Technical Support Document (TSD) for Carbon Pollution Guidelines for Existing Power Plants: Emission Guidelines for Greenhouse Gas Emissi ons from Existing Stationary Sources: Electric Utility Generating Units
Docket ID No. EPA-HQ-OAR-2013-0602



# GHG Abatement Measures

U.S. Environmental Protection Agency Office of Air and Radiation
September 16, 2014

### TABLE OF CONTENTS

Chapter 1: Intr oduction ........................................................................................ 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 Ge neration Sour ces ............................................................... 4-1 Chapter 5: Demand-side Ener gy Efficiency (EE) ................................................ 5-1 Chapter 6: Fuel Switching ................................................................................... 6-1 Chapter 7: Carbon Captu re & Storage ................................................................. 7-1 APPENDIX .......................................................................................................... A-1

This version of the GHG Abatement Me asures TSD (1) contains previously identified corrections to minor typograp hical errors EPA found in the version posted on the web on June 2, 2014, and (2) makes two cl arifying 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 st andards 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 achie ving such reductions and any non-air quality health and environmental imp act 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 calcula te the amount of emissi on reduction achievable through applying the BSER.
As a first step towards determination of BSER, the EPA recognized that, in general, reductions in carbon dioxide (CO 2 ) emissions from individual exis ting 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 im proving heat rates or switching to lower carbon fuels), and/or (2) reducing u tilization of greenhouse gas (GHG )-emitting EGUs (e.g., by reducing the overall demand for electricity or by shif ting dispatch from higher-GHG-emitting EGUs to lower-GHG-emitting and non-emitting units). Accord ingly, to determine BSER for reducing GHG emissions at affected units, the EPA eval uated numerous GHG abatement measures that utilize the above approaches. In its evaluation, th e EPA considered only those measures that have been adequately demonstrated to reduce CO 2 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 sequestrati on at individual EGUs, shifting dispatch from higher-GHG-emitting EGUs to lower-GHG-emitti ng and non-emitting units , and reducing the overall demand for electricity via improve ments in demand-side energy efficiency.

Based on its evaluation of the above GHG abat ement measures, the EPA identified four categories of demonstrated measures, or “build ing blocks,” that are technically viable and broadly applicable, and can provide cost-effective reductions in CO 2 emissions from individual existing EGUs. These building blocks include:
1. Reducing the carbon intensity of generation at individual aff ected EGUs through heat rate improvements;1 - 2 2. Reducing emissions from the most carbon-inte nsive affected EGUs in the amount that results from substituting generation at t hose EGUs with generation from less carbonintensive affected EGU s (including NGCC units under construction);
3. Reducing emissions from affected EGUs in the amount that results from substituting generation at those EGUs with expand ed low- or zero-car bon generation; and,
4. Reducing emissions from affected EGUs in the amount that results from the use of demand-side energy efficiency that redu ces 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 a ll four building blocks. The app lication of all four building
blocks as BSER is consistent with current trends in the electric power sect or and with strategies that companies and states are already taking to reduce GHG emissions. Also, the application of all four building blocks as BS ER supports achieving cost-effectiv e, and technically feasible
reductions of CO 2

. The subsequent chapters in this technical support document describe EPA’s evaluation of all adequately demonstrated GHG abatement meas ures. While evaluating each measure, the EPA considered its technical feasibility, applicability and use, application leve l appropriate for BSER,
and cost effectiveness associated with reducing GHG emissions at EGUs.2 - 1 

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

2.0 Introduction
Based on the range of operating efficiencies for existing coal-fir ed electric generating units (EGUs), it is evident that EGUs are gene rally 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 charac teristics and the year-t o-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 exis ting coal-fired EGUs, design factors that influence efficiency, technologies to improve efficiency, previ ous studies estimating potential efficiency improvements, the proposed EPA appro ach to calculate efficiency improvements, and the estimated capital costs of those improvements.

2.1 Overview of U.S. Existing Coal-F ired Electric Generating Units
Coal in the United States is predominately us ed for electric power generation. Most coalfired EGUs in the United States burn either bituminous or subbituminous coals. The largest
sources of bituminous coals burned in coal-f ired EGUs are mines in regions along the Appalachian Mountains and in southern Illinois, western Ke ntucky 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 bur ned in limited quantities. Existing coal-fired EGUs in the U.S. electric utility fleet use one of five basic coal combustion configurations: (1) pu lverized coal (PC) combusti on, (2) fluidized-bed combustion (FBC), (3) gasified coal combustion, (4) cyclone furnace combustion, or (5) stoker-fired coal2 - 2 combustion. Table 2-1 presents a summary of the operating characteristics of each of these coal combustion configurations.
Table 2-1. Characteristics of coal-firin g configurations used for U.S. EGUs
Coal-firing Configuration Coal Combustion Process Description Distinctive Design/Operating Characteristics

Pulverized Coal (PC) Combustion Coal is ground to a fine powder that is pneumatically fed to a burner where it is mixed with combustion air and then blown into the furnace. The pulverized-coal particles burn in suspension in the furnace. Unburned and partially burned coal particles are carried off with the flue gas. Wall-fired An array of burners fire into the furnace horizontally, and can be positioned on one wall or opposing walls depending on furnace design.

Tangential-fired

(Corner-fired) Multiple burners are positioned in opposite corners of the furnace producing a fireball that moves in a cyclonic motion and expands to fill the furnace.

Fluidized-bed Combustion (FBC) Coal is crushed into fine particles. The coal particles are suspended in a fluidized bed by upward-blowing jets of air creating a turbulent mixing of combustion air with the coal particles. Typically, the coal is mixed with a sorbent such as limestone (for SO 2 emission control). FBC have a greater fuel flexibility than PC EGUs and can be designed for combustion within the bed to occur at atmospheric or elevated pressures. FBC operating temperatures are in the range of 1,500 to 1,650  F (800 to 900 o C). Bubbling fluidized bed (BFB) Operates at relatively low gas stream velocities and with coarse-bed size particles. Air in excess of that required to fluidize the bed passes through the bed in form of bubbles.

Circulating fluidized bed (CFB) Operates at higher gas stream velocities and with finer-bed size particles. No defined bed surface. Must use high-volume, hot cyclone separators to recirculate entrained solid particles in flue gas to maintain the bed and achieve high combustion efficiency.

Integrated Coal

Gasification Combined Cycle (IGCC) Synthetic combustible gas (“syngas”) derived from an on-site coal gasification process is burned in a combustion turbine. The hot exhaust gases from the combustion turbine pass through a heat recovery steam generator to produce steam for driving a steam turbine/generator unit. Coal gasification units are unique among coal-firing configurations because a gase ous 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 ne w coal-fired power plants are pulverized coal, fluidized bed (FBC), and integrated coal gasification combined cycle (IGCC). Pulverized
coal combustion is the coal-firi ng configuration predominately us ed at existing EGUs. The most recent coal technology combustion development i nvolves the integration of coal gasification technologies with the combined cycle electric generation proce ss. The efficiency of an IGCC power plant is comparable to the latest a dvanced PC-fired and FBC EGU designs using supercritical steam cycles. The advantages of using IGCC technology can include greater fuel flexibility (e.g., capability to use a wider variety of coal ranks), potential improved control of PM, SO 2 emissions, and other air poll utants, the need for fewer post-combustion control devices (e.g., almost all of the sulfur a nd ash in the coal can be remove d once the fuel is gasified and prior to combustion), generation of less soli d waste, reduced water consumption, and the chemical process that creates a concentrated CO 2 stream that is more amenable to carbon capture

processes. Older combustion technologies, namely cyclone furnaces and stoker-fired coal combustion, have been replaced at new coal-fired EGUs by more efficient methods that provide superior coal combustion effici ency and other advantages. Howe ver, a few remaining old stokerfired EGUs and cyclone furnaces still remain in se rvice for a small number of existing EGUs in the U.S. electric utility market.

2.2 Influence of Heat Rate on Coal-Fired EGU CO 2 Emission Rate
Heat rate is a common way to measure EGU effi ciency. As the efficiency of a coal-fired EGU is increased, less coal is burned per kilo watt-hour (kWh) generated by the EGU resulting in a corresponding decrease in CO 2 and other air emissions. Heat rate is expressed as the number of British thermal units (Btu) or kilojoules (kJ) required to generate a kilowatt-hour (kWh) of electricity. Lower heat rates are associated with more efficient coal-fired EGUs. The electric energy output for an EGU can be expressed as either as “gross output” or “net output.” The gross output of an EGU is the total amount of electricity generated at the generator terminal. The net output of an EGU is the gross output minus the total amount of auxiliary (or parasitic) electri city used to operate the EGU (e.g., electricity to power fuel handling equipment, pumps, fans, pollution contro l equipment, and other on-site electricity needs), and thus is a measure of the electricity de livered to the transmission grid for distribution2 - 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 effici ency of a given coal-fired EGU. The actual overall efficiency that a given coal-fired EGU achieves is determined by the interaction of a combination of site-specific factors that impact efficiency to varying de grees. Examples of the factors affecting EGU efficiency at a given facility include:

 EGU thermodynamic cycle – EGU efficiency can be si gnificant improved by using a supercritical or ultra-su percritical steam cycle. Supercriti cal and ultra-supercritical boilers operate above the critical point of wa ter (approximately 374°C (705°F) and 22.1 MPa (3,210 psia)). As a general guideline, the therma l 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 he ating values of great er 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 equipmen t, b) larger units tend to be younger incorporating improvements from advanced te chnologies, and c) the economy of scale of larger units allows the use of higher co st improvements to be more economic.

 EGU pollution control systems – The electric power consum ed 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 optim ization, equipment maintenance, etc. can affect EGU efficiency.2 - 5  EGU cooling system – The temperature of the cooli ng water entering the condenser can have impacts on steam turbine performance. Once-through cooling systems can have an efficiency advantage over recirculating coo ling systems (e.g., cooling towers). However, once-though cooling systems typically have larg er 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 a nd less oxygen is availa ble for combustion per
unit volume of ambient air th an at lower elevations. Cooler ambient temperatures theoretically could increase the overall EGU e fficiency by increasing the draft pressure of the boiler flue gases and the condenser vac uum, 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 loca tions 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 lo ad following unit to respond to fluctuations in customer electricity demand.

 EGU plant components – EGUs using the optimum numberof feedwater heaters, highefficiency electric motors, variable speed driv es, better materials for heat exchangers, etc.
tend to be more efficient.

2.3 Technologies to Improve Existi ng 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) Nati onal Energy Technology Laboratory (NETL) is presented in Table 2-2. The efficiency percentage s were converted to a co mmon basis so that all of the data can be compared. All of the improve ment technologies presented in Table 2-2 cannot necessarily be implemented at every existing coal-f ired 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 engi neering study originally completed by Sargent and Lundy in 2009 titled “Coal-Fired Power Plant H eat Rate Reductions.” It describes numerous well-known and technically proven methods to im prove efficiency of coal-fired EGUs. The study lists possible efficiency improvements in the boiler, turbine, flue gas system, air pollution control equipment and the water treatment syst em. 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 contro l system, sootblowers, and the air heaters. 2.3.1.1 Materials Handling 2 The coal-handling portion of materials handling typically requires about 0.07% (7 Btu/kWh) of the gross electrical output of a pow er plant. Depending on the state of the motors and drives, replacing them with energy-efficient motors and variable freq uency 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% (6 0 Btu/kWh) of the gro ss electrical output and can be upgraded to provide more consistent size and finer coal partic les. The fine particles improve combustion efficiency, consequently reduc ing fuel cost and heat rate. The costs for changes to the pulverizer system are significant, and, historicall y, the projects have improved the heat rate justifiably only when th e existing equipment has degraded. The bottom ash handling system may be a candidate for heat rate improvement. Switching from a water-sluicing bo ttom ash system to a dry drag chain system can reduce the auxiliary requirements and reduce the amount of water to the wate r treatment plant. The typical power requirements are about 0.1% (10 Bt u/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 impr oves the efficiency of an EGU by recovering energy from the exhaust gases to preheat th e boiler feedwater. The replacement of the
economizer can lead to substa ntial heat rate improvements around 50-100 Btu/kWh, but is large capital investment (~$2-8M). Due to this hi gh cost, economizer upgrad es are not generally performed unless the existing equipment has degrad ed 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 ra te of the unit. The process control capabilities can control a nd evaluate many aspects of th e 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 system s are typically around $550,000-$750,000 and offer heat rate re ductions up to 150 Btu/kWh.
2.3.1.4 Sootblowers Intelligent sootblowers may be installed to improve system efficiency. The intelligent sootblowers system monitors the furnace exhaus t gas temperatures and steam temperatures. Other readings may be incorporated into th e 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 betw een 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 th e 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 fa ns and avoids capturing the heat transferred between the flue gas and pre-combustion air. The increased volume re quires 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 ra te 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. Typi cally the air heater outlet is maintained at 20-30°F above the sulfuric acid dew point to prev ent 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 te mperature. 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 turbin e drive and motor-driven feed

pumps. 2.3.2.1 Turbine Replacement or overhaul of existing steam tu rbines 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 reduc tions of 100-300 Btu/kWh.
2.3.2.2 Feedwater Heaters The feedwater heaters are heat exchangers us ed to heat the boiler feedwater by extracting heat from the steam leaving the turbine sect ion. The EGU efficiency can be increased by improving the heat transfer surface area. This en tails adding heat exchange surfaces to the existing heaters or adding additi onal heaters. The costs relative to the heat rate improvement associated with these projects typically prohi bit 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 c ondenser 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 th e turbine to extract as much en ergy 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 qua lity, closed cooling water syst ems 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 an d maintenances are 30-70 Btu/kWh with annual fixed costs of $30,000-$80,000. 2.3.2.4 Boiler Feed Pumps2 - 9 Boiler feed pumps require a large amount of auxiliary power to pump large amounts of boiler feedwater through the heat ers and the boiler. Due to the high use of these pumps, maintenance is extremely important to ensure reli ability and the most efficient operation. As the pumps wear and operate less e fficiently, 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 i nduced 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 frequenc y drives, and variable pi tch blades, available to control the flue gas flow allowing highly e fficient 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 driv es at an EGU can be $6-16M
with heat rate reducti ons 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 plan t. 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 si gnificantly while maintaining the level of
emissions reduction desired. Th e three technologies discu ssed below are the flue gas desulfurization, the elect rostatic precipitator, and the selective catalyt ic reduction systems. 2.3.4.1 Flue Gas Desulfurization Coal-fired power plants use many types of fl ue 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, a2 - 10 co-current spray tower quencher may replace the unit. The capital cost is about $2.5M with heat
rate reductions around 13 Btu/kWh. Another technology upgrade affects the vanes an d distribution plate in an absorber. The improvement of the gas flow coming into cont act with the absorber sorbent increases SO 2 capture, reduces maintenance due to erosion, an d reduces the amount of energy required for the induced draft fan. Turning vanes and a perforated gas distributi on 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, multip le spray levels are installed to deliver the limestone slurry. If a powe r plant is operating with SO 2 levels below its permit limit, turning off one spray level will reduce the auxiliary power re quired. If this is possible, a unit heat rate reduction of 16 Btu/kWh may be available. 2.3.4.2 Electrostatic Precipitator The best operation for an electrostatic pr ecipitator involves main taining the maximum applied voltage, but below the level at which sp ark-over occurs. Electrosta tic precipitator energy management system upgrades often help improve the electrostatic prec ipitator performance by maintaining the optimal performance and lowering power consumption. The installation for this technology can be from minimal to $0.8M a nd can lower heat rate by 5 Btu/kWh. 2.3.4.3 Selective Catalytic Reduction For the last 15 years, selectiv e catalytic reduction systems ha ve been in use to reduce
NO X emissions from power plants. Extensive mode ling was performed to achieve the necessary reduction with minimum ammonia slip. The result s showed that reducing pressure drop and using secondary air as dilution for the amm onia vaporizer can reduce the auxiliary power necessary. The heat rate reduc tion is 0-10 Btu/kWh and capita l 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 pl ant. The quality of the water is a key factor affecting the scale bu ildup 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 blow downs required, which allows for more steam in the turbine cycle. If the water is not properl y maintained, heat transfer may be reduced by up
to 10%.2 - 11 Similar to the boiler water, the cooling towe rs are also affected by the water quality. Fouling and scaling remain issues for heat transf er and purity of the water. By maintaining the cooling water system efficiently, the overall wate r quality is improved, whic h branches into other aspects already mentioned.2 - 12Table 2-2. Existing coal-fired EGU efficiency improvements reported for actualefficiency improvement projectsEfficiencyImprovementTechnologyDescriptionReportedEfficiencyIncreaseaCombustion ControlOptimizationCombustion controls adjust coal and air flow to optimize steam production for the steam turbine/generator set.However, combustion control for a coal-fired EGU is complex and impacts a number of important operating parametersincluding combustion efficiency, steam temperature, furnaceslagging and fouling, and NOXformation. Thetechnologies include instruments that measure carbon levels in ash, coal flow rates, air flow rates, CO levels, oxygenlevels, slag deposits, and burner metrics as well as advanced coal nozzles and plasmaassisted coal combustion.0.15 to0.84%Cooling System HeatLoss RecoveryRecover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circulationthorough a cooling tower or discharge to a water body. Theidentified technologies includereplacing the cooling towerfill (heat transfer surface) and tuningthe cooling tower and condenser.0.2 to 1%Flue Gas Heat RecoveryFlue gas exit temperature from the air preheater can range from 250 to 350°F depending on the acid dew pointtemperature of the flue gas, which is dependent on the concentration of vapor phase sulfuric acid and moisture. Forpower plants equipped with wet FGD systems, the flue gas is further cooled to approximately 125°F as it is sprayedwith the FGD reagent slurry. However, it may be possible to recover some of this lost energy in the flue gas to preheatboiler feedwater via use of a condensing heat exchanger.0.3 to 1.5%Low-rank Coal DryingSubbituminous and lignite coals contain relatively large amounts of moisture (15 to 40%) compared to bituminous coal(less than 10%). A significant amount of the heat released during combustion of low-rank coals is used to evaporate thismoisture, rather than generate steam for the turbine. As a result, boiler efficiency is typically lower for plants burninglow-rank coal. The technologies include using waste heat from the flue gas and/or cooling water systems to dry lowrank coal prior to combustion.0.1 to 1.7%SootblowerOptimizationSootblowers intermittently injecthigh velocity jets of steam or air to cleancoal ash deposits from boiler tube surfaces inorder to maintain adequate heat transfer. Proper controlof the timing and intensity of individual sootblowers isimportant to maintain steam temperature and boiler efficiency.The identified technologies include intelligent or neuralnetwork sootblowing (i.e., sootblowing inresponse to real-time conditions in the boiler) and detonation sootblowing.0.1 to 0.65%Steam Turbine DesignThere are recoverable energy losses that result from the mechanical design or physical condition of the steam turbine. Forexample, steam turbine manufacturers haveimproved the design of turbine blades and steam seals which can increaseboth efficiency and output (i.e., steam turbine dense pack technology).0.84 to 2.6Source: National Energy Technology Laboratory (NETL), 2008.Reducing CO2Emissions by Improving the Efficiencyof the Existing Coal-fired Power PlantFleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 2008. Available at:<http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>.aReported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual component efficiencies for a referenceplant as follows: 87% boiler efficiency, 40%turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assumptions, the referencepower plant has an overall efficiency of 32%and a net heat rate of 10,600 Btu/kWh. As a result, if a particular efficiency improvement method was reported toachieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewise, a reported 100 Btu/kWhdecrease in net heat rate would be converted toa 0.30% point increase in overall efficiency.2 - 13 2.4 Previous Studies on Heat Rate Improvements
A number of studies using varying appro aches have been performed to determine potential efficiency improvement s and associated resulting CO 2 emission reductions. These approaches include characterizi ng the current U.S. coal-fired EGU fleet, identifying potential efficiency improvements, and applying improveme nt actions to existing EGUs. The approach taken within each study varies. Five studies are briefly summarized and co mpared in Table 2-3. The NETL studies used a benchmarking approach that evaluated the design factors that are known to influence efficiency and grouped E GUs based on similar design characteristics. The studies categorized the indus try 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 improve d 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 th eoretically possible. The Lehi gh study used a less detailed approach and evaluated technol ogies applicable to bituminous and subbituminous coals to estimate potential flee t wide reductions. An alternative approach to evaluate heat ra te improvement is used by Resources for the Future. This study focused on the operating efficien cy (synonymous with heat rate) of the entire existing U.S. coal-fired EGU fl eet. The authors evaluated decad es of data from industrial responses to economic factors such as demand, co al price and energy polic ies. This approach sought to estimate overall change s in industry fleet efficiency in response to changes in fuel prices or carbon prices. In one specific exampl e, the coal price was assumed to be a 10%
increase and the CO 2 emissions tax at $1.64 per ton for h eat rate reductions of 0.3 to 0.9%. The National Resources Defense Council approa ch considered the fleet of coal-fired EGUs and assumes a target heat rate in order fo r 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 CO 2 emissions limit will be averaged, the paper disc usses many potential options that coal-fired2 - 14 EGUs may have to meet new standards. These op tions include additional options such as adding renewable, natural gas, and combined-cycle sources. The EPA observed that existing HRI studies using a benchmarking approach have relied on a single year annual average he at rate data, whereas many of the operational impacts on heat rate (diurnal temperatures, load following, etc) b ecome 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 re veal patterns of performance that should also inform estimates of HRI potential. For these re asons, the EPA has developed in this TSD an additional assessment of HRI potential that draw s on multiyear historical hourly data. While we understand that engineering judgment remains essent ial to a proper interpre tation of the results,
the EPA intends that this assessment be a more substantial basis for estimating the fleet-wide HRI potential for coal-fired EGUs. Table 2-3. Summary comparison of previous studies on EGU heat rate improvements
Study

ID Study Title Author Factors Used for Industry Grouping Key Study Assumptions Relevant HRI Results 1 “Reducing CO 2 Emissions by Improving the Efficiency of the Existing Coal-fired Power Plant Fleet” NETL Plant design: age and steam cycle Category improvement is the bottom 90% of EGUs in each group improving their heat rate to the average performance of top 10% in that category Units with capacity factors under 50% were removed from dataset 15% reduction in overall heat rate of coal-fired EGUs

2 “Improving theEfficiency of CoalFired Power Plants for Near Term Greenhouse Gas Emissions Reductions” NETL Plant design: coal type, steam cycle, and size Category improvement is the bottom 90% of EGUs in each group improving their heat rate to the average performance of top 10% in that category • Units with anomalous data, capacity factors under 10%, using less than 97% coal, and gasification plants were removed from dataset Low pressure subcritical units and 0-200 MW subbituminous units 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% improvementfor bituminous coalfired EGU2 - 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 CO 2 emissions of about $1.64 per ton, improving heat rates by 0.3 to 0.9 %

5 “Closing the Power Plant Carbon Pollution Loophole: Smart Ways the Clean Air Act Can Clean Up America’s Biggest Climate Polluters” NRDC Plant design: coal type and steam cycle Improvements are broken into 3 group • Top 10% for each subcategory, no change • Top 11% to 49%, improve heat rate by 50% of the difference between facility heat rate and performance of top 10% in class or 600 Btu/kWh, whichever is less • Bottom 50%, improve heat rate by 100% of the difference between facility heat rate and performance of top 10% in class or 600 Btu/kWh, whichever is less Broader analysis not strictly focused on heat rate improvements. 11% reduction on overall fossil fuel-fired EGU emission rates; includes switching from coal to natural gas.

2.5 EPA’s Heat Rate Improvement Assessment
This EPA assessment of fleet-wide HRI potential looks at historical data from coal-fired EGUs in the U.S. to identify changes to EGUs’ h eat rates – the amount of heat input required, on average, to generate 1 kWh of electricity – that can be attributed to operation and maintenance practices and equipment upgrades. These heat ra te 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 po rtion of the study relies on unit-le vel 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 se ction. Unit-level
evaluations allowed the EPA to r ecognize the significant heterogene ity of coal-fired EGUs; even2 - 16 ‘sister’ units, units built at the same time at a given facility, may disp lay different operating profiles and may have different equipment, c ontrols, fuel mixes or cooling systems. Based on literature reviews; informal interv iews with engineering experts, vendors, and plant operators; and historical information collect ed by the EPA, we believe EGUs achieve heat rate improvements by: 1) operating under reco mmended operation and maintenance conditions (best practices), and 2) instal ling 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 component s. Upgrades often involve higher costs and greater downtime, such as, extensive overhaul or upgrade of major equipment (turbine or boiler) or replacing existing component s with improved versions. The EPA developed unit-level statistics fr om over 60 million rows of hourly data. We
evaluate each unit on its individual performance using heat rate vari ability as an indicator of the application of best practices a nd potential for improvement. To estimate heat rate improvement through equipment upgrades we survey engineeri ng studies, examine year-to-year trends, and research EGUs where such methods were applied.

2.5.1 Study Population and Data The EGU study population consists of 884 coal - and petroleum coke-fired EGUs that reported both heat input 3 and electrical output to the EPA’ s Clean Air Markets Division in 2012. 4 It includes a wide range of c onfigurations, from 24 to 1,500 MW nameplate capacities, super and subcritical thermodynamic cycles, between 1 a nd 69 years old, and diffe rent 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 he at input (Btu), and electricity output (MWh) data from the Clean Air Markets Divi sion 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 th e fuel being combusted and the average hourly measurements of CO 2 flow and concentration.
4 Information on the Clean Air Markets Division data is available at http://ampd.epa.gov/ampd/2 - 17 meteorological data were used to account for temp erature impacts on heat ra tes. The eleven year study period is representative of a wide range of conditions, including gr owth and recessionary economic conditions, changing electricity genera tion from renewable and natural gas, and different regulatory constraints. The hourly heat input and ge neration 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 measuremen t and reporting to support emission trading programs, including the Acid Rain Program and Cl ean Air Interstate Rule (CAIR). However, the EPA recognized that there will be times when emission data are not available due to monitoring system malfunctions or maintenance, technical challenges, or missed qua lity 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 incu rs an economic penalty because, at the end of the compliance period, an EGU must surrender allowances equa l 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 reco rds) with atypical a nnual heat rates less than 6,500 or greater than 15,000 Btu/kWh resulting fr om a variety of factors including firing of natural gas, very low operating time, or errors in reported gro ss load. Table 2-4 summarizes the heat input, electric generation, heat rate and un it counts by year for the study population used in this work. This population corresponds to 9,388 un it-years of data at 884 distinct EGUs. Figure 2-1 displays the study population average gross heat rate by year and the 11-year average.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 Aver age Gross Heat Rate by Year

NOAA’s Integrated Surface Data (ISD) pr oduct 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 us e meteorological data from stations that are reasonably close to th e 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 10,00020022003200420052006200720082009201020112012Heat Rate (btu/kwh)

Annual Heat Rate 11-yr Avg Heat Rate2 - 19 impact of ambient conditions. For each of the plants in this st udy’s database, we identified the nearest stations that reported a minimum of 8,4 00 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 statio ns over the eleven-year study period. The average distance between the plant and the nearest sta tion was 22 miles and the average difference in elevation was 366 feet. At nine plants we used da ta 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 approxim ation of ambient meteorological conditions at each facility. Joining the trimmed heat rate a nd hourly temperature datasets resulted in 61,848,580 hourly records for the study popula tion of EGUs (see Table 2-4). The results of this study are based on analys es of data from the population of coal-fired EGUs shown in Table 2-4. These units emitted 1,605 million tons of CO 2 in 2012. In contrast,
emissions of CO 2 from coal-fired EGUs in the entire U. S. electric power sector in 2012 were at 1,669 million tons according preliminary data from the EIA. 7 Since the stu dy population of
EGUs accounted for over 96% of CO 2 emissions from the fleet of U.S. coal-fired EGUs, the
EPA considers the results of this study to be applicable to th e 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 de sign characteristics are not eas ily 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. Alt hough the EPA believes grouping by categories can provide a useful way of understand ing the operating profile of an E GU and the fleet, the range of heat rates for the broad categories has significa nt overlap (see box and wh isker chart in Figure 2- 2) and therefore makes it challenging to deve lop appropriate categoriz ation. The figure below displays available information on coal-fired E GUs 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 Subcategory 8

2.5.3 Observed Trends in the Period 2002-2012
Three trends are notable for the study population during the period 2002-2012. Comparing the averages of the first three ye ars (2002-2004) to the last three years (2010-2012), electric generation and gross heat rate declined by 9% and 2%, re spectively. Capacity factor for the study population fell by 14% comp aring the same time periods (see Table 2-5). The decrease in coal-fired generation and cap acity factor may be because of reduced demand for electricity resulting from the recession starti ng 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 shar e of generation. In addi tion, 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 a pproximately 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 supercr itical, 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)Subcategory2 - 21 Table 2-5. Reported Annual Capacity Factor 2002-2012 9

Year Capacity Factor (%) 2002 68 2003 69 2004 70 2005 71 2006 70 2007 71 2008 70 2009 62 2010 65 2011 61 2012 53
2.5.4 Startups and Shutdowns - Impact On the Results of This Study During periods of startup and shutdown, EGUs are known to operate at higher heat rates. Therefore, we evaluated the poten tial impact of such events in our study. A startup event, as defined here, occurs when an EGU begins combus ting fossil fuel and generates some measurable amount of electricity. Table 2-6 summarizes th e study population average, maximum and total starts by year. On average, coal-fired EGUs st art combusting fuel and generating electricity 11 times per year. The total number (approximately 9,000) of star ts for the study population of EGUs has remained stable over the study period. Ou r data reflects that some coal-fired units operate in a load following capacity and may repor t upwards of 200 starts in a single year, but these units tend to have low annual capacity fact ors. 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 numbe r of starts is an important va riable at a small number of EGUs, its impact on heat rate performance evaluate d 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 h eat rate improvement availabl e with existing coal-fired steam EGUs, the EPA conducted a number of quantitative analyses. These included: (1) regression analyses to understa nd the impact of capaci ty factor and ambient temperature; (2) using a bin model to determine the potential fr om 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 a ffect 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 typicall y use digital control systems to capture hundreds of data points in n ear 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 independe nt 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 consta ntly changing operating c onditions. We tested for collinearity between capacity factor and ambi ent 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 characteristic s 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 perf ormance 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 in crease. 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 opera ting. 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 year ly utilization levels becomes more complex. For example, an EGU may run at an annual 60% capacity factor by operating 8 months at near full cap acity and generating no electricity the rest of the year, or it may run at lower utilization levels for most hours of the year in response to weather, generation cost, and transmission constraints. Ambient temperature can affect heat rate in two ways: 1) the efficiency of the thermodynamic steam cycle 11 and, 2) in many regions of the country, as temperatures increase electricity demand and capacity factor follow. Figure 2-3 shows the average monthly capacity factor in 2012 alongside the clim ate 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 a nd generation in most parts of the U.S. occur on the hottest days of the year. Yet, the relationship between ambi ent temperature and capacity factor is complex. Each plant responds differently depending on de sign, 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 ke y 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 effectiv eness of the cooling system, the condenser, and, therefor e, overall plant efficiency.
12 Climate normal is the average of temperature (or other meas ure) over a prescribed 30-year interval and location. The chart shows the 1981-2010 climate normal monthl y 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 perfor med three regression analyses for each unityear: heat rate onto capacity factor, heat rate onto ambient temperat ure and heat rate onto capacity factor and ambient temper ature. Since this analysis seek s to evaluate heat rate under normal operating conditions, we re moved records with hourly heat rate values outside of +/- 2.6 standard deviations (1.9% of record s) 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 percen t 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 varyi ng 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 toge ther explain 26% of the change in heat rate for the study populat ion 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-s quared for each unit multiplied by its annu al generation divided by the sum of generation for all units. 0 20 40 60 80 100JanFebMarAprMayJunJulAugSepOctNovDec 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 attribut able to capacity factor and 10% to ambient temperature. These results, however, conceal consid erable 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 influe ncing changes in heat rate. At approximately one-fourth of the study popul ation 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 ar e typically, but not excl usively, units with oncethrough, fresh water, cooling systems. Identifyi ng temperature-responsive EGUs allows us to understand why heat rate may increase during pe riods of peak demand. These are the EGUs where the ambient temperature ‘signal’ is an important variable. At a typical EGU, summer month heat rates may increase by 2-4% comparedto winter months, but at a temperatureresponsive EGU that figure may be as high as 10%. Our analysis indicates that as EGUs moved from base lo ad to load following, capacity factor tended to have a larg er 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 cycl es in 2008 to 2012. A significant share of 2008 generation occurred at EGUs running at greate r than 84% annual capacit y factor. In 2012, little generation took place in that bin or above, and ge neration 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 Indica tion of Improvement Potential This study examines heat rate variability from the standpoint of statistical process control, which is utilized th roughout the power industry. Several years ago, the EPA introduced process control charts for auditing emissions da ta reported under Part 75. Sources and vendors adopted the EPA methodology to identify potential problems earl y, before they significantly
affect emission measurements. Heat rate lends its elf 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 opport unities 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 comp are with the study population. Each unit has up to eleven RSD values, i.e., one for each opera ting 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 essentia l to process control the introduction of increasingly sophisticated digital control systems (DCS) presents new opportunities for find ing 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 populati on 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 m ean RSD of 5.4%. Notably, the EGUs in the top quartile are not outliers; they re port a third of all generation – the most of any segment. The results display a wide range of heat rate variab ility in the study population and thereby indicate the potential for heat rate improvement.

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

Quartile RSD Average RSD Minimum RSD Maximum Share of Generation

1 3.5 1.6 4.2 33
2 4.8 4.2 5.3 26
3 6.1 5.3 7.0 24
4 9.8 7.1 25.2 16
The study also examined EGU heat rate vari ability using the resi dual heat rate. The residual in a regression analysis is the difference between the observed value of the dependent variable (heat rate) and the predicted value. The intercept is the value where the linear regression crosses the y-axis. For each EGU, we calculated the residual heat rate by summing the residual for each hour to the intercept value. The standard de viation of the residual heat rate statistic is
used to understand the amount of variability that is not explained by capacity factor and temperature. The average RSD corresponding to residual heat rate variability for each EGU is the generation-weighted average of up to eleven annual values. The study population generationweighted mean RSD over the study period is 4.5%. This perc entage represents the total variability across the study populat ion 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 te mporal 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 gene ration-weighted average RSD of residual heat rate over the study period in quartiles ordere d by the 11-year generation-wei ghted 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 quart ile is 2.7% – well below the study population average of 4.5 %. This means that the residual hour ly heat rates of these un its generally stay in a narrow range within a given year. The maximu m 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 quartil e is 6.9%, which is over twice that of the top quartile. This spread indica tes that there is likely room fo r 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 rate s calculated from reported data. The study population generation-weighted an nual RSD ranges between 5% and 6% during the study period. Figure 2-5 below summarizes the re sults of regressing RSD of heat rate onto annual heat rate. 19 The r-squared result is 57%. These results indica te that, other factors he ld equal, if an EGU reduces heat rate variability, generally heat rate performance will improve.

18 This table excludes nine units where RSD exceed ed 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 reporte d 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 leas t 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 thr ee categories: EGUs with at least one year-toyear decrease in heat rate > 8.5%; units with at least one year-to-year in crease in heat rate > 8.5%; and, units with both. We examined whether th e large heat rate chan ges were due to `yearto-year changes in capac ity factor and found no correlation betw een the year-to-year changes in heat rate and capacity factor for a ny 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 annu al capacity factor fell under 50 percent the count is 313 EGUs.
21 The correlation remains weak even when limited to cases wh ere 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 alternativ es. 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 usin g 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 9950RSD heat rate %(generation weighted)

Heat Rate2 - 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 qua rtile, in Table 2-11 below, is consistent with the study population results shown in Table 2-8. Th is does not imply that the changes associated with a large year-to-year d ecrease, 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 effici ency, heat rate variabil ity 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 improve ment potential via best practices As mentioned before, across the study period, the effects of hourly capacity factor and ambient temperature explain a generation-weight ed 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 ra te 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, th e range of variation i ndicates that significant potential for heat rate improvement is available through the application of best practices. To control for known factors, the EPA c onstructed 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-squ ared 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 va lue 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 repor ted hourly heat input greater th an p10 by a percentage of the distance between the reported va lue and p10 (e.g., 50% of the diffe rence). The same statistical procedure is applied to every hour of heat input data in each bin. These reduced hourly heat input values are then used to calculate a reduced 11-ye ar average heat rate for each unit. The percent difference between a unit’s reported 11-year average heat rate and the heat rate that corresponds to reduced heat inputs is the potential heat rate improvement for that unit. 26 Using this approach,
those units with the lowest vari ability (e.g., in the top quartile of residual heat rate variability) take proportionally smaller reductions.
Table 2-12 below shows the model results wi th 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. Al ternatively, a 50% reduction will result in a 6.7% study population wide improvement in heat rate. In eff ect, the model proportionate ly 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 minimu m of 30 values in each bin has a mode st 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 impr ovement potential via best practices

% Reduction from reported heat input to p10 Study population heat rate (Btu(kWh-gross) 27 Reduced study population heatrate (Btu/kWhgross) Study population heat rate improvement (%)

10

9,753 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 fi ltering out those cases that may have been the result of changes in capacity factor, reporting me thod, or other events, we identified 16 EGUs that reported a single year-to-year heat rate improvement of 3-8%. In two of these cases we were able to identify equipment upgrades 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 resear ch was unable to confirm specific equipment upgrades, based on the elimination of other possi ble 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 provid ed data for seven coal-fired units at three anonymous plants with details on specific actual and potential e quipment modifications. These included turbine efficiency and condenser pe rformance upgrades, installation of variable frequency drive fans, reducing boiler air in-leak age and others. Togeth er, these actual and potential measures were projecte d to achieve from 0.25% to 3.5% heat rate improvement at the
seven EGUs An EPA study (SRA, 2001) de scribes WEPCO’s two-phase e fficiency program at four coal-fired plants over a ten-year period. In the first phase, 1990 – 1994, WEPCO installed equipment upgrades that included re tractable turbine pack ing, 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 ra te improvements ranging from 2.3% – 4.1% as a result of the equipment upgrades. In th e second phase, 1995-2000, WEPCO implemented changes that would generally fall into the be st practices category: equipment control and metering upgrades, boiler cleaning, feed water he ater 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 engi neering studies available in th e literature and selected the Sargent & Lundy 2009 study as the basis for our asse ssment 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 bo ttom) shown in Table 2-13. The first nine items in Table 2-13 contribute about 15 percent of the to tal average $/kW cost fo r 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 be lieve properly fall into the category discussed here as upgr ades. 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 Replacement2 - 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 performan ce 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 cha nges: best practices th at 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 ar e supported by the va riability analysis using 11 years of hourly data applied to each un it. This analysis found that th e top quartile of EGUs reported significantly lower heat rate va riability than the st udy 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 cons ervatively 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 meas uring 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% improve ment and the best single year over 6%. EPA believes that the minimum three-year moving averag e heat rate is a reasonable target for the improvement potential from applying best practice s. Single year results could be due to unusual conditions, such as, an extended outage or weat her. Using three consecu tive years tends to smooth out the effect of equipment maintenan ce cycles and unusual mete orological patterns.2 - 35 The equipment upgrades results are supported by numerous studies 28 and by the EPA’s analysis of the costs and associated improvements in heat rate that can be attributed to equipment and system upgrades. We considered that a 4% reduction in heat rate might be achieved on a coal-steam unit by applying the four higher cost upgrade actions described in Table 2-13 above. However, because details of current actual un it configurations are unknown, and some units may have applied at least some of the upgrades, we co nservatively 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 st udy population of EGUs accounted for over 96% of 2012
electric sector CO 2 emissions from coal.

2.5.11 Sensitivity Analysis Removing Pl anned 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 reducti on of 4% under the 30 percent diffe rence 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 co st. These HRI capital co sts can be economic to incur if they yield sufficient redu ctions in other current or potentia l costs, particularly reductions in coal fuel cost and any cost related to CO 2 emissions. For the purpose of this TSD analysis, it is
assumed that HRI can be economic if the annua lized net savings (coal cost savings plus CO 2 emission cost savings minus capital cost) is positive: Annual Net HRI Savings = Coal Cost Savings + CO 2 Emission Cost Savings – Capital Cost 30

28 See discussion in Table 2-3 above, and the HRI Pa rtial 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 met hods. 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 ma terially affect the economics of broadl y combined HRI methods. The analysis therefore does not consider the small econom ic impact of HRI-related O&M costs.2 - 36 2.6.1 Heat Rate Improvement Ca pital Cost Assumption The 2009 Sargent & Lundy study describes numer ous 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 thes e methods and unit sizes are combined, as though they were all applied on a single EGU, th e following range of Sargent & Lundy estimated Btu reductions and associated range of capital cost s are obtained:
Combined Min-Max HRI Btu Reduction: 415-1205 Btu Combined Min-Max HRI Capital Cost: $40-150/kW 31 EPA Assumed Combined HRI Capital Cost: $100/kW The wide ranges of estimated HRI Btu and costs are indicative of the wide range of real differences in the many details of site specifi c EGU designs, fuel types, age, size, ambient conditions, current physical condition, etc. This TSD analysis therefore assumes $100/kW as a representative combined HRI cap ital 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 IPM v5.13 Base Case modeling. As indicated by the Sargent & Lundy estimates given above, HRI methods could possibly reduce this average coal fleet heat rate by about 400 to 1200 Btu/kWh, or by about 4% to 12% of the projected 2020 average, provided that all units were able to apply all of the combined HRI methods. The proviso is important to this analysis because the EPA e xpects that a significant fr action of the coal fleet has already applied some or ma ny of the available HRI methods. 32 The EPA does not have sufficient site speci fic information to accurately estimate what percentage of the fleet has adopted various HR I methods, nor how effectively, and is not aware of any other investigator havi ng sufficient information. HRI po tential 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 cu rrent equipment configura tions of the coal steamEGU fleet. The EPA therefore analyzed 11 years of historical heat rate da ta 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 a nnual 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 HR I methods reduce net heat rate w ithout 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 th at 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 Brea keven Economic Analysis Figure 2-6 presents a simple breakeven ec onomic analysis for combined HRI methods using the assumptions described a bove, also assuming there is no CO 2 emission cost that is
reduced via HRI.

Figure 2-6. HRI Breakeven Economics
Notes: 1. Capital cost S/MWh assumes the following: HR I capital cost = $100/kW; capital charge rate = 14.3%; IPM projected 2020 annua l 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/kW2 - 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. Fo r 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 le ss than the assumed $100/kW, 6% HRI would
then become economic at lower coal costs. Fo r example, if the aver age 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 bitumi nous 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 reduc tion were only 4% instead of th e assumed 6%, at a cost of $100/kW, average coal costs would have to exceed $4/MMBtu for 4% HRI to be economic fleet wide,

 But, if the average heat rate reduction were 4% at a cost of $50/kW, HRI could become economic at an average coal cost of about $2.50/MMBtu.

 If there were additional HRI sa vings due to avoided future CO 2 emission costs, HRI could become economic at lower coal costs, or at higher capital cost s, or at lower heat
rate reduction percentages.

2.6.4 U.S. Coal-steam EGUs – Estimated Fleet-wide CO 2 Reduction and Cost via HRI It is possible to make an order-of-magnitude estimate of the fleet-wide extent and costeffectiveness of HRI using reasonable a ssumptions as in the following example: Fleet-wide 2020 Assumpti ons (basis: similar to IPMv5.13 Base Case):

33 EIA, Electricity DataTable 7.4, Av erage Weighted Cost of Fossil Fuels for the Electric Power Industry 2002- 2012, http://www.eia.gov/electri city/annual/html/epa_07_04.html2 - 39  Coal fleet capacity applying combined HRI methods = 244,000 MW

 Average CO 2 emission rate = 0.976 tonne/MWh net

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

 Average capacity factor = 78%

 Pre-HRI CO 2 emissions = 1.62 billion tonne/yr (calculated)

 HRI Btu and CO 2 reduction = 6%

 HRI capital cost = $100/kW

 Annual capital charge rate = 14.3%

 Average coal cost = $2.62/MMBtu

Estimated Fleet-wide Results:

 Fleet-wide CO 2 reduction via HRI = 97 million tonne/yr

 Total HRI capital cost = $24 billion

 Annualized HRI capital cost = $3.5 billion

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

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

 Annual net savings (cost) of CO 2 reduction = ($7.7/tonne)

2.6.5 Conclusion - HRI Economics This necessarily simplified HRI economic analysis supports the following summary
conclusions:

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

 If a fleet-wide average 6% HR I is technically feasible, it would also be economic on the basis of fuel savings alone, before consid eration of the value of the associated CO 2 emission reductions, on a fleet-wid e 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 associ ated average capital cost is as much as $100/kW, 6% HRI coul d become economic on the basis of fuel savings alone, before consideration of the value of the associated CO 2 emission2 - 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 an d an IPM projected 2020 coal price of $2.62/MMBtu, the fleet-wide cost of CO 2 reduction via 6% HRI would be a relatively low $7.7/tonne. Thus, although there is currently some uncertain ty associated with the costs of achieving a particular fleet-wide amount of HR I, it is clear that HRI is an available low-cost approach to
CO 2 reduction for existing coal-fired EGUs2 - 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) CO 2 carbon dioxide EGU electric generating unit EIA Energy Information Administration EPA Environmental Protection Agency heat input amount of energy consumed in a combustion unit (e.g., boiler) expressed in Btu heat rate improvement decrease in the amount of heat input required to generate 1 kWh of electricity
heat rate gross heat input required to genera te 1 kWh of electricity, expressed in gross Btu/kWh. MMBtu million Btu 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/res ource/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 EG U 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 options2 - 42 HRI Partial Bibliography PowerEng 2002, “Heat Rate Optimization Pays Dividends,” Power E ngineering, January 1, 2002, available at http://www.power-eng.com/art icles/print/volume-106 /issue-1/features/heatrate-optimization-pays-dividends.html
NETL 2008, Reducing CO 2 Emissions by Improving the Efficien cy of the Existing Coal-fired Power Plant Fleet, DOE/NETL-2008/1329, July 2008
NETL 2009, Opportunities to Improve the Efficien cy of Existing Coal-fired Power Plants, Workshop Report, NETL July 2009, available at http://www.netl.doe.gov/energyanalyses/pubs/NETL%20Power%20Plant%20Eff iciency%20Workshop%20Report%20Final.pdf
NETL 2010, Improving the Thermal Efficiency of Co al-Fired Power Plants in the United States, DOE/NETL Technical Workshop Repor t, February 2010, available at http://www.netl.doe.gov/File%20Lib rary/Research/Energy%20Anal ysis/Publications/ThermalEf ficCoalFiredPowerPlants-TechWorkshopRpt.pdf
NETL 2010a, Improving the Efficiency of Coal -Fired Power Plants for Near Term GHG Emissions Reductions, DOE/NETL-2 010/1411, 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 H eat 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 H eat Rate Performance of Pulverized Coal Fueled Boilers, Storm Technologies, Inc, EPRI 2009 Heat Rate Conference, available at http://www.stormeng.com/pdf/EPRI 2009HeatRateConference%20FINAL.pdf
NRDC 2013, Closing the Power Plant Carbon Po llution Loophole, NRDC Report R:12-11-A, March 2013 available at http://www.nrdc.org/air/pollution-st andards/files/pollution-standardsreport.pdf
Lehigh 2009, Reducing Heat Rates Of Coal-Fired Power Plants, Lehigh Energy Update, January 2009, available at http://www.lehigh.edu/~i nenr/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 Existi ng 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 Re porting and Improvement under the Provision of the Indian Energy Conservation Act – Output 1.1”, Evonik/ VGB 2008, available at http://www.emt-india.net/Power PlantComponent/Output1.1/Output1.1.pdf2 - 43 IEA 2010, Power Generation from Coal - Measuri ng and Reporting Efficiency Performance and CO 2 Emissions, OECD/IEA-CIAB 2010, available at http://www.iea.org/ciab/papers/ power_generation_from_coal.pdf
EPRI 2009, Renewed Interest in Reducing Heat Rate, EPRI Jour nal, Spring 2009, available at http://mydocs.epri.com/docs/Corpor ateDocuments/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 Electr ic 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%2 Fdocuments%2Fpublica tions%2Fspecial_in terest_publications%2FEPRI_Electricit y_Use_Report_Final_1024651.pdf&ei=Qo9yUvmwMY Xb4AOZz4GYAw&usg=AFQjCNElzekbt oSNCR5SKFwkfbKx83p0Uw&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.pdf3 - 1 

### Chapter 3: CO2 Reduction Potential fr om Re-Dispatch of Existing Units

Overview
This chapter explores the dynamics of power s ector dispatch and the cost-effectiveness of lowering the carbon dioxide (CO 2 ) emissions intensity of the power sector by substituting generation from the most carbon-int ensive existing EGUs and increa sing utilization, to the extent possible, of less carbon-intensive existing fossi l fuel-fired EGUs. More specifically, the examination focuses on opportunities to improve em issions 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 productio n and delivery in the
context of cost-effective CO 2 emission reduction opportunities.

Introduction
Electric system dispatch is typically defined as “the operation of ge neration 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 generato rs 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 transmi ssion grids that extend over large regions. Through these interconnections, EGU balancing author ities treat the product (i.e., electricity) of EGUs as fungible, calling for el ectricity generation supply to m eet demand usually by deploying the least expensive power source first. 35 EGU operators and balancing authorities must take into account se veral constraints in dispatch, including transmission constraints as well as emissi on control programs and other environmental requirements. Such programs and re quirements 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.pdf3 - 2 many years, EGU operators throughout the country have considered the emissions implications for pollutants such as SO 2 and NO x when scheduling unit dispatch, in response to costs and regulatory requirements. For example, EGU operato rs in 10 states partic ipating in the Regional
Greenhouse Gas Initiative have several years of experience with factoring CO 2 emissions limits directly into bids for economic dispatch. The electric system’s carbon intensity can be lowered through re-dispatch among existing EGUs, particul arly 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 faci lities to produce energy at the lo west cost to reliably serve consumers, recognizing any operational limits of generation and tr ansmission facilities.” 36 The cost of operating electric generato rs varies based on a number of f actors, such as fuel used and generator efficiency. Regional Transmission Or ganizations (RTOs) a nd Independent System Operators (ISOs) help coordinate economic dispatch over larger ar eas to help keep the cost of meeting electricity demand as low as possi ble, subject to opera tional constraints. The decision by balancing authorities to call upon, or dispatch, any particular generating unit is driven by the relative operating cost, or marg inal cost, of generating electricity to meet the
last increment of electric demand. These cost s 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 ma rkets 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 technol ogies, like renewables, hydroelectric, and nuclear, have lit tle or no variable costs and ar e 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 (wit h higher variable costs) will be called upon sequentially, such that total system demand is met. The economic orde r 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-integra ted utilities who own generation, transmission, and distribution infrastruc ture, the utilit ies themselves often form the balancing authorities who determin e unit dispatch. Such utilities ar e presumed to dispatch their units in a cost-minimizing fashion (seeking the lo west marginal cost), and they can arrange to buy and sell power with othe r balancing authorities.
In states that have restruct ured regulation to allow for co mpetition between generators, RTOs and ISOs are generally responsible for movi ng electricity across larger areas in the most efficient and least-cost manner possible. 39 They coordinate, contro l, 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 el ectricity (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 wholesal e 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, en sure efficient and reliable delivery of electricity,

38 http://www.eia.gov/todayinenergy/detail.cfm?id=7590
39 http://www.ferc.gov/industrie s/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 te rm energy needs of the state and consumers. Depending on market structure, PUCs also al locate costs among customers, design price structures and set price levels, set service qua lity standards, approve capital expenditure by utilities, and arbitrate disputes among relevant pa rties and stakeholders. In restructured markets, the PUC’s authority is generally applicable to the transmission and distribution system, since the generation and dispatch component is governed by RTOs and ISOs. In cost-of-service states, the PUC also has oversight of the genera tion and capacity planning components

.
Spot and Day-Ahead Markets RTOs and ISOs operate spot markets for w holesale 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 balanci ng 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 sche dules 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 typica lly 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 u tility 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 tr ansmission 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 author ities are responsible for the reliable operation of the bulk electric system. The bulk electric syst em refers to a large interconnected electrical3 - 5 system made up of generation and transmission faci lities and their control systems. To ensure reliability, system operators continuously analyz e real-time and forecasted load and transmission conditions to ensure that scheduled generation di spatch 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 Amer ican 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 CO 2 emission rates 42 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 consid erably 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 MW h from an average NGCC will result in notable
CO 2 emission reductions. The lower emission rate of NGCC conveys th e potential of re-dispatch to reduce GHG emissions. However, the actual potential to r ealize emission reductions through this technology depends on the availability and capacity factor s 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 fl eet that could displace generation from more
CO 2 intensive generating resources. The term “ava ilability” 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 sin ce that year included a leap day). The unavailable period is generally attr ibuted 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 docket3 - 6 maintenance, and unplanned outages. The EPA assu mes that NGCC has an availability of 87%. 43 Other reports suggest that NGCC availab ility 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-d ispatch opportunities to unused NGC C generation potential in the system, the EPA reviewed recent NGCC fleet oper ating 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 generatio n 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 re lationship of a unit’s tota l net generation relative to its net generating capacity (i .e., capacity factor). Net gene rating capacity is a function of weather/temperature conditions at the site, which varies throu ghout the year. While some units may model actual weather adjusted capacity by th e hour/minute, these data are not reported for the fleet. Therefore, the EPA used the nameplat e capacity reported for units. The net generation was divided into the nameplate generation cap acity 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 genera ting capacity. Whereas availability refers to the maximum amount of generation that could be expect ed 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 fl eet had an average cap acity 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 histor ical data suggests that there is a significant potential for re-dispatch from higher CO 2 emitting resources to lower emitting NGCC generation.

Availability for NGCC fleet................................87% to 92% 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/a rticles/print/volume-115/issue-2/featur es/higher-availab ility-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 fr om re-dispatch, the EPA considered alternative capacity factor le vels at which the NGCC fleet c ould 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 collec tively 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 proj ections describing NGC C 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 availa bility factors.

Table 3-1: Existing NGCC Capacity, by Age 47

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 Ener gy Data Systems (NEEDS) file at http://www.epa.gov/powersectormodeling/BaseCasev513.html
47 See “Operable” worksheet in “Generator Y2012” Workbook in 2012 Zip file at http://www.eia.gov/electricity/data/eia860/3 - 8 Of 464 NGCC plants generating in 2012 and gr eater 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 fa ctor that was 70% or greater (see Figures 3-1 and 3-2). 48

Figure 3-1: NGCC Plant Distributio n by Capacity Factors (2012)

49
Table 3-2: Plant Distributio n 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 categories3 - 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 disp atched to provide base load power. While only 10% of plants operated at 70% or higher capacity factor on an annual ba sis, the fleet of NGCC units was relied upon heavily during certain period s of time, in response to higher demand. On a seasonal basis, a significant number of units ach ieved capacity factors gr eater 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 electric ity 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 wint er of 2011/2012 and 20% hit that level or higher during the

summer. 52 During periods where demand levels are t ypically lower, some NGCCs were idled or operated at lower capacity factors. Nonethele ss, a notable number of existing NGCCs have demonstrated the ability to achieve a 70% capac ity factor for extended periods of time. These units achieved high capacity factors without adve rse effects on the electric system. While many units demonstrated an ability to deliver net generation that was more than 70% of their nameplate capacity, the EPA assumed that 70% was a reasonable fleet-wide ceiling for each state. 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 th e production of natural gas have helped reduce natural gas prices and improved the competitive pos ition 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 Au gust. Winter defined as December, Ja nuary, and February. Estimates are for units for which data was provided to EPA. See f ile 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 CO 2 requirements. 2012 net generation from NGCC units grew to 981 TWh, up from 796 TWh in 2011 (22% growth in one year). The extent of this capability varies by region, based on factors such as the mix of EGU types and the amount of available NGCC capacity. An analysis of historical di spatch 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 di spatch, for each technology. While there are wideranging costs at the unit level, an aggregated a ssessment of the relative economics is informative and can provide a metric for assessing the implica tions of dispatch as it relates to emissions of
CO 2. The potential for redispatch from CO 2 intensive sources to less CO 2 intensive sources is
evidenced in historical data. EIA form 860 a nd 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 signifi cant redispatch from coal to gas over just a one year period demonstrates the ability for th e quick re-dispatch in response to market or
economic drivers.
The increase in the NGCC utilization was in la rge part driven by the decrease in natural gas prices to historic lows (s ee 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 incen tive that a $15/metric ton of CO 2 price signal would create. 53
This historical data also show s a sharp increase in the NGCC fl eet’s capacity factor from the high 30s to the mid 40s. During that same pe riod, net coal generation dropped by an amount similar to the increase observed in NGCC net ge neration. 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 con tinued and increasing redispatch to NGCC generating technology. 54

Table 3-3: 2011 and 2012 Gas and Coal Generation 55

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 u tilization level of the ~ 90 th 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 reas onable ceiling in the EPA’s BSER approach. For purposes of establishing state goals, hist orical 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 ex isting 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 calculat ed to collectively generate 1,390 TWh. Adding in the existing sources that were not yet onl ine in 2012 (under construction) increases total NGCC generation calculated in th e goal setting to 1,444 TWh. Although, states may choose to comply w ith 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/#rel ease=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-dispatc h 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 Genera tion 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 domesti c natural supplies. Increases in the natural gas resource base have led to fundamental changes in the outlook for natu ral gas. There is general agreement that recoverable natural gas resources will be substantially higher for the foreseeable future than previously anticipated, exerting downw ard 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% ove r that same timeframe (from 19.2 TCF to 25.3 TCF). EIA’s Annual Energy Outlook for 2014 proj ects that production w ill further increase to 29.1 TCF, due to increased supplies and favorab le 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/ipdbprojec t/IEDIndex3.cfm?tid=3&pid=3&aid=6
60 Assuming 1,024 Btu/cubic foot and 10,000 Btu/KWh3 - 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 natu ral gas is far bigger th an was thought even a few years ago,” after large increases in shale resource estimates. 61

Figure 3-2: U.S. Natural Gas Technicall y Recoverable Resources (from NPC, 2011) 62

Technical Considerations
Emission reductions through re-dispatch are larg ely 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 (h ttp://www.npc.org/PD_ update-80112.pdf)
62 http://www.npc.org/reports/rd.html3 - 14 For purposes of economic dispatc h, most NGCCs have historical ly been operated to serve
base load or intermediate demand due to their hi gh efficiency and flexib ility of operation, with national average annual capacity f actors 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 consid ered 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 availab ility 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 gene rating 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 availabi lities reported by curre nt vintage F class and H class turbines from General Electric Power Systems). 69 Data reported to NERC fr om NGCC units greater than 50 MW in 1994 through 1998 shows similar availabi lity factors (generally exceeding 89 percent).

Natural Gas Pipeline and Electricity Transmission

63 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayine nergy/detail.cfm?id=14611 (for recent data)
64 EIA, Electric Power Annual 2009, http://www.eia.gov/electric ity/annual/archive/03482009.pdf (Table 5-2 for 2009 and earlier data)
65 NERC, 2008-2012 Generating Unit Statis tical Brochure – All Units Reporting, http://www.nerc.com/pa/RAPA/ gads/Pages/Reports.aspx
66 Power Engineering, Negotiating Availability Gu arantees for Gas Turbine Plants, 03/01/2001, http://www.powereng.com/articles/print/volume-105/issue-3/features/negotia ting-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/hig her-availability-of-gas-turbine-combined-cycle.html
68 http://site.ge-energy.com/corporate/n etwork/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 transm ission 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 th at 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 spec ific 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 complia nce. As a consequence of this averaging flexibility, constraints that occur at peak tim es 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 hi storically low natural gas prices. Power plant use of natural gas use in 2012 increased by 20% over 2011 70 and resulted in a national average capacity factor for NGCC of 45.8% on av erage, 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 a bove 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 th is hourly pattern across years shows the pattern to be stable. Since the av erage 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 aver age capacity factor. Th ese peak hours are the period when there are most likel y 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 considerati ons (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 averag e 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/todayine nergy/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, 20143 - 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 Natura l Gas Combined Cycle Power Plant Fleet

Although there can be site-specific constraint s on utilization at some NGCC facilities, several factors support the ability of the power and natural gas pipeline systems to respond effectively with increases in infrastructure when needed to alleviate these barriers. For example, in recent years, the power transmission syst em 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 tran smission 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 tec hnical description of these upgrades, see: http://www.pjm.com/~/m edia/committees-groups/commi ttees/teac/20131211/20131211- december-2013-pjm-boardapproval-of-rtep-whitepaper.ashx

.3 - 17 meet additional use of existing facilities will gene rally 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 leve ls of end-use energy efficiency, which will reduce the load on the electricity transmission and na tural 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 la rge amounts of new NGCC capacity in 2001 to 2003, or the delivery to market of un conventional gas supplies since 2008. 76 These pipeline capacity increases have added significant deliverab ility to the natural gas pipeline network to meet the potential demands from increased us e of existing NGCCs. Over a longer time period, much more significant pipeline e xpansion is possible. In previ ous studies, when the pipeline system was expected to face very large demands for natural gas us e 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 nota ble capacity expansions over the past five years, in response to increased natu ral gas supply estimates and advances in drilling techniques. 78 To examine the potential for increases in pi peline deliverability, the EPA analyzed the pipeline flow data from the Energy Informa tion 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 leve l of gas supply that can be firmly delivered to a particular region de pends on the amount of natural gas the will be required to be delivered out of the region to othe r regions. Consequently, it is important to focus on the net capacity – the diffe rence between inflow capacity and outflow capacity -- in the relevant areas. The regions used by EIA for m easuring regional natural gas deliverability are shown in Figure 3-4. Of these regions, the key regi ons 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 Ener gy, 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#pipelines3 - 18 gas consumption as a result of re-dispatch to comply with the proposed rule. These are the Northeast, Southeast, Midwest and Western regi ons 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 Area 79 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 fr om the period 2005 to 2010 can be used as an estimator of the potential increase in pipelin e 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 faci lities 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 de liverability in gas consuming areas increased by 21%. Since the powe r sector currently uses approximately 30% of
the total national natura l gas consumption, and gas usage in other sectors is expected to be

79 Source: Energy Information Administration, http://www.eia.gov/naturalgas/data.cfm3 - 19 essentially flat through 2020, this historical incr ease 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 Administra tion Natural Gas Pipeline Regions

Source: Energy Information Administration.

Recent pipeline construction has continued to su pport 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 servic e 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 ar eas of the US, and include both new pipeline

80 See www.eia.gov/naturalgas/pipelines/E IA-NaturalGasPipe lineProjects.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 Proj ect 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 thou sand 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 b illion (this is only a portion of the total transmission investment anticipat ed). Approximately 75 percent of the reported projects are high voltage (345 kV and higher), re presenting 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 patt erns, EPA employed the Integrated Planning Model (IPM), a multi-regional, dynamic, determin istic 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 prospectiv e environmental policies. IPM provides a wide array of projections related to the electr ic power sector and its relate d markets (including least cost capacity expansion and electricity dispatch proj ections) while meeting fuel supply, transmission, dispatch, and reliability constraints. Natural gas supply, demand, transportation, st orage, and related costs are modeled directly in IPM through the incor poration of a natural gas module. The module includes a detail rich representation of the natural gas pipeline ne twork inclusive of discount curves that represent the marginal value of gas transmission as a func tion 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.pdf3 - 21 designed to account for the cycli ng capabilities of EGUs to ensure that the model properly reflects the distinct operating ch aracteristics of peaking, cycl ing, 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 CO 2 emissions through re-dispatch. As pa rt of this effort, the EPA conduc ted an initial set of analyses utilizing the Integrated Pla nning 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-eff ectively 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-dispa tch from more carbon-intensive to less carbonintensive EGUs, the EPA introduced two addi tional elements to the IPM framework:

1. The application of a CO 2 charge to the variable cost of dispatch for all existing coal steam boilers, IGCC units, and oil/gas steam boilers greater than 25 MW and with a CO 2 emissions rate greater than 1,100 lbs/MWh. 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 cap acity expansion, electricity dispatch , and emission control strategies while meeting energy demand and environmental, transmission, di spatch, and reliability constraints. Full documentation of the IPM model can be found at http://www.epa.gov/powersectormodeling
84 In practice, unit dispatch does not respect state boundaries because l east-cost supply must be balanced with demand in real time over grid interconnects which span mu ltiple states (with the exception of the Electric Reliability Council of Texas interconnect). The design of this modelin g 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 constr aints necessarily forgo cost-effective opportunities to re-d ispatch units in different states; as a result, costs and pr ices in this analysis are overstated.
85 The addition of CO 2 costs represents a simple analytic ap proach to estimating the cost-effective CO 2 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 ef fect 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 Case 86 from existing NGCC of any size, plus existing coal steam, IGCC and oil/gas steam boilers greater than 25 MW and with a CO 2 emissions rate >1,100 lbs/MWh.

These elements test the economic and technical potential for re-dispa tch 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 s ources be offset in its entirety by an increase in output from that state’s existing NGCC capac ity. Utilizing IPM to conduct this analysis ensures an integrated, least-cost , technically 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 characteris tics (e.g., operating and maintenance costs, heat rate, turndown/cycling behavior).

Cost and Availability of Econom ic Re-Dispatch Opportunities In executing this analysis, the EPA conducte d 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 associat ed average $/tonne of CO 2 reduced by comparing emissions and costs against the EPA’s Base Case (the difference in total system cost divided by the difference in power sector emissions). While the charge is applied exogenously in IP M, the average $/tonne shown below is calculated from the modeling resu lts and projections by dividing the increase in total system costs (compared to the base case) by the total change in CO 2 emissions (compared to the base case).

86 http://epa.gov/powersectormodeling/BaseCasev513.html3 - 23 Figure 3-5: NGCC National Capaci ty Factor, 2020 Initial Analyses

The EPA believes average $/tonne – which is distinct from the $/tonne CO 2 cost imposed in these analytic scenarios on affected coal st eam, IGCC, and O/G steam EGUs – to be the most relevant metric in evaluating cost-effectiveness. System cost changes necessarily encompass all elements of cost across power, transmission, a nd fuel markets and therefore provide the most comprehensive perspective regarding cost-effectiveness. 58%

62% 65% 68% 71% 74% 75%

0% 10% 20% 30% 40% 50% 60% 70% 80%

‐ 10 20 30 40 50 60AverageCapacityFactor

Average $/tonne 

### Projected Capacity Factor for Existing NGCCs (2020)3 - 24 Table 3-6: Power Sector Emission Reductions Due to Re-Dispatch

CO 2 Cost Level Imposed National Average
NGCC Capacity Factor Average Cost ($/tonne) 2020 Power Sector CO 2 Emissions (MMT / % Change from 2020) Base Case 52% N/A 2,161 $10/tonne 58% $17 2,038 / -6% $15/tonne 62% $18 1,997 / -8% $20/tonne 65% $21 1,961 / -9% $25/tonne 68% $27 1,928 / -11% $30/tonne 71% $34 1,901 / -12% $40/tonne 74% $44 1,866 / -14% $50/tonne 75% $50 1,852 / -14%

Although the EPA views this estimated range of average $/tonne costs as reasonable, we expect the costs of implementing this requirement in a compliance 87 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 init ial analyses utilized CO 2 charges on the variable cost of dispatch for existing coal steam, IGCC, and O/G st eam with emission rates greater than 1,100 lbs/MWh and a capacity greater than 25 MW, as an analytic construct to induce redispatch behavior in the model to existing NGCC facilities. The CO 2 charge was applied uniformly to all states in order to quantify the ultimate amount of in-state re-dispatch opportunities available as that ch arge is increased acr oss scenarios. In th e initial analyses,
low levels of CO 2 charges produce cost-effective re-d ispatch opportunities relative to the Base Case in almost all states. However, as the CO 2 costs are increased to higher levels, economic re-dispatch, opportunitie s within some states may ev entually plateau – a point clearly illustrated in the d eclining 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 CO 2 charge in all states pr oduces an outcome for many
states where the additional CO 2 costs imposed on affected coal steam, IGCC, and O/G steam are not able to produce incremental econo mic re-dispatch at units within that state; therefore, the additional costs imposed by th ese higher CO2 charges overstate the actual $/tonne necessary to induce achieva ble 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 stat e boundaries. In one set of analyses, redispatch was allowed to occur across the multi-state regions defined by NERC assessment areas (subject to othe r real-world constraints specif ied in the model, including transmission limits). In these scenarios with greater re-dispatch flexibility, the system was able to achieve 8% greater CO 2 emission reductions at an identical CO 2 charge (relative to a scenario where it was limited on a state basis), demonstrating that the main analysis’s imposition of artifi cial re-dispatch boundaries on state borders overstates the
cost-effectiveness of re-dispatch potential.

To evaluate how EGU owners and grid operato rs could respond to a state plan’s possible requirements, signals, or incentives to re-dis patch from more carbon-in tensive to less carbonintensive EGUs, the EPA also analyzed an additi onal series of scenarios in which the fleet of NGCC units nationwide was required, on average, to achieve a specified a nnual 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 spec ified operating and reliability constraints for the scenario, including the aforementioned state-by-stat e generation levels from the base case. This constraint effectively requires states that decrease coal genera tion to offset, in equal amounts, NGCC generation. Collectively, states must achie ve the required capacity factor for NGCCs. The costs and economic impacts of the vari ous 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 reflecti ng a 65, 70, and 75 percent NGCC utilization rate, comparison to the business-as-usual case in dicates that the average cost of the CO 2 reductions achieved over the 2020-2029 period was $21, $30, and $40 per metric ton of CO 2 , respectively. However, we also note that the costs just described are higher than we would expect to actually3 - 26 occur in real-world compliance with this proposa l’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 utilizat ion rate target for the existi ng NGCC fleet requires an average national capacity factor of 63 percent. The EPA also analyzed dispatch-only scen arios where shifting of generation among EGUs was limited by state boundaries. In these scen arios with less re-dispatch flexibility, the cost of achieving the quantity of CO 2 reductions corresponding to a nationwide average NGCC unit utilization of 70% was $33 per ton.

Table 3-7: IPM Results from Re-Dispatch Scenarios Existing NGCC Average National Capacity Factor Re-Dispatch Constraint Average Cost ($/tonne, 2020- 2029) Average CO 2 Emissions (MMT, 2020- 2029) Reductions from Base Case (%, 2020-2029)

Base Case NA NA 2,215 NA 65% Regional $21 2,022 9% 70% Regional $30 1,969 11% 75% Regional $40 1,915 14% 65% State $22 2,024 9% 70% State $33 1,971 11%

Natural Gas Price Impacts
The extent of re-dispatch estimated in th is building block can be achieved without causing significant economic impacts. For exam ple, in neither of the 70 percent NGCC unit
utilization rate scenar ios – re-dispatch limited to regional or state bound aries – did delivered natural gas price projections increase by more than 10 percent in the 2020-2029 period, which is well within the range of historic al natural gas price volatility. For example, the year-to-year percentage difference in Henry Hub prices reported by the En ergy 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.htm3 - 27 within the range of histor ic electric price variability. For exam ple, 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 estimat ed costs per ton of
CO 2 , the actual implementation is expected to result in notably lower economic impacts, including natural gas pri ce impacts, and are considerably la rger than would actually occur in real-world compliance with this rule’s proposed goals.

Table 3-8: National Average De livered 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 a pproach for reducing carbon dioxide (CO 2 ) emissions from fossil fuel-fired electric gene rating units (EGUs) thr ough the substitution of electricity generated from renewa ble resources, referred to in this document as renewable energy (RE). The portfolio of available RE sources en compasses a wide variet y 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, wi nd, water, organic matter, and other resources regularly replenished by physical a nd biological cycles. To integrate the rapidly increasing and evolving portfolio of RE into th e Best System of Emission Reductions (BSER), the EPA has developed a proposed approach th at builds upon current st ate policy encouraging increased production of RE taking into account rene wable potential in particular regions of the country. Additionally, the EPA believes that the pla nned expansion of new nuclear generating capacity and the preservation of existing nuclear generating capacity represent a cost-effective
means to reduce CO 2 emissions at fossil fuel-fired E GUs by providing carbon-free generation that can replace generation at those EGUs. Increas ing the amount of nuclear capacity relative to the amount that would otherwise be available to op erate is a technically vi able and economically efficient approach for reducing CO 2 emissions from affected fossil fuel-fired EGUs. This TSD is intended to support discussi on 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 representati on within the RIA. Results from this chapter feed into the technical support document (TSD) on state goal setting. Cleaner gene ration is also addressed in TSDs on Survey of Existing State Actions, St ate Plan Considerations, Projecting EGU CO 2 Emission Performance, and Legal Memorandum. 4.2. Proposed Approach To estimate the potential RE available for incl usion as part of BSER, EPA developed an RE generation scenario that provides a target for how much of each st ate’s generation can be produced by RE based upon the current goals of l eading states in the sa me region, and allows4 - 2 each state to grow RE generation over time toward s that target, based upon that state’s current
level of RE. The method can be summarized as follo ws. First, the country is divided into regions. Second, an RE generation target is calculate d for each region, based upon averaging all 2020 RPS requirements in that region. Third, an annual gr owth factor is calculated that would allow the region as a whole to reach the regional RE target in 2029 as suming that RE generation would increase from 2012 levels beginning in 2017. Fourt h, the annual growth factor for a given region is applied to individual states’ 2012 RE generation to calculate futu re 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 gene ration levels for each state are used to calculate interim and final RE targets for that state. The proposed approach is derived from st ate experience with policies that drive investment in RE and the generation that result s from those efforts. The EPA focused on statelevel RE policy for several reasons. Every stat e in the union is produc ing electricity from renewable resources, and some states have achie ved significant levels of renewable generation, surpassing a quarter of in-state generation. Stat e-level RE requirements have been implemented in 29 states plus Washington, DC , representing all regions of th e country. Nine states have voluntary goals. 90,91 These state-level goals and requi rements have been developed and implemented with technical assistance from state-level regulatory agencies and utility commissions such that they reflect expert asse ssments of RE technical and economic potential that can be cost-effectively developed fo r that state’s electricity consumers. The proposed approach focuses on RE re quirements established through Renewable Portfolio Standards (RPS), which provide specif ic 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 th e RPS-based targets as a reasonable benchmark of regionally cost-effective RE generation which stat es could grow towards over time. While EPA’s proposed approach is derived from RPS data, stat es may also consider a broad variety of other RE policies to increase generation, such as perf ormance-based incentives, financial assistance

90 Database of State Incentives for Renewables and Efficiency, last modified March 2013, accessed at http://www.dsireusa.o rg/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 th e development of renewa ble sources and their interconnection to the grid and “l ead by example” strategies integr ating RE generation into state properties. 92 Because the EPA did not quantify potentia l 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-effectiv e 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 antici pated 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 cap acity 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 trajectorie s. 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 pe rcent increase in RPS oblig ations 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 ach ievable level of total RE generation within states. It does not represent an EPA forecast of bus iness-as-usual impacts of state policies or an

92 See State Plan Considerations TSD for a discussion of how states can incorpor ate such RE policies into their state plans for this rule.
93 Barbose, Galen, “Renewables Portfolio Standards in th e 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. 65 89E, 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 complian ce measure cited is inclusive of credit multipliers and banked RECs utilized for compliance, but excludes a lternative compliance paymen ts, borrowed RE Cs, 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 th e 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/do cs/fy14osti/61042.pdf.4 - 4 EPA estimate of the full potential of RE availabl e to the power system; rather, it is intended to represent a feasible development scenario that enables reductions of CO 2 emissions from fossil fuel-fired EGUs in all states and that is ge nerally consistent with ongoing trends in RE development. The scenario uses a level of perf ormance that has already been demonstrated or required by policies of leading states, while considering each state’s uni que 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 developm ent 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 impro ve from start year to target level of performance
4.2.5 Calculate RE targets for in terim 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 “Pr oposed 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 generati on has also changed rapi dly in the past few years, and states with RE policies have signi ficantly increased their renewable capacity. To4 - 5 characterize the current level of RE generation and total generation 96 , we have used the most current state-level data on genera tion: 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 fac ilities. Large existing hydroelectric facilities provide a large percentage of RE generati on 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 ot her states. Because RPS policies were implemented to stimulate the development of new RE generation, existing hydr oelectric facilities ar e 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 ch apter includes only non-hydropower RE in the target-setting calculations and in the RE gene ration levels used to inform the state goals calculated in this proposal. In Appendix 4-1, we provide a differe nt version of the RE generation targets that includes existing hydropower genera tion from 2012 for each state in the state RE targets. These targets that incl ude 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 we re included in the qua ntification of BSER-re lated 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 fr om 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 w ith 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 Columbia 99 - 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 generatio n, 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 char acteristics (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 in creasing 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 onl y 2% of new capacity additions. 100 The economics of the fastest growing RE technologies – on-shore wi nd and solar photovoltaics (PV) – are improving and are competitive in many regions. In 2012, cu mulative installed wind capacity increased by nearly 28% and cumulative installed solar PV cap acity 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, instal led wind electricity capacity increased more than 23 fold
between 2000 and 2012. 102 Solar electricity generating capaci ty 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 installe d, bringing total solar U. S. solar capacity to
12.1 GW. 104 The National Renewable Energy Laborato ry (NREL) has also found that the continental U.S. has solar poten tial that exceeds high solar gene rating 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, h ttp://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/imag es/us_germany_spain/pvmap_usgermanyspai n%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.go v/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 Effec tive RE Levels from State-Level RPS Requirements The proposed approach is also based upon an an alysis of renewable por tfolio standards, a policy that facilitates the quantification of RE ta rgets. By only examining the impact of one type of policy, the analysis is inhere ntly 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 qu antitative information about mandatory state RPS requirements from the Database for State Incentives for Renewables and Efficiency (DSIRE). 107 This information enabled the EPA to dete rmine the effective RE levels in 2020 for states with mandatory RPS requirements. 108

107 Database of State Incentives for Renewables & E fficiency (DSIRE) is a very comprehensive source of information on incentives and policies that support renewabl es and energy efficiency in the United States. DSIRE is currently operated by the N.C. Solar Ce nter 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 annu al percentages requireme nts for the years 2000 to 2030. Many states have multiple compliance re quirements, including the main percentage requirements for eligible resources and additional resource-specific percen tage requirements that states are increasingly using to promote the deve lopment 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 dete rmine states’ effective RE leve ls 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 “seco ndary” and “tertiary” RPS requirement s for smaller utilities, municipal utilities, or cooperative util ities. The EPA only included the primary RPS requirements to simplify the analysis of primary and secondary R PS 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/RPSFiel dDefinitionsApril2011.pdf, p. 1.4 - 11 Figure 4.2 RPS Data Structure 111

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 Targ ets 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 st ate-level effective RE levels derived from RPS requirements to quantify regional RE targets consiste nt with states’ reasonable level of increased RE development. This methodology helps us to quan tify RE potential in st ates which do not have an RPS policy from which the renewable resource potential can be inferred. Specifically, the scenario estimates each region’s RE potential by assuming all states in each region can achieve by 2030 the average of the 2020 requirements of RPS states in that region. The regions assigned to states to quantify th eir RE generation target are based upon North American Electric Reliability Corporation (NERC) regions and Regional Transmission Organizations (RTOs) and are the same as the regions used in the modeling of the “regional” compliance scenarios as outlined in the proposal RIA (see Figure 4-3). 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 stat e-level technical potential reported in an NREL

112 For more information on the structure of these regions, please refer to the Regulatory Impact An alysis Chapter 3.4 - 13 GIS-based analysis. 113 The results show clear trends for th e regions used to create the proposed approach, with portfolios of par ticular technologies s howing clear dominance in specific regions. North Central and South Central regions have st rong on-shore wind resource potential. The East Central and Southeast regions s how moderate to strong resources in both biopower and rooftop PV potential. The West has notable potentia l in geothermal (hydrothermal) power and concentrating solar power, in addition to pot ential for increased hydropower generation. The Northeast has strong resources in off-shore wi nd and moderate biopower and solar resources
available. It should be no ted that high technical po tential in a particular renewable resource is not necessarily needed to re ach the generation levels quantified under this appro ach. For example, Maine produced 28% of its electricity ge neration in 2012 from biopower and onshore wind, while it is estimated in this report to have relatively moderate tec hnical potential for biopower and relatively low levels of onshore wind capacit y. Overall, results from the NREL GIS-based analysis show that the regiona l RE targets included in th is proposed approach assume development of only 0.5% to 4.5% of the RE res ources 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/do cs/fy12osti/51946.pdf.4 - 14 Figure 4.3. Proposed Approach Regions4 - 15 Table 4.3. List of States Include d in Proposed Approach Regions 114 Region States

East

Central Delaware, District of Columbia, Mary land, New Jersey, Ohio, Pennsylvania,
Virginia, West Virginia

North

Central Illinois, Indiana, Iowa, Michigan, Mi nnesota, 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 Caro lina, South Carolina,

Tennessee
West Arizona, California, Colorado, Ida ho, 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 generati on 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 calcula ted as the average of 2020 RPS requirements in Illinois (16%), Michigan (10%), Minnesota (3 0%), Missouri (10%), and Wisconsin (10%), which is equivalent to 15%. This average regi onal RE generation target offers a basis for the determination of state-level RE targets for inform ing state goals, as described below. Given their unique locations, Alaska and Hawaii are not grouped with other stat es 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, eq uivalent 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 deadline 115 , 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 perfor mance (as shown above using 2012 generation data). This appro ach 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 appro ach assumes an increasing trend over time of annual levels of RE generation that can carry th e performance level of each region in the start year (in 2017, assumed to be equivalent to its 2012 observed performance le vel) 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 re gion’s current renewable genera tion level and its RE target
level identified above.

115 See Preamble Section 8.E – Process for St ate 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 regi on’s RE target. The EPA then de termined the constant rate at which each region would need to increase its ge neration each year to reach the regional RPS target, if these rates are applied in the period 2017-2029. The consta nt rate of annual RE generation increase calculated from this approach is called the growth fa ctor. For example, the North Central region had 52,058,236 MWh of RE ge neration in 2012, while the North Central regional RE target of 15% appl ied 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 incr ease its RE generation, starting at its 2012 level, from the year 2017 onward and would achieve its RE target le vel 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 met hod to calculate growth factors for Alaska and Hawaii, calculating an annual growth factor base d 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 genera tion levels, assuming that the growth over that time had been constant in each year. This resulte d 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 give n region, that region’s annual gr owth factor was applied to each state’s historic (2012) RE ge neration level to calculate a new level of RE generation for that4 - 19 state in the initial year (2017). This calculati on 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 tim e 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 a nnual RE generation estimates represent the realization of the proposed approach for each stat e. 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 percenta ge (share of total generation) terms to all states in a given region; therefore, the ab solute megawatt-hour target will be smaller for states starting with a lower absolu te amount of RE generation and larger for a state starting with a higher absolute amount of RE generation. This approach applies the calcu lated growth factors and region al RE targets to state-level generation, whereas the state-level RPS requi rements upon which they are based are not necessarily applied in pr actice to generation that is produced w ithin 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 res ources 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 st ate but is instead used to calculate a regional growth factor that is then app lied 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 r each 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 Futu re 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 targ et 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 th e North Central region, but it do es not reach the North Central regional RE target generation level of 15% (w hich 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 targ et 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 Genera tion 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 4,8224 - 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 9,4284 - 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 in terim and final state goals The agency then translated the annual RE targ et performance levels for each state into state-level interim and final RE targets for info rming this rule’s quantification of state goals. Separate interim and final RE targets were ca lculated for the proposed state goals and the alternate state goals in this pr oposed rulemaking. For the proposed state goals, the interim RE target for each state was calculated as the averag e of that state’s RE ta rget 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 in terim RE target for each state was calculated as the average of that state’s RE target perfor mance 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 Illinoi s is provided below. State-le vel RE targets, expressed in absolute (megawatt-hour) terms and as a percenta ge level of each state’s RE generation as a share of its total generation, along with 2012 RE le vels for each state, are provided in Table 4.8. For an explanation of how these state-level RE targ ets 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 value4 - 27 Table 4.8. Proposed and Alternate State Target s 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 Targ ets 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 4,131,7914 - 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 target s under the proposed approach are comparable to rates that leader states have been able to appr oach in the past. Eleven states across four regions have already achieved over 10% of total gene ration from RE, surpassi ng the lowest regional target applied in the Southeast. Two states, Ma ine and Iowa, have alrea dy equaled or surpassed the highest regional target of 25% of generation, with Sout h Dakota close behind at 24%. Finally, five states have already re ached 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 fo r wind and solar. The economics of the fastest growing RE technologies – on-s hore wind and solar PV – are improving. According to recent analyses of wind and solar proj ect 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 agreemen ts 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 primaril y by lower installed PV project prices (which, in turn, have been driven primarily by declining module pri ces), as well as exp ectations 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 pri ce 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 regiona lly, several recent studies have found costeffective or economic RE resource s are available to serve future needs. The National Renewable Energy Laboratory (NREL) examined the future av ailability 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 ar eas—including any needed transmission and integration costs—with the cost of energy from a new natural gas-fired ge nerator built near the customers it serves. The report indicates that by 2025 wind a nd solar PV generation could become cost-competitive, if new RE developm ent 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 So lar 2012: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United States, Lawrence Berkel ey National Laboratory, LBNL-6408E. September 2013, p. 19.
119 Mark Bolinger and Samantha Weaver, Utility-Scale So lar 2012: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United States, Lawrence Berkel ey National Laboratory, LBNL-6408E. September 2013, p. ii.
120 Ibid.
121 Ryan H. Wiser and Mark Bolinger, 2012 Wind Tech nologies Market Report, La wrence 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 Futu re 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 incr eased 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 R PS-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-leve l 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 CO 2 reduction cost to be $3 per metric ton. 128 This finding has been confirmed with mo re 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 onl y two states experiencing price impacts of greater than three percent. 129

4.4. Nuclear Energy Nuclear generating capacity facilitates CO 2 emission reductions at fossil fuel-fired EGUs by providing carbon-free generation that can re place generation at those EGUs. Increasing the amount of nuclear capacity relative to the amount that would otherwis e 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: S outheast 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 Stat e 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 th e 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. 65 89E, http://www.nrel.gov/ docs/fy14osti/61042.pdf.4 - 33 therefore a technically viable appr oach 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 availabl e nuclear capacity is to build new nuclear EGUs. However, nuclear generating capacity is relatively expensiv e to build compared to other types of generating capacity, and li ttle new nuclear capacity has been constructed in the U.S. in recent years. Five new nuclear EGUs at three pl ants are currently under construction: Watts Bar 2 in Tennessee, Vogtle 3-4 in Georgia, and Su mmer 2-3 in South Carolina. The EPA believes
that since the decisions to construct these units we re made prior to this proposal, it is reasonable
to view the incremental co st associated with the CO 2 emission reductions available from completion of these units as zero for purposes of setting states’ CO 2 reduction goals. Completion of these units therefore represents a hi ghly cost-effective opportunity to reduce CO 2 emissions
from affected fossil fuel-fired EGUs. For this reason, we are proposi ng 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 availabl e 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 i ndividual 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 w ith 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 nuc lear fleet. EIA describes the projected capacity reductions – which are not tied to the retire ment 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 capac ity at risk of retirement.

130 Jeffrey Jones and Michael Leff, “I mplications of accelerated power pl ant retirements,” EIA, April 2014.4 - 34 We believe that, based on available inform ation 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 achie ving 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 elect ricity sales. 131 Assuming that such a revenue s hortfall is representative of the
incentive to retire at-risk nucle ar capacity, one can estimate the value of offsetting the revenue
loss at these at-risk nucle ar 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 ach ievable 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 quan tified 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 CO 2 /MWh or from the project ed average 2020 emissions intensity across the U.S. power system at 1,127 lbs CO 2 /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 retir e (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 45,0624 - 364 - 37 Appendix 4-1. RE Generation Targets Including Existing Table 4-1.1. State RE Generation Targets in cluding 2012 Existing Hydropower Generation (Gigawatt-hours)

State 2012 Existing Hydro 2012Nonhydro

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 17,22 8 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 1,6 81 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 5,61 5 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 7,60 0 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 4,13 7 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 4,02 8 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 5,3 06 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 2012Nonhydro

RE (GWh) 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 20294 - 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 16,7 25 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 8,08 5 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 10 5,212 106,172 107,190 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 6,9 59 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 Generati on 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 Target s 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 Targ ets 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 4,720,8 48 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 6,628,2514 - 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 7,907,1765 - 1 

### Chapter 5: Demand-side Energy Efficiency (EE)
Introduction This chapter provides information on dema nd-side energy effici ency (EE) as an abatement measure for reducing carbon dioxide (CO 2 ) emissions from fossil fuel-fired electric generating units (EGUs). Specifi cally, this chapter addresses EE as a component of both the “best system of emission reduction” (BSER) and state goals, and the incl usion of EE within the impacts assessment. Support is provided in this chapter for the discussion of the EE abatement measure throughout the preamble (most extensivel y in these sections: Building Blocks for Setting State Goals and Considera tions, State Goals, State Plans, and Impacts of the Proposed Rule) and its representation within the Regulator y Impact Analysis (RIA). Results from this chapter feed into the techni cal support document (TSD) on Go al Computation. EE is also addressed in TSDs on state plan considera tions 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 Assessment5 - 2 – Approach

– Inputs

– Calculations

– Results 5) Analysis Considerations 6) Appendices 7) References

Background
As discussed in the State Plan Considerati ons TSD (Appendix: “Survey of Existing State Policies and Programs that Reduce Power Sector CO 2 Emissions”), demand-side energy efficiency policies and programmatic efforts have existed for decades and are now used in all 50 states. These strategies are intended to help states achieve energy sa vings goals, reduce the environmental impacts (including CO 2 emissions) of meeting energy service needs, save energy and money for consumers, and provide a signific ant 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 – th rough their utilities, primarily – have been rapidly increasing their fu nding 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 repr esented 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 Laborator y (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 rela ted cost tests used by states to evaluate EE programs.
135 American Council for an Energy-Efficient Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-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 ma jor new policy developments,” such as requirements under Clean Air Act, Section 111(d). 137,138 This section provides relevant background for th e 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 technologie s 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, a nd discuss in more detail the key strategy of employing EE programs.

EE Technologies and Practices Energy efficiency is using less energy to provi de the same or greater level of service. Demand-side energy efficiency refers to an ex tensive array of technologies, practices and measures that are applied thr oughout all sectors of the economyto reduce energy demand while providing the same, and sometimes better, level a nd quality of service. Utilities employ a large array of strategies in implementing energy e fficiency programs, these include financial incentives such as rebates and loans, technical services such as audi ts and retrofits, and educational campaigns about the benefits of en ergy efficiency improvements. The purpose of these EE programs is to induce EE investments a nd 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 pur chase of more efficient produc ts 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 hom e retrofits. Opportunities for cost-effective EE in commercial buildings includ e optimization of heatin g, 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 cu rrent energy efficiency policies, we do not consider the potential impact of major new fede ral (or state) policy initiatives (e.g., a national energy efficiency resource standard, clean en ergy standard, or carbon policy) that could result in customer-funded energy efficiency program spending and savings th at 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-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend

.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 invest ment in EE is dynamic, growing over time as technologies and practices advance, as populat ions grow, and as investment occurs in the construction of new homes, buildings, and indus trial facilities. As ne w 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, st ates are finding renewed opportunities as they develop more sophisticated and effective strate gies, evolving from a focus on individual enduses and products to whole-building and syst ems-based strategies that account for the interactions between the many energy end-uses in build ings 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 ev en as the impact of past and ongoing efforts
have accumulated.

Barriers to EE Investment Despite the persistent and larg e potential for elec tricity 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 ma rket failures that lead to inefficiencies in energy use are well recognized by analysts and pract itioners, 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 CO 2 , SO 2 , and NO x that cause human health and environmental damages. Energy prices that do not correc tly reflect these externalities lead to investments in energy efficiency be low 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 Pr ograms. American Council for an En ergy-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. An nual Review of Resource Economics. Annual Review of Resource Economics 1: 597-619 and Gillingham and Palmer (2 013). “Bridging the Energy Efficiency Gap: insights for policy from economic theory and empirical analysis,” Resour ces for the Future DP 13-02.5 - 5  Imperfect information. Energy users often lack accurate information about energy savings
and other attributes of ener gy efficient products or practi ces 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 incenti ves of those who use and pay for energy. Examples include misalignment be tween 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 a nd the private entities. 141

 Credit constraints . Limited access to credit may prevent some consumers, especially lowincome consumers, from making cost-effec tive EE improvement decisions due to the higher upfront cost of energy e fficient 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 t echnology 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 informa tion 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 slow ing the adoption of EE technologies.

141 For example, see DeCanio, S. 1998. The efficiency pa radox: 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. St avins. 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 psychol ogy have identified potential behavioral phenomena that lead to consumer s to deviate from the standard theory of welfare maximizing in consumption and othe r decisions, including energy efficiency investments. Behavioral economics posits possible explanatio ns, 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 di scussed above explain why indi viduals do not always make energy efficiency investments that are seemingly in their own best interest to reduce their total expenditure, given preva iling electricity prices.
In addition to market failures and behavioral impediments, other factors, such as hidden costs, risk and uncertainty experienced by bot h 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 consumer s and firms do not always invest in EE measures. Suppliers also face risk and un certainty, without perfect information of consumer preferences for energy efficiency. In the presence of risk and uncertainties, consumers and suppliers alik e 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 phe nomenon, also termed ‘energy paradox,’ or ‘energy efficiency gap,’ has been studied exte nsively 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 Ch aracteristics 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 pr oducts at a disadvantage relativ e to less-efficient products with lower upfront costs.

 Capital market barriers . Consumers sometimes face higher interest rates to finance EE investments compared to other investments. Le nders can be reluctant to invest in EE loan portfolios in part because energy efficiency loans may lack standardization and financial markets have difficulty ascertaining the likely payoff from such investments. EE policies and programs can play an important role in correcting market failures and addressing the barriers to the investment and a doption of socially beneficial energy efficiency opportunities. Examples of eff ective EE policies and programs in clude public funding of R&D, information programs (such as energy labe ling, the voluntary ENERGY STAR Program, and consumer education), rebates for high-effi ciency products, product energy performance standards, financing and loan progr ams, and technical assistance.

EE Policies 145 Objectives and Role in Reducing CO 2 Emissions from the Power Sector EE policies are implemented by states to meet a number of closel y related policy goals 146
, including:

- Reducing costs to elec tricity customers,

- Providing a significant resource for mee ting power system capacity needs,

- Meeting energy savings goals,

- Stimulating local economic de velopment 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-eff ective strategies for contributing to each of these policy objectives. 147 While each of these objectives, and others,

145 Existing state EE policies are described extensiv ely in the State Plan Considerations TSD.
146 U.S. EPA and U.S. DOE. July 2006. National Ac tion 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 Ac tion Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf

.5 - 8 contribute to the motivation of state policymaker s to pursue EE policies, reducing energy costs over the long term is the leadi ng objective in pursui ng these policies. In addition, EE policies are central to meeting state objectives for reducing CO 2 emissions from the power sector. As noted in the State Plan Considerations TSD, EE policies are a leading tool for achieving CO 2 reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in

ten states 148 with statutory requirements for greenhouse gas reductions. Economy-wide studies of climate mitigation s cenarios confirm that energy efficiency plays a critical role in reduci ng the costs and enhancing the fl exibility 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 pot ential in 2035 derives from great er energy efficiency in the world economy. 150 Several recent Energy Modeling Forum (EMF) studies have investigated the role of technology in achieving climate policy obj ectives in the U.S. (“EMF 24” and “EMF 25” studies) and globally (“EMF 27” study). 151 These studies concluded th at compared to businessas-usual energy efficiency, improvements in en ergy efficiency in various economic sectors
would slow the increases of GHG emissions in th e short run, substantia lly 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 EMF 25 studies) examined the role of energy efficiency policies (such as energy efficiency stan dards 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. 20 14. 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 ener gy 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 deci sions. 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 ef ficiency measures or would tr eat 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. Climat e Policy Goals. The Energy Journal, 32: 61-87.5 - 9 policy instruments (such as car bon 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 re cognized to be an appropriat e response to demonstrated market failures and behavioral impediments, partic ularly in contexts where these failures have broader societal implications such as environmental externalities. 155 In addition to providing cost-effective oppor tunities for reducing GHG emissions, energy efficiency is recognized to provide other co-b enefits, including air qua lity and public health benefits, waste reductio n from energy generation, energy secu rity, energy system reliability, community economic and social development, and consumer amenities. 156 Energy efficiency investments and policies are also found to s pur 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 progr ams. These co-benefits have not been fully accounted for in the EPA analysis.

Policy Types EE policies come in many forms. The most pr ominent and impactful EE policies in most
states include those that drive de velopment and funding of EE programs 159 , and building energy codes. Other policies that ar e leading to significant impact s 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 Prov ision 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 Progr am Impacts’ and Environmental Compli ance Costs,” Report prepared by RAP and Synapse Energy Economics.
157 Boyd, GA and JX Pang (2000). “Estimating the lin kage between energy effi ciency and productivity,” Energy Policy , 28: 289-296; Worrell, E. (2011). “Pro ductivity benefits of industrial ener gy 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 co sts. Proceeding of National Acad emy 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 th e relative impact (achieved or potential) of the different policy types is cha llenging, particularly to do so comprehensively, across all states, and at the national level. EE pr ograms are the only state EE approach that has comprehensive and detailed repor ting of impacts, costs, and ot her characteristics from all 50

states. 160 This information is generally based upon measurement and verification studies submitted annually, most commonly to state uti lity 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 (res idential, commercial, industrial ). In 2012, utilities and other program administrators in 48 st ates reported savings from EE programs to EIA through form EIA-861. At a national level, the EPA is not aware of a comprehensive data set reported by states of the achieved impacts of strate gies other than those that lead to investment in EE programs. However, state and regional-leve l information does exist. For ex ample, the Northwest Power and Conservation Council (NPCC) has been compiling the impacts of EE policies (including utility and third-party EE programs, state building ener gy 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 c odes accounting for the remaining savings. 161 Another representation of the relative oppor tunity provided by different state EE strategies is presented by evalua tions 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.pdf5 - 11 1. EE programs account for 77% and 82% of achievable savings in ACEEE 162 and Georgia
Tech 163 studies, respectively. These studies indicate that the substantial majority of potential savings from state EE efforts are available th rough EE programs, and that state and local building energy codes can make a significant ad ditional contribution. Massachusetts provides a state example of the impacts of EE program s relative to other state EE policies. The Massachusetts Global Warming Solutions Ac t of 2008 established statewide limits on greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. To achieve this target, Massachusetts is relying upon an integrated portfolio of clean energy policies. State EE policies are expected to provide the largest cont ribution to meeting the 25 percent target with utility sponsored EE programs and state bui lding energy codes accounting for 76% and 17%, respectively, of those policies. 164 In their 2013 progress report, Mass achusetts 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 verificati on (M&V), penalties for non-compliance, and implementation status) in the State Plan C onsiderations TSD. Because EE programs have provided the majority of state EE-policy electricity savings to-d ate and offer the majority of potential savings going forward, we next summar ize key characteristics of this strategy.

162 Hayes, S., et. al. American Council for an Energy-E fficient Economy (ACEEE). Apr il 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/re search-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-Engineerin g 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-g as-and-climate-change/climate-changeadaptation/mass-clean-energ y-and-climate-plan.html

. 165 Commonwealth of Massachusetts. Global Warming So lutions 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 s ectors of electricity end-users. Figure 5-1 166 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 deta iled 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 part icipant 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 owne rship types (investor-owned, mu nicipals, and cooperatives), nonprofit and for-profit third-partie s (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 savings 168 ) are overseen by state utility commissions, which review and approve program plans, projected impacts, and associated budgets; and establish annual repo rting and M&V requirements.

Policy Drivers EE programs result from a number of diffe rent policy approaches or “drivers.” 169 These include energy efficiency resource standards (EERS) (26 states) 170 , system benefit charges (14 states), integrated resource planning (IRP) requ irements (34 states), demand-side management

166 Ian M. Hoffman, Megan A. Billingsley, Steven R. Sch iller, 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 di scussion 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 Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-policy/scorecard

.5 - 13 plan or multi-year energy efficiency budget (28 st ates), 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 thei r 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, whic h are supported through different funding
mechanisms.

FIGURE 5-1 174 Energy Efficiency Program Portfolio

171 Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend.
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 w ith 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/re search-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 ratepaye rs through electricity surcharges , typically ranging from $1 to $4 per megawatt-hour. 175 More recently adopted funding s ources include proceeds from the auction of allowances in the Regional Gree nhouse Gas Initiative (RGGI) states and from EE resources bid into the forward capacity mark et operated by the New England Independent System Operator (NE-ISO). Ratepayer-funding acc ounts 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 hi storically 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, a nd 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 el ectricity savings potential achievable throughexpanded 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 rapidl y in recent years driven by recent policy innovations and increasing eviden ce of the effectiveness of th ese 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 Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-policy/scorecard

. 176 American Council for an Energy-Efficient Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-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 ha ve helped to drive these ra pid increases in EE program funding include EERS, electricity savings goals , and “all cost-effectiv e energy efficiency” requirements. The adoption of EERS, in particul ar, increased through this period and clearly has been the primary driving force behind the increa sing 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 Me xico, 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 inform ation (including costs and impacts) are annual EE program reports required by utility commi ssions, or cooperative or municipal utility boards of directors. These reports are based on M&V studies of indivi dual 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 requ ired 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 progr ams). 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 data set for identifying hist oric impacts of EE programs by state. Specifically, the reported sales data, and incremental and cumulative electricity savings in the 2012 EIA 861 dataset ar e used to estimate electricity EE impacts by

state. 181 EIA data is reported by program administra tor (e.g., utility, third-party, or state agency) and requires the disaggregation of reported data by state for administrators with programs in multiple states (e.g., multi-state investor-owned util ities). 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/el ectricity/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 Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-policy/scorecard

. 181 EPA recognizes concerns associated with consistency an d 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 savings 182 in 2012 was 0.58%
of retail sales with individua l state values ranging from 0.00% to 2.19%. Cumulative electricity savings 183 (representing the remaining impacts of progr ams from all prior y ears) reported at the national level for 2012 represent 3.7 4% of retail sales wi th 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, taki ng 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 Laborator y (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 ga s utility customers in the Unite d States under three scenarios (high, medium, and low cases). 184 The scenarios represent “a ra nge 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 standa rd, clean energy standar d, or carbon policy” and specifies that such policy changes could “resu lt in customer-funded energy efficiency program spending and savings that exceed the values in our High Case.” The study concludes that effici ency programs are “poised for dramatic growth over the course of the next 10 to 15 years” with the mo st significant increases o ccurring in regions with lower levels of program spending, historicall y, including the Midwest an d 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 th e high scenario, spending more than doubles from 2012 levels to $12.2 billion in 2025. Incremen tal savings levels grow commensurately, to
0.8% and 1.1% of sales under the medium and hi gh scenarios, respectively. The study results indicate that under the high scen ario 20 states would be achie ving 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-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend

.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-Funde d 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 comm on tool for informing the development of EE program plans and budgets, as well as support ing the development of electricity savings targets, required savings le vels under an EERS, or “all co st-effective” EE requirement. In conducting these studies, states and utilities have develope d a methodology that is often described as a “bottom-up, e ngineering-based” approach. 186 EE potential studies are conducted at various geographic scopes (nationa l, regional, state, and utility service territory level) and at different degrees of aggregation (e.g., economy-wi de, sectoral, and program), and can be broadly grouped into a few types: techni cal, 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 pot ential” can vary to some degree by study. Some estimate economic potential by evalua ting technology upfront cost, operating costs that considers energy prices, product life time and discount rate, compared to a
conventional alternative or the supply-side energy res ources. Others incorporate consideration of consumer preferences in addition to consumers’ out-of-pocket expenditure when evaluati ng the economic potential. Bo th technical and economic potential estimates assume immediate implem entation of efficiency measures without regard to technology adoption process or re al-life program implementation. In addition, these estimates do not always reflect market failures or barriers that impede energy efficiency and often fail to capture tran saction costs (e.g., admi nistration, 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 am ount of energy savings that can realistically be achieved, taking into account factors such as tec hnology adoption process, market failures or barriers that inhibit technology adoption, transaction costs, co nsumer 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 pot ential that can be realized given specific program funding levels and designs. Program potenti al studies can consider scenarios ranging from a single progra m to a full portfolio of programs. 188 As mentioned, the EE industry standard for potential studie s is the bottom-up, engineering evaluation of energy efficiency potential of indi vidual end-use technologies and

measures. 189 Bottom-up analyses all empl oy 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 ec onomic 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 e nd-uses considered, study period, discount rate, pattern of technology penetra tion, whether economically jus tified early replacement of technologies is allowed for, and whether continue d improvement in efficiency of technology is provided for). As a result, estimated efficien cy potential can vary si gnificantly among studies. 190

Overview of Results Studies of energy efficiency potential are numerous. In recent years, dozens of studies have been conducted at regional, st ate, and utility levels. This s ection reviews rece nt studies and presents a summary of findings. We first addre ss 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 st udy periods, we present average annual achievable potential by dividing cumula tive percentage savings in the last year of the study by the duration (in years) of th e study period. This is a common method of normalization for energy effi ciency 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 potenti al, 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% pe r year in economic potential, a nd 1.5% per year in achievable potential. To supplement these studies with mo re recent data, the EPA has conducted a metaanalysis of twelve studies conduc ted between 2010 and 2014 at the ut ility, state or regional level (see Appendix 5-1). The EPA revi ew indicates an average annu al achievable pot ential 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 mark et and macroeconomic drivers of energy use and energy efficiency, and in some cases due to the lack of consistent data, quantifying energy effi ciency 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. Recen t estimates of energy efficiency pote ntial in the USA. Energy Efficiency.
192 Eldridge et. al. 2008. State-Level En ergy 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 th ree 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) evalua ted four major energy efficien cy 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 othe r 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 elec tricity savings potential in th e 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 poten tial. On a normalized basis, the EPRI 2009 study provides an achievable annualized potential rang e of 0.2-0.4% per year (realistically achievable and maximum achievable potential, respectivel y) through 2030 at the national level. Two more recent studies also provide natio nal 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 (ACE EE). April 2014. Select Stat e-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 ar e 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 st udies 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 budget s 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 sim ilar scale opportunity across all regions. For instance, EPRI (2014) shows achieva ble 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 i ndustrial sectors in every regi on. The ACEEE (2014) study also shows consistently large potential across all st ates 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, lo wer costs of meeting electricity supply need s, 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 cost s and benefits to include in these tests. The costs and benefits are compared on an equal f ooting by using present value analysis. This is necessary because EE typically requires primarily upfront e xpenditures (e.g., a whole home retrofit) while the economic benefits (e.g., elect ricity bill savings) accru e 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 st ates employ multiple tests, adju sting 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 thos e that are found to be cost-effectiv e, with benefits greater than costs, as determined by the utility applying me thods 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 installati on costs; benefits include incentive payments, bill savings, and applicable tax credits or incentives. (2) Utility/program admin istrator cost test from the perspective of utility, government agency or third-party implementing the pr ogram. Costs may include program incentive, installation, and overhead costs; benefits ma y include avoided energy and capacity costs - including generation, transmission a nd 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 we ll 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 Ac tion 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. Understandin g Cost-Effectiveness of Ener gy Efficiency Programs (Best Practices, Technical Methods, an d 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 Assi stance Project, “Energy Effi ciency 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 util ity 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 ca pacity costs, and addi tional resource savings. (5 ) Societal cost test from the social perspective. In addi tion to benefits considered in total resource cost test, may also include non-moneti zed benefits such as environmental and health
benefits. While many states consider more than one co st test in evaluating EE programs, the most commonly used (29 states) primary te st is the total resources cost test. This test is considered to
be the best measure of the interest s of all utility customers. The u tility 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 clim ate change policy su ggests that energy efficiency presents a large pot ential 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 fo r 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 an d climate policy strategies. Climatic Change, January 2014; Kyle P. , L. Clarke, S. Smith et al. 2011. Th e 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 Effi ciency as a Low-Cost Resource for Achieving Carbon Emissions Reductions: a Resource of the National Action Plan for Energy E fficiency. 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/la test_thinking/reducing_us_greenhouse_gas_emissions

.5 - 27 Costs of Saved Energy A common metric for comparing alternative electricity reso urce options within utility resource plans is the levelized cost of energy (L COE) or, for EE resources, the levelized cost of saved energy (LCSE). 201 LCSE EE is often compared favorab ly with LCOE of alternative new generation sources such as fossil-fueled or nucle ar power plants, or renewable energy resources like wind or solar-power generation. In these co mparisons, typically only utility (or program) costs are considered, not the total costs of saved energy that are di scussed 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/kW h, with a mean value of 2.8 cents/kWh. 203 Earlier reviews of utility EE programs identified a si milar 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) revi ewed cost-effectiveness analysis results in nine states and suggested that reported utility LC SE 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 modeli ng), although this body of studies is much smaller compared to the bottom-up, engineeri ng-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 Reso urce Planning with Energy Efficiency: a Resource of the National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/docu ments/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 measur e 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-Sect or Energy Efficiency Programs,” AC EEE Report No. U092. Available at http://www.aceee.org/re search-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 co st of saved energy. For example, a recent study by Auffhammer et al. (2008) ex amining 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 estimatedLCSE 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 program s between 1992 and 2006, Arimura et al. (2009) found that the estimated energy savings in electric ity 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-spe cific price elasticity, Paul et al. (2011) estimated that electricity savings of 1 to 3 per cent were available at a marginal cost of 5 cents/kWh and a correspon ding average cost of 2.5-3.5 cents/kWh. 209 A number of analytical and data considera tions related to LCSE estimation are also discussed in the literature, including th e issue of “free riders” in EE programs 210 , and the accuracy of utility reporte d costs and energy savings. 211 Energy efficiency practitioners also recognize the need to consider “free rider” a nd “spillover” effects in program evaluation. A slight majority of states adjust for free riders hip in energy savings esti mates, 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 es timation is the rebound effect. The economic literature has extensive discussion of the pot ential rebound effect, mark et 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. Retrosp ective 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 Electri city 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 Nega watt 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/re search-report/u122

.5 - 29 economy-wide response of energy efficiency polic ies and investments. An improvement in energy efficiency would effectively reduce the co st of a service or pr oduction input, potentially boosting its demand or production ou tput 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 ma y increase energy consumption (two forms of “indirect” rebound). Reviews suggest that both direct and indirect rebound effects exist and the size of such effects varies among different st udies, technologies, 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. 2000 214 ; Sorrell 2007 215 ; Davis 2008 216 ; Gillingham et al. 2013 217 ).

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 poli cy approaches. Increased use of, and impacts from, state energy efficiency policies is a lead ing industry trend over re cent years and the trend of increasing investment in EE pr ograms is projected to continue for the next decade, at least. These findings support the inclusi on of demand-side energy efficien cy as an abatement measure for reducing CO 2 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 reas onableness of the costs of this building block.

State Goal Setting Approach To estimate the potential CO 2 reductions at affected EGUs that could be achieved through implementation of demand-side energy effici ency policies as a part of state goals, the EPA developed a “best practices” demand-side energy efficiency scenario. This scenario provides an estimate of the potential for states to implement policies that increase investment in cost-effective demand-side energy efficiency tec hnologies and practices, a nd projects the annual impacts of the scenario for each state. The scenar io does not distinguish between policies that are currently in place and additional policies that in mo st states would be required to be implemented to realize the goals established. It does not represent an EPA forecast of business-as-usual impacts of state energy efficiency policies or an EPA estimate of the full potential of end-use energy efficiency available to the power system, but rather is intended to represent a feasible policy scenario showing the reductions of CO 2 emissions from fossil fuel-fired EGUs resulting from accelerated use of energy efficiency policies in all states, generally consistent with ongoing industry trends. The scenario uses : 1) a level of performance that has already been demonstrated or required by policies (e.g., energy efficiency res ource standards) of many leading states; 2) considers each state’s unique ex isting level of performance; and 3) allows appropriate time for
each state to increase from their current level of performance to the id entified 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 ar e reported by utility or other program administrator, by state, and compiled nati onally, using standardized elements and definitions, and5 - 31  EE program savings are projected and eval uated under requiremen ts established and overseen by state utility commi ssions, and by municipal and c ooperative utility boards of
directors.

While the approach is derived from info rmation about energy efficiency programs overseen by state utility commissi ons, other state energy efficien cy policies are available to realize a state’s goals 218 such as building energy codes, appl iance 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 establis h 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 ye ar level of performance

 Step 5: Determine pace at which states improve from start year to best practices level of performance

 Step 6: Determine average portfolio measur e life and distributi on 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 res ource standards and is readily calculated fro m 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 le vel to establish each state’s curre nt level of performance. These results were presented pr eviously in Table 5-4.

Step 2: Determine Best Prac tices Level of Performance
As discussed previously, achievable demand-si de energy efficiency potential exists at significant and comparable levels (on the basis of total cumulative potential over a period of ten to twenty years) in all regions of the country. While varied regi onal characteristics (e.g., avoided power system costs, economic grow th, sectoral mix, climate, and le vel of past energy efficiency efforts) affect estimates of achievable pot ential, ongoing improvements in energy-efficient technologies and practices, economic growth, popul ation increases, and continually improving strategies for program delivery have resulted in persistent and substantia l levels of achievable potential regardless of speci fic regional characteristics.
A direct indicator of the achievable incremen tal levels of energy savings performance is provided by past performance at th e state and utility levels, and by requirements states have put in place for levels of savings to be achiev ed by 2020. As discussed, these requirements are typically in the form of energy efficiency resour ce 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%, a nd >= 1.5%). As shown, three states achieved the highest level of performance (> 1.5%) and an additional eight stat es achieved the second highest level of perfor mance (1.0% to 1.49%). Table 5-9 summarizes incremental savings le vels 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 add itional five states are required to achieve the next highest level of pe rformance (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 En ergy Efficiency Resource Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/po licy-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 leve l of performance for Option 1 223 , the EPA has chosen 1.5% incremental savings as a percentage of retail sa les. This level was achie ved by three states (AZ, ME, and VT) in 2012 and an additional nine st ates (CO, IL, IN, MA, MN, NY, OH, RI, and WA), accounting for overlap, are ex pected to achieve this leve l by 2020. Thus, twelve states have either achieved or are required to achieve this level of performance by 2020. For Option 2 224 , the EPA has chosen 1.0% incrementa l savings as a percentage of retail sales as the best practices level of performan ce 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 th e two sets of states) have either achieved or are required to achieve this level of performance by 2020.

Step 3: Determine Start Year for State Efforts For construction of the best practices scen ario, the EPA has used 2017, the year following the required state plan submittal 225 , as the first year for state efforts.

Step 4: Determine Start Ye ar Level of Performance For construction of the best practices scen ario, the EPA has set each state’s level of performance (incremental savings) in the start ye ar (2017) to its current level of performance (aggregated to the state-level fr om reported EIA Form 861 data). Th is approach reflects neither improvement nor decline in performance between 2012 and 2017. Any improvement in EE savings performance between 2012 and 2017 will bene fit 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 sa vings levels from the 2017 level to the best practices level, the EPA considered past pe rformance of individual program administrators 227 as well as requirements of existing state energy e fficiency 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. Th e moderate group (47 entities) was defined as programs that achieved from 0.8% to 1.5% maxi mum incremental saving s levels and the high group (26 entities) was defined as programs that achieved greater than 1.5% maximum incremental savings levels. We then calculate d 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 desc riptions 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 co llect data from third-part y 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 s upporting data and analysis. The EPA also considered requirements of ex isting 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 st ates 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 Measur e Life and Distributi on 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 repr esent the total impacts of all EE measures put in place in that year and all pr ior years, due to EE program activities. The cumulative savings account for the continuing impact s of energy efficiency measures that remain in place for a period of time (the “measure lif e”) before being replaced. For example, the purchase of a high-efficiency refrigerator may le ad to savings for twelve years, before being replaced with a new model. To estimate cumulativ e impacts of a series of years of incremental savings, the industry uses the c oncept of an average measure lif e for the entire portfolio of EE programs. Rather than use a single, average meas ure 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 y ears (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 nati onal reviews of EE program costs and related program characteristic s, 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 th e 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 lifeti mes by state and customer class
Sector
All Sectors Residential Commercial/Business Industrial Average 8.1 12.5 9.5 10.6

Source: ACEEE 2014.

Other studies have found slightly higher valu es 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 live s across an EE portfolio, consistent with an average measure life of ten years, we have assu med an even distribution from one year in length to two times the average measure life (twenty y ears) in length. Our a pproach is generally supported by the substantial range in measure liv es reviewed and summarized in a 2014 study by LBNL which shows an interquartile range fr om five to 25 years across twelve program categories (e.g., low income, residential new cons truction, commercial/indus trial custom, etc.). Our approach represents a first- order approximation of the distri bution 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 m easure 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 c onservative 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/resear ch-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 Co st 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 gr eater portion of the savings occurr ing in later years than occurs with the more common, and simpler, approach. Th is results in lower cumulative impacts in earlier years and correspondingl y lower state goals through 2030.

Step 7: Determine Sustainability of Best Practices Level of Performance For construction of the best practices scenar io, once a state achieves the best practices level of performance, the EPA has kept the level of performance constant through 2030. For
states with lower levels of current performance (and, hence, later achievement of the best practices level of performance – as late as 2025 in some instances), this requires sustaining the target level for as little as five years. For states currently at or above th e best practices level of performance, this reflects an ability to sustai n the target level for thirteen years (2017 through 2030). Limited empirical data suggests the r easonableness of this approach; however, comprehensive data, across all regions and states, does not exist because these levels of performance have not been achieved and sustai ned nationwide previously . The Northwest Power Conservation Council (NPCC) provides one such example. NPCC has been conducting the most consistent and long-running series of evaluations of achievable cost-effective potential in the country, updated every five years, as part of their five-state 230 regional energy resource plans 231

. These analyses have become more detaile d, 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 co sts of saved energy achieved have remained flat), and exceeding plan targets every year since 2005. A study of the NPCC results concludes: “our research shows that when programs invest in hi gher levels of efficiency, this helps drive measurement improvements and technical innovati on, 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 Do cument 2010-09. Available at www.nwcouncil.org/ener gy/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 Ac hieved 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 w ith the rates resulting from the best practices scenario. We address this in a later section, Results in Context , after presenting those results.

Summary of Best Practices Scenarios Construction Table 5-12 provides a summary of inputs for the best practi ces scenarios for Options 1 and 2. The pace of improvements, average measur e life, and distribution of measure lives are each conservative and, therefore, contribute lo wer state goals than would otherwise result. Similarly, the best practices level of performa nce, being based solely on results from and requirements of EE programs, is le ss stringent than a le vel would be that accounted for potential impacts of other state EE policies such as bu ilding energy codes, build ing energy benchmarking requirements, and state appliance standards. The use of 2012 level of performance for the 2017

232 Gordon, Fred, Lakin Garth, Tom Ec kman, and Charles Grist, “Beyond Su pply 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 Do cument 2010-09. Available at www.nwcouncil.org/ener gy/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 fo r determining cumulative savings levels (cumulative savings as a percentage of baseline sales) for each stat e, 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 detailed5 - 40 description of how the demand-si de EE component is used as one of several inputs to the
calculation of interim and fina l state emission rate goals. Calculating the net cumulative savings as a perc ent of electricity sale s 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 savi ngs 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) fo r 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 fr om 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 th e 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 90,0055 - 41 Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales As discussed, the 2017 value for annual incremen tal EE savings as a percentage of sales is set at the 2012 value based upon EIA-861 report ed 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 th en held constant. The resulting
values are summarized in Ta ble 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 calculate d by multiplying the annual incremental savings as a percentage of sales times the prio r year sales after net EE. Sales after net EE are calculated by subtracting net cumulative saving s 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 (f irst-year) savings based upon an av erage measure life of 10 years and a linear decline in first-year savings over twen ty years. As an example, Figure 2 illustrates the decline in first-year savings from EE meas ures 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 ar e equal to annual incremental savings for that year minus total expiring savings for that ye ar 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 valu es 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 6,6595 - 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 ar e represented as cumulative savings as a percentage of retail sales by year for each op tion. 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 Ra tes 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 year5 - 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 av erage annual savings rate represented in the EE best practices scenario are below the range of values found in rece nt utility, state, and regional studies (1.2% to 1.5% per year) as summ arized in Table 5-6, an d within the range of
values found in the 2014 national studies from EPRI and ACEEE (0.5%/0.6% to 1.6% per year) as summarized in Table 5-7. These results provide additional support for the feasibility of the EE best practices scenario and asso ciated state-specific EE goals.

Impacts Assessment Approach In the Goal Computation TSD, state-specifi c 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 us ed to adjust electricity demand levels used as exogenous inputs to power sector modeling for th e 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 scen arios. 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 leve ls of performance over time. The EE goals represented in the illustrative compliance scenarios lead to substantial reductions in power system costs due to the redu ctions in specified electricity demand. Since EE is not represented endogenously as an abatemen t 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 addr essed 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-specifi c electricity savings by year

 Step 2: Determine first-year program costs of saved energy

 Step 3: Determine the ratio of program to participant costs

 Step 4: Determine the escalation rate of EE costs

Step 1: Determine State-Specific Electricity Savings by Year Results from the previous section, State Goal Setting, provide the starting point for estimation of EE costs. From those results, stat e-specific annual incremental savings (MWh) and yearly distribution of associated continuing saving s (MWh) in future years are used as inputs to
the cost estimation calculations.

Step 2: Determine First-Year Program Costs of Saved Energy First-year program costs refer to the full costs (e.g., administration, incentive payments, marketing, information to consumers, etc.) incu rred by a utility or othe r administrator of EE programs in a given year that lead to EE measur es (technologies and practices) put in place in that year and resulting in reducti ons 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, AC EEE conducted a national review of data on EE program costs from pr ogram annual reports, evaluation reports, and information compiled from contacts at program administers in 14 states. Compiled data was sourced from multiple EE program administrators in each state and over multiple years of data for each administrator. ACEEE found average first-year net 234 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 thei r analysis from 2009, expanding th e number of states to 20, and including a greater number of program admi nistrators 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 comprehe nsive study of EE program costs. The LBNL analysis reviewed program-level data from over 100 pr ogram administrators in 31 stat es. 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 th eir study and the lack of net savings information for many programs, LBNL focused on gross 235 , 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 de flating 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 conser vative, 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 r eadily known and consistently reported by the program administrator, particip ant costs require significant effort to estimate, and are less consistently estimated and reported. The ratio be tween program and participant costs will vary significantly from one program to the next with in a utility’s portfolio. The EPA has used a generic approach to estimate the ratio of program to participant costs across an entire portfolio, thus providing for the estimation of total costs on ce program costs are determined. To derive the ratio, the EPA reviewed 2012 EE annual reports fr om program administrators in 22 states identified as leaders in EE programs 236 based upon their magnitude of savings or their savings as a percentage of retail sales. Complete info rmation on full portfolio participant costs were
available for nine of the 22 states. Across the ni ne 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” re fers to electricity savings before any accoun ting for effects of free-ridership or spillover.
236 Leaders were identified using results from the 2013 AC EEE State Energy Efficiency Scorecard based on energy savings as a percentage of retail sales or total savings.5 - 52 data and analysis documenting th is review. Based on this review , the EPA has taken a slightly conservative approach 237 and used a ratio of 1:1 between pr ogram and participant costs. We use this ratio to derive participant and total cost s based upon program costs. Starting from program CSE of $275/MWh and applying th e 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 represente d in the state EE goals are substantial and represent a scenario that has not previously been achieved and sust ained at a national level in the U.S. Thus, even though the EPA has taken a cons ervative approach (i.e., leading to higher estimates of costs) to the devel opment 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 thei r historic levels of incremental savings to the best practices leve l of 1.5% of retails sa les). Economic theory suggests two mechanisms that would change EE costs as higher levels of performance are
achieved. Economies of scale in th e operation of larger EE programs and larger portfolios of EE programs, and learning and expertise gained ove r time from the continued implementation of programs, are two factors that would lower co sts 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 lim ited empirical data does provide support for significant economies of scale and/or cost reduc tions 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 es calating 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: Eviden ce from Experience to Date. ACEEE Su mmer Study on Energy Efficiency in Buildings. http://www.aceee.org/files/proceedi ngs/2008/data/p apers/8_434.pdf

.5 - 53 are realized. 239 In a recent analysis, Lawr ence Berkeley National Laboratory (LBNL) adopted an approach that generically repres ented both effects discussed above. 240 LBNL changed EE costs (first-year program costs) as a function of EE savings levels, decr easing 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% wh en 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 increm ental savings (as a percentage of electricity sales) at the state
level. Until a state reaches a 0.5% savings level, their costs are se t at the base level; for savings levels between 0.5% and 1.0%, stat e costs are escalated to 120% of the base level; and for savings levels over 1.0%, state cost s are escalated to 140% of the ba se 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 anal ysis including first-year costs of saved energy, ratio of program to particip ant 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,” C ouncil Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/ ; and McKinsey & Company. D ecember 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? Available at http://www.mckinsey.com/client_service/sustainability/la test_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-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programsunited-states-projected-spend

. 241 Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility Energy Efficiency Programs. ACEEE Repo rt 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 es timating the costs associated with the statespecific EE goals discussed above. The results of th ese calculations are then used within the RIA and preamble. Specifically, three values are calculate d (annual first-year cost s, levelized cost of saved energy (LCSE), and annualized costs); fo r each, program and participant components are then calculated using the 1:1 ra tio (i.e., 50% of total for each) derived above. Specific results from prior sections on state goal setting and im pacts assessment inputs are used as inputs for these calculations. For each state, the following steps are taken for each year (2017-2030) and for each option. Calculations for steps 2 and 3 are done using real discount rates of 3% and 7%. The steps are:
1. Calculate annual first-year costs
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 nationa l results (through 2030) are presented below.5 - 55 Step 1: Calculate Annual First-Year Costs Annual total first-year costs are calculat ed by multiplying annual total incremental savings (MWh) (from Table 5-15) by the first-year total CSE (f rom Table 5-23 with escalation based upon results from Table 5-14). Program and pa rticipant portions of th e 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-Yea r 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$) 75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.95 - 56 Step 2: Calculate Levelized Cost of Saved Energy Levelized costs of saved energy (LCSE) are ba sed on levelization of al l 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., ove r the entire distribution of pr ogram 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 th e 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 repr esented 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 distri bution of measure lifetimes. For each year in the analysis, the annualized costs resulting from a ll current and past investments ar e 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 75.7 114.6 158.8 206.6 251.3 293.15 - 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 = ∑ ሺܥܮܵܧ ௬௘௔௥	௜ି௧ ൈ ் ௧ୀ଴ ݅	ݎ݁ܽݕ ݅݊ ݏ݅݊݃ݒܽݏ ݈ܽݐ݁݉݁݊ݎ݅݊ܿ ݈ܽݑܽ݊݊ ௧ି௘௔௥	௜௬ ሻ , where LCSE year i-t is the LCSE of EE programs implemented in year i-t , ݅	ݎ݁ܽݕ	݅݊	ݏ݅݊݃ݒܽݏ	݈ܽݐ݁݉݁݊ݎ݅݊ܿ	݈ܽݑܽ݊݊ ௧ି௘௔௥	௜௬ estimated represents 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 su mmarizes national LCSE for Option 1 for the same years. Each of the three tables includes values fo r program, participant, and total

costs. TABLE 5-29 First-Year EE Costs (billions 2011$) (Continental U.S.)
Year
2018 2020 2025 2030 Program 10.2 15.4 21.8 21.8 Participant 10.2 15.4 21.8 21.8
Total 20.5 30.7 43.6 43.55 - 60 TABLE 5-30 Levelized Cost of Saved Energy (3% discount rate, 2011$/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 a nd 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 cost s. Historic data is from Tabl e 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 (Opt ion 1) Annual Growth Rate s 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 CO 2 Reduced To estimate the reductions in power system costs and CO 2 emissions associated with this building block, EPA analyzed a scenario incorp orating the resulting reduction in electricity demand (the “energy efficiency scenario”) and comp ared the results with the base case scenario. Both analyses were conducted using the Integr ated 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 CO 2 reductions for each year, EPA found that the average cost of the CO 2 reductions achieved ranged from $16 to $24 per
metric tonne of CO 2 . Although EPA considers this estimated range of average $/tonne to be reasonable, we expect the $/tonne would be lowe r in combination with th e other building blocks because, in that context, power system costs w ould be somewhat higher and, thus, avoided power system costs due to this building block would be higher as well, leading to lower $/tonne CO 2 avoided.

Analysis Considerations
Two considerations are worth noting in regards to the analysis describe d in the previous two sections, “Goal Setting” and “Impacts Asse ssment:” 1) state energy efficiency policies implicitly represented in the ba seline 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 th e power sector modeling di scussed in the RIA, is based upon the AEO 2013 reference case scenar io. 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 exampl e, 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 repr esented in the reference case. This implicit representation is captured in part through a calibratio n process that is affected by se veral 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 st ate goals (expressed in percentage cumulative savings by year, by state) are not affected by th e underlying electricity demand fore cast. 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 pr ograms because the assessment isbuilt partly from absolute energy savings values that are partly derived from the business-asusual (BAU) demand forecast. If the BAU forecast did not implicitly repres ent a continuation of existing energy efficiency programs, the forecast would indicate higher electricity demand, at least in the near term. However, the direction (h igher or lower) of the net cost impacts (energy efficiency program costs as well as power system cost reductions) is not clear as it is possible that program costs could increase while av oided power system costs also increase.

Energy Information Administration Form EIA-861 as Data Source Comprehensive data on energy efficiency programs’ spending and energy savings are limited for evaluating and comparing energy efficiency programs and their effectiveness at the utility, state, and national scale. Issues relate d to the lack of standardized definitions and5 - 63 reporting, and data quality are noted to limit evaluation of energy efficiency programs. 243 The EIA Form 861, “Annual Electric Power Industry Repor t,” remains the most comprehensive effort
that collects data annua lly 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 es timate 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 va riables. Specificall y, we use the 2012 sales data and reported incremental annual energy savi ngs of energy efficiency programs to estimate the current performance of energy efficiency programs to inform setting best practices performance level for th e state EE goal setting.

EPA notes potential concerns associated w ith consistency and qua lity 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 standardizatio n in data reporting a nd more detailed data categories are being reported. Fo r instance, in 2011, EIA began co llecting data from third-party administrators of programs. While now comprehe nsive, outside entities have found that the EIA861 data can be improved through supplementa tion with publicly av ailable 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 Coun cil 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) El ectric 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: St ate Goal Setting and Impacts Assessment5 - 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 (2010-2 014) for states, utilities, and other agencies across the U.S. Study periods ranged from five to twenty-one years in leng th. As Table 1 shows,
across the eleven studies that reported achievable potential, resu lts 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 Rive r 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. Delawa re Economic Energy Efficiency Potential. Prepared for the Delaware Department of Natu ral Resources and Environmental Control, May
24.

Illinois: ICF International. 2013. ComEd Ener gy Efficiency Potential Study Report, 2013-2018. August 20.

Michigan: GDS Associates, Inc. 2013. Michigan Electric and Na tural Gas Energy Efficiency Potential Study – Final Report. Prepared fo r the Michigan Public Service Commission,
November 1.

New Jersey: EnerNOC Utility Solutions C onsulting. 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 E fficiency Potential Study for the State of New Mexico, Volume 2: Electric En ergy 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 Po tential Report. March.

Pacific Northwest states (com bined): Barbose, Galen, and Al an Sanstad, Charles Goldman, Stuart McMenamin, Andy Sukenik. 2014. Incorpor ating Energy Efficiency into Western Interconnection Transmission Planning. Draft re port, 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 Partne rs. 2011. Tennessee Valley Author ity Potential Study, Final Report, Volume 1: Executive Summa ry. 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 tw o different perspectives: 1) hist orical data reflecting achieved savings of EE programs and 2) requirements of existing state energy efficiency resource standards (EERS). For the historic al perspective, we reviewed da ta from the Energy Information Administration’s Form EIA-861 on EE program elect ricity 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 follow ing two groups of energy efficiency program
administrators.
1. Top saver 1% - a group with 47 entities that achieved a maximum firs t-year savings level
of 0.8% to 1.5%.
2. Top saver 2% - a group with 26 entities that achieved a maximum firs t-year savings level
of 1.5% to 3.0%. 247 For the existing state requirements perspec tive, we reviewed energy savings ramp-up
schedules established under EERS for states that provi de clear ramp-up schedules. According to ACEEE’s 2013 State Energy Efficiency Scorecard 248 , 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 colle ct third-party administrator data in 2011. The third-party entities included in our analysis are Efficiency Vermont, Ener gy 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 El ectric Company (now part of National Grid).
247 In addition to these maximum first-year savings threshol ds, we screened program administrators for the following conditions: (a) the maximum savings levels occurs after the minimum savings levels; (b) sufficient amounts of increase in first-year savings are provide d to evaluate reasonable ramp-up schedul es 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 Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/st ate-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 historic al 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 a bout 3.4 years on average to in crease first-year savings by 1% (with a range of 1.6 years to 10 years) (s ee Table 1). The entitie s in this group have increased the level of first-year saving s 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 in crease 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% pe r 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%” groups 252 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 Sc orecard, Appendix B, November 2013,
250 This is a simple average estimate of the annual averag e increase in first-year savi ngs from each entity in this group. 251 This is the simple average estimate of the annual aver age increase in first-year sa vings from each entity in this group. 252 Data sources: The EIA 861 was the main data source. Ho wever, 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 Effici ency Vermont, Energy Trust of Oregon, Efficiency Maine Trust, and Cape Light Compact. In addition, we suppleme nted 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 Co mpany (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 electric ity savings ramp-up based on existing state EERS policies are:

 The states with EERS policies which exhibi t savings ramp-up schedules are requiring increases in first-year ener gy savings at a pace that ra nges from 0.11% (Colorado and Oregon) to 0.40% (Rhode Isla nd) as shown in Table 2.

 The first-year savings pace of increase averag es 0.21% per year across the 10 states. This savings level translates to a bout 4.7 years to achieve an incr emental 1% first-year savings
increase. Table 2. First-Year Energy Savings Ramp -up Review of State EERS Policies 253

State Minimum Target Maximum Target Climb
Time (years) Annual Average %

Increase Years to
Achieve 1%

Increase Min Year Max Year a b c d e=d-b f=(c-a)/e g=1/f
Arizona 1.25% 2011 2.5% 2016 5 0.25% 4.0
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% 4.85 - 73 

### Appendix 5-3
Review of the Ratio of Pr ogram to Participant Costs

Introduction and Summary This appendix summarizes and analyzes data on EE costs (program and participant) to develop a ratio to enable the estimation of participant costs from known program costs. We reviewed cost data from leading EE program admi nistrators in 22 states . Our research findings
are as follows:

 A 1:1 ratio between program and particip ant 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 informa tion (participant costs across entire portfolio of EE programs) to inform the analysis.

 Participant cost data from ten program admini strators in nine states indicate that the weighted average and simple average particip ant costs were 47 percent of total costs.

Participant Cost Analysis We first identified states having high incr emental electricity savings rates or high absolute savings levels based upon 2013 AC EEE State Energy Efficiency Scorecard. 254 These states represent a large portion of total EE savi ngs in the U.S. We identified 22 states meeting these criteria and collected publicly availa ble EE program reports for major program
administrators within each state. From th ese program reports we identified 10 program
administrators across nine states where we were able to identify both pr ogram administrator and participant costs across their full portfolio of EE programs. The table below provides the 2012 portfolio-level program administrator and part icipant costs for the nine states. Program

254 For the purpose of this research, we have defined leading or high impact states as th e top 15 states in the 2013 ACEEE State Energy Efficiency Sc orecard in terms of incremental savings as a percentage of retail sales or absolute annual energy savings in terms of total annual MWh saving s. 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 re present the customer costs to partake in the program. Total program costs are the sum of both costs. Each state’s program administrator and participant costs are presented as a percentage of total program costs in Table 3. The weighted and simple average program and participant cost s across all nine states are presented as a percentage of total program costs. The wei ghted average cost shares were based on each program’s spending by administ rator and participants.

Table 1 2012 Participant and Program Cost Info rmation from Reported Entities

In our analysis, the weighted average pr ogram 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 tota l costs. Participant cost results range fr om 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, leadi ng to slightly higher total costs than the precise average values would provide.

References

MidAmerican Energy Company, "Energy Efficien cy Plan Docket No. EEP-08-2, 2012 Annual Report to the Iowa Utilities Board," May 1, 2013, available at: http://www.state.ia.us/governmen t/com/util/energy/energy_effici ency/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 N o. 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_e fficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf

Burlington Electric Department , "2012 Energy Efficiency Annual Report," 2012, available at: https://www.burlingtonelectric.com/ELBO/a ssets/2012%20DSM%20Annual%20Report%20Mas ter.pdf5 - 76 Hawaii Energy, “Annual Report Program Year 2012 July 1, 2012 - June 30, 2013,” 2013,
available at http://www.hawaiienergy.c om/information-reports

Xcel Energy, "2012 Status Report & Associated Compliance Filings Minnesota Electric and Natural Gas Conservation Improvement Progr am Docket No. E,G002/CIP-09-198, " April 1, 2013, available at http://www.xcelenergy.com/sta ticfiles/xe/Regulatory/Regul atory%20PDFs/MN-DSM-CIP-2012- Status-Report.pdf

National Grid, "2012 Energy Efficiency Y ear-End Report," May 31, 2013, available at http://www.nationalgridus.com/non_htm l/eer/ri/4295-YearEnd%20Rept%20(PUC%205-31- 13).pdf

Southern California Edison, "2013 Energy Effici ency Annual Report," June 2013, available at http://eega.cpuc.ca.gov/Documents.aspx http://eega.cpuc.ca.gov/Documents.aspx5 - 77 

### Appendix 5-4 Comprehensive Results: State Go al 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, Firs t-year Costs, and Annualized Costs

 Option 1 – National Costs at 7% Discount Rate (2017-2030)

o Levelized Cost of Saved Energy, Firs t-year Costs, and Annualized Costs

 Option 2 – National Costs at 3% Discount Rate (2017-2025)

o Levelized Cost of Saved Energy, Firs t-year Costs, and Annualized Costs

 Option 2 – National Costs at 7% Discount Rate (2017-2025)

o Levelized Cost of Saved Energy, Firs t-year Costs, and Annualized Costs6 - 1 

### Chapter 6: Fuel Switching

Coal-to-Natural Gas Switching

Introduction
Firing natural gas in a boiler designed for coal-fired generation is one approach to reducing the output-based CO 2 emissions rate (lbs/MWh) in these boilers. The CO 2 emission rate is reduced when natural gas is substituted for coal because the gas has a much higher percentage of hydrogen and a lower percentage of carbon than the coal it replaces. When quantities of gas and coal are burned with oxygen from air to produce the same amounts of heat, the higher hydrogen content of natura l gas produces more water vapor (H 2 O) than coal, but far
less CO 2

. The discussion below focuses solely on the c onversion 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 boi ler from the exhaust of a newly installed gas turbine generator; and,

 Gasification of coal, producing substitute natu ral gas (SNG) that prov ides 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 th at will become apparent in this analysis. This chapter evaluates the cost-effectivene ss of widespread adoption of coal-to-gas switching at a national level fo r the purpose of setting CO 2 emissions goals consistently in each

state.

Description of Technology Engineering Considerations
Most existing coal-fired EGU boilers can be modified to switch to 100% gas input, or to co-fire gas with coal in any de sired proportion. This transition t ypically requires at least some6 - 2 plant modifications and might have some nega tive impact on the efficiency of the unit as described later in this chapter. A conversion from coal to gas firing first requ ires either an existi ng natural gas delivery system to the boiler or the installation of a ne w 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 mer it, given the significance of the change to the cost of fuel for generation from the converted boiler. Conversion to natural gas firi ng in a coal-fired boiler typi cally involves installation of new gas burners and supply piping, modifications to combustion air ducts and control dampers, and possibly modifications to th e 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 cha nges affecting heat transfer surfaces; the need for sizing of flue gas recirculation fans; and opera tional 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 th e flue gas. The increased moisture content, in turn, results in additional heat lost up the stack in stead of being directed towards electricity generation. Additionally, depe nding 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 over all net output efficiency of a coal-steam boiler EGU that switches from coal to natural gas fi ring may change only slig htly, depending on how much auxiliary load is converted to net output by avoiding the need to run coal pulverizers, conveyors, ash sluice pumps, and relevant air pollution control equipment (e.g., PM and SO 2 controls).6 - 3 Fuel Considerations Delivery of natural gas via pipe line is critical for conversion of a coal-fired boiler to a gas-fired boiler. Some coal boilers are connected to the na tural gas pipeline network for purposes of using gas as a startup fu el, 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 output 255 ) without constructing additional gas pipeline capac ity. For purposes of this analysis, the EPA conservatively assumed that gas use of 10% or great er at these boilers, or any gas use at boilers without an existing gas pipeline, would require construction of additiona l pipeline capacity. Unlike coal, natural gas cannot be stored in quan tities sufficient for sustained utilization on site. To the extent that firm (uninterruptible) gas supply is contractually unavailable or costprohibitive, any potential interruption in gas su pply could impact the ability of the unit to continue operating w ithout increasing its CO 2 emissions rate (since it would likely need to
substitute more CO 2 -intensive fuel for the unavailable natu ral gas). Additionally, for boilers that switch to 100% gas, interruption in gas impacts the ability of th e 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 s upply pipeline from separa te 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 hypotheti cal conversion of a boiler from burning 100% coal to burning va rying proportions of gas (10%, 50%, and 100%). The capital cost of modifying a coal boiler to switch to natural gas includes the ne w 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 th at 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 cost s for pulverized coal (PC) and cyclone boiler
modifications are as follows 256 : $/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 coul d burn any proportion of natural gas. For this illustrative example, to support 100% gas combustion we assume that a 50-mile gas pipeline 257 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 & Veat ch also used a similar cost level in a recent case study. 259 At a 14.3% capital charge rate 260 and 75% annual capacity f actor, 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 th e increase in fuel cost discussed later. Due to a reduced need for operators, maintena nce 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 anal ysis that there would be no derate in the net EGU output, and estimated that the impact on net h eat rate for an average unit would be a 3%
increase for a switch from coal to 100% natura l 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 perf ormance 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 Casev.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, th e 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 In ternational, December 2012, available at http://bv.com/redirects

. 260 Capital charge rate at 14.3% is the average of the re gulated 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 pr ojections for delivered gas prices that are about double projected delivered coal pr ices 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 fu el cost on an output ba sis 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 sw itching 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 CO 2 reduction potential is directly related to the amount of gas co-fired, and is due largely to the different carbon intensities of each fuel. More reductions in CO 2 rate are achieved at higher levels of gas co-firi ng as shown in Table 6-1. At 10 % gas co-firing, the net emissions rate (lbs/MWh net) of a typi cal unit would decrease by approx imately 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 expect ed to make similar conversi ons, 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. CO 2 Rates at Various Levels of Natural Gas Co-Firing
Case Heat Rate (Btu/kWh) CO 2 Rate (lbs/MWh net) Reduction in CO 2 Rate from 100% Coal (lbs/MWh net) 100% Coal 10,340 2,108 N/A 10% Gas 10,370 2,021 4% 50% Gas 10,490 1,673 21% 100% Gas 10,640 1,239 41%
In addition to reducing CO 2 emissions, natural gas co-firi ng at a coal-fired steam EGU will generally also reduce crit eria air pollution. Reducing CO 2 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 discu ssed in detail in Chapter 5 of th e RIA for this proposed rule. For this analysis, EPA estimated the PM 2.5 -related human health co-benefits of SO 2 , NO X , and
direct-PM 2.5 emission reductions attributable to a 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 NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impa irment. Only the unit-level emissions of SO 2 , NO X and
direct-PM 2.5 are considered in this illustrative ex ercise. Additionally, emissions from the extraction and transport of the fuels used by thes e technologies are not co nsidered. Furthermore, there may be differences in upstream greenhouse gas emissions (in particular, methane) from different technologies but those were not quantifie d for this assessment. The estimated avoided emissions under 10% gas co-firing and a 100% switch to gas are pr esented 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 SO 2 0.3 3.1 NO X 0.2 2.04 PM 2.5 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) oper ating at 75% capacity fact or. 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
PM 2.5 -related benefit-per-ton estimates for SO 2 , NO X , and direct-PM 2.5 emission reductions described in detail in Chapter 5 of the RIA for th is proposal. To estimate the benefits associated with co-firing, we determine the emission reducti ons for co-firing in Table 6-2 and apply the
2020 social benefit values discusse d in Chapter 5 of the RIA for th is proposal. Specifically, we multiply the reduction in SO 2 , NO X , and direct-PM 2.5 emissions by the PM 2.5 -related benefit perton estimates, and add those values to get a measure of 2020 benefits. Table 6-3 shows the
PM 2.5 -related benefits expected ba sed on the estimated emission re ductions that would occur in this illustrative example. These estimates are purely illustrative as the EPA does not assert a specific location for the illustrativ e electricity generation technologi es and is therefore unable to specifically determine the populatio n that would be affected by th eir emissions. Therefore, the benefits for any specific unit can be di fferent than the estimates shown here.

Table 6-3. Rounded PM2.5-related Co-benefit s ($/MWh net) of Gas Co-firing (2011$)

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 significa nt figures. Co-benefits are based on national benefit-per-ton estimates for directly emitted PM 2.5 and PM 2.5 precursors, SO 2 and NO X . It is important to note that the monetized health co -benefits do not include reduced health effects from ozone or direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment. Emissions of directly emitted par ticles 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 a ssociated with lower emissions would depend primarily on the location of the co-firing unit, th e 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 So ciety Study Linking Particulate Air Po llution 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 1 974 to 2009.” Environmental Health Perspectives, July, 120(7):965-70.6 - 8 account for these characteristics. However, th ese factors will not ch ange 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 ex isting coal boilers can be via gas reburning, a NOx reduction technology. 267 Gas reburning involves firing na tural gas (between 10 and 25% of total heat input) above the prim ary 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 downstrea m of the reburn zone to burn out th e remaining combustibles and convert the reduced nitrogenous species to molecula r nitrogen. The heat input from gas would approximately substitute for a similar heat input from coal, thus reducing CO 2 and other coal emissions in a manner similar to ga s co-firing as discussed above.

Cost of Reductions and Cost Effectiveness This analysis examines the average $/tonne 268 cost of avoided CO 2 that results from applying a range of natural gas co-f iring levels to a typical baseload coal boiler. We capture the capital costs of boiler modifi cations and new pipeline constr uction (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 CO 2 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%20Libra ry/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 Casev.5.13 Using the Integrated Planning Model, available at: http://www.epa.gov/airmarkets/progsregs/epaipm/BaseCasev513.html6 - 9 Table 6-4. Average Cost of Avoided CO 2 and CO 2 Emission Rate Reductions from 100%
Coal at Various Levels of Natural Gas Co -Firing at Base Case Projected Gas Price ($5.36/MMBtu) Case Average Cost of Avoided CO 2 ($/tonne) Change in CO 2 Rate from 100% Coal (lbs/MWh net) 100% Coal N/A N/A 10% Gas 150 4% 50% Gas 91 21% 100% Gas 83 41% Note: Based on a typical 500 MW bituminous coal steam unit operating at 75% capacity factor. Assumes construction of new 50-mile pipeline. EP A 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-f iring would increase overall gas demand and place upward pressure on the natural gas price, which would consequently increase the average
cost of avoided CO 2 of a potential boiler conversion.

Conclusion Switching from coal to gas is a re latively costly approach to CO 2 reductions at existing coal steam boilers when compared to other meas ures such as heat rate improvements and redispatch of generation supply to ot her existing capacity with lower CO 2 emission rates. Moreover, this analysis shows th at coal-to-gas conversion of an existing boiler is less efficient than constructing a new natural gas combined cycl e (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 CO 2 emission rate of the generati ng capacity by 62% at a cost of about $50/tonne of avoided CO 2 under the base case projected gas price and about $81/tonne of
avoided CO 2 at a future gas price 50% higher than th e base case projection. See preamble
section VI.C.3.c. The EPA is considering cost-effectiveness at a national level for th e purpose of setting emissions goals consistently in each state. While this analys is suggests that cost-effective
reductions of CO 2 are not available on a national basis fr om 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 CO 2 and other emissions, nor does it preclude states from f actoring in that unit-level potential into the design of stat e plans for compliance with the 111(d) standa rd. EPA notes that6 - 10 there are utilities that see mer it 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 pow er plants with natgas,” Reuters U.S. Edition, January 16, 2014 , available at http://www.reuters.c om/article/2014/01/16/utilities-southern-alabamaidUSL2N0KP1WA20140116 271 Dominion 2012, “Dominion Virginia Power Proposes To Convert Bremo Power Station From Coal To NaturalGas,” 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-Gas6 - 11 Biomass Co-firing
Introduction Co-firing biomass in existing boilers designe d for coal-fired gene ration, or converting those boilers to consume entirely biomass, is another approach to potentially reduce the outputbased CO 2 emissions rate (lbs/MWh) of these boilers. In the analysis presented in this technical support document, the physical CO 2 emissions rate at the boiler st ack could increase or decrease, depending on the amount of coal energy replaced by biomass energy and differences in the properties of a selected biomass and the coal it replaces. 272 There are many possible combinations of coals and biomass types that could be co-fired. Site-specific economics and accessibility would dete rmine which combinations might actually be feasible. This TSD analysis does not attempt to estimate an economically feasible national average increase or decrease in CO 2 emission rate via biomass co-f iring. Instead, this analysis simply employs one reasonably representative case to evaluate the cost effectiveness of biomass energy substitution in reducing the physical CO 2 emission rate based only on the CO 2 coming from coal. This analysis indicates that while th e co-firing of biomass with coal is technically feasible as a means of reducing the coal-based CO 2 emission rate due to the substitution of biomass for coal, it generally has limited economic feasibili ty 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 curren tly practiced in the U.S. It is also consistent with recent findings by others 273 , including an earlier study by the State of Maryland 274 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 woul d make the investments required to cofire with a more expensive and less efficient fuel.”

272 Fuel properties particularly affecting relative CO 2 emission rates are: higher hea ting 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.c om/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.pdf6 - 12 Based on the basic analysis of the cost e ffectiveness of biomass energy substitution in reducing the physical CO 2 emission rate based only on the CO 2 coming from coal presented below, the EPA concludes in this TSD that bi omass 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-firi ng in existing coal-fired boilers has been thoroughly investigated in many resear ch 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 biom ass 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; poten tial corrosive effects of biomass ash in the boiler furnace; potential imp acts of co-firing on boiler effi ciency even at low biomass percentages, and the likely re duction (derate) in unit generati ng output at very high biomass percentages.

275 This analysis does not include evaluation of stack biogenic CO 2 emissions relative to the net landscape and process-related carbon fluxes associated with the production and use of the biogenic feedstocks combusted. Issues related to methods for assessing biogenic CO 2 emissions from stationary sources are currently being evaluated by the EPA. In general, the overall net atmospheric contribution of CO 2 resulting from the use of a biogenic feedstock by a stationary source, such as an EGU, will ultimately depend on the stationary source process and the type of feedstock used, as well as the conditions under which that feedstock is grown and harvested. In September 2011, the EPA submitted a draft Accounting Framework to the Science Advisory Board (SAB) Biogenic Carbon Emissions (BCE) Panel for peer review. The SAB BCE Panel delivered its Peer Review Advisory to the EPA on September 28, 2012. In its Advisory, the SAB recommended revisions to the EPA's proposed accounting approach, and also noted that biomass cannot be considered carbon neutral a priori, without an evaluation of the carbon cycle effects related to the use of the type of biomass being considered.
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/PlantsTe rritory/Schiller-Station.aspx6 - 13 There are considerable physical differences between coal and biomass that will generally limit the extent to which biomass can be reasonably us ed to replace coal in a boiler. For example, compared to most coals, many solid biomass fuels have both a signifi cantly higher as-fired moisture content and a significantly lower heatin g value per unit of weig ht. Most solid biomass fuels are also significantly less dense than most co als. 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 th at the weight of biomass needed to provide a given amount of heat energy could be twice th e weight of the coal it replaces, and the volume (cubic feet) of biomass needed could be four-t o-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 sp ecific seasons in which the crop was grown. As noted above, the four-fol d or greater increase in volume occupied by
biomass relative to coal means that the necessa ry additional storage space could be large. However, if pre-prepared or condensed biomass fu els 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 cost s. Stored biomass can be at ev en greater risk of spontaneous combustion than stored coal; this may limit the sa fe 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 esti mated that a 500 MW baseload coal plant cofiring 10% biomass and receiving biomass deliv eries 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 hi gher transportation cost per unit of delivered energy, it may only be economically viable to tr ansport biomass a limited distance from where it is grown. This could limit the both the percentage of biomass co-f iring 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.pdf6 - 14 as torrefaction of biomass, could mitigate so me of these transportation, storage and energy content concerns, but are not yet commercially available. The relatively higher moisture content and lo wer 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 assume d for this analysis that coal-steam EGU boilers will generally only co-f ire with biomass to a limited degree. While the actual level at which any plant can co-fire with biomass is hi ghly site-specific, this analysis adopts the assumption used in EPA’s fleet wide IPM modeli ng 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 indi vidual 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 representati ve of EPA assumptions used in its IPM modeling and discus sed 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, th e 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 co al 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 increas e 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/bibliogr aphy/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.f ed.us/pnw/pubs/pnw_gtr867.pdf6 - 15 alone. At low biomass cofiring rates, this fact or slightly affects calculated biomass fuel consumption and any associated CO 2 emission from biomass.

Cost of Fuel For this analysis, the EPA uses a delivered bi omass cost of $4/MMBtu, representative of delivered woody crops grown specifica lly for energy-generating combustion, 283 and roughly 50% greater than IPM projected 202 0 average delivered coal costs. 284 This analysis also considers a sensitivity scenario assu ming 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 tran sportation 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 transp orted. For example, the EPA engineering staff estimate that for a one-way distance of 50 miles with a 20-ton semi-trail er 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 valu e (HHV) basis), transpor tation cost in this example case could account for 25-50 percent of th e 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 CO 2 reduction potential of biom ass 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 ratios 285 for a selected biomass fuel compared to the particular coal it replaces. The types of biomass typically available to E GUs in the United States include woody-based feedstocks such as wood chips, forest industr y byproducts, and to a le sser degree agricultural crop residues, as well as emerging dedicated ener gy 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 2 012 – 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=236 - 16 woody crops. 286 In general, when comparing coal-only versus co-firing coal with biomass, cofiring may result in either an in crease or decrease in the stack CO 2 emission rate. The extent to
which the use of biomass contributes to net emi ssions to the atmosphere is being considered in EPA’s current study on biogeni c 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 CO 2 emissions in a hypothetical scenario where biomass CO 2 emissions are
not included in total stack CO 2 emissions (in effect, biogenic CO 2 emissions are subtracted from
total CO 2 emissions measured at the stack). The es timated results presented below are based on a reasonably representati ve case using a baseload bituminous coal-fired boiler with a net heat rate of 10,340 btu/kWh that shifts from 100% bituminous coal to 90% coal and 10% biomass (assuming fuel prices of $2.62/MMBtu for coal in 2020 as projected in IPMv5.13 Base Case and $4/MMBtu for biomass as explained above). When biogenic stack emissions are not counted as part of total emissions, the cost of avoided CO 2 for a “typical” baseload coal boiler co-firing 10% biomass is $30/tonne. At higher delivered fuel pr ice differentials, the cost of avoided coal CO 2 emissions would increase (for example, at a biomass price of $6/MMBt u, cost of avoided CO 2 is $80/tonne if CO 2 emissions from biomass are not counted). 287 This estimated cost of avoided
coal CO 2 emissions, which ranges for $30 to $80/tonne , would increase if any portion of the biogenic CO 2 emissions from the co-fired biomass were included.

Conclusion Replacing some coal with low levels of biomass co-firing may result in stack CO 2 emission increases. 288 Even if biogenic CO 2 emissions are not counted as part of stack emissions, biomass co-firing is a relatively costly approach to CO 2 reductions at existing coal steam boilers when compared to other measures such as heat rate improvements and re-dispatch of generation supply to other ex isting capacity with lower CO 2 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 wo uld decrease at lower fuel price differentials.
288 Depending on biogenic feedstocks used and whether or not an assessment system is applied that evaluates biogenic CO 2 emissions from the stack in relation to the terrestr ial 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 th e purpose of setting emissions goals consistently in each state. Wh ile this analysis concludes that cost-effective
reductions of CO 2 are not available on a national basis fr om widespread adoption of biomass cofiring, it does not preclude the potential for individual EGUs to utilize co-firing as a way to
reduce overall CO 2 emissions, nor does it preclude states from factoring in that unit-level 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 Combus tion Options for Steam Generation, Presented at Power-Gen 97, Dallas, TX, December 9 – 11, 1997.

(b) Grusha, J and S. Woldehanna, K. McCarthy, a nd G. Heinz, Long Term Results from the First
US Low NOx Conversion of a Ta ngential Lignite Fired Unit, pr esented at 24th International Technical Conference on Coal & Fuel Sy stems, 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 Generatio n, Inc., Johnstown, PA: 1999. TR-113903.

(d) Laux S., J. Grusha, and D. Tillman, Co-firin g of Biomass and Opport unity Fuels in Low NOx Burners, PowerGen 2000 - Orlando, FL

(e) Tillman, D. A., Co-firing Biomass for Gr eenhouse Gas Mitigation, presented at Power-Gen 99, New Orleans, LA, November 30 – December 1, 1999.

289 EPA notes that states will need to consider any futu re EPA finding regarding an assessment framework that considers carbon fluxes on the biogenic feedstock production landscape applied when evaluating net stack CO 2 emissions from biomass co-firing.6 - 18 (f) Tillman, D. A. and P. Hus, Blending Oppor tunity Fuels with Coal for Efficiency and Environmental Benefit, presented at 25th Intern ational 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 char acteristics and biomass cofiring in pulverized
coal boilers. In: Electric Power, Baltimore, MD, USA, 18 - 20 May, 2010

(i) Renewable and Alternative Energy Fact Sheet – Co-firing Biomass with Coal, Pennsylvania State University, 2010, http://pubs.cas.psu.edu/Free Pubs/PDFs/ub044.pdf

(j) Fernando R, Cofiring High Ratios of Bioma ss with Coal, IEA Clean Coal Centre (CCC/194), January 2012

(k) DOE-EERE-ORNL, Biomass Energy Data Book- Edition 4, October 2012, http://cta.ornl.gov/bedb/index.shtml7 - 1 

### Chapter 7: Carbon Capture & Storage
Introduction Another possible approach for reducing CO 2 emissions from existing fossil fuel-fired EGUs is through the application of carbon capture and storage t echnology (CCS; sometimes also referred to as carbon capture and sequestratio n). In the recently proposed standards of performance for new fossil fuel-fired EGUs (79 FR 1430), the EPA proposed to find that the best system of emission reduction for new fossil fu el-fired boilers and IGCC units is partial application of CCS. In that proposal, the EPA found that, for new units, partial CCS has been adequately demonstrated; it is technically 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 foss il fuel-fired utility boilers and IGCC units.

Carbon Capture Options for Existing Fossil Fuel-fired EGUs In general, CO 2 capture technologies a pplicable to existing fo ssil fuel-fired power generation can be categorized into three appr oaches – (1) post-combustion capture; (2) precombustion capture; and (3) oxy-combustion. Each of these is described and discussed in more
detail below. Post-combustion Capture
Post-combustion CO 2 capture refers to removal of CO 2 from a combustion flue gas prior to discharging to the atmosphere. Separating CO 2 from such a gas stream can be challenging for
a number of reasons. Because CO 2 is a dilute fraction of the co mbustion 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 fr om 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 requi re added energy to release the captured CO 2 and regenerate the solvent. Many of th e chemical solvents require a flue gas stream that is free of or has very low quantities of components – such as SO 2 , NO X , and HCl – that can degrade the solvent. The captured CO 2 must then be compressed from near atmospheric pressure to much higher pipeline pressures (about 2,000 psia).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 foss il fuel (e.g., pet coke) is converted into a synthesis gas (or “syngas”) by applying heat under pressure in the pres ence of steam and limited
O 2 . The product syngas contains primarily H 2 and CO – and, depending on the fuel and gasification system – some lesser amount of CO 2 . The amount of CO 2 in the resulting syngas stream can be increased by “shifting” the com position via the catalytic water-gas shift (WGS) reaction. This process involves the catalytic reaction of steam (“wate r”) with CO (“gas”) to form
H 2 and CO 2 . The resulting CO 2 contained in the syngas is then captured before combustion of
the H 2 -enriched syngas for power generation in a co mbined cycle system. Contrary to the postcombustion capture flue gas, the IGCC syngas can contain a high volume of CO 2 and is pressurized. This allows the use of physical ab sorbents (e.g., SelexolTM, R ectisol®) that require much less added energy to release the captured CO 2 and require less compression to get to pipeline standards.

Oxy-combustion Oxy-combustion systems for CO 2 capture rely on combusting coal or other fuels with relatively pure O 2 diluted with recycled CO 2 or CO 2 /steam mixtures. Unde r these conditions, the primary products of combustion are water and CO 2 , with the CO 2 purified by condensing the water. Challenges associated with oxy-com bustion include the capit al cost and energy consumption for a cryogenic air separation unit (ASU) to produce oxyge n, introduction of N 2 via boiler air infiltration, and excess O 2 in the CO 2 product stream.

CO 2 Transportation and Storage
CO 2 Pipeline Infrastructure Carbon dioxide has been transported via pipe lines in the U.S. for nearly 40 years. Approximately 50 million metric tons of CO 2 are transported each year through 3,600 miles of pipelines. Moreover, a re view of the 500 largest CO 2 point sources in the U.S. shows that 95 percent are within 50 miles of a possible ge ologic sequestration site, which would lower transportation costs. There are multiple factors that contribute to the cost of CO 2 transportation via pipelines including but not li mited to: availability and acqui sition of rights-of-way for new7 - 3 pipelines, capital costs, operating costs, length and diameter of pipeline, terrain, flow rate of
CO 2 , and the number of sources utilizing the pipeline.

Geologic Storage Existing project and regulato ry experience, research, and analogs (e.g. naturally existing
CO 2 sinks, natural gas storage, and acid gas injec tion), indicate that geol ogic sequestration is a viable long term CO 2 storage option. The viability of geologic sequestration of CO 2 is based on a demonstrated understanding of the fate of CO 2 in the subsurface. Geologic storage potential for
CO 2 is widespread and available throughout the U.S. and Canada. Nearly every state in the U.S. has or is in close proximity to formations with carbon storage potenti al 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 billio n metric tons of CO 2 in deep saline formations, oil and gas reservoirs and un-mineable coal seams. 290 Other types of geologic formations such as organic rich shale an d basalt may also have the ability to store CO 2 ; and the DOE is currently evaluating their potential storage capacity.
Further evidence of the wide spread availability of CO 2 storage reserves in the U.S. comes from the Department of Interior’s U.S. Geological Survey (USGS) which has recently completed a comprehensive evaluation of the tech nically accessible storag e 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 CO 2 storage potential across the Un ited States. For comparison, this amount is 500 times the 2011 annual U.S. energy-related CO 2 emissions of 5.5 Gigatons (Gt). 292

Enhanced Oil Recovery (EOR) Geologic storage options also include use of CO 2 in EOR, which is th e injection of fluids
into a reservoir to incr ease oil production efficiency. EOR is typically conducted at a reservoir

290 The United States 2012 Carbon Utilization and Storage A tlas, 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 Su rvey Factsheet 2013-3020, 6p.http://pubs.usgs.gov/fs/2013/3020/.7 - 4 after production yields have decreased from primary production. EOR using CO 2 , sometimes
referred to as ’CO 2 flooding’ or CO 2 -EOR, involves injecting CO 2 into an oil reservoir to help mobilize the remaining oil and make it ava ilable for recovery. The crude oil and CO 2 mixture is produced, and sent to a separato r where the crude oil is separa ted from the gaseous hydrocarbons
and CO 2 . The gaseous CO 2 -rich stream then is typically dehydrated, purified to remove hydrocarbons, recompressed, and re-injected into the oil or natural gas reservoir to further enhance recovery.
CO 2 -EOR has been successfully used at many production fields throughout the U.S. to increase oil recovery. The oil and natural gas indu stry in the United States has over 40 years of experience of injecti on and monitoring of CO 2 in the deep subsurface for the purposes of enhancing oil and natural gas production. This experience provid es 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 feasib le 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-f ired boilers and IGCC units, it is much more
difficult to make that determination for the en tire fleet of existing fossil fuel-fired EGUs. Developers of new generating faci lities can select a physical loca tion that is more amenable to CCS – such as a site that is near an existing CO 2 pipeline or an existing oil field. Existing sources do not have the advantage of pr e-selecting an appropriate loca tion. Some existing facilities are
located in areas where CO 2 storage is not geologically favorab le and are not ne ar an existing CO 2 pipeline. Developers of new facilities also have the advantage of integrating the partial CCS system into the original design of the new fac ility. 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 be ing installed for large-scale demonstration at NRG’s WA Parish facility; and it will very soon be applied at commercial-scale as a retrofit at SaskPower’s Boundary Dam coal-fired EGU in Ca nada. However, all of these facilities are located in areas that are either near an existing oil field or in an area that is geologically
favorable for CO 2 storage. Thus, at some existing facili ties, 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 se rve as the best system of emission reduction for a broadly applicable GHG mitigation program. Ther efore, the EPA does not propose to find that CCS is a component of the best system of emission reduction for CO 2 emissions from existing
fossil fuel-fired EGUs.

Reasonableness of Cost In the proposed standard of performance fo r new fossil fuel-fired EGUs (79 FR 1430), the EPA found that the costs to implement partia l CCS (to a level to meet the proposed emission standard of 1,100 lb/MWh-gross) were consistent with costs for other non-natural gas-fired generating technologies – such as nuclear, biomass and 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 orpartial CCS as BSER for new natural gasfired stationary combustion turbines noting tech nical 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 part ial CCS would have more of an impact on the price of electricity than the few projected coal plants w ith CCS and the number of projects would make it difficult to implement in the short term. An emission standard for existing units base d on CCS (or even partial CCS) would most certainly have an even more significant effect on nationwide electricity pr ices and could affect the reliability of the supply of electricity. Theref ore, we do not find that the cost to implement7 - 6 existing source emission standards to be reason able, which further suppo rts the determination that CCS is not an appropriate component of the best system of emission reduction for CO 2 emissions from existing fossil fuel-fired EGUs.

Emission Reductions and Prom otion 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 sta ndard may not be technically or logistically
feasible in a number of cases and cannot be broadly implemente d 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 co st-effective reductions in CO 2 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 th e most carbon-intensive affected EGUs in the amount that results from substituting gene ration at those EGUs with generation from less carbon-intensive affected EGUs (i ncluding NGCC units under construction);

Building Block 3 - Reducing emissions from affected EGUs in the amount that results from substituting generation at those E GUs 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 efficien cy that reduces the amount of generation required.

Coal-fired Steam EGUs

For Building Block 1, the EPA evaluated the fl eet-wide potential fo r lowering the carbon intensity of generation at indivi dual 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 a nnual average gross heat rate by about 6%. Furthermore, the EPA understood that any HRI met hod that reduces gross heat rate will alsoA - 2 reduce net heat rate, and that some HRI methods reduce net heat rate with out reducing gross heat rate. As such, the EPA expects that the HRI pot ential on a net output basis is somewhat greater than on a gross output basis, primarily through upgr ades that result in re ductions in auxiliary loads. Therefore, the EPA conservatively assumed th at the coal-steam fleet average net heat rate can be reduced by 6% and included th is finding in its Building Block 1.

As discussed in the preamble, for purposes of developing the alte rnate set of goals on which we are taking comment, the EPA used an es timate 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 detaile d 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 signi ficantly less carbon pe r unit of heating value than coal. Oil and gas therefore produce significantly less CO 2 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 le vels compared to oil/gas
steam EGUs. Therefore the amount of CO2 reducti on that can be achieved via HRI at oil/gas EGUs is significantly smaller. For example, EPA modeling 293 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 CO 2 emissions from all oil/gas steam units could be reduced by 6% on average using HRI methods (as assumed on coal-steam units) th at reduction would amount to only a fraction of 1% of the HRI reduction th at 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 CO 2 reductions from fossil-fired sources other than coal-steam EGUs are never important. Such reduction might be significant in a few situ ations, 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, ha ve a lesser range of opportunities for implementing HRI. For example, oil/gas steam units do not typically use flue gas SO 2 scrubbers, particulate collection devices, coal mills, coal conveyors, ash handling systems, sootblowers, etc. Consequently, some of the HRI methods discus sed in the GHG Abatement Measures TSD are not applicable for oil/gas steam EGUs.

The above factors taken together explain why the potential for CO 2 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 conserva tively decided to not se parately itemize and include this potential in Building Block 1.

Natural Gas Combined Cycle (NGCC) EGUs

EPA modeling also projects that in 2020 na tural 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 CO 2 emissions from fossil fuels us ed in electrical generation.

The significantly lower amount of CO 2 produced by combustion of natural gas compared to coal (about 40% less for the same amount of heat input) is due pr imarily to the higher hydrogen content and lower carbon c ontent in natural gas compared to coal. Also, because a NGCC is typically more efficient than a coal-steam EGU, thus using less h eat input from fuel to make an equal electrical output, a very efficient NGCC can further reduce the CO 2 emission rate per MWh to about 60% less than that from coal-s team EGUs. Thus, natural gas, particularly as used in NGCCs, inherently reduces CO 2 emissions by more than one-half. Existing NGCC EGUs are therefore already significantly reducing CO 2 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 CO 2 emissions of NGCC EGUs via HRI. Ho wever, as with coal-steam EGUs, we do not have the unit-specifi c detailed design information on existing individual NGCCs thatA - 4 would be needed to make a detailed assessment of the HRI potential vi a 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 us e 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 th at only one-th ird of the NGCC fleet has historically reported complete (com bustion 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 di scussed 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 onl y 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 porti on of the power cycle: the HRSG (heat recovery steam generator), the st eam turbine-generator, and the heat rejection system (water or air-cooled condenser system s). The HRI potential asso ciated with the steam portion of the NGCC is significan tly less than in a coal-steam unit because the NGCC steam system is much simpler (gaseous fuel, no back-e nd 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 th e combustion turbine portion of the NGCC relate primarily to critic al components in the hot expansion side of the un it - components that are exposed to the products of co mbustion of fuel and air th at contain small amounts of corrosive/erosive contaminants at very hi gh temperatures. These critical components (combustors, nozzles/vanes, seals, rotating blades ) therefore require regul ar periodic removal and refurbishment or replacement to maintain high NGCC efficiency levels, and indeed to avoid potentially catastrophic mechanical failures. The gr eatest loss in the perfor mance (increased heatA - 5 rate) of a NGCC is this physical degradation that occurs in proportion to its hours of operation and number of starts. Consequently, it has l ong been an accepted practice by NGCC owners to closely follow the NGCC manufacturer’s main tenance recommendations, a practice that regularly restores the NGCCs efficiency and re liability. This close a dherence to manufacturer recommendations is financially motivated in pa rt by the fact that many NGCC owners have longterm maintenance contracts with the manufacturers, wherein th e manufacturer guarantees the service life and replacement costs of expensiv e critical components - pr ovided 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 bei ng applied across most of the NGCC fleet.

With NGCCs projected to produce 20% of fossil CO 2 emissions in 2020, and with a max sustained HRI potential for existing NGCCs of 2-3%, as mentioned earlier, the CO 2 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 CO 2 reductions expected from coal-steam EGUs via HRI. Because of this limited potential a nd the uncertainty associated with it, EPA conservatively decided to not separately itemi ze and include this NGCC potential in Building
Block 1.

Simple-cycle Combustion Turbine (CT) EGUs

Natural gas-fired CTs provide peaking ge neration, typically operating at very low capacity factors. This is prim arily because of their relatively low efficiency, which is economically only partially offset by their relati vely 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 be low 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 on ly about 1% of total fossil-fired electrical generation (GWh). Whether gas or oil-fired, CT capacity can therefore onlyA - 6 contribute CO 2 emissions amounting about 1% of total fossil CO 2 emissions, or perhaps 2% of
total coal-steam CO 2 emissions. Any single-digit percentage reduction in CT heat rates, can therefore only provide much less than a 1% reduction in total fossil-fired CO 2 emissions.

Most CTs likely benefit from the same regul ar preventive/restorative maintenance as the combustion turbine portion of a NGCC, as discu ssed above, and for the same reasons, Thus, the heat rates of most CTs are already periodi cally (even if not regularly, depending on their irregular operating hour s 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 CO 2 reduction potentials via HRI on oil/gas steam, NGCC, and CT EGUs as part of the CO 2 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 CO 2 reduction. Furthermore, we do not have the detailed site-speci fic information that would be needed to make a more precise engineering eval uation of the HRI potential for any individual EGU, including coal-steam units; only the owne rs/operators of these EGUs w ould 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 CO 2 reduction via HRI is available from th ese EGUs. States and sources would be free to use HRI at these EGUs to help reach the state CO 2 reduction goals. Further, we note that there are geographic differences in the proportions of total ge neration produced from various EGU types, and that in certain geographically isolated jurisdictions, HRI from non-coal fossil fuel-fired EGUs could be a more impor tant potential approach to reducing CO 2 emissions. For this reason, as noted in the preamble, we ar e taking comment on whether HRI from non-coal
fossil fuel-fired EGUs should be included as part of the basis supporting the BSER.