissions across all source categories covered by the state 

or multi-state program. This would be necessary to project the CO2 emissions performance of 

                                                            

40 Depending on the program, these cost containment allowance reserves make additional allowances available from 

within the base emission budget (i.e., from “within the emission cap”), or add to the base emission budget (i.e., 

increase the emission cap). 

41 For example, the Regional Greenhouse Gas Initiative (RGGI), which is an emission budget trading program 

limited to EGUs, includes EGUs that are not subject to CAA section 111(d). 

42 The California GHG emission trading program also includes large industrial sources and as of 2015 will include 

distributors of transportation, natural gas, and other fuels. 
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affected EGUs under the multi-sector emissions budget trading program. 

For emissions budget trading programs that regulate EGUs and include offsets, which we 

define here as emissions reductions from sources not regulated by the trading program, emissions 

reductions from offsets would not be counted when evaluating CO2 emission performance of 

affected EGUs, because those reductions would not come from those affected EGUs. However, 

state plan emissions projections would need to evaluate the use and impact of offsets, because 

the availability of offsets would affect the amount of CO2 emissions from affected EGUs. 

Addressing Multi-Sector Emission Trading 

A state or regional emissions budget trading program could potentially regulate sources 

from multiple emissions sectors beyond the electric generating sector that is the focus of the EPA 

emission guidelines. For example, the California GHG emissions budget trading program is a 

multi-sector emissions trading program that address emission sources outside the scope of the 

EPA emission guidelines for EGUs. If a multi-sector emissions budget trading program is 

included in a state plan, CO2 emission projections provided as part of the state plan will need to 

evaluate projected CO2 emissions across all covered emissions sectors under the program. This 

would be necessary to project the CO2 emissions or weighted average CO2 emission rate of 

affected EGUs that will be achieved under the multi-sector emissions budget trading program 

included in the state plan. 

Evaluating the projected impact of a multi-sector emissions budget trading program on 

the CO2 emissions performance of affected EGUs introduces an additional level of analytical 

complexity to emissions projections. For example, additional modeling capabilities may be 

necessary to adequately evaluate CO2 emissions performance across sectors in a multi-sector 

emissions budget trading program. 

Adequately projecting the CO2 emissions of affected EGUs subject to a multi-sector 

emissions budget trading program may require modeling using a multi-sector energy model.43

                                                            

43 For example, the Energy 2020 model was used by California to evaluate the impacts of its multi-sector GHG 

emissions budget trading program. Other multi-sector models are available with similar capabilities, such as the 

National Energy Modeling System (NEMS) developed and implemented by the U.S. Energy Information 

Administration (EIA). 

Comment [A7]: This language should match 

what is in the preamble (see interagency comments). 

Comment [A8]: We believe that a proposal of

this importance should be clearly stated in the

Preamble and that the EPA should specifically

request comments on alternatives, including

allowing the use of state plan components

incorporating out‐of‐sector offsets financed by

affected EGUs to comply with CO2 emission

performance goals established by this rule.  
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The use of an electricity sector dispatch and capacity expansion planning model, might also be 

required, as a complement to a multi-sector model. Some multi-sector models, include an 

electricity sector dispatch and capacity expansion planning module. However, use of a standalone electricity sector model, might also be necessary as a supplement to a multi-sector energy 

model, in order to project CO2 emissions or the weighted average CO2 emission rate from 

affected EGUs at a sufficient level of resolution for state plan emission projections. Under this 

approach, a multi-sector energy model might be used to project the level of CO2 emissions 

abatement achieved across the multiple emission sectors covered by the program. This multisector projection could be used to develop a CO2 marginal abatement cost curve representing 

cost-effective, non-electricity sector emission reduction opportunities at increasing cost levels. 

The marginal abatement cost curve would then serve as an input assumption for the electricity 

sector capacity expansion and dispatch planning model, which would be used to project the CO2

emission performance of affected EGUs (e.g., the CO2 emissions or CO2 emission rate of 

individual affected EGUs, or the total CO2 emissions or weighted average CO2 emission rate 

across multiple affected EGUs).44

Multi-sector emission budget trading programs also often address GHGs beyond CO2. If 

this is the case, emission projections in a state plan would need to account for emissions 

abatement opportunities for each of the GHGs regulated under the program, and project 

emissions reductions for each of the regulated GHGs. This would be necessary to project the 

extent to which affected emission sources in different industrial sectors reduce emissions of nonCO2 GHGs, which could impact projected CO2 emissions from affected EGUs. 

Addressing Emission Offsets 

Emission offsets in CO2 or GHG emissions budget trading programs represent projectbased GHG emissions reductions outside the sector or sectors regulated by the program.45

                                                            

44 This marginal abatement cost curve would be applied in a similar manner as marginal abatement cost curves are 

applied for GHG emission offsets in modeling analyses of emission budget trading programs. Form a modeling 

perspective, emission reductions from other sectors could be used by affected EGUs to demonstrate compliance with 

the emission limit. When economic to do so, emission reductions from affected EGUs would be foregone and 

replaced by emission reductions from emission sources in other sectors that are also subject to the multi-sector 

emission budget trading program. 

45 “Offsets” as used in the context of CO2 or GHG emissions budget trading programs are distinct from offsets in the 

NSR permitting context under the CAA, where in certain instances emissions of a criteria pollutant from a proposed 

Comment [A9]: There are several places in the 

various files where offsets are discussed. However, 

EPA has directly addressed this issue in the TSD 

entitled, “Projecting Emission Performance TSD_04-

10- 

14”. Therein EPA makes several statements that are 

not consistent with federal agency initiatives nor 

many state initiatives that do encourage offsets as a 

cost-effective means to achieve reductions in 

greenhouse gases. 

• Under this approach, state plans would ignore the 

CO2-equivalent emissions reductions projected to be 

achieved through offsets, when projecting the CO2 

emissions performance that will be achieved by the 

affected EGU source category through 

implementation of the state plan. 

• Rather, when demonstrating emission performance 

by affected EGUs, the projected CO2 emissions or 

weighted average CO2 emission rate for affected 

EGUs under the state plan would not incorporate a 

credit for the CO2-equivalent emissions reductions 

represented by offset allowances or credits used by 

affected EGUs for compliance with the emission 

budget trading program. 

EPA analysis of carbon cap‐and‐trade legislation

along with a large literature document the cost

savings afforded by including offsets in a national‐

level program aimed at achieving significant

reductions in GHG emissions. The memorandum

from the President also makes it clear that EPA

should to the greatest extent possible tailor the

111(d) regulation to reduce costs, allow market‐

based instruments, employ regulatory flexibility,

and be implemented in a manner that ensures

continued provision of reliable and affordable

electric power for consumers and businesses. Given

the severability approach taken by EPA with respect

to the building blocks, USDA thinks that the

inclusion of offsets is desirable, consistent with the

memorandum, and should be included as an option

for public comment. See edits.  

EPA response:

The approach to offsets explained in this section 

would not preclude offsets from being included in 

a state plan. However, credit or adjustment of 

affected EGU emission performance would not be 

provided for GHG emission reductions achieved 

through out-of-sector offsets. 

We have added text in the preamble (VIII.C.2) 

clarifying the treatment of offsets and how they 

could be included in state plans, as well 

explaining the potential policy value of offsets.
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Emissions reductions achieved through eligible offset projects are awarded allowances or 

“credits” that may be used by an affected source to meet a portion of its allowance compliance 

obligation. For example, under the RGGI program “CO2 offset allowances” awarded for GHG 

emissions reductions achieved through approved offset projects may be initially used by 

affected sources to meet up to 3.3 percent of their CO2 allowance compliance obligation.46 This 

percentage increases to five and ten percent of a CO2 allowance compliance obligation at 

specified $7 and $10 price triggers, respectively. 

The ability to use GHG emission offsets for compliance means that CO2 emissions from 

EGUs regulated under the emissions budget trading program may exceed the base CO2 emissions 

budget established for the program. Offset allowances or credits are awarded in addition to the 

existing CO2 emissions budget, in exchange for CO2-equivalent emissions reductions achieved 

outside the capped emissions sector.47 Consequently, the use of offsets by affected sources to 

meet a portion of their compliance obligation under an emissions budget trading program could 

result in higher projected gross CO2 emissions from affected EGUs in a state plan, because the 

use of offsets functionally expands the CO2 emissions budget for affected EGUs. This allows 

affected EGUs to emit more CO2 while meeting their compliance obligation under the emissions 

budget trading program, in exchange for CO2-equivalent. emissions reductions achieved outside 

of the affected source category. As a result, to properly project the CO2 emissions from affected 

EGUs in a state plan that includes an emissions budget trading program that allows for the use of 

offsets, it is necessary for modeling to also project the extent to which offsets are used by 

affected EGUs for compliance.48

Under this approach, state plans could demonstrate how would ignore the CO2-equivalent 

emissions reductions projected to be achieved through offsets, when projecting the CO2

                                                                                                                                                                                               

new facility must be offset with creditable emissions reductions at an existing facility, if the state or jurisdiction 

where the proposed facility would be located is in non-attainment status for the pollutant. 46 This percentage increases to five and ten percent of a CO2 allowance compliance obligation at specified $7 and 

$10 price triggers, respectively. 

47 A key criterion that must be met for the award of offset allowances or credits is a demonstration that the offset 

project is “additional” (i.e., that it would not have occurred absent the incentive provided through the award of the 

offset allowance or credit). 

48 Existing capacity expansion and dispatch planning models can project the use of offsets for compliance based on 

specified offset marginal abatement supply curves, which represent the amount of offset credits/allowances assumed 

to be available from different categories of offset projects at different GHG emissions abatement costs. 

Comment [A10]: EPA: We have not accepted 

this change because it is incorrect. See next 

comment. 

Comment [A11]: Suggest adding this and a 

footnote that states: 

This percentage increases to five and ten percent of

a CO2 allowance compliance obligation at specified

$7 and $10 price triggers, respectively.  

EPA response:

We have not accepted these changes, because they

are incorrect. The RGGI participating states

removed these offset “triggers” as part of program

revisions made in 2013. See

http://www.rggi.org/docs/ProgramReview/_FinalP

rogramReviewMaterials/Model_Rule_Summary.pd

f.

Comment [A12]: Can EPA take comment on 

how to incorporate offsets when gross emissions 

may exceed the base cO2 emissions budget. 

EPA response: 

The approach to offsets explained in this section 

of the TSD would not preclude offsets from being 

included in a state plan. However, credit or 

adjustment of affected EGU emission 

performance would not be provided for GHG 

emission reductions achieved through out-ofsector offsets. 

We have added text in the preamble (VIII.C.2) 

clarifying the treatment of offsets and how they 

could be included in state plans, as well 

explaining the potential policy value of offsets.

Comment [A13]: EPA: We have not accepted 

this change – see next comment. 
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emissions performance that will be achieved by the affected EGU source category through 

implementation of the meets the state plan goals. This does not mean that an emission budget 

trading program included in a state plan could not include an offset component. Effectively, such 

a system,Rather, when demonstrating emission performance by affected EGUs, the projected 

CO2 emissions or weighted average CO2 emission rate for affected EGUs under the state plan,

would not incorporate a credit for the CO2-equivalent emissions reductions represented by offset 

allowances or credits used by affected EGUs for compliance with the emission budget trading 

program.49 In the preamble, the EPA seeks comment on alternatives including allowing the use 

of state plan components incorporating offsets financed by affected EGUs to comply with CO2 

emission performance goals established by this rule. 

2. Multi-State Emission Trading Programs 

Emission budget trading programs may be multi-state in nature. For such programs, 

emissions reductions are achieved on a regional, rather than a state-by-state basis. 

For example, in the multi-state Regional Greenhouse Gas Initiative (RGGI) emissions 

budget trading program, individual participating states have established CO2 emissions budgets 

in state regulations. However, there is no requirement limiting total CO2 emissions from affected 

sources in an individual state. State regulations include reciprocity provisions allowing emission 

sources to use CO2 allowances issued by another participating state for compliance with the state 

program. This provides for state-to-state CO2 allowance flows (and the potential for differences 

in state-by-state CO2 emissions relative to state emissions budgets) based on where the 

allowance marketemission sources determines it is most economical to achieve CO2 emissions 

reductions. However, in aggregate, the CO2 emissions budgets in each of the participating state 

regulations establish a regional cap on CO2 emissions from affected EGUs. As a result, while a 

multi-state emission budget trading program may be projected to result in CO2 emissions from 

                                                            

49 In other words, the projected CO2 emissions or weighted average CO2 emission rate for affected EGUs would be 

based on the direct emissions from these EGUs alone, with no calculation of “net” EGU CO2 emissions that factor in 

the CO2-equivalent. emissions reductions represented by offset credits or allowances used by an EGU to meet a 

portion of its compliance obligation. 

Comment [A14]: Suggest deleting this, or 

making the suggested edits, and adding to the end of 

the paragraph: 

EPA requests comment and data to support this 

approach under a state implementation plan. 

EPA response: 

We have not accepted these edits as they are 

inconsistent with the proposed treatment of 

offsets discussed in the preamble. 

The approach to offsets explained in this section 

of the TSD would not preclude offsets from being 

included in a state plan. However, credit or 

adjustment of affected EGU emission 

performance would not be provided for GHG 

emission reductions achieved through out-ofsector offsets. 

We have added text in the preamble (VIII.C.2) in 

response to interagency comments clarifying the 

treatment of offsets and how they could be 

included in state plans, as well explaining the 

potential policy value of offsets.

Comment [A15]: Addition (5 25) 
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affected EGUs consistent with a multi-state mass-based CO2 emission performance goal, the 

CO2 emissions outcomes may vary by state. This necessitates evaluating a multi-state emission 

budget trading program as a whole, because the individual regulations of participating states 

function together as single integrated program. 

To address these issues, states participating in a multi-state emission budget trading 

program would jointly demonstrate that the multi-state program is achieving the required level of 

CO2 emission performance on a multi-state basis, based on the CO2 emission performance of all 

affected EGUs in the multi-state group implementing the program. 

Some state emission budget trading programs also include international partner 

jurisdictions.50 In such instances, the program could be treated in a similar fashion as a multistate program that involves only U.S. states. In this instance, emission projections evaluating an 

international program would include all jurisdictions participating in the program, but emission 

performance for state plans would be assessed based only on the CO2 emission performance of 

affected EGUs in the subset of the program represented by U.S. states. Although the CO2

emission performance of EGUs (or other emission sources) in a foreign country would not be 

addressed in the state plan, the entire international program would be evaluated as part of the 

emission projection included in the state plan. This would be necessary in order to project 

international allowance flows and CO2 emissions across all participating jurisdictions, as these 

cross-jurisdictional flows would impact projected CO2 emissions from affected EGUs in U.S. 

states. 

VI. Process Considerations 

As discussed in the preamble, in section VIII.F.7, the credibility of state plans under 

section 111(d) will depend in large part on ensuring credible and consistent emission 

performance projections in state plans. Therefore, the use of appropriate methods, tools, and 

assumptions for such projections is critical. Furthermore, considerations for projecting emission 

performance under a state plan will differ depending on the type of plan. This includes 

                                                            

50 Currently, the EPA is only aware of one such instance, which is the linkage of the California GHG emission 

budget trading program with a similar program in Quebec. 
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differences in how inputs to projections are derived; how projections are conducted, including 

tools and methods; and how aspects of a plan are represented in these projections. 

In the preamble, the EPA seeks comment on whether the EPA should develop guidance 

that describes acceptable projection approaches, tools, and methods for use in an approvable 

plan, as well as provide technical resources for conducting projections. This section of the TSD 

elaborates on these considerations. 

A. Provision of the EPA Guidance 

One approach to address these considerations is for the EPA to provide recommended

guidance for EGU CO2 emission projections included in state plans. Such guidance could include 

default modeling assumptions, or data sources for key assumptions. State modeling projections 

included in a state plan could include assumptions that deviate from the EPA’s recommended 

default assumptions, but a state plan would justify the reason for using alternative assumptions. 

The EPA technical guidance could specify recommended reference case assumptions for use 

with modeling or EGU utilization growth tools, for example: 

 Electricity load growth projections (energy and peak demand) 

 Fuel supply, delivery, and pricing assumptions 

 Cost and performance of electric generating technologies 

 Cost and performance of pollution control equipment 

 EGU firm builds and retirements (those scheduled with a regional transmission 

organization or independent system operator (RTO/ISO)51

 Transmission capability and ISO/RTO transmission expansion plans 

 Applicable federal regulations (other than the EPA emission guidelines) 

 Applicable state regulations and programs (other than the alternative standards that are 

included in the state plan) 

                                                            

51 ISOs and RTOs are independent organizations that administer a regional electric power pool (EGU dispatch and 

electricity transmission systems), and often also administer related wholesale electricity markets for electric energy 

and capacity. 

Comment [A16]: Suggest EPA consider 

developing guidance on best practices and other 

ways to encourage standardization. 

EPA response: 

We acknowledge the comment and note that we 

are seeking comment on this in the preamble at 

section VIII.F.7.
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It would be necessary in many instances to include assumptions about other state 

programs implemented by neighboring states in the same region. This would be especially 

relevant for states that are located within the same electric power pool (or adjoining power pools) 

that are administered by a common RTO/ISO. To address this need, the EPA technical guidance 

could provide documentation of state programs and policies included in a reference case, as well 

as those that are eligible for inclusion in a state plan as alternative standards. The guidance could 

compile information about state programs and provide model input assumptions related to these 

programs (e.g., MWh of electric generation needed to meet a state renewable portfolio 

standard).52 The EPA might also play a role in facilitating coordination among states as they 

develop their plans, to harmonize regional assumptions. 

ISOs and RTOs, in discussions with the EPA have also offered to support states in 

evaluating the emission performance of state plans on a regional basis. The ISO/RTO Council, 

an organization of electric grid operators, has suggested that ISOs and RTOs could provide 

analytic support to help states develop and implement their plans. The ISOs and RTOs have the 

capability to model the system-wide effects of individual state plans. Providing assistance in this 

way, they felt, would allow states with borders that fall within an RTO or ISO footprint to assess 

the system-wide impacts of potential state plan approaches. In addition, as the state implements 

its plan, ISO/RTO analytic support would allow the state to monitor the effects of its plan on the 

regional electricity system. ISO/RTO analytic capability could help states assure that their plans 

are consistent with region-wide system reliability. The ISO/RTO Council suggested that the EPA 

ask states to consult with the applicable ISO/RTO in developing their state plans. 

B. Party that Translates the Rate-Based Goal to a Mass-Based Goal 

In the preamble, in section VIII.F.7, The EPA seeks comment on whether it should 

develop guidance that describes acceptable projection approaches, tools, and methods for use in 

an approvable plan, as well as provide technical resources for conducting projections. 

                                                            

52 EPA’s manual, Roadmap for Incorporating Energy Efficiency/Renewable Energy Policies and Programs into 

State Implementation Plans/Tribal Implementation Plans (July 2012), could potentially support and be expanded 

upon to develop this component of EPA technical modeling guidance. In particular, see Appendix I: EPA’s Draft

Methodology for Estimating Energy Impacts of EE/RE Policies. The draft manual is available at 

http://www.epa.gov/airquality/eere.html. State environmental regulations addressing EGUs are itemized in the 

modeling documentation for the EPA IPM Base Case v.5.13. 
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One consideration for state plans that use a mass-based CO2 emission performance goal 

is the party that conducts the translation of the rate-based CO2 emission performance goal in the 

emission guidelines to a mass-based goal—either the EPA or the state. In the preamble, in 

section VIII.D.3, Tthe EPA seeks comment on whether to assist states that seek to translate the 

rate-based goal into a mass-based goal. 

One approach is for the EPA to provide a presumptive translation of the state-specific 

rate-based CO2 emission performance goal to an equivalent mass-based goal for all states, for 

those states that request it, and/or for multi-state regions. This could include default modeling 

assumptions and results of modeling runs for a Reference Case Scenario and an EPA MassBased CO2 Emission Goal Policy Scenario, as described above in section III.B. A state could 

utilize the presumptive mass-based CO2 emission performance goal for the state or multi-state 

region identified through these EPA modeling runs. If a state proposed modifications to EPA 

default modeling assumptions, it would need to justify these modifications as part of the CO2 

emission projection included in its state plan, and present an emission projection that supports a 

proposed modified mass-based CO2 emission performance goal. 

Another approach is for the EPA to provide guidance for states to use in translating a 

rate-based goal to a mass-based goal. This could include information about acceptable analytical 

methods and tools, as well as default input assumptions for key parameters that will likely 

influence projections, such as electricity load forecasts and projected fossil fuel prices. Under 

this approach, the EPA might also provide a coordinating function in addressing the assumptions 

applied by multiple states within a grid region, acknowledging that assumptions about state 

programs across a broader grid region that are included in an analysis scenario will influence 

projections of CO2 emissions by affected EGUs in any particular state. 

Under this approach, states could deviate from these default methods and assumptions 

with justification. Following the guidance could provide a streamlined path for the EPA approval 

of emissions projections, but states would still have flexibility to use other approaches, which the 

EPA would review. 

Comment [A17]: Suggest that EPA have more 

guidance/clarity on how compliance with state 

targets would be evaluated under a mass-based 

approach. 

EPA response: 

We acknowledge the comment and note that we 

are seeking comment on this in the preamble.
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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 

1Proposed State Goals

 Appendix 2 - State level goals, underlying state level data, and calculations for Option 

2tThe Alternative state goals

 Appendix 3 – Summary table of state goals

 Appendix 4 – Deconstruction of state goals 

 Appendix 5 – Block 1 & 2 only goals

 Appendix 6 - 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.  

[EPA Response]:  EPA participated in the requested

meeting.

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.”

[EPA Response]:  1990 and 2005 data are

comparison points for emission reductions.  2012 is

the basis for state goal calculation as it reflects the

most recent year for which we had complete data.  

Formatted: Indent: Before: 0.69", Hanging: 

0.19"

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.

[EPA Response]:  Dedicated biomass is included in

this total.  RE totals are grown from 2012 levels

starting in 2017, so there are only three years of RE

growth assumed to occur by the year 2020 in the

context of quantifying RE for incorporation into

state goals.  This methodology is further addressed

in the GHG abatement measures TSD, and this

comment is beyond the scope of this goal setting

TSD which deals with just the mechanics of

quantifying state goals – not the individual building

block levels themselves.
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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 1Proposed with state emission 

rate goals based on reasonable assumptions related to BSER, and (2) Option 2Alternative state goals with 

less ambitiousmore moderate assumptions relative to those used for the Option 1Proposed state goals. 

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 1Proposed – A 6% heat rate improvement in the state’s coal fleet 

Option 2Alternative – A 4% heat rate improvement in the state’s coal fleet 

b. Redispatch to Existing Natural Gas Combined Cycle Plants (NGCC) 

Option 1Proposed – a 70% capacity factor (CF) ceiling for the state’s NGCC fleet 

Option 2Alternative – 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 1Proposed and 2 include under construction (5.5 GW) and preserved nuclear 

capacity (~5.8% of existing nuclear capacity) 

Option 1Proposed – Non-Hydro RE at 13% by start of 2030 and thereafter* 

Option 2Alternative – Non-Hydro RE at 9.4% by start of 2025 and thereafter* 

d. End-use Energy Efficiency (EE)* 

Option 1Proposed – 10.65% cumulative savings by start of 2030 and each year thereafter 

Option 2Alternative – 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. The RE totals do 

not reflect existing hydro generation.

Comment [A4]: Initial text implies that option 1

was “immoderate.”

[EPA Response]:   Thank you, change made

Comment [A5]: Need to pick a single font,

suggest Times New Roman.

[EPA Response]:  Thank you, change made

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.

[EPA Response]:  State goals do not require any

particular dispatch pattern, and many of the states

that appear to have no coal generation in the

computation of state goals are still projected to

maintain some level of coal‐fired generation in the

compliance scenarios described in the RIA.  Also,

some of the indicated states are already expected

to fully reduce coal‐fired generation even without

the imposition of this rule (e.g., Oregon and

Washington, whose coal‐fired facilities are already

scheduled to retire).
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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 emission rate reduction capability 

and total NGCC capacity currently on the groundinstalled to date. 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 

                                                            

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.

[EPA Response]:  Thank you for your comment, we

have deleted the parenthetical and made clarifying

edits accordingly.

Formatted: Font: (Default) Times New Roman
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 State level NGCC capacity 

 “Oother” emissions2

 “Oother” 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 covered 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. A covered EGU is a steam generating unit or IGCC that is: (1) capable of combusting more

than 250 MMBtu/h heat input of fossil fuel and (2) constructed for the purpose of supplying

one‐third or more of its potential net‐ electric output capacity and more than 219,000 MWh to

any utility power distribution system for sale (that is, to the grid) on an annual basis. A covered

                                                            

2

 “Other” includes fossil sources that are likely subject to 111(d) rulemaking, but not subject to building block 

abatement measures (e.g., IGCC, low utilization CTs, useful thermal output at cogens). 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. 

Formatted: Font: (Default) Times New Roman
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EGU that is a stationary combustion turbine that serves a generator capable of selling greater

than 25 MW to a utility distribution system (that is, to the grid).4

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 

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 operating 

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 operatingexisting in 2012. For this small subset of “existing” 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.5

  In addition to those under construction NGCC units in NEEDS, the EPA identified 

three other NGCCfour other NGCC and IGCC plants that were under construction and would 

likely fit the rulemaking’s definition of “existing” unit. These were the Dominion Brunswick 

                                                            

4 See Preamble and Regulatory tText for full description of 111(d) applicability criteria 5 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.

[EPA Response]:  This rule’s applicability is limited

to units “built with the purpose of supplying

electricity to the grid”

Comment [A9]: The previous comment was

referring to reciprocating engines that are built

expressly to supply electricity to the grid

Comment [A10]: 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.

[EPA Response]:  Good point, modified language to

convey that while not captured in 2012 data, was

still legally an existing unit – so treated as such in

state goals
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plant in Virginia (1,358 MW), the Cheyenne Generating Station in Wyoming (220 MW), and the 

Cane Run plant in Kentucky (640 MW), and the Kemper IGCC plant. (582 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 

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. The mathematical calculations are fundamental to translating the building blocks into a quantifiable state goal.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 generationemissions and energy output associated with the useful thermal output not used for electricity production are included in the state goal and would be reported under the 111(d) reporting guidelines if the unit meets the 111(d) applicability criteria. The emissions and energy output associated with the useful thermal output are captured in the “Other” category. The emission rates shown reflect total emissions divided by total net energy output (e.g. net electricity generation + useful thermal output). (including that associated with useful thermal output not for electricity generation)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.6 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.7,8 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. 6 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 generationtheir net electricity generation in the denominator. This was necessary as IPM is a dispatch model that optimizes around net electricity demand, so it reflects total emissions associated with cogeneration operation, but only the generation associated with meeting net electricity demand. 7 http://www.theclimateregistry.org/downloads/2013/04/2013-Climate-Registry-Default-Emissions-Factors.pdf 8 Please see section VIII of the preamble for more information on biomass in the context of this rulemaking.
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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 

2012 Rate (lbs/MWh) 

2012 mass (lbs) 2012 Generation (MWh) 

2012 Capacity 

Starting 

Covered 

Fossil 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 2,126 1,332 963 284,732,506 86,473,075 321,602 20,907,183 214,178 4,343 1,897 

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 1,897946 lb/MWh. 

ሻܹ݄ܯሺ	݊݋݅ݐܽݎ݃݁݊݁	ݕݐ݅ܿ݅ݎݐ݈݁݁ܿ	݈݅ݏݏ݋݂	݈ܽݐ݋ݐ ሻܱ2ܥ	݈ܾሺ	ݏ݊݋݅ݏݏ݁݉݅	݈݅ݏݏ݋݂	݈ܽݐ݋ݐ ൌ ݁ݐܽݎ	݊݋݅ݏݏ݁݉݅	݈݅ݏݏ݋݂	݁݀ݐݏݑ݆݊ܽ݀ݑ	݈݅ܿܽݎ݋ݐݏ݄݅

This can be rewritten as 

Historical fossil 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) 
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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 the option 1Proposed state goals 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 2,12687lb/MWh to 1,9992,056 lb/MWh. This completes the building block 1 treatment for option 1the Proposed state goals.9

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 

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 1,999 1,332 963 284,732,506 86,473,075 321,602 20,907,183 214,178 4,343 1,795 

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 1,897946 lb/MWh to 1,795841 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. 9 EPA makes a similar adjustment for option 2the Alternative state goals, 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 3a: 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 plantsunits 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 unitsNGCCs 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.10 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 

10 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 

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 1,999 1,332 963 284,732,506 80,700,563 300,133 26,701,164 214,178 4,343 1,739 

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 

4,343 MW x 8784 hours x 70% CF = 26,701,164

4,343 MW x 8784 hours x 70% CF = 26,701,164
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calculation, the emission rate drops from 1,795841 lb/MWh in Step 2 to 1,73981 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.1112State 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: Step 3b: BSER Block Two, cont. - Calculation of state fossil emission rate goal resulting from heat rate improvement and redispatch to existing and “en route”under construction NGCC capacityStep 3a 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.13 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. 

11 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.

12 In option 2the Alternative 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 13 http://ampd.epa.gov/ampd/
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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.14 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. 

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 

14 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. 

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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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 1,999 1,332 963 2,791,474,084 79,993,008 297,502 27,411,350 2,818,195 4,343 539 1,715 

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 1,73981lb/MWh to 1,71554 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. Note the “Other” category is bolded here as it reflects 55% utilization of under construction NGCC.

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 4a5: BSER Block Three - Calculation of state fossil emission rate goal resulting from heat rate improvement, redispatch to existing and en route NGCC capacity, and under construction new and preserved preservednuclear 

4,343 MW x 8784 hours x 70% CF = 26,701,164

539 x 8784 x 15% CF = 710,186

Total NGCC = 27,411,350539 x 8784 x 55% CF =2,604,017Total “other”= 2,604,017 + 214,178 = 2,818,195
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In Step 4a5, the total under construction nuclear capacity is determined.15 Additionally, an amount of existing nuclear capacity that is “at risk” of being retired is identified as approximately 5.8% of the current nuclear fleet (see GHG Abatement Measures TSD for further discussion on how the percent assumption was derived). This under construction new and preserved nuclear capacity was incorporated into the state goals as zero emitting generation. This 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 “under 

construction 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 provided they maintain the same level of nuclear capacity.. 

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 

15 Watts Bar in TN, Vogtle in GA, and Summer in SC 
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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 1,999 1,332 963 2,791,474,084 79,993,008 297,502 27,411,350 2,818,195 4,343 539 

993,077 

1,699 

Ohio has 0 MW of under construction nuclear and 125.6271 MW of “at risk” nuclear capacity share as described in the “GHG Abatement Measures TSD”. This results in an expected nuclear generation amount of 933,0771,351,239 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 1,71554lb/MWh to 1,699733 lb/MWhState Emission Rate Post Block 3.1 = (coal gen. x coal emission rate)+) + (OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) + “Other” emissions 

 Coal gen. + OG gen. + NGCC gen. + “Other” gen. + NuclearN&P gen. 

Step 4b6: 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 from renewable generating sources is not considered “dispatchable”. In other words, is not expected to vary significantly between a business-asusual scenario and a policy scenario. Therefore, the generation from these sources is added into the denominator in a similar fashion as nuclear. Consequently, all non-hydro RE generation (existing and new build) would be eligible for demonstrating compliance with the state goal. 
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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 1The Proposed state goals is approximately 13% nonhydro RE by the end of 2029.16 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 

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 1,999 1,332 963 2,791,474,084 79,993,008 297,502 27,411,350 2,818,195 4,343 539 993,077 13,775,594 

1,512 

16 See “GHG Abatement Measures TSD” 
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Ohio had 1,738,622 MWh of non-hydro RE in 201217 (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 1,699733 lb/MWh to 1,512540 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 57: 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 1The Proposed state goals. A different set of annual percent savings are derived for 2020-2024 under for Option 2tThe Alternative state goals. 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.18 The 7.51% scaling factor effectively convertscoverts the retails sales figure into a corresponding total net generation value that accounts for transmission and distribution losses. 19 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 own in-state generation. 17 http://www.eia.gov/electricity/data/state/18 http://www.eia.gov/electricity/data/state/ “Retail Sales of Electricity by State and Sector” 19 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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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 fossilfuel 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 

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

2012 Capacity 

"En 

Route

" 

NGC

C 

New and 

Preserve

d 

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 

NGC

C MW MWh MWh 

MWh 

Ohio 1,999 1,332 963 2,791,474,084 

79,993,008 297,502 27,411,350 2,818,195 4,343 539 993,077 

13,775,59

4 

16,284,58

4 1,338 

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 11.5567% in Ohio. This value is then multiplied by the retail electricity sales in the state (152,456,864 MWh)20 and multiplied by 1.0751 to reflect the net generation commensurate with these sales (i.e., inclusive of transmission 20 http://www.eia.gov/electricity/data/state/ 
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and distribution losses). This results in 18,942,382 MWh of avoided generation. This value is then multiplied by 85.9792.43% (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 16,284,58417,507,556 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 1,51240 lbs/MWh to 1,33849 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 Adjustedadjusted 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. 

Steps 6 & 7 8: Interim and Final State Goals The completion of the previous five 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 two state goals – an interimerim and final state goal. Thus, goals are derived by averaging, or taken directly from and the annual adjusted 

emission rate values.  For Option 1The Proposed state goals:  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 2The Alternative state goals:  Interim State Goal – Average of the adjusted yearly rates for the period 2020-20249 Final State Goal – The 2024 emission rate (as calculated above) becomes the state goal for 202530 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”. For more discussion on emission reductions, please see the Regulatory Impact Analysis.
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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 choose 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 1Proposed state goals – ((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) – ((1,999 x 79,993,008) + (1,332 x 297,502) + (963 x 27,411,350) + 2,791,474,084) = 1,338 lb/MWh ((79,993,008 + 297,502 + 27,411,350 + 2,818,195 + 993,077 + 13,775,594 + 16,284,585) See the accompanying Excel Workbook for all state level data, calculations, and final state level emission rate goals used to calculate theo Interim and Final State Goals for Options 1 and 2. This workbook shows all the calculations used to transform the historical covered fossil emission rate (column BB) to the Interim and Final State Goal (columns BB and BC) for option 1the Proposed state goals, and (columns AM and AN) for option 2the Alternative state goals. 
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APPENDIX 

Appendix 1 - State level goals, underlying state level data, and calculations for Option 1The 

Proposed state goals

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 2The 

Alternative state goals

 See “Appendix 2” worksheet in the Excel Attachment titled “State Goal Data and 

 Computation”

*The above referenced workbook has the data and formulas embedded for state goal calculations. 

Therefore, a commenter suggesting an adjustment to a building block assumption or any historic statelevel data can replace the current assumed values with the suggested amount and see the resulting statelevel goal under such assumptions. 
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Appendix 353 – Summary of state goals under the proposed Existing Source Performance Standard

Option 1Proposed 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

State 

Interim Goal 

(2020-2029) 

 Final Goal 

(2030 and 

after) State 

Interim 

Goal 

(2020-

2029) 

 Final Goal 

(2030 and 

after) 

Alabama 1,147 1,059 Montana 1,882 1,771 

Alaska 1,097 1,003 Nebraska 1,596 1,479 

Arizona 735 702 Nevada 697 647 

Arkansas 968 910 New 546 486 

Comment [A32]: 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.  

[EPA Response]:  Thank you for your comment, we

believe these observations are fully consistent with

the process we have documented here for

expressing state goals for application to existing

sources subject to this rule.

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Hampshire 

California 556 537 New Jersey 647 531 

Colorado 1,159 1,108 New Mexico 1,107 1,048 

Connecticut 597 540 New York 635 549 

Delaware 913 841 North Carolina 1,077 992 

Florida 794 740 North Dakota 1,817 1,783 

Georgia 891 834 Ohio 1,452 1,338 

Hawaii 1,378 1,306 Oklahoma 931 895 

Idaho 244 228 Oregon 407 372 

Illinois 1,366 1,271 Pennsylvania 1,179 1,052 

Indiana 1,607 1,531 Rhode Island 822 782 

Iowa 1,341 1,301 South Carolina 840 772 

Kansas 1,578 1,499 South Dakota 800 741 

Kentucky 1,844 1,763 Tennessee 1,254 1,163 

Louisiana 948 883 Texas 853 791 

Maine 393 378 Utah 1,378 1,322 

Maryland 1,347 1,187 Virginia 884 810 

Massachusetts 655 576 Washington 264 215 

Michigan 1,227 1,161 West Virginia 1,748 1,620 

Minnesota 911 873 Wisconsin 1,281 1,203 

Mississippi 732 692 Wyoming 1,808 1,714 

Missouri 1,621 1,544 

   

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INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866 INTERAGENCY REVIEW.  SUBJECT

TO FURTHER POLICY REVIEW.

Option The Alternative  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   

State 

Interim 

Goal 

(2020-

2024) 

 Final 

Goal 

(2025 

and 

after) State 

Interim 

Goal 

(2020-

2024) 

 Final 

Goal 

(2025 

and 

after) 

Alabama 1,270 1,237 Montana 2,007 1,960 

Alaska 1,170 1,131 Nebraska 1,721 1,671 

Arizona 779 763 Nevada 734 713 

Arkansas 1,083 1,058 New Hampshire 598 557 

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INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866 INTERAGENCY REVIEW.  SUBJECT

TO FURTHER POLICY REVIEW.

California 582 571 New Jersey 722 676 

Colorado 1,265 1,227 New Mexico 1,214 1,176 

Connecticut 651 627 New York 736 697 

Delaware 1,007 983 North Carolina 1,199 1,156 

Florida 907 884 North Dakota 1,882 1,870 

Georgia 997 964 Ohio 1,588 1,545 

Hawaii 1,446 1,417 Oklahoma 1,019 986 

Idaho 261 254 Oregon 450 420 

Illinois 1,501 1,457 Pennsylvania 1,316 1,270 

Indiana 1,715 1,683 Rhode Island 855 840 

Iowa 1,436 1,417 South Carolina 930 897 

Kansas 1,678 1,625 South Dakota 888 861 

Kentucky 1,951 1,918 Tennessee 1,363 1,326 

Louisiana 1,052 1,025 Texas 957 924 

Maine 418 410 Utah 1,478 1,453 

Maryland 1,518 1,440 Virginia 1,016 962 

Massachusetts 715 683 Washington 312 284 

Michigan 1,349 1,319 West Virginia 1,858 1,817 

Minnesota 1,018 999 Wisconsin 1,417 1,380 

Mississippi 765 743 Wyoming 1,907 1,869 

Missouri 1,726 1,694 

Appendix 4 – State level goals (Deconstructed by building blocks) 

The below table shows the starting covered fossil rate in a state, the building block assumptions, and the

final composite state goal.

Proposed State Goals (Deconstruction of Final State Goal for Proposed Option)

State

2012

Covered

Fossil

Rate

(lb/MWh)

Post

Blocks 1 &

2 Rate

(lb/MWh)

Blocks 3

(RE % Gen)

Blocks 4

(% of

EE)

Final Rate

(lb/MWh)

Alabama

            

1,518   1,329 9% 9% 1,059

Alaska

            

1,368   1,252 2% 9% 1,003

Arizona

            

1,551   900 4% 11% 702

Arkansas

            

1,722   1,115 7% 10% 910

California              838 21% 12% 537

Comment [A33]: [EPA]EPA is still evaluating

different forms of providing this information

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INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866 INTERAGENCY REVIEW.  SUBJECT

TO FURTHER POLICY REVIEW.

900  

Colorado

            

1,959   1,521 21% 11% 1,108

Connecticut

            

844   809 9% 12% 540

Delaware

            

1,255   1,013 12% 9% 841

Florida

            

1,238   910 10% 10% 740

Georgia

            

1,598   1,296 10% 10% 834

Hawaii

            

1,783   1,751 10% 10% 1,306

Idaho

            

858   858 21% 11% 228

Illinois

            

2,189   1,865 9% 12% 1,271

Indiana

            

1,991   1,834 7% 11% 1,531

Iowa

            

2,197   1,846 15% 12% 1,301

Kansas

            

2,320   2,186 20% 10% 1,499

Kentucky

            

2,166   1,977 2% 10% 1,763

Louisiana

            

1,533   1,099 7% 9% 883

Maine

            

873   848 25% 12% 378

Maryland

            

2,029   1,868 16% 12% 1,187

Massachusetts

            

1,001   886 24% 12% 576

Michigan

            

1,814   1,511 7% 12% 1,161

Minnesota

            

2,013   1,369 15% 12% 873

Mississippi

            

1,140   843 10% 10% 692

Missouri

            

2,010   1,784 3% 10% 1,544

Montana

            

2,439   2,295 10% 11% 1,771

Nebraska

            

2,162   1,941 11% 10% 1,479

Nevada

            

1,091   882 18% 11% 647
INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866 INTERAGENCY REVIEW.  SUBJECT

TO FURTHER POLICY REVIEW.

New Hampshire

            

1,119   878 25% 11% 486

New Jersey

            

1,035   905 16% 10% 531

New Mexico

            

1,798   1,447 21% 11% 1,048

New York

            

1,096   927 18% 12% 549

North Carolina

            

1,772   1,329 10% 10% 992

North Dakota

            

2,368   2,226 15% 10% 1,783

Ohio

            

1,897   1,714 11% 12% 1,338

Oklahoma

            

1,562   1,186 20% 10% 895

Oregon

            

1,081   852 21% 11% 372

Pennsylvania

            

1,627   1,480 16% 12% 1,052

Rhode Island

            

918   918 6% 12% 782

South Carolina

            

1,791   1,514 10% 10% 772

South Dakota

            

2,256   1,456 15% 10% 741

Tennessee

            

2,015   1,798 6% 10% 1,163

Texas

            

1,420   1,083 20% 10% 791

Utah

            

1,874   1,559 7% 11% 1,322

Virginia

            

1,438   1,135 16% 9% 810

Washington

            

1,379   811 15% 11% 215

West Virginia

            

2,056   1,933 14% 10% 1,620

Wisconsin

            

1,988   1,619 11% 12% 1,203

Wyoming

            

2,331   2,145 19% 10% 1,714

Appendix 5 – Blocks 1 & 2 only State Goals
INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866 INTERAGENCY REVIEW.  SUBJECT

TO FURTHER POLICY REVIEW.

The table below provides the interim and final goals from just blocks 1 and 2, before the introduction of

nuclear, renewable, and EE into the denominator.  Note – there is no difference between the Interim

and Final rates through the first two building blocks as the increasing annual potential only applies to

block 3 and block 4.

Proposed State Goals Alternative State Goals

State

Interim Rate (2020 ‐ 

2029 average)

Final Rate (2029

Rate)

Interim Rate

(2020 ‐ 2024

average)

Final Rate ‐ 2025

and thereafter

Alabama 1,329 1,329 1,405 1,405

Alaska 1,252 1,252 1,252 1,252

Arizona 900 900 900 900

Arkansas 1,115 1,115 1,196 1,196

California 838 838 838 838

Colorado 1,521 1,521 1,586 1,586

Connecticut 809 809 812 812

Delaware 1,013 1,013 1,068 1,068

Florida 910 910 991 991

Georgia 1,296 1,296 1,375 1,375

Hawaii 1,751 1,751 1,762 1,762

Idaho 858 858 858 858

Illinois 1,865 1,865 1,924 1,924

Indiana 1,834 1,834 1,883 1,883

Iowa 1,846 1,846 1,900 1,900

Kansas 2,186 2,186 2,230 2,230

Kentucky 1,977 1,977 2,025 2,025

Louisiana 1,099 1,099 1,162 1,162

Maine 848 848 848 848

Maryland 1,868 1,868 1,908 1,908

Massachusetts 886 886 886 886

Michigan 1,511 1,511 1,574 1,574

Minnesota 1,369 1,369 1,446 1,446

Mississippi 843 843 843 843

Missouri 1,784 1,784 1,832 1,832

Montana 2,295 2,295 2,343 2,343

Nebraska 1,941 1,941 1,987 1,987

Nevada 882 882 882 882

New Hampshire 878 878 878 878

New Jersey 905 905 905 905

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INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866 INTERAGENCY REVIEW.  SUBJECT

TO FURTHER POLICY REVIEW.

New Mexico 1,447 1,447 1,511 1,511

New York 927 927 970 970

North Carolina 1,329 1,329 1,391 1,391

North Dakota 2,226 2,226 2,273 2,273

Ohio 1,714 1,714 1,766 1,766

Oklahoma 1,186 1,186 1,255 1,255

Oregon 852 852 852 852

Pennsylvania 1,480 1,480 1,537 1,537

Rhode Island 918 918 918 918

South Carolina 1,514 1,514 1,576 1,576

South Dakota 1,456 1,456 1,521 1,521

Tennessee 1,798 1,798 1,855 1,855

Texas 1,083 1,083 1,150 1,150

Utah 1,559 1,559 1,614 1,614

Virginia 1,135 1,135 1,216 1,216

Washington 811 811 811 811

West Virginia 1,933 1,933 1,974 1,974

Wisconsin 1,619 1,619 1,689 1,689

Wyoming 2,145 2,145 2,195 2,195

   
INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866 INTERAGENCY REVIEW.  SUBJECT

TO FURTHER POLICY REVIEW.

Appendix 65 - 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 in order to filterscreen out units that 

are not likely subject to the Rule’s applicability criteria. 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: 

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TO FURTHER POLICY REVIEW.

CATEGORY Category Name Includes 

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, Kemper21

                                                            

21 EPA assumed 800 lb/MWh and 70% capacity factor for the Kemper unit as it was not yet operating in 2012.  

http://www.netl.doe.gov/publications/proceedings/11/co2capture/presentations/4‐Thursday/25Aug11‐

%20Nelson‐Kemper‐Capture%20at%20Kemper%20IGCC.pdf

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Page 17: [1] Comment [A25] 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.

[EPA Response]:  Existing RE and existing nuclear are quantified differently because their BSER foundations are

different.  EPA is proposing to include only that portion of existing nuclear in BSER that is at risk of retirement,

whereas EPA is proposing to include total RE generation as part of BSER.  The difference in their quantification

for state goal purposes is consistent with their different circumstances in BSER.

Page 17: [2] Comment [A26] Author 

Yes – we looked at this comparison out to 2030 of base v. policy projections in the RIA, and it is quite disconcerting

that all this effort to empower RE has very little impact in 2030 according to the modeling.  Can EPA explain this –

should we be looking for more RE?

[EPA Response]:  This language was not actually intended to compare the quantity of RE generation represented

in the calculation of state goals with the compliance projections described in the RIA; such a comparison would

be outside the scope of this TSD, which is intended to demonstrate how the state goals are calculated. As such,

we have deleted this language.  That being said, in response to your question, the quantification of individual

BSER measures is not a prescription for what states may actually elect to do when considering all available

measures in developing state plans to demonstrate compliance with state goals.

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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

3-1 

MEMO: EMISSION REDUCTIONS, COSTS, BENEFITS AND ECONOMIC 

IMPACTS ASSOCIATED WITH BUILDING BLOCKS 1 AND 2 

1 Introduction 

This memo provides estimates of the costs, energy impacts, and the monetized climate 

benefits and air pollution health co-benefits associated with emission reductions for two 

illustrative compliance scenarios associated with building blocks 1 and 2 only. It also includes 

estimates of the labor impacts on the regulated sector. The methods applied to generate these 

estimates are described in detail in Chapter 3, 4, and 6 of the Regulatory Impact Analysis (RIA). 

For brevity, this memo just reports the results of these analyses. 

EPA used the Integrated Planning Model (IPM), developed by ICF International, to 

conduct this analysis. 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.

This analysis also evaluates the climate benefits associated with emission reductions of 

CO2 and the air pollution health co-benefits associated with reduced emissions of SO2, NOx, and 

PM2.5, which would lead to lower ambient concentrations of PM2.5 and ozone. Unlike the 

analysis of health co-benefits in Chapter 4 of the RIA, estimates of emissions reductions of 

directly emitted particles available are not available for this analysis. Similar to Chapter 4 of the 

RIA, unquantified co-benefits include exposure to several HAPs (including mercury and 

hydrogen chloride), carbon monoxide, SO2, and NO2, as well as ecosystem effects, and visibility 

impairment. 

2 State Goals for Building Blocks 1 and 2 

This analysis includes state-specific rate-based goals that reflect a two-building block 

approach. It is also based upon the same modeling framework, assumptions and calculations as 

presented in the RIA. The two-blocks incorporated in the following state goals include efficiency 

improvements at existing coal steam electric generating units (EGUs) and use of lower emitting 

power sources (increased utilization of existing NGCCs) and are identical to the proposed levels, 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

3-2 

included for purposes of establishing state-specific goals, associated with Option 1 (6% 

efficiency improvement and no more than 70% redisptatch). 

Table 1. State-specific Goals for Two-Block Approach for Existing Sources

State Interim Rate (2020 - 2029 average) Final Rate (2029 Rate) 

Alabama 1,329 1,329

Alaska 1,252 1,252 

Arizona 900 900 

Arkansas 1,115 1,115 

California 838 838 

Colorado 1,521 1,521 

Connecticut 809 809 

Delaware 1,013 1,013 

Florida 910 910 

Georgia 1,296 1,296 

Hawaii 1,751 1,751 

Idaho 858 858 

Illinois 1,865 1,865 

Indiana 1,834 1,834 

Iowa 1,846 1,846 

Kansas 2,186 2,186 

Kentucky 1,986 1,977 

Louisiana 1,099 1,099 

Maine 848 848 

Maryland 1,868 1,868 

Massachusetts 886 886 

Michigan 1,511 1,511 

Minnesota 1,369 1,369 

Mississippi 843 843 

Missouri 1,784 1,784 

Montana 2,295 2,295 

Nebraska 1,941 1,941 

Nevada 882 882 

New Hampshire 878 878 

New Jersey 905 905 

New Mexico 1,447 1,447 

New York 927 927 

North Carolina 1,329 1,329 

North Dakota 2,226 2,226 

Ohio 1,714 1,714 

Oklahoma 1,186 1,186 

Oregon 852 852 

Pennsylvania 1,480 1,480 

Rhode Island 918 918 

South Carolina 1,514 1,514 

South Dakota 1,456 1,456 

Tennessee 1,798 1,798 

Texas 1,083 1,083 

Utah 1,559 1,559 

Virginia 1,135 1,135 

Washington 811 811 

West Virginia 1,933 1,933 

Wisconsin 1,619 1,619 

Comment [A1]: Was this going to be re-termed 

“the Proposed” option, and the Alternative? 

Comment [A2]: This presentation is opaque to 

the state by state comparison comparing today’s rate 

in each state (by whatever math, see Goal Setting 

TSD) to the expected state rates after application of 

BB1 and BB2. For a state with all NGCC, there is 

no required reduction, at all, under BB1 and 2. For a 

state with all coal generation currently, there will be 

a 6% total rate reduction required as a result of BB1, 

but no additional as a result of BB2 because there is 

no redispatch potential since there is no existing 

NGCC in that state. For the states with a balance of 

NGCC and coal, there will be major reductions in 

the required emission rate goal based on i) a small 

percent reduction under BB1, and then ii) a 

substantial reduction added under BB2 depending on 

the amount of redispatching of the coal generation 

(potentially all the coal generation in some states) 

over to the NGCC capacity in that state. EPA should 

consider acknowledging this up front in the request 

for comment, since some states are certain to notice 

the equity issue, based on first principles 

understanding of what BBs 1 and 2 do, which is 

more evident than for the combined BB1,2,3,4 

approach where other RE and EE factors serve to 

smooth out disparities. 

Comment [A3]: If the columns don’t differ, 

suggest merging and explaining in text. 

But KY and WY do differ by very small amounts 

between columns. Suggest that this is going to need 

an explanation. 

Comment [A4]: Suggest right justifying columns 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

3-3 

Wyoming 2,151 2,145 

3 Emissions and Power Sector Impacts 

Tables 2 through 16 report the emissions and power sector impacts of the two-block approach. 

Table 2. Projected CO2 Emission Impacts 

 CO2 Emissions 

 (MM Tonnes) 

CO2 Emissions 

Reductions from Base 

Case 

(MM Tonnes)

CO2 Emissions Reductions: 

Percent Change from Base 

Case 

 2020 2025 2030 2020 2025 2030 2020 2025 2030 

Base Case 2,161 2,231 2,256 

Two-Block Regional 1,930 1,973 1,997 231 258 258 11% 12% 11% 

Two-Block State 1,908 1,947 1,963 252 284 292 12% 13% 13% 

Source: Integrated Planning Model run by EPA, 2014 

Table 3. Projected Non-CO2 Emissions Reductions, 2020-2030 

Base Case 

Two Block Two Block 

Regional State Regional State 

2020 

SO2 (thousand tons) 1,476 1,243 1,199 -15.7% -18.8% 

NOX (thousand tons) 1,559 1,319 1,286 -15.4% -17.5% 

Hg (tons) 8.3 7.2 7.1 -12.8% -13.9% 

HCl (thousand tons) 8 8 8 2.9% -1.3% 

2025 

SO2 (thousand tons) 1,515 1,228 1,171 -19.0% -22.7% 

NOX (thousand tons) 1,587 1,317 1,272 -17.0% -19.9% 

Hg (tons) 8.7 7.4 7.3 -14.9% -16.2% 

HCl (thousand tons) 8 9 9 0.8% 3.0% 

2030 

SO2 (thousand tons) 1,530 1,188 1,134 -22.3% -25.9% 

NOX (thousand tons) 1,537 1,274 1,224 -17.1% -20.4% 

Hg (tons) 8.8 7.4 7.3 -15.2% -16.8% 

HCl (thousand tons) 9 9 9 0.0% -5.1% 

Source: Integrated Planning Model run by EPA, 2014 

Table 4. Annualized Compliance Costs (billions of 2011$) 

 2020 2025 2030 

Two Block Regional 3.2 3.0 6.8 

Two Block State 4.4 4.6 9.8 

Source: Integrated Planning Model run by EPA, 2014 

Comment [A5]: Is there going to be a discussion 

of the option of setting up a re-evaluation schedule in 

2020 or 2025, since most of the benefits have 

already occurred by then in this two BB approach? 

Is there a legal opinion regarding whether the CAA 

supports such a re-evaluation under 111(d)? 

Comment [A6]: Why does this cost so much in 

2030, when there are no incremental emission 

reductions from 2025, indeed from 2020? If most of 

the action to implement BB1 and 2 is prior to 2020, 

what is the late hitting cost? 
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Table 51. Generation Mix (thousand GWh) 

 Base Case Two Block Two Block 

 Regional State Regional State 

2020 

Pulverized Coal 1,665 1,435 1,395 -14% -16% 

NG Combined Cycle (existing) 1,003 1,178 1,170 17% 17% 

NG Combined Cycle (new) 85 153 199 81% 134% 

Combustion Turbine 19 34 31 79% 63% 

Oil/Gas Steam 52 25 26 -51% -50% 

Non-Hydro Renewables 299 300 300 0% 0% 

Hydro 280 283 283 1% 1% 

Nuclear 817 819 819 0% 0% 

Other 8 8 9 11% 17% 

Total 4,227 4,235 4,232 0% 0%

2025 

Pulverized Coal 1,702 1,420 1,377 -17% -19% 

NG Combined Cycle (existing) 919 1,176 1,144 28% 24% 

NG Combined Cycle (new) 280 310 385 11% 37% 

Combustion Turbine 27 42 38 57% 42% 

Oil/Gas Steam 37 20 20 -47% -46% 

Non-Hydro Renewables 335 335 335 0% 0% 

Hydro 280 282 282 1% 1% 

Nuclear 817 819 819 0% 0% 

Other 6 7 8 15% 22% 

Total 4,404 4,411 4,408 0% 0%

2030 

Pulverized Coal 1,668 1,372 1,321 -18% -21% 

NG Combined Cycle (existing) 810 1,144 1,099 41% 36% 

NG Combined Cycle (new) 599 551 644 -8% 7% 

Combustion Turbine 23 43 40 92% 78% 

Oil/Gas Steam 23 15 17 -34% -29% 

Non-Hydro Renewables 350 352 353 1% 1% 

Hydro 280 282 282 1% 1% 

Nuclear 797 797 797 0% 0% 

Other 6 7 8 23% 31% 

Total 4,557 4,565 4,560 0% 0%

Note: “Other” mostly includes MSW and fuel cells. Source: Integrated Planning Model run by EPA, 2014

Table 6. Total Generation Capacity by 2020-2030 (GW) 

 Base 

Case 

Two Block Two Block 

 Reg. State Reg. State 

2020 

Pulverized Coal 244 219 211 -10% -13% 

NG Combined Cycle (existing) 219 221 221 1% 1% 

NG Combined Cycle (new) 12 23 30 93% 153% 

Comment [A7]: The different columns are going 

to need more explanation in the titles, like 

Generation, and Percent Change from Base Case. 

Also, in the other reports the State has been the 

column before Regional, I think. Check for 

consistency; if different, a brief explanation might be 

helpful.. 
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Combustion Turbine 146 148 149 1% 2% 

Oil/Gas Steam 83 80 79 -3% -5% 

Non-Hydro Renewables 93 93 93 0% 1% 

Hydro 101 101 101 0% 0% 

Nuclear 103 103 103 0% 0% 

Other 5 5 5 2% 2% 

Total 1,005 994 992 -1% -1% 

2025 

Pulverized Coal 243 219 211 -10% -13% 

NG Combined Cycle (existing) 219 221 221 1% 1% 

NG Combined Cycle (new) 39 49 59 25% 52% 

Combustion Turbine 149 160 160 8% 7% 

Oil/Gas Steam 82 80 79 -2% -4% 

Non-Hydro Renewables 103 104 104 0% 1% 

Hydro 101 101 101 0% 0% 

Nuclear 103 103 103 0% 0% 

Other 5 5 5 2% 2% 

Total 1,044 1,042 1,043 0% 0% 

2030 

Pulverized Coal 240 217 209 -10% -13% 

NG Combined Cycle (existing) 219 221 221 1% 1% 

NG Combined Cycle (new) 84 88 101 6% 20% 

Combustion Turbine 156 174 172 12% 10% 

Oil/Gas Steam 82 80 79 -2% -4% 

Non-Hydro Renewables 107 108 109 1% 1% 

Hydro 101 101 101 0% 0% 

Nuclear 101 101 101 0% 0% 

Other 5 5 5 2% 2% 

Total 1,095 1,095 1,096 0% 0% 

Source: Integrated Planning Model run by EPA, 2014 

Table 7. Projected Capacity Factor of Existing Coal Steam and Natural Gas Combined 

Cycle Capacity 

 Existing Coal Steam Existing Natural Gas Combined 

Cycle 

 2020 2025 2030 2020 2025 2030 

Base Case 78% 80% 79% 52% 48% 42% 

Option 2 Regional 75% 74% 72% 61% 61% 59% 

Option 2 State 75% 74% 72% 60% 59% 57% 

Source: Integrated Planning Model run by EPA, 2014
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Table 8. Projected Capacity Additions, Gas (GW) 

Cumulative Capacity Additions: Gas 

Combined Cycle 

Incremental Cumulative Capacity 

Additions: Gas Combined Cycle 

2020 2025 2030 2020 2025 2030 

Base Case 11.9 38.9 83.8 

Two Block Regional 23.1 48.7 88.5 11.1 9.8 4.7 

Two Block State 30.2 58.9 100.7 18.3 20.0 17.0 

Source: Integrated Planning Model run by EPA, 2014 

Table 9. Projected Capacity Additions, Non-hydro Renewable (GW) 

Cumulative Capacity Additions: 

Renewables 

Incremental Cumulative Capacity Additions: 

Renewables 

2020 2025 2030 2020 2025 2030 

Base Case 17.8 28.4 32.7 

Two Block Regional 18.1 28.8 33.6 0.3 0.4 0.8 

Two Block State 18.3 29.0 33.7 0.5 0.6 1.0 

Source: Integrated Planning Model run by EPA, 2014 

Table 10. Coal Production for the Electric Power Sector, 2020 

Coal Production (MM Tons) Percent Change from Base Case 

Two Block Two Block 

Base Case Regional State Regional State 

Appalachia 140 110 111 -22% -21% 

Interior 249 230 224 -8% -10% 

West 446 324 308 -27% -31% 

Waste Coal 9 11 11 20% 20% 

Imports 0 0 0 

Total 844 675 654 -20% -22% 

Source: Integrated Planning Model run by EPA, 2014 

Table 11. Power Sector Gas Use 

Power Sector Gas Use (TCF) Percent Change in Power Sector Gas Use 

2020 2025 2030 2020 2025 2030 

Base Case 8.35 8.88 9.89 

Two Block Regional 9.90 10.90 12.14 18.7% 22.7% 22.6% 

Two Block State 10.14 11.14 12.42 21.5% 25.5% 25.6% 

Table 12. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu) 

Minemouth Delivered - Electric Power Sector 

2020 2025 2030 2020 2025 2030 

Base Case 1.73 1.88 2.06 2.62 2.80 2.98 

Two Block Regional 1.55 1.69 1.81 2.32 2.47 2.59 

Two Block State 1.55 1.69 1.83 2.31 2.47 2.62 
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Table 13. Projected Average Minemouth and Delivered Coal Prices: Percent Change from 

Base Case Projections 

Minemouth Delivered - Electric Power Sector 

2020 2025 2030 2020 2025 2030 

Two Block Regional -10.6% -10.5% -12.1% -11.5% -11.8% -13.0% 

Two Block State -10.5% -10.4% -11.0% -11.7% -11.9% -12.2% 
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Table 14. Projected Natural Gas Prices (2011$/MMBtu) 

 Henry Hub Delivered - Electric Power 

Sector 

 2020 2025 2030 2020 2025 2030 

Base Case 4.98 5.68 6.00 5.36 6.11 6.39 

Two Block Regional 5.53 6.06 6.62 5.90 6.42 6.94 

Two Block State 5.56 6.10 6.73 5.93 6.45 7.05 

Table 15. Projected Natural Gas Prices: Percent Change from Base Case Projections 

 Henry Hub Delivered - Electric Power 

Sector 

 2020 2025 2030 2020 2025 2030 

Two Block Regional 10.9% 6.7% 10.3% 9.9% 5.0% 8.6% 

Two Block State 11.5% 7.3% 12.2% 10.6% 5.5% 10.3% 

Table 16. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices 

(cents/kWh) 

 

2020 Projected Retail Price 

(cents/kWh) 

Percent Change from Base 

Case 

 

Base 

Case 

Two Block 

Regional 

Two Block 

State 

Two Block 

Regional 

Two Block 

State 

ERCT 9.9 10.3 10.4 4.8% 5.1% 

FRCC 10.6 11.0 11.2 3.6% 5.1% 

MROE 10.4 10.5 10.5 0.6% 0.5% 

MROW 9.2 9.2 9.3 0.0% 1.8% 

NEWE 13.8 14.2 14.5 2.5% 5.0% 

NYCW 18.0 18.3 18.5 1.9% 2.6% 

NYLI 14.7 15.1 15.2 2.5% 3.3% 

NYUP 12.7 13.0 13.1 2.1% 2.8% 

RFCE 12.2 12.6 12.7 3.2% 3.6% 

RFCM 10.7 10.7 10.8 0.3% 1.1% 

RFCW 10.1 10.3 10.4 1.7% 2.5% 

SRDA 9.0 9.2 9.2 2.8% 2.6% 

SRGW 9.3 9.5 9.6 1.9% 2.9% 

SRSE 10.4 10.4 10.5 0.5% 0.7% 

SRCE 8.2 8.2 8.2 -0.9% -0.9% 

SRVC 10.7 10.6 10.7 -0.3% -0.1% 

SPNO 10.6 10.5 10.5 -1.0% -1.3% 

SPSO 8.3 8.8 8.8 5.0% 5.3% 

AZNM 10.5 10.7 10.8 1.7% 2.9% 
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CAMX 14.3 14.6 14.6 2.2% 2.4% 

NWPP 7.3 7.4 7.4 1.3% 1.4% 

RMPA 8.9 9.0 9.2 0.7% 2.9% 

Contiguous 

U.S. 

10.4 10.6 10.7 1.9% 2.5% 

Table 17. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices 

(cents/kWh) 

 2025 Projected Retail Price (cents/kWh) Percent Change from Base Case 

 Base Case Two Block 

Regional Two Block State Two Block 

Regional Two Block State 

ERCT 11.2 11.5 11.5 2.5% 3.0% 

FRCC 10.9 11.2 11.3 2.6% 4.0% 

MROE 10.5 10.5 10.4 0.3% -0.8% 

MROW 9.2 9.4 9.6 1.9% 4.2% 

NEWE 14.2 14.6 14.9 2.2% 4.4% 

NYCW 18.8 19.2 19.3 2.0% 2.3% 

NYLI 15.6 16.0 16.1 2.9% 3.3% 

NYUP 13.2 13.5 13.6 2.0% 2.3% 

RFCE 12.6 12.8 12.9 1.6% 2.0% 

RFCM 10.7 10.8 10.9 0.7% 1.8% 

RFCW 10.9 11.0 11.1 0.7% 1.0% 

SRDA 9.3 9.6 9.6 2.7% 3.0% 

SRGW 10.1 10.3 10.3 1.3% 1.9% 

SRSE 10.3 10.5 10.5 1.1% 1.1% 

SRCE 8.2 8.1 8.1 -1.5% -1.7% 

SRVC 10.6 10.6 10.6 -0.1% 0.1% 

SPNO 10.3 10.3 10.3 -0.2% -0.3% 

SPSO 8.8 9.3 9.2 4.9% 4.4% 

AZNM 10.8 11.1 11.3 2.7% 4.7% 

CAMX 13.9 14.1 14.1 1.0% 1.0% 

NWPP 7.4 7.4 7.5 0.7% 1.7% 

RMPA 9.4 9.5 9.7 0.9% 3.3% 

Contiguous U.S. 

10.8 10.9 11.0 1.4% 2.0% 

Table 18. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices 

(cents/kWh) 
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 2030 Projected Retail Price (cents/kWh) Percent Change from Base Case 

 Base Case Two Block 

Regional Two Block State Two Block Regional Two Block State 

ERCT 11.6 12.1 12.1 4.0% 4.8% 

FRCC 10.9 11.5 11.6 5.0% 6.3% 

MROE 10.5 10.7 10.7 2.3% 1.6% 

MROW 9.4 9.6 9.8 2.1% 4.9% 

NEWE 15.1 15.4 15.4 2.0% 1.8% 

NYCW 19.9 20.3 20.3 1.7% 1.6% 

NYLI 16.9 17.2 17.1 1.8% 1.3% 

NYUP 14.2 14.5 14.5 1.7% 1.6% 

RFCE 12.4 12.8 12.8 2.8% 3.4% 

RFCM 10.8 11.0 11.1 1.8% 2.9% 

RFCW 11.2 11.4 11.4 1.3% 1.8% 

SRDA 9.5 9.9 10.0 4.5% 5.0% 

SRGW 10.4 10.5 10.6 0.9% 1.6% 

SRSE 10.4 10.6 10.7 2.4% 2.6% 

SRCE 8.1 8.1 8.1 -1.2% -1.2% 

SRVC 10.4 10.4 10.5 0.6% 1.1% 

SPNO 10.2 10.2 10.2 -0.1% 0.2% 

SPSO 9.1 9.7 9.6 6.3% 6.1% 

AZNM 11.5 11.8 12.0 2.7% 4.4% 

CAMX 14.1 14.4 14.5 1.9% 2.3% 

NWPP 7.4 7.5 7.6 1.2% 2.6% 

RMPA 9.9 10.1 10.4 2.1% 5.7% 

Contiguous U.S. 

10.9 11.2 11.3 2.3% 2.9% 

4 Estimated Climate Benefits from CO2 Emission Reductions 

Tables 19 through 21 report the climate benefits estimated in three analysis years (2020, 

2025, and 2030) for the two illustrative compliance scenarios (i.e., state and regional) evaluated. 
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Table 19. Estimated Global Climate Benefits of CO2 Reductions for Building Blocks 1 and 

2 in 2020 (billions of 2011$)* 

Discount Rate and Statistic State Regional 

Million metric tonnes of CO2 reduced 252 231 

5% (average) $3.2 $3.0 

3% (average) $12 $11 

2.5% (average) $17 $16 

3% (95th percentile) $34 $31 

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

Table 20 Estimated Global Climate Benefits of CO2 Reductions in 2025 (billions of 2011$)* 

Discount Rate and Statistic State Regional 

Million metric tonnes of CO2 reduced 284 258 

5% (average) $4.2 $3.9 

3% (average) $14 $13 

2.5% (average) $21 $19 

3% (95th percentile) $43 $39 

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

Table 21 Estimated Global Climate Benefits of CO2 Reductions in 2030 (billions of 2011$)* 

Discount Rate and Statistic State Regional 

Million metric tonnes of CO2 reduced 292 258 

5% (average) $5.0 $4.4 

3% (average) $16 $14 

2.5% (average) $23 $21 

3% (95th percentile) $50 $44 

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

5 Estimated Human Health Co-Benefits 

Tables 22 through 24 provide the emission reductions estimated to occur in three analysis 

years (2020, 2025, and 2030) for two illustrative compliance scenarios (i.e., state and regional) 

by region (i.e., East, West, and California). Tables 25 through 27 report the health co-benefits 

estimated in three analysis years (2020, 2025, and 2030) for the two illustrative compliance 

scenarios (i.e., state and regional) evaluated. 

Table 22 Emission Reductions of Criteria Pollutants in 2020 (thousands of short tons) 

Region SO2 All-year NOx Ozone-Season NOx 

State 

East 253 233 96 

West 23 40 17 

California 0 0 0 
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Total 277 273 113 

Regional 216 209 86 

East 15 29 12 

West 1 2 1 

California 232 240 98 

Total 216 209 86 

Table 23 Emission Reductions of Criteria Pollutants in 2025 (thousands of short tons) 

Region SO2 All-year NOx Ozone-Season NOx 

State 

East 318 265 111 

West 26 41 17 

California 0 9 1 

Total 344 315 128 

Regional 

East 265 237 97 

West 22 26 11 

California 1 8 1 

Total 287 270 109 

Table 24 Emission Reductions of Criteria Pollutants in 2030 (thousands of short tons) 

Region SO2 All-year NOx Ozone-Season NOx 

State 

East 365 265 108 

West 31 48 21 

California 0 0 0 

Total 396 313 128 

Regional 

East 316 233 92 

West 26 29 12 

California 1 1 0 

Total 342 263 105 
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Table 25 Summary of Estimated Monetized Health Co-benefits in 2020 (millions of 

2011$)* 

Pollutant 3% Discount Rate 7% Discount Rate 

State 

SO2 $10,000 to $23,000 $9,300 to $21,000 

NOx (as PM2.5) $1,600 to $3,600 $1,400 to $3,200 

NOx (as Ozone) $450 to $1,900 $450 to $1,900 

Total $12,000 to $29,000 $11,000 to $26,000

Regional 

SO2 $8,900 to $20,000 $8,000 to $18,000 

NOx (as PM2.5) $1,500 to $3,300 $1,300 to $3,000 

NOx (as Ozone) $410 to $1,700 $410 to $1,700 

Total $11,000 to $25,000 $9,800 to $23,000

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which 

drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. 

Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-perton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). 
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Table 26 Summary of Estimated Monetized Health Co-benefits in 2025 (millions of 

2011$)* 

Pollutant 3% Discount Rate 7% Discount Rate 

State 

SO2 $14,000 to $32,000 $13,000 to $29,000 

NOx (as PM2.5) $2,100 to $4,800 $1,900 to $4,300 

NOx (as Ozone) $680 to $2,900 $680 to $2,900 

Total $17,000 to $40,000 $15,000 to $36,000

Regional 

SO2 $12,000 to $27,000 $11,000 to $24,000 

NOx (as PM2.5) $1,900 to $4,200 $1,700 to $3,800 

NOx (as Ozone) $600 to $2,600 $600 to $2,600 

Total $14,000 to $34,000 $13,000 to $30,000

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which 

drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. 

Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-perton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). 

Table 27 Summary of Estimated Monetized Health Co-benefits in 2030 (millions of 

2011$)* 

Pollutant 3% Discount Rate 7% Discount Rate 

State 

SO2 $17,000 to $39,000 $16,000 to $35,000 

NOx (as PM2.5) $2,100 to $4,700 $1,900 to $4,200 

NOx (as Ozone) $700 to $3,000 $700 to $3,000 

Total $20,000 to $47,000 $18,000 to $43,000 

Regional 

SO2 $15,000 to $34,000 $14,000 to $31,000 

NOx (as PM2.5) $1,800 to $4,100 $1,700 to $3,700 

NOx (as Ozone) $600 to $2,600 $600 to $2,600 

Total $18,000 to $41,000 $16,000 to $37,000 

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which 

drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. 

Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-perton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). 
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6 Combined Climate Benefits and Health Co-benefits 

Tables 28 through 30 provide the combined climate and health benefits for each 

compliance scenarios evaluated for 2020, 2025, and 2030. 

Table 28 Combined Climate Benefits and Health Co-Benefits in 2020 (billions of 2011$)* 

SCC Discount Rate 

Climate 

Benefits 

Only 

Climate Benefits and Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits) 

3% 7% 

State 252 million metric tonnes CO2

5% $3.2 $16 to $32 $14 to $29 

3% $12 $24 to $40 $23 to $38 

2.5% $17 $30 to $46 $28 to $43 

3% (95th percentile) $34 $47 to $63 $46 to $61 

Regional 231 million metric tonnes CO2

5% $3.0 $14 to $28 $13 to $26 

3% $11 $21 to $36 $20 to $33 

2.5% $16 $27 to $41 $26 to $39 

3% (95th percentile) $31 $42 to $57 $41 to $54 

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The 

monetized health co-benefits do not include reduced health effects from direct exposure to NO2, SO2, and HAP; 

ecosystem effects; or visibility impairment. 

Table 29 Combined Climate Benefits and Health Co-Benefits in 2025 (billions of 2011$)* 

SCC Discount Rate 

Climate 

Benefits 

Only 

Climate Benefits and Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits) 

3% 7% 

State 284 million metric tonnes CO2

5% $4.2 $21 to $44 $20 to $40 

3% $14 $31 to $54 $30 to $50 

2.5% $21 $38 to $61 $36 to $57 

3% (95th percentile) $43 $60 to $83 $59 to $79 

Regional 258 million metric tonnes CO2

5% $3.9 $18 to $37 $17 to $34 

3% $13 $27 to $46 $26 to $43 

2.5% $19 $33 to $52 $32 to $49 

3% (95th percentile) $39 $54 to $73 $52 to $70 

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is 

important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. 
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Table 30 Combined Climate Benefits and Health Co-Benefits in 2030 (billions of 2011$)* 

SCC Discount Rate 

Climate 

Benefits 

Only 

Climate Benefits and Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits) 

3% 7% 

State 292 million metric tonnes CO2

5% $5.0 $25 to $52 $23 to $48 

3% $16 $36 to $63 $35 to $59 

2.5% $23 $44 to $70 $42 to $66 

3% (95th percentile) $50 $70 to $97 $68 to $92 

Regional 258 million metric tonnes CO2

5% $4.4 $22 to $45 $20 to $41 

3% $14 $32 to $55 $30 to $51 

2.5% $21 $38 to $62 $37 to $58 

3% (95th percentile) $44 $61 to $85 $60 to $81 

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is 

important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. 

7 Net Benefits 

Tables 31 through 33 summarize the benefits, costs, and net benefits for 2020, 2025, and 

2030 for the two illustrative compliance scenarios evaluated for building blocks 1 and 2 only. 
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Table 31 Summary of Monetized Benefits, Compliance Costs, and Net Benefits in 2020 

(billions of 2011$) a

State 

3% Discount Rate 7% Discount Rate 

Climate Benefits b 

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$3.2 

$12 

$17 

$34 

Air pollution health co-benefits c $12 to $29 $11 to $26 

Total Compliance Costs d $4.4 

Net Benefits e $20 to $36 $18 to $33 

Non-Monetized Benefits 

Direct exposure to SO2 and NO2

1.1 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Regional 

3% Discount Rate 7% Discount Rate 

Climate Benefits b

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$3.0 

$11 

$16 

$31 

Air pollution health co-benefits c $11 to $25 $9.8 to $23 

Total Compliance Costs d $3.2 

Net Benefits e $18 to $32 $17 to $30 

Non-Monetized Benefits 

Direct exposure to SO2 and NO2

1.1 tons of Hg 

Ecosystem Effects 

Visibility impairment a

 All estimates are for 2020 and are rounded to two significant figures, so figures may not sum. 

b

 The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not account for 

changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO2

emissions are long-lived and subsequent damages occur over many years. The SCC estimates are year-specific and increase over 

time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission reductions of directly 

emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions from different epidemiology studies. 

The reduction in premature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM2.5 and ozone. 

These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature 

mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model and a discount 

rate of approximately 5%. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy 

efficiency program and participant costs. 

e

 The estimates of net benefits in this summary table are calculated using the global SCC at a 3 percent discount rate (model 

average). Tables 10-12 in this memo present combined climate and health estimates based on additional discount rates. 
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Table 32 Summary of Monetized Benefits, Compliance Costs, and Net Benefits in 2025 

(billions of 2011$) a

State 

3% Discount Rate 7% Discount Rate 

Climate Benefits b 

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$4.2 

$14 

$21 

$43 

Air pollution health co-benefits c $17 to $40 $15 to $36 

Total Compliance Costs d $4.6 

Net Benefits e $27 to $49 $25 to $46 

Non-Monetized Benefits 

Direct exposure to SO2 and NO2

1.4 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Regional 

3% Discount Rate 7% Discount Rate 

Climate Benefits b

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$3.9 

$13 

$19 

$39 

Air pollution health co-benefits c $14 to $34 $13 to $30 

Total Compliance Costs d $3.0 

Net Benefits e $24 to $43 $23 to $40 

Non-Monetized Benefits 

Direct exposure to SO2 and NO2

1.3 tons of Hg 

Ecosystem Effects 

Visibility impairment a

 All estimates are for 2025 and are rounded to two significant figures, so figures may not sum. 

b

 The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not account for 

changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO2

emissions are long-lived and subsequent damages occur over many years. The SCC estimates are year-specific and increase over 

time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission reductions of directly 

emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions from different epidemiology studies. 

The reduction in premature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM2.5 and ozone. 

These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature 

mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model and a discount 

rate of approximately 5%. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy 

efficiency program and participant costs. 

e

 The estimates of net benefits in this summary table are calculated using the global SCC at a 3 percent discount rate (model 

average). Tables 10-12 in this memo present combined climate and health estimates based on additional discount rates. 
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Table 33 Summary of Monetized Benefits, Compliance Costs, and Net Benefits in 2030 

(billions of 2011$) a

State 

3% Discount Rate 7% Discount Rate 

Climate Benefits b 

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$5.0 

$16 

$23 

$50 

Air pollution health co-benefits c $20 to $47 $18 to $43 

Total Compliance Costs d $9.8 

Net Benefits e $27 to $53 $25 to $49 

Non-Monetized Benefits 

Direct exposure to SO2 and NO2

1.5 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Regional 

3% Discount Rate 7% Discount Rate 

Climate Benefits b

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$4.4 

$14 

$21 

$44 

Air pollution health co-benefits c $18 to $41 $16 to $37 

Total Compliance Costs d $6.8 

Net Benefits e $25 to $48 $23 to $45 

Non-Monetized Benefits 

Direct exposure to SO2 and NO2

1.3 tons of Hg 

Ecosystem Effects 

Visibility impairment a

 All estimates are for 2030, and are rounded to two significant figures, so figures may not sum. 

b

 The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not account for 

changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO2

emissions are long-lived and subsequent damages occur over many years. The SCC estimates are year-specific and increase over 

time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission reductions of directly 

emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions from different epidemiology studies. 

The reduction in premature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM2.5 and ozone. 

These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature 

mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning and a discount rate of 

approximately 5%. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency 

program and participant costs. 

e

 The estimates of net benefits in this summary table are calculated using the global SCC at a 3 percent discount rate (model 

average). Tables 10-12 in this memo present combined climate and health estimates based on additional discount rates.
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8 Labor 

Table 34 presents the labor changes estimated in job-years associated with the illustrative 

building blocks 1 and 2 analysis. 

Table 34 Engineering-Baseda

 Changes in Labor Utilization, Building Blocks 1 & 2 (Number 

of Job-Years of Employment in Year)

Category Regional Compliance State Compliance 

2017-2020 2021-2025 2026-2030 2017-2020 2021-2025 2026-2030 

Construction-related (One-time) Changes*

Heat Rate Improvement: Total 37,400 0 0 35,300 0 0 

Boilermakers and General 

Construction 26,000 0 0 24,500 0 0 

Engineering and Management 6,900 0 0 6,500 0 0 

Equipment-related 3,300 0 0 3,200 0 0 

Material-related 1,200 0 0 1,100 0 0 

New Capacity Construction: 

Total 7,700 7,300 1,600 11,000 9,400 1,000 

Renewables 0 500 1,600 -700 500 1,600 

Natural Gas 7,700 6,800 0 11,700 8,900 -600 

Recurring Changes**

2020 2025 2030 2020 2025 2030 

Operation and Maintenance: 

Total -10,970 -9,500 -8,300 -14,100 -12,200 -10,600 

Changes in Gas 1,330 1,500 1,100 2,200 2,400 2,100 

Retired Coal -11,800 -10,700 -9,200 -15,600 -14,100 -12,300 

Retired Oil and Gas -500 -300 -200 -700 -500 -400 

Fuel Extraction: Total -1,900 -1,000 -700 -1,500 -1,000 -800 

Coal -9,700 -11,100 -12,000 -10,500 -12,400 -13,500 

Natural Gas 7,800 10,100 11,300 9,000 11,400 12,700 

Supply Side Employment 

Impacts – Quantified 32,230 -3,200 -7,400 30,700 -3,800 -10,400 

a

 Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates 

(tons coals or MMBtu gas)

b

All job-year estimates on this are Full Time Equivalent (FTE) jobs. Job estimates in the Demand-Side energy 

efficiency section (below) include both full time and part time jobs

*Construction-related job-year changes are one-time impacts, occurring during each year of the 2 to 4 year period 

during which construction and HRI installation activities occur. Figures in table are average job-years during each of 

the years in each range. Negative job-year estimates when additional generating capacity must be built in the base 

case, but is avoided in the Guideline implementation scenarios due to HRI or Demand-side energy efficiency 

programs. 

**Recurring Changes are job-years associated with annual recurring jobs including operating and maintenance 

activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of positive job-years, 

while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job-years. In 

addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 

2020; the recurring jobs prior to 2020 are not estimated.
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9 References 

Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987-

2000.” Journal of the American Medical Association, 292(19): pp. 2372-8. 

Krewski D; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes and Y. Shi, et al. 2009. Extended FollowUp and Spatial Analysis of the American Cancer Society Study Linking Particulate Air 

Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. 

Lepeule, J.; F. Laden; D. Dockery and J. Schwartz. 2012. “Chronic Exposure to Fine Particles 

and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 

2009.” Environmental Health Perspectives, July, 120(7): pp. 965-70. 

Levy, J.I.; S.M. Chemerynski and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An 

Empiric Bayes Meta-regression Analysis.” Epidemiology, 16(4): pp. 458-68. 
1 

May 20, 2014 

Aggregate Emissions Reduction Opportunities from Heat Rate Improvements at Coal-Fired Plants 

Dallas Burtraw, Josh Linn, Anthony Paul, Peter Vail

Resources for the Future1

 This memorandum describes the aggregate carbon dioxide (CO2) emissions reductions 

(measured in short tons) that could be achieved from improvements in heat rates across the fleet of U.S. 

coal-fired power plants. The emissions reduction opportunities are based on statistical estimates 

derived in “Regulating Greenhouse Gases from Coal Power Plants under the Clean Air Act,” Joshua Linn, 

Erin Mastrangelo and Dallas Burtraw, Journal of the Association of Environmental and Resource 

Economists 2014, 1:1 (forthcoming). Those estimates rely on panel data for a twenty-five year period 

1985–2009. Those data and estimated coefficients describe the change in heat rate that was observed in

response to a change in fuel input prices. The estimates are useful to predict the responsiveness of heat 

rates to regulatory initiatives that would in effect impose an increase in input prices, either directly 

through a price (such as cap and trade) or through regulatory constraints. 

 The observed heterogeneity in operating performance validates the general availability of 

emission reduction opportunities across the fleet identified by the EPA in previous technical documents. 

With no change in utilization and not accounting for fuel savings, EPA (2008. p. 16.) estimates that a 5 

percent efficiency improvement would produce emissions reductions of 90 million short tons per year, 

about 1.6 percent of total U.S. emissions in 2009 (Environmental Protection Agency, 2008. Technical 

Support Document for the Advance Notice of Proposed Rulemaking for Greenhouse Gases: Stationary 

Sources. Washington DC.) Drawing on estimates from Sargent & Lundy, L.L.C. (2009, Coal-Fired Power 

Plant Heat Rate Reductions) these reductions could be achieved at a cost of $10.74 to $63.91 per ton 

CO2 before accounting for the value of saved energy. 

The statistical estimates in Linn et al. describe a behavioral response that can be compared to 

engineering cost estimates of investment opportunities (Sargent & Lundy 2009). Linn et al. estimates 

suggest these emissions reductions could be achieved at costs that are consistent with Sargent & Lundy. 

For example, Linn et al. find that a 10 percent increase in coal prices, corresponding to a tax on CO2 

emissions of about $1.64 per ton, causes a 0.2 to 0.5 percent heat rate reduction with no change in 

utilization of the facilities, which is broadly consistent with engineering assessments. Change in 

utilization in Linn et al. means there is no change in the output from the plant, so the capacity factor 

remains unchanged; however, the estimates do account for savings associated with a reduction in fuel 

use and other variable costs resulting from heat rate improvements. 2 Linn et al. find an increase in coal 

prices of about 60 percent (commensurate with a $10 per ton tax on CO2 emissions) would stimulate a 1 

to 2 percent heat rate reduction (holding fixed utilization). This range of estimates encompasses the 

 

1

 Direct comments to: Burtraw@RFF.org; 202.328.5087 

2

 Among the values used from that paper is the coefficient on the log price of coal including the associated SO2

allowance cost, where relevant. The coefficient is interpreted as the elasticity of the heat rate to the price of coal 

in the data. The value that is used is the estimated elasticity with fixed effects, weighted by capacity. This elasticity 

value is -0.013. 
2 

estimates suggested in Sargent & Lundy but includes the possibility of somewhat lower costs than have 

been estimated. 

 Figure 1 describes the annualized investment costs (measured in 2009 dollars) and the 

emissions reduction (million short tons of CO2) that could be achieved by improving the heat rates at 

existing plants. The curves display cost effective schedules, wherein the marginal cost of investments

per ton of emissions reduction that are achieved strictly through heat rate reductions are equal across 

the industry. The analysis includes all coal plants operating in 2009. The two curves represent estimates 

based on utilization observed in 2009 and in 2012. There was lower average utilization of coal units in 

2012 due in part to the retirement of several small, relatively inefficient units, according to EIA. Hence, 

one would expect to find fewer opportunities for heat rate improvements in the remaining fleet and this 

is evident in the figure. However, there is a qualitative difference in the information represented in the 

two curves. The estimate based on 2009 utilization is consistent with other parameters such as relative 

fuel prices that are used to estimate the model. In other years the variable costs of operating a coal

facility including the relative cost of different fuels will change, making the estimate less reliable for 

other years. For example, the estimate for 2012 is less reliable because it is using utilization rates from 

2012 but other parameters associated with 2009. Therefore the curve for 2012 utilization is illustrated 

as a broken line and should be interpreted as suggestive directionally of trends, compared to 2009, but 

the specific estimate is less reliable than the curve for 2009. 

Figure 1. Emissions reduction versus total heat rate investment costs with constant utilization: 2009 

and 2012 utilization rates 

The evidence presented in Figure 1 indicates, for example, that in 2009 about 50 million tons of 

emissions reductions could have been achieved at a cost of $94 million dollars (see Table 1). This would 

correspond to 3 percent reduction in heat input or equivalently that same percent reduction in heat 
3 

rate. Emissions reductions of 100 million tons would incur annualized costs of about $1.1 billion dollars. 

These reductions would constitute 4.5 percent of total emissions from the electricity sector in 2012, and 

about 6 percent of emissions from just the coal fleet.3 

The values should be acknowledged to be uncertain not only due to the potential change in the 

utilization of facilities going forward, or their potential retirement, but also stemming from the 

estimated coefficients and model used in Linn et al. The values illustrated in Figure 1 use the estimated 

elasticity with fixed effects for each generating unit, weighted by capacity, of -0.013. 

Figure 2 repeats this information, which is the authors’ preferred estimate, but compares it with 

a model that does not include unit fixed effects, resulting in an estimated elasticity of -0.039. (In all 

figures we compare the preferred estimate with only one alternative.) The alternative elasticity estimate 

implies substantially more investments and associated emissions reductions are available for any given

cost. For example, at a marginal cost of $40 per short ton (2009$), which is proximate to the social cost 

of carbon identified by the Interagency Working Group (2013), the elasticity value (-0.013) estimated 

with unit fixed effects (Figure 1) achieves investments of about $830 million dollars and just over 95

million short tons of CO2 emissions reductions at a marginal cost of $40 per short ton. In contrast, the 

value estimated without unit fixed effects (-0.039) implies $2.3 billion would be invested yielding 270 

million short tons of CO2 emissions reduction. 

Figure 2. . Emissions reduction versus total heat rate investment costs with constant utilization: 2009 

utilization rates with alternative elasticity estimates 

We prefer the estimate of the heat rate cost function that derives from a regression that 

includes unit fixed effects because this regression controls for the large set of technical factors of the 

 

3

 Calculations are based on information obtained at: http://www.eia.gov/tools/faqs/faq.cfm?id=77&t=11. 
4 

plant that affect heat rates, but which are difficult to measure. The regression without fixed effects

includes some technical characteristics, such as the firing type of the boiler, but there may be omitted 

factors that bias these estimates. On the other hand, the regression without unit fixed effects may be

interpreted as more of a long run cost function, which would be more appropriate for projecting the 

costs of (presumably) long-term regulation. 

Figure 3 describes these annualized investment costs as a function of the percent reduction in 

generation-weighted heat rate across the fleet associated with the estimates in Figure 2. Only the 

information for 2009 is included in this figure to avoid potential confusion regarding the basis of the 

percent calculation. The curve displays the preferred elasticity estimate (-0.013) and the alternative

estimate (-0.039). 

Figure 3. Percent reduction in generation-weighted heat rate versus total heat rate investment costs 

with constant utilization 

Figure 4 describes the marginal investment cost per ton reduced at each level of emissions 

reductions. The solid curve represents estimates based on 2009 levels of plant utilization. The broken

curve represents estimates based on 2012 levels of utilization but within the model built with other 

parameters associated with 2009. Again, we interpret this comparison as indicative of trends but the 

estimates are less reliable because of the inconsistency in the data. 
5 

Figure 4. Emissions reduction versus marginal heat rate investment costs with constant utilization: 

2009 and 2012 utilization rates 

A companion Figure 5 displays only the 2009 level of utilization, and compares the marginal 

investment cost per ton reduced using the preferred elasticity value using unit fixed effects (-0.013) with 

the alternative elasticity value without fixed effects (-0.039). As seen previously, substantially more 

investment would be anticipated with the alternative model. 

Figure 5. Emissions reduction versus marginal heat rate investment costs with constant utilization: 

2009 utilization rates with alternative elasticity estimates 
6 

Figure 6 describes these marginal investment cost per ton of emissions reduction along with the 

percent reduction in generation-weighted heat rate that is achieved associated with the estimates in 

Figure 5. Only estimates based on utilization levels in 2009 appear in Figure 6 to avoid potential 

confusion regarding the basis of the percent calculation. The curve displays the preferred elasticity 

estimate (-0.013) and the alternative estimate (-0.039). Using the preferred elasticity estimate, the 

marginal cost of a 50 million ton reduction is about $5/ton; this corresponds to a 3 percent reduction in 

the generation-weighted average heat rate. The marginal cost of a reduction of 100 million tons is 

about $50/ton; this corresponds to a 6 percent reduction in heat rate. These values indicate strong 

convexity in the schedule of opportunities. For emissions reductions beyond this amount resulting from

investments at existing coal-fired plants the predicted marginal cost schedule begins to rise steeply. The 

alternative elasticity estimate suggests that substantially more emissions reductions would be available 

at each level of marginal investment cost, and the predicted marginal cost rises less steeply over the

range that is displayed. 

Figure 6. Percent reductions in generation-weighted heat rate versus the marginal cost per ton of 

emissions reduction with constant utilization 

These estimates rely strictly on investments and other measures that could be taken at existing 

coal plants. These estimates are input to the determination of equilibrium, but do not describe that 

equilibrium. Any policy that we can imagine would not result in the emissions reductions that are 

illustrated here. For example, an inflexible policy that mandated specific investments would lead to 

retirement of some units. At other units the reduced variable costs would result in an increase in 

utilization of those units, but utilization is assumed not to change in these calculations. Assuming no 

change in utilization also means there is no change in fuel prices or other factor markets that might be 

affected after the electricity market achieves a new equilibrium in response to these investments. 

Consequently, the estimates do not correspond to outcomes from any particular policy that could be 
7 

enacted. Rather, these estimates can be viewed as inputs to the evaluation of a policy or modeling 

effort. 

Table 1 reports estimates with the preferred elasticity value and with the two different 

utilization levels for 2009 and 2012. The table reports the schedule of emissions reductions that are 

predicted from the investments, their associated total and marginal investment cost, and the heat rate

improvement in percentage terms. The results with 2012 utilization rates are indicative of trends but 

less reliable because these rates are inserted into the model calibrated with the 2009 data. 

Table 2 reports information using utilization rates for 2009 and with the two different elasticity 

values - the preferred elasticity value (-0.013) and the alternative elasticity value (-0.039). The 

alternative value from the model without unit fixed effects suggests substantially more investment 

would occur and greater emissions reductions would be available at each level of marginal cost. 

In summary, the central estimates for 2009 using the preferred elasticity estimate (-0.013) are 

likely to underestimate the emissions (overstate the emissions reductions) that result in the future from 

various types of policy due to recent and expected changes in utilization. Second, one of the most 

important sources of uncertainty is the choice of econometric model. The alternative model that does 

not include unit fixed effects results in a greater elasticity value (-0.039) that would indicate that the 

values based on the lower elasticity value overestimate the emissions (understate the emissions 

reductions) that would result from various types of policy at any level of total or marginal investment 

cost. It is also important to note the magnitude of the emissions reductions and differences in the 

estimates will depend on the policy design. 
8 

Table 1: Total Investment Costs and Marginal Reduction Schedule: 2009 and 2012 Data 

Emission 

Reduction (Million 

Short Tons CO2) 

Change in 

GenerationWeighted Heat 

Rate (%) 

Total Investment 

Cost 

(Million $) 

Marginal 

Investment Cost 

($/tCO2) 

Change in 

GenerationWeighted Heat 

Rate (%) 

Total Investme

Cost 

(Million $) 

2009 Utilization / 2009 Model 2012 Utilization / 200

α = -0.013 α = -0.013 

0 0.00 0 0 0.00 0 5 0.29 2.6 0.59 0.37 2.7 

10 0.58 6.0 0.76 0.73 6.3 15 0.87 10.3 0.96 1.10 11.2 20 1.16 15.7 1.21 1.46 18.0 25 1.45 22.5 1.52 1.83 26.9 30 1.74 31.1 1.92 2.20 38.9 35 2.03 41.8 2.41 2.56 54.9 40 2.33 55.4 3.03 2.93 76.4 45 2.62 72.5 3.82 3.29 105.1 50 2.91 94.0 4.81 3.66 143.6 55 3.20 121.1 6.06 4.02 195.1 60 3.49 155.2 7.64 4.39 264.2 65 3.78 198.2 9.64 4.76 356.9 70 4.07 252.5 12.17 5.12 481.4 75 4.36 321.0 15.37 5.49 648.9 80 4.65 407.6 19.42 5.85 871.7 85 4.94 517.1 24.57 6.22 1,174.7 90 5.23 655.6 31.10 6.59 1,583.6 95 5.52 829.0 39.38 6.95 2,136.5 

100 5.81 1,050.8 49.92 7.32 2,883.0 
9 

Table 2: Total Investment Costs and Marginal Reduction Schedule: 2009 Utilization Rates with 

Alternative Elasticity Values 

Emission 

Reduction 

(Million 

Short Tons 

CO2) 

Change in 

GenerationWeighted 

Heat Rate 

(%) – 

2009 Only 

Total 

Investment 

Cost 

(Million $) 

Marginal 

Investment 

Cost 

($/tCO2) 

Total 

Investment 

Cost 

(Million $) 

Marginal 

Investment 

Cost 

($/tCO2) 

2009 Utilization / 

2009 Model 

2009 Utilization / 

2009 Model 

α = -0.013 α = -0.039

0.0 0.0 0.0 0.00 0.0 0.00 

8.6 0.5 5.0 0.71 4.3 0.54 

17.1 1.0 12.6 1.06 9.2 0.62 

25.7 1.5 23.7 1.58 14.9 0.71 

34.4 2.0 40.5 2.35 21.5 0.82 

43.1 2.5 65.2 3.49 29.1 0.94 

51.6 3.0 102.0 5.18 37.6 1.07 

60.2 3.5 156.8 7.72 47.5 1.23 

68.8 4.0 238.4 11.51 58.8 1.41 

77.4 4.5 360.2 17.21 71.6 1.61 

86.0 5.0 542.6 25.77 86.7 1.85 

94.6 5.5 816.0 38.67 103.8 2.12 

103.2 6.0 1,226.5 58.16 123.4 2.43 

111.8 6.5 1,844.8 87.65 145.8 2.79 

120.4 7.0 2,777.9 132.40 171.5 3.20 

129.0 7.5 4,367.3 209.07 201.1 3.68 

137.6 8.0 6,333.4 305.03 235.2 4.23 

146.2 8.5 9,576.6 462.51 274.1 4.87 

154.8 9.0 14,521.5 704.88 319.0 5.60 

163.4 9.5 22,072.9 1,077.05 370.9 6.45 

172.0 10.0 33,621.9 1,649.27 430.6 7.44