Technical Support Document (TSD)

Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electricity

Utility Generating Units Docket ID No. EPA - HQ - OAR- 2013 - 0602



## Incorporating RE **and Demand -**  **S ide EE**  **Impacts in**  **to**

**State Plan Demonstrations**

U.S. Environmental Protection Agency Office of Air and Radiation July 31, 20152



### I. **Introduction** This technical support document details certain demonstration requirements for Clean Power Plan (CPP) state plan submittals that must include a projection of carbon dioxide (CO 2 ) emission performance by

affected electric generating units (EGUs) , as identified in section VIII.D.2.a(3) of the preamble. 1 Development of a state’s CO 2 emission performance projection begins with an electricity demand forecast , which, depending upon the ty pe o f anal ytical method or model used, the state may

want to reflect the impacts of renewable energy (RE ) and demand - side energy efficiency ( EE ) policies and programs . In order to develop this electricity demand forecast, first, the state would either select an existing base case electricity demand and supply forecast or develop its own . Section II below addresses how to choo se or develop a base case demand and supply forecast as well as how to document any included RE or demand - side EE measures. Second, the state may

adjust this base case demand forecast to reflect the impacts of RE and demand - side EE measures included in the state plan. Section III describes how to quantify the impacts of RE and demand - side EE policies and programs and then adjust the base c ase demand forecast to reflect these impacts. 

### I **I .**  **Base C ase**  **Electricity Demand and Supply**  **Forecasts** The CO 2 emission performance projection of affe cted EGUs requires information from an electricity demand and supply forecast as a basis for predicting generation and emissions during the interim and final plan performance periods. This section describes the steps a state should take to demonstrate that the base case demand forecast transparentl y represents future demand and supply : Step 1 : Choose or develop a base case electricity demand and suppl y forecast . Step 2 : Document RE and demand - side EE already included in the base case electricity demand and suppl y forecast. **Step 1 : Choose or develop a**  **base case electricity demand**  **and supply forecast**  **.**

1 This includes state measures type plans as well as some emission standards type plans .3

A state can develop its own base case electricity demand and suppl y forecast or use a forecast from the following information sources : 2

 U.S. Department of Energy , Energy Information Administration, Annual Energy Outlook (AEO) ,  North American Electric Reliability Corporation (NERC) ,  Regional transmission organizations (RTOs)/independent s ys tem operators (I SOs) (e.g., PJM Interconnection, ISO -New England, NYISO) ,  Verticall y integrated utilities (e.g., a large power company that operates the electricity system for a specific region) , in combinations with another publicall y available source ,  State energy

agencies (e.g., State Energy Office or Public Utility

Commission (PUC)) , and  Regional councils that coordinate energy planning (e.g., Northwest Power and Conservation Council) To develop its own state - specific base case electricity demand and suppl y forecast, a state would apply an annual average electricity demand growth rate derived from one of the above data sources to state or regional specific historical electricity sales data . States may

also consider more comprehensive modeling approaches that calculate expected demand based on assumptions including: electricity

prices, technology

development, population, and economic forecasts. For the base case suppl y forecast, a state should inventory all generation resources available to meet state demand through the final plan performance periods, including in - state existing and anticipated capacity , and any expected imports. **Step 2 : Document**  **RE and**  **demand -**  **side EE**  **measures included in**  **the base case**  **electricity demand and**  **supply forecast.**  Demand forecasts from different information sources reflect on - the -books RE and demand side EE policies to varying degrees in demand and supply forecasts . The state must therefore research and document how on - the -books RE and demand - side EE program s included in the

2 States can either adopt an existing forecast from one of these sources or use data from them to develop their own forecast. States may request to use a different source and EPA would review those requests on an individual basis. Any s tate - specific base ca se forecasts developed by states must be approved by EPA per the final emission guidelines.4

demand or suppl y forecast used by the state are incorporated into that. This will clarify which program s are influencing the growth rate assumptions, and the EPA will use this information to assess whether the demand and supply forecast is reasonable . This also helps ensure that impacts captured in the base case forecast are not double - counted in the base case forecast or duplicated in the state plan performance projection. A state should work with the source of the forecast to understand whether and how RE and demand - side EE policies and programs , including but not limited to the list below, are included in the base case electricity demand and supply forecast :  EE policies or programs funded b y utility ratepayers ,  Existing federal appliance and lighting efficiency standards that are already in effec t ,  Forthcoming f

ederal appliance and lighting standards ,  State appliance or lighting efficiency standards (if applicable) ,  State building energy

codes ,  Renewable portfolio standards , and  C odified local policies . There are at least two ways that these programs are captured in an existing forecast: 1. Programs are explicitly identifie d , meaning the forecast show s a direct connection between the program and its impacts on energy demand or supply (MWhs) , 2. Programs are in directl y, and either full y or partially , captured through econometric or other assumptions in the relevant economic model (e.g., as captured in National Energy Modeling S ys tem (NEMS)) . 3

3 Demand - side EE programs explicitly included in a demand forecast are programs that the forecast’s documentation specifically states directly impact future demand levels. Some demand - side EE programs, however, are indirectly represented in the assumption and not explicitly identified. For example, the U.S. Energy Information Agency’s (EIA) Annual Energy Outlook (AEO) explicitly include s in the reference case for electricity demand and supply forecast existing federal appliance and equipment standards for residential and commercial sectors as well as state renewable portfolio standards. The AEO indirectly captures some impacts of state de mand - side EE programs in the forecast and does not explicitly call out their impacts. Indirect impacts do not need to be quantified, but on a case -by case basis, a discount factor toward any incremental savings in the CPP performance projection may be required to adjust for potential double counting. For more information visit: http://www.epa.gov/statelocalclimate/state/statepolicies.html.5

For all RE and demand - side EE program s included in the base case demand forecast, a state must document whether the program is captured explicitly or indirectly . In addition, a state must include the following information for on - the - books programs that are reflected in the base case forecast : 1. Policy or program name , 2. Whether the policy

is codified in state or local rule , 3. Year enacted , 4. When the policy

requirement s sunset . The above requirements apply regardless of whether an RE or demand - side EE program will be used to meet the emission guidelines (e.g., to generate ERCs or as state measures) . For example, assume a state with an Energy Efficiency

Resource Standard ( EERS ) in state law adopt s a rate -based emission standards ty pe state plan that requires a projection . The impacts of the on - the -books EERS may already be included in the base case demand forecast , and if so, should be documented . If the affected EGUs in the state do not need to use the energy savings (e.g., emission rate credits (ERCs) from demand - side EE) from the state’s EERS to adjust their CO 2 emission rate s , then the state’s plan demonstration would include the four pieces of information noted above about its EERS to support the reasonableness of the electricity demand forecast. 4 I f the state expect s affected EGUs to use the demand - side EE impacts from the EERS to adjust the ir rate s, then the state plan should document the eight pieces of information noted at the end of this section. Similarly , under a mass -based state measures approach where the state EERS is not included as a state measure , the state plan demonstration would include the four pieces of information noted above. If the on - the -books EERS is included as a state measure, then the plan should document the eight pieces of information noted below. This information will inform the EP A's evaluation of whether the emission standards in the state plan are achievable.

4 For example, affected units in the state might be able to achieve the rate -based emission standards with other measures or actions outside of the EERS.6

In addition to documenting for on - the -books RE or demand - side EE measures the four pieces of information noted above and whether they are directly or indirectl y captured in the base case demand and suppl y forecast, the state plan must provide additional information on certain types of on - the -books programs. There are at least two categories of on - the -books policies and programs that must be explicitly accounted for in the base case demand and/or supply forecast and in the following manner :

 Qualifying RE and demand - side EE programs that are implemented after 2012 and impact electricity generation in 2022 or later . 5 o Quantify

the (annual incremental and cumulative savings or generation (MWhs) ) x ( effective useful life) to estimate the savings that impacts demand or generation in 2022 or later .

 Qualifying RE and demand - side EE programs that impact electricity generation in 2020 or 2021 and meet eligibility requirements in the final rule regarding the Clean Energy In centive Program . 6 o Quantify

the (annual incremental and cumulative savings or generation (MWhs) ) x ( effective useful life) to estimate the savings that impacts demand or generation in 2020 and 2021 . Because these on - the -books RE and demand - side EE measures or programs are explicitly accounted for in the base case demand forecast, the same impacts must not be represented again in the state plan performance projection . In other words, such impacts cannot be included in the CO 2 emission performance projection, because they are already captured in that base case forecast. For any RE and demand - side EE programs that are explicitly included in an EGU base

5 Eligibility requirements related to the timing, geography and type of RE and demand- side EE measures that can be used to adjust a CO 2 performance rate of an affected EGU under the final rule are detailed in s ection VII I.K.1 . 6 For details refer to s ection s VIII.B.2 and VIII.K.1.a of the preamble **.**7

case forecast and will be used to meet the CPP emission guidelines , the state must document the following informatio n : 1. Policy or program name , 2. Whether the program is codified in state or local rule , 3. Year enacted , 4. When the p rogram requirement s sunset , 5. P rogram requirements (e.g., targets in megawatt hours (MWh) or percentage) , 6. Annual energy

savings or generation in the base year (MWh) , 7. Annual and cumulative energy savings or generation in the future interim and final periods (MWh) , and 8. Estimated useful lives for programs. 

### III. **Proper Characterization of Renewable Energy and Demand**  **- Side**  **Energy Efficiency in a**  **State Plan Performance Projection**  Section VIII.D.2.a.(3)(a) of the preamble explains which ty pes of state plans require CO 2 emission performance projections for affected EGUs in order to show that the state plan will lead to achievement of the CO 2 emission performance rates, state goal, or other emission guideline requirements . S tate plans that require such a projection must include a base case electricity demand and suppl y forecast as well as supporting documentation , as explain ed in section II above. In addition, such plans must quantify the combined future impact of all the emission standards and state measures included in the state plan. This section explains the proper characterization of expected RE and demand - side EE impacts in a CO 2 emission performance projection . States that plan to use RE and demand - side EE to meet the emission guidelines (e.g., to generate ERCs or as state measures) under applicable state plan ty pes that require a performance projection will need to project the expected incremental direct generation impacts and electricity8

savings of RE and demand - side EE . 7 While RE and demand - side EE measures, programs and policies may impact the generation , construction, retirement and CO 2 emissions of both affected and non - affected EGUs , a projection must demonstrate how RE and demand - side EE will enable affected EGUs to meet the emiss ion guidelines. There are different approaches to projecting electricity savings and renewable generation , depending up on the design of an RE and demand side EE program . Regardless of the approach chosen, a state plan that includes RE and demand side EE must document specific assumptions in their performance projections , as described below. 

### A. **Approaches to E**  **stimating F**  **uture RE Generation and EE**  **S avings**  Projections of expected annual electricity savings and/or renewable energy generation are necessary inputs when projecting EGU CO 2 emission performance. This is because increased RE generation will impact the generation of affected EGUs and resulting CO 2 emissions . Electricity savings estimates included in EE projections will lower electricity demand forecasts in the CO 2 emission performance projections. The EE projection can be used as an input, or modifier, to various models or other tools in order to demonstrate that the state plan will achieve emission guideline requirement s. A CO 2 emission performance projection must include the potential impacts of RE and demand - side EE , and the state must document how th ose RE and demand - side EE impacts are reflect ed in the projection. The state can incorporate RE and demand - side EE impacts at the individual project or program level , or it can aggregate RE and demand - side EE impacts across a portfolio of programs. 8 The EPA historically has accepted the estimation of RE and demand - side

7 Incremental direct effects refers to RE and demand- side EE beyond any impacts that are in the base case. If a new program is more stringent than an existing program already in the base case, it may not be appropriate to use the full magnitude of the new program. 8 This can include state and local sponsored programs, and RE and demand - side EE projects, such as ones done under performance contracting , provided they meet the eligibi lity requirements specified in s ection VIII.K.1 of the preamble . Other types of programs and projects that may be eligible for inclusion are described in s ection VIII.K.1.a .9

EE at an aggregated level as indicated in EPA’s Bundled Measures Guidance. 9 The state must also verify

that the assumptions about RE and demand - side EE availability are reasonable , such as based on an assessment of RE and demand - side EE potential . As discuss ed above, a state plan CO 2 emission performance projection must not include RE and demand - side EE impacts captured in the base case electricity demand forecast. Consequently , RE and demand - side EE impacts included in the CO 2 emission performance projection must be incremental to and non - duplicative of impacts included in the base case demand forecast. The performance projections, for example, could include impacts from RE and demand - side EE programs not reflected in the base case forec ast or from the expansion of existing programs beyond levels reflected in the base case forecast . A state must use the following general equations to quantify the expected incremental impacts of RE and demand side EE during the future interim and final performance periods . 10 Energy Efficiency :  (Incremental first - ye ar annual plus cumulative savings of EE program 11 ) x ( effective useful life) for each interim period and final period. Determine persistent level of savings for final period and beyond. Renewable Energy :  (Incremental MW installed capacity) x ( location 12 and technology- specific capacity factors) x (8760 hours) for each interim period and final period. Determine persistent level of renewable energy generation for final period and beyond.

9 Guidance on Incorporating Bundled Measures in a State Implementation Plan, www.epa.gov/ttn/oarpg/t1/memoranda/10885guideibminsip.pdf. 10 The EPA recognizes that for unique situations, states may not be able to use these exact equations. In those unique circumstances, states can propose an alternative equation to EPA for approval on a case -by case basis. 11 Projections of demand - side EE impacts must reflect the annual impacts that occur due to new EE impacts in a given year plus the impacts of EE investments made in previous years that are still generating savings (i.e. , have not exceed their measure life or otherwise ended).10

Several tools and methods are available to help states estimate the potential direct energy impacts of RE and demand - side EE that will be used to adjus t a state’s electricity demand and supply forecast. States can base their electricity savings projections on past evaluation, measurement and verification (EM&V) reports that document impacts of similar projects or programs and that have been submitted to public service commissions or evaluated b y third parties . States also can conduct their own surveys or studies to estimate the direct energy impacts of EE policies and/or use sophisticated methods, such as building simulation tools, vintaging models, production costing , and cost - of - energy analysis models. 13 Any

methodology

used must be EPA - approved, and each state is encouraged to consult with the EPA early during the plan development process about the tools and methods it intends to employ . Assessing the potential RE and demand - side EE impacts of measures, policies or programs can involve bottom - up economic and/or engineering -based estimation techniques that are based up on a representation of the fundamentals of the technology and economic behavior. These bottom - up approaches estimate potential energy savings at a very

detailed level and roll these estimates u

p to the statewide level . Alternatively , the potential impacts of RE and demand - side EE can be quantified based up on the design and requirements of state - level policies and regulations specified over a future time frame. The next two sections describe how a state plan CO 2 performance projection would calculate the generation impacts of two common state policies : energy

efficiency

resource standards and renewable portfolio standards. 

### B. **Projecting Energy Savings from Energy Efficiency Resource Standards**  Many states have adopted an EERS , which is a policy that sets targets for energy savings over a specified time frame from demand - side EE programs operated b y utilities or other

12 States can use location - specific capacity factors informed by historical data or weather-prediction models, such as the kind of data included in the National Renewable Energy Laboratory (NREL) Eastern Wind Integration and Transmission Study (EWITS) or in publicly available datasets from EIA and ISO/RTOs. 13 For more information about available tools for estimating direct energy impacts, see Assessing the Multiple Benefits Guide: A Resource for States, Chapter 2. http://www.epa.gov/statelocalclimate/documents/pdf/epa_assessing_benefits_ch2.pdf#page=4 .11

program administrators . States ty picall y specify

annual first - year or cumulative targets as percentages of electricity sales or as absolute energy savings . States use different bases for specifying EERS goals. S ome specify goals based on electricity sales (in MWhs) from investorowned utilities, whil e others have mandated en ergy savings targets based on total MWh sales or a subset of total MWh sales. A state can quantify

energy savings for use in its CO 2 emission performance projection using formulas specific to the state’s EERS, as described below . 14 For this approach a state would select the appropriate sales basis , and if the basis is not total sales, a state could u se recent year utility- level sales data from EIA and growth rates from EIA, an RTO/ISO, NERC or other publicl y available sources to deve lop base case forecasts of sales of the utilities required to comply with the EERS . The design of the EERS will affect whether a state , for purposes of its CO 2 emission performance projection, should assume energy savings at the EERS target level. For example, it may

not be appropriate to assume full achievement of energy saving s goals in an EERS that includes cost/rate caps or other design features that may not lead to incremental energy savings relative to the base case demand forecast (e.g., counting savings from building energy codes or historical EE programs) . The general formulas a state must use to quantify first - ye ar and cumulative energy savings from an EER S policy

for each year (t) are shown below , though a state may propose a different methodo logy

if it better represents the state’s EER S. Once these first - year and cumulative energy savings are quantified, the state can incorporate them into the demand forecast of its CO 2 emission performance projection . 15 **1. EERS with Annual First** **- Year EE**  **Savings Targets Specified in Percent Terms** A(t) = r(t) Z(t - 1)

14 Savings from purely voluntary demand - side EE goals are not state enforceable measures and are not eligible for use in a CO 2 emission performance projection for a state measures type plan . Quantification of eligible demand - side EE savings must be done in accordance with the requirements specified in s ection VIII.K.3 of the preamble. 15 These equations are the recommended calculation methods for inclusion in state plans.12

C(t) = A(t) + A(t - 1) + ... + A(t - L+1) I(t) = C(t) - E(t) Z(t) = B(t) - I(t) Where: r is the annual first - ye ar percent savings target, A is the annual first - ye ar energy savings, L is the measure lifetime, B is the base case sales of utilities affected by these specific policies, C is the cumulative energy savings, E is the cumulative savings embedded in the base case demand forecast, 16 I is the cumulative savings incremental to the base case demand forecast, and Z is the adjusted sales after application of cumulative incremental savings. For example, a representative state has an EERS first - ye ar target of 2% for each ye ar 2016 - 2018. Fo recasted baseline sales of utilities in 2015 are 50 ,000 GWh, growing b y 1% each year of the forecast period . Embedded savings are 50 GWh per year, applied to years in which first - year energy savings are realized. The measure lifetime assumed for both first - ye ar energy savings and embedded savings is four years. Table 1 applies this example to the above formula . This is the most common ty

pe of EERS structure, and states having adopted some version of it include Arizona, Illinois, Indiana, and Ohio. Table 1: Example EERS with Annual First- Year EE Savings Targets Specified in Percent Terms **2015 2016**  **2017 2018**  **2019 2020**  **2021 2022**  Baseline sales of affected utilities (GWh) (B) 50,000.0 50,500.0 51,005.0 51,515.1 52,030.2 52,550.5 53,076.0 53,606.8 First - year percent savings target (%) (r) 2% 2% 2% First - year energy savings (GWh) (A) 1,000.0 991.0 982.3 Cumulative energy savings (GWh) (C) 1,000.0 1,991.0 2,973.3 2,973.3 1,973.3 982.3 0.0 Cumulative savings embedded (GWh) (E) 50.0 100.0 150.0 150.0 100.0 50.0 0.0 Cumulative savings incremental (GWh) (I) 950.0 1,891.0 2,823.3 2,823.3 1,873.3 932.3 0.0

16 States using the AEO 2013 forecast should take note that it does not include state - level EE RS policies but does embed EE savings of policies. These EE savings are not reported out by the AEO and need to be determined independently.13

Adjusted sales after incremental savings (GWh) (Z) 50,000.0 49,550.0 49,114.0 48,691.8 49,206.9 50,677.2 52,143.7 53,606.8 **2. EERS with Annual First** **- Year EE Savings Targets Specified in Absolute Terms**  C(t) = A(t) + A(t - 1) + ... + A(t - L+1) I(t) = C(t) - E(t) Z(t) = B(t) - I(t) Where: A is the annual first - ye ar energy savings target, L is the measure lifetime, B is the base case sales of utilities affected by these specific policies, C is the cumulative energy savings, E is the cumulative savings embedded in the base case demand forecast , I is the cumulative savings incremental to the base case demand forecast , and Z is the adjusted sales after application of cumulative incremental savings. For example, a representative state has an EERS first - ye ar target of 1,000 GWh in 2016, increasing b y about 2% per year for 2017 - 20 18 to the absolute targets shown in the table below . Forecasted baseline sales of utilities in 2015 are 50 ,000 GWh, growing b y 1% each ye ar of the forecast period . Embedded savings are 50 GWh per year, applied to ye ars in which first - ye ar energy savings are realized. The measure lifetime assumed for both first - ye ar energy savings and embedded savings is four years. Table 2 applies this example to the above formula. S tates that applied this structure during their EERS implementation include Florida, Iowa, and Oregon. Table 2: Example EERS with Annual First- Year EE Savings Targets Specified in Absolute Terms **2015 2016**  **2017 2018**  **2019 2020**  **2021 2022**  Baseline sales of utilities (GWh) (B) 50,000.0 50,500.0 51,005.0 51,515.1 52,030.2 52,550.5 53,076.0 53,606.8 First - year energy savings (GWh) (A) 1,000.0 1,020.0 1,040.4 Cumulative energy savings (GWh) (C) 1,000.0 2,020.0 3,060.4 3,060.4 2,060.4 1,040.4 0.0 Cumulative savings embedded (GWh) (E) 50.0 100.0 150.0 150.0 100.0 50.0 0.0 Cumulative savings incremental (GWh) (I) 950.0 1,920.0 2,910.4 2,910.4 1,960.4 990.4 0.0 Adjusted sales after incremental savings (GWh) (Z) 50,000.0 49,550.0 49,085.0 48,604.7 49,119.8 50,590.1 52,085.6 53,606.8 **3. EERS with Cumulative EE Savings Targets Specified in Percent Terms** 14

A(t) = C(t)

– C(t - 1) + A(t - L) If r(t) available, C(t) = r(t) x B(t) I(t) = C(t) - E(t) Z(t) = B(t) - I(t) If r(t) not available, Z(t) calculated b y interpolation I(t) = B(t) - Z(t) C(t) = I(t) + E(t) Where: r is the cumulative percent savings target, A is the annual first - ye ar energy savings, L is the measure lifetime, B is the base case sales of utilities affected by these specifi c policies, C is the cumulative energy savings, E is the cumulative savings embedded in the base case demand forecast , I is the cumulative savings incremental to the base case demand forecast , and Z is the adjusted sales after application of cumulative incremental savings. For example, a representative state has an EERS cumulative target of 5% by 2018. Forecasted baseline sales of utilities in 2015 are 50 ,000 GWh, growing b y 1% each ye ar of the forecast period . Embedded savings are 50 GWh per year, app lied to years in which first - ye ar energy savings are realized. The measure lifetime assumed for both first - ye ar energy savings and embedded savings is four years. Distribution of the cumulative energy savings in 2018 to the first - ye ar savings for 2016 - 20 18 applies the simple assumption of an equal share of savings being implemented in each year. Table 3 applies this example to the above formula. This approach is uncommon, and states that applied this structure in earlier ye ars of their EERS policies include New York and Penns ylvania. Table 3: Example EERS with Cumulative EE Savings Targets Specified in Percent Terms **2015 2016**  **2017 2018**  **2019 2020**  **2021 2022**  Baseline sales of utilities (GWh) (B) 50,000.0 50,500.0 51,005.0 51,515.1 52,030.2 52,550.5 53,076.0 53,606.8 Cumulative percent savings target (%) (r) 5% First - year energy savings (GWh) (A) 858.6 858.6 858.6 Cumulative energy savings 858.6 1,717.2 2,575.8 2,575.8 1,717.2 858.6 0.015

(GWh) (C) Cumulative savings embedded (GWh) (E) 50.0 100.0 150.0 150.0 100.0 50.0 0.0 Cumulative savings incremental (GWh) (I) 808.6 1,617.2 2,425.8 2,425.8 1,617.2 808.6 0.0 Adjusted sales after incremental savings (GWh) (Z) 50,000.0 49,691.4 49,387.8 49,089.3 49,604.4 50,933.3 52,267.4 53,606.8 **4. EERS with Cumulative EE Savings Targets Specified in Absolute Terms**  A(t) = C(t)

– C(t - 1) + A(t - L) If C(t) available, I(t) = C(t) - E(t) Z(t) = B(t) - I(t) If C(t) not available, Z(t) calculated b y interpolation I(t) = B(t) - Z(t) C(t) = I(t) + E(t) Where: C is the cumulative energy savings target, A is the annual first - ye ar energy savings, L is the measure lifetime, B is the base case sales of utilities affected by these specific policies, E is the cumulative savings embedded in the base case demand forecast , I is the cumulative savings incremental to the base case demand forecast , and Z is the adjusted sales after application of cumulative incremental savings. For example, a representative state has an EERS cumulative target of 3,000 GWh by 2018. Forecasted baseline sales of utilities in 2015 are 50 ,000 GWh, growing b y 1% each ye ar of the forecast period . Embedded savings are 50 GWh per year, applied to ye ars in which first - ye ar energy s avings are realized. The measure lifetime assumed for both first- ye ar energy savings and embedded savings is four years. Distribution of the cumulative energy savings in 2018 to the first - ye ar savings for 2016 - 20 18 applies the simple assumption of an equal share of savings being implemented in each year. Table 4 applies this example to the above formula. States that utilized this structure during EERS implementation include California and Hawaii. Table 4: Example EERS with Cumulative EE Savings Targets Specified in Absolute Terms16 **2015 2016**  **2017 2018**  **2019 2020**  **2021 2022**  Baseline sales of utilities (GWh) (B) 50,000.0 50,500.0 51,005.0 51,515.1 52,030.2 52,550.5 53,076.0 53,606.8 First - year energy savings (GWh) (A) 1,000.0 1,000.0 1,000.0 Cumulative energy savings (GWh) (C) 1,000.0 2,000.0 3,000.0 3,000.0 2,000.0 1,000.0 0.0 Cumulative savings embedded (GWh) (E) 50.0 100.0 150.0 150.0 100.0 50.0 0.0 Cumulative savings incremental (GWh) (I) 950.0 1,900.0 2,850.0 2,850.0 1,900.0 950.0 0.0 Adjusted sales after incremental savings (GWh) (Z) 50,000.0 49,550.0 49,105.0 48,665.1 49,180.2 50,650.5 52,126.0 53,606.8 

### C. **Projecting Generation Impacts from Renewable Portfolio Standards**  Renewable p ortfolio s tandards (RPS) set RE targets across a specified period of time. Most often these state policies require that a specified percentage of the electricity sold by a utility come s from eligible renewable technologies (e.g., wind, solar, biomass ). A state must project annual RE generation using formulas specific to the state’s RPS policy , as shown below , or an EPA - approved alternative method . Similar to the EERS approach described above, a state would select the appropriate sales basis , and if the basis is not total sales, a state could use a recent year of utility- level sales data from EIA and growth rates from EIA, an RTO /IS O, NERC or other public ly available sources to develop base case forecasts of sales for the utilities required to comply with the RPS. The design of the RPS will affec t whether a state can assume full achievement of the RPS targets. Many RPS s include p rovisions for alternative c ompliance p ay ments, which allow an entity to pay a fee in lieu of achieving the RPS . Additionally, RPS policies may include sources that are not eligible for CPP compliance purposes. In such case s , only generation impacts from eligible RE sources should be included in a state’s CO 2 emission performance projection . 17

17 States must represent measures in their projection in accordance with the accounting and eligibility requirements specified in the guidelines , irrespective of the ways that RE is counted under a state requirement , such as an RPS , or for the purposes of renewable energy certificates. For example, some RPS apply an adjustment factor or allow credit different from the accounting method included in the final17

The general formulas a state must use to quantify annual generation impacts of an RPS policy for each year (t) are shown below , though a state may propose a different methodology if it better represents the state’s RPS. 18 **1. RPS with Annual Targets Specified in Percent of Retail Sales Terms**  A(t) = r(t) x Z(t)

– MWh ineligible (t) Where: A is the total MWh of CPP - qualifying renewable generation in a future ye ar, r is the annual RPS percent RE target, Z is the forecasted electricity sales that fall under the RPS policy, and MWh ineligible is the MWh of generation from technologies ineligible under CPP (t) is the year Note that each state should apply this general formula to its specific RPS design . For example, if a specified percentage of RE is required to be met by sales from investor- owned utilities and separate percentage from rural cooperatives, then the state should appl y the formula individually for each class and sum the MWh for each to calculate A(t) . Additionall y, each state plan should estimate the MWh of generation from any technologies that fall within the scope of its RPS but outside CPP . This estimate (MWh ineligible) can be based on planned and/or expected new capacities for these technologies and best - available historical capacity factors. For example, take a representative state with an RPS target of 10% in 2015, rising to 15% by 2020. Forecasted sales in 2015 are 75,000 gigawatthours (GWh), growing in subsequent ye ars. In 2015, the renewable requirement is 7,500 GWh, but 500 GWh of the RPS is met by technologies ineligible under the final CPP. As a result, eligible renewable generation is 7,000 GWh in 2015. Assuming the same MWh ineligible in future ye ars, the state would predict 11,500 rule. RE generation from purely voluntary RE goals are not state enforceable measures and are not eligible for use in a performance projection for a state measures plan type. Similarly, alternate compliance payments , state - level bonuses for RE and RE resources permitted under an RPS that are not permitted under the final guidelines are not eligible to demonstrate performance in a projection. For more information about acceptable accounting methods for RE, see s e ction s VIII.K.1 and VIII.K.2 of the preamble. For information about addressing interactions between renewable energy credits (RECs) and emission rate credits, see s ection VIII.K.2. 18 The equations in this section are the recommended calculation methods for inclusion in state plans.18

GWh of CPP eligible renewables in 2020. The table below applies this example to the above formula. This is a common ty pe of RPS policy . States with structures similar to this include Massachusetts, North Carolina, and Illinois. Table 5 : Example Qualifying Renewable Energy Calculation **2015 2016**  **2017 2018**  **2019 2020**  RPS Target (%) 10% 11% 12% 13% 14% 15% Forecast Sales (GWh) 75,000 76,000 77,000 78,000 79,000 80,000 Required RE Gen (GWh) 7,500 8,360 9,240 10,140 11,060 12,000 CPP Ineligible RE Gen (GWh) 500 500 500 500 500 500 CPP Eligible RE Gen (GWh) 7,000 7,860 8,740 9,640 10,560 11,500 **2. RPS with**  **Annual Targets Specified in Absolute Capacity Terms (MW)** RPS policies often include technology “carve - outs” that specify required contributions of particular technology

ty pes . Even where these carve - outs are not specified, many different technologies can contribute to meet an RPS . The formula below illustrates three different technologies contributing to an RPS . The distinction between technology ty pes is important in calculating RE generation from a capacity-based RPS due to the differences in the underl ying operating characteristics of technologies (i.e., capacity factors) . Each state should include the number of technology

types that best applies to its own RPS design . In this example , each technology

(1, 2, and 3) is an eligible RE re source per the final emission guidelines ; a technology that is not an eligible RE resource should not be included in the formula . 19 A(t) = [ f

1 (t) x C 1 (t) + f

2 (t) x C 2 (t) + f

3 (t) x C 3 (t) ] x H Where: A is the total MWh of eligible renewable generation in a future year,

19 For more information about what renewable resources are eligible under the emissions guidelines, see s ection VIII.K.3 .19

f 1 , f 2 , and f

3 are annual effective capacity factors for eligible technologies 1, 2, and 3, respectivel y, and C 1 , C 2 , and C 3 are the eligible renewable capacities for technologies 1, 2, and 3, respectivel y (in MW). H is hours in the year (8760, except leap years) For example, take a representative state with a target of 10,000MW of wind capacity in 2015, and 100MW of solar energy . The state average wind capacity factor is 28.4% and the state average solar capacity factor is 18.7%. Wind would be expected to produce 24,878,400 MWh of energy (10,000MW x 8760 hours x 28.4%), while solar would produce 163,812 MWh (100MW x 8760 hours x 18.7%). In total the state would estimate 25,042,212 MWh of renewable generation. Table 6 applies this example to the above formula. States with structures similar to this include Texas and Io wa . Some states have overall targets specified as a percentage of sales with a specific carve out for some amount (MWh) of wind or solar energy . In order to avoid double counting, the amount of the carve - out would not be added to the overall target, as it represents a subset of the overall generation (MWh) requirement. Table 6 : Example Qualifying Renewable Energy Calculation (Capacity Target) **2015 2016**  **2017 2018**  **2019 2020**  Wind Target (MW) 1,000 1,100 1,200 1,300 1,400 1,500 Wind Capacity Factor (%) 28.4% 28.4% 28.4% 28.4% 28.4% 28.4% Wind Energy (GWh) 2,488 2,737 2,985 3,234 3,483 3,732 Solar Target (MW) 150 175 200 225 250 275 Solar Capacity Factor (%) 18.2% 18.2% 18.2% 18.2% 18.2% 18.2% Solar Energy (GWh) 239 279 319 359 399 438 Total Eligible Gen (GWh) 2,727 3,016 3,304 3,593 3,882 4,170 The above equations are sufficient to calculate the expected annual energy output from renewable energy

associated with an RPS. As previously noted, annual capacity factor projections vary

by technology

and even within a technology category , depending on the location of the resource. As a result, if annual energy projections are based on aggregate capacity factors,20

a state should specify

the location of all assumed RE generation and the source of capacity factor projections. 

### D. **Documentation of Assumptions Related to RE**  **and Demand -**  **S ide**  **EE in a**  **State Plan CO 2**  

### Performance **Projection** As explained above, s tates that plan to use RE and demand - side EE policies and programs under a state plan approach that requires a demonstration using projections (as described in s ection VIII.D.2.a.(3)(a) of the preamble) must quantify for each requirement and program the expected direct electricity savings and generation impacts on affected EGUs. Further, the state plan performance demonstration must document the underl ying assumptions regarding the renew able electricity generation and electricity savings impacts on affected EGUs. Specific considerations and assumptions that should be described in the state plan, when relevant, include the following:

 Energy e

fficiency and/or renewable energy requirement or p rogram description: What is the requirement/program? Who will administer it? Does it require new legislation or can a state implement it under existing authorities?  Requirements/

p rogram time period: What year does each specific program start and end? What are the MWh target s for the interim and final performance periods, including interim steps and other applicable time periods ? How will the program meet the permanent requirement s in the CPP ?  Relationship to complementary or existing requirement/program(s): Will this program include any fed erally

enforceable limits ? Are state measure s an expansion or extension of existing requirement s or program s ? I f the plan includes an expansion of an existing program that allows for ineligible resources or unaffected EGUs , how does the demonstration address the se impacts?  Program focus: What sector or consumer ty pe is the focus of the measure?  Anticipated compliance or penetration rate: Which affected EGUs will be subject to the RE and demand - side EE policy or program ? Does the projection reflect characteristics of applicable RE technologies and demand - side EE programs and policies ?  Technology performance: What technologies and/or generating capacity will be implemented? For h ow many years will th e measure achieve electricity savings (e.g. ,21

measure life) or generate renewable electricity? How will measure performance degrade or decay over time?  Transmission and distribution (T &D ) loss: Is there an expected increase or decrease in T&D losses that w ould require adjustment of the energy savings estimate?  Resource Costs: What is the cost of saved and/or generated electricity for the RE and demand - side EE measures?  Program Costs and Funding: What is the cost of the program, including administration costs? How it be funded?  Resource availability: Are the projected electricity savings or renewable electricity generation levels realistic compared to the potential available?