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1 Technical Support Document (TSD) for Carbon Pollution Guidelines for Existing Power Plants: Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generating Units

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

Projecting EGU CO 2 Emission Performance in State Plans

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

June 2014DRAFT, DELIBERATIVE

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2 Table of Contents

I. Introduction .................................................................................................................. ......3 II. Analytic Approaches for Projecting CO 2 Emissions from Affected EGUs ..................5 A. Electricity System Modeling Approaches for Projecting EGU CO 2 Emissions .......5 B. Growth Tools for Projecti ng Future EGU Utilization and CO 2 Emissions...............9 III. Translating from a Rate-Based Emission Performance Goal to a Mass-Based Emission Performance Goal ............................................................................................11 A. Concept ...................................................................................................................1 1 B. Projection Scenarios ................................................................................................13 IV. Projecting CO 2 Emission Performance under a State Plan .........................................17 V. Applied Considerations for Different Types of State Plans .........................................18 A. End-Use Energy Efficien cy and Renewable Energy .............................................18 B. Emission Budget Trading Programs ......................................................................32 VI. Process Considerations ....................................................................................................38 A. Provision of the EPA Guidance .............................................................................38 B. Party that Translates the Rate-B ased Goal to a Mass-Based Goal ........................40DRAFT, DELIBERATIVE

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3 I. Introduction

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

CO 2 emissions from affected EGUs.

The EPA is also proposing that a state may translate the state rate-based CO 2 emission performance goal for affected EGUs to an equivalent mass-based CO 2 emission performance goal. If this translation option is used, a state pl an must also include a projection used to derive

the mass-based CO 2 emission performance goal. This translation will involve a projection of CO 2 emissions from affected EGUs during the plan period, under a scenario that assumes the ratebased goal in the emission guidelines is met.

As discussed in the preamble, the EPA is st riving to find a balance between providing state implementation flexibility and ensuring th at the emission performance required by CAA section 111(d) is properly define d in state plans and that plan performance projections have technical integrity. The credibility of state plans under section 111(d) will de pend in large part on ensuring credible and consistent emission performa nce projections in state plans. Therefore, the use of appropriate methods, tools, and assu mptions for such projections is critical.

The preamble, at section VIII.F.7, seeks comment on options presented for how CO 2 emission projections might be conducted in an a pprovable state plan, and how different types of state plan approaches are represented in thes e projections. These opti ons include the use of historical data and parameters for estimating th e future impact of individual state programs and measures. Alternatively, a projection could be based on modeling, such as use of a capacity expansion and dispatch planning mode l, or a dispatch simulation model. 1 This latter approach would be able to capture dynamic interactions within the electricity sector, based on system

1 In many cases, this approach will also require the developm ent of parameters for estimating the effect of individual state programs and measures, for use as input assumptions for modeling.DRAFT, DELIBERATIVE

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

In the preamble, the EPA further seeks co mment on whether the EPA should develop guidance that describes acceptable projection ap proaches, tools, and methods for use in an approvable plan, as well as wh ether the EPA should provide t echnical resources for conducting projections.

This technical support document (TSD) elabor ates these options and considerations. The TSD discusses possible analytic approaches for translating from a rate-based CO 2 emission performance goal to a mass-base d goal, and projecting the CO 2 emission performance that will be achieved through a state plan. It discusses both modeling and non-modeling approaches, such as electricity sector capacity expansion and dispatch planning models, dispatch simulation models, and growth tools that ba se projections on historical data and algorithms. The TSD also discusses possible approaches fo r developing inputs that are us ed for emission projections and applied considerations for di fferent 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 pr actice of projecting EGU CO 2 emission performance under a state plan

 Section V discusses applied considerations for projecting EGU CO 2 emission performance under different type s of state plans, including:

o Considerations that should be addres sed in conducting projections of emission performance for different types of plans

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

 Section VI discusses process considerations for conducting EGU CO 2 emission projections for state plans, including:

o Regional coordination among states in conducting projectionsDRAFT, DELIBERATIVE

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

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

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

A. Electricity System Modeling Approaches for Projecting EGU CO 2 Emissions

1. National-scale capacity expansion and dispatch planning models

National-scale electricity capacity expansion and dispatch planning models are typically used for fundamentals-based projections of the pow er sector (i.e., projections of the expected response of the sector to factors such as electricity de mand, fuel prices, and emission constraints) that may extend over a period of several decades. These models are built to evaluate the impacts of market, technical, and regulat ory factors on the electric powe r sector and related markets. Typical outputs of such models include EGU dispatch, fuel cons umption, fuel prices, wholesale electricity prices, emissions, EGU retirements, and infrastructure expenditures (e.g., addition of new EGU capacity and installation of re trofit pollution cont rol technologies).

National-scale electricity capacity expans ion and dispatch planning models have moderate spatial detail with broad scope, ge nerally encompassing the entire country or transmission system interconnects (i.e. Easter n, Western, and ERCOT), which are subdivided into smaller areas, such as balanc ing authorities or control areas. 2 For computational efficiency, these models generally model several representativ e hours of the year, or aggregate hours into representative bundles with simila r electricity demand profiles (i.e . peak, shoulder, off-peak). In these models, to reduce model size, existing EGUs may be aggregated into “model plants” to the

extent that such EGUs share ke y unit characteristics, such as location, size, efficiency, operating

2 A “balancing area” or “control area” refers to a specifi ed portion of the transmissi on system where electricity demand and generation are balanced in real time by a system administrator to maintain grid reliability.DRAFT, DELIBERATIVE

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6 costs, pollution retrofit control status, age, and fu el type use/availabilit y. These types of models are well suited to project dynamic entry and exit ( capacity expansion and un it retirement) to meet energy and capacity requirements while minimizing system costs, maintaining reliability criteria, and following other constraints, such as mini mum build times, transmission constraints, renewable energy availabilit y, or emission limitations.

2. Utility-Scale Capacity Expansion and Dispatch Planning Models

Utility-scale capacity expansion and dispatch planning models are similar in concept to

related national-scale models. Ho wever, utility-scale capacity e xpansion and dispatch planning models are typically used for evaluating narrower utility planning and investment decisions, such as procurement of a specific new electric generating facility and re tirement or retrofit decisions for existing capacity within a specific utility’s te rritory. Utility planning models typically project outcomes for periods of up to two decades. Ut ility-scale capacity expansion and dispatch planning models are an industry-standard, used regularly in state el ectricity regulatory proceedings. Within the electricity sector there is broad familiarity with these models at state PUCs. Multiple vertically integrated utilitie s use capacity expansion and dispatch planning models to conduct forward planning and revi ew the economics of specific EGU retrofit decisions. Utilities that submit integrated res ource plans (IRP) typically use a utility-scale capacity expansion and dispatch planning model to examine long-term strategies and develop short-term action plans. 3 Utilities have experience using these models to examine CO 2 emission reduction strategies or CO 2 emission constraints, as IRP scenarios may include greater penetration of end-use energy effici ency or renewable energy, proxy CO 2 emission prices, emission trading, and limits on CO 2 mass emissions.

3 See, for example: The Duke Energy Carolinas IRP (Annual Report), September 2012, available at http://www.energy.sc.gov/files/view/Duke_IRP 2012.pdf ; PacifiCorp 2013 IRP (Vol 1), April 2013, available at http://www.pacificorp.com/es/irp.html ; TVA IRP, March 2011, available at http://www.tva.gov/environment/reports/irp/archive/index.htm ; Georgia Power Company 2013 IRP, January 31, 2013, available at http://facts.psc.state.ga.us/Public/GetDocument.aspx?ID=145981 ; 2011 Joint IRP of Louisville Gas and Electric and Kentucky Utilities Company, April 2011, available at http://psc.ky.gov/PSCSCF/2011%20cases/2011- 00140/20110421_LG%26E-KU_IRP_Volume%20I.pdf ; Great River Energy IRP, November 2012, available at http://www.greatriverenergy.com/makingelectricity/resourceplan/pdoc295631.pdf ; Public Service Company of Oklahoma IRP, October 2012, available at http://occeweb.com/pu/PSO%202012%20IRP.pdf

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7 Utility-scale capacity expansion and dispatch planning models tend to have relatively high spatial detail with limited geographic sc ope, generally encompassing a utility service territory or a sub-regional scale. These models ge nerally have better temporal resolution than the national-scale capacity expansion and dispatch planning models, w ith each model year typically dispatched based on an annua l hourly load duration curve. 4 Utility-scale capacity expansion and dispatch planning models typically represen t individual EGUs, where each EGU has specific operational characteristics.

Due to the additional spatial and temporal resolution of these models as compared to the national-scale models, the numbe r of technology options for capacity expansion is generally

limited to reduce the runtime of the model. This can be done through an outside-the-model screening analysis to pre-select the resources most likely to be economic in a planner’s area of interest, or by running the model iteratively to eliminate rarely chosen technology alternatives.

3. Electricity System Dispatch Simulation Models

Dispatch simulation models are regularly used by utilities, grid operators, and independent power producers (IPP) for short-te rm planning, ratemaking, di spatch decisions, and market intelligence. Dispatch simulation models are typically driven by near-term economics, system restrictions and market constraints, including 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 ad d or retire generating capacity on an economic basis, although EGU additions a nd retirements may be exogenously input to these models. As a result, projections from these models tend to be considered more robus t in the shorter term. 5 Grid operators, including utili ties, and independent system operato rs (ISO) use dispatch simulation

models in near real-time to match demand with electric genera tion from available generating units and dispatch EGUs on a least-cost basi s. EGU owners and operators run dispatch simulation models to assist in fuel procuremen t, forecast revenues and costs, and calculate the

4 Load duration curves typically represent electri city load during a typical week for each month. 5 Some planners use dispatch simulation models in conj unction with national or utility-scale capacity expansion and dispatch planning models, where the capacity expansion and dispatch planning model indicates the disposition of new and existing generating resources, and the dispatch simula tion model is used to simulate operation of individual EGUs. In this case, these models can be used in the same time horizon as a capac ity expansion and dispatch planning model ( i.e ., decades).DRAFT, DELIBERATIVE

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8 avoided generation supply costs related to pr ocurement of end-use energy efficiency and renewable energy resources. 6 . Other utilities use dispatch simulation models to forecast retail rates for ratemaking proceedings and other planning purposes.

Electricity system dispatch simulation models (also called production cost models) utilize security constrained economic dispatch (SCED) algorithms to determine which EGUs operate on an hourly (or shorter) basis to meet electricity demand. 7 These models typically have a very broad spatial scope, generally covering multip le Regional Transmission Organization (RTO) regions, and often modeling entire interconnect s (i.e. Western, Eastern, and ERCOT). While individual EGUs are modeled in detail, incl uding fuel and variable costs and operational constraints, transmission is simplified to characterize thermal constraints between zones, which typically represent control areas or balancing authorities. Zones contain both load (electricity demand) and EGUs; EGU dispatch and electricity demand 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, wh ere transmission 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 typi cal weeks of the year. These models contain substantially more detail a bout individual EGUs than regional or national capacity expansion and dispatch planning models , including EGU ramp rates, minimum outages, maintenance schedules, emission rates, fuel use constraints, and heat rate curves depicting expected efficiency changes at various levels of output.

6 For example, see Hornby, R., P. Chernick, D. White, et al., 2013. Avoided Energy Supply Costs in New England: 2013 Report. http://www.rieermc.ri.gov/documents/2013%20 Evaluation%20Studies/AESC%20Report%20- %20With%20Appendices%20Attached.pdf 7 The Federal Energy Regulatory Commission (FERC) define s security constrained economic dispatch as “the 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 minimized”. SCED optimizes dispat ch based not only on the marginal costs of all available generating resources, but also constr aints on transmission availability and ancillary services. See FERC, “Security Constrained Economic Dispatch: De finition, Practices, Issues, and Recommendations” (2006), available at http://www.ferc. gov/industries/electric/indus-ac t/joint-boards/final-cong-rpt.pdf

. 8 “A “nodal” model represents the entire transmission network in a given area, without making the simplifying assumptions that load is served on a more aggregate “zona l” level.” A caveat is that while a nodal model has more “links” and places where supply must meet demand, it is stil l an approximation in terms of the electrical operation of the transmission network (the network is represented as a DC flow network, not an AC flow network).DRAFT, DELIBERATIVE

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9 4. Multi-Sector Models

Multi-sector energy models are typically us ed to examine the effect of energy and environmental policies that affected multiple eco nomic sectors, such as multi-sector emission trading systems. Multi-sector models are used to review trends in emissions, expected broadscale resource use, and energy sector im pacts under changing regulatory and economic conditions, and often review cha nges over a period of decades.

Multi-sector models cover a broad range of energy sectors beyond the electricity sector, and can better reflect energy demand and technol ogy choices by energy end-users. Such models typically have relatively limited spatial detail with broad scope, generally encompassing the entire country subdivided into from one to 30 regions. Such models tend to have much more limited temporal resolution and treatment of EGU dispatch. EGUs are typically aggregated to a few broad technology types. A key strength of multi-sector energy models is the ability to provide multi-sectoral feedback between energy resource use and price ( i.e. , tracking national fuel supplies and adjusting pr ice to account for demand).

The range of national- and u tility scale capacity expansion and dispatch planning models, and dispatch simulation models identified above all te nd to focus on properly characterizing the electricity sector in order to answer sector-specific questi ons. Multi-sector energy models attempt to include many other en ergy-using sectors of the economy in order to better capture interactions between these sectors. The more aggr egate representation of th e 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 understand variations in electricit y load that may result from changes outside th e electricity sector.

B. Growth Tools for Projecting EGU Utilization and CO 2 Emissions

Organizations use growth tools for a variety of reasons; to estimate future emissions

inventories for state and re gional air quality modeling, 9 to estimate the impact of load-reduction measures such as end-use energy efficiency a nd distributed renewable energy on individual EGU

9 See, for example, the ERTAC Lo ad Growth Model, available at http://www.ertac.us/index_egu.html

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10 emissions, and county, state, 10 and regional emissions rates. 11 Because these algorithms are generally based on publicly available data 12 and do not rely on economic data or proprietary information regarding individual EGUs, they prov ide 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, approximate future emissions and generation from existing and new fossil fuel-f ired EGUs under different assumed growth, retrofit, and load-reduction scenarios. These forecast tools do not simulate economic EGU dispatch, but could use demand growth rates and electricity production trends from other energy modeling forecasts as an input assumption. 13 The algorithms in these forecast tools assume that EGU dispatch behavior generally follows simple rules based on pa st operation. In the absence of significant shifts in fuel pri ces and electricity demand, EGUs may be expected to behave similarly in the future as they did in the past. Several common features of these forecast tools are that they (a) generally build on historical generation and emi ssions output from individual EGUs, (b) are insensitive to fuel and emission price forecasts, (c) do not solve for optimal economic EGU dispatch or EGU capacity expansion, and (d ) do not capture transmission constraints or limits. The algorithms in these forecast t ools generally divide the contiguous US 14 into regional power markets, following ISO boundaries, eG RID boundaries, NERC regional boundaries, or similar designations. These algori thms generally seek to exam ine how operation and emissions from individual EGUs could be expected to chan ge with changes in environmental regulations or installed pollution control retr ofits and additional or reduced hourly electricity demand. Some algorithms use the observed historical behavior of individual EGUs to approximate future behavior, while others add addi tional steps of differentiating E GUs into fuel groups and unit types, with implicit differentiation of economic outcomes for these different groups. Some of

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

. 11 See Flexibility Weighted Hourly Average Em issions Rate (FW-HAER) tool, available at http://nepis.epa.gov/Adobe/PDF/P1002UQO.pdf ; Time Matched Marginal (TMM) emissions tool, a proprietary algorithm used to estimate avoided em issions; D. Jacobson and C. High, “U .S. Policy Action Necessary to Ensure Accurate Assessment of the Air Emission Reduction Benefits of Increased Use of Energy Efficiency and Renewable Energy Technologies,” Journal of Energy & Environmental Law (2010)

. 12 Hourly emissions and generation data for all fossil fuel -fired EGUs greater than 25 MW are available from EPA’s Clean Air Markets Division (CAMD) throug h its Air Markets Program Data (AMPD). 13 For example, EIA’s AEO 2013 elect ricity load growth rates and EGU production trends are available at http://www.eia.gov/forecasts/aeo/a ssumptions/pdf/0 554(2013).pdf. 14 Hawaii and Alaska do not report hourly generation and emission data from individual EGUs to EPA, and are therefore generally excluded from these forecast tools.DRAFT, DELIBERATIVE

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11 these algorithms may contain subroutines to add new generating capacity automatically to meet load requirements.

III. Translating from a Rate-Based Emission Performance 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 (taki ng into account the qualif ying impacts of plan

measures that avoid CO 2 emissions from affected EGUs) must be equivalent to, or better than, the required CO 2 emission performance level in the state plan. This required level of emission performance in the state plan will reflect th e rate-based goal 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 t o, or lower than, the required level of CO 2 emission performance in the state plan would be considered to meet this plan approvability criterion. States may demonstrate such emission pe rformance by affected EGUs either by state or jointly on a multi-state basis.

This section of the TSD discusse s the concept of a mass-based CO 2 emission performance goal, considerations for constructing projection scen arios for such a translation, methods for conducting such a translation, and ke y input assumptions for such a translation.

A. Concept

A mass-based CO 2 emission performance goal translates the application of a rate-based emission performance goal to an expected CO 2 emissions outcome in tons during a plan performance period. A mass-based CO 2 emission performance goal is calculated by projecting

the tons of CO 2 that would be emitted during a stat e plan performance period (e.g., 2020-2029, 2030-2032) by affected EGUs in the state if they hypothetically were meeting the state ratebased CO 2 emission performance goal for affected EGUs established in the emission guidelines.

The translation of a rate-bas ed goal (expressed in lb CO 2 /MWh of useful energy output from affected EGUs) to tons (expr essed 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.DRAFT, DELIBERATIVE

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12 Importantly, this projection is conducted assumi ng the absence of qualifying state programs and measures contained in a state pl an, and applying the rate-based goa l in the emission guidelines as a proxy emission limit for affected EGUs. State pr ograms and measures in the state plan are not included in this projection, because the purpose of this analysis is to determine the tonnage CO 2 emissions that corresponds to the state-specific rate-based CO 2 emission performance goal for affected EGUs in the emission guidelines, withou t the eligible state programs and measures that are included in the state plan.

Translation of a rate goal to a mass goal seeks to answer the question, “What would happen to EGU CO 2 emissions if one applied the rate goal s in the emissions guidelines instead of the measures in the state plan?” Because the emission guidelines do not specify the emissions

reduction measures that a state mu st use in its plan, only the leve l of emission performance that must be achieved through a plan, there is no sp ecified “policy” or set of emission reduction measures in the emission guidelines to apply when conducting this projection. However, a proxy policy can be applied to project the emission performance in tons that would be achieved if the state plan were to include suitable measur es to achieve the requi red level of emission performance established thr ough the state rate-based CO 2 emission performance goals in the emission guidelines. This proxy involves applying the rate-based emission goal for a state as a rate-based average CO 2 emission limit for affected EGUs in a state and then projecting the CO 2 emissions that would occur if such a limit were applied.

When demonstrating projected emission pe rformance under a mass- based plan, a state would project the CO 2 emissions outcome that would be achieved under the suite of requirements, programs, and measures in its pl an. The state plan requ irements, programs, andmeasures substitute for EPA’s application of the best system, which is represented by the ratebased goal in the emission guidelines. If the CO 2 emissions outcome, in total tons of CO 2 emissions over each plan performance period, is e qual to or less than what would be emitted by affected EGUs through the application of the rate-based goal in the emission guidelines ( i.e. , equal to or less than the translated mass-based go al), the plan would be deemed to achieve the required emission performance criterion.DRAFT, DELIBERATIVE

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13 B. Projection Scenarios

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

Projection scenarios for translating from a rate-based CO 2 emission performance goal to a

mass-based CO 2 emission performance goal, and comp aring projected emission performance under a state plan to this goal, include the following:

1. A Reference Case Scenario . This scenario projects the average CO 2 emission rate and

CO 2 emissions from affected EGUs in the ab sence of the EPA emission guidelines or any enforceable requirements, programs, and measur es included in a state plan. This scenario does, however, include all current on-the -books state requirements, programs, and

measures that are not included as enforceable measures in a state plan. These measures are complementary to the state plan. 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. The 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 performance peri od in this scenario represents the translated mass-based CO 2 emission performance goal for the state plan. To construct this scenario, this emission lim it is added to the underlying reference case

scenario described above.

15 An “existing measure” refers to a state or utility requi rement, program, or measure that is currently “on the books.” For the purposes of this discussion, this may in clude a legal requirement that includes current and future obligations or current pr ograms and measures that are in place and are an ticipated to be continue d or expanded in the future in accordance with established plans. Existing measures may have past, current, and future impacts on EGU CO 2 emissions. 16 The proxy emission limit applied includes crediting for end-use energy efficiency, renewable energy, and nuclear generation included in building blocks three and four, which were used by EPA when calculating the state-specific CO 2 emission performance goals for affected EGUs in the proposed emission guidelines. As a result, the proxy emission limit is able to capture all four building blocks included by EPA when calculating these goals.DRAFT, DELIBERATIVE

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14 3. A State Plan Policy Scenario . This projection scenario includes the enforceable requirements, programs, and measures included in the state plan, and is used to project

CO 2 emission performance by affected EGUs und er the state plan. To construct this scenario, the enforceable requirements, programs, and measures included in the state plan are added to the underlying referenc e case scenario described above.

1. Constructing the Reference Case

A key consideration for transl ating from a rate-based CO 2 emission performance goal to a

mass-based CO 2 emission performance goal is proper cons truction of a reference case scenario. This includes assumptions for key variables that may drive EGU CO 2 emission projections, 17

such as:

 Electricity load growth projec tions (energy and peak demand)

 Fuel supply, delivery, and pricing assumptions

 Cost and performance of electr ic generating technologies

 EGU firm builds and retirements (those scheduled with a regional transmission organization or independent system operator (RTO/ISO) 18

 Transmission capability and ISO/RTO transmission expansion plans

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

 Applicable state regulations and programs (other than those that are included in the state plan)

It may be necessary in many instances to in clude assumptions about other state programs implemented by neighboring states in the same region. This would be especially relevant for

states that are located within the same electr ic power pool (or adjoini ng power pools) that are administered by a common RTO/ISO.

17 Process considerations related to constructing reference case scenarios are addressed in section VI of this TSD. 18 ISOs and RTOs are independent organizations that admini ster a regional electric powe r pool (EGU dispatch and electricity transmission systems) , and often also administer related wholesal e electricity markets for electric energy and capacity

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15 2. Treatment of Existing State Regulations and Programs in the Reference Case

A key aspect of the reference case is prope r treatment of existi ng state regulations, programs, and measures. As discussed previous ly, existing state regulations, programs, and

measures that are not enforceable measures in a state plan are complement ary to the state plan – as a result, they should be included in the refe rence case scenario rath er than the state plan scenario. These state regulations, programs, a nd measures should not, however, be ignored altogether, as they influence EGU CO 2 emissions. Instead, these stat e actions are occurring in the background – either when a proxy rate-based CO 2 emission limit is applied in the Mass-Based

CO 2 Emission Goal Policy Scenario ; or when the state plan requirements, programs, and measures are applied in the State Plan Policy Scenario . As a result, these state actions are properly addressed in the re ference case scenario.

In effect, when evaluating whether a state plan will meet the mass-based CO 2 emission performance goal, the two policy scenarios desc ribed above, each of which involve adding a

different CO 2 emission reduction policy on top of the reference case scenario, are compared to

one another. If the State Plan Policy Scenario achieves projected tonnage CO 2 emissions equal to or lower than the projected CO 2 emissions in the Mass-Based CO 2 Emission Goal Policy

Scenario , then the state plan can be deemed to meet the translated mass-based CO 2 emission performance goal.

As discussed in the preamble, the EPA is proposing that a state may apply toward its required emission performance level the emission reductions that are achieved by existing state requirements, programs, and measures during the plan period, due to actions 19 taken after the date of the proposal of the emission guidelines. 20 In practice, this means that emission reductions

that occur in 2020 and later due to actions taken pursuant to an existing state requirement, program, or measure could be applied toward meeting the required level of emission

19 An “action” as used here refers to an action taken pursuant to a state requirement, program, or measure. For example, the installation of an end-use energy efficiency measure, such as an energy-efficient refrigerator installed through a utility energy efficiency program to meet the u tility’s obligations under a state energy efficiency resource standard (EERS), w ould constitute an “action” taken pursua nt to an existing state requirement. 20 The preamble also takes comment on alternative dates for eligible actions, including: the start date of the plan period, the date of promulgation of emission guidelines, 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 energy efficiency), the end of 2005, or another date.DRAFT, DELIBERATIVE

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16 performance in a state plan if these actions o ccur after proposal of th e emission guidelines (e.g., as of June 2014 and in subsequent years). Fo r example, emission reductions during the plan period that result from end-use energy efficiency or renewable energy technologies and measures installed beginning in June 2014 c ould be applied toward meeting the required level of emission performance during the plan period. These invest ments might be made to meet requirements under an existing end-use energy efficiency reso urce standard (EERS) or renewable portfolio standard (RPS).

Under a rate-based plan, the approach descri bed above would address the eligibility date for qualifying renewable energy and demand-side energy efficiency measures that, through MWh generation or MWh savings, avoid CO 2 emissions from affected EGUs. Measures installed after the eligibility da te could generate MWh generation or MWh savings during the plan period,

and related avoided CO 2 emissions, that could be applied to ward meeting a required rate-based emission performance level. Under the proposed opti on, the eligibility date would be the date of proposal of the emission guidelines. For exampl e, under this approach, new renewable energy generating capacity installed in June 2014 or la ter to meet an existing, on-the-books RPS would be a qualifying measure in a state plan. Ho wever, only MWh genera tion beginning in 2020 and

related avoided CO 2 emissions could be applied toward meeting a required emission rate performance level.

Under a mass-based plan, the approach described above woul d be applied when establishing a reference case scenario projection th at is used to translat e a rate-based goal to a mass-based goal. For example, qualifying renewabl e energy and demand-side energy efficiency measures after a respective eligibility date woul d not be included in the reference case scenario. This could be achieved by not including an EER S or RPS requirement in a reference case projection, beginning at a specified date. The reference case would, however, include the level of such requirements up to the date at which such m easures would become eligible for inclusion in a state plan. This treatment would represent past actions taken under exis ting state programs and measures that are not eligible for inclusion in a state plan.

For example, the reference case might incl ude the required MWh of renewable energy generation necessary to meet a st ate RPS as of the date of pro posal of the emission guidelinesDRAFT, DELIBERATIVE

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17 (e.g., as of June 2014). Renewable en ergy generation that is used to meet a state RPS, but that is incremental to the amount of generation used to meet the existing RPS obligation included in the reference case as of June 2014, could be used toward demonstration of CO 2 emission performance under a state plan. This renewable energy generation would be incremental to the renewable energy generation already assu med in the reference case scenario.

IV. Projecting CO 2 Emission Performance under a State Plan

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 pla n. (This identified level of performance must be consistent with either the state-specific rate-based CO 2 emission performance goals for affected EGUs identified in the emission guidelines, or an equivalent tr anslated mass-based CO 2 emission performance goal.)

This emission projection is based on a scenario that includes the suite of requirements, programs, and measures in the state plan – the State Plan Policy Scenario described above. Depending on the plan approach, construction of this state plan scenario may be straightforward. For example, if the state plan consists solely of an emission limit, the state plan scenario would apply this emission limit to the underlying refere nce case scenario. Other types of state plan approaches are more involved, in particular wh en the use of end-use energy efficiency and renewable energy regulations, prog rams, or measures are included in a state plan. This could include a state plan that applies a rate-based CO 2 emission limit that provides credit for end-use energy efficiency and renewable energy measures that avoided CO 2 emissions, or the inclusion of such measures through a portfolio appro ach. These types of plans will require input assumptions for the amount of end-use energy efficiency and renewable energy resource (in MWh over the plan period) that will be realized through implem entation of the plan. Utilitydriven portfolio approaches may also include a di verse set of actions take n directly at affected EGUs that will need to be properly re presented in the state plan scenario.

Considerations for developing estimates of energy savings and energy generation that will be achieved through end-use energy efficien cy and renewable energy regulations, programs, and measures included in a state plan are discussed below in section VI.DRAFT, DELIBERATIVE

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18 V. Applied Considerations for Different Types of State Plans 21

There are a number of considerations for properly representing di fferent 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 assump tions that are used in CO 2 emission projections. This includes:

 Estimating the future effects of end-us e energy efficiency and renewable energy requirements, programs, and measures

 Properly characterizing flexibilities included in emission 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 efficiency requirement or program as part of a projection of CO 2 emissions from affected EGUs under a state plan involves projections of the impact of the requirement or program on energy and capacity savings, and the impact of this reduction in electricity load and peak demand on EGU dispatch and CO 2 emissions from affected

EGUs. 22 This involves projections of the level of cu rrent and future energy and capacity savings achieved through program investment or activities in each year, the distribution of those savings on a daily and seasonal basis (e.g., the load shap e of the energy and capacity savings), and the

effective useful life of those en ergy and capacity savings (e.g., th e life of installed measures representing the persiste nce of energy savings). Such projec tions may also need to address net program energy savings after accounting for poten tial “free-ridership,” which involves energy savings that were likely to have occurred in th e absence of program incentives, and “spillover,”

which involves broader market transformation impacts that result from the program. These

21 Different potential state plan approaches are described in detail in the accompanying State Plan Considerations TSD. 22 The evaluation discussed in this section is relevant for state plans that implement a rate-based CO 2 emission limit applicable to affected EGUs that provides for adjustment or crediting of the CO 2 emission rate of an affected EGU based on the effects of end-use energy efficiency and renewable energy. It is also relevant for state plans that take either a rate-based or mass-based port folio approach. Descriptions of these plan approaches are provided in the accompanying State Plan Considerations TSD.DRAFT, DELIBERATIVE

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19 energy efficiency program projections would then be used to adjust electricity load forecasts, as necessary, for modeling runs using an electricity sector dispatch and capacity expansion planning model. This modeling would evaluate the impact of the energy and capacity savings on the dispatch of EGUs, construction and retirement of EGUs, and related CO 2 emissions from

affected EGUs.

Similar considerations apply for projecting the impact of renewable energy requirements and programs included in a state plan on CO 2 emissions from affected EGUs. The renewable energy generation and generating capacity in a stat e or region that result s from renewable energy requirements, programs, and measures in a st ate plan will affect EGU dispatch and CO 2 emissions from affected EGUs. This section di scusses these considerations in more depth, including approaches and data sources for estimating the impact of end-use energy efficiency and renewable energy requirements and programs as part of state plan projections of CO 2 emission performance by affected EGUs.

1. Potential Approaches for Estimating the Future Effects of State Energy Efficiency Requirements, Programs, and Measures

There are two basic approaches for developing a ten-year or longer forecast of end-use energy efficiency resources that will result from state energy efficiency requirements and programs: a bottom-up or a top-down approach. Thes e forecasts would contain data that could be used to develop inputs to EGU CO 2 emission projections in a stat e plan, using modeling or an EGU growth tool, both of which are summarized above.

A bottom-up approach is based on annual eval uated and reported data from state and utility energy efficiency programs, and progr am-level 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 stat e-by-state end-use energy efficiency projection developed by ISO New England for us e in its system planning process. 23 In simple terms, ISO

23 Each year, ISO New England produces a Regional System Plan (RSP) that provides a comprehensive assessment of the New England bulk power system that is used as input data for evaluating the future reliability of the grid. One component of the annual RSP is a future estimate of peak loads and annual energy use th at is modified (reduced) by the energy efficiency forecas t. Information about the ISO New England energy efficiency forecasting method and assumptions is included in, ISO-New England (IS O-NE) Energy-Efficiency Forecast Working Group, Draft FinalDRAFT, DELIBERATIVE

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20 New England develops production cost curves for en ergy efficiency measures based on historical performance of energy efficiency as documented in evaluation, measurement, and verification (EM&V) studies. ISO New England then applies those production cost curves (with adjustments for inflation and other factors) to future state or 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 annual energy use (MWh) and peak demand (MW). ISO New England has produced an energy e fficiency forecast for the last three Regional System Plans (2011, 2012, and 2013), and the near-ter m estimates in these forecasts have been validated by the actual quantity of end-use ener gy efficiency that has qualified for the annual capacity auctions conducted by ISO New England as part of its forward capacity market. Other approaches to projecting energy efficiency im pacts, such as those used by New York ISO (NYISO), PJM Interconnection, and the Calif ornia Public Utilit ies Commission (CPUC), estimate the effects of energy efficiency requireme nts and programs over a shorter time frame. In these cases, energy efficiency projections are t ypically 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 acquired in ISO and RTO forward capacity markets.

Top-down projection methods analyze aggregat e energy use changes resulting from enduse energy efficiency requirements or programs, often for a geographic re gion, entire industry, or

economic sector. A top-down approach is determined on the basis of state energy efficiency policy requirements, and assumes that utilities and other parties implement efficiency programs necessary to achieve required en ergy and demand savings. In th is way, a top-down approach is typically based on an estimated annual percentage reduction in energy use that results from state energy efficiency requirements and programs. On e example of a top-down approach is the EPA’s state-level projection of th e impacts of “on-the-books” 24 energy requirements and programs. EPA’s approach uses these state requirements and program commitments as the basis for

Energy-Efficiency Forecast 2018-2023 (March 2014), available at: http://www.isone.com/committees/comm_wkgrps/othr/enrgy_effncy_frcst/ 24 As used here, “on the books” refers to state energy-e fficiency requirements and pr ograms currently in place and specified in state legislation or admi nistrative order. These requirements include energy efficiency resource standards (EERS) and dedicated sources of energy efficiency program funding.DRAFT, DELIBERATIVE

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21 adjusting national electricity load forecasts for use in electricity system modeling. 25 In this case, EIA Annual Energy Outlook (AEO) projections of fu ture electricity use (MWh) and peak loads (MW) are modified to account for the reduction in energy sales due to state and utility programs and associated energy savings requirements. 26 These energy savings requirements are examined on a state-by-state basis and adjusted, as necessa ry, to reflect known increases or decreases in energy efficiency program budgets and other f actors likely to affect whether state energy efficiency requirements are achieved. In additi on to EPA’s projections, a top-down approach is used to develop EE forecasts by LBNL, NREL 27 , and some state energy offices and

commissions.

The LBNL 28 and the EPA forecast models are useful for comparison with the bottom-up ISO/RTO approaches. Where discrepancies between the national top-down analyses of end-use energy efficiency savings and the more granular bottom-up energy efficiency forecasts conducted by ISOs and RTOs exist, review of key assumptions regarding state policies for the different approaches may be warranted. Ideall y, using the two approaches as complementary analyses may provide for helpful comparison and r econciliation of energy savings estimates used in state plan proj ections of EGU CO 2 emission performance.

The electricity load forecasts developed by ISOs and RTOs are important because they are often the base case from which adjusted lo ad forecasts that inco rporate projected energy efficiency program savings are developed. For reasons described above, ISOs and RTOs are beginning to include some adjustments to their base case load forecasts to account for energy efficiency program impacts. In most cases, th ese efforts are focused on including the future anticipated impacts of existing “on the books” energy efficiency requirements and programs and adjusting load forecasts accordingly. However, in so me cases there is also an effort to anticipate

25 While EPA’s approach utilizes AEO fo recasts of electricity use and peak loads, other credible and long-term forecasts could be used in a similar manner. U.S. EPA, Background and Draft Methodology for Estimating Energy Impacts of EE/RE Policies (March 2014), available at: http://epa.gov/statelocalclimate/state/statepolicies.html

. 26 For example, annual incremental and cumulative energy savings requirements for utilities included in a state EERS. 27 [Forthcoming April 2014 – Citation TBD] 28 Lawrence Berkeley National Laboratory (LBNL), The Future of Utility Custom er-Funded Energy Efficiency Programs in the United States: Projected Spending and Savings to 2015 (January 2013), available at: http://emp.lbl.gov/publications/future-utility-customer-funde d-energy-efficiency-programs -united-states-projectedspend

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22 new incremental energy efficiency investments in the future that are not tied to existing energy efficiency requirements and programs. 29 When conducting a projection of EGU CO 2 emission performance as part of a state plan that incl udes energy efficiency requirements and programs, it will be important to know how the base electricity load forecast for the state or region was developed, whether it already includes the eff ect of state and utility energy efficiency requirements and programs, and whether and how it should 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 Efficien cy Projections for State Plan Emission Projections

Whether developed through a bottom-up or top-down approach, projections of annual energy and peak load reductions that result fr om state energy efficiency requirements and programs included in a state plan are n ecessary inputs to projections of EGU CO 2 emission performance under the state plan. Energy and peak demand savings estimates included in energy efficiency projections are used as a decrement to fu ture electricity load forecasts that are used in conducting EGU CO 2 emission performance projections. Th e energy efficiency projection is used as an input, or modifier, to various models or other tools used to project the impact of energy efficiency resources on state or regional CO 2 emissions. For example capacity expansion and dispatch planning models are capable of in corporating energy efficiency program impact data, such as annual energy and peak load re ductions, when projecting EGU dispatch and capacity additions, and the avoided CO 2 emissions that are projec ted to result from these requirements and programs.

1.2 Data Issues and Considerations for Using and Developing Energy Efficiency Projections Depending on what data sources and approaches are used to project the future energy and demand savings from energy efficiency requireme nts and programs, there are certain strengths

29 In New England states that have policies to pursue “a ll cost-effective energy effici ency,” the ISO-NE energy efficiency forecast assumes that energy efficiency program investments will continue beyond the approved program funding period (typically documented three-year plans), either at a constant level or some discounted level based on assumptions about an increase in the cost of saved energy over time.DRAFT, DELIBERATIVE

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23 and limitations regarding the approach and me thodology used. Generally, these strengths and limitations relate to data availability and en ergy efficiency forecast assumptions, such as:

 Energy efficiency requireme nts and programs included

 Jurisdictions covered

 Planning timeframe

 Embedded energy and demand savings from energy efficiency requirements and programs in base electricity load forecasts

 Cost of saved energy for differe nt energy efficiency measures

 Energy efficiency measure life and measure energy performance decay

 Time of energy and demand savings for ener gy efficiency measures (availability of hourly data necessary to derive a load reducti on profile for an energy efficiency measure)

Bottom-up versus top-down forecasting approaches have different strengths and limitations related to the data and assumptions described above. In the case of bottom-up approaches, state energy efficiency requirement s and programs can be fully addressed in the analysis. In top-down approaches, such as EIA AE O analysis, greater focus is placed on federal policies (e.g., codes and standards) but less data is incorporated to reflect state energy efficiency requirements and programs. Bottom-up approaches t ypically include state-specific data that are rolled up to a regional level, while top-down national electricity load fore casts do not have a comparable level of granular state data de tail. ISO New England provides energy and demand savings forecasts for state energy efficiency programs (average annual and peak demand coincident with the ISO defined peak load peri ods). The ISO NE energy efficiency forecast is informed by data collected at the energy e fficiency measure, indi vidual program, program portfolio, sector, and state levels.

An important consideration with both top-down and bottom-up energy efficiency forecasts is the extent to which energy efficien cy program effects have become embedded into the long-range economic forecasts used as an inpu t in developing electricity load forecasts. In

some cases the economic forecast includes the eff ect of historical energy efficiency policies, either implicitly or explicitly, while in other cases the economic forecast may also anticipateDRAFT, DELIBERATIVE

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24 some limited quantity of future energy effici ency investment that is incremental to the anticipated effect of existing energy efficiency requirements and programs. An additional consideration is whether and how building energy codes and appliance standards (both current and future) are accounted for in the base electricity load forecast.

Another consideration, for all energy efficien cy projections, is ener gy efficiency program spending. While policies “on th e books” energy efficiency requirements and programs may include multi-year approved energy efficiency program budgets, actual program expenditures may differ. This has been the case in some of the New England states, for example, where ISO New England has made adjustments to “discount” the spending levels to reflect actual spending trends, and not used the stat e approved budgets identified th rough 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 generally assumed in most energy efficiency projections that the cost of installing energy efficiency measures will become more expensive into the future as state programs move beyond “low-hanging fruit” and increasingly focus on achieving deeper and broader energy savings through whole-building, multi-fuel programs addressing new buildi ngs and building retrofits). 30

While many existing sources of energy effi ciency projection data do not account for hourly savings, it is becoming increasingly possible for states to examine and incorporate information about the time-dimension of energy e fficiency impacts. Smart meters combined with data-sharing and analysis technologies are making it easier for util ities and other energy efficiency program administrators to more accu rately determine how the total energy savings achieved in a calendar year are spread out acros s the hours of that year . These data can be applied to energy efficiency projections to provi de a better estimate of the timing of energy and demand savings. Such time-differentiated data is valuable for identifying the marginal EGU or cohort of marginal EGUs that are affected by energy efficiency requirements and programs,

30 Evidence to date is mixed as to a relationship of larg er scale energy efficiency programs and broader energy efficiency measure portfolios, and the asso ciated deeper levels of energy savings, to increasing cost of saved energy. Economies of scale, and expertise gained by program administrators from managing larger programs for multiple years, can lead to cost reductions. Ho wever, at some point, as program administrators move away from initial lowcost strategies and end-use energy efficiency measures, it is assumed that the cost of saved energy will increase.DRAFT, DELIBERATIVE

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25 which can be used to provide more refined estimates of avoided CO 2 emissions due to changes in EGU dispatch and the addition of new generating capacity.

2. Potential Approaches for Estimating the Future Effects of State Renewable Energy Requirements, Programs, and Measures

This section describes data and analytic considerations for th e 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 us e of capacity expansion and di spatch planning models, and less sophisticated statistical or top-down projection appro aches. Examples of specific

considerations are described for three renewable energy policies that may be used as enforceable measures in a state plan— renewable portfolio standards (RPS), feed-in tariffs (FIT), and performance-based tax incentives.

2.1 Renewable Portfolio Standards

RPS requirements are typically se t at the state level with an increasing percentage of total retail sales over a set schedule. The RPS require ment defines the eligible resource types (e.g., wind, solar, etc.) and in some cases, may specify resource-specific requirements, such as a percentage of the overall RPS targ et that is set-aside for a specific resource. It also defines the allowable geographic boundary for obtaining renewa ble 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.

2.1.1 RPS as an Input to Electricity Sector Modeling

Electricity sector modeling includes the use of dispatch simulation models and capacity expansion and dispatch planning models that simulate the operation of individual EGUs (or aggregations of EGUs) on the el ectric system over time based on a detailed characterization of those EGUs, engineering and market operating constr aints, other market fa ctors (e.g., fuel prices, transmission constraints), emission constraints, and the requirement to meet a certain level energy and peak demand. These models may be ba sed on a small control area, but more likelyDRAFT, DELIBERATIVE

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26 are regional or national in scope. Some, such as capacity expansion and dispatch planning models, simulate EGU dispatch and also consid er the impact of long-term generating capacity investment decisions when optimizing system op eration and buildout over a long-term planning horizon. These are typically optimization framew orks that have as their objective meeting electricity demand subject to a broad range of operating, environmental and market constraints, including meeting RPS requirements.

When using these types of anal ytic tools to project EGU CO 2 emissions, a number of analytic and data considerations are rele vant for analysis of RPS impacts on EGU CO 2 emissions. These analytic and data c onsiderations are summarized below.

Translation of RPS Requirements to Re newable Energy Generation Requirements Most RPS are retail sales-based requirements, wh ile most electricity sector analytic tools are generation-based mode ls. When projecting CO 2 emission performance by affected EGUs using a generation-based analysis tool, it is necessary to translate the RPS sales-based requirement to a generation requirement using a transmission and distri bution loss rate to “ gross up” the sales estimate. For example, if an RPS requires 100 MWh of sales be met with eligible renewable energy resources, then the required re newable energy generation necessary to meet the RPS is 100 MWh / (1 + T&D Loss rate). 31

RPS-Eligible Resources vs. State Plan-Eligible Resources Some RPS rules define a broad range of elig ible renewable energy generating resources. These could include renewable energy EGUs that emit CO 2 , and EGUs that were constructed and began operation recently or many years ago. Some of these specific EGUs may not be eligible for use in a plan, in particular un its that began operation prior to eligibility dates for actions that may be included in a state plan. 32 Thus, the RPS requirement would need to be adjusted to reflect the use of only those resources e ligible for use in state plans. This would be addressed through the treatment of existing RPS in the analysis ba se case, as described a bove in section III.B.2.

31 According to EIA data, nationally, annual electricity transmission and distribution losses are equivalent to about seven percent of the electricity that is input to the transmission system in the United States. 32 As discussed in the preamble, and explained in section III.B.2 of this TSD, EPA is proposing that emission reductions that occur in 2020 and later due to actions taken pursuant to an existing state requirement, program, or measure could be applied toward meeting the required level of emission performance in a state plan if these actions occur after proposal of the emission guidelines (e .g., as of June 2014 and in subsequent years).DRAFT, DELIBERATIVE

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27 RPS Vintage Rules Some RPS requirements provide that only renewable energy EGUs that began operation after a certain date are eligible. To the extent these renewable energy E GUs are eligible under a state RPS but not under the state plan, the analysis should explicitly address these resources. This could be done either through an adjustment of the MWh needed to meet the RPS, to account for renewable energy resources that ar e not eligible for use in a stat e plan, or through the use of two modeled RPS requirements for each class of rene wable energy resource, with the RPS that

addresses resources not eligible in a state plan addresse d in the modeling reference case.

In/Out-of-State Contribution Some RPS allow some of the requirement to be met by out-of-state resources. Some modeling frameworks may allow the user to exp licitly specify the source region of eligible renewables. From a modeling perspective, this means a state would allo w a broader supply area and model a broader area generally. If the renewable energy resources have different

characteristics across the relevant geographic area, then data is needed on the characteristics of these resources by region (e.g., resource availab ility, performance, and capital and production costs). EIA (for NEMS), EPA (for IPM), a nd NREL (ReEDS) provide information on the characteristics and performance of renewable energy resources by region. 33

Characteristics of Renewable Resources In a dispatch modeling framework, better emission projections will result when the source of renewable energy is known and the energy profile is modeled. Renewable energy resources, such as wind or solar PV, may differ substantially in generation profile, as the supply of electricity to the grid from the EGU is based on availability of the renewable energy resource. The time during which electricity generation is s upplied will influence the marginal fossil fuelfired EGU (or cohort of EGUs) that is displa ced by renewable energy generation. Understanding the renewable energy resource base, including th e energy profile of the relevant renewable energy resource types will improve the modeling of EGU dispatch and projections of avoided

CO 2 emissions that result from renewable ener gy generation. Data may include a detailed 8760

33 EPA’s Integrated Planning Model generation profiles for wind and solar in the EPA IPM v.5.13 base case are available at http://www.epa.gov/powersector modeling/BaseCasev513.html

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28 energy output profile for renewable energy resources , or at a minimum, a seasonal diurnal profile

for such resources. If no state- specific data is available, ge neric output profiles for different renewable energy resources is available from NREL. 34

Renewable Energy Resource Availability and Economics Emission projections will be enhanced by a representation of the new renewable energy EGUs that are likely to be brought online as a result of a state RPS, based on an analysis of available renewable energy resources and econo mics. This requires understanding the relative economics (e.g., capital costs, fi xed operation and maintenance (F OM) costs, variable operation and maintenance (VO&M) costs, and fuel costs) and operating conditions for different types of renewable energy generators, as well as re newable energy resource availability (e.g., MW capacity available at different wind classes, sola r insolation levels, and the characteristics of biomass). Having this information allows for be tter projections of th e level and quality of renewable resources likely to be brought forward as the result of a state RPS. This will result in better emissions projections.

NREL has a suite of tools that can be used to estimate the potential costs of renewable energy under various assumptions. 35 For example, the CREST model is a cost-of-energy analysis tool intended to assist policy makers evaluating the appropriate payment rate for a cost-based renewable energy incentive policy. The model aims to determine the cost-of-energy, or minimum

34 NREL’s datasets are intended for use by energy profe ssionals, 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 plants. The Eastern Wind Dataset ( http://www.nrel.gov/electricity/transmission/eastern_wind_methodology.html ) contains modeled wind farm data points for the eastern United States for 2004, 2005, and 2006. The Western Wind Dataset ( http://www.nrel.gov/electricity/transm ission/western_wind_methodology.html ) includes information about the methodology used to develop the dataset, the accuracy of the data, site selection, and power output. The Solar Integration Datasets (http://www.nrel.gov/electricity/transmission/solar_integration_methodology.html ) are solar photovoltaic (PV) power plant five-minute and hourly day-ahead forecasts of generation output for approximately 6,000 simulated PV plants in the United States for the year 2006. 35 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 (CRE ST) and the System Advisor Model (SAM). CREST (https://financere.nrel.gov/finance/content/crest-cost-ener gy-models) makes performance predictions and cost of energy estimates for grid-conn ected power projects based on system design parameters that the user specifies. The SAM cash flow model (http://sam.nrel.gov/) helps assess solar, wind, or geothermal projects, design cost-based incentives, and evaluate the im pact of tax incentives or other support structures on renewable energy projects.DRAFT, DELIBERATIVE

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29 revenue per unit of production, ne eded for a sample (modeled) re newable energy project to meet

its investors' assumed minimum re quired after-tax ra te of return. 36

Retail Rate Impacts Many state RPSs have caps on the allowable im pact on retail rates or on customer bills that can result from implementation of an RPS. If these caps are triggered, it will delay further increases in the RPS targets, depending on RPS implementati on rules. This would lower estimates of renewable energy generation ne eded to satisfy the RPS requirements.

Market Impacts – Multiple State Players One important issue is to account for actions of others. Modeling result s that consider the

RPS-related actions of other stat es could differ substa ntially from those that consider a state’s actions in isolation. This require s having at least a re gional, if not national, modeling framework. A broader framework captures competition for rene wables (and increased pr ices, greater reliance on poorer resources), as well as market-wide im pacts on EGU dispatch an d capacity expansion.

2.1.2 Data Needs for Estimating Generation Resulting from State RPS

Projections of renewable en ergy generation will require assembling the following data:

 Resource availability, resource performance, a nd resource costs will be necessary for the more sophisticated modeling approaches described above.

 Projections of retail sales, by year, adjusted to account for any ex clusions from the RPS obligation (e.g. small utilities, cooperatives, municipalities, or perhaps certain large industrial loads). These covered sales must be calculated and projecte d using forecasts of

annual retail demand.

 RPS requirements by year, so the annual forecas t of covered retail sales can be multiplied by these percentages.

36 The CREST model is a product of a 2009-2010 partners hip between the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE) Solar Energy Technologies Program (SETP), and the National Association of Regulatory Utility Commissioners (NARUC). The model was developed by Sustainable Energy Advantage (SEA) under the direction of NREL. The report , user manual, and CREST models are free and available for download at: https://financere.nrel.gov/ finance/content/crest-cost-energy-models.DRAFT, DELIBERATIVE

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30  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.

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

 If an electricity sector ca pacity expansion and dispatch planning model is not used, estimates of the geographic origin, by state a nd grid-region, of generation used to satisfy the RPS will be necessary. If generation to sa tisfy the RPS is expected to come from out of state, the basis for these assumptions should be documented.

 CO 2 emission rate associated with renewa ble energy generation, by resource type (if applicable).

 Generation profile, by renewa ble energy resource type

2.2 Feed-in Tariff (FIT) and Othe r Performance-Based Incentives

A feed-in-tariff is a performance-based incen tive (similar to production tax credits) that typically guarantees utility cust omers who own a eligible renewabl e electricity generation system (e.g., roof-top solar PV system) will be paid a sp ecific amount by their utility for the electricity the system generates and provides to the grid ove r a fixed period of time. Such tariffs usually offer a fixed payment amount per kWh, but varia tions 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 amount of renewable energy generation that will occur exists for production-based tax credits. In the case of an RPS, the amount of renewable energy generation necessary to meet th e RPS is known given the required RPS level and a utility sales projection. 37 In the case of FITs the response to the program may be more uncertain. The

remainder of the discussion focuse s on estimating these impacts.

37 Most state RPS schedules are established as a percentage of utility sales in a given year, although some include specified MWh amounts or are based on MW of generating capacity.DRAFT, DELIBERATIVE

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31 The key information required to estimate the generation that will occur as a result of a

FIT include:

 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 requirements, net metering, and federal investment and production tax credits. If ge nerators receiving FIT payments are also eligible to satisfy an RPS, states should en sure their estimates of renewable electricity generation are not double-counted. For analysis using a detailed capacity expansion and dispatch planning model, market penetration of renewable energy generation to meet an RPS may be an output of the model. In this case, information is required on the capital costs, FOM costs, and VOM costs of the re newable energy generating technology. This is the same information required for modeling an RPS; however, the modeling or analysis mechanism may differ. For example, these co sts may be reduced directly in a modeling

framework to reflect the FIT payment (e.g., reducing VOM to re flect the FIT in the years it is available).

 Information on the types of renewable energy resources, including th eir location, output levels, energy output profiles (8760 or seas onally), and operating life of the EGU (by technology, so that they can be included in an electric dispatch model or mapped to an appropriate load shape or marginal avoided emission rate if using simpler analytical

methods.

 An estimate of whether the projected retail price impacts of the FIT will lead to lowering the amount paid to eligible renewable en ergy generators, if a FIT includes caps on generation subject to the tariff ba sed on rate impacts. If retail electricity rate impacts or budgetary impacts become unsustainable, the pr ojected generation resulting from the FIT

will be overstated.

2.2.1 Data Needs for Estimating Generatio n Resulting from Feed-In Tariff and Performance-Based Tax IncentivesDRAFT, DELIBERATIVE

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32 Projections of renewable gene ration that will occur as th e result of FITs and other performance-based incentives wi ll require assembling the followi ng data about the specific incentive programs that will suppor t renewable energy generation:

 Caps on generating capacity, if any, by year, because such caps will limit the amount of renewable energy generation resulting from the tariff or tax incentive.

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

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

incentive.

 Budget amounts, if known, by year, which may be expended to support renewable generation. When divided by the incentive payment levels , on a per-kWh or per-MWh basis, these budget amounts will yield an estimat e of the total electric generation that will be supported by the FIT or othe r production-based incentive.

 State experience and trends with FITs a nd other production-based incentives. Lacking budget or capacity limits, prior experience in the state, or the experien ce of other states and utilities, normalized for state or util ity size, could be the basis for estimated renewable energy generation at a given incen tive level.

 Because performance is metered at the busbar (i.e., the point of interconnection to the electricity transmission or di stribution system), no adjustme nts would be needed for T&D

losses.

 Estimates of the geographic origin, by state, of generation that receives support from these programs. In most cases, eligible generation will be in-state, but if generation is expected to come from out of state, the basis for th ese assumptions should be

documented.

 CO 2 emission of renewable energy generation, by resource type (if applicable).

B. Emission Budget Trading Programs

Emission budget trading programs establish an emission limit for a group of emission sources and establish a budget of tradable emission allowances e qual to the emission constraintDRAFT, DELIBERATIVE

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33 for the group of sources. An emission allowance t ypically represents a limited authorization to emit one ton of a regulated pollutant. These programs also typically include a number of additional flexibility mechanisms beyond the abi lity to trade allowances. These include multiyear compliance periods, the ability to bank allo wances issued in a previous compliance period for use in a subsequent compliance period, the use of out-of-sector project-based emission offsets, and cost-containment allowance reserves that make additional allowances available to the market if pre-established allowance price thresholds are achieved. 38 As a result, annual emissions from affected sources subject to an emission budget trading program often differ from the established annual emission budget for affected sources. In addition, th ese programs may be multi-sector in nature, regulating emissions fo r source categories in addition to EGUs. As a result, state plan emission projections will ne ed to accurately account for and represent these compliance flexibilities, as well as the scope of affected sources if they are broader than EGUs affected under CAA section 111(d). 39 In general, most electricity sector capacity expansion and dispatch planning models can be configured to evaluate these program flexibilities and project

CO 2 emissions from affected sources, cons idering these compliance flexibilities.

1. Addressing the Sectoral Scope of Emission Budget Trading Programs

Some existing state emission budget trad ing programs addressing GHG emissions regulate emission sources in addition to EGUs, such as industrial sources. 40 We refer to these here as multi-sector emission budget trading pr ograms. All existing state emission budget trading programs addressing GHG emissions include out-of -sector project based emission offsets, which may be used to cover a portion of the co mpliance obligation of affected sources.

For multi-sector emissions budget trading pr ograms, state plan emission projections would need to evaluate projected CO 2 emissions across all source ca tegories 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 budget trading program.

38 Depending on the program, these cost containment allowa nce reserves make additional allowances available from within the base emission budget (i.e., from “within the em ission cap”), or add to the base emission budget (i.e., increase the emission cap). 39 For example, the Regional Greenhouse Gas Initiative (RGGI), which is an emission budget trading program limited to EGUs, includes EGUs that are not subject to CAA section 111(d). 40 The California GHG emission trading program also include s large industrial sources and as of 2015 will include distributors of transportation, natural gas, and other fuels.DRAFT, DELIBERATIVE

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34 For emissions budget trading programs that regul ate EGUs and include offsets, which we

define here as emissions reductions from sources not regulated by the trading program, emissions

reductions from offsets would not be counted when evaluating CO 2 emission performance of affected EGUs, because those reductions would not come from those affected EGUs. However, state plan emissions projections would need to ev aluate the use and impact of offsets, because the availability of offsets would affect the amount of CO 2 emissions from affected EGUs.

Addressing Multi-Sector Emission Trading A state or regional emissions budget trading program could potentially regulate sources from multiple emissions sectors beyond the electric ge nerating sector that is the focus of the EPA emission guidelines. For example, the Califor nia GHG emissions budget trading program is a multi-sector emissions trading program that addr ess emission sources outside the scope of the EPA emission guidelines for EGUs. If a multi- sector emissions budget trading program is included in a state plan, CO 2 emission projections provided as part of the state plan will need to evaluate projected CO 2 emissions across all covered emissions sectors under the program. This would be necessary to project the CO 2 emissions or weighted average CO 2 emission rate of

affected EGUs that will be achieved under the multi-sector emissions budget trading program included in the state plan.

Evaluating the projected impact of a multi- sector emissions budget trading program on

the CO 2 emissions performance of affected EGUs in troduces an additional level of analytical complexity to emissions projections. For exam ple, additional modeling capabilities may be necessary to adequately evaluate CO 2 emissions performance across sectors in a multi-sector emissions budget trading program.

Adequately projecting the CO 2 emissions of affected EGUs subject to a multi-sector emissions budget trading program may require modeling using a multi-sector energy model. 41 The use of an electricity sector dispatch and capacity expansion planning model, might also be required, as a complement to a multi-sector mo del. Some multi-sector models, include an

41 For example, the Energy 2020 model was used by Calif ornia to evaluate the impacts of its multi-sector GHG emissions budget trading program. Other multi-sector mode ls are available with similar capabilities, such as the National Energy Modeling System (NEMS) developed and implemented by the U.S. Energy Information Administration (EIA).DRAFT, DELIBERATIVE

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35 electricity sector dispatch and capacity expans ion planning module. However, use of a standalone electricity sector model, might also be necessary as a supplement to a multi-sector energy model, in order to project CO 2 emissions or the weighted average CO 2 emission rate from

affected EGUs at a sufficient level of resoluti on for state plan emission projections. Under this approach, a multi-sector energy model might be used to project the level of CO 2 emissionsabatement achieved across the multiple emission sectors covered by the program. This multisector projection could be used to develop a CO 2 marginal abatement cost curve representing cost-effective, non-electricity se ctor emission reduction opportunities at increasing cost levels. The marginal abatement cost curve would then se rve as an input assumption for the electricity sector capacity expansion and di spatch planning model, which w ould be used to project the CO 2 emission performance of affected EGUs (e.g., the CO 2 emissions or CO 2 emission rate of individual affected EGUs, or the total CO 2 emissions or weighted average CO 2 emission rate across multiple affected EGUs). 42

Multi-sector emission budget trading progr ams also often address GHGs beyond CO 2 . If this is the case, emission projections in a st ate plan would need to account for emissions abatement opportunities for each of the GHGs regulated under the program, and project emissions reductions for each of the regulated GHGs. This would be necessary to project the

extent to which affected emissi on sources in different industria l sectors reduce emissions of nonCO 2 GHGs, which could impact projected CO 2 emissions from affected EGUs.

Addressing Emission Offsets

Emission offsets in CO 2 or GHG emissions budget tradi ng programs represent projectbased GHG emissions reductions outside the sect or or sectors regulated by the program. 43

Emissions reductions achieved th rough eligible offset project s are awarded allowances or “credits” that may be used by an affected sour ce to meet a portion of its allowance compliance

42 This marginal abatement co st curve would be applied in a similar manne r as marginal abatement cost curves are applied for GHG emission offsets in modeling analyses of emission budget trading programs. Form a modeling perspective, emission reductions from other sectors could be used by affected EGUs to demonstrate compliance with the emission limit. When economic to do so, emission reductions from affected EGUs would be foregone and replaced by emission reductions from emi ssion sources in other sectors that are also subject to the multi-sector emission budget trading program. 43 “Offsets” as used in the context of CO 2 or GHG emissions budget trading prog rams are distinct from offsets in the NSR permitting context under the CAA, where in certain inst ances emissions of a criteria pollutant from a proposed new facility must be offset with creditable emissions reduc tions at an existing facility, if the state or jurisdiction where the proposed facility would be located is in non-attainment status for the pollutant.DRAFT, DELIBERATIVE

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36 obligation. For example, under the RGGI program “CO 2 offset allowances” awarded for GHG

emissions reductions achieved thr ough approved offset projects ma y be used by affected sources to meet up to 3.3 percent of their CO 2 allowance compliance obligation. 44

The ability to use GHG emission offsets for compliance means that CO 2 emissions from EGUs regulated under the emissions budget trading program may exceed the base CO 2 emissions budget established for the program. Offset allowances or credits are awarded in addition to the existing CO 2 emissions budget, in exchange for CO 2 -equivalent emissions reductions achieved outside the capped emissions sector. 45 Consequently, the use of offsets by affected sources to meet a portion of their compliance obligation un der an emissions budget trading program could result in higher projected CO 2 emissions from affected EGUs in a state plan, because the use of offsets functionally expands the CO 2 emissions budget for affected EGUs. This allows affected

EGUs to emit more CO 2 while meeting their compliance obligation under the emissions budget trading program, in exchange for CO 2 -eq. emissions reductions achieve d outside of the affected source category. As a result, to properly project the CO 2 emissions from affected EGUs in a state plan that includes an emissions budget trading progr am that allows for the use of offsets, it is necessary for modeling to also project the extent to which offsets are used by affected EGUs for compliance. 46

Under this approach, stat e plans would ignore the CO 2 -equivalent emissions reductions projected to be achieved through offsets, when projecting the CO 2 emissions performance that will be achieved by the affected EGU source cate gory through implementation of the state plan. This does not mean that an emission budget tradi ng program included in a state plan could not include an offset component. Rather, when demonstrating 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 in corporate a credit for the CO 2 -equivalent emissions reductions

44 This percentage increases to five and ten percent of a CO 2 allowance compliance obligation at specified $7 and $10 price triggers, respectively. 45 A key criterion that must be met for the award of offset allowances or credits is a demonstration that the offset project is “additional” (i.e., that it would not have occurred absent the incentive provided through the award of the offset allowance or credit). 46 Existing capacity expansion and dispatch planning models can project the use of offsets for compliance based on specified offset marginal abatement su pply curves, which represent the amount of offset credits/allowances assumed to be available from different categ ories of offset projects at differe nt GHG emissions abatement costs.DRAFT, DELIBERATIVE

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37 represented by offset allowances or credits us ed by affected EGUs for compliance with the emission budget trading program. 47

2. Multi-State Emission Trading Programs

Emission budget trading programs may be mu lti-state in nature. For such programs, emissions reductions are achieved on a region al, rather than a state-by-state basis.

For example, in the multi-state Regional Greenhouse Gas Initiative (RGGI) emissions budget trading program, individual partic ipating states have established CO 2 emissions budgets in state regulations. However, there is no requirement limiting total CO 2 emissions from affected

sources in an individual state. State regulations include recipr ocity provisions allowing emission

sources to use CO 2 allowances issued by anot her participating state for compliance with the state program. This provides for state-to-state CO 2 allowance flows (and the potential for differences in state-by-state CO 2 emissions relative to state em issions budgets) based on where the

allowance market determines it is most economical to achieve CO 2 emissions reductions. However, in aggregate, the CO 2 emissions budgets in each of the participating state regulations establish a regional cap on CO 2 emissions from affected EGUs. As a result, while a multi-state emission budget trading program may be projected to result in 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 evaluating a multi-state emission budget trading program as a whole, because the individual regulat ions of participating states function together as sing le integrated program.

To address these issues, states particip ating in a multi-state emission budget trading program would jointly demonstrate that the multi-s tate 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 program s also include international partner

47 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 EG Us alone, with no calculation of “net” EGU CO 2 emissions that factor in the CO 2 -eq. emissions reductions represented by offset credits or allowances used by an EGU to meet a portion of its compliance obligation.DRAFT, DELIBERATIVE

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38 jurisdictions. 48 In such instances, the program could be treated in a similar fashion as a multistate program that involves only U.S. states. In this instance, emission pr ojections evaluating an international program would incl ude all jurisdictions participa ting 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 progr am represented by U.S. states. Although the CO 2 emission performance of EGUs (or other emission sources) in a foreign country would not be addressed in the state plan, the entire international program woul d be evaluated as part of the emission projection included in the state plan. Th is would be necessary in order to project

international allowance flows and CO 2 emissions across all participating jurisdictions, as these cross-jurisdictional flows would impact projected CO 2 emissions from affected EGUs in U.S.

states. VI. Process Considerations

As discussed in the preamble, in section V III.F.7, the credibility of state plans under section 111(d) will depend in large part on ensuring credible and consistent emission performance projections in state plans. Therefore, the use of appropriate methods, tools, and assumptions for such projections is critical. Furt hermore, considerations for projecting emission performance under a state plan will differ de pending on the type of plan. This includes differences in how inputs to projections are de rived; how projections are conducted, including tools and methods; and how aspects of a plan are represented in these projections.

In the preamble, the EPA seeks comment on whether the EPA should develop guidance that describes acceptable projecti on approaches, tools, and methods for use in an approvable plan, as well as provide techni cal resources for conducting projec tions. This section of the TSD

elaborates on these considerations.

A. Provision of the EPA Guidance

One approach to address these considerati ons is for the EPA to provide recommended guidance for EGU CO 2 emission projections included in stat e plans. Such guidance could include default modeling assumptions, or data sources for key assumptions. State modeling projections

48 Currently, the EPA is only aware of one such instance, which is the linkage of the California GHG emission budget trading program with a similar program in Quebec.DRAFT, DELIBERATIVE

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39 included in a state plan could include assumptions that deviate from the EPA’s recommended default assumptions, but a state plan would just ify the reason for using alternative assumptions. The EPA technical guidance could specify reco mmended reference case assumptions for use with modeling or EGU utilizati on growth tools, for example:

 Electricity load growth projec tions (energy and peak demand)

 Fuel supply, delivery, and pricing assumptions

 Cost and performance of electr ic generating technologies

 Cost and performance of pollution control equipment

 EGU firm builds and retirements (those scheduled with a regional transmission organization or independent system operator (RTO/ISO) 49

 Transmission capability and ISO/RTO transmission expansion plans

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

 Applicable state regulations and programs (oth er than the alternative standards that are included in the state plan)

It would be necessary in many instances to include assumptions about other state programs implemented by neighboring states in the same region. This would be especially

relevant for states that are located within the same electric power pool (or adjoining power pools) that are administered by a common RTO/ISO. To address this need, the EPA technical guidance could provide documentation of state programs and policies included in a reference case, as well as those that are eligible for in clusion in a state plan as alternative standards. The guidance could compile information about state programs and pr ovide model input assumptions related to these programs (e.g., MWh of electric generation need ed to meet a state renewable portfolio standard). 50 The EPA might also play a role in faci litating coordination am ong states as they develop their plans, to ha rmonize regional assumptions.

49 ISOs and RTOs are independent organizations that admini ster a regional electric powe r pool (EGU dispatch and electricity transmission systems) , and often also administer related wholesal e electricity markets for electric energy and capacity. 50 EPA’s manual, Roadmap for Incorporating Energy Efficiency/Re newable Energy Policies and Programs into State Implementation Plans/Tribal Implementation Plans (July 2012), could potentially support and be expanded upon to develop this component of EPA technical modelin g guidance. In particular, s ee Appendix I: EPA’s Draft Methodology for Estimating Energy Impacts of EE/ RE Policies. The draft manual is available atDRAFT, DELIBERATIVE

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40 ISOs and RTOs, in discussions with the EP A have also offered to support states in evaluating the emission performance of state pl ans on a regional basis. The ISO/RTO Council, an organization of electric grid operators, has suggested that ISOs a nd RTOs could provide analytic support to help states develop and implement their plans. The ISOs and RTOs have the capability to model the system-wide effects of indi vidual state plans. Providing assistance in this way, they felt, would allow states with borders that fall within an RTO or ISO footprint to assess the system-wide impacts of poten tial state plan approaches. In addition, as the state implements its plan, ISO/RTO analytic support would allow the state to monitor the effects of its plan on the regional electricity system. ISO/RTO analytic capability could help states assure that their plans are consistent with region-wide system reliab ility. The ISO/RTO Council suggested that the EPA ask states to consult with the applicable IS O/RTO in developing th eir state plans.

B. Party that Translates the Rate -Based Goal to a Mass-Based Goal

In the preamble, in sect ion VIII.F.7, The EPA seeks comment on whether it should develop guidance that describes a cceptable projection approaches, tools, and methods for use in an approvable plan, as well as provide t echnical resources for conducting projections.

One consideration for state pl ans that use a mass-based CO 2 emission performance goal is the party that conducts the tr anslation of the rate-based CO 2 emission performance goal in the emission guidelines to a mass-based goal—either the EPA or the state. In the preamble, in section VIII.D.3, 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 pr esumptive translation of the state-specific

rate-based CO 2 emission performance goal to an equivale nt mass-based goal for all states, for those states that request it, and/or for multi-state regions. This could include default modeling assumptions and results of modeling runs for a Reference Case Scenario and an EPA MassBased CO 2 Emission Goal Policy Scenario , as described above in section III.B. A state could utilize the presumptive mass-based CO 2 emission performance goal for the state or multi-state

http://www.epa.gov/airquality/eere.html . State environmental regulations ad dressing EGUs are itemized in the modeling documentation for the EPA IPM Base Case v.5.13.DRAFT, DELIBERATIVE

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41 region identified through these EPA modeling runs. If a state proposed modifications to EPA default modeling assumptions, it would need to ju stify these modifications as part of the CO2 emission projection included in its state plan, an d present an emission pr ojection 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 coul d include information about acceptable analytical methods and tools, as well as default input a ssumptions for key parameters that will likely influence projections, such as el ectricity load forecasts and proj ected fossil fuel prices. Under this approach, the EPA might also provide a co ordinating function in ad dressing the assumptions applied by multiple states within a grid re gion, acknowledging that assumptions about state programs across a broader grid region that are in cluded in an analysis scenario will influence projections of CO 2 emissions by affected EGUs in any particular state.

Under this approach, states could deviate from these default methods and assumptions with justification. Following the guidance could provide a streamlined path for the EPA approval of emissions projections, but states would still have flexibility to use other approaches, which the

EPA would review.