## 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 TS D_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 character istics and the year to year variability in

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

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

efficiencies would result in corresponding reductions in greenhouse gas emissions.

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

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

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

the estimated capital costs of those improvements.

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

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

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

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

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

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

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

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

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

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

coal or coal refuse is burned in limited quantities.

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

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

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

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

combustion configurations.

### 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

SO 2 

### emission control). FBC have a greater fuel flexibility than PC EGUs and can be designed for combustion

within the bed to occur at atmospheric or elevated pressures. FBC operating temperatures are in the range of 1,500 to 1,650  F (800 to

900 o 

### C). Bubbling

fluidized bed (BFB) Operates at relatively low gas stream

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

Circulating

fluidized bed (CFB) Operates at higher gas stream

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

Integrated Coal

Gasification

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

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

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

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

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

Cyclone

Furnace

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

Stoker-fired

Coal

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

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



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

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

coal combustion is the coal-firing configuration predominately used at existing EGUs. The most

### 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, SO 2 

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

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

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

chemical process that creates a concentrated CO 2 

### stream that is more amenable to carbon capture

processes.

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

combustion, have been replaced at new coal-fired EGUs by more efficient methods that 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 CO 2 

### Emission Rate

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

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

a corresponding decrease in CO 2 

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

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

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

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

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

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

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

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

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

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

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

heat and power, or CHP, facilities.

### DRAFT May 15, 2014

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

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

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

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

factors affecting EGU efficiency at a given facility include:

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

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

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

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

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

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

efficiencies are lower than design efficiencies.

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

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

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

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

8,300 British thermal units per pound.

 EGU 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 coolin g 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 driv es, better materials for heat exchangers, etc.

tend to be more efficient.

2.3 Technologies to Improve Existing Coal-Fired EGU Heat Rate

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

articles and technical papers identifying potentia l efficiency improvement techniques applicable

to existing coal-fired EGUs. 1 

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

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

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

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

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

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

technical feasibility of using a given technology.

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

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

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 im prove efficiency of coal-fired EGUs. The study

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

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

2.3.1 Boiler

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

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

2.3.1.1 Materials Handling 2



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

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

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

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

strain on the other equipment.

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

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

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

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

heat rate justifiably only when the existing equipment has degraded.

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

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

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

requirements are about 0.1% (10 Btu/kWh) of the plant’s gross output.

2.3.1.2 Economizer

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

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

economizer can lead substantial heat rate improvements around 50-100 Btu/kWh, but is large

capital investment (~$2-8M). Due to this high cost, economizers upgrades are not generally

2 

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

### 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 ma ny aspects of the plan t’s operations. Commonly

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

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

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

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

2.3.1.4 Sootblowers

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

sootblowers system monitors the furnace exhaus t gas temperatures and steam temperatures.

Other readings may be incorpor ated into the intelligent so otblowers system which also

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

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

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

maintenance costs.

2.3.1.5 Air Heaters

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

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

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

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

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

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

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

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

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

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

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

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

### 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 ex change surfaces to the existing heaters or adding

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

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

repair.

2.3.2.3 Condenser

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

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

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

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

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

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

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

$80,000.

### 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 relia bility and the most efficient operation. As the

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

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

2.3.3 Flue Gas System

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

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

of variable frequency drives.

2.3.3.1 Induced Draft Fans

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

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

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

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

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

2.3.3.2 Variable Frequency Drives

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

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

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

2.3.4 Emission Control Technologies

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

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

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

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

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

electrostatic precipitator, and the selective catalytic reduction systems.

### DRAFT May 15, 2014

10 2.3.4.1 Flue Gas Desulfurization

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

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

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

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

operation, a co-current spray tower quencher may replace the unit. The capital cost is about

$2.5M with heat rate reductions around 13 Btu/kWh.

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

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


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

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

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

In a wet flue gas desulfurization multiple spray levels are installed to deliver the limestone

slurry. If a power plant is operating with SO 2 

### levels below its permit li mit, turning off one spray

level will reduce the auxiliary power necessary. If this is possible, a unit heat rate reduction of 16

Btu/kWh may be available.

2.3.4.2 Electrostatic Precipitator

The best operation for an electrostatic pr ecipitator involves maintaining the maximum

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

management system upgrades often help improve the electrostatic precipitator performance by

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

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

2.3.4.3 Selective Catalytic Reduction

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

NO X 

### emissions from power plants. Extensive modeling was performed to achieve the necessary

reduction with minimum ammonia slip. The resu lts showed that reducing pressure drop and

using secondary air as dilution for the ammonia vaporizer can reduce the auxiliary power

necessary. The heat rate reduction is 0-10 Btu/kWh and capital costs between $0.5-$2M with

fixed and variable costs up to $100,000 each.

### 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. Th e boiler water is one of the most important

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

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

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

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

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

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

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

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

aspects already mentioned.DRAFT May 15, 201412Table 2-2. Existing coal-fired EGU efficiency improvements reported for actual efficiency improvement projectsEfficiencyImprovementTechnologyDescriptionReportedEfficiencyIncreaseaCombustionControlOptimizationCombustion controls adjust coal and air flow to optimize steam production for the steam turbine/generator set.However, combustion control for a coal-fired EGU is complex and impacts a number of important operatingparameters including combustion efficiency, steam temperature, furnace slagging and fouling, and NOXformation. The technologies include instruments that measure carbon levels in ash, coal flow rates, air flow rates,CO levels, oxygen levels, slag deposits, and burner metricsas well as advanced coal nozzles and plasma assistedcoal combustion.0.15 to 0.84%Cooling SystemHeat LossRecoveryRecover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circulationthorough a cooling tower or discharge to a water body. The identified technologies include replacing the coolingtower fill (heat transfer surface) and tuning the cooling tower and condenser.0.2 to 1%Flue Gas HeatRecoveryFlue gas exit temperature from the air preheater can range from 250 to 350°F depending on the acid dew pointtemperature of the flue gas, which is dependent on the concentration of vapor phase sulfuric acid and moisture.For power plants equipped with wet FGD systems, the flue gas is further cooled to approximately 125°F as it issprayed with the FGD reagent slurry. However, it may be possible to recover some of this lost energy in the fluegas to preheat boiler feedwater via use of a condensing heat exchanger.0.3 to 1.5%Low-rank CoalDryingSubbituminous and lignite coals contain relatively large amounts of moisture (15 to 40%) compared to bituminouscoal (less than 10%). A significant amount of the heatreleased during combustion of low-rank coals is used toevaporate this moisture, rather than generate steam for the turbine. As a result, boiler efficiency is typically lowerfor plants burning low-rank coal. The technologies include using waste heat from the flue gas and/or cooling watersystems to dry low-rank coal prior to combustion.0.1 to 1.7%SootblowerOptimizationSootblowers intermittently inject high velocity jets of steam or air to clean coal ash deposits from boiler tubesurfaces in order to maintain adequate heat transfer. Proper control of the timing and intensity of individualsootblowers is important to maintain steam temperature and boiler efficiency.The identified technologies includeintelligent or neural-network sootblowing (i.e., sootblowing in response to real-time conditions in the boiler) anddetonation sootblowing.0.1 to 0.65%Steam TurbineDesignThere are recoverable energy losses that result from themechanical design or physical condition of the steamturbine. For example, steam turbine manufacturers haveimproved the design of turbine blades and steam sealswhich can increase both efficiency and output (i.e., steam turbine dense pack technology).0.84 to 2.6Source: National Energy Technology Laboratory (NETL), 2008.Reducing CO2Emissions by Improving the Efficiency of the Existing Coal-fired Power PlantFleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 2008. Available at:<http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>.aReported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual component efficiencies for a referenceplant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assumptions, the referencepower plant has an overall efficiency of 32% and a net heat rate of 10,600 Btu/kWh. As a result, if a particular efficiency improvement method was reportedto achieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewise, a reported 100 Btu/kWhdecrease in net heat rate would be converted to a 0.30% point increase in overall efficiency.

### DRAFT May 15, 2014

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### W ORKING 

### C OMMENTS ON 

### D RAFT 

### L ANGUAGE UNDER 

### EO12866

I NTERAGENCY 

### R EVIEW



### . S UBJECT TO 

### F URTHER 

### P OLICY 

### R EVIEW



### .

13 2.4 Previous Studies on Heat Rate Improvements

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

potential efficiency improvements and associated resulting CO 2 

### emission reductions. These

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

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

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

The NETL studies used a benchmarking approa ch that evaluated the design factors that

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

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

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

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

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

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

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

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

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

approach and evaluated technologies applicable to bituminous and subbituminous coals to

estimate potential fleet wide reductions.

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

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

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

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

sought to estimate overall changes in industry fl eet efficiency in response to changes in fuel

prices or carbon prices. In this specific exam ple, the coal price was assumed to be a 10%

increase and the CO 2 

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

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

EGUs and assumes a target heat rate in order fo r the EGUs to comply w ith an inferred standard.

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

approach estimated how the industry will meet any imposed standards and calculated the heat

### DRAFT May 15, 2014

I NTERAGENCY 

### W ORKING 

### C OMMENTS ON 

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


### emissions limit will be averaged, the paper disc usses many potential options that coal-fired

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

renewable, natural gas, and combined-cycle sources.

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

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

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

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

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

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

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

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

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

HRI potential for coal-fired EGUs.

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

Study

ID Study Title

Author Factors Used for Industry Grouping Key Study Assumptions Relevant HRI

Results

1 “Reducing CO 2 

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

Power Plant Fleet”

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

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

under 50% were removed

from dataset • 15% reduction in

overall heat rate of

coal-fired EGUs

2 “Improving theEfficiency of CoalFired Power Plants

for Near Term

Greenhouse Gas

Emissions

Reductions”

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

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

removed from dataset • Low pressure subcritical

units and 0-200 MW

subbituminous units • 8.7% reduction in

overall heat rate of

coal-fired EGUs

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15 Study

ID Study Title

Author Factors Used for Industry Grouping Key Study Assumptions Relevant HRI

Results

assumed retired for goal • Lost generation made up by

more efficient coal-fired

EGUs

3 “Reducing Heat

Rates of Coal-Fired

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

moisture coal: • 10% improvement

for bituminous coalfired EGU • 15% improvement

for subbituminous

coal-fired EGU

4 “Regulating

Greenhouse Gases

from Coal Power

Plants under the

Clean Air Act”

Resources for the

Future • Analyze the actual operating efficiency of the

entire fleet of U.S. coalfired EGUs

• Assess abatement

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

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

tax on CO 2 

### emissions of about $1.64 per ton, improving heat rates by 0.3 to 0.9 %

5 “Closing the Power

Plant Carbon Pollution Loophole: Smart Ways the

Clean Air Act Can Clean Up America’s Biggest

Climate Polluters”

NRDC • Plant design: coal type and steam cycle Improvements are broken into 3 group • Top 10% for each subcategory, no change • Top 11% to 49%, improve heat rate by 50% of the difference between facility heat rate and performance of top 10% in class or 600 Btu/kWh, whichever is less • Bottom 50%, improve heat rate by 100% of the difference between facility heat rate and performance of top 10% in class or 600 Btu/kWh,

whichever is less Broader analysis not strictly focused

on heat rate improvements. 11%

reduction on overall

fossil fuel-fired EGU emission rates; includes switching

from coal to natural

gas.



### 2.5 EPA’s Heat Rate Improvement Assessment

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

or replacing existing components with improved versions.

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

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

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

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

research EGUs where such methods were applied.

2.5.1 Study Population and Data

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

reported both heat input 3 

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



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

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

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

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

3 

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

### flow and concentration.

4 

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

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

comparison of these units’ heat rates.

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

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

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

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

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

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

different regulatory constraints.

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

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

complete and accurate emission measurement and reporting to support emission trading

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

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

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

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

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

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

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

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

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

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

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

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

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

than 6,500 or greater th an 15,000 Btu/kWh resulting from a vari ety of factors incl uding firing of

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

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

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

2-1 displays the study population average gross heat rate by year and the 11-year average.

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18 Formatted: Paragraph, Indent: First line: 0.5", Space After: 6 pt, Line spacing: 1.5 lines

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19

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

2002 - 2012 5



### Year Heat Input (million MMBtu) Electric

Generation(million MWhgross) Heat Rate(Btu/kWhgross) Unit Count

2002 18,601 1,874 9,924 839

2003 18,428 1,864 9,886 834

2004 18,405 1,875 9,819 836

2005 18,665 1,910 9,774 838

2006 18,644 1,914 9,743 848

2007 18,704 1,920 9,740 846

2008 18,459 1,914 9,643 852

2009 16,588 1,719 9,649 864

2010 17,693 1,831 9,662 869

2011 16,934 1,744 9,708 878

2012 14,947 1,536 9,732 884

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 10,00020022003200420052006200720082009201020112012Heat Rate (btu/kwh)



### Annual Heat Rate

11-yr Avg Heat Rate

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20 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 stati on was 22 miles and the average difference in

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

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

weather station measurements are a good approxima tion of ambient meteorological conditions at

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

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

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

EGUs shown in Table 2-4. These units emitted 1,605 million tons of CO 2 

### in 2012. In contrast,

emissions of CO 2 

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

6 

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



### Annual Heat Rate

11-yr Avg Heat Rate

Formatted: Font: (Default) Times New Roman, 11 pt

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numbers

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21 1,669 million tons according preliminary data from the EIA. 7 

### Since the study population of

EGUs accounted for over 96% of CO 2 

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

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

2.5.2 Subcategorization

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

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

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

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

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

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

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

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

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

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

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

7 

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

.

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



### 2.5.3 Observed Trends in the Period 2002-2012

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

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

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

8 

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

SUB SUPER

BIT OVER 200

PC

BIT 100 to 200

SUB OVER 100

LIG OVER 100

BIT UNDER 100

SUB UNDER 100

LIG UNDER 100

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



### 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 Formatted: Font: (Default) Times New Roman, Complex Script Font: Times New Roman

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23 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 2002-2012 9



### Year Capacity Factor (%)

2002 68

2003 69

2004 70

2005 71

2006 70

2007 71

2008 70

2009 62

2010 65

2011 61

2012 53



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

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

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

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

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

9 

### Table 2-5 shows data as reported to EPA as of May 8, 2014. Formatted: Font: 11 pt, Not Bold

Formatted: Heading 1

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24 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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25 Table 2-6. EGU Start Count by Year

Year Average Maximum (at any single EGU) Total

2002 11.1 209 9,363

2003 10.6 194 8,936

2004 10.7 134 9,081

2005 11.0 183 9,265

2006 10.5 139 8,859

2007 10.5 164 8,908

2008 10.4 134 8,880

2009 10.3 178 8,902

2010 10.5 211 9,110

2011 10.6 206 9,295

2012 9.9 119 8,805



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

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

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

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

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

described in the following sections.

2.5.5 Impact of capacity factor and ambient temperature

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

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

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

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

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

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

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

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

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

for collinearity between capacity factor and ambient temperature using a zero-order correlation

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26 matrix on the entire hourly data set. The correlation between the independent variables was -.048

– well below an indication of collinearity.

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

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

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

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

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

heat rate improvement and fixed characteristics is typically low. 10



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

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

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

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

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

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

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

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

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

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

cost, and transmission constraints.

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

thermodynamic steam cycle 11 

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

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

factor in 2012 alongside the climate normal monthly temperature. 12 

### The lines intersect in the

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

10 

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

11 

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

12 

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

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

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27 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 100JanFebMarAprMayJunJulAugSepOctNovDec 

### Electric generation monthly 2012 capacity factor and normal temperature

Capacity Factor (Monthly %, 2012)

Climate Normal (BWI, MD)

0 20 40 60 80 100JanFebMarAprMayJunJulAugSepOctNovDec 

### Electric generation monthly 2012 capacity factor and normal temperature

Capacity Factor (Monthly %, 2012)

Climate Normal (BWI, MD)

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28 standard deviations (1.9% of records) before performing the regressions. 13 

### Similarly to partial

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

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

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

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

generation-weighted. 14 

### The average study population r-squared for the multivariate regression is

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

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

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

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

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

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

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

factor is a key variable influencing changes in heat rate.

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

is larger than the response to capacity factor. At some individu al 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 pe riods of peak demand. These are the EGUs

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

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

Our analysis indicates that as EGUs moved from base 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 lo ad bins. This can be seen from Figure 2-4,

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

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

13 

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

14 

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

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29 generation took place in that bin or above, and generation was reduced by about half in the next

lower bin (78-83% capacity factor).

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

2.5.6 Heat Rate Variability and Indication of Improvement Potential

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

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

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

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

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

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

### Therefore, in general,

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

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

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30eleven RSD values, i.e., one for each operating year between 2002 and 2012. The generationweighted mean RSD for the study population acr oss 11 years is 5.4%. Table 2-8 summarizes

eleven years of results for the study population by quartiles ordered by the 11-year generationweighted RSD 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 variab ility in the study population and thereby indicate

the potential for heat rate improvement.

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

Quartile RSD Average RSD

Minimum RSD

Maximum Share of

Generation

1 3.5 1.6 4.2 

### 33

2 4.8 4.2 5.3 

### 26

3 6.1 5.3 7.0 

### 24

4 9.8 7.1 25.2 

### 16



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

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

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

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

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

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

temperature.

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

generation-weighted average of up to eleven annual values. The study population 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 ab ove the mean and were removed from the results in the

table.

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

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

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

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



### Quartile RSD

average RSD

Minimum RSD

Maximum

1 2.7 0.0 3.2

2 3.6 3.2 4.1

3 4.7 4.1 5.3

4 6.9 5.3 10.4



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

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

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

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

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

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

operation to reduce variability and heat rate.

2.5.6.1 Heat rate variability and performance

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

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

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

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



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



### 2.5.7 Units with large heat rate changes

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

change in heat rate greater than +/- 8.5%. 20 

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

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

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

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

heat rate and capacity factor for any of the three groups in Table 2-10. 21 

### This would indicate that

20 

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

21 

### The correlation remains weak even when limited to cases where capacity fact or changed more than 30 percent. y = 0.002x - 14.254
R2 = 0.5704 5.0 5.2 5.4 5.6 5.8 6.0 6.2

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



### Heat Rate

y = 0.002x - 14.254
R2 = 0.5704 5.0 5.2 5.4 5.6 5.8 6.0 6.2

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



### Heat Rate

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33 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. Th is does not imply that the changes associated

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

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

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

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

Quartile RSD Average RSD

Minimum RSD

Maximum

1 4.0 2.3 4.8

2 5.3 4.8 5.9

3 6.6 5.9 7.5

4 10.5 7.6 26.5

22 

### EPA Reference method 2 specifies the normal procedure fo r measuring stack gas volumetric flow rate during a relative accuracy test audit. Methods 2F, 2G, 2H a nd CTM-041 are approved alternat ives. Methods 2F and 2G correct measured flow rates for angular (non-axial) flow , Method 2H (for circular stacks) and conditional test method CTM-041 (method J, for rectangular stacks and ducts) are used to correct measured flow rates for velocity decay near the stack wall, using a “wall effects adjustment factor”. These alternative methodologies are optional. Therefore, given the additional complexity and cost of using these alternatives a source is likely to use them only if 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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34 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 varia tion indicates that signifi cant potential for heat

rate improvement is available through the application of best practices.

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

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

matrix of 168 bins. 24 

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

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

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



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

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

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

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

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

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

to reduced heat inputs is the potential heat rate improvement for that unit. 26 

### Using this approach,

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

take proportionally smaller reductions.

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

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

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 wi de improvement in heat rate (rather than 4.0%).

26 

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

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

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

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

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

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

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

% Reduction from reported heat input to p10 Study population

heat rate (Btu(kWh-gross) 27 

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

heat rate improvement (%)

10

9,755 9,625 1.3

20 9,495 2.7

30 9,365 4.0

40 9,235 5.3

50 9,105 6.7



### 2.5.9 Assessment of potential heat rate improvement via equipment upgrades

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

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

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

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

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

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

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

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

some of the observed heat rate improvements.

Two other sources provide information about heat rate improvements after equipment

upgrades at existing plants. EPA Region 7 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 Tabl e 2-4 is 9,754; the 9,755 value is derived from the dataset that includes hourly temperature values.

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36 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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37 ESP Modification Boiler Feed Pump Rebuild Air Heater and Duct Leakage Control

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

Higher Cost Options Economizer Replacement

Acid Dew Point Control

Combined VFD and Fan

Turbine Overhaul

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

method of least squares. Using the slope of the tre nd line, 2002-2012, as an indicator of 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 slop e. This would imply that equipment maintenance

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

maintain the status quo.

2.5.10 Combined study population results

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

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

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

by 2%.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The equipment upgrades results are supported by numerous studies 28 

### and by the EPA’s

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

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

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

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

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

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

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

electric sector CO 2 

### emissions from coal.

2.5.11 Sensitivity Analysis Removing Planned or Announced EGU Retirements

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/e pa-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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39 incur if they yield sufficient reductions in other current or potential costs, particularly reductions

in coal fuel cost and any cost related to CO 2 

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

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


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

Annual Net HRI Savings = Coal Cost Savings + 

### CO 2 

### Emission Cost Savings – Capital Cost 30



### 2.6.1 Heat Rate Improvement Capital Cost Assumption

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

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

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

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

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

Btu reductions and associated range of capital costs are obtained:

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

Combined Min-Max HRI Ca pital Cost: $40-150/kW 31



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

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

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

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

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

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

2.6.2 Heat Rate Reduction Assumption

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

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

30 

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

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

31 

### Note that highest cost does not necessarily align with greatest heat rate improvement. A low cost HRI method can have a large HRI potential (e.g., upgraded digital contro l 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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40 Sargent & Lundy estimates given above, HRI methods could possibly reduce this average coal

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

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

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

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



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

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

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

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

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

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

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

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

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

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

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

6%.

2.6.3 Heat Rate Improvement Breakeven Economic Analysis

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

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

### emission cost that is

reduced via HRI.

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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41 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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42 sensitivity indicates that fuel cost savings alone would make it economic for some of

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

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

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

mentioned above.

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

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

economic fleet wide,

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

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

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

### emission costs, HRI

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

rate reduction percentages.

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

### Reduction and Cost via HRI

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

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

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

 Average CO 2 

### emission rate = 0.976 tonne/MWh net

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

 Average capacity factor = 78%

 Pre-HRI CO 2 

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

 HRI Btu and CO 2 

### reduction = 6%

 HRI capital cost = $100/kW

 Annual capital charge rate = 14.3%

 Average coal cost = $2.62/MMBtu

Estimated Fleet-wide Results:

 Fleet-wide CO 2 

### reduction via HRI = 97 million tonne/yr

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43  Total HRI capital cost = $24 billion

 Annualized HRI capital cost = $3.5 billion

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

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

 Annual net savings (cost) of CO 2 

### reduction = ($7.7/tonne)

2.6.5 Conclusion - HRI Economics

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

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

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

basis of fuel savings alone, before consideration of the value of the associated CO 2



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

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

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

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

savings alone, before consideration of the value of the associated CO 2 

### emission

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

$2.62/MMBtu in 2020).

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

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

### reduction via 6% HRI would be a relatively

low $7.7/tonne.

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

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


### reduction for existing coal-fired EGUs

Definitions/Abbreviations

Btu British thermal unit

capacity factor electricity generation expressed as a percentage of maximum electricity generation (i.e., actual generation / maximum potential generation)

CO 2 

### carbon dioxide

EGU electric generating unit

EIA Energy Information Administration

EPA Environmental Protection Agency

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

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

heat rate gross heat input required to genera te 1 kWh of electricity, expressed in gross Btu/kWh.

MMBtu million Btu

MW megawatt

PC pulverized coal (boiler)

RSD Relative standard deviation

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

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

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



### Docket Datasets

Name Description

hour_QA_data.txt 2002-2012 hourly dataset

hour_QA_regression_data.txt 2002-2012 hourly dataset for regression analysis

units_885.txt List of study units and characteristics

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

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45 HRI Partial Bibliography

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

NETL 2008, Reducing CO 2 

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

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

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

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

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

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

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

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

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

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

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

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

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

CO 2 

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

EPRI 2009, Renewed Interest in Reducing Heat Rate, EPRI Journal, Spring 2009, available at http://mydocs.epri.com/docs/CorporateDocuments/EPRI_Journal/2009- Spring/1019287_Heat%20Rate.pdf Formatted: No underline

Formatted: Heading 2, Line spacing: 1.5 lines

Formatted: Font: Not Bold

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

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

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

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

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1 

### GHG Abatement Measures Technical Support Document

Chapter 3: CO 2 

### Reduction Potential from Re-Dispatch of Existing Units

Table of Contents

Overview

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

Introduction

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

Power Sector Background

............................................................................................................................. 3

Historical Context

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

Natural Gas Supply

............................................................................................................................... ....... 15

Technical Considerations

............................................................................................................................ 17

Analysis of Cost

‐ Effectiveness

..................................................................................................................... 23

Natural Gas Price Impacts

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2 

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

lowering the carbon dioxide (CO 2 

### ) emissions intensity of the power sector by substituting

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 oppor tunities to improve emissions intensity by

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

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

production and delivery in the context of cost-effective CO 2 

### emission reduction opportunities.

Introduction

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

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

of generation and transmission facilities.” 1 

### Electricity demand varies across geography and time

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

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

manner possible.

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

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

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

the least expensive power source first. 2



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

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

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

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

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

for pollutants such as SO 2 

### and NO

x 

### when scheduling unit dispatch, in response to costs and

1 

### Energy Policy Act of 2005

2 

### A balancing authority is the responsible entity that inte grates resource plans ahead of time, maintains the balance between supply, demand, and generation within a balanci ng authority area, and supports interconnection frequency in real-time. http://www.nerc.com/files/glossary_of_terms.pdf Formatted: Heading 1

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3 

### regulatory requirements. For example, EGU operat ors in 10 states partic ipating in the Regional

Greenhouse Gas Initiative have several years of experience with factoring CO 2 

### emissions limits

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

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

units to natural gas combined cycle (NGCC) units.

Power Sector Background

Electric Dispatch

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

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

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

### The

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

generator efficiency. Regional Transmission Or ganizations (RTOs) and Independent System

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

meeting electricity demand as low as possi ble, subject to operational constraints.

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

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

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

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

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

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

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

associated with running pollution controls. 4 

### Other generating technologies, like renewables,

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

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

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

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

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 a nd maintenance, and costs of operating a pollution control and/or emission allowance charges. Formatted: Heading 1

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4 

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

“curve.” 5

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5 

### Balancing Authorities

In states with cost-of-service regulation of vertically-integrated utilities who own

generation, transmission, and distribution infrastru cture, the utilities themselves often form the

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

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

buy and sell power with other balancing authorities.

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

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

efficient and least-cost manner possible. 6 

### They coordinate, control, and monitor electricity

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

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

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

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

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

North America (including Canada).

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

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

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

power through these wholesale electricity markets.

State Public Utility Commissions (PUCs) 7



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

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

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

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

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

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

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

6 

### http://www.ferc.gov/industries/electric/indus-act/rto.asp

7 

### These entities are sometimes called Utilities Commissions, Utility Regulatory Commissions, or Public Service

Commissions

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### generation and dispatch component is governed by RTOs and ISOs. In cost-of-service states, the

PUC also has oversight of the generation and capacity planning components

.

Spot and Day-Ahead Markets

RTOs and ISOs operate spot markets for wholesale power supply and demand for their

designated area, including both day ahead and re al-time (hourly, or shorter time periods). These

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

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

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

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

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

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

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

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

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

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

balancing of power is conducted by the utili ty based on its own sche duling and pu rchasing

protocols and varies considerably from one utility to the next.

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

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

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

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

is taken when needed.

Reliability

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

bulk electric system. The bulk electric system refe rs to a large interconnected electrical system

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

system operators continuously an alyze real-time and forecasted lo ad and transmission conditions

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

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

adjusted by the system operator. The North Am erican 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 CO 2 

### emission rates 9 

### 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 – 9 0726 lbs/MWh

Coal- and oil/gas-fired boilers are consider ably higher-emitting sources than NGCCs, on

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

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

CO 2 

### emission reductions.

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

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

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

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

CO 2 

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

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

produce electricity (a period being 1 year, or 8,7 84 hours in 2012 since that year included a leap

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

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



### Other reports suggest that NGCC availab ility factors may reach as high as 92%. 11

8 

### http://www.nerc.com/

9 

### Emission rates in this document are shown on a net gene ration basis and reflect Hawaii and Alaska sources. See “2012 eGrid Data” file provided in the docket

10 

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

11 

### http://www.power-eng.com/articles/print/volume-115/is sue-2/features/higher-availability-of-gas-turbinecombined-cycle.html Formatted: Heading 1

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

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

those units

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

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

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

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

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

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

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

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

may model actual weather adjusted capacity by the hour/minute, 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 act ual 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 po tential for re-dispatch from higher CO 2 

### emitting resources to

lower emitting NGCC generation.

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



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

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

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

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

12 

### See “2012 eGrid Data” file provided in the docket for 44% figure. See EIA 860 and 923 for 46% value.

13 

### Because the 2012 capacity factor used in this exercise focuses on net generation as a percent of name plate capacity, the availability is converted from a gross genera tion/name plate value to a 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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9 

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

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

considered historical data and modeling proj ections describing NGCC characteristics and

operating behavior.

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

capacity has come online in the last 15 years. 14 

### Of this capacity, almost all are a highly efficient

class of NGCCs that are able to ach ieve high availability factors.

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



### Online Year Capacity

(Name Plate Capacity – MW) Percentage of

Total Existing NGCC

Fleet

Pre 1950 103 0%

1950-1959 1,769 0.7%

1960-1969 3,087 1.3%

1970-1979 6,909 2.8%

1980-1989 7,658 3.1%

1990-1999 28,467 11.7%

2000-2009 174,947 71.7%

2010+ 21,068 8.6%

Total 244,008 100%

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%CapacityFactor



### % of Units 

# Cumulative Capacity Factor

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



### % of Units 

# Cumulative Capacity Factor Comment [A3]:

Formatted: Font: (Default) Times New Roman, 12 pt, Complex Script Font: Times New Roman, 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 

### EIA Forms 860 and 923. CA and CT Prime Mover categories 0 20 40 60 80 100 120 140 160 180 200#ofUnits 

# Unit Distribution by Capacity Factor

0 20 40 60 80 100 120 140 160 180 200#ofUnits 

# 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 heav ily 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 p eak electricity demand timeframes nationwide,

more than 10% of NGCC s were operated at a capacity factor greater than 70%. 19 

### In fact, 19% of

NGCCs achieved 70% capacity factor during the winter of 2011/2012 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 defi ned as December, January, and February. Estimates are for units for which data was provided to EPA. Formatted: Font: Bold

Formatted: Centered, Indent: First line: 0" Comment [A6]: Are these data the same as
those plotted in the first figure above?

Formatted: Font: Bold

Formatted Table

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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 201 2. In addition, 16% of units (291 units) operated at 65%, or

higher.

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

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

units. As a result, operators have shifted signifi cant quantities of generatio n from coal units to

NGCCs, absent any federal CO 2 

### requirements. 2012 net generation from NGCC units grew to

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

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

NGCC capacity.

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

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

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

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

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

CO 2.



### The potential for redispatch from CO 2 

### intensive sources to less CO 2 

### intensive sources is

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

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

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

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

20 

### Air Markets Program Data ( http://ampd.epa.gov/ampd/ ). Winter includes December of 2011, January and February of 2012. Summer in cludes June, July, and August.

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### a one year period demonstrates the ability for th e quick re-dispatch in response to market or

economic drivers.

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

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

in 2011 and $2.76/mmBtu in 2012. This $1. 24/mmBtu creates an ad ditional incentive for

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

advantage is similar to the incentive that a $15/metric ton of CO 2 

### price signal would create. 21



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

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

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

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

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



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



### Year NGCC

Name Plate

Capacity

(GW) NGCC Heat Input for Electricity

(TBtu) NGCC Net

Generation

(TWh) NGCC Capacity

Factor H enry H ub

Natural Gas

Price ($/mmBtu Coal Net

Generation

(TWh)

2011 23 95 5,912 796 3 89 % $4.00 1,719

2012 24 42 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 ~ 90 th



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

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

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

approach.

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

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

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 Formatted Table Formatted: Line spacing: single

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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). Formatted ... [1]

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### recoverable natural gas resources will be substa ntially higher for the foreseeable future than

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



### According to EIA, natural gas proved reserves have doubled between 2000 and 2012. 27



### Domestic production has increased by 32% over that same timeframe (from 19.2 TCF to 25.3

TCF). EIA’s Annual Energy Outlook for 2014 pr ojects that production will further increase to

29.1 TCF, due to increased supplies and favorable market conditions. For comparison, NGCC

generation growth of 450 TWh (calculated in goal setting) would result in increased gas

consumption of roughly 3.54.4 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 natu ral 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 Formatted: Font: (Default) Times New Roman, 12 pt, Complex Script Font: Times New Roman, 12 pt

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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, relati ve to current utilization levels. Other influences

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

timing, and cost.

NGCC Availability

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

base load or intermediate dema nd 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 hist orical 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) Formatted: Font: (Default) Times New Roman, 12 pt, Complex Script Font: Times New Roman, 12 pt

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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 report ed availability factors of 90 to 92 percent to NERC (compared

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

from General Electric Power Systems). 37 

### Data reported to NERC from NGCC units greater than

50 MW in 1994 through 1998 shows similar availability factors (generally exceeding 89

percent).

Natural Gas Pipeline and Electricity Transmission

The EPA believes that the natural gas pipeline and electricity transmission networks can

support aggregate operation of the NGCC fleet at up to a 70% capacity factor on average, either

as they currently exist or with modifications that can be reasonably expected in the time frame

for compliance with this rule. Existing NGCCs are already connected to both the power and

natural gas networks and, while constraints to specific unit operations can occur in either or both

networks during peak pipeline flow or electricity us e, the rule allows for emission rate averaging

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

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

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

33 

### NERC, 2008-2012 Generating Unit Statistical Brochure – All Units Reporting, http://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx

34 

### Power Engineering, Negotiating Availability Guarantees for Gas Turbine Plants, 03/01/2001, http://www.powereng.com/articles/print/volume-105/issu e-3/features/negotiating-availability-guarantees-for-gas-turbine-plants.html

35 

### Power Engineering, Higher Availability of Gas Turbine Combined Cycle 02/01/2011, http://www.powereng.com/articles/print/volume-115/is sue-2/features/higher-availability-o f-gas-turbine-combined-cycle.html

36 

### http://site.ge-energy.com/corporate/network/downloads/7FA_Evolution.pdf

37 

### GE Power Systems submitted to U.S. Department of Energy, 2000. Utility Advanced Turbine Systems Technology Readiness Testing Phase 3 Restructured. DOE Cooperative Agreement No. DE-FC21-95MC31176—

30.

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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 2011 38 

### and resulted in a national average

capacity factor for NGCC of 4 5.89 % 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 constr aints on the pipeline or electricity transmission

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

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

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

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

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

higher levels at all hours withou t 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 constraint s on utilization at some NGCC facilities,

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

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

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

infrastructure when needed to allow the retirement of uneconomic coal plants. 43 

### This rule

provides for flexible implementation that will permit efficient scheduling of infrastructure

upgrades as needed. Upgrades to pipeline and transmission infrastructure potentially needed to

meet additional use of existing facilities will generally be less extensive than upgrades of that

infrastructure potentially needed for siting of new capacity. In addition, this proposed rule is

expected to result in significantly higher levels of end-use energy efficiency, which will reduce

the load on the electricity tr ansmission and natural gas pipelin e 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 tec hnical description of these upgrades, see:http://www.pjm.com/~/media/committees-groups/committees/teac/20131211/20131211-december-2013-pjm-boardapproval-of-rtep-whitepaper.ashx

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### In addition, natural gas pipeline capacity is regularly added in response to increased gas

demand and supply, such as the addition of large amounts of new NGCC capacity in 2001 to

2003, or the delivery to market of unconventional gas supplies since 2008. 44 

### These pipeline

capacity increases have added significant deliver ability to the natural gas pipeline network to

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

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

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

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

be possible by the pipeline industry. 45 

### There have also been notable capacity expansions over

the past five years, in response to increased natural gas supply estimates and advances in drilling

techniques. 46



### To examine the potential for in creases in pipeline deliverab ility, the EPA analyzed the

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

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

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

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

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

on the net capacity – the difference between inflow capacity and outflow capacity -- in the

relevant areas. The regions used by EIA for measuring regional natural gas deliverability are

shown in Figure 3- 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

Area 47

44 

### Energy Information Administration, Today in Energy , Natural Gas Pipeline Additions in 2011. Additions averaged around 20Bcf per day from 2008 to 2011.

45 

### Pipeline and Storage Infrastructure Requirements for a 30 Tcf Market, INGAA Foundati on, 1999 (Updated July, 2004); U.S. gas groups confident of 30-tcf market, Oil and Gas Journal, 1999.

46 

### http://www.eia.gov/naturalgas/data.cfm#pipelines

47 

### Source: Energy Information Administrati on, 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 complian ce 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 an d the capability of the electricity transmission

system to accommodate shifting generation patterns, EPA employed the Integrated Planning

Model (IPM), a multi-regional, dynamic, deterministic linear programming model of the U.S.

electric power sector that the EPA has used for over two decades to evaluate the economic and

emission impacts of prospective environmental policies. IPM provides a wide array of

projections related to the electric power sector and its related markets (including least cost

capacity expansion and electricity dispatch project ions) while meeting fuel supply, transmission,

dispatch, and reliability constraints.

Natural gas supply, demand, transportation, storage, and related costs are modeled

directly in IPM through the incorporation of a natural gas module. The module includes a detail

rich representation of the natural gas pipeline network inclusive of discount curves that represent Formatted: Space After: 10 pt, Line spacing: single

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### the marginal value of gas transmission as a functi on of the pipeline’s load factor. IPM’s natural

gas module has the capability to expand pipeline capacity on an economic basis.

At the unit level, IPM contains a detailed representation of new and existing resource

options, inclusive of key operational limitations. For example, turn down constraints are

designed to account for the cycling capabilities of EGUs to ensure that the model properly

reflects the distinct operating characteristics of peaking, cycling, and base load units. EPA

believes IPM represents a powerful tool to ev aluate the technical f easibility of requiring

increasing levels of re-dispatch from higher to lower-emitting EGUs.

The EPA has conducted extensive analysis to quantify the opportunity to reduce CO 2


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

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

broader economic and emissions implications of shifting generation from coal-fired steam EGUs

to NGCC units within defined areas. 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 restrictiv e 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 in centivizing re-dispatch from more carbon-intensive to less carbonintensive EGUs, the EPA introduced two addi tional 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 capac ity expansion, electricity dispatch, and emission control strategies while meeting energy demand and environmental, transmission, di spatch, and reliability constraints. Full documentation

of the IPM model can be found at http://www.epa.gov/powersectormodeling

49 

### In practice, unit dispatch does not respect state boundari es because least-cost supply must be balanced with demand in real time over grid interconnects which span mul tiple states (with the exception of the Electric Reliability Council of Texas interconnect). The design of this modeling scenario assumes artificial constraints on re-dispatch to force such behavior to respect state boundaries, given the context of this rulemaking’s quantification of individual state goals. These state boundary constraints necessarily forgo cost-effective opportunities to re-dispatch units in different states; as a result, costs and prices in this analysis are overstated.

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### 1. The application of a CO 2 

### charge to the variable cost of dispatch for all existing coal

steam boilers, IGCC units, and oil/gas steam boilers greater than 25 MW and with a CO 2


### emissions rate greater than 1,100 lbs/MWh. 50



### 2. Generation constraints that maintain the sum of state-level generation in the Base Case 51



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

boilers greater than 25 MW and with a CO 2 

### emissions rate >1,100 lbs/MWh.

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

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

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

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

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

reliability constraints, fuel market impacts, na tural gas transmission and distribution networks,

electric power transmission cons traints, and unit-specific characteristics (e.g., operating and

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

Cost and Availability of Economic Re-Dispatch Opportunities

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

relationship between the amount and cost of re-dispatch. Figure 3- 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 CO 2 

### reduced by comparing emissions

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

difference in power sector emissions). While the charge is applied exogenously in IPM, the

average $/tonne shown below is calculated from the modeling results and projections by dividing

the increase in total system costs (compared to the base case) by the total change in CO 2


### emissions (compared to the base case).

50 

### The addition of CO 2 

### costs represents a simple analytic a pproach to estimating the cost-effective CO 2 

### reductions under this building block and acts as a proxy for some exis ting state policies that shift dispatch. In actual plan implementation, states would be free to select any polic y approach that has the net effect of reducing the carbon intensity of generation and/or reducing overall emissions from affected sources.

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 – wh ich is distinct from the $/tonne CO 2 

### cost imposed in

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

relevant metric in evaluating cost-effectiveness. System cost changes necessarily encompass all 58%

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

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

‐ 10 20 30 40 50 60AverageCapacityFactor



### Average $/tonne 

## Projected Capacity Factor for Existing NGCCs (2020)

58%

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

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

‐ 10 20 30 40 50 60AverageCapacityFactor



### Average $/tonne 

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### elements of cost across power, transmission, a nd 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

CO 2 

### Cost Level

Imposed National Average

NGCC

Capacity Factor Average Cost

($/tonne) 2020 Power Sector CO 2



### Emissions

(MMT / % Change from

2020)

Base Case 52% N/A 2,161

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

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

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

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

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

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

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

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

expect the costs of implementing this requirement in a compliance 52 

### setting will be considerably

lower for several reasons:

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

chapter, the EPA’s initial analyses utilized CO 2 

### charges on the variable cost of dispatch

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

lbs/MWh and a capacity greater than 25 MW, as an analytic construct to induce redispatch behavior in the model to existing NGCC facilities. The CO 2 

### charge was applied

uniformly to all states in or der to quantify the ultimate am ount of in-state re-dispatch

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

low levels of CO 2 

### charges produce cost-effective re-dispatch opportunities relative to the

Base Case in almost all states. However, as the CO 2 

### costs are increased to higher levels,

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

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 slop e of the best-fit line in Figure 3- 65 . A uniform

application of the same rising CO 2 

### charge in all states produces an outcome for many

states where the additional CO 2 

### costs imposed on affected coal steam, IGCC, and O/G

steam are not able to produce incremental economic re-dispatch at units within that state;

therefore, the additional cost s imposed by these higher CO 2 charges overstate the actual

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

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

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

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

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

was able to achieve 8% greater CO 2 

### emission reductions at an identical CO 2 

### charge

(relative to a scenario where it was limited on a state basis), , demonstrating that the main

analysis’s imposition of artificial re-dispatc h boundaries on state borders overstates the

cost-effectiveness of re -dispatch potential.

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

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

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

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

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

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

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

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

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

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

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

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

indicates that the average cost of the CO 2 

### reductions achieved ranged from [$xx] to [$xx] per

metric ton of CO 2



### . 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 CO 2 

### reductions

achieved over the 2020-2029 period was $21, $30, and $40 per metric ton of CO 2 

### , respectively.

However, we also note that the costs just desc ribed are higher than we would expect to actually

occur in real-world compliance with this proposal’s goals. This is because only 29 state goals are

premised on the existing NGCC fleet achieving an average capacity factor of 70 percent.

Consequently, a 70 percent utiliza tion rate target for the existing NGCC fleet requires an average

national capacity factor of 63 percent.

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

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

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

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 ga s prices were projected to increase by a range of from [x] percent

to [x] percent, which is within the range of historic natural gas price variability. Projected

wholesale electricity price increases ranged from [x] to [x] percent, which similarly is within the

range of historic electric price variability.

The EPA also analyzed dispatch-only scenarios where shifting of generation among

EGUs was not limited by state boundaries. In one set of analyses, re-dispatch was simulated to

occur across the multi-state regions overseen by independent system operators (ISOs) and other

regional transmission organizations (RTOs), while in another set of analyses, national redispatch was simulated (subject to other constraints specified in the model, including

transmission limits). In these scenarios with grea ter re-dispatch flexibility, the cost of achieving

the quantity of CO 2 

### reductions corresponding to a nationw ide 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 Formatted: Subscript

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### cost of achieving the quantity of CO 2 

### reductions corresponding to a nationwide average NGCC

unit utilization of 70% was $33 per ton.

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

Existing NGCC
Average

National
Capacity

Factor Re-Dispatch

Constraint Average Cost ($/tonne, 2020-

2029) Average CO 2 

### Emissions
(MMT, 2020-
2029) Reductions

from Base Case
(%, 2020-2029)

Base Case NA NA 2,215 NA

65% Regional $21 2,022 9%

70% Regional $30 1,969 11%

75% Regional $40 1,915 14%

65% State $22 2,024 9%

70% State $33 1,971 11%

Natural Gas Price Impacts

The extent of re-dispatch estimated in this building block can be achieved without

causing significant economic impacts. For exampl e, in neither of the 70 percent NGCC unit

utilization rate scenarios – re-dispatch limited to regional or state boundaries – did delivered

natural gas price projections increase by more than 10 percent in the 2020-2029 period, which is

well within the range of historical natural ga s price volatility. For exam ple, the year-to-year

percentage difference in Henry Hub prices reported by the Energy Information Administration

averaged 18.5% over the period from 1981 to 2012. 53 

### Projected wholesal e electricity price

increases over the same period were less than 7 percent in both cases, which similarly is well

within the range of historic elect ric price variability. For example, the average year-to-year price

53 

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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

CO 2 

### , the actual implementation is expected to result in notably lower economic impacts,

including natural gas price impacts, and are considerably larger than would actually occur in

real-world compliance with this rule’s proposed goals.

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

Existing NGCC Average National Capacity Factor Re-Dispatch

Constraint Price ($/mmBtu) % Change

Base Case NA $5.94

65% Regional $6.36 7%

70% Regional $6.53 10%

75% Regional $6.69 13%

65% State $6.37 7%

70% State $6.52 10%

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

Price ($/mmBtu) % Change

Base Case $6.05

$10/tonne $6.21 3%

$15/tonne $6.33 5%

$20/tonne $6.42 6%

$25/tonne $6.57 9%

$30/tonne $6.61 9%

54 

### ISO Real

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## Chapter 5: Demand-side Energy Efficiency (EE)

Introduction

This chapter provides information on dema nd-side energy efficiency (EE) as an

abatement measure for reducing carbon dioxide (CO 2 

### ) emissions from fossil fuel-fired electric

generating units (EGUs). Specifically, this chapte r 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 Goal s, State Plans, and Impacts of the Proposed

Rule) and its representation within the RIA. Resu lts 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 Consideratio ns TSD (Appendix: “Survey of Existing State

Policies and Programs that Reduce Power Sector CO 2 

### 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 (CO 2 

### ) 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 objectives 1 

### . Moreover, states – through

their utilities, primarily – have been rapidly in creasing 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

billion i 

### . In 2012, the cumulative impacts of these programs represented a 3.7% reduction in

national electricity demand ii 

### . 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” an d “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 relate d 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 ex ceed 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 technol ogies and practices, and then describes the market

failures that limit cost-effective EE investment s. 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 cust omized whole home retrofits. Opportunities for

cost-effective EE in commercial buildings in clude 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 invest ment in EE is dynamic, growing over time

as technologies and practices advance, as popul ations 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 strate gies, 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 industry iv 

### . 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 invest ments. 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 CO 2 

### , SO 2 

### , and NO

x 

### that cause human health and environmental damages.

Energy prices that do not correctly reflect th ese 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 pro ducts 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 “pri ncipal-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 with in 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 developmen t 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 ex plain 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 decisions 3 

### . 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 inform ation 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 disadvan tage 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. Le nders 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 policie s 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 phenom enon, also termed ‘energy paradox,’ or ‘energy efficiency gap,’ has been studied extens ively 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 Char acteristics 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 Policies 4



### Objectives and Role in Reducing CO 2 

### Emissions from the Power Sector

EE policies are implemented by states to meet a number of closely related policy goals ix



### 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 objectives x 

### . 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 pur suing these policies. In addition, EE policies are

central to meeting state objectives for reducing CO 2 

### emissions from the power sector. As noted

in the State Plan Considerations TSD, EE policies are a leading tool for achieving CO 2


### reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in

ten states 5 

### with statutory requirements for greenhouse gas reductions.

Economy-wide studies of climate mitigation scenar ios confirm that energy efficiency plays a

critical role in reducing the costs and enhancin g the flexibility of meeting long-term climate

stabilization targets xi 

### . 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 fro m greater energy efficiency in the world

economy xii 

### . 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 effi ciency 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 goals xiii,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 opport unities 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 programs xx 

### . 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 gro ups, the EMF has focused on a series of energy and environmental topics that are of interest to policy d ecisions. 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 w ould 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 programs 8 

### , and building energy

codes. Other policies that are leading to si gnificant 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

states 9 

### . This information is generally based upon measurement and verification studies submitted

annually, most commonly to state utility commis sions, and reported to the Energy Information

Administration (EIA) for all program administrato r 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, in dustrial). In 2012, utilities and other program

administrators in 48 states reported savings fr om 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 ex ist. 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 co des accounting for th e remaining savings xxi



### .

8 

### EE programs are described in more detail in the follo wing 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 administ rators of programs. Prior to 2011, this was a significant shortcoming in the breadth of the data collected. The br eadth 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 regard s to data quality and consistency. See “Analysis Limitations” section for further

discussion.

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Another representation of the relative opp ortunity 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 ACEEE xxii 

### and Georgia

Tech xxiii 

### 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 Ac t 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 cont ribution to meeting the 25 percent target with

utility sponsored EE programs and state build ing energy codes accounting for 76% and 17%,

respectively, of those policies xxiv 

### . 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 C onsiderations TSD. Because EE programs have

provided the majority of state EE-policy electrici ty 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 5-1 xxvi 

### 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) repr esents a unique set of ch aracteristics including

costs of energy saved, ratio of program to pa rticipant 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 5-1 11



### Energy Efficiency Program Portfolio

Administrators

EE programs are admini stered 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., Vermon t Energy Investment Corporation), and state and

local government agencies (e.g., NYSERDA). Most EE programs (including all investor-owned

utilities which account for more th an 75% of reported savings xxvii 

### ) 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) requ irements (34 states), demand-side management

plan or multi-year energy efficiency budget (28 states), and statutory re quirement 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 tool xxx 

### . 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-hour xxxi 

### . 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 charact eristics. 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 ad ministrators, 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 el ectricity 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 eff ectiveness of these new strategies. Table 5-2

presents levels of EE program funding in the U.S. since 2006 xxxii 

### . 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 year xxxiii



### .

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.

Formatted: Centered

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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 co mmissions, 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 Admi nistration (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, supplemen ted it with additional sources including surveys

of utility commissions and program administrators , and published their own annual reports that

capture EE program impacts xxxiv

, 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 datase t 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 administra tor (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 utilitie s). 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 savings 16 

### in 2012

was 0.58% of retail sales with individual state values ranging from 0.00% to 2.19%. Cumulative

electricity savings 17 

### (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 consiste ncy and quality of 861 data that different reporting entities utilities and states may have used different method ologies 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 reporti ng. 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) represen t the reduction in electricity use in a given year from EE activities in that year and all preceding years, ta king into account the lifetim es 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 custom ers 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.” Formatted: Font: 10 pt, Not Bold, Not Italic, Complex Script Font: 10 pt

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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-Fu nded 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 th e 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” approach xxxvii 

### . EE potential studies are conducted

at various geographic scopes (national, regional, st ate, and utility service territory level) and at

different degrees of aggregation (e.g., economy-wi de, 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 oppo rtunities 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 potenti al” can vary to some degree by study.

Some estimate economic potential by evaluatin g 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 r eal-life program implemen tation. 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 programs 20



### .

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

measures xxxviii 

### . 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 subs et 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 continue d improvement in efficiency of technology is

provided for). As a result, estimated efficiency potential can vary significantly among studies 21



### .

Overview of Results

Studies of energy efficiency potential are numerous. In recent years, dozens of studies

have been conducted at regional, state, and util ity 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 th en 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 mark et and macroeconomic drivers of energy use and energy efficiency, and in some cases due to the lack of consiste nt 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 t echnology 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 f our 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 potentia l, 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 st udies show significant potential in every

region of the country including regions with lowe r electricity prices like the southeast, regions

with historically high levels of EE program budge ts 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 si milar scale opportunity across all regions. For

instance, EPRI (2014) shows achievable potential ra nging from 8% to 14% relative to baseline in

2035 across the thirteen regions of their analys is 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 electrici ty 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 benefits xliii 

### . Most states evaluate their EE

policy options through the application of cost te sts, weighing the projected benefits with the

costs of the energy efficiency technologies and practices xliv 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 expenditure s (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, adjustin g 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 type xlvi 

### . Policies that

are selected are those that are found to be cost-e ffective, with benefits greater than costs, as

determined by the utility applying methods de fined 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 a nd distribution - by the utility.

(3) Ratepayer impact measure test from the perspective of utilit y 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 utilit y 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

targets xlvii 

### . 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 potential xlviii 

### . 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 positiv e net present value if “persistent barriers to

market efficiency” could be addressed.

Costs of Saved Energy

A common metric for comparing alternative el ectricity 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 favora bly with LCOE of alternative new

generation sources such as fossil-fueled or nucl ear 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 esti mated 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 econometric sal , 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 analys ie s 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 a n 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 measur e 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. Dema nd Side Management and Energy Efficiency Revisited. Energy Journal 29(3): 91-104. It is noted that the econometri c 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?

Formatted: Font: (Default) Times New Roman, 10 pt, Complex Script Font: Times New Roman, 10 pt

Formatted: Font: (Default) Times New Roman, 10 pt, Complex Script Font: Times New Roman, 10 pt

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discount rate

. lvi 

### Using a top-down approach that evaluates the savings potential of EE

investments using state- and regi on-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 LCSE cost of 2.5-3.5 cents/kWh. lvii



### A number of analytical and data considera tions related to LCSE estimation are also

discussed in the literature, including the issue of “free riders” in EE programs lviii 

### , and the

accuracy of utility reported costs and energy savings

. lix



### . Energy efficiency practitioners also

recognize the need to consider “free rider” an d “spillover” effects in program evaluation. A

slight majority of states adjust for free riders hip 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 poten tial rebound effect, market interactions and

economy-wide response of energy efficiency policies and investments. An improvement in

energy efficiency would effectively reduce the co st 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 st udies, technologies, sect ors 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. 2000 lxii 

### ; Sorrell 2007 lxiii 

### ; Davis 2008 lxiv 

### ; Gillingham et al. 2013 lxv 

### ).

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

(CO 2 

### ) emissions from fossil fuel-fired electric generating units (EGUs). In the next section, we

address the setting of state-speci fic goals for electricity savings levels resulting from state

demand-side energy efficiency efforts. In the fina l 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 CO 2 

### 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 tec hnologies and practices, and projects the annual

impacts of the scenario for each state. The scenario does not distingu ish 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 CO 2 

### 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 stat e energy efficiency policies are available to

realize a state’s goals 24 

### 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&V 25



### . 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 prac tices 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 measur e 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 be st 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 savings 26 

### . 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. Formatted: Space After: 0 pt, Line spacing: single

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Step 2: Determine Best Practices Level of Performance

As discussed previously, achievable demand-si de 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 poten tial, ongoing improvements in energy-efficient

technologies and practices, economic growth, po pulation 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 utili ty 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 res ource standards or similar savings goals that are

applied to utilities in the state 27



### . Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA

Form 861 (2012) data lxvi 

### , 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 ranges lxvii 

### . 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 1 28 

### , the EPA has chosen 1.5%

incremental savings as a percentage of retail sale s. 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 2 29 

### , 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 additiona l twelve states are expected to achieve this

level by 2020. In total, twenty states (accounti ng for duplication between th e 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 submittal 30 

### , 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 fr om 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 period 31



### .

Step 5: Determine Pace at Which States Improve from Start Year to Best Practices Level of

Performance

To determine a trajectory of incremental savi ngs levels from the 2017 level to the best

practices level, the EPA considered past perf ormance of individual program administrators 32 

### 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 performi ng 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 ex isting state EERS and evaluated the rate at

which their incremental savings levels increase ove r 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 supplem ented EIA 861 data with data for third-party program administrators because prior to 2011 EIA did not co llect 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 year s . 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 Measur e 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 levels lxviii



### . In 2014, ACEEE updated their 2004 and 2009 national reviews of EE program costs and related program characteristics, including measur e 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

All Sectors Residential Commercial/Business Industrial

Average 8.1 12.5 9.5 10.6

Other studies have found slightly higher valu es for average measure life for EE portfolios,

ranging from 10 to 13 years lxix 

### . 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 fro m five to 25 years across twelve program

categories (e.g., low income, resi dential new construction, commer cial/industrial custom, etc.).

Our approach represents a first-order approximati on 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 whatso ever, 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 scenar io, 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 stat es, 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-state 33 

### regional energy resource plans lxx



### . These analyses have become more detailed, reliable, and purposeful over time. Since 1998,

NPCC’s estimates of achievable potential have mo re 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 hi gher 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 1998 lxxii



### .

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 Washingt on 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 w ith 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 fo r 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 perc ent 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 saving s 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.

Formatted: After: 0", Line spacing: single

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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 Formatted: After: 0", Line spacing: single

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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 ne t 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 percenta ge of BAU sales are equal to net

cumulative savings divided by BAU sales. Th e 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 Re sults 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 asso ciated 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 i nputs 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 implementati on, 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-specifi c 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, st ate-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.) incu rred 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 net 34 

### 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 incl uding 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 w asere 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 afte r 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 gross 35 

### , 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 with in 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 programs 36 

### based upon their magnitude of savings or their savings as

a percentage of retail sales. Complete inform ation 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 approach 37 

### 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 an y accounting for effects of free-ridership or spillover.

36 

### Leaders were identified using re sults from the 2013 ACEEE State Energy E fficiency 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 st ate 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 reduc tions over time as learning and expertise are

gained lxxiii 

### . “Supply curves” of EE as an energy resource, as well as EE as a measure represented

within a GHG abatement curve, provide support fo r escalating costs as higher levels of savings

are realized lxxiv 

### . In a recent analysis, Lawrence Berkeley National Laboratory (LBNL) adopted

an approach that generically represented both effects discussed above lxxv 

### . 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 rate lxxvi



### . In consideration of the above discussion, th e 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 Anal ysis 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 esti mating 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 im pacts 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%



### 1.25 to 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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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.

Formatted: After: 0", Line spacing: single

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Step 1: Calculate Annual First-Year Costs Annual total first-year costs are calculate d by multiplying annual total incremental

savings (MWh) (from Table 5-15) by the firs t-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 Formatted: After: 0", Line spacing: single

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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 Manual lxxvii 

### . The net present

value of all savings from a single year’s EE act ivities (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 annu al 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 Re sults 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 

### =
∑ ܧܵܥܮሺ ௧ି௘௔௥	௜௬ 

### ݅ ݎ݁ܽݕ ݅݊ ݏ݅݊݃ݒܽݏ ݈ܽݐ݁݉݁݊ݎ݅݊ܿ ݈ܽݑܽ݊݊ ൈ ௧ି௘௔௥	௜௬ 

### ሻ ௡ ௧ୀ଴ 

### ,

where LCSE year i-t 

### is the LCSE of EE progr ams implemented in year i-t

,

݈ܽݐ݁݉݁݊ݎ݅݊ܿ	݈ܽݑܽ݊݊ ݅	ݎ݁ܽݕ	݅݊	ݏ݅݊݃ݒܽݏ ௧ି௘௔௥	௜௬ 

### estimated represents

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 in cludes 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 Formatted: Line spacing: single

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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 sheet s 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 CO 2 

### Reduced

To estimate the reductions in power system costs and CO 2 

### 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 thes e net cost impacts by the associated CO 2


### reductions for each year, EPA found that the average cost of the CO 2 

### reductions achieved ranged

from $16 to $24 per metric tonne of CO 2 

### . 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 CO 2 

### avoided.

Analysis Considerations

Two considerations are worth noting in regards to the analysis described in the previous

two sections, “Goal Setting” and “Impacts Assessme nt:” 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 Formatted: Subscript

Formatted: Subscript

Formatted: Line spacing: 1.5 lines

Formatted: Font: Not Bold

Formatted: Underline

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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 evalua ted 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 (h igher 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 relate d 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,” Formatted: Underline

Formatted: Font: Not Bold, No underline

Formatted: Line spacing: 1.5 lines

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remains the most comprehensive effort that co llects 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 w ith consistency and quality of reported DSM

program data in Form EIA-861. Specifically, the data are se lf-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 standardiza tion 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 Formatted: Indent: First line: 0.5", Line spacing: 1.5 lines

Formatted: Line spacing: 1.5 lines

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Formatted: Line spacing: 1.5 lines

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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 Formatted: Line spacing: single

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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 Ri ver Project 2012-2017 Energy Efficiency Plan –

Final Report. Prepared for Salt River Project, April 16.

Colorado: Kema, Inc. 2010. Colorado DSM Ma rket Potential Assessment – Final Report.

Prepared for Xcel Energy, March 12.

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Delaware: Optimal Energy, Inc. 2013. Delawa re Economic Energy E fficiency 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 U tilities 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 pers pectives: 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 el ectricity savings (supplemented as needed

with program administrator reports) and identi fied 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 Scorecard lxxxi 

### , there are a total of 26 states that have

38 

### The EIA 861 was the main data source. However, we ha ve 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, En ergy Trust of Oregon, Efficiency Maine Trust, and Cape Light Compact. In addition, we supplemented the EIA 861 da tabase 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 threshol ds, 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 reas onable 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%” groups 43



### 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 averag e increase in first-year savi ngs from each entity in this

group.

42 

### This is the simple average estimate of the annual average increase in firs t-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, Effi ciency 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 electrici ty savings ramp-up based on existing state

EERS policies are:

 The states with EERS policies which exhi bit 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 averag es 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 Policies 44



### State Minimum

Target Maximum

Target Climb

Time

(years) Annual

Average %

Increase Years to

Achieve 1%

Increase Min Year Max Year

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 Boar d," 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 admi nistrators 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 inform ation (participant costs across entire portfolio

of EE programs) to inform the analysis.

 Participant cost data from ten program admini strators 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 thes e 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 par ticipant costs for the nine states. Program

45 

### For the purpose of this research, we have defined leadi ng or high impact states as the top 15 states in the 2013 ACEEE State Energy Efficiency Scorecard in terms of incremen tal 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 cost s 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 hi gher 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 Boar d," 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/a ssets/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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### Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy 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

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lxxix 

### More information on EIA Form 861 can be found at http://www.eia.gov/elec tricity/data/eia861/

lxxx 

### Add citation

lxxxi 

### American Council for an Ener gy-Efficient Economy (ACEEE). Nove mber 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

.

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1 

## 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 CO 2 

### emissions rate (lbs/MWh) in these boilers. The CO 2 

### 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 (H 2 

### O) than coal, but far less CO 2



### .

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 ex isting coal boiler, for reasons th at 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 CO 2 

### 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 tr ansition typically requires at least some plant

modifications and might have some negative imp act on the efficiency of the unit as described

later in this chapter.

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2 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 pipeli ne 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 chan ges affecting heat transfer surfaces; the need

for sizing of flue gas recirculation fans; and ope rational changes to sootbl owers, spray flows, air

heaters, and emission controls.

Whether co-firing with coal or switching complete ly 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 he at lost up the stack in stead of being directed

towards electricity generation. Additionally, depending on the design of the boiler and extent of

modifications, some boilers may in cur 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 over all 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 SO 2



### 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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3 gas as a startup fuel, or are located at facilities wi th onsite gas-fired generators. These boilers are

likely able to co-fire to some degr ee with gas (at least 10% total output 1 

### ) 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 quanti ties sufficient for sustained utilization on site.

To the extent that firm (uninterruptible) gas supply is contractually unavailable or costprohibitive, any potential interruption in gas su pply could impact the ability of the unit to

continue operating without increasing its CO 2 

### emissions rate (since it would likely need to

substitute more CO 2 

### -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 follows 2 

### :

$/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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4 $/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 com bustion we assume that a 50-mile gas pipeline 3 

### 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 rate 6 

### 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/air markets/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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5 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 outpu t 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 CO 2 

### 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 CO 2 

### 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. CO 2 

### Rates at Various Levels of Natural Gas Co-Firing

Case Heat Rate (Btu/kWh) CO 2 

### Rate (lbs/MWh net) Reduction in CO 2 

### 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 CO 2 

### emissions, natural gas co-firing at a coal-fired steam EGU will

generally also reduce criteria air pollution. Reducing CO 2 

### 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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6 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 PM 2.5 

### -related human health co-benefits of SO 2 

### , NO X 

### , and

direct-PM 2.5 

### emission reductions attributable to a ra nge of natural gas co-firing levels at an

illustrative coal steam unit burn ing bituminous coal in 2020. 11 

### The estimated monetized cobenefits do not include climate benefits or health effects from direct exposure to NO 2 

### , SO 2 

### , and

HAP; ecosystem effects; or visibility impairment. Only the unit-level emissions of SO 2 

### , NO X 

### and

direct-PM 2.5 

### are considered in this illustrative ex ercise. 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 Leve ls of Co-Firing, based on Illustrative Unit

(lbs/MWh net)

10%
Gas 100%
Gas

SO 2 

### 0.3 3.1

NO X 

### 0.2 2.04

PM 2.5 

### 0.02 0.2



### To estimate human health co-benefits for this illustrative coal steam unit, the EPA used PM 2.5 

### -
related benefit-per-ton estimates for SO 2 

### , NO X 

### , and direct-PM 2.5 

### emission reductions described

in detail in Chapter 5 of the RIA for this proposa l. 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 SO 2 

### , NO X 

### , and direct-PM 2.5 

### emissions by the PM 2.5 

### -related benefit per-ton estimates,

and add those values to get a measure of 2020 benefits. Table 6-3 shows the PM 2.5 

### -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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7 the illustrative electricity generation technol ogies 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

PM 2.5 and PM 2.5 precursors, SO 2 and NO X
. It is important to note that the monetized health co

‐ benefits do not include reduced health effects

from ozone or direct exposure to NO 2 , SO 2 , 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 huma n 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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8 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 CO 2 

### 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 $/tonne 14 

### cost of avoided CO 2 

### 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 pipeli ne 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 CO 2 

### 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 CO 2 

### and CO 2 

### 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 CO 2 

### ($/tonne) Change in CO 2 

### 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 CO 2 

### 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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9 Conclusion

Switching from coal to gas is a relatively costly approach to CO 2 

### 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 CO 2 

### emission rates. Moreover, this

analysis shows that coal-to-gas conversion of an ex isting 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 discusse d above with a new NGCC facility would

reduce the net CO 2 

### emission rate of the generating capacity by 62% at a cost of about $50/tonne

of avoided CO 2 

### under the base case projected gas price and about $81/tonne of avoided CO 2 

### 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 na tional level for the purpose of setting emissions

goals consistently in each state. While this an alysis suggests that cost-effective reductions of

CO 2 

### are not available on a national basis from wi despread adoption of natural gas co-firing, it

does not preclude the potential for individual E GUs to utilize co-firing as a way to reduce CO 2


### 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 un its 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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10 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

CO 2 

### emissions rate (lbs/MWh) of these boilers. In the analysis presented in this technical

support document, the physical CO 2 

### emissions rate at the boiler st ack 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 CO 2 

### 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 re ducing the physical CO 2 

### emission rate based only on the CO 2 

### coming

from coal. This analysis indicates that while th e co-firing of biomass with coal is technically

feasible as a means of reducing the coal-based CO 2 

### 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 others 19 

### and others , including a n earlier study by the State of Maryland 20 

### that

concluded as follows:

“Due to the higher cost of biomass fuels wh en 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 CO 2 

### 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/biblio graphy/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 CO 2 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.”

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11 Based on the basic analysis of the cost effectiv eness of biomass energy substitution in reducing

the physical CO 2 

### emission rate based only on the CO 2 

### 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 CO 2


### emissions relative to the net landscape and pr ocess-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 calculati ng 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 competitiv e. For example, state plans such as that

21 

### This analysis does not include evaluation of stack biogenic CO 2 

### 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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12 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 conditio ns, 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 CO 2 

### (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 gi ven the delivered cost of available biomass

versus the cost of coal. However, the use of co-fi ring 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 s ector (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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13 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 heati ng 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 n ecessary 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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14 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 situati ons. 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 tr ansportation, 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 un it 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-speci fic, 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 “transfo rm 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/biblio graphy/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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15 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 capab ility that are approximately repr esentative 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 CO 2 

### 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 hi ghly 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/air markets/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/electricit y/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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16 little to no other market value). The transporta tion component depends primarily on the distance

that biomass needs to be transported. For exampl e, 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 biom ass 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 CO 2 

### reduction potential of biomass co-firing is di rectly related to the amount and type of

biomass co-fired and is due to the difference in heating value, moisture content and

hydrogen/carbon ratios 31 

### 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 en ergy 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 CO 2 

### 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 emission s accounting. See preamble Section VIII.G.

European studies show that biomass can signifi cantly 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 potentia l reductions, the EPA first estimated the cost of

avoided coal CO 2 

### emissions in a hypothetical scenario where biomass CO 2 

### emissions are not

31 

### IFRF Combustion Handbook, Combustion File No. 23, What is Biomass? (Van Krevelen Diagram), http://www.handbook.ifrf.ne t/handbook/cf.html?id=23

32 

### Biomass Combined Heat and Power Catalog of Technologies, U.S. EPA, September 2007, http://www.epa.gov/chp/docume nts/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 CO 2 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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17 included in total stack CO 2 

### emissions (in effect, biogenic CO 2 

### emissions are subtracted from

total CO 2 

### 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 CO 2 

### for a “typical” baseload coal boiler co-firing 10%

biomass is $30/tonne. This estimated cost of avoided coal CO 2 

### emissions would increase if some

portion of the biogenic CO 2 

### emissions from the co-fired biomass were included. At higher

delivered fuel price differentials, the cost of avoided coal CO 2 

### emissions would increase (for

example, at a biomass price of $6/MMBtu, cost of avoided CO 2 

### is $80/tonne if CO 2 

### emissions

from biomass are not counted). Similarly, the costs of avoided coal CO 2 

### emissions would

decrease at lower fuel price differentials. deliver ed 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 CO 2


### 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 CO 2 

### emissions from the stack in relation to the terrestrial

carbon cycling associated with the production and us e of that biogenic feedstock. If biogenic

CO 2 

### 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 CO 2


### 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 CO 2 

### emission rates, the amount of cofiring that would be efficient will depend on f acility specific factors such as available feedstock

supplies and ability to retrofit the facility. For ex ample, 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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18 (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 CO 2 

### emission

increases. 33 

### Even if biogenic CO 2 

### emissions are not counted as pa rt of stack emissions, biomass

co-firing is a relatively costly approach to CO 2 

### 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 CO 2 

### emission rates.

The EPA is considering cost-effectiveness at a na tional level for the purpose of setting emissions

goals consistently in each state. While this analysis concludes that cost-effective reductions of

CO 2 

### are not available on a national basis from wide spread adoption of biomass co-firing, it does

not preclude the potential for individual EGUs to utilize co-firing as a way to reduce overall CO 2


### emissions, nor does it preclude states from factoring in that unit-level poten tial into the design of

state plans for compliance with the 111(d) standard. 34



### Partial Bibliography fo r Technical Feasibility of Biomass Co-firing

(a) Briggs, J. and J. M. Adams, Biomass Combus tion 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 System s, 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 CO 2 

### 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 CO 2 

### 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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19 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-firi ng of Biomass and Opportunity Fuels in Low NOx

Burners, PowerGen 2000 - Orlando, FL

(e) Tillman, D. A., Co-firing Biomass for Gr eenhouse Gas Mitigation, presented at Power-Gen

99, New Orleans, LA, November 30 – December 1, 1999.

(f) Tillman, D. A. and P. Hus, Blending Oppo rtunity 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/bib liography/PPES_06_ 02/PPES_06_02.pdf

(h) Tillman D, Conn R, Duong D (2010) Coal char acteristics 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/Free Pubs/PDFs/ub044.pdf

(j) Fernando R, Cofiring High Ratios of Bioma ss with Coal, IEA Clean Coal Centre (CCC/194),

January 2012

(k) DOE-EERE-ORNL, Biomass Energy Da ta Book- Edition 4, October 2012,

http://cta.ornl.gov/bedb/index.shtml

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1 

## Chapter 7: Carbon Capture & Storage

Introduction

Another possible approach for reducing CO 2 

### 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 fu el-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 feasib le; 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 fo ssil fuel-fired utility boilers and IGCC units.

Carbon Capture Options for Existing Fossil Fuel-fired EGUs

In general, CO 2 

### 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 CO 2 

### capture refers to removal of CO 2 

### from a combustion flue gas prior

to discharging to the atmosphere. Separating CO 2 

### from such a gas stream can be challenging for

a number of reasons. Because CO 2 

### 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 CO 2 

### 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 SO 2 

### , NO X 

### , and HCl – that can degrade the

solvent. The captured CO 2 

### must then be compressed from near atmospheric pressure to much

higher pipeline pressures (about 2,000 psia).

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2 Pre-combustion Capture

Pre-combustion capture systems are applicable to fossil fuel gasification power plants

(i.e., IGCC units) where coal or other solid foss il 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

O 2 

### . The product syngas contains primarily H 2 

### and CO – and, depending on the fuel and

gasification system – some lesser amount of CO 2 

### . The amount of CO 2 

### 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

H 2 

### and CO 2 

### . The resulting CO 2 

### contained in the syngas is then captured before combustion of

the H 2 

### -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 CO 2 

### and is

pressurized. This allows the use of physical absorbents (e.g., SelexolTM, Rectisol®) that require

much less added energy to release the captured CO 2 

### and require less compression to get to

pipeline standards.

Oxy-combustion

Oxy-combustion systems for CO 2 

### capture rely on combusting coal or other fuels with

relatively pure O 2 

### diluted with recycled CO 2 

### or CO 2 

### /steam mixtures. Under these conditions, the

primary products of combustion are water and CO 2 

### , with the CO 2 

### 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 N 2 

### via

boiler air infiltration, and excess O 2 

### in the CO 2 

### product stream.

CO 2 

### Transportation and Storage

CO 2 

### Pipeline Infrastructure

Carbon dioxide has been transported via pipelines in the U.S. for nearly 40 years.

Approximately 50 million metric tons of CO 2 

### are transported each year through 3,600 miles of

pipelines. Moreover, a review of the 500 largest CO 2 

### 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 fact ors that contribute to the cost of CO 2 

### transportation

via pipelines including but not limited to: availa bility and acquisition of rights-of-way for new

pipelines, capital costs, operating costs, length a nd diameter of pipeline, terrain, flow rate of

CO 2 

### , and the number of sources utilizing the pipeline.

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3

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4 Geologic Storage

Existing project and regulatory experience, research, and analogs (e.g. naturally existing

CO 2 

### sinks, natural gas storage, and acid gas inject ion), indicate that geologic sequestration is a

viable long term CO 2 

### storage option. The viability of geologic sequestration of CO 2 

### is based on a

demonstrated understanding of the fate of CO 2 

### in the subsurface. Geologic storage potential for

CO 2 

### 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 wi th carbon storage potential including vast areas

offshore. Estimates based on DOE studies indicat e 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 CO 2


### 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 CO 2 

### ;

and the DOE is currently evaluating their potential storage capacity.

Further evidence of the widespread availability of CO 2 

### 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 CO 2


### storage potential across the United States. For comparison, this amount is 500 times the 2011

annual U.S. energy-related CO 2 

### emissions of 5.5 Gigatons (Gt). 3



### Enhanced Oil Recovery (EOR)

Geologic storage options also include use of CO 2 

### in EOR, which is the injection of fluids

into a reservoir to increase oil production effici ency. EOR is typically conducted at a reservoir

after production yields have decreased from primary production. EOR using CO 2 

### , sometimes

referred to as ’CO 2 

### flooding’ or CO 2 

### -EOR, involves injecting CO 2 

### into an oil reservoir to help

mobilize the remaining oil and make it avai lable for recovery. The crude oil and CO 2 

### 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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5 produced, and sent to a separator where the crude oil is separated from the gaseous hydrocarbons

and CO 2 

### . The gaseous CO 2 

### -rich stream then is typically dehydrated, purified to remove

hydrocarbons, recompressed, and re-injected into the oil or natural ga s reservoir to further

enhance recovery.

CO 2 

### -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 CO 2 

### 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 technol ogies 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 feas ible 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 CO 2 

### 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 CO 2 

### storage is not geologically favorable and are not near an existing CO 2


### 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 Ca nada. 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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6 favorable for CO 2 

### storage. Thus, at some existing facil ities, 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. Ther efore, the EPA does not propose to find that

CCS is a component of the best system of emission reduction for CO 2 

### emissions from existing

fossil fuel-fired EGUs.

Reasonableness of Cost

In the proposed standard of performance fo r new fossil fuel-fired EGUs (79 FR 1430),

the EPA found that the costs to implement partia l 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 ge othermal – 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 th at 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 electri city 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 sour ce 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 CO 2 

### emissions from existing fossil fuel-fired EGUs.

Emission Reductions and Promotion of Advanced Technology

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7 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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1 

### Technical Support Document (TSD) for Carbon Pollution Guidelines for Existing Power Plants:

Emission Guidelines for Greenhouse Gas Emissi ons from Existing Stationary Sources: Electric Utility Generating Units

Docket ID No. EPA-HQ-OAR-2013-0602

Projecting EGU CO 2 

### Emission Performance in State Plans

U.S. Environmental Protection Agency

Office of Air and Radiation

June 2014

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2 

### Table of Contents

I. Introduction .................................................................................................................. ......3

II. Analytic Approaches for Projecting CO 2 

### Emissions from Affected EGUs ..................5 A. Electricity System Modeling Approaches for Projecting EGU CO 2 

### Emissions .......5 B. Growth Tools for Projecting Future EGU Utilization and CO 2 

### Emissions...............9

III. Translating from a Rate-Based Emission Performance Goal to a Mass-Based

Emission Performance Goal ............................................................................................11 A. Concept ...................................................................................................................1 1 B. Projection Scenarios ................................................................................................13

IV. Projecting CO 2 

### 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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3 

### I. Introduction

As discussed in the preamble, in section VIII. F.7, all state plans will need to include a

projection of the CO 2 

### emission performance by affected EGUs that will be achieved under a state

plan (inclusive of plan measures that avoid CO 2 

### 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 CO 2 

### emission rate achieved by affected EGUs or total

CO 2 

### emissions from affected EGUs.

The EPA is also proposing that a stat e may translate the state rate-based CO 2 

### emission

performance goal for affected EGUs to an equivalent mass-based CO 2 

### emission performance

goal. If this translation option is used, a state pl an must also include a projection used to derive

the mass-based CO 2 

### emission performance goal. This transl ation will involve a projection of CO 2


### emissions from affected EGUs during the plan pe riod, 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 th at the emission performance required by CAA

section 111(d) is properly defined in state plan s 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 performa nce projections in state plans. Therefore, the

use of appropriate methods, tools, and assu mptions for such projections is critical.

The preamble, at section VIII.F.7, seeks comment on options presented for how CO 2


### emission projections might be conducted in an a pprovable state plan, and how different types of

state plan approaches are represented in thes e projections. These options include the use of

historical data and parameters for estimating th e 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 developm ent of parameters for estimating the effect of individual state programs and measures, for use as input assumptions for modeling.

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4 

### 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 sh ould provide technical resources for conducting

projections.

This technical support document (TSD) elabor ates these options and considerations. The

TSD discusses possible analytic approaches for translating from a rate-based CO 2 

### emission

performance goal to a mass-based goal, and projecting the CO 2 

### 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 di spatch planning models, dispatch simulation

models, and growth tools that base projections on historical data and algorithms. The TSD also

discusses possible approaches for developing inpu ts 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 CO 2 

### emissions from affected

EGUs

 Section III discusses the concept and practice of translating from a rate-based CO 2


### emission performance goal to a mass-based CO 2 

### emission performance goal

 Section IV discusses the concept and practice of projecting EGU CO 2 

### emission

performance under a state plan

 Section V discusses applied considerations for projecting EGU CO 2 

### 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 CO 2 

### emission projections, for

different types of state plans

 Section VI discusses process considerations for conducting EGU CO 2 

### emission

projections for state plans, including:

o Regional coordination among states in conducting projections

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5 

### o Whether the EPA should provide guidance and other analytic support for conducting

projections in state plans and translation of rate-based CO 2 

### emission performance

goals to mass-based goals

II. Analytic Approaches for Projecting CO 2 

### Emissions from Affected EGUs

This section surveys different types of methods and tools for projecting CO 2 

### emissions

from affected EGUs, including modeling tools and other tools that base projections on historical

data and use algorithms to extrapolate future CO 2 

### emissions performance based on past

performance.

A. Electricity System Modeling Approaches for Projecting EGU CO 2 

### 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 po wer sector (i.e., projections of the expected

response of the sector to factors such as electri city 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 disp atch, fuel consumption, fuel prices, wholesale

electricity prices, emissi ons, EGU retirements, and infrastructu re 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 de mand 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 characteristic s, 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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6 

### costs, pollution retrofit control status, age, and fu el 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, maintainin g reliability criteria,

and following other constraints, such as mi nimum 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 pl anning 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 generati ng facility and retirement or retrofit decisions

for existing capacity within a specific utility’s te rritory. Utility planning m odels 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 re view 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 CO 2 

### emission

reduction strategies or CO 2 

### emission constraints, as IRP scenarios may include greater

penetration of end-use energy efficiency or renewable energy, proxy CO 2 

### emission prices,

emission trading, and limits on CO 2 

### mass emissions.

3 

### See, for example: The Duke Energy Caroli nas IRP (Annual Report), September 2012, available

at http://www.energy.sc.gov/file s/view/Duke_IRP 2012.pdf ; PacifiCorp 2013 IRP (Vol 1), April 2013, available

at http://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/Pu blic/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_L G%26E-KU_IRP_Volume%20I.pdf ; Great River Energy IRP, November 2012, available at http://www.greatriverenergy.com/makinge lectricity/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 represen t individual EGUs, where each EGU has specific

operational characteristics.

Due to the additional spatial and temporal re solution 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 si mulation models are typically driven by near-term economics,

system restrictions and market constraints, incl uding 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 sy stem 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 basi s. 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 conjun ction 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 simula tion 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 mode ls (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 char acterize thermal constraints between zones, which

typically represent control areas or balancing au thorities. 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, wher e transmission constraints between individual

EGUs and load are modeled as well. 8 

### Dispatch simulation m odels 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) define s security constrained economi c dispatch as “the level at which each available resource should be operated, given act ual 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 constr aints on transmission availability and ancillary services. See FERC, “Security Constrained Economic Dispatch: Defi nition, 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 “zona l” 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 affect ed 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 change s 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 ke y 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 capaci ty 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 secto r-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 aggregate d representation of the electricity sector in

multi-sector models may limit their use to providi ng input data for more specific analysis in an

electricity sector model, and to better understand ing variations in electricity load that may result

from changes outside the electricity sector.

B. Growth Tools for Projecting EGU Utilization and CO 2 

### 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 data 12 

### 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 wi ll respond to changing system conditions.

Non-modeling approaches, such as growth tools, approximate future emissions and

generation from existing and new fossil fuel-f ired EGUs under differ ent assumed growth,

retrofit, and load-reduction scenarios. These forecast tools do not simulate economic EGU

dispatch, but could use demand growth rates and el ectricity 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 electrici ty 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 gene ration 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 US 14 

### 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 cha nge with changes in environmental regulations or

installed pollution control retrofits and additio nal 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 Ho urly 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 a dd new generating capacity automatically to meet

load requirements.

III. Translating from a Rate-Based Emission P erformance 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

CO 2 

### emission performance by affected EGUs (taking into account the qualifying impacts of plan

measures that avoid CO 2 

### emissions from affected EGUs) must be equivalent to, or better than,

the required CO 2 

### emission performance level in the stat e 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 th e state chooses to use this approach. State plans

that are projected to achieve an average CO 2 

### emission rate (CO 2 

### lb/MWh) or tonnage CO 2


### emission outcome by all affected EGUs equal to, or lower than, the required level of CO 2


### emission performance in the state plan would be considered to meet this plan approvability

criterion. States may demonstrate such emission pe rformance 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 CO 2 

### 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 CO 2 

### emission performance goal translates the application of a rate-based

emission performance goal to an expected CO 2 

### emissions outcome in tons during a plan

performance period. A mass-based CO 2 

### emission performance goal is calculated by projecting

the tons of CO 2 

### 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 CO 2 

### emission performance goal for affected EGUs established in the emission guidelines.

The translation of a rate-based goal (expressed in lb CO 2 

### /MWh of useful energy output from

affected EGUs) to tons (expressed as total tons of CO 2 

### 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 th e rate-based goal in the emission guidelines as

a proxy emission limit for affected EGUs. State pr ograms and measures in the state plan are not

included in this projection, because the purpose of this analysis is to determine the tonnage CO 2


### emissions that corresponds to the state-specific rate-based CO 2 

### 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 CO 2 

### 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 CO 2 

### emission performance goals in the

emission guidelines. This proxy involves applying the rate-based emission goal for a state as a

rate-based average CO 2 

### emission limit for affected EGUs in a state and then projecting the CO 2


### 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 CO 2 

### 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 CO 2 

### emissions outcome, in total tons of CO 2


### 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 scenar io for translating from a rate-based CO 2


### emission performance goal to a mass-based CO 2 

### 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 CO 2 

### emission performance goal to a

mass-based CO 2 

### 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 CO 2 

### emission rate and

CO 2 

### emissions from affected EGUs in the ab sence 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. Becau se these measures will influence EGU CO 2


### emissions, they should, however, be included in the reference case projection scenario.

2. A Mass-Based CO 2 

### 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 CO 2


### emission limit to affected EGUs that is equi valent to the state-specific rate-based lb

CO 2 

### /MWh emission goal in the EPA emission guidelines. 16 

### The CO 2 

### emissions from

affected EGUs projected during the specified plan performance period in this scenario

represents the translated mass-based CO 2 

### emission performance goal for the state plan.

15 

### An “existing measure” refers to a state or utility re quirement, program, or measure that is currently “on the books.” For the purposes of this discussion, this may incl ude a legal requirement that includes current and future obligations or current programs and measur es that are in place and are anticipated to be continued or expanded in the future in accordance with established plans. Existing measur es may have past, current, and future impacts on EGU

CO 2 

### emissions.

16 

### The proxy emission limit applied includes crediting for end- use energy efficiency, rene wable energy, and nuclear generation included in building blocks three and four, which were used by EPA when calculating the state-specific

CO 2 

### 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 the se rate-based CO 2 

### 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 CO 2 emission

rate and CO 2 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 CO 2 goal

for affected EGUs in that state. The scenario then projects the CO 2 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

res p onse 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

CO 2 

### 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 CO 2 

### Emission Performance Goal Assumptions :

 State interim rate-based CO 2 

### emission performance goal for affected EGUs: 1,100 lb CO 2 

### /MWh

 State final rate-based CO 2 

### emission performance goal for affected EGUs: 1,000 lb CO 2 

### /MWh

Reference Case Scenario :

 Under this scenario, the projected average CO 2 

### emission rate for affected EGUs is 1,500 lb CO 2 

### /MWh during the 2020-2029 interim performance period and 1,500 lb CO 2 

### /MWh in 2030.

Mass-Based CO 2 

### Emission Goal Policy Scenario :

 A proxy emission rate limit is applied to affected EGUs (1,100 lb CO 2 

### /MWh on average during 2020-2029; 1,000 lb CO 2 

### /MWh in 2030 and subsequent years during the projection period).

 Under this scenario, the projected CO 2 

### mass emissions from affected EGUs during the 2020-2029 interim performance period are 100,000,000 tons of CO 2 

### (an average of 10,000,000 tons per year during this period) and 9,000,000 tons of CO 2 

### in 2030.

 The mass-based interim emission performance goal for affected EGUs is 100,000,000 tons of cumulative

CO 2 

### 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 CO 2 

### 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 CO 2 

### mass emissions from affected EGUs during the 2020-2029 interim plan performance period are 98,000,000 tons of CO 2 

### and 8,800,000 tons of CO 2 

### in 2030.

 Based on this analysis, the state plan is projected to achieve both the translated interim and final massbased CO 2 

### emission performance goals for affected EGUs.



### 1. Constructing the Reference Case

A key consideration for translating from a rate-based CO 2 

### emission performance goal to a

mass-based CO 2 

### 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 CO 2 

### 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 electri c 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, an d measures should not, however, be ignored

altogether, as they influence EGU CO 2 

### emissions. Instead, these stat e actions are occurring in the

background – either when a proxy rate-based CO 2 

### emission limit is applied in the Mass-Based

CO 2 

### 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 CO 2 

### emission

performance goal, the two policy scenarios described above, each of which involve adding a

different CO 2 

### emission reduction policy on top of the reference case scenario, are compared to

one another. If the State Plan Policy Scenario achieves projected tonnage CO 2 

### emissions equal

to or lower than the projected CO 2 

### emissions in the Mass-Based CO 2 

### Emission Goal Policy

Scenario , then the state plan can be deemed to meet the translated mass-based CO 2 

### 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 actions 19 

### 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 progr am 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, th e end date of the base period for the EPA’s BSER-based goals analysis (e.g., beginning of 2013 for blocks 1-3 and be ginning 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, pr ograms, 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 thes e actions occur after proposal of the emission

guidelines (e.g., as of June 2014 and in subseq uent 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 CO 2 

### emissions from affected EGUs. Measures installed

after the eligibility date could generate MWh generation or MWh savings during the plan period,

and related avoided CO 2 

### 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 CO 2 

### emissions could be applied toward meeting a required emission rate

performance level.

generation prior to the date of proposal of the em ission guidelines was factored into the state-specific CO 2 

### goals as part of BSER building block three.

22 

### Although such a limitation is not propo sed 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 Ju ne 2014 or later to meet an existing, on-the-books RPS would be a qualifying measure in a state plan. Howeve r, only MWh generation beginning in 2020 and related

avoided CO 2 

### emissions could be applied toward meeti ng a required emission rate performance level in a state plan

. Similarly, under this approach in th e context of a mass-based plan, the re ference case might include the required MWh of renewable energy generation neces sary to meet a state RPS as of the date of proposal of the emission guidelines (e.g., as of June 2014). Renewable energy gene ration 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 CO 2 

### 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 th e 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 CO 2 

### 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 CO 2 

### Emission Performance under a State Plan

As discussed in the preamble, in section V III.D.4, one of the required components of a

state plan is a projection that a plan will achieve the required level of CO 2 

### 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 CO 2 

### emission performance goals for affected

EGUs identified in the emission guidelines, or an equivalent translated mass-based CO 2 

### emission

performance goal.)

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### This emission projection is based on a scenar io 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 th is 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 wh en 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 CO 2 

### emission limit that provides credit for end-use

energy efficiency and renewable energy measures that avoided CO 2 

### 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 reali zed through implementati on of the plan. Utilitydriven portfolio approaches may also include a di verse 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 Plans 23



### There are a number of considerations for properly representing different state plan

approaches in CO 2 

### emission projection scenarios. This includes both accurate representation of

the attributes of state regulations, programs, and m easures in the scenario, as well as the methods

and data sources used to derive certain input assumptions that are used in CO 2 

### 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 CO 2 

### 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 CO 2 

### 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 CO 2 

### emissions from

affected EGUs.

Similar considerations apply for projecting the impact of renewable energy requirements

and programs included in a state plan on CO 2 

### 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 st ate plan will affect EGU dispatch and CO 2



### emissions from affected EGUs. This section disc usses 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 rele vant for state plans that implement a rate-based CO 2 

### emission limit applicable to affected EGUs that provides for adjustment or crediting of the CO 2 

### emission rate of an affected EGU

based on the effects of end-use energy efficiency and renewabl e energy. It is also relevant for state plans that take either a rate-based or mass-based po rtfolio 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 CO 2


### 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. Th ese forecasts would contain data that could be

used to develop inputs to EGU CO 2 

### 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 efficien cy programs, and program-level util ity 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 en ergy 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 utili ty 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 ef ficiency 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 (IS O-NE) Energy-Efficiency Forecast Working Group, Draft Final Energy-Efficiency Forecast 2018-2023 (March 2014), availa ble 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 Ca lifornia 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 ty pically 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 aggreg ate 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 an d 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 necessa ry, 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 requirement s 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 electri city 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 ener gy savings requirements for utilities included in a state

EERS.

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### used to develop EE forecasts by LBNL, NREL 29 

### , and some state energy offices and

commissions.

The LBNL 30 

### 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 CO 2 

### 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. Fo r 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, thes e 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 so me cases there is also an effort to anticipate

new incremental energy efficiency investments in th e future that are not tied to existing energy

efficiency requirements and programs. 31 

### When conducting a projection of EGU CO 2 

### emission

performance as part of a state plan that include s 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 al ready 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 effici ency,” 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 Efficien cy 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 nece ssary inputs to projections of EGU CO 2 

### emission

performance under the state plan. Energy and peak demand savings estimates included in energy

efficiency projections are used as a decrement to fu ture electricity load forecasts that are used in

conducting EGU CO 2 

### 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 CO 2 

### 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 CO 2 

### 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 a pproach and methodology used. Generally, these strengths and

limitations relate to data availability and en ergy 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 peri ods). The ISO NE energy efficiency forecast is

informed by data collected at the energy ef ficiency 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 eff ect of historical energy efficiency policies,

either implicitly or explicitly, while in othe r cases the economic forecast may also anticipate

some limited quantity of futu re 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 ov erall 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 installi ng 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-diffe rentiated 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 CO 2 

### 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 CO 2 

### 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 asso ciated deeper levels of energy saving s, to increasing cost of saved energy. Economies of scale, and expertise gained by program ad ministrators from managing larger programs for multipleyears, can lead to cost reductions. However, at some point, as program administrators move away from initial lowcost strategies and end-use en ergy 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 renewa ble energy or RECs. W ith this context, the

following issues may be important to consider when projecting the impact of state RPS on EGU

CO 2 

### 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 constr aints, 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 framew orks 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 CO 2 

### emissions, a number of

analytic and data considerations are relevant for analysis of RPS impacts on EGU CO 2


### emissions. These analytic and data considerations are summarized below.

Translation of RPS Requirements to Re newable Energy Generation Requirements

Most RPS are retail sales-based requirements, while most electricity sector analytic tools

are generation-based models. When projecting CO 2 

### 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 Ll oss rate). 33



### RPS-Eligible Resources vs. St ate Plan-Eligible Resources

Some RPS rules define a broad range of eligible renewable energy generating resources.

These could include renewable energy EGUs that emit CO 2 

### , 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 tr ansmission 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 ta ken 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 b ecause existing renewable energy generation prior to the date of proposal of the emission guidelines was factored into the state-specific CO 2 

### 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 analys is 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 exp licitly 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 s ubstantially 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 s upplied 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

CO 2 

### emissions that result from renewable energy generation. Data may include a detailed 8760

energy output profile for renewable energy resour ces, 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 economi cs. 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 rene wable energy resource availability (e.g., MW

capacity available at different wind classes, sola r insolation levels, and the characteristics of

biomass). Having this information allows for be tter 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 rese archers who perform wind and solar inte gration studies and need to estimate power production from hypothetical wind a nd 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 data set, the accuracy of the data, site selection, and power output. The Solar Integration Datasets (http://www.nrel.gov/electricity/transmissi on/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 Un ited 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-ener gy-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 so lar, 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 requ ired after-tax ra te 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 l east a regional, if not national, modeling framework.

A broader framework captures co mpetition for renewables (and incr eased prices, gr eater reliance

on poorer resources), as well as market-wide im pacts 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 foreca st of covered retail sa les can be multiplied

by these percentages.

38 

### The CREST model is a product of a 2009-2010 partners hip 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 comp liance 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 expansio n 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 sati sfy the RPS is expected to come from out

of state, the basis for these assumptions should be documented.

 CO 2 

### 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 include s :

 An estimate of the rene wable 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 en sure 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 pr ice 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 Genera tion Resulting from Feed-In Tariff and

Performance-Based Tax Incentives

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### Projections of renewable gene ration that will occur as the result of FITs and other

performance-based incentives will require asse mbling the following da ta about the specific

incentive programs that will suppor t 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 resour ce 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 estim ate of the total electric generation that will

be supported by the FIT or other production-based incentive.

 State experience and trends with FITs a nd 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 util ity 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 dist ribution 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.

 CO 2 

### 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

NO X Bud g et Tradin g Pro g ram.

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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 fo r 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

CO 2 

### emissions from affected sources, considering these compliance flexibilities.

1. Addressing the Sectoral Scope of Emission Budget Trading Programs

Some existing state emission budget trad ing 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 progr ams. 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 budg et trading programs, state plan emission projections

would need to evaluate projected CO 2 

### emissions across all source categories covered by the state

or multi-state program. This would be necessary to project the CO 2 

### emissions performance of

40 

### Depending on the program, these cost containment allowa nce 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 includ es 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 re gulate 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 CO 2 

### emission performance of

affected EGUs, because those reductions would not come from those affected EGUs. However,

state plan emissions projections would need to ev aluate the use and impact of offsets, because

the availability of offsets would affect the amount of CO 2 

### 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 Californ ia 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, CO 2 

### emission projections provided as part of the state plan will need to

evaluate projected CO 2 

### emissions across all covered emissions sectors under the program. This

would be necessary to project the CO 2 

### emissions or weighted average CO 2 

### emission rate of

affected EGUs that will be achieved under th e 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 CO 2 

### 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 CO 2 

### emissions performance across sectors in a multi-sector

emissions budget trading program.

Adequately projecting the CO 2 

### 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 Cali fornia 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 cap acity 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 expans ion planning module. However, use of a standalone electricity sector model, might also be n ecessary as a supplement to a multi-sector energy

model, in order to project CO 2 

### emissions or the weighted average CO 2 

### emission rate from

affected EGUs at a sufficient level of resoluti on for state plan emission projections. Under this

approach, a multi-sector energy model might be used to project the level of CO 2 

### emissions

abatement achieved across the multiple emission s ectors covered by the program. This multisector projection could be used to develop a CO 2 

### marginal abatement cost curve representing

cost-effective, non-electricity sector emission re duction 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 CO 2


### emission performance of affected EGUs (e.g., the CO 2 

### emissions or CO 2 

### emission rate of

individual affected EGUs, or the total CO 2 

### emissions or weighted average CO 2 

### emission rate

across multiple affected EGUs). 44



### Multi-sector emission budget trading programs also often address GHGs beyond CO 2 

### . 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 di fferent industrial sectors reduce emissions of nonCO 2 

### GHGs, which could impact projected CO 2 

### emissions from affected EGUs.

Addressing Emission Offsets

Emission offsets in CO 2 

### 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 co st curve would be applied in a similar manne r 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 re ductions from affected EGUs would be foregone and replaced by emission reductions from emission sources in othe r sectors that are also subject to the multi-sector emission budget trading program.

45 

### “Offsets” as used in the context of CO 2 

### or GHG emissions budget trading programs are distinct from offsets in the NSR permitting context under the CAA, where in certain in stances 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

ex p lainin g the p otential p olic y 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 “CO 2 

### 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 CO 2 

### 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 CO 2 

### emissions from

EGUs regulated under the emissions budget trading program may exceed the base CO 2 

### emissions

budget established for the program. Offset allowances or credits are awarded in addition to the

existing CO 2 

### emissions budget, in exchange for CO 2 

### -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 CO 2 

### emissions from affected EGUs in a state plan, because the

use of offsets functionally expands the CO 2 

### emissions budget for affected EGUs. This allows

affected EGUs to emit more CO 2 

### while meeting their compliance obligation under the emissions

budget trading program, in exchange for CO 2 

### -eq uivalent. emissions reductions achieved outside

of the affected source category. As a result, to properly project the CO 2 

### 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 CO 2 

### -equivalent

emissions reductions projected to be achieved through offsets, when projecting the CO 2



### new facility must be offset with cred itable 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 CO 2 

### 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 occurr ed absent the incentive provid ed 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 re present 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 GHGemission 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 achiev ed 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 performan ce by affected EGUs, the projected

CO 2 

### emissions or weighted average CO 2 

### emission rate for affected EGUs under the state plan

,

would not incorporate a credit for the CO 2 

### -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 fi nanced 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 CO 2 

### emissions budgets

in state regulations. However, there is no requirement limiting total CO 2 

### emissions from affected

sources in an individual state. State regulations include reciproc ity provisions allowing emission

sources to use CO 2 

### allowances issued by another participating state for compliance with the state

program. This provides for state-to-state CO 2 

### allowance flows (and the potential for differences

in state-by-state CO 2 

### emissions relative to state em issions budgets) based on where the

allowance marketemission sources determine s it is most economical to achieve CO 2 

### emissions

reductions. However, in aggregate, the CO 2 

### emissions budgets in each of the participating state

regulations establish a regional cap on CO 2 

### emissions from affected EGUs. As a result, while a

multi-state emission budget trading prog ram may be projected to result in CO 2 

### emissions from

49 

### In other words, the projected CO 2 

### emissions or weighted average CO 2 

### emission rate for affected EGUs would be based on the direct emissions from these EGUs alone, with no calculation of “net” EGU CO 2 

### emissions that factor in

the CO 2 

### -eq uivalent. 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 GHGemission 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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41 

### affected EGUs consistent with a multi-state mass-based CO 2 

### emission performance goal, the

CO 2 

### 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 participati ng in a multi-state emission budget trading

program would jointly demonstrate that the multi- state program is achieving the required level of

CO 2 

### emission performance on a multi-state basis, based on the CO 2 

### emission performance of all

affected EGUs in the multi-state group implementing the program.

Some state emission budget trading prog rams 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 CO 2 

### emission performance of

affected EGUs in the subset of the program represented by U.S. states. Although the CO 2


### 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 CO 2 

### emissions across all participating jurisdictions, as these

cross-jurisdictional flows would impact projected CO 2 

### 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. Fu rthermore, considerations for projecting emission

performance under a state plan will differ depe nding 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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42 

### differences in how inputs to projections are de rived; 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 CO 2 

### 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 jus tify 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 (oth er 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 stat e 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 pl ans on a regional basis. The ISO/RTO Council,

an organization of electric grid operators, ha s 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 indi vidual state plans. Providing assistance in this

way, they felt, would allow states with borders th at fall within an RTO or ISO footprint to assess

the system-wide impacts of potent ial state plan approaches. In a ddition, 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 reliabilit y. The ISO/RTO Council suggested that the EPA

ask states to consult with the applicable IS O/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/Ren ewable Energy Policies and Programs into State Implementation Plans/Tribal Implementation Plans (July 2012), could potentiall y support and be expanded upon to develop this component of EPA technical modelin g 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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44 

### One consideration for state plans that use a mass-based CO 2 

### emission performance goal

is the party that conducts the translation of the rate-based CO 2 

### 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, Tt he 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 pr esumptive translation of the state-specific

rate-based CO 2 

### 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 CO 2 

### Emission Goal Policy Scenario , as described above in section III.B. A state could

utilize the presumptive mass-based CO 2 

### 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 ju stify 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 CO 2 

### 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 assu mptions 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 coor dinating function in addressing the assumptions

applied by multiple states within a grid re gion, acknowledging that assumptions about state

programs across a broader grid region that are included in an analysis scenario will influence

projections of CO 2 

### 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 ha ve 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 seekin g comment on this in the p reamble.

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Technical Support Document (TSD) for the CAA Section 111(d) Emission Guidelines for Existing Power

Plants

Docket ID No. EPA-HQ-OAR-2014-XXXX



# Goal Computation Technical Support

Document

U.S Environmental Protection Agency

Office of Air and Radiation

June 2014

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State Emission Rate Goal Setting under 111(d)

This Technical Support Document (TSD) provides information that supports EPA’s determination of
state-level emission rate goals under the proposed Ru le. Section VII of the preamble discusses state

emission

- rate goals more broadly, and the GHG Abatemen t 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, underlyi ng state level data, and calculations for Option
1Proposed State Goals

 Appendix 2 - State level goals, underlyi ng 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 assumpti ons regarding application of each GHG reducing
technology were identified (see GHG Abatement Measures TSD). The EPA used these assumptions to
develop the two regulatory options provided in this 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 determini ng the state emission rate goals. That document
describes EPA’s historic data review and analysis underlying each technology and informing
EPA’s assessment of its feasibility and cost-eff ectiveness as part of a BSER. The technology
estimates determined through EPA’s analysis and documented in the GHG Abatement Measures

TSD are summarized below. These estimates wearewere used in EPA’s calculation of state
emission rate goals, as described in this TSD.

a. Heat Rate Improvement
Option 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 CO 2 

### emission rate goal is informed by state-specific non-hydro RE
and EE values that relate to its pre-existing non-hydro RE generation and EE savings
rates respectively as described in the GHG Abatement Measures TSD. 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 determin e each state’s emission rate goal. The EPA
carefully considered using a historic year data set, a projected year data set, or a hybrid of the two
as a starting point for applying the above t echnology assumptions and calculating the state’s
emission rate goals. 1 

### Ultimately the EPA chose the historic data approach as it reflected actual
historic performance at the state level. EPA chose the year 2012 as it represented the most recent
year for which complete data were available at the time of the analysis. The EPA also considered
the possibility of using average fossil generation an d emission rate values over a baseline period
(e.g., 2009 – 2012), but determined that there would be little variation in results compared to a
2012 base year data set results due to the rate-based nature of the goal.

The form of the goal is an emission rate representing the deployment of BSER measures, and that
deployment level is largely a function of technology

- specific 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 Em issions 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

 “ Oo ther” emissions 2



###  “ Oo ther” generation

All generation values are expressed as net generation. Emission rate values are net emission rates
and expressed as lbs/MWh. The capacity expressed is nameplate capacity in Megawatts.

B. Source of Data

i. Emissions & Generation Integrated Resource Database (eGRID)

eGRID is a comprehensive inventory of environmental attributes of the U.S. electric power

system. It is the preeminent source of air emi ssions data for the electric power sector, based on

available plant-specific data for all U.S. electricity generating plants that provide power to the

electric grid and report data to the U.S. government. eGRID integrates many different data

sources on power plants and power companies, in cluding, but not limited to: the EPA, the Energy

Information Administration (EIA), the North American Electric Reliability Corporation (NERC),

and the Federal Energy Regulatory Commission (FERC). Emissions data from the EPA are

carefully integrated with generation data from EIA to produce useful values like pounds per

megawatt-hour (lb/MWh) of emissions, which al lows direct comparison of the environmental

attributes of electricity generation. Although 2012 eGRID data had not yet been released, the

EPA applied its eGRID methodology for matching the publically available and reported 2012

emissions and generation data. The EPA relies on this most recent data to calculate state goals.

The state level totals for each technology category described in the above bullets are intended to

reflect the electric generating units (EGUs) meeting the following criteria: 3



### An 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 u ltimate universe of EGUs subject to this rulemaking may vary from that used in this goal setting exercise and will be determined by the rule’s applicability language and actual operating conditions at that plant. 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 th e inventory of units subjected to building blocks

one and two. Although they are covered units under the rule, they are excluded from the

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. IG CCs 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 efficien t 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 covere d 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 form s the starting point for the goal calculation,
the EPA’s BSER methodology also included under construction nuclear and NGCC capacity that

was not 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 NG CC 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 “existi ng” 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 appr opriateness of the state goals estimated using this

approach.INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.3.Emission Rate CalculationEPA’s methodology for calculating state goals is described in the steps noted below. The implementation of each step is illustrated in the tablebelow its description, using the state of Ohio as an example.The mathematical calculations are fundamental to translating the building blocks intoa quantifiable state goal.Step 1:Calculation of unadjusted 2012 state fossil emission rate for covered sourcesAs noted above, the EPA begins the state goal emission rate calculation by starting with actual historic data at the state level. Unit-level dataareissummed to state-level values describing:coal generation, coal emission rate, NGCC generation, NGCC emission rate, OG steam generation, OGsteam emission rate, “other” emissions, “other” generation, andNGCC capacity. These categories are identified as they represent the historicalgeneration that is subject to building block application and/or would likely be subject to the state goal. For combined heat and power (CHP) unitsthat are covered under the rule, theentire emissions and generationemissions and energy outputassociated with the useful thermal output not usedfor 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. Theemission rates shown reflect total emissions divided by total net energy output (e.g. net electricity generation + useful thermal output). (includingthat associated with useful thermal output not for electricity generation)steam-based)from the facility are counted when determining emissionrates because such facilities’ entire emissions and generationwould be considered when determining compliance with rule.6However, only theelectricity generation portion of CHP is available for redispatchpurposes, and therefore only this portion of the cogenerationoperation is reflectedin the generation totals. There are four coal facilities that co-fired biomass in 2012 that did not report emissions under Part75, in these cases anemission rate factor is used to estimate stack CO2emissions attributable to the type of biomass reported.7,8These technology specific valuesbecome the basis for calculating the state’s emission rate. All emission rates provided in the state goal determination are ona net basis.Therefore, they will appear slightly higherthan the emission rates viewed on a grossgenerationbasis.6A separate state goal rate is calculated for IPM power sector modeling that reflects total emissions from CHP units, but justthe electricity provided to gridportion of their generationtheir net electricity generation in the denominator. This was necessary as IPMis a dispatch model that optimizes around net electricitydemand, so itreflects total emissions associated with cogeneration operation, but only the generation associated with meeting net electricity demand.7http://www.theclimateregistry.org/downloads/2013/04/2013-Climate-Registry-Default-Emissions-Factors.pdf8PleaseseesectionVIIIofthepreambleformoreinformationonbiomassinthecontextofthisrulemaking.Comment [A11]:Youmightwanttoaddasectionbeforethisbeingexplicitregardingthefundamentalimportanceofthemathematicalformulatothesecalculations.[EPAResponse]:Thankyou,addedsomelanguagetoemphasize.Comment [A12]:Thisapproachseemslikeitmightconflictwiththeapproachdiscussedinthepreambleonpages103‐104,inthediscussionofBuildingBlock1,whichindicatesthatimprovementtousefulthermalenergywouldalsobecredited:“HeatrateimprovementsarechangesthatincreasetheefficiencywithwhichanEGUconvertsfuelenergytoelectricenergy(andusefulthermalenergyinthecaseofunitsthatcogeneratesteamforprocessuseaswellaselectricity),therebyreducingtheamountoffuelneededtoproducethesameamountofelectricityandloweringtheamountofCO2producedasabyproductoffuelcombustion.”&“TheheatrateofanEGUistheamountoffuelenergyinputneeded(Btu,higherheatingvaluebasis)toproduce1kWhofnetelectricalenergyoutput(andusefulthermalenergyinthecaseofcogenerationunits).”Commentersincludingusefulthermalenergywhenconsideringcogenerationunits.[EPAResponse]:Thankyou,wehaverevisedlanguagehereaccordingly.Comment [A13]:Forpurposesofcalculatingstategoals,co‐firedbiomassunitsarepresumedtoemitCO2atalevelbasedonthetypeofbiomass(TSD,p.8).Itshouldbenotedthatthecalculationsofbiomasscontributionsmaybeimpactedbyfuturebiomassrulemakings.Further,thesefuturebiomassrulemakingscouldcontributetofuturecoalunitretirementdecisions.Aclarifyingstatementaboutfuturecountingofbiomassemissionswouldbehelpfulinthiscontext.[EPAResponse]:Biomassrelatedemissionsareaddressedinthepreambleandwehaveinsertedafootnotewithacross‐referencehere.INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.2012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012 CapacityStarting coveredsource rate (lbs/MWh)ABCDEFGHIJCoal RateOG SteamRateNGCCRateOther EmissionsCoal GenOG Steam genNGCC GenOther GenNGCC MWOhio2,1872,196963084,726,936343,62720,907,18304,3431,9462012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012CapacityStartingCoveredFossil Rate(lbs/MWh)ABCDEFGHIJCoal RateOGSteamRateNGCCRateOtherEmissionsCoal GenOGSteamGenNGCCGenOtherGenNGCCMWOhio2,1261,332963284,732,50686,473,075321,60220,907,183214,1784,3431,897Note, when the values in the above cells shaded green and blue are applied to the equation below, the state’s 2012 “unadjustedfossil” emissionrate (or historical fossil rate) for likely covered sources is equal 1,897946lb/MWh.ൌ ݁ݐܽݎ	݊݋݅ݏݏ݁݉݅	݈݅ݏݏ݋݂	݁݀ݐݏݑ݆݊ܽ݀ݑ	݈݅ܿܽݎ݋ݐݏ݄݅ሻܱ2ܥ	݈ܾሺ	ݏ݊݋݅ݏݏ݁݉݅	݈݅ݏݏ݋݂	݈ܽݐ݋ݐሻܹ݄ܯሺ	݊݋݅ݐܽݎ݃݁݊݁	ݕݐ݅ܿ݅ݎݐ݈݁݁ܿ	݈݅ݏݏ݋݂	݈ܽݐ݋ݐThis can be rewritten asHistoricalfossilemission rate = (coal gen. x coal emission rate)+ (OG gen. xOG emission rate) + (NGCC gen. x NGCC emission rate) + “Other” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen.Step 2: BSER Block one- Calculation of state fossil emission rate goal for covered sources resultingfrom heat rate improvement (HRI)Formatted:Centered, Space Before: 0 pt,After: 0 ptFormatted:Space Before: 0 pt, After: 0 ptFormatted:Font: 10 pt, Underline, ComplexScript Font: 10 ptINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.After this historical data is collected for each state, the EPA begins to adjust particular variables to the data to reflect each building block elementof BSER. The EPA assumes that a 6% heat rate improvement atthe facility will directly translate to a 6% reduction in the netCO2emission rate.Therefore, in conjunction with determining state goals undertheoption 1Proposed state goalsin this proposal, the EPA adjusts the state-level coalemission rate (column A) downwards by 6% to reflect the 6% heatrate improvement. In the case of Ohio, this results in a dropfrom 2,12687lb/MWh to1,9992,056lb/MWh. This completes the building block 1 treatment foroption 1the Proposed state goals.92012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012 CapacityState Goal Post Block 1(lbs/MWh)ABCDEFGHIJCoal RateOG SteamRateNGCCRateOther EmissionsCoal GenOG Steam genNGCC GenOther GenNGCC MWOhio2,0562,196963084,726,936343,62720,907,18304,3431,8412012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012CapacityState GoalPost Block 1(lbs/MWh)ABCDEFGHIJCoal RateOGSteamRateNGCCRateOtherEmissionsCoal GenOGSteamGenNGCCGenOtherGenNGCCMWOhio1,9991,332963284,732,50686,473,075321,60220,907,183214,1784,3431,795Note that when the total emission rate is recalculated with heat rate improvement reflected in the adjustment to the state’s coal emission rate, thestate emission rate drops from 1,897946lb/MWh to 1,795841lbs/MWh. This is not the final state emission rate goal rate, rather an emission ratereflecting building block 1 application before moving onto the remaining blocks. The bold areas in the equation below reflectthe values that areadjusted from their historical level at this stage.9EPA makes a similar adjustment foroption 2the Alternative state goals, but uses a 4% HRI improvement assumption when reducing the coal rateFormatted:Font: (Default) Times NewRoman, 8 pt, Font color: Black, Complex ScriptFont: Times New Roman, 8 ptFormatted:Font: (Default) Times NewRoman, 8 pt, Font color: Black, Complex ScriptFont: Times New Roman, 8 ptINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.State Emission Rate Post Block 1= (coal gen.xcoal emission rate)+ (OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) + “Other” EmissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen.Step 3a: BSER Block Two -Calculation of state fossil emission rate goal resulting from heat rate improvement and redispatch toexisting NGCCcapacityIn the GHG Abatement Measures TSD, the EPA described how historical 2012 data illustrated that a significant number of NGCCplantsunitshada net generation that was greater than orequal to its nameplate capacity x 8784 *0.7 – in other words a 70% or greater net capacity factor. It alsoprovided analysis supporting a 70% capacity factor as cost-effective and historical data that illustrated the ability to increase utilization atcombined cycle unitsNGCCsunder favorable market conditions. That portion of the TSD also describes the engineering analysis that suggests theaverage availability of a NGCC is greater than 70%. Building block 2 was intended to reflect the potential redispatch to the state’s existing NGCCfleet 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 multipliedby 8,784 hours(the number of hours in the 2012 year) and then by 70% to get total potential net NGCC generation at a 70% capacity factor. However, this 70%capacity factor represents a redispatch ceiling, and the state’s NGCC generation is only adjusted up to this ceiling if its historic fossil sourcessupport such a level.10Therefore, if this ceiling value (MWh) isless thanthe sum of the state’s 2012 coal, OG steam, and NGCC net generation,the historic NGCC generation is increased to this calculated ceiling value representing the NGCC fleet at a 70% capacity factor, and historic coaland OG steam generation are ramped down by an equivalent amountof generation. If the generation of existing NGCC at a 70% capacity factor isgreater thanthe sum of the state’s 2012 coal, OG steam, and NGCC net generation, the historical NGCC generation is only adjusted to equalthetotal of the 2012 net fossil generation from these sources. In summary, adjusted NGCC net generation is equal to the lesser ofexisting NGCCfleet at 70% capacity factor or 2012 total fossil generation from BSER subjected sources. This preserves the historical total covered fossilgeneration in the state for BSER subjected sources, but reapportions it assuming a 70% state-average net capacity factor ceiling for the NGCCfleet. The increase in NGCC generation is subtracted from the coal and OG steam generation in proportion to the state’s historic generation. Forexample, if coal historically accounted for 90% of the total coal and OG steam generation in the state, then its historic generation would be10Fossil sources here refers to those units subject to BSER redispatch building block treatment (i.e., coal, o/g steam, and NGCC)Comment [A14]:Asnotedabove,wewouldbeinterestedindiscussingtheprosandconsofacoal“floor”thatmightbebasedonhistoricalempiricaldataregardinglowlevelsofcoaldispatchandidling.[EPAResponse]:EPAparticipatedinthisdiscussion.EPA’spowersectormodelingofillustrativecompliancescenariosforthisrule(presentedintheRIA)takeintoaccountreal‐worldlimitationssuchasturndownconstraintsoncoal‐firedunitoperation.Comment [A15]:Thismightnotcapturefullpotentialofredispatch–ifregionalscenarios/modelingisdone,existingNGCCinthesestatescouldpotentiallyincreaseto70%capacityfactorbyexportingtheadditionalgas/re‐dispatchingcoalinothercoal‐heavystates.IsitpossibleforEPAtoprovideitsregionalmodelingofpotentialredispatchforBSER?[EPAResponse]:ThisTSDisfocusedonthecomputationofstategoals;thisbuildingblockspecificissueisaddressedintheGHGabatementmeasuresTSD,Chapter3INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.reduced by 90% of the amount that NGCC generation increases. Likewise, the OG steam generation would be reduced by 10% of theamount bywhich NGCC generation is increased.2012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012 CapacityState Goal Post Block2.1 (lbs/MWh)ABCDEFGHIJCoal RateOG SteamRateNGCCRateOther EmissionsCoal GenOG Steam genNGCC GenOther GenNGCC MWOhio2,0562,196963078,956,358320,22326,701,16404,3431,7812012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012CapacityState GoalPost Block2.1(lbs/MWh)ABCDEFGHIJCoal RateOGSteamRateNGCCRateOtherEmissionsCoal GenOGSteamGenNGCCGenOtherGenNGCCMWOhio1,9991,332963284,732,50680,700,563300,13326,701,164214,1784,3431,739Note – in the above example, the total covered fossil generationhas not increased in the state from the historic totals shownin step 1. It has onlybeen reapportioned assuming a 70% capacity factor for NGCC.NGCC generation is adjusted upwards by approximately 6,000,000 MWh, andcoal and O/G are adjusted downward by the same amount. This increase in NGCC generation reflects the existing fleet increasingfrom ahistorical level of approximately 55% CF up to the ceiling of 70% CF. When these updated generation values are folded into thestate goal4,343MWx8784hoursx70%CF=26,701,1644,343MWx8784hoursx70%CF=26,701,164Formatted:Font: (Default) Times NewRoman, Font color: Black, Complex Script Font:Times New RomanFormatted:Font: (Default) Calibri, Font color:Black, Complex Script Font: Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.calculation, the emission rate drops from 1,795841lb/MWh in Step 2 to 1,73981lb/MWh. This completes building block two application. Thevalues in bold below reflect the historic data points that have been adjusted to reflect building block implementation at thispoint 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” emissionsCoal 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 toexisting and“en route”under constructionNGCC capacityStep 3ais repeated for “en route” NGCC capacity that would also be considered an existing covered fossil source. For this step, “enroute” isdefined as anything that came online in 2013 or that was under construction, site prep, or testing by January 8, 2014. The EPAlooked at reporteddata for 2012 and calculated the average performance of NGCCs thatcame online in the past 5 years and observed that 55% was the averagecapacity factor for these units under a business as usual scenario.13Therefore, the EPA assumed that a 55% capacity factor would be a reasonablerepresentation for the expected generation of “en route” NGCCs capacity under a business as usual scenario. The EPA conservatively designatedthe generation associated with this 55% capacityfactor as unavailable for redispatch to reduce CO2(i.e., not qualifying for building block 2),instead, reserving that amount of generation potential to meet other system needs presumed to have motivated the construction of the “en route”NGCCs. Because these sources are nevertheless covered under thestate emission rate goal, the emissions and generation from this 55%generation are added to the “other” category and averaged into the state goal calculation. The EPA assumes that while these units would operate at55% under a business as usual scenario, the average availability for these units is greater than 55%, and they too could ramp up to 70% CF ceiling,on average, under a BSER framework and displace relatively higher CO2-emitting generation. Thus, 15% of their ultimate CF (70% - 55%) isassumed to be available for redispatch purposes. The MWh associated with this 15% additional CF displaces coal and OG steam generation in thesame manner as in step 3.11While a 70% capacity factor ceiling is assumed in the state goal setting exercise, the majority of states are not expected to operate their existing fleet at thislevel in EPA’s projected compliance scenarios.12Inoption 2the Alternativestate goal calculations, the EPA makes similar adjustments to the historic data but utilize a 65% capacity factor ceiling in place ofthe 70% CF shown above13http://ampd.epa.gov/ampd/Formatted:Font: BoldComment [A16]:Isthisdifferentfrom“existing”underthelegaldefinitionforhowweseparate“new”from“existing”undertheNSPS?Asnotedabove,keepingtheterminologysimpleandconsistentisimportant,unlesstherearesomefundamentaltimingissuesthatweneedtoaddressbetweenthevarioussourcesofMWhrsandhowtheyarecomingonlinearoundtheJan8,2014period.[EPAResponse]:Thedateprovidedhereisindeedthedaterelevanttothelegaldistinctionof“existing”and“new”unitsundertheNSPS;however,inthisdocumentwemustfurtherdistinguishbetween“existing”unitsthatwereoperatingin2012and“existing”unitsthathadnotyetcommencedoperationsin2012,thelatterofwhichwerefertohereas“enroute”capacity.Thissectionexplainstheassumptionswehavetomakeregardinggenerationfrom“enroute”capacitygiventheirlackof2012operationaldata.Formatted:Font: (Default) Times New RomanFormatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.The portion of the “en route” NGCC unit’s generation that is not considered subject to BSER abatement measures (i.e., the first55%) is capturedin the “other” category. The generation and emissions from these sources are still included in the ultimate emission rate quantified as the stategoal by averaging in their unadjusted emissions and generation. For “en route” NGCC units that have no 2012 historical data, the emissions andgeneration associated with the 55% baseline capacity factor isobtained by using the “en route capacity” and historical averageemission rate forNGCCs in the state.14The emissions total for these units is based on multiplying the generation at a 55% CF by the average emission rate forexisting NGCCs in the state.2012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012Capacity"En Route"NGCCState Goal PostBlock 2.2(lbs/MWh)ABCDEFGHIJKCoal RateOGSteamRateNGCCRateOther EmissionsCoal GenOG SteamgenNGCC GenOther GenNGCC MWNGCC MWOhio2,0562,1969632,506,741,57878,249,041317,35427,411,3502,604,0174,3435391,75414Note, both Wyoming and Kentucky had “en route” NGCC capacity, but did not have existing covered NGCC in the state from which aproxy emission ratecould be derived for the “en route” capacity. Therefore, the nationwide average NGCC emissions rate of 919 lb/MWh (net basis)became the assumed emissionrate for the “en route” NGCC in these two states.4,343MWx8784hoursx70%CF=26,701,164539x8784x15%CF=710,186Total=27,411,350Formatted:Font: (Default) Times NewRoman, 10 pt, Bold, Complex Script Font:Times New Roman, 10 ptFormatted:Font: (Default) Times NewRoman, 10 pt, Bold, Complex Script Font:Times New Roman, 10 ptFormatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.2012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012Capacity"En Route"NGCCState GoalPost Block2.2(lbs/MWh)ABCDEFGHIJKCoal RateOGSteamRateNGCCRateOtherEmissionsCoal GenOG Steam GenNGCC GenOther GenNGCCMWNGCCMWOhio1,9991,3329632,791,474,08479,993,008297,50227,411,3502,818,1954,3435391,715Note that when the total emission rate is recalculated to reflect the “en route” NGCC capacity, the state’s emission rate at this step drops from1,73981lb/MWh to 1,71554lbs/MWh. This is not the final state emission rate goalrate, rather an interim rate reflecting building blocks one andtwo application before moving onto the remaining blocks. The bold areas in the equation below reflect the values that are adjusted from theirhistorical level at this stage.Note the “Other” category is bolded here as itreflects 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” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen.Step4a5: BSER Block Three -Calculation of state fossil emission rate goal resulting from heat rate improvement, redispatch to existing and enroute NGCC capacity, andunder construction newandpreserved preservednuclear4,343MWx8784hoursx70%CF=26,701,164539x8784x15%CF=710,186TotalNGCC=27,411,350539x8784x55%CF=2,604,017Total“other”=2,604,017+214,178=2,818,195Formatted TableFormatted:Font: (Default) Times NewRoman, 10 pt, Bold, Complex Script Font:Times New Roman, 10 ptComment [A17]:Forthisstep‐by‐stepequationdiscussion,Iwouldsuggestboldingonlythosetermsthatareaddedinthestepthatisbeingpresented.Thatwillhighlightthestepmoreclearly.INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.In Step4a5, the total under construction nuclear capacity is determined.15Additionally, an amount of existing nuclear capacity that is “at risk” ofbeing retired is identified as approximately5.8% of the current nuclear fleet (see GHG Abatement Measures TSD for further discussion on howthe percent assumption was derived). Thisunder construction newand preserved nuclear capacity was incorporated into the state goals as zeroemitting generation. This was achieved by applying a 90% capacityfactor to the nuclear capacity identified as under construction or “at risk” andthen applying the resulting MWh figure into the denominator of the state goal calculation. Nuclear is not considered a “dispatchable” resource asis NGCC capacity. That is, its utilization will remain fairly constant in both a business-as-usual and policy scenario due toits low variableoperating cost relative to fossil sources. The differing dispatcheconomics for NGCC and nuclear leads to a slightly differenttreatment in the goalsetting. Existing nuclear is not assumed to increase generationin the same manner as the NGCC fleet is assumed capable of inthe 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 issimply added to the state goal denominator – resulting in alovwerloverstate goal emission rate. However, it is important to note that this “underconstruction newand preserved” nuclear capacity is also part of the inventorythat the state can count towardscompliance 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 isequal to or less than thestate emission rate goal).Therefore, the fossil sources do not necessarily have to lower their own emission rate to help the state achieve the lowerstate goal reflected subsequent to adding in this nuclear technology provided that the nuclear capacity continues to operate ata 90% capacity factorprovided they maintain the same level of nuclear capacity..2012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012Capacity"En Route"NGCCNew andPreservedNuclearState GoalPost Block 3.1(lbs/MWh)ABCDEFGHIJKLCoalRateOGSteamRateNGCCRateOther EmissionsCoal GenOG SteamgenNGCC GenOther GenNGCCMWNGCC MWMWhOhio2,0562,1969632,506,741,57878,249,041317,35427,411,3502,604,0174,3435391,351,2391,73315Watts Bar in TN, Vogtle in GA, and Summer in SCComment [A18]:Terminologyissue–isthisNuclearatthefivereactorsactually“new”underthelegalterminology,orisitunderconstructionasofJan82014andhence“existing”inthekindoflegallanguageintheNSPS?Or,dowehaveamixofunderconstructionandnotquiteunderconstructionnuclear?Thisisnotaimedatbeingpedantic,becauseasyouwillseeundertheREcommentsbelowthereappearstobealogicalinconsistencyinthewaythatthisproposaldealswithRE(allexistingandnew)andnuclear(notexisting,buttheunderconstruction,atrisk,andaka“new”.)[EPAResponse]:Wehavemodifiedthelanguageaccordingly.Comment [A19]:Theapproachforpreservednuclearplantsassumesthatthoseat‐riskplantsthatwillbekeptactivewilloperateatacapacityfactorof90%.Giventheadvancedageoftheseunits,thatmaybeanaggressiveassumption.Acapacityfactorof85%maybemoreprudent.[EPAResponse]:Wehavenotfoundanystatisticallysignificantcorrelationbetweenageandcapacityfactoratnuclearunits.Comment [A20]:?[EPAResponse]:ModifiedlanguageaccordinglyComment [A21]:Alittlemisleading,asthe“newandpreserved”nuclearisactuallyaddedtoEPA’sgenerationofthestategoal,andhencethestateisgoingtohavetobuildandkeepthenuclearorelsefindanothersourceofzeroemissionMWhr[EPAResponse]:Goodpoint,modifiedlanguageFormatted:Font: (Default) Times NewRoman, 8 pt, Font color: Black, Complex ScriptFont: Times New Roman, 8 ptComment [A22]:Shouldthisnumberbe1,351,858?(171MW*0.9*8784)?Pleasecheck.[EPAResponse]:Hadactuallyused171.39MW*.9*8760.Butthe8,760shouldbeupdatedto8784forthe2012leapyearandtobeconsistentwithhrsusedelsewhere.Willupdate.INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.2012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012Capacity"EnRoute"NGCCNew andPreservedNuclearState GoalPost Block 3.1(lbs/MWh)ABCDEFGHIJKLCoal RateOG SteamRateNGCCRateOtherEmissionsCoal GenOG SteamGenNGCC GenOther GenNGCCMWNGCCMWMWhOhio1,9991,3329632,791,474,08479,993,008297,50227,411,3502,818,1954,343539993,0771,699Ohio has 0 MW of under construction nuclear and 125.6271MW of “at risk” nuclear capacity share as described in the “GHG AbatementMeasures TSD”. This results in an expected nuclear generation amount of933,0771,351,239MWh that is incorporated into the state goalcalculation (based on 90% capacity factor). When this value isadded into the state goal calculation, the state’s emission rate drops from 1,71554lb/MWh to 1,699733lb/MWhState Emission Rate Post Block 3.1= (coal gen. x coal emission rate)+) +(OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) +“Other” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen. + NuclearN&Pgen.Step4b6: BSER Block Three, cont. -Calculation of state fossil emission rate goal resulting from heat rate improvement, redispatch to existingand 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 haslow variable operating cost and utilization that is dictated bythe renewable resources available. Like nuclear generating capacity, its utilizationfrom 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 generationfrom these sources is added into the denominator in a similarfashion as nuclear.Consequently, all non-hydro RE generation (existing and new build)would be eligible for demonstrating compliance with the state goal.Formatted TableFormatted:Font: (Default) Times NewRoman, 8 pt, Font color: Black, Complex ScriptFont: Times New Roman, 8 ptFormatted:SubscriptComment [A23]:Thisvalueisnotwhatwewouldnormallycallnucleargensinceitisonlygenfromnew&preservedcapacity.ThemostclearandexplicitpracticetowriteequationswouldhavethispresentedwithsomekindofmodifiersuchasNuclear(subscriptNP).Comment [A24]:It’snotimmediatelyclearwhetherbiomassisincludedinbuildingblock3.TotheextentbiomassisincludedinstateRPSs(e.g.,NCincludesswineandpoultrywasteinitsRPS),commentersexpectsthatitis,butinthatcasethelevelofREdeploymentanticipatedbyEPAinthisTSDseemsquitelow.In2012,non‐hydroREexcludingbiomassaccountedfor170TWhofgeneration,andbiomassaccountedforanadditional58TWh.Weexpectthatcurrentin‐serviceandin‐constructionwind,solar,andgeothermalwouldaddanadditional~40TWh/year,leadingtoatotalof~270TWh.[EPAResponse]:ThequantificationofREforBSER(thatisthenincorporatedhereintostategoalcalculations)isdocumentedandexplainedintheGHGAbatementMeasuresTSD.Comment [A25]:ThisistheapparentlogicalinconsistencybetweennuclearandRE.Itwouldlikelybemathematicallydistortingtoaddallexistingnuclearunderthedenominator.Theconversedoesnotovertlyappeartobeproblematic,i.e.,toremovethesmallamountsofexistingnon‐hydroREfromthedenominator,anda)subtractthisexistingREfromtheultimatestategoalandb)givethestatekudosinthetext/tableforhavingthislevelof... [1]Comment [A26]:Yes–welookedatthiscomparisonoutto2030ofbasev.policyprojectionsintheRIA,anditisquitedisconcertingthatallthisefforttoempowerREhasverylittleimpactin2030accordingtothemodeling.CanEPAexplainthis–shouldwebelookingformoreRE?... [2]INTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.The GHG Abatement Measures TSD describes how the RE generationtotal for each state was derived from a combination of existingrenewablegeneration in the state and target RE levels informed by existing Renewable Portfolio Standards (RPS) in the region. Althoughthe amount ofassumed RE generation varies by state to reflect the different existing portfolios of renewable resources and the amount of renewable fuelsavailable in different regions of the country, the nationwide average assumed underOption 1The Proposed state goalsis approximately 13%nonhydroRE by the end of 2029.16A potentialnon-hydroRE generation value is calculated for each state annually for all years between 2020-2029to reflect the annual growth as states move toward a particular regionalnon-hydroRE benchmark. The state-specificnon-hydroRE generationvalues that relate to this goal nationwide average are available in the Excel attachment to this TSD and the GHG Abatement Measures TSD.Thesenon-hydroRE MWh totals include both existingnon-hydrorenewable resources in the state and newnon-hydrorenewable generation.2012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012Capacity"EnRoute"NGCCNew andPreservedNuclearExisting andNew NonHydro REState GoalPost Block 3.2(lbs/MWh)ABCDEFGHIJKLMCoalRateOGSteamRateNGCCRateOtherEmissionsCoal GenOGSteamgenNGCCGenOtherGenNGCCMWNGCCMWMWhMWhOhio2,0562,1969632,506,741,57878,249,041317,35427,411,3502,604,0174,3435391,351,23913,775,5941,5402012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012Capacity"EnRoute"NGCCNew andPreservedNuclearExistingand NewNonHydro REState GoalPost Block3.2(lbs/MWh)ABCDEFGHIJKLMCoal RateOG SteamRateNGCCRateOtherEmissionsCoal GenOG SteamGenNGCC GenOther GenNGCCMWNGCCMWMWhMWhOhio1,9991,3329632,791,474,08479,993,008297,50227,411,3502,818,1954,343539993,07713,775,5941,51216See “GHG Abatement Measures TSD”Comment [A27]:ThisismoreacommentfortheGoalsTSD,buttherearethreeconstraintsongeneratingastate’sREgoal:regionalaverage,stateREstartingpoint,andstatehistoricrateofprogress.Theendresultsaredisconcertingdifferencesbetweenstategoals–suchasMAat24%in2029,andKYat1.9%in2029.Also,asintheMAandMEexamples,onewonderswhywearereducingasmallnumberofstateREgoalsbelowwhatthestatehasitselfset.[EPAResponse]:ThisTSDisfocusedonthecomputationofstategoals;thisbuildingblockspecificissueisaddressedintheGHGAbatementMeasuresTSD.Formatted:Font: (Default) Times NewRoman, Bold, Font color: Black, Complex ScriptFont: Times New RomanFormatted TableFormatted:Font: (Default) Times NewRoman, Bold, Font color: Black, Complex ScriptFont: Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.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 thebuilding block three assumptions. This 13,775,594 MWh ofnon-hydroRE generationwould be added into the state goal by adding it in the denominator. The RE value and resulting state goal shown above reflectthe 2029 year.When this is added into the state goal calculation, the state’s emission rate drops from 1,699733lb/MWh to 1,512540lb/MWh.State Emission Rate Post Block 3.2= (coal gen. x coal emission rate)+) +(OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) +“Other” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen. + Nuclear gen. + RE gen.Step57: BSER Block 4 -Calculation of state fossil emission rate goal resulting from heat rate improvement, redispatch to existing NGCC, “enroute”NGCC, under construction and preserved nuclear, RE,anddemand-side EEA state-specific percent value is determined for each state reflecting the amount of total MWh sales that could potentially beavoided throughdemand-side energy efficiency measures (see GHG Abatement MeasuresTSD). Annual percentage savings rates for MWh sales are derived foreach state for all years between 2020 and 2029 inOption 1The Proposed state goals. A different set of annual percent savings are derived for2020-2024under 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 BSERcomponents that increase each year. The GHG Abatement Measures TSDexplains the rationale and derivation of these annual savings rates. Once these state-specific percent savings rates for MWh sales are derived, theyare 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.18The7.51% scaling factor effectivelyconvertscovertsthe retailssales figure into a corresponding total net generation value that accounts fortransmission and distribution losses.19The lesser of 1) this avoided net generation value,or 2) the avoided net generation value multiplied by thestate’s generation share of its sales is then applied to the denominator. This last step helps assure that building block fourreductions for netimporter states are linked to theirownin-state generation.17http://www.eia.gov/electricity/data/state/18http://www.eia.gov/electricity/data/state/“Retail Sales of Electricity by State and Sector”19The 7.51% scaler represents the historical difference betweentotal net generation of electricity (4,036 TWh) and retail salesof electricity (3,754 TWh).http://www.eia.gov/electricity/state/pdf/sep2010.pdfComment [A28]:ThisTSDdoesnotexplainanothermathcalculationthatsetsthedenominatorrequirementforREMWhr,namelymultiplyingthestateREgoalbythetotalstateMWhrpurchasesfor2012,whichincludesexistinghydropower,nuclear,andREthatexistsin2012.Thisstronglydifferentiallyimpactsasmallnumberofstatesthathavelargehydropowerin2012,e.g.,IdahoandWashington,becausetherelativelysubstantialnewnon‐hydroREgoalrequirement(generatedbecauseoflargeexistingzeroemissionhydro)isassignedtoarelativelysmallfossilenergyMWhrratebase.Thisisbestdiscussedthroughlookingatdetails(appendix,graphs)onindividualstateratereductionsresultingspecificallyfromBB3.[EPAResponse]:Thankyouforyourcomment.TheGHGAbatementMeasuresTSDdocumentstheprocessbywhichREgenerationwasquantifiedforincorporationintostategoals.Comment [A29]:T&Dlossesvaryregionally.Itmaybehelpfultoincorporatethisvariation.[EPAResponse]:EPAdoesnotbelievesuchvariationissignificantenoughtowarrantaddingsuchcomplexityintothestategoalcomputationapproachhere.Comment [A30]:Theremaybeotheroptionstoconsiderthatwouldbemoreappropriate.Commenterslookforwardtodiscussingthis.[EPAResponse]:EPAparticipatedinthisdiscussion.Formatted:Font: (Default) Times New RomanFormatted:Default Paragraph Font, Font:(Default) Times New RomanFormatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.This step further reduces the state goal. However, the state may also count any future EE measures towards compliance with itsstate goal.Therefore, if a state achieves the assumed avoided generation based on EE, it does not have to further lower its emission ratefrom covered fossilfuel fired generators determined in the previous steps. Instead, adding the avoided generation to the denominator (i.e., averaging in the avoidedgeneration at a 0 lbs/MWh emission rate) will lower the state’s emission rate used for compliance with the state goal.2012 Rate (lbs/MWh)2012 mass(lbs)2012 Generation (MWh)2012Capacity"EnRoute"NGCCNew andPreservedNuclearExisting andNew NonHydro REAvoidedSales viademandside EEPost Block4(lbs/MWh)ABCDEFGHIJKLMNCoalRateOGSteamRateNGCCRateOtherEmissionsCoal GenOGSteamgenNGCCGenOtherGenNGCCMWNGCCMWMWhMWhMWhOhio2,0562,1969632,506,741,57878,249,041317,35427,411,3502,604,0174,3435391,351,23913,775,59417,507,5561,3492012 Rate (lbs/MWh)2012 mass (lbs)2012 Generation (MWh)2012Capacity"EnRoute"NGCCNew andPreservedNuclearExistingand NewNonHydro REAvoidedSales viademandside EEPost Block4(lbs/MWh)ABCDEFGHIJKLMNCoalRateOGSteamRateNGCCRateOtherEmissionsCoal GenOG SteamGenNGCC GenOther GenNGCCMWNGCC MWMWhMWhMWhOhio1,9991,3329632,791,474,08479,993,008297,50227,411,3502,818,1954,343539993,07713,775,59416,284,5841,338As described in theDdemandDemand-side energy efficiency section of theGHG Abatement Measures TSD, the 2029 percent of generationsavings due to energy efficiency iscalculatedassumedto be 11.5567% in Ohio. This value is then multiplied by the retail electricity sales in thestate (152,456,864 MWh)20and multiplied by 1.0751 to reflect the net generation commensurate with these sales (i.e., inclusive of transmission20http://www.eia.gov/electricity/data/state/Formatted:Font: (Default) Times NewRoman, Font color: Black, Complex Script Font:Times New RomanFormatted:Font: (Default) Times NewRoman, Font color: Black, Complex Script Font:Times New RomanFormatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.and distribution losses). This results in 18,942,382 MWh ofavoided generation. This value is then multiplied by85.9792.43% (Ohio’s share ofsales coming from in state power generation) to reflect the amountof avoided in-state generation that results from the energyefficiencyinvestments. This final adjustment results in16,284,58417,507,556MWh of avoided generation for Ohio that is assumed for 2029. This avoidedgeneration value is added into the denominator andresults in the state goal being decreased from 1,51240lbs/MWh to 1,33849lbs/MWh. Thisconcludes the application of all four building blocks for the year 2029, and the 1,349 lb/MWh rate reflects Ohio’sAdjustedadjustedemission ratefor the year 2029.State Emission Rate Post Block 4= (coal gen. x coal emission rate)+) +(OG gen. x OG emission rate) + (NGCC gen. x NGCC emission rate) +“Other” emissionsCoal gen. + OG gen. + NGCC gen. + “Other” gen. + Nuclear gen. + RE gen. + EE gen.Steps 6 & 7 8: Interim and Final State GoalsThe completion of the previousfive sevensteps results in a 2029 emission rate for the state. However, as noted above, the RE and EEassumptions change for each year from 2020 through 2029. Thus this procedure is repeated for each of those years using the corresponding REand EE assumption for that year. This results in a set ofannual adjusted emission rates for the years 2020-2029. However, this rulemaking onlyissues two state goals – an interimerimand final state goal. Thus,goals are derived by averaging,or taken directly from and the annual adjustedemission rate values.ForOption 1The Proposed state goals:Interim State Goal – Average of the adjusted yearly emission rates for the period 2020-2029Final State Goal – The 2029 emission rate (as calculated above) becomes the state goal for 2030 and each year thereafterForOption 2The Alternative state goals:Interim State Goal – Average of the adjusted yearly rates for the period 2020-20249Final State Goal – The 2024 emission rate (as calculated above) becomes the state goal for 202530and each year thereafterThe EPA recognizes that the emission rate goals calculated hereinclude measures that may yield reductions in mass emissions but may not yieldemission rate reductions in practice when observing monitored and reported emission and generation data from affected units. As such, it is notfully informative to compare the state goals calculated here toobservable emission rates across affected units either historically or in futureyears. For more discussion about how states can demonstrate compliance with these emission rate goals, please see the “State PlansConsiderations TSD”.For more discussion on emission reductions, please see the Regulatory Impact Analysis.Formatted:Font: (Default) Times New RomanComment [A31]:Andviceversa–maybethiswouldbeagoodspottomentiontheIPMmodelingintheRIA,whichprovidesanoverallestimateoftheactualcumulativeCO2reductionsnationwideby2030.[EPAResponse]:Thankyou.Formatted:Font: (Default) Times New RomanFormatted:Font: (Default) Times New RomanINTERAGENCYWORKINGCOMMENTSONDRAFTLANGUAGEUNDEREO12866INTERAGENCYREVIEW.SUBJECTTOFURTHERPOLICYREVIEW.Finally, the assumptions used to arriveat each state’s goal are not prescriptive ofnecessary actions that a state must take.As described in thepreamble, these values are used only forcalculating a state goal. A state may choose to comply with that state goal through any combination ofabatement measures that differ in class and magnitude from those assumed in the state goal calculation.Example of Final State Goal Calculation for Ohio:Final State Goal Rate for Ohio,Option 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 theoInterimand Final State Goals for Options 1 and 2. This workbook shows all the calculations used to transform the historicalcovered fossilemission rate(columnBB) to the Interim and Final State Goal (columns BB and BC) foroption 1the Proposed state goals, and (columns AM and AN) foroption2the Alternative state goals.Formatted:Font: (Default) Times New RomanFormatted:Font: (Default) Times New Roman

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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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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 Formatted: Font: (Default) Times New Roman, Complex Script Font: Times New Roman

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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 Formatted Table

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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 (Deco nstructed 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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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

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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

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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 Formatted: Font: Not Bold

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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

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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 emissi ons 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 ce rtain gaseous and liquid fuels, fuel flow meters and
fuel testing as required under appendix D and E of 40 CFR Part 75.

For units that do not report to the EPA under 40 CFR Pa rt 75, CO2 emissions are calculated from fuel use
reported in the EIA-923 and emission factors under EPA’s Inventory of U.S. Greenhouse Gas Emissions
and Sinks or The Climate Registry Default Emission Factors. If no unit level fuel use exists in the EIA923 dataset and prime mover fuel data exists, and there are more than one unit at a plant that have the
same prime mover (e.g. steam turbine, combustion turbine, etc.), then prime mover fuel level emissions
are distributed to each generator in the prime mover by proportionately by nameplate capacity. For most
cases (where all units in the prime mover would likely be covered by the rule), this apportionment does

not matter because emissions are summed to the state level. However, if there is a smaller unit in the
prime mover that would not likely be covered by the rule, then this apportionment may not exactly match
the amount of fuel actually burned and the associated emissions for each unit.

Net generation is taken from EIA-923 data. If no uni t level net generation exists in the EIA-923 dataset
and prime mover fuel data exists, and there are more than one unit at a plant that have the same prime
mover (e.g. steam turbine, combustion turbine, etc. ), then prime mover fuel level net generation is
distributed to each generator in the prime mover by proportionately by nameplate capacity.

Logic for determination of source CATEGORY: Formatted: Font: (Default) Times New Roman

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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 , Kemper 21

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/publ ications/proceedings/11/co2capture/presentations/4

‐ Thursday/25Aug11

‐ %20Nelson

‐ Kemper

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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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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 en ergy demand and environmental, transmission,

dispatch, and reliability constraints.

This analysis also evaluates the climate benefits associated with emission reductions of

CO 2 

### and the air pollution health co-benefits associated with reduced emissions of SO 2 

### , NOx, and

PM 2.5 

### , which would lead to lower ambient concentrations of PM 2.5 

### 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 HAP s (including mercury and

hydrogen chloride), carbon monoxide, SO 2 

### , and NO 2 

### , as well as ecosystem effects

, and visibility

impairment.

2 State Goals for Building Blocks 1 and 2

This analysis includes state-specific rate-bas ed 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 gene rating units (EGUs) and use of lower emitting

power sources (increased utilization of existing NGCCs) and are identical to the proposed levels,

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3-2 included for purposes of establishing state-specific goals, associated with Option 1 (6%

efficiency improvement and no more than 70% redisp t a t ch).

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

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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 CO 2 

### Emission Impacts

CO 2 

### Emissions (MM Tonnes) CO 2 

### Emissions

Reductions from Base

Case (MM Tonnes) CO 2 

### 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-CO 2 

### Emissions Reductions, 2020-2030

Base Case Two Block Two Block

Regional State Regional State

2020

SO 2 

### (thousand tons) 1,476 1,243 1,199 -15.7% -18.8%

NO X 

### (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

SO 2 

### (thousand tons) 1,515 1,228 1,171 -19.0% -22.7%

NO X 

### (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

SO 2 

### (thousand tons) 1,530 1,188 1,134 -22.3% -25.9%

NO X 

### (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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3-4 Table 5 1 . 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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5 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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6 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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7 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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8 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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9 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/k Wh) 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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10 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 CO 2 

### 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 a nd regional) evaluated.

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11 Table 19. Estimated Global Climate Benefits of CO 2 

### Reductions for Building Blocks 1 and 2 in 2020 (billions of 2011$)*

Discount Rate and Statistic State Regional

Million metric tonnes of CO 2 

### reduced 252 231 5% (average) $3.2 $3.0 3% (average) $12 $11 2.5% (average) $17 $16

3% (95 th 

### 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 CO 2 

### Reductions in 2025 (billions of 2011$)*

Discount Rate and Statistic State Regional

Million metric tonnes of CO 2 

### reduced 284 258 5% (average) $4.2 $3.9 3% (average) $14 $13 2.5% (average) $21 $19

3% (95 th 

### 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 CO 2 

### Reductions in 2030 (billions of 2011$)*

Discount Rate and Statistic State Regional

Million metric tonnes of CO 2 

### reduced 292 258 5% (average) $5.0 $4.4 3% (average) $16 $14 2.5% (average) $23 $21

3% (95 th 

### 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 Po llutants in 2020 (thousands of short tons)

Region SO 2 

### All-year NOx Ozone-Season NOx

State

East 253 233 96

West 23 40 17

California 0 0 0

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12 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 Po llutants in 2025 (thousands of short tons)

Region SO 2 

### 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 Po llutants in 2030 (thousands of short tons)

Region SO 2 

### 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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13 Table 25 Summary of Estimated Monetized Health Co-benefits in 2020 (millions of 2011$)*

Pollutant 3% Discount Rate 7% Discount Rate

State

SO 2 

### $10,000 to $23,000 $9,300 to $21,000

NOx (as PM 2.5 

### ) $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

SO 2 

### $8,900 to $20,000 $8,000 to $18,000

NOx (as PM 2.5 

### ) $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 NO 2 

### , SO 2 

### , ecosystem effects, or visibility impairme nt. 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 PM 2.5 

### levels, which drive population exposure. The moneti zed co-benefits incorporate the conv ersion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 

### 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 95 th 

### percentile confidence interval for monetized PM 2.5 

### benefits ranges from approximately -90 percent to +180 per cent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).

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14 Table 26 Summary of Estimated Monetized Health Co-benefits in 2025 (millions of 2011$)*

Pollutant 3% Discount Rate 7% Discount Rate

State

SO 2 

### $14,000 to $32,000 $13,000 to $29,000

NOx (as PM 2.5 

### ) $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

SO 2 

### $12,000 to $27,000 $11,000 to $24,000

NOx (as PM 2.5 

### ) $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 NO 2 

### , SO 2 

### , ecosystem effects, or visibility impairme nt. 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 PM 2.5 

### levels, which drive population exposure. The moneti zed co-benefits incorporate the conv ersion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 

### 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 95 th 

### percentile confidence interval for monetized PM 2.5 

### benefits ranges from approximately -90 percent to +180 per cent 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

SO 2 

### $17,000 to $39,000 $16,000 to $35,000
NOx (as PM 2.5 

### ) $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

SO 2 

### $15,000 to $34,000 $14,000 to $31,000

NOx (as PM 2.5 

### ) $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 NO 2 

### , SO 2 

### , ecosystem effects, or visibility impairme nt. All fine particles are assumed to ha ve equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM 2.5 

### levels, which drive population exposure. The moneti zed co-benefits incorporate the conv ersion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 

### 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 95 th 

### percentile confidence interval for monetized PM 2.5 

### benefits ranges from approximately -90 percent to +180 per cent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).

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15 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 Heal th 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 CO 2



### 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% (95 th 

### percentile) $34 $47 to $63 $46 to $61
Regional 231 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $31 $42 to $57 $41 to $54

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 

### emissions. Co-benefits are based on regional benefit-per-ton estimat es. Co-benefits for ozone are based on ozone season NOx

emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 

### and ozone co-benefits and reflect the ra nge 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 redu ced health effects from direct exposure to NO 2 

### , SO 2 

### , 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 CO 2



### 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% (95 th 

### percentile) $43 $60 to $83 $59 to $79
Regional 258 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $39 $54 to $73 $52 to $70
*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 

### emissions. Co-benefits are based on regional benefit-per-ton estimat es. Co-benefits for ozone are based on ozone season NOx

emissions. Ozone co-benefits occur in an alysis year, so they are the same fo r all discount rates. The health cobenefits reflect the sum of the PM 2.5 

### and ozone co-benefits and reflect the ra nge 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

NO 2 

### , SO 2 

### , and HAP; ecosystem effects; or visibility impairment.

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16 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 CO 2



### 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% (95 th 

### percentile) $50 $70 to $97 $68 to $92
Regional 258 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $44 $61 to $85 $60 to $81
*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 

### emissions. Co-benefits are based on regional benefit-per-ton estimat es. Co-benefits for ozone are based on ozone season NOx

emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 

### and ozone co-benefits and reflect the ra nge 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

NO 2 

### , SO 2 

### , 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 scenario s evaluated for building blocks 1 and 2 only.

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17 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

95 th 

### 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 SO 2 

### and NO 2 

### 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

95 th 

### 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 SO 2 

### and NO 2 

### 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 CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Also, different discount rates are a pplied to SCC than to the other estimates because CO 2 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 PM 2.5 and ozone associated with emission reductions of directly

emitted PM 2.5 , SO 2 and NO X . 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 PM 2.5 and ozone. These models assume that all fine particles, regardless of thei r 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 cost s 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 calcu lated using the global SCC at a 3 percent discount rate (model average). Tables 10-12 in this memo present combined cl imate and health estimates based on additional discount rates.

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18 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

95 th 

### 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 SO 2 

### and NO 2 

### 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

95 th 

### 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 SO 2 

### and NO 2 

### 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 CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Also, different discount rates are a pplied to SCC than to the other estimates because CO 2 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 PM 2.5 and ozone associated with emission reductions of directly

emitted PM 2.5 , SO 2 and NO X . 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 PM 2.5 and ozone. These models assume that all fine particles, regardless of thei r 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 cost s 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 calcu lated 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.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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19 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

95 th 

### 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 SO 2 

### and NO 2 

### 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

95 th 

### 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 SO 2 

### and NO 2 

### 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 CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Also, different discount rates are a pplied to SCC than to the other estimates because CO 2 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 PM 2.5 and ozone associated with emission reductions of directly

emitted PM 2.5 , SO 2 and NO X . 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 PM 2.5 and ozone. These models assume that all fine particles, regardless of thei r 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 o f approximately 5%. This estimate includes monitoring, recordke eping, and reporting costs and demand side energy efficiency program and participant costs.

e The estimates of net benefits in this summary table are calcu lated using the global SCC at a 3 percent discount rate (model average). Tables 10-12 in this memo present combined clim ate and health estimates based on additional discount rates.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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20 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-Based a 

### 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 impact s, occurring during each year of the 2 to 4 year period during which construction and HRI instal lation activities occur. Figures in tabl e 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 s cenarios due to HRI or Demand-side energy efficiency

programs. **Recurring Changes are job-years associated with a nnual 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.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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21 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 th e 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 He at Rate Improvements at Coal-Fired Plants

Dallas Burtraw, Josh Linn, Anthony Paul, Peter Vail

Resources for the Future 1



## This memorandum describes the aggregate carbon dio xide (CO 2 

## ) emissions reductions

(measured in short tons) that could be achieved fro m improvements in heat rates across the fleet of U. S.
coal-fired power plants. The emissions reduction op portunities are based on statistical estimates

derived in “Regulating Greenhouse Gases from Coal P ower Plants under the Clean Air Act,” Joshua Linn,

Erin Mastrangelo and Dallas Burtraw, Journal of the Association of Environmental and Res ource

Economists 2014, 1:1 (forthcoming). Those estimates rely on panel data f or a twenty-five year period

1985–2009. Those data and estimated coefficients de scribe the change in heat rate that was observed in

response to a change in fuel input prices. The esti mates are useful to predict the responsiveness of h eat
rates to regulatory initiatives that would in effec t impose an increase in input prices, either direct ly

through a price (such as cap and trade) or through regulatory constraints.

The observed heterogeneity in operating performanc e validates the general availability of

emission reduction opportunities across the fleet i dentified by the EPA in previous technical document s. With no change in utilization and not accounting fo r fuel savings, EPA (2008. p. 16.) estimates that a 5
percent efficiency improvement would produce emissi ons 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

CO 2 

## 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 opportunit ies (Sargent & Lundy 2009). Linn et al. estimates

suggest these emissions reductions could be achieve d at costs that are consistent with Sargent & Lundy

. For example, Linn et al. find that a 10 percent inc rease in coal prices, corresponding to a tax on CO 2 

## 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 con sistent with engineering assessments. Change in

utilization in Linn et al. means there is no change in the output from the plant, so the capacity fact or remains unchanged; however, the estimates do accoun t for savings associated with a reduction in fuel

use and other variable costs resulting from heat ra te improvements. 2 

## Linn et al. find an increase in coal
prices of about 60 percent (commensurate with a $10 per ton tax on CO 2 

## emissions) would stimulate a 1

to 2 percent heat rate reduction (holding fixed uti lization). 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 coeff icient on the log price of coal including the assoc iated SO 2 

## allowance cost, where relevant. The coefficient is interpreted as the elasticity of the heat rate to t he price of coal

in the data. The value that is used is the estimate d elasticity with fixed effects, weighted by capaci ty. 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 CO 2 

## ) that could be achieved by improving the heat rate s at
existing plants. The curves display cost effective schedules, wherein the marginal cost of investments

per ton of emissions reduction that are achieved st rictly through heat rate reductions are equal acros s the industry. The analysis includes all coal plants operating in 2009. The two curves represent estima tes

based on utilization observed in 2009 and in 2012. There was lower average utilization of coal units i n 2012 due in part to the retirement of several small , relatively inefficient units, according to EIA. H ence, one would expect to find fewer opportunities for he at rate improvements in the remaining fleet and thi s is evident in the figure. However, there is a quali tative difference in the information represented in the

two curves. The estimate based on 2009 utilization is consistent with other parameters such as relativ e 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 f uels 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. The refore the curve for 2012 utilization is illustrate d

as a broken line and should be interpreted as sugge stive directionally of trends, compared to 2009, bu t
the specific estimate is less reliable than the cur ve for 2009.

Figure 1. Emissions reduction versus total heat rat e investment costs with constant utilization: 2009

and 2012 utilization rates

The evidence presented in Figure 1 indicates, for e xample, 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 wou ld

correspond to 3 percent reduction in heat input or equivalently that same percent reduction in heat

## 3

rate. Emissions reductions of 100 million tons woul d incur annualized costs of about $1.1 billion doll ars. These reductions would constitute 4.5 percent of to tal emissions from the electricity sector in 2012, and

about 6 percent of emissions from just the coal fle et. 3



## The values should be acknowledged to be uncertain n ot only due to the potential change in the

utilization of facilities going forward, or their p otential retirement, but also stemming from the

estimated coefficients and model used in Linn et al . The values illustrated in Figure 1 use the estima ted

elasticity with fixed effects for each generating u nit, weighted by capacity, of -0.013.

Figure 2 repeats this information, which is the aut hors’ preferred estimate, but compares it with

a model that does not include unit fixed effects, r esulting in an estimated elasticity of -0.039. (In all

figures we compare the preferred estimate with only one alternative.) The alternative elasticity estim ate
implies substantially more investments and associat ed emissions reductions are available for any given
cost. For example, at a marginal cost of $40 per sh ort ton (2009$), which is proximate to the social c ost
of carbon identified by the Interagency Working Gro up (2013), the elasticity value (-0.013) estimated

with unit fixed effects (Figure 1) achieves investm ents of about $830 million dollars and just over 95

million short tons of CO 2 

## 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 27 0

million short tons of CO 2 

## emissions reduction.

Figure 2. . Emissions reduction versus total heat r ate investment costs with constant utilization: 200 9

utilization rates with alternative elasticity estim ates

We prefer the estimate of the heat rate cost functi on 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 difficu lt to measure. The regression without fixed effects

includes some technical characteristics, such as th e firing type of the boiler, but there may be omitt ed

factors that bias these estimates. On the other han d, the regression without unit fixed effects may be

interpreted as more of a long run cost function, wh ich would be more appropriate for projecting the

costs of (presumably) long-term regulation.

Figure 3 describes these annualized investment cost s as a function of the percent reduction in

generation-weighted heat rate across the fleet asso ciated with the estimates in Figure 2. Only the

information for 2009 is included in this figure to avoid potential confusion regarding the basis of th e percent calculation. The curve displays the preferr ed 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 ba sed 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 interpre t this comparison as indicative of trends but the

estimates are less reliable because of the inconsis tency 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 o f 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 effe cts (-0.039). As seen previously, substantially mor e investment would be anticipated with the alternativ e 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 p er 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 level s in 2009 appear in Figure 6 to avoid potential

confusion regarding the basis of the percent calcul ation. 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 abou t $5/ton; this corresponds to a 3 percent reduction in

the generation-weighted average heat rate. The mar ginal cost of a reduction of 100 million tons is
about $50/ton; this corresponds to a 6 percent redu ction in heat rate. These values indicate strong

convexity in the schedule of opportunities. For emi ssions reductions beyond this amount resulting from

investments at existing coal-fired plants the predi cted marginal cost schedule begins to rise steeply. The

alternative elasticity estimate suggests that subst antially more emissions reductions would be availab le

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 ot her measures that could be taken at existing

coal plants. These estimates are input to the deter mination of equilibrium, but do not describe that

equilibrium. Any policy that we can imagine would n ot 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 reduce d variable costs would result in an increase in

utilization of those units, but utilization is assu med not to change in these calculations. Assuming n o 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 ne w equilibrium in response to these investments.

Consequently, the estimates do not correspond to ou tcomes from any particular policy that could be

## 7

enacted. Rather, these estimates can be viewed as i nputs to the evaluation of a policy or modeling

effort.

Table 1 reports estimates with the preferred elasti city value and with the two different

utilization levels for 2009 and 2012. The table rep orts the schedule of emissions reductions that are

predicted from the investments, their associated to tal and marginal investment cost, and the heat rate

improvement in percentage terms. The results with 2 012 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) an d 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 th e preferred elasticity estimate (-0.013) are

likely to underestimate the emissions (overstate th e emissions reductions) that result in the future f rom 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 e conometric 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 overesti mate the emissions (understate the emissions

reductions) that would result from various types of policy at any level of total or marginal investmen t
cost. It is also important to note the magnitude of the emissions reductions and differences in the

estimates will depend on the policy design.8Table 1: Total Investment Costs and Marginal Reduction Schedule: 2009 and 2012 DataEmissionReduction (MillionShort Tons CO2)Change inGenerationWeighted HeatRate (%)Total InvestmentCost(Million $)MarginalInvestment Cost($/tCO2)Change inGenerationWeighted HeatRate (%)Total InvestmentCost(Million $)MarginalInvestment Cost($/tCO2)2009 Utilization / 2009 Model2012 Utilization / 2009 Modelα = -0.013α = -0.01300.000 00.000 050.292.6 0.590.372.7 0.62100.586.0 0.760.736.3 0.85150.8710.3 0.961.1011.2 1.15201.1615.7 1.211.4618.0 1.54251.4522.5 1.521.8326.9 2.07301.7431.1 1.922.2038.9 2.76352.0341.8 2.412.5654.9 3.70402.3355.4 3.032.9376.4 4.95452.6272.5 3.823.29105.1 6.62502.9194.0 4.813.66143.6 8.87553.20121.1 6.064.02195.1 11.88603.49155.2 7.644.39264.2 15.94653.78198.2 9.644.76356.9 21.41704.07252.5 12.175.12481.4 28.77754.36321.0 15.375.49648.9 38.71804.65407.6 19.425.85871.7 52.13854.94517.1 24.576.221,174.7 70.31905.23655.6 31.106.591,583.6 94.89955.52829.0 39.386.952,136.5 128.321005.811,050.8 49.927.322,883.0 173.66

## 9

Table 2: Total Investment Costs and Marginal Reduct ion Schedule: 2009 Utilization Rates with

Alternative Elasticity Values

Emission

Reduction (Million

Short Tons

CO 2 

## ) Change in

GenerationWeighted

Heat Rate (%) –
2009 Only Total

Investment

Cost (Million $) Marginal

Investment

Cost ($/tCO 2 

## ) Total

Investment

Cost (Million $) Marginal

Investment

Cost ($/tCO 2 

## )

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