Technical Support Document (TSD) for

Carbon Pollution Emission Guidelines for Existing S tationary Sources: Electric Utility Generating Units
Docket ID No. EPA-HQ-OAR-2013-0602



# Projecting EGU CO 2 

# Emission Performance in

State Plans

U.S. Environmental Protection Agency Office of Air and Radiation

June 20142 Table of Contents

I. Introduction ......................................

................................................... ...............................3 II. Analytic Approaches for Projecting CO 2 Emissions from Affected EGUs ..................6

A. Electricity System Modeling Approaches for Proje cting EGU CO 2 Emissions .......6 B. Growth Tools for Projecting Future EGU Utilizati on and CO 2 Emissions.............10 III. Translating from a Rate-Based Emission Performance Goal to a Mass-Based Emission Performance Goal..........................

................................................... ...............13 A. Concept ........................................

................................................... ........................13 B. Projection Scenarios ...........................

................................................... ..................15 IV. Projecting CO 2 Emission Performance under a State Plan – Overview .....................20 V. Applied Considerations for Different Types of State Plans .........................................21

A. End-Use Energy Efficiency and Renewable Energy . ............................................21 B. Emission Budget Trading Programs ...............

................................................... ....36 VI. Process Considerations ............................

................................................... .....................43

A. Provision of the EPA Guidance ..................

................................................... ........43 B. Party that Translates the Rate-Based Goal to a M ass-Based Goal ........................453

I. Introduction
As discussed in the preamble, in section VIII.F.7, all state plans will need to include a projection of the CO 2 emission performance by affected EGUs that will be achieved under a state plan (inclusive of plan measures that avoid CO 2 emissions from affected EGUs, such as end-use energy efficiency and renewable energy). Depending on the type of plan approach, this will include either a projection of the average CO 2 emission rate achieved by affected EGUs or total
CO 2 emissions from affected EGUs.

The EPA is also proposing that a state may translat e the state rate-based CO 2 emission performance goal for affected EGUs to an equivalent mass-based CO 2 emission performance goal. If this translation option is used, a state p lan must also include a projection used to derive
the mass-based CO 2 emission performance goal. This translation will i nvolve a projection of CO 2 emissions from affected EGUs during the initial 202 0-2029 plan performance period and in 2030, under a scenario that assumes the rate-based goal in the emission guidelines is met.

As discussed in the preamble, the EPA is striving t o find a balance between providing state implementation flexibility and ensuring that the emission performance required by CAA section 111(d) is properly defined in state plans a nd that plan performance projections have technical integrity. The credibility of state plans under section 111(d) will depend in large part on ensuring credible and consistent emission performan ce projections in state plans. Therefore, the use of appropriate methods, tools, and assumptions for such projections is critical.

The preamble, at section VIII.F.7, seeks comment on options presented for how CO 2 emission projections might be conducted in an appro vable state plan, and how different types of state plan approaches are represented in these proj ections. These options include the use of historical data and parameters for estimating the f uture impact of individual state programs and measures. Alternatively, a projection could be base d on modeling, such as use of a capacity expansion and dispatch planning model, or a dispatc h simulation model. 1 This latter approach would be able to capture dynamic interactions withi n the electricity sector, based on system

1 In many cases, this approach will also require the development of parameters for estimating the effec t of individual state programs and measures, for use as input assum ptions for modeling.4

operation and market forces, including interactions among state programs and measures and the dynamics of market-based measures.

In the preamble, the EPA further seeks comment on w hether the EPA should develop guidance that describes acceptable projection appro aches, tools, and methods for use in an approvable plan, as well as whether the EPA should provide technical resources for conducting projections.

This technical support document (TSD) elaborates th ese options and considerations. The TSD discusses possible analytic approaches for tran slating from a rate-based CO 2 emission performance goal to a mass-based goal, and projecti ng the CO 2 emission performance that will be achieved through a state plan. It discusses both modeling and non-modeling approaches, such as electricity sector capacity expansion and dispat ch planning models, dispatch simulation models, and growth tools that base projections on h istorical data and algorithms. The TSD also discusses possible approaches for developing inputs that are used for emission projections and applied considerations for different types of state plans. Topics addressed include:

• Section II

discusses analytic approaches for projecting CO 2 emissions from affected
EGUs

• Section III

discusses the concept and practice of translating from a rate-based CO 2 emission performance goal to a mass-based CO 2 emission performance goal

• Section IV discusses the concept and practice of projecting EG U CO 2 emission performance under a state plan

• Section V

discusses applied considerations for projecting EG U CO 2 emission performance under different types of state plans, i ncluding:

o Considerations that should be addressed in conducti ng projections of emission performance for different types of plans

o Data needs and methods for developing inputs to EGU CO 2 emission projections, for different types of state plans

• Section VI

discusses process considerations for conducting EG U CO 2 emission projections for state plans, including:

o Regional coordination among states in conducting pr ojections5

o Whether the EPA should provide guidance and other a nalytic support for conducting projections in state plans and translation of rate- based CO 2 emission performance goals to mass-based goals6

II. Analytic Approaches for Projecting CO 2 Emissions from Affected EGUs
This section surveys different types of methods and tools for projecting CO 2 emissions from affected EGUs, including modeling tools and ot her tools that base projections on historical data and use algorithms to extrapolate future CO 2 emissions performance based on past performance.

A. Electricity System Modeling Approaches for Proje cting EGU CO 2 Emissions
1. National-scale capacity expansion and dispatch p lanning models
National-scale electricity capacity expansion and d ispatch planning models are typically used for fundamentals-based projections of the powe r sector (i.e., projections of the expected response of the sector to factors such as electrici ty demand, fuel prices, and emission constraints) that may extend over a period of several decades. T hese models are built to evaluate the impacts of market, technical, and regulatory factors on the electric power sector and related markets. Typical outputs of such models include EGU dispatch , fuel consumption, fuel prices, wholesale electricity prices, emissions, EGU retirements, and infrastructure expenditures (e.g., addition of new EGU capacity and installation of retrofit pollu tion control technologies).

National-scale electricity capacity expansion and d ispatch planning models have moderate spatial detail with broad scope, generally encompassing the entire country or transmission system interconnects (i.e. Eastern, We stern, and ERCOT), which are subdivided into smaller areas, such as balancing authorities o r control areas. 2 For computational efficiency, these models generally model several representative hours of the year, or aggregate hours into representative bundles with similar electricity dem and profiles (i.e. peak, shoulder, off-peak). In these models, to reduce model size, existing EGUs m ay be aggregated into “model plants” to the extent that such EGUs share key unit characteristic s, such as location, size, efficiency, operating costs, pollution retrofit control status, age, and fuel type use/availability. These types of models are well suited to project dynamic entry and exit ( capacity expansion and unit retirement) to meet energy and capacity requirements while minimizing s ystem costs, maintaining reliability criteria,

2 A “balancing area” or “control area” refers to a s pecified portion of the transmission system where e lectricity demand and generation are balanced in real time by a system administrator to maintain grid reliability

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and following other constraints, such as minimum bu ild times, transmission constraints, renewable energy availability, or emission limitati ons.

2. Utility-Scale Capacity Expansion and Dispatch Pl anning Models
Utility-scale capacity expansion and dispatch plann ing models are similar in concept to related national-scale models. However, utility-sca le capacity expansion and dispatch planning models are typically used for evaluating narrower u tility planning and investment decisions, such as procurement of a specific new electric generatin g facility and retirement or retrofit decisions for existing capacity within a specific utility’s t erritory. Utility planning models typically project outcomes for periods of up to two decades. Utility- scale capacity expansion and dispatch planning models are an industry standard, used regu larly in state electricity regulatory proceedings. Within the electricity sector there is broad familiarity with these models at state PUCs. Multiple vertically integrated utilities use capacity expansion and dispatch planning models to conduct forward planning and review the e conomics of specific EGU retrofit decisions. Utilities that submit integrated resourc e plans (IRP) typically use a utility-scale capacity expansion and dispatch planning model to e xamine long-term strategies and develop short-term action plans. 3 Utilities have experience using these models to ex amine CO 2 emission reduction strategies or CO 2 emission constraints, as IRP scenarios may include greater penetration of end-use energy efficiency or renewab le energy, proxy CO 2 emission prices, emission trading, and limits on CO 2 mass emissions.

Utility-scale capacity expansion and dispatch plann ing models tend to have relatively high spatial detail with limited geographic scope, generally encompassing a utility service territory or a sub-regional scale. These models gen erally have better temporal resolution than the national-scale capacity expansion and dispatch plan ning models, with each model year typically

3 See, for example: The Duke Energy Carolinas IRP (A nnual Report), September 2012, available at http://www.energy.sc.gov/files/view/Duke_IRP 2012.p df ; PacifiCorp 2013 IRP (Vol 1), April 2013, availabl e at http://www.pacificorp.com/es/irp.html ; TVA IRP, March 2011, available at http://www.tva.gov/environment/reports/irp/archive/ index.htm ; Georgia Power Company 2013 IRP, January 31, 2013, available at http://facts.psc.state.ga.us/Public/GetDocument.asp x?ID=145981 ; 2011 Joint IRP of Louisville Gas and Electric and Kentucky Utilities Company, Ap ril 2011, available at http://psc.ky.gov/PSCSCF/2011%20cases/2011-00140/20 110421_LG%26E-KU_IRP_Volume%20I.pdf ; Great River Energy IRP, November 2012, available at http://www.greatriverenergy.com/makingelectricity/r esourceplan/pdoc295631.pdf ; Public Service Company of Oklahoma IRP, October 2012, available at http://occeweb.com/pu/PSO%202012%20IRP.pdf

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dispatched based on an annual hourly load duration curve. 4 Utility-scale capacity expansion and dispatch planning models typically represent indivi dual EGUs, where each EGU has specific operational characteristics.

Due to the additional spatial and temporal resoluti on of these models as compared to the national-scale models, the number of technology opt ions for capacity expansion is generally
limited to reduce the runtime of the model. This ca n be done through an outside-the-model screening analysis to pre-select the resources most likely to be economic in a planner’s area of interest, or by running the model iteratively to el iminate rarely chosen technology alternatives.

3. Electricity System Dispatch Simulation Models
Dispatch simulation models are regularly used by ut ilities, grid operators, and independent power producers (IPP) for short-term pl anning, ratemaking, dispatch decisions, and market intelligence. Dispatch simulation models are typically driven by near-term economics, system restrictions and market constraints, includi ng a typically more detailed representation of an EGU’s operational constraints ( e.g. , ramp rates, heat input curves, and unit downtime for maintenance). These models typically do not add or retire generating capacity on an economic basis, although EGU additions and retirements may b e exogenously input to these models. As a result, projections from these models tend to be co nsidered more robust in the shorter term. 5 Grid operators, including utilities, and independent sys tem operators (ISO) use dispatch simulation
models in near real-time to match demand with elect ric generation from available generating units and dispatch EGUs on a least-cost basis. EGU owners and operators run dispatch simulation models to assist in fuel procurement, fo recast revenues and costs, and calculate the avoided generation supply costs related to procurem ent of end-use energy efficiency and

4 Load duration curves typically represent electrici ty load during a typical week for each month. 5 Some planners use dispatch simulation models in co njunction with national or utility-scale capacity e xpansion and dispatch planning models, where the capacity expans ion and dispatch planning model indicates the dispo sition of new and existing generating resources, and the disp atch simulation model is used to simulate operation of individual EGUs. In this case, these models can be used in the same time horizon as a capacity expansion and disp atch planning model ( i.e ., decades).9

renewable energy resources. 6 Other utilities use dispatch simulation models to forecast retail rates for ratemaking proceedings and other planning purposes.

Electricity system dispatch simulation models (also called production cost models) utilize security constrained economic dispatch (SCED) algor ithms to determine which EGUs operate on an hourly (or shorter) basis to meet electricity de mand. 7 These models typically have a very broad spatial scope, generally covering multiple Re gional Transmission Organization (RTO) regions, and often modeling entire interconnects (i .e. Western, Eastern, and ERCOT). While individual EGUs are modeled in detail, including fu el and variable costs and operational constraints, transmission is simplified to characte rize thermal constraints between zones, which typically represent control areas or balancing auth orities. Zones contain both load (electricity demand) and EGUs; EGU dispatch and electricity dema nd are balanced to maintain transmission system reliability while providing least-cost servi ce on a variable cost basis. Some versions of these models operate at a “nodal” level, where tran smission constraints between individual
EGUs and load are modeled as well. 8 Dispatch simulation models typically operate chronologically, modeling either all 8760 hours of the year, or typical weeks of the year. These models contain substantially more detail about indi vidual EGUs than regional or national capacity expansion and dispatch planning models, in cluding EGU ramp rates, minimum outages, maintenance schedules, emission rates, fuel use con straints, and heat rate curves depicting expected efficiency changes at various levels of ou tput.

6 For example, see Hornby, R., P. Chernick, D. White , et al., 2013. Avoided Energy Supply Costs in New England: 2013 Report. http://www.rieermc.ri.gov/documents/2013%20Evaluati on%20Studies/AESC%20Report%20- %20With%20Appendices%20Attached.pdf
7 The Federal Energy Regulatory Commission (FERC) de fines security constrained economic dispatch as “th e level at which each available resource should be operated , given actual load and grid conditions, such that reliability is maintained and overall production costs are minimiz ed”. SCED optimizes dispatch based not only on the marginal costs of all available generating resources, but al so constraints on transmission availability and anc illary services. See FERC, “Security Constrained Economic Dispatch: Definition, Practices, Issues, and Recommendations” (2006), available at http://www.ferc.gov/industries/electric/indus-act/j oint-boards/final-cong-rpt.pdf

. 8 “A “nodal” model represents the entire transmissio n network in a given area, without making the simpl ifying assumptions that load is served on a more aggregate “zonal” level.” A caveat is that while a nodal mod el has more “links” and places where supply must meet demand, i t is still an approximation in terms of the electri cal operation of the transmission network (the network is represente d as a DC flow network, not an AC flow network).10

4. Multi-Sector Models
Multi-sector energy models are typically used to ex amine the effect of energy and environmental policies that affect multiple economi c sectors, such as multi-sector emission trading systems. Multi-sector models are used to re view trends in emissions, expected broadscale resource use, and energy sector impacts under changing regulatory and economic conditions, and often review changes over a period of decades.

Multi-sector models cover a broad range of energy s ectors beyond the electricity sector, and can better reflect energy demand and technology choices by energy end-users. Such models typically have relatively limited spatial detail wi th broad scope, generally encompassing the entire country subdivided into from one to 30 regio ns. Such models tend to have much more limited temporal resolution and treatment of EGU di spatch. EGUs are typically aggregated to a few broad technology types. A key strength of multi -sector energy models is the ability to provide multi-sectoral feedback between energy reso urce use and price ( i.e. , tracking national fuel supplies and adjusting price to account for de mand).

The range of national- and utility scale capacity e xpansion and dispatch planning models, and dispatch simulation models identified above all tend to focus on properly characterizing the electricity sector in order to answer sector-specif ic questions. Multi-sector energy models attempt to include many other energy-using sectors of the economy in order to better capture interactions between these sectors. The more aggreg ated representation of the electricity sector in multi-sector models may limit their use to providin g input data for more specific analysis in an electricity sector model, and to better understandi ng variations in electricity load that may result from changes outside the electricity sector.

B. Growth Tools for Projecting EGU Utilization and CO 2 Emissions
Organizations use growth tools for a variety of rea sons, including to estimate future emissions inventories for state and regional air qu ality modeling, 9 and to estimate the impact of load-reduction measures such as end-use energy effi ciency and distributed renewable energy on

9 See, for example, the ERTAC Load Growth Model, ava ilable at http://www.ertac.us/index_egu.html

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individual EGU emissions, and county, state, 10 and regional emissions rates. 11 Because these algorithms are generally based on publicly availabl e data 12 and do not rely on economic data or proprietary information regarding individual EGUs, they provide a low-cost, simple, and often transparent framework for estimating how EGUs will respond to changing system conditions.

Non-modeling approaches, such as growth tools, appr oximate future emissions and generation from existing and new fossil fuel-fired EGUs under different assumed growth, retrofit, and load-reduction scenarios. These forec ast tools do not simulate economic EGU dispatch, but could use demand growth rates and ele ctricity production trends from other energy modeling forecasts as an input assumption. 13 The algorithms in these forecast tools assume that EGU dispatch behavior generally follows simple rule s based on past operation. In the absence of significant shifts in fuel prices and electricity d emand, EGUs may be expected to behave similarly in the future as they did in the past. Se veral common features of these forecast tools are that they (a) generally build on historical generat ion and emissions output from individual EGUs, (b) are insensitive to fuel and emission price fore casts, (c) do not solve for optimal economic EGU dispatch or EGU capacity expansion, and (d) do not capture transmission constraints or limits. The algorithms in these forecast tools gene rally divide the contiguous US 14 into regional power markets, following ISO boundaries, eGRID boun daries, NERC regional boundaries, or similar designations. These algorithms generally se ek to examine how operation and emissions from individual EGUs could be expected to change wi th changes in environmental regulations or installed pollution control retrofits and additiona l or reduced hourly electricity demand. Some algorithms use the observed historical behavior of individual EGUs to approximate future behavior, while others add additional steps of diff erentiating EGUs into fuel groups and unit

10 See EPA’s AVERT (Avoided Emissions and Generation Tool), available at http://epa.gov/statelocalclimate/resources/avert/in dex.html

. 11 See Flexibility Weighted Hourly Average Emissions Rate (FW-HAER) tool, available at http://nepis.epa.gov/Adobe/PDF/P1002UQO.pdf ; Time Matched Marginal (TMM) emissions tool, a pro prietary algorithm used to estimate avoided emissions; D. Ja cobson and C. High, “U.S. Policy Action Necessary t o Ensure Accurate Assessment of the Air Emission Reduction B enefits of Increased Use of Energy Efficiency and R enewable Energy Technologies,” Journal of Energy & Environmental Law (2010)

. 12 Hourly emissions and generation data for all fossi l fuel-fired EGUs greater than 25 MW are available from EPA’s Clean Air Markets Division (CAMD) through its Air M arkets Program Data (AMPD). 13 For example, EIA’s AEO 2013 electricity load growt h rates and EGU production trends are available at http://www.eia.gov/forecasts/aeo/assumptions/pdf/05 54(2013).pdf. 14 Hawaii and Alaska do not report hourly generation and emission data from individual EGUs to EPA, and are therefore generally excluded from these forecast to ols.12

types, with implicit differentiation of economic ou tcomes for these different groups. Some of these algorithms may contain subroutines to add new generating capacity automatically to meet load requirements.13

III. Translating from a Rate-Based Emission Perfor mance Goal to a Mass-Based Emission Performance Goal
As discussed in the preamble, in section VIII.C.2, the EPA is proposing that the projected
CO 2 emission performance by affected EGUs (taking into account the qualifying impacts of plan
measures that avoid CO 2 emissions from affected EGUs) must be equivalent t o, or better than, the required CO 2 emission performance level in the state plan. This required level of emission performance in the state plan is the rate-based goa l for the state in the emission guidelines, or a translated mass-based goal if the state chooses to use this approach. State plans that are projected to achieve an average CO 2 emission rate (CO 2 lb/MWh) or tonnage CO 2 emission outcome by all affected EGUs equal to, or lower than, the required level of CO 2 emission performance in the state plan would be considered to meet this plan ap provability criterion. States may demonstrate such emission performance by affected EGUs either b y state or jointly on a multi-state basis.

This section of the TSD discusses the concept of a mass-based CO 2 emission performance goal, considerations for constructing p rojection scenarios for such a translation, methods for conducting such a translation, and key input assumptions for such a translation.

A. Concept

A mass-based CO 2 emission performance goal translates the applicati on of a rate-based emission performance goal to an expected CO 2 emissions outcome in tons during a plan performance period. A mass-based CO 2 emission performance goal is calculated by project ing
the tons of CO 2 that would be emitted during a state plan performa nce period (e.g., 2020-2029, 2030-2032) by affected EGUs in the state if they hy pothetically were meeting the state ratebased CO 2 emission performance goal for affected EGUs establ ished in the emission guidelines. The translation of a rate-based goal (expressed in lb CO 2 /MWh of useful energy output from affected EGUs) to tons (expressed as total tons of CO 2 emissions from affected EGUs over a specified time period) is based on a projection of affected EGU utilization and dispatch mix. Importantly, this projection is conducted assuming the absence of qualifying state programs and measures contained in a state plan, and applying th e rate-based goal in the emission guidelines as a proxy emission limit for affected EGUs. State pro grams and measures in the state plan are not included in this projection, because the purpose of this analysis is to determine the tonnage CO 214

emissions that corresponds to the state-specific ra te-based CO 2 emission performance goal for affected EGUs in the emission guidelines, without t he eligible state programs and measures that are included in the state plan.

Translation of a rate goal to a mass goal seeks to answer the question, “What would happen to EGU CO 2 emissions if one applied the rate goals in the emi ssions guidelines instead of the measures in the state plan?” Because the emissi on guidelines do not specify the emissions reduction measures that a state must use in its pla n, only the level of emission performance that must be achieved through a plan, there is no specif ied “policy” or set of emission reduction measures in the emission guidelines to apply when c onducting this projection. However, a proxy policy can be applied to project the emission perfo rmance in tons that would be achieved if the state plan were to include suitable measures to ach ieve the required level of emission performance established through the state rate-base d CO 2 emission performance goals in the emission guidelines. This proxy involves applying t he rate-based emission goal for a state as a rate-based average CO 2 emission limit for affected EGUs in a state and th en projecting the CO 2 emissions that would occur if such a limit were app lied.

When demonstrating projected emission performance u nder a mass-based plan, a state would project the CO 2 emissions outcome that would be achieved under the suite of requirements, programs, and measures in its plan. T he state plan requirements, programs, and measures substitute for EPA’s application of the be st system, which is represented by the ratebased goal in the emission guidelines. If the CO 2 emissions outcome, in total tons of CO 2 emissions over each plan performance period, is equ al to or less than what would be emitted by affected EGUs through the application of the rate-b ased goal in the emission guidelines ( i.e. , equal to or less than the translated mass-based goa l), the plan would be deemed to achieve the required emission performance criterion.15

B. Projection Scenarios
As described above, the projection scenario for tra nslating from a rate-based CO 2 emission performance goal to a mass-based CO 2 emission performance goal does not include requirements, programs, and measures included in a state plan. Construction of this scenario must therefore carefully consider treatment of elig ible “on-the-books” state requirements, programs and measures included in the state plan. 15

Projection scenarios for translating from a rate-ba sed CO 2 emission performance goal to a
mass-based CO 2 emission performance goal, and comparing projected emission performance under a state plan to this goal, include the follow ing:

1. A Reference Case Scenario . This scenario projects the average CO 2 emission rate and
CO 2 emissions from affected EGUs in the absence of the EPA emission guidelines or any enforceable requirements, programs, and measures in cluded in a state plan. This scenario does, however, include all current on-the-books sta te requirements, programs, and

measures that are not included as enforceable measures in a state plan. These mea sures are complementary to the state plan. Because these measures will influence EGU CO 2 emissions, they should, however, be included in the reference case projection scenario.
2. A Mass-Based CO 2 Emission Goal Policy Scenario . This projection scenario is used to translate a rate-based goal to a mass-based goal. T he scenario applies a rate-based CO 2 emission limit to affected EGUs that is equivalent to the state-specific rate-based lb
CO 2 /MWh emission goal in the EPA emission guidelines. 16 The CO 2 emissions from affected EGUs projected during the specified plan p erformance period in this scenario represents the translated mass-based CO 2 emission performance goal for the state plan.

15 An “existing measure” refers to a state or utility requirement, program, or measure that is currently “on the books.” For the purposes of this discussion, this m ay include a legal requirement that includes curren t and future obligations or current programs and measures that a re in place and are anticipated to be continued or expanded in the future in accordance with established plans. Existi ng measures may have past, current, and future impa cts on EGU CO 2 emissions. 16 The proxy emission limit applied includes creditin g for end-use energy efficiency, renewable energy, and nuclear generation included in building blocks three and fo ur, which were used by EPA when calculating the sta te-specific CO 2 emission performance goals for affected EGUs in the proposed emission guidelines. In essence, these me asures can be used by affected EGUs as “compliance” flexib ility mechanisms when “complying” with the proxy em ission rate limit in this projection scenario. As a result , the proxy rate-based emission limit is able to ca pture all four building blocks included by EPA when calculating th e rate-based CO 2 emission performance goals.16

To construct this scenario, this emission limit is added to the underlying reference case
scenario described above.
3. A State Plan Policy Scenario . This projection scenario includes the enforceable requirements, programs, and measures included in th e state plan, and is used to project
CO 2 emission performance by affected EGUs under the st ate plan. To construct this scenario, the enforceable requirements, programs, a nd measures included in the state plan are added to the underlying reference case scenario described above.

An applied example is provided below in Box 1.
Box 1. Example Mass-Based Goal Translation and Proj ection of Plan Performance

Example State Rate-Based CO 2 Emission Performance Goal Assumptions :

• State interim rate-based CO 2 emission performance goal for affected EGUs: 1,100 lb CO 2 /MWh

• State final

rate-based CO 2 emission performance goal for affected EGUs: 1,000 lb CO 2 /MWh
Reference Case Scenario :

• Under this scenario, the projected average CO 2 emission rate for affected EGUs is 1,500 lb CO 2 /MWh during the 2020-2029 interim performance period and 1,500 lb CO 2 /MWh in 2030.

Mass-Based CO 2 Emission Goal Policy Scenario :

• A proxy emission rate limit is applied to affected EGUs (1,100 lb CO 2 /MWh on average during 2020-2029; 1,000 lb CO 2 /MWh in 2030 and subsequent years during the projec tion period).

• Under this scenario, the projected CO 2 mass emissions from affected EGUs during the 2020- 2029 interim performance period are 100,000,000 tons of CO 2 (an average of 10,000,000 tons per year during thi s period) and 9,000,000 tons of CO 2 in 2030.

• The mass-based interim emission performance goal for affected EGUs is 100 ,000,000 tons of cumulative CO 2 emissions during the 2020-2029 interim plan perform ance period.

• The mass-based final emission performance goal for affected EGUs is 9,0 00,000 tons of CO 2 per year, in 2030 and subsequent years. During plan implementation, this final goal can be met on a three-year rolling average basis, beginning with the period 2030-2032, as discussed in the preamble at section VIII.B.2.c

. State Plan Policy Scenario:

• This scenario includes the suite of requirements, p rograms, and measures included in the state plan.

• Under this scenario, the projected CO 2 mass emissions from affected EGUs during the 2020- 2029 interim plan performance period are 98,000,000 tons of CO 2 and 8,800,000 tons of CO 2 in 2030.

• Based on this analysis, the state plan is projected to achieve both the translated interim and final m assbased CO 2 emission performance goals for affected EGUs.

1. Constructing the Reference Case
A key consideration for translating from a rate-bas ed CO 2 emission performance goal to a
mass-based CO 2 emission performance goal is proper construction o f a reference case scenario.17

This includes assumptions for key variables that ma y drive EGU CO 2 emission projections, 17
such as:

• Electricity load growth projections (energy and pea k demand)

• Fuel supply, delivery, and pricing assumptions

• Cost and performance of electric generating technol ogies

• EGU firm builds and retirements (e.g., those schedu led with a regional transmission organization or independent system operator (RTO/IS O)) 18

• Transmission capability and ISO/RTO transmission ex pansion plans

• Applicable federal regulations (other than the EPA emission guidelines)

• Applicable state regulations and programs (other th an those that are included in the state plan)

It may be necessary in many instances to include as sumptions about other state programs implemented by neighboring states in the same regio n. This would be especially relevant for states that are located within the same electric po wer pool (or adjoining power pools) that are administered by a common RTO/ISO.

2. Treatment of Existing State Regulations and Prog rams in the Reference Case
A key aspect of the reference case is proper treatm ent of existing state regulations, programs, and measures. As discussed previously, ex isting state regulations, programs, and
measures that are not enforceable measures in a sta te plan are complementary to the state plan – as a result, they should be included in the referen ce case scenario rather than the state plan scenario. These state regulations, programs, and me asures should not, however, be ignored altogether, as they influence EGU CO 2 emissions. Instead, these state actions are occurr ing in the background – either when a proxy rate-based CO 2 emission limit is applied in the Mass-Based
CO 2 Emission Goal Policy Scenario ; or when the state plan requirements, programs, an d

17 Process considerations related to constructing ref erence case scenarios are addressed in section VI o f this TSD. 18 ISOs and RTOs are independent organizations that a dminister a regional electric power pool (EGU dispa tch and electricity transmission systems), and often also a dminister related wholesale electricity markets for electric energy and capacity

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measures are applied in the State Plan Policy Scenario . As a result, these state actions are properly addressed in the reference case scenario.

In effect, when evaluating whether a state plan wil l meet the mass-based CO 2 emission performance goal, the two policy scenarios describe d above, each of which involve adding a
different CO 2 emission reduction policy on top of the reference case scenario, are compared to
one another. If the State Plan Policy Scenario achieves projected tonnage CO 2 emissions equal to or lower than the projected CO 2 emissions in the Mass-Based CO 2 Emission Goal Policy
Scenario , then the state plan can be deemed to meet the tra nslated mass-based CO 2 emission performance goal.

As discussed in the preamble, the EPA is proposing that a state may apply toward its required emission performance level the emission re ductions that are achieved by existing state requirements, programs, and measures during a plan performance period, due to actions 19 taken after the date of the proposal of the emission guid elines. 20, 21 In practice, this means that emission
reductions that occur in 2020 and later due to acti ons taken pursuant to an existing state requirement, program, or measure could be applied t oward meeting the required level of emission performance in a state plan if these actio ns occur after proposal of the emission guidelines (e.g., as of June 2014 and in subsequent years). For example, emission reductions during the initial plan performance period that res ult from end-use energy efficiency technologies and measures installed beginning in Ju ne 2014 could be applied toward meeting the required level of emission performance during the p lan performance period. These investments might be made to meet requirements under an existin g end-use energy efficiency resource standard (EERS).

19 An “action” as used here refers to an action taken pursuant to a state requirement, program, or measu re. For example, the installation of an end-use energy effi ciency measure, such as an energy-efficient refrige rator installed through a utility energy efficiency program to meet the utility’s obligations under a state energy eff iciency resource standard (EERS), would constitute an “action” taken pursuant to an existing state requirement. 20 The preamble also takes comment on alternative dat es for eligible actions, including: the start date of the plan period, the date of promulgation of emission guidel ines, the end date of the base period for the EPA’s BSER-based goals analysis (e.g., beginning of 2013 for blocks 1-3 and beginning of 2017 for block 4, end-use ener gy efficiency), the end of 2005, or another date.
21 As discussed in the preamble, at section VIII.F.2. b, the EPA is also proposing that this proposed lim itation would not apply to existing renewable energy requirements , programs, and measures because existing renewable energy generation prior to the date of proposal of the emi ssion guidelines was factored into the state-specif ic CO 2 goals as part of BSER building block three.19

Under a rate-based plan, the approach described abo ve would address the eligibility date for demand-side energy efficiency measures that, th rough MWh savings, avoid CO 2 emissions
from affected EGUs. Measures installed after the el igibility date could generate MWh savings during the plan period, and related avoided CO 2 emissions, that could be applied toward meeting a required rate-based emission performance level. U nder the proposed option, the eligibility date would be the date of proposal of the emission guide lines. 22

Under a mass-based plan, the approach described abo ve would be applied when establishing a reference case scenario projection t hat is used to translate a rate-based goal to a mass-based goal. For example, the assumed amount of energy savings from demand-side energy efficiency measures installed subsequent to the eli gibility date that are used to meet an existing EERS would not be included in the electricity load forecast used in the reference case scenario. Energy efficiency measures installed prior to the e ligibility date to meet an existing EERS would be factored into the electricity load forecast used in the reference case scenario. This treatment would represent in the reference case past actions taken under existing state programs and measures that are not eligible for inclusion in a s tate plan.

22 Although such a limitation is not proposed for RE measures, we also describe here how such a limitati on could be applied to RE measures under such an approach. For example, under this approach in the context of a ra te-based plan, new renewable energy generating capacity inst alled in June 2014 or later to meet an existing, on -the-books RPS would be a qualifying measure in a state plan. However, only MWh generation beginning in 2020 and related avoided CO 2 emissions could be applied toward meeting a requir ed emission rate performance level in a state plan. Similarly, under this approach in the context of a mass-based plan, the reference case might include t he required MWh of renewable energy generation necessary to mee t a state RPS as of the date of proposal of the emi ssion guidelines (e.g., as of June 2014). Renewable energ y generation that is used to meet a state RPS, but that is incremental to the amount of generation used to mee t the existing RPS obligation included in the refer ence case as of June 2014, could be used toward demonstration of CO 2 emission performance under a state plan. This rene wable energy generation would be incremental to the renew able energy generation already assumed in the refer ence case scenario.20

IV. Projecting CO 2 Emission Performance under a State Plan – Overview
As discussed in the preamble, in section VIII.D.4, one of the required components of a state plan is a projection that a plan will achieve the required level of CO 2 emission performance by affected EGUs that is specified in the plan. (Th is identified level of performance must be consistent with either the state-specific rate-base d CO 2 emission performance goals for affected EGUs identified in the emission guidelines, or an e quivalent translated mass-based CO 2 emission performance goal.)

This emission projection is based on a scenario tha t includes the suite of requirements, programs, and measures in the state plan – the State Plan Policy Scenario described above. Depending on the plan approach, construction of thi s state plan scenario may be straightforward. For example, if the state plan consists solely of a n emission limit, the state plan scenario would apply this emission limit to the underlying referen ce case scenario. Other types of state plan approaches are more involved, in particular when th e use of end-use energy efficiency and renewable energy regulations, programs, or measures are included in a state plan. This could include a state plan that applies a rate-based CO 2 emission limit that provides credit for end-use energy efficiency and renewable energy measures tha t avoided CO 2 emissions, or the inclusion of such measures through a portfolio approach. Thes e types of plans will require input assumptions for the amount of end-use energy effici ency and renewable energy resource (in MWh over the plan period) that will be realized thr ough implementation of the plan. Utilitydriven portfolio approaches may also include a dive rse set of actions taken directly at affected EGUs that will need to be properly represented in t he state plan scenario.

Considerations for developing estimates of energy s avings and energy generation that will be achieved through end-use energy efficiency and renewable energy regulations, programs, and measures included in a state plan are discussed below in section VI.21

V. Applied Considerations for Different Types of S tate Plans 23
There are a number of considerations for properly r epresenting different state plan approaches in CO 2 emission projection scenarios. This includes both accurate representation of the attributes of state regulations, programs, and measures in the scenario, as well as the methods and data sources used to derive certain input assum ptions that are used in CO 2 emission projections. This includes:

• Estimating the future effects of end-use energy eff iciency and renewable energy requirements, programs, and measures

• Properly characterizing flexibilities included in e mission limits, such as emission budget trading programs

• Properly addressing characteristics of multi-state regulations and programs

A. End-Use Energy Efficiency and Renewable Energy
Evaluating the impact of a state end-use energy eff iciency requirement or program included in a state plan, as part of a projection o f CO 2 emissions from affected EGUs under a state plan, involves projections of the impact of t he requirement or program on energy and capacity savings, and the impact of this reduction in electricity load and peak demand on EGU dispatch and CO 2 emissions from affected EGUs. 24 This involves projections of the level of current and future energy and capacity savings achi eved through program investment or activities in each year, the distribution of those savings on a daily and seasonal basis (e.g., the load shape of the energy and capacity savings), and the effective useful life of those energy and capacity savings (e.g., the life of installed measu res representing the persistence of energy savings). Such projections may also need to address net program energy savings after accounting for potential “free-ridership,” which involves ener gy savings that were likely to have occurred in the absence of program incentives, and “spillover,” which involves broader market

23 Different potential state plan approaches are desc ribed in detail in the accompanying State Plan Cons iderations TSD. 24 The evaluation discussed in this section is releva nt for state plans that implement a rate-based CO 2 emission limit applicable to affected EGUs that provides for adjus tment or crediting of the CO 2 emission rate of an affected EGU based on the effects of end-use energy efficiency a nd renewable energy. It is also relevant for state plans that take either a rate-based or mass-based portfolio approac h. Descriptions of these plan approaches are provid ed in the accompanying State Plan Considerations TSD.22

transformation impacts that result from the program . These energy efficiency program projections would then be used to adjust electricit y load forecasts, as necessary, for modeling runs using an electricity sector dispatch and capac ity expansion planning model. This modeling would evaluate the impact of the energy and capacit y savings on the dispatch of EGUs, construction and retirement of EGUs, and related CO 2 emissions from affected EGUs.

Similar considerations apply for projecting the imp act of renewable energy requirements and programs included in a state plan on CO 2 emissions from affected EGUs. The renewable energy generation and generating capacity in a stat e or region that results from renewable energy requirements, programs, and measures in a state pla n will affect EGU dispatch and CO 2 emissions from affected EGUs. This section discusse s these considerations in more depth, including approaches and data sources for estimatin g the impact of end-use energy efficiency and renewable energy requirements and programs as p art of state plan projections of CO 2 emission performance by affected EGUs.

1. Potential Approaches for Estimating the Future E ffects of State Energy Efficiency Requirements, Programs, and Measures
There are two basic approaches for developing a ten -year or longer forecast of end-use energy efficiency resources that will result from s tate energy efficiency requirements and programs: a bottom-up or a top-down approach. These forecasts would contain data that could be used to develop inputs to EGU CO 2 emission projections in a state plan, using modeli ng or an EGU growth tool, both of which are summarized above

.
A bottom-up approach is based on annual evaluated a nd reported data from state and utility energy efficiency programs, and program-lev el utility compliance reports under state energy efficiency requirements, such as an end-use energy efficiency resource standard (EERS). One example of a bottom-up approach is the state-by -state end-use energy efficiency projection developed by ISO New England for use in its system planning process. 25 In simple terms, ISO

25 Each year, ISO New England produces a Regional Sys tem Plan (RSP) that provides a comprehensive assess ment of the New England bulk power system that is used a s input data for evaluating the future reliability of the grid. One component of the annual RSP is a future estimate of peak loads and annual energy use that is modified (reduced) by the energy efficiency forecast. Information about t he ISO New England energy efficiency forecasting me thod and assumptions is included in, ISO-New England (ISO-NE ) Energy-Efficiency Forecast Working Group, Draft Final23

New England develops production cost curves for ene rgy efficiency measures based on historical performance of energy efficiency as documented in e valuation, measurement, and verification (EM&V) studies. ISO New England then applies those production cost curves (with adjustments for inflation and other factors) to future state or utility energy efficiency program budgets for each of the six New England states over a ten-year horizon. The ISO New England energy efficiency forecast includes both reductions in ann ual energy use (MWh) and peak demand (MW). ISO New England has produced an energy effici ency forecast for the last three Regional System Plans (2011, 2012, and 2013), and the near-t erm estimates in these forecasts have been validated by the actual quantity of end-use energy efficiency that has qualified for the annual capacity auctions conducted by ISO New England as p art of its forward capacity market. Other approaches to projecting energy efficiency impacts, such as those used by New York ISO (NYISO), PJM Interconnection, and the California Pu blic Utilities Commission (CPUC), estimate the effects of energy efficiency requireme nts and programs over a shorter time frame. In these cases, energy efficiency projections are typi cally consistent with the periods for which energy efficiency programs or program budgets have been approved by state PUCs, or consistent with the period for which energy and demand savings are acquired in ISO and RTO forward capacity markets.

Top-down projection methods analyze aggregate energ y use changes resulting from enduse energy efficiency requirements or programs, oft en for a geographic region, entire industry, or
economic sector. A top-down approach is determined on the basis of s tate energy efficiency policy requirements, and assumes that utilities and other parties implement efficiency programs necessary to achieve required energy and demand sav ings. In this way, a top-down approach is typically based on an estimated annual percentage r eduction in energy use that results from state energy efficiency requirements and programs. One ex ample of a top-down approach is the EPA’s state-level projection of the impacts of “on-the-bo oks” 26 energy requirements and programs. EPA’s approach uses these state requirements and pr ogram commitments as the basis for

Energy-Efficiency Forecast 2018-2023 (March 2014), available at: http://www.isone.com/committees/comm_wkgrps/othr/enrgy_effncy_frc st/ 26 As used here, “on the books” refers to state energ y-efficiency requirements and programs currently in place and specified in state legislation or administrative or der. These requirements include energy efficiency r esource standards (EERS) and dedicated sources of energy ef ficiency program funding.24

adjusting national electricity load forecasts for u se in electricity system modeling. 27 In this case, EIA Annual Energy Outlook (AEO) projections of futu re electricity use (MWh) and peak loads (MW) are modified to account for the reduction in e nergy sales due to state and utility programs and associated energy savings requirements. 28 These energy savings requirements are examined on a state-by-state basis and adjusted, as necessar y, to reflect known increases or decreases in energy efficiency program budgets and other factors likely to affect whether state energy efficiency requirements are achieved. In addition t o EPA’s projections, a top-down approach is used to develop EE forecasts by LBNL 29 , NREL 30 , and some state energy offices and
commissions.

The LBNL and the EPA forecast models are useful for comparison with the bottom-up ISO/RTO approaches. Where discrepancies between the national top-down analyses of end-use energy efficiency savings and the more granular bot tom-up energy efficiency forecasts conducted by ISOs and RTOs exist, review of key ass umptions regarding state policies for the different approaches may be warranted. Ideally, usi ng the two approaches as complementary analyses may provide for helpful comparison and rec onciliation of energy savings estimates used in state plan projections of EGU CO 2 emission performance.

The electricity load forecasts developed by ISOs an d RTOs are important because they are often the base case from which adjusted load fo recasts that incorporate projected energy efficiency program savings are developed. For reaso ns described above, ISOs and RTOs are beginning to include some adjustments to their base case load forecasts to account for energy efficiency program impacts. In most cases, these ef forts are focused on including the future anticipated impacts of existing “on the books” ener gy efficiency requirements and programs and

27 While EPA’s approach utilizes AEO forecasts of el ectricity use and peak loads, other credible and lo ng-term forecasts could be used in a similar manner. U.S. E PA, Background and Draft Methodology for Estimating Ene rgy Impacts of EE/RE Policies (March 2014), available at: http://epa.gov/statelocalclimate/state/statepolicie s.html

. 28 For example, annual incremental and cumulative ene rgy savings requirements for utilities included in a state EERS. 29 Lawrence Berkeley National Laboratory (LBNL), The Future of Utility Customer-Funded Energy Effici ency Programs in the United States: Projected Spending a nd Savings to 2015 (January 2013), available at: http://emp.lbl.gov/publications/future-utility-cust omer-funded-energy-efficiency-programs-united-state s-projectedspend

. 30 National Renewable Energy Laboratory, State Energy Efficiency Resource Standards: Policy Design, Status, and Impacts (May 2014), available at: http://www.nrel.gov/publications/

.25

adjusting load forecasts accordingly. However, in s ome cases there is also an effort to anticipate new incremental energy efficiency investments in th e future that are not tied to existing energy efficiency requirements and programs. 31 When conducting a projection of EGU CO 2 emission performance as part of a state plan that includes e nergy efficiency requirements and programs, it will be important to know how the base electricity load forecast for the state or region was developed, whether it already includes the effect o f state and utility energy efficiency requirements and programs, and whether and how it s hould be adjusted to account for the future effects of existing on-the-books and incremental (i .e., new) energy efficiency requirements and programs that are included in the state plan.

1.1 Potential Uses of Energy Efficiency Projection s for State Plan Emission Projections
Whether developed through a bottom-up or top-down approach, projections of annual energy and peak load reductions that result from st ate energy efficiency requirements and programs included in a state plan are necessary inp uts to projections of EGU CO 2 emission performance under the state plan. Energy and peak d emand savings estimates included in energy efficiency projections are used as a decrement to f uture electricity load forecasts that are used in conducting EGU CO 2 emission performance projections. The energy effic iency projection is used as an input, or modifier, to various models or other tools used to project the impact of energy efficiency resources on state or regional CO 2 emissions. For example, capacity expansion and dispatch planning models are capable of incorpo rating energy efficiency program impact data, such as annual energy and peak load reduction s, when projecting EGU dispatch and capacity additions, and the avoided CO 2 emissions that are projected to result from these requirements and programs.

31 In New England states that have policies to pursue “all cost-effective energy efficiency,” the ISO-NE energy efficiency forecast assumes that energy efficiency program investments will continue beyond the approv ed program funding period (typically documented three-year pla ns), either at a constant level or some discounted level based on assumptions about an increase in the cost of saved energy over time.26

1.2 Data Issues and Considerations for Using and D eveloping Energy Efficiency Projections
Depending on what data sources and approaches are u sed to project the future energy and demand savings from energy efficiency requirements and programs, there are certain strengths and limitations regarding the approach and methodol ogy used. Generally, these strengths and limitations relate to data availability and energy efficiency forecast assumptions, such as:

• Energy efficiency requirements and programs include d

• Jurisdictions covered

• Planning timeframe

• Embedded energy and demand savings from energy effi ciency requirements and programs in base electricity load forecasts

• Cost of saved energy for different energy efficienc y measures

• Energy efficiency measure life and measure energy p erformance decay

• Time of energy and demand savings for energy effici ency measures (availability of hourly data necessary to derive a load reduction pr ofile for an energy efficiency measure)

Bottom-up versus top-down forecasting approaches ha ve different strengths and limitations related to the data and assumptions des cribed above. In the case of bottom-up approaches, state energy efficiency requirements an d programs can be fully addressed in the analysis. In top-down approaches, such as EIA AEO a nalysis, greater focus is placed on federal policies (e.g., codes and standards) but less data is incorporated to reflect state energy efficiency requirements and programs. Bottom-up approaches typ ically include state-specific data that are rolled up to a regional level, while top-down natio nal electricity load forecasts do not have a comparable level of granular state data detail. ISO New England provides energy and demand savings forecasts for state energy efficiency progr ams (average annual and peak demand coincident with the ISO defined peak load periods). The ISO NE energy efficiency forecast is informed by data collected at the energy efficiency measure, individual program, program portfolio, sector, and state levels.27

An important consideration with both top-down and b ottom-up energy efficiency forecasts is the extent to which energy efficiency program effects have become embedded into the long-range economic forecasts used as an input in developing electricity load forecasts. In
some cases the economic forecast includes the effec t of historical energy efficiency policies, either implicitly or explicitly, while in other cas es the economic forecast may also anticipate some limited quantity of future energy efficiency i nvestment that is incremental to the anticipated effect of existing energy efficiency re quirements and programs. An additional consideration is whether and how building energy co des and appliance standards (both current and future) are accounted for in the base electrici ty load forecast.

Another consideration, for all energy efficiency pr ojections, is energy efficiency program spending. While “on the books” energy efficiency re quirements and programs may include multi-year approved energy efficiency program budge ts, actual program expenditures may differ. This has been the case in some of the New England s tates, for example, where ISO New England has made adjustments to “discount” the spending lev els to reflect actual spending trends, and not used the state approved budgets identified through stakeholder discussions. Additionally, assumptions are made about how the overall cost of saved energy for a portfolio of energy efficiency programs will change over time. It is ge nerally assumed in most energy efficiency projections that the cost of installing energy effi ciency measures will become more expensive into the future as state programs move beyond “low- hanging fruit” and increasingly focus on achieving deeper and broader energy savings through whole-building, multi-fuel programs addressing new buildings and building retrofits). 32

While many existing sources of energy efficiency pr ojection data do not account for hourly savings, it is becoming increasingly possibl e for states to examine and incorporate information about the time dimension of energy effi ciency impacts. Smart meters combined with data-sharing and analysis technologies are making i t easier for utilities and other energy

32 Evidence to date is mixed as to a relationship of larger scale energy efficiency programs and broader energy efficiency measure portfolios, and the associated d eeper levels of energy savings, to increasing cost of saved energy. Economies of scale, and expertise gained by program administrators from managing larger programs for m ultiple years, can lead to cost reductions. However, at som e point, as program administrators move away from i nitial lowcost strategies and end-use energy efficiency measu res, it is assumed that the cost of saved energy wi ll increase.28

efficiency program administrators to more accuratel y determine how the total energy savings achieved in a calendar year are spread out across t he hours of that year. These data can be applied to energy efficiency projections to provide a better estimate of the timing of energy and demand savings. Such time-differentiated data is va luable for identifying the marginal EGU or cohort of marginal EGUs that are affected by energy efficiency requirements and programs, which can be used to provide more refined estimates of avoided CO 2 emissions due to changes in EGU dispatch and the addition of new generating cap acity.

2. Potential Approaches for Estimating the Future E ffects of State Renewable Energy Requirements, Programs, and Measures
This section describes data and analytic considerat ions for the representation of renewable energy requirements and programs included in state plans, when conducting projections of EGU CO 2 emission performance that will be achieved under a plan. A range of data and analytic considerations are examined that may be relevant when using different modeling approaches, including use of capacity expa nsion and dispatch planning models, and less sophisticated statistical or top-down projecti on approaches. Examples of specific
considerations are described for three renewable en ergy policies that may be used as enforceable measures in a state plan— renewable portfolio stand ards (RPS), feed-in tariffs (FIT), and performance-based tax incentives.

2.1 Renewable Portfolio Standards
RPS requirements are typically set at the state lev el with an increasing percentage of total retail sales over a set schedule. The RPS requireme nt defines the eligible resource types (e.g., wind, solar, etc.) and in some cases, may specify r esource-specific requirements, such as a percentage of the overall RPS target that is set-as ide for a specific resource. It also defines the allowable geographic boundary for obtaining renewab le energy or RECs. With this context, the following issues may be important to consider when projecting the impact of state RPS on EGU
CO 2 emissions.29

2.1.1 RPS as an Input to Electricity Sector Modeli ng Electricity sector modeling includes the use of dis patch simulation models and capacity expansion and dispatch planning models that simulat e the operation of individual EGUs (or aggregations of EGUs) in the electric system over t ime based on a detailed characterization of those EGUs, engineering and market operating constr aints, other market factors (e.g., fuel prices, transmission constraints), emission constraints, an d the requirement to meet a certain level energy and peak demand. These models may be based o n a small control area, but more likely are regional or national in scope. Some, such as ca pacity expansion and dispatch planning models, simulate EGU dispatch and also consider the impact of long-term generating capacity investment decisions when optimizing system operati on and buildout over a long-term planning horizon. These are typically optimization framework s that have as their objective meeting electricity demand subject to a broad range of oper ating, environmental and market constraints, including meeting RPS requirements.

When using these types of analytic tools to project EGU CO 2 emissions, a number of analytic and data considerations are relevant for a nalysis of RPS impacts on EGU CO 2 emissions. These analytic and data considerations a re summarized below.

Translation of RPS Requirements to Renewable Energy Generation Requirements Most RPS are retail sales-based requirements, while most electricity sector analytic tools are generation-based models. When projecting CO 2 emission performance by affected EGUs using a generation-based analysis tool, it is neces sary to translate the RPS sales-based requirement to a generation requirement using a tra nsmission and distribution loss rate to “ gross up” the sales estimate. For example, if an RPS require s 100 MWh of sales be met with eligible renewable energy resources, then the required renew able energy generation necessary to meet the RPS is 100 MWh / (1 + T&D loss rate). 33

33 According to EIA data, nationally, annual electric ity transmission and distribution losses are equiva lent to about seven percent of the electricity that is input to t he transmission system in the United States.30

RPS-Eligible Resources vs. State Plan-Eligible Reso urces Some RPS rules define a broad range of eligible ren ewable energy generating resources. These could include renewable energy EGUs that emit CO 2 , and EGUs that were constructed and began operation recently or many years ago. Some of these specific EGUs may not be eligible for use in a plan, in particular units that began o peration prior to eligibility dates for actions tha t may be included in a state plan (if such limitation s were applied to renewable energy measures in state plans). 34 Thus, the RPS requirement would need to be adjuste d to reflect the use of only those resources eligible for use in state plans. Th is would be addressed through the treatment of existing RPS in the analysis base case, as describe d above in section III.B.2.

RPS Vintage Rules Some RPS requirements provide that only renewable e nergy EGUs that began operation after a certain date are eligible. To the extent th ese renewable energy EGUs are eligible under a state RPS but not under the state plan, the analysi s should explicitly address these resources. This could be done either through an adjustment of the M Wh needed to meet the RPS, to account for renewable energy resources that are not eligible fo r use in a state plan, or through the use of two modeled RPS requirements for each class of renewabl e energy resource, with the RPS that addresses resources not eligible in a state plan ad dressed in the modeling reference case.

In/Out-of-State Contribution Some RPS allow some of the requirement to be met by out-of-state resources. Some modeling frameworks may allow the user to explicitl y specify the source region of eligible renewables. From a modeling perspective, this means a state would allow a broader supply area and model a broader area generally. If the renewabl e energy resources have different characteristics across the relevant geographic area , then data is needed on the characteristics of these resources by region (e.g., resource availabil ity, performance, and capital and production

34 As discussed in the preamble, and explained in sec tion III.B.2 of this TSD, EPA is proposing that emi ssion reductions that occur in 2020 and later due to acti ons taken pursuant to an existing state requirement , program, or measure could be applied toward meeting the require d level of emission performance in a state plan if these actions occur after proposal of the emission guidelines (e. g., as of June 2014 and in subsequent years). As di scussed in the preamble, at section VIII.F.2.b, the EPA is also pr oposing that this proposed limitation would not app ly to existing renewable energy requirements, programs and measure s because existing renewable energy generation prio r to the date of proposal of the emission guidelines was fac tored into the state-specific CO 2 goals as part of BSER building block three.31

costs). EIA (for NEMS), EPA (for IPM), and NREL (Re EDS) provide information on the characteristics and performance of renewable energy resources by region. 35

Characteristics of Renewable Resources In a dispatch modeling framework, better emission p rojections will result when the source of renewable energy is known and the energy profile is modeled. Renewable energy resources, such as wind or solar PV, may differ sub stantially in generation profile, as the supply of electricity to the grid from the EGU is based on availability of the renewable energy resource. The time during which electricity generation is sup plied will influence the marginal fossil fuelfired EGU (or cohort of EGUs) that is displaced by renewable energy generation. Understanding the renewable energy resource base, including the e nergy profile of the relevant renewable energy resource types will improve the modeling of EGU dispatch and projections of avoided
CO 2 emissions that result from renewable energy genera tion. Data may include a detailed 8760- hour energy output profile for renewable energy res ources, or at a minimum, a seasonal diurnal profile for such resources. If no state-specific da ta is available, generic output profiles for different renewable energy resources are available from NREL. 36

Renewable Energy Resource Availability and Economic s Emission projections will be enhanced by a represen tation of the new renewable energy EGUs that are likely to be brought online as a resu lt of a state RPS, based on an analysis of available renewable energy resources and economics. This requires understanding the relative economics (e.g., capital costs, fixed operation and maintenance (FOM) costs, variable operation and maintenance (VO&M) costs, and fuel costs) and o perating conditions for different types of renewable energy generators, as well as renewable e nergy resource availability (e.g., MW

35 EPA’s Integrated Planning Model generation profile s for wind and solar in the EPA IPM v.5.13 base cas e are available at http://www.epa.gov/powersectormodeling/BaseCasev513 .html

. 36 NREL’s datasets are intended for use by energy pro fessionals, such as transmission planners, utility planners, project developers, and university researchers who perform wind and solar integration studies and need to estimate power production from hypothetical wind and solar p lants. The Eastern Wind Dataset ( http://www.nrel.gov/electricity/transmission/easter n_wind_methodology.html ) contains modeled wind farm data points for the eastern United States for 2004, 2005 , and 2006. The Western Wind Dataset ( http://www.nrel.gov/electricity/transmission/wester n_wind_methodology.html ) includes information about the methodology used to develop the dataset, the accura cy of the data, site selection, and power output. T he Solar Integration Datasets (http://www.nrel.gov/electricity/transmission/solar _integration_methodology.html ) are solar photovoltaic (PV) power plant five-minute and hourl y day-ahead forecasts of generation output for appr oximately 6,000 simulated PV plants in the United States for the year 2006.32

capacity available at different wind classes, solar insolation levels, and the characteristics of biomass). Having this information allows for better projections of the level and quality of renewable resources likely to be brought forward as the result of a state RPS. This will result in better emissions projections.

NREL has a suite of tools that can be used to estim ate the potential costs of renewable energy under various assumptions. 37 For example, the CREST model is a cost-of-energy a nalysis tool intended to assist policy makers evaluating th e appropriate payment rate for a cost-based renewable energy incentive policy. The model aims t o determine the cost-of-energy, or minimum revenue per unit of production, needed for a sample (modeled) renewable energy project to meet its investors' assumed minimum required after-tax r ate of return. 38

Retail Rate Impacts Many state RPSs have cost containment mechanisms or effective caps on the allowable impact on retail rates or on customer bills that ca n result from implementation of an RPS. If these caps are triggered, it will delay further increases in the RPS targets, depending on RPS implementation rules. This would lower estimates of renewable energy generation needed to satisfy the RPS requirements. Likewise, to limit ra te impacts, many state RPS include an alternative compliance payment (ACP) provision, which requires obligated entities to pay a predetermined fee to th e state for each MWh of RPS shortfall. Although these ACP payments may be directed to programs to promote the deployment of renewable energy technolo gies, these payments are not equivalent to renewable energy generation and should not be accou nted as such.

37 NREL's Energy Analysis web site ( http://www.nrel.gov/analysis/models_tools.html ) has links to tools such as the Cost of Renewable Energy Spreadsheet Tool (CREST) a nd the System Advisor Model (SAM). CREST (https://financere.nrel.gov/finance/content/crest-c ost-energy-models) makes performance predictions an d cost of energy estimates for grid-connected power projects based on system design parameters that the user spe cifies. The SAM cash flow model (http://sam.nrel.gov/) helps as sess solar, wind, or geothermal projects, design co st-based incentives, and evaluate the impact of tax incentiv es or other support structures on renewable energy projects. 38 The CREST model is a product of a 2009-2010 partne rship between the National Renewable Energy Laborat ory (NREL), the U.S. Department of Energy (DOE) Solar E nergy Technologies Program (SETP), and the National Association of Regulatory Utility Commissioners (NA RUC). The model was developed by Sustainable Energy Advantage (SEA) under the direction of NREL. The re port, user manual, and CREST models are free and av ailable for download at: https://financere.nrel.gov/finance /content/crest-cost-energy-models.33

Market Impacts – Multiple State Players One important issue is to account for actions of ot hers. Modeling results that consider the
RPS-related actions of other states could differ su bstantially from those that consider a state’s actions in isolation. This requires having at least a regional, if not national, modeling framework. A broader framework captures competition for renewa bles (and increased prices, greater reliance on poorer resources), as well as market-wide impact s on EGU dispatch and capacity expansion.

2.1.2 Data Needs for Estimating Generation Resulti ng from State RPS
Projections of renewable energy generation will req uire assembling the following data:

• Resource availability, resource performance, and re source costs will be necessary for the more sophisticated modeling approaches described ab ove.

• Projections of retail sales, by year, adjusted to a ccount for any exclusions from the RPS obligation (e.g. small utilities, cooperatives, mun icipalities, or perhaps certain large industrial loads). These covered sales must be calc ulated and projected using forecasts of
annual retail demand.

• RPS requirements by year, so the annual forecast of covered retail sales can be multiplied by these percentages.

• The extent to which credit multipliers are used for compliance. Credit multipliers reduce the number of MWh needed for RPS compliance, and a downward adjustment should be made based on the percentage of compliance achieved by credit multipliers.

• Representation of alternative compliance payment (A CP) provisions, if applicable, and other relevant compliance flexibility provisions pr ovided through the RPS.

• Estimates of transmission and distribution (T&D) lo sses, so that retail sales can be “grossed up” as described above. T&D losses estimat es are available at the national level and can also be obtained for various grid regions f rom the appropriate regional grid operator.

• If an electricity sector capacity expansion and dis patch planning model is not used, estimates of the geographic origin, by state and gr id-region, of generation used to satisfy the RPS will be necessary. If generation to satisfy the RPS is expected to come from out of state, the basis for these assumptions should be documented.34

• CO 2 emission rate associated with renewable energy gen eration, by resource type (if applicable).

• Generation profile, by renewable energy resource ty pe

2.2 Feed-in Tariff and Other Performance-Based Inc entives
A feed-in-tariff (FIT) is a performance-based incen tive (similar to production tax credits) that typically guarantees utility customers who own a eligible renewable electricity generation system (e.g., roof-top solar PV system) will be pai d a specific amount by their utility for the electricity the system generates and provides to th e grid over a fixed period of time. Such tariffs usually offer a fixed payment amount per kWh, but v ariations may require competitive bids. In some cases, FIT payments may be in addition to othe r incentives (such as a production tax credit). With a FIT, the amount of renewable energy that will be generated is not specified in the tariff schedule. A similar uncertainty about the am ount of renewable energy generation that will occur exists for production-based tax credits. In t he case of an RPS, the amount of renewable energy generation necessary to meet the RPS is know n given the required RPS level and a utility sales projection. 39 In the case of FITs the response to the program m ay be more uncertain. The remainder of the discussion focuses on estimating t hese impacts.

The key information required to estimate the genera tion that will occur as a result of a
FIT includes:

• An estimate of the renewable energy generation that will be provided by year for the FIT, along with estimates of payments to be made. These should capture the impacts of interactions with other policies, including RPS req uirements, net metering, and federal investment and production tax credits. If generator s receiving FIT payments are also eligible to satisfy an RPS, states should ensure th eir estimates of renewable electricity generation are not double-counted. For analysis usi ng a detailed capacity expansion and dispatch planning model, market penetration of rene wable energy generation to meet an RPS may be an output of the model. In this case, in formation is required on the capital

39 Most state RPS schedules are established as a perc entage of utility sales in a given year, although s ome include specified MWh amounts or are based on MW of generat ing capacity.35

costs, FOM costs, and VOM costs of the renewable en ergy generating technology. This is the same information required for modeling an RPS; however, the modeling or analysis mechanism may differ. For example, these costs may be reduced directly in a modeling framework to reflect the FIT payment (e.g., reducin g VOM to reflect the FIT in the years it is available).

• Information on the types of renewable energy resour ces, including their location, output levels, energy output profiles (across all 8,760 ho urs of the year or seasonally), and operating life of the EGU (by technology, so that t hey can be included in an electric dispatch model or mapped to an appropriate load sha pe or marginal avoided emission rate if using simpler analytical methods.

• An estimate of whether the projected retail price i mpacts of the FIT will lead to lowering the amount paid to eligible renewable energy genera tors, if a FIT includes caps on generation subject to the tariff based on rate impa cts. If retail electricity rate impacts or budgetary impacts become unsustainable, the project ed generation resulting from the FIT
will be overstated.

2.2.1 Data Needs for Estimating Generation Resulti ng from Feed-In Tariff and Performance-Based Tax Incentives
Projections of renewable generation that will occur as the result of FITs and other performance-based incentives will require assemblin g the following data about the specific incentive programs that will support renewable ener gy generation:

• Caps on generating capacity, if any, by year, becau se such caps will limit the amount of renewable energy generation resulting from the tari ff or tax incentive.

• Capacity amounts by renewable energy resource type that will be subject to a FIT or other performance-based incentives, and the capacit y factors for these resource types, so that total generation in MWh can be calculated.

• Payment levels per kWh or MWh for each year of the FIT or other production-based
incentive.

• Budget amounts, if known, by year, which may be exp ended to support renewable generation. When divided by the incentive payment l evels, on a per-kWh or per-MWh36

basis, these budget amounts will yield an estimate of the total electric generation that will be supported by the FIT or other production-based i ncentive.

• State experience and trends with FITs and other pro duction-based incentives. Lacking budget or capacity limits, prior experience in the state, or the experience of other states and utilities, normalized for state or utility size , could be the basis for estimated renewable energy generation at a given incentive le vel.

• Because performance is metered at the busbar (i.e., the point of interconnection to the electricity transmission or distribution system), n o adjustments would be needed for T&D
losses.

• Estimates of the geographic origin, by state, of ge neration that receives support from these programs. In most cases, eligible generation will be in-state, but if generation is expected to come from out of state, the basis for t hese assumptions should be
documented.

• CO 2 emission of renewable energy generation, by resour ce type (if applicable).

B. Emission Budget Trading Programs
Emission budget trading programs establish an emiss ion limit for a group of emission sources and establish a budget of tradable emission allowances equal to the emission constraint for the group of sources. An emission allowance typ ically represents a limited authorization to emit one ton of a regulated pollutant. These progra ms also typically include a number of additional flexibility mechanisms beyond the abilit y to trade allowances. These include multiyear compliance periods, the ability to bank allowa nces issued in a previous compliance period for use in a subsequent compliance period, the use of out-of-sector project-based emission offsets, and cost-containment allowance reserves th at make additional allowances available to the market if pre-established allowance price thres holds are achieved. 40 As a result, annual emissions from affected sources subject to an emiss ion budget trading program often differ from the established annual emission budget for affected sources. In addition, these programs may be multi-sector in nature, regulating emissions for so urce categories in addition to EGUs. As a

40 Depending on the program, these cost containment a llowance reserves make additional allowances availa ble from within the base emission budget (i.e., from “within the emission cap”), or add to the base emission bu dget (i.e., increase the emission cap).37

result, state plan emission projections will need t o accurately account for and represent these compliance flexibilities, as well as the scope of a ffected sources if they are broader than EGUs affected under CAA section 111(d). 41 In general, most electricity sector capacity expan sion and dispatch planning models can be configured to evalu ate these program flexibilities and project
CO 2 emissions from affected sources, considering these compliance flexibilities.

1. Addressing the Sectoral Scope of Emission Budget Trading Programs
Some existing state emission budget trading program s addressing GHG emissions regulate emission sources in addition to EGUs, such as industrial sources. 42 We refer to these here as multi-sector emission budget trading progra ms. All existing state emission budget trading programs addressing GHG emissions include out-of-se ctor project-based emission offsets, which may be used to cover a portion of the compliance ob ligation of affected sources.

For multi-sector emissions budget trading programs, state plan emission projections would need to evaluate projected CO 2 emissions across all source categories covered by the state or multi-state program. This would be necessary to project the CO 2 emissions performance of affected EGUs under the multi-sector emissions budg et trading program.

For emission budget trading programs that regulate EGUs and include offsets, which we
define here as emissions reductions from sources no t regulated by the trading program, emissions
reductions from offsets would not be counted when e valuating CO 2 emission performance of affected EGUs, because those reductions would not c ome from those affected EGUs. However, state plan emissions projections would need to eval uate the use and impact of offsets, because the availability of offsets would affect the amount of CO 2 emissions from affected EGUs.

Addressing Multi-Sector Emission Trading A state or regional emission budget trading program could potentially regulate sources from multiple emissions sectors beyond the electric generating sector that is the focus of the EPA emission guidelines. For example, the California GH G emission budget trading program is a

41 For example, the Regional Greenhouse Gas Initiativ e (RGGI), which is an emission budget trading progr am limited to EGUs, includes EGUs that are not subject to CAA section 111(d). 42 The California GHG emission trading program also i ncludes large industrial sources and as of 2015 wil l include distributors of transportation, natural gas, and ot her fuels.38

multi-sector emissions trading program that address emission sources outside the scope of the EPA emission guidelines for EGUs. If a multi-sector emission budget trading program is included in a state plan, CO 2 emission projections provided as part of the state plan will need to evaluate projected CO 2 emissions across all covered emissions sectors und er the program. This would be necessary to project the CO 2 emissions or weighted average CO 2 emission rate of
affected EGUs that will be achieved under the multi -sector emission budget trading program included in the state plan.

Evaluating the projected impact of a multi-sector e mission budget trading program on the
CO 2 emission performance of affected EGUs introduces a n additional level of analytical complexity to emissions projections. For example, a dditional modeling capabilities may be necessary to adequately evaluate CO 2 emission performance across sectors in a multi-sec tor emission budget trading program.

Adequately projecting the CO 2 emissions of affected EGUs subject to a multi-sect or emissions budget trading program may require modeli ng using a multi-sector energy model. 43 The use of an electricity sector dispatch and capac ity expansion planning model, might also be required, as a complement to a multi-sector model. Some multi-sector models, include an electricity sector dispatch and capacity expansion planning module. However, use of a standalone electricity sector model, might also be neces sary as a supplement to a multi-sector energy model, in order to project CO 2 emissions or the weighted average CO 2 emission rate from
affected EGUs at a sufficient level of resolution f or state plan emission projections. Under this approach, a multi-sector energy model might be used to project the level of CO 2 emissions abatement achieved across the multiple emission sec tors covered by the program. This multisector projection could be used to develop a CO 2 marginal abatement cost curve representing cost-effective, non-electricity sector emission red uction opportunities at increasing cost levels. The marginal abatement cost curve would then serve as an input assumption for the electricity sector capacity expansion and dispatch planning mod el, which would be used to project the CO 2

43 For example, the Energy 2020 model was used by Cal ifornia to evaluate the impacts of its multi-sector GHG emissions budget trading program. Other multi-secto r models are available with similar capabilities, s uch as the National Energy Modeling System (NEMS) developed an d implemented by the U.S. Energy Information Administration (EIA).39

emission performance of affected EGUs (e.g., the CO 2 emissions or CO 2 emission rate of individual affected EGUs, or the total CO 2 emissions or weighted average CO 2 emission rate across multiple affected EGUs). 44

Multi-sector emission budget trading programs also often address GHGs beyond CO 2 . If this is the case, emission projections in a state p lan would need to account for emissions abatement opportunities for each of the GHGs regula ted under the program, and project emissions reductions for each of the regulated GHGs . This would be necessary to project the
extent to which affected emission sources in differ ent industrial sectors reduce emissions of nonCO 2 GHGs, which could impact projected CO 2 emissions from affected EGUs.

Addressing Emission Offsets
Emission offsets in CO 2 or GHG emission budget trading programs represent projectbased GHG emissions reductions outside the sector o r sectors regulated by the program. 45 Emissions reductions achieved through eligible offs et projects are awarded allowances or “credits” that may be used by an affected source to meet a portion of its allowance compliance obligation. For example, under the RGGI program “CO 2 offset allowances” awarded for GHG emissions reductions achieved through approved offs et projects may be used by affected sources to meet up to 3.3 percent of their CO 2 allowance compliance obligation.

The ability to use GHG emission offsets for complia nce means that CO 2 emissions from EGUs regulated under the emission budget trading pr ogram may exceed the base CO 2 emissions budget established for the program. Offset allowanc es or credits are awarded in addition to the existing CO 2 emission budget, in exchange for CO 2 -equivalent emissions reductions achieved

44 This marginal abatement cost curve would be applie d in a similar manner as marginal abatement cost cu rves are applied for GHG emission offsets in modeling analys es of emission budget trading programs. Form a mode ling perspective, emission reductions from other sectors could be used by affected EGUs to demonstrate comp liance with the emission limit. When economic to do so, emissio n reductions from affected EGUs would be foregone a nd replaced by emission reductions from emission sourc es in other sectors that are also subject to the mu lti-sector emission budget trading program. 45 “Offsets” as used in the context of CO 2 or GHG emissions budget trading programs are disti nct from offsets in the NSR permitting context under the CAA, where in cert ain instances emissions of a criteria pollutant fro m a proposed new facility must be offset with creditable emissio ns reductions at an existing facility, if the state or jurisdiction where the proposed facility would be located is in non-attainment status for the pollutant.40

outside the capped emissions sector. 46 Consequently, the use of offsets by affected sourc es to meet a portion of their compliance obligation under an emission budget trading program could result in higher projected CO 2 emissions from affected EGUs in a state plan, beca use the use of offsets functionally expands the CO 2 emission budget for affected EGUs. This allows aff ected
EGUs to emit more CO 2 while meeting their compliance obligation under th e emission budget trading program, in exchange for CO 2 -equivalent emissions reductions achieved outside o f the affected source category. As a result, to properly project the CO 2 emissions from affected EGUs in a state plan that includes an emissions budget t rading program that allows for the use of offsets, it is necessary for modeling to also proje ct the extent to which offsets are used by affected EGUs for compliance. 47

Under this approach, state plans would ignore the C O 2 -equivalent emissions reductions projected to be achieved through offsets, when proj ecting the CO 2 emissions performance that will be achieved by the affected EGU source categor y through implementation of the state plan. This does not mean that an emission budget trading program included in a state plan could not include an offset component. Rather, when demonstra ting emission performance by affected EGUs, the projected CO 2 emissions or weighted average CO 2 emission rate for affected EGUs under the state plan would not incorporate a credit for the CO 2 -equivalent emissions reductions represented by offset allowances or credits used by affected EGUs for compliance with the emission budget trading program. 48

2. Multi-State Emission Trading Programs
Emission budget trading programs may be multi-state in nature. For such programs, emissions reductions are achieved on a regional, ra ther than a state-by-state basis.

46 A key criterion that must be met for the award of offset allowances or credits is a demonstration tha t the offset project is “additional” (i.e., that it would not ha ve occurred absent the incentive provided through t he award of the offset allowance or credit). 47 Existing capacity expansion and dispatch planning models can project the use of offsets for complianc e based on specified offset marginal abatement supply curves, which represent the amount of offset credits/allowa nces assumed to be available from different categories of offset projects at different GHG emissions abatement cost s. 48 In other words, the projected CO 2 emissions or weighted average CO 2 emission rate for affected EGUs would be based on the direct emissions from these EGUs alone , with no calculation of “net” EGU CO 2 emissions that factor in the CO 2 -equivalent emissions reductions represented by off set credits or allowances used by an EGU to meet a portion of its compliance obligation.41

For example, in the multi-state Regional Greenhouse Gas Initiative (RGGI) emission budget trading program, individual participating st ates have established CO 2 emission budgets in state regulations. However, there is no requirement limiting total CO 2 emissions from affected sources in an individual state. State regulations i nclude reciprocity provisions allowing emission
sources to use CO 2 allowances issued by another participating state f or compliance with the state program. This provides for state-to-state CO 2 allowance flows (and the potential for differences in state-by-state CO 2 emissions relative to state emissions budgets) bas ed on where emission
sources determine it is most economical to achieve CO 2 emissions reductions. However, in aggregate, the CO 2 emission budgets in each of the participating stat e regulations establish a regional cap on CO 2 emissions from affected EGUs. As a result, while a multi-state emission budget trading program may be projected to result i n CO 2 emissions from affected EGUs
consistent with a multi-state mass-based CO 2 emission performance goal, the CO 2 emissions outcomes may vary by state. This necessitates evalu ating a multi-state emission budget trading program as a whole, because the individual regulati ons of participating states function together as single integrated program.

To address these issues, states participating in a multi-state emission budget trading program would jointly demonstrate that the multi-st ate program is achieving the required level of
CO 2 emission performance on a multi-state basis, based on the CO 2 emission performance of all affected EGUs in the multi-state group implementing the program.

Some state emission budget trading programs also in clude international partner jurisdictions. 49 In such instances, the program could be treated in a similar fashion as a multistate program that involves only U.S. states. In th is instance, emission projections evaluating an international program would include all jurisdictio ns participating in the program, but emission performance for state plans would be assessed based only on the CO 2 emission performance of affected EGUs in the subset of the program represen ted by U.S. states. Although the CO 2 emission performance of EGUs (or other emission sou rces) in a foreign country would not be addressed in the state plan, the entire internation al program would be evaluated as part of the

49 Currently, the EPA is only aware of one such insta nce, which is the linkage of the California GHG emi ssion budget trading program with a similar program in Qu ebec.42

emission projection included in the state plan. Thi s would be necessary in order to project
international allowance flows and CO 2 emissions across all participating jurisdictions, as these cross-jurisdictional flows would impact projected C O 2 emissions from affected EGUs in U.S.

states.43

VI. Process Considerations
As discussed in the preamble, in section VIII.F.7, the credibility of state plans under section 111(d) will depend in large part on ensurin g credible and consistent emission performance projections in state plans. Therefore, the use of appropriate methods, tools, and assumptions for such projections is critical. Furth ermore, considerations for projecting emission performance under a state plan will differ dependin g on the type of plan. This includes differences in how inputs to projections are derive d; how projections are conducted, including tools and methods; and how aspects of a plan are re presented in these projections.

In the preamble, the EPA seeks comment on whether t he EPA should develop guidance that describes acceptable projection approaches, to ols, and methods for use in an approvable plan, as well as provide technical resources for co nducting projections. This section of the TSD
elaborates on these considerations.

A. Provision of the EPA Guidance
One approach to address these considerations is for the EPA to provide guidance for
EGU CO 2 emission projections included in state plans. Such guidance could include default modeling assumptions, or data sources for key assum ptions. State modeling projections included in a state plan could include assumptions that devi ate from the EPA’s recommended default assumptions, but a state plan would justify the rea son for using alternative assumptions. The EPA technical guidance could specify recommended re ference case assumptions for use with modeling or EGU utilization growth tools, for examp le:

• Electricity load growth projections (energy and pea k demand)

• Fuel supply, delivery, and pricing assumptions

• Cost and performance of electric generating technol ogies

• Cost and performance of pollution control equipment44

• EGU firm builds and retirements (those scheduled wi th a regional transmission organization or independent system operator (RTO/IS O) 50

• Transmission capability and ISO/RTO transmission ex pansion plans

• Applicable federal regulations (other than the EPA emission guidelines)

• Applicable state regulations and programs (other th an the alternative standards that are included in the state plan)

It would be necessary in many instances to include assumptions about other state programs implemented by neighboring states in the s ame region. This would be especially
relevant for states that are located within the sam e electric power pool (or adjoining power pools) that are administered by a common RTO/ISO. To addre ss this need, the EPA technical guidance could provide documentation of state programs and p olicies included in a reference case, as well as those that are eligible for inclusion in a state plan as alternative standards. The guidance could compile information about state programs and provid e model input assumptions related to these programs (e.g., MWh of electric generation needed t o meet a state renewable portfolio standard). 51 The EPA might also play a role in facilitating coo rdination among states as they develop their plans, to harmonize regional assumpti ons.

ISOs and RTOs, in discussions with the EPA have als o offered to support states in evaluating the emission performance of state plans on a regional basis. The ISO/RTO Council, an organization of electric grid operators, has sug gested that ISOs and RTOs could provide analytic support to help states develop and impleme nt their plans. The ISOs and RTOs have the capability to model the system-wide effects of indi vidual state plans. Providing assistance in this way, they felt, would allow states with borders tha t fall within an RTO or ISO footprint to assess the system-wide impacts of potential state plan app roaches. In addition, as the state implements

50 ISOs and RTOs are independent organizations that a dminister a regional electric power pool (EGU dispa tch and electricity transmission systems), and often also a dminister related wholesale electricity markets for electric energy and capacity. 51 EPA’s manual, Roadmap for Incorporating Energy Efficiency/Renewab le Energy Policies and Programs into State Implementation Plans/Tribal Implementation Pl ans (July 2012), could potentially support and be expa nded upon to develop this component of EPA technical mod eling guidance. In particular, see Appendix I: EPA’ s Draft Methodology for Estimating Energy Impacts of EE/RE Policies. The draft manual is available at http://www.epa.gov/airquality/eere.html . State environmental regulations addressing EGUs a re itemized in the modeling documentation for the EPA IPM Base Case v. 5.13.45

its plan, ISO/RTO analytic support would allow the state to monitor the effects of its plan on the regional electricity system. ISO/RTO analytic capab ility could help states assure that their plans are consistent with region-wide system reliability. The ISO/RTO Council suggested that the EPA ask states to consult with the applicable ISO/RTO i n developing their state plans.

B. Party that Translates the Rate-Based Goal to a M ass-Based Goal
In the preamble, in section VIII.F.7, The EPA seeks comment on whether it should develop guidance that describes acceptable projecti on approaches, tools, and methods for use in an approvable plan, as well as provide technical re sources for conducting projections.

One consideration for state plans that use a mass-b ased CO 2 emission performance goal is the party that conducts the translation of the r ate-based CO 2 emission performance goal in the emission guidelines to a mass-based goal—either the EPA or the state. In the preamble, in section VIII.D.3, the EPA seeks comment on whether to assist states that seek to translate the rate-based goal into a mass-based goal.

One approach is for the EPA to provide a presumptiv e translation of the state-specific
rate-based CO 2 emission performance goal to an equivalent mass-ba sed goal for all states, for those states that request it, and/or for multi-stat e regions. This could include default modeling assumptions and results of modeling runs for a Reference Case Scenario and an EPA MassBased CO 2 Emission Goal Policy Scenario , as described above in section III.B. A state coul d utilize the presumptive mass-based CO 2 emission performance goal for the state or multi-s tate region identified through these EPA modeling runs. If a state proposed modifications to EPA default modeling assumptions, it would need to just ify these modifications as part of the CO2 emission projection included in its state plan, and present an emission projection that supports a proposed modified mass-based CO 2 emission performance goal.

Another approach is for the EPA to provide guidance for states to use in translating a rate-based goal to a mass-based goal. This could in clude information about acceptable analytical methods and tools, as well as default input assumpt ions for key parameters that will likely influence projections, such as electricity load for ecasts and projected fossil fuel prices. Under this approach, the EPA might also provide a coordin ating function in addressing the assumptions46

applied by multiple states within a grid region, ac knowledging that assumptions about state programs across a broader grid region that are incl uded in an analysis scenario will influence projections of CO 2 emissions by affected EGUs in any particular state

.
Under this approach, states could deviate from thes e default methods and assumptions with justification. Following the guidance could pr ovide a streamlined path for the EPA approval of emissions projections, but states would still ha ve flexibility to use other approaches, which the
EPA would review.