Technical Support Document (TSD) for 

Carbon Pollution Guidelines for Existing Power Plants: 

Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: 

Electric Utility Generating Units 

Docket ID No. EPA-HQ-OAR-2013-0602 

GHG Abatement Measures 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

September 16, 2014 

 
TABLE OF CONTENTS 

Chapter 1: Introduction ........................................................................................ 1-1 

Chapter 2: Heat Rate Improvement at Existing Coal-fired EGUs ....................... 2-1 

Chapter 3: CO2 Reduction Potential from Re-Dispatch of Existing Units ......... 3-1 

Chapter 4: Cleaner Generation Sources ............................................................... 4-1 

Chapter 5: Demand-side Energy Efficiency (EE) ................................................ 5-1 

Chapter 6: Fuel Switching ................................................................................... 6-1 

Chapter 7: Carbon Capture & Storage ................................................................. 7-1 

APPENDIX .......................................................................................................... A-1 

This version of the GHG Abatement Measures TSD (1) contains previously 

identified corrections to minor typographical errors EPA found in the version 

posted on the web on June 2, 2014, and (2) makes two clarifying edits to page 2-

32 of the TSD docketed on June 18, 2014, at EPA-HQ-OAR-2013-0602-0437. 
1 - 1 

Chapter 1: Introduction 

CAA section 111(d) requires that state plans must establish standards of performance that 

reflect the degree of emission limitation achievable through the application of the best system of 

emission reduction (BSER) that, taking into account the cost of achieving such reductions and 

any non-air quality health and environmental impact and energy requirements, the Administrator 

determines has been adequately demonstrated. Under CAA section 111(a)(1) and (d), the EPA is 

authorized to determine the BSER and to calculate the amount of emission reduction achievable 

through applying the BSER. 

As a first step towards determination of BSER, the EPA recognized that, in general, 

reductions in carbon dioxide (CO2) emissions from individual existing electric generating units 

(EGUs) can be achieved by implementing either of two basic approaches: (1) making emission 

rate improvements at affected EGUs (e.g., by improving heat rates or switching to lower carbon 

fuels), and/or (2) reducing utilization of greenhouse gas (GHG)-emitting EGUs (e.g., by reducing 

the overall demand for electricity or by shifting dispatch from higher-GHG-emitting EGUs to 

lower-GHG-emitting and non-emitting units). Accordingly, to determine BSER for reducing 

GHG emissions at affected units, the EPA evaluated numerous GHG abatement measures that 

utilize the above approaches. In its evaluation, the EPA considered only those measures that have 

been adequately demonstrated to reduce CO2 emissions from fossil fuel-fired EGUs. These 

measures included: heat rate improvements at individual EGUs, switching to lower carbon fuels 

at individual EGUs, carbon capture and sequestration at individual EGUs, shifting dispatch from 

higher-GHG-emitting EGUs to lower-GHG-emitting and non-emitting units, and reducing the 

overall demand for electricity via improvements in demand-side energy efficiency. 

Based on its evaluation of the above GHG abatement measures, the EPA identified four 

categories of demonstrated measures, or “building blocks,” that are technically viable and 

broadly applicable, and can provide cost-effective reductions in CO2 emissions from individual 

existing EGUs. These building blocks include: 

1. Reducing the carbon intensity of generation at individual affected EGUs through heat rate 

improvements; 
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2. Reducing emissions from the most carbon-intensive affected EGUs in the amount that 

results from substituting generation at those EGUs with generation from less carbonintensive affected EGUs (including NGCC units under construction); 

3. Reducing emissions from affected EGUs in the amount that results from substituting 

generation at those EGUs with expanded low- or zero-carbon generation; and, 

4. Reducing emissions from affected EGUs in the amount that results from the use of 

demand-side energy efficiency that reduces the amount of generation required. 

The EPA believes that for purposes of CAA section 111(d), as applied to the power 

sector, the BSER encompasses all four building blocks. The application of all four building 

blocks as BSER is consistent with current trends in the electric power sector and with strategies 

that companies and states are already taking to reduce GHG emissions. Also, the application of 

all four building blocks as BSER supports achieving cost-effective, and technically feasible 

reductions of CO2. 

The subsequent chapters in this technical support document describe EPA’s evaluation of 

all adequately demonstrated GHG abatement measures. While evaluating each measure, the EPA 

considered its technical feasibility, applicability and use, application level appropriate for BSER, 

and cost effectiveness associated with reducing GHG emissions at EGUs. 
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Chapter 2: Heat Rate Improvement at Existing Coal-fired EGUs 

2.0 Introduction 

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

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

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

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

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

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

efficiencies would result in corresponding reductions in greenhouse gas emissions. 

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

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

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

the estimated capital costs of those improvements. 

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

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

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

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

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

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

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

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

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

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

coal or coal refuse is burned in limited quantities. 

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

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

(FBC), (3) gasified coal combustion, (4) cyclone furnace combustion, or (5) stoker-fired coal 
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combustion. Table 2-1 presents a summary of the operating characteristics of each of these coal 

combustion configurations. 

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

Coal-firing 

Configuration 

Coal Combustion Process 

Description Distinctive Design/Operating Characteristics 

Pulverized 

Coal (PC) 

Combustion 

Coal is ground to a fine powder that 

is pneumatically fed to a burner 

where it is mixed with combustion 

air and then blown into the furnace. 

The pulverized-coal particles burn in 

suspension in the furnace. Unburned 

and partially burned coal particles 

are carried off with the flue gas. 

Wall-fired 

An array of burners fire into the 

furnace horizontally, and can be 

positioned on one wall or opposing 

walls depending on furnace design. 

Tangential-fired 

(Corner-fired) 

Multiple burners are positioned in 

opposite corners of the furnace 

producing a fireball that moves in a 

cyclonic motion and expands to fill 

the furnace. 

Fluidized-bed 

Combustion 

(FBC) 

Coal is crushed into fine particles. 

The coal particles are suspended in a 

fluidized bed by upward-blowing jets 

of air creating a turbulent mixing of 

combustion air with the coal 

particles. Typically, the coal is mixed 

with a sorbent such as limestone (for 

SO2 emission control). FBC have a 

greater fuel flexibility than PC EGUs 

and can be designed for combustion 

within the bed to occur at 

atmospheric or elevated pressures. 

FBC operating temperatures are in 

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

900o

C). 

Bubbling 

fluidized bed 

(BFB) 

Operates at relatively low gas stream 

velocities and with coarse-bed size 

particles. Air in excess of that 

required to fluidize the bed passes 

through the bed in form of bubbles. 

Circulating 

fluidized bed 

(CFB) 

Operates at higher gas stream 

velocities and with finer-bed size 

particles. No defined bed surface. 

Must use high-volume, hot cyclone 

separators to recirculate entrained 

solid particles in flue gas to maintain 

the bed and achieve high combustion 

efficiency. 

Integrated Coal 

Gasification 

Combined 

Cycle (IGCC) 

Synthetic combustible gas (“syngas”) 

derived from an on-site coal 

gasification process is burned in a 

combustion turbine. The hot exhaust 

gases from the combustion turbine 

pass through a heat recovery steam 

generator to produce steam for 

driving a steam turbine/generator 

unit. 

Coal gasification units are unique among coal-firing 

configurations because a gaseous fuel (synfuel or syngas) 

is burned instead of solid coal because the combustion 

and power generation process and combines the Rankine 

and Brayton thermodynamic cycles as is the case for a 

combined cycle power plant. 

Cyclone 

Furnace 

Combustion 

Coal is crushed into small pieces and 

fed through a burner into the cyclone 

furnace. A portion of the combustion 

air enters the burner tangentially 

creating a whirling motion to the 

incoming coal. 

Designed to burn coals with low-ash fusion temperatures 

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

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

Stoker-fired 

Coal 

Combustion 

Coal is crushed into large lumps and 

burned in a fuel bed on a moving, 

vibrating, or stationary grate. Coal is 

fed to the grate by a mechanical 

device called a “stoker.” 

One of three types of stoker mechanisms can be used that 

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

unto the grate. 
2 - 3 

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 

recent coal technology combustion development involves the integration of coal gasification 

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

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

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

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

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

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

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

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

processes. 

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

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

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

the U.S. electric utility market. 

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

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

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

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

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

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

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

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

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

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

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

needs), and thus is a measure of the electricity delivered to the transmission grid for distribution 
2 - 4 

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

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

heat and power, or CHP, facilities. 

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

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

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

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

factors affecting EGU efficiency at a given facility include: 

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

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

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

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

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

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

efficiencies can be lower than design efficiencies. 

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

to be more efficient than EGUs burning lower quality coals with higher moisture contents 

(e.g., lignite). Bituminous coals have higher 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 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 c) 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. 
2 - 5 

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

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

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

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

recirculating cooling systems. 

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

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

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

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

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

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

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

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

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

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

in customer electricity demand. 

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

tend to be more efficient. 

2.3 Technologies to Improve Existing Coal-Fired EGU Heat Rate 

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

articles and technical papers identifying potential efficiency improvement techniques applicable 

to existing coal-fired EGUs.1

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

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

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

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

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

                                                            

1

 See HRI Partial Bibliography at the end of this chapter. 
2 - 6 

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

technical feasibility of using a given technology. 

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

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

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

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

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

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

below. 

2.3.1 Boiler 

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

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

2.3.1.1 Materials Handling2

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

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

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

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

on the other equipment. 

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

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

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

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

heat rate justifiably only when the existing equipment has degraded. 

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

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

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

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

2.3.1.2 Economizer

                                                            

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

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

from other efficiency activities.
2 - 7 

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

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

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

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

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

installation of new control equipment. 

2.3.1.3 Boiler Control System

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

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

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

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

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

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

2.3.1.4 Sootblowers

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

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

Other readings may be incorporated into the intelligent sootblower system, which also 

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

areas need sootblowing. Boiler efficiency improvements range from 30-150 Btu/kWh with 

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

costs. 

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. For 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 and avoids capturing the heat transferred between the flue gas 

and pre-combustion air. The increased volume requires more power to move more flue gas. 

Improvements to seals on the air heaters reduce the leakages. Improvements to reduce air heater 

and duct leakages generally reduce the heat rate by 10-40 Btu/kWh with capital costs between 

$0.3-1.2M. 
2 - 8 

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

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

sulfuric acid dew point to prevent corrosion of cold-end baskets. Injection of sorbents such as 

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

heater, it may need to be modified in order to optimize the lower outlet temperature. The capital 

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

2.3.2 Turbine 

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

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

pumps. 

2.3.2.1 Turbine

Replacement or overhaul 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 used to heat the boiler feedwater by extracting 

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

improving the heat transfer surface area. This entails adding heat exchange surfaces to the 

existing heaters or adding additional heaters. The costs relative to the heat rate improvement 

associated with these projects typically prohibit the advancement of the project unless the 

feedwater heaters are in need of repair. 

2.3.2.3 Condenser

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

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

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

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

water quality. To improve water quality, closed cooling water systems can be used to provide 

better control over water quality and tube cleaning can be performed as needed. Heat rate 

reductions observed from condenser upgrades and maintenances are 30-70 Btu/kWh with annual 

fixed costs of $30,000-$80,000. 

2.3.2.4 Boiler Feed Pumps
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Boiler feed pumps require a large amount of auxiliary power to pump large amounts of 

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

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

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

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

2.3.3 Flue Gas System 

Two aspects of the flue gas system that can contribute to improvements in the plant heat 

rate are: (1) improve the forced draft and induced draft fan efficiencies, and (2) implement 

variable frequency drives. 

2.3.3.1 Induced Draft Fans

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

Many fans have dampers, which are the least efficient option. There are many other methods, 

such as variable inlet vanes, variable 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 

Variable frequency drives facilitate more efficient plant operation by reducing the 

auxiliary load significantly. The capital costs for upgrading all drives at an EGU can be $6-16M 

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

2.3.4 Emission Control Technologies 

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

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

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

sometimes decrease the power requirements significantly while maintaining the level of 

emissions reduction desired. The three technologies discussed below are the flue gas 

desulfurization, the electrostatic precipitator, and the selective catalytic reduction systems. 

2.3.4.1 Flue Gas Desulfurization

Coal-fired power plants use many types of flue gas desulfurization systems. Older units 

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

pressure drop and greater power to operate the induced draft fans. To improve this operation, a 
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co-current spray tower quencher may replace the unit. The capital cost is about $2.5M with heat 

rate reductions around 13 Btu/kWh. 

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

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

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

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

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

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

limestone slurry. If a power plant is operating with SO2 levels below its permit limit, turning off 

one spray level will reduce the auxiliary power required. If this is possible, a unit heat rate 

reduction of 16 Btu/kWh may be available. 

2.3.4.2 Electrostatic Precipitator

The best operation for an electrostatic precipitator involves maintaining the maximum 

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

management system upgrades often help improve the electrostatic precipitator performance by 

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

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

2.3.4.3 Selective Catalytic Reduction

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

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

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

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

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

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

2.3.5 Water Treatment System 

The boiler water is one of the most important aspects of the power plant. The quality of 

the water is a key factor affecting the scale buildup on the boiler tubes, which reduces the heat 

transfer in the tubes or can cause tube failures. Proper use of chemicals to maintain pure water is 

key. Also, high-quality water can reduce the blowdowns required, which allows for more steam 

in the turbine cycle. If the water is not properly maintained, heat transfer may be reduced by up 

to 10%. 
2 - 11 

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

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

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

aspects already mentioned. 
 2 - 12 

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

Efficiency 

Improvement 

Technology

Description 

Combustion Control 

Optimization 

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

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

Cooling System Heat 

Loss Recovery 

Recover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circuthorough a cooling tower or discharge to a water body. The identified technologies include replacing the coo

fill (heat transfer surface) and tuning the cooling tower and condenser. 

Flue Gas Heat Recovery 

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

Low-rank Coal Drying 

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

rank coal prior to combustion. 

Sootblower 

Optimization 

Sootblowers intermittently inject high velocity jets of steam or air to clean coal ash deposits from boiler tube 

order to maintain adequate heat transfer. Proper control of the timing and intensity of individual sootblowers important to maintain steam temperature and boiler efficiency. The identified technologies include intelligen

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

Steam Turbine Design 

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

Source: National Energy Technology Laboratory (NETL), 2008. Reducing CO2 Emissions by Improving the Efficiency of the Existing Co

Fleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 2008. Av

<http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>. 

a Reported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual component effici

plant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assum

power plant has an overall efficiency of 32% and a net heat rate of 10,600 Btu/kWh. As a result, if a particular efficiency improvement achieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewise, a repo

decrease in net heat rate would be converted to a 0.30% point increase in overall efficiency. 
2 - 13 

2.4 Previous Studies on Heat Rate Improvements 

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

potential efficiency improvements and associated resulting CO2 emission reductions. These 

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

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

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

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

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

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

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

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

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

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

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

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

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

approach and evaluated technologies applicable to bituminous and subbituminous coals to 

estimate potential fleet wide reductions. 

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

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

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

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

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

prices or carbon prices. In one specific example, the coal price was assumed to be a 10% 

increase and the CO2 emissions tax at $1.64 per ton for heat rate reductions of 0.3 to 0.9%. 

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

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

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

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

rates necessary to meet a standard. As it has not been determined with regard to how the CO2

emissions limit will be averaged, the paper discusses many potential options that coal-fired 
2 - 14 

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

renewable, natural gas, and combined-cycle sources. 

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

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

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

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

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

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

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

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

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

HRI potential for coal-fired EGUs. 

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

Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping 

Key Study 

Assumptions 

Relevant HRI 

Results 

1 “Reducing CO2 

Emissions by 

Improving the 

Efficiency of the 

Existing Coal-fired 

Power Plant Fleet” 

NETL 

Plant design: age and steam 

cycle 

 Category improvement is the 

bottom 90% of EGUs in each 

group improving their heat 

rate to the average 

performance of top 10% in 

that category 

 Units with capacity factors 

under 50% were removed 

from dataset 

 15% reduction in 

overall heat rate of 

coal-fired EGUs 

2 “Improving the 

Efficiency of CoalFired Power Plants 

for Near Term 

Greenhouse Gas 

Emissions 

Reductions” 

NETL 

Plant design: coal type, 

steam cycle, and size 

 Category improvement is the 

bottom 90% of EGUs in each 

group improving their heat 

rate to the average 

performance of top 10% in 

that category • Units with 

anomalous data, capacity 

factors under 10%, using less 

than 97% coal, and 

gasification plants were 

removed from dataset 

 Low pressure subcritical units 

and 0-200 MW subbituminous 

units assumed retired for goal 

 Lost generation made up by 

more efficient coal-fired 

EGUs 

 8.7% reduction in 

overall heat rate of 

coal-fired EGUs 

3 “Reducing Heat 

Rates of Coal-Fired 

Power Plants” 

 All possible heat rate 

improvements are made 

• 10% improvement 

for bituminous coalfired EGU 
2 - 15 

Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping 

Key Study 

Assumptions 

Relevant HRI 

Results 

Lehigh Energy 

Update 

including drying of high 

moisture coal: 

• 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. coal-fired EGUs 

 Assess abatement 

opportunities and costs by 

observing how coal plants 

respond to market and 

regulatory incentives to 

improve energy efficiency 

Overall efficiency 

improvements of 2 to 5%, 

from other literature studies 

10% coal price 

increase, 

corresponding to a 

tax on CO2

emissions of about 

$1.64 per ton, 

improving heat rates 

by 0.3 to 0.9 % 

5 “Closing the Power 

Plant Carbon 

Pollution Loophole: 

Smart Ways the 

Clean Air Act Can 

Clean Up 

America’s Biggest 

Climate Polluters” 

NRDC 

Plant design: coal type and 

steam cycle 

Improvements are broken into 

3 group 

• Top 10% for each 

subcategory, no change 

• Top 11% to 49%, improve 

heat rate by 50% of the 

difference between facility 

heat rate and performance of 

top 10% in class or 600 

Btu/kWh, whichever is less 

• Bottom 50%, improve heat 

rate by 100% of the difference 

between facility heat rate and 

performance of top 10% in 

class or 600 Btu/kWh, 

whichever is less 

 Broader analysis 

not strictly focused 

on heat rate 

improvements. 11% 

reduction on overall 

fossil fuel-fired EGU 

emission rates; 

includes switching 

from coal to natural 

gas. 

2.5 EPA’s Heat Rate Improvement Assessment 

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

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

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

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

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

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

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

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

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

evaluations allowed the EPA to recognize the significant heterogeneity of coal-fired EGUs; even 
2 - 16 

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

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

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

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

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

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

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

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

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

or replacing existing components with improved versions. 

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

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

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

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

research EGUs where such methods were applied. 

2.5.1 Study Population and Data

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

reported both heat input3

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

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

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

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

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

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 

                                                            

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

hourly measurements of CO2 flow and concentration. 4

 Information on the Clean Air Markets Division data is available at http://ampd.epa.gov/ampd/
2 - 17 

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

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

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

different regulatory constraints. 

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

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

complete and accurate emission measurement and reporting to support emission trading 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2-1 displays the study population average gross heat rate by year and the 11-year average. 
2 - 18 

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

2002 - 2012 5

Year 

Heat Input 

(million 

MMBtu) 

Electric 

Generation 

(million MWhgross) 

Heat Rate 

(Btu/kWhgross) Unit Count 

2002 18,601 1,874 9,924 839 

2003 18,428 1,864 9,886 834 

2004 18,405 1,875 9,819 836 

2005 18,665 1,910 9,774 838 

2006 18,644 1,914 9,743 848 

2007 18,704 1,920 9,740 846 

2008 18,459 1,914 9,643 852 

2009 16,588 1,719 9,649 864 

2010 17,693 1,831 9,662 869 

2011 16,934 1,744 9,708 878 

2012 14,947 1,536 9,732 884 

Figure 2-1. Study Population 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 

                                                            

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

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

9,500

9,550

9,600

9,650

9,700

9,750

9,800

9,850

9,900

9,950

10,000

20022003200420052006200720082009201020112012

Heat Rate (btu/kwh)

Annual Heat Rate

11-yr Avg Heat Rate
2 - 19 

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

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

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

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

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

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

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

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

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

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

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

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

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

1,669 million tons according preliminary data from the EIA.7 Since the study population of 

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

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

2.5.2 Subcategorization 

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

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

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

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

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

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

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

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

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

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

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

                                                            

7

 Preliminary 2012 results from http://www.eia.gov/tools/faqs/faq.cfm?id=77&t=3.    
2 - 20 

 

Figure 2-2. Three-Year Average Heat Rates by Subcategory8

2.5.3 Observed Trends in the Period 2002-2012 

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

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

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

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. 

                                                            

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

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

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

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

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

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

BIT SUPER

SUB SUPER

BIT OVER 200

PC

BIT 100 to 200

SUB OVER 100

LIG OVER 100

BIT UNDER 100

SUB UNDER 100

LIG UNDER 100

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

Subcategory
2 - 21 

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

Year Capacity 

Factor (%) 

2002 68 

2003 69 

2004 70 

2005 71 

2006 70 

2007 71 

2008 70 

2009 62 

2010 65 

2011 61 

2012 53 

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

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

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

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

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

starts by year. On average, coal-fired EGUs start combusting fuel and generating electricity 11 

times per year. The total number (approximately 9,000) of starts for the study population of 

EGUs has remained stable over the study period. Our data reflects that some coal-fired units 

operate in a load following capacity and may report upwards of 200 starts in a single year, but 

these units tend to have low annual capacity factors. The subset of EGUs with more than 20 

annual startup and shutdown events is responsible for less than 4% of total generation in any 

study year. Therefore while the number of starts is an important variable at a small number of 

EGUs, its impact on heat rate performance evaluated in this study is considered to be marginal. 

                                                            

9 Table 2-5 shows data as reported to EPA as of May 8, 2014.
2 - 22 

Table 2-6. EGU Start Count by Year 

Year Average Maximum (at 

any single EGU) Total 

2002 11.1 209 9,363 

2003 10.6 194 8,936 

2004 10.7 134 9,081 

2005 11.0 183 9,265 

2006 10.5 139 8,859 

2007 10.5 164 8,908 

2008 10.4 134 8,880 

2009 10.3 178 8,902 

2010 10.5 211 9,110 

2011 10.6 206 9,295 

2012 9.9 119 8,805 

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

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

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

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

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

described in the following sections. 

2.5.5 Impact of capacity factor and ambient temperature

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

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

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

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

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

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

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

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

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

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

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

– well below an indication of collinearity. 
2 - 23 

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

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

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

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

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

heat rate improvement and fixed characteristics is typically low.10 

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

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

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

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

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

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

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

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

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

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

cost, and transmission constraints. 

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

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

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

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

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

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 

                                                            

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

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

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

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

the condenser, and, therefore, overall plant efficiency. 12 Climate normal is the average of temperature (or other measure) over a prescribed 30-year interval and location. 

The chart shows the 1981-2010 climate normal monthly temperature at Baltimore-Washington Airport, MD.
2 - 24 

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 

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

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

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

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

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

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

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

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

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

                                                            

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

generation for all units.

0

20

40

60

80

100

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Electric generation monthly 2012 capacity 

factor and normal temperature

Capacity Factor

(Monthly %, 2012)

Climate Normal

(BWI, MD)
2 - 25 

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

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

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

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

factor is a key variable influencing changes in heat rate. 

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

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

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

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

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

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

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

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

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

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

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

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

lower bin (78-83% capacity factor). 
2 - 26 

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

2.5.6 Heat Rate Variability and Indication of Improvement Potential

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

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

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

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

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

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

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

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

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

eleven RSD values, i.e., one for each operating year between 2002 and 2012. The generation-

                                                            

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

Process Control Methods in Performance Monitoring. Available at 

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

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

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

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

technologies. The improvement can be even higher if system-wide real-time optimization is included. 
2 - 27 

weighted mean RSD for the study population across 11 years is 5.4%. Table 2-8 summarizes 

eleven years of results for the study population by quartiles ordered by the 11-year generationweighted RSD average (ascending).17 The RSD of the top quartile (3.5%) is significantly lower 

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

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

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

the potential for heat rate improvement. 

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

Quartile RSD 

Average 

RSD 

Minimum 

RSD 

Maximum 

Share of 

Generation 

1 3.5 1.6 4.2 33

2 4.8 4.2 5.3 26

3 6.1 5.3 7.0 24

4 9.8 7.1 25.2 16

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

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

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

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

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

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

temperature. 

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

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

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

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

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

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

                                                            

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

table.
2 - 28 

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

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

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

Quartile RSD 

average 

RSD 

Minimum 

RSD 

Maximum 

1 2.7 0.0 3.2 

2 3.6 3.2 4.1 

3 4.7 4.1 5.3 

4 6.9 5.3 10.4 

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

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

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

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

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

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

operation to reduce variability and heat rate. 

2.5.6.1 Heat rate variability and performance

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

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

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

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

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

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

                                                            

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

outside 2.6 standard deviations.
2 - 29 

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

2.5.7 Units with large heat rate changes

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

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

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

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

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

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

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. 

                                                            

20 After removing unit-years where annual capacity factor fell under 50 percent the count is 313 EGUs. 21 The correlation remains weak even when limited to cases where capacity factor changed more than 30 percent. 22 EPA Reference method 2 specifies the normal procedure for measuring stack gas volumetric flow rate during a 

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

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

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

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

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

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

effect of changes in flow reporting methods on fleet heat rate performance.

y = 0.002x - 14.254

R2 = 0.5704

5.0

5.2

5.4

5.6

5.8

6.0

6.2

9600 9650 9700 9750 9800 9850 9900 9950

RSD heat rate % 

(generation weighted)

Heat Rate
2 - 30 

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

Description Count 

Correlation of 

heat rate to 

capacity factor 

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

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

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

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

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

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

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

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

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

decrease’ EGUs (generation weighted) 

Quartile RSD 

Average 

RSD 

Minimum 

RSD 

Maximum 

1 4.0 2.3 4.8 

2 5.3 4.8 5.9 

3 6.6 5.9 7.5 

4 10.5 7.6 26.5 

2.5.8 Assessment of heat rate improvement potential via best practices 

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

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

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

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

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

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

rate improvement is available through the application of best practices. 

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

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

                                                            

23 The 26% result is the generation weighted average of r-squared values from the multivariate regression analysis.
2 - 31 

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

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

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

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

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

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

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

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

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

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

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

take proportionally smaller reductions. 

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

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

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

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

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

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

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

                                                            

24 The matrix provides up to 168 bins but only 164 contained hourly values. Temperature bins ranged from -20 to 

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

each. 

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

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

for the given population and time period.  
2 - 32 

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

% Reduction from 

reported heat input 

to p10 

Study population 

heat rate 

(Btu(kWh-gross)27

Reduced study 

population heat 

rate (Btu/kWhgross) 

Study population 

heat rate 

improvement (%) 

10 

9,753 

9,623 1.3 

20 9,493 2.7 

30 9,363 4.0 

40 9,233 5.3 

50 9,103 6.7 

2.5.9 Assessment of potential heat rate improvement via equipment upgrades 

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

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

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

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

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

applicable estimates from the Sargent & Lundy 2009 study (see “Allen S. King materials” and 

“Gorgas materials” available in the rulemaking docket. Docket ID: EPA-HQ-OAR-2013-0602). 

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 actual and potential equipment modifications. These 

included turbine efficiency and condenser performance upgrades, installation of variable 

frequency drive fans, reducing boiler air in-leakage and others. Together, these actual and 

potential measures were projected to achieve 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 

                                                            

27 Fleet heat rate for study population as described in Table 2-4 is 9,753; the 9,753 value is derived from the dataset 

that includes hourly temperature values.
2 - 33 

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 

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 
2 - 34 

Acid Dew Point Control 

Combined VFD and Fan 

Turbine Overhaul 

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

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

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

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

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

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

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

maintain the status quo. 

2.5.10 Combined study population results

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

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

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

by 2%. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

smooth out the effect of equipment maintenance cycles and unusual meteorological patterns. 
2 - 35 

The equipment upgrades results are supported by numerous studies28 and by the EPA’s 

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

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

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

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

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

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

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

electric sector CO2 emissions from coal. 

2.5.11 Sensitivity Analysis Removing Planned or Announced EGU Retirements

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

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

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

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

practices. 

2.6 Heat Rate Improvement – Economics 

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

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

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

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

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

emission cost savings minus capital cost) is positive: 

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

                                                            

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

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

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

http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/NEEDS_v513.xlsx 30 This TSD analysis assesses a broadly combined application of multiple HRI methods. As estimated in the 2009 

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

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

therefore does not consider the small economic impact of HRI-related O&M costs. 
2 - 36 

2.6.1 Heat Rate Improvement Capital Cost Assumption 

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

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

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

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

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

Btu reductions and associated range of capital costs are obtained: 

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

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

EPA Assumed Combined HRI Capital Cost: $100/kW 

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

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

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

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

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

2.6.2 Heat Rate Reduction Assumption 

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

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

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

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

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

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

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

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

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

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

                                                            

31 Note that highest cost does not necessarily align with greatest heat rate improvement. A low cost HRI method can 

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

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

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

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

individual EGUs. The EPA believes that many EGU owners consider such information to be confidential. 
2 - 37 

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

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

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

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

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

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

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

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

6%. 

2.6.3 Heat Rate Improvement Breakeven Economic Analysis 

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

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

reduced via HRI. 

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% 

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
2 - 38 

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 

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

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

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

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

mentioned above. 

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

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

economic fleet wide, 

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

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

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

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

rate reduction percentages. 

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

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

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

                                                            

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 
2 - 39 

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

 Average CO2 emission rate = 0.976 tonne/MWh net 

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

 Average capacity factor = 78% 

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

 HRI Btu and CO2 reduction = 6% 

 HRI capital cost = $100/kW 

 Annual capital charge rate = 14.3% 

 Average coal cost = $2.62/MMBtu 

Estimated Fleet-wide Results: 

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

 Total HRI capital cost = $24 billion 

 Annualized HRI capital cost = $3.5 billion 

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

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

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

2.6.5 Conclusion - HRI Economics 

This necessarily simplified HRI economic analysis supports the following summary 

conclusions: 

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

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

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

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

capital cost is about $75/kW or less. 

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

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

savings alone, before consideration of the value of the associated CO2 emission 
2 - 40 

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

$2.62/MMBtu in 2020). 

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

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

low $7.7/tonne. 

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

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

CO2 reduction for existing coal-fired EGUs 
2 - 41 

Definitions/Abbreviations

Btu British thermal unit 

capacity factor 

electricity generation expressed as a percentage of maximum 

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

generation) 

CO2 carbon dioxide 

EGU electric generating unit 

EIA Energy Information Administration 

EPA Environmental Protection Agency 

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

expressed in Btu 

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

electricity 

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

in gross Btu/kWh. 

MMBtu million Btu 

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 
2 - 42 

HRI Partial Bibliography 

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

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

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

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

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

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

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

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

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

ficCoalFiredPowerPlants-TechWorkshopRpt.pdf 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

http://www.emt-india.net/PowerPlantComponent/Output1.1/Output1.1.pdf
2 - 43 

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

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

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

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

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

Spring/1019287_Heat%20Rate.pdf 

EPRI/Korellis 2011, Identifying Ways to Improve Plant Heat Rate, Energy-Tech Magazine, 

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

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

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

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

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

terest_publications%2FEPRI_Electricity_Use_Report_Final_1024651.pdf&ei=Qo9yUvmwMY

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

dmg 

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

DP 13-05, February 2013, available at http://www.rff.org/RFF/documents/RFF-DP-13-05.pdf
3 - 1 

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

Overview 

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

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

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

possible, of less carbon-intensive existing fossil fuel-fired EGUs. More specifically, the 

examination focuses on opportunities to improve emissions intensity by increasing the utilization 

of existing natural gas combined cycle units. The TSD provides background on existing power 

plants, power system operation, and the economics of electricity production and delivery in the 

context of cost-effective CO2 emission reduction opportunities. 

Introduction 

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

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

of generation and transmission facilities.”34 Electricity demand varies across geography and time 

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

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

manner possible. 

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

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

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

the least expensive power source first.35 

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 

                                                            

34 Energy Policy Act of 2005 

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

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

in real-time. http://www.nerc.com/files/glossary_of_terms.pdf 
3 - 2 

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

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

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

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

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

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

units to natural gas combined cycle (NGCC) units. 

Power Sector Background 

Electric Dispatch 

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

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

consumers, recognizing any operational limits of generation and transmission facilities.”36 The 

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

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

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

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

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

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

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

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

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

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

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

associated with running pollution controls.37 Other generating technologies, like renewables, 

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

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

                                                            

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

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

operating a pollution control and/or emission allowance charges. 
3 - 3 

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

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

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

“curve.”38 

Balancing Authorities 

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

generation, transmission, and distribution infrastructure, the utilities themselves often form the 

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

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

buy and sell power with other balancing authorities. 

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

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

efficient and least-cost manner possible.39 They coordinate, control, and monitor electricity 

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

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

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

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

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

North America (including Canada). 

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

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

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

power through these wholesale electricity markets. 

State Public Utility Commissions (PUCs)40

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

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

                                                            

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

39 http://www.ferc.gov/industries/electric/indus-act/rto.asp 40 These entities are sometimes called Utilities Commissions, Utility Regulatory Commissions, or Public Service 

Commissions.
3 - 4 

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

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

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

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

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

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

PUC also has oversight of the generation and capacity planning components. 

Spot and Day-Ahead Markets 

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

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

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

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

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

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

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

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

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

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

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

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

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

protocols and varies considerably from one utility to the next. 

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

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

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

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

is taken when needed. 

Reliability 

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

of the bulk electric system. The bulk electric system refers to a large interconnected electrical 
3 - 5 

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

reliability, system operators continuously analyze real-time and forecasted load and transmission 

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

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

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

develops and enforces the procedures to ensure reliability, in accordance with Federal laws and 

regulations, and with FERC oversight.41 

Historical Context 

In 2012, average CO2 emission rates42 across all the following technology categories on a 

net generation basis were: 

 Coal Steam - 2,220 lbs/MWh 

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

 NGCC – 907 lbs/MWh 

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

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

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

CO2 emission reductions. 

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

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

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

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

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

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

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

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

                                                            

41 http://www.nerc.com/ 

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

“2012 unit-level data using the eGrid methodology” file provided in the docket 
3 - 6 

maintenance, and unplanned outages. The EPA assumes that NGCC has an availability of 87%.43 

Other reports suggest that NGCC availability factors may reach as high as 92%.44 

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

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

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

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

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

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

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

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

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

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

may model actual weather adjusted capacity by the hour/minute, these data are not reported for 

the fleet. Therefore, the EPA used the nameplate capacity reported for units. The net generation 

was divided into the nameplate generation capacity of a unit multiplied by the number of hours 

in a year. This calculation of capacity factor provides an indication of how much net generation 

a unit is providing as a percent of its total generating capacity. Whereas availability refers to the 

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

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

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

44% to 46% range for 2012.45 Since the fleet-wide capacity factor in 2012 was less than the 

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

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

generation. 

Availability for NGCC fleet................................87% to 92% 

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

                                                            

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

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

45 See “2012 unit-level data using the eGrid methodology” file provided in the docket for 44% figure. See EIA 860 

and 923 for 46% value. 
3 - 7 

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

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

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

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

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

considered historical data and modeling projections describing NGCC characteristics and 

operating behavior. 

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

capacity has come online in the last 15 years.46 Of this capacity, almost all are a highly efficient 

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

Table 3-1: Existing NGCC Capacity, by Age47

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% 

                                                            

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

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

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

http://www.eia.gov/electricity/data/eia860/ 
3 - 8 

Of 464 NGCC plants generating in 2012 and greater than 25 MW, the EPA observed that 

50 plants (more than 10% of NGCC plants) had a net generation value that was greater than or 

equal to its nameplate capacity x 8784 hours * 70%. That is, a capacity factor that was 70% or 

greater (see Figures 3-1 and 3-2).48 

Figure 3-1: NGCC Plant Distribution by Capacity Factors (2012) 

49 

Table 3-2: 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% 

                                                            

48 See “2012 unit-level data using the eGrid methodology” file provided in the docket 

49 EIA Forms 860 and 923. CA and CT Prime Mover categories
3 - 9 

60%-64% 36 7.76% 

65%-69% 27 5.82% 

70%-74% 20 4.31% 

75%-79% 13 2.80% 

80-84% 10 2.16% 

85-89% 4 0.86% 

Greater than 90% 3 0.65% 

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

higher. This subset of NGCCs was largely dispatched to provide base load power. While only 

10% of plants operated at 70% or higher capacity factor on an annual basis, the fleet of NGCC 

units was relied upon heavily during certain periods of time, in response to higher demand. On a 

seasonal basis, a significant number of units achieved capacity factors greater than 50%, and 

even up to 80%. Using data reported to the EPA,50 and looking more closely at data during the 

summer and winter peak electricity demand timeframes nationwide, more than 10% of NGCCs 

were operated at a capacity factor greater than 70%.51 In fact, 19% of NGCCs achieved 70% 

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

summer.52 During periods where demand levels are typically lower, some NGCCs were idled or 

operated at lower capacity factors. Nonetheless, a notable number of existing NGCCs have 

demonstrated the ability to achieve a 70% capacity factor for extended periods of time. These 

units achieved high capacity factors without adverse effects on the electric system. While many 

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

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

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

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

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

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

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

                                                            

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

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

for units for which data was provided to EPA. See file titled “NGCC capacity factors for summer months”. 52 Air Markets Program Data (http://ampd.epa.gov/ampd/). Winter includes December of 2011, January and 

February of 2012. Summer includes June, July, and August. 
3 - 10 

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

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

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

NGCC capacity. 

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

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

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

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

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

CO2. 

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

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

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

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

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

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

economic drivers. 

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

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

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

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

advantage is similar to the incentive that a $15/metric ton of CO2 price signal would create.53 

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

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

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

                                                            

53 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”) 
3 - 11 

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

to foster the potential continued and increasing redispatch to NGCC generating technology.54

Table 3-3: 2011 and 2012 Gas and Coal Generation55

Year 

NGCC 

Name Plate 

Capacity 

(GW) 

NGCC Heat 

Input for 

Electricity 

(TBtu) 

NGCC Net 

Generation 

(TWh) 

NGCC 

Capacity 

Factor 

Henry Hub 

Natural Gas 

Price 

($/mmBtu 

Coal Net 

Generation 

(TWh) 

2011 239 5,912 796 38% $4.00 1,719 

2012 244 7,224 981 46% $2.76 1,503 

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

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

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

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

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

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

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

applicability criteria was 959 TWh.56 After the application of NGCC re-dispatch to the 70% 

level,57 these same existing sources were calculated to collectively generate 1,390 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 1,444 TWh. 

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

measures, the EPA believes that upwards of 1,400 TWh from existing and under construction 

NGCCs is achievable. As a reference point, NGCC generation increased by approximately 430 

TWh (an 81% increase) between 2005 and 2012. EPA is calculating that NGCC generation in 

                                                            

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

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

55 EIA form 860 and EIA form 923 

56 For covered sources. 57 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). 
3 - 12 

2020 could increase by approximately 47% form today’s levels. This reflects a smaller ramp rate 

in NGCC generation than has been observed from 2005 to 2012. 

Table 3-4: Historic and Assumed Generation Patterns for State Goal Setting 

 

NGCC 

Name Plate 

Capacity 

(GW) 

NGCC Net 

Generation 

(TWh) 

Growth in 

NGCC 

Generation 

from 2005 to 

2012 

Growth in 

NGCC 

Generation 

from 2012 to 

2020 

Nationwide 

NGCC 

Capacity 

Factor 

2005 199 551 NA NA 32% 

2012 244 981 81% NA 46% 

2020 State 

Goal 

Calculation 

256 1,444 

47% 

64% 

Natural Gas Supply 

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

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

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

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

previously anticipated, exerting downward pressure on natural gas prices.58

According to EIA, natural gas proved reserves have doubled between 2000 and 2012.59 

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

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

29.1 TCF, due to increased supplies and favorable market conditions. For comparison, NGCC 

generation growth of 450 TWh (calculated in goal setting) would result in increased gas 

consumption of roughly 3.5 TCF for the electricity sector.60 

                                                            

58 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). 59 http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=3&pid=3&aid=6 60 Assuming 1,024 Btu/cubic foot and 10,000 Btu/KWh 
3 - 13 

The National Petroleum Council (NPC), a privately funded advisory committee 

established by the Secretary of Energy, recently updated a major resource study and concluded 

that “the potential supply of North American natural gas is far bigger than was thought even a 

few years ago,” after large increases in shale resource estimates.61 

Figure 3-2: U.S. Natural Gas Technically Recoverable Resources (from NPC, 2011)62

Technical Considerations 

Emission reductions through re-dispatch are largely determined by the ability to change 

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

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

timing, and cost. 

NGCC Availability 

                                                            

61 National Petroleum Council, 2012 (http://www.npc.org/PD_update-80112.pdf) 62 http://www.npc.org/reports/rd.html 
3 - 14 

For purposes of economic dispatch, most NGCCs have historically been operated to serve 

base load or intermediate demand due to their high efficiency and flexibility of operation, with 

national average annual capacity factors in the range of 40-50%.63,64 However, NGCCs are 

designed for, and are demonstrably capable of, reliable and efficient operation at much higher 

annual capacity factors, as shown in observed historical data for particular units and their design 

and engineering specifications. 

The capability of NGCCs to operate at capacity factors of 70% and greater is indicated, in 

part, by statistics on the average availability factor of NGCCs. 65 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.66,67 

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).68

These high-efficiency and high-availability NGCC units were first introduced around 

1995 and have consistently reported availability factors of 90 to 92 percent to NERC (compared 

to 95 percent or greater availabilities reported by current vintage F class and H class turbines 

from General Electric Power Systems).69 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 

                                                            

63 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayinenergy/detail.cfm?id=14611 (for recent data) 64 EIA, Electric Power Annual 2009, http://www.eia.gov/electricity/annual/archive/03482009.pdf (Table 5-2 for 

2009 and earlier data) 

65 NERC, 2008-2012 Generating Unit Statistical Brochure – All Units Reporting, 

http://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx 66 Power Engineering, Negotiating Availability Guarantees for Gas Turbine Plants, 03/01/2001, http://www.powereng.com/articles/print/volume-105/issue-3/features/negotiating-availability-guarantees-for-gas-turbine-plants.html 67 Power Engineering, Higher Availability of Gas Turbine Combined Cycle 02/01/2011, http://www.powereng.com/articles/print/volume-115/issue-2/features/higher-availability-of-gas-turbine-combined-cycle.html 68 http://site.ge-energy.com/corporate/network/downloads/7FA_Evolution.pdf 69 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. 
3 - 15 

The EPA believes that the natural gas pipeline and electricity transmission networks can 

support aggregate operation of the NGCC fleet at up to a 70% capacity factor on average, either 

as they currently exist or with modifications that can be reasonably expected in the time frame 

for compliance with this rule. Existing NGCCs are already connected to both the power and 

natural gas networks and, while constraints to specific unit operations can occur in either or both 

networks during peak pipeline flow or electricity use, the rule allows for emission rate averaging 

across multiple units and across time for compliance. As a consequence of this averaging 

flexibility, constraints that occur at peak times are unlikely to be a barrier to achieving 

compliance with the rule, because these peak times are only a small percentage of the year and 

will constrain only a limited percentage of the state-wide NGCC fleet. The ability for the current 

fleet to ramp up significantly to meet changes in demand can be seen from the increased use of 

natural gas that occurred in 2012 in response to historically low natural gas prices. Power plant 

use of natural gas use in 2012 increased by 20% over 201170 and resulted in a national average 

capacity factor for NGCC of 45.8% on average, and higher in some states.71

During the peak hours of the day (which vary by region and season), NGCC capacity 

factors are typically well above average capacity factors.72 The pattern of capacity utilization by 

hour for 2005 to 2010 is shown in Figure 3-3. In this figure, capacity factors in 2010 are 

approximately 50% from the Hour 11 to Hour 21.73 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%74, this indicates that the current system can 

support levels of approximately 11% above the average capacity factor. These peak hours are the 

period when there are most likely to be constraints on the pipeline or electricity transmission 

networks; during other hours of the day, continued NGCC operation at equal, or higher levels, 

are technically feasible but may be limited by economic considerations (e.g., whether NGCCs 

can offer least-cost electricity compared to other sources at those times). As a result, the current 

system is already able to support national average capacity factors in the mid to high 50’s for 

                                                            

70 Energy Information Administration, U.S. Natural Gas Consumption by End Use, 

http://www.eia.gov/dnav/ng/ng_cons_sum_dcu_nus_a.htm. 

71 Source: Air Markets Program Data (AMPD), ampd.epa.gov, EPA, 2014 

72 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# 73 In this figure, hour 11 is the hour ending at 11 AM, and similarly for other hours. 

74 Air Markets Program Data (AMPD), ampd.epa.gov, EPA, 2014
3 - 16 

NGCC for peak. It is reasonable to expect that average capacity factors could be extended to 

higher levels at all hours without experiencing technical feasibility barriers from either pipeline 

supplies or electricity transmission. 

Figure 3-3. Average Utilization of Natural Gas Combined Cycle Power Plant Fleet 

Although there can be site-specific constraints on utilization at some NGCC facilities, 

several factors support the ability of the power and natural gas pipeline systems to respond 

effectively with increases in infrastructure when needed to alleviate these barriers. For example, 

in recent years, the power transmission system has responded with increased transmission 

infrastructure when needed to allow the retirement of uneconomic coal plants.75 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 

                                                            

75 See http://www.pjm.com/~/media/about-pjm/newsroom/2013-releases/20131211-pjm-board-authorizes-4.6-

billion-in-chnages-to-regional-electric-grid.ashx for an example of short term transmission upgrades performed to 

facilitate environmental compliance. For technical description of these upgrades, see: 

http://www.pjm.com/~/media/committees-groups/committees/teac/20131211/20131211-december-2013-pjm-boardapproval-of-rtep-whitepaper.ashx. 
3 - 17 

meet additional use of existing facilities will generally be less extensive than upgrades of that 

infrastructure potentially needed for siting of new capacity. In addition, this proposed rule is 

expected to result in significantly higher levels of end-use energy efficiency, which will reduce 

the load on the electricity transmission and natural gas pipeline infrastructure, while also 

providing other system wide benefits, such as decreased need for new generating units and 

reduced peak demands. 

In addition, natural gas pipeline capacity is regularly added in response to increased gas 

demand and supply, such as the addition of large amounts of new NGCC capacity in 2001 to 

2003, or the delivery to market of unconventional gas supplies since 2008.76 These pipeline 

capacity increases have added significant deliverability to the natural gas pipeline network to 

meet the potential demands from increased use of existing NGCCs. Over a longer time period, 

much more significant pipeline expansion is possible. In previous studies, when the pipeline 

system was expected to face very large demands for natural gas use by electric utilities about 10 

years ago, increases of up to 30% in total deliverability out of the pipeline system were judged to 

be possible by the pipeline industry.77 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.78 

To examine the potential for increases in pipeline deliverability, the EPA analyzed the 

pipeline flow data from the Energy Information Administration. These data provide pipeline 

capacity for inflows and outflows by state. However, since the natural gas pipeline system is a 

network for flows into, across, and out of states and broader area, the level of gas supply that can 

be firmly delivered to a particular region depends on the amount of natural gas the will be 

required to be delivered out of the region to other regions. Consequently, it is important to focus 

on the net capacity – the difference between inflow capacity and outflow capacity -- in the 

relevant areas. The regions used by EIA for measuring regional natural gas deliverability are 

shown in Figure 3-4. Of these regions, the key regions for the analysis of the potential impact of 

the proposed rule are those natural gas consuming areas where there could be increases in natural 

                                                            

76 Energy Information Administration, Today in Energy, Natural Gas Pipeline Additions in 2011. Additions 

averaged around 20Bcf per day from 2008 to 2011. 

77 Pipeline and Storage Infrastructure Requirements for a 30 Tcf Market, INGAA Foundation, 1999 (Updated July, 

2004); U.S. gas groups confident of 30-tcf market, Oil and Gas Journal, 1999. 

78 http://www.eia.gov/naturalgas/data.cfm#pipelines 
3 - 18 

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 35. The net pipeline capacity for 

these regions from 2005 to 2011 is shown in Table 3-5 below. 

Table 3-5. Natural Gas Pipeline Net Capacity by Region, 2005 - 2011 by Gas Consuming 

Area79

Region 2005 2006 2007 2008 2009 2010 2011

Capacity in MMCF/day 

Midwest 17,102 17,232 17,452 17,302 18,714 18,564 18,414 

Northeast 11,199 11,219 11,384 11,929 12,079 12,229 12,379 

Southeast 12,921 12,901 12,736 15,741 18,241 20,797 20,797 

Western 11,882 11,882 12,496 12,496 12,496 12,641 14,407 

All Areas 53,104 53,234 54,068 57,468 61,530 64,231 65,997 

Percent Change from 2005 

Midwest 0.0% 0.8% 2.0% 1.2% 9.4% 8.5% 7.7%

Northeast 0.0% 0.2% 1.7% 6.5% 7.9% 9.2% 10.5%

Southeast 0.0% -0.2% -1.4% 21.8% 41.2% 61.0% 61.0%

Western 0.0% 0.0% 5.2% 5.2% 5.2% 6.4% 21.3%

All Areas 0.0% 0.2% 1.8% 8.2% 15.9% 21.0% 24.3%

As a conservative assumption, the increase from the period 2005 to 2010 can be used as 

an estimator of the potential increase in pipeline capacity to accommodate compliance with the 

rule between 2015 and 2020. This is an extremely conservative assumption, since compliance is 

measured over a longer period and is not limited to re-dispatch approaches. Moreover, increased 

use of natural gas in existing facilities can be largely met with expansions to existing pipeline 

facilities and corridors, so that the types of capacity expansion required will be less expensive 

and take less time than new pipelines. Over 2005-2010, the total gas deliverability in gas 

consuming areas increased by 21%. Since the power sector currently uses approximately 30% of 

the total national natural gas consumption, and gas usage in other sectors is expected to be 

                                                            

79 Source: Energy Information Administration, http://www.eia.gov/naturalgas/data.cfm
3 - 19 

essentially flat through 2020, this historical increase indicates that pipeline capacity will be 

adequate to support any compliance changes in re-dispatch of natural gas power plants. 

Figure 3-4. Energy Information Administration Natural Gas Pipeline Regions

Source: Energy Information Administration. 

Recent pipeline construction has continued to support the increasing need for natural gas. 

According to information released in April, 2014 by the EIA,80 118 pipeline projects were 

completed and placed into service from 2010 to 2014, totaling 4,699 miles of pipe, and 44,107 

MMcf per day of additional pipeline capacity. 

For projects expected to be in service from April, 2014 through 2016, EIA reports 47 

projects, with planned capacity additions of 20,505 MMcf per day and 1,567 miles of pipe. 

These projects cover all major gas consuming areas of the US, and include both new pipeline 

                                                            

80 See www.eia.gov/naturalgas/pipelines/EIA-NaturalGasPipelineProjects.xls. 
3 - 20 

construction, such as the Spectra Energy’s NEXUS Gas Transmission project in the Upper 

Midwest and pipeline expansions such as the Tennessee Gas Pipeline Project in Connecticut. 

The electric transmission system has also been expanded over the past few years, and 

continued investment is expected. As of 2012, The EIA reports 187 thousand circuit miles of 

high voltage transmission in the US at 100 kV. There are 8 thousand miles of planned expansion 

in 2013, with a total 26 thousand miles proposed from 2013 to 2018.81

According to the Edison Electric Institute,82 member companies are planning over 170 

projects through 2024, totaling approximately $60.6 billion (this is only a portion of the total 

transmission investment anticipated). Approximately 75 percent of the reported projects are high 

voltage (345 kV and higher), representing over 13,000 line miles. 

Analysis of Cost-Effectiveness 

To further evaluate the technical capability of the natural gas supply and delivery system 

to provide increased quantities of natural gas and the capability of the electricity transmission 

system to accommodate shifting generation patterns, EPA employed the Integrated Planning 

Model (IPM), a multi-regional, dynamic, deterministic linear programming model of the U.S. 

electric power sector that the EPA has used for over two decades to evaluate the economic and 

emission impacts of prospective environmental policies. IPM provides a wide array of 

projections related to the electric power sector and its related markets (including least cost 

capacity expansion and electricity dispatch projections) while meeting fuel supply, transmission, 

dispatch, and reliability constraints. 

Natural gas supply, demand, transportation, storage, and related costs are modeled 

directly in IPM through the incorporation of a natural gas module. The module includes a detail 

rich representation of the natural gas pipeline network inclusive of discount curves that represent 

the marginal value of gas transmission as a function of the pipeline’s load factor. IPM’s natural 

gas module has the capability to expand pipeline capacity on an economic basis. 

At the unit level, IPM contains a detailed representation of new and existing resource 

options, inclusive of key operational limitations. For example, turn down constraints are 

                                                            

81 http://www.eia.gov/electricity/annual/ 

82 http://www.eei.org/issuesandpolicy/transmission/Documents/Trans_Project_lowres_bookmarked.pdf
3 - 21 

designed to account for the cycling capabilities of EGUs to ensure that the model properly 

reflects the distinct operating characteristics of peaking, cycling, and base load units. EPA 

believes IPM represents a powerful tool to evaluate the technical feasibility of requiring 

increasing levels of re-dispatch from higher to lower-emitting EGUs. 

The EPA has conducted extensive analysis to quantify the opportunity to reduce CO2

emissions through re-dispatch. As part of this effort, the EPA conducted an initial set of analyses 

utilizing the Integrated Planning Model (IPM) to provide a framework for understanding the 

broader economic and emissions implications of shifting generation from coal-fired steam EGUs 

to NGCC units within defined areas.83 In the most restrictive scenarios, re-dispatch was 

simulated only between EGUs located in the same state. These scenarios were designed to 

consider, even under a restrictive interpretation of the degree of re-dispatch that might constitute 

a component of BSER under CAA section 111(d),84 to what extent existing NGCC units could 

increase their dispatch cost-effectively taking into account the impact of that behavior on prices 

of natural gas and electricity. To evaluate how EGU operators and balancing authorities could 

respond to a state’s goal by incentivizing re-dispatch from more carbon-intensive to less carbonintensive EGUs, the EPA introduced two additional elements to the IPM framework: 

1. The application of a CO2 charge to the variable cost of dispatch for all existing coal 

steam boilers, IGCC units, and oil/gas steam boilers greater than 25 MW and with a CO2

emissions rate greater than 1,100 lbs/MWh.85

                                                            

83 IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector. It 

provides forecasts of least cost capacity expansion, electricity dispatch, and emission control strategies while

meeting energy demand and environmental, transmission, dispatch, and reliability constraints. Full documentation 

of the IPM model can be found at http://www.epa.gov/powersectormodeling 84 In practice, unit dispatch does not respect state boundaries because least-cost supply must be balanced with 

demand in real time over grid interconnects which span multiple states (with the exception of the Electric Reliability 

Council of Texas interconnect). The design of this modeling scenario assumes artificial constraints on re-dispatch to 

force such behavior to respect state boundaries, given the context of this rulemaking’s quantification of individual 

state goals. These state boundary constraints necessarily forgo cost-effective opportunities to re-dispatch units in 

different states; as a result, costs and prices in this analysis are overstated. 85 The addition of CO2 costs represents a simple analytic approach to estimating the cost-effective CO2 reductions 

under this building block and acts as a proxy for some existing state policies that shift dispatch. In actual plan 

implementation, states would be free to select any policy approach that has the net effect of reducing the carbon 

intensity of generation and/or reducing overall emissions from affected sources. 
3 - 22 

2. Generation constraints that maintain the sum of state-level generation in the Base Case86

from existing NGCC of any size, plus existing coal steam, IGCC and oil/gas steam 

boilers greater than 25 MW and with a CO2 emissions rate >1,100 lbs/MWh. 

These elements test the economic and technical potential for re-dispatch by: (1) increasing 

dispatch costs for affected coal steam, IGCC, and O/G steam EGUs within each state, and (2) 

requiring that any reduction in output from those sources be offset in its entirety by an increase 

in output from that state’s existing NGCC capacity. Utilizing IPM to conduct this analysis 

ensures an integrated, least-cost, technically feasible solution subject to power sector system 

reliability constraints, fuel market impacts, natural gas transmission and distribution networks, 

electric power transmission constraints, and unit-specific characteristics (e.g., operating and 

maintenance costs, heat rate, turndown/cycling behavior). 

Cost and Availability of Economic Re-Dispatch Opportunities

In executing this analysis, the EPA conducted a number of scenarios to quantify the 

relationship between the amount and cost of re-dispatch. Figure 3-5 below presents the projected 

national average capacity factor for NGCCs in 2020 (the first year of the assumed re-dispatch 

incentive) and the associated average $/tonne of CO2 reduced by comparing emissions and costs 

against the EPA’s Base Case (the difference in total system cost divided by the difference in 

power sector emissions). While the charge is applied exogenously in IPM, the average $/tonne 

shown below is calculated from the modeling results and projections by dividing the increase in 

total system costs (compared to the base case) by the total change in CO2 emissions (compared to 

the base case). 

 

                                                            

86 http://epa.gov/powersectormodeling/BaseCasev513.html
3 - 23 

Figure 3-5: NGCC National Capacity Factor, 2020 Initial Analyses 

The EPA believes average $/tonne – which is distinct from the $/tonne CO2 cost imposed 

in these analytic scenarios on affected coal steam, IGCC, and O/G steam EGUs – to be the most 

relevant metric in evaluating cost-effectiveness. System cost changes necessarily encompass all 

elements of cost across power, transmission, and fuel markets and therefore provide the most 

comprehensive perspective regarding cost-effectiveness. 

 

58%

62%

65%

68% 71%

74% 75%

0%

10%

20%

30%

40%

50%

60%

70%

80%

 ‐  10 20 30 40 50 60

Average Capacity Factor

Average $/tonne

Projected Capacity Factor for Existing NGCCs (2020)
3 - 24 

Table 3-6: Power Sector Emission Reductions Due to Re-Dispatch 

CO2 Cost Level 

Imposed 

National Average 

NGCC 

Capacity Factor 

Average Cost 

($/tonne) 

2020 Power Sector CO2

Emissions 

(MMT / % Change from 

2020) 

Base Case 52% N/A 2,161 

$10/tonne 58% $17 2,038 / -6% 

$15/tonne 62% $18 1,997 / -8% 

$20/tonne 65% $21 1,961 / -9% 

$25/tonne 68% $27 1,928 / -11% 

$30/tonne 71% $34 1,901 / -12% 

$40/tonne 74% $44 1,866 / -14% 

$50/tonne 75% $50 1,852 / -14% 

 

Although the EPA views this estimated range of average $/tonne costs as reasonable, we 

expect the costs of implementing this requirement in a compliance87 setting will be considerably 

lower for several reasons: 

 Analytic construct used to simulate re-dispatch incentive: As described earlier in this 

chapter, the EPA’s initial analyses utilized CO2 charges on the variable cost of dispatch 

for existing coal steam, IGCC, and O/G steam with emission rates greater than 1,100 

lbs/MWh and a capacity greater than 25 MW, as an analytic construct to induce redispatch behavior in the model to existing NGCC facilities. The CO2 charge was applied 

uniformly to all states in order to quantify the ultimate amount of in-state re-dispatch 

opportunities available as that charge is increased across scenarios. In the initial analyses, 

low levels of CO2 charges produce cost-effective re-dispatch opportunities relative to the 

Base Case in almost all states. However, as the CO2 costs are increased to higher levels, 

economic re-dispatch, opportunities within some states may eventually plateau – a point 

clearly illustrated in the declining slope of the best-fit line in Figure 3-5. A uniform 

                                                            

87 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. 
3 - 25 

application of the same rising CO2 charge in all states produces an outcome for many 

states where the additional CO2 costs imposed on affected coal steam, IGCC, and O/G 

steam are not able to produce incremental economic re-dispatch at units within that state; 

therefore, the additional costs imposed by these higher CO2 charges overstate the actual 

$/tonne necessary to induce achievable re-dispatch in each state. 

 Potential for multi-state compliance: The EPA also analyzed scenarios where shifting of 

generation among EGUs was not limited by state boundaries. In one set of analyses, redispatch was allowed to occur across the multi-state regions defined by NERC 

assessment areas (subject to other real-world constraints specified in the model, including 

transmission limits). In these scenarios with greater re-dispatch flexibility, the system 

was able to achieve 8% greater CO2 emission reductions at an identical CO2 charge 

(relative to a scenario where it was limited on a state basis), demonstrating that the main 

analysis’s imposition of artificial re-dispatch boundaries on state borders overstates the 

cost-effectiveness of re-dispatch potential. 

To evaluate how EGU owners and grid operators could respond to a state plan’s possible 

requirements, signals, or incentives to re-dispatch from more carbon-intensive to less carbonintensive EGUs, the EPA also analyzed an additional series of scenarios in which the fleet of 

NGCC units nationwide was required, on average, to achieve a specified annual utilization rate. 

Specifically, the scenarios required average NGCC unit utilization rates of 65, 70, and 75 

percent. For each scenario, dispatch decisions are allowed such that electricity demand is met at 

the lowest total cost, subject to all other specified operating and reliability constraints for the 

scenario, including the aforementioned state-by-state generation levels from the base case. This 

constraint effectively requires states that decrease coal generation to offset, in equal amounts, 

NGCC generation. Collectively, states must achieve the required capacity factor for NGCCs. 

 The costs and economic impacts of the various scenarios were evaluated by comparing 

the total costs and emissions from each scenario to the costs and emissions from a business-asusual scenario. For the scenarios reflecting a 65, 70, and 75 percent NGCC utilization rate, 

comparison to the business-as-usual case indicates that the average cost of the CO2 reductions 

achieved over the 2020-2029 period was $21, $30, and $40 per metric ton of CO2, respectively. 

However, we also note that the costs just described are higher than we would expect to actually 
3 - 26 

occur in real-world compliance with this proposal’s goals. This is because only 29 state goals are 

premised on the existing NGCC fleet achieving an average capacity factor of 70 percent. 

Consequently, a 70 percent utilization rate target for the existing NGCC fleet requires an average 

national capacity factor of 63 percent. 

The EPA also analyzed dispatch-only scenarios where shifting of generation among 

EGUs was limited by state boundaries. In these scenarios with less re-dispatch flexibility, the 

cost of achieving the quantity of CO2 reductions corresponding to a nationwide average NGCC 

unit utilization of 70% was $33 per ton. 

Table 3-7: IPM Results from Re-Dispatch Scenarios 

Existing NGCC 

Average 

National 

Capacity 

Factor 

Re-Dispatch 

Constraint 

Average Cost 

($/tonne, 2020-

2029) 

Average CO2

Emissions 

(MMT, 2020-

2029) 

Reductions 

from Base Case 

(%, 2020-2029) 

Base Case NA NA 2,215 NA 

65% Regional $21 2,022 9% 

70% Regional $30 1,969 11% 

75% Regional $40 1,915 14% 

65% State $22 2,024 9% 

70% State $33 1,971 11% 

Natural Gas Price Impacts 

The extent of re-dispatch estimated in this building block can be achieved without 

causing significant economic impacts. For example, in neither of the 70 percent NGCC unit 

utilization rate scenarios – re-dispatch limited to regional or state boundaries – did delivered 

natural gas price projections increase by more than 10 percent in the 2020-2029 period, which is 

well within the range of historical natural gas price volatility. For example, the year-to-year 

percentage difference in Henry Hub prices reported by the Energy Information Administration 

averaged 18.5% over the period from 1981 to 2012.88 Projected wholesale electricity price 

increases over the same period were less than 7 percent in both cases, which similarly is well 

                                                            

88 http://www.eia.gov/dnav/ng/hist/rngwhhdA.htm
3 - 27 

within the range of historic electric price variability. For example, the average year-to-year price 

change for the PJM region was 19.5 percent over the period from 2000 to 2013. For all the ISOs 

in the East, the variation is virtually unchanged from the PJM example (at 19.6%).89 

However, for the reasons previously discussed with respect to estimated costs per ton of 

CO2, the actual implementation is expected to result in notably lower economic impacts, 

including natural gas price impacts, and are considerably larger than would actually occur in 

real-world compliance with this rule’s proposed goals. 

Table 3-8: 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% 

                                                            

89 ISO Real-Time data for all hours, from Ventx Velocity Suite data across Eastern ISOs (PJM, NYISO,ISO-NE and 

Midcontinent ISO). 
4 - 1 

Chapter 4: Cleaner Generation Sources 

4.1. Introduction 

Renewable energy is a cost-effective approach for reducing carbon dioxide (CO2) 

emissions from fossil fuel-fired electric generating units (EGUs) through the substitution of 

electricity generated from renewable resources, referred to in this document as renewable energy 

(RE). The portfolio of available RE sources encompasses a wide variety of technologies from 

utility-scale RE plants to smaller-scale distributed generation sited at residential, commercial, or 

industrial facilities. RE technologies are fueled by the sun, wind, water, organic matter, and other 

resources regularly replenished by physical and biological cycles. To integrate the rapidly 

increasing and evolving portfolio of RE into the Best System of Emission Reductions (BSER), 

the EPA has developed a proposed approach that builds upon current state policy encouraging 

increased production of RE taking into account renewable potential in particular regions of the 

country. 

Additionally, the EPA believes that the planned expansion of new nuclear generating 

capacity and the preservation of existing nuclear generating capacity represent a cost-effective 

means to reduce CO2 emissions at fossil fuel-fired EGUs by providing carbon-free generation 

that can replace generation at those EGUs. Increasing the amount of nuclear capacity relative to 

the amount that would otherwise be available to operate is a technically viable and economically 

efficient approach for reducing CO2 emissions from affected fossil fuel-fired EGUs. 

This TSD is intended to support discussion of cleaner generation sources (RE and 

nuclear) as a component of BSER in the preamble (most extensively in these sections: Building 

Blocks for Setting State Goals and Considerations, State Goals, State Plans, and Impacts of the 

Proposed Rule) and its representation within the RIA. Results from this chapter feed into the 

technical support document (TSD) on state goal setting. Cleaner generation is also addressed in 

TSDs on Survey of Existing State Actions, State Plan Considerations, Projecting EGU CO2

Emission Performance, and Legal Memorandum. 

4.2. Proposed Approach 

To estimate the potential RE available for inclusion as part of BSER, EPA developed an 

RE generation scenario that provides a target for how much of each state’s generation can be 

produced by RE based upon the current goals of leading states in the same region, and allows 
4 - 2 

each state to grow RE generation over time towards that target, based upon that state’s current 

level of RE. The method can be summarized as follows. First, the country is divided into regions. 

Second, an RE generation target is calculated for each region, based upon averaging all 2020 

RPS requirements in that region. Third, an annual growth factor is calculated that would allow 

the region as a whole to reach the regional RE target in 2029 assuming that RE generation would 

increase from 2012 levels beginning in 2017. Fourth, the annual growth factor for a given region 

is applied to individual states’ 2012 RE generation to calculate future RE generation in that state 

from 2017 through 2029, not to exceed a maximum RE generation level equivalent to the 

regional RE target. Finally, these annual RE generation levels for each state are used to calculate 

interim and final RE targets for that state. 

The proposed approach is derived from state experience with policies that drive 

investment in RE and the generation that results from those efforts. The EPA focused on statelevel RE policy for several reasons. Every state in the union is producing electricity from 

renewable resources, and some states have achieved significant levels of renewable generation, 

surpassing a quarter of in-state generation. State-level RE requirements have been implemented 

in 29 states plus Washington, DC, representing all regions of the country. Nine states have 

voluntary goals.90,91 These state-level goals and requirements have been developed and 

implemented with technical assistance from state-level regulatory agencies and utility 

commissions such that they reflect expert assessments of RE technical and economic potential 

that can be cost-effectively developed for that state’s electricity consumers. 

The proposed approach focuses on RE requirements established through Renewable 

Portfolio Standards (RPS), which provide specific quantifiable RE generation requirements over 

time. The EPA used these RPS-mandated quantities as the basis for deriving regional targets to 

be applied to states as part of BSER, using the RPS-based targets as a reasonable benchmark of 

regionally cost-effective RE generation which states could grow towards over time. While EPA’s 

proposed approach is derived from RPS data, states may also consider a broad variety of other 

RE policies to increase generation, such as performance-based incentives, financial assistance 

                                                            

90 Database of State Incentives for Renewables and Efficiency, last modified March 2013, accessed at 

http://www.dsireusa.org/rpsdata/index.cfm. 91 Alaska House Bill 306, Signed by Governor Sean Parnell June 16, 2010,

http://www.legis.state.ak.us/basis/get_bill_text.asp?hsid=HB0306Z&session=26.
4 - 3 

programs, regulatory changes to facilitate the development of renewable sources and their 

interconnection to the grid and “lead by example” strategies integrating RE generation into state 

properties.92 Because the EPA did not quantify potential that could be tapped through any of 

those policy approaches, the agency believes that the RE targets derived from RPS mandates 

represent a conservative estimate of cost-effective generation that could actually be developed by 

states. 

While future RPS requirements will necessitate more RE generation and capacity beyond 

current levels, the EPA does not expect the anticipated rate of growth required to meet those 

requirements to exceed the historical rate of RE deployment. Full compliance with current RPS 

requirements through 2035 would necessitate the deployment of approximately 3 to 5 GW of 

new renewable capacity per year through 2020 and 2 to 3 GW through 2035. Average 

deployment of RPS-supported renewable capacity from 2007-2012 has exceeded 6 GW per 

year.93 In addition, recent improvements in RPS compliance rates indicate to the EPA the 

reasonableness of current RPS growth trajectories. Weighted average compliance rates among all 

states have improved in each of the past three reported years (2008 - 2011) from 92.1 percent to 

95.2 percent despite a 40 percent increase in RPS obligations during this period.94 As the 

Lawrence Berkeley National Laboratory (LBNL) RPS Status Update found, in the period 1998-

2012, 67% of all non-hydro U.S. RE capacity additions, totaling roughly 46,000 MW, was built 

in states with RPS requirements.95

This scenario provides an estimate of an achievable level of total RE generation within 

states. It does not represent an EPA forecast of business-as-usual impacts of state policies or an 

                                                            

92 See State Plan Considerations TSD for a discussion of how states can incorporate such RE policies into their state 

plans for this rule. 

93 Barbose, Galen, “Renewables Portfolio Standards in the United States: A Status Update,” Lawrence Berkeley 

National Lab, November 2013. Also, Heeter, J., Barbose, G., Bird, L., Weaver, S., Flores-Espino, F., KuskovaBurns, K., and Wiser, R., “A Survey of State-Level Cost and Benefit Estimates of Renewable Portfolio Standards,” 

NREL Report No. 6A20-61042, LBNL Report No. 6589E, http://www.nrel.gov/docs/fy14osti/61042.pdf. 94 Barbose, Galen, “RPS Compliance Summary Data,” Lawrence Berkeley National Lab, May 2013, accessed 

March 2014 at http://emp.lbl.gov/rps. The RPS compliance measure cited is inclusive of credit multipliers and 

banked RECs utilized for compliance, but excludes alternative compliance payments, borrowed RECs, deferred 

obligations, and excess compliance. This estimate does not represent official compliance statistics, which vary in 

methodology by state. 

95 Barbose, Galen, “Renewables Portfolio Standards in the United States: A Status Update,” Lawrence Berkeley 

National Lab, November 2013. Slide 8. Also, Heeter, J., Barbose, G., Bird, L., Weaver, S., Flores-Espino, F., 

Kuskova-Burns, K., and Wiser, R., “A Survey of State-Level Cost and Benefit Estimates of Renewable Portfolio 

Standards.” NREL Report No. 6A20-61042, LBNL Report No. 6589E, 

http://www.nrel.gov/docs/fy14osti/61042.pdf. 
4 - 4 

EPA estimate of the full potential of RE available to the power system; rather, it is intended to 

represent a feasible development scenario that enables reductions of CO2 emissions from fossil 

fuel-fired EGUs in all states and that is generally consistent with ongoing trends in RE 

development. The scenario uses a level of performance that has already been demonstrated or 

required by policies of leading states, while considering each state’s unique existing level of RE 

performance and allowing appropriate time for each state to increase from their current level of 

performance to the identified target level. In the context of this rulemaking, RE “performance” 

and RE targets are measured as the share of total generation represented by renewables as 

explained further below. 

The following steps were taken to establish the inputs for development of the proposed 

approach for each state. The implementation of each step is illustrated in the table below its 

description, using the state of Illinois as an example. 

4.2.1 Determine current level of performance 

4.2.2 Determine target level of performance 

4.2.3 Determine start year for state efforts 

4.2.4 Determine pace at which states improve from start year to target level of 

performance 

4.2.5 Calculate RE targets for interim and final state targets 

Note that an accompanying excel file that contains the aggregate state level data, 

calculations, and proposed state RE targets is also available in the Docket for this rulemaking. 

The title of this document is “Proposed RE Approach Data File.” 

4.2.1 Determine current level of performance 

The type and extent of current RE capacity varies significantly across states, and is 

influenced by the renewable resources available, the economics of the power sector to date in 

different regions, and the state policies that affect renewable sources specifically and energy 

production generally. The extent of that generation has also changed rapidly in the past few 

years, and states with RE policies have significantly increased their renewable capacity. To 
4 - 5 

characterize the current level of RE generation and total generation96, we have used the most 

current state-level data on generation: 2012 net generation data by state. 97

For the purposes of calculating a baseline level of RE generation in each state, the EPA 

adopted a broad interpretation of RE generation to include any non-fossil renewable fuel type, 

with the exception of generation from existing hydroelectric power facilities. Large existing 

hydroelectric facilities provide a large percentage of RE generation in a few states (hydropower 

is America’s largest existing source of RE, for which generating capacity has remained relative 

constant over the last 20 years), and inclusion of this generation in current and projected levels of 

performance would distort the proposed approach by presuming future development potential of 

large hydroelectric capacity in other states. Because RPS policies were implemented to stimulate 

the development of new RE generation, existing hydroelectric facilities are often excluded from 

RPS accounting. No states are expected to develop any new large facilities. 98 The RE targetsetting method presented in the body of this chapter includes only non-hydropower RE in the 

target-setting calculations and in the RE generation levels used to inform the state goals 

calculated in this proposal. In Appendix 4-1, we provide a different version of the RE generation 

targets that includes existing hydropower generation from 2012 for each state in the state RE 

targets. These targets that include existing hydropower generation as of 2012 reflect the potential 

incorporation of existing hydropower in the state RE targets that could inform the calculation of 

state goals if such generation were included in the quantification of BSER-related RE generation. 

The analysis informing regional RE targets does not explicitly account for the potential of 

building new hydroelectric facilities as a source under RPS policies; however, states may choose 

to encourage such development, and generation from such facilities would not be excluded from 

compliance with a state’s goal under this rule. The most recent 2012 performance data for all 

states is shown in Table 4-1. Consistent with the design of a number RPS policies, RE 

“performance” is measured here as the share of total generation represented by non-hydro RE. 

 

                                                            

96 EIA state-level total generation has been adjusted to remove utility-scale fossil generation located in Indian 

Country. 

97 U.S. EIA state level data available at http://www.eia.gov/electricity/data/state/. 98 U.S. EIA, Annual Energy Outlook 2014, p. 121, http://www.eia.gov/forecasts/archive/aeo13/index.cfm.   
4 - 6 

Table 4-1. 2012 RE Performance by State (MWh) 

State RE 

Generation

Total 

Generation

Performance 

Level 

 Alabama 2,776,554 152,878,688 2% 

 Alaska 39,958 6,946,419 1% 

 Arizona 1,697,652 95,016,925 2% 

 Arkansas 1,660,370 65,005,678 3% 

 California 29,966,846 199,518,567 15% 

 Colorado 6,192,082 52,556,701 12% 

 Connecticut 666,525 36,117,544 2% 

 Delaware 131,051 8,633,694 2% 

District of 

Columbia99 - 71,787 0% 

 Florida 4,523,798 221,096,136 2% 

 Georgia 3,278,536 122,306,364 3% 

 Hawaii 924,815 10,469,269 9% 

 Idaho 2,514,502 15,499,089 16% 

 Illinois 8,372,660 197,565,363 4% 

 Indiana 3,546,367 114,695,729 3% 

 Iowa 14,183,424 56,675,404 25% 

 Kansas 5,252,653 44,424,691 12% 

 Kentucky 332,879 89,949,689 0% 

 Louisiana 2,430,042 103,407,706 2% 

 Maine 4,098,795 14,428,596 28% 

 Maryland 898,152 37,809,744 2% 

 Massachusetts 1,843,419 36,198,121 5% 

 Michigan 3,785,439 108,166,078 3% 

 Minnesota 9,453,871 52,193,624 18% 

 Mississippi 1,509,190 54,584,295 3% 

 Missouri 1,298,579 91,804,321 1% 

 Montana 1,261,752 27,804,784 5% 

 Nebraska 1,346,762 34,217,293 4% 

 Nevada 2,968,630 35,173,263 8% 

 New Hampshire 1,381,285 19,264,435 7% 

 New Jersey 1,280,715 65,263,408 2% 

 New Mexico 2,573,851 2,289,4524 11% 

 New York 5,192,427 135,768,251 4% 

                                                            

99 The District of Columbia has no utility-scale RE generation, but the District does have distributed RE resources 

contributing to the electrical grid. 
4 - 7 

 North Carolina 2,703,919 116,681,763 2% 

 North Dakota 5,280,052 36,125,159 15% 

 Ohio 1,738,622 129,745,731 1% 

 Oklahoma 8,520,724 77,896,588 11% 

 Oregon 7,207,229 60,932,715 12% 

 Pennsylvania 4,459,118 223,419,715 2% 

 Rhode Island 101,895 8,309,036 1% 

 South Carolina 2,143,473 96,755,682 2% 

 South Dakota 2,914,666 12,034,206 24% 

 Tennessee 836,458 77,724,264 1% 

 Texas 34,016,697 429,812,510 8% 

 Utah 1,099,724 36,312,527 3% 

 Vermont 465,169 6,569,670 7% 

 Virginia 2,358,444 70,739,235 3% 

 Washington 8,214,350 116,835,474 7% 

 West Virginia 1,296,563 73,413,405 2% 

 Wisconsin 3,223,178 63,742,910 5% 

 Wyoming 4,369,107 49,588,606 9% 

4.2.2 Determine target level of performance 

Achievable RE potential exists at significant and comparable levels in all regions of the 

country. While varied regional characteristics (e.g., the extent of renewable resources available, 

cost of competing sources of power, and level of past RE development) affect estimates of 

achievable potential, ongoing improvements in technologies and practices, and continually 

improving strategies for RE development are increasing the extent of economically utilized 

renewable resources across all regions of the United States. RE has been capturing a growing 

percentage of new capacity additions in the past few years. In 2012, RE accounted for more than 

56% of all new electrical capacity installations in the U.S. – a major increase from 2004 when 

renewable installations captured only 2% of new capacity additions.100 The economics of the 

fastest growing RE technologies – on-shore wind and solar photovoltaics (PV) – are improving 

and are competitive in many regions. In 2012, cumulative installed wind capacity increased by 

nearly 28% and cumulative installed solar PV capacity grew more than 83% from the previous 

                                                            

100 U.S. Department of Energy. 2012 Renewable Energy Data Book. DOE/GO-102013-4291.October 2013, p. 3. 
4 - 8 

year.101 In the United States, installed wind electricity capacity increased more than 23 fold 

between 2000 and 2012.102 Solar electricity generating capacity grew by a factor of over 21 

between 2000 and 2012 and currently accounts for 0.3% of annual U.S. electricity generation.103

In 2013, 4.8 GW of solar PV capacity was installed, bringing total solar U.S. solar capacity to 

12.1 GW.104 The National Renewable Energy Laboratory (NREL) has also found that the 

continental U.S. has solar potential that exceeds high solar generating countries like Germany, 

which is now generating over 6% of their electricity from solar.105 Looking forward, the U.S. 

Department of Energy has found that 46 states would have substantial wind development by 

2030 under a scenario in which 20% of national generation is provided by wind. The distribution 

of that deployment is shown in Figure 4.1.106 

 

                                                            

101 Ibid. p. 18. 102 Ibid. p. 53. 103 Ibid. p. 63. 

104 GTM Research and the Solar Energy Industries Association (SEIA), “SEIA Solar Market Insight Report 2013: 

Year in Review”, 2014, http://www.seia.org/research-resources/solar-market-insight-report-2013-year-review.

105 NREL. Photovoltaic Solar Resource: The United States, Spain and Germany. 2009, 

http://www.nrel.gov/gis/images/us_germany_spain/pvmap_usgermanyspain%20poster-01.jpg. Also IEA, PVPS 

Snapshot of Global PV 1992-2013. Report IEA-PVPS T1-24:2014, March 31, 2014, http://www.ieapvps.org/fileadmin/dam/public/report/statistics/PVPS_report_-_A_Snapshot_of_Global_PV_-_1992-2013_-

_final_3.pdf. 

106 U.S. Department of Energy, 20% Wind Energy by 2030: Increasing Wind Energy’s Contribution to U.S. 

Electricity Supply – Executive Summary, December 2008, http://www.nrel.gov/docs/fy09osti/42864.pdf. 
4 - 9 

Figure 4.1. DOE Projected Installed Wind Capacity by State under 20% National Generation Scenario 

4.2.2.1. Quantification of Effective RE Levels from State-Level RPS Requirements 

The proposed approach is also based upon an analysis of renewable portfolio standards, a 

policy that facilitates the quantification of RE targets. By only examining the impact of one type 

of policy, the analysis is inherently conservative, as many other policy options are also available 

to states in addition to RPS. 

In order to apply the various RPS policies to the development of a target level of 

performance, the EPA used publicly-available quantitative information about mandatory state 

RPS requirements from the Database for State Incentives for Renewables and Efficiency 

(DSIRE).107 This information enabled the EPA to determine the effective RE levels in 2020 for 

states with mandatory RPS requirements.108 

                                                            

107 Database of State Incentives for Renewables & Efficiency (DSIRE) is a very comprehensive source of 

information on incentives and policies that support renewables and energy efficiency in the United States. DSIRE is 

currently operated by the N.C. Solar Center at N.C. State University, with support from the Interstate Renewable 

Energy Council, Inc. DSIRE is funded by the U.S. Department of Energy. http://www.dsireusa.org/. 108 EPA did not include targets that were capacity-based. 
4 - 10 

DSIRE provides regularly-updated RPS Data Spreadsheets that detail state RPS 

requirements by year, resources, and other key component parts.109 The RPS compliance 

schedules are broken down into the specific annual percentages requirements for the years 2000 

to 2030. Many states have multiple compliance requirements, including the main percentage 

requirements for eligible resources and additional resource-specific percentage requirements that 

states are increasingly using to promote the development of a particular set of resources or 

technologies (e.g., solar PV). DSIRE called each of these sets of resource requirements “tiers” 

and applied a standardized approach to them, “in order to compare RPS policies on equal 

footing.”110 The benefit of this approach is that state resource requirements become additive and 

facilitate a process of selection and exclusion. The EPA added together each state’s tiers, as 

standardized by DSIRE, to determine states’ effective RE levels for 2020, but excluded tiers, 

other than main tiers, that include energy efficiency or any fossil fuel. 

In addition, six states have established more than one set of RPS requirements for in-state 

utilities, including “secondary” and “tertiary” RPS requirements for smaller utilities, municipal 

utilities, or cooperative utilities. The EPA only included the primary RPS requirements to 

simplify the analysis of primary and secondary RPS requirements in determining states’ effective 

RE levels for 2020. By only considering primary requirements, there is additional inherent 

conservatism in the RPS estimates, as additional state-level RPS obligations are not included in 

the calculated targets. 

   

                                                            

109 DSIRE, RPS Data Spreadsheet. April 2013, http://www.dsireusa.org/rpsdata/index.cfm. 110 DSIRE, DSIRE RPS Field Definitions, April 2011. 

http://www.dsireusa.org/rpsdata/RPSFieldDefinitionsApril2011.pdf, p. 1. 
4 - 11 

Figure 4.2 RPS Data Structure111

The RPS compliance schedules in six states implement maximum requirements prior to 

2020; their effective RE levels for 2020 are set to those maximum levels. In fact, most states 

maintain their percentage requirements indefinitely. 

TABLE 4.2. Effective RE Levels Derived from RPS Requirements 

RPS 

States 

Primary 

Target 

Target 

Year 

2020 

Effective 

RE 

Levels 

Exclusions 

AZ 15% 2025 10% 

CA 33% 2020 33% 

CO 30% 2020 30% Secondary RPS requirement 

CT 23% 2020 23% Class 3 includes non-RE 

DC 20% 2023 20% 

DE 25% 2027 19% 

HI 40% 2030 25% 

IL 25% 2025 16% Secondary RPS requirement 

KS 20% 2020 20% 

MA 33% 2030 22% 

MD 20% 2022 18% 

ME 40% 2017 40% 

MI 10% 2015 10% 

                                                            

111 Ibid.
4 - 12 

MN 30% 2020 30% Secondary RPS requirement 

MO 15% 2021 10% 

MT 15% 2015 15% 

NC 13% 2021 10% Secondary RPS requirement 

NH 25% 2025 20% 

NJ 24% 2021 22% 

NM 20% 2020 20% Secondary RPS requirement 

NV 25% 2025 22% 

NY 29% 2015 29% 

OH 13% 2024 9% 

OR 25% 2025 20% Secondary & tertiary RPS 

requirements 

PA 8% 2021 8% Class 2 includes non-RE 

RI 16% 2019 16% 

WA 15% 2020 15% 

WI 10% 2015 10% 

4.2.2.2. Development of Regional RE Generation Targets from State-level Effective RE Levels

To take into account the varied availability of different renewable resources across 

regions of the United States, the EPA uses the state-level effective RE levels derived from RPS 

requirements to quantify regional RE targets consistent with states’ reasonable level of increased 

RE development. This methodology helps us to quantify RE potential in states which do not have 

an RPS policy from which the renewable resource potential can be inferred. Specifically, the 

scenario estimates each region’s RE potential by assuming all states in each region can achieve 

by 2030 the average of the 2020 requirements of RPS states in that region. 

The regions assigned to states to quantify their RE generation target are based upon North 

American Electric Reliability Corporation (NERC) regions and Regional Transmission 

Organizations (RTOs) and are the same as the regions used in the modeling of the “regional” 

compliance scenarios as outlined in the proposal RIA (see Figure 4-3).112 States within each 

region exhibit similar profiles of RE potential or have similar levels of renewable resources. The 

regional similarities can be inferred from the state-level technical potential reported in an NREL 

                                                            

112 For more information on the structure of these regions, please refer to the Regulatory Impact Analysis Chapter 3. 
4 - 13 

GIS-based analysis.113 The results show clear trends for the regions used to create the proposed 

approach, with portfolios of particular technologies showing clear dominance in specific regions. 

North Central and South Central regions have strong on-shore wind resource potential. The East 

Central and Southeast regions show moderate to strong resources in both biopower and rooftop 

PV potential. The West has notable potential in geothermal (hydrothermal) power and 

concentrating solar power, in addition to potential for increased hydropower generation. The 

Northeast has strong resources in off-shore wind and moderate biopower and solar resources 

available. It should be noted that high technical potential in a particular renewable resource is not 

necessarily needed to reach the generation levels quantified under this approach. For example, 

Maine produced 28% of its electricity generation in 2012 from biopower and onshore wind, 

while it is estimated in this report to have relatively moderate technical potential for biopower 

and relatively low levels of onshore wind capacity. Overall, results from the NREL GIS-based 

analysis show that the regional RE targets included in this proposed approach assume 

development of only 0.5% to 4.5% of the RE resources in those regions. See Figure 4.3.1 for a 

graph showing the state RE targets in each region represented as a percentage of the renewable 

resources available in the state. 

   

                                                            

113 NREL. U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. NREL/TP-A20-51946. July 2012. 

http://www.nrel.gov/docs/fy12osti/51946.pdf.
4 - 14 

Figure 4.3. Proposed Approach Regions 
4 - 15 

Table 4.3. List of States Included in Proposed Approach Regions114

Region States 

East 

Central 

Delaware, District of Columbia, Maryland, New Jersey, Ohio, Pennsylvania, 

Virginia, West Virginia 

North 

Central 

Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, North Dakota, South 

Dakota, Wisconsin 

Northeast Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, 

Vermont 

South 

Central 

Arkansas, Kansas, Louisiana, Nebraska, Oklahoma, Texas 

Southeast Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, 

Tennessee 

West Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, 

Utah, Washington, Wyoming 

   

                                                            

114 Alaska and Hawaii are not grouped with other states into regions. 
4 - 16 

Figure 4.3.1. Regional RE Targets of each State as a Percentage of the State’s Renewable Resources 

The EPA compiled the state-level effective RE levels derived above, organized them by 

region, and determined an average percent generation target for that region. This average did not 

include any values for states in which there is no binding RPS requirement. For example, the RE 

target for the North Central region is calculated as the average of 2020 RPS requirements in 

Illinois (16%), Michigan (10%), Minnesota (30%), Missouri (10%), and Wisconsin (10%), 

which is equivalent to 15%. This average regional RE generation target offers a basis for the 

determination of state-level RE targets for informing state goals, as described below. Given their 

unique locations, Alaska and Hawaii are not grouped with other states into these regions. As a 

conservative approach to estimating cost-effective RE generation potential in Alaska and Hawaii, 

the EPA developed RE generation targets for each of those states based on the lowest values for 

the six regions evaluated here, equivalent to the regional target for the Southeast region. The 

calculated regional RE targets are shown in Table 4.4. 

   
4 - 17 

Table 4.4. Regional RE Generation Targets 

Region 

Regional RE 

Generation Targets

Alaska 10% 

East Central 16% 

Hawaii 10% 

North Central 15% 

Northeast 25% 

South Central 20% 

Southeast 10% 

West 21% 

4.2.3 Determine start year for state efforts 

The proposed approach assumes that RE generation will begin increasing in 2017, the 

year following the initial state plan submission deadline115, and continues through 2029, by 

which time the EPA assumes the regions can achieve the identified regional RE target level of 

performance. The EPA has set each state’s level of performance prior to the start year of the 

scenario (2017) to be equal to its current level of performance (as shown above using 2012 

generation data). This approach assumes neither improvement nor decline in performance 

between 2012 and 2017. 

4.2.4 Determine pace at which states improve from start year to target level of 

performance 

In order to account for the time needed to plan and construct the required additional 

amounts of renewable capacity, the proposed approach assumes an increasing trend over time of 

annual levels of RE generation that can carry the performance level of each region in the start 

year (in 2017, assumed to be equivalent to its 2012 observed performance level) to that region’s 

RE generation target by 2029. This 2017-2029 trend yields an annual growth factor that is unique 

to each region and based upon each region’s current renewable generation level and its RE target 

level identified above. 

                                                            

115 See Preamble Section 8.E – Process for State Plan Submittal and Review for further 

discussion of timing requirements for state plan submittals. 
4 - 18 

To derive the annual growth factor, the EPA determined the amount of additional 

renewable generation (in megawatt-hours) that would be required beyond each region’s historic 

(2012) generation to reach that region’s RE target. The EPA then determined the constant rate at 

which each region would need to increase its generation each year to reach the regional RPS 

target, if these rates are applied in the period 2017-2029. The constant rate of annual RE 

generation increase calculated from this approach is called the growth factor. For example, the 

North Central region had 52,058,236 MWh of RE generation in 2012, while the North Central 

regional RE target of 15% applied to total 2012 generation across states in that region would 

yield an RE generation level of 110,786,042 MWh. This approach assumes that the North 

Central region would begin to increase its RE generation, starting at its 2012 level, from the year 

2017 onward and would achieve its RE target level by 2029. Under those conditions, an annual 

growth rate of 6% per year for RE generation would occur in the North Central region. Due to 

their unique location, the EPA used a different method to calculate growth factors for Alaska and 

Hawaii, calculating an annual growth factor based on the growth between each states’ individual 

historical 2002 and 2012 RE generation. Similar to the method for other states, EPA calculated 

the constant rate of growth that would have been required to take each of these two states from 

their 2002 RE generation to their 2012 RE generation levels, assuming that the growth over that 

time had been constant in each year. This resulted in an 8% growth factor for Hawaii, and an 

11% growth factor for Alaska. 

Table 4.5. Regional Annual RE Growth Factors 

Region Growth Factor 

Alaska 11%

East Central 17%

Hawaii 9%

North Central 6%

Northeast 13%

South Central 8%

Southeast 13%

West 6%

Then, for all states in a given region, that region’s annual growth factor was applied to 

each state’s historic (2012) RE generation level to calculate a new level of RE generation for that 
4 - 19 

state in the initial year (2017). This calculation is then repeated for each year in the 2017-2029 

time period. If, as the growth factor is applied annually, a state reaches an RE generation level 

that equals or exceeds the regional RE percent generation target, their RE generation target is 

made equal to the RE percent generation target as applied to that state’s 2012 generation and is 

kept at that level for the remainder of the time period. If a state’s RE generation in 2012 has 

already exceeded the regional RE target, their annual RE generation levels are held to the 

regional RE target for all years in the 2017-2029 time period. For all other states, the annual 

growth factor is applied through 2029. These annual RE generation estimates represent the 

realization of the proposed approach for each state. These RE generation levels are provided in 

absolute and percentage (share of total generation) terms in Table 4.6 and Table 4.7. 

This approach imposes the same regional RE target in percentage (share of total 

generation) terms to all states in a given region; therefore, the absolute megawatt-hour target will 

be smaller for states starting with a lower absolute amount of RE generation and larger for a state 

starting with a higher absolute amount of RE generation. 

This approach applies the calculated growth factors and regional RE targets to state-level 

generation, whereas the state-level RPS requirements upon which they are based are not 

necessarily applied in practice to generation that is produced within the relevant state. However, 

the EPA notes that state-level RPS policies are often established with the aim of developing instate renewables generation.116 This intention is evident in RPS policies that include minimum 

requirements for specific types of renewable resources whose development is desired in that 

state. Regional analysis by NREL has also shown that many states in the west are satisfying RPS 

requirements with in-state generation.117 Furthermore, the regional RE target is not applied 

directly as an immediate requirement of each state but is instead used to calculate a regional 

growth factor that is then applied to each state’s pre-existing RE generation, such that historic 

RE performance acts as a limiting factor on the extent to which a state is assumed to reach the 

regional target. Over the program period, several states do not reach the RE percentage target in 

the proposed approach, such as Kentucky in the Southeast and Nevada in the West. Thus, this 

                                                            

116 Wiser, Ryan H., and Galen L. Barbose. 2008. Renewable Portfolio Standards in the United States: A Status 

Report with Data Through 2007, LBNL-154E, Berkeley, CA: Lawrence Berkeley National Laboratory, p. 7. 117 Hurlbut, David, Joyce McLaren, and Rachel Gelman, Beyond Renewable Portfolio Standards: An Assessment of 

Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West, NREL/TP-6A20-

57830, Golden, CO: NREL, August 2013. 
4 - 20 

approach is designed to respect each state’s ability to improve toward the RE targets developed 

above. 

 An illustrative calculation for Illinois’s target RE generation level is provided below. 

Generation levels for all states, in gigawatt-hours and percentage terms, are provided in Tables 

4.6 and 4.7. Under this approach, Illinois grows its own historic RE generation level by the 6% 

growth factor calculated for the North Central region, but it does not reach the North Central 

regional RE target generation level of 15% (which would be 29,860 GWh for Illinois) between 

2017 and 2029. 

State

2012 RE

(MWh) Assigned

Region

Regional

RE

Generation

Targets (%)

Annual

Regional

Growth

Factor (%)

(source:

EIA)

Illinois 8,373 North

Central 15% 6%

    

Illinois RE Generation Targets

Year GWh % of 2012

generation

2017 8,873 4.50%

2018 9,404 4.80%

2019 9,967 5.00%

2020 10,563 5.30%

2021 11,195 5.70%

2022 11,864 6.00%

2023 12,574 6.40%

2024 13,326 6.70%

2025 14,123 7.10%

2026 14,968 7.60%

2027 15,863 8.00%

2028 16,812 8.50%

2029 17,818 9.00%

An illustrative calculation for Minnesota’s target RE generation level is provided here, as 

an example of a state which has already reached its RE target, with 9,454 GWh of RE generation 

in 2012, and thus its obligation under the target is capped at its share of the 15% regional RE 

target, 7,889 GWh of RE generation. 

   

Calculation for 2017 Generation Target = 8,372 x 1.06 = 8,873

Calculation for 2018 Generation Target = 8,873 x 1.06 = 9,404

Similar calculations are performed for all years from 2017 through 2029,

with quantified RE targets in any year not to exceed the regional RE

target level (e.g., 15% for states in the North Central region).
4 - 21 

State

2012 RE

(MWh) Assigned

Region

Regional

RE

Generation

Targets (%)

Annual

Regional

Growth

Factor (%) (source:

EIA)

Minnesota 9,453 North

Central 15% 6%

    

Minnesota RE Generation Targets

Year GWh % of 2012

generation

2017 7,889 15%

2018 7,889 15%

2019 7,889 15%

2020 7,889 15%

2021 7,889 15%

2022 7,889 15%

2023 7,889 15%

2024 7,889 15%

2025 7,889 15%

2026 7,889 15%

2027 7,889 15%

2028 7,889 15%

2029 7,889 15%

   
4 - 22 

Table 4.6. State Target RE Generation Levels (Gigawatt-hours) 

State 

Historic 

RE RE Generation Targets (GWh) 

2012 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 

 AK 40 

 

45 

 

50 

 

55 

 

62 

 

69 

 

76 

 

85 

 

95 

 

106 

 

118 

 

131 

 

146 

 

163 

 AL 

2,777 

 

3,150 

 

3,573 

 

4,053 

 

4,597 

 

5,214 

 

5,915 

 

6,709 

 

7,611 

 

8,633 

 

9,793 

 

11,108 

 

12,600 

 

14,293 

 AR 

1,660 

 

1,799 

 

1,949 

 

2,112 

 

2,288 

 

2,479 

 

2,686 

 

2,911 

 

3,154 

 

3,417 

 

3,702 

 

4,011 

 

4,346 

 

4,709 

 AZ 

1,698 1,801 1,911 2,027 2,151 2,282 2,421 2,569 2,725 2,891 3,068 3,255 3,453 3,663 

 CA 

29,967 

 

31,793 

 

33,731 

 

35,787 

 

37,968 

 

40,282 

 

41,151 

 

41,151 

 

41,151 

 

41,151 

 

41,151 

 

41,151 

 

41,151 

 

41,151 

 CO 

6,192 

 

6,569 

 

6,970 

 

7,395 

 

7,845 

 

8,324 

 

8,831 

 

9,369 

 

9,940 

 

10,546 

 

10,840 

 

10,840 

 

10,840 

 

10,840 

 CT 

667 

 

750 

 

845 

 

951 

 

1,071 

 

1,206 

 

1,358 

 

1,529 

 

1,721 

 

1,938 

 

2,182 

 

2,457 

 

2,766 

 

3,114 

 DE 

131 

 

154 

 

180 

 

211 

 

248 

 

291 

 

341 

 

399 

 

468 

 

549 

 

644 

 

755 

 

886 

 

1,038 

 FL 

4,524 

 

5,131 

 

5,821 

 

6,603 

 

7,490 

 

8,496 

 

9,637 

 

10,931 

 

12,400 

 

14,066 

 

15,955 

 

18,098 

 

20,529 

 

22,110 

GA 

3,279 

 

3,719 

 

4,219 

 

4,785 

 

5,428 

 

6,157 

 

6,984 

 

7,922 

 

8,987 

 

10,194 

 

11,563 

 

12,231 

 

12,231 

 

12,231 

 HI 

925 1005 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047 1,047 

 IA 

14,183 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 

8,566 

 ID 

2,515 

 

2,668 

 

2,830 

 

3,003 

 

3,186 

 

3,197 

 

3,197 

 

3,197 

 

3,197 

 

3,197 

 

3,197 

 

3,197 

 

3,197 

 

3,197 

 IL 

8,373 

 

8,873 

 

9,404 

 

9,967 

 

10,563 

 

11,195 

 

11,864 

 

12,574 

 

13,326 

 

14,123 

 

14,968 

 

15,863 

 

16,812 

 

17,818 

 IN 

3,546 

 

3,758 

 

3,983 

 

4,222 

 

4,474 

 

4,742 

 

5,025 

 

5,326 

 

5,645 

 

5,982 

 

6,340 

 

6,719 

 

7,121 

 

7,547 

 KS 

5,253 

 

5,691 

 

6,166 

 

6,681 

 

7,239 

 

7,843 

 

8,498 

 

8,885 

 

8,885 

 

8,885 

 

8,885 

 

8,885 

 

8,885 

 

8,885 

 KY 

333 

 

378 

 

428 

 

486 

 

551 

 

625 

 

709 

 

804 

 

912 

 

1,035 

 

1,174 

 

1,332 

 

1,511 

 

1,714 

 LA 

 

2,430 

 

2,633 

 

2,853 

 

3,091 

 

3,349 

 

3,629 

 

3,931 

 

4,260 

 

4,615 

 

5,001 

 

5,418 

 

5,870 

 

6,361 

 

6,892 

 MA 

 

1,843 

 

2,076 

 

2,337 

 

2,631 

 

2,962 

 

3,335 

 

3,755 

 

4,228 

 

4,761 

 

5,360 

 

6,035 

 

6,795 

 

7,650 

 

8,613 

 MD 

898 

 

1,053 

 

1,235 

 

1,448 

 

1,698 

 

1,991 

 

2,335 

 

2,738 

 

3,210 

 

3,764 

 

4,414 

 

5,176 

 

5,982 

 

5,982 

 ME 

4,099 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 

3,612 

 MI 

 

3,785 

 

4,012 

 

4,252 

 

4,506 

 

4,776 

 

5,061 

 

5,364 

 

5,685 

 

6,025 

 

6,385 

 

6,767 

 

7,172 

 

7,601 

 

8,056 

 MN 

9,454 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 

7,889 

 MO 

1,299 

 

1,376 

 

1,459 

 

1,546 

 

1,638 

 

1,736 

 

1,840 

 

1,950 

 

2,067 

 

2,190 

 

2,322 

 

2,460 

 

2,608 

 

2,764 

 MS 

1,509 

 

1,712 

 

1,942 

 

2,203 

 

2,499 

 

2,834 

 

3,215 

 

3,647 

 

4,137 

 

4,692 

 

5,323 

 

5,458 

 

5,458 

 

5,458 

 MT 

1,262 

 

1,343 

 

1,430 

 

1,523 

 

1,621 

 

1,726 

 

1,837 

 

1,956 

 

2,082 

 

2,217 

 

2,360 

 

2,513 

 

2,675 

 

2,848 

 NC 

2,704 

 

3,067 

 

3,479 

 

3,946 

 

4,477 

 

5,078 

 

5,760 

 

6,534 

 

7,412 

 

8,407 

 

9,536 

 

10,817 

 

11,668 

 

11,668 

 ND 

5,280 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 

5,460 

 NE 

1,347 

 

1,459 

 

1,581 

 

1,713 

 

1,856 

 

2,011 

 

2,179 

 

2,361 

 

2,558 

 

2,771 

 

3,003 

 

3,254 

 

3,525 

 

3,819 

 NH 

1,381 

 

1,555 

 

1,751 

 

1,971 

 

2,220 

 

2,499 

 

2,814 

 

3,168 

 

3,567 

 

4,016 

 

4,522 

 

4,822 

 

4,822 

 

4,822 
4 - 23 

State Historic 

RE RE Generation Targets (GWh) 

2012 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 

 NJ 

1,281 

 

1,502 

 

1,761 

 

2,065 

 

2,421 

 

2,839 

 

3,329 

 

3,904 

 

4,577 

 

5,367 

 

6,294 

 

7,380 

 

8,654 

 

10,147 

 NM 

 

2,574 2,731 2,897 3,074 3,261 3,460 3,671 3,894 4,132 4,384 4,651 4,722 4,722 4,722 

 NV 

 

2,969 

 

3,150 

 

3,342 

 

3,545 

 

3,761 

 

3,991 

 

4,234 

 

4,492 

 

4,766 

 

5,056 

 

5,364 

 

5,691 

 

6,038 

 

6,406 

 NY 

 

5,192 

 

5,846 

 

6,582 

 

7,411 

 

8,344 

 

9,395 

 

10,578 

 

11,910 

 

13,409 

 

15,098 

 

16,999 

 

19,139 

 

21,549 

 

24,262 

 OH 

 

1,739 

 

2,039 

 

2,391 

 

2,803 

 

3,287 

 

3,854 

 

4,519 

 

5,299 

 

6,214 

 

7,287 

 

8,544 

 

10,019 

 

11,748 

 

13,776 

 OK 

8,521 

 

9,232 

 

10,003 

 

10,838 

 

11,743 

 

12,723 

 

13,785 

 

14,936 

 

15,579 

 

15,579 

 

15,579 

 

15,579 

 

15,579 

 

15,579 

 OR 

7,207 

 

7,647 

 

8,113 

 

8,607 

 

9,132 

 

9,688 

 

10,279 

 

10,905 

 

11,570 

 

12,275 

 

12,567 

 

12,567 

 

12,567 

 

12,567 

 PA 

4,459 

 

5,229 

 

6,131 

 

7,189 

 

8,430 

 

9,885 

 

11,591 

 

13,592 

 

15,938 

 

18,688 

 

21,914 

 

25,696 

 

30,131 

 

35,331 

 RI 

102 

 

115 

 

129 

 

145 

 

164 

 

184 

 

208 

 

234 

 

263 

 

296 

 

334 

 

376 

 

423 

 

476 

 SC 

2,143 

 

2,431 

 

2,758 

 

3,128 

 

3,549 

 

4,025 

 

4,566 

 

5,180 

 

5,875 

 

6,665 

 

7,560 

 

8,575 

 

9,676 

 

9,676 

 SD 

2,915 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 

1,819 

 TN 

836 

 

949 

 

1,076 

 

1,221 

 

1,385 

 

1,571 

 

1,782 

 

2,021 

 

2,293 

 

2,601 

 

2,950 

 

3,346 

 

3,796 

 

4,306 

 TX 

34,017 

 

36,857 

 

39,934 

 

43,268 

 

46,880 

 

50,794 

 

55,034 

 

59,629 

 

64,607 

 

70,001 

 

75,845 

 

82,177 

 

85,963 

 

85,963 

 UT 

1,100 1,167 1,238 1,313 1,393 1,478 1,568 1,664 1,765 1,873 1,987 2,108 2,237 2,373 

 VA 

2,358 

 

2,765 

 

3,243 

 

3,802 

 

4,459 

 

5,228 

 

6,131 

 

7,189 

 

8,429 

 

9,884 

 

11,192 

 

11,192 

 

11,192 

 

11,192 

 VT 

465 

 

524 

 

590 

 

664 

 

748 

 

842 

 

948 

 

1,067 

 

1,201 

 

1,353 

 

1,523 

 

1,645 

 

1,645 

 

1,645 

 WA 

8,214 

 

8,715 

 

9,246 

 

9,810 

 

10,408 

 

11,042 

 

11,715 

 

12,429 

 

13,186 

 

13,990 

 

14,843 

 

15,747 

 

16,707 

 

17,726 

 WI 

3,223 

 

3,416 

 

3,620 

 

3,837 

 

4,066 

 

4,310 

 

4,567 

 

4,841 

 

5,130 

 

5,437 

 

5,762 

 

6,107 

 

6,472 

 

6,859 

 WV 

1,297 

 

1,520 

 

1,783 

 

2,090 

 

2,451 

 

2,874 

 

3,370 

 

3,952 

 

4,634 

 

5,434 

 

6,372 

 

7,471 

 

8,761 

 

10,273 

 WY 

4,369 

 

4,635 

 

4,918 

 

5,218 

 

5,536 

 

5,873 

 

6,231 

 

6,611 

 

7,014 

 

7,441 

 

7,895 

 

8,376 

 

8,886 

 

9,428 

   
4 - 24 

Table 4.7. State Target RE Generation Levels (% of Total Generation) 

State 

Historic 

RE RE Generation Targets (GWh) 

2012 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 

 AK 0.6% 0.6% 0.7% 0.8% 0.9% 1.0% 1.1% 1.2% 1.4% 1.5% 1.7% 1.9% 2.1% 2.3% 

 AL 2% 2.1% 2.3% 2.7% 3.0% 3.4% 3.9% 4.4% 5.0% 5.6% 6.4% 7.3% 8.2% 9.3% 

 AR 3% 2.8% 3.0% 3.2% 3.5% 3.8% 4.1% 4.5% 4.9% 5.3% 5.7% 6.2% 6.7% 7.2% 

 AZ 2% 1.9% 2.0% 2.1% 2.3% 2.4% 2.5% 2.7% 2.9% 3.0% 3.2% 3.4% 3.6% 3.9% 

 CA 15% 16.0% 17.0% 18.1% 19.3% 20.5% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6% 

 CO 12% 12.5% 13.4% 14.2% 15.1% 16.1% 17.2% 18.3% 19.4% 20.6% 20.6% 20.6% 20.6% 20.6% 

 CT 2% 2.1% 2.3% 2.6% 3.0% 3.3% 3.8% 4.2% 4.8% 5.4% 6.0% 6.8% 7.7% 8.6% 

 DE 2% 1.8% 2.1% 2.4% 2.9% 3.4% 3.9% 4.6% 5.4% 6.4% 7.5% 8.7% 10.3% 12.0% 

 FL 2% 2.3% 2.6% 3.0% 3.4% 3.8% 4.4% 4.9% 5.6% 6.4% 7.2% 8.2% 9.3% 10.0% 

 GA 3% 3.0% 3.4% 3.9% 4.4% 5.0% 5.7% 6.5% 7.3% 8.3% 9.5% 10.0% 10.0% 10.0% 

 HI 9% 9.6% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% 

 IA 25% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 

 ID 16% 17.3% 18.4% 19.6% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6% 20.6% 

 IL 4% 4.5% 4.8% 5.0% 5.3% 5.7% 6.0% 6.4% 6.7% 7.1% 7.6% 8.0% 8.5% 9.0% 

 IN 3% 3.3% 3.5% 3.7% 3.9% 4.1% 4.4% 4.6% 4.9% 5.2% 5.5% 5.9% 6.2% 6.6% 

 KS 12% 12.8% 13.9% 15.0% 16.3% 17.7% 19.1% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0% 

 KY 0% 0.4% 0.5% 0.5% 0.6% 0.7% 0.8% 0.9% 1.0% 1.2% 1.3% 1.5% 1.7% 1.9% 

 LA 2% 2.5% 2.8% 3.0% 3.2% 3.5% 3.8% 4.1% 4.5% 4.8% 5.2% 5.7% 6.2% 6.7% 

 MA 5% 5.7% 6.5% 7.3% 8.2% 9.2% 10.4% 11.7% 13.2% 14.8% 16.7% 18.8% 21.1% 23.8% 

 MD 2% 2.8% 3.3% 3.8% 4.5% 5.3% 6.2% 7.2% 8.5% 10.0% 11.7% 13.7% 15.8% 15.8% 

 ME 28% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 

 MI 3% 3.7% 3.9% 4.2% 4.4% 4.7% 5.0% 5.3% 5.6% 5.9% 6.3% 6.6% 7.0% 7.4% 

 MN 18% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 

 MO 1% 1.5% 1.6% 1.7% 1.8% 1.9% 2.0% 2.1% 2.3% 2.4% 2.5% 2.7% 2.8% 3.0% 

 MS 3% 3.1% 3.6% 4.0% 4.6% 5.2% 5.9% 6.7% 7.6% 8.6% 9.8% 10.0% 10.0% 10.0% 

 MT 5% 4.8% 5.1% 5.5% 5.8% 6.2% 6.6% 7.0% 7.5% 8.0% 8.5% 9.0% 9.6% 10.2% 

 NC 2% 2.6% 3.0% 3.4% 3.8% 4.4% 4.9% 5.6% 6.4% 7.2% 8.2% 9.3% 10.0% 10.0% 

 ND 15% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 

 NE 4% 4.3% 4.6% 5.0% 5.4% 5.9% 6.4% 6.9% 7.5% 8.1% 8.8% 9.5% 10.3% 11.2% 

 NH 7% 8.1% 9.1% 10.2% 11.5% 13.0% 14.6% 16.4% 18.5% 20.8% 23.5% 25.0% 25.0% 25.0% 

 NJ 2% 2.3% 2.7% 3.2% 3.7% 4.4% 5.1% 6.0% 7.0% 8.2% 9.6% 11.3% 13.3% 15.5% 

 NM 11% 11.9% 12.7% 13.4% 14.2% 15.1% 16.0% 17.0% 18.0% 19.1% 20.3% 20.6% 20.6% 20.6% 

 NV 8% 9.0% 9.6% 10.2% 10.8% 11.5% 12.3% 13.1% 13.9% 14.8% 15.8% 16.8% 17.9% 19.0% 

 NY 4% 4.3% 4.8% 5.5% 6.1% 6.9% 7.8% 8.8% 9.9% 11.1% 12.5% 14.1% 15.9% 17.9% 

 OH 1% 1.6% 1.8% 2.2% 2.5% 3.0% 3.5% 4.1% 4.8% 5.6% 6.6% 7.7% 9.1% 10.6% 

 OK 11% 11.9% 12.8% 13.9% 15.1% 16.3% 17.7% 19.2% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0% 

 OR 12% 12.6% 13.4% 14.3% 15.2% 16.2% 17.2% 18.3% 19.5% 20.6% 20.6% 20.6% 20.6% 20.6% 
4 - 25 

State 

Historic 

RE RE Generation Targets (GWh) 

2012 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 

 PA 2% 2.3% 2.7% 3.2% 3.8% 4.4% 5.2% 6.1% 7.1% 8.4% 9.8% 11.5% 13.5% 15.8% 

 RI 1% 1.4% 1.6% 1.8% 2.0% 2.2% 2.5% 2.8% 3.2% 3.6% 4.0% 4.5% 5.1% 5.7% 

 SC 2% 2.5% 2.9% 3.2% 3.7% 4.2% 4.7% 5.4% 6.1% 6.9% 7.8% 8.9% 10.0% 10.0% 

 SD 24% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 15.1% 

 TN 1% 1.2% 1.4% 1.6% 1.8% 2.0% 2.3% 2.6% 2.9% 3.3% 3.8% 4.3% 4.9% 5.5% 

 TX 8% 8.6% 9.3% 10.1% 10.9% 11.8% 12.8% 13.9% 15.0% 16.3% 17.6% 19.1% 20.0% 20.0% 

 UT 3% 3.2% 3.4% 3.6% 3.8% 4.1% 4.3% 4.6% 4.9% 5.2% 5.5% 5.8% 6.2% 6.5% 

 VA 3% 3.9% 4.6% 5.4% 6.3% 7.4% 8.7% 10.2% 11.9% 14.0% 15.8% 15.8% 15.8% 15.8% 

 VT 7% 8.0% 9.0% 10.1% 11.4% 12.8% 14.4% 16.2% 18.3% 20.6% 23.2% 25.0% 25.0% 25.0% 

 WA 7% 7.5% 8.0% 8.5% 9.0% 9.6% 10.2% 10.9% 11.6% 12.4% 13.2% 14.0% 14.9% 15.9% 

 WI 5% 5.4% 5.7% 6.0% 6.4% 6.8% 7.2% 7.6% 8.0% 8.5% 9.0% 9.6% 10.2% 10.8% 

 WV 2% 2.1% 2.4% 2.8% 3.3% 3.9% 4.6% 5.4% 6.3% 7.4% 8.7% 10.2% 11.9% 14.0% 

 WY 9% 9.4% 10.0% 10.6% 11.3% 12.0% 12.8% 13.7% 14.5% 15.5% 16.5% 17.5% 18.7% 19.9% 

4.2.5. Calculate RE targets for interim and final state goals 

The agency then translated the annual RE target performance levels for each state into 

state-level interim and final RE targets for informing this rule’s quantification of state goals. 

Separate interim and final RE targets were calculated for the proposed state goals and the 

alternate state goals in this proposed rulemaking. For the proposed state goals, the interim RE 

target for each state was calculated as the average of that state’s RE target performance level 

from 2020-2029, and the final target is equivalent to that state’s RE target performance level in 

the year 2029. For the alternate state goals, the interim RE target for each state was calculated as 

the average of that state’s RE target performance level from 2020-2024, and the final target is 

equivalent to that state’s RE target performance level in the year 2024. 

A sample calculation for Illinois is provided below. State-level RE targets, expressed in 

absolute (megawatt-hour) terms and as a percentage level of each state’s RE generation as a 

share of its total generation, along with 2012 RE levels for each state, are provided in Table 4.8. 

For an explanation of how these state-level RE targets informed the calculations of state goals in 

this rule, please refer to the Goal Computation TSD. 

   
4 - 26 

Illinois RE Generation Targets

Year GWh % of 2012

generation

2017 8,873 4.50%

2018 9,404 4.80%

2019 9,967 5.00%

2020 10,563 5.30%

2021 11,195 5.70%

2022 11,864 6.00%

2023 12,574 6.40%

2024 13,326 6.70%

2025 14,123 7.10%

2026 14,968 7.60%

2027 15,863 8.00%

2028 16,812 8.50%

2029 17,818 9.00%

    

Interim & Final Target Calculation

Proposed   Alternate

Interim

(2020‐

2029)

Final

(2030)

Interim

(2020‐

2024)

Final

(2025)

13,910,775 17,818,004 11,904,488 13,326,217

7.00% 9.00% 6.00% 6.70%

   

Option 1 Interim = average of 2020‐2029 values

Option 1 Final = 2029 value

Option 2 Interim = average of 2020‐2024 values

Option 2 Final = 2024 value
4 - 27 

Table 4.8. Proposed and Alternate State Targets for RE Generation as a Percentage of 

Total Generation, with 2012 Historical RE Generation 

State 2012 

Proposed Targets Alternate Targets 

Interim 

Level Final Level Interim Level Final Level 

 Alabama 2% 6% 9% 4% 5%

 Alaska 1% 2% 2% 1% 1%

 Arizona 2% 3% 4% 3% 3%

 Arkansas 3% 5% 7% 4% 5%

 California 15% 20% 21% 20% 21%

 Colorado 12% 19% 21% 17% 19%

 Connecticut 2% 5% 9% 4% 5%

 Delaware 2% 7% 12% 4% 5%

 Florida 2% 6% 10% 4% 6%

 Georgia 3% 8% 10% 6% 7%

 Hawaii 9% 10% 10% 10% 10%

 Idaho 16% 21% 21% 21% 21%

 Illinois 4% 7% 9% 6% 7%

 Indiana 3% 5% 7% 4% 5%

 Iowa 25% 15% 15% 15% 15%

 Kansas 12% 19% 20% 19% 20%

 Kentucky 0% 1% 2% 1% 1%

 Louisiana 2% 5% 7% 4% 4%

 Maine 28% 25% 25% 25% 25%

 Maryland 2% 10% 16% 6% 8%

 Massachusetts 5% 15% 24% 11% 13%

 Michigan 3% 6% 7% 5% 6%

 Minnesota 18% 15% 15% 15% 15%

 Mississippi 3% 8% 10% 6% 8%

 Missouri 1% 2% 3% 2% 2%

 Montana 5% 8% 10% 7% 7%

 Nebraska 4% 8% 11% 6% 7%

 Nevada 8% 14% 18% 12% 14%

 New Hampshire 7% 19% 25% 15% 19%

 New Jersey 2% 8% 16% 5% 7%
4 - 28 

State 2012 

Proposed Targets Alternate Targets 

Interim 

Level Final Level Interim Level Final Level 

 New Mexico 11% 18% 21% 16% 18%

 New York 4% 11% 18% 8% 10%

 North Carolina 2% 7% 10% 5% 6%

 North Dakota 15% 15% 15% 15% 15%

 Ohio 1% 6% 11% 4% 5%

 Oklahoma 11% 19% 20% 18% 20%

 Oregon 12% 19% 21% 17% 19%

 Pennsylvania 2% 9% 16% 5% 7%

 Rhode Island 1% 4% 6% 3% 3%

 South Carolina 2% 7% 10% 5% 6%

 South Dakota 24% 15% 15% 15% 15%

 Tennessee 1% 3% 6% 2% 3%

 Texas 8% 16% 20% 13% 15%

 Utah 3% 5% 7% 4% 5%

 Virginia 3% 12% 16% 9% 12%

 Washington 7% 12% 15% 10% 11%

 West Virginia 2% 8% 14% 5% 6%

 Wisconsin 5% 8% 11% 7% 8%

 Wyoming 9% 15% 19% 13% 14%

   
4 - 29 

Table 4.9. Proposed and Alternate State Targets for RE Generation in Megawatt-hours, 

with 2012 Historical RE Generation 

State 2012 

Proposed Targets Alternate Targets 

Interim 

Level Final Level Interim Level Final Level 

Alabama 2,776,554 8,647,278 14,292,801 6,009,218 7,610,632 

Alaska 39,958 105,136 163,089 77,373 94,950 

Arizona 1,697,652 2,847,759 3,663,325 2,429,595 2,725,233 

Arkansas 1,660,370 3,370,253 4,708,823 2,703,555 3,153,509 

California 29,966,846 40,745,587 41,150,704 40,340,469 41,150,704 

Colorado 6,192,082 9,821,423 10,839,820 8,861,798 9,940,119 

Connecticut 666,525 1,934,220 3,114,375 1,376,991 1,721,274 

Delaware 131,051 561,909 1,038,351 349,356 468,394 

 Florida 4,523,798 13,971,137 22,109,614 9,790,728 12,399,889 

 Georgia 3,278,536 9,392,695 12,230,636 7,095,644 8,986,583 

 Hawaii 924,815 1,046,927 1,046,927 1,046,927 1,046,927 

 Idaho 2,514,502 3,195,606 3,196,687 3,194,526 3,196,687 

 Illinois 8,372,660 13,910,775 17,818,004 11,904,488 13,326,217 

 Indiana 3,546,367 5,892,120 7,547,086 5,042,327 5,644,522 

 Iowa 14,183,424 8,565,921 8,565,921 8,565,921 8,565,921 

 Kansas 5,252,653 8,577,482 8,884,938 8,270,026 8,884,938 

 Kentucky 332,879 1,036,717 1,713,556 720,442 912,434 

 Louisiana 2,430,042 4,932,549 6,891,619 3,956,800 4,615,333 

 Maine 4,098,795 3,611,728 3,611,728 3,611,728 3,611,728 

Maryland 898,152 3,728,926 5,982,069 2,394,301 3,210,129 

Massachusetts 1,843,419 5,349,504 8,613,477 3,808,366 4,760,555 

Michigan 3,785,439 6,289,326 8,055,859 5,382,246 6,025,037 

Minnesota 9,453,871 7,888,544 7,888,544 7,888,544 7,888,544 

Mississippi 1,509,190 4,272,197 5,458,430 3,266,297 4,136,743 

 Missouri 1,298,579 2,157,527 2,763,528 1,846,357 2,066,863 

 Montana 1,261,752 2,116,550 2,722,706 1,805,757 2,025,485 

 Nebraska 1,346,762 2,733,684 3,819,427 2,192,912 2,557,879 

 Nevada 2,968,630 4,979,784 6,405,939 4,248,556 4,765,528 

 New Hampshire 1,381,285 3,727,303 4,822,223 2,853,632 3,567,113 

 New Jersey 1,280,715 5,491,354 10,147,466 3,414,138 4,577,463 

 New Mexico 2,573,851 4,161,824 4,721,996 3,683,568 4,131,791 
4 - 30 

State 2012

Proposed Targets Alternate Targets

Final Level Interim 

Level Final Level Interim Level

 New York 5,192,427 15,068,148 24,261,905 10,727,168 13,409,233 

 North Carolina 2,703,919 8,135,750 11,668,176 5,852,016 7,411,538 

 North Dakota 5,280,052 5,459,957 5,459,957 5,459,957 5,459,957 

 Ohio 1,738,622 7,454,735 13,775,594 4,634,830 6,214,090 

 Oklahoma 8,520,724 14,666,348 15,579,318 13,753,378 15,579,318 

 Oregon 7,207,229 11,411,751 12,567,372 10,314,627 11,569,730 

 Pennsylvania 4,459,118 19,119,477 35,330,855 11,887,147 15,937,543 

 Rhode Island 101,895 295,694 476,110 210,507 263,140 

 South Carolina 2,143,473 6,534,613 9,675,568 4,639,057 5,875,334 

 South Dakota 2,914,666 1,818,850 1,818,850 1,818,850 1,818,850 

 Tennessee 836,458 2,605,058 4,305,814 1,810,322 2,292,760 

 Texas 34,016,697 67,689,311 85,962,502 55,388,864 64,607,260 

 Utah 1,099,724 1,844,752 2,373,069 1,573,870 1,765,381 

 Virginia 2,358,444 8,608,808 11,192,008 6,287,155 8,429,425 

 Washington 8,214,350 13,779,314 17,725,558 11,755,968 13,186,456 

 West Virginia 1,296,563 5,559,307 10,273,036 3,456,386 4,634,107 

 Wisconsin 3,223,178 5,355,156 6,859,301 4,582,807 5,130,122 

 Wyoming 4,369,107 7,329,040 9,427,996 6,252,848 7,013,706 

The annual rates used to set state RE targets under the proposed approach are comparable 

to rates that leader states have been able to approach in the past. Eleven states across four regions 

have already achieved over 10% of total generation from RE, surpassing the lowest regional 

target applied in the Southeast. Two states, Maine and Iowa, have already equaled or surpassed 

the highest regional target of 25% of generation, with South Dakota close behind at 24%. 

Finally, five states have already reached their region’s required target. 

4.3 Cost Effectiveness of RE 

The costs of building new RE capacity and generating more RE have changed 

significantly in the past decade, particularly for wind and solar. The economics of the fastest 

growing RE technologies – on-shore wind and solar PV – are improving. According to recent 

analyses of wind and solar project costs and pricing trends by U.S. Department of Energy, 
4 - 31 

levelized long-term power purchase agreement (PPA) prices have been declining. PPA prices, in 

general, reflect actual agreements to pay for power from wind or solar projects over a long-term 

and cover the cost of installing, operating, and maintaining a wind or solar project, along with a 

profit margin.118 For utility-scale solar PV, those levelized PPA prices have fallen by more than 

two-thirds in the past five years “driven primarily by lower installed PV project prices (which, in 

turn, have been driven primarily by declining module prices), as well as expectations for further 

cost reductions in future years.”119 More recent PPAs in the West are reporting levelized PPA 

prices in the range of $50-60/MWh (in 2012 dollars).120 For wind, PPA prices have fallen since 

2009 despite a trend within the wind industry to build projects at lower-quality wind resource 

sites.121 “The average levelized long-term price from wind PAs signed in 2011/2012—many of 

which were for projects built in 2012—fell to around $40/MWh nationwide.”122 

Examining RE resource availability regionally, several recent studies have found costeffective or economic RE resources are available to serve future needs. The National Renewable 

Energy Laboratory (NREL) examined the future availability of RE in the West after Western 

state RPS requirements level off in 2025.123 The study compares the cost of RE generation from 

the West's most productive RE resource areas—including any needed transmission and 

integration costs—with the cost of energy from a new natural gas-fired generator built near the 

customers it serves. The report indicates that by 2025 wind and solar PV generation could 

become cost-competitive, if new RE development occurs in the most productive locations.124 It 

also has shown that a cost decrease of 10% in 2025 would bring solar power to cost parity with 

NGCC in the West, with similar possibilities for utility scale geothermal. In 2010, the Southeast 

                                                            

118 Mark Bolinger and Samantha Weaver, Utility-Scale Solar 2012: An Empirical Analysis of Project Cost, 

Performance, and Pricing Trends in the United States, Lawrence Berkeley National Laboratory, LBNL-6408E. 

September 2013, p. 19. 

119 Mark Bolinger and Samantha Weaver, Utility-Scale Solar 2012: An Empirical Analysis of Project Cost, 

Performance, and Pricing Trends in the United States, Lawrence Berkeley National Laboratory, LBNL-6408E. 

September 2013, p. ii. 

120 Ibid. 121 Ryan H. Wiser and Mark Bolinger, 2012 Wind Technologies Market Report, Lawrence Berkeley National 

Laboratory, August 2013, p. viii. 

122 Ibid. 123 David Hurlbut, Joyce McLaren, and Rachel Gelman, Beyond Renewable Portfolio Standards: An Assessment of 

Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West. NREL/TP-6A20-

57830, Golden, CO: NREL, August 2013. 

124 Ibid. p. xvi. 
4 - 32 

Energy Efficiency Alliance published a report by Marilyn Brown et al. titled Renewable Energy 

in the South.125 In addition to highlighting significant RE resources in different parts of the 

region, it stated, “Under realistic renewable expansion and policy scenarios, the region could 

economically supply a large proportion of its future electricity needs from both utility-scale and 

customer-owned RE sources.”126 This study suggested that increased RE utilization should not 

necessarily lead to significant rate increases in part because RE resources may moderate 

forecasted rate increases in the next decade or two.127

Several studies have found the cost of RPS-driven RE deployment to be modest. One 

comparative analysis that "synthesize[d] and analyze[d] the results and methodologies of 28 

distinct state or utility-level RPS cost impact analyses" found the median change in retail 

electricity price to be $0.0004 per kilowatt-hour (only a 0.7 percent increase), the median 

monthly bill impact to be between $0.13 and $0.82, and the median CO2 reduction cost to be $3 

per metric ton.128 This finding has been confirmed with more recent RPS cost data, including a 

report that determined 2010-2012 retail electricity price impacts due to state RPS policies to be 

less than two percent, with only two states experiencing price impacts of greater than three 

percent.129

4.4. Nuclear Energy 

Nuclear generating capacity facilitates CO2 emission reductions at fossil fuel-fired EGUs 

by providing carbon-free generation that can replace generation at those EGUs. Increasing the 

amount of nuclear capacity relative to the amount that would otherwise be available to operate is 

                                                            

125 Marilyn A. Brown, Etan Gumerman, Youngsun Baek, Joy Wang, Cullen Morris, and Yu Wang, 2010, Renewable 

Energy in the South. Atlanta, GA: Southeast Energy Efficiency Alliance, December 2010. Also Marilyn A. Brown, 

Etan Gumerman, Xiaojing Sun, Kenneth Sercy, and Gyungwon Kim, 2012, “Myths and Facts about Clean 

Electricity in the U.S. South,” Energy Policy, 40: 231-241. 

126 Ibid. p. xxii. 127 Ibid. p. 109. 128 Chen et al., "Weighing the Costs and Benefits of State Renewable Portfolio Standards: A Comparative Analysis 

of State-Level Policy Impact Projections," Lawrence Berkeley National Laboratory, March 2007, 

http://emp.lbl.gov/publications/weighing-costs-and-benefits-state-renewables-portfolio-standards-comparativeanalysis-s. 

129 Galen Barbose, “Renewables Portfolio Standards in the United States: A Status Update,” Lawrence Berkeley 

National Lab, November 2013. Also, Heeter, J., Barbose, G., Bird, L., Weaver, S., Flores-Espino, F., KuskovaBurns, K., and Wiser, R., “A Survey of State-Level Cost and Benefit Estimates of Renewable Portfolio Standards.” 

NREL Report No. 6A20-61042, LBNL Report No. 6589E, http://www.nrel.gov/docs/fy14osti/61042.pdf. 
4 - 33 

therefore a technically viable approach that states may consider in the development of state plans 

for reducing CO2 emissions from affected fossil fuel-fired EGUs. 

 One way to increase the amount of available nuclear capacity is to build new nuclear 

EGUs. However, nuclear generating capacity is relatively expensive to build compared to other 

types of generating capacity, and little new nuclear capacity has been constructed in the U.S. in 

recent years. Five new nuclear EGUs at three plants are currently under construction: Watts Bar 

2 in Tennessee, Vogtle 3-4 in Georgia, and Summer 2-3 in South Carolina. The EPA believes 

that since the decisions to construct these units were made prior to this proposal, it is reasonable 

to view the incremental cost associated with the CO2 emission reductions available from 

completion of these units as zero for purposes of setting states’ CO2 reduction goals. Completion 

of these units therefore represents a highly cost-effective opportunity to reduce CO2 emissions 

from affected fossil fuel-fired EGUs. For this reason, we are proposing that the emission 

reductions achievable at affected sources due to the generation provided at the identified new 

nuclear units should be factored into the state goals for the respective states where these new 

units are located. 

 Another way to increase the amount of available nuclear capacity is to preserve existing 

nuclear EGUs that would otherwise be retired. While each retirement decision is based on the 

unique circumstances of that individual unit, the EPA recognizes that a host of factors – 

increasing fixed operation and maintenance costs, relatively low wholesale electricity prices, and 

additional capital investment associated with ensuring plant security and emergency 

preparedness – have altered the outlook for the U.S. nuclear fleet in recent years. Reflecting 

similar concern for these challenges, EIA in its most recent Annual Energy Outlook has 

projected an additional 5.7 GW of capacity reductions to the nuclear fleet. EIA describes the 

projected capacity reductions – which are not tied to the retirement of any specific unit – as 

necessary to recognize the “continued economic challenges” faced by the higher-cost nuclear 

units.130 Likewise, without making any judgment about the likelihood that any individual EGU 

will retire, we view this 5.7 GW, which comprises an approximately six percent share of nuclear 

capacity, as a reasonable proxy for the amount of nuclear capacity at risk of retirement. 

                                                            

130 Jeffrey Jones and Michael Leff, “Implications of accelerated power plant retirements,” EIA, April 2014. 
4 - 34 

We believe that, based on available information regarding the cost and performance of 

the nuclear fleet, preserving the operation of at-risk nuclear capacity is likely to be a relatively 

cost-effective approach to achieving CO2 reductions from affected EGUs. According to a recent 

report, nuclear units may be experiencing up to a $6/MWh shortfall in covering their operating 

costs with electricity sales.131 Assuming that such a revenue shortfall is representative of the 

incentive to retire at-risk nuclear capacity, one can estimate the value of offsetting the revenue 

loss at these at-risk nuclear units to be about $12 to $17 per metric ton. 132 The EPA views this 

cost as reasonable. We therefore propose that the emission reductions achievable by retaining in 

operation approximately six percent of each state’s historical nuclear capacity should be factored 

into the state goals for the respective states. 133 

 The amount of at-risk nuclear generation quantified for each state is displayed in Table 

4.10: 

Table 4.10. Nuclear At-Risk Generation by State 

State 2012 Nuclear Fleet 

(MW)* 

At-Risk 

Nuclear 

Capacity 

(MW) 

At-Risk Nuclear 

Generation 

(GWh) 

Alabama 5,043 295 2,330 

Arizona 3,937 230 1,818 

Arkansas 1,823 107 842 

California 2,240 131 1,035 

Connecticut 2,103 123 971 

Florida 3,514 205 1,623 

Georgia 4,061 237 1,876 

Illinois 11,486 671 5,305 

                                                            

131 Eggers, et al., “Nuclear... The Middle Age Dilemma?” Credit Suisse, February 2013. 132 The derivation of $12 to $17 per metric ton assumes that replacement power for at-risk nuclear capacity is 

sourced either from new NGCC capacity at 800 lbs CO2/MWh or from the projected average 2020 emissions 

intensity across the U.S. power system at 1,127 lbs CO2/MWh(from EPA’s IPM Base Case). 133 Historical nuclear fleet excludes Watts Bar 2, Vogtle 3-4, and Summer 2-3, as well as all units that have retired or 

are committed to retire (as of May 2014). 
4 - 35 

Iowa 601 35 278 

Kansas 1,175 69 543 

Louisiana 2,133 125 985 

Maryland 1,705 100 788 

Massachusetts 685 40 316 

Michigan 3,957 231 1,828 

Minnesota 1,819 106 840 

Mississippi 1,368 80 632 

Missouri 1,190 70 550 

Nebraska 1,245 73 575 

New Hampshire 1,246 73 576 

New Jersey 3,499 204 1,616 

New York 5,219 305 2,411 

North Carolina 4,970 290 2,296 

Ohio 2,150 126 993 

Pennsylvania 9,700 567 4,480 

South Carolina 6,486 379 2,996 

Tennessee 3,401 199 1,571 

Texas 4,960 290 2,291 

Virginia 3,562 208 1,645 

Washington 1,097 64 507 

Wisconsin 1,184 69 547 

Total 97,559 5,700 45,062
4 - 36 

 
4 - 37 

Appendix 4-1. RE Generation Targets Including Existing 

Table 4-1.1. State RE Generation Targets including 2012 Existing Hydropower Generation 

(Gigawatt-hours) 

State 

2012 

Existing 

Hydro 

2012 

Nonhydro 

RE 

(GWh) 

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 

Alabama 7,435 2,777 10,585 11,008 11,488 12,032 12,650 13,350 14,145 15,046 16,068 17,228 18,543 20,035 21,728 

Alaska 1,575 40 1,620 1,625 1,630 1,637 1,644 1,652 1,660 1,670 1,681 1,693 1,706 1,721 1,738 

Arizona 6,717 1,698 8,518 8,628 8,744 8,868 8,999 9,138 9,286 9,442 9,608 9,784 9,971 10,170 10,380 

Arkansas 2,198 1,660 3,997 4,148 4,310 4,487 4,678 4,885 5,109 5,352 5,615 5,901 6,210 6,544 6,907 

California 26,837 29,967 58,631 60,568 62,624 64,805 67,120 67,988 67,988 67,988 67,988 67,988 67,988 67,988 67,988 

Colorado 1,497 6,192 8,067 8,467 8,892 9,343 9,821 10,328 10,866 11,437 12,043 12,337 12,337 12,337 12,337 

Connecticut 312 667 1,063 1,157 1,263 1,383 1,518 1,670 1,841 2,033 2,250 2,494 2,769 3,078 3,427 

Delaware - 131 154 180 211 248 291 341 399 468 549 644 755 886 1,038 

Florida 151 4,524 5,282 5,971 6,753 7,640 8,646 9,787 11,082 12,550 14,216 16,105 18,249 20,680 22,260 

Georgia 2,236 3,279 5,955 6,455 7,021 7,664 8,393 9,220 10,159 11,223 12,430 13,799 14,467 14,467 14,467 

Hawaii 115 925 1,119 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 

Idaho 10,940 2,515 13,608 13,771 13,943 14,126 14,137 14,137 14,137 14,137 14,137 14,137 14,137 14,137 14,137 

Illinois 111 8,373 8,985 9,515 10,078 10,674 11,306 11,976 12,685 13,437 14,235 15,079 15,975 16,924 17,929 

Indiana 434 3,546 4,192 4,417 4,655 4,908 5,175 5,459 5,759 6,078 6,416 6,773 7,153 7,555 7,981 

Iowa 766 14,183 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 

Kansas 10 5,253 5,702 6,177 6,692 7,249 7,854 8,508 8,895 8,895 8,895 8,895 8,895 8,895 8,895 

Kentucky 2,362 333 2,739 2,790 2,848 2,913 2,987 3,071 3,166 3,274 3,397 3,536 3,694 3,872 4,075 

Louisiana 680 2,430 3,313 3,533 3,771 4,029 4,308 4,611 4,940 5,295 5,681 6,098 6,550 7,041 7,572 

Maine 3,733 4,099 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 

Maryland 1,657 898 2,710 2,891 3,105 3,355 3,648 3,991 4,394 4,867 5,421 6,070 6,832 7,639 7,639 

Massachusetts 912 1,843 2,988 3,249 3,544 3,875 4,248 4,668 5,141 5,673 6,272 6,947 7,707 8,563 9,526 

Michigan 1,215 3,785 5,227 5,467 5,721 5,991 6,276 6,579 6,900 7,240 7,600 7,982 8,387 8,816 9,271 

Minnesota 561 9,454 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 

Mississippi - 1,509 1,712 1,942 2,203 2,499 2,834 3,215 3,647 4,137 4,692 5,323 5,458 5,458 5,458 

Missouri 714 1,299 2,091 2,173 2,260 2,353 2,451 2,554 2,664 2,781 2,905 3,036 3,175 3,322 3,478 

Montana 11,283 1,262 12,622 12,704 12,790 12,882 12,980 13,083 13,193 13,309 13,432 13,563 13,702 13,850 14,006 

Nebraska 1,257 1,347 2,716 2,838 2,970 3,113 3,268 3,436 3,618 3,815 4,028 4,260 4,511 4,782 5,076 

Nevada 2,440 2,969 5,590 5,782 5,986 6,202 6,431 6,674 6,932 7,206 7,496 7,805 8,131 8,478 8,846 

New 

Hampshire 1,289 1,381 2,845 3,040 3,261 3,509 3,789 4,103 4,458 4,856 5,306 5,811 6,112 6,112 6,112 

New Jersey 11 1,281 1,513 1,772 2,076 2,432 2,850 3,340 3,914 4,588 5,378 6,305 7,391 8,665 10,158 

New Mexico 223 2,574 2,954 3,120 3,297 3,484 3,683 3,894 4,117 4,355 4,606 4,874 4,945 4,945 4,945 

State 

2012 

Existing 

Hydro 

2012 

Nonhydro 

RE 

(GWh) 

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 
4 - 38 

New York 24,652 5,192 30,499 31,235 32,064 32,997 34,047 35,230 36,562 38,062 39,750 41,651 43,791 46,201 48,914 

North 

Carolina 3,728 2,704 6,795 7,207 7,674 8,205 8,806 9,488 10,262 11,139 12,135 13,264 14,545 15,396 15,396 

North Dakota 2,477 5,280 7,937 7,937 7,937 7,937 7,937 7,937 7,937 7,937 7,937 7,937 7,937 7,937 7,937 

Ohio 414 1,739 2,453 2,805 3,217 3,701 4,268 4,934 5,714 6,628 7,701 8,958 10,433 12,162 14,190 

Oklahoma 1,146 8,521 10,378 11,148 11,983 12,888 13,869 14,931 16,082 16,725 16,725 16,725 16,725 16,725 16,725 

Oregon 39,410 7,207 47,057 47,523 48,017 48,542 49,098 49,689 50,315 50,980 51,685 51,978 51,978 51,978 51,978 

Pennsylvania 2,242 4,459 7,471 8,373 9,431 10,672 12,127 13,833 15,834 18,179 20,930 24,156 27,938 32,373 37,573 

Rhode Island 4 102 119 133 150 168 189 212 238 267 301 338 380 427 480 

South 

Carolina 1,420 2,143 3,852 4,178 4,549 4,969 5,446 5,986 6,600 7,296 8,085 8,980 9,996 11,096 11,096 

South Dakota 5,981 2,915 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 

Tennessee 8,296 836 9,244 9,372 9,517 9,681 9,867 10,078 10,317 10,588 10,896 11,246 11,642 12,092 12,601 

Texas 584 34,017 37,441 40,518 43,852 47,464 51,378 55,619 60,213 65,192 70,586 76,430 82,762 86,547 86,547 

Utah 748 1,100 1,915 1,986 2,061 2,141 2,226 2,316 2,412 2,513 2,621 2,735 2,856 2,985 3,121 

Virginia 1,044 2,358 3,809 4,287 4,846 5,503 6,272 7,174 8,232 9,473 10,928 12,236 12,236 12,236 12,236 

Washington 89,464 8,214 98,179 98,711 99,274 99,872 100,506 101,179 101,893 102,651 103,455 104,307 105,212 106,172 107,190 

West Virginia 1,431 1,297 2,952 3,214 3,522 3,883 4,306 4,802 5,383 6,066 6,865 7,803 8,903 10,192 11,704 

Wisconsin 1,522 3,223 4,938 5,143 5,359 5,589 5,832 6,090 6,363 6,652 6,959 7,284 7,629 7,994 8,382 

Wyoming 893 4,369 5,529 5,811 6,111 6,429 6,767 7,124 7,504 7,907 8,335 8,788 9,269 9,780 10,321 

Table 4-1.2. State RE Generation Targets (% of Total Generation) including 2012 Existing 

Hydropower Generation 

State 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 

Alabama 6.9% 7.2% 7.5% 7.9% 8.3% 8.7% 9.3% 9.8% 10.5% 11.3% 12.1% 13.1% 14.2% 

Alaska 23.3% 23.4% 23.5% 23.6% 23.7% 23.8% 23.9% 24.0% 24.2% 24.4% 24.6% 24.8% 25.0% 

Arizona 9.0% 9.1% 9.2% 9.3% 9.5% 9.6% 9.8% 9.9% 10.1% 10.3% 10.5% 10.7% 10.9% 

Arkansas 6.1% 6.4% 6.6% 6.9% 7.2% 7.5% 7.9% 8.2% 8.6% 9.1% 9.6% 10.1% 10.6% 

California 29.4% 30.4% 31.4% 32.5% 33.6% 34.1% 34.1% 34.1% 34.1% 34.1% 34.1% 34.1% 34.1% 

Colorado 15.3% 16.1% 16.9% 17.8% 18.7% 19.7% 20.7% 21.8% 22.9% 23.5% 23.5% 23.5% 23.5% 

Connecticut 2.9% 3.2% 3.5% 3.8% 4.2% 4.6% 5.1% 5.6% 6.2% 6.9% 7.7% 8.5% 9.5% 

Delaware 1.8% 2.1% 2.4% 2.9% 3.4% 3.9% 4.6% 5.4% 6.4% 7.5% 8.7% 10.3% 12.0% 

Florida 2.4% 2.7% 3.1% 3.5% 3.9% 4.4% 5.0% 5.7% 6.4% 7.3% 8.3% 9.4% 10.1% 

Georgia 4.9% 5.3% 5.7% 6.3% 6.9% 7.5% 8.3% 9.2% 10.2% 11.3% 11.8% 11.8% 11.8% 

Hawaii 10.6% 11.1% 11.1% 11.1% 11.1% 11.1% 11.1% 11.1% 11.1% 11.1% 11.1% 11.1% 11.1% 

Idaho 87.8% 88.8% 90.0% 91.1% 91.2% 91.2% 91.2% 91.2% 91.2% 91.2% 91.2% 91.2% 91.2% 

Illinois 4.5% 4.8% 5.1% 5.4% 5.7% 6.1% 6.4% 6.8% 7.2% 7.6% 8.1% 8.6% 9.1% 

State 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 

Indiana 3.7% 3.9% 4.1% 4.3% 4.5% 4.8% 5.0% 5.3% 5.6% 5.9% 6.2% 6.6% 7.0% 

Iowa 16.5% 16.5% 16.5% 16.5% 16.5% 16.5% 16.5% 16.5% 16.5% 16.5% 16.5% 16.5% 16.5% 
4 - 39 

Kansas 12.8% 13.9% 15.1% 16.3% 17.7% 19.2% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0% 

Kentucky 3.0% 3.1% 3.2% 3.2% 3.3% 3.4% 3.5% 3.6% 3.8% 3.9% 4.1% 4.3% 4.5% 

Louisiana 3.2% 3.4% 3.6% 3.9% 4.2% 4.5% 4.8% 5.1% 5.5% 5.9% 6.3% 6.8% 7.3% 

Maine 50.9% 50.9% 50.9% 50.9% 50.9% 50.9% 50.9% 50.9% 50.9% 50.9% 50.9% 50.9% 50.9% 

Maryland 7.2% 7.6% 8.2% 8.9% 9.6% 10.6% 11.6% 12.9% 14.3% 16.1% 18.1% 20.2% 20.2% 

Massachusetts 8.3% 9.0% 9.8% 10.7% 11.7% 12.9% 14.2% 15.7% 17.3% 19.2% 21.3% 23.7% 26.3% 

Michigan 4.8% 5.1% 5.3% 5.5% 5.8% 6.1% 6.4% 6.7% 7.0% 7.4% 7.8% 8.2% 8.6% 

Minnesota 16.2% 16.2% 16.2% 16.2% 16.2% 16.2% 16.2% 16.2% 16.2% 16.2% 16.2% 16.2% 16.2% 

Mississippi 3.1% 3.6% 4.0% 4.6% 5.2% 5.9% 6.7% 7.6% 8.6% 9.8% 10.0% 10.0% 10.0% 

Missouri 2.3% 2.4% 2.5% 2.6% 2.7% 2.8% 2.9% 3.0% 3.2% 3.3% 3.5% 3.6% 3.8% 

Montana 45.4% 45.7% 46.0% 46.3% 46.7% 47.1% 47.4% 47.9% 48.3% 48.8% 49.3% 49.8% 50.4% 

Nebraska 7.9% 8.3% 8.7% 9.1% 9.6% 10.0% 10.6% 11.1% 11.8% 12.4% 13.2% 14.0% 14.8% 

Nevada 15.9% 16.4% 17.0% 17.6% 18.3% 19.0% 19.7% 20.5% 21.3% 22.2% 23.1% 24.1% 25.2% 

New 

Hampshire 14.8% 15.8% 16.9% 18.2% 19.7% 21.3% 23.1% 25.2% 27.5% 30.2% 31.7% 31.7% 31.7% 

New Jersey 2.3% 2.7% 3.2% 3.7% 4.4% 5.1% 6.0% 7.0% 8.2% 9.7% 11.3% 13.3% 15.6% 

New Mexico 12.9% 13.6% 14.4% 15.2% 16.1% 17.0% 18.0% 19.0% 20.1% 21.3% 21.6% 21.6% 21.6% 

New York 22.5% 23.0% 23.6% 24.3% 25.1% 25.9% 26.9% 28.0% 29.3% 30.7% 32.3% 34.0% 36.0% 

North Carolina 5.8% 6.2% 6.6% 7.0% 7.5% 8.1% 8.8% 9.5% 10.4% 11.4% 12.5% 13.2% 13.2% 

North Dakota 22.0% 22.0% 22.0% 22.0% 22.0% 22.0% 22.0% 22.0% 22.0% 22.0% 22.0% 22.0% 22.0% 

Ohio 1.9% 2.2% 2.5% 2.9% 3.3% 3.8% 4.4% 5.1% 5.9% 6.9% 8.0% 9.4% 10.9% 

Oklahoma 13.3% 14.3% 15.4% 16.5% 17.8% 19.2% 20.6% 21.5% 21.5% 21.5% 21.5% 21.5% 21.5% 

Oregon 77.2% 78.0% 78.8% 79.7% 80.6% 81.5% 82.6% 83.7% 84.8% 85.3% 85.3% 85.3% 85.3% 

Pennsylvania 3.3% 3.7% 4.2% 4.8% 5.4% 6.2% 7.1% 8.1% 9.4% 10.8% 12.5% 14.5% 16.8% 

Rhode Island 1.4% 1.6% 1.8% 2.0% 2.3% 2.5% 2.9% 3.2% 3.6% 4.1% 4.6% 5.1% 5.8% 

South Carolina 4.0% 4.3% 4.7% 5.1% 5.6% 6.2% 6.8% 7.5% 8.4% 9.3% 10.3% 11.5% 11.5% 

South Dakota 64.8% 64.8% 64.8% 64.8% 64.8% 64.8% 64.8% 64.8% 64.8% 64.8% 64.8% 64.8% 64.8% 

Tennessee 11.9% 12.1% 12.2% 12.5% 12.7% 13.0% 13.3% 13.6% 14.0% 14.5% 15.0% 15.6% 16.2% 

Texas 8.7% 9.4% 10.2% 11.0% 12.0% 12.9% 14.0% 15.2% 16.4% 17.8% 19.3% 20.1% 20.1% 

Utah 5.3% 5.5% 5.7% 5.9% 6.1% 6.4% 6.6% 6.9% 7.2% 7.5% 7.9% 8.2% 8.6% 

Virginia 5.4% 6.1% 6.9% 7.8% 8.9% 10.1% 11.6% 13.4% 15.4% 17.3% 17.3% 17.3% 17.3% 

Washington 84.0% 84.5% 85.0% 85.5% 86.0% 86.6% 87.2% 87.9% 88.5% 89.3% 90.1% 90.9% 91.7% 

West Virginia 4.0% 4.4% 4.8% 5.3% 5.9% 6.5% 7.3% 8.3% 9.4% 10.6% 12.1% 13.9% 15.9% 

Wisconsin 7.7% 8.1% 8.4% 8.8% 9.1% 9.6% 10.0% 10.4% 10.9% 11.4% 12.0% 12.5% 13.1% 

Wyoming 11.1% 11.7% 12.3% 13.0% 13.6% 14.4% 15.1% 15.9% 16.8% 17.7% 18.7% 19.7% 20.8% 

   
4 - 40 

Table 4-1.3. Proposed and Alternate State Targets for RE Generation as a Percentage of 

Total Generation, with 2012 Historical RE Generation 

State 2012 

Proposed Targets Alternate Targets 

Interim 

Level 

Final 

Level 

Interim 

Level 

Final 

Level 

 Alabama 7% 11% 14% 9% 10%

 Alaska 23% 24% 25% 24% 24%

 Arizona 9% 10% 11% 10% 10%

 Arkansas 6% 9% 11% 8% 8%

 California 28% 34% 34% 34% 34%

 Colorado 15% 22% 23% 20% 22%

 Connecticut 3% 6% 9% 5% 6%

 Delaware 2% 7% 12% 4% 5%

 Florida 2% 6% 10% 4% 6%

 Georgia 5% 10% 12% 8% 9%

 Hawaii 10% 11% 11% 11% 11%

 Idaho 87% 91% 91% 91% 91%

 Illinois 4% 7% 9% 6% 7%

 Indiana 3% 6% 7% 5% 5%

 Iowa 26% 16% 16% 16% 16%

 Kansas 12% 19% 20% 19% 20%

 Kentucky 3% 4% 5% 3% 4%

 Louisiana 3% 5% 7% 4% 5%

 Maine 54% 51% 51% 51% 51%

 Maryland 7% 14% 20% 11% 13%

 Massachusetts 8% 17% 26% 13% 16%

 Michigan 5% 7% 9% 6% 7%

 Minnesota 19% 16% 16% 16% 16%

 Mississippi 3% 8% 10% 6% 8%

 Missouri 2% 3% 4% 3% 3%

 Montana 45% 48% 50% 47% 48%

 Nebraska 8% 12% 15% 10% 11%

 Nevada 15% 21% 25% 19% 20%

 New 

Hampshire 14% 26% 32% 22% 25%

 New Jersey 2% 8% 16% 5% 7%

 New Mexico 12% 19% 22% 17% 19%

 New York 22% 29% 36% 26% 28%

 North 

Carolina 6% 10% 13% 8% 10%

 North Dakota 21% 22% 22% 22% 22%
4 - 41 

 Ohio 2% 6% 11% 4% 5%

State 2012

Proposed Targets Alternate Targets

Interim 

Level

Final 

Level

Interim 

Level

Final 

Level

 Oklahoma 12% 20% 21% 19% 21%

 Oregon 77% 83% 85% 82% 84%

 Pennsylvania 3% 10% 17% 6% 8%

 Rhode Island 1% 4% 6% 3% 3%

 South 

Carolina 4% 8% 11% 6% 8%

 South Dakota 74% 65% 65% 65% 65%

 Tennessee 12% 14% 16% 13% 14%

 Texas 8% 16% 20% 13% 15%

 Utah 5% 7% 9% 6% 7%

 Virginia 5% 14% 17% 10% 13%

 Washington 84% 88% 92% 87% 88%

 West Virginia 4% 10% 16% 7% 8%

 Wisconsin 7% 11% 13% 10% 10%

 Wyoming 11% 17% 21% 14% 16%

   
4 - 42 

Table 4-1.4. Proposed and Alternate State Targets for RE Generation in Megawatt-hours, 

with 2012 Historical RE Generation 

State 2012 

Proposed Targets Alternate Targets 

Interim 

Level 

Final 

Level 

Interim 

Level 

Final 

Level 

Alabama 10,211,777 16,082,501 21,728,024 13,444,441 15,045,855 

Alaska 1,615,003 1,680,181 1,738,134 1,652,418 1,669,995 

Arizona 8,414,586 9,564,693 10,380,259 9,146,529 9,442,167 

Arkansas 3,858,842 5,568,725 6,907,295 4,902,027 5,351,981 

California 56,804,216 67,582,957 67,988,075 67,177,840 67,988,075 

Colorado 7,689,291 11,318,632 12,337,029 10,359,007 11,437,328 

Connecticut 978,666 2,246,361 3,426,516 1,689,132 2,033,415 

Delaware 131,051 561,909 1,038,351 349,356 468,394 

Florida 4,674,309 14,121,648 22,260,125 9,941,239 12,550,400 

Georgia 5,514,836 11,628,995 14,466,936 9,331,944 11,222,883 

Hawaii 1,039,396 1,161,508 1,161,508 1,161,508 1,161,508 

Idaho 13,454,907 14,136,011 14,137,092 14,134,931 14,137,092 

Illinois 8,483,868 14,021,983 17,929,212 12,015,696 13,437,425 

Indiana 3,979,872 6,325,625 7,980,591 5,475,832 6,078,027 

Iowa 14,949,615 9,332,112 9,332,112 9,332,112 9,332,112 

Kansas 5,263,052 8,587,881 8,895,337 8,280,425 8,895,337 

Kentucky 2,694,661 3,398,499 4,075,338 3,082,224 3,274,216 

Louisiana 3,109,986 5,612,493 7,571,563 4,636,744 5,295,277 

Maine 7,831,400 7,344,333 7,344,333 7,344,333 7,344,333 

Maryland 2,554,691 5,385,465 7,638,608 4,050,840 4,866,668 

Massachusetts 2,755,901 6,261,986 9,525,959 4,720,848 5,673,037 

Michigan 5,000,293 7,504,180 9,270,713 6,597,100 7,239,891 

Minnesota 10,014,892 8,449,566 8,449,566 8,449,566 8,449,566 

Mississippi 1,509,190 4,272,197 5,458,430 3,266,297 4,136,743 

Missouri 2,012,848 2,871,796 3,477,797 2,560,626 2,781,132 

Montana 12,545,217 13,400,015 14,006,171 13,089,222 13,308,950 

Nebraska 2,603,816 3,990,738 5,076,481 3,449,966 3,814,933 

Nevada 5,409,045 7,420,199 8,846,354 6,688,971 7,205,943 

New 

Hampshire 2,670,671 5,016,689 6,111,609 4,143,018 4,856,499 

New Jersey 1,291,470 5,502,109 10,158,221 3,424,893 4,588,218 

New Mexico 2,796,670 4,384,643 4,944,815 3,906,387 4,354,610 

New York 29,844,923 39,720,644 48,914,401 35,379,664 38,061,729 

North Carolina 6,431,857 11,863,688 15,396,114 9,579,954 11,139,476 

North Dakota 7,757,282 7,937,187 7,937,187 7,937,187 7,937,187 

Ohio 2,152,783 7,868,896 14,189,755 5,048,991 6,628,251 
4 - 43 

State 2012

Proposed Targets Alternate Targets

Interim Level Final Level Interim Level Final Level

Oklahoma 9,666,238 15,811,862 16,724,832 14,898,892 16,724,832 

Oregon 46,617,408 50,821,930 51,977,551 49,724,805 50,979,909 

Pennsylvania 6,701,038 21,361,397 37,572,775 14,129,067 18,179,463 

Rhode Island 106,161 299,960 480,376 214,773 267,406 

South Carolina 3,563,745 7,954,885 11,095,840 6,059,329 7,295,606 

South Dakota 8,895,631 7,799,815 7,799,815 7,799,815 7,799,815 

Tennessee 9,132,118 10,900,718 12,601,474 10,105,982 10,588,420 

Texas 34,601,171 68,273,785 86,546,976 55,973,339 65,191,734 

Utah 1,847,510 2,592,538 3,120,855 2,321,656 2,513,167 

Virginia 3,402,218 9,652,582 12,235,782 7,330,929 9,473,199 

Washington 97,678,705 103,243,669 107,189,913 101,220,323 102,650,811 

West Virginia 2,728,003 6,990,747 11,704,476 4,887,826 6,065,547 

Wisconsin 4,745,413 6,877,391 8,381,536 6,105,042 6,652,357 

Wyoming 5,262,577 8,222,510 10,321,466 7,146,318 7,907,176 
5 - 1 

Chapter 5: Demand-side Energy Efficiency (EE) 

Introduction 

This chapter provides information on demand-side energy efficiency (EE) as an 

abatement measure for reducing carbon dioxide (CO2) emissions from fossil fuel-fired electric 

generating units (EGUs). Specifically, this chapter addresses EE as a component of both the 

“best system of emission reduction” (BSER) and state goals, and the inclusion of EE within the 

impacts assessment. Support is provided in this chapter for the discussion of the EE abatement 

measure throughout the preamble (most extensively in these sections: Building Blocks for 

Setting State Goals and Considerations, State Goals, State Plans, and Impacts of the Proposed 

Rule) and its representation within the Regulatory Impact Analysis (RIA). Results from this 

chapter feed into the technical support document (TSD) on Goal Computation. EE is also 

addressed in TSDs on state plan considerations and projecting emissions performance. 

This chapter is organized as follows: 

1) Background 

– EE Technologies and Practices 

– Barriers to EE Investment 

– EE Policies 

– EE Programs 

2) 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 

3) State Goal Setting 

– Approach 

– Inputs 

– Calculations 

– Results 

4) Impacts Assessment 
5 - 2 

– Approach 

– Inputs 

– Calculations 

– Results 

5) Analysis Considerations 

6) Appendices 

7) References 

Background 

As discussed in the State Plan Considerations TSD (Appendix: “Survey of Existing State 

Policies and Programs that Reduce Power Sector CO2 Emissions”), demand-side energy 

efficiency policies and programmatic efforts have existed for decades and are now used in all 50 

states. These strategies are intended to help states achieve energy savings goals, reduce the 

environmental impacts (including CO2 emissions) of meeting energy service needs, save energy 

and money for consumers, and provide a significant resource for meeting power system capacity 

requirements. EE policies currently in place are considered by states to be cost-effective 

strategies for contributing to these policy objectives.134 Moreover, states – through their utilities, 

primarily – have been rapidly increasing their funding of EE programs in recent years, more than 

tripling budgets in the five years from 2006 to 2011, from $1.6 billion to $5.9 billion.135 In 2012, 

the cumulative impacts of these programs represented a 3.7% reduction in national electricity 

demand.136 And, EE spending is projected to continue to grow at a substantial rate. A recent 

study by Lawrence Berkeley National Laboratory (LBNL) projects EE program spending to 

reach $8.1 billion to $12.2 billion (“Medium Case” and “High Case,” respectively) in 2025 even 

                                                            

134 See below for discussion of cost-effectiveness and related cost tests used by states to evaluate EE programs. 135 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. 136 U.S. Energy Information Administration Form EIA-861 data files. 2012. Available at 

http://www.eia.gov/electricity/data/eia861/. 
5 - 3 

“without considering possible major new policy developments,” such as requirements under 

Clean Air Act, Section 111(d).137,138 

This section provides relevant background for the subsequent sections that address the EE 

opportunity, EE as a component of BSER, EE within state goal setting, and the integration of EE 

within the benefit, cost, and impacts assessments as reported in the RIA and elsewhere. This 

section begins with a discussion of EE technologies and practices, and then describes the market 

failures that limit cost-effective EE investments. We then summarize EE policy objectives and 

discuss policy types, their relative impacts, and discuss in more detail the key strategy of 

employing EE programs. 

EE Technologies and Practices 

Energy efficiency is using less energy to provide the same or greater level of service. 

Demand-side energy efficiency refers to an extensive array of technologies, practices and 

measures that are applied throughout all sectors of the economy to reduce energy demand while 

providing the same, and sometimes better, level and quality of service. Utilities employ a large 

array of strategies in implementing energy efficiency programs, these include financial 

incentives such as rebates and loans, technical services such as audits and retrofits, and 

educational campaigns about the benefits of energy efficiency improvements. The purpose of 

these EE programs is to induce EE investments and practices that would not otherwise occur in 

the presence of market failures and behavioral impediments. In the residential sector, examples 

of EE activities include the purchase of more efficient products and equipment (e.g., ENERGY 

STAR labeled), the upgrading of insulation in attics and walls, sealing of air leaks, and 

undertaking home energy audits leading to customized whole home retrofits. Opportunities for 

cost-effective EE in commercial buildings include optimization of heating, ventilation, and air 

conditioning (HVAC) systems, upgrades of windows, and use of more efficient office equipment 

                                                            

137 Specifically, the LBNL study states: “By virtue of limiting the analysis to current energy efficiency policies, we 

do not consider the potential impact of major new federal (or state) policy initiatives (e.g., a national energy 

efficiency resource standard, clean energy standard, or carbon policy) that could result in customer-funded energy 

efficiency program spending and savings that exceed the values in our High Case.” 138 Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend. 
5 - 4 

at replacement. In the industrial sector key EE strategies include motor upgrades and 

maintenance programs, recovery of waste heat streams, and optimization of processes through 

modern instrumentation and controls systems. 

The opportunity presented for economic investment in EE is dynamic, growing over time 

as technologies and practices advance, as populations grow, and as investment occurs in the 

construction of new homes, buildings, and industrial facilities. As new policies are enacted, 

leading to the acceleration of investment in EE, an additional portion of the expanding 

opportunity is realized. After decades of experience implementing policies to accelerate 

investment in cost-effective energy efficiency, states are finding renewed opportunities as they 

develop more sophisticated and effective strategies, evolving from a focus on individual enduses and products to whole-building and systems-based strategies that account for the 

interactions between the many energy end-uses in buildings and industry.139 As will be 

discussed, the experience in the U.S. has been that on balance, a persistent and large potential for 

achievable and cost-effective EE has remained even as the impact of past and ongoing efforts 

have accumulated. 

Barriers to EE Investment 

Despite the persistent and large potential for electricity savings through investment in EE 

technologies and practices, market failures, as well as non-market failures, limit the realization of 

the many benefits of these investments. Several market failures that lead to inefficiencies in 

energy use are well recognized by analysts and practitioners, and are discussed extensively in the 

economic literature.140 Some of the most common examples of these market failures include: 

 Pollution externalities. Energy consumption is associated with negative externalities, 

such as emissions of CO2, SO2, and NOx that cause human health and environmental 

damages. Energy prices that do not correctly reflect these externalities lead to 

investments in energy efficiency below the socially optimal levels. 

                                                            

139 Seth Nowak, Martin Kushler, Patti Witte, and Dan York. Leaders of the Pack: ACEEE’s Third National Review 

of Exemplary Energy Efficiency Programs. American Council for an Energy-Efficient Economy (ACEEE). 

Research Report U132. Available at http://www.aceee.org/research-report/u132. 140 See reviews of market failures and barriers related to energy efficiency in Gillingham, K, R Newell, and K 

Palmer. 2009. Energy Efficiency Economics and Policy. Annual Review of Resource Economics. Annual Review of 

Resource Economics 1: 597-619 and Gillingham and Palmer (2013). “Bridging the Energy Efficiency Gap: insights 

for policy from economic theory and empirical analysis,” Resources for the Future DP 13-02. 
5 - 5 

 Imperfect information. Energy users often lack accurate information about energy savings 

and other attributes of energy efficient products or practices to understand the costs and 

benefits of EE investments. Market failure due to information imperfection leads to 

underinvestment in energy efficiency by consumers. 

 Split incentives (or the “principal-agent problem”). Incentives of individuals who make 

EE investment decisions are not always aligned with incentives of those who use and pay 

for energy. Examples include misalignment between landlords and tenants, and between 

builders and homeowners. Split incentives also persist within organizations and 

institutions that lead to underinvestment in EE in both public and the private entities.141

 Credit constraints. Limited access to credit may prevent some consumers, especially lowincome consumers, from making cost-effective EE improvement decisions due to the 

higher upfront cost of energy efficient products or practices. 

 Under-provision of research and development (R&D). Because of the public good nature 

of knowledge, technology innovation invested by one firm likely spills over to other 

firms. As a result, firms involved in technology development may be less willing to 

invest in R&D, leading to sub-optimal levels of EE investments from a social 

perspective.142 

 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. 

                                                            

141 For example, see DeCanio, S. 1998. The efficiency paradox: bureaucratic and organizational barriers to profitable 

energy-saving investments. Energy Policy 26(5): 441-458; McKinsey & Co and The Conference Board. 2007. 

Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? pp. (52-53). 

142 See discussion in Jaffe, A.B., R.G. Newell, and R.N. Stavins. 2003. Technological Change and the Environment. 

Chapter 11 in the Handbook of Environmental Economics. Volume 1, Edited by K.-G. Maler and J.R. Vincent. 

Elsevier Science B.V. 461-516. 
5 - 6 

 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.143 

In the presence of market failures, users of electricity, or those making energy efficiency 

investments, face prices or incentives that prevent them from weighing the social benefits and 

costs of their investments and thus under-invest in approaches to reduce electricity consumption. 

The behavioral impediments discussed above explain why individuals do not always make 

energy efficiency investments that are seemingly in their own best interest to reduce their total 

expenditure, given prevailing electricity prices. 

In addition to market failures and behavioral impediments, other factors, such as hidden 

costs, risk and uncertainty experienced by both consumers and suppliers of energy efficient 

products, and heterogeneity among consumers, producers and markets, also influence EE 

investment decisions.144 Examples of such factors include: 

 Risk and uncertainty. Adopting an unfamiliar, typically more expensive EE technology 

can be an uncertain undertaking given the lack of credible information on product 

performance and future energy prices, and the irreversibility of the investment. Imperfect 

or asymmetric information can exacerbate the perceived risk of energy efficiency 

investments and help explain why consumers and firms do not always invest in EE 

measures. Suppliers also face risk and uncertainty, without perfect information of 

consumer preferences for energy efficiency. In the presence of risk and uncertainties, 

consumers and suppliers alike will underinvest in EE. 

                                                            

143 See discussion in Gillingham, K and K Palmer. 2014. Bridging the Energy Efficiency Gap: Policy Insights from 

Economic Theory and Empirical Analysis. Review of Environmental Economics & Policy, 8(1): 18-38. 

144 It has been recognized that there is a difference between cost-effective energy efficiency investment levels, based 

on cost-minimizing consideration, and observed levels of energy efficiency. This phenomenon, also termed ‘energy 

paradox,’ or ‘energy efficiency gap,’ has been studied extensively in the literature. See, for example, Jaffe, AB, and 

RN Stavins. 1994. “The Energy Paradox and the Diffusion of Conservation Technology.” Resource and Energy 

Economics 16(2): 91–122; Sanstad, A. H. and R. B. Howarth. 1994. ‘Normal’ markets, market imperfections and 

energy efficiency, Energy Policy, 22: 811-818; DeCanio 1998; DeCanio, SJ and WE Watkins. 2008. Investment in 

Energy Efficiency: Do the Characteristics of Firms Matter? The Review of Economics and Statistics, 80: 95-107;

Allcott, H, and M. Greenstone. 2012. Is There an Energy Efficiency Gap? Journal of Economic Perspectives 26 

(1):3-28. 
5 - 7 

 Transaction costs. Consumers face transaction costs in searching, assessing and acquiring 

energy efficient technologies and services. It can be time-consuming and difficult for 

consumers to estimate lifetime operating costs of a product. The complexity of the search 

process puts many efficient products at a disadvantage relative to less-efficient products 

with lower upfront costs. 

 Capital market barriers. Consumers sometimes face higher interest rates to finance EE 

investments compared to other investments. Lenders can be reluctant to invest in EE loan 

portfolios in part because energy efficiency loans may lack standardization and financial 

markets have difficulty ascertaining the likely payoff from such investments. 

EE policies and programs can play an important role in correcting market failures and 

addressing the barriers to the investment and adoption of socially beneficial energy efficiency 

opportunities. Examples of effective EE policies and programs include public funding of R&D, 

information programs (such as energy labeling, the voluntary ENERGY STAR Program, and 

consumer education), rebates for high-efficiency products, product energy performance 

standards, financing and loan programs, and technical assistance. 

EE Policies145

Objectives and Role in Reducing CO2 Emissions from the Power Sector 

EE policies are implemented by states to meet a number of closely related policy goals146, 

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.147 While each of these objectives, and others, 

                                                            

145 Existing state EE policies are described extensively in the State Plan Considerations TSD. 146 U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf. 147 U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf. 
5 - 8 

contribute to the motivation of state policymakers to pursue EE policies, reducing energy costs 

over the long term is the leading objective in pursuing these policies. In addition, EE policies are 

central to meeting state objectives for reducing CO2 emissions from the power sector. As noted 

in the State Plan Considerations TSD, EE policies are a leading tool for achieving CO2

reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in 

ten states148 with statutory requirements for greenhouse gas reductions. 

Economy-wide studies of climate mitigation scenarios confirm that energy efficiency 

plays a critical role in reducing the costs and enhancing the flexibility of meeting long-term 

climate stabilization targets.149 Analysis by the International Energy Agency (IEA) suggested 

that in order to stabilize carbon concentration in the atmosphere at 450 ppm, as much as 44% of 

the estimated global abatement potential in 2035 derives from greater energy efficiency in the 

world economy.150 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) and globally (“EMF 27” study).151 These studies concluded that compared to businessas-usual energy efficiency, improvements in energy efficiency in various economic sectors 

would slow the increases of GHG emissions in the short run, substantially reduce the costs of 

GHG mitigation (on average, by about 50%152), and ease the technology transformation 

pathways to achieve long-term carbon reduction goals.153

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 

                                                            

148 States with GHG reduction laws include: California, Connecticut, Hawaii, Maine, Maryland, Massachusetts, 

Minnesota, New Jersey, Oregon, and Washington. 

149 Kriegler, E., J. P. Weyant, G. J. Blanford et al. 2014. The role of technology for achieving climate policy 

objectives: overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change. 

January 2014; Clarke, L, A Fawcett, J Weyant et al. Technology and U.S. Emissions Reductions Goals: Results of 

the EMF 24 Modeling Exercise. [forthcoming]

150 International Energy Agency (IEA). 2012. World Energy Outlook 2012. Paris. 151 Energy Modeling Forum (EMF) is a consortium of energy economists and energy economic modeling teams that 

was established in 1976. Through ad hoc working groups, the EMF has focused on a series of energy and 

environmental topics that are of interest to policy decisions. In recent years, the EMF is recognized for its 

contribution to the advancement of economics of climate change and the reports of the Intergovernmental Panel on 

Climate Change (IPCC).   152 It should be noted that these energy-economy modeling studies do not typically include the costs of 

implementing energy efficiency measures or would treat such costs as exogenous.   153 E.g., Kriegler et al. (2014) cited above and Kyle P., L. Clarke, S. Smith et al. 2011. The Value of Advanced EndUse Energy Technologies in Meeting U.S. Climate Policy Goals. The Energy Journal, 32: 61-87.
5 - 9 

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.154 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.155

In addition to providing cost-effective opportunities for reducing GHG emissions, energy 

efficiency is recognized to provide other co-benefits, including air quality and public health 

benefits, waste reduction from energy generation, energy security, energy system reliability, 

community economic and social development, and consumer amenities.156 Energy efficiency 

investments and policies are also found to spur productivity growth, technology learning and 

innovation.157, 158 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. These co-benefits have not been fully 

accounted for in the EPA analysis. 

Policy Types 

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 programs159, and building energy 

codes. Other policies that are leading to significant impacts include state appliance and 

equipment standards, building energy disclosure requirements, innovative financing strategies 

                                                            

154 See, for example, Comstock, O, and E Boedecker. 2011. Energy and Emissions in the Building Sector: A 

Comparison of Three Policies and Their Combinations. The Energy Journal, 32: 23-41; Fischer, C. (2005) “On the 

importance of the supply side in demand side management." in Energy Economics, 27: 165-180; Fischer, C. 2010. 

Imperfect Competition, Consumer Behavior, and the Provision of Fuel Efficiency in Light-Duty Vehicles. 

Resources for the Future DP 10-60. Washington, DC. 

155 E.g., Gillingham, K, R Newell, and K Palmer. 2009. Energy Efficiency Economics and Policy. Annual Review of 

Resource Economics. Annual Review of Resource Economics 1: 597-619. 156 Woolf, T. W. Steinhurst, E. Malone, K. Takahashi. 2012. “Energy Efficiency Cost-Effectiveness Screening: How 

to Properly Account for ‘Other Program Impacts’ and Environmental Compliance Costs,” Report prepared by RAP 

and Synapse Energy Economics. 

157 Boyd, GA and JX Pang (2000). “Estimating the linkage between energy efficiency and productivity,” Energy 

Policy, 28: 289-296; Worrell, E. (2011). “Productivity benefits of industrial energy efficiency measures.” Lawrence 

Berkeley National Laboratory Paper LBNL-52727. 

158 Van Buskirk, R, C. Kantner, B. Gerke et al. The benefits of energy efficiency standards and how policies may 

accelerate declines in appliance costs. Proceeding of National Academy of Sciences. [forthcoming] 159 EE programs are described in more detail in the following section of this chapter and in the State Plan 

Considerations TSD. 
5 - 10 

(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.160 This information is generally based upon measurement and verification studies 

submitted annually, most commonly to state utility commissions, and reported to the Energy 

Information Administration (EIA) for all program administrator types (all utility types, thirdparties, and government agencies). EE program data reported to EIA includes incremental and 

cumulative energy and peak demand savings, program costs broken down by component, and 

composition by end-use sector (residential, commercial, industrial). In 2012, utilities and other 

program administrators in 48 states reported savings from EE programs to EIA through form 

EIA-861. At a national level, the EPA is not aware of a comprehensive dataset reported by states 

of the achieved impacts of strategies other than those that lead to investment in EE programs. 

However, state and regional-level information does exist. For example, the Northwest Power and 

Conservation Council (NPCC) has been compiling the impacts of EE policies (including utility 

and third-party EE programs, state building energy codes, and federal appliance standards) 

across their member states (ID, MT, OR, WA) for more than three decades. For the past decade, 

EE programs have accounted for more than 75% of the cumulative energy savings from state EE 

policies for NPCC, with building energy codes accounting for the remaining savings.161 

Another representation of the relative opportunity provided by different state EE 

strategies is presented by evaluations of EE achievable potential or projections of the impacts of 

EE policies. The results from two recent evaluations at a national level are presented in Table 5-

                                                            

160 In 2011, EIA began collecting data from third-party administrators of programs. Prior to 2011, this was a 

significant shortcoming in the breadth of the data collected. The breadth and quality of information collected 

through Form EIA-861 has improved over time, however, outside entities (e.g., ACEEE) have found that the data 

can be improved through expert review and supplementation with other data sources. While now fairly 

comprehensive, the EIA data can be improved further with regards to data quality and consistency. See “Analysis 

Limitations” section for further discussion. 

161 Sixth Northwest Electric Power and Conservation Plan, Northwest Power and Conservation Council. February 

2010. Council Document 2010-09. http://www.nwcouncil.org/media/6284/SixthPowerPlan.pdf 
5 - 11 

1. EE programs account for 77% and 82% of achievable savings in ACEEE162 and Georgia 

Tech163 studies, respectively. These studies indicate that the substantial majority of potential 

savings from state EE efforts are available through EE programs, and that state and local 

building energy codes can make a significant additional contribution. Massachusetts provides a 

state example of the impacts of EE programs relative to other state EE policies. The 

Massachusetts Global Warming Solutions Act of 2008 established statewide limits on 

greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. To achieve this 

target, Massachusetts is relying upon an integrated portfolio of clean energy policies. State EE 

policies are expected to provide the largest contribution to meeting the 25 percent target with 

utility sponsored EE programs and state building energy codes accounting for 76% and 17%, 

respectively, of those policies.164 In their 2013 progress report, Massachusetts indicates that they 

are generally on track for meeting or exceeding these projections.165

TABLE 5-1 

Relative Opportunities Provided by Key EE Programs and Building Codes 

Study Year EE Programs Building Codes Other 

ACEEE 2030 77% 13% 10% 

Georgia Tech 2035 82% 18% 0% 

The full range of EE policies are addressed in greater detail (including designs, authority, 

obligated parties, measurement and verification (M&V), penalties for non-compliance, and 

implementation status) in the State Plan Considerations TSD. Because EE programs have 

provided the majority of state EE-policy electricity savings to-date and offer the majority of 

potential savings going forward, we next summarize key characteristics of this strategy. 

                                                            

162 Hayes, S., et. al. American Council for an Energy-Efficient Economy (ACEEE). April 2014. Change is in the 

Air: How States Can Harness Energy Efficiency to Strengthen the Economy and Reduce Pollution. Report Number 

E1401. Available at http://www.aceee.org/research-report/E1401. 163 Yu Wang and Marilyn A. Brown. February 2013. Policy Drivers for Improving Electricity End-Use Efficiency in 

the U.S.: An Economic-Engineering Analysis. Energy Efficiency. 164 Ian A. Bowles. December 29, 2010. Massachusetts Clean Energy and Climate Plan for 2020. Available at 

http://www.mass.gov/eea/waste-mgnt-recycling/air-quality/green-house-gas-and-climate-change/climate-changeadaptation/mass-clean-energy-and-climate-plan.html. 165 Commonwealth of Massachusetts. Global Warming Solutions Act: 5-Year Progress Report. December 2013. 

Available at http://www.mass.gov/eea/docs/eea/gwsa/ma-gwsa-5yr-progress-report-1-6-14.pdf. 
5 - 12 

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 5-1166 illustrates 

the multi-level composition and breadth of EE program portfolios. The diversity represented by a 

typical portfolio of EE programs implemented by a utility (or other program administrator) is an 

important characteristic relevant to analysis of EE policies. Every detailed program type (as 

illustrated in the lower half of the figure) represents a unique set of characteristics including 

costs of energy saved, ratio of program to participant costs, investment life, scale, M&V 

approach, etc.167

Administrators 

EE programs are administered by a variety of entities (“program administrators”) 

including utilities of all ownership types (investor-owned, municipals, and cooperatives), nonprofit and for-profit third-parties (e.g., Vermont Energy Investment Corporation), and state and 

local government agencies (e.g., NYSERDA). Most EE programs (including all investor-owned 

utilities which account for more than 75% of reported savings168) 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. 

Policy Drivers 

EE programs result from a number of different policy approaches or “drivers.”169 These 

include energy efficiency resource standards (EERS) (26 states)170, system benefit charges (14 

states), integrated resource planning (IRP) requirements (34 states), demand-side management 

                                                            

166 Ian M. Hoffman, Megan A. Billingsley, Steven R. Schiller, Charles A. Goldman and Elizabeth Stuart. Lawrence 

Berkeley National Laboratory. August 28, 2013. Energy Efficiency Program Typology and Data Metrics: Enabling 

Multi-State Analyses Through the Use of Common Terminology. LBNL-6370E. Available at 

http://eetd.lbl.gov/news/article/56865/new-policy-brief-energy-efficie. 167 See following sections for discussion of these factors. 168 U.S. Energy Information Administration Form EIA-861 data files (2012). Available at 

http://www.eia.gov/electricity/data/eia861/. 169 These policies are discussed in depth in State Plan Considerations TSD. 170 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. 
5 - 13 

plan or multi-year energy efficiency budget (28 states), and statutory requirement to acquire “allcost-effective EE” (6 states).171,172 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.173 These policy drivers lead to the evaluation, planning, and adoption of 

EE programs and associated budgets, which are supported through different funding 

mechanisms. 

FIGURE 5-1174

Energy Efficiency Program Portfolio 

                                                            

171 Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend. 

172 The number of EERS states is from ACEEE (see endnote) and includes states with explicit EERS, those with 

long-term energy savings targets for individual program administrators, and those with EE incorporated as an 

eligible resource in a renewable portfolio standard. The numbers for the other policy approaches are from LBNL 

(see endnote). 

173 Sciortino, M., et. al. American Council for an Energy-Efficient Economy (ACEEE). June 2011. Energy 

Efficiency Resources Standards: A Progress Report on State Experience. Report Number U112. Available at 

http://www.aceee.org/research-report/u112. 174 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. 
5 - 14 

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.175 More recently adopted funding sources include proceeds from the 

auction of allowances in the Regional Greenhouse Gas Initiative (RGGI) states and from EE 

resources bid into the forward capacity market operated by the New England Independent 

System Operator (NE-ISO). Ratepayer-funding accounts for more than 90% of total EE program 

support nationally. 

The EE Opportunity 

As discussed, states are employing a number of EE strategies with EE programs yielding 

the most significant impacts both historically as well as in terms of future potential. Furthermore, 

EE programs are unique among state EE strategies in the comprehensiveness and transparency of 

their reported impacts, funding, and other characteristics. In this section we address the rapid 

growth in EE programs, estimated impacts of EE programs to-date and projections of the impacts 

of existing EE programs and trends, and the electricity savings potential achievable through 

expanded use of EE policies and programs. Finally, we will discuss the costs and costeffectiveness of EE programs, specifically. 

Rapid Growth in EE 

Funding for EE programs has increased rapidly in recent years driven by recent policy 

innovations and increasing evidence of the effectiveness of these new strategies. Table 5-2 

presents levels of EE program funding in the U.S. since 2006.176 In the previous five years, 

funding increased by more than 250%, from $1.6 billion in 2006 to $5.9 billion in 2011. 

                                                            

175 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. 176 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. 
5 - 15 

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 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.177

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 

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 

Source: ACEEE, 2014. 

                                                            

177 American Council for an Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency 

Resource Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/policy-brief/eers-02-

2014.pdf. 
5 - 16 

EE Program Impacts 

Impacts to-date 

The primary sources for EE program information (including costs and impacts) are 

annual EE program reports required by utility commissions, or cooperative or municipal utility 

boards of directors. These reports are based on M&V studies of individual EE programs within 

the program portfolio. The EIA has been collecting data on EE programs through Form 861, 

“Annual Electric Power Industry Report,” for more than three decades.178 The data collection 

reflects an increasing degree of breadth and detail over time. For example, third-partyadministered programs were not initially required to report but were added beginning in 2011. 

Data fields have been added over the years to reflect industry trends (e.g., EE programs are now 

reported separately from load management programs). Outside organizations have taken the EIA 

data, supplemented it with additional sources including surveys of utility commissions and 

program administrators, and published their own annual reports that capture EE program 

impacts.179, 180 

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 savings in the 2012 EIA 861 dataset are used to estimate electricity EE impacts by 

state.181 EIA data is reported by program administrator (e.g., utility, third-party, or state agency) 

and requires the disaggregation of reported data by state for administrators with programs in 

multiple states (e.g., multi-state investor-owned utilities). Program administrators in 48 states 

reported savings in 2012. The EPA has compiled this information and aggregated key data to the 

state level. Table 5-4 provides a summary of this data by state for the 2012 reporting year, the 

                                                            

178 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/. 179 Consortium for Energy Efficiency. March 28, 2013. 2012 State of the Efficiency Program Industry. Available at 

http://library.cee1.org/content/2012-state-efficiency-program-industry-report/. 180 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. 181 EPA recognizes concerns associated with consistency and quality of 861 data that different reporting entities may 

have used different methodologies to estimate savings and the EIA 861 data are self-reported. Over time, there has 

been increased standardization in data reporting. We believe his dataset remains to be the most comprehensive 

publically available dataset. See “Analysis Limitations” section below for further discussion.
5 - 17 

most recent available. At the national level, incremental electricity savings182 in 2012 was 0.58% 

of retail sales with individual state values ranging from 0.00% to 2.19%. Cumulative electricity 

savings183 (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% 

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% 

                                                            

182 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. 183 Cumulative savings (also known as annual savings) represent the reduction in electricity use in a given year from 

EE activities in that year and all preceding years, taking into account the lifetimes of installed measures. 
5 - 18 

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% 

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% 
5 - 19 

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 Form 861 data. 

Projected Spending and Savings from EE Programs 

In 2013, Lawrence Berkeley National Laboratory (LBNL) published an update to a 2009 

analysis and projected future spending levels and savings through 2025 from energy efficiency 

programs funded by electric and gas utility customers in the United States under three scenarios 

(high, medium, and low cases).184 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.” 

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 

                                                            

184 Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend. 
5 - 20 

incremental savings, with 11 of those reaching or exceeding 2.0%.185 Table 5-5 summarizes the 

results of the LBNL analysis. 

Table 5-5 

Summary of Impacts: 

Scenarios of Future Utility Customer-Funded Electric Energy Efficiency Programs 

Case

2025 

Incremental Savings 

(% of Sales) 

Program Costs 

(billions of $, nominal) 

Programs Costs 

(% of Revenues) 

Low 0.5% 5.5 1.1% 

Medium 0.8% 8.1 1.7% 

High 1.1% 12.2 2.7% 

EE Potential

Evaluations of EE Potential 

Energy efficiency potential studies are a common tool for informing the development of 

EE program plans and budgets, as well as supporting the development of electricity savings 

targets, required savings levels under an EERS, or “all cost-effective” EE requirement. In 

conducting these studies, states and utilities have developed a methodology that is often 

described as a “bottom-up, engineering-based” approach.186 EE potential studies are conducted at 

various geographic scopes (national, regional, state, and utility service territory level) and at 

different degrees of aggregation (e.g., economy-wide, sectoral, and program), and can be broadly 

grouped into a few types: technical, economic, market, and program.187

 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 

                                                            

185 LBNL provided these unpublished results from their analysis. 186 U.S. EPA and U.S. DOE. November 2007. Guide for Conducting Energy Efficiency Potential Studies: a 

Resource of the National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/potential_guide.pdf. 187 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). 
5 - 21 

assumes immediate implementation of all technologically feasible energy saving 

measures, with additional efficiency opportunities assumed as they arise. 

 Economic potential refers to the subset of the technical potential that is economically 

cost-effective. Definition of “economic potential” can vary to some degree by study. 

Some estimate economic potential by evaluating technology upfront cost, operating costs 

that considers energy prices, product lifetime and discount rate, compared to a 

conventional alternative or the supply-side energy resources. Others incorporate 

consideration of consumer preferences in addition to consumers’ out-of-pocket 

expenditure when evaluating the economic potential. Both technical and economic 

potential estimates assume immediate implementation of efficiency measures without 

regard to technology adoption process or real-life program implementation. In addition, 

these estimates do not always reflect market failures or barriers that impede energy 

efficiency and often fail to capture transaction costs (e.g., administration, marketing, 

analysis, etc.) beyond the costs of efficiency measures. 

 Market potential (or “achievable” potential) refers to the subset of economic potential 

that reflects the estimated amount of energy savings that can realistically be achieved, 

taking into account factors such as technology adoption process, market failures or 

barriers that inhibit technology adoption, transaction costs, consumer preferences, social 

and institutional constraints, and possibly the capability of programs and administrators 

to ramp up program activity over time. 

 Program potential refers to the subset of market potential that can be realized given 

specific program funding levels and designs. Program potential studies can consider 

scenarios ranging from a single program to a full portfolio of programs.188 

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.189 Bottom-up analyses all employ a similar methodology but can vary significantly in 

                                                            

188 Each subsequent potential estimate described above is a subset of the previous potential estimate, e.g., the market 

potential is a subset of the economic potential, and the economic potential is a subset of the technical potential. 189 U.S. EPA and U.S. DOE. November 2007. Guide for Conducting Energy Efficiency Potential Studies: a 

Resource of the National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/potential_guide.pdf. 
5 - 22 

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 

technologies is allowed for, and whether continued improvement in efficiency of technology is 

provided for). As a result, estimated efficiency potential can vary significantly among studies.190

Overview of Results 

Studies of energy efficiency potential are numerous. In recent years, dozens of studies 

have been conducted at regional, state, and utility levels. This section reviews recent studies and 

presents a summary of findings. We first address meta-analyses that summarize results from 

multiple utility, state, and regional studies, and then we address the few national studies that have 

been conducted. To normalize results of analyses addressing different study periods, we present 

average annual achievable potential by dividing cumulative percentage savings in the last year of 

the study by the duration (in years) of the study period. This is a common method of 

normalization for energy efficiency potential studies. 

At the regional and state level, two meta-analyses, Sreedharan (2013)191 and Eldridge et 

al. (2008)192, 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) 

                                                            

190 Because of the complex consumer behavior, energy market and macroeconomic drivers of energy use and energy 

efficiency, and in some cases due to the lack of consistent data, quantifying energy efficiency potential and energy 

savings from policies and programs remains a challenging analytical task. Assumptions about consumer technology 

adoption behavior, market barriers and failures, and how technology diffusion occurs can also affect estimated 

potential. 

191 Sreedharan, P. 2013. Recent estimates of energy efficiency potential in the USA. Energy Efficiency. 192 Eldridge et. al. 2008. State-Level Energy Efficiency Analysis: Goals, Methods, and Lessons Learned. 2008 

ACEEE Summer Study on Energy Efficiency in Buildings. 
5 - 23 

Electric Energy Efficiency Potential Studies) for complete results from the EPA research. 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) 2010-2014 12 1.5%/year 

In addition to the numerous studies conducted at the utility, state, or regional levels since 

2001, a number of studies have evaluated efficiency potential at the national level, applying a 

generally consistent methodology and employing a common data set, across all regions of the 

country. Sreedharan (2013) evaluated four major energy efficiency potential studies at the 

national level, namely, McKinsey and Co. (2007), McKinsey and Co. (2009), EPRI (2009), and 

AEO (2008) Energy Efficiency Side Case. All four studies used the AEO 2008 reference case as 

the baseline but differed in other key respects (e.g., breadth of end-uses, assumed technology 

improvement over time, and definition of cost test for economic potential screening). These 

studies suggest technical electricity savings potential in the range of 25-40% and economic 

potential in the range of 10-25%, as a percentage of total demand in 2020. Of these studies, only 

EPRI provided an estimate of achievable potential. On a normalized basis, the EPRI 2009 study 

provides an achievable annualized potential range of 0.2-0.4% per year (realistically achievable 

and maximum achievable potential, respectively) through 2030 at the national level. 

Two more recent studies also provide national estimates of achievable EE potential: 

EPRI (2014)193 updates their 2009 analysis, using a conventional bottom-up engineering 

approach, and ACEEE (2014)194, using a top-down, policy-based approach derived from state 

experience and their evaluated results. EPRI (2014) results show an average annual achievable 

                                                            

193 Electric Power Research Institute (EPRI). April 2014. U.S. Energy Efficiency Potential Analysis through 2035. 

[forthcoming] 

194 American Council for an Energy-Efficient Economy (ACEEE). April 2014. Select State-Level Energy Efficiency 

Policy Opportunities 2016-2035. [forthcoming] 
5 - 24 

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. 

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 1.5%/year 

 Notably, each of these national potential studies show significant potential in every 

region of the country including regions with lower electricity prices like the southeast, regions 

with historically high levels of EE program budgets like the northeast and west coast, and across 

regions with varied sectoral composition (e.g., higher manufacturing regions like the midwest 

and south, as well as higher service industry regions like the northeast and California). Both 

EPRI studies illustrate the substantial and similar scale opportunity across all regions. For 

instance, EPRI (2014) shows achievable potential ranging from 8% to 14% relative to baseline in 

2035 across the thirteen regions of their analysis as well as significant opportunity in the 

residential, commercial and industrial sectors in every region. The ACEEE (2014) study also 

shows consistently large potential across all states and regions through 2030, with an average 

potential of 24% and a range of 20% to 36% across 50 states. 

Costs and Cost-Effectiveness of State EE Policies

EE Cost-Effectiveness 

States enact EE policies to meet multiple policy objectives including reduction of 

customer electricity bills, lower costs of meeting electricity supply needs, energy reduction, 
5 - 25 

environment and health benefits, and local economic development benefits.195 Most states 

evaluate their EE policy options through the application of cost tests, weighing the projected 

benefits with the costs of the energy efficiency technologies and practices.196 197 Each state 

determines their own policies for the specific costs and benefits to include in these tests. The 

costs and benefits are compared on an equal footing by using present value analysis. This is 

necessary because EE typically requires primarily upfront expenditures (e.g., a whole home 

retrofit) while the economic benefits (e.g., electricity bill savings) accrue over the life of the 

investment (“measure life”) which can range from a few to twenty or more years. As such, the 

choice of discount rate and the estimation of measure life are significant determinants of the 

cost-effectiveness results. Most states employ multiple tests, adjusting cost and benefit categories 

depending upon the economic perspective of interest (e.g., utility, ratepayer, program participant, 

society), and consider the results from each one, usually with an identified primary test type. 

Policies that are selected are those that are found to be cost-effective, with benefits greater than 

costs, as determined by the utility applying methods defined by their state utility commission. 

There are five primary cost-effective tests used in the U.S.: 

(1) Participant cost test from the perspective of the customer installing the measure. Costs 

may include incremental equipment and installation costs; benefits include incentive payments, 

bill savings, and applicable tax credits or incentives. 

(2) Utility/program administrator cost test from the perspective of utility, government 

agency or third-party implementing the program. Costs may include program incentive, 

installation, and overhead costs; benefits may include avoided energy and capacity costs - 

including generation, transmission and distribution - by the utility. 

(3) Ratepayer impact measure test from the perspective of utility ratepayers not participating 

in available energy efficiency programs. This text includes the costs and benefits that will affect 

utility rates, including program and administration costs, as well as “lost revenues” to the utility; 

benefits include avoided energy and capacity costs, and additional resource savings. 

                                                            

195 U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf. 196 U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs 

(Best Practices, Technical Methods, and Emerging Issues for Policy-Makers): a Resource of the National Action 

Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/cost-effectiveness.pdf. 197 Woolf, T., et. al. November 2012. Regulatory Assistance Project, “Energy Efficiency Cost-Effectiveness 

Screening. Available at http://ww.raponline.org/document/download/id/6149. 
5 - 26 

(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. 

(5) Societal cost test from the social perspective. In addition to benefits considered in total 

resource cost test, may also include non-monetized benefits such as environmental and health 

benefits. 

While many states consider more than one cost test in evaluating EE programs, the most 

commonly used (29 states) primary test is the total resources cost test. This test is considered to 

be the best measure of the interests of all utility customers. The utility and societal cost tests are 

the next most commonly used primary tests, used by five states each. The utility cost test is 

considered to be the most comparable metric to compare with supply-side resource investments 

from a utility resource planning perspective. 

Economic and modeling analyses of climate change policy suggests that energy 

efficiency presents a large potential in reducing greenhouse gas emissions and plays a critical 

role in offsetting the costs and enhancing the flexibility to achieve long-term GHG reduction 

targets.198 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.199 For example, McKinsey (2007) found that EE accounted for 

more than 60% of their mid-range potential for greenhouse gas reductions from the U.S. power 

sector and that it was available at positive net present value if “persistent barriers to market 

efficiency” could be addressed. 200

                                                            

198 See, for instance, Kriegler, E., J. P. Weyant, G. J. Blanford et al. 2014. The role of technology for achieving 

climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. 

Climatic Change, January 2014; Kyle P., L. Clarke, S. Smith et al. 2011. The Value of Advanced End-Use Energy 

Technologies in Meeting U.S. Climate Policy Goals. The Energy Journal, 32: 61-87. 199 U.S. EPA and U.S. DOE. September 2009. Energy Efficiency as a Low-Cost Resource for Achieving Carbon 

Emissions Reductions: a Resource of the National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/ee_and_carbon.pdf. 200 McKinsey & Company. December 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? 

Available at 

http://www.mckinsey.com/client_service/sustainability/latest_thinking/reducing_us_greenhouse_gas_emissions. 
5 - 27 

Costs of Saved Energy 

A common metric for comparing alternative electricity resource options within utility 

resource plans is the levelized cost of energy (LCOE) or, for EE resources, the levelized cost of 

saved energy (LCSE).201 LCSE EE is often compared favorably with LCOE of alternative new 

generation sources such as fossil-fueled or nuclear power plants, or renewable energy resources 

like wind or solar-power generation. In these comparisons, typically only utility (or program) 

costs are considered, not the total costs of saved energy that are discussed later in this chapter. 

The energy efficiency analysis literature reports average LCSE in the range of 1-6 cents/kWh 

based on program administrator cost.202 A recent review by ACEEE (2014) examined studies 

across 20 states between 2009 and 2012, and estimated LCSE for electricity energy efficiency 

programs in the range of 1.3-5.6 cents/kWh, with a mean value of 2.8 cents/kWh.203 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.204 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.205 

The economic literature also evaluates the LCSE from EE measures using other 

techniques (e.g., econometrics, 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 

                                                            

201 U.S. EPA and U.S. DOE. November 2007. Guide for Resource Planning with Energy Efficiency: a Resource of 

the National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/suca/resource_planning.pdf. 202 Unless otherwise noted, estimates of LCSE discussed in this section refer to program administrator cost (also 

known as utility cost). The discount rates, average measure lives, and other assumptions affecting the calculation of 

LCSE were not always consistent or reported in all studies. 

203 Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility Energy 

Efficiency Programs. ACEEE Report No. U1402. Washington, DC. Available at http://www.aceee.org/researchreport/u1402. 204 Friedrich, K., M. Eldridge, D. York et al. 2009. Saving Energy Cost-Effectively: A National Review of the Cost 

of Energy Saved Through Utility-Sector Energy Efficiency Programs,” ACEEE Report No. U092. Available at 

http://www.aceee.org/research-report/u092. 205 Kushler, M., D. York, and P. Witte. American Council for an Energy-Efficient Economy (ACEEE). 2004. Five 

Years In: An Examination of the First Half-Decade of Public Benefits Energy Efficiency Policies. 
5 - 28 

these empirical analyses present a wider range of estimates of cost of saved energy. For example, 

a recent study by Auffhammer et al. (2008) examining utility DSM programs estimated the 

average utility cost of saved energy in the range of 5.1 to 14.6 cents per kWh.206 Some other 

studies in the economic literature suggest estimated LCSE in a similar range as from the bottomup analyses. Gillingham et al. (2004) estimated an average cost of 3.4 cents per kWh saved from 

utility EE programs.207 In a 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.208 Using a top-down approach that 

evaluates the savings potential of EE investments using state- and region-specific price elasticity, 

Paul et al. (2011) estimated that electricity savings of 1 to 3 percent were available at a marginal 

cost of 5 cents/kWh and a corresponding average cost of 2.5-3.5 cents/kWh.209 

A number of analytical and data considerations related to LCSE estimation are also 

discussed in the literature, including the issue of “free riders” in EE programs210, and the 

accuracy of utility reported costs and energy savings.211 Energy efficiency practitioners also 

recognize the need to consider “free rider” and “spillover” effects in program evaluation. A 

slight majority of states adjust for free ridership in energy savings estimates, leading to higher 

LCSE values than otherwise would be the case. A smaller number of states adjust for spillover 

effects which reduce LCSE values when addressed.212

 Another consideration related to LCSE estimation is the rebound effect. The economic 

literature has extensive discussion of the potential rebound effect, market interactions and 

                                                            

206 Auffhammer M., C. Blumstein, M. Fowlie. 2008. Demand Side Management and Energy Efficiency Revisited. 

Energy Journal 29(3): 91-104. 207 Gillingham, K., R. Newell, K. Palmer. 2004. Retrospective Examination of Demand-Side Energy Efficiency 

Policies. Resources for the Future Working Paper DP 04-19 REV. Washington, DC. 

208 Arimura, T. S. Li, R. Newell, and K. Palmer, 2012. Cost-Effectiveness of Electricity Energy Efficiency 

Programs, The Energy Journal Vol 33(2). 209 Paul, A., K. Palmer and M. Woerman. 2011. Supply Curves for Conserved Electricity. Resources for the Future 

Discussion Paper 11-11. Washington, DC. 

210 Trains, K. 1994. Estimation of Net Savings from Energy-Conservation Programs. Energy 19 (4):423-441. 211 Joskow, P., and D. Marron. 1992. What Does a Negawatt Really Cost? Evidence from Utility Conservation 

Programs. The Energy Journal 13 (4):41-74. 

212 Kushler, M., S. Nowak, and P. Witte. February 2012. A National Survey of State Policies and Practices for the 

Evaluation of Ratepayer-Funded Energy Efficiency Programs. ACEEE Report No. U122. Available at 

http://www.aceee.org/research-report/u122. 
5 - 29 

economy-wide response of energy efficiency policies and investments. An improvement in 

energy efficiency would effectively reduce the cost of a service or production input, potentially 

boosting its demand or production output thus increasing energy use (“direct” rebound). In 

addition, money saved from energy efficiency can be used for consumption or investment that 

can increase energy consumption in other markets of the economy and lower energy prices as a 

result of energy efficiency improvement may increase energy consumption (two forms of 

“indirect” rebound). Reviews suggest that both direct and indirect rebound effects exist and the 

size of such effects varies among different studies, technologies, sectors and income groups.213 

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. 2000214; Sorrell 2007215; Davis 2008216; Gillingham et al. 2013217). 

EE as an Abatement Measure 

Demand-side energy efficiency is a technically viable and broadly applicable measure for 

achieving significant reductions in the amount of generation required and associated emissions 

from affected EGUs. Moreover, this measure has been adopted by 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 CO2 emissions from fossil fuel-fired EGUs. In the next section, we address the 

setting of state-specific goals for electricity savings levels resulting from state demand-side 

                                                            

213 Sorrell, S. 2007. “The Rebound Effect: an assessment of the evidence for economy-wide energy savings from 

improved energy efficiency.” A report produced by the Sussex Energy Group for the Technology and Policy 

Assessment function of the UK Energy Research Centre. ISBN 1-903144-0-35. 

214 Greening, LA, DL Greene, and C Difiglio. 2000. Energy efficiency and consumption — the rebound effect — a 

survey, Energy Policy, 28: 389-401. 215 Sorrell, S. 2007. “The Rebound Effect: an assessment of the evidence for economy-wide energy savings from 

improved energy efficiency.” A report produced by the Sussex Energy Group for the Technology and Policy 

Assessment function of the UK Energy Research Centre. ISBN 1-903144-0-35 

216 Davis, LW 2008. Durable goods and residential demand for energy and water: evidence from a field trial. The 

RAND Journal of Economics, 39: 530–546. 217 Gillingham K, MJ Kotchen, DS Rapson, G Wagner. 2013. Energy policy: the rebound effect is overplayed. 

Nature 493: 475-476. 
5 - 30 

energy efficiency efforts. In the final section, the integration of these goals into the impacts 

assessment is presented and we consider the reasonableness of the costs of this building block.

State Goal Setting 

Approach 

To estimate the potential CO2 reductions at affected EGUs that could be achieved 

through implementation of demand-side energy efficiency policies as a part of state goals, the 

EPA developed a “best practices” demand-side energy efficiency scenario. This scenario 

provides an estimate of the potential for states to implement policies that increase investment in 

cost-effective demand-side energy efficiency technologies and practices, and projects the annual 

impacts of the scenario for each state. The scenario does not distinguish between policies that are 

currently in place and additional policies that in most states would be required to be implemented 

to realize the goals established. It does not represent an EPA forecast of business-as-usual 

impacts of state energy efficiency policies or an EPA estimate of the full potential of end-use 

energy efficiency available to the power system, but rather is intended to represent a feasible 

policy scenario showing the reductions of CO2 emissions from fossil fuel-fired EGUs resulting 

from accelerated use of energy efficiency policies in all states, generally consistent with ongoing 

industry trends. The scenario uses: 1) a level of performance that has already been demonstrated 

or required by policies (e.g., energy efficiency resource standards) of many leading states; 2) 

considers each state’s unique existing level of performance; and 3) allows appropriate time for 

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 
5 - 31 

 EE program savings are projected and evaluated under requirements established and 

overseen by state utility commissions, and by municipal and cooperative utility boards of 

directors. 

While the approach is derived from information about energy efficiency programs 

overseen by state utility commissions, other state energy efficiency policies are available to 

realize a state’s goals218 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.219

The following steps were taken to establish the inputs for development of the best 

practices scenario for each state: 

 Step 1: Determine current level of performance 

 Step 2: Determine best practices level of performance 

 Step 3: Determine start year for state efforts 

 Step 4: Determine start year level of performance 

 Step 5: Determine pace at which states improve from start year to best practices level of 

performance 

 Step 6: Determine average portfolio measure life and distribution of measure lives 

 Step 7: Determine sustainability of best practices level of performance 

Inputs 

Step 1: Determine Current Level of Performance 

A fundamental indicator of the level of energy efficiency program performance is 

incremental annual savings as a percent of retail sales. This is a common metric defining savings 

levels for energy efficiency resource standards and is readily calculated from EIA Form 861 data 

for each state. Incremental annual savings are also more directly estimated and evaluated than 

are cumulative savings.220 For the best practices scenario, we aggregated the most recent year of 

                                                            

218 See State Plan Considerations TSD. 219 See State Plan Considerations TSD. 220 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. 
5 - 32 

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. 

Step 2: Determine Best Practices Level of Performance 

As discussed previously, achievable demand-side energy efficiency potential exists at 

significant and comparable levels (on the basis of total cumulative potential over a period of ten 

to twenty years) in all regions of the country. While varied regional characteristics (e.g., avoided 

power system costs, economic growth, sectoral mix, climate, and level of past energy efficiency 

efforts) affect estimates of achievable potential, ongoing improvements in energy-efficient 

technologies and practices, economic growth, population increases, and continually improving 

strategies for program delivery have resulted in persistent and substantial levels of achievable 

potential regardless of specific regional characteristics. 

A direct indicator of the achievable incremental levels of energy savings performance is 

provided by past performance at the state and utility levels, and by requirements states have put 

in place for levels of savings to be achieved by 2020. As discussed, these requirements are 

typically in the form of energy efficiency resource standards or similar savings goals that are 

applied to utilities in the state.221

Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA 

Form 861 (2012) data, 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 5-9 summarizes incremental savings levels required by state policy on or before 

2020 and categorized into the same four ranges.222 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%). 

                                                            

221 See State Plan Considerations TSD for more information. 222 American Council Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency Resource 

Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/policy-brief/eers-02-2014.pdf. 
5 - 33 

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 

 Source: EPA calculation based on EIA Form 861. 

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 

Source: ACEEE, 2014. 

 For the best practices level of performance for Option 1223, the EPA has chosen 1.5% 

incremental savings as a percentage of retail sales. This level was achieved by three states (AZ, 

ME, and VT) in 2012 and an additional nine states (CO, IL, IN, MA, MN, NY, OH, RI, and 

WA), accounting for overlap, are expected to achieve this level by 2020. Thus, twelve states 

have either achieved or are required to achieve this level of performance by 2020. 

For Option 2224, the EPA has chosen 1.0% incremental savings as a percentage of retail 

sales as the best practices level of performance for this alternate approach. This level was 

achieved by eleven states in 2012 and an additional twelve states are expected to achieve this 

                                                            

223 See Preamble and Goal Computation TSD for description of Option 1. 224 See Preamble and Goal Computation TSD for description of Option 2. 
5 - 34 

level by 2020. In total, twenty states (accounting for duplication between the two sets of states) 

have either achieved or are required to achieve this level of performance by 2020. 

Step 3: Determine Start Year for State Efforts 

For construction of the best practices scenario, the EPA has used 2017, the year following 

the required state plan submittal225, as the first year for state efforts. 

Step 4: Determine Start Year Level of Performance 

For construction of the best practices scenario, the EPA has set each state’s level of 

performance (incremental savings) in the start year (2017) to its current level of performance 

(aggregated to the state-level from reported EIA Form 861 data). This approach reflects neither 

improvement nor decline in performance between 2012 and 2017. Any improvement in EE 

savings performance between 2012 and 2017 will benefit a state in meeting its state EE goals for 

the 2020-2029 interim compliance period.226

Step 5: Determine Pace at Which States Improve from Start Year to Best Practices Level of 

Performance 

To determine a trajectory of incremental savings levels from the 2017 level to the best 

practices level, the EPA considered past performance of individual program administrators227 as 

well as requirements of existing state energy efficiency resource standards. For the past 

performance of individual program administrators, we first screened the data and divided them 

into moderate and high performing sub-groups. The moderate group (47 entities) was defined as 

programs that achieved from 0.8% to 1.5% maximum incremental savings levels and the high 

group (26 entities) was defined as programs that achieved greater than 1.5% maximum 

incremental savings levels. We then calculated the rate at which each entity had increased 

savings over time and calculated average values for each sub-group. For the moderate group, the 

average rate of improvement of incremental annual savings rate was 0.30% per year. For the 

                                                            

225 See Preamble and State Plan Considerations TSD for descriptions of the schedule for state plan submittals. 226 See Preamble for description of interim and final compliance periods. 227 EIA 861 was the primary data source; however, we supplemented EIA 861 data with data for third-party program 

administrators because prior to 2011 EIA did not collect data from third-party program administrators. 
5 - 35 

high group, the average rate of improvement of incremental annual savings rate was 0.38% per 

year. See Appendix 5-3 for supporting data and analysis. 

 The EPA also considered requirements of existing state EERS and evaluated the rate at 

which their incremental savings levels increase over time. For several EERS, we were unable to 

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. See Appendix 5-3 for 

supporting data and analysis. 

Based on these results, for the best practices rate of improvement the EPA has chosen 

0.2% per year and 0.15% per year for Options 1 and 2, respectively. These values are 

conservative by comparison with our analysis of past state performance and future state 

requirements. 

Step 6: Determine Average Portfolio Measure Life and Distribution of Measure Lives 

The next step in defining the best practices scenario requires projecting the cumulative 

future impacts of the annual incremental savings levels for each state. The incremental savings 

impacts reflect the savings from EE measures put in place in that year, driven by EE program 

activities in that year. The cumulative annual savings represent the total impacts of all EE 

measures put in place in that year and all prior years, due to EE program activities. The 

cumulative savings account for the continuing impacts of energy efficiency measures that remain 

in place for a period of time (the “measure life”) before being replaced. For example, the 

purchase of a high-efficiency refrigerator may lead to savings for twelve years, before being 

replaced with a new model. To estimate cumulative impacts of a series of years of incremental 

savings, the industry uses the concept of an average measure life for the entire portfolio of EE 

programs. Rather than use a single, average measure life to represent a diverse portfolio of 

programs, that range in measure lives from as little as a few years (e.g., certain lighting 

technologies and applications) to as long as fifteen or twenty years (e.g., adding insulation to an 

attic), the EPA is assuming a distribution of measure lives around the average to account for 

future impacts of incremental savings levels. 
5 - 36 

 In 2014, ACEEE updated their 2004 and 2009 national reviews of EE program costs and 

related program characteristics, including measure lives.228 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. 

TABLE 5-10 

Average Electricity Measure lifetimes by state and customer class 

Sector 

Residential Commercial/Business Industrial All Sectors 

Average 8.1 12.5 9.5 10.6 

Source: ACEEE 2014. 

Other studies have found slightly higher values for average measure life for EE portfolios, 

ranging from 10 to 13 years.229 Our assumption of 10 years is conservative by comparison and 

leads to lower cumulative impacts over time and correspondingly lower state goals. 

To approximate a distribution of measure lives across an EE portfolio, consistent with an 

average measure life of ten years, we have assumed an even distribution from one year in length 

to two times the average measure life (twenty years) in length. Our approach is generally 

supported by the substantial range in measure lives reviewed and summarized in a 2014 study by 

LBNL which shows an interquartile range from five to 25 years across twelve program 

categories (e.g., low income, residential new construction, commercial/industrial custom, etc.). 

Our approach represents a first-order approximation of the distribution of measure lives across a 

diverse portfolio of programs. The more common approach in other studies is to assume a 

portfolio with no diversity of measure lives whatsoever, with the entirety of incremental savings 

being realized in each year from the first through the full average measure life and then dropping 

to zero in the following year. Our approach is a conservative one, leading to the same quantity of 

                                                            

228 Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility Energy 

Efficiency Programs. ACEEE Report No. U1402. Available at http://www.aceee.org/research-report/u1402. 229 Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence 

Berkeley National Laboratory. March 2014. The Program Administrator Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf. 
5 - 37 

total energy savings, but with a greater portion of the savings occurring in later years than occurs 

with the more common, and simpler, approach. This results in lower cumulative impacts in 

earlier years and correspondingly lower state goals through 2030. 

Step 7: Determine Sustainability of Best Practices Level of Performance 

For construction of the best practices scenario, once a state achieves the best practices 

level of performance, the EPA has kept the level of performance constant through 2030. For 

states with lower levels of current performance (and, hence, later achievement of the best 

practices level of performance – as late as 2025 in some instances), this requires sustaining the 

target level for as little as five years. For states currently at or above the best practices level of 

performance, this reflects an ability to sustain the target level for thirteen years (2017 through 

2030). 

 Limited empirical data suggests the reasonableness of this approach; however, 

comprehensive data, across all regions and states, does not exist because these levels of 

performance have not been achieved and sustained nationwide previously. The Northwest Power 

Conservation Council (NPCC) provides one such example. NPCC has been conducting the most 

consistent and long-running series of evaluations of achievable cost-effective potential in the 

country, updated every five years, as part of their five-state230 regional energy resource plans231. 

These analyses have become more detailed, reliable, and purposeful over time. Since 1998, 

NPCC’s estimates of achievable potential have more than tripled even as evaluated electricity 

savings from energy efficiency programs have increased rapidly, more than quadrupling between 

1998 and 2010 (while levelized costs of saved energy achieved have remained flat), and 

exceeding plan targets every year since 2005. A study of the NPCC results concludes: “our 

research shows that when programs invest in higher levels of efficiency, this helps drive 

measurement improvements and technical innovation, resulting in larger and more reliable 

                                                            

230 NPCC’s resource plans cover Idaho, Oregon, and Washington in their entirety, and western regions of Montana 

and Wyoming. 

231 Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and 

Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/. 
5 - 38 

conservation supply estimates.”232 Table 5-11 summarizes the NPCC’s achievable potential 

estimates and evaluated savings since 1998.233

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 

 Additional substantiation of this approach is provided by average annual achievable rates 

from reviewed studies, as discussed previously, and comparison of those with the rates resulting 

from the best practices scenario. We address this in a later section, Results in Context, after 

presenting those results. 

Summary of Best Practices Scenarios Construction 

 Table 5-12 provides a summary of inputs for the best practices scenarios for Options 1 

and 2. The pace of improvements, average measure life, and distribution of measure lives are 

each conservative and, therefore, contribute lower state goals than would otherwise result. 

Similarly, the best practices level of performance, being based solely on results from and 

requirements of EE programs, is less stringent than a level would be that accounted for potential 

impacts of other state EE policies such as building energy codes, building energy benchmarking 

requirements, and state appliance standards. The use of 2012 level of performance for the 2017 

                                                            

232 Gordon, Fred, Lakin Garth, Tom Eckman, and Charles Grist, “Beyond Supply Curves,” Proceedings of 2008 

ACEEE Summer Study on Energy Efficiency in Buildings, August 17, 2008 available at: 

http://aceee.org/files/proceedings/2008/data/papers/8_419.pdf. 233 Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and 

Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/. 
5 - 39 

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 

(increase in incremental 

savings rate per year) 

0.20% per year 0.15% per year 

Average Measure Life and 

Distribution of Measure Lives 

10 years; evenly distributed 

across 20 years 

10 years; evenly distributed 

across 20 years 

Continued Performance 

Once achieved, best 

practices level sustained 

through 2030 

Once achieved, best practices 

level sustained through 2025 

Calculations 

This section addresses the calculations for determining cumulative savings levels 

(cumulative savings as a percentage of baseline sales) for each state, for each year of the interim 

and final compliance periods for Options 1 and 2. The cumulative savings levels are derived 

based upon the key inputs summarized in Table 5-12. These levels represent the demand-side EE 

component of the state goals for each state. See the Goal Computation TSD for a detailed 
5 - 40 

description of how the demand-side EE component is used as one of several inputs to the 

calculation of interim and final state emission rate goals. 

Calculating the net cumulative savings as a percent of electricity sales for each state involves 

six steps. For each state, for each year (2017-2030 for Option 1 and 2017-2025 for Option 2) the 

following steps are taken: 

1. Determine annual business as usual (BAU) sales 

2. Determine annual incremental EE savings as a percentage of sales 

3. Determine annual incremental EE savings (GWh) and sales after net EE (GWh) 

4. Determine annual expiring EE savings (GWh) 

5. Determine net cumulative EE savings (GWh) 

6. Determine net cumulative EE savings as a percentage of BAU sales 

To illustrate these calculations, each step is described and results provided for one state 

(using South Carolina as an example) for 2017 through 2025 for Option 1. We truncate the 

results at 2025 for simplicity, but full results are presented for all states in the section. 

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 
5 - 41 

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

Annual incremental EE savings are calculated by multiplying the annual incremental 

savings as a percentage of sales times the prior year sales after net EE. Sales after net EE are 

calculated by subtracting net cumulative savings from BAU sales. The resulting values are 

summarized in Table 5-15 for South Carolina. 

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. 
5 - 42 

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 

FIGURE 1 

Generalized Distribution of First-Year Savings over Time. 

 
5 - 43 

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. 

TABLE 5-17 

Net Cumulative EE Savings for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Net 

Cumulative 

EE Savings 

(GWh) 

274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659 

Step 6: Determine the Net Cumulative EE Savings as a Percentage of BAU Sales

Net cumulative EE savings as a percentage of BAU sales are equal to net cumulative 

savings divided by BAU sales. The resulting values are summarized for South Carolina in Table 

5-18. 

 
5 - 44 

TABLE 5-18 

Net Cumulative EE Savings as a Percentage of BAU Sales for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Net 

Cumulative 

EE Savings 

as % of 

BAU Sales 

0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40% 

Summary of General Formulas and Results by Step for South Carolina 

 Tables 5-19 and 5-20 provide summaries of the generic formulas and results for South 

Carolina for each step. 

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 - 45 

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 

EE Savings 

(% sales) 

0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50% 

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 
5 - 46 

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 each year, including both annual incremental and cumulative 

savings as a percentage of retail sales, for each options, as well as cumulative energy savings 

(MWh). 

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 5.24% 11.42% 3.52% 5.98%

Arkansas 1.52% 9.71% 1.24% 4.10%

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%
5 - 47 

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 3.80% 11.10% 3.28% 5.88%

Illinois 4.36% 11.63% 3.52% 6.15%

Indiana 3.20% 11.11% 2.89% 5.70%

Iowa 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 5.37% 12.13% 3.61% 6.25%

Maryland 4.21% 11.51% 3.47% 6.10%

Massachusetts 4.43% 11.77% 3.55% 6.21%

Michigan 4.59% 11.77% 3.59% 6.22%

Minnesota 4.80% 11.72% 3.58% 6.17%

Mississippi 1.40% 9.59% 1.12% 3.93%

Missouri 1.58% 9.92% 1.29% 4.20%

Montana 3.36% 10.90% 3.01% 5.69%

Nebraska 2.20% 10.40% 1.91% 4.89%

Nevada 2.95% 10.69% 2.67% 5.45%

New Hampshire 2.84% 11.00% 2.56% 5.49%

New Jersey 1.25% 9.58% 0.96% 3.74%

New Mexico 3.10% 10.60% 2.81% 5.50%

New York 4.42% 11.76% 3.54% 6.20%

North Carolina 2.37% 10.26% 2.09% 4.98%

North Dakota 1.39% 9.71% 1.11% 3.95%

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%
5 - 48 

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 5.37% 12.13% 3.61% 6.25%

Virginia 1.23% 9.33% 0.95% 3.67%

Washington 4.24% 11.26% 3.45% 6.00%

West Virginia 1.77% 10.11% 1.49% 4.44%

Wisconsin 4.68% 11.79% 3.60% 6.22%

Wyoming 1.61% 9.73% 1.33% 4.19%

Continental U.S. 3.05% 10.66% 2.44% 5.18%

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 
5 - 49 

2 

2017-

2025 

8 4.3% to 6.8% 0.54%/year to 

0.85%/year 0.72% per year 

 The state range and national values for the average annual savings rate represented in the 

EE best practices scenario are below the range of values found in recent utility, state, and 

regional studies (1.2% to 1.5% per year) as summarized in Table 5-6, and within the range of 

values found in the 2014 national studies from EPRI and ACEEE (0.5%/0.6% to 1.6% per year) 

as summarized in Table 5-7. These results provide additional support for the feasibility of the EE 

best practices scenario and associated state-specific EE goals. 

Impacts Assessment 

Approach 

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 RIA. In addition, the EE state goals, 

resulting from the EE best practices scenario, are used to adjust electricity demand levels used as 

exogenous inputs to power sector modeling for the illustrative compliance scenarios. In other 

words, the degree to which EE is employed as an abatement resource is not determined 

endogenously within the power sector modeling based upon optimization of costs but, rather, 

“hard wired” into the illustrative compliance scenarios. This approach is taken because the EPA 

has determined, as discussed previously, that EE is cost-effective at the established EE goal 

levels. The EE goal levels were constrained by practical considerations of state EE policy 

implementation, specifically, the current levels of EE performance and the pace at which states 

can feasibly improve their levels of performance over time. 

The EE goals represented in the illustrative compliance scenarios lead to substantial 

reductions in power system costs due to the reductions in specified electricity demand. Since EE 

is not represented endogenously as an abatement measure within the power sector modeling, the 

costs associated with the EE best practice scenario must be estimated outside of the power sector 

modeling and integrated with the results from that modeling. These EE cost estimates, their 

basis, and calculations are addressed in the following sections. 
5 - 50 

Inputs 

The following steps were taken to establish the inputs for development of the EE cost 

estimates for each state. 

 Step 1: Determine state-specific electricity savings by year 

 Step 2: Determine first-year program costs of saved energy 

 Step 3: Determine the ratio of program to participant costs 

 Step 4: Determine the escalation rate of EE costs 

Step 1: Determine State-Specific Electricity Savings by Year

 Results from the previous section, State Goal Setting, provide the starting point for 

estimation of EE costs. From those results, state-specific annual incremental savings (MWh) and 

yearly distribution of associated continuing savings (MWh) in future years are used as inputs to 

the cost estimation calculations. 

Step 2: Determine First-Year Program Costs of Saved Energy

 First-year program costs refer to the full costs (e.g., administration, incentive payments, 

marketing, information to consumers, etc.) incurred by a utility or other administrator of EE 

programs in a given year that lead to EE measures (technologies and practices) put in place in 

that year and resulting in reductions in electricity demand in that and future years (driven by the 

mix of measure lives across the portfolio of EE programs employed). Unlike participant costs, 

program costs are readily known by the administrator of EE programs and are, therefore, an 

appropriate starting point for EE program cost analysis. In 2009, ACEEE conducted a national 

review of data on EE program costs from program annual reports, evaluation reports, and 

information compiled from contacts at program administers in 14 states. Compiled data was 

sourced from multiple EE program administrators in each state and over multiple years of data 

for each administrator. ACEEE found average first-year net234 costs of $275/MWh (2011$). The 

EPA has used this value for our analysis. 

                                                            

234 “Net costs” refers to costs per electricity saved after accounting for effects of free-ridership on those savings. 

Depending upon the state, spillover effects may also be accounted for in net costs. 
5 - 51 

 Two recent national analyses have found lower program costs than the 2009 ACEEE 

study. In 2014, ACEEE updated their analysis from 2009, expanding the number of states to 20, 

and including a greater number of program administrators and years. In this analysis ACEEE 

found average first-year net costs of $230/MWh (2011$). In 2014, an LBNL study presented 

results from a uniquely comprehensive study of EE program costs. The LBNL analysis reviewed 

program-level data from over 100 program administrators in 31 states. Data were 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 focused on gross235, rather than net, savings 

values. LBNL found national average first-year cost of gross savings of $162/MWh (2012$). 

Applying an average net-to-gross ratio of 0.9 and deflating costs at 3%, results in an estimated 

national average first-year cost of net savings of $175 (2011$). The up-to-date, more 

comprehensive results from the ACEEE and LBNL studies, indicate that the value of $275/MWh 

used for this analysis is conservative, resulting in comparatively higher total costs than would be 

the case based upon the newer studies. 

Step 3: Determine the Ratio of Program to Participant Costs

 As noted above, while program costs are readily known and consistently reported by the 

program administrator, participant costs require significant effort to estimate, and are less 

consistently estimated and reported. The ratio between program and participant costs will vary 

significantly from one program to the next within a utility’s portfolio. The EPA has used a 

generic approach to estimate the ratio of program to participant costs across an entire portfolio, 

thus providing for the estimation of total costs once program costs are determined. To derive the 

ratio, the EPA reviewed 2012 EE annual reports from program administrators in 22 states 

identified as leaders in EE programs236 based upon their magnitude of savings or their savings as 

a percentage of retail sales. Complete information on full portfolio participant costs were 

available for nine of the 22 states. Across the nine states, the average program and participant 

costs as a percentage of total costs were 53% and 47%, respectively. See Appendix 5-3 for the 

                                                            

235 “Gross savings” refers to electricity savings before any accounting for effects of free-ridership or spillover. 236 Leaders were identified using results from the 2013 ACEEE State Energy Efficiency Scorecard based on energy 

savings as a percentage of retail sales or total savings. 
5 - 52 

data and analysis documenting this review. Based on this review, the EPA has taken a slightly 

conservative approach237 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

 The level of EE program impacts represented in the state EE goals are substantial and 

represent a scenario that has not previously been achieved and sustained at a national level in the 

U.S. Thus, even though the EPA has taken a conservative approach (i.e., leading to higher 

estimates of costs) to the development of the EE state goals as well as to other factors that affect 

the EE cost estimates, we have also chosen to take a cautious approach to the escalation of EE 

costs at higher levels of performance (i.e., as states improve from their historic levels of 

incremental savings to the best practices level of 1.5% of retails sales). Economic theory 

suggests two mechanisms that would change EE costs as higher levels of performance are 

achieved. Economies of scale in the operation of larger EE programs and larger portfolios of EE 

programs, and learning and expertise gained over time from the continued implementation of 

programs, are two factors that would lower costs as programs scale up and expand to realize 

higher levels of performance. However, the limited supply of EE abatement measures and the 

need to employ higher cost measures, over time, to reach higher levels of performance, and to 

sustain high levels of performance, are factors that would increase costs as higher levels of 

performance are achieved. Analysis based upon limited empirical data does provide support for 

significant economies of scale and/or cost reductions over time as learning and expertise are 

gained.238 “Supply curves” of EE as an energy resource, as well as EE as a measure represented 

within a GHG abatement curve, provide support for escalating costs as higher levels of savings 

                                                            

237 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. 

238 Kenji Takahashi and David Nichols, Synapse Energy Economics, Inc. 2008. The Sustainability and Costs of 

Increasing Efficiency Impacts: Evidence from Experience to Date. ACEEE Summer Study on Energy Efficiency in 

Buildings. http://www.aceee.org/files/proceedings/2008/data/papers/8_434.pdf. 
5 - 53 

are realized.239 In a recent analysis, Lawrence Berkeley National Laboratory (LBNL) adopted an 

approach that generically represented both effects discussed above.240 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.241 

In consideration of the above discussion, the EPA has chosen to escalate EE costs over 

three steps as a function of incremental savings (as a percentage of electricity sales) at the state 

level. Until a state reaches a 0.5% savings level, their costs are set at the base level; for savings 

levels between 0.5% and 1.0%, state costs are escalated to 120% of the base level; and for 

savings levels over 1.0%, state costs are escalated to 140% of the base level. This approach leads 

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. 

                                                            

239 For example: Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest 

Conservation and Electric Power Plan,” Council Document 2010-09. Available at 

www.nwcouncil.org/energy/powerplan/6/plan/; and McKinsey & Company. December 2007. Reducing U.S. 

Greenhouse Gas Emissions: How Much at What Cost? Available at 

http://www.mckinsey.com/client_service/sustainability/latest_thinking/reducing_us_greenhouse_gas_emissions. 240 Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend. 241 Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility Energy 

Efficiency Programs. ACEEE Report No. U1402. Available at http://www.aceee.org/research-report/u1402. 
5 - 54 

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: 

$770/MWh (2011$) 

Calculations 

This section addresses the calculations for estimating the costs associated with the statespecific EE goals discussed above. The results of these calculations are then used within the RIA 

and preamble. Specifically, three values are calculated (annual first-year costs, levelized cost of 

saved energy (LCSE), and annualized costs); for each, program and participant components are 

then calculated using the 1:1 ratio (i.e., 50% of total for each) derived above. Specific results 

from prior sections on state goal setting and impacts assessment inputs are used as inputs for 

these calculations. For each state, the following steps are taken for each year (2017-2030) and for 

each option. Calculations for steps 2 and 3 are done using real discount rates of 3% and 7%. 

 The steps are: 

1. Calculate annual first-year costs 

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. 
5 - 55 

Step 1: Calculate Annual First-Year Costs

Annual total first-year costs are calculated by multiplying annual total incremental 

savings (MWh) (from Table 5-15) by the first-year total CSE (from Table 5-23 with escalation 

based upon results from Table 5-14). Program and participant portions of the first-year costs are 

then calculated as 50% of total first-year costs for each per Table 5-23. 

The resulting values are summarized for South Carolina in Table 5-24. 

TABLE 5-24 

Calculation of Annual First-Year Costs for South Carolina 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Annual 

Incremental 

Savings 

(GWh) 

274 440 608 777 945 1,113 1,250 1,249 1,249 

First-Year 

Total Cost of 

Saved Energy 

(2011$/MWh) 

$550 $660 $660 $660 $770 $770 $770 $770 $770 

First-Year 

Total Cost 

(millions 

2011$) 

151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

First-Year 

Program 

(millions of 

2011$) 

75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9 

First-Year 

Participant 

(millions of 

2011$) 

75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9 
5 - 56 

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.242 The net present value of all savings from a 

single year’s EE activities (i.e., over the entire distribution of program lifetimes) is calculated 

using the real discount rate. The levelized cost of saved energy is then calculated by dividing the 

annual first-year costs (from Table 5-24) by the levelized savings. The resulting values are 

summarized for South Carolina in Table 5-25. 

TABLE 5-25 

Levelized Cost of Saved Energy for South Carolina (at 3% discount rate) 

Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Levelized 

Savings 

(GWh) 

2,313 3,720 5,139 6,563 7,987 9,405 10,562 10,553 10,554 

First-Year 

Total Cost 

(millions 

2011$) 

151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 

Total LCSE 

(cents/kWh) 

6.51 7.81 7.81 7.81 9.11 9.11 9.11 9.11 9.11 

Program 

LCSE 

(cents/kWh) 

3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56 

                                                            

242 State of California Governor’s Office of Planning and Research. July 2002. California Standard Practice Manual: 

Economic Analysis of Demand-Side Programs and Projects. Available at 

http://www.calmac.org/events/SPM_9_20_02.pdf. 
5 - 57 

Participant 

LCSE 

(cents/kWh) 

3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56 

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 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 
5 - 58 

Summary of General Formulas and Results by Step for South Carolina 

 Tables 5-27 and 5-28 provide summaries of the generic formulas and results for South 

Carolina for each step. 

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. 

2 

LCSE 

(2011$/MWh) 

Levelized Cost of Saved Energy year i = Annual First-Year Cost year i / Levelized 

Savings year i

3 

Annualized 

Costs (2011$) 

Annualized Cost of Saved Energy year i = ∑ ሺܥܮܵܧ௬௘௔௥	௜ି௧ ൈ ்

௧ୀ଴

 ,௧ሻି௘௔௥	௜௬	݅	ݎ݁ܽݕ ݅݊ ݏ݅݊݃ݒܽݏ ݈ܽݐ݁݉݁݊ݎ݅݊ܿ ݈ܽݑܽ݊݊

where LCSE year i-t is the LCSE of EE programs implemented in year i-t, 

 estimated represents ௧ି௘௔௥	௜௬	݅	ݎ݁ܽݕ	݅݊	ݏ݅݊݃ݒܽݏ	݈ܽݐ݁݉݁݊ݎ݅݊ܿ	݈ܽݑܽ݊݊ 

incremental savings in year i from EE programs implemented in year i-t. 

 
5 - 59 

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 

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 

Results 

Summary of National Results 

Tables 5-29 and 5-31 summarize the national first-year and annualized EE costs for 

Option 1 for 2018, 2020, 2025, and 2030. Table 5-30 summarizes national LCSE for Option 1 

for the same years. Each of the three tables includes values for program, participant, and total 

costs. 

TABLE 5-29 

First-Year EE Costs (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 

 
5 - 60 

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 

TABLE 5-31 

Annualized EE Costs (3% discount rate, billions 2011$) 

(Continental U.S.) 

Year 

2018 2020 2025 2030 

Program 2.0 5.1 14.4 21.4 

Participant 2.0 5.1 14.4 21.4 

Total 4.1 10.2 28.9 42.7 

See Appendix 5-4 for comprehensive data sheets of EE cost results at the national level 

by year for Options 1 and 2, and at discount rates of 3% and 7%. These data sheets provide 

results of LCSE (total, program and participant), first-year costs (total, program and participant), 

and annualized costs (total, program and participant). 

Results in Context

 To provide context for the pace of increase in EE program spending levels represented by 

Option 1, we consider the compound annual growth rate (CAGR) of the recent rapid increase in 

historic investment (2006 to 2011) and the CAGR from 2011 through 2018, 2020, and 2025 

represented by Option 1 program costs. Historic data is from Table 5-2 and Option 1 data is from 

Table 5-29. Table 5-32 provides a summary of the results. The CAGRs represented by Option 1 

through 2018, 2020, and 2025 vary from 8% to 11%. The historic growth rate reflecting the rapid 

recent growth in EE program spending is 30%, roughly three times the Option 1 values. 
5 - 61 

TABLE 5-32 

Historic and Projected (Option 1) Annual Growth Rates of EE Program Spending 

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% 

Costs per Tonne CO2 Reduced

 To estimate the reductions in power system costs and CO2 emissions associated with this 

building block, EPA analyzed a scenario incorporating the resulting reduction in electricity 

demand (the “energy efficiency scenario”) and compared the results with the base case scenario. 

Both analyses were conducted using the Integrated Planning Model (IPM) described in earlier 

chapters. Combining the resulting power system cost reductions with the energy efficiency cost 

estimates associated with the energy efficiency scenario, EPA derived net cost impacts for 2020, 

2025, and 2030. Dividing these net cost impacts by the associated CO2 reductions for each year, 

EPA found that the average cost of the CO2 reductions achieved ranged from $16 to $24 per 

metric tonne of CO2. Although EPA considers this estimated range of average $/tonne to be 

reasonable, we expect the $/tonne would be lower in combination with the other building blocks 

because, in that context, power system costs would be somewhat higher and, thus, avoided power 

system costs due to this building block would be higher as well, leading to lower $/tonne CO2 

avoided. 

Analysis Considerations 

 Two considerations are worth noting in regards to the analysis described in the previous 

two sections, “Goal Setting” and “Impacts Assessment:” 1) state energy efficiency policies 

implicitly represented in the baseline electricity demand and 2) Form EIA-861 as a data source. 
5 - 62 

State Energy Efficiency Policies in the Baseline Electricity Demand

 The baseline electricity demand forecast used for the state goal setting approach 

represented in this chapter, as well as for the power sector modeling discussed in the RIA, is 

based upon the AEO 2013 reference case scenario. AEO 2013 does not explicitly represent 

existing utility energy efficiency programs or future requirements (e.g., EERS) to achieve 

savings goals through such programs. For example, existing state EERS are not evaluated and 

represented in the AEO 2013 reference case. However, to some degree, AEO 2013 does 

implicitly reflect a continuation of the effects of existing state energy efficiency programs in the 

electricity demand projections represented in the reference case. This implicit representation is 

captured in part through a calibration process that is affected by several historic factors including 

reported electricity sales and sectoral energy consumption surveys. 

 As noted, EPA’s state goal setting approach for demand-side energy efficiency is built 

upon the AEO 2013 forecast of electricity demand. However, because the goal setting approach 

uses percentage incremental savings by year to derive percentage cumulative savings by year 

(for each state), the resulting state goals (expressed in percentage cumulative savings by year, by 

state) are not affected by the underlying electricity demand forecast. The impacts assessment of 

the demand-side energy efficiency building block is affected, to some degree, by the implicit 

representation of a continuation of existing energy efficiency programs because the assessment is 

built partly from absolute energy savings values that are partly derived from the business-asusual (BAU) demand forecast. If the BAU forecast did not implicitly represent a continuation of 

existing energy efficiency programs, the forecast would indicate higher electricity demand, at 

least in the near term. However, the direction (higher or lower) of the net cost impacts (energy 

efficiency program costs as well as power system cost reductions) is not clear as it is possible 

that program costs could increase while avoided power system costs also increase. 

Energy Information Administration Form EIA-861 as Data Source 

Comprehensive data on energy efficiency programs’ spending and energy savings are 

limited for evaluating and comparing energy efficiency programs and their effectiveness at the 

utility, state, and national scale. Issues related to the lack of standardized definitions and 
5 - 63 

reporting, and data quality are noted to limit evaluation of energy efficiency programs.243 The 

EIA Form 861, “Annual Electric Power Industry Report,” remains the most comprehensive effort 

that collects data annually on utility demand-side management (DSM) programs, including their 

spending and energy savings impacts, nationally.244 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 uses only two EIA-861 data variables. Specifically, we use the 2012 sales 

data and reported incremental annual energy savings of energy efficiency programs to estimate 

the current performance of energy efficiency programs to inform setting best practices 

performance level for the state EE goal setting. 

EPA notes potential concerns associated with consistency and quality of reported DSM 

program data in Form EIA-861. Specifically, the data are self-reported by utilities and DSM 

program administrators. The definition and data categories may not be consistently applied 

across utility, state, and data year. Over time, however, the data quality has improved 

significantly and there is increased standardization in data reporting and more detailed data 

categories are being reported. For instance, in 2011, EIA began collecting data from third-party 

administrators of programs. While now comprehensive, outside entities have found that the EIA861 data can be improved through supplementation with publicly available annual energy 

efficiency program reports.

245

 

                                                            

243 MJ Bradley & Associates, LLC. 2011. Benchmarking Electric Utility Energy Efficiency Portfolios in the U.S. 244 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/ 245 See, for example, American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 

State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. 
5 - 64 

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 

 
5 - 65 

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% 
5 - 66 

Michigan 

Michigan PSC GDS Associates 2013 

2013-

2023 

33.8% 15% 3.1% 1.4% 

New Jersey 

Rutgers 

University 

EnerNOC Utility 

Solutions 

2012 

2010-

2016 

12.8% 5.90% 1.8% 0.8% 

New Mexico 

State of New 

Mexico 

Global Energy 

Partners 

2011 

2012-

2025 

14.7% 11.1% 1.1% 0.8% 

New York 

ConEd Global Energy 

Partners 

2010 

2010-

2018 

26% 15% 2.9% 1.7% 

Pacific 

Northwest 

(Idaho, 

Montana, 

Oregon, 

Washington) 

US 

Department of 

Energy 

Lawrence 

Berkeley 

National 

Laboratory 

2014 

2011-

2021 

11% Not reported 1.9% Not reported 

Pennsylvania 

Pennsylvania 

PUC 

GDS Associates 

and Nexant 

2012 

2013-

2018 

27.2% 17.3% 4.5% 2.9% 

Tennessee 

Tennessee 

Valley 

Authority 

Global Energy 

Partners 

2011 

2009-

2030 

24.8% 19.8% 1.1% 0.9% 

Range 

0.8% - 2.9% 

per year 

Average 

1.5% 

Per year 

References 

Arizona: The Cadmus Group, Inc. 2010. Salt River Project 2012-2017 Energy Efficiency Plan – 

Final Report. Prepared for Salt River Project, April 16. 

Colorado: Kema, Inc. 2010. Colorado DSM Market Potential Assessment – Final Report. 

Prepared for Xcel Energy, March 12. 
5 - 67 

Delaware: Optimal Energy, Inc. 2013. Delaware Economic Energy Efficiency Potential. 

Prepared for the Delaware Department of Natural Resources and Environmental Control, May 

24. 

Illinois: ICF International. 2013. ComEd Energy Efficiency Potential Study Report, 2013-2018. 

August 20. 

Michigan: GDS Associates, Inc. 2013. Michigan Electric and Natural Gas Energy Efficiency 

Potential Study – Final Report. Prepared for the Michigan Public Service Commission, 

November 1. 

New Jersey: EnerNOC Utility Solutions Consulting. 2012. New Jersey Energy Efficiency 

Market Potential Assessment, Volume 1: Executive Summary. Report Number 1401, Prepared 

for Rutgers, The State University of New Jersey, October 17, 2012. 

New Mexico: Global Energy Partners. 2011. Energy Efficiency Potential Study for the State of 

New Mexico, Volume 2: Electric Energy Efficiency Analysis. June 30. 

New York: Global Energy Partners, LLC. 2010. Energy Efficiency Potential Study for 

Consolidated Edison Company of New York, Inc., Volume 2: Electric Potential Report. March. 

Pacific Northwest states (combined): Barbose, Galen, and Alan Sanstad, Charles Goldman, 

Stuart McMenamin, Andy Sukenik. 2014. Incorporating Energy Efficiency into Western 

Interconnection Transmission Planning. Draft report, Lawrence Berkeley National Laboratory, 

January. 

Pennsylvania: GDS Associates, Inc., and Nexant. 2012. Electric Energy Efficiency Potential for 

Pennsylvania – Final Report. Prepared for the Pennsylvania Public Utilities Commission, May 

10. 
5 - 68 

Tennessee: Global Energy Partners. 2011. Tennessee Valley Authority Potential Study, Final 

Report, Volume 1: Executive Summary. Report Number 1360, December 21. 
5 - 69 

Appendix 5-2 

Incremental Electricity Savings Pace of Improvement Analysis 

This appendix summarizes and analyzes data to characterize the pace of improvement of 

incremental (or first-year) savings as a percentage of retail sales for electricity energy efficiency 

(EE) programs. We considered two different perspectives: 1) historical data reflecting achieved 

savings of EE programs and 2) requirements of existing state energy efficiency resource 

standards (EERS). For the historical perspective, we reviewed data from the Energy Information 

Administration’s Form EIA-861 on EE program electricity savings (supplemented as needed 

with program administrator reports) and identified the pace at which entities reaching higher 

savings levels have historically increased energy savings over time.246 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%.247

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 Scorecard248, there are a total of 26 states that have 

                                                            

246 The EIA 861 was the main data source. However, we have supplemented the EIA 861 with third-party program 

administrator data because the EIA 861 just started to collect third-party administrator data in 2011. The third-party 

entities included in our analysis are Efficiency Vermont, Energy Trust of Oregon, Efficiency Maine Trust, and Cape 

Light Compact. In addition, we supplemented the EIA 861 database with additional data for two utilities that we 

found achieved high energy savings, but did not report savings data in the EIA 861 data for one or two years. These 

entities are Burlington Electric and Massachusetts Electric Company (now part of National Grid). 247 In addition to these maximum first-year savings thresholds, we screened program administrators for the following 

conditions: (a) the maximum savings levels occurs after the minimum savings levels; (b) sufficient amounts of 

increase in first-year savings are provided to evaluate reasonable ramp-up schedules to gain an incremental 1% firstyear savings; and (c) savings data series are continuous between the years for the minimum and maximum savings 

levels.

248 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. 
5 - 70 

mandatory EERS policies.249 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 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).250 

 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 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 firstyear savings levels (with a range of 0.14% per year to 1.28% per year).251 

Table 1. Energy savings ramp-up trends in first-year savings for “Top Saver 1%” and 

“Top Saver 2%” groups252

 

Top Saver 1% Top Saver 2% 

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 

                                                            

249 ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, 250 This is a simple average estimate of the annual average increase in first-year savings from each entity in this 

group. 

251 This is the simple average estimate of the annual average increase in first-year savings from each entity in this 

group. 

252 Data sources: The EIA 861 was the main data source. However, we have supplemented the EIA 861 with thirdparty program administrator data because the EIA 861 just started to collect third-party administrator data in 2011. 

The third-party entities included in our analysis are Efficiency Vermont, Energy Trust of Oregon, Efficiency Maine 

Trust, and Cape Light Compact. In addition, we supplemented the EIA 861 database with additional data for two 

utilities that we found achieved high energy savings, but did not report savings data in the EIA 861 data for one or 

two years. These entities are Burlington Electric and Massachusetts Electric Company (now part of National Grid). 
5 - 71 

Median 0.29% 3.4 0.34% 3.0 

Max 0.63% 1.6 1.28% 0.8 

Min 0.10% 10 0.14% 7.3 

# of sample entities 47 26 

Our research findings on incremental electricity savings ramp-up based on existing state 

EERS policies are: 

 The states with EERS policies which exhibit savings ramp-up schedules are requiring 

increases in first-year energy savings at a pace that ranges from 0.11% (Colorado and 

Oregon) to 0.40% (Rhode Island) as shown in Table 2. 

 The first-year savings pace of increase averages 0.21% per year across the 10 states. This 

savings level translates to about 4.7 years to achieve an incremental 1% first-year savings 

increase. 

Table 2. First-Year Energy Savings Ramp-up Review of State EERS Policies253

State 

Minimum 

Target 

Maximum 

Target 

Climb 

Time 

(years) 

Annual 

Average % 

Increase 

Years to 

Achieve 1% 

Min Year Max Year Increase 

 a b c d e=d-b f=(c-a)/e g=1/f 

Arizona 1.25% 2011 2.5% 2016 5 0.25% 4.0 

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 

                                                            

253 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. 
5 - 72 

Average 0.21% 4.8 

 
5 - 73 

Appendix 5-3 

Review of the Ratio of Program to Participant Costs 

Introduction and Summary 

This appendix summarizes and analyzes data on EE costs (program and participant) to 

develop a ratio to enable the estimation of participant costs from known program costs. We 

reviewed cost data from leading EE program administrators in 22 states. Our research findings 

are as follows: 

 A 1:1 ratio between program and participant costs is a reasonable and slightly 

conservative (i.e., slightly higher total costs) basis for estimating participant costs from 

known program costs. 

 Reported data was reviewed from 22 states; however, program administrator reports from 

only nine states contained sufficient information (participant costs across entire portfolio 

of EE programs) to inform the analysis. 

 Participant cost data from ten program administrators in nine states indicate that the 

weighted average and simple average participant costs were 47 percent of total costs. 

Participant Cost Analysis 

We first identified states having high incremental electricity savings rates or high 

absolute savings levels based upon 2013 ACEEE State Energy Efficiency Scorecard.254 These 

states represent a large portion of total EE savings in the U.S. We identified 22 states meeting 

these criteria and collected publicly available EE program reports for major program 

administrators within each state. From these program reports we identified 10 program 

administrators across nine states where we were able to identify both program administrator and 

participant costs across their full portfolio of EE programs. The table below provides the 2012 

portfolio-level program administrator and participant costs for the nine states. Program 

                                                            

254 For the purpose of this research, we have defined leading or high impact states as the top 15 states in the 2013 

ACEEE State Energy Efficiency Scorecard in terms of incremental savings as a percentage of retail sales or absolute 

annual energy savings in terms of total annual MWh savings. These criteria resulted in a total of 22 states which 

include Arizona, California, Connecticut, Florida, Hawaii, Illinois, Indiana, Iowa, Maine, Massachusetts, Michigan, 

Minnesota, New Jersey, New York, North Carolina, Ohio, Oregon, Pennsylvania, Rhode Island, Texas, Vermont, 

and Washington. 
5 - 74 

administrator costs represent the program administrator’s program development and 

implementation costs, and the participant costs represent the customer costs to partake in the 

program. Total program costs are the sum of both costs. Each state’s program administrator and 

participant costs are presented as a percentage of total program costs in Table 3. The weighted 

and simple average program and participant costs across all nine states are presented as a 

percentage of total program costs. The weighted average cost shares were based on each 

program’s spending by administrator and participants. 

Table 1 

2012 Participant and Program Cost Information from Reported Entities 

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 

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%
5 - 75 

52.6% and 47.4%, respectively, of total costs. Participant cost results range from a low of 17% of 

total costs (National Grid in Rhode Island) to a high of 65% (Xcel Energy in Minnesota). When 

deriving participant costs from program costs, using a ratio of 1:1 is consistent with these results 

and slightly conservative, leading to slightly higher total costs than the precise average values 

would provide. 

References 

MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual 

Report to the Iowa Utilities Board," May 1, 2013, available at: 

http://www.state.ia.us/government/com/util/energy/energy_efficiency/ee_plans_reports.html 

Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 

2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012-

Annual-Report.pdf 

National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas 

Energy Efficiency Plan," Docket No. 12-109, November 2, 2012, available at: 

http://www.env.state.ma.us/dpu/docs/electric/13-120/8113ngptapb.xlsx 

PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission, 

for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215, 

November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf 

Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at: 

http://www.efficiencyvermont.com/docs/about_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf 

Burlington Electric Department, "2012 Energy Efficiency Annual Report," 2012, available at: 

https://www.burlingtonelectric.com/ELBO/assets/2012%20DSM%20Annual%20Report%20Mas

ter.pdf
5 - 76 

Hawaii Energy, “Annual Report Program Year 2012 July 1, 2012 - June 30, 2013,” 2013, 

available at http://www.hawaiienergy.com/information-reports 

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 

 
5 - 77 

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 1 – Cumulative Savings (GWh) by State (2017-2030) 

 Option 2 – Incremental Savings as % of Sales by State (2017-2025) 

 Option 2 – Cumulative Savings as % of Sales by State (2017-2025) 

 Option 2 – Cumulative Savings (GWh) by State (2017-2025) 

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 (2017-2025) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 

 Option 2 – National Costs at 7% Discount Rate (2017-2025) 

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 
6 - 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 CO2 emissions rate (lbs/MWh) in these boilers. The CO2 emission 

rate is reduced when natural gas is substituted for coal because the gas has a much higher 

percentage of hydrogen and a lower percentage of carbon than the coal it replaces. When 

quantities of gas and coal are burned with oxygen from air to produce the same amounts of heat, 

the higher hydrogen content of natural gas produces more water vapor (H2O) than coal, but far 

less CO2. 

The discussion below focuses solely on the conversion of an existing coal-fired boiler to 

burn natural gas instead of, or along with, coal. There are other technical options for gas 

substitution in an existing coal-steam EGU that are not examined in any detail here. They 

include: 

 Repowering an existing coal EGU by providing heat input to the boiler from the exhaust 

of a newly installed gas turbine generator; and, 

 Gasification of coal, producing substitute natural gas (SNG) that provides heat input to 

the existing coal-fired boiler. 

These other options have higher capital cost and thus would not be as economic as the direct 

substitution of natural gas in an existing coal boiler, for reasons that will become apparent in this 

analysis. 

This chapter evaluates the cost-effectiveness of widespread adoption of coal-to-gas 

switching at a national level for the purpose of setting CO2 emissions goals consistently in each 

state. 

Description of Technology 

 Engineering Considerations 

Most existing coal-fired EGU boilers can be modified to switch to 100% gas input, or to 

co-fire gas with coal in any desired proportion. This transition typically requires at least some 
6 - 2 

plant modifications and might have some negative impact on the efficiency of the unit as 

described later in this chapter. 

A conversion from coal to gas firing first requires either an existing natural gas delivery 

system to the boiler or the installation of a new gas pipeline to serve the boiler. While it is 

sometimes assumed that a need to install a new gas pipeline would render the conversion 

uneconomic, this analysis will show that the cost of a new gas pipeline is not likely to be the 

determining factor for the project’s economic merit, given the significance of the change to the 

cost of fuel for generation from the converted boiler. 

Conversion to natural gas firing in a coal-fired boiler typically involves installation of 

new gas burners and supply piping, modifications to combustion air ducts and control dampers, 

and possibly modifications to the boiler’s steam superheater, reheater, and economizer heating 

surfaces that transfer heat from the hot flue gas exiting the boiler furnace. The conversion may 

also involve some modification and possible deactivation of some downstream air pollution 

emission control equipment. Engineering studies are performed to assess changes in furnace heat 

absorption and exit gas temperature; material changes affecting heat transfer surfaces; the need 

for sizing of flue gas recirculation fans; and operational changes to sootblowers, spray flows, air 

heaters, and emission controls. 

Whether co-firing with coal or switching completely to natural gas, boilers will become 

less efficient due to the high hydrogen content of natural gas. When combusted, the additional 

hydrogen yields increased moisture content (water vapor) in the flue gas. The increased 

moisture content, in turn, results in additional heat lost up the stack instead of being directed 

towards electricity generation. Additionally, depending on the design of the boiler and extent of 

modifications, some boilers may incur some derate (reduction in generating capacity) in order to 

maintain steam temperatures at or within design limits, or for other technical reasons. 

Even with a decrease in boiler efficiency, the overall net output efficiency of a coal-steam boiler 

EGU that switches from coal to natural gas firing may change only slightly, depending on how 

much auxiliary load is converted to net output by avoiding the need to run coal pulverizers, 

conveyors, ash sluice pumps, and relevant air pollution control equipment (e.g., PM and SO2

controls). 

 
6 - 3 

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 gas as a startup fuel, or are located at facilities with onsite gas-fired generators. 

These boilers are likely able to co-fire to some degree with gas (at least 10% total output255) 

without constructing additional gas pipeline capacity. For purposes of this analysis, the EPA 

conservatively assumed that gas use of 10% or greater at these boilers, or any gas use at boilers 

without an existing gas pipeline, would require construction of additional pipeline capacity. 

Unlike coal, natural gas cannot be stored in quantities sufficient for sustained utilization on site. 

To the extent that firm (uninterruptible) gas supply is contractually unavailable or costprohibitive, any potential interruption in gas supply could impact the ability of the unit to 

continue operating without increasing its CO2 emissions rate (since it would likely need to 

substitute more CO2-intensive fuel for the unavailable natural gas). Additionally, for boilers that 

switch to 100% gas, interruption in gas impacts the ability of the unit to continue generating at 

all if gas is unavailable. For these reasons, an EGU switching to a large percentage of gas use 

may elect to install more than one new gas supply pipeline from separate sources. Although the 

EPA assumes the addition of one gas pipeline in the simplified cost analysis presented below, it 

will be seen that pipeline cost will generally not be the main driver of economic feasibility. 

Cost and Performance Impacts of Coal-to-Gas Switching 

The analysis described in this section presents a hypothetical conversion of a boiler from 

burning 100% coal to burning varying proportions of gas (10%, 50%, and 100%). The capital 

cost of modifying a coal boiler to switch to natural gas includes the new gas burners and piping, 

combustion air ductwork and control damper modifications, air heater upgrades, gas 

recirculating fans, control systems modifications, and other site-specific modifications, as well as 

any pipeline installation costs that would be necessary to supply the unit’s assumed level of gas 

combustion following the conversion. 

                                                            

255 Based on assumed use of Class 1 igniters (10% of burner capacity) as defined in NFPA 85 Boiler and 

Combustion Systems Hazards Code. 
6 - 4 

For this analysis, the EPA assumes capital costs for pulverized coal (PC) and cyclone boiler 

modifications are as follows256: 

$/kW = 267*(75/MW)0.35 (pulverized coal) 

$/kW = 374*(75/MW)0.35 (cyclone) 

Based on the above formula, a 500 MW pulverized coal unit would have a capital cost of 

$137/kW to convert the boiler such that it could burn any proportion of natural gas. For this 

illustrative example, to support 100% gas combustion we assume that a 50-mile gas pipeline257 at 

$50 million, 258 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.259 At a 14.3% capital charge rate260 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. 

                                                            

256 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 

257 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/epaipm/BaseCasev513.html 

258 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. 

259 A Case Study on Coal to Natural Gas Fuel Switch, Black & Veatch, Power-Gen International, December 2012, 

available at http://bv.com/redirects . 260 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. 
6 - 5 

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 gas).261 As a result, the fuel cost for a typical converted boiler burning 100% 

gas is expected to be at least double its prior fuel cost on an output basis as well ($27/MWh for 

coal, $57/MWh for gas).262,263 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.264 This 

difference would increase with higher gas prices, which would be projected to result from an 

increase in overall gas demand caused by widespread adoption of gas co-firing. 

Emission Reduction Potential 

The CO2 reduction potential is directly related to the amount of gas co-fired, and is due 

largely to the different carbon intensities of each fuel. More reductions in CO2 rate are achieved 

at higher levels of gas co-firing as shown in Table 6-1. At 10% gas co-firing, the net emissions 

rate (lbs/MWh net) of a typical unit would decrease by approximately 4%. At 100% gas cofiring, the net emissions rate (lbs/MWh net) of a typical unit would decrease by approximately 

40%. 

 

                                                            

261 EPA Base Case 5.13, projections for 2020 262 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. 

263 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. 

264 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. 
6 - 6 

Table 6-1. CO2 Rates at Various Levels of Natural Gas Co-Firing 

Case Heat Rate 

(Btu/kWh) 

CO2 Rate 

(lbs/MWh net)

Reduction in CO2 Rate from 100% Coal

(lbs/MWh net) 

100% Coal 10,340 2,108 N/A 

10% Gas 10,370 2,021 4% 

50% Gas 10,490 1,673 21% 

100% Gas 10,640 1,239 41% 

In addition to reducing CO2 emissions, natural gas co-firing at a coal-fired steam EGU 

will generally also reduce criteria air pollution. Reducing CO2 and criteria air pollution will 

result in climate benefits and human health co-benefits. The impacts of these pollutants on the 

environment and health are discussed in detail in Chapter 5 of the RIA for this proposed rule. 

For this analysis, EPA estimated the PM2.5-related human health co-benefits of SO2, NOX, and 

direct-PM2.5 emission reductions attributable to a range of natural gas co-firing levels at an 

illustrative coal steam unit burning bituminous coal in 2020.265 The estimated monetized cobenefits do not include climate benefits or health effects from direct exposure to NO2, SO2, and 

HAP; ecosystem effects; or visibility impairment. Only the unit-level emissions of SO2, NOX and 

direct-PM2.5 are considered in this illustrative exercise. Additionally, emissions from the 

extraction and transport of the fuels used by these technologies are not considered. Furthermore, 

there may be differences in upstream greenhouse gas emissions (in particular, methane) from 

different technologies but those were not quantified for this assessment. The estimated avoided 

emissions under 10% gas co-firing and a 100% switch to gas are presented Table 6-2. 

Table 6-2. Avoided Emissions at Various Levels of Co-Firing, based on Illustrative Unit 

(lbs/MWh net) 

 

10% 

Gas 

100% 

Gas 

SO2 0.3 3.1 

NOX 0.2 2.04 

PM2.5 0.02 0.2 

                                                            

265 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. 
6 - 7 

To estimate human health co-benefits for this illustrative coal steam unit, the EPA used 

PM2.5-related benefit-per-ton estimates for SO2, NOX, and direct-PM2.5 emission reductions 

described in detail in Chapter 5 of the RIA for this proposal. To estimate the benefits associated 

with co-firing, we determine the emission reductions for co-firing in Table 6-2 and apply the 

2020 social benefit values discussed in Chapter 5 of the RIA for this proposal. Specifically, we 

multiply the reduction in SO2, NOX, and direct-PM2.5 emissions by the PM2.5-related benefit perton estimates, and add those values to get a measure of 2020 benefits. Table 6-3 shows the 

PM2.5-related benefits expected based on the estimated emission reductions that would occur in 

this illustrative example. These estimates are purely illustrative as the EPA does not assert a 

specific location for the illustrative electricity generation technologies and is therefore unable to 

specifically determine the population that would be affected by their emissions. Therefore, the 

benefits for any specific unit can be different than the estimates shown here. 

Table 6-3. Rounded PM2.5-related Co-benefits ($/MWh net) of Gas Co-firing (2011$) 

 Health Co-benefit Discount Rate 

 3% Discount Rate 7% Discount Rate 

Gas Co-firing 10% $6.5 to $15 $5.9 to $13 

Gas Co-firing 100% $67 to $150 $61 to $140 

Note: All estimates are rounded to two significant figures. Co-benefits are based on national 

benefit-per-ton estimates for directly emitted PM2.5 and PM2.5 precursors, SO2 and NOX. It is 

important to note that the monetized health co-benefits do not include reduced health effects 

from ozone or direct exposure to NO2, SO2, and HAP; ecosystem effects; or visibility 

impairment. Emissions of directly emitted particles are disaggregated into EC+OC or crustal 

components using the method discussed in Appendix.266 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 

                                                            

266 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. 
6 - 8 

account for these characteristics. However, these factors will not change the qualitative 

conclusion. There will always be incremental human health co-benefits associated with co-firing 

natural gas in an existing coal steam boiler, independent of the location, coal type, and operating 

pollution controls. 

A related beneficial use of natural gas in existing coal boilers can be via gas reburning, a 

NOx reduction technology.267 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 

air is injected downstream of the reburn zone to burn out the remaining combustibles and convert 

the reduced nitrogenous species to molecular nitrogen. The heat input from gas would 

approximately substitute for a similar heat input from coal, thus reducing CO2 and other coal 

emissions in a manner similar to gas co-firing as discussed above. 

Cost of Reductions and Cost Effectiveness 

This analysis examines the average $/tonne268 cost of avoided CO2 that results from 

applying a range of natural gas co-firing levels to a typical baseload coal boiler. We capture the 

capital costs of boiler modifications and new pipeline construction (assuming 50 miles of new 

pipeline),269 decreased FOM and VOM costs, and incremental fuel costs (based on IPMv5.13 

Base Case average delivered fuel price projections for coal and gas in 2020). For a typical coal 

boiler at current base case fuel prices, the average cost of avoided CO2 ranges from $83/tonne for 

100% gas switch to $150/tonne for co-firing at 10% (see Table 6-4). 

 

                                                            

267 DOE/NETL 2001, Evaluation of Gas Reburning and Low-NOx Burners on a Wall-Fired Boiler, DOE/NETL2001/1143, February 2001, available at 

http://www.netl.doe.gov/File%20Library/Research/Coal/major%20demonstrations/cctdp/Round3/GRLNBPPA.pdf 268 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.” 

269 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/epaipm/BaseCasev513.html
6 - 9 

Table 6-4. Average Cost of Avoided CO2 and CO2 Emission Rate Reductions from 100% 

Coal at Various Levels of Natural Gas Co-Firing at Base Case Projected Gas Price 

($5.36/MMBtu) 

Case Average Cost of Avoided 

CO2 ($/tonne) 

Change in CO2 Rate from 100% 

Coal (lbs/MWh net) 

100% Coal N/A N/A 

10% Gas 150 4% 

50% Gas 91 21% 

100% Gas 83 41% 

Note: Based on a typical 500 MW bituminous coal steam unit operating at 75% capacity factor. 

Assumes construction of new 50-mile pipeline. EPA estimated reduced total capital costs for the 

50% and 10% gas cases; for example, total capital cost for 10% gas was estimated to be about 

one-half of the capital costs for the 100% gas case. 

However, widespread adoption of gas co-firing would increase overall gas demand and 

place upward pressure on the natural gas price, which would consequently increase the average 

cost of avoided CO2 of a potential boiler conversion. 

Conclusion 

Switching from coal to gas is a relatively costly approach to CO2 reductions at existing 

coal steam boilers when compared to other measures such as heat rate improvements and redispatch of generation supply to other existing capacity with lower CO2 emission rates. 

Moreover, this analysis shows that coal-to-gas conversion of an existing boiler is less efficient 

than constructing a new natural gas combined cycle (NGCC) turbine in its place. For example, 

EPA analysis indicates that replacing the coal steam plant discussed above with a new NGCC 

facility would reduce the net CO2 emission rate of the generating capacity by 62% at a cost of 

about $50/tonne of avoided CO2 under the base case projected gas price and about $81/tonne of 

avoided CO2 at a future gas price 50% higher than the base case projection. See preamble 

section VI.C.3.c. 

The EPA is considering cost-effectiveness at a national level for the purpose of setting 

emissions goals consistently in each state. While this analysis suggests that cost-effective 

reductions of CO2 are not available on a national basis from widespread adoption of natural gas 

co-firing, it does not preclude the potential for individual EGUs to utilize co-firing as a way to 

reduce CO2 and other emissions, nor does it preclude states from factoring in that unit-level 

potential into the design of state plans for compliance with the 111(d) standard. EPA notes that 
6 - 10 

there are utilities that see merit in converting some existing coal units to burn 100% gas, and 

several are currently doing so.270,271

 

                                                            

270 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-alabamaidUSL2N0KP1WA20140116 

271 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-VirginiaPower-Proposes-To-Convert-Bremo-Power-Station-From-Coal-To-Natural-Gas 
6 - 11 

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 outputbased CO2 emissions rate (lbs/MWh) of these boilers. In the analysis presented in this technical 

support document, the physical CO2 emissions rate at the boiler stack could increase or decrease, 

depending on the amount of coal energy replaced by biomass energy and differences in the 

properties of a selected biomass and the coal it replaces.272 

There are many possible combinations of coals and biomass types that could be co-fired. 

Site-specific economics and accessibility would determine which combinations might actually be 

feasible. This TSD analysis does not attempt to estimate an economically feasible national 

average increase or decrease in CO2 emission rate via biomass co-firing. Instead, this analysis 

simply employs one reasonably representative case to evaluate the cost effectiveness of biomass 

energy substitution in reducing the physical CO2 emission rate based only on the CO2 coming 

from coal. This analysis indicates that while the co-firing of biomass with coal is technically 

feasible as a means of reducing the coal-based CO2 emission rate due to the substitution of 

biomass for coal, it generally has limited economic feasibility due to the generally higher cost of 

energy from biomass as compared to coal. This general finding largely explains the very limited 

amount of biomass co-firing currently practiced in the U.S. It is also consistent with recent 

findings by others273 , including an earlier study by the State of Maryland274 that concluded as 

follows: 

“Due to the higher cost of biomass fuels when compared to coal, cofiring with biomass 

will lead to an increase in fuel costs. Without consideration for any environmental benefits, it is 

unlikely that any Maryland coal-fired facility would make the investments required to cofire with 

a more expensive and less efficient fuel.” 

                                                            

272 Fuel properties particularly affecting relative CO2 emission rates are: higher heating value, carbon and hydrogen 

contents, and as-fired moisture content. 

273 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 274 The Potential for Biomass Cofiring in Maryland, Maryland Department of Natural Resources, March 2006, (pg 

53) http://esm.versar.com/pprp/bibliography/PPES_06_02/PPES_06_02.pdf 
6 - 12 

Based on the basic analysis of the cost effectiveness of biomass energy substitution in 

reducing the physical CO2 emission rate based only on the CO2 coming from coal presented 

below, the EPA concludes in this TSD that biomass co-firing would not be a cost-effective 

measure on which to base state goals. 275 

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.276 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.277 

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. 

                                                            

275 This analysis does not include evaluation of stack biogenic CO2 emissions relative to the net landscape and 

process-related carbon fluxes associated with the production and use of the biogenic feedstocks combusted. Issues 

related to methods for assessing biogenic CO 2 emissions from stationary sources are currently being evaluated by 

the EPA. In general, the overall net atmospheric contribution of CO 2 resulting from the use of a biogenic feedstock 

by a stationary source, such as an EGU, will ultimately depend on the stationary source process and the type of 

feedstock used, as well as the conditions under which that feedstock is grown and harvested. In September 2011, the 

EPA submitted a draft Accounting Framework to the Science Advisory Board (SAB) Biogenic Carbon Emissions 

(BCE) Panel for peer review. The SAB BCE Panel delivered its Peer Review Advisory to the EPA on September 28, 

2012. In its Advisory, the SAB recommended revisions to the EPA's proposed accounting approach, and also noted 

that biomass cannot be considered carbon neutral a priori, without an evaluation of the carbon cycle effects related 

to the use of the type of biomass being considered. 

276 See Partial Bibliography – Biomass Co-firing at end of this section. 277 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
6 - 13 

There are considerable physical differences between coal and biomass that will generally limit 

the extent to which biomass can be reasonably used to replace coal in a boiler. For example, 

compared to most coals, many solid biomass fuels have both a significantly higher as-fired 

moisture content and a significantly lower heating value per unit of weight. Most solid biomass 

fuels are also significantly less dense than most coals. For example, a typical biomass might have 

twice the moisture, half the heating value, and less than half the density of coal.278 Important 

consequences of these physical differences are that the weight of biomass needed to provide a 

given amount of heat energy could be twice the weight of the coal it replaces, and the volume 

(cubic feet) of biomass needed could be four-to-eight times the volume of coal replaced. 

Biomass requires space for storage after delivery to a facility, and the length of time that the 

biogenic material would remain on site prior to use can differ. For example, wood chips could 

be delivered year-round while crop residue delivery would follow specific seasons in which the 

crop was grown. As noted above, the four-fold or greater increase in volume occupied by 

biomass relative to coal means that the necessary additional storage space could be large. 

However, if pre-prepared or condensed biomass fuels such as pelletized or torrefied biomass is 

used, some of these concerns may be lessened, recognizing that such pre-preparations of the 

feedstock will 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.279

The volumetric differences alone can have other unexpected consequences. For this 

analysis, experienced EPA engineering staff estimated that a 500 MW baseload coal plant cofiring 10% biomass and receiving biomass deliveries 10 hours per day and 5 days per week 

would require a 20-ton truck delivery to the plant every 10 minutes, in addition to the ongoing 

coal deliveries. Limiting traffic issues may arise in some situations. Also, because of the low 

energy density of biomass and its relatively higher transportation cost per unit of delivered 

energy, it may only be economically viable to transport biomass a limited distance from where it 

is grown. This could limit the both the percentage of biomass co-firing in a single boiler and the 

maximum MW output from biomass at a single site. New technologies under development, such 

                                                            

278 Biomass Energy Data Book- Edition 4, October 2012, DOE-EERE-ORNL, http://cta.ornl.gov/bedb/index.shtml 279 Properties of Wood Waste Stored for Energy Production, Purdue University, 2011, 

http://www.extension.purdue.edu/extmedia/ID/ID-421-W.pdf 
6 - 14 

as torrefaction of biomass, could mitigate some of these transportation, storage and energy 

content concerns, but are not yet commercially available. 

The relatively higher moisture content and lower heat content of biomass reduces boiler 

efficiency, and typically requires a derating in unit generation at very high co-firing percentages 

as furnace volume and boiler fan capacities become inadequate. 

For all of the above reasons, the EPA assumed for this analysis that coal-steam EGU 

boilers will generally only co-fire with biomass to a limited degree. While the actual level at 

which any plant can co-fire with biomass is highly site-specific, this analysis adopts the 

assumption used in EPA’s fleet wide IPM modeling of the electric power sector: a reasonable 

average limit on biomass co-firing is up to 10% on any single boiler, not to exceed 50 MW total 

biomass powered output at an individual plant site (which aligns with the magnitude of some of 

the larger such entities currently in the U.S.). This amount of co-firing has been used as 

representative practical limit in other studies as well.280 

Costs and Performance Impacts of Retrofitted Biomass Co-firing 

For this analysis the EPA adopted capital and O&M costs, and performance impacts for 

retrofitted biomass co-firing capability that are approximately representative of EPA assumptions 

used in its IPM modeling and discussed in the documentation for IPM v.5.13.281

EPA estimated that the capital cost to install 50 MW of biomass co-firing capability would be at 

least $10 million.282 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 

                                                            

280 The Potential for Biomass Cofiring in Maryland, Maryland Department of Natural Resources, March 2006, 

http://esm.versar.com/pprp/bibliography/PPES_06_02/PPES_06_02.pdf 281 See Sec 5.3, pg 5-19 at: http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/Chapter_5.pdf 282 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 
6 - 15 

alone. At low biomass cofiring rates, this factor slightly affects calculated biomass fuel 

consumption and any associated CO2 emission from biomass. 

Cost of Fuel 

For this analysis, the EPA uses a delivered biomass cost of $4/MMBtu, representative of 

delivered woody crops grown specifically for energy-generating combustion,283 and roughly 

50% greater than IPM projected 2020 average delivered coal costs.284 This analysis also 

considers a sensitivity scenario assuming a higher $6/MMBtu biomass price. 

The EPA recognizes that the cost of biomass is highly site-specific, and in some cases 

could be largely comprised of collection and transportation cost (as is the case for opportunity 

fuels with little to no other market value). The transportation component depends primarily on 

the distance that biomass needs to be transported. For example, the EPA engineering staff 

estimate that for a one-way distance of 50 miles with a 20-ton semi-trailer truck, transportation 

costs could be $10-20/ton. For biomass at a total delivered price of $4/MMBtu with an indicative 

heating value of 5,000 Btu/lb (higher heating value (HHV) basis), transportation cost in this 

example case could account for 25-50 percent of the total delivered biomass cost. In any case, it 

is the total delivered price of biomass on a $/MMBtu basis that will primarily determine the 

economic feasibility of biomass co-firing. 

Emission Reduction Potential 

The CO2 reduction potential of biomass co-firing is directly related to the amount and 

type of biomass co-fired and is due to the difference in heating value, moisture content and 

hydrogen/carbon ratios285 for a selected biomass fuel compared to the particular coal it replaces. 

The types of biomass typically available to EGUs in the United States include woody-based 

feedstocks such as wood chips, forest industry byproducts, and to a lesser degree agricultural 

crop residues, as well as emerging dedicated energy crops such as switchgrass and short-rotation 

                                                            

283 Average biomass price as projected by EPA modeling in IPMv5.13 Base Case 284 EIA, Electric Power Annual 2012 – Electricity (Table 7.4) 

http://www.eia.gov/electricity/annual/html/epa_07_04.html 

285 IFRF Combustion Handbook, Combustion File No. 23, What is Biomass? (Van Krevelen Diagram), 

http://www.handbook.ifrf.net/handbook/cf.html?id=23 
6 - 16 

woody crops.286 In general, when comparing coal-only versus co-firing coal with biomass, cofiring may result in either an increase or decrease in the stack CO2 emission rate. The extent to 

which the use of biomass contributes to net emissions to the atmosphere is being considered in 

EPA’s current study on biogenic emissions accounting. See preamble Section VIII.G. 

Cost of Reductions and Cost Effectiveness 

In order to evaluate cost-effectiveness of potential reductions, the EPA first estimated the 

cost of avoided coal CO2 emissions in a hypothetical scenario where biomass CO2 emissions are 

not included in total stack CO2 emissions (in effect, biogenic CO2 emissions are subtracted from 

total CO2 emissions measured at the stack). The estimated results presented below are based on 

a reasonably representative case using a baseload bituminous coal-fired boiler with a net heat 

rate of 10,340 btu/kWh that shifts from 100% bituminous coal to 90% coal and 10% biomass 

(assuming fuel prices of $2.62/MMBtu for coal in 2020 as projected in IPMv5.13 Base Case and 

$4/MMBtu for biomass as explained above). When biogenic stack emissions are not counted as 

part of total emissions, the cost of avoided CO2 for a “typical” baseload coal boiler co-firing 10% 

biomass is $30/tonne. At higher delivered fuel price differentials, the cost of avoided coal CO2

emissions would increase (for example, at a biomass price of $6/MMBtu, cost of avoided CO2 is 

$80/tonne if CO2 emissions from biomass are not counted).287 This estimated cost of avoided 

coal CO2 emissions, which ranges for $30 to $80/tonne, would increase if any portion of the 

biogenic CO2 emissions from the co-fired biomass were included. 

Conclusion 

Replacing some coal with low levels of biomass co-firing may result in stack CO2

emission increases.288 Even if biogenic CO2 emissions are not counted as part of stack 

emissions, biomass co-firing is a relatively costly approach to CO2 reductions at existing coal 

steam boilers when compared to other measures such as heat rate improvements and re-dispatch 

of generation supply to other existing capacity with lower CO2 emission rates. 

                                                            

286 Biomass Combined Heat and Power Catalog of Technologies, U.S. EPA, September 2007, 

http://www.epa.gov/chp/documents/biomass_chp_catalog.pdf

287 Similarly, the costs of avoided CO2 emissions would decrease at lower fuel price differentials. 288 Depending on biogenic feedstocks used and whether or not an assessment system is applied that evaluates 

biogenic CO2 emissions from the stack in relation to the terrestrial carbon cycling associated with the production and 

use of that biogenic feedstock. 
6 - 17 

The EPA is considering cost-effectiveness at a national level for the purpose of setting 

emissions goals consistently in each state. While this analysis concludes that cost-effective 

reductions of CO2 are not available on a national basis from widespread adoption of biomass cofiring, it does not preclude the potential for individual EGUs to utilize co-firing as a way to 

reduce overall CO2 emissions, nor does it preclude states from factoring in that unit-level 

potential into the design of state plans for compliance with the 111(d) standard.289 

Partial Bibliography for Technical Feasibility of Biomass Co-firing 

(a) Briggs, J. and J. M. Adams, Biomass Combustion Options for Steam Generation, Presented at 

Power-Gen 97, Dallas, TX, December 9 – 11, 1997. 

(b) Grusha, J and S. Woldehanna, K. McCarthy, and G. Heinz, Long Term Results from the First 

US Low NOx Conversion of a Tangential Lignite Fired Unit, presented at 24th International 

Technical Conference on Coal & Fuel Systems, Clearwater, FL., March 8 – 11, 1999. 

(c) EPRI, Biomass Co-firing: Field Test Results: Summary of Results of the Bailly and Seward 

Demonstrations, Palo Alto, CA, supported by U.S. Department of Energy Division of Energy 

Efficiency and Renewable Energy, Washington D.C.; U.S. Department of Energy Division 

Federal Energy Technology Center, Pittsburgh PA; Northern Indiana Public Service Company, 

Merrillville, IN; and GPU Generation, Inc., Johnstown, PA: 1999. TR-113903. 

(d) Laux S., J. Grusha, and D. Tillman, Co-firing of Biomass and Opportunity Fuels in Low NOx 

Burners, PowerGen 2000 - Orlando, FL 

(e) Tillman, D. A., Co-firing Biomass for Greenhouse Gas Mitigation, presented at Power-Gen 

99, New Orleans, LA, November 30 – December 1, 1999. 

                                                            

289 EPA notes that states will need to consider any future EPA finding regarding an assessment framework that 

considers carbon fluxes on the biogenic feedstock production landscape applied when evaluating net stack CO2

emissions from biomass co-firing. 
6 - 18 

(f) Tillman, D. A. and P. Hus, Blending Opportunity Fuels with Coal for Efficiency and 

Environmental Benefit, presented at 25th International Technical Conference on Coal Utilization 

& Fuel Systems, Clearwater, FL., March 6 – 9, 2000 

(g) Tillman D A, Harding N S (2004) Fuels of opportunity: characteristics and uses in 

combustion systems. Elsevier, Oxford, UK 

(h) Tillman D, Conn R, Duong D (2010) Coal characteristics and biomass cofiring in pulverized 

coal boilers. In: Electric Power, Baltimore, MD, USA, 18 - 20 May, 2010 

(i) Renewable and Alternative Energy Fact Sheet – Co-firing Biomass with Coal, Pennsylvania 

State University, 2010, http://pubs.cas.psu.edu/FreePubs/PDFs/ub044.pdf 

(j) Fernando R, Cofiring High Ratios of Biomass with Coal, IEA Clean Coal Centre (CCC/194), 

January 2012 

(k) DOE-EERE-ORNL, Biomass Energy Data Book- Edition 4, October 2012, 

http://cta.ornl.gov/bedb/index.shtml 
7 - 1 

Chapter 7: Carbon Capture & Storage 

Introduction 

Another possible approach for reducing CO2 emissions from existing fossil fuel-fired 

EGUs is through the application of carbon capture and storage technology (CCS; sometimes also 

referred to as carbon capture and sequestration). In the recently proposed standards of 

performance for new fossil fuel-fired EGUs (79 FR 1430), the EPA proposed to find that the best 

system of emission reduction for new fossil fuel-fired boilers and IGCC units is partial 

application of CCS. In that proposal, the EPA found that, for new units, partial CCS has been 

adequately demonstrated; it is technically feasible; it can be implemented at reasonable costs; it 

provides meaningful emission reductions; and its implementation will serve to promote further 

development and deployment of the technology. This chapter examines the potential for 

implementation of CCS technology at existing fossil fuel-fired utility boilers and IGCC units. 

Carbon Capture Options for Existing Fossil Fuel-fired EGUs 

In general, CO2 capture technologies applicable to existing fossil fuel-fired power 

generation can be categorized into three approaches – (1) post-combustion capture; (2) precombustion capture; and (3) oxy-combustion. Each of these is described and discussed in more 

detail below. 

Post-combustion Capture 

Post-combustion CO2 capture refers to removal of CO2 from a combustion flue gas prior 

to discharging to the atmosphere. Separating CO2 from such a gas stream can be challenging for 

a number of reasons. Because CO2 is a dilute fraction of the combustion flue gas – typically 13-

15 % in coal-fired systems and 3-4 % in natural gas-fired systems – a large volume of flue gas 

must be treated. The flue gas from typical combustion systems is usually at near atmospheric 

pressure. Therefore, most of the available capture systems rely on chemical absorption 

(chemisorption) options (e.g., amines) that require added energy to release the captured CO2 and 

regenerate the solvent. Many of the chemical solvents require a flue gas stream that is free of or 

has very low quantities of components – such as SO2, NOX, and HCl – that can degrade the 

solvent. The captured CO2 must then be compressed from near atmospheric pressure to much 

higher pipeline pressures (about 2,000 psia). 
7 - 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 fossil fuel (e.g., pet coke) is converted into a 

synthesis gas (or “syngas”) by applying heat under pressure in the presence of steam and limited 

O2. The product syngas contains primarily H2 and CO – and, depending on the fuel and 

gasification system – some lesser amount of CO2. The amount of CO2 in the resulting syngas 

stream can be increased by “shifting” the composition via the catalytic water-gas shift (WGS) 

reaction. This process involves the catalytic reaction of steam (“water”) with CO (“gas”) to form 

H2 and CO2. The resulting CO2 contained in the syngas is then captured before combustion of 

the H2-enriched syngas for power generation in a combined cycle system. Contrary to the postcombustion capture flue gas, the IGCC syngas can contain a high volume of CO2 and is 

pressurized. This allows the use of physical absorbents (e.g., SelexolTM, Rectisol®) that require 

much less added energy to release the captured CO2 and require less compression to get to 

pipeline standards. 

Oxy-combustion 

Oxy-combustion systems for CO2 capture rely on combusting coal or other fuels with 

relatively pure O2 diluted with recycled CO2 or CO2/steam mixtures. Under these conditions, the 

primary products of combustion are water and CO2, with the CO2 purified by condensing the 

water. Challenges associated with oxy-combustion include the capital cost and energy 

consumption for a cryogenic air separation unit (ASU) to produce oxygen, introduction of N2 via 

boiler air infiltration, and excess O2 in the CO2 product stream. 

CO2 Transportation and Storage 

CO2 Pipeline Infrastructure 

Carbon dioxide has been transported via pipelines in the U.S. for nearly 40 years. 

Approximately 50 million metric tons of CO2 are transported each year through 3,600 miles of 

pipelines. Moreover, a review of the 500 largest CO2 point sources in the U.S. shows that 95 

percent are within 50 miles of a possible geologic sequestration site, which would lower 

transportation costs. There are multiple factors that contribute to the cost of CO2 transportation 

via pipelines including but not limited to: availability and acquisition of rights-of-way for new 
7 - 3 

pipelines, capital costs, operating costs, length and diameter of pipeline, terrain, flow rate of 

CO2, and the number of sources utilizing the pipeline. 

Geologic Storage 

 Existing project and regulatory experience, research, and analogs (e.g. naturally existing 

CO2 sinks, natural gas storage, and acid gas injection), indicate that geologic sequestration is a 

viable long term CO2 storage option. The viability of geologic sequestration of CO2 is based on a 

demonstrated understanding of the fate of CO2 in the subsurface. Geologic storage potential for 

CO2 is widespread and available throughout the U.S. and Canada. Nearly every state in the U.S. 

has or is in close proximity to formations with carbon storage potential including vast areas 

offshore. Estimates based on DOE studies indicate that areas of the U.S. with appropriate 

geology have a storage potential of 2,300 billion to more than 20,000 billion metric tons of CO2

in deep saline formations, oil and gas reservoirs and un-mineable coal seams.290 Other types of 

geologic formations such as organic rich shale and basalt may also have the ability to store CO2; 

and the DOE is currently evaluating their potential storage capacity. 

 Further evidence of the widespread availability of CO2 storage reserves in the U.S. 

comes from the Department of Interior’s U.S. Geological Survey (USGS) which has recently 

completed a comprehensive evaluation of the technically accessible storage resource for carbon 

storage for 36 sedimentary basins in the onshore areas and State waters of the United 

States.291 The USGS assessment estimates a mean of 3,000 billion metric tons of subsurface CO2

storage potential across the United States. For comparison, this amount is 500 times the 2011 

annual U.S. energy-related CO2 emissions of 5.5 Gigatons (Gt).292

Enhanced Oil Recovery (EOR) 

Geologic storage options also include use of CO2 in EOR, which is the injection of fluids 

into a reservoir to increase oil production efficiency. EOR is typically conducted at a reservoir 

                                                            

290 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).

291 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/. 292 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/.
7 - 4 

after production yields have decreased from primary production. EOR using CO2, sometimes 

referred to as ’CO2 flooding’ or CO2-EOR, involves injecting CO2 into an oil reservoir to help 

mobilize the remaining oil and make it available for recovery. The crude oil and CO2 mixture is 

produced, and sent to a separator where the crude oil is separated from the gaseous hydrocarbons 

and CO2. The gaseous CO2-rich stream then is typically dehydrated, purified to remove 

hydrocarbons, recompressed, and re-injected into the oil or natural gas reservoir to further 

enhance recovery. 

CO2-EOR has been successfully used at many production fields throughout the U.S. to 

increase oil recovery. The oil and natural gas industry in the United States has over 40 years of 

experience of injection and monitoring of CO2 in the deep subsurface for the purposes of 

enhancing oil and natural gas production. This experience provides a strong foundation for the 

injection and monitoring technologies that will be needed for successful deployment of CCS. 

Evaluation of Retrofit CCS as BSER for Existing Fossil Fuel-fired EGUs 

Technical Feasibility 

In evaluating partial CCS as the BSER for new fossil fuel-fired boilers and IGCC units, 

the EPA determined that the technology is feasible and adequately demonstrated for new units 

because the major components of CCS – the capture, the transportation, and the storage – are all 

proven technologies that have been demonstrated at large scale. While the EPA found that partial 

CCS is technically feasible for new fossil fuel-fired boilers and IGCC units, it is much more 

difficult to make that determination for the entire fleet of existing fossil fuel-fired EGUs. 

Developers of new generating facilities can select a physical location that is more amenable to 

CCS – such as a site that is near an existing CO2 pipeline or an existing oil field. Existing sources 

do not have the advantage of pre-selecting an appropriate location. Some existing facilities are 

located in areas where CO2 storage is not geologically favorable and are not near an existing CO2

pipeline. Developers of new facilities also have the advantage of integrating the partial CCS 

system into the original design of the new facility. Integrating a retrofit CCS system into an 

existing facility is much more challenging. Some existing sources have a limited footprint and 

may not have the land available to add partial CCS system. Integration of the existing steam 

system with a retrofit CCS system can be particularly challenging. 
7 - 5 

Partial CCS has been demonstrated at existing EGUs. It has been demonstrated at a pilotscale at Southern Company’s Plant Barry; it is being installed for large-scale demonstration at 

NRG’s WA Parish facility; and it will very soon be applied at commercial-scale as a retrofit at 

SaskPower’s Boundary Dam coal-fired EGU in Canada. However, all of these facilities are 

located in areas that are either near an existing oil field or in an area that is geologically 

favorable for CO2 storage. Thus, at some existing facilities, the implementation of partial CCS 

may be a viable GHG mitigation option and some utilities may choose to pursue that option. 

However, the EPA does not believe that it can serve as the best system of emission reduction for 

a broadly applicable GHG mitigation program. Therefore, the EPA does not propose to find that 

CCS is a component of the best system of emission reduction for CO2 emissions from existing 

fossil fuel-fired EGUs. 

Reasonableness of Cost 

In the proposed standard of performance for new fossil fuel-fired EGUs (79 FR 1430), 

the EPA found that the costs to implement partial CCS (to a level to meet the proposed emission 

standard of 1,100 lb/MWh-gross) were consistent with costs for other non-natural gas-fired 

generating technologies – such as nuclear, biomass and geothermal – that utilities are considering 

for new intermediate and base load generating capacity. The EPA also noted in the proposal, 

that most of the relatively few new projects that are in the development phase are already 

planning to implement CCS; and, as a result, the standard would not have a significant impact on 

nationwide energy prices. 

In contrast, the EPA did not identify full or partial CCS as BSER for new natural gasfired stationary combustion turbines noting technical challenges to implementation of CCS at 

NGCC units as compared to implementation at new solid fossil fuel-fired 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. 

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 
7 - 6 

existing source emission standards to be reasonable, which further supports the determination 

that CCS is not an appropriate component of the best system of emission reduction for CO2

emissions from existing fossil fuel-fired EGUs. 

Emission Reductions and Promotion of Advanced Technology 

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 may not be technically or logistically 

feasible in a number of cases and cannot be broadly implemented at a reasonable cost at this 

time. 
A - 1 

APPENDIX 

Technical Memorandum 

Consideration of Heat Rate Improvement (HRI) Potential at Existing Oil/Gas-fired Steam, 

Natural Gas Combined Cycle, and Combustion Turbine EGUs for Inclusion in Building 

Block 1 

As described in the GHG Abatement Measures TSD, the EPA identified four categories 

of demonstrated measures, or “building blocks,” that are technically viable and broadly 

applicable, and can provide cost-effective reductions in CO2 emissions from individual existing 

EGUs. These building blocks include: 

Building Block 1 - Reducing the carbon intensity of generation at individual affected 

EGUs through heat rate improvements; 

Building Block 2 - Reducing emissions from the most carbon-intensive affected EGUs in 

the amount that results from substituting generation at those EGUs with generation from 

less carbon-intensive affected EGUs (including NGCC units under construction); 

Building Block 3 - Reducing emissions from affected EGUs in the amount that results 

from substituting generation at those EGUs with expanded low- or zero-carbon 

generation; and, 

Building Block 4 - Reducing emissions from affected EGUs in the amount that results 

from the use of demand-side energy efficiency that reduces the amount of generation 

required. 

Coal-fired Steam EGUs 

For Building Block 1, the EPA evaluated the fleet-wide potential for lowering the carbon 

intensity of generation at individual affected coal-fired steam EGUs by improving heat rates at 

these EGUs (see the GHG Abatement Measures TSD). The EPA analyzed 11 years of historical 

heat rate data and the literature on HRI methods 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%. 

Furthermore, the EPA understood that any HRI method that reduces gross heat rate will also 
A - 2 

reduce net heat rate, and that some HRI methods reduce net heat rate without reducing gross heat 

rate. As such, the EPA expects that the HRI potential on a net output basis is somewhat greater 

than on a gross output basis, primarily through upgrades that result in reductions in auxiliary 

loads. Therefore, the EPA conservatively assumed that the coal-steam fleet average net heat rate 

can be reduced by 6% and included this finding in its Building Block 1. 

As discussed in the preamble, for purposes of developing the alternate set of goals on 

which we are taking comment, the EPA used an estimate of a 4% HRI from affected coal-fired 

steam EGUs on average. The EPA views the 4% estimate as a reasonable minimum estimate of 

the technical potential for HRI on average across affected coal-fired EGUs. 

Oil/Gas Steam EGUs 

As summarized above, the EPA made a detailed assessment of the fleet-wide potential for 

HRI at existing affected coal-fired steam EGUs in Building Block 1. However, we did not make 

a detailed assessment of this potential for existing affected oil and gas steam units at this time, 

for the three main reasons described below. 

First, oil and gas contain significantly less carbon per unit of heating value than coal. Oil 

and gas therefore produce significantly less CO2 than coal for the same amount of heat. (This is 

discussed further under NGCCs, below.) 

Second, coal-fired steam EGUs are utilized at much higher levels compared to oil/gas 

steam EGUs. Therefore the amount of CO2 reduction that can be achieved via HRI at oil/gas 

EGUs is significantly smaller. For example, EPA modeling293 projects that in 2020 coal-steam 

units will provide 59% of all fossil-fired electrical generation, while oil/gas steam units will 

provide only 2%. Even if CO2 emissions from all oil/gas steam units could be reduced by 6% on 

average using HRI methods (as assumed on coal-steam units) that reduction would amount to 

only a fraction of 1% of the HRI reduction that might be obtained from coal-steam units. 294

                                                            

293 IPM Base Case v5.13 modeling results as presented in RIA Chapter 3. 294 The EPA is not suggesting that CO2 reductions from fossil-fired sources other than coal-steam EGUs are never 

important. Such reduction might be significant in a few situations, and states are free to make use of these reductions 

in meeting their goals. 
A - 3 

 Third, oil/gas steam EGUs employ less extensive systems and equipment compared to 

coal steam EGUs and therefore, in general, have a lesser range of opportunities for implementing 

HRI. For example, oil/gas steam units do not typically use flue gas SO2 scrubbers, particulate 

collection devices, coal mills, coal conveyors, ash handling systems, sootblowers, etc. 

Consequently, some of the HRI methods discussed in the GHG Abatement Measures TSD are 

not applicable for oil/gas steam EGUs. 

The above factors taken together explain why the potential for CO2 reduction achieved 

via HRI at oil and gas steam EGUs would be quite small compared to that from the existing fleet 

of coal-fired EGUs. Therefore the EPA conservatively decided to not separately itemize and 

include this potential in Building Block 1. 

Natural Gas Combined Cycle (NGCC) EGUs 

EPA modeling also projects that in 2020 natural gas-fired NGCCs will provide about 

39% of the U.S. electrical generation from fossil fuels, compared to 59% from coal-steam EGUs. 

Also, as explained below, NGCCs in 2020 would emit only about 20% of the total CO2

emissions from fossil fuels used in electrical generation. 

The significantly lower amount of CO2 produced by combustion of natural gas compared 

to coal (about 40% less for the same amount of heat input) is due primarily to the higher 

hydrogen content and lower carbon content in natural gas compared to coal. Also, because a 

NGCC is typically more efficient than a coal-steam EGU, thus using less heat input from fuel to 

make an equal electrical output, a very efficient NGCC can further reduce the CO2 emission rate 

per MWh to about 60% less than that from coal-steam EGUs. Thus, natural gas, particularly as 

used in NGCCs, inherently reduces CO2 emissions by more than one-half. Existing NGCC EGUs 

are therefore already significantly reducing CO2 emissions compared to existing coal EGUs, per 

MWh of output, before considering whether NGCCs might be able to further reduce their CO2 

emissions via HRI methods. 

The EPA has preliminarily considered that there may be some potential for a further 

reduction in the CO2 emissions of NGCC EGUs via HRI. However, as with coal-steam EGUs, 

we do not have the unit-specific detailed design information on existing individual NGCCs that 
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would be needed to make a detailed assessment of the HRI potential via best practices and 

upgrades for each NGCC unit. While it would be possible for EPA to make a “variability 

analysis” of NGCC historical hourly heat rate data (as was done for coal-steam EGUs), we are 

aware that the various NGCC configurations in use and the historically lower capacity factors of 

the NGCC fleet (less run time per start, and more part load operation) would require a NGCC 

analysis that includes more complexity and likely more uncertainty than in the coal-steam 

analysis. In addition, the analysis would be limited by the fact that only one-third of the NGCC 

fleet has historically reported complete (combustion turbine and steam turbine generator) load 

data to EPA. 

To preliminarily gauge the HRI potential for NGCCs, EPA engineering staff familiar 

with NGCC design and operation informally discussed the NGCC HRI potential with power 

sector engineering firms and NGCC suppliers. Our preliminary conclusion is that the fleet-wide 

HRI potential for existing NGCC EGUs may be only about 2-3% at most, on a sustained basis, 

for the following two reasons. 

First, as a “combined” combustion turbine and steam turbine power cycle, some of the 

available HRI methods would be applicable only to the steam turbine portion of the power cycle: 

the HRSG (heat recovery steam generator), the steam turbine-generator, and the heat rejection 

system (water or air-cooled condenser systems). The HRI potential associated with the steam 

portion of the NGCC is significantly less than in a coal-steam unit because the NGCC steam 

system is much simpler (gaseous fuel, no back-end scrubbers, less parasitic power, no air heater 

leakage, no feedwater heaters, etc) and its flue gas exit temperature is typically already much 

lower than in a coal-steam unit. 

Second, the HRI methods applicable to the combustion turbine portion of the NGCC 

relate primarily to critical components in the hot expansion side of the unit - components that are 

exposed to the products of combustion of fuel and air that contain small amounts of 

corrosive/erosive contaminants at very high temperatures. These critical components 

(combustors, nozzles/vanes, seals, rotating blades) therefore require regular periodic removal and 

refurbishment or replacement to maintain high NGCC efficiency levels, and indeed to avoid 

potentially catastrophic mechanical failures. The greatest loss in the performance (increased heat 
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rate) of a NGCC is this physical degradation that occurs in proportion to its hours of operation 

and number of starts. Consequently, it has long been an accepted practice by NGCC owners to 

closely follow the NGCC manufacturer’s maintenance recommendations, a practice that 

regularly restores the NGCCs efficiency and reliability. This close adherence to manufacturer 

recommendations is financially motivated in part by the fact that many NGCC owners have longterm maintenance contracts with the manufacturers, wherein the manufacturer guarantees the 

service life and replacement costs of expensive critical components - provided that the regular 

preventive/restorative maintenance schedule is followed. Regularly scheduled maintenance 

practices are the most effective HRI methods that can be applied on NGCCs, and the EPA 

concludes that they are likely already being applied across most of the NGCC fleet. 

With NGCCs projected to produce 20% of fossil CO2 emissions in 2020, and with a max 

sustained HRI potential for existing NGCCs of 2-3%, as mentioned earlier, the CO2 reduction 

potential for NGCCs would amount to only a fraction of 1% of total fossil emissions in 2020, 

which would be only about 10% of the potential CO2 reductions expected from coal-steam EGUs 

via HRI. Because of this limited potential and the uncertainty associated with it, EPA 

conservatively decided to not separately itemize and include this NGCC potential in Building 

Block 1. 

Simple-cycle Combustion Turbine (CT) EGUs 

Natural gas-fired CTs provide peaking generation, typically operating at very low 

capacity factors. This is primarily because of their relatively low efficiency, which is 

economically only partially offset by their relatively low capital cost. As peaking capacity, any 

CT may have many starts/shutdowns in the course of a year. It may also “load follow,” with an 

average electric power output that may be well below its most efficient load point. CTs have an 

operational flexibility well suited to their role as peakers, but this role requires them to be 

inherently less efficient than they could be if it were economic to operate them at higher capacity 

factors. 

EPA modeling projects that the power sector CT capacity in 2020 (Base Case) will be 

about 21% of total fossil-fired capacity (GW), and that it will provide only about 1% of total 

fossil-fired electrical generation (GWh). Whether gas or oil-fired, CT capacity can therefore only 
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contribute CO2 emissions amounting about 1% of total fossil CO2 emissions, or perhaps 2% of 

total coal-steam CO2 emissions. Any single-digit percentage reduction in CT heat rates, can 

therefore only provide much less than a 1% reduction in total fossil-fired CO2 emissions. 

Most CTs likely benefit from the same regular preventive/restorative maintenance as the 

combustion turbine portion of a NGCC, as discussed above, and for the same reasons, Thus, the 

heat rates of most CTs are already periodically (even if not regularly, depending on their 

irregular operating hours and starts) restored to a level that allows them to be both reliable and as 

efficient as reasonably possible. Therefore the EPA decided to not include HRI for CTs as an 

additional potential in Building Block 1. 

Conclusion 

This technical memorandum outlines the EPA’s reasons for not including CO2 reduction 

potentials via HRI on oil/gas steam, NGCC, and CT EGUs as part of the CO2 reduction target of 

Building Block 1 at this time. For each non-coal technology the EPA concludes that the total 

additional potential reduction is small compared to the potential coal-steam CO2 reduction. 

Furthermore, we do not have the detailed site-specific information that would be needed to make 

a more precise engineering evaluation of the HRI potential for any individual EGU, including 

coal-steam units; only the owners/operators of these EGUs would have that information. 

The EPA notes, however, that although we did not include an HRI potential for these 

non-coal classes of existing fossil-fired EGUs in Building Block 1, we do expect that some 

amount of CO2 reduction via HRI is available from these EGUs. States and sources would be 

free to use HRI at these EGUs to help reach the state CO2 reduction goals. Further, we note that 

there are geographic differences in the proportions of total generation produced from various 

EGU types, and that in certain geographically isolated jurisdictions, HRI from non-coal fossil 

fuel-fired EGUs could be a more important potential approach to reducing CO2 emissions. For 

this reason, as noted in the preamble, we are taking comment on whether HRI from non-coal 

fossil fuel-fired EGUs should be included as part of the basis supporting the BSER.