Technical Support Document (TSD) for Carbon Pollution Guidelines for Existing Power Plan ts: Emission Guidelines for Greenhouse Gas Emissions fr om 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

June 10, 2014

### TABLE OF CONTENTS

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

................................................... .......... 1-1 Chapter 2: Heat Rate Improvement at Existing Coal-f ired EGUs ....................... 2-1 Chapter 3: CO2 Reduction Potential from Re-Dispatch of Existing Units ......... 3-1 Chapter 4: Cleaner Generation Sources .............

.................................................. 4-1 Chapter 5: Demand-side Energy Efficiency (EE)...... .......................................... 5-1 Chapter 6: Fuel Switching .........................

................................................... ....... 6-1 Chapter 7: Carbon Capture & Storage ...............

.................................................. 7-1 APPENDIX ..........................................

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

This version of the GHG Abatement Measures TSD corr ects minor typographical errors EPA found in the version poste d on the web on June 2, 2014.



# `1 - 1 

### Chapter 1: Introduction CAA section 111(d) requires that state plans must e stablish standards of performance that reflect the degree of emission limitation achievabl e 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 impact and energy requirements, the Administrator determines has been adequately demonstrated. Under CAA section 111(a)(1) and (d), the EPA is

authorized to determine the BSER and to calculate t he amount of emission reduction achievable through applying the BSER.

As a first step towards determination of BSER, the EPA recognized that, in general, reductions in carbon dioxide (CO 2 ) emissions from individual existing electric gener ating units (EGUs) can be achieved by implementing either of tw o basic approaches: (1) making emission rate improvements at affected EGUs (e.g., by improv ing heat rates or switching to lower carbon fuels), and/or (2) reducing utilization of greenhou se gas (GHG)-emitting EGUs (e.g., by reducing the overall demand for electricity or by shifting d ispatch from higher-GHG-emitting EGUs to lower-GHG-emitting and non-emitting units). Accordi ngly, to determine BSER for reducing GHG emissions at affected units, the EPA evaluated numerous GHG abatement measures that utilize the above approaches. In its evaluation, 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 indivi dual EGUs, switching to lower carbon fuels at individual EGUs, carbon capture and sequestratio n at individual EGUs, shifting dispatch from higher-GHG-emitting EGUs to lower-GHG-emitting and non-emitting units, and reducing the overall demand for electricity via improvements in demand-side energy efficiency.

Based on its evaluation of the above GHG abatement measures, the EPA identified four categories of demonstrated measures, or “building b locks,” 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 i ndividual affected EGUs through heat rate improvements;1 - 2

2. Reducing emissions from the most carbon-intensiv e affected EGUs in the amount that results from substituting generation at those EGUs with generation from less carbonintensive affected EGU s (including NGCC units under construction);

3. Reducing emissions from affected EGUs in the amo unt that results from substituting generation at those EGUs with expanded low- or zero -carbon generation; and,

4. Reducing emissions from affected EGUs in the amo unt that results from the use of demand-side energy efficiency that reduces the amou nt of generation required.

The EPA believes that for purposes of CAA section 1 11(d), as applied to the power sector, the BSER encompasses all four building bloc ks. The application of all four building

blocks as BSER is consistent with current trends in the electric power sector and with strategies that companies and states are already taking to red uce GHG emissions. Also, the application of all four building blocks as BSER supports achieving cost-effective, and technically feasible

reductions of CO 2

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



### Chapter 2: Heat Rate Improvement at Existing Coal-f ired EGUs

2.0 Introduction

Based on the range of operating efficiencies for ex isting coal-fired electric generating units (EGUs), it is evident that EGUs are generally less efficient at converting fuel into electricity than is technically and economically po ssible. For example, the difference in operating efficiency of EGUs with similar design characterist ics and the year-to-year variability in individual EGU efficiency indicates that there is p otential for broadly applicable efficiency improvements through cost-effective operational and maintenance practices. These improved efficiencies would result in corresponding reductio ns in greenhouse gas emissions. This chapter presents an overview of existing coal- fired EGUs, design factors that influence efficiency, technologies to improve effic iency, previous studies estimating potential efficiency improvements, the proposed EPA approach to calculate efficiency improvements, and the estimated capital costs of those improvements.

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

Coal in the United States is predominately used forelectric power generation. Most coalfired EGUs in the United States burn either bitumin ous or subbituminous coals. The largest

sources of bituminous coals burned in coal-fired EG Us are mines in regions along the Appalachian Mountains and in southern Illinois, wes tern Kentucky and Indiana. Additional bituminous coals are supplied from mines in Utah an d Colorado. The vast majority of subbituminous coals are supplied from mines in Wyom ing and Montana, with many coal-fired EGUs burning subbituminous coals from the Powder Ri ver Basin (PRB) region in Wyoming. In general, the burning of lignite by U.S. electric ut ilities is limited to coal-fired EGUs that are located near the mines that supply the lignite in T exas, Louisiana, Mississippi, Montana, and North Dakota. At a few power plant locations in the Eastern United States, recovered anthracite coal or coal refuse is burned in limited quantities

. Existing coal-fired EGUs in the U.S. electric utili ty fleet use one of five basic coal combustion configurations: (1) pulverized coal (PC) combustion, (2) fluidized-bed combustion (FBC), (3) gasified coal combustion, (4) cyclone fu rnace combustion, or (5) stoker-fired coal2 - 2

combustion. Table 2-1 presents a summary of the ope rating characteristics of each of these coal combustion configurations. Table 2-1. Characteristics of coal-firing configur ations 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-firin g configurations because a gaseous fuel (synfuel or s yngas) is burned instead of solid coal because the combust ion and power generation process and combines the Ranki ne 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 temperat ures that are difficult to burn in PC boilers. The major ity 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 flippi ng coal unto the grate.2 - 3

The three technologies currently used for new coal- fired power plants are pulverized coal, fluidized bed (FBC), and integrated coal gasi fication combined cycle (IGCC). Pulverized coal combustion is the coal-firing configuration pr edominately used at existing EGUs. The most recent coal technology combustion development invol ves the integration of coal gasification technologies with the combined cycle electric gener ation process. The efficiency of an IGCC power plant is comparable to the latest advanced PC -fired and FBC EGU designs using supercritical steam cycles. The advantages of using IGCC technology can include greater fuel flexibility (e.g., capability to use a wider variet y of coal ranks), potential improved control of PM, SO 2 emissions, and other air pollutants, the need for fewer post-combustion control devices (e.g., almost all of the sulfur and ash in the coal can be removed once the fuel is gasified and prior to combustion), generation of less solid wast e, 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 furna ces and stoker-fired coal combustion, have been replaced at new coal-fired EG Us by more efficient methods that provide superior coal combustion efficiency and other advan tages. However, a few remaining old stokerfired EGUs and cyclone furnaces still remain in ser vice 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 efficiency . As the efficiency of a coal-fired EGU is increased, less coal is burned per kilowatt- hour (kWh) generated by the EGU resulting in a corresponding decrease in CO 2 and other air emissions. Heat rate is expressed as the number of British thermal units (Btu) or kilojoules (kJ) requ ired to generate a kilowatt-hour (kWh) of electricity. Lower heat rates are associated with m ore efficient coal-fired EGUs. The electric energy output for an EGU can be expres sed as either as “gross output” or “net output.” The gross output of an EGU is the tot al amount of electricity generated at the generator terminal. The net output of an EGU is the gross output minus the total amount of auxiliary (or parasitic) electricity used to operat e the EGU (e.g., electricity to power fuel handling equipment, pumps, fans, pollution control equipment, and other on-site electricity needs), and thus is a measure of the electricity 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 compar ing the effectiveness of heat rate improvement technologies to increase the efficiency of a given coal-fired EGU. The actual overall efficiency that a given coal-fired EGU achi eves is determined by the interaction of a combination of site-specific factors that impact ef ficiency to varying degrees. Examples of the factors affecting EGU efficiency at a given facilit y include:

• EGU thermodynamic cycle – EGU efficiency can be significant improved by us ing a supercritical or ultra-supercritical steam cycle. S upercritical and ultra-supercritical boilers operate above the critical point of water (approxim ately 374°C (705°F) and 22.1 MPa (3,210 psia)). As a general guideline, the thermal design efficiencies for subcritical EGUs are in the range of 35% to 37%, supercritical EGUs are in the range of 39% to 40%, and ultra-supercritical EGUs in the range of 42% to 45% . However, actual operating efficiencies can be lower than design efficiencies.

• EGU coal rank and quality – EGUs burning higher quality coals (e.g., bitumin ous) tend to be more efficient than EGUs burning lower qualit y coals with higher moisture contents (e.g., lignite). Bituminous coals have higher heati ng values of greater than 10,500 British thermal units per pound and lignite coals have high er heating values of less than 8,300 British thermal units per pound.

• EGU size –EGU efficiency generally increases somewhat with size (e.g., from 200 MW to 800 MW) because: a) the boiler and steam turbine losses are lower for larger equipment compared to smaller equipment, b) larger units tend to be younger incorporating improvements from advanced technologi es, and c) the economy of scale of larger units allows the use of higher cost improvem ents to be more economic.

• EGU pollution control systems – The electric power consumed by air pollution con trol 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 optimizatio n, equipment maintenance, etc. can affect EGU efficiency.2 - 5

• EGU cooling system – The temperature of the cooling water entering th e condenser can have impacts on steam turbine performance. Once-thr ough cooling systems can have an efficiency advantage over recirculating cooling sys tems (e.g., cooling towers). However, once-though cooling systems typically have larger w ater 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 h ave an impact on EGU efficiency. At higher elevations, air pressure is lower and less o xygen is available for combustion per

unit volume of ambient air than at lower elevations . Cooler ambient temperatures theoretically could increase the overall EGU effici ency by increasing the draft pressure of the boiler flue gases and the condenser vacuum, and by increasing the efficiency of the cooling system. Also, geographic location influence s the type of cooling system that can be used (e.g., EGUs located in arid locations often cannot use once through cooling)

• EGU load generation flexibility requirements – Operating an EGU as a baseload unit is more efficient than operating an EGU as a load foll owing unit to respond to fluctuations in customer electricity demand.

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

tend to be more efficient.

2.3 Technologies to Improve Existing Coal-Fired EGU Heat Rate

A number of studies have been conducted involving l iterature 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 st udy conducted by the Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) is presented in Table 2-2. The efficiency percentages were converted to a common basis so that all of the data can be compared. All of the improvement technologies presented in Table 2-2 cannot necessarily be implemented at every existing coal-f ired EGU facility in the U.S. electric utility

1 See HRI Partial Bibliography at the end of this ch apter.2 - 6

fleet. The existing EGU design configuration and ot her site-specific factors may prevent the technical feasibility of using a given technology. Typically, these studies share as a common basis th e estimated potential efficiency improvement percentages and costs from the engineer ing study originally completed by Sargent and Lundy in 2009 titled “Coal-Fired Power Plant He at Rate Reductions.” It describes numerous well-known and technically proven methods to improv e efficiency of coal-fired EGUs. The study lists possible efficiency improvements in the boiler, turbine, flue gas system, air pollution control equipment and the water treatment system. E ach of these main areas are expanded upon

below.

2.3.1 Boiler The systems to focus on for improving heat input wi thin the boiler area include the materials handling, combustion system, boiler contr ol system, sootblowers, and the air heaters. 2.3.1.1 Materials Handling 2 The coal-handling portion of materials handling typ ically requires about 0.07% (7 Btu/kWh) of the gross electrical output of a power plant. Depending on the state of the motors and drives, replacing them with energy-efficient mo tors and variable frequency drives can reduce the auxiliary power requirements. The variable freq uency drives also limit the stress and strain on the other equipment. Coal pulverizers typically require about 0.6% (60 B tu/kWh) of the gross electrical output and can be upgraded to provide more consistent size and finer coal particles. The fine particles improve combustion efficiency, consequently reducin g fuel cost and heat rate. The costs for changes to the pulverizer system are significant, a nd, historically, the projects have improved the heat rate justifiably only when the existing equipm ent has degraded. The bottom ash handling system may be a candidate f or heat rate improvement. Switching from a water-sluicing bottom ash system t o a dry drag chain system can reduce the auxiliary requirements and reduce the amount of wat er to the water treatment plant. The typical power requirements are about 0.1% (10 Btu/kWh) of t he plant’s gross output. 2.3.1.2 Economizer

2 The Sargent and Lundy report did not provide potent ial savings for material handling operations. Energ y use in Btu/kWh has been provided to compare the energy use of materials handling relative to the potential en ergy savings from other efficiency activities.2 - 7

An economizer is a heat exchanger that improves the efficiency of an EGU by recovering energy from the exhaust gases to preheat the boiler feedwater. The replacement of the economizer can lead to substantial heat rate improv ements around 50-100 Btu/kWh, but is large capital investment (~$2-8M). Due to this high cost, economizer upgrades are not generally performed unless the existing equipment has degrade d or a replacement is necessary due to the installation of new control equipment. 2.3.1.3 Boiler Control System The boiler control system has a large impact on the heat rate of the unit. The process control capabilities can control and evaluate many aspects of the plant’s operations. Commonly referred to as Neural Network, computer models are able to control the plant’s processes by predicting performance during static and dynamic ch anges. Many vendors offer Neural Network systems to improve the overall efficiency. Neural n etwork systems are typically around $550,000-$750,000 and offer heat rate reductions up to 150 Btu/kWh.

2.3.1.4 Sootblowers Intelligent sootblowers may be installed to improve system efficiency. The intelligent sootblowers system monitors the furnace exhaust gas temperatures and steam temperatures. Other readings may be incorporated into the intelli gent sootblower system, which also communicates with the boiler control system. This s ystem uses real-time data to identify which areas need sootblowing. Boiler efficiency improveme nts range from 30-150 Btu/kWh with capital costs around $300,000-$500,000 and $50,000/ year for fixed operating and maintenance

costs. 2.3.1.5 Air Heaters Air heaters operate to transfer heat between the in coming pre-combustion air and the effluent flue gas. These systems are critical to ma intain an efficient power plant. For these systems to operate most efficiently, air heater lea kages must be maintained below 6% of incoming air flow. Most leakage is due to the pre-c ombustion air leaking across the rotating section and leaving with the flue gas. This increas es the flue gas volume going through the forced draft and induced draft fans and avoids capt uring the heat transferred between the flue gas and pre-combustion air. The increased volume requir es more power to move more flue gas. Improvements to seals on the air heaters reduce the leakages. Improvements to reduce air heater and duct leakages generally reduce the heat rate by 10-40 Btu/kWh with capital costs between $0.3-1.2M.2 - 8

A second method to improve the heat rate is to lowe r the air heater outlet temperature by controlling the acid dew point. Typically the air h eater outlet is maintained at 20-30°F above the sulfuric acid dew point to prevent corrosion of col d-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 opti mize the lower outlet temperature. The capital costs can range from $1.5-18M for heat rate reducti ons of 50-120 Btu/kWh.

2.3.2 Turbine The systems within the turbine area on which to foc us heat rate improvements are the turbine, the feedwater heaters, the condenser, and the turbine drive and motor-driven feed

pumps. 2.3.2.1 Turbine Replacement or overhaul of existing steam turbines with advanced turbine designs improves the efficiency of converting the energy in the steam to electrical energy. The capital costs for these projects ranges from $2-25M with he at rate reductions of 100-300 Btu/kWh.

2.3.2.2 Feedwater Heaters The feedwater heaters are heat exchangers used to h eat the boiler feedwater by extracting heat from the steam leaving the turbine section. Th e EGU efficiency can be increased by improving the heat transfer surface area. This enta ils adding heat exchange surfaces to the existing heaters or adding additional heaters. The costs relative to the heat rate improvement associated with these projects typically prohibit t he advancement of the project unless the feedwater heaters are in need of repair.

2.3.2.3 Condenser To obtain the most efficiency from the condenser se ction, the most effective operation

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

the condenser. This allows for the turbine to extra ct as much energy from the steam as possible. Condensers are subject to fouling and plugging, whi ch directly impact the heat transfer rates and water quality. To improve water quality, closed coo ling water systems can be used to provide better control over water quality and tube cleaning can be performed as needed. Heat rate reductions observed from condenser upgrades and mai ntenances 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 auxilia ry power to pump large amounts of boiler feedwater through the heaters and the boiler . Due to the high use of these pumps, maintenance is extremely important to ensure reliab ility and the most efficient operation. As the pumps wear and operate less efficiently, a pump ove rhaul 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 contrib ute to improvements in the plant heat rate are: (1) improve the forced draft and induced draft fan efficiencies, and (2) implement variable frequency drives. 2.3.3.1 Induced Draft Fans One of the most important features in the fans is b eing able to control the flue gas flow. Many fans have dampers, which are the least efficie nt option. There are many other methods, such as variable inlet vanes, variable frequency dr ives, and variable pitch blades, available to control the flue gas flow allowing highly efficient fan performance. These upgrades or replacements provide a heat rate reduction of 10-50 Btu/kWh and cost between $6-$16M. 2.3.3.2 Variable Frequency Drives Variable frequency drives facilitate more efficient plant operation by reducing the auxiliary load significantly. The capital costs for upgrading all drives at an EGU can be $6-16M

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

2.3.4 Emission Control Technologies With the passage of environmental regulations, addi tional emission control devices have been and must be implemented in the power plant. Th ese systems typically require large amounts of auxiliary power with their benefit being improve d air quality. Even small upgrades can sometimes decrease the power requirements significa ntly while maintaining the level of emissions reduction desired. The three technologies discussed below are the flue gas desulfurization, the electrostatic precipitator, an d the selective catalytic reduction systems. 2.3.4.1 Flue Gas Desulfurization Coal-fired power plants use many types of flue gas desulfurization systems. Older units typically contained a venturi throat that increased the velocity of the fluid, but resulted in a large pressure drop and greater power to operate the indu ced draft fans. To improve this operation, a2 - 10

co-current spray tower quencher may replace the uni t. The capital cost is about $2.5M with heat

rate reductions around 13 Btu/kWh. Another technology upgrade affects the vanes and di stribution plate in an absorber. The improvement of the gas flow coming into contact wit h the absorber sorbent increases SO 2 capture, reduces maintenance due to erosion, and re duces the amount of energy required for the induced draft fan. Turning vanes and a perforated g as distribution plate improve gas distribution. The cost of the vanes is around $250,000 with heat rate reductions of 1-2 Btu/kWh. In a wet flue gas desulfurization system, multiple spray levels are installed to deliver the limestone slurry. If a power plant is operating wit h SO 2 levels below its permit limit, turning off one spray level will reduce the auxiliary power req uired. 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 precipitato r involves maintaining the maximum applied voltage, but below the level at which spark -over occurs. Electrostatic precipitator energy management system upgrades often help improve the e lectrostatic precipitator performance by maintaining the optimal performance and lowering po wer consumption. The installation for this technology can be from minimal to $0.8M and can low er heat rate by 5 Btu/kWh. 2.3.4.3 Selective Catalytic Reduction For the last 15 years, selective catalytic reductio n systems have been in use to reduce

NO X emissions from power plants. Extensive modeling wa s performed to achieve the necessary reduction with minimum ammonia slip. The results sh owed that reducing pressure drop and using secondary air as dilution for the ammonia vap orizer can reduce the auxiliary power necessary. The heat rate reduction is 0-10 Btu/kWh and capital costs between $0.5-$2M with fixed and variable costs up to $100,000 each.

2.3.5 Water Treatment System The boiler water is one of the most important aspec ts of the power plant. The quality of the water is a key factor affecting the scale build up on the boiler tubes, which reduces the heat

transfer in the tubes or can cause tube failures. P roper use of chemicals to maintain pure water is key. Also, high-quality water can reduce the blowdo wns required, which allows for more steam in the turbine cycle. If the water is not properly maintained, heat transfer may be reduced by up

to 10%.2 - 11

Similar to the boiler water, the cooling towers are also affected by the water quality. Fouling and scaling remain issues for heat transfer and purity of the water. By maintaining the cooling water system efficiently, the overall water quality is improved, which branches into other aspects already mentioned.2 - 12Table 2-2. Existing coal-fired EGU efficiency improvements reported for actual efficiency improvementprojectsEfficiencyImprovementTechnologyDescriptionReportedEfficiencyIncreaseaCombustion 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 iscomplex and impacts a number of important operating parametersincluding combustion efficiency, steam temperature,furnace slagging 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 plasma assisted 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. The identified technologies include replacing the cooling towerfill (heat transfer surface) and tuning the coolingtower 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 dewpointtemperature of the flue gas, which is dependent onthe concentration of vapor phase sulfuric acid andmoisture. 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 relativelylarge 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 usedto 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 wasteheat from the flue gas and/or cooling water systems to dry lowrank coal prior to combustion.0.1 to 1.7%SootblowerOptimizationSootblowers intermittently inject high velocity jets of steam or air to clean coal ash deposits from boiler tube surfaces inorder to maintain adequate heat transfer. Proper control of the timing and intensity of individual sootblowers isimportant to maintain steam temperature and boilerefficiency.The identified technologies include intelligent orneuralnetwork sootblowing (i.e., sootblowing in responseto 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 have improvedthe design of turbine blades and steam seals whichcan 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 Efficiency of the Existing Coal-fired Power PlantFleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 2008. Available at:<http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>.aReported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual component efficiencies for a referenceplant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assumptions, the referencepower plant has an overall efficiency of 32% and anet heat rate of 10,600 Btu/kWh. As a result, if aparticular efficiency improvement method was reported toachieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase inoverall efficiency. Likewise, a reported 100 Btu/kWhdecrease in net heat rate would be converted to a 0.30% point increase in overall efficiency.2 - 13

2.4 Previous Studies on Heat Rate Improvements

A number of studies using varying approaches have b een performed to determine potential efficiency improvements and associated re sulting CO 2 emission reductions. These approaches include characterizing the current U.S. coal-fired EGU fleet, identifying potential efficiency improvements, and applying improvement a ctions to existing EGUs. The approach taken within each study varies. Five studies are br iefly summarized and compared in Table 2-3. The NETL studies used a benchmarking approach that evaluated the design factors that are known to influence efficiency and grouped EGUs based on similar design characteristics. The studies categorized the industry based on fuel type, location, steam cycle, and age of the boilers. Potential efficiency improvements were cal culated based on an assumption that the lower-performing EGUs in each group should to be ab le to do as well as the better performing EGUs in that group. Specifically, the goal for pote ntial improvement: for each subcategory was that the bottom 90% of EGUs in each group improved their heat rate to the average performance of top 10% in that group. While the studies are dif ferent in the level of detail and assumptions, the results of these studies overall suggest that a U.S. coal-fired EGU fleet-wide improvement ranging from 9% to 15% is theoretically possible. T he Lehigh study used a less detailed approach and evaluated technologies applicable to b ituminous and subbituminous coals to estimate potential fleet wide reductions. An alternative approach to evaluate heat rate impro vement is used by Resources for the Future. This study focused on the operating efficie ncy (synonymous with heat rate) of the entire existing U.S. coal-fired EGU fleet. The authors eva luated decades of data from industrial responses to economic factors such as demand, coal price and energy policies. This approach sought to estimate overall changes in industry flee t efficiency in response to changes in fuel prices or carbon prices. In one specific example, t he coal price was assumed to be a 10%

increase and the CO 2 emissions tax at $1.64 per ton for heat rate reduc tions of 0.3 to 0.9%. The National Resources Defense Council approach con sidered the fleet of coal-fired EGUs and assumes a target heat rate in order for th e 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 i mposed standards and calculated the heat rates necessary to meet a standard. As it has not b een determined with regard to how the CO 2 emissions limit will be averaged, the paper discuss es many potential options that coal-fired2 - 14

EGUs may have to meet new standards. These options include additional options such as adding renewable, natural gas, and combined-cycle sources. The EPA observed that existing HRI studies using a benchmarking approach have relied on a single year annual average heat rate data, whe reas many of the operational impacts on heat rate (diurnal temperatures, load following, etc) be come more apparent in the variability of hourly performance data. We further recognized that an exa mination of heat rate data over a multiple year period, perhaps a decadal time frame might rev eal patterns of performance that should also inform estimates of HRI potential. For these reason s, the EPA has developed in this TSD an additional assessment of HRI potential that draws o n multiyear historical hourly data. While we understand that engineering judgment remains essent ial to a proper interpretation of the results,

the EPA intends that this assessment be a more subs tantial basis for estimating the fleet-wide HRI potential for coal-fired EGUs. Table 2-3. Summary comparison of previous studies o n 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 b y 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 loo ks at historical data from coal-fired EGUs in the U.S. to identify changes to EGUs’ heat rates – the amount of heat input required, on average, to generate 1 kWh of electricity – that ca n be attributed to operation and maintenance practices and equipment upgrades. These heat rate c hanges are analyzed to determine their applicability to the rest of the coal-fired EGU fle et and to determine the potential heat rate improvement that, on average, could be achieved by the fleet. This data analysis portion of the study relies on u nit-level heat input and gross generation data reported to the EPA by owners or operators of EGUs to assess in detail the changes in gross heat rates. Potential changes in net heat rates are then addressed later in this section. Unit-level evaluations allowed the EPA to recognize the signif icant heterogeneity of coal-fired EGUs; even2 - 16

‘sister’ units, units built at the same time at a g iven facility, may display different operating profiles and may have different equipment, controls , fuel mixes or cooling systems. Based on literature reviews; informal interviews wi th 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 recommende d operation and maintenance conditions (best practices), and 2) installing and using equip ment upgrades. Best practices include no-cost

or low-cost methods such as the installation or mor e frequent tuning of control systems and the like-kind replacement of worn existing components. Upgrades often involve higher costs and greater downtime, such as, extensive overhaul or up grade of major equipment (turbine or boiler) or replacing existing components with improved vers ions. The EPA developed unit-level statistics from over 6 0 million rows of hourly data. We evaluate each unit on its individual performance us ing heat rate variability as an indicator of the application of best practices and potential for imp rovement. To estimate heat rate improvement through equipment upgrades we survey engineering st udies, 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 Marke ts Division in 2012. 4 It includes a wide range of configurations, from 24 to 1,500 MW nameplate capacities, super and subcritical thermodynamic cycles, between 1 and 69 years old, and different coal ranks. It excludes any EGUs at any facility that reported cog eneration to the EPA or the EIA. These units are excluded because a portion of the heat input wa s used to generate electricity and/or steam heat. Therefore, it is difficult for the EPA, using available data, to make a meaningful comparison of these units’ heat rates. The EPA performed this study using hourly heat inpu t (Btu), and electricity output (MWh) data from the Clean Air Markets Division and meteorological data from NOAA’s National Climatic Data Center for the years 2002-20 12. As described later in this section, these

3 Sources calculate heat input using an ‘F factor’ fo r the carbon content of the fuel being combusted an d 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 temper ature impacts on heat rates. The eleven year study period is representative of a wide range of c onditions, including growth and recessionary economic conditions, changing electricity generatio n from renewable and natural gas, and different regulatory constraints. The hourly heat input and generation data used in t his study is collected under the authority of 40 CFR part 75 (hereafter, Part 75). T he EPA designed Part 75 to encourage complete and accurate emission measurement and repo rting to support emission trading programs, including the Acid Rain Program and Clean Air Interstate Rule (CAIR). However, the EPA recognized that there will be times when emissi on data are not available due to monitoring system malfunctions or maintenance, technical chall enges, or missed quality assurance/quality control (QA/QC) tests. When data are not available or deemed invalid (e.g., when a QA/QC test was not performed as required), the EPA has specifi ed data substitution methods that are designed to overestimate emissions. This conservati ve 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 all owances equal to total reported emissions. Because of this conservative bias and the impact it would have on the results of this study, the EPA excluded substitute data reported by EGUs from this study’s dataset. These substitute data represent approximately 2% of all reported operatin g hours. In addition, we excluded partial hours of operation that occur during the first hour of startup and the last hour of shutdown. We also excluded 40 unit-years (0.5% of records) wi th atypical annual heat rates less than 6,500 or greater than 15,000 Btu/kWh resulting from a variety of factors including firing of natural gas, very low operating time, or errors in reported gross load. Table 2-4 summarizes the heat input, electric generation, heat rate and unit counts by year for the study population used in this work. This population corresponds to 9,388 uni t-years of data at 884 distinct EGUs. Figure 2-1 displays the study population average gross hea t rate by year and the 11-year average.2 - 18

Table 2-4. Study Population Annual Heat Input, Gene ration, Heat Rate and Unit Count 2002 - 2012 5

Year Heat Input

(million MMBtu) Electric

Generation(million MWhgross) Heat Rate

(Btu/kWhgross) Unit Count

2002 18,601 1,874 9,924 839 2003 18,428 1,864 9,886 834 2004 18,405 1,875 9,819 836 2005 18,665 1,910 9,774 838 2006 18,644 1,914 9,743 848

2007 18,704 1,920 9,740 846 2008 18,459 1,914 9,643 852 2009 16,588 1,719 9,649 864 2010 17,693 1,831 9,662 869 2011 16,934 1,744 9,708 878 2012 14,947 1,536 9,732 884

Figure 2-1. Study Population Average Gross Heat Rat e by Year

NOAA’s Integrated Surface Data (ISD) product provid es hourly temperature for over 20,000 weather stations worldwide. 6 Since EGU heat rate performance is sensitive to ai r temperature and barometric pressure, which vary wit h elevation, we use meteorological data from stations that are reasonably close to the EGU' s location and elevation to account for the

5 The study population for each year includes those E GUs that reported both heat input and electric gene ration.

6 Temperature data is from NOAA’s Integrated Surface Data at http://www.ncdc.noaa.gov/data-access/land-b asedstation-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 plant s in this study’s database, we identified the nearest stations that reported a minimum of 8,400 h ourly observations in the calendar year (i.e., greater than 95% of the hours in a year). Generally , each plant was associated with meteorological data from the two closest stations o ver the eleven-year study period. The average distance between the plant and the nearest station was 22 miles and the average difference in elevation was 366 feet. At nine plants we used data with as few as 7,000 observations in order to keep the maximum difference in elevation under 1,00 0 feet. The EPA believes that nearby weather station measurements are a good approximati on of ambient meteorological conditions at each facility. Joining the trimmed heat rate and ho urly temperature datasets resulted in 61,848,580 hourly records for the study population of EGUs (see Table 2-4). The results of this study are based on analyses of data from the population of coal-fired EGUs shown in Table 2-4. These units emitted 1,605 million tons of CO 2 in 2012. In contrast,

emissions of CO 2 from coal-fired EGUs in the entire U.S. electric p ower sector in 2012 were at 1,669 million tons according preliminary data from the EIA. 7 Since the study population of

EGUs accounted for over 96% of CO 2 emissions from the fleet of U.S. coal-fired EGUs, the EPA considers the results of this study to be appli cable to the coal-fired fleet at large.

2.5.2 Subcategorization In this analysis, units are not categorized by unit specific design characteristics or fuel because: (1) EGU-specific detailed design informati on on all factors that influence heat rate is not available, and (2) certain design characteristi cs are not easily categorized (e.g., EGUs use a large range of steam conditions). Several other stu dies do categorize EGUs broadly by capacity, thermodynamic cycle, and/or fuel rank. Although the EPA believes grouping by categories can provide a useful way of understanding the operating profile of an EGU and the fleet, the range of heat rates for the broad categories has significant overlap (see box and whisker chart in Figure 2- 2) and therefore makes it challenging to develop ap propriate categorization. The figure below displays available information on coal-fired EGUs c onsidered in this work for the years 2009- 2011 in typical subcategories of capacity, fuel ran k, 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 Subcat egory 8

2.5.3 Observed Trends in the Period 2002-2012 Three trends are notable for the study population d uring the period 2002-2012. Comparing the averages of the first three years (20 02-2004) to the last three years (2010-2012), electric generation and gross heat rate declined by 9% and 2%, respectively. Capacity factor for the study population fell by 14% comparing the same time periods (see Table 2-5). The decrease in coal-fired generation and capacity factor may be because of reduced demand for electricity resulting from the recession starting in late 2008 and greater use of natural gas and renewables to generate electricity. The 11-year average annual gross heat rate for the study population of coal-fired EGUs (see Table 2-4) was 9,754 (Btu/kWh). The decrease i n study population annual heat rate between 2002 and 2012 may be due to several factors. Unit e fficiency may have improved or units with lower heat rates may have taken up a larger share o f generation. In addition, changes in reporting methodology described later in this chapter may be partly responsible. The minimum annual heat rate (9,643 (Btu/kWh)) occurred in 2008 and was app roximately 1% below the 11-year average.

8 Abbreviations in the figure: BIT means bituminous, SUB means subbituminous, PC means petroleum coke, LIG means lignite, SUPER means supercritical, OVER/UNDE R means greater/less than indicated MW capacity. Un it 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 2002-201 2 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 Result s of This Study During periods of startup and shutdown, EGUs are kn own to operate at higher heat rates. Therefore, we evaluated the potential impact of suc h events in our study. A startup event, as defined here, occurs when an EGU begins combusting fossil fuel and generates some measurable amount of electricity. Table 2-6 summarizes the stu dy population average, maximum and total starts by year. On average, coal-fired EGUs start c ombusting fuel and generating electricity 11 times per year. The total number (approximately 9,0 00) of starts for the study population of EGUs has remained stable over the study period. Our data reflects that some coal-fired units operate in a load following capacity and may report upwards of 200 starts in a single year, but these units tend to have low annual capacity factor s. The subset of EGUs with more than 20 annual startup and shutdown events is responsible f or less than 4% of total generation in any study year. Therefore while the number of starts is an important variable at a small number of EGUs, its impact on heat rate performance evaluated in this study is considered to be marginal.

9 Table 2-5 shows data as reported to EPA as of May 8 , 2014.2 - 22 Table 2-6. EGU Start Count by Year

Year Average Maximum (at any single EGU) Total

2002 11.1 209 9,363 2003 10.6 194 8,936 2004 10.7 134 9,081 2005 11.0 183 9,265 2006 10.5 139 8,859 2007 10.5 164 8,908 2008 10.4 134 8,880 2009 10.3 178 8,902 2010 10.5 211 9,110 2011 10.6 206 9,295 2012 9.9 119 8,805

To understand the potential for heat rate improveme nt available with existing coal-fired steam EGUs, the EPA conducted a number of quantitat ive analyses. These included: (1) regression analyses to understand the impact of cap acity factor and ambient temperature; (2) using a bin model to determine the potential from b est practices; and, (3) evaluating available data and information to assess the potential from e quipment upgrades. These analyses are described in the following sections.

2.5.5 Impact of capacity factor and ambient tempera ture Two important factors that affect heat rate at an E GU are hourly capacity factor and ambient temperature. In this section, we examine th e impact of these two variables on heat rates of the EGUs in the study population. Power plant op erators today typically use digital control systems to capture hundreds of data points in near real-time that are summarized in the unit heat

rate statistic. EPA has access to a small fraction of that information. A key reason this study used capacity factor and ambient temperature as independ ent variables is that both were available as hourly data. Preliminary analyses of heat rate at h igher 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 constant ly changing operating conditions. We tested for collinearity between capacity factor and ambien t temperature using a zero-order correlation matrix on the entire hourly data set. The correlati on between the independent variables was -.048 – well below an indication of collinearity.2 - 23

We also considered fixed unit characteristics such as unit type, fuel rank and age as independent variables. As noted above in the discus sion on subcategorization, these factors can be helpful to understanding the heat rate performan ce of EGUs. The purpose of this study, however, is to find the potential for heat rate imp rovement across the fleet. We use heat rate variability as a key statistic to measure this pote ntial. The correlation between the potential for heat rate improvement and fixed characteristics is typically low. 10 Coal-fired units are designed to operate most effic iently at full capacity. As a unit drops below this level, in general, heat rate will increa se. The average capacity factor over 11 years for the study population is 67%, but as noted above, ha s moved markedly over the study period. This study looks at utilization level at both hour and y ear time scales. The two are related but reveal different information about how an EGU is operating . For example, for a unit to achieve a high annual capacity factor (e.g., over 90%) it must ope rate at a high load for most hours in a year. At lower capacity factors interpreting the relationshi p between hourly and yearly utilization levels becomes more complex. For example, an EGU may run a t an annual 60% capacity factor by operating 8 months at near full capacity and genera ting 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 way s: 1) the efficiency of the thermodynamic steam cycle 11 and, 2) in many regions of the country, as tempera tures increase electricity demand and capacity factor follow. Figu re 2-3 shows the average monthly capacity factor in 2012 alongside the climate normal monthly temperature. 12 The lines intersect in the spring as temperatures begin to rise and the need f or cooling drives electricity demand. Generally, peak capacity factor and generation in m ost parts of the U.S. occur on the hottest days of the year. Yet, the relationship between ambient temperature and capacity factor is complex. Each plant responds differently depending on design , meteorological conditions and electricity

10 For example, the correlation between annual unit he at rate variability (discussed below as relative st andard deviation) and unit nameplate capacity (MW) is in t he -0.1 range.

11 The availability of a cold heat sink in the conden ser is a key factor in that cycle. The design of th e heat exchanger, type of cooling system and availability of water al l have an impact on performance. An increase in amb ient air temperature, and consequent increase in water tempe rature, typically lower the effectiveness of the co oling system, the condenser, and, therefore, overall plant effici ency. 12 Climate normal is the average of temperature (or ot her measure) 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 coll inearity between these two variables is low.

Figure 2-3. Monthly Capacity Factor, 2012.

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

Heat rate Using the hourly data set, the EPA performed three regression analyses for each unityear: heat rate onto capacity factor, heat rate ont o ambient temperature and heat rate onto capacity factor and ambient temperature. Since this analysis seeks to evaluate heat rate under normal operating conditions, we removed records wit h hourly heat rate values outside of +/- 2.6 standard deviations (1.9% of records) before perfor ming the regressions. 13 Similarly to partial operating hours, these outliers tend to occur durin g low load conditions. The records trimmed amount to one-fourth of a percent of the total stud y 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 contribut ion of varying unit capacities all results are generation-weighted. 14 The average study population r-squared for the mul tivariate regression is 26%. This means that hourly ambient temperature and capacity factor together explain 26% of the change in heat rate for the study population ov er the study period. The average study population r-squared from the single variable analy sis of capacity factor is 16%; the

13 The 2.6 standard deviation bound is used in other E PA regulatory analyses.

14 In a weighted average, each component is multiplie d by a factor reflecting its importance. In this ca se, generationweighted r-squared is the sum of r-squared for each unit multiplied by its annual generation divided b y 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 attributable to capacity fac tor and 10% to ambient temperature. These results, however, conceal considerable variability. Some EGUs, typically load-following, have an 11-year average r-squared for capacity factor ex ceeding 50%. At those EGUs, the capacity factor is a key variable influencing changes in hea t rate. At approximately one-fourth of the study population the response to ambient temperature is larger than the response to capacity factor. At some individual EGUs, temperature may explain up to 30% of the change in heat rate. These are typ ically, but not exclusively, units with oncethrough, fresh water, cooling systems. Identifying temperature-responsive EGUs allows us to understand why heat rate may increase during period s of peak demand. These are the EGUs where the ambient temperature ‘signal’ is an import ant variable. At a typical EGU, summer month heat rates may increase by 2-4% compared to w inter months, but at a temperatureresponsive EGU that figure may be as high as 10%. Our analysis indicates that as EGUs moved from base load to load following, capacity factor tended to have a larger effect on heat rate. Since 2008, the study population capacity factors moved from the top load bin into lower load bins. This can be seen from Figure 2-4, which compares the study population duty cycles in 2008 to 2012. A significant share of 2008 generation occurred at EGUs running at greater than 84% annual capacity factor. In 2012, little generation took place in that bin or above, and gen eration 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 20 12

2.5.6 Heat Rate Variability and Indication of Impro vement Potential This study examines heat rate variability from the standpoint of statistical process control, which is utilized throughout the power ind ustry. Several years ago, the EPA introduced process control charts for auditing emissions data reported under Part 75. Sources and vendors adopted the EPA methodology to identify potential p roblems early, before they significantly

affect emission measurements. Heat rate lends itsel f to process control since it is the principal indicator that defines the quality of the electric generation process. 15,16 Therefore, in general, high variability in heat rate values would reflect opportunities for process improvement. We use the relative standard deviation (RSD) of the hourly heat rates to evaluate each unit against its past performance and to compare wi th the study population. Each unit has up to eleven RSD values, i.e., one for each operating yea r between 2002 and 2012. The generation-

15 “The principal indicator that defines the quality of the process is heat rate.” (Fredrick & Todd, 199 3. Statistical Process Control Methods in Performance Monitoring. Available at famos.scientech.us/Papers/1993/1993section11.pdf)

16 Since accurate measurement is essential to process control the introduction of increasingly sophistica ted digital control systems (DCS) presents new opportunities fo r finding inefficiencies. Vendors (ABB, Siemens, Em erson) claim heat rate improvements of 2-5 percent can res ult from upgrading to a modern DCS and advanced con trol 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 1 1 years is 5.4%. Table 2-8 summarizes eleven years of results for the study population by quartiles ordered by the 11-year generationweighted RSD average (ascending). 17 The RSD of the top quartile (3.5%) is significantl y lower than the study population generation-weighted mean RSD of 5.4%. Notably, the EGUs in the top quartile are not outliers; they report a third of a ll generation – the most of any segment. The results display a wide range of heat rate variabili ty in the study population and thereby indicate the potential for heat rate improvement.

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

Quartile RSD

Average RSD

Minimum RSD

Maximum Share of

Generation

1 3.5 1.6 4.2 33 2 4.8 4.2 5.3 26 3 6.1 5.3 7.0 24 4 9.8 7.1 25.2 16

The study also examined EGU heat rate variability u sing the residual heat rate. The residual in a regression analysis is the difference between the observed value of the dependent variable (heat rate) and the predicted value. The i ntercept is the value where the linear regression crosses the y-axis. For each EGU, we calculated the residual heat rate by summing the residual for each hour to the intercept value. The standard deviation of the residual heat rate statistic is used to understand the amount of variability that i s 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%. Th is percentage represents the total variability across the study population that our an alysis could not explain by hourly capacity factor or ambient temperature. Possible causes of t his variability include changes in plant equipment, operating procedures and maintenance, fu els (particularly coal rank), reporting methodology, and unexplained factors. There is no t emporal trend evident in the RSD. Table 2-9

17 Nine EGU RSD values exceeded 2.6 standard deviation s above the mean and were removed from the results in the table.2 - 28

summarizes the study population generation-weighted average RSD of residual heat rate over the study period in quartiles ordered by the 11-year ge neration-weighted RSD average (ascending).

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

Quartile RSD

average RSD

Minimum RSD

Maximum

1 2.7 0.0 3.2 2 3.6 3.2 4.1 3 4.7 4.1 5.3 4 6.9 5.3 10.4

The weighted average RSD of the top quartile is 2.7 % – well below the study population average of 4.5 %. This means that the residual hour ly heat rates of these units generally stay in a narrow range within a given year. The maximum RSD i n the top quartile is 3.2%. From the statistical process control point of view, these un its appear to have low variability. The weighted average RSD of the bottom quartile is 6.9%, which is over twice that of the top quartile. This spread indicates that there is l ikely room for improvement in study population operation to reduce variability and heat rate.

2.5.6.1 Heat rate variability and performance To examine the association between heat rate variab ility and heat rate performance this study examined the RSD for unit-year heat rates cal culated from reported data. The study population generation-weighted annual RSD ranges be tween 5% and 6% during the study period. Figure 2-5 below summarizes the results of regressi ng RSD of heat rate onto annual heat rate. 19 The r-squared result is 57%. These results indicate that, other factors held equal, if an EGU reduces heat rate variability, generally heat rate performance will improve.

18 This table excludes nine units where RSD exceeded 2 .6 standard deviations from the mean.

19 The regression analysis was performed on 9,388 unit -years of study data which were trimmed to remove v alues outside 2.6 standard deviations.2 - 29

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

2.5.7 Units with large heat rate changes Over one third of the study population (355 units) reported at least one year-to-year change in heat rate greater than +/- 8.5%. 20 We consider this magnitude to be in the upper rang e of what would be expected due to changes in fuel ra nk, operations and maintenance, or plant equipment. Table 2-10 presents the counts by three categories: EGUs with at least one year-toyear decrease in heat rate > 8.5%; units with at le ast one year-to-year increase in heat rate > 8.5%; and, units with both. We examined whether the large heat rate changes were due to `yearto-year changes in capacity factor and found no cor relation between the year-to-year changes in heat rate and capacity factor for any of the three groups in Table 2-10. 21 This would indicate that other factors account for these large changes to he at 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 accura te heat input data. 22 The large changes noted at the remaining one-third could not be expla ined by changes in reporting methodology. Moreover, we found no correlation between changes i n reporting method and heat rate RSD.

20 After removing unit-years where annual capacity fac tor fell under 50 percent the count is 313 EGUs.

21 The correlation remains weak even when limited to c ases where capacity factor changed more than 30 per cent.

22 EPA Reference method 2 specifies the normal procedu re for measuring stack gas volumetric flow rate dur ing a relative accuracy test audit. Methods 2F, 2G, 2H an d CTM-041 are approved alternatives. Methods 2F and 2G correct measured flow rates for angular (non-axial) flow, Method 2H (for circular stacks) and conditio nal test method CTM-041 (method J, for rectangular stacks an d ducts) are used to correct measured flow rates fo r velocity decay near the stack wall, using a “wall effects ad justment factor”. These alternative methodologies a re optional. Therefore, given the additional complexity and cost of using these alternatives a source is likely to use them only if the results are significantly lower volumetric stac k gas flow. The EPA was unable to draw conclusions about the effect of changes in flow reporting methods on flee t 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 quart ile, in Table 2-11 below, is consistent with the study population results shown in Table 2-8. Th is does not imply that the changes associated with a large year-to-year decrease, which may inclu de operations and maintenance, more accurate reporting methods, or new equipment, do no t affect heat rate variability. If they occur as part of an engineering effort to improve efficiency , heat rate variability may also be reduced.

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

Quartile RSD

Average RSD

Minimum RSD

Maximum

1 4.0 2.3 4.8 2 5.3 4.8 5.9 3 6.6 5.9 7.5 4 10.5 7.6 26.5

2.5.8 Assessment of heat rate improvement potential via best practices As mentioned before, across the study period, the e ffects of hourly capacity factor and ambient temperature explain a generation-weighted a verage of 26% of the change in study population heat rate. 23 This means that on average 74% of the change remai ns unexplained after controlling for those factors. The residual heat ra te analysis determined there is significant variation in the operation of EGUs. Since lower hea t rate variability is associated with lower heat rate, other factors held equal, the range of variat ion indicates that significant potential for heat rate improvement is available through the applicati on of best practices. To control for known factors, the EPA constructed a model that groups each EGU’s hourly heat rate data into 14 temperature bins and 12 capacity factor bins, resulting in a 12 by 14

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

matrix of 168 bins. 24 For a given EGU, a temperature and capacity factor bin will have all the relevant hourly values over the eleven-year study p eriod. For each bin with 15 or more values the model finds the reported hourly heat input value co rresponding to the 10th percentile (p10). 25 This means that approximately 90% of the heat input values in that bin exceed p10. For each unit, the model reduces the reported hourly heat in put greater than p10 by a percentage of the distance between the reported value and p10 (e.g., 50% of the difference). The same statistical procedure is applied to every hour of heat input da ta in each bin. These reduced hourly heat input values are then used to calculate a reduced 11-year average heat rate for each unit. The percent difference between a unit’s reported 11-year averag e heat rate and the heat rate that corresponds to reduced heat inputs is the potential heat rate i mprovement for that unit. 26 Using this approach, those units with the lowest variability (e.g., in t he top quartile of residual heat rate variability) take proportionally smaller reductions. Table 2-12 below shows the model results with optio ns of 10% to 50% stringency. For example, reducing reported heat input 10 % of the d istance to the p10 value achieves a 1.3% study population wide reduction. Alternatively, a 5 0% reduction will result in a 6.7% study population wide improvement in heat rate. In effect , the model proportionately reduces heat rate variability and improves performance for each unit while controlling for temperature and capacity factor. The heat rate improvement for the study population is derived from the performance of each individual EGU as compared to i ts own record.

24 The matrix provides up to 168 bins but only 164 co ntained hourly values. Temperature bins ranged from -20 to greater than 110 with 10 degrees F in each. Capacit y factor bins ranged from 0% to greater than 110% w ith 10% in each. 25 Performing the calculation with a minimum of 30 va lues in each bin has a modest effect on the results in Table 2- 12. For example, a 30% reduction obtains a 3.9% fle et 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 gen eration (kWh) for the given population and time period.2 - 32

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

% Reduction from reported heat i nput 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 exam ples of EGUs that made significant year-to-year improvements in heat rate. After filte ring out those cases that may have been the result of changes in capacity factor, reporting met hod, or other events, we identified 16 EGUs that reported a single year-to-year heat rate impro vement of 3-8%. In two of these cases we were able to identify equipment upgrades responsible for 2-3% heat rate improvement using the applicable estimates from the Sargent & Lundy 2009 study. Similarly, in the other cases, while our research was unable to confirm specific equipme nt upgrades, based on the elimination of other possible explanations we believe that equipme nt upgrades were the most likely cause of some of the observed heat rate improvements. Two other sources provide information about heat ra te improvements after equipment upgrades at existing plants. EPA Region 7 provided data for seven coal-fired units at three anonymous plants with details on specific equipment modifications. These included turbine efficiency and condenser performance upgrades, inst allation of variable frequency drive fans, reducing boiler air in-leakage and others. Together , these measures achieved from 0.25% to 3.5% heat rate improvement at the seven EGUs. An EPA study (SRA, 2001) describes WEPCO’s two-phas e efficiency program at four coal-fired plants over a ten-year period. In the f irst phase, 1990 – 1994, WEPCO installed equipment upgrades that included retractable turbin e packing, variable speed drives on the forced and induced draft fans, feed water heater replaceme nts and new performance monitoring instrumentation. The four units reported heat rate improvements ranging from 2.3% – 4.1% as a

27 Fleet heat rate for study population as described in Table 2-4 is 9,753; the 9,753 value is derived f rom the dataset that includes hourly temperature values.2 - 33

result of the equipment upgrades. In the second pha se, 1995-2000, WEPCO implemented changes that would generally fall into the best pra ctices category: equipment control and metering upgrades, boiler cleaning, feed water heat er improvements and reduced condenser air in-leakage and thermal losses. These gained an addi tional 0.5% per year heat rate improvement (for a total of 2.5%). The EPA also reviewed the engineering studies avail able in the literature and selected the Sargent & Lundy 2009 study as the basis for our ass essment of heat rate improvement potentials from equipment and system upgrades. We focused on s ome 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 bot tom) shown in Table 2-13. The first nine items in Table 2-13 contribute about 15 percent of the to tal average $/kW cost for all items. We believe

it is reasonable to consider those nine no-cost and low-cost heat rate improvement methods as belonging in the category of what has been describe d above as best practices. The remaining four methods are higher cost heat rate improvement items that we believe properly fall into the category discussed here as upgrades. Using an avera ge of the ranges of potential Btu improvements estimated by Sargent & Lundy for the f our upgrade methods, upgrades, as defined here, could provide a 4% heat rate improvement if a ll were applied on an EGU that has not already made these upgrades.

Table 2-13. Sargent & Lundy Heat Rate Improvement M ethods

No-Cost and Low-Cost Options Condenser Cleaning Intelligent Soot Blowers ESP Modification Boiler Feed Pump Rebuild Air Heater and Duct Leakage Control

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

Higher Cost Options Economizer Replacement Acid Dew Point Control Combined VFD and Fan Turbine Overhaul2 - 34

We also examined the annual heat rate trend line fo r each unit developed using the method of least squares. Using the slope of the tre nd line, 2002-2012, as an indicator of the heat rate performance of an EGU, a negative slope would indicate that the heat rate has improved. The annual trend line incorporates performance due to operating conditions (capacity factor, temperature), coal rank, maintenance, reporting met hod changes, equipment upgrades, and other factors. Over 40% of units have a positive slope. T his 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 ra te improvements for the coal study population can be achieved through two types of cha nges: best practices that have the potential to improve heat rate by 4% and equipment upgrades t hat have the potential to improve heat rate by 2%. The best practices results are supported by the var iability analysis using 11 years of hourly data applied to each unit. This analysis fou nd that the top quartile of EGUs reported significantly lower heat rate variability than the study population average. Reducing heat rate variability will generally also improve heat rate p erformance, other factors held equal. We found that a 4% improvement is determined by conservative ly reducing heat input by 30% of the difference between the reported value and p10 in ea ch unit’s capacity factor and ambient temperature bins. The 30% approach is in the middle of the range of options shown in Table 2-12 and is comparable to other approaches for measuring potential fleet heat rate improvement. For example, if each unit achieved heat rate performanc e equal to its best three-year moving average, the study population as a whole would post a 3.9% h eat rate improvement. The best two-year moving average would achieve nearly a 5% improvemen t and the best single year over 6%. EPA believes that the minimum three-year moving average heat rate is a reasonable target for the improvement potential from applying best practices. Single year results could be due to unusual conditions, such as, an extended outage or weather. Using three consecutive years tends to smooth out the effect of equipment maintenance cycl es and unusual meteorological patterns.2 - 35

The equipment upgrades results are supported by num erous studies 28 and by the EPA’s analysis of the costs and associated improvements i n heat rate that can be attributed to equipment and system upgrades. We considered that a 4% reduct ion in heat rate might be achieved on a coal-steam unit by applying the four higher cost up grade actions described in Table 2-13 above. However, because details of current actual unit con figurations are unknown, and some units may have applied at least some of the upgrades, we cons ervatively estimate the heat rate improvement potential for upgrades at 2%. The EPA considers the results of this study to be applicable to the U.S. coal-fired fleet at large since the study popu lation of EGUs accounted for over 96% of 2012

electric sector CO 2 emissions from coal.

2.5.11 Sensitivity Analysis Removing Planned or Ann ounced EGU Retirements The EPA’s research found 233 coal-fired, non-cogene ration 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 r esults are identical to the full population –

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

2.6 Heat Rate Improvement – Economics

Most of the methods that can be applied to achieve a sustained Heat Rate Improvement (HRI) on a coal-steam EGU will entail a capital cos t. These HRI capital costs can be economic to incur if they yield sufficient reductions in other current or potential costs, particularly reductions in coal fuel cost and any cost related to CO 2 emissions. For the purpose of this TSD analysis, i t is

assumed that HRI can be economic if the annualized net savings (coal cost savings plus CO 2 emission cost savings minus capital cost) is positi ve: 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 Part ial Bibliography at the end of this section 29 IPM documentation includes a list of the announc ed retirements. See Table 4-36 of IPM Documentation : http://www.epa.gov/airmarkets/progsregs/epa-ipm/doc s/v513/Chapter_4.pdf and http://www.epa.gov/airmarkets/progsregs/epa-ipm/doc s/v513/NEEDS_v513.xlsx 30 This TSD analysis assesses a broadly combined application of multiple HRI methods. As estimated in the 2009 Sargent & Lundy study most HRI-related O&M costs ar e sufficiently small relative to the associated ann ualized capital costs, such that they do not materially aff ect the economics of broadly combined HRI methods. The analysis therefore does not consider the small economic impa ct of HRI-related O&M costs.2 - 36

2.6.1 Heat Rate Improvement Capital Cost Assumption The 2009 Sargent & Lundy study describes numerous w ell 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 ca pital cost for each HRI method, for units ranging in size from 200 MW to 900 MW. If these met hods and unit sizes are combined, as though they were all applied on a single EGU, the f ollowing 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 specific EG U designs, fuel types, age, size, ambient conditions, current physical condition, etc. This T SD analysis therefore assumes $100/kW as a representative combined HRI capital cost to achieve whatever HRI Btu reduction is possible at an average site. The effect of a lower HRI cost is also examined.

2.6.2 Heat Rate Reduction Assumption The weighted average annual net heat rate of the U. S. coal-steam EGU fleet in 2020 is projected at 10,450 Btu/kWh in the EPA’s IPMv5.13 B ase 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 expec ts that a significant fraction of the coal fleet has already applied some or many of the available H RI methods. 32 The EPA does not have sufficient site specific info rmation to accurately estimate what percentage of the fleet has adopted various HRI met hods, nor how effectively, and is not aware of any other investigator having sufficient informa tion. HRI potential can therefore not be

31 Note that highest cost does not necessarily align with greatest heat rate improvement. A low cost HRI method can have a large HRI potential (e.g., upgraded digital control system, neural network). Also, economy of s cale causes most HRI methods to be more costly ($/kW) on smalle r 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 exte nt to which specific HRI methods have already been applied at individual EGUs. The EPA believes that many EGU own ers consider such information to be confidential.2 - 37

estimated at this time through analysis of the curr ent equipment configurations of the coal steamEGU fleet. The EPA therefore analyzed 11 years of h istorical heat rate data and the literature on HRI methods, as discussed earlier in this TSD, to e stimate that the U.S. coal-steam EGU fleet might reasonably be expected to reduce its annual a verage gross heat rate by about 6%. The EPA understands that any HRI method that reduce s gross heat rate will also reduce net heat rate, and that some HRI methods reduce net heat rate without reducing gross heat rate. We expect that the HRI potential on a net output ba sis is somewhat greater than on a gross output basis, primarily through upgrades that result in re ductions in auxiliary loads. For purposes of this TSD the EPA conservatively assumes that the coal fl eet average net heat rate can be reduced by

6%.

2.6.3 Heat Rate Improvement Breakeven Economic Anal ysis Figure 2-6 presents a simple breakeven economic ana lysis for combined HRI methods using the assumptions described above, also assumin g 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: HRI capital cost = $100/kW; capital charge rate = 14.3%; IPM projected 2020 annual capacity factor = 78% 2. Coal fleet average 2020 net heat rate = 10,450 Btu/kWh; heat rate reduction = 6% 0.00 0.50 1.00 1.50 2.00 2.50 3.00

1.00 2.00 3.00 4.00$/MWh-net

Coal Cost $/MMBtu Coal Cost Savings $/MWh-net Capital Cost $/MWh-net

HRI Capital Cost = $100/kW2 - 38

Figure 2-6 shows that the average fleet-wide saving s in coal cost would become greater than the annualized capital cost of an average 6% r eduction in heat rate when the average fleetwide coal cost exceeds about $3.25/MMBtu. For comp arison, 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 eco nomic breakeven point would change directionally as follows:

• If the HRI capital cost were on average less than t he assumed $100/kW, 6% HRI would then become economic at lower coal costs. For examp le, if the average capital cost were actually $75/kW, a fleet-wide 6% HRI would become e conomic at an average coal cost of about $2.50/MMBtu, which is comparable to the U. S. power sector average costs of $2.38/MMBtu for all coal ranks and $2.89/MMBtu for bituminous coals in 2012. 33 This sensitivity indicates that fuel cost savings alone would make it economic for some of those EGUs currently using high cost bituminous coa ls 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 co sts lower than the values

mentioned above.

• If the average heat rate reduction were only 4% ins tead of the assumed 6%, at a cost of $100/kW, average coal costs would have to exceed $4 /MMBtu for 4% HRI to be economic fleet wide,

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

• If there were additional HRI savings due to avoided future CO 2 emission costs, HRI could become economic at lower coal costs, or at hi gher capital costs, or at lower heat rate reduction percentages.

2.6.4 U.S. Coal-steam EGUs – Estimated Fleet-wide C O 2 Reduction and Cost via HRI It is possible to make an order-of-magnitude estima te of the fleet-wide extent and costeffectiveness of HRI using reasonable assumptions a s in the following example: Fleet-wide 2020 Assumptions (basis: similar to IPMv 5.13 Base Case):

33 EIA, Electricity DataTable 7.4, Average Weighted C ost of Fossil Fuels for the Electric Power Industry 2002- 2012, http://www.eia.gov/electricity/annual/html/ep a_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 s upports the following summary

conclusions:

• Some degree of HRI is already economic for high hea t rate – high coal cost EGUs

• If a fleet-wide average 6% HRI is technically feasi ble, it would also be economic on the basis of fuel savings alone, before consideration o f the value of the associated CO 2 emission reductions, on a fleet-wide basis at today ’s coal prices if the associated average capital cost is about $75/kW or less.

• If a fleet-wide average 6% HRI is technically feasi ble, and the associated average capital cost is as much as $100/kW, 6% HRI could become eco nomic 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 abo ut $3.25/MMBtu (IPM projects coal at $2.62/MMBtu in 2020).

• Even at a capital cost of $100/kW and an IPM proje cted 2020 coal price of $2.62/MMBtu, the fleet-wide cost of CO 2 reduction via 6% HRI would be a relatively low $7.7/tonne. Thus, although there is currently some uncertainty associated with the costs of achieving a particular fleet-wide amount of HRI, it is clear that HRI is an available low-cost approach to

CO 2 reduction for existing coal-fired EGUs2 - 41 Definitions/Abbreviations Btu British thermal unit

capacity factor electricity generation expressed as a percentage of maximum electricity generation (i.e., actual generation / m aximum 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 ge nerate 1 kWh of electricity

heat rate gross heat input required to generate 1 kWh of elec tricity, 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 versi on) [Available at: http://www.epa.gov/airmarket/resource/docs/coalfire d.pdf]

start a startup event in which an EGU begins combusting f ossil fuel and generates some measurable amount of electricity bef ore ceasing fossil fuel combustion unit-year data for one EGU over a one year period

Docket Datasets Name Description hour_QA_data.txt 2002-2012 hourly dataset hour_QA_regression_data.txt 2002-2012 hourly datase t for regression analysis units_885.txt List of study units and characteristi cs year_bin_10_50_data.txt 2002-2012 unit-year binned results at 10 – 50% diff erence options2 - 42

HRI Partial Bibliography PowerEng 2002, “Heat Rate Optimization Pays Dividen ds,” Power Engineering, January 1, 2002, available at http://www.power-eng.com/articles/print/volume-106/ issue-1/features/heatrate-optimization-pays-dividends.html

NETL 2008, Reducing CO 2 Emissions by Improving the Efficiency of the Exist ing Coal-fired Power Plant Fleet, DOE/NETL-2008/1329, July 2008

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

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

NETL 2010a, Improving the Efficiency of Coal-Fired Power Plants for Near Term GHG Emissions Reductions, DOE/NETL-2010/1411, April 201 0, available at http://www.alrc.doe.gov/energy-analyses/refshelf/Pu bDetails.aspx?Action=View&PubId=307

Sargent & Lundy 2009, Coal-Fired Power Plant Heat R ate Reductions, SL-009597, Final Report, January 2009, available at http://www.epa.gov/airmarkets/resource/docs/coalfir ed.pdf

Storm 2009, “Applying the Fundamentals for Best Hea t Rate Performance of Pulverized Coal Fueled Boilers, Storm Technologies, Inc, EPRI 2009 Heat Rate Conference, available at http://www.stormeng.com/pdf/EPRI2009HeatRateConfere nce%20FINAL.pdf

NRDC 2013, Closing the Power Plant Carbon Pollution Loophole, NRDC Report R:12-11-A, March 2013 available at http://www.nrdc.org/air/pollution-standards/files/p ollution-standardsreport.pdf

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

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

CRS 2013, Increasing the Efficiency of Existing Coa l-Fired Power Plants, Congressional Research Service, December 2013, available at http://www.fas.org/sgp/crs/misc/R43343.pdf

Evonik/VGB 2008, “Power Plant Performance Reporting and Improvement under the Provision of the Indian Energy Conservation Act – Output 1.1” , Evonik/VGB 2008, available at http://www.emt-india.net/PowerPlantComponent/Output 1.1/Output1.1.pdf2 - 43

IEA 2010, Power Generation from Coal - Measuring an d Reporting Efficiency Performance and CO 2 Emissions, OECD/IEA-CIAB 2010, available at http://www.iea.org/ciab/papers/power_generation_fro m_coal.pdf

EPRI 2009, Renewed Interest in Reducing Heat Rate, EPRI Journal, Spring 2009, available at http://mydocs.epri.com/docs/CorporateDocuments/EPRI _Journal/2009- Spring/1019287_Heat%20Rate.pdf

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

EPRI 2011a , Opportunities to Enhance Electric Ener gy Efficiency in the Production and Delivery of Electricity, EPRI Technical Report 1024 651, November 2011, available at http://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm= 1&source=web&cd=1&ved=0CCsQF jAA&url=http%3A%2F%2Fwww.pserc.wisc.edu%2Fdocuments %2Fpublications%2Fspecial_in terest_publications%2FEPRI_Electricity_Use_Report_F inal_1024651.pdf&ei=Qo9yUvmwMY Xb4AOZz4GYAw&usg=AFQjCNElzekbtoSNCR5SKFwkfbKx83p0Uw &bvm=bv.55819444,d. dmg

RFF 2013, Regulating Greenhouse Gases from Coal Pow er 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 from Re-Dispatch of Existing Units

Overview

This chapter explores the dynamics of power sector dispatch and the cost-effectiveness of lowering the carbon dioxide (CO 2 ) emissions intensity of the power sector by substi tuting generation from the most carbon-intensive existing EGUs and increasing utilization, to the extent possible, of less carbon-intensive existing fossil fuel-fired EGUs. More specifically, the examination focuses on opportunities to improve emi ssions 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 o f electricity production and delivery in the

context of cost-effective CO 2 emission reduction opportunities.

Introduction

Electric system dispatch is typically defined as “t he operation of generation facilities to produce energy at the lowest cost to reliably serve consumers, recognizing any operational limits of generation and transmission facilities.” 34 Electricity demand varies across geography and ti me in response to numerous conditions, such that elect ricity generators are constantly responding to changes in demand and “re-dispatching” to meet dema nd in the most reliable and cost-effective manner possible. The nation’s EGUs are connected by transmission gri ds that extend over large regions. Through these interconnections, EGU balancing autho rities treat the product (i.e., electricity) of EGUs as fungible, calling for electricity generatio n supply to meet demand usually by deploying the least expensive power source first. 35 EGU operators and balancing authorities must take i nto account several constraints in dispatch, including transmission constraints as wel l as emission control programs and other environmental requirements. Such programs and requi rements can change the relative cost of generating electricity among plants and/or limit th e number of hours that a plant can run. For

34 Energy Policy Act of 2005

35 A balancing authority is the responsible entity th at integrates resource plans ahead of time, maintai ns the balance between supply, demand, and generation within a bal ancing authority area, and supports interconnection frequency in real-time. http://www.nerc.com/files/glossary_o f_terms.pdf3 - 2

many years, EGU operators throughout the country ha ve considered the emissions implications for pollutants such as SO 2 and NO x when scheduling unit dispatch, in response to cost s and regulatory requirements. For example, EGU operators in 10 states participating in the Regional Greenhouse Gas Initiative have several years of exp erience with factoring CO 2 emissions limits directly into bids for economic dispatch. The elect ric system’s carbon intensity can be lowered through re-dispatch among existing EGUs, particular ly by shifting generation from coal-fired units to natural gas combined cycle (NGCC) units.

Power Sector Background

Electric Dispatch Electricity generation conforms to the principle of least-cost economic dispatch, which is “the operation of generation facilities to produce energy at the lowest cost to reliably serve consumers, recognizing any operational limits of ge neration and transmission facilities.” 36 The cost of operating electric generators varies based on a number of factors, such as fuel used and generator efficiency. Regional Transmission Organiz ations (RTOs) and Independent System Operators (ISOs) help coordinate economic dispatch over larger areas to help keep the cost of meeting electricity demand as low as possible, subj ect to operational constraints. The decision by balancing authorities to call upon, or dispatch, any particular generating unit is driven by the relative operating cost, or m arginal cost, of generating electricity to meet the

last increment of electric demand. These costs chan ge over time depending upon a variety of factors like fuel prices, weather conditions, and o verall demand levels. Since the fixed cost of power plants is a sunk cost, plant operators bid in to electricity markets such that their variable costs are covered. For fossil fuel-fired electric g enerating units, variable costs are dominated by the cost of the fuel, although coal-fired units oft en also have considerable variable costs associated with running pollution controls. 37 Other generating technologies, like renewables, hydroelectric, and nuclear, have little or no varia ble costs and are generally dispatched to the extent possible. In order to maintain least-cost di spatch, the units with the lowest variable costs

36 Federal Energy Regulatory Commission, 2005. Econom ic Dispatch: Concepts, Practices, and Issues

37 In addition to fuel costs, variable costs also inc lude costs associated operating and maintenance, an d costs of operating a pollution control and/or emission allow ance charges.3 - 3

will be called upon first, then other units (with h igher variable costs) will be called upon sequentially, such that total system demand is met. The economic order in which units are dispatched to meet demand, at any particular point in time, is commonly called a dispatch

“curve.” 38

Balancing Authorities In states with cost-of-service regulation of vertic ally-integrated utilities who own generation, transmission, and distribution infrastr ucture, the utilities themselves often form the balancing authorities who determine unit dispatch. Such utilities are presumed to dispatch their units in a cost-minimizing fashion (seeking the low est marginal cost), and they can arrange to buy and sell power with other balancing authorities

. In states that have restructured regulation to allo w for competition between generators, RTOs and ISOs are generally responsible for moving electricity across larger areas in the most efficient and least-cost manner possible. 39 They coordinate, control, and monitor electricity transmission systems to ensure cost-effective and r eliable delivery of power, and they are independent from market participants. ISOs grew out of the Federal Energy Regulatory Commission (FERC) requirements for existing power p ools to satisfy the requirement of providing non-discriminatory access to transmission . Subsequently, FERC encouraged the voluntary formation of RTOs to administer the trans mission grid on a regional basis throughout North America (including Canada). RTOs and ISOs administer wholesale power markets, w hich match the generation of electricity with the purchase of electricity (and a ncillary services) prior to delivery to end-users. Companies that provide retail electricity (e.g., ut ilities and energy service companies) procure power through these wholesale electricity markets.

State Public Utility Commissions (PUCs) 40 Each state has a governing body that is tasked with regulating retail electricity rates and electric services to protect the public interest, e nsure efficient and reliable delivery of electricit y,

38 http://www.eia.gov/todayinenergy/detail.cfm?id=759 0

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

40 These entities are sometimes called Utilities Comm issions, Utility Regulatory Commissions, or Public Service Commissions

.3 - 4

and plan appropriately for the short and long term energy needs of the state and consumers. Depending on market structure, PUCs also allocate c osts among customers, design price structures and set price levels, set service qualit y 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 RT Os and ISOs. In cost-of-service states, the PUC also has oversight of the generation and capaci ty planning components

.

Spot and Day-Ahead Markets RTOs and ISOs operate spot markets for wholesale po wer 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 balan cing power supply and load in their area and typically serve as the balancing authority for the same area. For areas not administered by RTOs and ISOs, dispat ch is scheduled both day ahead and hourly, but is typically driven by the power supply costs and schedules of traditional utilities. This dispatch will depend, to a certain degree, on spot markets for power, since utilities will dispatch purchased power from other suppliers when that power can be obtained at a cost savings. There is an active wholesale market for th is power in the spot market, from individual sales and from exchanges. These markets typically s ell power day-ahead but not hourly, and also sell power for longer periods, such as weekly or mo nthly. However, the actual dispatch and balancing of power is conducted by the utility base d on its own scheduling and purchasing protocols and varies considerably from one utility to the next. As a balancing authority, the RTO or utility will b alance demand, generation, and imports/exports in real time while maintaining syst em 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 a re appropriate, and appropriate corrective action

is taken when needed.

Reliability As reliability coordinators, balancing authorities are responsible for the reliable operation of the bulk electric system. The bulk electric syst em refers to a large interconnected electrical3 - 5

system made up of generation and transmission facil ities and their control systems. To ensure reliability, system operators continuously analyze real-time and forecasted load and transmission conditions to ensure that scheduled generation disp atch can meet load without adverse impacts. If the scheduled dispatch is not feasible within th e limits of the transmission system, it must be adjusted by the system operator. The North American Electric Reliability Corporation (NERC) develops and enforces the procedures to ensure reli ability, 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 considerably hi gher-emitting sources than NGCCs, on average. Therefore, the replacement, or re-dispatc h, of each megawatt-hour (MWh) from the average fossil fuel-fired boiler with each MWh from an average NGCC will result in notable

CO 2 emission reductions. The lower emission rate of NGCC conveys the potenti al of re-dispatch to reduce GHG emissions. However, the actual potential to realize emission reductions through this technology depends on the availability and capacity factors ofthe 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 fle et that could displace generation from more

CO 2 intensive generating resources. The term “availabi lity” 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 8,78 4 hours in 2012 since that year included a leap day). The unavailable period is generally attribute d 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 sou rces. See “2012 unit-level data using the eGrid methodology” file provided in the docket3 - 6

maintenance, and unplanned outages. The EPA assumes that NGCC has an availability of 87%. 43 Other reports suggest that NGCC availability factor s 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 e xisting generating system for re-dispatch to those units. To evaluate re-dispatch opportunities to unused NGCC generation potential in the system, the EPA reviewed recent NGCC fleet operatin g data to determine capacity factors. Redispatch for GHG abatement purposes would require one net MWh of a lower emitting technology displacing one net MWh of generation fro m 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 relat ionship of a unit’s total net generation relative to its net generating capacity (i.e., capacity fact or). Net generating capacity is a function of weather/temperature conditions at the site, which v aries throughout the year. While some units may model actual weather adjusted capacity by the h our/minute, these data are not reported for the fleet. Therefore, the EPA used the nameplate c apacity reported for units. The net generation was divided into the nameplate generation capacity of a unit multiplied by the number of hours in a year. This calculation of capacity factor pro vides an indication of how much net generation a unit is providing as a percent of its total gener ating capacity. Whereas availability refers to the maximum amount of generation that could be expected from a given source, the capacity factor

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

over 1800 NGCC units and observed that the NGCC fle et had an average capacity factor in the 44% to 46% range for 2012. 45 Since the fleet-wide capacity factor in 2012 was less than the availability assumed for the technology, the 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/po wersectormodeling/BaseCasev513.html

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

45 See “2012 unit-level data using the eGrid methodol ogy” file provided in the docket for 44% figure. S ee EIA 860 and 923 for 46% value.3 - 7

To quantify the GHG reduction potential from re-dis patch, the EPA considered alternative capacity factor levels at which the NGC C fleet could be dispatched. Although the availability for NGCC units is assumed to be in the mid to high 80s, the EPA did not assume that each state’s NGCC fleet could collectively operate at this level on an annual basis. To determine reasonable average capacity factor ceilings for a s tate’s NGCC fleet as part of BSER, the EPA considered historical data and modeling projections describing NGCC characteristics and operating behavior. As seen in Table 3-1, the existing NGCC fleet is re latively young. More than 80% of the capacity has come online in the last 15 years. 46 Of this capacity, almost all are a highly efficie nt 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 Energy Data Systems ( NEEDS) file at http://www.epa.gov/powersectormodeling/BaseCasev513 .html

47 See “Operable” worksheet in “GeneratorY2012” Workb ook in 2012 Zip file at http://www.eia.gov/electricity/data/eia860/3 - 8

Of 464 NGCC plants generating in 2012 and greater than 25 MW, the EPA observed that 50 plants (more than 10% of NGCC plants) had a net generation value that was greater than or equal to its nameplate capacity x 8784 hours * 70%. That is, a capacity factor that was 70% or greater (see Figures 3-1 and 3-2). 48

Figure 3-1: NGCC Plant Distribution by Capacity Fac tors (2012)

49 Table 3-2: Plant Distribution of Existing NGCCs (20 12)

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 methodol ogy” file provided in the docket

49 EIA Forms 860 and 923. CA and CT Prime Mover categ ories3 - 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 a n annual capacity factor of 70% or higher. This subset of NGCCs was largely dispatched to provide base load power. While only 10% of plants operated at 70% or higher capacity fa ctor on an annual basis, 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 achie ved capacity factors greater than 50%, and even up to 80%. Using data reported to the EPA, 50 and looking more closely at data during the summer and winter peak electricity demand timeframe s nationwide, more than 10% of NGCCs were operated at a capacity factor greater than 70%

. 51 In fact, 19% of NGCCs achieved 70% capacity factor during the winter of 2011/2012 and 20% hit that level or higher during the

summer. 52 During periods where demand levels are typically lower, some NGCCs were idled or operated at lower capacity factors. Nonetheless, a notable number of existing NGCCs have demonstrated the ability to achieve a 70% capacity factor for extended periods of time. These units achieved high capacity factors without advers e effects on the electric system. While many units demonstrated an ability to deliver net genera tion 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 unit s (107 units) operated at a 75% capacity factor, or higher, in 2012. In addition, 16% of units (291 units) operated at 65%, or higher. Over the last several years, advances in the produc tion 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 signific ant quantities of generation from coal units to

50 Air Markets Program Data (at http://ampd.epa.gov/a mpd/).

51 Summer defined as June, July and August. Winter d efined as December, January, and February. Estimat es 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 A ugust.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 dispatch across the gener ating fleet of coal and NGCC units for 2011 and 2012 provides some implicit measures of th e cost dynamics between the two technologies. For example, one is able to look at t he change in the prices of coal and gas to gauge the relative costs of generating, or dispatch , for each technology. While there are wideranging costs at the unit level, an aggregated asse ssment of the relative economics is informative and can provide a metric for assessing the implicat ions 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 and 923 data demonstrate an increase in NGCC generation and fuel use between 2011 and 2012 of mo re 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 signi ficant redispatch from coal to gas over just a one year period demonstrates the ability for the quick re-dispatch in response to market or

economic drivers. The increase in the NGCC utilization was in large p art driven by the decrease in natural gas prices to historic lows (see Table 3-3). Henry Hub natural gas prices averaged $4.00/mmBtu in 2011 and $2.76/mmBtu in 2012. This $1.24/mmBtu c reates an additional incentive for redispatch from coal generation to NGCC relative t o 2011 dispatch economics. The fuel advantage is similar to the incentive that a $15/me tric ton of CO 2 price signal would create. 53 This historical data also shows a sharp increase in the NGCC fleet’s capacity factor from the high 30s to the mid 40s. During that same period, n et coal generation dropped by an amount similar to the increase observed in NGCC net genera tion. Furthermore, natural gas supply is

53 Assumes 11,000 Btu/KWh heat rate and 2354 lb/MWh e mision rate for coal, 8000 Btu/KWh and 926 lbs/MWh for NGCC (based of “2012 eGrid file”)3 - 11

expected to grow more than 20% by 2020 relative to its 2012 levels, creating more fuel resources to foster the potential continued and increasing re dispatch 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 cons istently operate at levels greater than 70% of their nameplate capacity (e.g., this wa s the utilization 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 reasonable ceiling in the EPA’s BSER approach. For purposes of establishing state goals, historica l 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 existi ng NGCC units likely subject to the 111(d) applicability criteria was 959 TWh. 56 After the application of NGCC re-dispatch to the 70% level, 57 these same existing sources were calculated to col lectively generate 1,390 TWh. Adding in the existing sources that were not yet online in 2012 (under construction) increases total NGCC generation calculated in the goal setting to 1 ,444 TWh. Although, states may choose to comply with state go als through other abatement measures, the EPA believes that upwards of 1,400 TW h from existing and under construction NGCCs is achievable. As a reference point, NGCC ge neration increased by approximately 430 TWh (an 81% increase) between 2005 and 2012. EPA i s calculating that NGCC generation in

54 http://www.eia.gov/oiaf/aeo/tablebrowser/#release= AEO2014ER&subject=0-AEO2014ER&table=13- AEO2014ER®ion=0-0&cases=ref2014er-d102413a

55 EIA form 860 and EIA form 923

56 For covered sources. 57 This dispatch level is a ceiling dependent upon av ailable existing steam generation that can be decre ased. As a result, not all states achieve the assumed 70% re-d ispatch for purposes of goal setting (see Goal Sett ing chapter).3 - 12

2020 could increase by approximately 47% form today ’s levels. This reflects a smaller ramp rate in NGCC generation than has been observed from 2005 to 2012.

Table 3-4: Historic and Assumed Generation Patterns for State Goal Setting

NGCC Name Plate Capacity (GW) NGCC Net

Generation (TWh) Growth in

NGCC Generation from 2005 to 2012 Growth in

NGCC Generation from 2012 to 2020 Nationwide

NGCC Capacity Factor

2005 199 551 NA NA 32%

2012 244 981 81% NA 46%

2020 State Goal Calculation 256 1,444 47% 64%

Natural Gas Supply

The EPA expects the growth in NGCC generation assum ed in goal setting to be feasible and consistent with domestic natural supplies. Inc reases in the natural gas resource base have led to fundamental changes in the outlook for natural g as. There is general agreement that recoverable natural gas resources will be substanti ally higher for the foreseeable future than previously anticipated, exerting downward pressure on natural gas prices. 58 According to EIA, natural gas proved reserves have doubled between 2000 and 2012. 59 Domestic production has increased by 32% over that same timeframe (from 19.2 TCF to 25.3 TCF). EIA’s Annual Energy Outlook for 2014 project s that production will further increase to 29.1 TCF, due to increased supplies and favorable m arket conditions. For comparison, NGCC generation growth of 450 TWh (calculated in goal se tting) would result in increased gas consumption of roughly 3.5 TCF for the electricity sector. 60

58 National Petroleum Council. 2011. Prudent Developm ent: Realizing the Potential of North America's Abu ndant Natural Gas and Oil Resources. http://www.npc.org/r eports/rd.html (see Figure 1.2 on p. 47). 59 http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cf m?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 f unded advisory committee established by the Secretary of Energy, recently up dated a major resource study and concluded that “the potential supply of North American natura l gas is far bigger than was thought even a few years ago,” after

large increases in shale resource estimates. 61

Figure 3-2: U.S. Natural Gas Technically Recoverabl e Resources (from NPC, 2011) 62

Technical Considerations

Emission reductions through re-dispatch are largely determined by the ability to change the utilization of existing generating units, relat ive to current utilization levels. Other influences include physical limitations of the electric transm ission system and considerations for reliability, timing, and cost.

NGCC Availability

61 National Petroleum Council, 2012 (http://www.npc.o rg/PD_update-80112.pdf) 62 http://www.npc.org/reports/rd.html3 - 14

For purposes of economic dispatch, most NGCCs have historically been operated to serve base load or intermediate demand due to their high efficiency and flexibility of operation, with national average annual capacity factors in the ran ge of 40-50%. 63 , 64 However, NGCCs are designed for, and are demonstrably capable of, reli able and efficient operation at much higher annual capacity factors, as shown in observed histo rical data for particular units and their design and engineering specifications. The capability of NGCCs to operate at capacity fact ors of 70% and greater is indicated, in part, by statistics on the average availability fac tor of NGCCs. 65 Annual availability is the ratio of annual hours that an EGU is operating or conside red able to operate (not in a forced or maintenance outage) to the hours in a year. The ave rage availability factor for NGCCs in the U.S. generally exceeds 85%, and can exceed 90% for selected groups, as reported to NERC. 66 , 67 Advanced NGCCs being built today have availability factors of over 95%. According to one NGCC manufacturer, these highly efficient units alr eady represent over 15 percent of total installed capacity nationwide, including all electr ic generating sources (as of 2010). 68 These high-efficiency and high-availability NGCC un its were first introduced around 1995 and have consistently reported availability fa ctors of 90 to 92 percent to NERC (compared to 95 percent or greater availabilities reported by current vintage F class and H class turbines from General Electric Power Systems). 69 Data reported to NERC from NGCC units greater tha n 50 MW in 1994 through 1998 shows similar availabili ty factors (generally exceeding 89 percent).

Natural Gas Pipeline and Electricity Transmission

63 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayinenergy/detail.cfm?id=1461 1 (for recent data)

64 EIA, Electric Power Annual 2009, http://www.eia.gov/electricity/annual/archive/03482 009.pdf (Table 5-2 for 2009 and earlier data)

65 NERC, 2008-2012 Generating Unit Statistical Brochu re – All Units Reporting, http://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx

66 Power Engineering, Negotiating Availability Guaran tees for Gas Turbine Plants, 03/01/2001, http://www.powereng.com/articles/print/volume-105/issue-3/features/ negotiating-availability-guarantees-for-gas-turbine -plants.html

67 Power Engineering, Higher Availability of Gas Turb ine Combined Cycle 02/01/2011, http://www.powereng.com/articles/print/volume-115/issue-2/features/ higher-availability-of-gas-turbine-combined-cycle.h tml

68 http://site.ge-energy.com/corporate/network/downlo ads/7FA_Evolution.pdf

69 GE Power Systems submitted to U.S. Department of E nergy, 2000. Utility Advanced Turbine Systems Technology Readiness Testing Phase 3 Restructured. DOE Cooperative Agreement No. DE-FC21-95MC31176— 30.3 - 15

The EPA believes that the natural gas pipeline and electricity transmission networks can support aggregate operation of the NGCC fleet at up to a 70% capacity factor on average, either as they currently exist or with modifications that can be reasonably expected in the time frame for compliance with this rule. Existing NGCCs are a lready 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 u se, the rule allows for emission rate averaging across multiple units and across time for complianc e. As a consequence of this averaging flexibility, constraints that occur at peak times a re 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 sta te-wide NGCC fleet. The ability for the current fleet to ramp up significantly to meet changes in d emand 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% ove r 2011 70 and resulted in a national average capacity factor for NGCC of 45.8% on average, and h igher in some states. 71 During the peak hours of the day (which vary by reg ion and season), NGCC capacity factors are typically well above average capacity f actors. 72 The pattern of capacity utilization by hour for 2005 to 2010 is shown in Figure 3-3. In th is figure, capacity factors in 2010 are approximately 50% from the Hour 11 to Hour 21. 73 The persistence of this hourly pattern across years shows the pattern to be stable. Since the ave rage 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 avera ge capacity factor. These peak hours are the period when there are most likely to be constraints on the pipeline or electricity transmission networks; during other hours of the day, continued NGCC operation at equal, or higher levels, are technically feasible but may be limited by econ omic considerations (e.g., whether NGCCs can offer least-cost electricity compared to other sources at those times). As a result, the current system is already able to support national average capacity factors in the mid to high 50’s for

70 Energy Information Administration, U.S. Natural Ga s Consumption by End Use, http://www.eia.gov/dnav/ng/ng_cons_sum_dcu_nus_a.ht m. 71 Source: Air Markets Program Data (AMPD), ampd.epa. gov, EPA, 2014

72 Energy Information Administration, Today in Energy , July 9, 2011. Average utilization of the nation’s natural gas combined-cycle power plant fleet is rising. http:/ /www.eia.gov/todayinenergy/detail.cfm?id=1730#

73 In this figure, hour 11 is the hour ending at 11 A M, 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 aver age capacity factors could be extended to higher levels at all hours without experiencing tec hnical feasibility barriers from either pipeline supplies or electricity transmission.

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

Although there can be site-specific constraints on utilization at some NGCC facilities, several factors support the ability of the power an d natural gas pipeline systems to respond effectively with increases in infrastructure when n eeded to alleviate these barriers. For example, in recent years, the power transmission system has responded with increased transmission

infrastructure when needed to allow the retirement of uneconomic coal plants. 75 This rule provides for flexible implementation that will perm it efficient scheduling of infrastructure upgrades as needed. Upgrades to pipeline and transm ission infrastructure potentially needed to

75 See http://www.pjm.com/~/media/about-pjm/newsroom/2013- releases/20131211-pjm-board-authorizes-4.6- billion-in-chnages-to-regional-electric-grid.ashx for an example of short term transmission upgrades performed to facilitate environmental compliance. For technical description of these upgrades, see: http://www.pjm.com/~/media/committees-groups/commit tees/teac/20131211/20131211-december-2013-pjm-board

approval-of-rtep-whitepaper.ashx

.3 - 17

meet additional use of existing facilities will gen erally be less extensive than upgrades of that infrastructure potentially needed for siting of new capacity. In addition, this proposed rule is expected to result in significantly higher levels o f end-use energy efficiency, which will reduce the load on the electricity transmission and natura l gas pipeline infrastructure, while also providing other system wide benefits, such as decre ased need for new generating units and reduced peak demands. In addition, natural gas pipeline capacity is regul arly added in response to increased gas demand and supply, such as the addition of large am ounts of new NGCC capacity in 2001 to 2003, or the delivery to market of unconventional g as 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 use of ex isting NGCCs. Over a longer time period, much more significant pipeline expansion is possibl e. In previous studies, when the pipeline system was expected to face very large demands for natural gas use by electric utilities about 10 years ago, increases of up to 30% in total delivera bility out of the pipeline system were judged to be possible by the pipeline industry. 77 There have also been notable capacity expansions over the past five years, in response to increased natural g as supply estimates and advances in drilling techniques. 78 To examine the potential for increases in pipeline deliverability, the EPA analyzed the pipeline flow data from the Energy Information Admi nistration. 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 a nd broader area, the level of gas supply that can be firmly delivered to a particular region depends on the amount of natural gas the will be required to be delivered out of the region to other regions. Consequently, it is important to focus on the net capacity – the difference between inflow capacity and outflow capacity -- in the relevant areas. The regions used by EIA for measuri ng 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 a reas where there could be increases in natural

76 Energy Information Administration, Today in Energ y, Natural Gas Pipeline Additions in 2011. Addition s averaged around 20Bcf per day from 2008 to 2011.

77 Pipeline and Storage Infrastructure Requirements f or 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 compl y with the proposed rule. These are the Northeast, Southeast, Midwest and Western regions i n 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 Reg ion, 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 from the period 2005 to 2010 can be used as an estimator of the potential increase in pipeline capacity to accommodate compliance with the rule between 2015 and 2020. This is an extremely co nservative assumption, since compliance is measured over a longer period and is not limited to re-dispatch approaches. Moreover, increased use of natural gas in existing facilities can be la rgely met with expansions to existing pipeline facilities and corridors, so that the types of capa city expansion required will be less expensive and take less time than new pipelines. Over 2005-20 10, the total gas deliverability in gas consuming areas increased by 21%. Since the power sector currently uses approximately 30% of the total national natural gas consumption, and gas usage in other sectors is expected to be

79 Source: Energy Information Administration, http://w ww.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-di spatch of natural gas power plants.

Figure 3-4. Energy Information Administration Natur al Gas Pipeline Regions

Source: Energy Information Administration.

Recent pipeline construction has continued to suppo rt the increasing need for natural gas. According to information released in April, 2014 by the EIA, 80 118 pipeline projects were completed and placed into service from 2010 to 2014 , totaling 4,699 miles of pipe, and 44,107 MMcf per day of additional pipeline capacity. For projects expected to be in service from April, 2014 through 2016, EIA reports 47 projects, with planned capacity additions of 20,505 MMcf per day and 1,567 miles of pipe. These projects cover all major gas consuming areas of the US, and include both new pipeline

80 See www.eia.gov/naturalgas/pipelines/EIA-NaturalGasPipe lineProjects.xls

.3 - 20

construction, such as the Spectra Energy’s NEXUS Ga s Transmission project in the Upper Midwest and pipeline expansions such as the Tenness ee Gas Pipeline Project in Connecticut. The electric transmission system has also been expa nded over the past few years, and continued investment is expected. As of 2012, The E IA reports 187 thousand circuit miles of high voltage transmission in the US at 100 kV. The re are 8 thousand miles of planned expansion in 2013, with a total 26 thousand miles proposed fr om 2013 to 2018. 81 According to the Edison Electric Institute, 82 member companies are planning over 170 projects through 2024, totaling approximately $60.6 billion (this is only a portion of the total transmission investment anticipated). Approximately 75 percent of the reported projects are high voltage (345 kV and higher), representing over 13,0 00 line miles.

Analysis of Cost-Effectiveness To further evaluate the technical capability of th e natural gas supply and delivery system to provide increased quantities of natural gas and the capability of the electricity transmission system to accommodate shifting generation patterns, EPA employed the Integrated Planning Model (IPM), a multi-regional, dynamic, determinist ic linear programming model of the U.S. electric power sector that the EPA has used for ove r two decades to evaluate the economic and emission impacts of prospective environmental polic ies. IPM provides a wide array of projections related to the electric power sector an d its related markets (including least cost capacity expansion and electricity dispatch project ions) while meeting fuel supply, transmission, dispatch, and reliability constraints. Natural gas supply, demand, transportation, storage , and related costs are modeled directly in IPM through the incorporation of a natu ral gas module. The module includes a detail rich representation of the natural gas pipeline net work inclusive of discount curves that represent the marginal value of gas transmission as a functio n of the pipeline’s load factor. IPM’s natural gas module has the capability to expand pipeline ca pacity on an economic basis. At the unit level, IPM contains a detailed represen tation 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/Doc uments/Trans_Project_lowres_bookmarked.pdf3 - 21

designed to account for the cycling capabilities of EGUs to ensure that the model properly reflects the distinct operating characteristics of peaking, cycling, and base load units. EPA believes IPM represents a powerful tool to evaluate the technical feasibility of requiring increasing levels of re-dispatch from higher to low er-emitting EGUs. The EPA has conducted extensive analysis to quantif y the opportunity to reduce CO 2 emissions through re-dispatch. As part of this effo rt, the EPA conducted an initial set of analyses utilizing the Integrated Planning Model (IPM) to pr ovide a framework for understanding the broader economic and emissions implications of shif ting generation from coal-fired steam EGUs

to NGCC units within defined areas. 83 In the most restrictive scenarios, re-dispatch wa s simulated only between EGUs located in the same sta te. These scenarios were designed to consider, even under a restrictive interpretation o f the degree of re-dispatch that might constitute a component of BSER under CAA section 111(d), 84 to what extent existing NGCC units could increase their dispatch cost-effectively taking int o 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 additional e lements to the IPM framework:

1. The application of a CO 2 charge to the variable cost of dispatch for all ex isting coal steam boilers, IGCC units, and oil/gas steam boiler s 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 li near programming model of the U.S. electric power s ector. It provides forecasts of least cost capacity expansion , electricity dispatch, and emission control strate gies while meeting energy demand and environmental, transmissi on, dispatch, and reliability constraints. Full do cumentation of the IPM model can be found at http://www.epa.gov/powersectormodeling

84 In practice, unit dispatch does not respect state boundaries because least-cost supply must be balanc ed with demand in real time over grid interconnects which s pan multiple states (with the exception of the Elec tric Reliability Council of Texas interconnect). The design of this modeling scenario assumes artificial constraints o n re-dispatch to force such behavior to respect state boundaries, gi ven the context of this rulemaking’s quantification of individual state goals. These state boundary constraints nece ssarily forgo cost-effective opportunities to re-di spatch units in different states; as a result, costs and prices in this analysis are overstated.

85 The addition of CO 2 costs represents a simple analytic approach to est imating the cost-effective CO 2 reductions under this building block and acts as a proxy for s ome existing state policies that shift dispatch. I n actual plan implementation, states would be free to select any policy approach that has the net effect of reducing the carbon intensity of generation and/or reducing overall emi ssions from affected sources.3 - 22

2. Generation constraints that maintain the sum of sta te-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 pote ntial for re-dispatch by: (1) increasing dispatch costs for affected coal steam, IGCC, and O /G steam EGUs within each state, and (2) requiring that any reduction in output from those s ources be offset in its entirety by an increase in output from that state’s existing NGCC capacity. Utilizing IPM to conduct this analysis ensures an integrated, least-cost, technically feas ible solution subject to power sector system reliability constraints, fuel market impacts, natur al gas transmission and distribution networks, electric power transmission constraints, and unit-s pecific characteristics (e.g., operating and maintenance costs, heat rate, turndown/cycling beha vior).

Cost and Availability of Economic Re-Dispatch Oppor tunities In executing this analysis, the EPA conducted a num ber of scenarios to quantify the relationship between the amount and cost of re-disp atch. Figure 3-5 below presents the projected national average capacity factor for NGCCs in 2020 (the first year of the assumed re-dispatch incentive) and the associated average $/tonne of CO 2 reduced by comparing emissions and costs against the EPA’s Base Case (the difference in tota l system cost divided by the difference in power sector emissions). While the charge is appli ed exogenously in IPM, the average $/tonne shown below is calculated from the modeling results and projections by dividing the increase in total system costs (compared to the base case) by t he total change in CO 2 emissions (compared to the base case).

86 http://epa.gov/powersectormodeling/BaseCasev513.htm l3 - 23 Figure 3-5: NGCC National Capacity Factor, 2020 In itial Analyses

The EPA believes average $/tonne – which is distinc t from the $/tonne CO 2 cost imposed in these analytic scenarios on affected coal steam, IGCC, and O/G steam EGUs – to be the most relevant metric in evaluating cost-effectiveness. S ystem cost changes necessarily encompass all elements of cost across power, transmission, and fu el markets and therefore provide the most comprehensive perspective regarding cost-effectiven ess. 58% 62% 65% 68% 71% 74% 75%

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

- 10 20 30 40 50 60Average Capacity Factor

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 aver age $/tonne costs as reasonable, we expect the costs of implementing this requirement i n a compliance 87 setting will be considerably

lower for several reasons:

• Analytic construct used to simulate re-dispatch inc entive: As described earlier in this chapter, the EPA’s initial analyses utilized CO 2 charges on the variable cost of dispatch for existing coal steam, IGCC, and O/G steam with e mission rates greater than 1,100 lbs/MWh and a capacity greater than 25 MW, as an an alytic construct to induce redispatch behavior in the model to existing NGCC fac ilities. The CO 2 charge was applied uniformly to all states in order to quantify the ul timate amount of in-state re-dispatch opportunities available as that charge is increased across scenarios. In the initial analyses,

low levels of CO 2 charges produce cost-effective re-dispatch opportu nities relative to the Base Case in almost all states. However, as the CO 2 costs are increased to higher levels, economic re-dispatch, opportunities within some sta tes may eventually plateau – a point clearly illustrated in the declining slope of the b est-fit line in Figure 3-5. A uniform

87 The Regulatory Impact Analysis supporting the prop osal examines, in an illustrative manner, how the p ower sector could respond to the state goals that are ca lculated from all of the building blocks in a cost effective manner.3 - 25

application of the same rising CO 2 charge in all states produces an outcome for many

states where the additional CO 2 costs imposed on affected coal steam, IGCC, and O/ G steam are not able to produce incremental economic re-dispatch at units within that state; therefore, the additional costs imposed by these hi gher CO2 charges overstate the actual $/tonne necessary to induce achievable re-dispatch in each state.

• Potential for multi-state compliance: The EPA also analyzed scenarios where shifting of generation among EGUs was not limited by state boun daries. In one set of analyses, redispatch was allowed to occur across the multi-stat e regions defined by NERC assessment areas (subject to other real-world const raints specified in the model, including transmission limits). In these scenarios with great er 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 s tate basis), demonstrating that the main analysis’s imposition of artificial re-dispatch bou ndaries on state borders overstates the cost-effectiveness of re-dispatch potential.

To evaluate how EGU owners and grid operators could respond to a state plan’s possible requirements, signals, or incentives to re-dispatch from more carbon-intensive to less carbonintensive EGUs, the EPA also analyzed an additional series of scenarios in which the fleet of NGCC units nationwide was required, on average, to achieve a specified annual utilization rate. Specifically, the scenarios required average NGCC u nit 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 specifi ed operating and reliability constraints for the scenario, including the aforementioned state-by-sta te generation levels from the base case. This constraint effectively requires states that decreas e coal generation to offset, in equal amounts, NGCC generation. Collectively, states must achieve the required capacity factor for NGCCs. The costs and economic impacts of the various scen arios were evaluated by comparing

the total costs and emissions from each scenario to the costs and emissions from a business-asusual scenario. For the scenarios reflecting a 65, 70, and 75 percent NGCC utilization rate, comparison to the business-as-usual case indicates that the average cost of the CO 2 reductions achieved over the 2020-2029 period was $21, $30, an d $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 proposal’s goals. This is because only 29 state goals are premised on the existing NGCC fleet achieving an av erage capacity factor of 70 percent. Consequently, a 70 percent utilization rate target for the existing NGCC fleet requires an average national capacity factor of 63 percent. The EPA also analyzed dispatch-only scenarios where shifting of generation among EGUs was limited by state boundaries. In these scen arios with less re-dispatch flexibility, the cost of achieving the quantity of CO 2 reductions corresponding to a nationwide average N GCC 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 this buildin g block can be achieved without causing significant economic impacts. For example, in neither of the 70 percent NGCC unit utilization rate scenarios – re-dispatch limited to regional or state boundaries – did delivered natural gas price projections increase by more than 10 percent in the 2020-2029 period, which is well within the range of historical natural gas pri ce volatility. For example, the year-to-year percentage difference in Henry Hub prices reported by the Energy Information Administration averaged 18.5% over the period from 1981 to 2012. 88 Projected wholesale electricity price increases over the same period were less than 7 per cent in both cases, which similarly is well

88 http://www.eia.gov/dnav/ng/hist/rngwhhdA.htm3 - 27

within the range of historic electric price variabi lity. For example, the average year-to-year price change for the PJM region was 19.5 percent over the period from 2000 to 2013. For all the ISOs in the East, the variation is virtually unchanged f rom the PJM example (at 19.6%). 89 However, for the reasons previously discussed with respect to estimated costs per ton of

CO 2 , the actual implementation is expected to result i n notably lower economic impacts, including natural gas price impacts, and are consid erably larger than would actually occur in real-world compliance with this rule’s proposed goa ls.

Table 3-8: National Average Delivered Natural Gas P rice,

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 Veloc ity Suite data across Eastern ISOs (PJM, NYISO,ISO- NE and Midcontinent ISO).4 - 1 

### Chapter 4: Cleaner Generation Sources

4.1. Introduction Renewable energy is a cost-effective approach for r educing carbon dioxide (CO 2 ) emissions from fossil fuel-fired electric generatin g units (EGUs) through the substitution of electricity generated from renewable resources, ref erred to in this document as renewable energy (RE). The portfolio of available RE sources encompa sses a wide variety of technologies from utility-scale RE plants to smaller-scale distribute d generation sited at residential, commercial, or industrial facilities. RE technologies are fueled b y the sun, wind, water, organic matter, and other resources regularly replenished by physical and bio logical cycles. To integrate the rapidly increasing and evolving portfolio of RE into the Be st System of Emission Reductions (BSER), the EPA has developed a proposed approach that buil ds upon current state policy encouraging increased production of RE taking into account rene wable potential in particular regions of the country. Additionally, the EPA believes that the planned exp ansion of new nuclear generating capacity and the preservation of existing nuclear g enerating capacity represent a cost-effective

means to reduce CO 2 emissions at fossil fuel-fired EGUs by providing c arbon-free generation that can replace generation at those EGUs. Increasi ng the amount of nuclear capacity relative to the amount that would otherwise be available to ope rate is a technically viable and economically efficient approach for reducing CO 2 emissions from affected fossil fuel-fired EGUs. This TSD is intended to support discussion of clean er generation sources (RE and nuclear) as a component of BSER in the preamble (mo st extensively in these sections: Building Blocks for Setting State Goals and Considerations, State Goals, State Plans, and Impacts of the Proposed Rule) and its representation within the RI A. Results from this chapter feed into the technical support document (TSD) on state goal sett ing. Cleaner generation is also addressed in TSDs on Survey of Existing State Actions, State Pla n Considerations, Projecting EGU CO 2 Emission Performance, and Legal Memorandum. 4.2. Proposed Approach To estimate the potential RE available for inclusio n as part of BSER, EPA developed an RE generation scenario that provides a target for h ow much of each state’s generation can be produced by RE based upon the current goals of lead ing states in the same region, and allows4 - 2

each state to grow RE generation over time towards that target, based upon that state’s current

level of RE. The method can be summarized as follow s. First, the country is divided into regions. Second, an RE generation target is calculated for e ach region, based upon averaging all 2020 RPS requirements in that region. Third, an annual g rowth factor is calculated that would allow the region as a whole to reach the regional RE targ et in 2029 assuming that RE generation would increase from 2012 levels beginning in 2017. Fourth , the annual growth factor for a given region is applied to individual states’ 2012 RE generation to calculate future RE generation in that state from 2017 through 2029, not to exceed a maximum RE generation level equivalent to the regional RE target. Finally, these annual RE gener ation levels for each state are used to calculate interim and final RE targets for that state. The proposed approach is derived from state experie nce with policies that drive investment in RE and the generation that results fr om those efforts. The EPA focused on statelevel RE policy for several reasons. Every state in the union is producing electricity from renewable resources, and some states have achieved significant levels of renewable generation, surpassing a quarter of in-state generation. State- level RE requirements have been implemented in 29 states plus Washington, DC, representing all regions of the country. Nine states have voluntary goals. 90,91 These state-level goals and requirements have been developed and implemented with technical assistance from state-le vel regulatory agencies and utility commissions such that they reflect expert assessmen ts of RE technical and economic potential that can be cost-effectively developed for that sta te’s electricity consumers. The proposed approach focuses on RE requirements es tablished through Renewable Portfolio Standards (RPS), which provide specific q uantifiable RE generation requirements over time. The EPA used these RPS-mandated quantities as the basis for deriving regional targets to be applied to states as part of BSER, using the RPS -based targets as a reasonable benchmark of regionally cost-effective RE generation which state s could grow towards over time. While EPA’s proposed approach is derived from RPS data, states may also consider a broad variety of other RE policies to increase generation, such as perform ance-based incentives, financial assistance

90 Database of State Incentives for Renewables and Ef ficiency, last modified March 2013, accessed at http://www.dsireusa.org/rpsdata/index.cfm.

91 Alaska House Bill 306, Signed by Governor Sean Par nell June 16, 2010, http://www.legis.state.ak.us/basis/get_bill_text.as p?hsid=HB0306Z&session=26

.4 - 3

programs, regulatory changes to facilitate the deve lopment of renewable sources and their interconnection to the grid and “lead by example” s trategies integrating RE generation into state properties. 92 Because the EPA did not quantify potential that co uld be tapped through any of those policy approaches, the agency believes that t he RE targets derived from RPS mandates represent a conservative estimate of cost-effective generation that could actually be developed by

states. While future RPS requirements will necessitate more RE generation and capacity beyond current levels, the EPA does not expect the anticip ated rate of growth required to meet those requirements to exceed the historical rate of RE de ployment. Full compliance with current RPS requirements through 2035 would necessitate the dep loyment of approximately 3 to 5 GW of new renewable capacity per year through 2020 and 2 to 3 GW through 2035. Average deployment of RPS-supported renewable capacity from 2007-2012 has exceeded 6 GW per

year. 93 In addition, recent improvements in RPS compliance rates indicate to the EPA the reasonableness of current RPS growth trajectories. Weighted average compliance rates among all states have improved in each of the past three repo rted years (2008 - 2011) from 92.1 percent to 95.2 percent despite a 40 percent increase in RPS o bligations during this period. 94 As the Lawrence Berkeley National Laboratory (LBNL) RPS St atus Update found, in the period 1998- 2012, 67% of all non-hydro U.S. RE capacity additio ns, totaling roughly 46,000 MW, was built in states with RPS requirements. 95 This scenario provides an estimate of an achievable level of total RE generation within states. It does not represent an EPA forecast of bu siness-as-usual impacts of state policies or an

92 See State Plan Considerations TSD for a discussion of how states can incorporate such RE policies int o their state plans for this rule. 93 Barbose, Galen, “Renewables Portfolio Standards in the United States: A Status Update,” Lawrence Berk eley National Lab, November 2013. Also, Heeter, J., Barb ose, G., Bird, L., Weaver, S., Flores-Espino, F., K uskovaBurns, K., and Wiser, R., “A Survey of State-Level Cost and Benefit Estimates of Renewable Portfolio S tandards,” NREL Report No. 6A20-61042, LBNL Report No. 6589E, http://www.nrel.gov/docs/fy14osti/61042.pdf. 94 Barbose, Galen, “RPS Compliance Summary Data,” Law rence Berkeley National Lab, May 2013, accessed March 2014 at http://emp.lbl.gov/rps. The RPS compl iance measure cited is inclusive of credit multipli ers and banked RECs utilized for compliance, but excludes a lternative compliance payments, borrowed RECs, defe rred obligations, and excess compliance. This estimate d oes not represent official compliance statistics, w hich vary in methodology by state. 95 Barbose, Galen, “Renewables Portfolio Standards in the United States: A Status Update,” Lawrence Berk eley National Lab, November 2013. Slide 8. Also, Heeter, J., Barbose, G., Bird, L., Weaver, S., Flores-Espi no, F., Kuskova-Burns, K., and Wiser, R., “A Survey of Stat e-Level Cost and Benefit Estimates of Renewable Por tfolio Standards.” NREL Report No. 6A20-61042, LBNL Report No. 6589E, http://www.nrel.gov/docs/fy14osti/61042.pdf.4 - 4

EPA estimate of the full potential of RE available to the power system; rather, it is intended to represent a feasible development scenario that enab les reductions of CO 2 emissions from fossil fuel-fired EGUs in all states and that is generally consistent with ongoing trends in RE development. The scenario uses a level of performan ce that has already been demonstrated or required by policies of leading states, while considering each state’s unique existing level of R E 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 g eneration represented by renewables as explained further below. The following steps were taken to establish the inp uts for development of the proposed approach for each state. The implementation of each step is illustrated in the table below its description, using the state of Illinois as an exam ple.

4.2.1 Determine current level of performance

4.2.2 Determine target level of performance

4.2.3 Determine start year for state efforts

4.2.4 Determine pace at which states improve from start year to target level of

performance

4.2.5 Calculate RE targets for interim and final state t argets

Note that an accompanying excel file that contains the aggregate state level data, calculations, and proposed state RE targets is also available in the Docket for this rulemaking. The title of this document is “Proposed RE Approach Data File.”

4.2.1 Determine current level of performance The type and extent of current RE capacity varies s ignificantly across states, and is influenced by the renewable resources available, th e economics of the power sector to date in different regions, and the state policies that affe ct renewable sources specifically and energy production generally. The extent of that generation has also changed rapidly in the past few years, and states with RE policies have significant ly 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 generation: 2012 net ge neration 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 hydr oelectric power facilities. Large existing hydroelectric facilities provide a large percentage of RE generation in a few states (hydropower is America’s largest existing source of RE, for whi ch generating capacity has remained relative constant over the last 20 years), and inclusion of this generation in current and project ed levels of performance would distort the proposed approach by presuming future development potential of large hydroelectric capacity in other states. Becau se RPS policies were implemented to stimulate the development of new RE generation, existing hydr oelectric facilities are often excluded from RPS accounting. No states are expected to develop a ny new large facilities. 98 The RE targetsetting method presented in the body of this chapte r includes only non-hydropower RE in the target-setting calculations and in the RE generatio n levels used to inform the state goals calculated in this proposal. In Appendix 4-1, we p rovide a different version of the RE generation targets that includes existing hydropower generatio n from 2012 for each state in the state RE targets. These targets that include existing hydrop ower generation as of 2012 reflect the potential incorporation of existing hydropower in the state R E targets that could inform the calculation of state goals if such generation were included in the quantification of BSER-related RE generation. The analysis informing regional RE targets does not explicitly account for the potential of building new hydroelectric facilities as a source under RPS policies; however, states may choose to encourage such development, and generation from such facilities would not be excluded from compliance with a state’s goal under this rule. The most recent 2012 performance data for all

states is shown in Table 4-1. Consistent with the d esign of a number RPS policies, RE “performance” is measured here as the share of tota l 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.cf m

.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 g eneration, but the District does have distributed R E 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 c omparable levels in all regions of the country. While varied regional characteristics (e.g ., the extent of renewable resources available, cost of competing sources of power, and level of pa st RE development) affect estimates of achievable potential, ongoing improvements in techn ologies and practices, and continually improving strategies for RE development are increas ing the extent of economically utilized renewable resources across all regions of the Unite d States. RE has been capturing a growing percentage of new capacity additions in the past fe w years. In 2012, RE accounted for more than 56% of all new electrical capacity installations in the U.S. – a major increase from 2004 when renewable installations captured only 2% of new cap acity additions. 100 The economics of the fastest growing RE technologies – on-shore wind and solar photovoltaics (PV) – are improving and are competitive in many regions. In 2012, cumul ative installed wind capacity increased by nearly 28% and cumulative installed solar PV capaci ty grew more than 83% from the previous

100 U.S. Department of Energy. 2012 Renewable Energy D ata Book. DOE/GO-102013-4291.October 2013, p. 3.4 - 8

year. 101 In the United States, installed wind electricity c apacity increased more than 23 fold

between 2000 and 2012. 102 Solar electricity generating capacity grew by a fa ctor of over 21 between 2000 and 2012 and currently accounts for 0. 3% of annual U.S. electricity generation. 103 In 2013, 4.8 GW of solar PV capacity was installed, bringing total solar U.S. solar capacity to

12.1 GW. 104 The National Renewable Energy Laboratory (NREL) ha s also found that the continental U.S. has solar potential that exceeds h igh solar generating countries like Germany, which is now generating over 6% of their electricit y 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 gene ration 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 Associ ation (SEIA), “SEIA Solar Market Insight Report 201 3: Year in Review”, 2014, http://www.seia.org/research -resources/solar-market-insight-report-2013-year-re view. 105 NREL. Photovoltaic Solar Resource: The United State s, Spain and Germany. 2009, http://www.nrel.gov/gis/images/us_germany_spain/pvm ap_usgermanyspain%20poster-01.jpg. Also IEA, PVPS Snapshot of Global PV 1992-2013. Report IEA-PVPS T1 -24:2014, March 31, 2014, http://www.ieapvps.org/fileadmin/dam/public/report/statistics/PVP S_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 20 08, http://www.nrel.gov/docs/fy09osti/42864.pdf.4 - 9

Figure 4.1. DOE Projected Installed Wind Capacity b y State under 20% National Generation Scenario

4.2.2.1. Quantification of Effective RE Levels from State-Level RPS Requirements The proposed approach is also based upon an analysi s of renewable portfolio standards, a policy that facilitates the quantification of RE ta rgets. By only examining the impact of one type of policy, the analysis is inherently conservative, as many other policy options are also available

to states in addition to RPS. In order to apply the various RPS policies to the d evelopment of a target level of performance, the EPA used publicly-available quanti tative information about mandatory state RPS requirements from the Database for State Incent ives for Renewables and Efficiency (DSIRE). 107 This information enabled the EPA to determine the effective RE levels in 2020 for states with mandatory RPS requirements. 108

107 Database of State Incentives for Renewables & Effi ciency (DSIRE) is a very comprehensive source of information on incentives and policies that support renewables and energy efficiency in the United Sta tes. DSIRE is currently operated by the N.C. Solar Center at N.C. State University, with support from the Interstate Renewable Energy Council, Inc. DSIRE is funded by the U.S. De partment of Energy. http://www.dsireusa.org/. 108 EPA did not include targets that were capacity-bas ed.4 - 10

DSIRE provides regularly-updated RPS Data Spreadshe ets that detail state RPS requirements by year, resources, and other key comp onent parts. 109 The RPS compliance schedules are broken down into the specific annual percentages requirements for the years 2000 to 2030. Many states have multiple compliance requi rements, including the main percentage requirements for eligible resources and additional resource-specific percentage requirements that states are increasingly using to promote the develo pment 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 or der to compare RPS policies on equal footing.” 110 The benefit of this approach is that state resourc e requirements become additive and facilitate a process of selection and exclusion. Th e EPA added together each state’s tiers, as standardized by DSIRE, to determine states’ effecti ve RE levels for 2020, but excluded tiers, other than main tiers, that include energy efficien cy or any fossil fuel. In addition, six states have established more than one set of RPS requirements for in-state utilities, including “secondary” and “tertiary” RPS requirements for smaller utilities, municipal utilities, or cooperative utilities. The EPA only i ncluded the primary RPS requirements to simplify the analysis of primary and secondary RPS requirements in determining states’ effective RE levels for 2020. By only considering primary req uirements, there is additional inherent conservatism in the RPS estimates, as additional st ate-level RPS obligations are not included in the calculated targets.

109 DSIRE, RPS Data Spreadsheet. April 2013, http://ww w.dsireusa.org/rpsdata/index.cfm. 110 DSIRE, DSIRE RPS Field Definitions, April 2011. http://www.dsireusa.org/rpsdata/RPSFieldDefinitions April2011.pdf, p. 1.4 - 11 Figure 4.2 RPS Data Structure 111

The RPS compliance schedules in six states implemen t 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 Req uirements

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 dif ferent renewable resources across regions of the United States, the EPA uses the stat e-level effective RE levels derived from RPS requirements to quantify regional RE targets consis tent with states’ reasonable level of increased RE development. This methodology helps us to quanti fy RE potential in states which do not have an RPS policy from which the renewable resource pot ential can be inferred. Specifically, the scenario estimates each region’s RE potential by as suming all states in each region can achieve by 2030 the average of the 2020 requirements of RPS states in that region. The regions assigned to states to quantify their RE generation target are based upon North American Electric Reliability Corporation (NERC) re gions and Regional Transmission Organizations (RTOs) and are the same as the region s used in the modeling of the “regional” compliance scenarios as outlined in the proposal RI A (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 reg ions, please refer to the Regulatory Impact Analysi s Chapter 3.4 - 13

GIS-based analysis. 113 The results show clear trends for the regions used to create the proposed approach, with portfolios of particular technologie s showing clear dominance in specific regions. North Central and South Central regions have strong on-shore wind resource potential. The East Central and Southeast regions show moderate to stro ng resources in both biopower and rooftop PV potential. The West has notable potential in geo thermal (hydrothermal) power and concentrating solar power, in addition to potential for increased hydropower generation. The Northeast has strong resources in off-shore wind an d moderate biopower and solar resources available. It should be noted that high technical p otential in a particular renewable resource is not necessarily needed to reach the generation levels q uantified under this approach. For example, Maine produced 28% of its electricity generation in 2012 from biopower and onshore wind, while it is estimated in this report to have relati vely moderate technical potential for biopower and relatively low levels of onshore wind capacity. Overall, results from the NREL GIS-based analysis show that the regional RE targets included in this proposed approach assume development of only 0.5% to 4.5% of the RE resource s in those regions. See Figure 4.3.1 for a graph showing the state RE targets in each region r epresented as a percentage of the renewable

resources available in the state.

113 NREL. U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. NREL/TP-A20-51946. July 2012. http://www.nrel.gov/docs/fy12osti/51946.pdf.4 - 14 Figure 4.3. Proposed Approach Regions4 - 15 Table 4.3. List of States Included in Proposed Appr oach Regions 114 Region States

East

Central Delaware, District of Columbia, Maryland, New Jerse y, Ohio, Pennsylvania,

Virginia, West Virginia

North

Central Illinois, Indiana, Iowa, Michigan, Minnesota, Misso uri, North Dakota, South

Dakota, Wisconsin

Northeast Connecticut, Maine, Massachusetts, New Ha mpshire, New York, Rhode Island,

Vermont

South

Central Arkansas, Kansas, Louisiana, Nebraska, Oklahoma, Te xas

Southeast Alabama, Florida, Georgia, Kentucky, Miss issippi, North Carolina, South Carolina,

Tennessee

West Arizona, California, Colorado, Idaho, Montana, Neva da, New Mexico, Oregon,

Utah, Washington, Wyoming

114 Alaska and Hawaii are not grouped with other state s 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 level s derived above, organized them by region, and determined an average percent generatio n target for that region. This average did not include any values for states in which there is no binding RPS requirement. For example, the RE target for the North Central region is calculated a s the average of 2020 RPS requirements in Illinois (16%), Michigan (10%), Minnesota (30%), Mi ssouri (10%), and Wisconsin (10%), which is equivalent to 15%. This average regional RE generation target offers a basis for the determination of state-level RE targets for informi ng state goals, as described below. Given their unique locations, Alaska and Hawaii are not grouped with other states into these regions. As a conservative approach to estimating cost-effective RE generation potential in Alaska and Hawaii, the EPA developed RE generation targets for each of those states based on the lowest values for the six regions evaluated here, equivalent to the r egional 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 wi ll begin increasing in 2017, the year following the initial state plan submission de adline 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 performance (as shown above using 2012 generation data). This approach assumes neither imp rovement nor decline in performance

between 2012 and 2017.

4.2.4 Determine pace at which states improve from s tart 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 approac h assumes an increasing trend over time of annual levels of RE generation that can carry the p erformance level of each region in the start year (in 2017, assumed to be equivalent to its 2012 observed performance level) to that region’s RE generation target by 2029. This 2017-2029 trend yields an annual growth factor that is unique to each region and based upon each region’s current renewable generation level and its RE target

level identified above.

115 See Preamble Section 8.E – Process for State Plan Submittal and Review for further discussion of timing requirements for state plan su bmittals.4 - 18

To derive the annual growth factor, the EPA determi ned the amount of additional renewable generation (in megawatt-hours) that would be required beyond each region’s historic (2012) generation to reach that region’s RE target. The EPA then determined the constant rate at which each region would need to increase its genera tion each year to reach the regional RPS target, if these rates are applied in the period 2017-2029. The constant rate of annual R E generation increase calculated from this approach i s called the growth factor. For example, the North Central region had 52,058,236 MWh of RE gener ation in 2012, while the North Central regional RE target of 15% applied to total 2012 gen eration across states in that region would yield an RE generation level of 110,786,042 MWh. T his approach assumes that the North Central region would begin to increase its RE gener ation, starting at its 2012 level, from the year 2017 onward and would achieve its RE target level b y 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 based o n 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 re quired to take each of these two states from their 2002 RE generation to their 2012 RE generatio n levels, assuming that the growth over that time had been constant in each year. This resulted in an 8% growth factor for Hawaii, and an 11% growth factor for Alaska.

Table 4.5. Regional Annual RE Growth Factors Region Growth Factor Alaska 11% East Central 17% Hawaii 9% North Central 6% Northeast 13% South Central 8% Southeast 13% West 6%

Then, for all states in a given region, that region ’s annual growth factor was applied to each state’s historic (2012) RE generation level to calculate a new level of RE generation for that4 - 19

state in the initial year (2017). This calculation is then repeated for each year in the 2017-2029 time period. If, as the growth factor is applied an nually, a state reaches an RE generation level that equals or exceeds the regional RE percent gene ration target, their RE generation target is made equal to the RE percent generation target as a pplied to that state’s 2012 generation and is kept at that level for the remainder of the time pe riod. If a state’s RE generation in 2012 has already exceeded the regional RE target, their annu al RE generation levels are held to the regional RE target for all years in the 2017-2029 t ime period. For all other states, the annual growth factor is applied through 2029. These annual RE generation estimates represent the realization of the proposed approach for each state . These RE generation levels are provided in absolute and percentage (share of total generation) terms in Table 4.6 and Table 4.7. This approach imposes the same regional RE target i n percentage (share of total generation) terms to all states in a given region; therefore, the absolute megawatt-hour target will be smaller for states starting with a lower absolut e amount of RE generation and larger for a state starting with a higher absolute amount of RE genera tion. This approach applies the calculated growth factors and regional RE targets to state-level generation, whereas the state-level RPS requirement s upon which they are based are not necessarily applied in practice to generation that is produced within the relevant state. However, the EPA notes that state-level RPS policies are oft en established with the aim of developing instate renewables generation. 116 This intention is evident in RPS policies that inc lude minimum requirements for specific types of renewable resour ces whose development is desired in that state. Regional analysis by NREL has also shown tha t many states in the west are satisfying RPS requirements with in-state generation. 117 Furthermore, the regional RE target is not applied directly as an immediate requirement of each state but is instead used to calculate a regional growth factor that is then applied to each state’s pre-existing RE generation, such that historic RE performance acts as a limiting factor on the ext ent to which a state is assumed to reach the regional target. Over the program period, several s tates do not reach the RE percentage target in the proposed approach, such as Kentucky in the Sout heast 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 Nation al Laboratory, p. 7.

117 Hurlbut, David, Joyce McLaren, and Rachel Gelman, Beyond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West, NREL/TP-6A 20- 57830, Golden, CO: NREL, August 2013.4 - 20

approach is designed to respect each state’s abilit y to improve toward the RE targets developed

above. An illustrative calculation for Illinois’s target RE generation level is provided below. Generation levels for all states, in gigawatt-hours and percentage terms, are provided in Tables 4.6 and 4.7. Under this approach, Illinois grows i ts own historic RE generation level by the 6% growth factor calculated for the North Central regi on, but it does not reach the North Central regional RE target generation level of 15% (which w ould be 29,860 GWh for Illinois) between

2017 and 2029.

State 2012 RE

(MWh) Assigned

Region Regional

RE Generation Targets (%) Annual

Regional Growth Factor (%) (source: EIA)

Illinois 8,373 North Central 15% 6%

Illinois RE Generation Targets

Year GWh % of 2012 generation

2017 8,873 4.50%

2018 9,404 4.80%

2019 9,967 5.00%

2020 10,563 5.30%

2021 11,195 5.70%

2022 11,864 6.00%

2023 12,574 6.40%

2024 13,326 6.70%

2025 14,123 7.10%

2026 14,968 7.60%

2027 15,863 8.00%

2028 16,812 8.50%

2029 17,818 9.00%

An illustrative calculation for Minnesota’s target RE generation level is provided here, as an example of a state which has already reached its RE target, with 9,454 GWh of RE generation in 2012, and thus its obligation under the target i s capped at its share of the 15% regional RE target, 7,889 GWh of RE generation. Calculation for 2017 Ge neration 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 fr om 2017 through 2029, with quantified RE targets in any year not to excee d the regional RE target level (e.g., 15% for states in the North Cen tral region).4 - 21 State 2012 RE

(MWh) Assigned

Region Regional

RE Generation Targets (%) Annual

Regional Growth Factor (%) (source: EIA)

Minnesota 9,453 North Central 15% 6%

Minnesota RE Generation Targets

Year GWh % of 2012 generation

2017 7,889 15%

2018 7,889 15%

2019 7,889 15%

2020 7,889 15%

2021 7,889 15%

2022 7,889 15%

2023 7,889 15%

2024 7,889 15%

2025 7,889 15%

2026 7,889 15%

2027 7,889 15%

2028 7,889 15%

2029 7,889 15%4 - 22 Table 4.6. State Target RE Generation Levels (Gigaw att-hours)

State Historic

RE RE Generation Targets (GWh)

2012 2017 2018 2019 2020 2021 2022 2023 2024 2025 2 026 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 2,8 91 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 2 026 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 4,3 84 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 1,8 73 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 2 026 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% 1 0.0% 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 2 026 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% 1 5.5% 16.5% 17.5% 18.7% 19.9%

4.2.5. Calculate RE targets for interim and final s tate goals The agency then translated the annual RE target per formance levels for each state into state-level interim and final RE targets for inform ing this rule’s quantification of state goals. Separate interim and final RE targets were calculat ed for the proposed state goals and the alternate state goals in this proposed rulemaking. For the proposed state goals, the interim RE target for each state was calculated as the average of that state’s RE target performance level from 2020-2029, and the final target is equivalent to that state’s RE target performance level in the year 2029. For the alternate state goals, the i nterim RE target for each state was calculated as the average of that state’s RE target performance l evel from 2020-2024, and the final target is equivalent to that state’s RE target performance le vel in the year 2024. A sample calculation for Illinois is provided below . State-level RE targets, expressed in absolute (megawatt-hour) terms and as a percentage level of each state’s RE generation as a share of its total generation, along with 2012 RE l evels 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% Op tion 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 Targets for RE Generation as a Percentage of Total Generation, with 2012 Historical RE Generatio n

State 2012 Proposed Targets Alternate Targets

Interim Level Final Level Interim Level Final Level

Alabama 2% 6% 9% 4% 5% Alaska 1% 2% 2% 1% 1% Arizona 2% 3% 4% 3% 3% Arkansas 3% 5% 7% 4% 5% California 15% 20% 21% 20% 21% Colorado 12% 19% 21% 17% 19% Connecticut 2% 5% 9% 4% 5% Delaware 2% 7% 12% 4% 5% Florida 2% 6% 10% 4% 6% Georgia 3% 8% 10% 6% 7% Hawaii 9% 10% 10% 10% 10% Idaho 16% 21% 21% 21% 21% Illinois 4% 7% 9% 6% 7% Indiana 3% 5% 7% 4% 5% Iowa 25% 15% 15% 15% 15% Kansas 12% 19% 20% 19% 20% Kentucky 0% 1% 2% 1% 1% Louisiana 2% 5% 7% 4% 4% Maine 28% 25% 25% 25% 25% Maryland 2% 10% 16% 6% 8% Massachusetts 5% 15% 24% 11% 13% Michigan 3% 6% 7% 5% 6% Minnesota 18% 15% 15% 15% 15% Mississippi 3% 8% 10% 6% 8% Missouri 1% 2% 3% 2% 2% Montana 5% 8% 10% 7% 7% Nebraska 4% 8% 11% 6% 7% Nevada 8% 14% 18% 12% 14% New Hampshire 7% 19% 25% 15% 19% New Jersey 2% 8% 16% 5% 7%4 - 28 State 2012 Proposed Targets Alternate Targets

Interim Level Final Level Interim Level Final Level

New Mexico 11% 18% 21% 16% 18% New York 4% 11% 18% 8% 10% North Carolina 2% 7% 10% 5% 6% North Dakota 15% 15% 15% 15% 15% Ohio 1% 6% 11% 4% 5% Oklahoma 11% 19% 20% 18% 20% Oregon 12% 19% 21% 17% 19% Pennsylvania 2% 9% 16% 5% 7% Rhode Island 1% 4% 6% 3% 3% South Carolina 2% 7% 10% 5% 6% South Dakota 24% 15% 15% 15% 15% Tennessee 1% 3% 6% 2% 3% Texas 8% 16% 20% 13% 15% Utah 3% 5% 7% 4% 5% Virginia 3% 12% 16% 9% 12% Washington 7% 12% 15% 10% 11% West Virginia 2% 8% 14% 5% 6% Wisconsin 5% 8% 11% 7% 8% Wyoming 9% 15% 19% 13% 14%4 - 29

Table 4.9. Proposed and Alternate State Targets for RE Generation in Megawatt-hours, with 2012 Historical RE Generation

State 2012 Proposed Targets Alternate Targets Interim Level Final Level Interim Level Final Level

Alabama 2,776,554 8,647,278 14,292,801 6,009,218 7,610,632 Alaska 39,958 105,136 163,089 77,373 94,950 Arizona 1,697,652 2,847,759 3,663,325 2,429,595 2,725,233 Arkansas 1,660,370 3,370,253 4,708,823 2,703,555 3,153,509 California 29,966,846 40,745,587 41,150,704 40,340,469 41,150,704 Colorado 6,192,082 9,821,423 10,839,820 8,861,798 9,940,119 Connecticut 666,525 1,934,220 3,114,375 1,376,991 1,721,274 Delaware 131,051 561,909 1,038,351 349,356 468,394 Florida 4,523,798 13,971,137 22,109,614 9,790,728 12,399,889 Georgia 3,278,536 9,392,695 12,230,636 7,095,644 8,986,583 Hawaii 924,815 1,046,927 1,046,927 1,046,927 1,046,927 Idaho 2,514,502 3,195,606 3,196,687 3,194,526 3,196,687 Illinois 8,372,660 13,910,775 17,818,004 11,904,488 13,326,217 Indiana 3,546,367 5,892,120 7,547,086 5,042,327 5,644,522 Iowa 14,183,424 8,565,921 8,565,921 8,565,921 8,565,921 Kansas 5,252,653 8,577,482 8,884,938 8,270,026 8,884,938 Kentucky 332,879 1,036,717 1,713,556 720,442 912,434 Louisiana 2,430,042 4,932,549 6,891,619 3,956,800 4,615,333 Maine 4,098,795 3,611,728 3,611,728 3,611,728 3,611,728 Maryland 898,152 3,728,926 5,982,069 2,394,301 3,210,129 Massachusetts 1,843,419 5,349,504 8,613,477 3,808,366 4,760,555 Michigan 3,785,439 6,289,326 8,055,859 5,382,246 6,025,037 Minnesota 9,453,871 7,888,544 7,888,544 7,888,544 7,888,544 Mississippi 1,509,190 4,272,197 5,458,430 3,266,297 4,136,743 Missouri 1,298,579 2,157,527 2,763,528 1,846,357 2,066,863 Montana 1,261,752 2,116,550 2,722,706 1,805,757 2,025,485 Nebraska 1,346,762 2,733,684 3,819,427 2,192,912 2,557,879 Nevada 2,968,630 4,979,784 6,405,939 4,248,556 4,765,528 New Hampshire 1,381,285 3,727,303 4,822,223 2,853,632 3,567,113 New Jersey 1,280,715 5,491,354 10,147,466 3,414,138 4,577,463 New Mexico 2,573,851 4,161,824 4,721,996 3,683,568 4,131,7914 - 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 targets under the proposed approach are comparable to rates that leader states have been able to appro ach in the past. Eleven states across four regions have already achieved over 10% of total generation from RE, surpassing the lowest regional target applied in the Southeast. Two states, Maine and Iowa, have already equaled or surpassed the highest regional target of 25% of generation, w ith South Dakota close behind at 24%. Finally, five states have already reached their reg ion’s required target.

4.3 Cost Effectiveness of RE The costs of building new RE capacity and generatin g more RE have changed significantly in the past decade, particularly for wind and solar. The economics of the fastest growing RE technologies – on-shore wind and solar P V – are improving. According to recent analyses of wind and solar project costs and pricin g trends by U.S. Department of Energy,4 - 31

levelized long-term power purchase agreement (PPA) prices have been declining. PPA prices, in general, reflect actual agreements to pay for power from wind or solar projects over a long-term and cover the cost of installing, operating, and ma intaining a wind or solar project, along with a profit margin. 118 For utility-scale solar PV, those levelized PPA pr ices have fallen by more than two-thirds in the past five years “driven primarily by lower installed PV project prices (which, in turn, have been driven primarily by declining modul e prices), as well as expectations for further cost reductions in future years.” 119 More recent PPAs in the West are reporting leveliz ed 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 bu ild projects at lower-quality wind resource

sites. 121 “The average levelized long-term price from wind P As signed in 2011/2012—many of which were for projects built in 2012—fell to aroun d $40/MWh nationwide.” 122 Examining RE resource availability regionally, seve ral recent studies have found costeffective or economic RE resources are available to serve future needs. The National Renewable Energy Laboratory (NREL) examined the future availa bility of RE in the West after Western state RPS requirements level off in 2025. 123 The study compares the cost of RE generation from the West's most productive RE resource areas—includ ing any needed transmission and integration costs—with the cost of energy from a ne w natural gas-fired generator built near the customers it serves. The report indicates that by 2 025 wind and solar PV generation could become cost-competitive, if new RE development occu rs 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 ut ility scale geothermal. In 2010, the Southeast

118 Mark Bolinger and Samantha Weaver, Utility-Scale S olar 2012: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United State s, Lawrence Berkeley National Laboratory, LBNL-6408 E. September 2013, p. 19. 119 Mark Bolinger and Samantha Weaver, Utility-Scale S olar 2012: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United State s, Lawrence Berkeley National Laboratory, LBNL-6408 E. September 2013, p. ii. 120 Ibid. 121 Ryan H. Wiser and Mark Bolinger, 2012 Wind Technol ogies Market Report, Lawrence Berkeley National Laboratory, August 2013, p. viii.

122 Ibid. 123 David Hurlbut, Joyce McLaren, and Rachel Gelman, B eyond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West. NREL/TP-6A 20- 57830, Golden, CO: NREL, August 2013.

124 Ibid. p. xvi.4 - 32

Energy Efficiency Alliance published a report by Ma rilyn Brown et al. titled Renewable Energy

in the South. 125 In addition to highlighting significant RE resourc es in different parts of the region, it stated, “Under realistic renewable expan sion and policy scenarios, the region could economically supply a large proportion of its futur e electricity needs from both utility-scale and

customer-owned RE sources.” 126 This study suggested that increased RE utilization should not necessarily lead to significant rate increases in p art because RE resources may moderate

forecasted rate increases in the next decade or two

. 127

Several studies have found the cost of RPS-driven R E deployment to be modest. One comparative analysis that "synthesize[d] and analyz e[d] the results and methodologies of 28 distinct state or utility-level RPS cost impact ana lyses" 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 more recent R PS cost data, including a report that determined 2010-2012 retail electricity price impacts due to state RPS policies to be less than two percent, with only two states experie ncing 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 replac e generation at those EGUs. Increasing the amount of nuclear capacity relative to the amount t hat would otherwise be available to operate is

125 Marilyn A. Brown, Etan Gumerman, Youngsun Baek, Jo y Wang, Cullen Morris, and Yu Wang, 2010, Renewable Energy in the South. Atlanta, GA: Southeast Energy Efficiency Alliance, December 2010. Also Marilyn A. Brown, Etan Gumerman, Xiaojing Sun, Kenneth Sercy, and Gyu ngwon 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 S tate Renewable Portfolio Standards: A Comparative A nalysis 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-compa rativeanalysis-s. 129 Galen Barbose, “Renewables Portfolio Standards in the United States: A Status Update,” Lawrence Berke ley National Lab, November 2013. Also, Heeter, J., Barb ose, G., Bird, L., Weaver, S., Flores-Espino, F., K uskovaBurns, K., and Wiser, R., “A Survey of State-Level Cost and Benefit Estimates of Renewable Portfolio S tandards.” NREL Report No. 6A20-61042, LBNL Report No. 6589E, http://www.nrel.gov/docs/fy14osti/61042.pdf.4 - 33

therefore a technically viable approach that states may consider in the development of state plans for reducing CO2 emissions from affected fossil fue l-fired EGUs. One way to increase the amount of available nuclea r capacity is to build new nuclear EGUs. However, nuclear generating capacity is relat ively expensive to build compared to other types of generating capacity, and little new nuclea r capacity has been constructed in the U.S. in recent years. Five new nuclear EGUs at three plants are currently under construction: Watts Bar 2 in Tennessee, Vogtle 3-4 in Georgia, and Summer 2 -3 in South Carolina. The EPA believes

that since the decisions to construct these units w ere made prior to this proposal, it is reasonable

to view the incremental cost associated with the CO 2 emission reductions available from completion of these units as zero for purposes of s etting states’ CO 2 reduction goals. Completion of these units therefore represents a highly cost-e ffective opportunity to reduce CO 2 emissions

from affected fossil fuel-fired EGUs. For this reas on, we are proposing that the emission

reductions achievable at affected sources due to th e generation provided at the identified new nuclear units should be factored into the state goa ls for the respective states where these new

units are located. Another way to increase the amount of available nu clear capacity is to preserve existing

nuclear EGUs that would otherwise be retired. While each retirement decision is based on the unique circumstances of that individual unit, the E PA recognizes that a host of factors – increasing fixed operation and maintenance costs, r elatively low wholesale electricity prices, and additional capital investment associated with ensur ing 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 mo st recent Annual Energy Outlook has projected an additional 5.7 GW of capacity reductio ns to the nuclear fleet. EIA describes the projected capacity reductions – which are not tied to the retirement of any specific unit – as necessary to recognize the “continued economic chal lenges” faced by the higher-cost nuclear

units. 130 Likewise, without making any judgment about the li kelihood that any individual EGU will retire, we view this 5.7 GW, which comprises a n approximately six percent share of nuclear capacity, as a reasonable proxy for the amount of n uclear capacity at risk of retirement.

130 Jeffrey Jones and Michael Leff, “Implications of a ccelerated power plant retirements,” EIA, April 201 4.4 - 34

We believe that, based on available information reg arding the cost and performance of the nuclear fleet, preserving the operation of at-r isk nuclear capacity is likely to be a relatively cost-effective approach to achieving CO2 reductions from affected EGUs. According to a recent report, nuclear units may be experiencing up to a $ 6/MWh shortfall in covering their operating costs with electricity sales. 131 Assuming that such a revenue shortfall is represen tative of the incentive to retire at-risk nuclear capacity, one c an estimate the value of offsetting the revenue loss at these at-risk nuclear units to be about $12 to $17 per metric ton. 132 The EPA views this cost as reasonable. We therefore propose that the e mission reductions achievable by retaining in operation approximately six percent of each state’s historical nuclear capacity should be factored into the state goals for the respective states. 133 The amount of at-risk nuclear generation quantifie d 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 assume s that replacement power for at-risk nuclear capaci ty is sourced either from new NGCC capacity at 800 lbs CO 2 /MWh or from the projected 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, Vog tle 3-4, and Summer 2-3, as well as all units that have retired or are committed to retire (as of May 2014).4 - 35 Iowa 601 35 278

Kansas 1,175 69 543 Louisiana 2,133 125 985 Maryland 1,705 100 788 Massachusetts 685 40 316 Michigan 3,957 231 1,828 Minnesota 1,819 106 840 Mississippi 1,368 80 632 Missouri 1,190 70 550

Nebraska 1,245 73 575

New Hampshire 1,246 73 576 New Jersey 3,499 204 1,616

New York 5,219 305 2,411

North Carolina 4,970 290 2,296

Ohio 2,150 126 993

Pennsylvania 9,700 567 4,480

South Carolina 6,486 379 2,996

Tennessee 3,401 199 1,571 Texas 4,960 290 2,291 Virginia 3,562 208 1,645 Washington 1,097 64 507

Wisconsin 1,184 69 547

Total 97,559 5,700 45,0624 - 364 - 37 Appendix 4-1. RE Generation Targets Including Exist ing Table 4-1.1. State RE Generation Targets including 2012 Existing Hydropower Generation (Gigawatt-hours)

State 2012

Existing Hydro 2012

Nonhydro RE (GWh) 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2 027 2028 2029

Alabama 7,435 2,777 10,585 11,008 11,488 12,032 12,650 13,350 14,145 15,046 16,068 17,228 18,543 20,035 21,728 Alaska 1,575 40 1,620 1,625 1,630 1,637 1,644 1,652 1,660 1,670 1,681 1,693 1,706 1,721 1,738 Arizona 6,717 1,698 8,518 8,628 8,744 8,868 8,999 9,138 9,286 9,442 9,608 9,784 9,971 10,170 10,380 Arkansas 2,198 1,660 3,997 4,148 4,310 4,487 4,678 4,885 5,109 5,352 5,615 5,901 6,210 6,544 6,907 California 26,837 29,967 58,631 60,568 62,624 64,805 67,120 67,988 67,988 67,988 67,988 67,988 67,988 67,988 67,988 Colorado 1,497 6,192 8,067 8,467 8,892 9,343 9,821 10,328 10,866 11,437 12,043 12,337 12,337 12,337 12,337

Connecticut 312 667 1,063 1,157 1,263 1,383 1,518 1,670 1,841 2,033 2,250 2,494 2,769 3,078 3,427

Delaware - 131 154 180 211 248 291 341 399 468 549 644 755 886 1,038

Florida 151 4,524 5,282 5,971 6,753 7,640 8,646 9,787 11,082 12,550 14,216 16,105 18,249 20,680 22,260 Georgia 2,236 3,279 5,955 6,455 7,021 7,664 8,393 9,220 10,159 11,223 12,430 13,799 14,467 14,467 14,467

Hawaii 115 925 1,119 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 1,162 Idaho 10,940 2,515 13,608 13,771 13,943 14,126 14,137 14,137 14,137 14,137 14,137 14,137 14,137 14,137 14,137

Illinois 111 8,373 8,985 9,515 10,078 10,674 11,306 11,976 12,685 13,437 14,235 15,079 15,975 16,924 17,929

Indiana 434 3,546 4,192 4,417 4,655 4,908 5,175 5,459 5,759 6,078 6,416 6,773 7,153 7,555 7,981

Iowa 766 14,183 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332 9,332

Kansas 10 5,253 5,702 6,177 6,692 7,249 7,854 8,508 8,895 8,895 8,895 8,895 8,895 8,895 8,895 Kentucky 2,362 333 2,739 2,790 2,848 2,913 2,987 3,071 3,166 3,274 3,397 3,536 3,694 3,872 4,075

Louisiana 680 2,430 3,313 3,533 3,771 4,029 4,308 4,611 4,940 5,295 5,681 6,098 6,550 7,041 7,572 Maine 3,733 4,099 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 7,344 Maryland 1,657 898 2,710 2,891 3,105 3,355 3,648 3,991 4,394 4,867 5,421 6,070 6,832 7,639 7,639

Massachusetts 912 1,843 2,988 3,249 3,544 3,875 4,248 4,668 5,141 5,673 6,272 6,947 7,707 8,563 9,526 Michigan 1,215 3,785 5,227 5,467 5,721 5,991 6,276 6,579 6,900 7,240 7,600 7,982 8,387 8,816 9,271

Minnesota 561 9,454 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 8,450 Mississippi - 1,509 1,712 1,942 2,203 2,499 2,834 3,215 3,647 4,137 4,692 5,323 5,458 5,458 5,458

Missouri 714 1,299 2,091 2,173 2,260 2,353 2,451 2,554 2,664 2,781 2,905 3,036 3,175 3,322 3,478

Montana 11,283 1,262 12,622 12,704 12,790 12,882 12,980 13,083 13,193 13,309 13,432 13,563 13,702 13,850 14,006 Nebraska 1,257 1,347 2,716 2,838 2,970 3,113 3,268 3,436 3,618 3,815 4,028 4,260 4,511 4,782 5,076 Nevada 2,440 2,969 5,590 5,782 5,986 6,202 6,431 6,674 6,932 7,206 7,496 7,805 8,131 8,478 8,846 New Hampshire 1,289 1,381 2,845 3,040 3,261 3,509 3,789 4,103 4,458 4,856 5,306 5,811 6,112 6,112 6,112 New Jersey 11 1,281 1,513 1,772 2,076 2,432 2,850 3,340 3,914 4,588 5,378 6,305 7,391 8,665 10,158

New Mexico 223 2,574 2,954 3,120 3,297 3,484 3,683 3,894 4,117 4,355 4,606 4,874 4,945 4,945 4,945

State 2012

Existing Hydro 2012

Nonhydro RE (GWh) 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2 027 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 16,725 16,725 16,725 16,725 Oregon 39,410 7,207 47,057 47,523 48,017 48,542 49,098 49,689 50,315 50,980 51,685 51,978 51,978 51,978 51,978 Pennsylvania 2,242 4,459 7,471 8,373 9,431 10,672 12,127 13,833 15,834 18,179 20,930 24,156 27,938 32,373 37,573

Rhode Island 4 102 119 133 150 168 189 212 238 267 301 338 380 427 480 South Carolina 1,420 2,143 3,852 4,178 4,549 4,969 5,446 5,986 6,600 7,296 8,085 8,980 9,996 11,096 11,096 South Dakota 5,981 2,915 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 7,800 Tennessee 8,296 836 9,244 9,372 9,517 9,681 9,867 10,078 10,317 10,588 10,896 11,246 11,642 12,092 12,601

Texas 584 34,017 37,441 40,518 43,852 47,464 51,378 55,619 60,213 65,192 70,586 76,430 82,762 86,547 86,547

Utah 748 1,100 1,915 1,986 2,061 2,141 2,226 2,316 2,412 2,513 2,621 2,735 2,856 2,985 3,121 Virginia 1,044 2,358 3,809 4,287 4,846 5,503 6,272 7,174 8,232 9,473 10,928 12,236 12,236 12,236 12,236 Washington 89,464 8,214 98,179 98,711 99,274 99,872 100,506 101,179 101,893 102,651 103,455 104,307 105,212 106,172 107,190 West Virginia 1,431 1,297 2,952 3,214 3,522 3,883 4,306 4,802 5,383 6,066 6,865 7,803 8,903 10,192 11,704 Wisconsin 1,522 3,223 4,938 5,143 5,359 5,589 5,832 6,090 6,363 6,652 6,959 7,284 7,629 7,994 8,382 Wyoming 893 4,369 5,529 5,811 6,111 6,429 6,767 7,124 7,504 7,907 8,335 8,788 9,269 9,780 10,321

Table 4-1.2. State RE Generation Targets (% of Tota l 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% 4 7.9% 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% 1 5.9% 16.8% 17.7% 18.7% 19.7% 20.8%4 - 40

Table 4-1.3. Proposed and Alternate State Targets f or RE Generation as a Percentage of Total Generation, with 2012 Historical RE Generatio n

State 2012 Proposed Targets Alternate Targets Interim Level Final

Level Interim

Level Final

Level Alabama 7% 11% 14% 9% 10% Alaska 23% 24% 25% 24% 24% Arizona 9% 10% 11% 10% 10% Arkansas 6% 9% 11% 8% 8% California 28% 34% 34% 34% 34% Colorado 15% 22% 23% 20% 22% Connecticut 3% 6% 9% 5% 6% Delaware 2% 7% 12% 4% 5% Florida 2% 6% 10% 4% 6% Georgia 5% 10% 12% 8% 9% Hawaii 10% 11% 11% 11% 11% Idaho 87% 91% 91% 91% 91% Illinois 4% 7% 9% 6% 7% Indiana 3% 6% 7% 5% 5% Iowa 26% 16% 16% 16% 16% Kansas 12% 19% 20% 19% 20% Kentucky 3% 4% 5% 3% 4% Louisiana 3% 5% 7% 4% 5% Maine 54% 51% 51% 51% 51% Maryland 7% 14% 20% 11% 13% Massachusetts 8% 17% 26% 13% 16% Michigan 5% 7% 9% 6% 7% Minnesota 19% 16% 16% 16% 16% Mississippi 3% 8% 10% 6% 8% Missouri 2% 3% 4% 3% 3% Montana 45% 48% 50% 47% 48% Nebraska 8% 12% 15% 10% 11% Nevada 15% 21% 25% 19% 20% New Hampshire 14% 26% 32% 22% 25% New Jersey 2% 8% 16% 5% 7% New Mexico 12% 19% 22% 17% 19% New York 22% 29% 36% 26% 28% North Carolina 6% 10% 13% 8% 10% North Dakota 21% 22% 22% 22% 22%4 - 41

Ohio 2% 6% 11% 4% 5%

State 2012 Proposed Targets Alternate Targets Interim Level Final

Level Interim

Level Final

Level Oklahoma 12% 20% 21% 19% 21% Oregon 77% 83% 85% 82% 84% Pennsylvania 3% 10% 17% 6% 8% Rhode Island 1% 4% 6% 3% 3% South Carolina 4% 8% 11% 6% 8% South Dakota 74% 65% 65% 65% 65% Tennessee 12% 14% 16% 13% 14% Texas 8% 16% 20% 13% 15% Utah 5% 7% 9% 6% 7% Virginia 5% 14% 17% 10% 13% Washington 84% 88% 92% 87% 88% West Virginia 4% 10% 16% 7% 8% Wisconsin 7% 11% 13% 10% 10% Wyoming 11% 17% 21% 14% 16%4 - 42

Table 4-1.4. Proposed and Alternate State Targets f or RE Generation in Megawatt-hours,

with 2012 Historical RE Generation

State 2012 Proposed Targets Alternate Targets Interim Level Final

Level Interim

Level Final

Level Alabama 10,211,777 16,082,501 21,728,024 13,444,441 15,045,855 Alaska 1,615,003 1,680,181 1,738,134 1,652,418 1,669,995 Arizona 8,414,586 9,564,693 10,380,259 9,146,529 9,442,167 Arkansas 3,858,842 5,568,725 6,907,295 4,902,027 5,351,981 California 56,804,216 67,582,957 67,988,075 67,177,840 67,988,075 Colorado 7,689,291 11,318,632 12,337,029 10,359,007 11,437,328 Connecticut 978,666 2,246,361 3,426,516 1,689,132 2,033,415 Delaware 131,051 561,909 1,038,351 349,356 468,394 Florida 4,674,309 14,121,648 22,260,125 9,941,239 12,550,400 Georgia 5,514,836 11,628,995 14,466,936 9,331,944 11,222,883 Hawaii 1,039,396 1,161,508 1,161,508 1,161,508 1,161,508 Idaho 13,454,907 14,136,011 14,137,092 14,134,931 14,137,092 Illinois 8,483,868 14,021,983 17,929,212 12,015,696 13,437,425 Indiana 3,979,872 6,325,625 7,980,591 5,475,832 6,078,027 Iowa 14,949,615 9,332,112 9,332,112 9,332,112 9,332,112 Kansas 5,263,052 8,587,881 8,895,337 8,280,425 8,895,337 Kentucky 2,694,661 3,398,499 4,075,338 3,082,224 3,274,216 Louisiana 3,109,986 5,612,493 7,571,563 4,636,744 5,295,277 Maine 7,831,400 7,344,333 7,344,333 7,344,333 7,344,333 Maryland 2,554,691 5,385,465 7,638,608 4,050,840 4,866,668 Massachusetts 2,755,901 6,261,986 9,525,959 4,720,848 5,673,037 Michigan 5,000,293 7,504,180 9,270,713 6,597,100 7,239,891 Minnesota 10,014,892 8,449,566 8,449,566 8,449,566 8,449,566 Mississippi 1,509,190 4,272,197 5,458,430 3,266,297 4,136,743 Missouri 2,012,848 2,871,796 3,477,797 2,560,626 2,781,132 Montana 12,545,217 13,400,015 14,006,171 13,089,222 13,308,950 Nebraska 2,603,816 3,990,738 5,076,481 3,449,966 3,814,933 Nevada 5,409,045 7,420,199 8,846,354 6,688,971 7,205,943 New Hampshire 2,670,671 5,016,689 6,111,609 4,143,018 4,856,499 New Jersey 1,291,470 5,502,109 10,158,221 3,424,893 4,588,218 New Mexico 2,796,670 4,384,643 4,944,815 3,906,387 4,354,610 New York 29,844,923 39,720,644 48,914,401 35,379,664 38,061,729 North Carolina 6,431,857 11,863,688 15,396,114 9,579,954 11,139,476 North Dakota 7,757,282 7,937,187 7,937,187 7,937,187 7,937,187 Ohio 2,152,783 7,868,896 14,189,755 5,048,991 6,628,2514 - 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 demand-side en ergy efficiency (EE) as an abatement measure for reducing carbon dioxide (CO 2 ) emissions from fossil fuel-fired electric generating units (EGUs). Specifically, this chapter addresses EE as a component of both the “best system of emission reduction” (BSER) and stat e goals, and the inclusion of EE within the impacts assessment. Support is provided in this cha pter for the discussion of the EE abatement measure throughout the preamble (most extensively i n these sections: Building Blocks for Setting State Goals and Considerations, State Goals , State Plans, and Impacts of the Proposed Rule) and its representation within the Regulatory Impact Analysis (RIA). Results from this chapter feed into the technical support document (T SD) on Goal Computation. EE is also addressed in TSDs on state plan considerations and projecting emissions performance.

This chapter is organized as follows: 1) Background

– EE Technologies and Practices

– Barriers to EE Investment

– EE Policies

– EE Programs 2) The EE Opportunity

– Rapid Growth in EE

– EE Program Impacts

– EE Potential

– Costs and Cost-Effectiveness of State EE Policies

– EE as an Abatement Measure 3) State Goal Setting

– Approach

– Inputs

– Calculations

– Results 4) Impacts Assessment5 - 2 – Approach

– Inputs

– Calculations

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

Background As discussed in the State Plan Considerations TSD ( Appendix: “Survey of Existing State Policies and Programs that Reduce Power Sector CO 2 Emissions”), demand-side energy efficiency policies and programmatic efforts have e xisted for decades and are now used in all 50 states. These strategies are intended to help state s achieve energy savings goals, reduce the environmental impacts (including CO 2 emissions) of meeting energy service needs, save e nergy and money for consumers, and provide a significant resource for meeting power system capacity requirements. EE policies currently in place are co nsidered by states to be cost-effective strategies for contributing to these policy objecti ves. 134 Moreover, states – through their utilities, primarily – have been rapidly increasing their fund ing of EE programs in recent years, more than tripling budgets in the five years from 2006 to 201 1, from $1.6 billion to $5.9 billion. 135 In 2012, the cumulative impacts of these programs represente d a 3.7% reduction in national electricity

demand. 136 And, EE spending is projected to continue to grow at a substantial rate. A recent study by Lawrence Berkeley National Laboratory (LBN L) projects EE program spending to reach $8.1 billion to $12.2 billion (“Medium Case” and “High Case,” respectively) in 2025 even

134 See below for discussion of cost-effectiveness and related cost tests used by states to evaluate EE p rograms. 135 American Council for an Energy-Efficient Economy ( ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

. 136 U.S. Energy Information Administration Form EIA-86 1 data files. 2012. Available at http://www.eia.gov/electricity/data/eia861/

.5 - 3

“without considering possible major new policy deve lopments,” such as requirements under Clean Air Act, Section 111(d). 137,138 This section provides relevant background for the s ubsequent sections that address the EE opportunity, EE as a component of BSER, EE within s tate goal setting, and the integration of EE within the benefit, cost, and impacts assessments a s reported in the RIA and elsewhere. This section begins with a discussion of EE technologies and practices, and then describes the market

failures that limit cost-effective EE investments. We then summarize EE policy objectives and discuss policy types, their relative impacts, and d iscuss in more detail the key strategy of employing EE programs.

EE Technologies and Practices Energy efficiency is using less energy to provide t he same or greater level of service. Demand-side energy efficiency refers to an extensiv e array of technologies, practices and measures that are applied throughout all sectors of the economy to reduce energy demand while providing the same, and sometimes better, level and quality of service. Utilities employ a large array of strategies in implementing energy efficien cy programs, these include financial incentives such as rebates and loans, technical ser vices such as audits and retrofits, and educational campaigns about the benefits of energy efficiency improvements. The purpose of these EE programs is to induce EE investments and p ractices that would not otherwise occur in the presence of market failures and behavioral impe diments. In the residential sector, examples of EE activities include the purchase of more effic ient products and equipment (e.g., ENERGY STAR labeled), the upgrading of insulation in attic s and walls, sealing of air leaks, and undertaking home energy audits leading to customize d whole home retrofits. Opportunities for cost-effective EE in commercial buildings include o ptimization of heating, ventilation, and air conditioning (HVAC) systems, upgrades of windows, a nd use of more efficient office equipment

137 Specifically, the LBNL study states: “By virtue of limiting the analysis to current energy efficiency policies, we do not consider the potential impact of major new f ederal (or state) policy initiatives (e.g., a natio nal energy efficiency resource standard, clean energy standard , or carbon policy) that could result in customer-f unded energy efficiency program spending and savings that exceed the values in our High Case.” 138 Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer- Funded Energy Efficiency Programs in the United States: Pr ojected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-cust omer-funded-energy-efficiency-programsunited-states-projected-spend

.5 - 4

at replacement. In the industrial sector key EE str ategies include motor upgrades and maintenance programs, recovery of waste heat stream s, and optimization of processes through modern instrumentation and controls systems. The opportunity presented for economic investment i n EE is dynamic, growing over time as technologies and practices advance, as populatio ns grow, and as investment occurs in the construction of new homes, buildings, and industria l facilities. As new policies are enacted, leading to the acceleration of investment in EE, an additional portion of the expanding opportunity is realized. After decades of experienc e implementing policies to accelerate investment in cost-effective energy efficiency, sta tes are finding renewed opportunities as they develop more sophisticated and effective strategies , evolving from a focus on individual enduses and products to whole-building and systems-bas ed strategies that account for the interactions between the many energy end-uses in bu ildings and industry. 139 As will be discussed, the experience in the U.S. has been that on balance, a persistent and large potential for

achievable and cost-effective EE has remained even as the impact of past and ongoing efforts

have accumulated.

Barriers to EE Investment Despite the persistent and large potential for elec tricity savings through investment in EE technologies and practices, market failures, as wel l as non-market failures, limit the realization of the many benefits of these investments. Several mar ket failures that lead to inefficiencies in energy use are well recognized by analysts and prac titioners, 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 exte rnalities,

such as emissions of CO 2 , SO 2 , and NO x that cause human health and environmental damages. Energy prices that do not correctly reflec t these externalities lead to investments in energy efficiency below the socially optimal levels.

139 Seth Nowak, Martin Kushler, Patti Witte, and Dan Y ork. Leaders of the Pack: ACEEE’s Third National Re view of Exemplary Energy Efficiency Programs. American C ouncil for an Energy-Efficient Economy (ACEEE). Research Report U132. Available at http://www.aceee.org/research-report/u132

. 140 See reviews of market failures and barriers relate d to energy efficiency in Gillingham, K, R Newell, and K Palmer. 2009. Energy Efficiency Economics and Polic y. Annual Review of Resource Economics. Annual Revi ew of Resource Economics 1: 597-619 and Gillingham and Pa lmer (2013). “Bridging the Energy Efficiency Gap: i nsights for policy from economic theory and empirical analy sis,” Resources for the Future DP 13-02.5 - 5

• Imperfect information. Energy users often lack accurate information about energy savings and other attributes of energy efficient products o r practices to understand the costs and

benefits of EE investments. Market failure due to i nformation imperfection leads to underinvestment in energy efficiency by consumers.

• Split incentives (or the “principal-agent problem”)

. Incentives of individuals who make EE investment decisions are not always aligned with incentives of those who use and pay for energy. Examples include misalignment between l andlords and tenants, and between builders and homeowners. Split incentives also pers ist within organizations and

institutions that lead to underinvestment in EE in both public and the private entities. 141

• Credit constraints . Limited access to credit may prevent some consume rs, especially lowincome consumers, from making cost-effective EE imp rovement decisions due to the higher upfront cost of energy efficient products or practices.

• Under-provision of research and development (R&D). Because of the public good nature of knowledge, technology innovation invested by one firm likely spills over to other firms. As a result, firms involved in technology de velopment may be less willing to invest in R&D, leading to sub-optimal levels of EE investments from a social perspective. 142

• Supply market imperfections. Market for energy efficient products is incomplete. Manufacturers do not have perfect information about consumer preferences and may supply limited menu of products to consumers. High start-up costs and the existence of patents may create barriers to entry in markets and result in oligopolistic or monopolistic behavior. Supply chains of EE products is fragmente d, leading to underinvestment in innovation and energy efficiency by suppliers. In a ddition, supply chain fragmentation may also add complexity to the purchase and install ation of otherwise economically rational investments, thereby slowing the adoption of EE technologies.

141 For example, see DeCanio, S. 1998. The efficiency paradox: bureaucratic and organizational barriers t o profitable energy-saving investments. Energy Policy 26(5): 441 -458; McKinsey & Co and The Conference Board. 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? pp. (52-53).

142 See discussion in Jaffe, A.B., R.G. Newell, and R. N. Stavins. 2003. Technological Change and the Envi ronment. Chapter 11 in the Handbook of Environmental Economi cs. Volume 1, Edited by K.-G. Maler and J.R. Vincen t. Elsevier Science B.V. 461-516.5 - 6

• Behavioral impediments . Behavioral economics and psychology have identifi ed potential behavioral phenomena that lead to consumers to devi ate from the standard theory of welfare maximizing in consumption and other decisio ns, including energy efficiency investments. Behavioral economics posits possible e xplanations, including bounded rationality, heuristic decision-making, and non-sta ndard preference and belief. 143 In the presence of market failures, users of electr icity, or those making energy efficiency investments, face prices or incentives that prevent them from weighing the social benefits and

costs of their investments and thus under-invest in approaches to reduce electricity consumption. The behavioral impediments discussed above explain why individuals do not always make energy efficiency investments that are seemingly in their own best interest to reduce their total expenditure, given prevailing electricity prices. In addition to market failures and behavioral imped iments, other factors, such as hidden costs, risk and uncertainty experienced by both con sumers and suppliers of energy efficient products, and heterogeneity among consumers, produc ers 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 c redible information on product performance and future energy prices, and the irrev ersibility of the investment. Imperfect or asymmetric information can exacerbate the percei ved risk of energy efficiency investments and help explain why consumers and firm s do not always invest in EE measures. Suppliers also face risk and uncertainty, without perfect information of consumer preferences for energy efficiency. In the presence of risk and uncertainties, consumers and suppliers alike will underinvest in E E.

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 E nvironmental Economics & Policy, 8(1): 18-38. 144 It has been recognized that there is a difference b etween cost-effective energy efficiency investment levels, based on cost-minimizing consideration, and observed leve ls of energy efficiency. This phenomenon, also term ed ‘energy paradox,’ or ‘energy efficiency gap,’ has been stud ied extensively in the literature. See, for example , Jaffe, AB, and RN Stavins. 1994. “The Energy Paradox and the Diffu sion of Conservation Technology.” Resource and Energy Economics 16(2): 91–122; Sanstad, A. H. and R. B. Howarth. 19 94. ‘Normal’ markets, market imperfections and energy efficiency, Energy Policy , 22: 811-818; DeCanio 1998; DeCanio, SJ and WE Wat kins. 2008. Investment in Energy Efficiency: Do the Characteristics of Firms Matter? The Review of Economics and Statistics , 80: 95-107; Allcott, H, and M. Greenstone. 2012. Is There an En ergy Efficiency Gap? Journal of Economic Perspectives 26 (1):3-28.5 - 7

• Transaction costs . Consumers face transaction costs in searching, as sessing and acquiring energy efficient technologies and services. It can be time-consuming and difficult for consumers to estimate lifetime operating costs of a product. The complexity of the search process puts many efficient products at a disadvant age relative to less-efficient products with lower upfront costs.

• Capital market barriers . Consumers sometimes face higher interest rates to finance EE investments compared to other investments. Lenders can be reluctant to invest in EE loan portfolios in part because energy efficiency loans may lack standardization and financial markets have difficulty ascertaining the likely pay off from such investments. EE policies and programs can play an important role in correcting market failures and addressing the barriers to the investment and adopt ion of socially beneficial energy efficiency opportunities. Examples of effective EE policies an d programs include public funding of R&D, information programs (such as energy labeling, the voluntary ENERGY STAR Program, and consumer education), rebates for high-efficiency pr oducts, product energy performance standards, financing and loan programs, and technic al 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 num ber of closely related policy goals 146

, including:

- Reducing costs to electricity customers,

- Providing a significant resource for meeting power system capacity needs,

- Meeting energy savings goals,

- Stimulating local economic development and new jobs , and

- Reducing the environmental impacts of meeting elect ricity service needs. EE policies currently in place are considered by st ates to be cost-effective strategies for contributing to each of these policy objectives. 147 While each of these objectives, and others,

145 Existing state EE policies are described extensive ly in the State Plan Considerations TSD. 146 U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee _report.pdf

. 147 U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee _report.pdf

.5 - 8

contribute to the motivation of state policymakers to pursue EE policies, reducing energy costs over the long term is the leading objective in purs uing 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 a re 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 red uctions. Economy-wide studies of climate mitigation scenario s confirm that energy efficiency plays a critical role in reducing the costs and enh ancing the flexibility of meeting long-term climate stabilization targets. 149 Analysis by the International Energy Agency (IEA) suggested

that in order to stabilize carbon concentration in the atmosphere at 450 ppm, as much as 44% of the estimated global abatement potential in 2035 de rives from greater energy efficiency in the world economy. 150 Several recent Energy Modeling Forum (EMF) studies have investigated the role of technology in achieving climate policy obje ctives in the U.S. (“EMF 24” and “EMF 25” studies) and globally (“EMF 27” study). 151 These studies concluded that compared to business

as-usual energy efficiency, improvements in energy efficiency in various economic sectors

would slow the increases of GHG emissions in the sh ort run, substantially reduce the costs of GHG mitigation (on average, by about 50% 152 ), and ease the technology transformation pathways to achieve long-term carbon reduction goal s. 153 Several economic studies (including EMF25 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, Massachuset ts, Minnesota, New Jersey, Oregon, and Washington. 149 Kriegler, E., J. P. Weyant, G. J. Blanford et al. 2 014. The role of technology for achieving climate p olicy 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: Re sults of the EMF 24 Modeling Exercise. [forthcoming] 150 International Energy Agency (IEA). 2012. World Ene rgy Outlook 2012. Paris. 151 Energy Modeling Forum (EMF) is a consortium of ene rgy economists and energy economic modeling teams t hat was established in 1976. Through ad hoc working gro ups, the EMF has focused on a series of energy and environmental topics that are of interest to policy decisions. In recent years, the EMF is recognized for its contribution to the advancement of economics of cli mate change and the reports of the Intergovernmenta l Panel on Climate Change (IPCC).

152 It should be noted that these energy-economy modeli ng studies do not typically include the costs of implementing energy efficiency 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. Climate Pol icy Goals. The Energy Journal, 32: 61-87.5 - 9

policy instruments (such as carbon taxes). These st udies found that when energy efficiency policies address market failures, they are welfare improving and can complement climate policy. 154 In addition, EE policies are recognized to be an a ppropriate response to demonstrated market failures and behavioral impediments, particu larly in contexts where these failures have broader societal implications such as environmental externalities. 155 In addition to providing cost-effective opportuniti es for reducing GHG emissions, energy efficiency is recognized to provide other co-benefi ts, including air quality and public health benefits, waste reduction from energy generation, e nergy security, energy system reliability, community economic and social development, and cons umer amenities. 156 Energy efficiency investments and policies are also found to spur pro ductivity growth, technology learning and

innovation. 157, 158 Recently, more attention has been paid to developi ng methods for recognizing these co-benefits and integrating them into the cos t-benefit analysis framework used by state utility commissions and administrators of EE progra ms. These co-benefits have not been fully accounted for in the EPA analysis.

Policy Types EE policies come in many forms. The most prominent and impactful EE policies in most states include those that drive development and fun ding of EE programs 159 , and building energy codes. Other policies that are leading to significa nt impacts include state appliance and equipment standards, building energy disclosure req uirements, innovative financing strategies

154 See, for example, Comstock, O, and E Boedecker. 2011. Energy and Emis sions 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 manage ment." in Energy Economics , 27: 165-180; Fischer, C. 2010. Imperfect Competition, Consumer Behavior, and the P rovision 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 Rev iew 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 Screeni ng: How to Properly Account for ‘Other Program Impacts’ and Environmental Compliance Costs,” Report prepared b y RAP and Synapse Energy Economics. 157 Boyd, GA and JX Pang (2000). “Estimating the linka ge between energy efficiency and productivity,” Energy Policy , 28: 289-296; Worrell, E. (2011). “Productivity be nefits of industrial energy efficiency measures.” L awrence Berkeley National Laboratory Paper LBNL-52727. 158 Van Buskirk, R, C. Kantner, B. Gerke et al. The be nefits of energy efficiency standards and how polic ies may accelerate declines in appliance costs. Proceeding of National Academy of Sciences. [forthcoming] 159 EE programs are described in more detail in the fo llowing section of this chapter and in the State Pl an Considerations TSD.5 - 10

(e.g., Property Assessed Clean Energy or “PACE”), s tate tax policies, and “lead by example” strategies targeting energy use in state operations . Comparing the relative impact (achieved or potential) of the different policy types is challen ging, 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 reporting of impacts, co sts, and other characteristics from all 50

states. 160 This information is generally based upon measureme nt and verification studies submitted annually, most commonly to state utility commissions, and reported to the Energy Information Administration (EIA) for all program ad ministrator types (all utility types, thirdparties, and government agencies). EE program data reported to EIA includes incremental and cumulative energy and peak demand savings, program costs broken down by component, and composition by end-use sector (residential, commerc ial, industrial). In 2012, utilities and other program administrators in 48 states reported saving s from EE programs to EIA through form EIA-861. At a national level, the EPA is not aware of a comprehensive dataset reported by states of the achieved impacts of strategies other than th ose that lead to investment in EE programs. However, state and regional-level information does exist. For example, the Northwest Power and Conservation Council (NPCC) has been compiling the impacts of EE policies (including utility and third-party EE programs, state building energy codes, and federal appliance standards) across their member states (ID, MT, OR, WA) for mor e than three decades. For the past decade, EE programs have accounted for more than 75% of the cumulative energy savings from state EE policies for NPCC, with building energy codes accou nting for the remaining savings. 161 Another representation of the relative opportunity provided by different state EE strategies is presented by evaluations of EE achiev able potential or projections of the impacts of EE policies. The results from two recent evaluation s at a national level are presented in Table 5-

160 In 2011, EIA began collecting data from third-part y administrators of programs. Prior to 2011, this w as a significant shortcoming in the breadth of the data collected. T he breadth and quality of information collected through Form EIA-861 has improved over time, howeve r, outside entities (e.g., ACEEE) have found that t he data can be improved through expert review and supplemen tation 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 Pl an, Northwest Power and Conservation Council. Febru ary 2010. Council Document 2010-09. http://www.nwcouncil.org/media/6284/SixthPowerPlan. pdf5 - 11

1. EE programs account for 77% and 82% of achievabl e 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 through EE programs, and that state and local building energy codes can make a significant additi onal contribution. Massachusetts provides a state example of the impacts of EE programs relativ e to other state EE policies. The Massachusetts Global Warming Solutions Act of 2008 established statewide limits on greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. To achieve this target, Massachusetts is relying upon an integrated portfolio of clean energy policies. State EE policies are expected to provide the largest contri bution to meeting the 25 percent target with utility sponsored EE programs and state building en ergy codes accounting for 76% and 17%, respectively, of those policies. 164 In their 2013 progress report, Massachusetts indic ates that they are generally on track for meeting or exceeding the se 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 grea ter detail (including designs, authority, obligated parties, measurement and verification (M& V), penalties for non-compliance, and implementation status) in the State Plan Considerat ions TSD. Because EE programs have provided the majority of state EE-policy electricit y savings to-date and offer the majority of potential savings going forward, we next summarize key characteristics of this strategy.

162 Hayes, S., et. al. American Council for an Energy- Efficient Economy (ACEEE). April 2014. Change is in the Air: How States Can Harness Energy Efficiency to St rengthen the Economy and Reduce Pollution. Report N umber E1401. Available at http://www.aceee.org/research-report/E1401

. 163 Yu Wang and Marilyn A. Brown. February 2013. Polic y Drivers for Improving Electricity End-Use Efficie ncy in the U.S.: An Economic-Engineering Analysis. Energy Efficiency. 164 Ian A. Bowles. December 29, 2010. Massachusetts Cl ean Energy and Climate Plan for 2020. Available at http://www.mass.gov/eea/waste-mgnt-recycling/air-qu ality/green-house-gas-and-climate-change/climate-ch angeadaptation/mass-clean-energy-and-climate-plan.html

. 165 Commonwealth of Massachusetts. Global Warming Solu tions Act: 5-Year Progress Report. December 2013. Available at http://www.mass.gov/eea/docs/eea/gwsa/ma-gwsa-5yr-p rogress-report-1-6-14.pdf

.5 - 12

EE Programs EE programs (actually portfolios of programs) are c omprised of numerous measures and measure types that are applied across all sectors o f electricity end-users. Figure 5-1 166 illustrates the multi-level composition and breadth of EE progr am portfolios. The diversity represented by a typical portfolio of EE programs implemented by a u tility (or other program administrator) is an important characteristic relevant to analysis of EE policies. Every detailed program type (as illustrated in the lower half of the figure) repres ents a unique set of characteristics including costs of energy saved, ratio of program to particip ant costs, investment life, scale, M&V approach, etc. 167

Administrators EE programs are administered by a variety of entiti es (“program administrators”) including utilities of all ownership types (investo r-owned, municipals, and cooperatives), nonprofit and for-profit third-parties (e.g., Vermont Energy Investment Corporation), and state and local government agencies (e.g., NYSERDA). Most EE programs (including all investor-owned utilities which account for more than 75% of report ed savings 168 ) are overseen by state utility commissions, which review and approve program plans , projected impacts, and associated budgets; and establish annual reporting and M&V req uirements.

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

166 Ian M. Hoffman, Megan A. Billingsley, Steven R. Sc hiller, Charles A. Goldman and Elizabeth Stuart. La wrence Berkeley National Laboratory. August 28, 2013. Ener gy Efficiency Program Typology and Data Metrics: En abling Multi-State Analyses Through the Use of Common Term inology. LBNL-6370E. Available at http://eetd.lbl.gov/news/article/56865/new-policy-b rief-energy-efficie. 167 See following sections for discussion of these fac tors. 168 U.S. Energy Information Administration Form EIA-86 1 data files (2012). Available at http://www.eia.gov/electricity/data/eia861/

. 169 These policies are discussed in depth in State Pla n Considerations TSD. 170 American Council for an Energy-Efficient Economy ( ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

.5 - 13

plan or multi-year energy efficiency budget (28 sta tes), and statutory requirement to acquire “allcost-effective EE” (6 states). 171,172 EERS is a more recently developed strategy and has quickly become the leading driver of the rapid growth in EE programs due to their clear goals and proven success as a policy tool. 173 These policy drivers lead to the evaluation, plann ing, and adoption of EE programs and associated budgets, which are suppo rted 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- Funded Energy Efficiency Programs in the United States: Pr ojected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications /future-utility-customer-funded-energy-efficiency-p rogramsunited-states-projected-spend.

172 The number of EERS states is from ACEEE (see endno te) and includes states with explicit EERS, those w ith long-term energy savings targets for individual pro gram administrators, and those with EE incorporated as an eligible resource in a renewable portfolio standard . The numbers for the other policy approaches are f rom LBNL (see endnote). 173 Sciortino, M., et. al. American Council for an Ene rgy-Efficient Economy (ACEEE). June 2011. Energy Efficiency Resources Standards: A Progress Report on State Experience. Report Number U112. Available at http://www.aceee.org/research-report/u112

. 174 The “EM&V” box is not comparable to the other prog ram types and is not relevant to this discussion. It was included in the referenced source to indicate that EM&V is a key activity within a program portfolio.5 - 14

Funding Sources Funding sources for EE programs are varied but for most states are dominated by revenues collected from ratepayers through electric ity surcharges, typically ranging from $1 to $4 per megawatt-hour. 175 More recently adopted funding sources include proc eeds from the auction of allowances in the Regional Greenhouse Ga s Initiative (RGGI) states and from EE resources bid into the forward capacity market oper ated by the New England Independent System Operator (NE-ISO). Ratepayer-funding account s for more than 90% of total EE program support nationally.

The EE Opportunity As discussed, states are employing a number of EE s trategies with EE programs yielding the most significant impacts both historically as w ell as in terms of future potential. Furthermore, EE programs are unique among state EE strategies in the comprehensiveness and transparency of their reported impacts, funding, and other characte ristics. In this section we address the rapid growth in EE programs, estimated impacts of EE prog rams to-date and projections of the impacts of existing EE programs and trends, and the electri city savings potential achievable through expanded use of EE policies and programs. Finally, we will discuss the costs and costeffectiveness of EE programs, specifically.

Rapid Growth in EE Funding for EE programs has increased rapidly in re cent years driven by recent policy innovations and increasing evidence of the effectiv eness of these new strategies. Table 5-2 presents levels of EE program funding in the U.S. s ince 2006. 176 In the previous five years, funding increased by more than 250%, from $1.6 bill ion in 2006 to $5.9 billion in 2011.

175 American Council for an Energy-Efficient Economy ( ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

. 176 American Council for an Energy-Efficient Economy ( ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

.5 - 15 TABLE 5-2 U.S. Electric Utility EE Program Funding (2006-2011 )

Year

2006 2007 2008 2009 2010 2011 2012 Electric Efficiency Program Budgets (billions of $s, nominal) 1.6 2.2 2.6 3.4 4.6 5.9 5.9

Key new state policies that have helped to drive t hese rapid increases in EE program funding include EERS, electricity savings goals, an d “all cost-effective energy efficiency” requirements. The adoption of EERS, in particular, increased through this period and clearly has been the primary driving force behind the increasin g 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 S tandards

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 C arolina 5

2008 Maryland, Michigan, New Mexico, New Year, Ohio 5

2009 Arizona, Indiana, Iowa, Maine, Massachusetts 5

2010 Arkansas, Oregon 2

2011 Wisconsin 1

1997-2011 26

Source: ACEEE, 2014.

177 American Council for an Energy-Efficient Economy ( ACEEE). February 24, 2014. State Energy Efficiency Resource Standard (EERS) Activity Policy Brief. Ava ilable at www.aceee.org/files/pdf/policy-brief/eers-02- 2014.pdf

.5 - 16

EE Program Impacts Impacts to-date The primary sources for EE program information (inc luding 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 individual EE programs within the program portfolio. The EIA has been collecting data on EE programs through Form 861, “Annual Electric Power Industry Report,” for more than three decades. 178 The data collection reflects an increasing degree of breadth and detail over time. For example, third-partyadministered programs were not initially required t o report but were added beginning in 2011. Data fields have been added over the years to refle ct industry trends (e.g., EE programs are now reported separately from load management programs). Outside organizations have taken the EIA data, supplemented it with additional sources inclu ding surveys of utility commissions and program administrators, and published their own ann ual reports that capture EE program impacts. 179, 180

The EPA has relied on the EIA Form 861 dataset for identifying historic impacts of EE programs by state. Specifically, the reported sales data, and incremental and cumulative electricity savings in the 2012 EIA 861 dataset are used to estimate electricity EE impacts by

state. 181 EIA data is reported by program administrator (e.g ., utility, third-party, or state agency) and requires the disaggregation of reported data by state for administrators with programs in multiple states (e.g., multi-state investor-owned u tilities). 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 d ata by state for the 2012 reporting year, the

178 More information on EIA Form 861 can be found at h ttp://www.eia.gov/electricity/data/eia861/. 179 Consortium for Energy Efficiency. March 28, 2013. 2012 State of the Efficiency Program Industry. Avai lable at http://library.cee1.org/content/2012-state-efficien cy-program-industry-report/

. 180 American Council for an Energy-Efficient Economy ( ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

. 181 EPA recognizes concerns associated with consistency and quality of 861 data that different reporting e ntities may have used different methodologies to estimate savin gs and the EIA 861 data are self-reported. Over tim e, there has been increased standardization in data reporting. We believe his dataset remains to be the most comp rehensive publically available dataset. See “Analysis Limitat ions” section below for further discussion.5 - 17

most recent available. At the national level, incre mental electricity savings 182 in 2012 was 0.58% of retail sales with individual state values rangin g from 0.00% to 2.19%. Cumulative electricity savings 183 (representing the remaining impacts of programs fr om all prior years) reported at the national level for 2012 represent 3.74% of retail s ales with individual state values ranging from

0.0% to 15.44%.

TABLE 5-4 2012 Reported Electricity Savings by State

State Incremental Savings as a % of Retail Sales (2012) Cumulative Savings as a % of Retail Sales (2012)

Alabama 0.07% 0.78%

Arizona 1.61% 5.39%

Arkansas 0.11% 0.39%

California 1.24% 13.67%

Colorado 0.84% 4.67%

Connecticut 1.05% 13.37%

Delaware 0.00% 0.00%

District of Columbia 0.00% 0.57%

Florida 0.27% 3.60% Georgia 0.18% 0.67%

Idaho 0.79% 6.20%

Iowa 1.05% 7.80%

Illinois 0.93% 2.15%

Indiana 0.58% 1.72%

Kansas 0.02% 0.24% Kentucky 0.23% 1.04%

Louisiana 0.00% 0.00%

182 Incremental savings (also known as first-year savi ngs) 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 alre ady existed in the previous years, or new DSM programs that existe d for the first time in the current year. 183 Cumulative savings (also known as annual savings) represent the reduction in electricity use in a giv en year from EE activities in that year and all preceding years, taking into account the lifetimes of installed mea sures.5 - 18

Maine 1.96% 5.42% Maryland 0.89% 2.47%

Massachusetts 0.94% 6.27% Michigan 1.01% 2.77%

Minnesota 1.12% 13.10% Mississippi 0.08% 0.50%

Missouri 0.12% 0.55%

Montana 0.66% 5.85%

Nebraska 0.30% 0.99%

Nevada 0.54% 6.19% New Hampshire 0.48% 4.90% New Jersey 0.03% 1.04%

New Mexico 0.60% 1.86%

New York 0.93% 6.89%

North Carolina 0.37% 1.26%

North Dakota 0.07% 0.22%

Ohio 0.87% 3.20%

Oklahoma 0.21% 0.70% Oregon 1.09% 7.72% Pennsylvania 1.06% 3.08%

Rhode Island 0.78% 11.22%

South Carolina 0.35% 1.12%

South Dakota 0.13% 0.33%

Tennessee 0.31% 1.76%

Texas 0.19% 1.54%

Utah 0.74% 6.59%

Vermont 2.19% 15.44% Virginia 0.03% 0.30% Washington 0.93% 7.37% West Virginia 0.18% 0.20%5 - 19

Wisconsin 1.05% 6.61% Wyoming 0.14% 0.71%

Continental U.S. Total 0.58% 3.75%

Alaska 0.02% 0.10%

Hawaii 0.04% 0.25%

U.S. Total 0.58% 3.74%

Source: EPA calculation based on 2012 EIA Form 861 data.

Projected Spending and Savings from EE Programs In 2013, Lawrence Berkeley National Laboratory (LBN L) published an update to a 2009 analysis and projected future spending levels and s avings through 2025 from energy efficiency programs funded by electric and gas utility custome rs in the United States under three scenarios (high, medium, and low cases). 184 The scenarios represent “a range of potential outc omes under the current policy environment” and were based on d etailed, bottom-up analysis of existing state energy efficiency policies. Significantly, the stu dy presumes no new major policy developments such as a “national energy efficiency standard, cle an energy standard, or carbon policy” and specifies that such policy changes could “result in customer-funded energy efficiency program spending and savings that exceed the values in our High Case.” The study concludes that efficiency programs are “p oised for dramatic growth over the course of the next 10 to 15 years” with the most si gnificant increases occurring in regions with lower levels of program spending, historically, inc luding the Midwest and South. For example, under the medium scenario total U.S. spending on el ectric efficiency programs increase by 40% to $8.1 billion in 2025 from 2012 levels. Under the high scenario, spending more than doubles from 2012 levels to $12.2 billion in 2025. Incremen tal savings levels grow commensurately, to 0.8% and 1.1% of sales under the medium and high sc enarios, respectively. The study results indicate that under the high scenario 20 states wou ld be achieving 1.5% or higher levels of

184 Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer- Funded Energy Efficiency Programs in the United States: Pr ojected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-cust omer-funded-energy-efficiency-programsunited-states-projected-spend

.5 - 20

incremental savings, with 11 of those reaching or e xceeding 2.0%. 185 Table 5-5 summarizes the results of the LBNL analysis.

Table 5-5 Summary of Impacts: Scenarios of Future Utility Customer-Funded Electri c Energy Efficiency Programs

Case 2025

Incremental Savings (% of Sales) Program Costs

(billions of $, nominal) Programs Costs (% of Revenues)

Low 0.5% 5.5 1.1%

Medium 0.8% 8.1 1.7% High 1.1% 12.2 2.7%

EE Potential Evaluations of EE Potential Energy efficiency potential studies are a common to ol for informing the development of EE program plans and budgets, as well as supporting the development of electricity savings targets, required savings levels under an EERS, or “all cost-effective” EE requirement. In conducting these studies, states and utilities have developed a methodology that is often described as a “bottom-up, engineering-based” appro ach. 186 EE potential studies are conducted at various geographic scopes (national, regional, stat e, and utility service territory level) and at different degrees of aggregation (e.g., economy-wid e, sectoral, and program), and can be broadly grouped into a few types: technical, economic, mark et, and program. 187

 Technical potential represents the theoretical maximum amount of energy use that could be displaced by efficiency, without regard to non-e ngineering constraints such as costs and the willingness of energy consumers to adopt th e 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 Co nducting Energy Efficiency Potential Studies: a Resource of the National Action Plan for Energy Eff iciency. Available at http://www.epa.gov/cleanenergy/documents/suca/poten tial_guide.pdf

. 187 The definitions discussed below largely follow tha t 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 technologic ally feasible energy saving measures, with additional efficiency opportunities assumed as they arise.

 Economic potential refers to the subset of the technical potential tha t is economically cost-effective. Definition of “economic potential” can vary to some degree by study. Some estimate economic potential by evaluating tech nology upfront cost, operating costs that considers energy prices, product lifetime and discount rate, compared to a conventional alternative or the supply-side energy resources. Others incorporate consideration of consumer preferences in addition t o consumers’ out-of-pocket expenditure when evaluating the economic potential. Both technical and economic potential estimates assume immediate implementation of efficiency measures without regard to technology adoption process or real-life program implementation. In addition, these estimates do not always reflect market failur es or barriers that impede energy efficiency and often fail to capture transaction co sts (e.g., administration, marketing, analysis, etc.) beyond the costs of efficiency meas ures.

 Market potential (or “achievable” potential ) refers to the subset of economic potential that reflects the estimated amount of energy saving s that can realistically be achieved, taking into account factors such as technology adop tion process, market failures or barriers that inhibit technology adoption, transact ion costs, consumer preferences, social and institutional constraints, and possibly the cap ability of programs and administrators to ramp up program activity over time.

 Program potential refers to the subset of market potential that can b e realized given specific program funding levels and designs. Progra m potential studies can consider scenarios ranging from a single program to a full p ortfolio of programs. 188 As mentioned, the EE industry standard for potentia l studies is the bottom-up, engineering evaluation of energy efficiency potenti al of individual end-use technologies and

measures. 189 Bottom-up analyses all employ a similar methodolog y 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, an d the economic potential is a subset of the technic al potential. 189 U.S. EPA and U.S. DOE. November 2007. Guide for Co nducting Energy Efficiency Potential Studies: a Resource of the National Action Plan for Energy Eff iciency. Available at http://www.epa.gov/cleanenergy/documents/suca/poten tial_guide.pdf

.5 - 22

key assumptions (e.g., breadth of sectors and end-u ses considered, study period, discount rate, pattern of technology penetration, whether economic ally justified early replacement of technologies is allowed for, and whether continued improvement in efficiency of technology is provided for). As a result, estimated efficiency po tential can vary significantly among studies. 190

Overview of Results Studies of energy efficiency potential are numerous . In recent years, dozens of studies have been conducted at regional, state, and utility levels. This section reviews recent studies and presents a summary of findings. We first address me ta-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 ad dressing different study periods, we present average annual achievable potential by dividing cum ulative percentage savings in the last year of the study by the duration (in years) of the study p eriod. This is a common method of normalization for energy efficiency potential studi es. 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 ann ual values of 4.1% per year in technical potential, 2.7% per year in economic potential, and 1.2% per year in maximum achievable potential. In comparison, Eldridge et al. (2008) es timated average annual values of 2.3% per year in technical potential, 1.8% per year in economic p otential, and 1.5% per year in achievable potential. To supplement these studies with more re cent data, the EPA has conducted a metaanalysis of twelve studies conducted between 2010 a nd 2014 at the utility, state or regional level (see Appendix 5-1). The EPA review indicates an ave rage annual achievable potential of 1.5% per year across the reviewed studies. See Appendix 5-2 (Summary of Recent (2010-2014)

190 Because of the complex consumer behavior, energy m arket and macroeconomic drivers of energy use and e nergy efficiency, and in some cases due to the lack of co nsistent data, quantifying energy efficiency potent ial and energy savings from policies and programs remains a challe nging analytical task. Assumptions about consumer t echnology adoption behavior, market barriers and failures, an d how technology diffusion occurs can also affect e stimated potential. 191 Sreedharan, P. 2013. Recent estimates of energy ef ficiency potential in the USA. Energy Efficiency. 192 Eldridge et. al. 2008. State-Level Energy Efficien cy Analysis: Goals, Methods, and Lessons Learned. 2008 ACEEE Summer Study on Energy Efficiency in Building s.5 - 23

Electric Energy Efficiency Potential Studies) for c omplete results from the EPA research. Table 5-6 presents a summary of these three meta-analyses of EE potential.

TABLE 5-6 Summary of Meta-Analyses of EE Potential at Utility , State, and Regional Levels Study Dates of Studies Number of

Studies Average Annual Achievable

Potential Sreedharan (2013) 2001-2009 10 1.2%/year Eldridge (2008) 2001-2007 20 1.5%/year EPA (2014) 2010-2014 12 1.5%/year

In addition to the numerous studies conducted at th e 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 co mmon data set, across all regions of the country. Sreedharan (2013) evaluated four major ene rgy efficiency potential studies at the national level, namely, McKinsey and Co. (2007), Mc Kinsey and Co. (2009), EPRI (2009), and AEO (2008) Energy Efficiency Side Case. All four st udies used the AEO 2008 reference case as the baseline but differed in other key respects (e. g., breadth of end-uses, assumed technology improvement over time, and definition of cost test for economic potential screening). These studies suggest technical electricity savings poten tial in the range of 25-40% and economic potential in the range of 10-25%, as a percentage o f total demand in 2020. Of these studies, only EPRI provided an estimate of achievable potential. On a normalized basis, the EPRI 2009 study provides an achievable annualized potential range o f 0.2-0.4% per year (realistically achievable and maximum achievable potential, respectively) thr ough 2030 at the national level. Two more recent studies also provide national estim ates 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 f rom state experience and their evaluated results. EPRI (2014) results show an average annual achievable

193 Electric Power Research Institute (EPRI). April 20 14. U.S. Energy Efficiency Potential Analysis throu gh 2035. [forthcoming] 194 American Council for an Energy-Efficient Economy ( ACEEE). April 2014. Select State-Level Energy Effic iency Policy Opportunities 2016-2035. [forthcoming]5 - 24

potential range of 0.5% to 0.6% per year (achievabl e and high achievable potential, respectively). ACEEE found average annual achievabl e potential of 1.5% per year. The results of

the EPRI and ACEEE studies are summarized in Table 5-7.

TABLE 5-7 Summary of National EE Potential Studies Study Study Type Average Annual Achievable

Potential EPRI (2009) Bottom-up, engineering 0.2%-0.4%/year (realistic to maximum achievable) EPRI (2014) Bottom-up, engineering 0.5%-0.6%/year (achievable to high achievable) ACEEE (2014) Top-down, policy-based 1.5%/year

Notably, each of these national potential studies show significant potential in every region of the country including regions with lower electricity prices like the southeast, regions with historically high levels of EE program budgets like the northeast and west coast, and across regions with varied sectoral composition (e.g., hig her manufacturing regions like the midwest and south, as well as higher service industry regio ns like the northeast and California). Both

EPRI studies illustrate the substantial and similar scale opportunity across all regions. For instance, EPRI (2014) shows achievable potential ra nging from 8% to 14% relative to baseline in 2035 across the thirteen regions of their analysis as well as significant opportunity in the residential, commercial and industrial sectors in e very region. The ACEEE (2014) study also shows consistently large potential across all state s and regions through 2030, with an average potential of 24% and a range of 20% to 36% across 5 0 states.

Costs and Cost-Effectiveness of State EE Policies

EE Cost-Effectiveness States enact EE policies to meet multiple policy ob jectives including reduction of customer electricity bills, lower costs of meeting electricity supply needs, energy reduction,5 - 25

environment and health benefits, and local economic development benefits. 195 Most states evaluate their EE policy options through the applic ation of cost tests, weighing the projected benefits with the costs of the energy efficiency te chnologies 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 footing by using present value analysis. This is necessary because EE typically requires primarily u pfront expenditures (e.g., a whole home retrofit) while the economic benefits (e.g., electr icity bill savings) accrue over the life of the investment (“measure life”) which can range from a few to twenty or more years. As such, the

choice of discount rate and the estimation of measu re life are significant determinants of the cost-effectiveness results. Most states employ mult iple tests, adjusting cost and benefit categories depending upon the economic perspective of interest (e.g., utility, ratepayer, program participant, society), and consider the results from each one, u sually with an identified primary test type.

Policies that are selected are those that are found to be cost-effective, with benefits greater than costs, as determined by the utility applying method s 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 th e measure. Costs may include incremental equipment and installation costs; benefits include incentive payments, bill savings, and applicable tax credits or incenti ves. (2) Utility/program administrator cost test from the perspective of utility, government agency or third-party implementing the program. Cos ts may include program incentive, installation, and overhead costs; benefits may incl ude avoided energy and capacity costs - including generation, transmission and distribution - by the utility. (3) Ratepayer impact measure test from the perspective of utility ratepayers not par ticipating in available energy efficiency programs. This text includes the costs and benefits that will affect utility rates, including program and administration costs, as well as “lost revenues” to the utility; benefits include avoided energy and capacity costs, and additional resource savings.

195 U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee _report.pdf

. 196 U.S. EPA and U.S. DOE. November 2008. Understandin g Cost-Effectiveness of Energy Efficiency Programs (Best Practices, Technical Methods, and Emerging Is sues 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 Assis tance Project, “Energy Efficiency Cost-Effectivenes s Screening. Available at http://ww.raponline.org/document/download/id/6149

.5 - 26

(4) Total resource cost test from the perspective of all utility customers in t he service area. Costs may include the full incremental cost of the measure, program installation and overhead costs; benefits may include avoided energy and capa city costs, and additional resource savings. (5 ) Societal cost test

from the social perspective. In addition to benefi ts considered in total resource cost test, may also include non-monetized benefits such as environmental and health

benefits. While many states consider more than one cost test in evaluating EE programs, the most commonly used (29 states) primary test is the total resources cost test. This test is considered to be the best measure of the interests of all utility customers. The utility and societal cost tests are the next most commonly used primary tests, used by five states each. The utility cost test is considered to be the most comparable metric to comp are with supply-side resource investments from a utility resource planning perspective. Economic and modeling analyses of climate change po licy suggests that energy efficiency presents a large potential in reducing g reenhouse gas emissions and plays a critical role in offsetting the costs and enhancing the flex ibility to achieve long-term GHG reduction targets. 198 Consistently, evaluations of the economic potentia l for carbon dioxide reductions from the United States’ power sector identify demand-sid e 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 account ed for more than 60% of their mid-range potential for gree nhouse gas reductions from the U.S. power sector and that it was available at positive net pr esent 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. 2 014. The role of technology for achieving climate policy objectives: overview of the EMF 27 s tudy on global technology and climate policy strate gies. Climatic Change, January 2014; Kyle P., L. Clarke, S. Smith et al. 2011. The Value of Advanced End-Use Energy Technologies in Meeting U.S. Climate Policy Goals. The Energy Journal, 32: 61-87. 199 U.S. EPA and U.S. DOE. September 2009. Energy Effi ciency as a Low-Cost Resource for Achieving Carbon Emissions Reductions: a Resource of the National Ac tion Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/ee_an d_carbon.pdf

. 200 McKinsey & Company. December 2007. Reducing U.S. G reenhouse Gas Emissions: How Much at What Cost? Available at http://www.mckinsey.com/client_service/sustainabili ty/latest_thinking/reducing_us_greenhouse_gas_emiss ions

.5 - 27

Costs of Saved Energy A common metric for comparing alternative electrici ty resource options within utility resource plans is the levelized cost of energy (LCO E) or, for EE resources, the levelized cost of saved energy (LCSE). 201 LCSE EE is often compared favorably with LCOE of a lternative new generation sources such as fossil-fueled or nuclear power plants, or renewable energy resources like wind or solar-power generation. In these compa risons, typically only utility (or program) costs are considered, not the total costs of saved energy that are discussed later in this chapter. The energy efficiency analysis literature reports a verage 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 estimat ed LCSE for electricity energy efficiency programs in the range of 1.3-5.6 cents/kWh, with a mean value of 2.8 cents/kWh. 203 Earlier reviews of utility EE programs identified a similar range of LCSE. Friedrich et al. (2009) reviewed 14 utility studies of LCSE and found a ran ge from 1.6 to 3.3 cents/kWh, with a mean

value of 2.5 cents/kWh. 204 An earlier ACEEE study (2004) reviewed cost-effec tiveness analysis results in nine states and suggested that reported utility LCSE ranged between 2.3-4.4 cents/kWh, with a mean value of 3 cents/kWh. 205

The economic literature also evaluates the LCSE fro m EE measures using other techniques (e.g., econometrics, top-down modeling), although this body of studies is much smaller compared to the bottom-up, engineering-base d 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 d irect comparison challenging, but overall

201 U.S. EPA and U.S. DOE. November 2007. Guide for Re source Planning with Energy Efficiency: a Resource of the National Action Plan for Energy Efficiency. Ava ilable at http://www.epa.gov/cleanenergy/documents/suca/resou rce_planning.pdf

. 202 Unless otherwise noted, estimates of LCSE discusse d in this section refer to program administrator co st (also known as utility cost). The discount rates, average measure lives, and other assumptions affecting the calculation of LCSE were not always consistent or reported in all studies. 203 Molina, M. 2014. The Best Value for America’s Ener gy Dollar: A National Review of the Cost of Utility Energy Efficiency Programs. ACEEE Report No. U1402. Washin gton, DC. Available at http://www.aceee.org/researchreport/u1402

. 204 Friedrich, K., M. Eldridge, D. York et al. 2009. S aving Energy Cost-Effectively: A National Review of the Cost of Energy Saved Through Utility-Sector Energy Effic iency Programs,” ACEEE Report No. U092. Available a t http://www.aceee.org/research-report/u092

. 205 Kushler, M., D. York, and P. Witte. American Counc il for an Energy-Efficient Economy (ACEEE). 2004. F ive Years In: An Examination of the First Half-Decade o f Public Benefits Energy Efficiency Policies.5 - 28

these empirical analyses present a wider range of e stimates of cost of saved energy. For example, a recent study by Auffhammer et al. (2008) examinin g utility DSM programs estimated the average utility cost of saved energy in the range o f 5.1 to 14.6 cents per kWh. 206 Some other studies in the economic literature suggest estimate d LCSE in a similar range as from the bottomup analyses. Gillingham et al. (2004) estimated an average cost of 3.4 cents per kWh saved from utility EE programs. 207 In a recent econometric analysis of utility rate- payer funded demand-side management and energy efficiency programs between 1 992 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 u sing state- and region-specific price elasticity, Paul et al. (2011) estimated that electricity savin gs of 1 to 3 percent were available at a marginal cost of 5 cents/kWh and a corresponding average cos t of 2.5-3.5 cents/kWh. 209 A number of analytical and data considerations rela ted to LCSE estimation are also discussed in the literature, including the issue of “free riders” in EE programs 210 , and the accuracy of utility reported costs and energy savin gs. 211 Energy efficiency practitioners also recognize the need to consider “free rider” and “sp illover” effects in program evaluation. A slight majority of states adjust for free ridership in energy savings estimates, leading to higher

LCSE values than otherwise would be the case. A sma ller number of states adjust for spillover

effects which reduce LCSE values when addressed. 212

Another consideration related to LCSE estimation i s the rebound effect. The economic literature has extensive discussion of the potentia l rebound effect, market interactions and

206 Auffhammer M., C. Blumstein, M. Fowlie. 2008. Dema nd Side Management and Energy Efficiency Revisited. Energy Journal

29(3): 91-104. 207 Gillingham, K., R. Newell, K. Palmer. 2004. Retros pective Examination of Demand-Side Energy Efficienc y Policies. Resources for the Future Working Paper DP 04-19 REV. Washington, DC. 208 Arimura, T. S. Li, R. Newell, and K. Palmer, 2012. Cost-Effectiveness of Electricity Energy Efficiency Programs, The Energy Journal Vol 33(2). 209 Paul, A., K. Palmer and M. Woerman. 2011. Supply C urves for Conserved Electricity. Resources for the Future Discussion Paper 11-11. Washington, DC. 210 Trains, K. 1994. Estimation of Net Savings from En ergy-Conservation Programs. Energy 19 (4):423-441.

211 Joskow, P., and D. Marron. 1992. What Does a Negaw att 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 Pr ograms. ACEEE Report No. U122. Available at http://www.aceee.org/research-report/u122

.5 - 29

economy-wide response of energy efficiency policies and investments. An improvement in energy efficiency would effectively reduce the cost of a service or production input, potentially boosting its demand or production output thus incre asing energy use (“direct” rebound). In addition, money saved from energy efficiency can be used for consumption or investment that can increase energy consumption in other markets of the economy and lower energy prices as a result of energy efficiency improvement may increas e energy consumption (two forms of “indirect” rebound). Reviews suggest that both dir ect and indirect rebound effects exist and the size of such effects varies among different studies , technologies, sectors and income groups. 213 Overall, however, rebound effects are found to be r elatively modest compared to the importance of energy efficiency as an effective way of reducin g 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 viab le and broadly applicable measure for achieving significant reductions in the amount of g eneration 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 leading industry trend over recent years and the trend of increasing investment in EE programs is project ed to continue for the next decade, at least. These findings support the inclusion of demand-side energy efficiency 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 sav ings levels resulting from state demand-side

213 Sorrell, S. 2007. “The Rebound Effect: an assessme nt of the evidence for economy-wide energy savings from improved energy efficiency.” A report produced by t he Sussex Energy Group for the Technology and Polic y Assessment function of the UK Energy Research Centr e. ISBN 1-903144-0-35.

214 Greening, LA, DL Greene, and C Difiglio. 2000. Ene rgy efficiency and consumption — the rebound effect — a survey, Energy Policy , 28: 389-401. 215 Sorrell, S. 2007. “The Rebound Effect: an assessme nt of the evidence for economy-wide energy savings from improved energy efficiency.” A report produced by t he Sussex Energy Group for the Technology and Polic y Assessment function of the UK Energy Research Centr e. 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. 201 3. Energy policy: the rebound effect is overplayed. Nature 493: 475-476.5 - 30

energy efficiency efforts. In the final section, th e integration of these goals into the impacts assessment is presented and we consider the reasona bleness 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 techno logies and practices, and projects the annual impacts of the scenario for each state. The scenari o does not distinguish between policies that are currently in place and additional policies that in most states would be required to be implemented to realize the goals established. It does not repr esent an EPA forecast of business-as-usual impacts of state energy efficiency policies or an E PA estimate of the full potential of end-use energy efficiency available to the power system, bu t 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 p erformance that has already been demonstrated or required by policies (e.g., energy efficiency re source standards) of many leading states; 2) considers each state’s unique existing level of per formance; and 3) allows appropriate time for

each state to increase from their current level of performance to the identified best practices

level. The best practices scenario is derived from state e xperience 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 p rograms for several reasons:

 EE programs have achieved significant levels of sav ings and are being used in almost every state,

 EE program spending and savings levels are reported by utility or other program administrator, by state, and compiled nationally, u sing standardized elements and definitions, and5 - 31

 EE program savings are projected and evaluated unde r requirements established and overseen by state utility commissions, and by munic ipal and cooperative utility boards of

directors.

While the approach is derived from information abou t energy efficiency programs overseen by state utility commissions, other state energy efficiency policies are available to realize a state’s goals 218 such as building energy codes, appliance standards , and building energy benchmarking requirements. All policies included in a state plan will need to meet established requirements or guidance for EM&V. 219 The following steps were taken to establish the inp uts for development of the best practices scenario for each state:

 Step 1: Determine current level of performance

 Step 2: Determine best practices level of performan ce

 Step 3: Determine start year for state efforts

 Step 4: Determine start year level of performance

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

 Step 6: Determine average portfolio measure life an d distribution of measure lives

 Step 7: Determine sustainability of best practices level of performance Inputs Step 1: Determine Current Level of Performance A fundamental indicator of the level of energy effi ciency program performance is incremental annual savings as a percent of retail s ales. This is a common metric defining savings levels for energy efficiency resource standards and is readily calculated from EIA Form 861 data for each state. Incremental annual savings are also more directly estimated and evaluated than are cumulative savings. 220 For the best practices scenario, we aggregated the most recent year of

218 See State Plan Considerations TSD. 219 See State Plan Considerations TSD. 220 Estimates of cumulative savings impacts in a given year are derived from incremental savings values a nd information on measure lives. Information on measur e lives is less consistently gathered than is infor mation on incremental savings values.5 - 32

EIA Form 861 data to the state level to establish e ach state’s current level of performance. These results were presented previously in Table 5-4.

Step 2: Determine Best Practices Level of Performan ce As discussed previously, achievable demand-side ene rgy 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. Whi le varied regional characteristics (e.g., avoided power system costs, economic growth, sectoral mix, climate, and level of past energy efficiency efforts) affect estimates of achievable potential, ongoing improvements in energy-efficient technologies and practices, economic growth, popula tion increases, and continually improving strategies for program delivery have resulted in pe rsistent and substantial levels of achievable potential regardless of specific regional character istics.

A direct indicator of the achievable incremental le vels of energy savings performance is provided by past performance at the state and utili ty levels, and by requirements states have put in place for levels of savings to be achieved by 20 20. As discussed, these requirements are typically in the form of energy efficiency resource standards or similar savings goals that are applied to utilities in the state. 221 Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA Form 861 (2012) data, aggregated to the state level , and categorized into four ranges of savings levels (< 0.5%, 0.5% to 0.99%, 1.0% to 1.49%, and > = 1.5%). As shown, three states achieved the highest level of performance (> 1.5%) and an ad ditional eight states achieved the second highest level of performance (1.0% to 1.49%). Table 5-9 summarizes incremental savings levels req uired 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 additi onal five states are required to achieve the next highest level of performance (1.0% to 1.49%).

221 See State Plan Considerations TSD for more informa tion. 222 American Council Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency Resourc e Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/policy-brief/eers-02-2014.p df

.5 - 33 TABLE 5-8 2012 Reported State Levels of Incremental Annual Sa vings 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 Pol icy on or before 2020 Incremental Savings as %

of Retail Sales # of States States

>= 1.5% 11 AZ, CO, IL, IN, MA, MN, NY, OH, RI, VT, WA

1.0% to 1.49% 5 HI, IA, ME, MI, OR

0.5% to 0.99% 3 AR, CA, WI

< 0.5% 1 TX

Source: ACEEE, 2014.

For the best practices level of performance for Op tion 1 223 , the EPA has chosen 1.5% incremental savings as a percentage of retail sales . This level was achieved by three states (AZ, ME, and VT) in 2012 and an additional nine states ( CO, IL, IN, MA, MN, NY, OH, RI, and WA), accounting for overlap, are expected to achiev e this level by 2020. Thus, twelve states have either achieved or are required to achieve thi s level of performance by 2020. For Option 2 224 , the EPA has chosen 1.0% incremental savings as a percentage of retail sales as the best practices level of performance fo r 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 descript ion of Option 1.

224 See Preamble and Goal Computation TSD for descript ion of Option 2.5 - 34

level by 2020. In total, twenty states (accounting for duplication between the two sets of states) have either achieved or are required to achieve thi s level of performance by 2020.

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

Step 4: Determine Start Year Level of Performance For construction of the best practices scenario, th e EPA has set each state’s level of performance (incremental savings) in the start year (2017) to its current level of performance (aggregated to the state-level from reported EIA Fo rm 861 data). This approach reflects neither improvement nor decline in performance between 2012 and 2017. Any improvement in EE savings performance between 2012 and 2017 will 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 savings le vels from the 2017 level to the best practices level, the EPA considered past performanc e of individual program administrators 227 as well as requirements of existing state energy effic iency resource standards. For the past performance of individual program administrators, w e first screened the data and divided them into moderate and high performing sub-groups. The m oderate group (47 entities) was defined as programs that achieved from 0.8% to 1.5% maximum in cremental savings levels and the high group (26 entities) was defined as programs that ac hieved greater than 1.5% maximum incremental savings levels. We then calculated the rate at which each entity had increased savings over time and calculated average values for each sub-group. For the moderate group, the average rate of improvement of incremental annual s avings rate was 0.30% per year. For the

225 See Preamble and State Plan Considerations TSD for descriptions of the schedule for state plan submit tals. 226 See Preamble for description of interim and final compliance periods. 227 EIA 861 was the primary data source; however, we s upplemented EIA 861 data with data for third-party program administrators because prior to 2011 EIA did not co llect data from third-party program administrators.5 - 35

high group, the average rate of improvement of incr emental annual savings rate was 0.38% per year. See Appendix 5-3 for supporting data and anal ysis. The EPA also considered requirements of existing s tate EERS and evaluated the rate at which their incremental savings levels increase ove r time. For several EERS, we were unable to clearly identify ramp-up schedules. We identified t en states with clear schedules and calculated the average rate of improvement for each. The avera ge rate of improvement of incremental annual savings rate required for these ten states i s 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 pas t state performance and future state requirements.

Step 6: Determine Average Portfolio Measure Life an d Distribution of Measure Lives The next step in defining the best practices scenar io requires projecting the cumulative future impacts of the annual incremental savings le vels 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 savi ngs represent the total impacts of all EE measures put in place in that year and all prior ye ars, due to EE program activities. The cumulative savings account for the continuing impac ts of energy efficiency measures that remain in place for a period of time (the “measure life”) before being replaced. For example, the purchase of a high-efficiency refrigerator may lead to savings for twelve years, before being replaced with a new model. To estimate cumulative i mpacts of a series of years of incremental savings, the industry uses the concept of an averag e measure life for the entire portfolio of EE programs. Rather than use a single, average measure life to represent a diverse portfolio of programs, that range in measure lives from as littl e as a few years (e.g., certain lighting technologies and applications) to as long as fiftee n or twenty years (e.g., adding insulation to an attic), the EPA is assuming a distribution of measu re lives around the average to account for future impacts of incremental savings levels.5 - 36

In 2014, ACEEE updated their 2004 and 2009 nationa l reviews of EE program costs and related program characteristics, including measure lives. 228 They reviewed electricity EE program data from 20 states and summarized average measure lifetimes by state and customer

class. Table 5-10 summarizes the results from the A CEEE study and shows an average across all sectors for these states of 10.6 years.

TABLE 5-10

Average Electricity Measure lifetimes by state and customer class

Sector

All Sectors Residential Commercial/Business Industrial Average 8.1 12.5 9.5 10.6

Source: ACEEE 2014.

Other studies have found slightly higher values for average measure life for EE portfolios, ranging from 10 to 13 years. 229 Our assumption of 10 years is conservative by comp arison and leads to lower cumulative impacts over time and cor respondingly lower state goals. To approximate a distribution of measure lives acro ss an EE portfolio, consistent with an average measure life of ten years, we have assumed an even distribution from one year in length to two times the average measure life (twenty years ) in length. Our approach is generally supported by the substantial range in measure lives reviewed and summarized in a 2014 study by LBNL which shows an interquartile range from five t o 25 years across twelve program categories (e.g., low income, residential new const ruction, commercial/industrial custom, etc.). Our approach represents a first-order approximation of the distribution of measure lives across a diverse portfolio of programs. The more common appr oach in other studies is to assume a portfolio with no diversity of measure lives whatso ever, with the entirety of incremental savings being realized in each year from the first through the full average measure life and then dropping to zero in the following year. Our approach is a co nservative one, leading to the same quantity of

228 Molina, M. 2014. The Best Value for America’s Ener gy Dollar: A National Review of the Cost of Utility Energy Efficiency Programs. ACEEE Report No. U1402. Availa ble at http://www.aceee.org/research-report/u1402. 229 Billingsley, Megan A., I. M. Hoffman, E. Stuart, S . R. Schiller, C. A. Goldman, K. LaCommare, Lawrenc e Berkeley National Laboratory. March 2014. The Progr am Administrator Cost of Saved Energy for Utility C ustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-en ergy-for-ee-programs.pdf

.5 - 37

total energy savings, but with a greater portion of the savings occurring in later years than occurs with the more common, and simpler, approach. This r esults in lower cumulative impacts in earlier years and correspondingly lower state goals through 2030.

Step 7: Determine Sustainability of Best Practices Level of Performance For construction of the best practices scenario, on ce 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 (an d, 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 state s currently at or above the best practices level of performance, this reflects an ability to sustain th e target level for thirteen years (2017 through 2030). Limited empirical data suggests the reasonableness of this approach; however, comprehensive data, across all regions and states, does not exist because these levels of performance have not been achieved and sustained na tionwide previously. The Northwest Power Conservation Council (NPCC) provides one such examp le. NPCC has been conducting the most consistent and long-running series of evaluations o f 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 detailed, reliable, and purposeful over time. Since 1998, NPCC’s estimates of achievable potential have more than tripled even as evaluated electricity savings from energy efficiency programs have increa sed rapidly, more than quadrupling between 1998 and 2010 (while levelized costs of saved energ y achieved have remained flat), and exceeding plan targets every year since 2005. A stu dy of the NPCC results concludes: “our research shows that when programs invest in higher levels of efficiency, this helps drive measurement improvements and technical innovation, resulting in larger and more reliable

230 NPCC’s resource plans cover Idaho, Oregon, and Was hington in their entirety, and western regions of M ontana and Wyoming. 231 Northwest Power & Conservation Council (NPCC). Feb ruary 1, 2010. “Sixth Northwest Conservation and Electric Power Plan,” Council Document 2010-09. Ava ilable at www.nwcouncil.org/energy/powerplan/6/plan/

.5 - 38

conservation supply estimates.” 232 Table 5-11 summarizes the NPCC’s achievable potent ial estimates and evaluated savings since 1998. 233

TABLE 5-11

NPCC Achievable EE Potential and Achieved Increment al 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 prov ided by average annual achievable rates from reviewed studies, as discussed previously, and comparison of those with the rates resulting from the best practices scenario. We address this i n 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 be st practices scenarios for Options 1 and 2. The pace of improvements, average measure li fe, and distribution of measure lives are each conservative and, therefore, contribute lower state goals than would otherwise result. Similarly, the best practices level of performance, being based solely on results from and requirements of EE programs, is less stringent than a level would be that accounted for potential impacts of other state EE policies such as building energy codes, building energy benchmarking requirements, and state appliance standards. The us e of 2012 level of performance for the 2017

232 Gordon, Fred, Lakin Garth, Tom Eckman, and Charles Grist, “Beyond Supply Curves,” Proceedings of 2008 ACEEE Summer Study on Energy Efficiency in Building s, August 17, 2008 available at: http://aceee.org/files/proceedings/2008/data/papers /8_419.pdf

. 233 Northwest Power & Conservation Council (NPCC). Feb ruary 1, 2010. “Sixth Northwest Conservation and Electric Power Plan,” Council Document 2010-09. Ava ilable at www.nwcouncil.org/energy/powerplan/6/plan/

.5 - 39

start year, allows states that increase their use o f effective EE policies prior to submitting their implementation plan to benefit.

Table 5-12

Summary of EE Best Practices Scenario Inputs Input Option 1 Option 2

Current Level of Performance (incremental savings as % of retail sales) Data from 2012 EIA 861 (2012) Data from 2012 EIA 861 (2012)

Best Practices Level of

Performance (incremental savings as % of retails sales) 1.5% 1.0%

Start Year 2017 2017

Start Year Level of

Performance Data from 2012 EIA 861 (2012) Data from 2012 EIA 861 (2012) Pace of Improvement (increase in incremental savings rate per year) 0.20% per year 0.15% per year

Average Measure Life and

Distribution of Measure Lives 10 years; evenly distributed across 20 years 10 years; evenly distributed across 20 years

Continued Performance Once achieved, best practices level sustained through 2030 Once achieved, best practices

level sustained through 2025

Calculations

This section addresses the calculations for determi ning cumulative savings levels (cumulative savings as a percentage of baseline sal es) for each state, for each year of the interim and final compliance periods for Options 1 and 2. T he 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 th e Goal Computation TSD for a detailed5 - 40

description of how the demand-side EE component is used as one of several inputs to the

calculation of interim and final state emission rat e goals. Calculating the net cumulative savings as a percent of electricity sales for each state involves six steps. For each state, for each year (2017-2030 for Option 1 and 2017-2025 for Option 2) the following steps are taken:

1. Determine annual business as usual (BAU) sales

2. Determine annual incremental EE savings as a percen tage of sales

3. Determine annual incremental EE savings (GWh) and s ales 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 desc ribed and results provided for one state (using South Carolina as an example) for 2017 throu gh 2025 for Option 1. We truncate the results at 2025 for simplicity, but full results ar e presented for all states in the section.

Step 1: Determine the Annual Business as Usual (BAU ) Sales

BAU sales are derived by taking 2012 sales from EIA Form 861 data for the state and increasing them for each subsequent year by the ave rage annual growth rate from the AEO 2013 Reference Case for the region corresponding to the state. For South Carolina the corresponding region is SERC and the average annual growth rate f rom 2012 to 2040 is 1.10% per year. The resulting values are summarized in Table 5-13 for S outh 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 incremental EE savings as a percentage of sales is set at the 2012 value based upon EIA-861 reporte d 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 rea ched and then held constant. The resulting

values are summarized in Table 5-14 for South Carol ina.

TABLE 5-14 Annual Incremental EE Savings as a Percentage of Sa les 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% 1.5 0%

Step 3: Determine Annual Incremental EE Savings (GW h) and Sales after net EE Annual incremental EE savings are calculated by mul tiplying the annual incremental savings as a percentage of sales times the prior ye ar sales after net EE. Sales after net EE are calculated by subtracting net cumulative savings fr om 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 al l expired savings in a given year from all prior years’ incremental (first-year) savings b ased upon an average measure life of 10 years and a linear decline in first-year savings over twe nty years. As an example, Figure 2 illustrates the decline in first-year savings from EE measures installed in 2017. The resulting values for expiring EE savings are summarized in Table 5-16 fo r South Carolina.5 - 42 TABLE 5-15 Annual Incremental EE Savings and Sales after Net E E 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 (GW h) Net cumulative EE savings in a given year are equal to annual incremental savings for that year minus total expiring savings for that yea r plus net cumulative savings for the prior year. The resulting values are summarized for South Carol ina 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 sa les are equal to net cumulative savings divided by BAU sales. The resulting values are summarized for South Carolina in Table

5-18.5 - 44 TABLE 5-18 Net Cumulative EE Savings as a Percentage of BAU Sa les 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% 7.4 0%

Summary of General Formulas and Results by Step for South Carolina Tables 5-19 and 5-20 provide summaries of the gene ric 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 r eached)

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% 1.5 0%

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% 7.4 0%

Results Summary of Results As discussed, the EE goals for each state are repre sented as cumulative savings as a percentage of retail sales by year for each option. Table 5-21 summarizes these values for the first and last year of the interim compliance perio d for Options 1 and 2. See Appendix 5-4 for comprehensive results by state, for each year, incl uding both annual incremental and cumulative savings as a percentage of retail sales, for each o ptions, 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 % 6.2 1 % 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 % 2.5 6% 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 re sults presented in Table 5-21 and Appendix 5-4, the average annual savings were calcu lated for each state through 2025 and 2030, starting from 2017. Table 5-22 summarizes the resul ts.

TABLE 5-22 Summary of Average Annual Savings Rates of Best Pra ctices 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 averag e annual savings rate represented in the EE best practices scenario are below the range of v alues found in recent utility, state, and regional studies (1.2% to 1.5% per year) as summari zed in Table 5-6, and within the range of

values found in the 2014 national studies from EPRI and ACEEE (0.5%/0.6% to 1.6% per year) as summarized in Table 5-7. These results provide a dditional support for the feasibility of the EE best practices scenario and associated state-specif ic EE goals.

Impacts Assessment Approach In the Goal Computation TSD, state-specific EE goal s 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 e mission rate goals are then represented as requirements within the power sector modeling for t he RIA. In addition, the EE state goals, resulting from the EE best practices scenario, are used to adjust electricity demand levels used as exogenous inputs to power sector modeling for the i llustrative compliance scenarios. In other words, the degree to which EE is employed as an aba tement resource is not determined endogenously within the power sector modeling based upon optimization of costs but, rather, “hard wired” into the illustrative compliance scena rios. 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 prac tical considerations of state EE policy implementation, specifically, the current levels of EE performance and the pace at which states can feasibly improve their levels of performance ov er time. The EE goals represented in the illustrative compli ance scenarios lead to substantial reductions in power system costs due to the reducti ons in specified electricity demand. Since EE is not represented endogenously as an abatement mea sure within the power sector modeling, the costs associated with the EE best practice scenario must be estimated outside of the power sector modeling and integrated with the results from that modeling. These EE cost estimates, their basis, and calculations are addressed in the follow ing sections.5 - 50

Inputs The following steps were taken to establish the inp uts for development of the EE cost

estimates for each state.

 Step 1: Determine state-specific electricity saving s by year

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

 Step 3: Determine the ratio of program to participa nt costs

 Step 4: Determine the escalation rate of EE costs

Step 1: Determine State-Specific Electricity Saving s by Year Results from the previous section, State Goal Sett ing, provide the starting point for estimation of EE costs. From those results, state-s pecific 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.) incurred by a utility or other administrator of EE programs in a given year that lead to EE measures ( technologies and practices) put in place in that year and resulting in reductions in electricit y demand in that and future years (driven by the mix of measure lives across the portfolio of EE pro grams employed). Unlike participant costs, program costs are readily known by the administrato r of EE programs and are, therefore, an appropriate starting point for EE program cost anal ysis. In 2009, ACEEE conducted a national review of data on EE program costs from program ann ual reports, evaluation reports, and information compiled from contacts at program admin isters 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-y ear 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 t hose savings. Depending upon the state, spillover effects may als o be accounted for in net costs.5 - 51

Two recent national analyses have found lower prog ram costs than the 2009 ACEEE study. In 2014, ACEEE updated their analysis from 2009, expanding the number of states to 20, and including a greater number of program administr ators and years. In this analysis ACEEE found average first-year net costs of $230/MWh (201 1$). In 2014, an LBNL study presented results from a uniquely comprehensive study of EE p rogram costs. The LBNL analysis reviewed program-level data from over 100 program administra tors in 31 states. Data were collected from over 1,700 individual programs for up to three year s (2009-2011), covering more than 4,000 individual program years of data points. Because of the broad scope of their study and the lack of net savings information for many programs, LBNL foc used 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 d eflating 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 studi es, indicate that the value of $275/MWh used for this analysis is conservative, resulting i n comparatively higher total costs than would be the case based upon the newer studies.

Step 3: Determine the Ratio of Program to Participa nt Costs As noted above, while program costs are readily kn own and consistently reported by the program administrator, participant costs require si gnificant effort to estimate, and are less consistently estimated and reported. The ratio betw een program and participant costs will vary significantly from one program to the next within a utility’s portfolio. The EPA has used a generic approach to estimate the ratio of program t o 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 from program administrators in 22 states identified as leaders in EE programs 236 based upon their magnitude of savings or their sav ings as a percentage of retail sales. Complete information on full portfolio participant costs were

available for nine of the 22 states. Across the nin e states, the average program and participant costs as a percentage of total costs were 53% and 4 7%, respectively. See Appendix 5-3 for the

235 “Gross savings” refers to electricity savings befo re any accounting for effects of free-ridership or spillover. 236 Leaders were identified using results from the 201 3 ACEEE State Energy Efficiency Scorecard based on energy savings as a percentage of retail sales or total sa vings.5 - 52

data and analysis documenting this review. Based on this review, the EPA has taken a slightly conservative approach 237 and used a ratio of 1:1 between program and partic ipant costs. We use this ratio to derive participant and total costs ba sed upon program costs. Starting from program CSE of $275/MWh and applying the 1:1 ratio, we esti mate participant CSE of $275/MWh and total CSE of $550/MWh (all values 2011$).

Step 4: Determine the escalation rate of EE costs The level of EE program impacts represented in the state EE goals are substantial and represent a scenario that has not previously been a chieved and sustained at a national level in the U.S. Thus, even though the EPA has taken a conserva tive approach (i.e., leading to higher estimates of costs) to the development of the EE st ate goals as well as to other factors that affect the EE cost estimates, we have also chosen to take a cautious approach to the escalation of EE costs at higher levels of performance (i.e., as sta tes improve from their historic levels of incremental savings to the best practices level of 1.5% of retails sales). Economic theory suggests two mechanisms that would change EE costs as higher levels of performance are achieved. Economies of scale in the operation of la rger EE programs and larger portfolios of EE programs, and learning and expertise gained over ti me from the continued implementation of programs, are two factors that would lower costs as programs scale up and expand to realize higher levels of performance. However, the limited supply of EE abatement measures and the need to employ higher cost measures, over time, to reach higher levels of performance, and to sustain high levels of performance, are factors tha t would increase costs as higher levels of performance are achieved. Analysis based upon limit ed empirical data does provide support for significant economies of scale and/or cost reductio ns over time as learning and expertise are gained. 238 “Supply curves” of EE as an energy resource, as we ll as EE as a measure represented within a GHG abatement curve, provide support for e scalating costs as higher levels of savings

237 If we had used the 53% and 47% values, starting fr om program costs, total costs would have been sligh tly lower than calculated with the 1:1 split used. 238 Kenji Takahashi and David Nichols, Synapse Energy Economics, Inc. 2008. The Sustainability and Costs of Increasing Efficiency Impacts: Evidence from Experi ence to Date. ACEEE Summer Study on Energy Efficien cy in Buildings. http://www.aceee.org/files/proceedings/2008/data/pa pers/8_434.pdf

.5 - 53

are realized. 239 In a recent analysis, Lawrence Berkeley National L aboratory (LBNL) adopted an approach that generically represented both effects discussed above. 240 LBNL changed EE costs (first-year program costs) as a function of EE savi ngs levels, decreasing costs at savings levels up to 0.5%, leaving costs constant at the base leve l at savings levels from 0.5% to 1.5%, and increasing costs at savings levels above 1.5%. Anot her recent analysis, by ACEEE, provides weak statistical support for a cost increase of 20% when going from 0.5% to 1.0% savings rate and an additional cost increase of 20% when going f rom 1.0% to 1.5% savings rate. 241 In consideration of the above discussion, the EPA h as chosen to escalate EE costs over three steps as a function of incremental savings (a s a percentage of electricity sales) at the state level. Until a state reaches a 0.5% savings level, their costs are set at the base level; for savings levels between 0.5% and 1.0%, state costs are escal ated to 120% of the base level; and for savings levels over 1.0%, state costs are escalated to 140% of the base level. This approach leads to higher costs relative to the one used by LBNL wh en applied to EPA’s EE best practices

scenario.

Summary of Inputs for EE Cost Analysis Table 5-23 provides a summary of inputs for the EE cost analysis including first-year costs of saved energy, ratio of program to particip ant costs, and escalation of costs as a function of the rate of incremental savings. Each of these f actors incorporates some level of conservatism, leading to higher costs than would otherwise result

.

239 For example: Northwest Power & Conservation Counci l (NPCC). February 1, 2010. “Sixth Northwest Conservation and Electric Power Plan,” Council Docu ment 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/ ; and McKinsey & Company. December 2007. Reducing U .S. Greenhouse Gas Emissions: How Much at What Cost? A vailable at http://www.mckinsey.com/client_service/sustainabili ty/latest_thinking/reducing_us_greenhouse_gas_emiss ions

. 240 Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer- Funded Energy Efficiency Programs in the United States: Pr ojected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility-cust omer-funded-energy-efficiency-programsunited-states-projected-spend

. 241 Molina, M. 2014. The Best Value for America’s Ener gy Dollar: A National Review of the Cost of Utility Energy Efficiency Programs. ACEEE Report No. U1402. Availa ble 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 estimat ing the costs associated with the statespecific EE goals discussed above. The results of t hese calculations are then used within the RIA and preamble. Specifically, three values are calcul ated (annual first-year costs, levelized cost of saved energy (LCSE), and annualized costs); for eac h, program and participant components are then calculated using the 1:1 ratio (i.e., 50% of t otal for each) derived above. Specific results from prior sections on state goal setting and impac ts assessment inputs are used as inputs for these calculations. For each state, the following s teps are taken for each year (2017-2030) and for each option. Calculations for steps 2 and 3 are don e 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 desc ribed and results are provided for one state (using South Carolina as an example) for 2017 through 2025 for Option 1. The results are truncated at 2025 for simplicity, but full national results (through 2030) are presented below.5 - 55

Step 1: Calculate Annual First-Year Costs Annual total first-year costs are calculated by mul tiplying annual total incremental savings (MWh) (from Table 5-15) by the first-year t otal CSE (from Table 5-23 with escalation based upon results from Table 5-14). Program and pa rticipant portions of the first-year costs are then calculated as 50% of total first-year costs fo r each per Table 5-23. The resulting values are summarized for South Carol ina in Table 5-24.

TABLE 5-24

Calculation of Annual First-Year Costs for South Ca rolina

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 based on levelization of all savings (first and future years) resulting from EE activities in a giv en year. The levelization algorithm is based on

the 2002 California Standard Practice Manual. 242 The net present value of all savings from a single year’s EE activities (i.e., over the entire distribution of program lifetimes) is calculated using the real discount rate. The levelized cost of saved energy is then calculated by dividing the annual first-year costs (from Table 5-24) by the le velized 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 1 0,554

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 Practi ce Manual: Economic Analysis of Demand-Side Programs and Proje cts. Available at http://www.calmac.org/events/SPM_9_20_02.pdf

.5 - 57

Participant

LCSE (cents/kWh) 3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56

Step 3: Calculate Annualized Costs The costs of the EE program can also be represented as annualized costs in a given year. Annualized costs are calculated by multiplying the LCSE for each year by the estimated savings in each year through the full distribution of measu re lifetimes. For each year in the analysis, the annualized costs resulting from all current and pas t investments are summed to calculate the total annualized costs in that year. The resulting values are summarized for South Carolina in Table 5-

26.

TABLE 5-26

Annualized Costs for South Carolina

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Annualized

Total Costs (millions 2011$) 17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2

Annualized Program

Costs (millions 2011$) 8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1

Annualized Participant

Costs (millions of 2011$) 8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.15 - 58

Summary of General Formulas and Results by Step for South Carolina Tables 5-27 and 5-28 provide summaries of the gene ric 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 = ∑  	

    (
)      , 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 = ∑ (  !" # 

$ × 

  &''(&) *'+,-.-'/&) 0&1*'20 *' 3-&, * #
	$ )

, where LCSE year i-t

is the LCSE of EE programs implemented in year i-t , &''(&)	*'+,-.-'/&)	0&1*'20	*'	3-&,	* #
	$ represents estimated 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-y ear and annualized EE costs for Option 1 for 2018, 2020, 2025, and 2030. Table 5-30 summarizes national LCSE for Option 1 for the same years. Each of the three tables includ es values for program, participant, and total

costs. TABLE 5-29 First-Year EE Costs (billions 2011$) (Continental U.S.)

Year

2018 2020 2025 2030 Program 10.2 15.4 21.8 21.8 Participant 10.2 15.4 21.8 21.8

Total 20.5 30.7 43.6 43.55 - 60 TABLE 5-30 Levelized Cost of Saved Energy (3% discount rate, 2 011$/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 201 1$)

(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 E E cost results at the national level by year for Options 1 and 2, and at discount rates of 3% and 7%. These data sheets provide results of LCSE (total, program and participant), f irst-year costs (total, program and participant), and annualized costs (total, program and participan t).

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 ra te (CAGR) of the recent rapid increase in historic investment (2006 to 2011) and the CAGR fro m 2011 through 2018, 2020, and 2025 represented by Option 1 program costs. Historic dat a is from Table 5-2 and Option 1 data is from Table 5-29. Table 5-32 provides a summary of the re sults. The CAGRs represented by Option 1 through 2018, 2020, and 2025 vary from 8% to 11%. T he historic growth rate reflecting the rapid recent growth in EE program spending is 30%, roughl y three times the Option 1 values.5 - 61 TABLE 5-32 Historic and Projected (Option 1) Annual Growth Rat es 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 a nd CO 2 emissions associated with this building block, EPA analyzed a scenario incorporati ng the resulting reduction in electricity demand (the “energy efficiency scenario”) and compa red the results with the base case scenario. Both analyses were conducted using the Integrated P lanning Model (IPM) described in earlier chapters. Combining the resulting power system cost reductions with the energy efficiency cost estimates associated with the energy efficiency sce nario, 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 av erage $/tonne to be reasonable, we expect the $/tonne would be lower in combination with the other building blocks because, in that context, power system costs would be somewhat higher and, thus, avoided power system costs due to this building block would be hi gher as well, leading to lower $/tonne CO 2 avoided.

Analysis Considerations Two considerations are worth noting in regards to the analysis described in the previous two sections, “Goal Setting” and “Impacts Assessmen t:” 1) state energy efficiency policies implicitly represented in the baseline electricity demand and 2) Form EIA-861 as a data source.5 - 62

State Energy Efficiency Policies in the Baseline El ectricity Demand The baseline electricity demand forecast used for the state goal setting approach represented in this chapter, as well as for the pow er sector modeling discussed in the RIA, is based upon the AEO 2013 reference case scenario. AE O 2013 does not explicitly represent existing utility energy efficiency programs or futu re requirements (e.g., EERS) to achieve savings goals through such programs. For example, e xisting state EERS are not evaluated and represented in the AEO 2013 reference case. However , to some degree, AEO 2013 does implicitly reflect a continuation of the effects of existing state energy efficiency programs in the electricity demand projections represented in the r eference case. This implicit representation is captured in part through a calibration process that is affected by several historic factors including reported electricity sales and sectoral energy cons umption surveys. As noted, EPA’s state goal setting approach for de mand-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 deri ve percentage cumulative savings by year (for each state), the resulting state goals (expres sed in percentage cumulative savings by year, by state) are not affected by the underlying electrici ty demand forecast. The impacts assessment of the demand-side energy efficiency building block is affected, to some degree, by the implicit representation of a continuation of existing energy efficiency programs because the assessment is built partly from absolute energy savings values th at are partly derived from the business-asusual (BAU) demand forecast. If the BAU forecast di d not implicitly represent a continuation of existing energy efficiency programs, the forecast w ould indicate higher electricity demand, at least in the near term. However, the direction (hig her or lower) of the net cost impacts (energy efficiency program costs as well as power system co st reductions) is not clear as it is possible that program costs could increase while avoided pow er system costs also increase.

Energy Information Administration Form EIA-861 as D ata Source Comprehensive data on energy efficiency programs’ s pending and energy savings are limited for evaluating and comparing energy efficie ncy programs and their effectiveness at the utility, state, and national scale. Issues related to the lack of standardized definitions and5 - 63

reporting, and data quality are noted to limit eval uation of energy efficiency programs. 243 The EIA Form 861, “Annual Electric Power Industry Repor t,” remains the most comprehensive effort that collects data annually on utility demand-side management (DSM) programs, including their spending and energy savings impacts, nationally. 244 The form is requested for electric utilities, electric power producers, energy service providers, wholesale power marketers, and all DSM program managers and entities responsible to estima te the DSM activity for the reporting year using their best available data , including costs and incremental and cumulative en ergy savings from energy efficiency programs and load management programs.

This analysis uses only two EIA-861 data variables. Specifically, we use the 2012 sales data and reported incremental annual energy savings of energy efficiency programs to estimate the current performance of energy efficiency progra ms to inform setting best practices performance level for the state EE goal setting.

EPA notes potential concerns associated with consis tency and quality of reported DSM program data in Form EIA-861. Specifically, the data are self-reported by utiliti es and DSM program administrators. The definition and data categories may not be consi stently 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 and more detailed data categories are being reported. For instance, in 20 11, EIA began collecting data from third-party administrators of programs. While now comprehensive , outside entities have found that the EIA861 data can be improved through supplementation wi th publicly available annual energy efficiency program reports

. 245

243 MJ Bradley & Associates, LLC. 2011. Benchmarking E lectric Utility Energy Efficiency Portfolios in the U.S.

244 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/ 245 See, for example, American Council for an Energy-E fficient 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) Electri c Energy Efficiency Potential Studies Appendix 5-2: Incremental Electricity Savings Pace of Improvement Analysis Appendix 5-3: Review of the Ratio of Program to Par ticipant Costs Appendix 5-4: Comprehensive Results: State Goal Se tting and Impacts Assessment5 - 65 

### Appendix 5-1 Summary of Recent (2010-2014) Electric Energy Effic iency Potential Studies

The following table summarizes estimates of economi c and achievable energy efficiency potential from a number of recent studies (2010-201 4) for states, utilities, and other agencies across the U.S. Study periods ranged from five to t wenty-one years in length. As Table 1 shows, across the eleven studies that reported achievable potential, results for average annual achievable potential range from 0.8% per year to 2.9% per year (of baseline sales) with an average of 1.5%

per year.

TABLE 1 Summary of Recent (2010-2014) Electric Energy Effic iency Potential Studies

State Client Analyst Study

Year Study

Period End-year Projected

Potential as % of

Baseline Sales Average Annual Projected

Potential as % of Baseline

Sales

Economic Achievable Economic Achievable

Arizona Salt River

Project Cadmus Group 2010 2012-

2020 29% 20% 3.2% 2.2%

California California

Energy

Commission California

Energy

Commission 2013 2014-

2024 Not

reported 9.6% N/A 0.9%

Colorado Xcel Energy Kema, Inc. 2010 2010-

2020 20% 15% 1.8% 1.4%

Delaware Delaware

DNR/DEC Optimal Energy,

Inc. 2013 2014-

2025 26.3% Not reported 2.2% N/A

Illinois ComEd ICF International 2013 2013-

2018 32% 10% 5.3% 1.7%5 - 66

Michigan Michigan PSC GDS Associates 2013 2013-

2023 33.8% 15% 3.1% 1.4%

New Jersey Rutgers

University EnerNOC Utility

Solutions 2012 2010-

2016 12.8% 5.90% 1.8% 0.8%

New Mexico State of New

Mexico Global Energy

Partners 2011 2012-

2025 14.7% 11.1% 1.1% 0.8%

New York ConEd Global Energy

Partners 2010 2010-

2018 26% 15% 2.9% 1.7%

Pacific

Northwest (Idaho, Montana, Oregon, Washington) US

Department of Energy Lawrence

Berkeley

National Laboratory 2014 2011-

2021 11% Not reported 1.9% Not reported

Pennsylvania Pennsylvania

PUC GDS Associates

and Nexant 2012 2013-

2018 27.2% 17.3% 4.5% 2.9%

Tennessee Tennessee

Valley Authority Global Energy

Partners 2011 2009-

2030 24.8% 19.8% 1.1% 0.9%

Range 0.8% - 2.9%

per year

Average 1.5% Per year

References Arizona: The Cadmus Group, Inc. 2010. Salt River Pr oject 2012-2017 Energy Efficiency Plan – Final Report. Prepared for Salt River Project, Apri l 16.

Colorado: Kema, Inc. 2010. Colorado DSM Market Pote ntial Assessment – Final Report. Prepared for Xcel Energy, March 12.5 - 67

Delaware: Optimal Energy, Inc. 2013. Delaware Econ omic Energy Efficiency Potential. Prepared for the Delaware Department of Natural Res ources and Environmental Control, May

24.

Illinois: ICF International. 2013. ComEd Energy Eff iciency Potential Study Report, 2013-2018. August 20.

Michigan: GDS Associates, Inc. 2013. Michigan Elect ric and Natural Gas Energy Efficiency Potential Study – Final Report. Prepared for the Mi chigan Public Service Commission,

November 1.

New Jersey: EnerNOC Utility Solutions Consulting. 2 012. New Jersey Energy Efficiency Market Potential Assessment, Volume 1: Executive Su mmary. Report Number 1401, Prepared for Rutgers, The State University of New Jersey, Oc tober 17, 2012.

New Mexico: Global Energy Partners. 2011. Energy Ef ficiency Potential Study for the State of New Mexico, Volume 2: Electric Energy Efficiency An alysis. June 30.

New York: Global Energy Partners, LLC. 2010. Energy Efficiency Potential Study for Consolidated Edison Company of New York, Inc., Volu me 2: Electric Potential Report. March.

Pacific Northwest states (combined): Barbose, Galen , and Alan Sanstad, Charles Goldman, Stuart McMenamin, Andy Sukenik. 2014. Incorporating Energy Efficiency into Western Interconnection Transmission Planning. Draft report , Lawrence Berkeley National Laboratory, January.

Pennsylvania: GDS Associates, Inc., and Nexant. 201 2. Electric Energy Efficiency Potential for Pennsylvania – Final Report. Prepared for the Penns ylvania Public Utilities Commission, May

10.5 - 68

Tennessee: Global Energy Partners. 2011. Tennessee Valley Authority Potential Study, Final Report, Volume 1: Executive Summary. Report Number 1360, December 21.5 - 69 

### Appendix 5-2 Incremental Electricity Savings Pace of Improvement Analysis

This appendix summarizes and analyzes data to chara cterize the pace of improvement of incremental (or first-year) savings as a percentage of retail sales for electricity energy efficiency (EE) programs. We considered two different perspect ives: 1) historical data reflecting achieved savings of EE programs and 2) requirements of exist ing state energy efficiency resource standards (EERS). For the historical perspective, w e reviewed data from the Energy Information Administration’s Form EIA-861 on EE program electri city savings (supplemented as needed with program administrator reports) and identified the pace at which entities reaching higher savings levels have historically increased energy s avings over time. 246 Specifically, we reviewed the historical savings data in the following two gr oups of energy efficiency program

administrators.

1. Top saver 1% - a group with 47 entities that achiev ed a maximum first-year savings level

of 0.8% to 1.5%.

2. Top saver 2% - a group with 26 entities that achiev ed a maximum first-year savings level

of 1.5% to 3.0%. 247 For the existing state requirements perspective, we reviewed energy savings ramp-up schedules established under EERS for states that pr ovide 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 prog ram administrator data because the EIA 861 just started to collect third-party administrator data in 2011. The third-party entities included in our analysis are Efficiency Ve rmont, Energy Trust of Oregon, Efficiency Maine Tru st, and Cape Light Compact. In addition, we supplemented the EIA 861 database with additional data for two utilitie s that we found achieved high energy savings, but did not rep ort savings data in the EIA 861 data for one or two years. These entities are Burlington Electric and Massachusetts Electric Company (now part of National Grid). 247 In addition to these maximum first-year savings th resholds, we screened program administrators for th e following conditions: (a) the maximum savings levels occurs a fter the minimum savings levels; (b) sufficient amo unts of increase in first-year savings are provided to eval uate reasonable ramp-up schedules to gain an increm ental 1% firstyear savings; and (c) savings data series are conti nuous between the years for the minimum and maximum savings levels. 248 American Council for an Energy-Efficient Economy ( ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

.5 - 70

mandatory EERS policies. 249 Our analysis contains 10 states for which clear ra mp-up schedules

were identifiable. Our research findings on historical savings perform ance are:

• The “Top Saver 1%” group (savings between 0.8% and 1.5%) exhibits a trend that these entities took or would take about 3.4 years on aver age to increase first-year savings by 1% (with a range of 1.6 years to 10 years) (see Tab le 1). The entities in this group have increased the level of first-year savings by 0.30% per year on average from their minimum to their maximum first-year savings levels (with a range of 0.10% per year to 0.63% per year). 250

• The “Top Saver 2%” group (savings between 1.5% and 3%) exhibits a trend that took or would take about 2.6 years on average to increase s avings by 1% (with a range of 0.8 years to 7.3 years) (see Table 1). The entities in this group have increased the level of first-year savings by 0.38% per year on average fro m 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-yea r 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 Scorecard, App endix B, November 2013, 250 This is a simple average estimate of the annual av erage increase in first-year savings from each enti ty in this group. 251 This is the simple average estimate of the annual average increase in first-year savings from each en tity in this group. 252 Data sources: The EIA 861 was the main data source . However, we have supplemented the EIA 861 with th irdparty program administrator data because the EIA 86 1 just started to collect third-party administrator data in 2011. The third-party entities included in our analysis a re Efficiency Vermont, Energy Trust of Oregon, Effi ciency Maine Trust, and Cape Light Compact. In addition, we supp lemented the EIA 861 database with additional data for two utilities that we found achieved high energy saving s, but did not report savings data in the EIA 861 d ata for one or two years. These entities are Burlington Electric a nd Massachusetts Electric Company (now part of Nati onal Grid).5 - 71

Median 0.29% 3.4 0.34% 3.0

Max 0.63% 1.6 1.28% 0.8

Min 0.10% 10 0.14% 7.3 # of sample entities 47 26

Our research findings on incremental electricity sa vings ramp-up based on existing state EERS policies are:

• The states with EERS policies which exhibit savings ramp-up schedules are requiring increases in first-year energy savings at a pace th at ranges from 0.11% (Colorado and Oregon) to 0.40% (Rhode Island) as shown in Table 2

.

• The first-year savings pace of increase averages 0. 21% per year across the 10 states. This savings level translates to about 4.7 years to achi eve an incremental 1% first-year savings

increase. Table 2. First-Year Energy Savings Ramp-up Review o f 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 Publ ic 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 Program to Participant Costs

Introduction and Summary This appendix summarizes and analyzes data on EE co sts (program and participant) to develop a ratio to enable the estimation of partici pant costs from known program costs. We reviewed cost data from leading EE program administ rators in 22 states. Our research findings

are as follows:

• A 1:1 ratio between program and participant costs i s a reasonable and slightly conservative (i.e., slightly higher total costs) ba sis for estimating participant costs from known program costs.

• Reported data was reviewed from 22 states; however, program administrator reports from only nine states contained sufficient information ( participant costs across entire portfolio of EE programs) to inform the analysis.

• Participant cost data from ten program administrato rs in nine states indicate that the weighted average and simple average participant cos ts were 47 percent of total costs.

Participant Cost Analysis We first identified states having high incremental electricity savings rates or high absolute savings levels based upon 2013 ACEEE State Energy Efficiency Scorecard. 254 These states represent a large portion of total EE saving s in the U.S. We identified 22 states meeting these criteria and collected publicly available EE program reports for major program administrators within each state. From these progra m reports we identified 10 program

administrators across nine states where we were abl e to identify both program administrator and participant costs across their full portfolio of EE programs. The table below provides the 2012 portfolio-level program administrator and participa nt costs for the nine states. Program

254 For the purpose of this research, we have defined leading or high impact states as the top 15 states in the 2013 ACEEE State Energy Efficiency Scorecard in terms of incremental savings as a percentage of retail sale s or absolute annual energy savings in terms of total annual MWh savings. These criteria resulted in a total of 22 s tates which include Arizona, California, Connecticut, Florida, Hawaii, Illinois, Indiana, Iowa, Maine, Massachuset ts, Michigan, Minnesota, New Jersey, New York, North Carolina, Oh io, Oregon, Pennsylvania, Rhode Island, Texas, Verm ont, and Washington.5 - 74

administrator costs represent the program administr ator’s program development and implementation costs, and the participant costs rep resent the customer costs to partake in the program. Total program costs are the sum of both co sts. Each state’s program administrator and participant costs are presented as a percentage of total program costs in Table 3. The weighted and simple average program and participant costs ac ross all nine states are presented as a percentage of total program costs. The weighted ave rage cost shares were based on each program’s spending by administrator and participant s.

Table 1 2012 Participant and Program Cost Information from Reported Entities

In our analysis, the weighted average program and p articipant costs are 53.4% and 46.6%, respectively, of total costs. On a simple av erage 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 $ 6 8 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 3 5.1% 64.9% Pennsylvania PECO $ 68 $ 109 $ 178 38.5% 61. 5% Rhode Island National Grid $ 63 $ 13 $ 7 5 83.2% 16.8%

Vermont Efficiency

Vermont; Burlington Electric Department $ 34 $ 23 $ 57 5 9.3% 40.7% Weighted Average 53.4% 46.6% Simple Average 52.6% 47.4%5 - 75

52.6% and 47.4%, respectively, of total costs. Part icipant cost results range from a low of 17% of total costs (National Grid in Rhode Island) to a hi gh of 65% (Xcel Energy in Minnesota). When deriving participant costs from program costs, usin g a ratio of 1:1 is consistent with these results and slightly conservative, leading to slightly high er total costs than the precise average values would provide.

References

MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual Report to the Iowa Utilities Board," May 1, 2013, a vailable at: http://www.state.ia.us/government/com/util/energy/e nergy_efficiency/ee_plans_reports.html

Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012- Annual-Report.pdf

National Grid Electric, "2013-2015 Massachusetts Jo int Statewide Three-Year Electric and Gas Energy Efficiency Plan," Docket No. 12-109, Novembe r 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 P ennsylvania 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 1 8, 2013, available at: http://www.efficiencyvermont.com/docs/about_efficie ncy_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf

Burlington Electric Department, "2012 Energy Effici ency Annual Report," 2012, available at: https://www.burlingtonelectric.com/ELBO/assets/2012 %20DSM%20Annual%20Report%20Mas ter.pdf5 - 76

Hawaii Energy, “Annual Report Program Year 2012 Jul y 1, 2012 - June 30, 2013,” 2013,

available at http://www.hawaiienergy.com/information-reports

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

National Grid, "2012 Energy Efficiency Year-End Rep ort," May 31, 2013, available at http://www.nationalgridus.com/non_html/eer/ri/4295- YearEnd%20Rept%20(PUC%205-31- 13).pdf

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

### Appendix 5-4 Comprehensive Results: State Goal Setting and Impa cts 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 Sta te (2017-2030)

• Option 1 – Cumulative Savings as % of Sales by Stat e (2017-2030)

• Option 1 – Cumulative Savings (GWh) by State (2017- 2030)

• Option 2 – Incremental Savings as % of Sales by Sta te (2017-2025)

• Option 2 – Cumulative Savings as % of Sales by Stat e (2017-2025)

• Option 2 – Cumulative Savings (GWh) by State (2017- 2025)

Impacts Assessment Sheets

• Option 1 – National Costs at 3% Discount Rate (2017 -2030)

o Levelized Cost of Saved Energy, First-year Costs, a nd Annualized Costs

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

o Levelized Cost of Saved Energy, First-year Costs, a nd Annualized Costs

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

o Levelized Cost of Saved Energy, First-year Costs, a nd Annualized Costs

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

o Levelized Cost of Saved Energy, First-year Costs, a nd Annualized Costs6 - 1 

### Chapter 6: Fuel Switching

Coal-to-Natural Gas Switching

Introduction

Firing natural gas in a boiler designed for coal-fi red 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 ca rbon than the coal it replaces. When quantities of gas and coal are burned with oxygen f rom air to produce the same amounts of heat, the higher hydrogen content of natural gas produces more water vapor (H 2 O) than coal, but far

less CO 2

. The discussion below focuses solely on the conversi on of an existing coal-fired boiler to burn natural gas instead of, or along with, coal. T here 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 i nput to the boiler from the exhaust of a newly installed gas turbine generator; and,

• Gasification of coal, producing substitute natural gas (SNG) that provides heat input to the existing coal-fired boiler. These other options have higher capital cost and th us would not be as economic as the direct substitution of natural gas in an existing coal boi ler, for reasons that will become apparent in this analysis. This chapter evaluates the cost-effectiveness of wi despread adoption of coal-to-gas switching at a national level for the purpose of se tting CO 2 emissions goals consistently in each

state.

Description of Technology Engineering Considerations

Most existing coal-fired EGU boilers can be modifie d to switch to 100% gas input, or to co-fire gas with coal in any desired proportion. T his transition typically requires at least some6 - 2

plant modifications and might have some negative im pact on the efficiency of the unit as described later in this chapter. A conversion from coal to gas firing first requires either an existing natural gas delivery system to the boiler or the installation of a new g as 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 o f a new gas pipeline is not likely to be the determining factor for the project’s economic merit , given the significance of the change to the cost of fuel for generation from the converted boil er. Conversion to natural gas firing in a coal-fired bo iler typically involves installation of new gas burners and supply piping, modifications to combustion air ducts and control dampers, and possibly modifications to the boiler’s steam su perheater, reheater, and economizer heating surfaces that transfer heat from the hot flue gas e xiting the boiler furnace. The conversion may also involve some modification and possible deactiv ation of some downstream air pollution emission control equipment. Engineering studies are performed to assess changes in furnace heat absorption and exit gas temperature; material chang es affecting heat transfer surfaces; the need for sizing of flue gas recirculation fans; and oper ational 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 v apor) in the flue gas. The increased moisture content, in turn, results in additional he at lost up the stack instead 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 lim its, or for other technical reasons. Even with a decrease in boiler efficiency, the over all net output efficiency of a coal-steam boiler EGU that switches from coal to natural gas firing m ay change only slightly, depending on how much auxiliary load is converted to net output by a voiding the need to run coal pulverizers, conveyors, ash sluice pumps, and relevant air pollu tion control equipment (e.g., PM and SO 2 controls).6 - 3

Fuel Considerations Delivery of natural gas via pipeline is critical fo r conversion of a coal-fired boiler to a gas-fired boiler. Some coal boilers are connected to the natural gas pipeline network for purposes of using gas as a startup fuel, or are loc ated at facilities with onsite gas-fired generators

. These boilers are likely able to co-fire to some de gree with gas (at least 10% total output 255 ) without constructing additional gas pipeline capaci ty. 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 con struction of additional pipeline capacity. Unlike coal, natural gas cannot be stored in quanti ties sufficient for sustained utilization on site. To the extent that firm (uninterruptible) gas suppl y is contractually unavailable or costprohibitive, any potential interruption in gas supp ly could impact the ability of the unit to continue operating without increasing its CO 2 emissions rate (since it would likely need to

substitute more CO 2 -intensive fuel for the unavailable natural gas). Additionally, for boilers that switch to 100% gas, interruption in gas impacts the ability of the unit to continue generating at all if gas is unavailable. For these reasons, an EG U switching to a large percentage of gas use may elect to install more than one new gas supply p ipeline from separate sources. Although the EPA assumes the addition of one gas pipeline in the simplified cost analysis presented below, it will be seen that pipeline cost will generally not be the main driver of economic feasibility.

Cost and Performance Impacts of Coal-to-Gas Switchi ng The analysis described in this section presents a h ypothetical conversion of a boiler from burning 100% coal to burning varying proportions of gas (10%, 50%, and 100%). The capital cost of modifying a coal boiler to switch to natura l gas includes the new gas burners and piping, combustion air ductwork and control damper modifica tions, air heater upgrades, gas recirculating fans, control systems modifications, and other site-specific modifications, as well as any pipeline installation costs that would be neces sary to supply the unit’s assumed level of gas combustion following the conversion.

255 Based on assumed use of Class 1 igniters (10% of b urner capacity) as defined in NFPA 85 Boiler and Combustion Systems Hazards Code.6 - 4

For this analysis, the EPA assumes capital costs fo r 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 coa l unit would have a capital cost of $137/kW to convert the boiler such that it could bu rn any proportion of natural gas. For this illustrative example, to support 100% gas combustio n 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 & Vea tch also used a similar cost level in a recent case study. 259 At a 14.3% capital charge rate 260 and 75% annual capacity factor, the total capital cost in this example equates to an annualized capit al cost of about $5/MWh. This $/MWh capital cost is relatively insignificant compared to the in crease in fuel cost discussed later. Due to a reduced need for operators, maintenance ma terials, 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 cos ts are assumed to be reduced by 25% due to reduced waste disposal, reduced auxiliary power req uirement, and miscellaneous other costs. EPA engineering staff also assumed for this analysi s that there would be no derate in the net EGU output, and estimated that the impact on net he at rate for an average unit would be a 3% increase for a switch from coal to 100% natural gas firing. The assumed 3% increase in net heat rate is conservative compared to the 2% assumption used by Black & Veatch in their previously mentioned case study.

256 EPA assumptions on costing and performance associa ted with coal-to-gas conversion and pipeline additi ons in this analysis are generally consistent with assumpt ions 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.pd f 257 Based on EPA analysis, the majority of existing co al units would require less than 50 miles of new ga s pipelines to switch fuels from coal to 100% natural gas. See Chapter 5 and Table 5-22 of Documentation for EPA Base Case v.5.13 Using the Integrated Planning Model, availab le at: http://www.epa.gov/airmarkets/progsregs/epaipm/BaseCasev513.html 258 For those plants that require additional pipeline c apacity, the average capital cost of constructing n ew pipelines is assumed to be approximately $1 million per mile of pipeline built, which is consistent with assumption s used in EPA’s IPM modeling. 259 A Case Study on Coal to Natural Gas Fuel Switch, B lack & Veatch, Power-Gen International, December 20 12, available at http://bv.com/redirects

. 260 Capital charge rate at 14.3% is the average of the regulated utility and unregulated merchant rates a s used in IPMv5.13 for environmental retrofits having a 15 ye ar book life.6 - 5

Cost of Fuel For this analysis, the EPA uses base case projectio ns for delivered gas prices that are about double projected delivered coal prices on ave rage ($2.62/MMBTU for coal, $5.36/MMBTU for gas). 261 As a result, the fuel cost for a typical converte d boiler burning 100% gas is expected to be at least double its prior fue l cost on an output basis as well ($27/MWh for coal, $57/MWh for gas). 262,263 Compared to the estimated $5/MWh capital cost impa ct presented above, a $30/MWh increase in fuel cost would make t he difference in fuel costs the most significant driver of project economics when switch ing from coal to gas in a coal boiler. 264 This difference would increase with higher gas prices, w hich 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 amo unt of gas co-fired, and is due largely to the different carbon intensities of each fuel. More reductions in CO 2 rate are achieved at higher levels of gas co-firing as shown in Table 6-1. At 10% gas co-firing, the net emissions rate (lbs/MWh net) of a typical unit would decrease by approximately 4%. At 100% gas cofiring, the net emissions rate (lbs/MWh net) of a t ypical unit would decrease by approximately

40%.

261 EPA Base Case 5.13, projections for 2020 262 This estimated fuel cost also accounts for the dec rease in efficiency that results from switching fro m coal to gas in a boiler, as well as the decrease in parasitic p ower consumption. 263 Combusting natural gas using combined-cycle turbin e technology can remain economically attractive notwithstanding these types of fuel price different ials because combined cycle turbine technology conv erts a substantially higher share of the fuel’s heat input into electricity output as compared to boiler tech nology. The $/MWh impact of a higher gas price in that instance is significantly mitigated by higher MWh output pr oduced for a given amount of heat input from the fuel purchased. 264 This demonstration assumes that the converting boi ler in question remains a “price taker” in the fuel marketplace, such that the projected gas and coal prices would b e unaffected by this hypothetical unit’s potential decision to convert. However, if enough other units might be e xpected to make similar conversions, the aggregate increased demand for natural gas would be likely to further i ncrease the price differential between coal and gas , making fuel costs an even more influential factor in the evalua tion 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-firing at a coal-fired s team EGU will generally also reduce criteria air pollution. Reducing CO 2 and criteria air pollution will

result in climate benefits and human health co-bene fits. The impacts of these pollutants on the

environment and health are discussed in detail in C hapter 5 of the RIA for this proposed rule. For this analysis, EPA estimated the PM 2.5 -related human health co-benefits of SO 2 , NO X , and

direct-PM 2.5 emission reductions attributable to a range of nat ural gas co-firing levels at an illustrative coal steam unit burning bituminous coa l 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 impairment. O nly the unit-level emissions of SO 2 , NO X and

direct-PM 2.5 are considered in this illustrative exercise. Add itionally, emissions from the extraction and transport of the fuels used by these technologies are not considered. Furthermore, there may be differences in upstream greenhouse gas emissions (in particular, methane) from different technologies but those were not quantifie d for this assessment. The estimated avoided emissions under 10% gas co-firing and a 100% switch to gas are presented Table 6-2.

Table 6-2. Avoided Emissions at Various Levels of Co-Firing, based on Illustrative Unit (lbs/MWh net)

10% Gas 100% Gas 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) operating at 75% capacity factor. Furthermore, this unit was assume d to operate a wet scrubber, cold-side ESP, and SNCR.6 - 7

To estimate human health co-benefits for this illus trative 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 thi s proposal. To estimate the benefits associated with co-firing, we determine the emission reduction s for co-firing in Table 6-2 and apply the 2020 social benefit values discussed in Chapter 5 o f the RIA for this proposal. Specifically, we multiply the reduction in SO 2 , NO X , and direct-PM 2.5 emissions by the PM 2.5 -related benefit perton estimates, and add those values to get a measur e of 2020 benefits. Table 6-3 shows the

PM 2.5 -related benefits expected based on the estimated e mission reductions that would occur in this illustrative example. These estimates are pure ly illustrative as the EPA does not assert a specific location for the illustrative electricity generation technologies and is therefore unable to specifically determine the population that would be affected by their emissions. Therefore, the benefits for any specific unit can be different tha n the estimates shown here.

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

Health Co-benefit Discount Rate 3% Discount Rate 7% Discount Rate Gas Co-firing 10% $6.5 to $15 $5.9 to $13 Gas Co-firing 100% $67 to $150 $61 to $140 Note: All estimates are rounded to two significant figures. Co-benefits are based on national benefit-per-ton estimates for directly emitted PM 2.5 and PM 2.5 precursors, SO 2 and NO X . It is important to note that the monetized health co-bene fits do not include reduced health effects from ozone or direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment. Emissions of directly emitted particles are disaggregated into EC+OC or crustal components using the method discussed in Appendix. 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. (200 9) to Lepeule et al. (2012)).

The precise incremental health co-benefits associat ed 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 th at unit. This illustrative assessment is unable to

266 Krewski D.; M. Jerrett; R.T. Burnett; R. Ma; E. Hu ghes; Y. Shi, et al. 2009. Extended Follow-Up and S patial Analysis of the American Cancer Society Study Linki ng Particulate Air Pollution and Mortality. HEI Res earch Report, 140, Health Effects Institute, Boston, MA. Lepeule, J.; F. Laden; D. Dockery; J. Schwartz. 201 2. “Chronic Exposure to Fine Particles and Mortalit y: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectiv es, July, 120(7):965-70.6 - 8

account for these characteristics. However, these factors will not change the qualitative conclusion. There will always be incremental human health co-benefits associated with co-firing natural gas in an existing coal steam boiler, indep endent of the location, coal type, and operating pollution controls. A related beneficial use of natural gas in existing coal boilers can be via gas reburning, a NOx reduction technology. 267 Gas reburning involves firing natural gas (between 10 and 25% of total heat input) above the primary combustion zone in the boiler furnace. This upper-level firing creates a slightly fuel-rich zone. NOx produced in the primary zone of the furnace is "reburned" in this zone and converted to molecular nitrogen an d other reduced nitrogenous species. Overfire air is injected downstream of the reburn zone to bu rn out the remaining combustibles and convert the reduced nitrogenous species to molecular nitrog en. The heat input from gas would approximately substitute for a similar heat input f rom coal, thus reducing CO 2 and other coal emissions in a manner similar to gas co-firing as d iscussed 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-firing levels to a typical baseload coal boiler. We capture the capital costs of boiler modifications and new pipel ine construction (assuming 50 miles of new pipeline), 269 decreased FOM and VOM costs, and incremental fuel costs (based on IPMv5.13 Base Case average delivered fuel price projections for coal and gas in 2020). For a typical coal boiler at current base case fuel prices, the averag e 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%20Library/Research/Coa l/major%20demonstrations/cctdp/Round3/GRLNBPPA.pdf 268 This document uses “tonne” to refer to a metric to nne. All control costs in this analysis are presen ted in dollars per metric tonne, or “$/tonne.”

269 Based on EPA analysis, the majority of existing co al units would require less than 50 miles of new ga s pipelines to switch fuels from coal to 100% natural gas. See Chapter 5 and Table 5-22 of Documentation for EPA Base Case v.5.13 Using the Integrated Planning Model, availab le 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 ste am unit operating at 75% capacity factor. Assumes construction of new 50-mile pipeline. EPA e stimated reduced total capital costs for the 50% and 10% gas cases; for example, total capital c ost for 10% gas was estimated to be about one-half of the capital costs for the 100% gas case

. However, widespread adoption of gas co-firing would increase overall gas demand and place upward pressure on the natural gas price, whi ch would consequently increase the average

cost of avoided CO 2 of a potential boiler conversion.

Conclusion Switching from coal to gas is a relatively costly a pproach to CO 2 reductions at existing coal steam boilers when compared to other measures such as heat rate improvements and redispatch of generation supply to other existing cap acity with lower CO 2 emission rates. Moreover, this analysis shows that coal-to-gas conv ersion of an existing boiler is less efficient than constructing a new natural gas combined cycle (NGCC) turbine in its place. For example, EPA analysis indicates that replacing the coal stea m plant discussed above with a new NGCC facility would reduce the net CO 2 emission rate of the generating capacity by 62% at a cost of about $50/tonne of avoided CO 2 under the base case projected gas price and about $81/tonne of

avoided CO 2 at a future gas price 50% higher than the base cas e projection. See preamble

section VI.C.3.c. The EPA is considering cost-effectiveness at a nati onal level for the purpose of setting emissions goals consistently in each state. While this analysis suggests that cost-effective

reductions of CO 2 are not available on a national basis from widespr ead adoption of natural gas co-firing, it does not preclude the potential for i ndividual EGUs to utilize co-firing as a way to

reduce CO 2 and other emissions, nor does it preclude states f rom factoring in that unit-level potential into the design of state plans for compli ance with the 111(d) standard. EPA notes that6 - 10

there are utilities that see merit in converting so me existing coal units to burn 100% gas, and several are currently doing so. 270,271

270 Reuters 2014, “Southern to repower three Alabama c oal power plants with natgas,” Reuters U.S. Edition , January 16, 2014 , available at http://www.reuters.com/art icle/2014/01/16/utilities-southern-alabamaidUSL2N0KP1WA20140116 271 Dominion 2012, “Dominion Virginia Power Propose s To Convert Bremo Power Station From Coal To Natur al Gas,” Dominion News, September 5, 2012, available a t http://dom.mediaroom.com/2012-09-05-Dominion-Virg iniaPower-Proposes-To-Convert-Bremo-Power-Station-From- Coal-To-Natural-Gas6 - 11

Biomass Co-firing

Introduction Co-firing biomass in existing boilers designed for coal-fired generation, or converting those boilers to consume entirely biomass, is anoth er 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 stack could increase or decrease, depending on the amount of coal energy replaced by biomass energy and differences in the properties of a selected biomass and the coal it re places. 272 There are many possible combinations of coals and b iomass types that could be co-fired. Site-specific economics and accessibility would det ermine which combinations might actually be feasible. This TSD analysis does not attempt to est imate an economically feasible national average increase or decrease in CO 2 emission rate via biomass co-firing. Instead, this analysis simply employs one reasonably representative case t o 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 the c o-firing of biomass with coal is technically feasible as a means of reducing the coal-based CO 2 emission rate due to the substitution of biomass for coal, it generally has limited economic feasibility due to the generally higher cost of energy from biomass as compared to coal. This gener al finding largely explains the very limited amount of biomass co-firing currently practiced in the U.S. It is also consistent with recent findings by others 273 , including an earlier study by the State of Maryl and 274 that concluded as

follows: “Due to the higher cost of biomass fuels when compa red to coal, cofiring with biomass

will lead to an increase in fuel costs. Without con sideration 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 heating value, carbon a nd hydrogen contents, and as-fired moisture content. 273 Nowling, Una, Black & Veatch, “Utility Biomass Use : Turning Over a New Leaf?, Power , May 2014, available at http://accessintelligence.imirus.com/Mpowered/book/ vpow14/i5/p52 274 The Potential for Biomass Cofiring in Maryland, Ma ryland 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 effectivene ss of biomass energy substitution in reducing the physical CO 2 emission rate based only on the CO 2 coming from coal presented below, the EPA concludes in this TSD that biomass c o-firing would not be a cost-effective measure on which to base state goals. 275

Description of Technology Engineering/Economic Considerations The technical feasibility of biomass co-firing in e xisting coal-fired boilers has been thoroughly investigated in many research and engine ering 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 relat ed systems of almost any existing coal-fired EGU can a ccept 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-fire d boiler for biomass co-firing at any percentage level depends on numerous technical and economic factors, including reliable availability of suitable biomass at an economic cos t; adequate onsite space for biomass receiving, storage, preparation, and handling systems; potenti al corrosive effects of biomass ash in the boiler furnace; potential impacts of co-firing on b oiler efficiency even at low biomass percentages, and the likely reduction (derate) in u nit generating 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 p roduction and use of the biogenic feedstocks combus ted. Issues related to methods for assessing biogenic CO 2 emissions from stationary sources are currently be ing evaluated by the EPA. In general, the overall net atmospheric contributio n of CO 2 resulting from the use of a biogenic feedstock by a stationary source, such as an EGU, will ultima tely depend on the stationary source process and th e type of feedstock used, as well as the conditions under whi ch that feedstock is grown and harvested. In Septem ber 2011, the EPA submitted a draft Accounting Framework to the S cience Advisory Board (SAB) Biogenic Carbon Emissio ns (BCE) Panel for peer review. The SAB BCE Panel deli vered its Peer Review Advisory to the EPA on Septem ber 28, 2012. In its Advisory, the SAB recommended revisions to t he EPA's proposed accounting approach, and also not ed 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 en d of this section. 277 For example, one unit at Schiller Station (NH) was converted in 2006 to burn biomass exclusively. Se e: https://www.psnh.com/PlantsTerritory/Schiller-Stati on.aspx6 - 13

There are considerable physical differences between coal and biomass that will generally limit the extent to which biomass can be reasonably used to replace coal in a boiler. For example, compared to most coals, many solid biomass fuels ha ve both a significantly higher as-fired moisture content and a significantly lower heating value per unit of weight. Most solid biomass fuels are also significantly less dense than most c oals. For example, a typical biomass might have twice the moisture, half the heating value, and les s than half the density of coal. 278 Important consequences of these physical differences are that the weight of biomass needed to provide a given amount of heat energy could be twice the weig ht of the coal it replaces, and the volume (cubic feet) of biomass needed could be four-to-eig ht times the volume of coal replaced. Biomass requires space for storage after delivery t o a facility, and the length of time that the biogenic material would remain on site prior to use can differ. For example, wood chips could be delivered year-round while crop residue delivery would follow specific seasons in which the crop was grown. As noted above, the four-fold or g reater increase in volume occupied by biomass relative to coal means that the necessary a dditional storage space could be large. However, if pre-prepared or condensed biomass fuels such as pelletized or torrefied biomass is used, some of these concerns may be lessened, recog nizing that such pre-preparations of the

feedstock will entail additional costs. Stored biom ass can be at even greater risk of spontaneous combustion than stored coal; this may limit the saf e height of biomass piles and further increase storage area requirements. 279

The volumetric differences alone can have other une xpected consequences. For this analysis, experienced EPA engineering staff estimat ed that a 500 MW baseload coal plant cofiring 10% biomass and receiving biomass deliveries 10 hours per day and 5 days per week would require a 20-ton truck delivery to the plant every 10 minutes, in addition to the ongoing coal deliveries. Limiting traffic issues may arise in some situations. Also, because of the low energy density of biomass and its relatively higher transportation cost per unit of delivered energy, it may only be economically viable to trans port biomass a limited distance from where it is grown. This could limit the both the percentage of biomass co-firing in a single boiler and the maximum MW output from biomass at a single site. N ew technologies under development, such

278 Biomass Energy Data Book- Edition 4, October 2012, DOE-EERE-ORNL, http://cta.ornl.gov/bedb/index.shtm l 279 Properties of Wood Waste Stored for Energy Product ion, Purdue University, 2011, http://www.extension.purdue.edu/extmedia/ID/ID-421- W.pdf6 - 14

as torrefaction of biomass, could mitigate some of these transportation, storage and energy content concerns, but are not yet commercially avai lable. The relatively higher moisture content and lower he at content of biomass reduces boiler efficiency, and typically requires a derating in un it generation at very high co-firing percentages as furnace volume and boiler fan capacities become inadequate. For all of the above reasons, the EPA assumed for t his analysis that coal-steam EGU boilers will generally only co-fire with biomass to a limited degree. While the actual level at which any plant can co-fire with biomass is highly site-specific, this analysis adopts the assumption used in EPA’s fleet wide IPM modeling of the electric power sector: a reasonable average limit on biomass co-firing is up to 10% on any single boiler, not to exceed 50 MW total biomass powered output at an individual plant site (which aligns with the magnitude of some of the larger such entities currently in the U.S.). T his amount of co-firing has been used as representative practical limit in other studies as well. 280

Costs and Performance Impacts of Retrofitted Biomas s Co-firing For this analysis the EPA adopted capital and O&M c osts, and performance impacts for retrofitted biomass co-firing capability that are a pproximately representative of EPA assumptions used in its IPM modeling and discussed in the docum entation for IPM v.5.13. 281 EPA estimated that the capital cost to install 50 M W of biomass co-firing capability would be at least $10 million. 282 As applied to a 500 MW coal unit, the minimum cost of this 10% co-firing capability would then be $20/kW. Fixed O&M cost was estimated by EPA engineering staff to be 10% greater than with coal alone, and variable O &M cost was estimated to remain unchanged. The heat rate impact (Btu/kWh) of 10% biomass co-fi ring as estimated by EPA engineering staff for this analysis was an increase of slightly more than 1% compared to coal

280 The Potential for Biomass Cofiring in Maryland, Ma ryland Department of Natural Resources, March 2006, http://esm.versar.com/pprp/bibliography/PPES_06_02/ PPES_06_02.pdf 281 See Sec 5.3, pg 5-19 at: http://www.epa.gov/airmar kets/progsregs/epa-ipm/docs/v513/Chapter_5.pdf 282 Generally consistent with EPA assumptions in IPM m odeling; also see the following source using the sa me retrofit capital cost assumption: Cofiring Biomass and Coal for Fossil Fuel Reduction and Other Benefi ts – Status of North American Facilities in 2010, USDA, August 201 2, http://www.fs.fed.us/pnw/pubs/pnw_gtr867.pdf6 - 15

alone. At low biomass cofiring rates, this factor s lightly affects calculated biomass fuel consumption and any associated CO 2 emission from biomass.

Cost of Fuel For this analysis, the EPA uses a delivered biomass cost of $4/MMBtu, representative of delivered woody crops grown specifically for energy -generating combustion, 283 and roughly 50% greater than IPM projected 2020 average deliver ed coal costs. 284 This analysis also considers a sensitivity scenario assuming a higher $6/MMBtu biomass price. The EPA recognizes that the cost of biomass is high ly site-specific, and in some cases could be largely comprised of collection and transp ortation cost (as is the case for opportunity fuels with little to no other market value). The tr ansportation component depends primarily on the distance that biomass needs to be transported. For example, the EPA engineering staff estimate that for a one-way distance of 50 miles wi th a 20-ton semi-trailer truck, transportation costs could be $10-20/ton. For biomass at a total d elivered price of $4/MMBtu with an indicative heating value of 5,000 Btu/lb (higher heating value (HHV) basis), transportation cost in this example case could account for 25-50 percent of the total delivered biomass cost. In any case, it is the total delivered price of biomass on a $/MMBt u basis that will primarily determine the economic feasibility of biomass co-firing.

Emission Reduction Potential

The CO 2 reduction potential of biomass co-firing is direct ly related to the amount and type of biomass co-fired and is due to the differen ce in heating value, moisture content and hydrogen/carbon ratios 285 for a selected biomass fuel compared to the partic ular coal it replaces. The types of biomass typically available to EGUs in the United States include woody-based feedstocks such as wood chips, forest industry bypr oducts, and to a lesser degree agricultural crop residues, as well as emerging dedicated energy crops such as switchgrass and short-rotation

283 Average biomass price as projected by EPA modeling in IPMv5.13 Base Case

284 EIA, Electric Power Annual 2012 – Electricity (Tab le 7.4) http://www.eia.gov/electricity/annual/html/epa_07_0 4.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-fi ring coal with biomass, cofiring may result in either an increase or decrease in the stack CO 2 emission rate. The extent to

which the use of biomass contributes to net emiss ions to the atmosphere is being considered in EPA’s current study on biogenic emissions accountin g. See preamble Section VIII.G.

Cost of Reductions and Cost Effectiveness In order to evaluate cost-effectiveness of potentia l reductions, the EPA first estimated the

cost of avoided coal CO 2 emissions in a hypothetical scenario where biomass CO 2 emissions are

not included in total stack CO 2 emissions (in effect, biogenic CO 2 emissions are subtracted from

total CO 2 emissions measured at the stack). The estimated r esults presented below are based on a reasonably representative case using a baseload b ituminous coal-fired boiler with a net heat rate of 10,340 btu/kWh that shifts from 100% bitumi nous coal to 90% coal and 10% biomass (assuming fuel prices of $2.62/MMBtu for coal in 20 20 as projected in IPMv5.13 Base Case and $4/MMBtu for biomass as explained above). When bio genic 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 pri ce differentials, the cost of avoided coal CO 2 emissions would increase (for example, at a biomass price of $6/MMBtu, cost of avoided CO 2 is $80/tonne if CO 2 emissions from biomass are not counted). 287 This estimated cost of avoided

coal CO 2 emissions, which ranges for $30 to $80/tonne, woul d 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-f iring 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 existing capacity wit h lower CO 2 emission rates.

286 Biomass Combined Heat and Power Catalog of Technol ogies, U.S. EPA, September 2007, http://www.epa.gov/chp/documents/biomass_chp_catalo g.pdf 287 Similarly, the costs of avoided CO2 emissions woul d decrease at lower fuel price differentials. 288 Depending on biogenic feedstocks used and whether or not an assessment system is applied that evaluat es biogenic CO 2 emissions from the stack in relation to the terres trial carbon cycling associated with the production and use of that biogenic feedstock.6 - 17

The EPA is considering cost-effectiveness at a nati onal level for the purpose of setting emissions goals consistently in each state. While this analysis concludes that cost-effective

reductions of CO 2 are not available on a national basis from widespr ead adoption of biomass cofiring, it does not preclude the potential for indi vidual EGUs to utilize co-firing as a way to

reduce overall CO 2 emissions, nor does it preclude states from factor ing in that unit-level potential into the design of state plans for compli ance with the 111(d) standard. 289

Partial Bibliography for Technical Feasibility of B iomass Co-firing (a) Briggs, J. and J. M. Adams, Biomass Combustion Options for Steam Generation, Presented at Power-Gen 97, Dallas, TX, December 9 – 11, 1997.

(b) Grusha, J and S. Woldehanna, K. McCarthy, and G . Heinz, Long Term Results from the First US Low NOx Conversion of a Tangential Lignite Fired Unit, presented at 24th International Technical Conference on Coal & Fuel Systems, Clearw ater, FL., March 8 – 11, 1999.

(c) EPRI, Biomass Co-firing: Field Test Results: Su mmary of Results of the Bailly and Seward Demonstrations, Palo Alto, CA, supported by U.S. De partment of Energy Division of Energy Efficiency and Renewable Energy, Washington D.C.; U .S. Department of Energy Division Federal Energy Technology Center, Pittsburgh PA; No rthern Indiana Public Service Company, Merrillville, IN; and GPU Generation, Inc., Johnsto wn, PA: 1999. TR-113903.

(d) Laux S., J. Grusha, and D. Tillman, Co-firing o f Biomass and Opportunity Fuels in Low NOx Burners, PowerGen 2000 - Orlando, FL

(e) Tillman, D. A., Co-firing Biomass for Greenhous e 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 fu ture EPA finding regarding an assessment framework that considers carbon fluxes on the biogenic feedstock p roduction landscape applied when evaluating net sta ck CO 2 emissions from biomass co-firing.6 - 18

(f) Tillman, D. A. and P. Hus, Blending Opportunity Fuels with Coal for Efficiency and Environmental Benefit, presented at 25th Internatio nal Technical Conference on Coal Utilization & Fuel Systems, Clearwater, FL., March 6 – 9, 2000

(g) Tillman D A, Harding N S (2004) Fuels of opport unity: characteristics and uses in combustion systems. Elsevier, Oxford, UK

(h) Tillman D, Conn R, Duong D (2010) Coal characte ristics and biomass cofiring in pulverized

coal boilers. In: Electric Power, Baltimore, MD, USA, 18 - 20 May, 2010

(i) Renewable and Alternative Energy Fact Sheet – C o-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 Biomass wit h Coal, IEA Clean Coal Centre (CCC/194), January 2012

(k) DOE-EERE-ORNL, Biomass Energy Data Book- Editio n 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 a nd storage technology (CCS; sometimes also referred to as carbon capture and sequestration). I n the recently proposed standards of performance for new fossil fuel-fired EGUs (79 FR 1 430), the EPA proposed to find that the best system of emission reduction for new fossil fuel-fi red 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 im plementation will serve to promote further development and deployment of the technology. This chapter examines the potential for implementation of CCS technology at existing fossil fuel-fired utility boilers and IGCC units.

Carbon Capture Options for Existing Fossil Fuel-fir ed EGUs In general, CO 2 capture technologies applicable to existing fossil fuel-fired power generation can be categorized into three approaches – (1) post-combustion capture; (2) precombustion capture; and (3) oxy-combustion. Each of these is described and discussed in more

detail below. Post-combustion Capture

Post-combustion CO 2 capture refers to removal of CO 2 from a combustion flue gas prior to discharging to the atmosphere. Separating CO 2 from such a gas stream can be challenging for

a number of reasons. Because CO 2 is a dilute fraction of the combustion flue gas – typically 13- 15 % in coal-fired systems and 3-4 % in natural gas -fired systems – a large volume of flue gas must be treated. The flue gas from typical combusti on systems is usually at near atmospheric pressure. Therefore, most of the available capture systems rely on chemical absorption (chemisorption) options (e.g., amines) that require added energy to release the captured CO 2 and regenerate the solvent. Many of the chemical solven ts 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 pres sure to much higher pipeline pressures (about 2,000 psia).7 - 2

Pre-combustion Capture Pre-combustion capture systems are applicable to fo ssil fuel gasification power plants (i.e., IGCC units) where coal or other solid fossil fuel (e.g., pet coke) is converted into a synthesis gas (or “syngas”) by applying heat under pressure in the presence of steam and limited

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 compositi on via the catalytic water-gas shift (WGS) reaction. This process involves the catalytic react ion of steam (“water”) with CO (“gas”) to form

H 2 and CO 2 . The resulting CO 2 contained in the syngas is then captured before co mbustion of

the H 2 -enriched syngas for power generation in a combined cycle system. Contrary to the postcombustion capture flue gas, the IGCC syngas can co ntain a high volume of CO 2 and is pressurized. This allows the use of physical absor bents (e.g., SelexolTM, Rectisol®) that require much less added energy to release the captured CO 2 and require less compression to get to pipeline standards.

Oxy-combustion Oxy-combustion systems for CO 2 capture rely on combusting coal or other fuels wit h relatively pure O 2 diluted with recycled CO 2 or CO 2 /steam mixtures. Under these conditions, the primary products of combustion are water and CO 2 , with the CO 2 purified by condensing the water. Challenges associated with oxy-combustion in clude the capital cost and energy consumption for a cryogenic air separation unit (AS U) to produce oxygen, introduction of N 2 via boiler air infiltration, and excess O 2 in the CO 2 product stream.

CO 2 Transportation and Storage

CO 2 Pipeline Infrastructure Carbon dioxide has been transported via pipelines i n the U.S. for nearly 40 years. Approximately 50 million metric tons of CO 2 are transported each year through 3,600 miles of pipelines. Moreover, a review of the 500 largest CO 2 point sources in the U.S. shows that 95 percent are within 50 miles of a possible geologic sequestration site, which would lower transportation costs. There are multiple factors th at contribute to the cost of CO 2 transportation via pipelines including but not limited to: availab ility and acquisition of rights-of-way for new7 - 3

pipelines, capital costs, operating costs, length a nd diameter of pipeline, terrain, flow rate of

CO 2 , and the number of sources utilizing the pipeline.

Geologic Storage Existing project and regulatory experience, resear ch, and analogs (e.g. naturally existing

CO 2 sinks, natural gas storage, and acid gas injection ), indicate that geologic sequestration is a viable long term CO 2 storage option. The viability of geologic sequestr ation 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. an d Canada. Nearly every state in the U.S. has or is in close proximity to formations with car bon storage potential including vast areas

offshore. Estimates based on DOE studies indicate t hat areas of the U.S. with appropriate geology have a storage potential of 2,300 billion t o more than 20,000 billion metric tons of CO 2 in deep saline formations, oil and gas reservoirs a nd un-mineable coal seams. 290 Other types of geologic formations such as organic rich shale and basalt may also have the ability to store CO 2 ; and the DOE is currently evaluating their potential storage capacity. Further evidence of the widespread availability of CO 2 storage reserves in the U.S. comes from the Department of Interior’s U.S. Geolog ical Survey (USGS) which has recently completed a comprehensive evaluation of the technic ally accessible storage resource for carbon storage for 36 sedimentary basins in the onshore ar eas and State waters of the United

States. 291 The USGS assessment estimates a mean of 3,000 bill ion metric tons of subsurface CO 2 storage potential across the United States. For co mparison, 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 the injection of fluids into a reservoir to increase oil production efficie ncy. EOR is typically conducted at a reservoir

290 The United States 2012 Carbon Utilization and Stor age Atlas, Fourth Edition, U.S Department of Energy , Office of Fossil Energy, National Energy Technology Labora tory (NETL). 291 U.S. Geological Survey Geologic Carbon Dioxide Sto rage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resou rces – Results: U.S. Geological Survey Circular 138 6, 41 p., http://pubs.usgs.gov/fs/2013/1386/

. 292 U.S. Geological Survey Geologic Carbon Dioxide Sto rage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resou rces – Summary: U.S. Geological Survey Factsheet 20 13-3020, 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 available fo r recovery. The crude oil and CO 2 mixture is produced, and sent to a separator where the crude o il is separated from the gaseous hydrocarbons

and CO 2 . The gaseous CO 2 -rich stream then is typically dehydrated, purified to remove hydrocarbons, recompressed, and re-injected into th e 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 injection and monitoring of CO 2 in the deep subsurface for the purposes of enhancing oil and natural gas production. This expe rience provides a strong foundation for the injection and monitoring technologies that will be needed for successful deployment of CCS.

Evaluation of Retrofit CCS as BSER for Existing Fos sil Fuel-fired EGUs Technical Feasibility In evaluating partial CCS as the BSER for new fossi l fuel-fired boilers and IGCC units, the EPA determined that the technology is feasible and adequately demonstrated for new units because the major components of CCS – the capture, the transportation, and the storage – are all proven technologies that have been demonstrated at large scale. While the EPA found that partial CCS is technically feasible for new fossil fuel-fir ed boilers and IGCC units, it is much more

difficult to make that determination for the entire fleet of existing fossil fuel-fired EGUs. Developers of new generating facilities can select a physical location that is more amenable to CCS – such as a site that is near an existing CO 2 pipeline or an existing oil field. Existing source s do not have the advantage of pre-selecting an appro priate location. Some existing facilities are

located in areas where CO 2 storage is not geologically favorable and are not near an existing CO 2 pipeline. Developers of new facilities also have th e advantage of integrating the partial CCS system into the original design of the new facility . Integrating a retrofit CCS system into an existing facility is much more challenging. Some ex isting 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 particular ly 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 bein g installed for large-scale demonstration at NRG’s WA Parish facility; and it will very soon be applied at commercial-scale as a retrofit at SaskPower’s Boundary Dam coal-fired EGU in Canada. However, all of these facilities are located in areas that are either near an existing o il field or in an area that is geologically

favorable for CO 2 storage. Thus, at some existing facilities, the im plementation of partial CCS may be a viable GHG mitigation option and some util ities may choose to pursue that option. However, the EPA does not believe that it can serve as the best system of emission reduction for a broadly applicable GHG mitigation program. Theref ore, the EPA does not propose to find that CCS is a component of the best system of emission r eduction for CO 2 emissions from existing

fossil fuel-fired EGUs.

Reasonableness of Cost In the proposed standard of performance for new fos sil fuel-fired EGUs (79 FR 1430), the EPA found that the costs to implement partial C CS (to a level to meet the proposed emission standard of 1,100 lb/MWh-gross) were consistent wit h 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 capac ity. The EPA also noted in the proposal, that most of the relatively few new projects that a re in the development phase are already planning to implement CCS; and, as a result, the st andard would not have a significant impact on nationwide energy prices. In contrast, the EPA did not identify full or parti al CCS as BSER for new natural gasfired stationary combustion turbines noting technic al challenges to implementation of CCS at NGCC units as compared to implementation at new sol id fossil fuel-fired sources. The EPA also noted that, because virtually all new fossil fuel-f ired power is projected to use NGCC technology, requiring full or partial CCS would hav e more of an impact on the price of electricity than the few projected coal plants with CCS and the number of projects would make it difficult to implement in the short term. An emission standard for existing units based on CC S (or even partial CCS) would most certainly have an even more significant effect on n ationwide electricity prices and could affect the reliability of the supply of electricity. There fore, we do not find that the cost to implement7 - 6

existing source emission standards to be reasonable , which further supports the determination that CCS is not an appropriate component of the bes t system of emission reduction for CO 2 emissions from existing fossil fuel-fired EGUs.

Emission Reductions and Promotion of Advanced Techn ology An emission standard for existing units based on CC S (or even partial CCS) would clearly result in significant emission reductions a nd would certainly serve to promote further deployment, development and improvement in the most advanced technology. However, the

EPA has determined that such an emission standard m ay not be technically or logistically feasible in a number of cases and cannot be broadly implemented at a reasonable cost at this

time.A - 1 

### APPENDIX

Technical Memorandum

Consideration of Heat Rate Improvement (HRI) Potent ial 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,” tha t are technically viable and broadly applicable, and can provide cost-effective reductio ns 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 the most carbon-intensive affected EGUs in the amount that results from substituting generatio n at those EGUs with generation from less carbon-intensive affected EGUs (including NGCC units under construction);

Building Block 3 - Reducing emissions from affected EGUs in the amount that results from substituting generation at those EGUs with exp anded low- or zero-carbon generation; and,

Building Block 4 - Reducing emissions from affected EGUs in the amount that results from the use of demand-side energy efficiency that reduces the amount of generation required.

Coal-fired Steam EGUs

For Building Block 1, the EPA evaluated the fleet-w ide potential for lowering the carbon intensity of generation at individual affected coal -fired steam EGUs by improving heat rates at these EGUs (see the GHG Abatement Measures TSD). Th e EPA analyzed 11 years of historical

heat rate data and the literature on HRI methods to estimate that the U.S. coal-steam EGU fleet might reasonably be expected to reduce its annual a verage gross heat rate by about 6%. Furthermore, the EPA understood that any HRI method that reduces gross heat rate will alsoA - 2

reduce net heat rate, and that some HRI methods red uce net heat rate without reducing gross heat rate. As such, the EPA expects that the HRI potenti al on a net output basis is somewhat greater than on a gross output basis, primarily through upg rades that result in reductions 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 this finding in i ts Building Block 1.

As discussed in the preamble, for purposes of devel oping the alternate set of goals on which we are taking comment, the EPA used an estima te of a 4% HRI from affected coal-fired steam EGUs on average. The EPA views the 4% estimat e as a reasonable minimum estimate of the technical potential for HRI on average across a ffected coal-fired EGUs.

Oil/Gas Steam EGUs

As summarized above, the EPA made a detailed assess ment of the fleet-wide potential for HRI at existing affected coal-fired steam EGUs in B uilding Block 1. However, we did not make a detailed assessment of this potential for existin g affected oil and gas steam units at this time,

for the three main reasons described below.

First, oil and gas contain significantly less carbo n per 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 levels compared to oil/gas

steam EGUs. Therefore the amount of CO2 reduction t hat can be achieved via HRI at oil/gas EGUs is significantly smaller. For example, EPA mod eling 293 projects that in 2020 coal-steam units will provide 59% of all fossil-fired electric al generation, while oil/gas steam units will provide only 2%. Even if CO 2 emissions from all oil/gas steam units could be re duced by 6% on average using HRI methods (as assumed on coal-steam units) that reduction would amount to only a fraction of 1% of the HRI reduction that mig ht 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 co al-steam EGUs are never important. Such reduction might be significant in a few situations, and states are free to make use of these reductions in meeting their goals.A - 3

Third, oil/gas steam EGUs employ less extensive sy stems and equipment compared to coal steam EGUs and therefore, in general, have a l esser range of opportunities for implementing HRI. For example, oil/gas steam units do not typica lly use flue gas SO 2 scrubbers, particulate collection devices, coal mills, coal conveyors, ash handling systems, sootblowers, etc. Consequently, some of the HRI methods discussed in the GHG Abatement Measures TSD are not applicable for oil/gas steam EGUs.

The above factors taken together explain why the po tential for CO 2 reduction achieved via HRI at oil and gas steam EGUs would be quite sm all compared to that from the existing fleet

of coal-fired EGUs. Therefore the EPA conservativel y decided to not separately itemize and include this potential in Building Block 1.

Natural Gas Combined Cycle (NGCC) EGUs

EPA modeling also projects that in 2020 natural gas -fired NGCCs will provide about 39% of the U.S. electrical generation from fossil f uels, 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 used in electrical gene ration.

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 primarily to the higher hydrogen content and lower carbon content in natura l gas compared to coal. Also, because a NGCC is typically more efficient than a coal-steam EGU, thus using less heat input from fuel to make an equal electrical output, a very efficient N GCC can further reduce the CO 2 emission rate per MWh to about 60% less than that from coal-steam EGUs. Thus, natural gas, particularly as used in NGCCs, inherently reduces CO 2 emissions by more than one-half. Existing NGCC EGU s are therefore already significantly reducing CO 2 emissions compared to existing coal EGUs, per MWh of output, before considering whether NGCCs mig ht 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. However, as with c oal-steam EGUs, we do not have the unit-specific detailed design in formation on existing individual NGCCs thatA - 4

would be needed to make a detailed assessment of th e HRI potential via best practices and upgrades for each NGCC unit. While it would be poss ible for EPA to make a “variability analysis” of NGCC historical hourly heat rate data (as was done for coal-steam EGUs), we are aware that the various NGCC configurations in use a nd the historically lower capacity factors of the NGCC fleet (less run time per start, and more p art load operation) would require a NGCC analysis that includes more complexity and likely m ore uncertainty than in the coal-steam analysis. In addition, the analysis would be limite d by the fact that only one-third of the NGCC fleet has historically reported complete (combustio n turbine and steam turbine generator) load

data to EPA.

To preliminarily gauge the HRI potential for NGCCs, EPA engineering staff familiar with NGCC design and operation informally discussed the NGCC HRI potential with power sector engineering firms and NGCC suppliers. Our pr eliminary conclusion is that the fleet-wide HRI potential for existing NGCC EGUs may be only ab out 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 t he steam turbine portion of the power cycle: the HRSG (heat recovery steam generator), the steam turbine-generator, and the heat rejection system (water or air-cooled condenser systems). The HRI potential associated with the steam portion of the NGCC is significantly less than in a coal-steam unit because the NGCC steam system is much simpler (gaseous fuel, no back-end s crubbers, less parasitic power, no air heater leakage, no feedwater heaters, etc) and its flue ga s exit temperature is typically already much

lower than in a coal-steam unit.

Second, the HRI methods applicable to the combustio n turbine portion of the NGCC relate primarily to critical components in the hot expansion side of the unit - components that are exposed to the products of combustion of fuel and a ir that contain small amounts of corrosive/erosive contaminants at very high tempera tures. These critical components (combustors, nozzles/vanes, seals, rotating blades) therefore require regular periodic removal and refurbishment or replacement to maintain high NGCC efficiency levels, and indeed to avoid potentially catastrophic mechanical failures. The g reatest loss in the performance (increased heatA - 5

rate) of a NGCC is this physical degradation that o ccurs in proportion to its hours of operation and number of starts. Consequently, it has long bee n an accepted practice by NGCC owners to closely follow the NGCC manufacturer’s maintenance recommendations, a practice that regularly restores the NGCCs efficiency and reliabi lity. This close adherence to manufacturer recommendations is financially motivated in part by the fact that many NGCC owners have longterm maintenance contracts with the manufacturers, wherein the manufacturer guarantees the service life and replacement costs of expensive cri tical components - provided that the regular preventive/restorative maintenance schedule is foll owed. Regularly scheduled maintenance practices are the most effective HRI methods that c an be applied on NGCCs, and the EPA concludes that they are likely already being applie d 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 and the uncertainty associated with it, EPA conservatively decided to not separately itemize an d include this NGCC potential in Building

Block 1.

Simple-cycle Combustion Turbine (CT) EGUs

Natural gas-fired CTs provide peaking generation, t ypically operating at very low capacity factors. This is primarily because of thei r relatively low efficiency, which is economically only partially offset by their relativ ely 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 belo w its most efficient load point. CTs have an operational flexibility well suited to their role a s 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 capa city in 2020 (Base Case) will be about 21% of total fossil-fired capacity (GW), and that it will provide only about 1% of total fossil-fired electrical generation (GWh). Whether g as 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 i n CT heat rates, can therefore only provide much less than a 1% reductio n in total fossil-fired CO 2 emissions.

Most CTs likely benefit from the same regular preve ntive/restorative maintenance as the combustion turbine portion of a NGCC, as discussed above, and for the same reasons, Thus, the heat rates of most CTs are already periodically (ev en if not regularly, depending on their irregular operating hours and starts) restored to a level that allows them to be both reliable and as efficient as reasonably possible. Therefore the EP A decided to not include HRI for CTs as an additional potential in Building Block 1.

Conclusion

This technical memorandum outlines the EPA’s reason s for not including CO 2 reduction potentials via HRI on oil/gas steam, NGCC, and CT E GUs as part of the CO 2 reduction target of Building Block 1 at this time. For each non-coal te chnology 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 evaluation of the HRI po tential for any individual EGU, including coal-steam units; only the owners/operators of thes e EGUs would have that information.

The EPA notes, however, that although we did not in clude an HRI potential for these non-coal classes of existing fossil-fired EGUs in B uilding Block 1, we do expect that some

amount of CO 2 reduction via HRI is available from these EGUs. St ates and sources would be free to use HRI at these EGUs to help reach the sta te CO 2 reduction goals. Further, we note that there are geographic differences in the proportions of total generation produced from various EGU types, and that in certain geographically isola ted jurisdictions, HRI from non-coal fossil fuel-fired EGUs could be a more important potential approach to reducing CO 2 emissions. For this reason, as noted in the preamble, we are takin g comment on whether HRI from non-coal fossil fuel-fired EGUs should be included as part o f the basis supporting the BSER.