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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 CO2 Emission Performance in State Plans 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

June 2014 

 
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Table of Contents 

I. Introduction ........................................................................................................................3 

II. Analytic Approaches for Projecting CO2 Emissions from Affected EGUs ..................5 

A. Electricity System Modeling Approaches for Projecting EGU CO2 Emissions .......5 

B. Growth Tools for Projecting Future EGU Utilization and CO2 Emissions...............9 

III. Translating from a Rate-Based Emission Performance Goal to a Mass-Based 

Emission Performance Goal ............................................................................................11 

A. Concept ...................................................................................................................11 

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

IV. Projecting CO2 Emission Performance under a State Plan .........................................17 

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

A. End-Use Energy Efficiency 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-Based Goal to a Mass-Based Goal ........................40 

 
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I. Introduction 

As discussed in the preamble, in section VIII.F.7, all state plans will need to include a 

projection of the CO2 emission performance by affected EGUs that will be achieved under a state 

plan (inclusive of plan measures that avoid CO2 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 CO2 emission rate achieved by affected EGUs or total 

CO2 emissions from affected EGUs. 

The EPA is also proposing that a state may translate the state rate-based CO2 emission 

performance goal for affected EGUs to an equivalent mass-based CO2 emission performance 

goal. If this translation option is used, a state plan must also include a projection used to derive 

the mass-based CO2 emission performance goal. This translation will involve a projection of CO2

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 striving to find a balance between providing 

state implementation flexibility and ensuring that the emission performance required by CAA 

section 111(d) is properly defined in state plans and that plan performance projections have 

technical integrity. The credibility of state plans under section 111(d) will depend in large part on 

ensuring credible and consistent emission performance projections in state plans. Therefore, the 

use of appropriate methods, tools, and assumptions for such projections is critical. 

The preamble, at section VIII.F.7, seeks comment on options presented for how CO2

emission projections might be conducted in an approvable state plan, and how different types of 

state plan approaches are represented in these projections. These options include the use of 

historical data and parameters for estimating the 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 model, 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 development of parameters for estimating the effect of individual 

state programs and measures, for use as input assumptions for modeling. 
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operation and market forces, including interactions among state programs and measures and the 

dynamics of market-based measures. 

In the preamble, the EPA further seeks comment on whether the EPA should develop 

guidance that describes acceptable projection approaches, tools, and methods for use in an 

approvable plan, as well as whether the EPA should provide technical resources for conducting 

projections. 

This technical support document (TSD) elaborates these options and considerations. The 

TSD discusses possible analytic approaches for translating from a rate-based CO2 emission 

performance goal to a mass-based goal, and projecting the CO2 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 base projections on historical data and algorithms. The TSD also 

discusses possible approaches for developing inputs that are used for emission projections and 

applied considerations for different types of state plans. Topics addressed include: 

 Section II discusses analytic approaches for projecting CO2 emissions from affected 

EGUs 

 Section III discusses the concept and practice of translating from a rate-based CO2

emission performance goal to a mass-based CO2 emission performance goal 

 Section IV discusses the concept and practice of projecting EGU CO2 emission 

performance under a state plan 

 Section V discusses applied considerations for projecting EGU CO2 emission 

performance under different types of state plans, including: 

o Considerations that should be addressed in conducting projections of emission 

performance for different types of plans 

o Data needs and methods for developing inputs to EGU CO2 emission projections, for 

different types of state plans 

 Section VI discusses process considerations for conducting EGU CO2 emission 

projections for state plans, including: 

o Regional coordination among states in conducting projections 
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o Whether the EPA should provide guidance and other analytic support for conducting 

projections in state plans and translation of rate-based CO2 emission performance 

goals to mass-based goals 

II. Analytic Approaches for Projecting CO2 Emissions from Affected EGUs 

This section surveys different types of methods and tools for projecting CO2 emissions 

from affected EGUs, including modeling tools and other tools that base projections on historical 

data and use algorithms to extrapolate future CO2 emissions performance based on past 

performance. 

A. Electricity System Modeling Approaches for Projecting EGU CO2 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 power sector (i.e., projections of the expected 

response of the sector to factors such as electricity demand, 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 regulatory factors on the electric power sector and related markets. 

Typical outputs of such models include EGU dispatch, fuel consumption, fuel prices, wholesale 

electricity prices, emissions, EGU retirements, and infrastructure expenditures (e.g., addition of 

new EGU capacity and installation of retrofit pollution control technologies). 

 

National-scale electricity capacity expansion and dispatch planning models have 

moderate spatial detail with broad scope, generally encompassing the entire country or 

transmission system interconnects (i.e. Eastern, Western, and ERCOT), which are subdivided 

into smaller areas, such as balancing authorities or control areas.2

 For computational efficiency, 

these models generally model several representative hours of the year, or aggregate hours into 

representative bundles with similar electricity 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 key unit characteristics, such as location, size, efficiency, operating 

                                                            

2

 A “balancing area” or “control area” refers to a specified portion of the transmission system where electricity 

demand and generation are balanced in real time by a system administrator to maintain grid reliability. 
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costs, pollution retrofit control status, age, and fuel type use/availability. These types of models 

are well suited to project dynamic entry and exit (capacity expansion and unit retirement) to meet 

energy and capacity requirements while minimizing system costs, maintaining reliability criteria, 

and following other constraints, such as minimum build times, transmission constraints, 

renewable energy availability, 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. However, utility-scale capacity expansion 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 retirement or retrofit decisions 

for existing capacity within a specific utility’s territory. Utility planning models typically project 

outcomes for periods of up to two decades. Utility-scale capacity expansion and dispatch 

planning models are an industry-standard, used regularly in state electricity regulatory 

proceedings. Within the electricity sector there is broad familiarity with these models at state 

PUCs. Multiple vertically integrated utilities use capacity expansion and dispatch planning 

models to conduct forward planning and review the economics of specific EGU retrofit 

decisions. Utilities that submit integrated resource 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 CO2 emission 

reduction strategies or CO2 emission constraints, as IRP scenarios may include greater 

penetration of end-use energy efficiency or renewable energy, proxy CO2 emission prices, 

emission trading, and limits on CO2 mass emissions. 

                                                            

3

 See, for example: The Duke Energy Carolinas IRP (Annual Report), September 2012, available 

athttp://www.energy.sc.gov/files/view/Duke_IRP 2012.pdf; PacifiCorp 2013 IRP (Vol 1), April 2013, available 

athttp://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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Utility-scale capacity expansion and dispatch planning models tend to have relatively 

high spatial detail with limited geographic scope, generally encompassing a utility service 

territory or a sub-regional scale. These models generally have better temporal resolution than the 

national-scale capacity expansion and dispatch planning models, with each model year typically 

dispatched based on an annual hourly load duration curve.4 Utility-scale capacity expansion and 

dispatch planning models typically represent 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 number 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-term planning, ratemaking, dispatch 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 add or retire generating capacity on an economic 

basis, although EGU additions and retirements may be exogenously input to these models. As a 

result, projections from these models tend to be considered more robust in the shorter term.5

 Grid 

operators, including utilities, and independent system operators (ISO) use dispatch simulation 

models in near real-time to match demand with electric generation from available generating 

units and dispatch EGUs on a least-cost basis. EGU owners and operators run dispatch 

simulation models to assist in fuel procurement, forecast revenues and costs, and calculate the 

                                                            

4

 Load duration curves typically represent electricity load during a typical week for each month. 5

 Some planners use dispatch simulation models in conjunction 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 simulation model is used to simulate operation of individual 

EGUs. In this case, these models can be used in the same time horizon as a capacity expansion and dispatch 

planning model (i.e., decades). 
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avoided generation supply costs related to procurement 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 multiple Regional Transmission Organization (RTO) 

regions, and often modeling entire interconnects (i.e. Western, Eastern, and ERCOT). While 

individual EGUs are modeled in detail, including 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 service on a variable cost basis. Some versions of 

these models operate at a “nodal” level, where 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 typical weeks of the year. These 

models contain substantially more detail about 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%20Evaluation%20Studies/AESC%20Report%20-

%20With%20Appendices%20Attached.pdf 7

 The Federal Energy Regulatory Commission (FERC) defines 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 dispatch based not only on the marginal 

costs of all available generating resources, but also constraints on transmission availability and ancillary services. 

See FERC, “Security Constrained Economic Dispatch: Definition, Practices, Issues, and Recommendations” (2006), 

available at http://www.ferc.gov/industries/electric/indus-act/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 “zonal” level.” A caveat is that while a nodal model has more 

“links” and places where supply must meet demand, it is still 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).
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4. Multi-Sector Models 

Multi-sector energy models are typically used to examine the effect of energy and 

environmental policies that affected multiple economic 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 impacts under changing regulatory and economic 

conditions, and often review changes over a period of decades. 

Multi-sector models cover a broad range of energy sectors beyond the electricity sector, 

and can better reflect energy demand and technology choices by energy end-users. Such models 

typically have relatively limited spatial detail 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 price to account for demand). 

The range of national- and utility scale capacity expansion and dispatch planning models, 

and dispatch simulation models identified above all tend to focus on properly characterizing the 

electricity sector in order to answer sector-specific questions. Multi-sector energy models 

attempt to include many other energy-using sectors of the economy in order to better capture 

interactions between these sectors. The more aggregate representation of the electricity sector in 

multi-sector models may limit their use to providing input data for more specific analysis in an 

electricity sector model, and to better understand variations in electricity load that may result 

from changes outside the electricity sector. 

B. Growth Tools for Projecting EGU Utilization and CO2 Emissions 

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

inventories for state and regional air quality modeling,9

 to estimate the impact of load-reduction 

measures such as end-use energy efficiency and distributed renewable energy on individual EGU 

                                                            

9

 See, for example, the ERTAC Load Growth Model, available at http://www.ertac.us/index_egu.html. 
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emissions, and county, state,10 and regional emissions rates.11 Because these algorithms are 

generally based on publicly available data12 and do not rely on economic data or proprietary 

information regarding individual EGUs, they provide a low-cost, simple, and often transparent 

framework for estimating how EGUs will respond to changing system conditions. 

Non-modeling approaches, such as growth tools, approximate future emissions and 

generation from existing and new fossil fuel-fired 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 past operation. In the absence of 

significant shifts in fuel prices 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 emissions 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 tools generally divide the contiguous US14 into regional 

power markets, following ISO boundaries, eGRID boundaries, NERC regional boundaries, or 

similar designations. These algorithms generally seek to examine how operation and emissions 

from individual EGUs could be expected to change with changes in environmental regulations or 

installed pollution control retrofits and additional or reduced hourly electricity demand. Some 

algorithms use the observed historical behavior of individual EGUs to approximate future 

behavior, while others add additional steps of differentiating EGUs 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 Emissions 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 emissions; 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) through its Air Markets Program Data (AMPD). 13 For example, EIA’s AEO 2013 electricity load growth rates and EGU production trends are available at 

http://www.eia.gov/forecasts/aeo/assumptions/pdf/0554(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. 
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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 

CO2 emission performance by affected EGUs (taking into account the qualifying impacts of plan 

measures that avoid CO2 emissions from affected EGUs) must be equivalent to, or better than, 

the required CO2 emission performance level in the state plan. This required level of emission 

performance in the state plan will reflect the 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 CO2 emission rate (CO2 lb/MWh) or tonnage CO2

emission outcome by all affected EGUs equal to, or lower than, the required level of CO2

emission performance in the state plan would be considered to meet this plan approvability 

criterion. States may demonstrate such emission performance by affected EGUs either by state or 

jointly on a multi-state basis. 

This section of the TSD discusses the concept of a mass-based CO2 emission 

performance goal, considerations for constructing projection scenarios for such a translation, 

methods for conducting such a translation, and key input assumptions for such a translation. 

A. Concept 

A mass-based CO2 emission performance goal translates the application of a rate-based 

emission performance goal to an expected CO2 emissions outcome in tons during a plan 

performance period. A mass-based CO2 emission performance goal is calculated by projecting 

the tons of CO2 that would be emitted during a state plan performance period (e.g., 2020-2029, 

2030-2032) by affected EGUs in the state if they hypothetically were meeting the state ratebased CO2 emission performance goal for affected EGUs established in the emission guidelines. 

The translation of a rate-based goal (expressed in lb CO2/MWh of useful energy output from 

affected EGUs) to tons (expressed as total tons of CO2 emissions from affected EGUs over a 

specified time period) is based on a projection of affected EGU utilization and dispatch mix. 
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Importantly, this projection is conducted assuming the absence of qualifying state programs and 

measures contained in a state plan, and applying the rate-based goal in the emission guidelines as 

a proxy emission limit for affected EGUs. State programs and measures in the state plan are not 

included in this projection, because the purpose of this analysis is to determine the tonnage CO2

emissions that corresponds to the state-specific rate-based CO2 emission performance goal for 

affected EGUs in the emission guidelines, without 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 CO2 emissions if one applied the rate goals 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 must use in its plan, only the level of emission performance that 

must be achieved through a plan, there is no specified “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 measures to achieve the required level of emission 

performance established through the state rate-based CO2 emission performance goals in the 

emission guidelines. This proxy involves applying the rate-based emission goal for a state as a 

rate-based average CO2 emission limit for affected EGUs in a state and then projecting the CO2

emissions that would occur if such a limit were applied. 

When demonstrating projected emission performance under a mass-based plan, a state 

would project the CO2 emissions outcome that would be achieved under the suite of 

requirements, programs, and measures in its plan. The state plan requirements, programs, and 

measures substitute for EPA’s application of the best system, which is represented by the ratebased goal in the emission guidelines. If the CO2 emissions outcome, in total tons of CO2

emissions over each plan performance period, is equal 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 goal), the plan would be deemed to achieve the 

required emission performance criterion. 

 
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B. Projection Scenarios 

As described above, the projection scenario for translating from a rate-based CO2

emission performance goal to a mass-based CO2 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 CO2 emission performance goal to a 

mass-based CO2 emission performance goal, and comparing projected emission performance 

under a state plan to this goal, include the following: 

1. A Reference Case Scenario. This scenario projects the average CO2 emission rate and 

CO2 emissions from affected EGUs in the absence of the EPA emission guidelines or any 

enforceable requirements, programs, and measures 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 CO2

emissions, they should, however, be included in the reference case projection scenario. 

2. A Mass-Based CO2 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 CO2

emission limit to affected EGUs that is equivalent to the state-specific rate-based lb 

CO2/MWh emission goal in the EPA emission guidelines.16 The CO2 emissions from 

affected EGUs projected during the specified plan performance period in this scenario 

represents the translated mass-based CO2 emission performance goal for the state plan. 

To construct this scenario, this emission limit is added to the underlying reference case 

scenario described above. 

                                                            

15 An “existing measure” refers to a state or utility requirement, program, or measure that is currently “on the 

books.” For the purposes of this discussion, this may include a legal requirement that includes current and future 

obligations or current programs and measures that are in place and are anticipated to be continued or expanded in the 

future in accordance with established plans. Existing measures may have past, current, and future impacts on EGU 

CO2 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 

CO2 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. 
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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 

CO2 emission performance by affected EGUs under the state plan. To construct this 

scenario, the enforceable requirements, programs, and measures included in the state plan 

are added to the underlying reference case scenario described above. 

1. Constructing the Reference Case 

A key consideration for translating from a rate-based CO2 emission performance goal to a 

mass-based CO2 emission performance goal is proper construction of a reference case scenario. 

This includes assumptions for key variables that may drive EGU CO2 emission projections,17

such as: 

 Electricity load growth projections (energy and peak demand) 

 Fuel supply, delivery, and pricing assumptions 

 Cost and performance of electric 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 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. 

 

                                                            

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 administer a regional electric power pool (EGU dispatch and 

electricity transmission systems), and often also administer related wholesale electricity markets for electric energy 

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

A key aspect of the reference case is proper treatment of existing state regulations, 

programs, and measures. As discussed previously, existing state regulations, programs, and 

measures that are not enforceable measures in a state plan are complementary to the state plan – 

as a result, they should be included in the reference case scenario rather than the state plan 

scenario. These state regulations, programs, and measures should not, however, be ignored 

altogether, as they influence EGU CO2 emissions. Instead, these state actions are occurring in the 

background – either when a proxy rate-based CO2 emission limit is applied in the Mass-Based 

CO2 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 reference case scenario. 

In effect, when evaluating whether a state plan will meet the mass-based CO2 emission 

performance goal, the two policy scenarios described above, each of which involve adding a 

different CO2 emission reduction policy on top of the reference case scenario, are compared to 

one another. If the State Plan Policy Scenario achieves projected tonnage CO2 emissions equal 

to or lower than the projected CO2 emissions in the Mass-Based CO2 Emission Goal Policy 

Scenario, then the state plan can be deemed to meet the translated mass-based CO2 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 actions19 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 utility’s obligations under a state energy efficiency resource 

standard (EERS), would constitute an “action” taken pursuant 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. 
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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). For 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 could be applied toward meeting the required level of emission 

performance during the plan period. These investments might be made to meet requirements 

under an existing end-use energy efficiency resource standard (EERS) or renewable portfolio 

standard (RPS). 

Under a rate-based plan, the approach described above would address the eligibility date 

for qualifying renewable energy and demand-side energy efficiency measures that, through 

MWh generation or MWh savings, avoid CO2 emissions from affected EGUs. Measures installed 

after the eligibility date could generate MWh generation or MWh savings during the plan period, 

and related avoided CO2 emissions, that could be applied toward meeting a required rate-based 

emission performance level. Under the proposed option, the eligibility date would be the date of 

proposal of the emission guidelines. For example, under this approach, new renewable energy 

generating capacity installed in June 2014 or later to meet an existing, on-the-books RPS would 

be a qualifying measure in a state plan. However, only MWh generation beginning in 2020 and 

related avoided CO2 emissions could be applied toward meeting a required emission rate 

performance level. 

Under a mass-based plan, the approach described above would be applied when 

establishing a reference case scenario projection that is used to translate a rate-based goal to a 

mass-based goal. For example, qualifying renewable energy and demand-side energy efficiency 

measures after a respective eligibility date would not be included in the reference case scenario. 

This could be achieved by not including an EERS 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 measures would become eligible for inclusion in 

a state plan. This treatment would represent past actions taken under existing state programs and 

measures that are not eligible for inclusion in a state plan. 

For example, the reference case might include the required MWh of renewable energy 

generation necessary to meet a state RPS as of the date of proposal of the emission guidelines 
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(e.g., as of June 2014). Renewable energy 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 CO2 emission 

performance under a state plan. This renewable energy generation would be incremental to the 

renewable energy generation already assumed in the reference case scenario. 

IV. Projecting CO2 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 CO2 emission performance 

by affected EGUs that is specified in the plan. (This identified level of performance must be 

consistent with either the state-specific rate-based CO2 emission performance goals for affected 

EGUs identified in the emission guidelines, or an equivalent translated mass-based CO2 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 reference case scenario. Other types of state plan 

approaches are more involved, in particular when the use of end-use energy efficiency and 

renewable energy regulations, programs, or measures are included in a state plan. This could 

include a state plan that applies a rate-based CO2 emission limit that provides credit for end-use 

energy efficiency and renewable energy measures that avoided CO2 emissions, or the inclusion 

of such measures through a portfolio approach. 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 implementation of the plan. Utilitydriven portfolio approaches may also include a diverse set of actions taken directly at affected 

EGUs that will need to be properly represented in the state plan scenario. 

Considerations for developing estimates of energy savings and energy generation that 

will be achieved through end-use energy efficiency and renewable energy regulations, programs, 

and measures included in a state plan are discussed below in section VI. 
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V. Applied Considerations for Different Types of State Plans21

There are a number of considerations for properly representing different state plan 

approaches in CO2 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 assumptions that are used in CO2 emission 

projections. This includes: 

 Estimating the future effects of end-use 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 CO2 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 CO2 emissions from affected 

EGUs.22 This involves projections of the level of current 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 shape of the energy and capacity savings), and the 

effective useful life of those energy and capacity savings (e.g., the life of installed measures 

representing the persistence of energy savings). Such projections may also need to address net 

program energy savings after accounting for potential “free-ridership,” which involves energy 

savings that were likely to have occurred in the 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 CO2 emission limit 

applicable to affected EGUs that provides for adjustment or crediting of the CO2 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 portfolio approach. Descriptions of these plan approaches are provided in the 

accompanying State Plan Considerations TSD. 
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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 CO2 emissions from 

affected EGUs. 

Similar considerations apply for projecting the impact of renewable energy requirements 

and programs included in a state plan on CO2 emissions from affected EGUs. The renewable 

energy generation and generating capacity in a state or region that results from renewable energy 

requirements, programs, and measures in a state plan will affect EGU dispatch and CO2

emissions from affected EGUs. This section discusses 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 CO2

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. These forecasts would contain data that could be 

used to develop inputs to EGU CO2 emission projections in a state plan, using modeling or an 

EGU growth tool, both of which are summarized above. 

A bottom-up approach is based on annual evaluated and reported data from state and 

utility energy efficiency programs, and program-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 state-by-state end-use energy efficiency projection 

developed by ISO New England for use 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 that is modified (reduced) by 

the energy efficiency forecast. Information about the ISO New England energy efficiency forecasting method and 

assumptions is included in, ISO-New England (ISO-NE) Energy-Efficiency Forecast Working Group, Draft Final 
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New England develops production cost curves for energy 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 efficiency forecast for the last three Regional 

System Plans (2011, 2012, and 2013), and the near-term estimates in these forecasts have been 

validated by the actual quantity of end-use energy efficiency that has qualified for the annual 

capacity auctions conducted by ISO New England as part of its forward capacity market. Other 

approaches to projecting energy efficiency impacts, such as those used by New York ISO 

(NYISO), PJM Interconnection, and the California Public Utilities Commission (CPUC), 

estimate the effects of energy efficiency requirements and programs over a shorter time frame. In 

these cases, energy efficiency projections are typically 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 aggregate energy use changes resulting from enduse energy efficiency requirements or programs, often for a geographic region, 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 energy and demand savings. In this 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. One example of a top-down approach is the EPA’s 

state-level projection of the 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-efficiency requirements and programs currently in place and 

specified in state legislation or administrative order. These requirements include energy efficiency resource 

standards (EERS) and dedicated sources of energy efficiency program funding.  
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adjusting national electricity load forecasts for use in electricity system modeling.25 In this case, 

EIA Annual Energy Outlook (AEO) projections of future 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 necessary, to reflect known increases or decreases in 

energy efficiency program budgets and other factors likely to affect whether state energy 

efficiency requirements are achieved. In addition to EPA’s projections, a top-down approach is 

used to develop EE forecasts by LBNL, NREL27, and some state energy offices and 

commissions. 

The LBNL28 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. Ideally, using the two approaches as complementary 

analyses may provide for helpful comparison and reconciliation of energy savings estimates used 

in state plan projections of EGU CO2 emission performance. 

The electricity load forecasts developed by ISOs and RTOs are important because they 

are often the base case from which adjusted load forecasts that incorporate 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, these 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 some cases there is also an effort to anticipate 

                                                            

25 While EPA’s approach utilizes AEO forecasts 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 Customer-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-funded-energy-efficiency-programs-united-states-projectedspend. 
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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 CO2 emission 

performance as part of a state plan that includes 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 effect 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 Efficiency 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 from state energy efficiency requirements and 

programs included in a state plan are necessary inputs to projections of EGU CO2 emission 

performance under the state plan. Energy and peak demand savings estimates included in energy 

efficiency projections are used as a decrement to future electricity load forecasts that are used in 

conducting EGU CO2 emission performance projections. The 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 CO2 emissions. For example capacity expansion 

and dispatch planning models are capable of incorporating energy efficiency program impact 

data, such as annual energy and peak load reductions, when projecting EGU dispatch and 

capacity additions, and the avoided CO2 emissions that are projected 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 requirements and programs, there are certain strengths 

                                                            

29 In New England states that have policies to pursue “all cost-effective energy efficiency,” the ISO-NE energy 

efficiency forecast assumes that energy efficiency program investments will continue beyond the 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. 
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and limitations regarding the approach and methodology used. Generally, these strengths and 

limitations relate to data availability and energy efficiency forecast assumptions, such as: 

 Energy efficiency requirements 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 different energy efficiency measures 

 Energy efficiency measure life and measure energy performance decay 

 Time of energy and demand savings for energy efficiency measures (availability of 

hourly data necessary to derive a load reduction 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 requirements and programs can be fully addressed in the 

analysis. In top-down approaches, such as EIA AEO 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 typically include state-specific data that are 

rolled up to a regional level, while top-down national electricity load forecasts do not have a 

comparable level of granular state data detail. ISO New England provides energy and demand 

savings forecasts for state energy efficiency programs (average annual and peak demand 

coincident with the ISO defined peak load periods). The ISO NE energy efficiency forecast is 

informed by data collected at the energy efficiency measure, individual program, program 

portfolio, sector, and state levels. 

An important consideration with both top-down and bottom-up energy efficiency 

forecasts is the extent to which energy efficiency program effects have become embedded into 

the long-range economic forecasts used as an input in developing electricity load forecasts. In 

some cases the economic forecast includes the effect of historical energy efficiency policies, 

either implicitly or explicitly, while in other cases the economic forecast may also anticipate 
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some limited quantity of future energy efficiency 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 efficiency projections, is energy efficiency program 

spending. While policies “on the 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 state approved budgets identified through stakeholder discussions. 

Additionally, assumptions are made about how the overall cost of saved energy for a portfolio of 

energy efficiency programs will change over time. It is 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 buildings and building retrofits).30 

While many existing sources of energy efficiency 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 efficiency impacts. Smart meters combined with 

data-sharing and analysis technologies are making it easier for utilities and other energy 

efficiency program administrators to more accurately determine how the total energy savings 

achieved in a calendar year are spread out across the hours of that year. These data can be 

applied to energy efficiency projections to provide a better estimate of the timing of energy and 

demand savings. Such time-differentiated data is 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 larger scale energy efficiency programs and broader energy 

efficiency measure portfolios, and the associated 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. However, 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. 
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which can be used to provide more refined estimates of avoided CO2 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 the representation of 

renewable energy requirements and programs included in state plans, when conducting 

projections of EGU CO2 emission performance that will be achieved under a plan. A range of 

data and analytic considerations are examined that may be relevant when using different 

modeling approaches, including use of capacity expansion and dispatch planning models, and 

less sophisticated statistical or top-down projection approaches. 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 set at the state level with an increasing percentage of total 

retail sales over a set schedule. The RPS requirement 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 target that is set-aside for a specific resource. It also defines the 

allowable geographic boundary for obtaining renewable energy or RECs. With this context, the 

following issues may be important to consider when projecting the impact of state RPS on EGU 

CO2 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 electric system over time based on a detailed characterization of 

those EGUs, engineering and market operating constraints, other market factors (e.g., fuel prices, 

transmission constraints), emission constraints, and the requirement to meet a certain level 

energy and peak demand. These models may be based on a small control area, but more likely 
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are regional or national in scope. Some, such as capacity expansion and dispatch planning 

models, simulate EGU dispatch and also consider the impact of long-term generating capacity 

investment decisions when optimizing system operation and buildout over a long-term planning 

horizon. These are typically optimization frameworks 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 analytic tools to project EGU CO2 emissions, a number of 

analytic and data considerations are relevant for analysis of RPS impacts on EGU CO2

emissions. These analytic and data considerations are summarized below. 

Translation of RPS Requirements to Renewable Energy Generation Requirements

Most RPS are retail sales-based requirements, while most electricity sector analytic tools 

are generation-based models. When projecting CO2 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 distribution 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 renewable 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 eligible renewable energy generating resources. 

These could include renewable energy EGUs that emit CO2, and EGUs that were constructed and 

began operation recently or many years ago. Some of these specific EGUs may not be eligible 

for use in a plan, in particular units that began 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 eligible for use in state plans. This would be addressed through 

the treatment of existing RPS in the analysis base case, as described above 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). 
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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 EGUs 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 are not eligible for use in a state plan, or through the use of two 

modeled RPS requirements for each class of renewable energy resource, with the RPS that 

addresses resources not eligible in a state plan addressed 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 explicitly specify the source region of eligible 

renewables. From a modeling perspective, this means a state would allow a broader supply area 

and model a broader area generally. If the 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 availability, performance, and capital and production 

costs). EIA (for NEMS), EPA (for IPM), and 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 supplied will influence the marginal fossil fuelfired EGU (or cohort of EGUs) that is displaced by renewable energy generation. Understanding 

the renewable energy resource base, including the energy profile of the relevant renewable 

energy resource types will improve the modeling of EGU dispatch and projections of avoided 

CO2 emissions that result from renewable energy 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/powersectormodeling/BaseCasev513.html. 
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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, generic 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 economics. This requires understanding the relative 

economics (e.g., capital costs, fixed operation and maintenance (FOM) costs, variable operation 

and maintenance (VO&M) costs, and fuel costs) and operating conditions for different types of 

renewable energy generators, as well as renewable energy resource availability (e.g., MW 

capacity available at different wind classes, solar insolation levels, and the characteristics of 

biomass). Having this information allows for better projections of the level and quality of 

renewable resources likely to be brought forward as the result of a state RPS. This will result in 

better emissions projections. 

NREL has a suite of tools that can be used to 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 professionals, 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/transmission/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 (CREST) and the System Advisor Model (SAM). CREST 

(https://financere.nrel.gov/finance/content/crest-cost-energy-models) makes performance predictions and cost of 

energy estimates for grid-connected 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 impact of tax incentives or other support structures on renewable energy projects. 
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revenue per unit of production, needed for a sample (modeled) renewable energy project to meet 

its investors' assumed minimum required after-tax rate of return. 36 

Retail Rate Impacts

Many state RPSs have caps on the allowable impact 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 implementation rules. This would lower 

estimates of renewable energy generation needed to satisfy the RPS requirements. 

Market Impacts – Multiple State Players

One important issue is to account for actions of others. Modeling results that consider the 

RPS-related actions of other states could differ substantially from those that consider a state’s 

actions in isolation. This requires having at least a regional, if not national, modeling framework. 

A broader framework captures competition for renewables (and increased prices, greater reliance 

on poorer resources), as well as market-wide impacts on EGU dispatch and capacity expansion. 

2.1.2 Data Needs for Estimating Generation Resulting from State RPS 

Projections of renewable energy generation will require assembling the following data: 

 Resource availability, resource performance, and resource costs will be necessary for the 

more sophisticated modeling approaches described above. 

 Projections of retail sales, by year, adjusted to account for any exclusions from the RPS 

obligation (e.g. small utilities, cooperatives, municipalities, or perhaps certain large 

industrial loads). These covered sales must be calculated and projected using forecasts of 

annual retail demand. 

 RPS requirements by year, so the annual forecast of covered retail sales can be multiplied 

by these percentages. 

                                                            

36 The CREST model is a product of a 2009-2010 partnership 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. 
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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 estimates 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 capacity expansion and dispatch planning model is not used, 

estimates of the geographic origin, by state and grid-region, of generation used to satisfy 

the RPS will be necessary. If generation to satisfy the RPS is expected to come from out 

of state, the basis for these assumptions should be documented. 

 CO2 emission rate associated with renewable energy generation, by resource type (if 

applicable). 

 Generation profile, by renewable energy resource type 

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

A feed-in-tariff is a performance-based incentive (similar to production tax credits) that 

typically guarantees utility customers who own a eligible renewable electricity generation system 

(e.g., roof-top solar PV system) will be paid a specific amount by their utility for the electricity 

the system generates and provides to the grid over a fixed period of time. Such tariffs usually 

offer a fixed payment amount per kWh, but variations may require competitive bids. In some 

cases, FIT payments may be in addition to other 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 the 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 focuses 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. 
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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 generators receiving FIT payments are also 

eligible to satisfy an RPS, states should ensure 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 renewable energy generating technology. This is 

the same information required for modeling an RPS; however, the modeling or analysis 

mechanism may differ. For example, these costs may be reduced directly in a modeling 

framework to reflect the FIT payment (e.g., reducing VOM to reflect the FIT in the years 

it is available). 

 Information on the types of renewable energy resources, including their location, output 

levels, energy output profiles (8760 or seasonally), 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 energy generators, if a FIT includes caps on 

generation subject to the tariff based on rate impacts. If retail electricity rate impacts or 

budgetary impacts become unsustainable, the projected generation resulting from the FIT 

will be overstated. 

2.2.1 Data Needs for Estimating Generation Resulting from Feed-In Tariff and 

 Performance-Based Tax Incentives 
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Projections of renewable generation that will occur as the result of FITs and other 

performance-based incentives will require assembling the following data about the specific 

incentive programs that will support 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 capacity 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 estimate of the total electric generation that will 

be supported by the FIT or other production-based incentive. 

 State experience and trends with FITs and other production-based incentives. Lacking 

budget or capacity limits, prior experience in the state, or the experience of other states 

and utilities, normalized for state or utility size, could be the basis for estimated 

renewable energy generation at a given incentive level. 

 Because performance is metered at the busbar (i.e., the point of interconnection to the 

electricity transmission or distribution system), no adjustments 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 these assumptions should be 

documented. 

 CO2 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 equal to the emission constraint 
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33

for the group of sources. An emission allowance typically 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 ability to trade allowances. These include multiyear compliance periods, the ability to bank allowances 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, these programs may be 

multi-sector in nature, regulating emissions for source categories in addition to EGUs. As a 

result, state plan emission projections will need 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 

CO2 emissions from affected sources, considering these compliance flexibilities. 

1. Addressing the Sectoral Scope of Emission Budget Trading Programs 

Some existing state emission budget trading 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 programs. 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 compliance obligation of affected sources. 

For multi-sector emissions budget trading programs, state plan emission projections 

would need to evaluate projected CO2 emissions across all source categories covered by the state 

or multi-state program. This would be necessary to project the CO2 emissions performance of 

affected EGUs under the multi-sector emissions budget trading program. 

                                                            

38 Depending on the program, these cost containment allowance reserves make additional allowances available from 

within the base emission budget (i.e., from “within the emission 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 includes large industrial sources and as of 2015 will include 

distributors of transportation, natural gas, and other fuels. 
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For emissions budget trading programs that regulate 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 CO2 emission performance of 

affected EGUs, because those reductions would not come from those affected EGUs. However, 

state plan emissions projections would need to evaluate the use and impact of offsets, because 

the availability of offsets would affect the amount of CO2 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 generating sector that is the focus of the EPA 

emission guidelines. For example, the California GHG emissions budget trading program is a 

multi-sector emissions trading program that address 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, CO2 emission projections provided as part of the state plan will need to 

evaluate projected CO2 emissions across all covered emissions sectors under the program. This 

would be necessary to project the CO2 emissions or weighted average CO2 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 CO2 emissions performance of affected EGUs introduces an additional level of analytical 

complexity to emissions projections. For example, additional modeling capabilities may be 

necessary to adequately evaluate CO2 emissions performance across sectors in a multi-sector 

emissions budget trading program. 

Adequately projecting the CO2 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 model. Some multi-sector models, include an 

                                                            

41 For example, the Energy 2020 model was used by California to evaluate the impacts of its multi-sector GHG 

emissions budget trading program. Other multi-sector models are available with similar capabilities, such as the 

National Energy Modeling System (NEMS) developed and implemented by the U.S. Energy Information 

Administration (EIA). 
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electricity sector dispatch and capacity expansion 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 CO2 emissions or the weighted average CO2 emission rate from 

affected EGUs at a sufficient level of resolution for state plan emission projections. Under this 

approach, a multi-sector energy model might be used to project the level of CO2 emissions 

abatement achieved across the multiple emission sectors covered by the program. This multisector projection could be used to develop a CO2 marginal abatement cost curve representing 

cost-effective, non-electricity sector emission reduction opportunities at increasing cost levels. 

The marginal abatement cost curve would then serve as an input assumption for the electricity 

sector capacity expansion and dispatch planning model, which would be used to project the CO2

emission performance of affected EGUs (e.g., the CO2 emissions or CO2 emission rate of 

individual affected EGUs, or the total CO2 emissions or weighted average CO2 emission rate 

across multiple affected EGUs).42

Multi-sector emission budget trading programs also often address GHGs beyond CO2. If 

this is the case, emission projections in a state 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 emission sources in different industrial sectors reduce emissions of nonCO2 GHGs, which could impact projected CO2 emissions from affected EGUs. 

Addressing Emission Offsets 

Emission offsets in CO2 or GHG emissions budget trading programs represent projectbased GHG emissions reductions outside the sector or sectors regulated by the program.43

Emissions reductions achieved through eligible offset projects are awarded allowances or 

“credits” that may be used by an affected source to meet a portion of its allowance compliance 

                                                            

42 This marginal abatement cost curve would be applied in a similar manner 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 emission sources in other sectors that are also subject to the multi-sector 

emission budget trading program. 

43 “Offsets” as used in the context of CO2 or GHG emissions budget trading programs are distinct from offsets in the 

NSR permitting context under the CAA, where in certain instances emissions of a criteria pollutant from a proposed 

new facility must be offset with creditable emissions reductions at an existing facility, if the state or jurisdiction 

where the proposed facility would be located is in non-attainment status for the pollutant. 
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obligation. For example, under the RGGI program “CO2 offset allowances” awarded for GHG 

emissions reductions achieved through approved offset projects may be used by affected sources 

to meet up to 3.3 percent of their CO2 allowance compliance obligation.44 

The ability to use GHG emission offsets for compliance means that CO2 emissions from 

EGUs regulated under the emissions budget trading program may exceed the base CO2 emissions 

budget established for the program. Offset allowances or credits are awarded in addition to the 

existing CO2 emissions budget, in exchange for CO2-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 under an emissions budget trading program could 

result in higher projected CO2 emissions from affected EGUs in a state plan, because the use of 

offsets functionally expands the CO2 emissions budget for affected EGUs. This allows affected 

EGUs to emit more CO2 while meeting their compliance obligation under the emissions budget 

trading program, in exchange for CO2-eq. emissions reductions achieved outside of the affected 

source category. As a result, to properly project the CO2 emissions from affected EGUs in a state 

plan that includes an emissions budget trading program 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, state plans would ignore the CO2-equivalent emissions reductions 

projected to be achieved through offsets, when projecting the CO2 emissions performance that 

will be achieved by the affected EGU source category through implementation of the state plan. 

This does not mean that an emission budget trading program included in a state plan could not 

include an offset component. Rather, when demonstrating emission performance by affected 

EGUs, the projected CO2 emissions or weighted average CO2 emission rate for affected EGUs 

under the state plan would not incorporate a credit for the CO2-equivalent emissions reductions 

                                                            

44 This percentage increases to five and ten percent of a CO2 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 supply curves, which represent the amount of offset credits/allowances assumed 

to be available from different categories of offset projects at different GHG emissions abatement costs. 
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represented by offset allowances or credits used by affected EGUs for compliance with the 

emission budget trading program.47 

2. Multi-State Emission Trading Programs 

Emission budget trading programs may be multi-state in nature. For such programs, 

emissions reductions are achieved on a regional, rather than a state-by-state basis. 

For example, in the multi-state Regional Greenhouse Gas Initiative (RGGI) emissions 

budget trading program, individual participating states have established CO2 emissions budgets 

in state regulations. However, there is no requirement limiting total CO2 emissions from affected 

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

sources to use CO2 allowances issued by another participating state for compliance with the state 

program. This provides for state-to-state CO2 allowance flows (and the potential for differences 

in state-by-state CO2 emissions relative to state emissions budgets) based on where the 

allowance market determines it is most economical to achieve CO2 emissions reductions. 

However, in aggregate, the CO2 emissions budgets in each of the participating state regulations 

establish a regional cap on CO2 emissions from affected EGUs. As a result, while a multi-state 

emission budget trading program may be projected to result in CO2 emissions from affected 

EGUs consistent with a multi-state mass-based CO2 emission performance goal, the CO2

emissions outcomes may vary by state. This necessitates evaluating a multi-state emission 

budget trading program as a whole, because the individual regulations of participating states 

function together as single integrated program. 

To address these issues, states participating in a multi-state emission budget trading 

program would jointly demonstrate that the multi-state program is achieving the required level of 

CO2 emission performance on a multi-state basis, based on the CO2 emission performance of all 

affected EGUs in the multi-state group implementing the program. 

Some state emission budget trading programs also include international partner 

                                                            

47 In other words, the projected CO2 emissions or weighted average CO2 emission rate for affected EGUs would be 

based on the direct emissions from these EGUs alone, with no calculation of “net” EGU CO2 emissions that factor in 

the CO2-eq. emissions reductions represented by offset credits or allowances used by an EGU to meet a portion of 

its compliance obligation. 
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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 projections evaluating an 

international program would include all jurisdictions participating in the program, but emission 

performance for state plans would be assessed based only on the CO2 emission performance of 

affected EGUs in the subset of the program represented by U.S. states. Although the CO2

emission performance of EGUs (or other emission sources) in a foreign country would not be 

addressed in the state plan, the entire international program would be evaluated as part of the 

emission projection included in the state plan. This would be necessary in order to project 

international allowance flows and CO2 emissions across all participating jurisdictions, as these 

cross-jurisdictional flows would impact projected CO2 emissions from affected EGUs in U.S. 

states. 

VI. Process Considerations 

As discussed in the preamble, in section VIII.F.7, the credibility of state plans under 

section 111(d) will depend in large part on ensuring credible and consistent emission 

performance projections in state plans. Therefore, the use of appropriate methods, tools, and 

assumptions for such projections is critical. Furthermore, considerations for projecting emission 

performance under a state plan will differ depending on the type of plan. This includes 

differences in how inputs to projections are derived; 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 projection approaches, tools, and methods for use in an approvable 

plan, as well as provide technical resources for conducting projections. This section of the TSD 

elaborates on these considerations. 

A. Provision of the EPA Guidance 

One approach to address these considerations is for the EPA to provide recommended 

guidance for EGU CO2 emission projections included in state 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. 
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included in a state plan could include assumptions that deviate from the EPA’s recommended 

default assumptions, but a state plan would justify the reason for using alternative assumptions. 

The EPA technical guidance could specify recommended reference case assumptions for use 

with modeling or EGU utilization growth tools, for example: 

 Electricity load growth projections (energy and peak demand) 

 Fuel supply, delivery, and pricing assumptions 

 Cost and performance of electric 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 (other 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 inclusion in a state plan as alternative standards. The guidance could 

compile information about state programs and provide model input assumptions related to these 

programs (e.g., MWh of electric generation needed to meet a state renewable portfolio 

standard).50 The EPA might also play a role in facilitating coordination among states as they 

develop their plans, to harmonize regional assumptions. 

                                                            

49 ISOs and RTOs are independent organizations that administer a regional electric power pool (EGU dispatch and 

electricity transmission systems), and often also administer related wholesale electricity markets for electric energy 

and capacity. 

50 EPA’s manual, Roadmap for Incorporating Energy Efficiency/Renewable 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 modeling guidance. In particular, see Appendix I: EPA’s Draft

Methodology for Estimating Energy Impacts of EE/RE Policies. The draft manual is available at 
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ISOs and RTOs, in discussions with the EPA have also offered to support states in 

evaluating the emission performance of state plans on a regional basis. The ISO/RTO Council, 

an organization of electric grid operators, has suggested that ISOs and 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 individual 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 potential 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 reliability. The ISO/RTO Council suggested that the EPA 

ask states to consult with the applicable ISO/RTO in developing their state plans. 

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

In the preamble, in section VIII.F.7, The EPA seeks comment on whether it should 

develop guidance that describes acceptable projection approaches, tools, and methods for use in 

an approvable plan, as well as provide technical resources for conducting projections. 

One consideration for state plans that use a mass-based CO2 emission performance goal 

is the party that conducts the translation of the rate-based CO2 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 presumptive translation of the state-specific 

rate-based CO2 emission performance goal to an equivalent 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 CO2 Emission Goal Policy Scenario, as described above in section III.B. A state could 

utilize the presumptive mass-based CO2 emission performance goal for the state or multi-state 

                                                                                                                                                                                               

http://www.epa.gov/airquality/eere.html. State environmental regulations addressing EGUs are itemized in the 

modeling documentation for the EPA IPM Base Case v.5.13. 
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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 justify these modifications as part of the CO2 

emission projection included in its state plan, and present an emission projection that supports a 

proposed modified mass-based CO2 emission performance goal. 

Another approach is for the EPA to provide guidance for states to use in translating a 

rate-based goal to a mass-based goal. This could include information about acceptable analytical 

methods and tools, as well as default input assumptions for key parameters that will likely 

influence projections, such as electricity load forecasts and projected fossil fuel prices. Under 

this approach, the EPA might also provide a coordinating function in addressing the assumptions 

applied by multiple states within a grid region, acknowledging that assumptions about state 

programs across a broader grid region that are included in an analysis scenario will influence 

projections of CO2 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.