## From: Culligan, Kevin [mailto:Culligan.Kevin@epa.gov]

Sent: Thursday, May 29, 2014 9:14 PM

To: Higgins, Cortney

Cc: Wayland, Robertj

Subject: FW: several more TSDs

Cortney – The Existing State Plans TSD is actually now an appendix to state plan considerations –

we sent you a version with edits on 5-16. Will make sure it is correctly referenced in the preamble.

From: Culligan, Kevin

Sent: Friday, May 16, 2014 5:49 PM

To: Higgins, Cortney

Cc: Wayland, Robertj

Subject: several more TSDs

Note – we’ve also included a memo on efficiency improvements on oil/gas referenced in the new

version of the preamble. I am still tracking down the Treasury contact.

<< State Plan Considerations TSD_04-10-14_InteragencyCommentsUnderEO12866_EPA

Responses.docx >>

<< State Plan Considerations TSD-A ppendix_04-03-14_InteragencyCommentsUnderEO12866

__revised by EPA 5-16-14.docx >>

<< TechMemo - HRI at OilGas NGCC CT 05162014.docx >>

<< GHG Abatement Measures Chapter 5 TS D Interagency Comments EPA Response May 15

PM.docx >>

### Technical Support Document (TSD) for

Carbon Pollution Guidelines for Existing Power Plants:

Emission Guidelines for Greenhouse Gas Emissi ons from Existing Stationary Sources: Electric

Utility Generating Units

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



# State Plan Considerations

U.S. Environmental Protection Agency

Office of Air and Radiation

June 2014

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2 Table of Contents

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

II. Description of State Plan Pathways .................................................................................4

A. Direct Emission Limits .............................................................................................6

B. Portfolio Approach ....................................................................................................8

III. Enforceability Considerations under Different State Plan Scenarios .........................12

A. Parties Regulated by the State other than Affected EGUs ......................................13

B. Private or Public Third-Party Entity not Regulated by the State ............................15

C. State Agency, Authority, or Entity ..........................................................................16

D. Multi-State Approaches

............................... ........................ ........................ ...........17

IV. Incorporating End-Use Energy Efficiency and Renewable Energy Programs and

Measures under a Rate-Based Approach ......................................................................19

A. Concept of Adjusting EGU CO 2 

### Emission Rates based on the Effects of End-Use

Energy Efficiency and Renewable Energy ..................................................................19

B. Approaches for Adjusting EGU CO 2 

### Emission Rates ............................................20

C. Methods and Tools for Quantifying Avoided CO 2 

### Emissions from End-Use Energy

Efficiency and Renewable Energy .........................................................................23

V. Quantification, Monitoring, and Verifica tion of End-Use Energy Efficiency and

Renewable Energy Programs and Measures .................................................................32

A. Quantification, Monitoring, and Verification for End-Use Energy Efficiency .....35

B. Quantification, Monitoring, and Verification for Renewable Energy Measures ...45

VI. Reporting and Recordkeeping for End-Use Energy Efficiency and Renewable

Energy Programs and Measures ....................................................................................71

A. Reporting for End-Use Energy Efficiency Programs and Measures .....................72

B. Reporting for Renewable Energy Programs and Measures ...................................74

VII. Treatment of Interstate Emission Effects ......................................................................79

A. Background ............................................................................................................81

B. Summary of Possible Approaches for Treat ment of Interstate Emission Effects..83

VIII. Appendix ...................................................................................................................... .....92

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3 I. Introduction

As discussed in the preamble, in section VIII.F, the EPA is proposing to give states broad

discretion to develop state plans that best su it their circumstances and policy objectives. In

developing its plan, a state will need to make a number of decisions th at will require careful

consideration, in order to ensure that its plan both meets a state’s policy objectives and is

approvable by EPA. The preamble, in section VIII. F, identified several key decision points and

factors that states should consider when developi ng their plans. In this section of the preamble,

the EPA also raised a number of considerations for how it will apply the proposed general plan

approvability criteria to different types of state plan approaches. This includes a number of

considerations related to appropriate approaches, methods, and materials that are submitted for

state plan components in an approvable plan, under different types of state plans. This technical

support document (TSD) explains and discusses these considerations in depth, and elaborates

options proposed in the preamble where relevant. Topics addressed in this TSD include:

 Description of state plan pathways

o Provided as context for the discussion of applied considerations that follows

 Enforceability considerations un der different plan scenarios

o Summary of potential enforceable mechanisms under different state plan

scenarios

 Incorporating energy efficiency and re newable energy (EE/RE) requirements and

programs in state plans, including:

o Options for adjusting EGU CO 2 

### emission rates based on the effect of EE/RE

requirements and programs

o Methods for estimating avoided CO 2 

### emissions that result from EE/RE

requirements and programs , for use in projecting emission performance under a

state plan and in ex post adjustment of CO 2 

### emission rates during plan

implementation

 Quantification, monitoring, and verification of EE/RE requirements and programs

o Survey of quantification, monitoring, and verification under existing state and

utility EE/RE requirements and programs Comment [A1]: 1. This TSD should echo the

emphasis in the preamble on the role of the

ISO/RTO or other planning authority with respect

to understanding the system/reliability

implications of a state plan design on the grid and

about including “guidance” to states about

utilizing flexibility to address events (including

retirements) that may occur during the

compliance period while ensuring grid reliability.

EPA response:

A companion TSD, Projecting EGU CO 2 Emission

Performance in State Plans, addresses the

potential role of ISO/RTO in facilitating state plan

development in a respective grid region, including

the role that an ISOs/RTOs could play in providing

analytical support to states during plan

development.

Deleted:

Comment [A2]: The discussion of avoided CO 2

calculations conflates two separate issues:

their use as part of an ex ante determination of

whether a state plan (such as a portfolio approach)

will achieve a given level of emissions (or emissions

rate) as part of an equivalency determination and

their use ex post to ‘adjust’ emissions. It would help

clarify things if this distinction were discussed. With

regards to the ex post use, all emissions are already

measured at the stack, so an avoided emissions

calculation is unnecessary and would constitute

double counting of emissions reductions.

EPA response:

We have added text here in response to this

comment. The referenced TSD section addresses

both considerations.

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4 o Discussion of possible approaches for minimum requirements or guidance for

quantification, monitoring, and verification for EE/RE requirements and programs

included in state plans, building off exis ting state processes and infrastructure

o Discussion of areas where supplement al information would be useful for

estimating avoided CO 2 

### emissions from EE/RE requirements and programs

 Reporting and recordkeeping for responsible parties subject to EE/RE requirements or

implementing EE/RE programs included in state plans

o Survey of reporting and recordkeeping under existing state and utility EE/RE

requirements and programs

o Discussion of possible approaches for EE/RE reporting requirements for state

plans, building off existing state processes and infrastructure

o Discussion of areas where supplement al information would be useful for

estimating avoided CO 2 

### emissions from EE/RE requirements and programs

 Treatment of interstate emission effects

o Further elaboration of proposed approaches and alternatives discussed in the

preamble

It should be noted that the preamble discusses, and solicits comment on, legal issues

concerning whether CAA section 111(d) authorizes some of the types of state plans described in

this TSD and whether it authorizes state plans to include some of these types of measures. This

TSD does not further discuss those legal issues, but solely for the purpose of describing all of the

available types of state plans and measures, assu mes that all of them are authorized under CAA

section 111(d).

II. Description of State Plan Pathways

As discussed in the preamble, the EPA is proposing a state plan approach that could

accommodate a diverse set of state requirements, programs, and measures, through two basic

approaches – direct emission limits and a po rtfolio approach. These two basic approaches

provide four distinct state plan “pathways” under CAA section 111(d). These pathways include:

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5  Rate-based CO 2 

### emission limits applied to affected EGUs;

 Mass-based CO 2 

### emission limits applied to affected EGUs;

 A state-driven portfolio approach

 A utility-driven por tfolio approach

Under this flexible approach, a state plan coul d include a combination of measures that reduce

CO 2 

### emissions at affected EGUs through the appli cation of emission limits as well as measures

that involve actions within the interconnected electri city system that reduce utilization at affected

EGUs and thereby avoid EGU CO 2 

### emissions. Examples of these latter measures include, among

others, end-use energy efficiency resource standa rds and renewable energy portfolio standards,

as well as certain components of utility integrated resource plans. A state could either rely solely

on CO 2 

### emission limits that are enforceable against affected EGUs or, alternatively, rely on a

portfolio approach, which would include those limits as well as other enforceable measures.

Table 1 provides practical examples of possibl e state plan approaches under each of these

pathways, which are discussed in more detail below.

This section elaborates the different plan appr oaches that are discussed in section VIII.B

of the preamble, to provide context for the applied considerations that are discussed throughout

the remainder of the TSD. In particular, some of these considerations apply to different types of

state plan approaches. For example, section III of this TSD addresses enforceable legal

mechanisms that might apply under different types of state plans, such as utility- and state-driven

portfolio approaches, which would apply enforceable obligations to different entities. Section IV

of this TSD applies to state pl ans that implement rate-based CO 2 

### emission limits for affected

EGUs that also provide for the adjustment of CO 2 

### emission rates based on the effect of

enforceable end-use energy efficiency and renewable energy requirements and programs that are

incorporated in a plan. Section V of this TSD addresses quantification, monitoring, and

verification of end-use energy efficiency an d renewable energy programs and measures.

Considerations addressed in this section woul d apply to states implementing a rate-based

emission limit approach that provides for adjustment of CO 2 

### emission rates based on the effect of

end-use energy efficiency and renewable energy, as well as states implementing utility- or statedriven portfolio approaches that incorporate en d-use energy efficiency and renewable energy

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6 requirements and programs. Likewise, section VI of this TSD addresses considerations for

reporting and recordkeeping for end-use energy efficiency and renewable energy requirements

and programs, which would apply for these types of state plans.

Table 1. Different Illustrative Plan Approaches

A. Direct Emission Limits

The first basic state plan approach is CO 2 

### emission limits that apply directly to affected

EGUs, and includes two pathways: 1.) rate-based CO 2 

### emission limits applied to affected EGUs;

and 2.) mass-based CO 2 

### emission limits applied to affected EGUs. For both types of emission

limits, end-use energy efficiency and rene wable energy measures that avoid EGU CO 2 

### emissions

could be a major component of a state’s overall strategy for cost-effectively reducing EGU CO 2



### emissions.

1. Rate-Based CO 2 

### Emission Limits Applied to Affected EGUs Comment [A3]: It would be helpful to have the

equivalent formula for rate

‐ based equivalency. In

particular, all of the above considerations apply to

the numerator of this formula. In addition, the

contributions to the denominator should be

specified. Specific questions include:

 Does generation from new NGCC units

count? New nuclear or CCS?

 What amount of generation from existing

clean units (including non

‐ hydro RE, hydro,

nuclear, etc.) is included in the denominator?

How are uprates dealt with?

 Is the EE generation included in the

denominator gross savings or net savings?

EPA response:

This topic is addressed in a companion TSD,

Projecting EGU CO 2 Emission Performance in State

Plans. We have made revisions in that TSD in

response to similar comments in that document.

The referenced section in this TSD describes

different types of possible state plans.

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7 Rate-based emission limits would apply a lb CO 2 

### /MWh emission limit to affected EGUs.

Depending on a state’s approach, compliance flex ibility could be provided through different

mechanisms, such as averaging among affected sour ces, or the use of tradable credits for avoided

CO 2 

### emissions resulting from end-use energy effi ciency and renewable energy measures as

discussed below. In the case of the latter approach, such credits could be used by an affected

EGU to adjust its CO 2 

### emission rate when demonstrating compliance with a rate-based emission

limit. 1 

### Rate-based emission limits could be implemen ted on a state-by-state basis, or through

multi-state averaging or trading. Rate-based em ission limits might also be a component of a

portfolio approach (described below), where the emission rate limit would not assure full

achievement of the required leve l of emission performance specified for affected EGUs in the

state plan, in which case the emission limit would be supplemented with other enforceable

measures.

Rate-based emissions limits could incorporat e end-use energy efficiency and renewable

energy measures that avoid EGU CO 2 

### emissions, through an administrative adjustment by the

state or tradable crediting system. 2 

### These adjustment credits could be used by an affected EGU to

comply with the rate-based emission li mit, by adjusting the unit’s reported CO 2 

### emission rate. 3



### Under this approach, end-use energy efficiency and renewable energy measures that avoid EGU

CO 2 

### emissions would be enforceable components of a state plan. These actions would need to be

enforceable components of a state plan to pr ovide assurance that a sufficient amount of

adjustment credits will be available to facilita te EGU compliance with the emission rate limit,

1 Section IV of this TSD discusses possi ble approaches for such adjustments.

2 Under a tradable credit system, a state would issue adjustment credits based on avoided CO 2 emissions achieved

through end-use energy efficiency and renewable ener gy measures. EGUs could then apply these tradable

adjustment credits when demonstrat ing compliance with a rate-based CO 2 emission limit. These tradable credits

might be issued to affected EGUs for free or through pur chase. Alternatively, end-use energy efficiency and

renewable energy actions undertaken by private parties (i ncluding EGU owners and operato rs) might be eligible for

the issuance of adjustment credits. Under an administra tive adjustment approach, a state program administrator

might administratively adjust an affected EGU’s CO 2 emission rate based on avoided CO 2 emissions achieved

through state end-use energy efficiency and renewabl e energy deployment programs or utility compliance with

energy efficiency resource standards or renewable energy portfolio standards. These adjustments might be

apportioned to affected EGUs based on a predetermined me tric. Affected EGUs would then begin with an adjusted

CO 2 emission rate when considering additional actions necessary for compliance with the rate-based CO 2 emission

limit.

3 Section IV of this TSD discusses po ssible approaches for such adjustments.

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8 and that end-use energy efficiency and renewable energy measures that generate adjustment

credits are properly quantified, monitored, and verified. 4



### 2. Mass-Based CO 2 

### Emission Limits Applied to Affected EGUs

Mass-based emission limits would apply either an individual limit on CO 2 

### tons emitted

from an affected EGU or establish a finite CO 2 

### emissions budget for a group of affected EGUs.

The latter approach is typically implemented through a tradable allowance system.

With mass-based emission limits, end-use en ergy efficiency measures that avoid EGU

CO 2 

### emissions could be a major component of a state’s overall strategy for cost-effectively

reducing EGU CO 2 

### emissions, but would be complementary to the enforceable state plan ( i.e.

,

not included as enforceable measures in a state pl an). These actions could be used to help a state

cost-effectively achieve the CO 2 

### emissions limits, or to achieve other policy goals, but CO 2



### emissions performance would be assured through the enforceable limit on mass emissions from

affected EGUs.

B. Portfolio Approach

The second basic state plan approach uses a portfolio of actions, in which a state plan

includes multiple programs and measures that are designed to achieve either a rate-based or

mass-based emissions performance goal for affected EGUs. This approach includes two

pathways: 1.) a state-driven portfolio approach; and 2.) a utility-driven portfolio approach. A

portfolio approach would include emission limits for affected EGUs along with other enforceable

end-use energy efficiency and renewable energy measures that avoid EGU CO 2 

### emissions. A

portfolio approach could be state-driven or utility-driven, dependi ng on the utility regulatory

structure in a state.

In general, a portfolio approach is distinguished from an emission limit approach by the

fact that achievement of the full level of required emission performance for affected EGUs

4 Section V of this TSD addresses considerations for quant ification, monitoring, and verification of end-use energy

efficiency and renewable energy measures. Comment [A4]: It would be very helpful to have

the explicit formula that is the basis for determining

mass

‐ based equivalence. In particular, the precise

list of sources whose emissions count needs to be

specified. Specific questions include:

 Do emissions from new NGCC units count?

 Do emissions from new CCS units count?

 Do emissions from units with a capacity

factor of less than 1/3 or other uncovered

sources count?

 How will biomass emissions be incorporated

when the final guidance on this is available?

EPA response:

This topic is addressed in a companion TSD,

Projecting EGU CO 2 Emission Performance in State

Plans. We have made revisions in that TSD in

response to similar comments in that document.

The referenced section in this TSD describes

different types of possible state plans.

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9 specified in the plan is not ensured through the ap plication of direct emissi on limits that apply to

affected EGUs.

A portfolio approach would include both dir ect emission limits that apply to affected

EGUs and other indirect measures that avoid EGU CO 2 

### emissions. Under a portfolio approach,

end-use energy efficiency and renewable energy measures that avoid EGU CO 2 

### emissions would

be enforceable components of a state plan. This would be necessary because the emission limit

applied directly to affected EGUs would not as sure full achievement of the required level of

emission performance specified in the state plan. 5



### As discussed below, due to differences in st ate utility regulatory st ructure, a portfolio

approach implemented in a restructured stat e with retail competition will likely look quite

different from one implemented in a state with ve rtically integrated, regulated electric utilities.

This includes the process for developing the portfolio approach, the mechanisms for

implementing it, the responsible parties, and the regulatory and legal relationships among parties

and state regulators.

1. State-Driven Portfolio Approach

A state-driven portfolio approach – rather than a utility-driven approach – is more likely

to be adopted in a state with a restructured el ectricity sector. In these states, rate-regulated

electric utilities have typically divested electric generation assets and there is often also retail

competition where non-utility entities can s upply retail customers with electricity. 6 

### Electric

distribution utilities in these states typically purchase electricity from competitive wholesale

markets. As a result, utilities in these states ty pically do not engage in a utility integrated

resource planning (IRP) process. IRP processes, which are a natural fit for implementing a

5 Under a portfolio approach, either a rate-based or mass- based emission limit might be applied. Such plans might

also include application of a direct emission limit to a subset of affected EGUs. Both scenarios would necessitate

inclusion of supplemental measures, such as end-use energy efficiency and renewable energy, or other measures that

directly apply to affected EGUs (e.g., repowering or retirement of one or more high-emitting EGUs), in order to

achieve the required level of emission performance for affected EGUs specified in the state plan.

6 In some restructured states, the holding company that ow ns the distribution utility may also own generating assets

which are operated on a competitive basis. In these inst ances, there typically are legal provisions in place that

mandate the independent management and opera tion of generation and distribution assets.

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10 portfolio approach, are mu ch more common in states with regu lated vertically integrated utilities

that own and operate electric generating assets.

In restructured states, policies for increasing end-use energy efficiency and renewable

energy are often established throu gh regulations that apply to electric distribution utilities, such

as end-use energy efficiency resource standard s and renewable portfolio standards. Many of

these states have also established independent n on-profit entities to administer end-use energy

efficiency and renewable energy deployment programs funded through regulated electricity

rates.

Under a state-driven portfolio approach a mix of entities might have enforceable

obligations under a state plan. This includes owne rs and operators of affected EGUs subject to

direct emission limits, as well as electric distri bution utilities, private or public third-party

entities, and state agencies or authorities th at administer end-use energy efficiency and

renewable energy deployment programs or are subject to portfolio requirements.

2. Utility-Driven Portfolio Approach

A state with vertically integrated, state-regulate d electric utilities is more likely to adopt a

utility-driven portfolio approach. In such stat es, private utilities own and operate electric

generation, transmission, and distribution system s necessary to supply retail customers with

electricity. 7 

### These utilities are overseen by state public utility commissions that approve utility

capital investments and oversee utility opera tions, and allow utilities to recover approved

investments and operating costs, along with a specifi ed financial rate of return, through regulated

retail electricity rates. State utility commissions often require regulated utilities to engage in an

integrated resource planning (IRP) process, which seeks to identify the least-cost set of resources

available to provide electricity to retail customers over a multi-year period, and often includes

evaluation of measures such as end-use energy efficiency, demand-side management, and

7 In some instances these regulated utilities may supplem ent electric generation with purchases through wholesale

electricity markets and may also sell surplus generati on in these wholesale markets on a competitive basis.

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11 renewable energy. 8 

### Once an IRP is approved by a state public utility commission, a utility’s cost

recovery and rate of return is often linked to identified measures and metrics in the IRP. 9



### Under a utility-driven portfolio approach, a vertically integrated utility would develop

and implement a portfolio of measures designed to meet the rate-based or mass-based emission

performance level for its affected EGUs specified in the state plan. This plan would likely be

developed and approved through an IRP-like process overseen by the state public utility

commission. If there is more than one rate-regulat ed electric utility in the state, the state might

apportion the state emission performance le vel for affected EGUs among utilities.

A utility plan under this approach might rely heavily on end-use energy efficiency and

renewable energy actions, but also might focus on direct actions at af fected EGUs, such as

repowering to fire a lower-carbon fuel or retirement of high-emitting units. Such plans might

also include direct emission limits on affected EGUs, or implementation of an environmental

dispatch approach that incorporates CO 2 

### emission rate into the dispatch protocol used by the

utility to schedule electric generation. 10



### Under a utility-driven portfolio approach, the entire suite of obligations under the plan

would be enforceable against the utility company, which would also be an owner and operator of

affected EGUs. If there are other affected EGUs in the state that are not owned and operated by a

vertically integrated utility, a state plan might need to include other measures that address CO 2



### emission performance by these affected EGUs.

A similar approach could be taken by munici pally owned utilities or utility cooperatives,

which often also engage in an IRP process. However, state public utility commissions (PUCs)

8 Depending on the state, an IRP process may also a ssess factors such as fuel diversity and environmental

performance, among others, when identifying th e least-cost mix of resources for a utility.

9 In some cases, an IRP prescribes or authorizes specific actions. In others, an IRP serves as a guide for the utility

and the public utility commission when evaluating acquisiti on or implementation of specific utility resources or

programs. In such cases, the specific resource or progra m is approved through a PUC order that authorizes or

requires actions and identifies performance obligations. Thes e orders may or may not be fully consistent with

provisions in an IRP.

10 Vertically integrated utilities, even if they operate within the footprint of a competitive wholesale electricity

market, may self-schedule generation assets.

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12 often do not regulate th ese utilities. As a result, implementati on of a portfolio approach by these

entities would introduce practical enforceab ility considerations under a state plan.

III. Enforceability Considerations under Different State Plan Scenarios

As discussed in the preamble of the propos al, in section VIII.F.1, a state plan must

include enforceable measures. To ensure that its plan is enforceable, a state will need to:

 Identify in its plan the entity or entities responsible for meeting compliance and other

enforceable obligations under the plan

 Include mechanisms for demonstrating compliance with plan requirements or

demonstrating that other binding obligations are met

 Provide a mechanism(s) for legal action if aff ected EGUs are not in compliance with plan

requirements or if other entities fail to meet enforceable plan obligations

As discussed in the preamble, responsible entities in an approvable state plan may include

an owner or operator of an affected EGU, other entities with responsibilities assigned by a state,

or the state itself. Other entities might include an en tity that is regulated by the state, such as an

electric distribution utility, or a private or pub lic third-party entity. State responsibility might

include obligations that are assumed directly by a state agency, authority, or other state entity to

carry out aspects of a state plan.

While this approach provides states with broad discretion to develop plans that best suit

their circumstances and policy objectives, assigni ng responsibility to other parties regulated by

the state, private or public third-party enti ties, or state entities raises enforceability

considerations. This section discusses how the general enforceability approach discussed in the

preamble, and described above, might apply in practice under different state plan approaches.

This section describes possible scenarios of responsible entities and legal mechanisms

and approaches that might be used to address en forceability considerations under different types

of state plans. These s cenarios were developed to capture th e range of entities that are currently

implementing end-use energy efficiency and renewa ble energy deployment programs in states, or

are subject to states requirements such as end- use energy efficiency resource standards (EERS)

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13 or renewable portfolio standards (RPS). For each of these examples, this section describes

current legal relationships between these entities and the state, and discusses possible legal

instruments that might provide the state with the authority to ensure that obligations in a state

plan are met and to address failure to meet those obligations. The mechanisms discussed take

different forms, but would specify the three el ements described above: obligations, compliance

demonstration, and enforcement mechanisms. This section also discusses enforceability

considerations in cases where states act jointly through a multi-state plan.

A. Parties Regulated by the State other than Affected EGUs

One likely state plan scenario involves incl usion of enforceable obligations for stateregulated entities other than affected EGUs. An exam ple of a state-regulated entity that is not an

owner or operator of affected EGUs may be an electric distribution utility. 11 

### These entities are

typically regulated by a state public utility commi ssion. An example of an enforceable state plan

measure that might apply to an electric distribution utility is a compliance obligation under a

state end-use energy efficiency resource standard (EERS) or renewable portfolio standard (RPS),

or implementation of incentive programs for the deployment of end-use energy efficiency and

renewable energy technologies.

Another example is where a vertically integr ated, state-regulated utility implements a

portfolio of enforceable actions under a state plan , which may include actions that apply directly

to affected EGUs as well other actions such as end-use energy efficiency and renewable energy

deployment programs. While vertically integrat ed utilities may own and operate affected EGUs,

some of the measures implemented may require different enforceability mechanisms than an

emission limit applied to an affected EGU.

11 Electric distribution utilities are also often referred to as local distribution companies (LDCs). Here we refer to an

electric distribution utility as an entity that owns and oper ates an electric distribution network but is not the owner

and operator of EGUs. As discussed further, vertically integrated utilities own and operate electric distribution

networks as well as EGUs.

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14 1. Electric Distribution Utility with Obligat ions to Meet an EERS or RPS Pursuant

to State Regulations

EERS and RPS requirements are typically implemented through state regulations, but

may also be implemented through a public u tility commission order. State EERS and RPS

regulations provide legal instruments generally comparable in enforceability to regulatory

emission limits applied to EGUs. These regula tions typically specify compliance obligations,

reporting, and enforcement. However, many state RPS regulations and some EERS regulations

include alternative compliance paym ent (ACP) provisions that prov ide the utility with the option

of making a payment in lieu of full compliance with the portfolio requirement. Thus, state EERS

and RPS mandates may not guarantee achievement of a given level of end-use energy efficiency

or renewable energy deployment during a plan performance period. 12



### 2. Vertically Integrated Electric Utility with Obligations under a State-Approved

Integrated Resource Plan

A utility integrated resource plan (IRP) may include a number of direct and indirect

actions that affect EGU CO 2 

### emissions, and may also include compliance with EERS and RPS

regulations. Broadly, IRPs may prescribe or au thorize actions for which utilities can recover

capital investments and operating costs through regulated retail electricity rates. 13 

### This creates

strong financial incentives for implementing an action, but may not mandate an action.

For a state plan under this scenario, an enforceability consideration is whether an IRP,

and related public utility commission orders, must include additional requirements to implement

12 We note that some direct emission limits for CO 2 emissions include somewhat similar provisions. For example,

both the RGGI state CO 2 emission budget trading programs and the California GHG emission budget trading

program include cost containment prov isions where more emission allowances are made available to affected

sources at certain allowance price thresholds. In both thes e instances, such relevant characteristics of the state

regulations would need to be taken into account when projecting the CO 2 emission performance that will be

achieved by affected EGUs under the state plan.

13 In some cases, an IRP prescribes or authorizes specific actions. In others, an IRP serves as a guide for the utility

and the public utility commission when evaluating acquisiti on or implementation of specific utility resources or

programs. In such cases, the specific resource or program is approved through a PUC order that authorizes or

requires actions and identifies performance obligations. Thes e orders may or may not be fully consistent with

provisions in an IRP.

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15 certain actions, beyond denial of rate recovery or a change to utility tariffs if a utility fails to

meet specified obligations in the IRP. If so, this may require state legislation to provide

additional authority to state public utility commis sions in some states, or confer additional

authority to other agencies (e.g., a state environmental agency).

B. Private or Public Third-Party Entity not Regulated by the State

Another state plan scenario involves public or private third-party entities with

enforceable obligations under a state plan. A private or public third-party entity could be a utility

entity that is not regulated by a state public u tility commission, such as a municipal utility or a

utility cooperative. 14 

### It could also be a private non-profit en tity established to administer end-use

energy efficiency and renewable energy deployment programs. 15 

### In most cases, since they often

expend electricity ratepayer funds, such non-profit entities are created by state legislation and

overseen by state public utility commission s or state-regulated private utilities.

An appropriate legal instrument or agreement applicable to such entities included in a

state plan might include legal arrangements similar to those currently used to establish

independent entities that expend el ectricity ratepayer dollars in multiple states. For entities not

subject to state oversight, such a mechanism might also include mechanisms where an entity

voluntarily submits to the authority of a state, pursuant to state statutory or regulatory authority

specified in a state plan. Such agreements might be attached to a funding source. For example,

the entity would voluntarily submit to such authority as a condition of receiving certain funds,

such as state appropriated funds or funds coll ected through state-regulated electricity rates.

Alternatively, a municipal utility or utility cooper ative might voluntarily su bmit to state authority

as a condition of the state agreeing to let the enti ty implement a portfolio approach, in lieu of the

application of certain direct CO 2 

### emission limits for affected EGUs owned and operated by such

entities through a state regulation. In some cases , new state statutory authority might be enacted

14 Here, “not regulated” refers to regulation of electricity rates by a state public utility commission. To the extent

that such entities are owners and oper ators of affected EGUs, these EGUs may be subject to state environmental and

other regulations. In some cases these utility entities are al so subject to state EERS and RPS regulations, as specified

under state law.

15 Examples include the Energy Trust of Oregon, the Delawa re Sustainable Energy Utility, and Efficiency Vermont.

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16 to support a state plan, specifying enforceable obligations for these private or public third-party

entities under the plan.

An additional consideration is whether such le gal instruments or agreements, if related to

a renewable energy or end-use energy efficiency deployment program, should specify a stable

budget authority and funding source through each plan performance period. Such authority and

funding might be necessary to ensure that suitable funds are made available to achieve the level

of energy savings or renewable energy deployme nt projected in the state plan, which may be

necessary to achieve the level of emission performance by affected EGUs that is projected will

be achieved through implementation of the plan.

C. State Agency, Authority, or Entity

This state plan scenario involves a state entity with an enforceable obligation in a state

plan. For example, state authorities in some states implement renewable energy and end-use

energy efficiency deployment programs. 16 

### In this scenario, the requirement for the state entity

would be an enforceable component of the state plan.

One type of legal arrangement that might be applied under this scenario is legislation

directing state executive branch agencies or i ndependent state authorities to follow through on

obligations under a state plan.

Such legislation might provide independent legal authority under state law to compel

executive branch actions, or actions by independent state authorities under the plan, if

obligations are not met. Depending on the form of legislation, this could also provide citizens

with the ability to compel state action under stat e law, if obligations are not met under a state

plan. 17

16 A prominent example is the New Yo rk State Energy Research and Development Authority (NYSERDA), which

administers energy efficiency and renewable energy deployment programs.

17 We note that under the CAA, measures included in an a pproved 111(d) state plan woul d be federally enforceable

by EPA, and that citizens would also have the ability to file citizen suits to compel enforcement of state plan

obligations, under CAA Section 304 (42 U.S. Code Section 7604).

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17 An additional consideration is whether such legal arrangements, if related to a renewable

energy or end-use energy efficiency deployment program, should also specify a stable budget

authority and funding source through the plan performance period, or other provisions, to ensure

that programs are implemented as projected under the state plan.

D. Multi-State Approaches

As discussed in the preamble, in section V III.F.1, multi-state appr oaches introduce crosscutting enforceability considerations.

For these multi-state approaches, states would demonstrate emission performance from

affected EGUs in aggregate jointly with partner states. For states participating in a multi-state

approach, the individual state performance goals in the emission guidelines would be replaced

with an equivalent multi-state performance goal. For example, states taking a rate-based

approach would demonstrate that all affected E GUs subject to the multi-state plan achieve a

weighted average CO 2 

### emission rate that is consistent, in aggregate, with an aggregation of the

state-specific rate-based CO 2 

### emission performance goals establis hed in the emission guidelines

that apply to each of the participating states. If states were taking a mass-based approach,

participating states would demonstr ate that all affected EGUs subject to the multi-state plan emit

a total tonnage of CO 2 

### emissions consistent with a translat ed multi-state mass-based goal. This

multi-state mass-based goal would be based on tr anslation of an aggregation of the state-specific

rate-based CO 2 

### emission performance goals established in the emission guidelines that apply to

each of the participating states.

1. Multi-State Emission Budget or Rate-Based Emission Trading Programs

The Regional Greenhouse Gas In itiative (RGGI) is an example of a multi-state approach

to regulation of CO 2 

### emissions. Through this initiative, nine states are currently implementing

coordinated CO 2 

### emission budget trading programs. The program works as a coordinated

regional whole through a shared emission and allowance tracking system and allowance auction

process, but is implemented in accordance with materially consistent stand-alone state

regulations and individual statutory au thority. These regulations recognize CO 2 

### allowances

issued by other participating states for use by affected EGUs when comp lying with each state’s

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18 emission limitation, but contain all the nece ssary components to administer the program

requirements on an individual state basis. 18 

### As a result, while the initiative is implemented

regionally, each CO 2 

### emission budget trading program regula tion is enforceable against affected

EGUs at the state level and functions as a discrete program.

As a result, a multi-state emission budget trad ing program approach, such as RGGI, is

enforceable in practice at the state level. A multi-state rate-based emission trading program could

also be established in much the same manner as a multi-state emission budget trading program,

and could therefore be enforceable at the state level. 19



### 2. Multi-State Portfolio Approaches

A multi-state portfolio approach could intr oduce novel enforceability considerations. If it

were based on interdependent emission reduction strategies among states that are not tied to

emission limits that directly apply to affected EGUs, the emission performance of affected EGUs

in one participating state may be dependent, in pa rt, on actions taken in other participating states.

If a state (or states) failed to implement commitmen ts under the multi-state plan, this raises the

question of whether the EPA should address non-performance of one or more participating states

in the context of failure to achieve the required level of multi-state emission performance under

the plan, or instead enforce actions at the individua l state level for those states that are failing to

meet commitments under the multi-state plan.

18 The emission limitation consists of a requirement to submit CO 2 allowances equal to reported CO 2 emissions

during a compliance period. While states have individu al emission budgets, representing the total number of

allowances issued for a given year th at are available for allocation, there are no individual state emission limits. The

CO 2 emission constraint is regional, based on the sum of state CO 2 emission budgets.

19 Enforceability would be contingent, in part, on states having comparable enforcement mechanisms.

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19 IV. Incorporating End-Use Energy Efficiency and Renewable Energy Programs and

Measures under a Rate-Based Approach

A. Concept of Adjusting EGU CO 2 

### Emission Rates based on the Effects of End-Use

Energy Efficiency and Renewable Energy

As discussed in the preamble, in section VIII.F.3, the EPA is proposing that RE and

demand-side EE requirements, programs, and measures may be incorporated into a rate-based

plan approach. 20 

### Measures that avoid CO 2 

### emissions from affected EGUs, such as quantified and

verified end-use energy savings and renewable energy generation, could be used to adjust the

CO 2 

### emission rate of an affected EGU when demonstrating compliance with a rate-based CO 2



### emission limit. Alternatively, a state could use the effect of such measures as a basis for

administratively adjusting the average CO 2 

### emission rate of affected EGUs when demonstrating

achievement of the required emission rate performance level in a state plan.

Under this approach, affected EGUs could comply with a rate-based CO 2 

### emission limit

through actions at the EGU, 21 

### as well as through the use of credits 22 

### for actions that occur

elsewhere in the interconnected electricity system that avoid CO 2 

### emissions from affected EGUs.

If a state is implementing a portfolio approach, then the state could administratively adjust the

average CO 2 

### emission rate of affected EGUs through a similar process when demonstrating

achievement of the required emission rate performance level in the state plan.

This section explores in depth the mechanic s and implications of the different possible

approaches for adjusting CO 2 

### emission rates that are summarized in the preamble. Aspects of

this discussion may apply to both retrospective demonstration of CO 2 

### emission performance

20 The EPA is also proposing that RE and demand-side EE measures could be used under a mass-based portfolio

approach in an approvable state plan. However, the focus of this section is limited to application of such measures

under a rate-based approach.

21 This could include an individual affected EGU or group of affected EGUs if a rate-based averaging or trading

approach is used.

22 These credits could be tradable, or re present non-tradable credits administra tively apportioned to affected EGUs.

This latter approach effectively represents an ad ministrative adjustment applied by the state.

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20 achieved by affected EGUs under an approved state plan and projections of CO 2 

### emission

performance by affected EGUs included in a submitted state plan.

B. Approaches for Adjusting EGU CO 2 

### Emission Rates

As discussed in the preamble, cred its or adjustment to an EGU CO 2 

### emission rate, based

on the effect of RE and demand-side EE programs and measures, might represent avoided MWh

of electric generation or avoided tons of CO 2 

### emissions. 23 

### If adjustment or credits represent

avoided MWh, they would be added to the denominator of the lb CO 2 

### /MWh emission rate when

determining an adjusted lb CO 2 

### /MWh emission rate. If adjustment or credits represent avoided

CO 2 

### emissions, they would be subtracted from the numerator when determining an adjusted lb

CO 2 

### /MWh emission rate. The approach chosen could affect the amount of credit or adjustment

provided for RE and demand-side EE programs and measures. These implications are discussed

below.

1. Adjustment of CO 2 

### Emission Rate based on Avoided MWh

One approach is to adjust an EGU’s CO 2 

### emission rate based on avoided MWh of

generation from an EGU, or cohort of EGUs, re sulting from RE and demand-side EE programs

and measures. 24 

### A MWh crediting or adjustment approach implicitly assumes that the avoided

CO 2 

### emissions come directly from the particular affected EGU (or group of EGUs) to which the

adjustment or credits are applied. It assumes, in effect, that an additional emission-free MWh is

being generated by that respective EGU, and that the RE or demand-side EE measure reduces

CO 2 

### emissions from that individual EGU or group of EGUs. 25 

### In practice, the average or

marginal CO 2 

### emission rate in the power pool or identified region – representing the avoided

CO 2 

### emissions from the generating sources being displaced by a MWh of energy savings or a

23 The preamble solicits comment on the appropriateness of these different approaches, which are further elaborated

in this section.

24 It should be noted that this was the process used by EPA for incorporating end-use energy efficiency, renewable

energy, and nuclear energy when calculating state CO 2 emission performance goals for affected EGUs. As discussed

in the preamble, states may have flexibility to use a di fferent approach when demonstrating the effect of these

measures in a rate-based state plan.

25 As a result, the assumed avoided CO 2 emissions from an individual MWh of energy savings or generation from

renewable energy will differ based on the reported CO 2 emission rate of the individual EGU to which the MWh is

applied as an adjustment to its MWh output.

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21 MWh of renewable energy generation – could differ significantly from the calculated avoided

CO 2 

### emissions derived by adjusting the MWh output of an affected EGU. The following

examples highlight these concepts.

Example 1 : Assume an EGU with a stack emission rate of 1,500 lb CO 2 

### /MWh generates

1,000 MWh. Also assume that 1,000 emission-free MWh credits for the effect of demand-side

EE measures are added to the denominator when calculating the EGU’s adjusted CO 2 

### emission

rate. The adjusted CO 2 

### emission rate is 1,500,000 lb CO 2 

### divided by 2,000 MWh, which equals a

CO 2 

### emission rate of 750 lb CO 2 

### /MWh. In this example, each MWh credit represents 750 lb of

avoided CO 2 

### emissions.

Example 2 : The same calculation applied to an affected EGU with a 2,000 lb CO 2 

### /MWh

rate would yield a different result. In this instance the adjusted emission rate is 2,000,000 lb CO 2



### divided by 2,000 MWh, which equals a CO 2 

### emission rate of 1,000 lb CO 2 

### /MWh. In this

example, each MWh credit represents 1,000 lb of avoided CO 2 

### emissions.

2. Adjustment of CO 2 

### Emission Rate based on Avoided CO 2 

### Emissions

An alternative approach is to provide an adjustment to the CO 2 

### emission rate of an EGU,

or cohort of EGUs, based on the estimated CO 2 

### emissions that are avoided in the power pool or

identified region as a result of RE and demand-side EE programs and measures. This approach

acknowledges that the avoided CO 2 

### emissions may come from the electric power pool or other

identified region as a whole, rather than an individual EGU. The avoided CO 2 

### emissions are

determined based on the MWh saved or generated, multiplied by a CO 2 

### emission rate for the

power pool or region.

This CO 2 

### emission rate could be based on the average or marginal emission rate in the

power pool, region, or state. A marginal avoided emission rate represents the generation that is

displaced at the margin for every MWh saved or generated through an RE or demand-side EE

program or measure. An average avoided emision ra te is based on either all fossil generation in a

region or total generation. This approach assumes that every MWh saved or generated equally

displaces generation from every generator in a power pool or region.

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22 Another approach that has been suggested by some stakeholders is crediting or

adjustment based on the level of the rate-based emission limit for affected EGUs (or the overall

rate-based level of emission performance for affected EGUs specified in a state plan). For

example, under this approach, if the emission rate limit were 1,500 lb CO 2 

### /MWh, one MWh of

energy savings or renewable energy generation would result in a credit or adjustment

representing 1,500 lb of CO 2 

### that could be subtracted from the numerator of an EGU’s CO 2



### emission rate. This approach assumes that affected EGUs jointly emit CO 2 

### from the stack on an

average basis at this required compliance level (i.e., consistent with a “closed” averaging

system), and that a MWh saved or generated avoids CO 2 

### emissions from affected EGUs at this

average compliance level. 26



### 3. Other Considerations

Some of the CO 2 

### emissions avoided through RE and demand-side EE measures may be

from non-affected EGUs. This may result because affected EGUs are only a subset of the fossil

fuel-fired EGU fleet, which also includes (or will in clude) existing non-affected fossil fuel-fired

EGUs, such as simple-cycle combustion turbines used to meet peak demand, as well as new

fossil fuel-fired EGUs subject to emission standards under CAA section 111(b). Furthermore, as

the fleet capital stock slowly turns over, affect ed EGUs under section 111(d) will comprise a

slowly shrinking subset of the total fossil fuel-f ired EGU fleet. These dynamics may need to be

addressed in a state plan when crediting or adjusting CO 2 

### emission rates of affected EGUs based

on the effects of RE and demand-side EE measures. The approaches described in more detail

later in this section may be able make these di stinctions between affected and non-affected fossil

fuel-fired EGUs.

26 This outcome might be expected in a closed averaging sy stem, without the use of crediting or adjustment for the

avoided emission effects of RE and demand-side EE meas ures. In this instance an EGU that emitted above the

compliance rate would need to averag e its performance with (or submit tradable credits obtained from) an EGU that

performed below the required rate. On balance, all EGUs subject to the emission rate limit would perform at or

below the compliance rate.

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23 C. Methods and Tools for Quantifying Avoided CO 2 

### Emissions from End-Use Energy

Efficiency and Renewable Energy

1. Introduction

There are a number of approaches for quantifying the avoided CO 2 

### emissions resulting

from end-use energy efficiency and renewable energy (EE/RE) programs, requirements and

measures 27 

### in the electric sector. These approaches ra nge from the application of basic avoided

emission rates to using sophis ticated electric sector models. 28 

### Annual average avoided emission

rates have often been used for rough approximations of CO 2 

### emissions avoided from reduced

electric energy use. 29 

### An annual average avoided emission rate assumes that EE/RE programs

and measures reduce electric generation from all generating types on a proportional basis

consistent with the generation mix in a region. A marginal emission rate represents the emission

rate of an electric generating unit (EGU) or cohort of EGUs likely to be displaced by EE/RE

measures (i.e., a marginal avoided emissions rate), based on the last unit(s) to come online to

meet electricity load and the first unit(s) to be brought offline when electricity load is reduced.

The primary question underlying estimating CO 2 

### emissions reductions from EE/RE

measures is the determination of which electric generators will be displaced (i.e. , cease

generation or reduce generation output) in the presence of incremental EE/RE. This section

briefly describes a range of avoided emission rates approaches, their underlying assumptions,

and considerations associated with the use of different avoided emission rate approaches.

2. Average Emission Rate Approach

An average emission rate (typically expressed in tons of CO 2 

### /MWh) is usually

understood to mean the average of all generators’ emissions rates, weighted by annual

generation. The rate (r

avg 

### ) is calculated as:

27 As used here, the term “EE/RE measures” refers to any program actions or regulatory requirements that result in

the increased use of energy-efficient equipment or practices, or renewable energy generating resources.

28 Electric sector models include simulation dispatch m odels (i.e. production-cost models) and capacity expansion

planning models.

29 See, for example, EPA “Household Carbon Footprint Calculator,” available at

http://www.epa.gov/climatechange/ghgemissions/ind-calculator.html

.

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24 ݎ ௔௩௚ 

### ൌ ∑ ݁ ௜ ௜



### ∑ ݃ ௜

௜



### Where:

e = annual emissions (tCO 2 

### ) from all sources i and

g = annual generation (MWh) from all sources i

.

Because the average emission rate puts the sum of all generation in the denominator (as MWh),

including nuclear, hydroelectric, and renewable generating resources, the rate fundamentally

assumes that EE/RE reduces all generating types by an equal proportion, regardless of their type

or contribution to the margin. 30 

### EGUs are generally dispatched on an economic merit order,

where the least-cost EGUs (on a variable cost basis) are dispatched first, and higher cost

resources are dispatched later. Since nuclear, hydr o, wind, and solar resources operate at a very

low cost, 31 

### they are generally dispatched before most fossil units. 32 

### Under current and historical

operating conditions, there are few circumstances in which non-fossil resources reduce output in

the presence of lower demand. As a result, in states with a moderate contribution of non-emitting

resources to total generation, the average emission rate may be lower than could reasonably be

expected for reductions from EE/RE programs and measures. 33 

### Conversely, in states that have

significant baseload fossil generation and few no n-emitting resources, a state-average emission

rate may reflect an emission rate that is too high. This may occur by incorporating emissions

from coal-fired EGUs that are less likely to reduce generation with a reduction in electricity load,

compared with other lower-emitt ing fossil fuel-fired EGUs.

30 In other words, a two-percent reduction in energy use would cause all EGUs in the system to reduce generation

output by two percent, including coal, gas, nucl ear, hydro, and renewable generating resources.

31 For representative variable costs for new EGU, see supporting documentation for the Electricity Market Module

of the Annual Energy Outlook (AEO) 2013, table 8.2., “Cos t and performance characteristics of new central station

electricity generating technologies”. Geothermal, wind, and solar generating resources are assumed to have a

variable operating cost of $0/MWh. Hydroelectric generati ng resources have an assumed variable operating cost of

$2.60/MWh and incur no fuel cost, while nuclear generating resources have an assumed variable operating cost of

$2.10/MWh (exclusive of fuel costs). In contrast, gas a nd coal units are assumed to have variable operating costs

from $3-$7/MWh (exclusive of fuel costs.

32 The exception is rare curtailment events for renewable resources when more energy is produced than required, and

due to operational constraints, where wind turbines are stal led to maintain system energy balance. Renewable energy

generators, such as wind and solar, are sometimes referred to as “non-dispatchable” resources, since the renewable

energy resource is intermittent and these generators cannot be called upon to run when the renewable energy

resource is unavailable. However, here we refer to ec onomic dispatch of these resources at times when the

renewable energy resource is available. In these instances, th ese generators are often one of the first resources to be

called upon, due to their low variable operating costs.

33 Counting hydroelectric, nuclear, and renewable resources in the denominator would re nder the emissions rate too

low, and thus the avoided emissions smaller than actually realized.

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25 3. Marginal Emission Rate Approach

A marginal emission rate represents the emi ssion rate of the EGU or cohort of EGUs

likely to be displaced by EE/RE ( i.e., an avoided emission rate). A marginal unit is the highestcost unit dispatched at any point in time. 34 

### Under most circumstances, and at any given time, the

marginal unit is the last unit to be brought online to meet electricity demand and the first unit to

be brought offline when electricity load is reduced. Due to constraints on generating unit ramp

rates 35 

### and transmission availability, it is not unc ommon for multiple units to be dispatched

incrementally simultaneously, thus creating a cohort of marginal units. Marginal unit(s) change

on a moment-to-moment basis, determined by load requirements and the variable cost of each

unit available to generate another unit of power. A marginal unit can either be a unit brought

online to meet load or may be an EGU that is alr eady operating, but that is dispatched at a greater

level of output to meet load.

The marginal emission rate ( r marginal 

### ) can be expressed for any given hour ( t ) as a function

of the difference between two distinct cases – the reference case ( i.e., in the absence of

incremental EE/RE programs and measures) and the change case, where the EE/RE programs

and measures have been implemented. A formula describing this rate is written as follows:

ݎ ௟௔௡௜௚௥௔௠,௧ 

### ൌ ∑ ݁ ௥௘௙,௜,௧ ௜ 

### െ ∑ ݁ ாாோா,௜,௧ ௜



### ∑ ݃ ௥௘௙,௜,௧

௜ 

### െ ∑ ݃ ாாோா,௜,௧ ௜



### Where:

e ref 

### and e EERE 

### = emissions (tCO 2 

### ) from EGUs i in hour t in the reference case ( ref ) and the change

case ( EERE ), respectively; and

g ref 

### and g EERE 

### = generation (MWh) from EGUs i in hour t in the reference case ( ref ) and the

change case ( EERE ), respectively.

34 See EPA’s Roadmap for Incorporating EE/RE Policies and Programs in State Implementation Plans: Appendix B,

available at http://www.epa.gov/statelocalclimate/doc uments/pdf/overview_paper_4-28-2011.pdf

35 Ramp rate refers to the ability of an EGU to respond to increasing load, based on its ability to increase output.

Ramp rates are typically determined by technology type, wi th some technologies, such as combustion turbines, able

to more quickly increase output in response to increasing load.

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26 The magnitude of the EE/RE program and the EE/RE load impact shape 36 

### is a key

element in determining marginal emissions reductions . In order to obtain a valid estimate of the

emission reduction effect of an EE/RE program, an annual marginal avoided emission rate

should be calculated that reflects the EE/RE program’s load impact shape and magnitude. This

annual marginal avoided emission rate may then be applied to other EE/RE programs with

similar load impact shapes and magnitudes. The ma rginal emission rate is highly time sensitive,

as are the impacts of EE/RE programs and measur es. For example, the EGU that is on the margin

and is most likely to be displaced from EE/RE in each hour of the year. In addition, different

EE/RE programs may have very different load impact shapes, reducing energy requirements in

different hours of the year. The interaction between the pattern of EGUs on the margin and the

load impact shape of EE/RE programs and measures results in a specific marginal avoided

emission rate. Applying an annual marginal avoided emission rate calculated based on the impact

of one specific set of EE/RE programs and measures to another set of EE/RE programs and

measures that is substantively different in timi ng or magnitude of energy savings or generation

(i.e ., with a different load impact shape and magnitude) may result in erroneous results ( i.e. the

assumption that the wrong EGUs are displaced). 37



### Several mechanisms have been proposed to estimate the marginal emission rate without

the use of a formal electric dispatch simulation model. These mechanisms rely on historic hourly

generation and emissions data, collected by EP A’s Clean Air Markets Division, to estimate

hourly marginal emissions rates for a past historical year. The benefit of these mechanisms is that

they are simple to apply, but (a) are difficult to verify or validate without the benefit of a formal

model and (b) rely on historical data and patterns of dispatch, which may not represent future

patterns of dispatch.

36 The load impact shape of an EE/RE program or measure is the hourly (or, if necessary, daily or seasonal) pattern

of either energy savings from an EE program or measure, or generation from an RE generator that is supplied to the

grid.

37 For example, an EE program focused on measures that reduce peak electricity demand, such as more efficient air

conditioning, or a solar RE program, may result in significan t reductions in electricity use from the grid during peak

demand hours, and little electricity use reduction during ov ernight “trough” hours. In contrast, an industrial EE

program may result in a relatively constant electricity load reduction over most hours. An EE program focused on

peak reduction measures may reduce generating output from primarily peaking units, while an EE program targeting

EE measures with a more constant load impact shape ma y significantly reduce baseload generation during overnight

trough hours.

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27 4. Methods and Tools for Estimating Avoided CO 2 

### Emission Rates and Avoided

CO 2 

### Emissions

4.1. Calculation Tool Method

The EPA has developed a user-friendly tool to estimate the emission reduction impacts of

EE/RE requirements, programs and measures. The “ AVoided Emissions and geneRation Tool ”

(AVERT) 38 

### was developed to help air quality planners quantify NOx and SO 2 

### emission impacts,

as specified in EPA’s Roadmap for Incorporating EE/RE Programs in NAAQS SIPs. 39 

### AVERT

can also be used to quantify the displaced CO 2 

### emissions of EE/RE measures within the

continental United States.

The AVERT method uses historical hourly emissions rates based on recent the EPA data

on fossil fuel-fired EGUs’ hourly generation and emissions reported through EPA’s Acid Rain

Program. 40 

### This method couples historical hourly generation and emissions with the hourly load

reduction profiles of EE/RE programs and measures to determine hourly emissions reductions on

the margin. AVERT can be used to estimate EE/RE-related emissions reductions in a current or

near-future year. However, AVERT estimates for current or future years are based on historical

behavior rather than projected economic behavi or. As a result, AVERT does not use projections

of future fuel or electricity market prices that affected EGU dispatch, and is therefore not an

appropriate tool for longer-term projections.

Users of AVERT can analyze how the different load profiles of a variety of EE programs

and measures, as well as wind and solar technologies, affect the magnitude and location of CO 2



### emissions at the county, state, and regional level. AVERT has a flexible framework with a

simple user interface designed specifically to meet the needs of state air quality planners and

other interested stakeholders.

38 More information about AVERT, including documentation and a user’s manual, is available at

(http://www.epa.gov/avert).

39 See Appendix I of the Roadmap for Incorporating Energy Efficiency/Renewable Energy Policies and Programs

,

available at http://epa.gov/airquality/eere/pdfs/appendixI.pdf ), for details about how this approach can be used in

the different NAAQS SIP pathways.

40 http://ampd.epa.gov/ampd/. Deleted:

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28 AVERT may be used to derive marginal emission reductions from historical generation

and emissions data, which can be used to derive a marginal avoided CO 2 

### emission rate.

However, AVERT does not quantify average emissions rates. AVERT approximates historical

dispatch behavior using a statisti cal algorithm. It does not repres ent transmission constraints, or

significant changes in grid structures or futu re economic conditions. To estimate a recent

historical marginal CO 2 

### emissions reduction from existing EE/RE programs or measures, a user

would input the MWh related to results from EE/RE programs or measures (representing either

MWh of electricity savings or MWh of generation) in a representative historical baseline year as

a positive increment to electricity load, and record the emissions incrementally added in the

tool. 41 

### The calculated marginal CO 2 

### emission rate is the incremental CO 2 

### emissions added by

AVERT, based on historical EGU dispatch pattern s, divided by the incremental MWh of EE/RE

savings or generation input to AVERT as an increase in electricity load. This approach reflects

the marginal impact of EE/RE measures based on historical recorded patterns of emissions and

generation. 42



### 4.2. Electricity Sector Modeling Method

Quantification of avoided CO 2 

### emissions from EE/RE requirements, programs, and

measures can be achieved through retrospective modeling approaches. Models can be used to

calculate avoided CO 2 

### emissions by comparing actual realized EGU CO 2 

### emissions to projected

EGU CO 2 

### emissions that would have occurred in a historical reference case that does not include

implementation of the EE/RE that is being evaluated. The appropriate choice of model depends

on the look-back period. For short look-back periods of one to three years, an electricity system

simulation dispatch model can de termine the marginal generation contribution to emissions in a

41 Because AVERT represents a historical baseline year, EE and RE programs that occurred in a past year are

already recorded by the statistics of AVERT. To estimate the emissions impacts of historic EE/RE programs, a user

would increase the load by the EE/RE in crement to estimate what generation and emissions would have been in the

reference case look-back year.

42 AVERT reflects fuel and emission control technologies to the extent they have influenced dispatch during the

base year chosen. However, AVERT cannot change dispat ch based on future economic or regulatory conditions,

such as expected fuel prices, emission allowance prices, or specific emissions limits. AVERT should not be used for

this type of analysis. When used to review historical da ta, AVERT will capture the impact of historical fuel prices

and other impacts on the variable cost of produc tion, but cannot capture specific emission limits.

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29 historical reference case ( i.e. absent incremental EE/RE programs). 43 

### Over the short term,

simulation dispatch models properly account for EGU economic dispatch considerations, such as

fuel and emission allowance prices, and operational constraints, such as ramp rates, outages, and

heat rate curves. Look-back periods beyond three to five years would benefit from use of a

utility-scale capacity expansion and dispatch plan ning model to understand the change in build

out of new generating capacity, as well as transm ission and distribution infrastructure, and its

impact on generation between the historical reference case and actual realized historical

outcome.

Electricity System Simulation Dispatch Models

Quantifying the CO 2 

### emissions reductions achieved by EE/RE measures through

modeling of a near-term look-back period require s a counterfactual historical reference case

model run, which examines how the electric system would have operated in the absence of the

EE/RE emission reduction measures under considera tion. The emissions projected in this model

run may be compared against actual realized emissions during the historical period. The lookback model should be calibrated by running the same model with the EE/RE measures in place

and comparing the outcome of that model against realized generation and emissions at an

appropriate spatial scale. 44



### Simulation dispatch models can be readily run for historical years provided they are

loaded with the accurate input assumptions, incl uding actual historic fuel costs, emission

allowance prices, and transmission constraints. Wh ile these models will not choose economically

optimal EGU retrofit or retirement decisions, they will provide a change in EGU dispatch and the

associated change in emissions across a large region in a more detailed manner than capacity

expansion planning models.

43 “Reference case” here refers to the case in which addi tional incremental carbon emi ssions reduction mechanisms

are not employed, in this case resulting from EE/RE programs and measures.

44 Generally, simulation models will not capture exact output of individual EGUs relative to reality due to a variety

of factors, including outages and other non-economic considerations not reflected in the model. Therefore, a

comparison at an individual EGU scale may not be meani ngful, but aggregate emissions at a state or regional scale

should be expected to be comparable.

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30 Simulation dispatch models may be most relevant as part of ex-post plan reporting, for

estimating the avoided CO 2 

### emissions from affected EGUs that occurred as a result of EE/RE

measures included in a plan, during a specified plan reporting period.

Utility-Scale Capacity Expansion and Dispatch Planning Models

Quantifying the CO 2 

### emissions reductions achieved by EE/RE measures through

modeling over a longer-term look-back period requires a counterfactual historical reference case

model run, which examines how the electric syst em would have operated and have been built

out, in the absence of the EE/RE measures under consideration. The critical difference between

this type modeling approach and the use of a simulation dispatch model is the assessment of

changes made at the “build margin” – i.e.

, new additions to generating capacity that may have

been avoided or compelled, or retirements of existing units that may not have occurred in the

reference case. The emissions projected in this model run may be compared against actual

realized emissions during the historical period . The look-back model should be calibrated by

running the same model with the EE/RE measures in place and comparing the outcome of that

model run against actual realized generation and emissions at an appropriate spatial scale.

Capacity expansion and dispatch planni ng models could be run for a longer-term

historical period to better reflect what the el ectricity system would have looked like in the

absence of the EE/RE measures. When EE or RE resources have been added over the course of

three to five years, these models will reflect ho w these resources have avoided new power plants,

retrofits, or fuel switch decisions. Some models may also be able to reflect avoided transmission

investments.

Capacity expansion and dispatch planning models may be more relevant than simulation

dispatch models for projecting the emission perform ance that will be achieved by affected EGUs

under a plan. As discussed below, these models are able to assess both the “operating” and

“build” margins that impact EGU CO 2 

### emissions as the result of EE/RE measures.

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31 5. Considerations Associated with use of an Avoided Emissions Rate

Operational versus Build Margin

Avoided emission rates approaches that do not include a capacity expansion model all

assume that EE/RE measures only impact the operational margin – i.e., they impact moment-tomoment operations of EGUs in the existing grid. While this is true, deployment of EE/RE over a

period of years may have impacts beyond the operational margin. Deployment of significant

EE/RE measures over time often impacts decision s to build new fossil ge nerating capacity, retire

existing aging generating capacity, and make diff erent decisions about transmission expansion.

These different decisions about what infrastructu re to build or retire are known as the “build

margin,” and may ultimately have a greater impact on long-term emissions outcomes than the

operational margin.

While a new wind farm brought online next year has an impact on patterns of generation

from existing fossil EGUs, decadal-scale planning associated with a policy meant to encourage

EE/RE has an impact on the choice of whether or not to pursue the construction of new fossil

generating capacity. A utility pursuing aggressive EE/RE programs may avoid the construction

of new fossil generating capacity and expansion of transmission and distribution capability, and

may even allow the utility to retire non-economic generating units no longer required for

generation or reliability purposes.

The use of an avoided emissions rate calculated based on the operational margin alone

may fail to represent the impact of EE/RE measures that occur at the build margin. This

differentiation is particularly important in re gions where the pursuit of EE/RE measures may

result in the retirement of high-emitting, non-economic generating assets. The use of a capacity

expansion and dispatch planning model would he lp accurately assess avoided emissions due to

changes at both the build and operational margins.

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32 Quantifying In-State versus Out- of-State Emissions Reductions

The electric system is not confined by stat e boundaries, and emissions displaced by

EE/RE measures may occur over a wide geographic area, 45 

### including outside of the state that

implemented the EE/RE measures. Without the use of a simulation dispatch model (or, under

some circumstances, use of a tool such as AVERT) 46 

### it can be challenging to attribute emissions

reductions that occur within th e implementing state versus emissions reductions that occur across

state boundaries. If a state sought to recognize the effect of EE/RE measures and quantified

emissions reductions using an approach that did not account for avoided emissions at an

appropriate spatial scale, it may inappropriately account for emissions reductions that occur in

other states, or conclude that all emissions reductions occur within the state’s boundaries. 47 

### In

interconnected areas in which some states pursue mass-based emissions reductions approaches

through their state plans while other states pursue rate-based approaches that include adjustment

or crediting for avoided CO 2 

### emissions from implementation of EE/RE measures, avoiding

double-counting of emission eff ects among states will be an important consideration when

quantifying the avoided emissions resulting from EE/RE measures. These considerations are

discussed below in section VII of this TSD.

V. Quantification, Monitoring, and Verifi cation of End-Use Energy Efficiency and

Renewable Energy Programs and Measures

As discussed in the preamble, in section VII.F.4, a key consideration for state plans is the

process and requirements for quantifying, monito ring, and verifying the effect of renewable

45 See California Energy Commission, Emissions Reductions from Renewable Energy and Energy Efficiency in

California Air Quality Management Districts (2011), available at

( http://ucciee.org/downloads/CAEmissionsReductions.pdf ).

46 AVERT captures the magnitude of in-state versus out-of -state emissions reductions, but may not be an accurate

assessment tool for edge-cases – i.e. states that fall near the boundaries of the AVERT regions – because AVERT

does not capture inter-regional transmission.

47 In the simplest case, a state without any fossil genera ting capacity that implements EE/RE measures would cause

emissions reductions in neighboring states, and none with in its own borders. Similarly, a state with significant,

predominantly baseload fossil generation may also cause littl e in-state emissions reductions from implementation of

EE/RE measures because those emissions reductions may occur at marginal generators located outside of the state.

Conversely, a state with significant marginal fossil gene rating capacity might realize reductions in emissions from

EE/RE measures implemented in other states. These complications may arise regardless of if a state is a net importer

or net exporter of energy. The key differentiation is if th e state’s EGUs are generally on the margin relative to

interconnected EGUs in neighboring states. Comment [A5]: 2. Can renewable energy that

is physically located in a state with a mass

‐ based

policy be used for out

‐ of

‐ state compliance in a

state with a rate

‐ based policy?

EPA response:

This question is related to treatment of interstate

emission effects, which is addressed later in

section VII of the TSD.

Comment [A6]: 3. Commenters suggest

separating the RE and EE EM&V discussion

completely into separate sections. They are

different issues. For EE, the Uniform Methods

Project was developed to provide a national

approach to EM&V. Commenters suggest citing

UMP as an optional but preferred approach to

EM&V and are willing to provide assistance.

EPA response:

This section is divided into two subsections that

address EE and RE separately. We have maintained

both within a single major section of the TSD

because state plans containing either enforceable

RE or EE measures would be subject to a similar

requirement to include an EM&V plan for those

measures in the state plan.

We have highlighted the role of the Uniform

Methods Project (UMP) in subsequent edits later in

this section.

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33 energy and demand-side energy efficiency measures that result in electricity generation or

savings. 48 

### In the preamble, the EPA proposes that a state plan that includes enforceable RE and

demand-side EE measures must include an evaluation, measurement, and verification (EM&V)

plan that explains how the effect of these measures will be determined in the course of plan

implementation. An EM&V plan will specify the analytic methods, assumptions, and data

sources that the state will employ during the st ate plan performance periods to determine the

energy generation and energy savings related to RE and demand-side EE measures. As discussed

in the preamble, an EM&V plan would be subj ect to EPA approval as part of a state plan. In the

preamble, the EPA also discusses its intent to develop guidance for acceptable EM&V methods

that could be incorporated in an approvable EM&V plan included as part of an approvable state

plan. The EPA seeks comment in the preamble on the critical features of such guidance,

including scope, applicability, and minimum requirements, as well as the appropriate basis for

and technical resources used to establish such guidance, including existing state and utility

protocols and existing international, national, and regional consensus standards or protocols. This

section further elaborates these considerations discussed in the preamble , with individual subsections addressing RE and demand-side EE programs and measures

.

The appropriate type of EM&V for RE and demand-side EE programs and m easures will

depend on the state plan approach. For states im plementing a mass-based portfolio approach, the

effect of renewable energy and demand-side energy efficiency requirements, programs, and

measures in helping to achieve the required level of CO 2 

### emission performance under a state

plan will be directly evident in reductions in the monitored CO 2 

### emissions from affected EGUs. 49



### In effect, the overall impact of these measures could be tracked through CO 2 

### emission

monitoring, reporting, and record-keeping requirements applied to affected EGUs. However, for

states implementing a rate-based plan approach, an approvable plan will need to include

48 In particular, this consideration applies to states impl ementing a rate-based emission limit approach that provides

for adjustment of CO 2 emission rates based on the effect of end-use en ergy efficiency and renewable energy, as well

as states implementing utility- or state-driven portfolio ap proaches that incorporate e nd-use energy efficiency and

renewable energy requirements and programs.

49 There could be exceptions, for example where a st ate plan includes acknowledgement of avoided CO 2 emissions

that occur outside state borders as a result of state plan measures. See section VII of this TSD for further discussion

of the treatment of interstate effects. Comment [A7]: EPA: Note, this revised text

conforms with revisions made to the preamble, at

section VIII.F.4, in response to interagency

comments. We have also made additional changes

throughout this section to conform with the

revised preamble text at section VIII.F.4.

Deleted:

Deleted: .seeks comment on the suitability of

current state and utility quantification, monitoring,

and verification approaches for end-use energy

efficiency and renewable energy in the context of an

approvable state plan, and on whether harmonization

of state approaches, or supplemental actions and

procedures, should be required in an approvable state

plan. The preamble also discusses generally whether

the EPA should establish good practice guidance for

quantification, monitoring, and verification of

renewable energy and demand-side energy efficiency

measures included in an approvable state plan, and

solicits comment on the content of such guidance.

Deleted: quantification, monitoring, and

verification

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34 quantification, monitoring, and reporting requi rements related to RE and demand-side EE

requirements, programs, and measures incorporated in a state plan.

Utilities and states have conducted ongoing EM&V of end-use energy efficiency and

renewable energy measures and programs for several decades. These evaluations, which include

quantification, monitoring and verification of results, generally rely upon a well-defined set of

industry -standard practices and procedures. However, measurement approaches vary by state

based on multiple factors, including the measure and program type being evaluated, the level and

nature of regulatory oversight, the degree of stat e and utility experience with these measures and

programs, and the overall magnitude of program impacts.

This section of the TSD discusses current st ate and utility evaluation, monitoring, and

verification approaches for end-use energy efficiency and renewable energy programs and

mandates. This section also discusses the potential suitability of these approaches in the context

of an approvable state plan, and whether harmon ization of state approaches, or supplemental

actions and procedures, might be warranted in an approvable state plan. In particular, this section

discusses considerations related to the establishment of requirements and guidance for

quantification, monitoring, and verification of en d-use energy efficiency and renewable energy

measures for an approvable state plan. It also discusses the possible appropriate basis for and

resources used to establish such requirements and guidance. This discussion includes

consideration of existing state and utility protocol s, as well as any international, national, and

regional consensus standards or protocols.

This section also discusses the types of end- use energy efficiency and renewable energy

measures and programs for which EM&V of results is relatively straightforward. Such

approaches might be subject to streamlined review of EM&V protocols included in an

approvable plan, provided that such protocols are applied in accordance with EPA requirements

and guidance . For example, many utilities have implemented a similar core set of end-use energy

efficiency and renewable energy measures and prog rams for utility customers. For these types of

measures and programs, a substantial base of experience has been established nationally for

quantification, monitoring, and verification of measure and program outcomes. Deleted: evaluation

Deleted: good practice guidance or

Deleted: , if the EPA were to take this approach

Deleted: quantification, monitoring, and

verification

Deleted: quantification, monitoring, and

verification

Deleted: industry best practices

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35 In the preamble, at section VIII.F.4, the EPA notes that it is not proposing to limit the

types of RE and demand-side EE programs and measures that may be included in a state plan.

However, less established types of measures and programs, such as new and innovative demandside EE programs that seek to alter consumer and building occupant behavior may pose

quantification and verification challenges. Still ot her types of measures, such as state energyefficient appliance standards and building codes, have not typically been subject to similar

evaluation of energy savings results. These types of approaches may have substantial impacts,

but may require additional documentation of EM&V methods in accordance with EPA guidance,

including development of appropriate quantification, monitoring, and verification protocols if

they do not currently exist

. Deleted: Oother

Deleted: those

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36 A. Quantification, Monitoring, and Veri fication for End-Use Energy Efficiency

1. Introduction

For rate-based state plans, a key element of the plan is a demonstration of how the state,

and related entities with enforceable obligations under the plan, will measure and verify energy

savings to be achieved the through the implementation of end-use energy efficiency measures

incorporated in the plan. 50 

### In the context of demand-side energy efficiency programs currently

overseen by state PUCs, this function is typically addressed through an evaluation, measurement,

and verification plan (EM&V plan). This sec tion discusses current state and utility EM&V

practice for end-use energy efficiency programs and discusses considerations related to

acceptable EM&V plan s and evaluation approaches for a state plan under CAA section 111(d).

2. Background on Evaluation, Monitoring, and Verification (EM&V) of Energy

Efficiency Measures

From the time that demand-side energy efficiency (EE) emerged as an important energy

strategy in the 1970s, 51 

### efforts to evaluate the impacts of EE actions have been critical to their

success, credibility, and expansion. Starting with measurement and verification (M&V) of

individual projects, these efforts have evolved to the point where there is now a mature and

rigorous evaluation, measurement, and verifica tion (EM&V) industry. This industry includes

many professional firms, protocols and guidelines, training and certification programs,

regulatory oversight, and established conferences with a rich library of published reports and

publically available data and analyses.

State agencies responsible for planning, implementing, and evaluating demand-side

energy efficiency programs and polici es utilize EE savings values, as follows: 52

50 In this section we use the term “end-use energy efficien cy measure” or “energy efficien cy measure” to refer to an

end-use energy efficiency requirement (such as an EERS), and energy efficiency program, or individual installed

energy efficiency measure, such as installation of an energy-efficient air conditioner through an energy efficiency

program.

51 National Energy Program Fact Sheet on the President's Program, April 20, 1977.

52 State and Local Energy Efficiency Action Network. 2012. Energy Efficiency Program Impact Evaluation Guide

. Prepared by Steven R. Schiller, Sch iller Consulting, Inc., available at http://www.seeaction.energy.gov

. Deleted: possible

Deleted: protocol

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37  Projected savings: values reported by a program implementer or administrator before the

efficiency activities are completed

 Claimed savings: values reported by a program implementer or administrator after the

efficiency activities have been completed, prior to independent evaluation of savings

 Evaluated savings: values reported by an in dependent third-party evaluator after the

efficiency activities and an impact evaluation have been completed.

Both claimed and evaluated energy savings involve real-time and/or retrospective

assessments of the performance and implementation of an energy efficiency program or a

portfolio of programs. Important impacts for evaluation include energy and demand savings and

non-energy benefits (e.g., avoided emissions, health benefits, job creation and local economic

development, energy security, transmission and distribution benefits, and water savings). Impact

evaluations also support cost-effectiveness analys es aimed at determining the value of energy

efficiency programs to utility cu stomers and identifying relative program costs an d benefits of

energy efficiency compared to other energy resources, including both demand- and supply-side

options.

Regardless of how the energy savings of an energy efficiency measure are determined, all

energy savings values are estimates of savings and not directly measured. Savings are

determined by comparing energy use after an ener gy efficiency project or measure is installed

(the reporting period) with what is assumed to be the energy use in the absence of the project or

measure (the “counterfactual” scenario, or baseline). Savings therefore depend critically on

baseline assumptions, which are necessarily estimated with varying degrees of accuracy. Figure

1-1 illustrates this concept.

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38 Figure 1. Energy Use Before, During, and After an Energy Efficiency Project is Installed 53



### 3. Current EM&V Practices in States

3.1 EM&V for Demand-Side Energy Efficiency Programs Overseen by State PUCs

Current practice with EM&V for demand-side energy efficiency programs in the U.S. is

primarily defined by state utility commission (PUC) 54 

### requirements for customer-funded

efficiency programs. The level of PUC oversight varies from state to state, but this oversight

process has generated the majority of the industry guidance and protocols for documenting

energy savings from energy efficiency programs. Typically, impact evaluation reports are

prepared based on the requirements established by PUCs and submitted (usually annually) for

PUC review, approval, and use in resource planning and performance assessment. According to a

recent survey, most states (79 percent) rely on independent consultants and contractors to

conduct evaluations, while some states (21 percent) use utility and/or government agency staff. 55

53 Ibid.

54 In some states these government entities are referred to as a public service commission (PSCs) or board of public

utilities (BPU), as well as other names.

55 Kushler, M.; Nowak, S.; Witte, P. , A National Survey of State Policies and Practices for the Evaluation of

Ratepayer-Funded Energy Efficiency Programs, American Council for an Energy-Efficient Economy (ACEEE)

(February 2012), Report Number U122 Available at http://www.aceee.org/ research-report/u122.

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39 The range of EM&V budgets varies signifi cantly between states, typically from two

percent to six percent of total energy efficiency program expenditures. 56 

### The average EM&V

budget in 2011 was about 3.6 percent of program expenditures. Reasons for this disparity may

include the fact that as states expand energy efficiency programs, they may implement more

complex programs, which require additional EM&V. 57 

### EM&V effort in states also typically

increases as the magnitude of program expenditures and energy savings impacts increase, and as

states and utilities gain experience in im plementing energy efficiency programs.

States at the low end of this EM&V expenditure range typically rely heavily on deemed

savings approaches, which are a common and relatively low-cost strategy for documenting

energy savings. Deemed savings are measure-sp ecific stipulated values based on historical and

verified data (in some cases using the results of prior EM&V studies). Unlike other EM&V

approaches, with deemed savings there are no – or very limited – measurement activities.

Instead, only the quantity of energy efficiency measures implemented is verified (e.g., number of

motors installed correctly, number of energy-efficient air conditioners that were purchased using

a program rebate). The verified installed energy efficiency measures are then multiplied by the

estimated (or deemed) energy savings per measure to derive energy savings for each measure

and energy savings for the total number of measures installed through an energy efficiency

program. The use of deemed energy savings is only considered appropriate for efficiency actions

with well-known characteristics. A variant of this approach is the deemed savings calculation

,

which involves the use of one or more agreed-upon (stipulated) engineering algorithms used to

generate energy and/or demand savings associated with energy efficiency measures. These

calculations may include predetermined assumptions for one or more parameters in the

algorithm, but typically require users to input data associated with the actual installed measure

into the algorithm. 58

56 Wallace, P.; Forster, H.J. (2012). State of the Effici ency Program Industry Budgets , Expenditures, and Impacts

2011. Consortium for Energy Efficiency. www.cee1.org/files/2011 CEE An nual Industry Report.pdf

. 57 Expansion of energy efficiency programs may also lead to a reduction in EM&V costs per unit of energy savings,

as programs achieve economies of scale and experience in conducting EM&V activities.

58 Examples of equipment types that are commonly evaluate d using deemed savings values and calculations include

energy-efficient washing machines, computer equipment, and refrigerators, and lighting retrofit projects with wellunderstood operating hours. For deemed savings calculati ons, evaluators collect information about the actual Comment [A8]: The opposite effect can also be

seen: additional programs mean more experience

with EM&V and hence reduced costs.

EPA response:

We have added a footnote in response to this

comment.

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40 The deemed savings values, themselves, are typically centrally located in a “Technical

Reference Manual” (TRM). The content and format of these TRMs vary, but in most cases

consists of a database of standardized, state- or region-specific algorithms (deemed calculations)

and associated energy savings estimates (deemed savings values) for energy efficiency measures.

TRMs also include various data assumptions, sample calculations, and other inputs that the state

uses to develop energy savings values for the range of energy efficiency programs in place. The

benefits to energy efficiency program administrators of using a TRM include reduced EM&V

costs and greater certainty regarding projected, claimed, and evaluated energy savings values

(see definitions above). There are about 20 states currently using TRM databases. 59



### It should be noted that TRM values for individual energy efficiency measures are not

always formally vetted in a regulatory process, although this is a good practice. 60 

### A recent

survey of TRMs found that deemed energy savings values for comparable energy efficiency

measures vary across states and regions. 61 

### The reasons for these variations include the use of

different calculation methodologies, technical a ssumptions, and input variables. Some of these

differences are expected based on relevant differences in weather and baseline assumptions (e.g.,

existing building stock and common practices vary from one state to another). However, other

differences are related to out-of-date input assu mptions and calculation errors. In the context of

state plans, this variation, and in particular data quality issues with some TRMs, raises

consideration of whether complete reliance on ex isting TRM resources for state plans is prudent

and appropriate, including how such reliance could or should be circumscribed.

In addition to the use of deemed savings, states on the higher end of the EM&V

expenditure range rely to a greater extent on a variety of direct measurement approaches for

documenting energy savings. Rather than mandating which EM&V methods must be used in a

installed measures -- such as hours of usage, wattage, and/or equipment capacity -- and combine this with

predetermined assumptions in the algorithm.

59 State and Local Energy Efficiency Action Network, Scoping Study to Evaluate Feasibility of National Databases

for EM&V Documents and Measure Savings , prepared by Jayaweera, T.; Haeri, H.; Lee, A.; Bergen, S.; Kan, C.;

Velonis, A.; Gurin, C.; Visser, M.; Grant, A.; Buckman, A.; The Cadmus Group Inc. (June 2011), available at

http://www.eere.energy. gov/seeaction/pdfs/ emvscoping__databasefeasibility.pdf.

60 State and Local Energy Efficiency Action Network, Energy Efficiency Program Impact Evaluation Guide

, prepared by Steven R. Schiller, Schiller Consulting, Inc. (2012), available at http://www.seeaction.energy.gov

. 61 Ibid.

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41 particular situation, PUCs typically allow uti lities and other program administrators to select

from a range of appropriate EM&V approaches that are consistent with standard practice in the

energy efficiency industry. EM&V analyses and cal culations are then carried out, in most cases

by an independent, third-party evaluator, throu gh a process that is unbiased, uses technically

rigorous methods, effective peer review, and is subject to public review and comment. In

addition, energy savings are frequently certified by the PUC as compliant with requirements

defined in a pre-approved EM&V plan.

EM&V requirements in states with the most experience implementing and overseeing

energy efficiency programs are typically based upon the following industry best practices:

 Use of one or more of the industry-standard EM&V protocols or guidelines (listed

below), as well as the use of deemed sa vings values for well-understood energy

efficiency programs and measures

 Consideration of local factors, such as climate, building type, and occupancy.

 Involvement of stakeholders and solicitation of expert advice regarding EM&V processes

and resulting energy savings impacts.

 Conduct of EM&V activities (e.g., direct equipment measurements, application of

deemed savings, and reporting of impacts) on a regular basis.

 Provision of interim and annual reporting of achieved energy savings.

Despite this well-defined and generally accepted set of industry best practices, many

states with energy efficiency programs use different input values and assumptions (e.g., net

versus gross savings, run-time of equipment, measure lifetime) in applying these practices. This

can result in significant differences in claimed energy savings values for similar energy

efficiency measures between states, even when the same measure type is installed under

otherwise identical circumstances. In response to a growing awareness of this lack of cross-state

comparability, policy makers, regulatory agencies, and other stakeholders are increasingly

advocating for the use of common evaluation approaches across jurisdictions. Several national

and regional EM&V efforts have emerged to promote collaboration and information sharing

across states. These initiatives include the Northeast Energy Efficiency Partnership’s (NEEP)

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42 EM&V Forum, which is active in New England, New York, and the Mid-Atlantic, and the

Pacific Northwest’s Regional Technical Forum (R TF). Both efforts aim to promote multi-state

coordination in EM&V practices, make EM&V results more transparent and publicly available,

and support the adoption of similar definitions, methods, and input assumptions. 62



### In addition to stakeholder efforts to promote EM&V collaboration and information

sharing, a growing number of EM&V protocols and guidelines, some of which have recently

been developed, are being used in the U.S. to promote greater consistency of measurement

techniques and methodologies:

a.

b. U.S. DOE Uniform Methods Project (UMP) Protocols 64



### c. International Performance Measurement and Verification Protocol (IPMVP) 65



### d. Federal Energy Management Program (FEMP) M&V Guidelines 66



### e. American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE)

Guideline 14, Measurement of Energy and Demand Savings 67



### f. California Evaluation Protocols 68



### g. ISO New England (ISO-NE) Manual for Measurement & Verification of Demand

Resources 69

62 For a list of EM&V resources, including more informa tion about these regional EM&V collaboratives, see

http://www1.eere.energy.gov/seeaction/evaluation.html

. 64 U.S. Department of Energy (DOE), Uniform Methods Project (April 2013) , available at:

http://energy.gov/eere/about-us/initiatives-and-projects/uniform-methods-project-determining-energy-efficiencyprogram-savings

. 65 Efficiency Valuation Organization (EVO), International Performance Measurement and Verification

Protocol (2012), available at: http://www.evo-world.org/.

66 FEMP, M&V Guidelines: Measurement and Verification for Federal Energy Projects, Version 3.0 (April 2008),

available at: https://www1.eere.energy.gov/femp/pdfs/mv_guidelines.pdf

. 67 American Society of Heating, Refrigerati ng and Air-Conditioning Engineers (ASHRAE), Guideline 14:

Measurement of Energy and Demand Savings (June 2002) , available at:

https://gaia.lbl.gov/people/ryin/public/Ashrae_guideline14-

2002_Measurement%20of%20Energy%20and%20Demand%20Saving%20.pdf

. 68 California Public Utilities Commissions (CPUC), California Energy Efficiency Evaluation Protocols: Technical,

Methodological, and Reporting Requirements for Evaluation Professionals (2006), available

at: http://www.calmac.org/events/EvaluatorsProtoco ls_Final_AdoptedviaR uling_06-19-2006.pdf. Moved down [1]: International Performance

Measurement and Verification Protocol

(IPMVP) 63 ¶

Moved (insertion) [1]

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43 h. PJM Energy Efficiency Measurement and Verification Manual 70



### i. State and Local Energy Efficiency Action Ne twork, Energy Efficiency Program Impact

Evaluation Guide 71



### j. U.S. DOE Superior Energy Performance M easurement and Verification Protocol for

Industry 72



### While many states are currently relying upon these protocols and guidelines, other states

and regional organizations (e.g., ISOs and RTOs) take the additional step of specify ing accuracy

and uncertainty requirements for energy savings estimates. For example, ISO-NE requires that

energy efficiency bids into its Forward Capacity Market (FCM) ensure that impact evaluations

achieve ±10 percent statistical precision at the 80% confidence interval (see below for more on

FCMs).

Regardless of the evaluation approach followed, the majority of state PUCs and energy

efficiency program administrators aim to strike a balance between the transaction costs of

EM&V activities (i.e., expense, time, staff effo rt) and the resulting reliability, validity, and

usefulness of the estimated energy savings re sults. The appropriate balance between EM&V

costs and the rigor of EM&V – and the related cer tainty of energy savings estimates – is often

determined based on the type of program (including program purpose and goals), level of

program expenditures, and magnitude of anticipated energy savings.

3.2 EM&V for Energy Efficiency Measures used in ISO Forward Capacity Markets

Two Independent System Operators (ISOs) re sponsible for operating regional electricity

grids and overseeing wholesale electricity markets in certain regions of the country have

69 ISO-New England (ISO-NE), Measurement and Verification of Demand Reduction Value from Demand

Resources (2013), available at http://www.iso-ne.com/rules_proced s/isone_mnls/MMVDR/index.html.

70 PJM, Manual 18B: Energy Efficiency Measurement & Verification (2010) , available at

http://www.pjm.com/~/media/documents/manuals/m18b.ashx

. 71 U.S. DOE State and Local Energy Efficiency Action Network (SEE Action), Energy Efficiency Program Impact

Evaluation Guide (December 2012), available

at: http://www1.eere.energy.gov/seeaction/pdfs/emv_ee_program_impact_guide.pdf.

72 U.S. DOE, Superior Energy Performance Measurement and Verification Protocol for Industry (2012), available

at: http://superiorenergyperformance.en ergy.gov/pdfs/sep_mv_protocol.pdf. Comment [A9]: EPA: Added based on

interagency comment.

Comment [A10]: This approach is not separate

from or inconsistent with the listed guides. Please

clarify the language.

EPA response:

We have made edits in response to this comment

to clarify this point.

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44 established forward capacity markets (FCMs) that pay suppliers to ensure sufficient electric

generating capacity is available to meet future peak electricity demand. In operating these

markets, ISO New England (ISO-NE) and PJM both allow demand-side energy efficiency

programs and other demand-side resources to compete directly with electric generators to meet

the regional capacity needs. One requirement for utilities and other energy efficiency program

administrators seeking to bid into the market is to submit an evaluation plan. ISO acceptance of

this evaluation plan “qualifies” energy efficiency programs and projects as prospective market

resources. The evaluation plan specifies the amount of energy and demand savings to be

delivered over the contract period (typically three years into the future), and documents how the

requirements of the ISO’s M&V standards manual will be satisfied.

Based on experience to-date, states bidding their energy efficiency programs into both the

ISO-NE and PJM forward capacity markets are typically subjected to EM&V requirements that

go beyond the evaluation already conducted for the purpose of meeting PUC requirements. This

is because, while PUCs typically require evaluation protocols and procedures for documenting

the cost-effectiveness of annual energy (MWh ) savings, FCMs require the measurement and

verification of capacity (MW) savings during specific peak demand hours. In addition to the

evaluation of energy savings, a separate set of measurement techniques and data collection

protocols are required to document peak demand reduction impacts. Furthermore, attaining the

level of statistical precision and confidence de scribed above typically requires additional

sampling 73 

### than is required by PUCs. One considerat ion is whether EM&V requirements in ISO

capacity markets include components that woul d facilitate better estimation of avoided CO 2



### emissions related to energy efficiency programs include in state plans.

For the states located in ISO-NE and PJM, the common evaluation requirements for FCM

participation have created an impetus for regional collaboration on EM&V practices. New

England and Mid-Atlantic states continue to wo rk together to establish consistent evaluation

protocols through the creation of an “EM&V Forum,” which is convened by the Northeast

73 With additional sampling requirements, energy efficiency program evaluators are typically required to measure a

larger percentage of the energy efficiency measures instal led within the total population of all measures installed

through an energy e fficiency program.

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45 Energy Efficiency Partnerships (NEEP) and supports common evaluation methods, reporting

metrics, and cost sharing on research studies. 74 

### The Forum also serves as a venue for information

exchange to address common EM&V challeng es encountered with FCM participation.

74 More information about the EM&V Forum is available through Northeast Energy Efficiency Partnerships, at

https://neep.org/emv-forum/index

.

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46 3.3 EM&V for Programs and Policies Not Typically Overseen by PUCs

In contrast to energy efficiency programs overseen by PUCs, EM&V is less common for

other types of energy efficiency requirements or programs, especially for minimum energy

efficiency requirements that do not involve the expenditure of electricity ratepayer dollars.

Examples include building energy codes, appliance efficiency standards, various energy

efficiency financing programs, behavioral change programs, and market transformation

programs that target both the suppliers of energy-efficient products and increasing consumer

demand for those products. While these approaches often have substantial impacts in reducing

energy use, they may also face EM&V challenges. In some cases, appropriate evaluation

protocols and approaches have not been developed for some programs and measures. In cases

where appropriate EM&V methods do exist, there may also be less experience applying them. 75



### 4. Considerations for EM&V of End-Use Ener gy Efficiency Programs and Measures in

State Plans

A key consideration for state plans is the process and requirements for EM&V of RE and

demand-side EE measures that result in electrici ty generation or savings. As described in the

preamble, at section VII.F.4, the EPA in tends to develop guidance on acceptable methods that

can be incorporated in an EM&V plan included as part of an approvable state plan. Critical

features of such EM&V guidance, including sc ope, applicability, and minimum criteria, are

discussed in this section.

4.1 Accuracy of Energy Savings Estimates

To document and verify that avoided CO 2 

### emissions from energy efficiency programs

and measures are real and persistent, impact eval uation must be rigorous and transparent. Impact

assessment should also consider the appropriate balance between certainty of results and the

EM&V costs to achieve a specified level of certainty. Because energy savings data are estimates

,

their use as part of the basis for determining the avoided CO 2 

### emissions resulting from energy

75 Examples of such EM&V methods are described in the PNNL report, “Building Energy Codes Program: National

Benefits Assessment: 1992-2040.”; and in Meyers et al, “Energy and Economic Impacts of U.S. Federal Energy and

Water Conservation Standards Adopted From 1987 through 2012” LBNL-6217E. Deleted: may

Deleted: many currently Comment [A11]: This text may overemphasize

EM&V challenges for this subset of measures. For

example, extensive analysis goes into appliance

standards, both ex ante to set the standard and ex

post when standards are reviewed for possible

update.

EPA response:

We have made edits to this text in response to this

comment.

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47 efficiency programs and measures in a state plan will depend upon the level of accuracy of this

information. Therefore, evaluation results should be reported as “expected values”—that is,

energy savings values are expected to be correct within an associated range of certainty. Key

considerations for EM&V of energy efficiency programs and measures in state plans are similar

to those faced in the design of any program evaluation approach: (1) the level of certainty that is

required given a program’s objectives and requirements, and (2) how achievement of that

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48 necessary level of certainty is balanced with the amount of effort (e.g., resources, time, money)

used to obtain that level of certainty.

4.2 EM&V Technical Considerations for State Plans

Using energy efficiency requirements, program s, and measures as an emission reduction

approach in state plans requires considerat ion of several evaluation-related technical

considerations. The following sections describe these considerations.

4.2.1 Qualifying Demand-Side Energy Efficiency Actions

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49 States are currently implementing a wide range of demand-side energy efficiency

requirements, programs, and measures. However, EM&V of some of these programs and

measures are associated with greater levels of measurement precision and certainty than others,

based in part on the EM&V procedures currently in place. For example, energy efficiency

programs subject to PUC oversight and review are frequently evaluated using rigorous

evaluation procedures that are based upon several decades of research and experience. These

energy efficiency programs are subject to a quasi-judicial, public, and transparent review

process, which can lead to adoption of EM&V protocols that convey a relatively high level of

certainty for EM&V results.

In contrast, other energy efficiency requirements and measures, some of which may result

in very significant and cost-effective energy savings (e.g., building energy codes, local tax

credits, EE loan programs, etc.), are not subject to PUC oversight and typically have comparably

fewer EM&V requirements. There are also some newer EE program designs (e.g., certain

behavior-based programs and some market transformation programs 76 

### ) for which rigorous

EM&V methodologies or a track record of energy savings persistence do not yet exist. 77

76 Behavior-based energy efficiency programs aim to affect consumer energy-use behaviors in order to

achieve energy and/or peak demand savings. Techniques to measure the impacts of these program designs

are emerging and currently under development. Market transformation programs are characterized by

strategic intervention in a market to address market barriers and market failures to accelerate market

adoption of energy-efficient technologies and practices, and create lasting market change. While market

transformation approaches can have very high energy savings impacts, by creating sustained deployment

of energy-efficient technologies and practices, evalua tion of these strategies is challenging due to the

involvement of numerous market players and the multi-year timeframe for achieving energy savings. 77 We

note that EM&V approaches and protocols for behavior-based end-use energy efficiency programs do exist, but they

have not been widely applied. For examples, see State and Local Energy Efficiency Action Network (SEE Action),

Evaluation, Measurement, and Verification (EM&V) of Re sidential Behavior-Based Energy Efficiency Programs:

Issues and Recommendations (2012), Prepared by A. Todd, E. Stuart, S. Schiller, and C. Goldman, Lawrence

Berkeley National Laboratory. Available at

http://www1.eere.energy.gov/seeaction/pdfs /emv_behaviorbased_eeprograms.pdf.

77 We note that EM&V approaches and protocols for behavior-based end-use energy efficiency programs do exist,

but they have not been widely applied. For examples, see State and Local Energy Efficiency Action Network (SEE

Action), Evaluation, Measurement, and Verification (EM&V) of Residential Behavior-Based Energy Efficiency

Programs: Issues and Recommendations (2012), Prepared by A. Todd, E. Stuart, S. Schiller, and C. Goldman, Deleted: various

Comment [A12]: One commenter suggests

behavior based programs have been used for

decades. Evaluation approaches exist for them, for

example,

http://www1.eere.energy.gov/seeaction/pdfs/emv_

behaviorbased_eeprograms.pdf.

EPA response:

We have made edits to this text in response to this

comment and have added a footnote referencing

the existence of EM&V protocols for behavior

‐

based programs.

Deleted: ¶

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50 As discussed in the preamble, while the EPA does not intend to limit the types of RE and

demand-side EE measures and programs that can be included in a state plan – provided that

supporting EM&V is rigorous, complete, and consistent with EPA requirements and guidance –

the level and type of documentation required by EPA in an approvable state plan may depend on

whether EM&V practices for that type of program or measure are well established. One option

for organizing these variations in EM&V practices is with a qualitative hierarchy, as follows:

 EM&V procedures and protocols well established – for example, rebate and direct

install programs for appliances , HVAC, and lighting equipment

 EM&V procedures and protocols moderately well established – for example,

building codes and standards

 EM&V procedures and protocols less well established – for example, building

disclosure and labeling programs

Table 2 provides an illustrative characterizatio n of the relative level of EM&V

uncertainty for different types of energy efficiency programs and measures under this approach.

This information is illustrative and generalized . In particular, there are numerous exceptions to

the categorization of the relative uncertainty of EM&V results for different types of programs

and measures listed in Table 2

.

Table 2. Illustrative Characterization of EM&V Procedures and Protocols for

Common Energy Efficiency Programs and Polices 78



### EM&V Procedures and EM&V Procedures and EM&V Procedures and

Lawrence Berkeley National Laboratory. Available at

http://www1.eere.energy.gov/seeaction/pdfs /emv_behaviorbased_eeprograms.pdf.

78 This table is intended as a generalized description, ba sed upon numerous conversations with professional energy

efficiency program evaluators. It should be noted that there are states with building energy codes, behavior

programs, and market transformation programs that are well documented and subject to rigorous EM&V. In

addition, such characterizations will change over time, as EM&V approaches for new and innovative programs and

measures become standard practice. 79 This includes energy efficiency progr ams and measures for which there is

significant experience with EM&V, a robust set of existing EM&V studies and reports, and relatively

straightforward EM&V approaches. Deleted: W

Deleted: 4.1

Comment [A13]: The three tier approach for

relative establishment of EM&V approaches could

be problematic. Some of the approaches in the

middle column on page 47, such as building

commissioning and CHP, have well established

procedures. The discussion seems to omit EM&V

currently used by the ESCO industry. If a tiered

approach were used, it would need to be updated

over time as new approaches become standard

practice. Has EPA considered that and how it will be

done?

EPA response:

We have made edits to this subsection and table 2

in response to this comment. We have also added

text acknowledging the need to update such

categorization over time as EM&V becomes

standard practice for different types of EE

programs and measures. Finally, we note that the

text states that Table 2 is intended to be

illustrative only, not a definitive indication of the

degree to which EM&V protocols are established

for different types of EE programs and measures.

Deleted: 4.1

Deleted: ¶

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51 Protocols Well Established Protocols Moderately Well

Established Protocols Less Well

Established

 Direct install incentive

programs for building

equipment (retrofits and

new construction),

including:

o lighting

o heating,

ventilation, and

air conditioning

(HVAC)

o refrigeration

o motors

 Building commissioning

and retro-commissioning

 Combined heat and

power (CHP)

installations/retrofits

 Electrical distribution

system and transmission

system upgrades  Building energy codes

(requirements and incentive

programs for new

construction, remodels)

 State government

building/operations

programs (procurement,

design standards, etc.)

 Product-specific mid-stream

and upstream market

transformation programs

 Industrial energy efficiency

new construction or retrofits  General education

programs for consumers,

contractors, distributers,

suppliers

 Targeted training

programs

 Building labeling and

disclosure programs

 Point-of-sale product

sales

 Targeted consumer

behavior programs

As discussed in the preamble, at section VIII.F.4, EPA is proposing to allow a wide (or

unlimited) set of energy efficiency program and measure types in state plans, as long as the

energy savings are adequately documented according to rigorous EM&V methods and

appropriate state regulatory oversight. Recognizing these variations in EM&V procedures and

protocols, one option for EM&V requirements and guidance for state plans is to streamline

review of EM&V plans for a pre-defined list of well-understood program types for which

evaluation is straightforward and energy savings results are subject to a relatively low level of

uncertainty. 79 

### Other programs and measures with less well developed EM&V approaches would

require greater documentation in state plans of EM&V methods that will be applied. This

proposed approach is intended to maximize state flexibility and accommodate the full range of

state energy efficiency programs, while simultaneously maintain ing EM&V rigor and

79 This includes energy efficiency pr ograms and measures for which there is significant experience with EM&V, a

robust set of existing EM&V studies and reports, and relatively straightforward EM&V approaches. Deleted: 

### <#> Building commissioning and

retro-commissioning ¶

Deleted: 

### <#> Combined heat and power (CHP)

installations/retrofits¶

Deleted: or

Deleted: limit the eligible types of energy

efficiency programs and measures that could be

included in a state plan to

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52 transparency. As discussed in the preamble, at section VIII.F.4, EPA is also taking comment on

the option of limiting the eligible types of energy efficiency programs and measures that could

be included in a state plan to a pre-defined list of well-understood program types for which

evaluation is straightforward and energy savings results are subject to a relatively low level of

uncertainty.

4.2.2 Avoided Transmission and Distribution (T&D) Losses from End-Use Energy

Efficiency Measures

In general, the difference between the amount of electricity input to the transmission

system by an EGU and the amount ultimately de livered to an end-user constitutes transmission

and distribution (T&D) line losses. 80 

### 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 sy stem in the United States. For every unit of energy use avoided at the

end-use site, energy efficiency also avoids the lo sses that would otherwise occur as electricity is

delivered to consumers through the T&D system. 81 

### Many state PUCs are aware of this

additional benefit of demand-side energy efficiency programs, and actively credit program

energy savings results to account for program contributions to avoided line losses, albeit using a

range of measurement approaches and calculations. A consideration for EM&V requirements

and guidance for state plans is whether to account for avoided T&D losses, and how to do so in a

consistent manner across states.

80 The T&D system includes all the power lines and related equipment used to deliver electricity from an electric

generating plant to an end-use site. Along the way, some of the supplied by the generator is lost due to the resistance

of the wires and equipment that the electricity passes throu gh, as well as reactive power losses in alternating current

systems due to inductance and capacitance. Most of this lost energy is converted to heat. The magnitude of losses in

the T&D system depends on the physical ch aracteristics of the system in question, as well as how it is operated.

81 U.S. EPA, Assessing the Multiple Benefits of Clean Energy: A Resource for States (February 2010), available at:

http://epa.gov/statelocalclimate/documen ts/pdf/epa_assessing_benefits_ch3.pdf Deleted: An alternative approach is

Deleted: EPA is proposing to allow a wide (or

unlimited) set of energy efficiency program and

measure types in state plans, as long as the energy

savings are adequately documented according to

rigorous EM&V methods and appropriate regulatory

oversight. The former option potentially reduces

measurement uncertainty, while the latter approach

may provide more flexibility.

Deleted: in

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53 4.2.3 Reported Energy Savings Values

Energy savings results for energy efficiency programs are often expressed in terms of

annual MWh of savings per year. However, for an assessment of the associated avoided CO 2



### emissions impacts, it may be useful to utilize time-differentiated (i.e., hourly, seasonal) energy

savings data. 82 

### Information about the timing of energy savings has direct implications for

estimating the avoided CO 2 

### emissions that result from an efficiency program or portfolio of

programs. The temporal energy savings profile that results from the application of energyefficient technologies and practices to different end-uses can vary significantly. For example, air

conditioner programs save energy primarily on hot summer days, whereas a refrigerator program

saves energy every hour of the year. Time-differentiated information related to electricity

generation is useful in estimating avoided CO 2 

### emissions. In particular time-differentiated data is

necessary to estimate the marginal avoided CO 2 

### emissions related to electric generation, as

discussed above in section IV.C.3.

In practice, state PUCs around the country have substantially different requirements and

recommendations for evaluating and reporting time-differentiated energy savings. Some energy

efficiency program administrators report annual energy savings impacts, where savings are

typically based on either tracking data (i.e., data used to estimate savings for planning purposes)

or evaluated (i.e., ex-post) savings data. Other programs supplement reporting of annual energy

savings with data on the timing of energy savi ngs, which can be used to identify the marginal

EGU or cohort of marginal EGUs that would ha ve provided generation in the absence of the

energy savings. This supplemental data can be used to more accurately estimate avoided CO 2



### emissions that result from program energy savings. Due to improved evaluation software and

data availability, it is increasingly common fo r energy efficiency program EM&V plans to

include calculation of estimated seasonal or even hourly energy savings as part of the program

evaluation process. A consideration for EM&V requirements and guidance for state plans is the

extent to which time-differentia ted data on energy savings from energy efficiency programs is

available, and whether states can readily acquire such data and information for use in

implementing their state plans.

82 This is often referred to as the “load shape” of energy savings achieved through an energy efficiency measure. Deleted: or

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54 A related consideration is metr ics reported for electricity savings. The primary metric

required to understand the avoided CO 2 

### emissions impacts of energy efficiency programs and

measures is annual MWh of energy saved. In addition, a secondary metric is MW demand

reduction impacts, which may be desirable because it is helpful in identifying the marginal EGU

or cohort of EGUs, in particular units on the build margin. This information is necessary to

estimate the marginal avoided CO 2 

### emissions related to electric generation, when reporting on

avoided CO 2 

### emissions achieved through energy efficiency programs and measures during a plan

reporting period, as discussed above in section IV.C.3. Identifying EGUs on the build margin,

based on estimates of MW demand savings that will be achieved through the implementation of

energy efficiency programs and measures included in a state plan, may also be useful in

projecting emission performance by affected EGUs that will be achieved under the state plan.

4.2.4 Savings Definitions: Net and Gross Savings

As described above, state PUCs typically specify whether energy efficiency program

administrators are required to report either gross or net energy savings, or both. Gross savings

are the change in energy use (MWh) and demand (MW) that results directly from programrelated actions taken by program participants, rega rdless of why they part icipated in a program.

Net savings refer to the change in energy use and demand that is directly attributable to a

particular energy efficiency program. 83 

### Reporting of net savings helps a PUC ensure that energy

efficiency program budgets are being used to pr omote technologies and practices that are not

otherwise being adopted in the marketplace. A consideration for EM&V requirements and

guidance for energy efficiency requirements and programs included in state plans is whether

83 Calculations of net energy savings involve excluding energy efficiency measures undertaken by "free riders" (i.e.,

EE program participants who receive a program rebate ev en though they would have taken the efficiency action

anyway), or adjusting energy savings estimates to account for these effects. Free riders increase program costs

without producing additional energy savings impacts beyond what would have occurred in the absence of the EE

program. Free ridership may also be addressed in the setting of baselines that are used to calculate energy savings.

Estimates of net energy savings may also involve an assessment of "free drivers" (sometimes referred to as

"spillover effects"). These are individuals who do not di rectly participate in an EE program, but who undertake

efficiency actions in response to program activity (e.g., marketing/advertising, greater availability of energyefficient equipment in a marketplace as the result of EE programs). Accurate estimation of free ridership and

spillover effects is complex, especially in areas of the country with robust energy efficiency markets with multiple

non-program influences competing for customers’ attention. Deleted: of

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55 required reporting of energy savings should be specifi ed on either a gross or net basis, or both, to

promote national consistency in measuring the imp act of energy efficiency measures across state

plans. 84



### 4.2.5 “Measure Life” and Persistence of Savings

Measure life and persistence of energy savings describe the ongoing effects of an

installed energy efficiency measure, including the retention of the measure ( i.e. , is it still in

place) and the performance degradation of that measure, which reduces a measure’s achieved

energy savings over time. Typically, program administrators estimate the impact of energy

efficiency programs in terms of first-year savings (in MWh), plus the cumulative MWh savings

realized from that program (or measure) over an assumed “measure lifetime”. Depending on the

mix of energy efficiency measures and thei r assumed measure lives, these energy-savings

benefits may extend from 10 to 15 years, or more into the future from the point of measure

installation. In practice, evaluators determin e measure lifetimes on the basis of engineering

judgment, manufacturer specifications, and some empirical field studies. These values are

frequently entered into PUC-managed state technical databases for ongoing and repeated use in

evaluation studies. For state plans, a key consideration for EM&V is whether measure life and

persistence values for energy efficiency measures documented by states are accurate, up-to-date,

and consistent with those utilized in other states (after accounting for appropriate non-energy

factors, such as weather and building occupancy type).

5. Options for EM&V Requirements and Guidance for State Plans

As EPA develops guidance on acceptable evaluation methods that can be incorporated in

an EM&V plan included as part of an approvable state plan, the agency (as discussed in the

preamble at section VIII.F.4 ) is seeking comment on the appropriate basis for and technical

resources used to establish such guidance, incl uding consideration of existing state and utility

84 If both gross and net savings were requi red to be reported, this would increase the transparency of reported energy

savings estimates, but only one savings value would be used to evaluate the effect of energy efficiency programs and

measures on CO 2 emissions from affected EGUs.

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56 protocols, as well as existing international, national, and regional consensus standards or

protocols, as described in this section.

As summarized in the preamble, and discussed in more de pth in this section, utilities and

states have conducted ongoing evaluation of en d-use energy efficiency and renewable energy

measures and programs for several decades. These evaluations, which include quantification,

monitoring and verification of results, gene rally rely upon a well-defined set of industry -standard

practices and procedures. As a result, existi ng state and utility EM&V requirements and

processes generally provide a solid foundation for minimum EM&V requirements that can be

utilized by the EPA in the development of EM&V requirements and guidance for state plans.

However, measurement approaches vary by state based on multiple factors, including the

measure and program type being evaluated, the level and nature of regulatory oversight, the

degree of state and utility experience with th ese measures and progr ams, and the overall

magnitude of program impacts. Due to this variation in state EM&V approaches, as well as the

specific objectives of a state plan under CAA section 111(d), harmonization of state EM&V

approaches, or inclusion of supplemental EM&V actions and procedures, may be warranted in an

approvable state plan.

As discussed previously, current state EM&V practices involve aligning the level of

EM&V effort (i.e., rigor, reliability, validity, and uncertainty of energy savings estimates) with

the appropriate level of certainty of evaluation results , while taking into consideration the

magnitude of energy efficiency program impacts. This approach is consistent with the objective

of achieving environmental results, ensur ing minimum levels of cross-state consistency, and

supporting and encourag ing the use of energy efficiency re quirements, programs, and measures

in state plans. To advance these objectives, the EPA could take several possible approaches for

documenting energy efficiency savings from measures in state plans.

Options for EM&V requirements and guidance for state plans that incorporate energy

efficiency requirements, programs, and measures include: Deleted: might

Deleted: established

Deleted: best

Deleted: require the

Deleted: to be aligned

Deleted: required

Deleted: and

Deleted: These best practices are

Deleted: credible

Deleted: e

Deleted: e

Deleted: state

Deleted: support

Deleted: the following process considerations are

relevant for documenting

Deleted: or

Comment [A14]: Options listed omit a “middle

of the road” approach such as the Uniform Methods

Project.

EPA response:

We have included a “middle

‐ of

‐ the

‐ road” option in

response to this comment and have included the

Uniform Methods Project (UMP) as an example of

such an option.

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57  Establishing specific EM&V requirements with a level of defined rigor – such as a

required minimum level of precision and accuracy (see discussion of ISO forward

capacity markets above) – for all energy efficiency programs and measures

 Establishing specific EM&V requirements for certain types of widely used energy

efficiency programs and measures – such as those addressed by DOE’s Uniform Methods

Project (UMP) – while establishing a generalized EM&V approach that states can apply

to programs that are relatively new, innovative, or untested

 Establishing a set of generalized, process-oriented EM&V requirements that apply to all

energy efficiency programs and measures, while providing flexibility to customize

EM&V approaches, as appropriate for different types of programs and measures,

provided that EM&V meets these minimum requirements

At one end of this spectrum, establishing program- specific EM&V requirements and an

associated level of rigor for EM&V provides certainty to states in terms of required energy

savings documentation, and do es so in a manner that ensures a consistent level of EM&V rigor

across all state plans. However, this approach may require significant effort by the EPA to

establish such requirements, and could potentiall y duplicate state efforts currently under way to

harmonize EM&V practices. This approach may also limit the variety of valid EM&V

approaches applied at the state level, and by extension the types of energy efficiency programs

and measures that could be included in a state plan . It could also inhibit the development of

innovative EM&V approaches that improve the accuracy of energy savings estimates. At the

other end of the spectrum, if only generalized, process-oriented EM&V requirements and

guidance are established, then a state has maximum flexibility, but also faces somewhat greater

uncertainty about whether the EM&V approach incl uded in a state plan will be approved by the

EPA. This could increase the transaction costs incu rred by states during the development of their

plans, and could possibly delay the full implementation of energy efficiency programs

incorporated in state plans.

Alternatively, a middle-ground approach involves a combination of specific EM&V

criteria for common energy efficiency program and measure types, along with generalized Deleted: ;

Deleted: Establishing no EM&V requirements

at all, beyond basic requirements to quantify and

verify energy savings, and allowing states to

establish their own EM&V protocols;

Formatted: Indent: Before: 0.5", No bullets

or numbering Comment [A15]: Under a rate

‐ based approach,

this is risky and should not be allowed (states with

lax EM&V could potentially generate false efficiency

credits that lower their compliance costs). For rate

‐

based approaches, EPA should consider requiring

strong national EM&V standards for efficiency (and

indicating its preference for this approach here),

drawing from DOE’s uniform methods project and

other state of the art approaches. On the other

hand, for mass

‐ based approaches, EPA should

consider allowing states to use their own EM&V for

efficiency.

EPA response:

We have deleted discussion of this option in

response to this comment.

Deleted: minimum specified

Deleted: , and

Deleted: no specific

Deleted: increased

Deleted: proposed

Comment [A16]: Though this comment

bookends the options, a lack of EM&V requirements

would be extremely problematic.

EPA response:

We have deleted discussion of this option in

response to this comment and the previous

comment.

Deleted: In the middle of the spectrum,

Deleted: some minimum

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58 guidance for emerging program designs and measures

. Such an approach would provide some

level of certainty regarding acceptable EM&V approaches in state plans, while maintaining a

certain degree of flexibility for states to determine an appropriate mix of EM&V approaches

,

given the types of energy efficiency programs and measures included in their plan.

In addition, one option for supplementing either approach described above is to prescribe

who can conduct EM&V activities and prepare energy savings documentation, and to specify

their needed qualifications. This approach is analogous to professional certification requirements

in the accounting and engineering fields, in which a minimum level of credibility, rigor, and

accountability is imparted to the services provided by qualified individuals and firms. Criteria for

eligible evaluators might include a demonstration of independence from those implementing or

administering the energy efficiency programs and measures ( i.e. , identification and mitigation of

potential conflicts of interest) and required minimum levels of training, experience, or

certification. This approach recognizes that th e qualifications, integrity, and independence of

those conducting EM&V of energy efficiency programs and measures, and preparing energy

savings estimates, is critical to assuring best-p ractice EM&V. However, such requirements alone

may not ensure sufficient evaluation rigor.

5.1 Use of EM&V Protocols

Establishing requirements and guidance for EM&V of energy efficiency programs and

measures included in state plans may involve:

 Relying on existing EM&V infrastructure and protocols, most of which have been

established for utility-customer funded energy efficiency programs overseen by state

PUCs

 Establishing new protocols and procedures

Relying on existing approaches has the benefit of utilizing existing resources that can be

relatively quickly ramped up for use in state plans. However, existing state and utility EM&V

infrastructure and protocols may not be applicable to the full range of energy efficiency

programs and measures that a state may want to in clude in its plan. In addition, because existing Deleted: could be specified for all energy

efficiency programs and measures, using general

guidance, and/or establishing EM&V criteria for

specific widely used energy efficiency programs and

measures

Deleted: States would have

Deleted: about

Deleted: as well as

Deleted: some

Deleted: in establishing appropriate

Deleted: for

Deleted: A supplemental approach, in addition to

those

Deleted:

,

Deleted: or

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59 state and utility EM&V infrastructure and protocol s were established to support the goals of state

energy efficiency programs, in current form they may not adequately support the level of EM&V

required for state plans under CAA section 111(d). In particular, this may include the form and

precision of energy savings data and reporting necessary to evaluate avoided CO 2 

### emissions that

result from energy efficiency programs and measure included in state plans.

5.2 EPA Review of EM&V Plans as Part of the State Plan Review Process

As discussed in the preamble, at section VIII.F.4, the EPA is proposing that a state plan

must include an EM&V plan, which is subject to approval by EPA, as part of plan review and

approval. Under this approach, the EPA would review these EM&V plans as part of its review of

submitted state plans. One option is that an approvable EM&V plan could rely primarily on

state- or utility-level EM&V plan review and approval processes, consistent with established

EPA requirements and guidance for EM&V, with open public involvement and state lead-agency

approval

. Using existing state EM&V plan review processes may better ensure that energy

savings estimates are transparent, peer reviewed, and address stakeholder input. Using state

processes also minimizes duplication of state an d the EPA requirements, and balances the need

for EM&V credibility and rigor with an interest in encouraging the deployment of cost-effective

energy efficiency programs and measures through incorporation in state plans.

5.3 General Quality Standards for EM&V Rigor and Accuracy

Since existing state EM&V process vary, EM&V guidance established by EPA may

need to identify and establish minimum criteria for EM&V rigor, accuracy and reliability, and

quality control. Requirements could be (a) a single set of requirements that apply to all energy

efficiency programs and measures in all states or (b) a variable or flexible set of requirements

with increasing levels of EM&V effort and rigor depending on the relative degree of uncertainty

of energy savings from the energy efficiency programs and measures in a state plan. For

example, well understood energy efficiency meas ures with a higher degree of energy savings Deleted:

Deleted: Development of EM&V plans that are

included in a state plan

Deleted: of energy savings reported. Under this

approach, states would develop EM&V plans as part

of the state plan development process. The EPA

would then review these EM&V plans as part of its

review of a submitted state plan.

Deleted: However, this approach could create

uncertainty for states about the approvability of a

proposed EM&V approach in a state plan, and could

also reduce the consistency and comparability of

EM&V approaches used in state plans. At the same

time, existing state EM&V processes currently vary

in the level of regulatory oversight, which could

necessitate establishment of minimum criteria for

EM&V plans.

Deleted: the EPA

Deleted: requirements

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60 certainty might require a lower level of EM&V effort, while measures with greater complexity or

uncertainty of energy savings effect s would require greater EM&V effort. 85



### For s imple, well-understood and straightforw ard energy efficiency programs and

measures, such as lighting retrofits , EPA guidance might specify only verification that measures

were installed and the use of deemed energy savings values ( i.e., lower EM&V effort level)

. In

contrast, EPA guidance might specify more detailed EM&V (i.e., a higher level of EM&V effort)

for less well-understood or more complex energy efficiency programs and measures, such as

behavior programs and market transformation programs

.

A prescri ptive EM&V approach in EPA guidance for different types of energy efficiency

programs and measures would provide states with certainty while supporting a consistent level of

EM&V rigor across all states. A flexible EM&V approach, based on an individual assessment of

the measurement uncertainty related to the energy efficiency programs and measures included in

a state plan could provide states with greater flexibility when selecting energy efficiency

programs and measures. However, the lack of a prescribed EM&V approach in EPA guidance

could increase uncertainty about the approvability of different plan approaches.

6. EM&V Documentation in a 111(d) State Implementation Plan

EM&V documentation will be an important component of state plans that incorporate

energy efficiency programs and measures, because transparency and reproducibility increase

overall confidence in reported energy savings results. A crucial component of EPA’s proposed

approach for evaluating energy efficiency progra ms and measures included in a state plan is a

requirement that state plans that include enfor ceable energy efficiency and renewable energy

measures must include an EM&V plan for these measures. These EM&V plan s would specif y

how achieved energy savings will be retrospectively evaluated at appropriate increments during

the plan period. Decisions about the level of EM &V documentation that is necessary in a state

85 For the purposes of this discussion, lower, medium, and high levels of “EM&V effort” are intentionally

indeterminate. One possibility is that “lower EM&V effo rt” could refer to greater reliance on deemed savings

values, smaller sample sizes for measured savings, fewer direct measurements, and pro portionately greater reliance

on ex-ante estimates. “Medium” and “high” levels of EM&V effort could require incrementally more effort in each

of these areas. Other interpretations of this concept are possible. Deleted: S

Deleted: require

Deleted: , while

Deleted: , would require more detailed EM&V

(high level of EM&V effort).

Deleted: bed

Deleted: all

Deleted: this EM&V documentation is an

Deleted: , which

Deleted: ies a priori

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61 plan must consider tradeoffs between provision of more information and greater transparency,

and the level of EM&V effort required. Excessive documentation requirements may not add

value in terms of transparency, but may discourage the inclusion of cost-effective energy

efficiency options in state plans. However, two basic criteria for EM&V documentation should

be applied in state plans:

 Energy savings documentation should be provided at a level of detail that allows for

recalculation of program energy savings totals; and

 EM&V information in state plans should be provided in a consistent manner across states

to allow for comparison, benchmarking, and more efficient review of plans by the public

and EPA

6.1 Illustrative Example of an EM&V Plan for End-Use Energy Efficiency Programs

Measures in a State Plan

The following is an example of a possible outline of the type s of information that might

be included in an EM&V plan for energy effici ency programs and measures included in a state

plan. An EM&V plan would speci fy how EM&V activities will be conducted and reported for

relevant energy efficiency programs and measur es during a state plan performance period. An

EM&V plan could apply to utility energy efficiency programs that are incorporated into a state

plan on a stand-alone basis, and might also apply to such programs when used to meet

mandatory energy efficiency requirements, such as an EERS, that are incorporated into a state

plan.

Who Will Document Savings and When

 Name of the organization that will prep are evaluated energy savings reports

 Relationship of the organization to the subject energy efficiency program(s) and program

administrator(s)

 Schedule of when the reports will be prepared and what period of time they will cover

 Name of the state or regional government entity, or non-governmental entity, which will

review and certify the evaluated savings Deleted: and their Comment [A17]: Incomplete sentence

– please

clarify.

EPA response:

This was a typo, as this bullet was originally

combined with the second bullet. Thank you for

spotting.

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62  How evaluated energy savings reports will be made publicly available and what the

primary use of the reports will be

Documentation Procedures

 List of energy savings metrics to be report ed (e.g., annual MWh, monthly MWh, hourly

MWh, average MW), and whether gross or net savings, or both, will be reported

 Name of impact evaluation protocols, guid ance documents, and other methods that will

be followed in preparing evaluated energy savings reports

 Description of the range of uncertainty for energy savings estimates indicated in

evaluated energy savings reports, including sources of uncertainty

 Description of assumptions concerning availability of data and data collection methods

 Indicate how the following issues, if applicable, will be addressed in the claimed and

evaluated savings estimates:

o Inclusion of estimates of avoided electrici ty transmissions and distribution losses

o Adjustment of gross savings estimates to net savings estimates (if applicable

o Sources of uncertainty

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63 B. Quantification, Monitoring, and Verifi cation for Renewable Energy Measures

For rate-based state plans, a key element of the plan is a demonstration of how the state,

and related entities with enforceable obligations under the plan, will measure and verify electric

generation that is achieved through the implementation of renewable energy measures

incorporated in the plan. 86 

### This section discusses current state and utility quantification,

monitoring, and verification practices for renewable energy measures, and discusses

considerations related to possible acceptable quantification, monitoring and verification

approaches for a state plan under CAA section 111(d).

States and utilities use a variety of policy instruments to increase the production and use

of renewable energy. The principal mechanisms include: renewable portfolio standards (RPSs),

feed-in tariffs (FITs), tax incentives (e.g., property tax exemptions; production-based tax

incentives; etc.), financial assistance programs (e.g., grants, loans, and other direct financial

assistance based on generating capacity or investme nt level), and other policies (R&D support;

manufacturing incentives; workforce training; net me tering; etc.). Experience to date indicates

that RPSs have led to the vast majority of the increase in renewable energy generating capacity

and generation resulting from state policies. However, FITs and production-based tax incentives

have been among the most important incentives used by states and utilities to help achieve RPS

requirements, as well as to spur additional production and use of renewable energy. Further,

these types of programs rely on measurable electric generation as the basis for compliance or

incentive payments. As a result, the following discussion focuses on quantification, monitoring,

and verification mechanisms related to these stat e policies. Other types of programs (e.g., certain

grant and rebate programs) may not currently qu antify electric generation output from funded

renewable energy projects. However, if such programs were modified to require the collection of

such data, many of the quantification, monitoring, and verification considerations discussed in

this section would also generally apply.

86 In this section we use the term “renewable energy meas ure” to refer to a renewable energy requirement (such as

an RPS), a renewable energy deployment program, or individual installed renewable energy measures, such as

installation of a solar photovoltaic system through a renewable energy deployment program.

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64 1. Renewable Portfolio Standards

A RPS is designed to increas e the amount of renewable energy a distribution utility or

load-serving entity provides to retail electricity customers. 87 

### . This increased customer demand in

turn increases the production of renewable energy to meet demand. To achieve compliance with

a RPS, an increasing share of a distribution utility’s electricity retail sales is required to be

produced or acquired from renewable energy resources and delivered to customers. To verify

compliance, RPSs have been complemented by tracking systems for renewable energy

generation and use. These tracking systems account for the growing amount of renewable energy

that is produced for obligated retail sellers as well as large and small retail energy consumers that

purchase renewable energy on a voluntary basis. 88



### The point of regulation for state RPSs is typically investor-owned electric distribution

utilities, because most RPS apply to entities under the jurisdiction of state PUCs. In a number of

states, municipally-owned utilit ies and electric cooperatives are exempt from state RPS, have

lower RPS requirements, or are required to develop their own renewable energy procurement

targets. Additionally, some states have created separate renewable energy requirements for each

of their affected distribution utilities.

The absolute amount of renewable energy that each distribution utility is obligated to

deliver will vary, with requirements in the form of a fixed amount of renewable energy (either

MWh or MW of capacity) or percentage of retail sales.

There are several pathways that affected distri buted utilities typically have to meet state

RPS requirements, including building and operating renewable energy generating capacity,

purchasing electricity from renewable energy ge nerators, and purchasing the attributes from

renewable energy generation. Many state RPSs take this latter approach. Rather than requiring

87 In states that have restructured their electricity sect ors and introduced retail competition, entities other than a

utility may supply electricity to a retail customer. These en tities use a regulated distribution utility’s network to

deliver electricity to a retail consumer. These entities either generate their own electricity or contract for supply from

wholesale electricity market participants. In this section, we use the term “distribution utility” to refer broadly to

both local distribution companies (LDCs) and other load-serving entities that supply electricity to retail customers.

88 “Voluntary” renewable energy purchases, as used here, refers to renewable energy purchases in addition to the

renewable energy required by RPSs.

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65 each distribution utility to gene rate electricity from its own renewable energy facilities or

purchase electricity from a renewable facility ow ned by others, many states require distribution

utilities to acquire renewable energy certificates (REC s) that represent the attributes of the unit of

renewable electricity produced. 89



### By allowing REC trading, many states have created markets for RECs based on specific

state RPS requirements. Renewable energy generato rs can sell RECs as another product bundled

with the underlying power they produce or sell RECs separately to different customers. Once the

RECs are separated from the power generated, the power has no attributes associated with it and

is considered generic or “null” power. 90



### There are a number of key aspects of RPS design and implementation that affect the

quantification, monitoring, and verification of re newable energy generation used to meet a RPS:

 Eligible renewable energy resources . While most RPS-eligible resources in most states

will result in avoided CO 2 

### emissions from fossil fuel-fired EGUs, some RPS-eligible

resources in some states are responsible for greenhouse gas (GHG) emissions or do not

meet common definitions of renewable energy (e.g., waste coal, coal-bed methane, and

fuel cell operation using fossil-fuel feedstocks).

 Existing and new resources. RPS-eligible re sources can include facilities that began

operation prior to the enactment of the RPS and, more importantly, prior to the proposal

of the emission guidelines by EPA. 91

89 RECs are contractual instruments that convey ownership of th e attributes of a unit of energy generated, but do not

represent the energy itself. The attri butes conveyed with RECs include info rmation about the generator, such as:

type of resource (e.g., wind), plant-level air emissions (if any), geographic location, nameplate capacity (MW),

commercial operation date, ownership, and the eligibility for RPS compliance or voluntary market certification.

90 These distinctions are typically made as part of utility disclosure to customers about the energy resource mix and

emissions of the electricity used by a customer that was su pplied from by the distribution utility. “Generic power” or

“null power” refers to energy th at has no generation attributes or descriptive information ( i.e. , the remaining system

mix of power, after assignment of specified power to diffe rent utility customers, through power purchase contracts

or purchase of generation attributes through RECs). For electric ity labeling, disclosure to customers, or other market

claims, generic or null power is typically assigned the attr ibutes of the remaining system mix of power, after the

assignment of attributes as described above.

91 In the preamble, the EPA is proposing that, for an ex isting state requirement, program or measure, a state may

apply toward its required emission performance level th e emission reductions that existing state programs and

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66  Scope of coverage . In some states a RPS applies to all retail sales in a state, but in others

only a subset of retail sales (e.g., only invest or-owned utility retail sales) are subject to a

RPS.

 Credit multipliers . Some states provide additional incentives for specific eligible

resources in the form of bonus credit toward compliance in their RPS accounting

framework. For example, these states may favor certain resources (e.g., distributed solar

PV), or locally-important resources or t echnologies. The MWh produced or renewable

energy certificates (RECs) related to MW h production from such facilities may be

counted twice or three times toward compliance with a RPS in such states. However,

these credit multipliers and bonuses are not an accurate representation of the amount of

renewable energy generation that is attributable to a RPS. For the purpose of quantifying

the amount of renewable energy produced as a result of a RPS included as a measure in a

state plan, only the actual renewable energy generation used to comply with an RPS is

relevant.

 Banking . Some states permit the carryover of renewable energy produced in one year to

satisfy RPS requirements in a subsequent year. Accounting for year-to-year carryover

should be addressed in a state plan, in order to determine the renewable energy

generation that occurred in a respective reporting year or compliance period.

 Alternative compliance payments (ACP) . Many states allow a compliance alternative

which requires obligated entities to pay a pred etermined fee to the state for each MWh of

RPS shortfall. Although these ACP payments may be directed to programs to promote the

deployment of renewable energy technologies, these payments are not equivalent to

renewable energy generation and should not be accounted as such.

 Interstate issues . While treatment of interstate emission effects is discussed in detail in

section VII, for quantification, monitoring, and verification of renewable energy

measures achieve during the plan period due to actions taken after the date on which the emission guidelines are

proposed (i.e., from June 2014 onward).

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67 generation under a RPS it is important to note that most states allow use of eligible

renewable energy resources located in other st ates to satisfy the state RPS requirements. 92



### Considerations related to the quantif ication, monitoring, and verification of renewable

energy generation used to meet a RPSs depends on the design and implementation of the RPS.

Distribution utilities subject to a RPS may meet their RPS oblig ations by building and operating

their own renewable energy generating facilities , entering into bilateral contracts with other

parties to purchase renewable energy, and participating in the REC market. Each compliance

method has specific implications for the quantification, monitoring, and verification of

renewable energy generation used to meet RPS obligations. Implications under these different

pathways are discussed below.

Build renewable generating facilities

In many states, utilities with RPS obligatio ns may build, own, and operate their own

renewable energy generating facilities. This pathway is often used by vertically integrated

utilities subject to a RPS. For large renewabl e energy generating facilities, production is

measured through a revenue-grade utility meter as it enters the grid at the point of

interconnection. This meter is subject to the same verification standards as for any other

generator participating in the wholesale market.

Some utilities with RPS obligations also bu ild, own, and operate smaller distributed

renewable energy generating facilities. Smaller generators, such as residential rooftop solar PV

systems of less than 10 kW capacity, often don’t have discrete metering of their total

generation. 93 

### State RPS requirements may permit these distributed generators to qualify for use

in meeting utility RPS obligations based on an engineering estimate of their renewable energy

generation output , provided the distributed genera tors are registered with a REC tracking system

and the generation output is verified according to tracking system and RPS rules.

92 This may also include international renewable energy resources, such as Canadian hydroelectric and wind energy

resources, which may be used to comply with some state RPSs.

93 Metered data for these PV systems may only track net el ectricity supply to or from the grid, representing either

surplus generation that is not used to serve on-site electricity load or additional net electri city supplied from the grid

if the system does not meet a building’s total electricity load. Comment [A18]: Suggest including this

clarification in the preamble.

EPA response:

This is discussed in the preamble, in the section

discussing treatment of interstate emission effects

(section VIII.F.6).

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68 Bilateral contract model

Under the bilateral contract mo del, distribution utilities with RPS obligations contract

with renewable energy generators for supply. These contracts typically specify a delivery amount

in MWh over a specified contract period. Thes e supply contracts may be short- or long-term,

may specify generation from certain renewable energy EGUs, and may be solicited through an

RFP or entered into through negotiation. Quantification of renewable energy generation (in

MWh) is accomplished through the use of a revenue-grade meter that measures the flow of

electricity from the generator into the transmi ssion grid. A contract may also stipulate an

adjustment to the metered MWh generation data to account for transmission losses that occur

between the point of injection of electricity to th e transmission grid and the point of receipt at a

utility transmission or distribution system. A rene wable energy supply contract also generally

addresses the ownership of the RECs related to the renewable energy generation. Under such a

contract, purchase of the RECs should accompany the purchase of the electricity, in order for the

utility to satisfy its RPS obligations through the contract. 94



### State RPS compliance processes may provide for PUC review of supply contracts,

including inspection of meters and verification of electricity delivery from the generator to the

utility distribution network through a specified contract path (e.g., through evidence of

transmission rights held or scheduled). The purch asing utility also reports their purchase and

delivery of RPS-compliant renewable energy pursuant to the contract to the state agency

responsible for RPS enforcement, typically the st ate PUC or state energy office. Verification is

accomplished by audit of electricity supply cont racts along with REC tracking system reports of

RECs held by the utility and submitted for retireme nt by the tracking system administrator. Some

state RPS require that electricity from qualifying renewable energy sources be produced within

the state or a specified grid region, or if outside the state or specified grid region, that electricity

be delivered to the state or grid region. In such cases, the verification of electricity delivery is

typically done by the REC tracking system admi nistrator before issuing RECs for the imported

energy. Verification can be provided, for example, by demonstration of scheduled delivery

94 This ensures that multiple parties are not using the sa me MWh of renewable energy generation to comply with

their RPS obligations.

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69 through the ISO or RTO serving the state or region, through demonstration that the seller holds

transmission rights for delivery or poss ession of NERC tags for the energy. 95 

### Bilateral contracts

also typically require certification by the seller that attributes related to the sold electricity have

not been and will not be otherwise sold, retired, claimed, represented as part of energy sold

elsewhere, or used to satisfy obligations in another jurisdiction.

REC model

Under the REC model, renewable energy genera tors register their EGU with a renewable

energy tracking system, which have been established by several regional groupings of states, as

well as a few individual states. 96 

### The registration process collects data about the generator’s

attributes: type of resource (e.g., wind), plant- level emissions, geograph ic location, nameplate

capacity (MW), commercial operation date, ownership, and the eligibility for RPS compliance or

voluntary market certification. After the generator is registered, revenue-meter data is

transmitted to the tracking system. Meter accuracy is verified for renewable energy generators in

the same manner as for any other generator pa rticipating in wholesale electricity markets.

Each MWh of renewable energy generation reported to the tracking system by a

registered generator results in the issuance of a REC, with its own unique serial number and

information about the generator, location, resource type, and the month in which the MWh was

generated, and the month or quarter in which the certificate was issued. The renewable energy

95 A NERC Tag, sometimes referred to as an E Tag, is an electronic tag that is used to track wholesale electricity

transactions that involve the transfer of electricity across or through control areas. NERC Tags allow transmission

system operators to track electricity transactions in real time in order to assess any potential reliability implications

of scheduled power transactions. NERC Tags define the physi cal path of an electricity transaction from the point of

generation to the point of receipt, and also define the fi nancial path, including all parties to a transaction. All

wholesale electricity transactions that will result in the transf er of electricity from one control area to another, or that

involve transfers through a control area, must be accompanie d by a NERC Tag. Based on a real-time assessment of

NERC Tags, system operators can curtail transfers if reliabili ty issues would arise as a result of the transfer. NERC

tags are issued through an electronic system in accordance with specifications established by the North American

Electric Reliability Corporation (NERC).

96 Currently, nine regional renewable energy certificate (REC) tracking systems operate in different regions of

North America, including: Texas Renewable Energy Credit Program, run by the Electric Reliability Council of

Texas (ERCOT), NEPOOL Generation Information System (GIS), PJM Generation Attribute Tracking System

(PJM-GATS), Western Renewable Energy Generation Info rmation System (WREGIS), Midwest Renewable Energy

Tracking System (M-RETS), North American Renewables Registry (NARR), Michigan Renewable Energy

Certification System (MIRECS), Nevada Tracks Rene wable Energy Credits (NVTREC), and North Carolina

Renewable Energy Tracking System (NC-RETS). For more information see

http://apps3.eere.energy.gov/greenpower /markets/certificates.shtml?page=3

.

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70 generator can then sell the renewable electricity as a bundle (both the commodity electricity and

the associated REC) or unbundle the RECs fro m the electricity and sell the two products

separately. Other market participants, such as brokers, REC marketers, and load-serving entities

also maintain accounts with the tracking system so that REC electronic transactions can be

recorded within the tracking system platfo rm. The system tracks each REC through these

transactions and ultimately “retires” the RE C when the final purchaser designates it for

retirement. Retirement could result from the REC bein g used to satisfy a state RPS, or as a result

of a voluntary buyer retiring the REC to demonstrate that they had purchased and used

renewable energy to meet their electricity demand.

Because the tracking system follows the REC from the point of issuance to retirement,

including all interim transactions, it minimizes the opportunity for renewable energy to be

double-counted across, for example, two diff erent state RPSs, or between two voluntary

purchasers. In recent years, the various tracking systems have developed interchange standards

so that RECs generated within one tracking system can be transferred to and used within another

tracking system. 97 

### Note that not every potential interchange possibility is currently supported,

and that many states have additional eligibility restrictions within their RPS that may limit the

use of RECs related to electric generation from distant locations. These include requirements in

some state RPSs for the seller to hold firm transmission rights for delivery of the accompanying

electricity from the renewable energy generator into the respective ISO/RTO system or grid

system in which a state is located.

Small distributed generators

Smaller distributed generators, such as reside ntial rooftop solar PV systems of less than

10 kW capacity, often don’t have discrete metering of their total generation. State RPS

requirements may permit these distributed genera tors to qualify for use in meeting utility RPS

obligations based on an engineering estimate of their renewable energy generation output ,

provided the distributed generators are registered with a REC tracking system and the generation

output is verified according to tracking system and RPS rules. Where such projects are third-

97 Additional linkages between tracking systems are bei ng established. More information can be found at

http://www.narecs.com/resources/registries/

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71 party owned and operated, the pr oject developer will own the RE Cs and factor their revenue

value into their pricing offered to the site host for electricity suppl y. Note also that many utilitysponsored renewable energy incentive programs sti pulate that all RECs resulting from the project

must be transferred from the generation owner to the utility as a condition of participation in the

incentive program. These RECs can then be used by the utility to meet its RPS obligations, or

can be sold to other parties.

State agency role

In many states, the PUC or its equivalent is responsible for establishing the detailed rules

and procedures that obligated parties must foll ow to comply with a RPS. The PUC is usually

responsible for receipt and review of obligated parties’ periodic compliance reports, imposing

compliance penalties as needed, and for evaluating the impacts of the program on energy costs,

generation diversity, and market operations. The list of eligible resources and MWh requirements

are often set through state legislation, but these decisions may also be delegated to the PUC for

study and promulgation through regulations of commission orders.

In some states the energy office may be responsible for certifying the eligibility of

specific generators to participate in the RPS a nd for making siting determinations. In New York,

for example, the New York State Energy Research and Development Authority (NYSERDA) is

responsible for the centralized procurement of the renewable energy needed to meet the RPS for

all of the state’s investor-owned utilities.

2. Feed-in Tariffs

Feed-in tariffs (FITs) are offered by some individual electric dist ribution utilities and

some states for renewable energy systems that meet eligibility criteria. 98 

### Under a FIT, the utility

offers to purchase specific kinds of electricity (e.g., solar) from sellers at posted prices or under a

published pricing formula for a specified period of time. FITs typically have caps on the amount

98 There are several states that offer FITs, including Ca lifornia, Hawaii, Maine, Oregon, Rhode Island, South

Carolina, Vermont and Washington, as well as numerous electric utilities. Deleted: Public Utilities Commission (

Deleted: )

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72 of renewable energy that will be purchased by a utility (in MWh of energy or MW of capacity).

FITs may also include customer impact caps for the tariff as a whole (in total dollars spent or in

specified retail rate impacts allowable), and may also have limits on the size of any participating

renewable energy generator from which the utility will purchase electricity through the FIT. FITs

may also have pricing formula s that are differentiated by resource or that change through time as

specified benchmarks are achieved (e.g., MW of renewable energy generating capacity subject to

the FIT, amount of electricity purchased through a FIT as a percentage of utility sales, or retail

rate impact level reached). Typically, the tariff treats all similarly-situated generators in a

consistent manner.

Quantification of renewable energy generation output under a FIT is accomplished

through the use of a revenue-grade meter to measur e the generator’s injection of electricity into

the grid. The utility’s tariff will typically specify the minimum performance characteristics

and/or certifications that a meter must meet in orde r to be used on its system. Utilities retain the

right to inspect and test the calibration of meters connected to their systems. As the utility will be

paying the generator each month based on the meter reading, it is in the utility’s interest to

ensure that the meter is reading precisely and accurately through time.

Both the utility and the state will need to consider the ownership of the environmental

attributes arising from the renewable energy ge neration purchased by a utility through a FIT, and

whether the renewable energy can be counted toward RPS compliance. If renewable energy

generation purchased by a utility through a FIT may be counted toward RPS compliance, then it

should not be counted separately as another renewable energy program in a state plan.

State agency role

FITs are usually authorized by state statutes that specify which utilities must offer a FIT,

eligibility criteria (e.g., renewable energy resource type, location, project MW generating

capacity limits), and sometimes overall program ta rgets (e.g., total installed MW of generating

capacity subject to the FIT). State statutes may al so specify whether the utilities offering a FIT

will receive the RECs related to purchased electricity generation output for use in complying

with a state RPS, and whether customers recei ving FIT payments may also receive incentives

under other utility and state programs. Typically st ate statutes leave implementation details to the Deleted: e

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73 PUC (or other utility governing body, if applicable, for municipal and cooperative utilities), but

may provide guidance on what to consider in se tting FIT payment levels. Based on this statutory

authority, PUCs develop detailed rules governing implementation, which can include payment

levels and contract length. PUCs may direct the affected utilities to develop standard contracts

with all the terms and conditions spelled out, and these standard contracts must be approved by

the PUC. As with state RPS, the PUC is respons ible for receipt and review of the utility’s

periodic status reports, approving changes to a tariff if needed, and evaluating the impacts of the

tariff on retail prices, generation diversity, and electricity system operations.

Several FITs are offered by distribution utili ties not overseen by PUCs, such as municipal

utilities and rural electric cooperatives. These ut ilities have a variety of governance structures

( e.g. , municipal government, cooper ative board of directors). The utility governing bodies in

these situations will be responsible for receipt an d review of the utility’s status reports, taking

corrective action if needed, and evaluating the impacts of the tariff.

3. State Tax Incentives

Several states offer a variety of tax incentives to promote the production and use of

renewable energy. These currently include sales tax exemptions for certain kinds of equipment

(e.g., PV panels), property tax abatement for impr ovements to a building or facility related to the

asset value of the renewable energy generating system, and income tax credits for the

installation of renewable energy systems based on capacity or investment level. Several states

provide a renewable energy production tax credit based on the amount of renewable energy

generated. This approach is useful because it results in a measureable quantity of renewable

energy electricity generation.

With a production-based tax incentive, the renewable energy generator might claim a tax

credit for each MWh of qualifying renewable energy generation within the state. One design

consideration for a production-based tax incentive th at affects quantification of renewable energy

generation output is whether the electricity must be sold to a third party as opposed to being used

by the site host. In the former case, a revenue-gra de utility meter would be present at the point of

interconnection to the electricity grid, which pr ovides measurement of MWh generation output

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74 for tax compliance purposes. Site-host use might necessitate the installation of an additional

meter within the project site to permit reliable measurement of the renewable energy generator’s

output.

State agency role

Currently, existing state tax policies are primarily under the authority of the state revenue

agency. The state revenue agency might have the primary responsibility for establishing the rules

for production-based tax incentives, although it may seek advice from state energy agencies

regarding the technical aspects of renewable energy generator operation and the behavior of

energy markets. The renewable energy generator might claim the tax incentive through the state

tax collection process and report MWh generation to claim the tax credit. The revenue agency

could receive tax filings from the owner and operators of renewable energy generators and be

responsible for determining whether the taxpayer’s claim for tax incentives is supported by the

MWh generation evidence. Assuming the state revenue authority retains its ability to audit the

taxpayer’s return, it could verify claimed MWh generation.

As with FITs, the renewable energy gene ration resulting from production-based tax

incentives might be used for RPS compliance. If that were to become the case, then it should not

be counted separately in a state plan from MWh generation used to comply with a state RPS.

4. Options for Quantification, Monitoring , and Verification of Renewable Energy

Measures in State Plans

As summarized in the preamble, and discussed in more de pth in this section, utilities and

states have conducted ongoing evaluation of renewable energy measures and programs for

several decades. These evaluations, which include quantification, monitoring and verification of

results, generally rely upon a set of standard practices and procedures. In addition, states have

designed and implemented REC tracking systems to facilitate compliance with state RPS. This

resource provides the ability to track the location and attributes of renewable energy generators,

and the electric generation from these generators , as well as the parties that use RECs for

compliance with state RPS. As a result, existing state and utility requirements and processes for

quantification, monitoring, and verification of renewable energy programs and measures

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75 generally provide a solid foundation for minimum requirements and guidance for EM&V for RE

measures in state plans that are established by the EPA.

The programs discussed above (RPS, FIT, and performance-based tax incentives) all

require quantification, monitoring, and verification of electricity generation from renewable

energy generators, as well as provisions of ot her key information, to determine eligibility and

track program activity or complian ce with regulatory requirements (if applicable). Quantification

of electricity generation is typically through the use of revenue-quality meters, 99 

### or engineering

estimates for small distributed generators. Thes e data are essential to program management,

verification of compliance or payments, budget control, and tracking progress toward goals. For

example, PUCs overseeing compliance with state RPS receive compliance information from

each obligated utility, including retail sales, compliance status based on MWh of electricity

generated by eligible utility-owned renewable en ergy sources, or electricity or RECs purchased

from eligible renewable energy generators, which may also consider application of multipliers or

alternative compliance payments. 100 

### PUCs overseeing utility FIT and state agencies overseeing

performance-based tax incentives managers receive reports containing MWh of electric

generation from qualified electric generators that received payments under either a FIT or tax

incentive. These data are essential for normal program management and accountability.

Current state data requirements under RPS, FIT, and production-based tax incentives are

tailored to the objectives of these programs a nd facilitating effective regulatory oversight.

Typically, avoiding CO 2 

### emissions, while considered a relevant co-benefit, is not a primary

objective of these regulations and programs. As a result, additional information and reporting

may be necessary to accurately quantify the avoided CO 2 

### emissions associated with the

renewable energy generated through an RPS, FIT, or production-based tax incentive that is

included in a state plan.

The following types of information will increas e the accuracy and verifiability of avoided

CO 2 

### emission estimates related to renewable energy requirements, programs, and measures

99 “Revenue-quality meter” refers to a meter used for b illing purposes in wholesale electricity markets, which

typically need to meet ISO or RTO precision requirements or other specifications.

100 Compliance status will also consider the application of credit multipliers or alternat ive compliance payments, if

relevant under an RPS. Deleted: or

Deleted: might be

Deleted: for state plans

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76 included in a state plan. For example, information on the location of the renewable energy

generation (e.g., in-by state or within a specified grid region) of the renewable energy generation

used for compliance with state requirements and programs would be helpful in determining

avoided CO 2 

### emissions. Information about the locati on of electric generators that supplied

electricity generation that was imported to a stat e or grid region, will also be important. Timedifferentiated information related to electricity generation is also useful in estimating avoided

CO 2 

### emissions. In particular time-differentiated data is necessary to estimate the marginal

avoided CO 2 

### emissions related to electric generation. Such time-differentiated data could be

based on metering or engineering estimates for a technology type that indicate the typical

generation profile for the renewable energy resource. This could include time differentiation on

an hourly, daily, or seasonal basis. If RECs can be banked and used or RPS compliance at a later

time than the year in which the electricity ge neration related to the REC occurred, information

about the quantity and vintage of RECs from prior year(s) generation that is used for RPS

compliance will also be useful.

VI. Reporting and Recordkeeping for End-Use Energy Efficiency and Renewable

Energy Programs and Measures

As discussed in the preamble, in section VIII.F.5, reporting and recordkeeping for enduse energy efficiency and renewable energy requ irements and programs will be an important

component of certain types of sate plans. If a state plan incorporates renewable energy and

demand-side energy efficiency requirements and programs under a rate-based approach or

implements a mass-based portfolio approach with such measures, reporting and recordkeeping

requirements for an approvable plan would differ from those applicable to an affected EGU. For

example, these requirements may include complian ce reporting by an electric distribution utility

subject to an end-use energy efficiency res ource standard (EERS) or renewable portfolio

standard (RPS). They may also include reporting by a vertically integrated utility implementing

an approved integrated resource plan. In the latte r instance, the utility may also be the owner and

operator of affected EGUs, but additional reporting of quantified effects of renewable energy and

demand-side energy efficiency measures under the utility plan would be necessary to

demonstrate emissions performance under the state plan. In other instances, a state agency or

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77 entity or a private or public third-party entity may be implementing programs and measures that

support the deployment of clean energy technologie s that are incorporated in a state plan. In each

of these instances, reporting of program compliance or program outcomes is a necessary part of

an approvable plan to demonstrate performance under the plan.

In the preamble, the EPA seeks comment on appropriate reporting and recordkeeping

requirements for entities implementing end-us e energy efficiency and renewable energy

programs included as enforceable measures in a stat e plan, or for entities subject to requirements,

such as an EERS or RPS, that are included as an enforceable state plan measure. This section

provides examples of current reporting and r ecordkeeping under state energy efficiency

requirements and programs, such as EERS, RPS, and utility and state deployment programs for

energy efficiency and renewable energy. The section then examines the suitability of these

reporting and recordkeeping practices as potent ial approaches in an approvable state plan.

A. Reporting for End-Use Energy Efficiency Programs and Measures

Reporting requirements and time frames (i.e. how often reports are required) for entities

implementing energy efficiency programs and measur es are key considerations for state plans. In

a state-regulatory context with PUC oversight, impact reports are the mechanism by which

utilities and other program administrators doc ument energy (MWh) and demand (MW) savings.

These reports serve as basis for PUC review of total achieved energy savings relative to program

goals or regulatory requirements, as well as for determining financial performance incentives for

utilities, where they exist.

In most states, impact reporting is initially conducted at the level of efficiency

“programs” (each consisting of numerous “project” installations or efficiency measures

occurring at individual homes, commercial buildings , or industrial facilities). Program level data

are then aggregated to the “portfolio” level to capture the full impact of energy efficiency

investments occurring under a PUC’s jurisd iction for the timeframe of interest.

Impacts reports are typically submitted annually, but in some cases program

administrators also provide interim (e.g., quarterly) reports. This added step can help inform

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78 progress towards goals, as well as provide for corrections in cases where evaluated (ex-post)

energy savings are not achieving projected or claimed (ex-ante) savings levels.

The information provided below presents a range of common reporting elements and

common practices implemented by state PUCs. Th ese reporting elements and practices – which

raise important considerations for the reporting of energy-efficiency impacts in state plans –

include:

 State EM&V guidelines, protocols, and/ or framework utilized, where applicable

 Energy efficiency policy or program information reported to PUCs in annual reports:

o Short description of the polic ies or programs implemented

o Implementation schedules and timeframes

 Energy-savings impacts reported to PUCs in annual reports:

o Incremental annual and lifetime MWh savings for reporting (in the case of an

energy efficiency program) or compliance years ( in the case of utility compliance

with a multi-year Energy Efficiency Resource Standard)

o Peak demand (MW) impacts (reported in many, but not all states)

 Verification documentation, which shows that installation of energy efficiency measures

occurred, and the installed measures are capable of generating energy savings

 EM&V process followed:

o Date and location of on-site facility visits and field observations

o Description of public process for review of overall EM&V approach, EM&V

plan, and EM&V results

o Information about evaluators:

 Name of firms and individuals performing EM&V activities, and

qualifications

 Certification that evaluators were selected through a public bid process,

and are third-parties unaffiliated with efficiency program administrators or

the state government

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79  EM&V methods used:

o Deemed savings values - name, date, and public location of technical reference

manual (TRM) 101 

### used for deemed savings values

o Direct measurement approaches - description of the measurement approaches and

reference to the EM&V protocols, standards, and guidance documents used

 Other documentation:

o Data about the quantity of measures/projects on which the full program-level

energy savings impacts are based (i.e., information describing the sample size and

sampling procedures used)

o Whether net or gross energy savings 102 

### are estimated, definitions used for gross

and net savings, and the basis for gross to net calculations, if applicable

o Avoided transmission and di stribution (T&D) impacts assumptions, if applied

The energy savings EM&V reporting elements listed above vary from state to state in

terms of type and level of documentation, and reports are provided in different formats from state

to state. This significant variation among states in reporting contents and format raises several

considerations for states utilizing demand-side en ergy efficiency programs in state plans. One is

whether the reporting processes, timeframes, and documentation required by state PUCs,

described above, are sufficient and appropriat e in the context of state plans. Another

consideration is whether lead state agencies that oversee energy efficiency programs should be

required to certify reported energy efficiency savings impacts on behalf of the state, potentially

including certification that the values are approp riate and conservative, and meet their approval.

A final consideration is whether and how energy savings impact reports are made available for

public input and comment prior to finalization, recognizing that impact reports in many states

101 A technical reference manual (TRM) is a document cons isting of predetermined savings values, assumptions,

methods, and calculation approaches for conducting EM&V of state demand-side energy efficiency programs. Most

states with robust efficiency programs rely on a TRM.

102 Gross savings are the change in energy use and/or energy demand that results directly from program-related

actions taken by program participants, regardless of why th ey participated in the program. Net savings refer to the

change in energy use and/or energy demand that is directly attributable to a particular energy efficiency program.

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80 exist but are not easily located or widely accessible by the public, nor are they provided in

consistent formats from state to state.

B. Reporting for Renewable Energy Programs and Measures

1. Typical Reporting and Compliance Requirements under State RPS

Each state has different reporting and compliance requirements for its RPS, but all states

with mandatory RPS require obligated entities to provide compliance reports to the state PUC or

equivalent state oversight agency. Compliance oblig ations are typically specified in authorizing

legislation, regulations, or PUC orders. Compliance is typically on an annual basis, and includes

a list of required reporting elements. Some states also require distribution utilities to provide an

implementation plan describing how they will comp ly with the state RPS rules in the future.

Data requirements for reporting may vary based on the design and implementation of a

RPS. However, for nearly all state RPS requirements, annual compliance report data is based on

measurable electric generation results and verified through tracking system data. 103 

### In some

states, compliance reports may also include st ate-level projections of renewable energy

generation resulting from current or proposed state RPS policies.

The most common form of tracking system for RPS compliance is a regional or state

REC tracking system or registry. These systems track RECs for both the compliance and

voluntary markets. RECs are typically provided with a unique identification number and may be

certified by a third-party verifier. Annual comp liance reports containing REC data typically

include the number of RECs the utility or load -serving entity procured and retired, what

renewable energy generators supplied the RECs, and how much the utility spent on procuring the

RECs.

2. Typical Reporting for Renewable Energy Deployment Programs 104

103 Some state RPS require electric utilities to procure a specified amount of renewable energy generating capacity,

rather than supply a specified number of MWh or percentage of electricity supply from renewable energy generation

to retail customers.

104 In this section, “deployment programs” refers to incentive programs and market transformation programs

designed to accelerate the market deployment of renewable energy technologies.

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81 Renewable energy deployment programs involve the provision of a payment or credit for

a renewable energy project, or for a quantified amount of electricity generation, in the case of

performance-based incentives. Qualification of eligible projects and payment for qualifying

electric generation (or related attributes) require reporting of electric generation and other project

for each specific program. Program administrators use this information to track program progress

and report to PUCs or other oversight entities. The summary below addresses typical reporting

required for utility-administered programs, as well as programs administered by non-profit

entities and state agencies and authorities.

Reporting for utility administered renewable energy incentive programs

Some utilities offer incentives to electricity consumers to acc elerate the deployment of

renewable energy technologies, such as rebates, feed-in tariffs, and net metering programs. As

mentioned previously, it is easier to quantify th e renewable electricity generation resulting from

some programs than it is for others. Utilities administering FITs, for example, will track the

number of customer contracts, resource type , capacity of each contracted project, MWh

generated, and utility expenditures for that ge neration under the tariff. In contrast, utilities

administering a rebate or loan program are more likely to track the number of customer

participants, the type and size of the projects, the cost of the projects, and the amount of rebates

paid or loans provided. Measuring the electric generation output of these projects may not

necessary to evaluate program status.

Net-metering is a renewable energy incentive program that is based on performance, but

where the total output of the net-metered devi ce is often unknown. Utilities reporting on netmetering programs track the number of participating customers, the type and size of net-metered

systems, and overall net-metered capacity. However, the gross amount of electricity generated

may not be known if a single bi-directional meter is used. Such meters only record net electricity

withdrawn from the grid or net electricity productio n supplied to the grid during an identified

time period. Utilities and the customers where the renewable energy generating system is located

may not necessarily know the total electric generation from the renewable energy system, unless

two meters are installed – one to measure to tal output from the customer-sited system, and

another to measure the total electricity purchased from the utility.

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82 Many of these renewable energy deployment programs are developed as part of

requirements by PUCs, and therefore utilities mu st provide reporting on a routine basis to the

PUC about program expenditures and outcomes (typically quarterly or annually). These records

should be readily accessible to states for estimating the impacts of their renewable energy

deployment programs included in a state plan. However, some utility incentive programs are

administered by distribution util ities that are not regulated by a state PUC (e.g., municipal and

cooperative electric utilities). In these instan ces program reporting data may not be readily

available to a state, unless separately required by a state if such programs are included in a state

plan.

Renewable energy deployment programs may be designed and implemented under the

auspices of a utility integrated resource plan (IRP). 105 

### IRPs document how a utility will meet

forecasted annual peak and energy demand over a defined period of time through a combination

of supply-side and demand-side resources. IRPs are typically mandated through state legislation

or PUC orders and may include renewable energy generation planning, particularly as it relates

to compliance with in-state requirements. IRPs are typically submitted on an annual basis to a

state utility commission and/or other state entity, and address a multi-year period (e.g., 10 years

is a typical period for an IRP). IRPs are a good resource for tracking and forecasting utility

renewable energy developments within a state. Though they may not include renewable energy

production data, data included in IRPs may help states project renewable energy generation

trends in subsequent years, and are a good resource for the development of state plans.

Reporting for renewable energy incentive programs administered by non-profit or state

entities

State renewable energy financial incentives are typically administered by a PUC, state

revenue agency, other state agency (e.g., state energy office), or a private non-profit or for-profit

entity contracted by a state agency. These programs may include an administrative process for

105 IRPs are typically required in states with vertically inte grated electric utilities, but are less common in states that

have deregulated their electricity sectors and introduced co mpetition for the supply of electricity to retail customers.

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83 pre-qualification, which in some instances may be competitive (e.g., performance-based

contracts) or available on a first-come, first-serve basis (e.g., capped production tax incentive).

Applications to incentive programs are good sources of data, and program administrators usually

compile data from approved applications to track program status. Additional reporting data is

also typically required to receive the incentive. For example, a production-based tax incentive is

calculated based on the amount of electricity gene rated by a renewable energy installation, which

may be tracked and verified through utility or third-party metering protocols. These reports,

which are currently used for internal reporting for budgetary control and performance evaluation,

and to track other performance metrics for regular public program reporting, could form much of

the basis for reporting under state plans for such measures.

3. Considerations for Reporting Requirements for Renewable Energy Measures

in State Plans

State renewable energy requirements, such as RPS and FITs, and incentive programs

typically include robust reporting requirements. For nearly all state RPS requirements, annual

compliance report data is based on measureable electric generation, using revenue

- quality

meters, and verified through tracking system data. 106 

### Other requirements and programs that

provide performance-based payments and incentives, such as FITs, net metering, and

performance-based tax incentives, also require reporting of metered generation output. 107 

### State

and utility incentive programs where payment of incentives is not based on electric generation

may not currently be sufficient for repor ting under a state plan. Additional reporting

requirements may be necessary if these programs are included as enforceable measures in a state

plan.

In addition to the reporting states currently require for renewable energy requirements

and programs, supplemental reporting information or adjustments may be necessary for state

106 Small distributed renewable energy systems, such as those below 10 kW in capacity, are often allowed to use

engineering estimates to determine annual output.

107 Some of these programs, such as net metering, may also require supplemental reporting in order to track total

generation that avoids CO 2 emissions. In many instances, net meteri ng programs only track the net electricity

supplied to a customer or supplied to the grid by the re newable energy system, rather than total generation output. Deleted:

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84 plans to demonstrate the avoided CO 2 

### emissions associated with these requirements, programs,

and measures. States may need to require additi onal reporting detail, such as the location of

renewable energy generating units that supplie d output used to comply with a state RPS.

Additional reporting detail about when renewable en ergy was generated may also be valuable for

estimating the avoided CO 2 

### emissions from renewable energy generation, especially if a

marginal avoided emission rate approach is used. This includes reporting of the typical

generating profile of a renewable energy generating unit, group of units, or renewable energy

resource type. 108 

### For distributed renewable energy resources, reporting of the MW capacity of

generating systems that are installed as a re sult of state requirements or programs during a

reporting period would also be useful for estimating avoided CO 2 

### emissions. These distributed

resources, since they are located “behind” the utility meter at a customer location, have a similar

effect in reducing the demand for electricity supplied from the grid as end-use energy efficiency

measures.

Example reporting requirements that provide sufficient data for estimating the avoided

CO 2 

### emissions from renewable energy requirements and programs might include the following:

 Metered MWh generation, using a revenue quality meter, or estimates of annual output

for small systems below 10 kW in capacity

 MW capacity of “behind-the-meter” distributed renewable energy generating systems

added during a reporting period as the result of a state program

 For renewable energy resources reported, including through REC data, the typical

generating profile of a renewable energy generating unit, group of units, or renewable

energy resource type

 For REC data, information including the follow ing generator attributes: type of resource

(e.g., wind), plant-level emissions, geographic location, nameplate capacity (MW),

commercial operation date, ownership, and the eligibility for RPS compliance or

voluntary market certification

108 This might include information about the seasonal or da ily generating profile of the generating unit or renewable

energy resource type.

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85 VII. Treatment of Interstate Emission Effects

Programs and measures in a state plan, such as RE and demand-side EE measures, may

affect the emission performance of the interconnect ed electricity system beyond a state border. In

addition, many state measures allow for actions in neighboring states to meet the in-state

requirement, or explicitly address CO 2 

### emissions in neighboring states. For example, many state

renewable portfolio standards allow for generation by qualifying renewable energy sources in

other states to count toward meeting the state po rtfolio requirement. Some states also apply CO 2



### emission requirements related to the generation of power purchased by regulated utilities,

including power imported from out of state.

As discussed in the preamble to the proposal, in section VIII.F.6, the EPA recognizes the

complexity of accounting for interstate effects as sociated with measures in a state plan in a

consistent manner, to minimize the likelihood of double counting. The EPA also realizes that

interstate effects on CO 2 

### emissions from affected EGUs could be attributed in different ways in

the context of an approvable state plan. This section discusses in more detail the options and

alternatives for treatment of interstate CO 2 

### emission effects presented in the preamble. These

options and alternatives could be applied to both projections of plan performance and

demonstration of achieved emission performance under a plan. These options and alternatives

may not be mutually exclusive – in some instances states could apply different approaches,

without introducing the potential for double coun ting of emission effects. One option presented

could lead to double counting of em ission effects, and we highlight these aspects of this option in

the discussion below.

In general, the options and alternatives address different possible state plan scenarios, and

consider the range of interstate approaches that states are currently using to implement electricity

sector policies, such as multi-state emission bu dget trading programs and regional renewable

energy certificate markets for state RPSs. Th e options and alternatives reflect possible

accounting approaches for interstate emission effects under CAA section 111(d) that could

potentially align with these current state program s and measures that we anticipate states may

want to include in a state plan.

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86 A. Background

Electricity flows across state lines. Often el ectricity load centers (i.e., areas of high

electricity demand) in one state are supplied in pa rt by generating units in another state. As a

result, some states are net exporters or import ers of electricity on an annual basis. Reducing

electricity load through improved end-use energy efficiency (e.g., through state energy efficiency

programs) or deploying new renewable energy el ectric generating capacity (e.g., through a state

RPS) therefore can result in CO 2 

### emission effects that are realized outside the state that

implements the regulation or program that produ ces the effects. Reducing electricity demand or

increasing available electric generating capacity also often impacts the economic dispatch curve

and locational economics that are used to dispatch EGUs on a regional basis. As a result, state

end-use energy efficiency and renewable energy regulations and programs often have regional

effects on electricity generation and avoided CO 2 

### emissions. In addition, many state regulations

explicitly address CO 2 

### emissions in neighboring states, or a llow for actions in neighboring states

to meet an in-state regulatory requirement. For example, many state RPS allow for generation in

other states to count toward meeti ng a utility portfolio requirement.

End-use energy efficiency actions reduce elect ricity load, and ultimately impact electric

generation. In some instances improving en d-use energy efficiency will reduce electric

generation nearby a load center (e.g., in the case of a load pocket with limited access to

electricity transmission capacity). In such cases, it may be feasible to directly link in-state enduse energy efficiency programs and measures to avoided CO 2 

### emissions from specific in-state

EGUs. More often, reduction of electricity load will impact EG U dispatch across a regional

generation control area, based on factors such as power plant economics and electricity

transmission capability, and could also impact flows between control areas. 109 

### In these cases,

state end-use energy efficiency programs and meas ures will affect electricity generation in the

state that reduces load, as well as in neighboring states.

109 Often these dispatch economics differ by location, based on electricity demand, transmission constraints, and

generation economics of individual power plants necessa ry for meeting demand (e.g., in competitive wholesale

markets, these factors are represente d through locational marginal prices (LMPs), which determine dispatch).

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87 State RPS regulations also impact electricity generation at a regional level. Over time,

state RPSs result in the introduction of new, incremental renewable energy generating capacity to

regional generation control areas, which affects EG U dispatch at the regional level. State RPSs

are typically applied to electric distribution utiliti es as a percentage of sales (e.g., a specified

percentage of delivered electricity must come from qualifying renewable energy sources). Many

state RPSs do not require the qualifying renewa ble energy electric generation to take place

within the state, or even be delivered into the state, but instead require that the renewable energy

be supplied within (or delivered into) the ISO/RTO in which the state resides. Often, utility

compliance with state RPS is through the submis sion of renewable energy credits (RECs), which

represent the attributes of renewable energy generation but not the actual electricity generated.

As a result, in many cases the intent of the state policy is often to affect the characteristics of the

regional electric generation mix, rather than the state generation mix.

The approach to implementing an RPS may differ in vertically integrated, cost-of-service

states where the distribution utility also owns electric generating capacity and dispatches

generation resources within its se rvice territory. In such cases, the RPS may require a utility to

increase its renewable energy generating capacity, rather than supplying a percentage of the

electricity it delivers to retail customers from renewable energy sources to meet load. A number

of state RPS also include “carve-outs” or “set-a sides” where a portion of the renewable energy

supplied to retail customers must come from renewable energy generating capacity located inside

the state. Most of these carve-outs are for distributed solar photovoltaic generating capacity,

which are located at the point of customer end-use (e.g., rooftop mounted solar PV on residential

homes and commercial buildings). 110 

### Distributed solar PV capacity of ten provides benefits to the

electric distribution system by im proving distribution system reliability and avoiding the need for

distribution system capacity upgrades. This type of distributed renewable energy generation has

effects similar to end-use energy efficiency, as it reduces the customer electricity load that must

110 A number of RPS also have carve-outs for other renewable energy resources, typically those where there is a

significant renewable energy resource located in the state. An example is carve-outs for offshore wind energy in

coastal states.

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88 be met through large EGUs connected to the grid. 111 

### Regardless of the approach taken, state RPS

regulations typically have impacts on EGU dispatch, and related avoided CO 2 

### emissions, beyond

the state border.

B. Summary of Possible Approaches for Treatment of Interstate Emission Effects

As discussed in the preamble, the EPA is proposing a set of approaches for addressing

interstate emission effects that result from the implementation of state plans that incorporate enduse energy efficiency and renewable energy programs. The preamble also solicits comment on

additional alternatives. The proposed approaches in the preamble include:

 For EE programs and measures:

o A state may take into account in its plan only those CO 2 

### emission reductions

occurring in the state that result from demand-side energy efficiency programs

and measures implemented in the state.

o States participating in multi-state plans w ould have the flexibility to distribute the

CO 2 

### emission reductions among states in the multi-state area.

o States could jointly demonstrate CO 2 

### emission performance by affected EGUs

through a multi-state plan in a contiguous electric grid region, in which case

attribution among states of emission reductions from demand-side energy

efficiency measures would not be necessary.

 For RE programs and measures:

o Consistent with existing state RPS policies, a state could take into account all of

the CO 2 

### emision reductions from renewable energy programs and measures

implemented by the state, whether they occur in the state and/or in other states.

o States participating in multi-state plans w ould have the flexibility to distribute the

CO 2 

### emission reductions among states in the multi-state area.

o States could jointly demonstrate CO 2 

### emission performance by affected EGUs

through a multi-state plan in a contiguous electric grid region, in which case

111 Distributed solar PV is also typically peak coincident , meaning it provides its greatest electric generation output

at times of peak system electricity demand. At many times of the day and year, solar PV systems supply electricity

back to the grid, when PV system out put exceeds building electricity demand.

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89 attribution among states of emission reduc tions from renewable energy measures

would not be necessary

This section surveys the range of potential approaches that could be applied for

individual state plans, as well as approaches that could be applied on a regional basis. The

surveyed approaches include those proposed, as well as alternatives. These basic approaches,

including variants of some approaches, include:

 State may only claim the impact of a measure in reducing in-state EGU CO 2 

### emissions

For plan measures such as end-use energy efficiency and renewable energy regulations

and programs, estimating the avoided CO 2 

### emissions from in-state versus out-of-state

EGUs could be addressed through modeling, other analytical tools, or proxy metrics (e.g.,

net import factor).

 State that implements the measure cl aims the emissions reduction benefit

Under this approach, the state that impl ements the measure (e.g., end-use energy

efficiency and renewable energy regulations or programs, or an emission limit that

addresses out-of-state generation) claims the avoided CO 2 

### emissions, regardless of where

they occur.

 Cooperative multi-state accounting

Multiple states are allowed to mutually ag ree to how they will distribute avoided CO 2



### emissions from state plan measures (e.g., end-use energy efficiency and renewable

energy regulations or programs, or an emission limit that addresses out-of-state

generation) across their respective EGU fleets. Avoided CO 2 

### emissions are distributed

among states by agreed formula they derive – an accounting “credit” in one state for

out-of-state avoided CO 2 

### emissions is complemented by an accounting “debit” in

the other state where the avoided CO 2 

### emissions occurred (i.e., through an increase in

reported CO 2 

### emissions or CO 2 

### emission rate).

 Tradable regional EE/RE credit market

This is a variant of the multi-state accounting approach, which could be applicable where

multiple states in a region are implementing rate-based state plans. Under this approach,

state end-use energy efficiency and renewable energy regulations and programs that meet

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90 EM&V guidelines or requirements are allowed to generate credits, based on MWh of

energy savings or renewable energy generation. These credits, which are denoted in

avoided tons of CO 2 

### or avoided MWh, could be used by affected EGUs toward

demonstration of compliance with state rate-based CO 2 

### emission limits within a

designated region. EE/RE credit issuance could be on a project and/or program basis.

State accounting for interstate emission effects would be addressed through the credit

market and determined based on credits held by affected EGUs.

 Regional demonstration by states of EGU emission performance

States are allowed to regionally demonstrat e emission performance by affected EGUs.

Stets jointly demonstrate emission performan ce for affected EGUs, in terms of total CO 2



### emissions (under a mass-based multi-state plan) or weighted average CO 2 

### emission rate

(under a rate-based multi-state plan).

 The EPA jointly assesses regional performance achieved in aggregate by all individual

state plans in a grid region

The EPA assesses interstate effects on a region al basis during the plan review process.

The EPA requires states to agree to an interstate attribution process only if necessary (i.e.,

if regional performance falls short of the aggregated identified performance levels for

affected EGUs in individual st ate plans). Alternatively, the EPA requires plan revisions if

regional performance falls short of the aggregated regional performance level (i.e., the

aggregated identified performance levels for affected EGUs in individual state plans).

Table 3 includes illustrative examples of the application of some of the different

approaches for addressing interstate emission eff ects summarized above. The table explains how

these approaches might be applied in different state plan contexts. The illustrative examples

consider the range of approaches states are currently using to implement electricity sector

policies, all of which interstate effects, such as multi-state emission budget trading programs,

regional renewable energy certificate markets used for state RPS compliance, and end-use

energy efficiency programs.

Table 3. Applied Examples of Intersate Emission Effects Attribution Approaches

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91 1. State May Claim the Impact of a Measure on CO 2 

### Emissions from Affected EGUs

Within its Borders

Under this approach, the effect of a state measure could be applied to help demonstrate

emission performance by affected EGUs in th e state if it has the effect of avoiding CO 2



### emissions from those in-state EGUs. This could be done regardless of whether the action taken to

implement the state measure occurs within or ou tside the state. For example, renewable energy

generation that occurs outside the state as a result of a state renewable portfolio standard

obligation would still be a valid state plan action, provided the out-of-state renewable energy

generation has the effect of avoiding CO 2 

### emissions from affected EGUs inside the state. As

another example, for a state that is a net importer of electricity, improvements in demand-side

EE and related reductions in electricity demand may reduce the need for generation from both

affected in-state and out-of-state EGUs. These electricity demand reductions could be applied to

help demonstrate emission performance by affected EGUs in the state if the reduction in

electricity demand has the effect of avoiding CO 2 

### emissions from those in-state EGUs.

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92 Estimating the effect of RE and demand-side EE measures on in-state versus out-of-state

EGU CO 2 

### emissions could be addressed through modeling, other analytical tools, or proxy

metrics such as a net import factor.

Modeling could be used to assess the interstate effects of state measures on EGU CO 2



### emissions, both for projections of emission performance under the plan and ex post

demonstration of performance achieved. Under th is approach, both projected plan performance

and performance achieved is assessed on a state-by-state basis.

Ex ante projections of plan performance

To project the effect of EE/RE measures un der a state plan, a dispatch model would be

applied to a grid region to estimate the marginal or average avoided CO 2 

### emissions impact of the

plan measures on a state-by-state basis within the region. To the extent that a state’s EE/RE

measures were projected to avoid CO 2 

### emissions from its own in-state EGUs, these effects could

be applied to meet the required level of CO 2 

### emission performance for affected EGUs in the state

plan. (See section IV.C of this TSD for a full discussion of using a dispatch modeling approach

to projected avoided CO 2 

### emissions that will be achieved through a plan.)

Ex post demonstration of plan performance

To assess the state-by-state avoided CO 2 

### emissions that result from the implementation of

a plan, a dispatch model would also be applied to a grid region, on a retrospective “look-back”

basis. This modeling would assess the avoided CO 2 

### emissions resulting from reported MWh of

energy savings and MWh of reported renewable energy generation, as a result of implementation

of EE/RE measures in the plan. Under a rate-based plan approach, modeled estimates of avoided

CO 2 

### emissions, based on reported EE savings and RE generation, could be applied through an

administrative adjustment by the state program ad ministrator or through the issuance of tradable

EE/RE credits within the state. For ex post demonstration under a mass-based plan approach,

performance would be determined based on reported stack CO 2 

### emissions from affected EGUs—

no further analysis would be necessary. (See section IV.C of this TSD for a full discussion of

using a modeling look-back appr oach to estimate avoided CO 2 

### emissions.)

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93 Use of simplified proxy metrics to apportion effects among states

Simplified metrics, such as a net electricity import or export factor, might also be applied

to assess the impact of state actions in avoiding CO 2 

### emissions from in-state affected EGUs. For

example, in a state that imports 30% of electricit y on average during a year, energy savings from

EE measures might be multiplied by a factor of 0.70. 112 

### Avoided CO 2 

### emissions might then be

calculated by multiplying this adjusted energy savings number by the average or marginal CO 2



### emission rate for affected EGUs in the state. This type of approach could be employed in both

projections of plan performance and ex post de monstrations of performance. However, this

method would be subject to uncertainty, as electri city net imports may vary significantly on an

annual basis, due to changes in system dispatch. (See section IV.C of this TSD for a discussion

of dispatch dynamics that affect avoided CO 2 

### emissions.)

This approach would avoid double counting of emission effects of state measures among

states. However, it could reduce incentives for states to employ measures that have a systemwide, regional effect in reducing EGU CO 2 

### emissions. In effect, because an adjustment factor

would be applied to energy savings under this approach, a net importer state would need to

achieve greater energy savings through end-use energy efficiency requirements and programs to

achieve a ton of avoided CO 2 

### emissions under its plan than a state that is not a net importer.

112 In this instance, it would be assumed that 30% of th e reduction in electricity load resulted in avoided CO 2 emissions from out-of-state EGUs that serve electricity load in the importing state.

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94 2. State that Implements the Meas ure Claims the Emission Effects

Under this approach, the state that implements the measure (e.g., an EERS or RPS, or an

emission limit that addresses the attributes of purchased electricity from out-of-state generation)

claims the avoided CO 2 

### emissions, regardless of where they occur.

If the avoided CO 2 

### emissions from state plan measures at the regional level are greater

than avoided emissions from affected EGUs within the state, these interstate effects would need

to be accounted for and applied to affected EGUs within the state. This could be achieved

through an administrative adjustment by the state, or through a tradable credit system that is

limited to affected EGUs in the state.

Under an administrative adjustment approach , out-of-state avoided emissions would be

applied to the in-state EGU fleet by the state program administrator when determining average

fleet CO 2 

### emission rate or tonnage CO 2 

### emissions. Under a tradable credit approach, credits

would be issued for all avoided CO 2 

### emissions resulting from appl icable state plan measures,

without regard to where the avoided emissions occurred. Since the tradable credit system would

be limited to affected EGUs in the state, use of the credits by affected EGUs when demonstrating

compliance with a rate-based emission limi ts would functionally apply the avoided CO 2



### emissions to the state that was responsible for the measure.

This approach provides a clear policy signal an d incentives that reward state actions that

reduce EGU CO 2 

### emissions on a system-wide, regional basis. However, this approach, absent

cooperative accounting among states in a grid region, as described below, will likely lead to

double counting of emission impacts among states, which could reduce the overall emissions

reductions achieved through state plans on a national basis under CAA section 111(d). We also

note below that other approaches could also provide incentives for a regional, system-based

approach to achieving CO 2 

### emissions reductions from affected EGUs, without raising the

prospect of double counting of emission effects among state.

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95 3. Cooperative Multi-State Accounting of Interstate Emission Effects

Under this approach, multiple states would be allowed to mutually agree on how they

will distribute avoided CO 2 

### emissions from RE and demand-side EE measures across their

respective EGU fleets. Avoided CO 2 

### emissions would be distribute d among states according to a

formula that they specify. Based on this agreed formula, each state would adjust its demonstrated

emission performance by affected EGUs accordin gly. In effect, a “credit” for out-of-state

emission effects in one state would be complemented by a “debit” for such effects in another

state.

This approach provides states with discretion about how to attribute interstate effects,

based on their situations and policy preferences in a grid region. Importantly, this approach also

avoids the potential for double counting of interstate emission effects among states. However,

this approach is premised on regional collaboration among all states in a grid region. Not all

states in a grid region may be willing to cooperate in implementing such an accounting approach.

4. Tradable Regional EE/RE Credit Market

Under this approach, RE and demand-side EE ac tions that meet applicable quantification,

monitoring, and verification requirements would be issued tradable credits that could be applied

by affected EGUs to their reported CO 2 

### emission rates when demonstrating compliance with an

emission limitation in a state plan. 113 

### A credit issued in one state could be used by an affected

EGU in another state toward meeting its respective rate limit. 114



### A regional credit market would be premised on agreement among states that credits

issued throughout a region could be used in multiple states. The distribution among different

states of usage of the credits would be determined by economic factors such as credit prices and

113 These credits might be denoted in avoided CO 2 emissions or MWh of electricity savings or electricity generation,

as described above in Section VI.E.3., incorporating RE and demand-side EE measures under a rate-based approach.

Depending on a state’s circumstances and its plan approach, these tradable credits might represent a new instrument

created for use under a state plan, or a state might use an existing instrument, such as RECs.

114 Credits could be issued on a program or project basis. The types of measures for which credits could be issued

and the basis for issuing credits would be an enforceable element of a state plan.

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96 EGU marginal emissions abatement costs. In eff ect, accounting of interstate effects would be

allocated among states based on prices in the credit market.

This approach is applicable if multiple states are implementing rate-based state plans.

Where states were implementing a mix of rate-based and mass-based state plans in a shared grid

region, this approach would lead to double count ing of emission effects among plans, unless this

market-based EE/RE credit approach was also coupled with a cooperative accounting agreement

among states. In this latter instance, for st ates implementing a mass-based approach, where

credits for avoided CO 2 

### emissions are transferred to affected EGUs located in another state for

compliance purposes, the state from which credits were transferred would adjust its reported CO 2



### mass emission from affected EGUs when demonstrating achievement of the required CO 2



### emission performance level by affected EGUs identified in the state plan. 115



### 5. Regional Demonstration by States of Emission Performance

Under this approach, multiple states would demonstrate CO 2 

### emission performance by

affected EGUs on a regional basis. 116 

### This could allow states in a contiguous grid region to

implement a portfolio of RE and demand-side EE measures without the need for state-by-state

attribution of avoided CO 2 

### emissions. Instead, states would asse ss the impact of state measures in

avoiding CO 2 

### emissions from the fleet of affected EGUs in the multi-state region.

This approach creates incentives for the implementation of system-based approaches that

collaboratively reduce EGU CO 2 

### emissions on a regional basis, while also avoiding the need to

attribute interstate emission effects among states . However, regional collaboration will require

more time for the development of multi-state pl ans. This approach is also premised on the

115 Note that in this example, state reporting of overall achieved CO 2 emission performance by affected EGUs

under a state plan is distinct from demonstration of compliance by affected EGUs subject to a mass-based CO 2 emission limit. For EGU compliance, no adjustment would be made to CO 2 emissions reported by affected EGUs

subject to the mass-based emission limit, even though emis sions from these affected EGUs may have been reduced

as a result of EE/RE regulations and programs implemented in a neighboring state. In this case, the state would

adjust the overall CO 2 emissions from the affected fleet to account for the “export” of avoided CO 2 emission credits,

in order to demonstrate the overall level of CO 2 emission performance that is assumed to have been achieved by the

affected EGU fleet under the plan.

116 This approach could be applied for CO 2 emission performance on either a rate or mass basis.

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97 willingness of all states in a gr id region to participate in the development and implementation of

a multi-state plan. Some states in a grid re gion may be unwilling to collaborate regionally.

6. Assessment of Interstate Effects by the EPA in the Course of State Plan Review

Under this approach, the EPA would evaluate interstate effects on a regional basis during

the plan review process. 117 

### The EPA would assess the emissions performance of affected EGUs

on a regional basis, considering the measures contained in the group of state plans for a

respective grid region. Under this approach, the EPA might conduct an analysis that considers all

of the state program measures together on a combined basis and evaluates projected emissions

performance achieved by affected EGUs in the region.

To the extent that all affected EGUs in a regi on are projected to achieve the required level

of performance represented in individual state pl ans, or are projected to achieve an aggregate

regional level of performance consistent with the level of required performance included in all

state plans in the region, instances of double count ing of interstate effects among states are less

important. The EPA could indicate as part of pl an approval that it will review actual emission

performance achieved by affected EGUs during the plan period on a regional basis.

VIII. Appendix

Survey of Existing State Policies and Programs that Reduce Power Sector CO 2 

### Emissions

[This appendix will include the content in the document, Survey of Existing State Policies and

Programs that Reduce Power Sector CO 2 

### Emissions. This draft document was submitted to OMB

on April 3, 2014.]

117 For example, this assessment could be for a multi-sta te region that generally aligns with a contiguous grid

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## Appendix for State Plan Considerations

Technical Support Document (TSD)

Survey of Existing State Policies and Programs that

Reduce Power Sector CO 2 

## Emissions

DRAFT

April 3, 2014

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

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## Contents

I. Overview of state climate and energy policies and programs that reduce power sector CO 2

............. 7

II. Existing state and utility policies, programs, and measures that affect EGU CO 2 emissions

............... 9

a. Actions that reduce EGU CO 2 Directly............................................................................................. 10

i. Market

‐ based Emission Limits

.................................................................................................... 10

ii. CO 2 emission performance standards

........................................................................................ 18

b. Energy Efficiency Policies, Programs and Measures

....................................................................... 23

i. Energy Efficiency Resource Standards

........................................................................................ 24

ii. Demand

‐ side Energy Efficiency Programs

.................................................................................. 27

iii. Building energy codes

................................................................................................................. 30

iv. Appliance and equipment efficiency standards

......................................................................... 34

v. Incentives and Finance Mechanisms for Energy Efficiency

........................................................ 35

c. Renewable Energy Policies and Programs

...................................................................................... 37

i. Renewable Portfolio Standards

.................................................................................................. 37

ii. Performance

‐ Based Incentives and Finance Mechanisms for Renewable Energy

..................... 40

d. Utility Planning Approaches and Requirements

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## List of Figures

Figure 1: Status of Electricity Restructuring by State

................................................................................... 8

Figure 2: States with Active Greenhouse Gas Cap and Trade Programs

.................................................... 11

Figure 3: Historical GDP and Greenhouse Gas Emissions in the RGGI Region

............................................ 17

Figure 4: States with Greenhouse Gas Performance Standards

................................................................. 19

Figure 5: Status of Energy Efficiency Resource Standards by State

............................................................ 26

Figure 6: Residential State Energy Code Status

.......................................................................................... 33

Figure 7: Commercial State Energy Code Status

......................................................................................... 33

Figure 8: States with Renewable Portfolio Standards

................................................................................ 38

Figure 9: States with Integrated Resource Planning or Similar Processes

.................................................. 47



## List of Tables

Table 1: Comparison of RGGI and California Emissions Budget Trading Programs

.................................... 12

Table 2: Examples of State CO 2 Performance Standards

............................................................................ 20

Table 3: Examples of Measurement and Verification Requirements for CO 2 Performance Standards

..... 21

Table 4: Examples of Penalties for Noncompliance

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## List of Acronyms

ACEEE

‐ American Council for an Energy Efficient Economy

ACP

‐ Alternative Compliance Payment

BSER

– Best System of Emission Reduction

CACJA

‐ Clean Air Clean Jobs Act

CCR

– Cost Containment Reserve

CHP

– Combined Heat and Power

CEMS

– Continuous Emissions Monitoring System

CO 2 – Carbon Dioxide

CO 2 e

– Carbon Dioxide Equivalent

CDPHE

– Colorado Department of Public Health and Environment

DOE

– Department of Energy

DSIRE

‐ Database of State Incentives for Renewable Energy

EERS

– Energy Efficiency Resource Standard

EGU

– Electricity Generating Unit

EIA

– Energy Information Administration

EM&V

– Evaluation, Measurement, and Verification

EPA

– Environmental Protection Agency

ERP

– Electric Resource Plan

ESCO

– Energy Service Company

GDP

– Gross Domestic Product

GHG

– Greenhouse Gas

GW

– Gigawatt (1 GW = 1,000 MW)

GWh

– Gigawatt

‐ hour (1 GWh

= 1,000 MWh)

IECC

‐ International Energy Conservation Code

IOU

– Investor

‐ Owned Utility

IRP

– Integrated Resource Planning

kWh

– Kilowatt

‐ hour

LBNL

– Lawrence Berkeley National Laboratory

LDC

– Local Distribution Company

MERP

‐ Metropolitan Reduction Proposal

MMBTU

– Million British Thermal Units

MW

– Megawatt

MWh

– Megawatt

‐ hour (1 MWh

= 1,000 kWh)

NOx

– Nitrogen Oxides

PBF

– Public Benefit Funds

PBI

– Performance

‐ based Incentives

RGGI

– Regional Greenhouse Gas Initiative

REC

– Renewable Energy Certificate

RES

– Renewable Energy StandardI NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW

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– Renewable Portfolio Standard

PUC

– Public Utility Commission

SO 2 – Sulfur Dioxide

VEIC

– Vermont Energy Investment Corporation

WAP

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## Survey of Existing State Policies and Programs that

Reduce Power Sector CO 2 

## Emissions

I. Overview of state climate and energy policies and programs that reduce

power sector CO 2



### Across the nation, many states and regions have shown strong leadership in creating and

implementing policies, programs and measures that reduce greenhouse gas (GHG) emissions

from the power sector, while achieving other economic, environmental, and energy benefits.

These policies and programs can serve as a strong foundation as states develop plans to meet

goals under the Carbon Pollution Standards for existing sources.

This document provides a survey of many of these activities. Policies and programs range from

market

‐ based programs and GHG performance standards that require direct GHG emission

reductions from EGUs, to others, such as renewable portfolio standards (RPS) and energy

efficiency resource standards (EERS), that reduce GHG emissions by altering the mix of energy

supply and reducing energy demand. States have developed their policies and programs with

stakeholder input and tailored them to their own circumstances and priorities. Their leadership

and experiences provided the EPA with important information about best practices to build

upon in this proposed rule.

States vary in their regulatory structures, electricity generation and consumption patterns,

while geography affects factors such as the availability of fuels, transmission networks, and

seasonal energy demand. States have tailored their climate and energy policies and programs

accordingly. For example, in some states, utilities are vertically integrated, meaning that the

one company is responsible for electricity generation, transmission, and distribution over a

given service territory. The state utility regulators have authority over these utilities. In other

states, where the electric power industry has been restructured, generation has been

decoupled from distribution, and retail customers have their choice of electricity suppliers. In

states where restructuring is active (see

Figure 1 ), the utility regulators do not have authority or control over the utilities responsible for

generation, only over the distribution utilities. States rely upon and have access to different fuel

types and house a variety of EGU types within state borders. States are part of regional

electricity grids that do not align with state boundaries. Electricity is imported and exported by

utilities across states throughout each regional grid. Formatted: Font: Not Bold, Font color: Auto

Formatted: Line spacing: Multiple 1.15 li

Deleted: actively restructured Comment [A1]: Revised awkward phrasing

Formatted: Font: Not Bold, Font color: Auto

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### Figure 1: Status of Electricity Restructuring by State

Source: “Status of Restructuring by State”, U.S. Energy Information Administration, accessed March 19, 2014,

http://www.eia.gov/electric ity/policies/restructuring /restructure_elect.html.



### States also have different economic considerations, drivers and approaches when

implementing climate change, energy efficiency and/or renewable energy policies, programs or

measures. Actions can be motivated by state environmental, energy and/or economic concerns.

For example, ten states have passed legislation requiring GHG emission reductions and are

using a combination of c arbon limits, performance standards

, energy efficiency and /or

renewable energy strategies to achieve these targets. 2 

### Other state policies are motivated by

public utility commission (PUC) requirements to achieve all cost

‐ effective energy efficiency

improvements and still others by renewable energy generation requirements. Policies,

programs and measures vary from state to state in their implementation levels and

administration, some run by state agencies and others by utilities with varying mechanisms for

ensuring compliance.

2 States include California, Connecticut, Hawaii, Maine, Maryland, Massachusetts, Minnesota, New Jersey, Oregon,

and Washington. Targets are typically defined on a 1990 base year, aiming to achieve reductions of between 0 and

10 percent by 2020, although Maryland and Minnesota have chosen targets of 25 percent below 2006 levels by

2020, and 15 percent below 2005 levels by 2015 respectively. Deleted: (September 2010) 1

Deleted: DOE,

Deleted: ¶

Deleted: limate change

Comment [A2]: It is unclear what’s meant by

“climate change

... strategies”.

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### This appendix is not exhaustive and is only intended to provide information about strategies

states have used to achieve GHG emission reductions, advance energy efficiency and/or

increase the use of renewable resources. For example, states may also consider policies states

have used to support other low to zero emitting generation beyond what is addressed here.

Policies and programs included in this appendix are not necessarily approvable in the context of

a 111(d) state plan. In order to be approvable, they must meet criteria laid out in this proposed

rule, as described in the earlier part of this Technical Support Document, State Plans

Considerations.



## II. Existing state and utility policies, programs, and measures that affect EGU

CO 2 

## emissions

Some state and utility policies, programs and measures directly target CO 2 

### emissions by

creating specific limits or standards for CO 2 

### in the power sector. Other policies and programs,

such as those that advance energy efficiency and renewable energy, are designed to reduce

energy demand or promote an increase of supply from low or non

‐ GHG emitting sources which

reduces CO 2 

### emissions. Many states that are aggressively pursuing climate change mitigation

look to energy efficiency and renewable energy first, recognizing the potential for low cost GHG

emissions reductions and the economic, reliability and diversity benefits these resources

provide

For example, according to California, “the integrated nature of the grid means that policies

which displace the need for fossil generation can often cut emissions from covered sources

more deeply, and more cost

‐ effectively than can engineering changes at the plants alone,

though these source

‐ level control efforts are a vital starting point.” 3 

### In working to meet its

statewide goal of reducing GHG emissions to 1990 levels by 2020 and 80 percent below 1990

levels by 2050, the state calls their energy efficiency standards “the bedrock upon which

climate policies are built” and uses renewable energy to fill any remaining energy needs.” 4



### Compared to the costs of other climate policies, they find that “energy efficiency provides

substantial emissions reductions and should be an essential element of the BSER CO 2 

### reduction

target.” 5 

### Another state, Connecticut, has a law that requires the state to reduce GHG emissions

to 10 percent below 1990 emissions levels by 2020 and 80 percent from 2001 levels by 2050. 6



### They consider energy efficiency investments, expanded renewable energy and participation in

3 Mary Nichols (Chairman of California Air Resources Board), letter to EPA Administrator Gina McCarthy, December

27, 2013

.

4 Ibid.

5 Ibid.

6 Environment Northeast, Connecticut House Bill No. 5600: An Act Concerning Connecticut Global Warming

Solutions

, accessed March 19, 2014, http://www.env

‐ ne.org/public/resources/pdf/CT_Global_Warming_Bill_Summary.pdf

. Deleted: multiple

Comment [A4]: This document should also

address policies used to further other forms of clean

generation. For example, one that should be added

is Construction Work in Progress for new nuclear

plants. It is unlikely that the four AP1000s under

construction in the Southeast would have been

pursued in the fashion that they were without this

state policy to offset some of the up

‐ front

construction risk.

Comment [A5]: Added a sentence to reinforce

that there are other options available but that this is

not exhaustive.

Deleted: While states often enact energy

efficiency and renewable energy programs for

economic or reliability purposes, such as for

reducing demand for electricity rates Comment [A6]: Can EPA provide an example of

when a state has enacted energy efficiency and

renewable energy programs to reduce electricity

rates? Improving system resilience could be a goal

of an EERS, however.

Comment [A7]: Reworded text to convey

intended message.

Deleted: or avoiding the need to build new

generation, many of the states aggressively pursuing

climate change mitigation look to both energy

efficiency and renewable energy for the bulk of

their reductions.

Formatted: Font: +Body (Calibri)

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of California Air Resources Board, to EPA

Administrator Gina McCarthy.

Deleted: Global Warming Solutions Act

Deleted:

,

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### the Regional Greenhouse Gas Initiative among its top ten strategies to reduce GHGs when

considering cost

‐ effectiveness and GHG

‐ reduction potential. 7



### Beyond these specific policies and programs, some states implement planning requirements

that can affect emissions both directly and indirectly. This section describes a range of existing

state actions that fall into all of these categories.



### a. Actions that reduce EGU CO 2 

### Directly

Existing state actions that directly reduce EGU CO 2 

### emissions tend to fall in one of two

categories: market

‐ based emission limits or emission performance standards.



### i. Market

‐ based Emission Limits

Description



### An emissions budget trading program (or “cap

‐ and

‐ trade” program) is a market

‐ based tool for

reducing pollution. The basic approach, which involves the allocation and trade of

environmental permits, has been used across environmental media, including air pollution

control, clean water regulation, and land

‐ use applications.

As shown in Figure 2 below, t en states have implemented emissions budget trading programs

addressing CO 2 

### and other GHG emissions. These include California’s cap and trade program and

7 States’ §111(d) Implementation Group Input to EPA on Carbon Pollution Standards for Existing Power Plants,

Joint comments from 15 states on Carbon Pollution Standards for Existing Power Plants sent to USEPA

Administrator McCarthy on December 16, 2013. Signatories include: Mary D. Nichols, Chairman of California Air

Resources Board, Robert B. Weisenmiller, California Energy Commission, Michael R. Peevey, Chair of California

Public Utilities Commission, Larry Wolk, MD, MSPH, Executive Director and Chief Medical Offices of Colorado

Department of Public Health and Environment, Dan Esty, Commissioner of Connecticut Department of

Environmental Protection, Collin O’Mara, Secretary of Delaware Department of Natural Resources and

Environmental Control, Dallas Winslow, Chairman of Delaware Public Service Commission, Douglas Scott, Chair of

Illinois Commerce Commission, David Littell, Commissioner of Maine Public Utilities Commission, Robert M.

Summers, Secretary of Maryland Department of the Environment, Kelly Speakes

‐ Backman, Commissioner of

Maryland Public Service Commission, Ken Kimmell, Commissioner of Massachusetts Department of Environmental

Protection, Mark Sylvia, Commissioner of Massachusetts Department of Energy resources, John Linc Stine,

Commissioner of Minnesota Pollution Control Agency, Mike Rothman, Commissioner of Minnesota Department of

Commerce, Thomas S. Burack, Commissioner of New Hampshire Department of Environmental Service, Joseph

Martens, Commissioner of New York State Department of Environmental Conservation, Audrey Zibelman, Chief of

New York State Public Commission, Dick Pederson, Director Oregon department of Environmental Quality, Janet

Coit, Director of Rhode Island Department of Environmental Management, Marion Gold, Commissioner of Rhode

Island Office of Energy resources, Deborah Markowitz, Secretary of Vermont Agency of Natural Resources, James

Volz, Chairman of Vermont Public Service Board, Maia Bellon, Director of Washington State Department of Ecology.

Letter hereafter referred to as “State environmental agency leaders from CA, CO, DE, IL, ME, MD, MA, MN, NH, NY,

OR, RI, VT, WA, Open Letter to the EPA Administrator Gina McCarthy on Emission Standards Under Clean Air Act

Section 111(d), December 16, 2013.” Deleted: TI NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW

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### the nine northeast and mid

‐ Atlantic states participating in the Regional Greenhouse Gas

Initiative (RGGI), consisting of Connecticut, Delaware, Maine, Maryland, Massachusetts, New

Hampshire, New York, Rhode Island, and Vermont. 89



### Figure 2: States with Active Greenhouse Gas Cap and Trade Programs

Policy Mechanics

Design



### An emissions budget trading program establishes an aggregate limit on pollution through an

emissions cap that specifies the total allowable emissions over some time period for all of the

sources subject to the program. To comply with the program, each source must acquire

allowances, through purchases or by allocation from the state, equal to their emissions and

surrender them at the end of each compliance period.

8 “ RGGI Homepage”, Regional Greenhouse Gas Initiative Incorporated, accessed March 19, 2014

http://www.rggi.org/.

9 “Cap and Trade Program”, California Air Resources Board

, accessed March 19, 2014

http://www.arb.ca.gov/cc/ca pandtrade/capandtrade.htm

. 

### Greenhouse Gas Cap and Trade

Last updated 3/24/2014 Formatted: Complex Script Font: +Body

(Calibri)

Deleted: website

Deleted: :

Deleted: http://www.rggi.org/rggi

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### Allowances may be traded among both regulated and non

‐ regulated parties, creating a market

for emission allowances. In turn, the allowance market establishes a price signal for emissions

(a market price for emitting a unit of pollution), which triggers broad economic incentives for

reducing emissions across the covered sector(s) and encourages innovation in developing

emissions control strategies and new pollution control technologies.

There are several key design elements that can vary from program to program:

 Scope of coverage (e.g., sectors and types of facilities covered)

 Applicability (criteria for inclusion of emitting facilities and units in the program)

 Emissions budget (i.e., “cap”) and emissions reduction schedule

 Flexibility provisions in addition to ability to trade emission allowances, including:

o Multi

‐ year compliance periods,

o Allowance banking,

o Offsets (e.g., project

‐ based emissions reductions occurring outside the capped

sector/sources)

 Additional provisions to mitigate price volatility and overall costs

o Auction reserve price

o Cost containment reserve of allowances provided for sale at set price thresholds.

Once the allowance price hits a threshold, an extra supply of allowances are made

available

Table 1 summarizes some of the key design elements of the RGGI and California programs.

Table 1: Comparison of RGGI and California Emissions Budget Trading Programs

Element RGGI California

Applicability 

###  All fossil fuel fired power plants with a

capacity of 25 MW or greater. 10 

###  All facilities in covered sectors emitting at

least 25,000 metric tons CO 2 equivalent

(CO

2 e) or greater. 11

10 RGGI

, Overview of RGGI CO 2 Budget Trading Program (Regional Greenhouse Gas Initiative, 2007), accessed on

March 19, 2014, http://www.rggi.org/docs/program_summary_10_07.pdf

. Formatted: Font: +Body (Calibri)

Formatted Table

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###  Facilities in electric utility sector. 12 

###  Facilities in utility and large industrial sectors

(plus fuel distributors in 2015) 13

Emissions

budget 

###  Recently reduced 45 percent to 91

million tons CO 2 in 2014. Beginning in

2014, cap was reduced a further 2.5

percent per year to 2020. 14 

###  Emissions cap was set at 2 percent below

expected 2012 emissions, declining by 2

percent in 2014 and 3 percent annually from

2015 to 2020. 15

Compliance

period 

###  Must demonstrate compliance every

three years and hold at least 50 percent

of their obligations during first two

years of each period. 16 

###  Facilities must demonstrate full compliance

every three years, but the must surrender

allowances and offsets to cover 30 percent

of their compliance obligations during the

first two years of each three year cycle. 17

Allowance

allocation

method  Each state distributes their share of

allowances in an amount determined by

their applicable statute or regulation.

Approximately 90 percent of CO 2

allowances are auctioned off to EGUs or

the secondary market. 18 

###  Large industrial facilities and electric utilities

are allocated the majority of their

allowances for free, set at 90 percent of the

average efficiency of a given sector. The

remaining allowances are auctioned off. The

share of allowances allocated for free

declines over time. 19

Cost

containment

provisions  An additional five million allowances

became available March 2014 when

market price exceeded $4 per ton. 20



###  Trigger price increases until 2020 when

10 million allowances become available

if price per ton exceeds $10.75 21 

###  A strategic reserve is starting at 1 percent of

allowances, rising over the years to 7

percent. Shares of allowances held in the

reserve will be released at three price trigger

points; $40, $45, and $50 per ton and rise by

5 percent per year including inflation. 22

11 CARB

, Cap and Trade Regulat i on Instructional Guidance, Chapter 2 : Is My Company Subject to the Cap

‐ and

‐ Trade

Regulation (California Air Resources Board, 2012), accessed on March 19, 2014,

http://www.arb.ca.gov/cc/capa ndtrade/guidance/chapter2.pdf

.

12 “Regulated Sources”, Regional Greenhouse Gas Initiative Inc., accessed on May 14, 2014,

http://www.rggi.org/design/ov erview/regulated_sources.

13 CARB

, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014, http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf

. 14 RGGI Inc. “RGGI States Make Major Cuts to Greenhouse Gas Emissions from Power Plants

, ” Regional Greenhouse

Gas Initiative Press Release ( January 13, 2014 ), accessed on March 19, 2014,

http://www.rggi.org/docs/PressReleases/PR011314_AuctionNotice23.pdf

.

15 CARB

, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014, http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf.

16 “CO

2 Offsets” Regional Greenhouse Gas Initiative Inc., accessed on March 19, 2014

http://www.rggi.org/market/offsets

.

17 CARB

, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014, http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf

. 18 “2013 Allowance Allocation”, Regional Greenhouse Gas Initiative, Inc., accessed on March 19, 2014,

http://rggi.org/design/overview/allowance

‐ allocation/2013

‐ allocation

.

19 CARB

, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014, http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf.

20 Thomson Reuters Point Carbon, “RGGI, You’ve Broken through the Cost Containment Reserve Price! Now What?”

Thomson Reuters

, March 17, 2014, accessed March 19, 2014,

http://www.pointcarbon.com/research/promo/research/1.4523252?&ref=searchlist

. Deleted:

‐

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Deleted: :

Deleted: RGGI Fact Sheet

–

Deleted: ;

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eet.pdf

Deleted: :

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Deleted: Press Release

Deleted:

Deleted:

,

Formatted: Subscript Deleted:

,

Deleted:

Deleted: Regional Greenhouse Gas Initiative 2013

Allowance Allocation

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###  Allows unlimited allowance banking 23 

###  Allows unlimited banking of allowances. 24

Offsets 

###  Power plants covered under RGGI are

allowed to use offsets within the RGGI

region 25 to meet 3.3 percent of their

compliance obligation. 26

.  Facilities may use domestic offsets for up to

8 percent of their compliance obligations. 27

A framework has been established to include

international offsets but these are currently

not allowed in the program. 28

Authority



### State and regional GHG emissions budget trading programs are authorized through individual

state legislation and implemented through state regulations. For example, California

implemented their emissions trading program under the authority of its 2006 Global Warming

Solutions Act, which requires the state to reduce its 2020 GHG emissions to 1990 levels. 29 

### Each

RGGI state has separate authorizing legislation, and in some cases their legislation specifically

directs the use of auction proceeds. For example, Maine authorized its participation in RGGI

through Stature 580

‐ A. Title 38 Chapter 3B: Regional Greenhouse Gas Initiative. This statute

21 R GGI Inc.

, Summary of RGGI Model Rule Changes ( Regional Greenhouse Gas Initiative, Inc

., 2013 ), accessed on

March 19, 2014,

http://www.rggi.org/docs/Progra mReview/_FinalProgramReviewMat erials/Model_Rule_Summary.pdf

.

22 CARB Proposed Regulation to Implement the California Cap

‐ and

‐ Trade Program (California Air Resources Board,

2010), accessed on March 19, 2014, http://www.arb.ca.gov/regact/2 010/capandtrade10/capisor.pdf

.

23 RGGI, Overview of RGGI CO 2 Budget Trading Program (Regional Greenhouse Gas Initiative, 2007), accessed on

March 19, 2014, http://www.rggi.org/docs/program_summary_10_07.pdf

.

24 CARB Proposed Regulation to Implement the California Cap

‐ and

‐ Trade Program (California Air Resources Board,

2010), accessed on March 19, 2014

, http://www.arb.ca.gov/regact/2 010/capandtrade10/capisor.pdf

.

25 “CO

2 Offsets” Regional Greenhouse Gas Initiative, Inc., accessed on March 19, 2014

http://www.rggi.org/market/offsets

. 26 Ibid.

Eligible offsets under RGGI include: landfill methane capture and destruction, sulfur hexafluoride (SF

6 ) reduction

from power transmission, afforestation, end use energy efficiency, and agricultural manure management

. 27 CARB

, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014, http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf.

Offsets are initially limited to forestry, urban forestry, livestock methane capture and destruction, and destruction

of ozone depleting substances. However, rice cultivation and coal mine methane are proposed for inclusion in the

program. See: CARB

– Potential New Compliance Offset Projects:

http://www.arb.ca.gov/cc/capa ndtrade/offsets/offsets.htm for more information.

28 Ibid.

29 Assembly Bill 32, California Global Warming Solutions Act of 2006, Division 25.5 (September 27, 2006), accessed

March 19, 2014, http://www.leginfo.ca.gov/pub/05

‐ 06/bill/asm/ab_0001

‐ 0050/ab_32_bill_20060927_chaptered.pdf

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Fact Sheet:

http://www.arb.ca.gov/newsrel/2011/cap_trade_ov

erview.pdf

Deleted: State of

Deleted: Chapter

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### also requires that 100 percent of auction proceeds to go towards carbon reduction and energy

conservation efforts. 30



### The regulatory authority issues individual authorizations to emit a specific quantity of emissions

(“allowances”), which typically represent one ton of a pollutant, in an amount that equals the

emissions cap.

Obligated Parties



### Obligated parties in trading programs are generally the covered facilities. It is the facilities that

are responsible for acquiring enough emission allowances to cover their emissions at the end of

each compliance period. For example, as stated above, RGGI covers fossil fuel fired power

plants 25 megawatts or greater in size. 31 

### The California cap and trade program covers electricity

generators, importers of electricity, and industrial facilities whose annual emissions exceed

25,000 metric tons CO 2 

### e. Starting in 2015, the California trading program will cover distributors

of transportation, natural gas, and other fuels with emissions greater than 25,000 metric tons

CO 2 

### e as well as electricity imports from all sources. 32

Measurement and Verification



### Emissions budget trading programs include requirements for emissions monitoring and

reporting, holding and transfer of allowances, and surrender of allowances (and offsets) in an

amount equal to reported emissions. This is often referred to, generally, as the program

“compliance obligation”).

For example, facilities obligated under the RGGI program must report emissions quarterly to

environmental agencies in their respective state as well as annually to the U.S. EPA Greenhouse

Gas Reporting Program. 34 

### They must provide verification of emissions as well as third party

verification of emission reductions from CO 2 

### offsets. GHG emissions reporting for affected

sources in California is addressed through the California mandatory GHG reporting regulations,

30 Maine revised statutes

, Title 38, Chapter 3

‐ B, section 580

‐ B, the Regional Greenhouse Gas Initiative Act of 2007,

accessed on March 19, 2014, http://www.mainelegislature.org/legis/statutes/38/title38sec580

‐ B.html

. 31 “Regulated Sources”, Regional Greenhouse Gas Initiative Inc., accessed on May 14, 2014,

http://www.rggi.org/design/ov erview/regulated_sources.

32 CARB, Overview of ARB Emissions Trading Program (California Air Resources Board, 2011) accessed on March 19,

2014, http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf.

34 RGGI, Overview of RGGI CO 2 Budget Trading Program ( Regional Greenhouse Gas Initiative

, 2007), accessed on

March 19, 2014, http://www.rggi.org/docs/program_summary_10_07.pdf. Deleted: 33

Deleted: :

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– Regional Greenhouse

Gas Initiative

http://www.rggi.org/docs/RGGI_Fact_Sheet.pdf

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### using a modified version of the reporting platform administered through the EPA Greenhouse

Gas Reporting Program. 35

Penalties for Non

‐ compliance



### Failure to submit allowances in an amount equal to reported emissions can result in emissions

penalties. Penalties can come in the form of additional allowance submission requirements (e.g.,

three

‐ to

‐ one submission requirements to account for any shortfall), as well as other

administrative fines and penalties. For example, if facilities are found to be short of the

allowances necessary to meet their obligation under the RGGI program, they can be fined for

up to three times the price of the value of the allowances they lack. Additional penalties may

also be applied at the state level. 36 

### If facilities under the California program are unable to meet

their compliance obligations in time, they must surrender four allowances for every one

allowance they lack. 37

Implementation Status



### The RGGI program was established in 2009. From 2009 through 2012, the nine RGGI states

invested auction proceeds of more than $700 million in programs that lower costs for energy

consumers and reduce CO 2 

### emissions, including approximately $460 million into energy

efficiency programs. 38 

### The participating RGGI states estimate that those investments are

providing benefits of more than $1.8 billion in lifetime energy savings to energy consumers in

the region. 39

35 CARB, Overview of ARB Emissions Trading Program ( California Air Resources Board

, 2011) accessed on March 19,

2014, http://www.arb.ca.gov/newsrel/2011/cap_trade_overview.pdf.

36 Bifera, Lucas, Regional Greenhouse Gas Initiative ( Center for Climate and Energy Solutions

, 2013),,

http://www.c2es.org /docUploads/rggi

‐ brief

‐ 12

‐ 18

‐ 13

‐ updated.pdf

.

37 C2ES

, California Cap

‐ and

‐ Trade Program Summary Table, (Center for Climate and Energy Solutions, 2012),

available at http://www.c2es.org/docUploads/California

‐ Cap

‐ Trade

‐ Summary.pdf

.

38 RGGI Inc., Regional Investment of RGGI CO 2 Allowance Proceeds, 2012 (Regional Greenhouse Gas Initiative Inc.,

2014), accessed on March 19, 2014, http://www.rggi.org/docs/Documents/2012

‐ Investment

‐ Report.pdf,

Programs include residential, commercial, and industrial programs. Of the $707 million in auction proceeds

invested by RGGI participating states through 2012, 65 percent supported end

‐ use energy efficiency programs.

39 Ibid. Formatted: Default Paragraph Font, Font: 11

pt, Complex Script Font: +Body CS (Arial), 11 pt Deleted: Environmental Protection Agency

, Air

Resources Board

, 10/20/11,

http://www.arb.ca.gov/newsrel/2011/cap_trade_ov

erview.pdf

.

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### Figure 3: Historical GDP and Greenhouse Gas Emissions in the RGGI Region

Between 2005, when RGGI was first announced, and 2012, power sector CO 2 

### emissions in the

RGGI participating states fell by more than 40 percent while GDP in the region grew (see Figure

3)

. 40 

### The RGGI program was not a primary driver for the reductions in RGGI states but the lower

emissions led participating states to adjust the CO 2 

### emission limits. 43 

### In January 2014, the

participating states lowered the overall allowable CO 2 

### emission level in 2014 by 45 percent,

40 Regional Greenhouse Gas Initiative, Open Letter to Gina McCarthy on Emission Standards Under Clean Air Act

Section 111(d), December 2, 2013, accessed on March 19, 2014,

http://www.rggi.org/docs/RGGI_S tates_111d_Letter_Comments.pdf

. By contrast, total U.S. power sector CO 2 emissions fell by 16 percent during the same period of time. See 2014 U.S.

Greenhouse Gas Inventory for more detail:

U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990

‐ 2012 (U.S. Environmental Protection Agency,

2014), accessed May 14, 2014, http://www.epa.gov/climatechange/gh gemissions/usinventoryreport.html.

43 Environment Northeast, RGGI Emissions Trends (Environment Northeast, 2011), accessed on March 19, 2014,

http://www.env

‐ ne.org/public/resources/pdf/ENE_RGGI _Emissions_Report_110502_FINAL.pdf

. The first three

‐ year control period under RGGI, establishing CO 2 emission limits for EGUs, began on January 1, 2009.

Low gas prices, increased renewables, decreased electric demand and weather are considered four primary drivers

of the reductions through 2010 as reported by Environment Northeast in May 2011. Field Code Changed

Deleted: ¶

Formatted: Indent: First line: 0"

Deleted: 41 By contrast, total U.S. power sector

CO 2 emissions fell by 16 percent during the same

period of time. 42

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10 pt

Deleted: Report on Emission Reduction Efforts of

the States Participating in the Regional Greenhouse

Gas Initiative and Recommendations for Guidelines

under Section 111(d) of the Clean Air Act (2013).

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### setting a multi

‐ state CO 2 

### emission limit for affected EGUs of 91 million short tons of CO 2 

### in 2014

and 78 million short tons of CO 2 

### in 2020, more than 50 percent below 2008 levels. 44



### The California economy

‐ wide market

‐ based GHG emissions trading program, which addresses

GHG emissions from multiple sectors, was implemented in 2012 with emissions limitations

beginning in 2013. 45,46 

### While California’s emission trading program, like its state emission limit,

is multi

‐ sector in scope, the state projects that the emissions trading program and related

complementary measures will reduce power sector GHG emissions to less than 80 million

metric tons of CO 2 

### equivalent by 2025, a 25 percent reduction from 2005 power sector

emission levels. 47 

### Prior to the implementation of the emission trading program, California

reports that it reduced CO 2 

### power sector emissions by 16 percent from 2005 to a 2010

‐ 2012

averaging period, a reduction of 16 million metric tons of CO 2 

### equivalent. 48



### ii. CO 2 

### E mission P erformance S tandards

Description



### CO 2 

### emission performance standards can apply either directly to EGUs or to the local

distribution company (LDC) that sells electricity to the customers. (For more information about

electricity is generated and distributed, see Chapter 2 of the Regulatory Impact Analysis).

As of March 2014, four states

‐ California, New York, Oregon and Washington

‐ have enacted

mandatory GHG emission standards that impose enforceable emission limits on new and/or

expanded electric generating units. Three states

‐ California, Oregon and Washington

‐ have

enacted mandatory GHG emission performance standards that set an emission rate for

electricity purchased by electric utilities. 49 

### In addition to these states, Illinois and Montana have

44 RGGI Inc. “RGGI States Make Major Cuts to Greenhouse Gas Emissions from Power Plants,” Regional Greenhouse

Gas Initiative Press Release ( January 13, 2014 ), accessed on March 19, 2014,

http://www.rggi.org/docs/PressReleases/PR011314_AuctionNotice23.pdf.

45 “Cap

‐ and

‐ Trade Program,” California Air Resources Board

, accessed March 19, 2014,

http://www.arb.ca.gov/cc/ca pandtrade/capandtrade.htm

. 46 The California program was developed in coordination with U.S. state and Canadian province WCI partners.

47 State environmental agency leaders from CA, CO, DE, IL, ME, MD, MA, MN, NH, NY, OR, RI, VT, WA, Open Letter

to the EPA Administrator Gina McCarthy on Emission Standards Under Clean Air Act Section 111(d), December 16,

2013

, accessed on March 19, 2014,

http://www.georgetownclimat e.org/sites/defaul t/files/EPA_Submi ssion_from_States

‐ FinalCompl.pdf

. Preliminary California Air Resources Board analyses, based in part on CARB 2008 to 2012 Emissions for Mandatory

GHG reporting Summary (2013), cited in this l etter.

48 Ibid.

49 “Standards and Caps for Electricity GHG Emissions,” Center for Climate and Energy Solutions, accessed on March

19, 2014, http://www.c2es.org/us

‐ states

‐ regions/policy

‐ maps/electricity

‐ emissions

‐ caps

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### requirements that new coal plants capture at least 50 percent of their CO 2 

### emissions (see

Figure 4)

. 50



### Figure 4: States with Greenhouse Gas Performance Standards

Policy Mechanics

Design



### States have implemented three different types of CO 2 

### performance standards that affect EGUs

and/or LDCs differently. The first requires power plant emissions per electricity generated to

be less than or equivalent to an established standard and is directly applicable to EGUs. The

second type places conditions on the emissions attributes of electricity procured by electric

utilities. It consists of standards that are applicable to LDCs that provide electricity to retail

customers. A third type requires that new coal

‐ fired power plants must capture and store a

specific percentage of CO 2 

### emissions. Table 2 provides state examples for each of the types of

CO 2 

### performance standards.

50 Ibid. 

### Greenhouse Gas Performance Standards

Applies to new units and power purchase agreements

Last updated 8/8/2013 Requires carbon capture and storage at new coal-fired power Applies only to new units Deleted: ¶

Deleted: C2

Deleted: ES: http://www.c2es.org/us

‐ states

‐ regions/policy

‐ maps/electricity

‐ emissions

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### In some states, programs are regulated through the Public Utilities Commission (California,

Oregon). New York’s program is regulated through the Department of Environmental

Conservation. Washington’s program is regulated through two different sets of entities

depending on the ownership of the utilities. The Washington Utilities and Transportation

Commission regulate investor owned utilities, and the utility's governing board, Washington

Department of Ecology, and the State Auditor oversees consumer owned utilities.

Obligated Parties



### The emission performance standard can apply either directly to EGUs or to the local distribution

company (LDC) that sells electricity to the customer.

Measurement and Verification



### Obligated parties must measure and report on electricity generation and CO 2 

### emissions on a

regular basis to verify their compliance with the standard. The reporting requirements and

timing varies from state to state and are typically set by the agency that oversees the program

as described under authority above. Table 2 provides an overview of different CO 2 

### performance

standards, while Table 3 provides examples regarding measurement and verification

requirements across California, New York, Oregon, and Washington

Table 2: Examples of State CO 2 

### Performance Standards

51 N ew Y ork Department of Environmental Conservation, Part 251: CO2 Performance Standards for Major Electric

Generating Facilities

, June 12, 2012, accessed March 19, 2014, http://www.dec.ny.gov/regs/83094.html#83097

. 52 Oregon Department of Energy

, Oregon’s Carbon Dioxide Emission Standards f or New Energy Facilities (Oregon

Department of Energy, 2010) http://www.oregon.gov/energy/Siti ng/docs/Reports/CO2Standard.pdf

. 

### What It Does State Examples

Requires power

plant emissions

per electricity

generated to be

less than or

equivalent to the

established

standard; Applies

to EGUs  New York (Part 251, 2012)

‐ New or expanded baseload plants (25 MW and larger)

must meet an emission rate of either 925 lb CO 2 /MWh (output based) or 120 lbs

CO 2 /MMBTU (input based). Non

‐ baseload plants (25 MW and larger) must meet an

emission rate of either 1450 lbs CO 2 /MWh (output based) or 160 lbs CO 2 /MMBTU

(input based). 51

 Oregon (HB 3283; 1997, 2007)

‐ New natural gas

‐ fired power plants (baseload and

non

‐ baseload) must meet an emission rate of 675 lb CO 2 /MWh. Cogeneration and

offsets may be used to comply with the emission standard. 52

 Washington (RCW 80

‐ 70

‐ 010; 2004)

‐ New EGUs 25 MW and larger must have an

approved CO 2 mitigation plan that results in mitigation of 20 percent of the total CO 2 Formatted: Font: +Body (Calibri), Not Bold,

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### Table 3: Examples of Measurement and Verification Requirements for CO 2 

### Performance

Standards

State Measurement and Verification Details

California  The California PUC is responsible for approving any long term financial commitment by

an electric utility and must adopt rules to enforce these requirements as well as

verification procedures. 60



### New York  CO 2 emission regulations require recordkeeping, monitoring and reporting consistent

53 Washington State Legislature

, Chapter 80.70 RCW: Carbon Dioxide Mitigation

, Accessed on March 19, 2014,

http://apps.leg.wa.gov/rcw/default.aspx?cite=80.70&full=true

.

54 California Energy Commission, California SB 1368

, Chapter 598 : Emission Performance Standards, September 29,

2006, accessed March 19, 2014, http://www.energy.ca.go v/emission_standards/

. 55 U.S. EPA, eGRID 2010 data files (U.S. Environmental Protection Agency, 2014), accessed on March 19, 2014,

http://www.epa.gov/cleanenergy/energy

‐ resources/egrid/index.html#

. 56 Oregon Department of Energy, Oregon’s Carbon Dioxide Emission Standards for New Energy Facilities (Oregon

Department of Energy, 2010) http://www.oregon.gov/energy/Siti ng/docs/Reports/CO2Standard.pdf

.

57 Washington State Legislature, Chapter 80.70 RCW: Carbon Dioxide Mitigation, Accessed on March 19, 2014,

http://apps.leg.wa.gov/rcw/default.aspx?cite=80.70&full=true

.

58 Illinois General Assembly

, Public Act 095

‐ 1027, SB1987, Clean Coal Portfolio Standard Law

, January 12, 2009,

accessed March 19, 2014 http://ilga.gov/legislation/publicacts/95/PDF/095

‐ 1027.pdf

. 59 Montana State Legislature

, H

. B. 00 25 .05, An Act Generally Revising the Electric Utility Industry and Customer

Choice Laws

, May 14, 2007

, accessed March 19, 2014, http://leg.mt.gov/bills/2007/billpdf/HB0025.pdf

. 60 “ SB 1368 Emission Performance Standards,” California Energy Commission, Accessed March 19, 2014,

http://www.energy.ca.go v/emission_standards/

. emissions over the life of the facility. Includes modifications to existing EGUs that

result in an increase in CO 2 emissions of 15 percent or more. The CO 2 mitigation plan

may include one or more of a list of eligible measures (includes indirect measures,

such as EE/RE and offsets). 53

Places conditions

on the emissions

attributes of

electricity

procured by

electric utilities;

Applies to LDCs  California (SB 1368; 2006)

‐ Electric utilities may only enter into long

‐ term power

purchase agreements for baseload power if the electric generator supplying the power

has a CO 2 emission rate that does not exceed that of a natural gas combined cycle

plant. The California Energy Commission promulgated regulations establishing an

emission rate of 1,100 lb CO 2 /MWh. 54 By comparison, the average emissions rate of

gas plants in the U.S. is 945 lb CO 2 /MWh, while the average emissions rate of

pulverized coal plants is 2,154 lb CO 2 /MWh. 55

 Oregon (HB 101; 2009) and Washington (SB 6001; 2007)

‐ Electric utilities may only

enter into long

‐ term power purchase agreements for baseload power if the electric

generator supplying the power has a CO 2 emission rate of 1,100 lb CO 2 /MWh or

less. 5657

Requires that new

coal

‐ fired power

plants must

capture and store

a specific

percentage of CO 2

emissions 

###  Illinois (SB 1987; 2009) Illinois utilities and retailers must purchase at least 5 percent of

their electricity from Clean Coal Facilities in 2015 and beyond. To be designated a

Clean Coal Facility, new coal

‐ fired power plants must capture and store 50 percent of

carbon emissions from 2009

‐ 2015, 70 percent for 2016

‐ 2017, and 90 percent after

2017. 58



###  Montana (HB 25; 2007). The Public Service Commission may not approve new plants

constructed after January 2007 that are primarily coal

‐ fired unless at least 50 percent

of the plant’s CO 2 emissions are captured and stored. 59 Formatted: Font: 12 pt

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 Each applicable emissions source must install Continuous Emissions Monitoring Systems

(CEMS) subject to Federal CO 2 reporting requirements for 40 CFR part 75, successfully

complete certification tests, and record, report, and quality assure the data from the

CEMS.

 The owner or operator must report the CO 2 mass emissions data and heat input data on

a semi

‐ annual basis to the Department of Environmental Conservation.

 On a quarterly basis, the owner or operator must report all of the data and information

required in either 40 CFR part 60 or subpart H of 40 CFR part 75. 61



### Washington  Mitigation projects must be approved by the appropriate council, department, or

authority, and made a condition of the proposed and final site certification agreement

or order of approval.

 Direct investment projects are approved if they provide reasonable certainty that the

performance requirements of the projects will be achieved and that they were

implemented after July 1, 2004.

 For facilities under the jurisdiction of a council, the implementation of a carbon dioxide

mitigation project, other than purchase of carbon credits, is monitored by an

independent entity for conformance with the performance requirements of the carbon

dioxide mitigation plan. The independent entity shares the project monitoring results

with the council.

 For facilities under jurisdiction of the department or authority, the implementation of a

carbon dioxide mitigation project, other than a purchase of carbon credits, is monitored

by the department or authority issuing the order of approval. 62

Oregon  It is up to the Council during the certificate application phase to determine the gross

CO 2 emissions over a 30 year lifetime of the proposed facility to determine whether it

meets the CO 2 performance standard.

 During the operation phase of approved facilities, there are CO 2 reporting requirements

to the Oregon Department of Environmental Quality and US EPA.

 New facilities must pass a 100 hour test in their first year of operation to show they

meet the performance standards. 64

Penalties for Noncompliance



### For policies that affect target new electric generating units, utilities must prove any proposed

units are in compliance at the time of permitting. In Oregon, if facilities do not meet the

performance standard in their first year of operation during a 100 hour test 65



### , they must

purchase offsets to account for any excess emissions. 66

61 N ew Y ork Department of Environmental Conservation, Part 251: CO 2 Performance Standards for Major Electric

Generating Facilities, June 12, 2012, Accessed March 19, 2014, http://www.dec.ny.gov/regs/83094.html#83097.

62 Washington State Legislature, Chapter 80.70 RCW: Carbon Dioxide Mitigation, Accessed on March 19, 2014,

http://apps.leg.wa.gov/rcw/default.aspx?cite=80.70&full=true

64 Oregon Department of Energy, Oregon’s Carbon Dioxide Emission Standards for New Energy Facilities (Oregon

Department of Energy, 2010) Accessed on March 19, 2014

http://www.oregon.gov/energy/Siti ng/docs/Reports/CO2Standard.pdf

.

65 During the first year of operation new power plants test their equipment to ensure compliance with standards

for commercial equipment. Initial CO 2 performance requirements can be validated during this test.

66 Oregon Department of Energy, Oregon’s Carbon Dioxide Emission Standards for New Energy Facilities

, Accessed

on March 19, 2014

, http://www.oregon.gov/energy/Siti ng/docs/Reports/CO2Standard.pdf

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### Since enacting the performance standard, California’s carbon emissions rates have fallen from

approximately 1,245 lbs CO 2 

### e/MWh for fossil generation (considering both in

‐ state and

imported power) and 875 lbs CO 2 

### e/MWh for all power in 2005 to an average of approximately

1,090 lbs CO 2 

### e/MWh and 775 lbs CO 2 

### e/MWh in the three years before 2012. 67



### b. Energy Efficiency Policies, Programs and Measures

Demand

‐ side energy efficiency policies and programs reduce utilization of EGUs and avoid

greenhouse gas emissions associated with electricity generation. These electricity demand

reductions can be achieved through enabling policies that incentivize investment in demand

‐

side energy efficiency improvements by overcoming market barriers that otherwise prevent

these investments, such as lack of information on energy efficient options, high transaction

costs, split

‐ incentives, lack of product availability, and perceptions of organizational risks.

Reducing electricity demand also reduces the associated transmission and distribution losses

that occur across the grid between the sites of electricity generation and the end use.

Demand

‐ side energy efficiency is considered a central part of climate change mitigation in

states that currently have mandatory GHG targets, accounting for roughly 35 percent to 70

percent of expected reductions of state's power sector emissions. 68 

### For example, California

expects to achieve reductions of 21.9 MMTCO 2 

### e in 2020 from energy efficiency programs

targeting electricity reductions. Taking into account expected reductions of 21.3 MMTCO 2 

### e

expected from California's RPS and 2.1 MMTCO 2 

### e from the million solar roofs program, energy

efficiency makes up 48 percent of power sector reductions based on California's Climate

Change Scoping Plan. 69 

### Another state, Washington, expects to reduce 9.7 MMTCO 2 

### e from

energy efficiency measures in 2020. Taking into account expected reductions of 4.1 MMTCO 2 

### e

from Washington's RPS, energy efficiency makes up 70 percent of expected emission reductions

from stationary energy within the state. 70

67 State environmental agency leaders from CA, CO, DE, IL, ME, MD, MA, MN, NH, NY, OR, RI, VT, WA, Open Letter

to EPA Administrator Gina McCarthy on Emission Standards Under Clean Air Act Section 111(d)

, December 16,

2013, accessed on March 19, 2014,

http://www.georgetownclimat e.org/sites/defaul t/files/EPA_Submi ssion_from_States

‐ FinalCompl.pdf.

68 These reduction target ranges are based on a review of state GHG reduction laws in California, Connecticut,

Hawaii, Maine, Maryland, Massachusetts, Minnesota, New Jersey, Oregon, and Washington.

69 California Air Resources Board, Climate Change Scoping Plan, accessed March 19, 2014,

http://www.arb.ca.gov/cc/scopingpla n/document/adopted_scoping_plan.pdf

. 70 Washington Department of Ecology, Growing Washington’s Economy in a Carbon

‐ Constrained World, Accessed

March 19, 2014, https://fortress.wa.gov/ecy/publications/publications/0801025.pdf

. Deleted: enhances system efficiency by reducing

the

Comment [A8]: Any reduction in T&D losses

should be proportional to the demand reduction

–

it’s not making the system more efficient; it’s just

minimizing demand.

Comment [A9]: Revised text accordingly.

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### States have employed a variety of strategies to increase investment in demand

‐ side energy

efficiency technologies and practices, including (1) energy efficiency resource standards, (2)

demand

‐ side energy efficiency programs, (3) building energy codes, (4) appliance standards and

(5) tax credits. Each of these strategies are described below.



### i. Energy Efficiency Resource Standards

Description



### Energy Efficiency Resource Standards (EERS) set multiyear targets for energy savings that

utilities or third

‐ party program administrators typically meet through customer energy

efficiency programs but also through other approaches, such as peak demand reductions,

building codes and combined heat and power (CHP). An EERS can apply to retail distributors of

either electricity or natural gas, or both, depending on the state. To date, at least 29 states

have some type of EE requirement or goal. 71

Policy Mechanics

Design



### EERS design and implementation details vary by state, and may be expressed as a percentage

reduction in annual retail electricity sales, as a percentage reduction in retail electricity sales

growth, or as a specific electricity savings amount over a long

‐ term period. A typical EERS sets

multiyear targets for energy savings that drive investment in EE programs implemented by

utilities or third party administrators. Over the compliance period, an EERS reduces electricity

demand by a target amount that utilities must meet. As a result, an EERS indirectly affects

utility CO 2 

### emissions by reducing the use of fossil

‐ fuel

‐ fired EGUs.

Authority



### Most state EERS policies are established through legislation. However, there are several

instances in which they have been established by PUC orders under broader statutory authority,

such as by setting quantitative targets consistent with the achievement of ‘all cost

‐ effective

energy efficiency. 72

71 D aniel C. Steinberg and O wen Zinaman

, State Energy Efficiency Resource Standards: Policy Design, Status, and

Impacts, Technical Report NREL /TP

‐ 6A20

‐ 61023, (National Renewable Energy Laboratory, April 2014), Accessed on

March 19, 2014.

72 Ernest Orlando Lawrence Berkeley National Laboratory, Benefits and Costs of Aggressive Energy Efficiency

Programs and the Impacts of Alternative Sources of Funding: Case Study of Massachusetts

, accessed on May 14,

2014, http://emp.lbl.gov/sites/all/files/REPORT%20lbnl

‐ 3833e.pdf

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### Retail electricity suppliers, which are utilities that sell electricity to customers for end

‐ use

purposes, are the obligated parties under an EERS.

Measurement and Verification



### PUCs generally oversee EERS. Retail electricity suppliers comply with EERS requirements by

developing a portfolio of end

‐ use energy efficiency programs that encourage electric utility

customers to invest in more energy efficient technologies and practices as described below.

Transmission and distribution infrastructure improvements may also count towards EERS

programs in some states. PUCs typically rely on independent program evaluators to perform

evaluation, measurement and verification (EM&V) activities that estimate the incremental

annual and cumulative energy savings attributable to the programs. 73 

### These estimates are

typically the basis for compliance reports submitted by retail electricity suppliers. Table 4, for

examples of penalties for program noncompliance. For more information about measurement

and verification of energy efficiency policies or programs, see earlier in the State Plan

Considerations Technical Support Document.

An important policy driver for EE programs in six states is a statutory requirement for utilities to acquire "all cost

‐

effective energy efficiency". This policy typically requires utilities and other program administrators to pursue

energy efficiency up to the point at which it is no longer cost effective, as defined by cost

‐ benefit tests and

procedures REQUIRED by state PUCs. States with all

‐ cost effective energy efficiency policies include: CA, CT, MA,

RI, VT, WA. For MA, this goals has translated into achieving annual electric energy savings equivalent to a 2.4%

reduction in retail sales from energy efficiency programs in 2012

. 73 Evaluation, measurement, and verification (EM&V) refers to set of techniques and approaches used to estimate

the quantity of energy savings from an EE program or policy. Since energy savings cannot be directly measured,

efficiency program impacts are estimated by taking the difference between: (a) actual energy consumption after

efficiency measures are installed, and (b) the energy consumption that would have occurred during the same

period had the efficiency measures not been installed (i.e., the baseline). Comment [A10]: Some state EERS, for example

Ohio, allow transmission and distribution

infrastructure improvements to count towards their

EERS.

http://www.dsireusa.org/incentives/incentive.cfm?I

ncentive_Code=OH16R&re=0&ee=0

Comment [A11]: Revised text to acknowledge

this.

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### Table 4: Examples of Penalties for Noncompliance

Penalties for

Noncomplianc

e



### If the

obligated

parties do not

demonstrate

compliance

with the EERS,

they may face

financial

penalties. The existence and amount of penalties varies across the states. Table 3 provides

examples of financial penalties in three states, Pennsylvania, Ohio and Illinois.

Implementation Status



### As of January 2014, more than 20 states had an active EERS in place, while at least four have EE

targets or goals that are voluntary, underfunded or uncertain at this time (see Figure 5)

. In

addition, two states have renewable portfolio standard that allow the option for energy

efficiency to meet requirements. 78



### Figure 5: Status of Energy Efficiency Resource Standards by State

74 “Energy Efficiency and Conservation Requirements for Utilities: Pennsylvania” Database of State Incentives for

Renewables & Efficiency, accessed on March 19, 2014,

http://www.dsireusa.org/inc entives/incentive.cfm?Incentive_Code=PA14R&re=0&ee=0

.

75 RECs represent the non

‐ energy attributes, including all the environmental attributes, of electricity generation

from renewable energy sources. RECs are typically issued in single MWh increments. See the section on Renewable

Portfolio Standards for more detail.

76 “Energy Efficiency and Conservation Requirements for Utilities: Ohio” Database of State Incentives for

Renewables & Efficiency, accessed on March 19, 2014,

http://www.dsireusa.org/inc entives/incentive.cfm?Ince ntive_Code=OH16R&re=0&ee=0

.

77 “Energy Efficiency and Conservation Requirements for Utilities: Illinois” Database of State Incentives for

Renewables & Efficiency, accessed on March 19, 2014,

http://www.dsireusa.org/inc entives/incentive.cfm?Incentive_Code=IL19R&re=0&ee=0

.

78 D aniel C. Steinberg and O wen Zinaman, State Energy Efficiency Resource Standards: Policy Design, Status, and

Impacts, Technical Report NREL/TP

‐ 6A20

‐ 61023

, (National Renewable Energy Laboratory, April 2014 ), Accessed on

March 19, 2014. 

### State Direct Financial Penalties

Pennsylvania Failure to achieve the requisite reductions in electricity consumption and peak demand

during Phase 1 results in one

‐ time fines from $1 million to $20 million. Failure to file a

plan with the public utilities commission is also punishable by a fine of $100,000 per day.

Costs associated with any such fines may not be passed on to ratepayers. 74



### Ohio Failure to comply with energy efficiency or peak demand reduction requirements results

in the state public utilities commission assessing a forfeiture upon the utility, to be

credited to the Advanced Energy Fund. The amount of the forfeiture is either: an

amount, per day per under

‐ compliance or non

‐ compliance, not greater than $10,000 per

violation; or an amount equal to the then existing market value of one renewable energy

credit (REC) 75 per megawatt hour of under

‐ compliance or noncompliance. 76



### Illinois For both natural gas and electric utilities, failure to submit an energy reduction plan will

result in a fine of $100,000 per day until the plan is filed. This penalty is deposited in the

Energy Efficiency Trust Fund and may not be recovered by rate payers. 77 Formatted Table

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### Most states are meeting or on track to meet their incremental savings goals, which typically

range from an annual reduction in electricity of about 0.25

‐ 2.5 percent. 79 

### In 2011, across the

50 states, incremental savings were equivalent to 0.62 percent of retail electricity sales. 80 

### For

those states with EERS policies in place for more than two years as of 2011

, thirteen of twenty

states are achieving 100 percent or more of their goals, three states are achieving over 90

percent of their goals, and only three states are realizing savings below 80 percent of their

goals. 81



### ii. Demand

‐ side Energy Efficiency Programs

Description



### Demand

‐ side energy efficiency programs are programs designed to advance energy efficiency

improvements within a state or utility service area. They are typically implemented to help

meet state policies, standards or objectives, such as energy efficiency resource standards (EERS),

79 Ibid.

80 “The 2013 State Energy Efficiency Scorecard ”, A merican C ouncil for an E nergy

‐ E fficient E conomy, Accessed on

March 19, 2014, http://www.aceee.org/research

‐ report/e13k

.

81 M ichael Sciortino, S eth Nowak, P atti White, D an York

, and M artin Kushler. “Energy Efficiency Resource

Standards: A Progress Report on State Experience.” American Council for an Energy Efficient Economy (2011). Deleted: ¶

¶

¶

¶

¶

¶

¶

¶

¶

¶

¶

¶

¶

¶

Comment [A12]: Recommend starting the

sentence with “As of 2011,”

Comment [A13]: Edited text.

Deleted: State Policy Table

– Internal estimate

drawing on multiple sources

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### ‘all cost effective’ energy efficiency goals, integrated resource planning, and other demand

‐ side

management program and budget processes. As of January 2013, utilities in 48 states

implement demand

‐ side energy efficiency programs. 82

Policy Mechanics



### Design

Demand

‐ side energy efficiency programs include financial incentives to use energy efficient

products, make energy efficiency upgrades to improve the performance of residential

,

commercial

, and industrial buildings, and provide technical assistance and information

programs to address market and information barriers. Funding for these programs typically

comes from charges added to customer utility bills and from revenues raised through emission

allowance auctions, such as under RGGI. The RGGI auction proceeds go to a variety of sources

with the authority to run demand

‐ side energy efficiency programs, including those also funded

via independent trusts, DOE’s Weatherization Assistance Program (WAP), and state

‐ run energy

efficiency grant programs for municipalities. 83



### States are also funding energy efficiency programs using revenues from “forward capacity

markets” operated by regional electricity operators. Forward capacity markets allow energy

suppliers to bid against each other for the amount of capacity they can supply into the

electricity market in a future year. Demand

‐ side management programs have been allowed to

bid into these markets as an energy source, demonstrating that energy efficiency programs can

compete with more traditional forms of electricity supply in meeting the needs of the power

grid.

Authority

Demand

‐ side programs that are a part of EERS programs are typically established through

legislation or PUC authority. Other demand

‐ side management program s can arise as a result of

utility planning processes and state and local government efforts to ensure all cost

‐ effective

energy efficiency and other policy goals are met.

82 “The 2013 State Energy Efficiency Scorecard”, A merican C ouncil for an E nergy

‐ E fficient E conomy, Accessed on

March 19, 2014, http://www.aceee.org/research

‐ report/e13k

.

83 RGGI, “ Regional Investment of RGGI CO2 Allowance Proceeds, 2012 ”, Regional Greenhouse Gas Initiative

(February 2014), accessed on March 19, 2014, http://www.rggi.org/docs/Documents/2012

‐ Investment

‐ Report.pdf

. Deleted: and

Comment [A14]: Demand

‐ side energy efficiency

financial incentives are also used to improve

industrial facilities, not just residential and

commercial buildings.

Comment [A15]: Edited text accordingly.

Deleted: Funding may also come

Comment [A16]: This sentence mischaracterizes

forward capacity markets (FCM) as providing

funding for EE programs. Energy efficiency programs

can be bid into the FCM but the FCM does not

provide funding in the same way that ratepayers

fund efficiency programs.

Comment [A17]: Edited accordingly.

Deleted: and integrated resource planning

Deleted: Additional

Deleted: and budget processes also

Deleted: exist via

Deleted:

, as well as

Comment [A18]: This paragraph incorrectly uses

IRP and utility planning as two different concepts.

Comment [A19]: Revised text to address.

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### Obligated Parties

Energy efficiency programs can be administered by investor

‐ owned, municipal or cooperative

utilities; third party administrators; or state and local government agencies.

Measurement and Verification

PUCs generally oversee demand

‐ side energy efficiency programs. Program administrators

typically rely on independent evaluators to perform evaluation, measurement and verification

(EM&V) activities that estimate the incremental annual and cumulative energy savings

attributable to the programs. These estimates are typically the basis for annual performance

reports submitted by retail electricity suppliers or third party administrators to the PUCs. In the

case of state and local government agency run programs that are not overseen by the PUC,

energy savings are typically estimated to assure proper use of grants or other funds. For more

information about the evaluation, measurement and verification of energy efficiency policies

and programs, see earlier in the State Plan Considerations Technical Support Document.

Penalties for Noncompliance



### As discussed above, some states with an EERS levy direct fines for missing energy efficiency

targets or failure to submit an energy efficiency plan. For some programs under PUC oversight,

failure to reach certain performance levels may result in an inability to receive an incentive

payment or recover all incurred costs. Demand

‐ side programs funded by RGGI proceeds or

grants typically do not have penalties for noncompliance. However, state agencies play a role in

evaluating these programs and deciding whether funding should continue to flow to them.

Implementation Status



### Well

‐ established state demand

‐ side energy efficiency programs have demonstrated their ability

to reduce electricity demand. 84 

### For example, data reported to the U.S. Energy Information

Administration (EIA) show that in 2012 California avoided 35,482 GWh of electricity

consumption through its demand

‐ side efficiency programs, while Illinois avoided 3,084 GWh

and Maryland avoided 1,528 GWh,

. 85 

### These reductions are equivalent to 13.7 percent, 2.1

percent, and 2.5 percent of total 2012 retail electricity sales in those states, respectively. 86

84

“ The Future of Utility Customer

‐ Funded Energy Efficiency Programs in the United States ”, L awrence B erkeley

N ational L aboratory, Accessed on March 19, 2014, http://emp.lbl.gov/ publications/future

‐ utility

‐ customer

‐ funded

‐

energy

‐ efficiency

‐ programs

‐ united

‐ states

‐ projected

‐ spend

.

85 “Electric Power Sales, Revenue, and Energy Efficiency Form EIA

‐ 861 Detailed Data Files”, Energy Information

Administration

, Accessed on March 19, 2014

, http://www.eia.gov/elec tricity/data/eia861/

. 86 “Electricity: Detailed State Data”, Energy Information Administration, Accessed on March 19, 2014,

http://www.eia.gov/elec tricity/data/state/

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### According to data and analyses from sources including Lawrence Berkeley National Lab (LBNL),

the U.S. Department of Energy’s Energy Information Administration, and the American Council

for an Energy Efficient Economy (ACEEE), as well as the EPA’s own analysis, 12 leading states

have either achieved

– or have established requirements that will lead them to achieve

‐ annual

incremental savings rates of at least 1.5 percent of the electricity consumption that would

otherwise have occurred. 87



### In 2011, state demand

‐ side energy efficiency programs are estimated to have reduced CO 2



### emissions by 75 million metric tons, or 3.5 percent of national power sector emissions. 8889



### iii. Building Energy C odes

Description



### Building e nergy codes establish minimum efficiency requirements for new and renovated

residential and commercial buildings. These measures are intended to eliminate inefficient

technologies with minimal impact on up

‐ front project costs. This can reduce the need for

energy generation capacity and new infrastructure while reducing energy bills. Energy codes

lock in future energy savings during the building design and construction phase, rather than

through a renovation.

Policy Mechanics

Design



### Codes specify “thermal resistance” improvements to the building shell and windows, minimum

air leakage, and minimum efficiency for heating and cooling equipment.

Mandatory building energy codes establish minimum efficiency requirements for residential

and commercial construction. The International Energy Conservation Code (IECC) is the

prevailing model code for the residential sector. ASHRAE 90.1

‐ 2010 is the model commercial

code.

87 See the Greenhouse Gas Abatement Measures TSD for more information.

88 T he Edison Foundation: Innovation, Electricity Efficiency, Summary of Customer

‐ Funded Electric Efficiency

Savings, Expenditures, and Budgets (2011

‐ 2012)

, March 2013, Accessed on March 19, 2014,

http://www.edisonfoundation.net/iei/Documents/FIN AL_IEE%20Whitepaper_2012_US%20Energy%20Efficiency.p

df.

89 EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990

‐ 2011 Executive Summary

, (Environment

Protection Agency, 2013), accessed on March 19, 2014.

http://www.epa.gov/climatechang e/Downloads/ghgemissions/US

‐ GHG

‐ Inventory

‐ 2013

‐ ES.pdf

. Deleted: ten Comment [A20]: The preamble says 12 states

have achieved or have established requirements to

achieve savings of 1.5%.

Comment [A21]: Thank you for catching that

error. Number is now updated.

Deleted:

Comment [A22]: This section should be titled

Building Energy Codes, not just “building codes”.

Similarly, the first paragraph should begin with

“Building energy codes establish...”

Comment [A23]: Revised text accordingly.

Deleted: E

Deleted: (an Institute of ), (), available at

http://www.edisonfoundation.net/iei/ourwork/Pag

es/issuebriefs.aspx

.

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### By locking in efficiency measures at the time of construction, codes are intended to capture

energy savings that are more cost

‐ effective than retrofit opportunities available after a building

has been constructed. Energy code requirements are also intended to overcome market

barriers to efficient construction in both the commercial and residential sectors, such as the

complexity of advanced codes, lack of local

‐ level implementation resources, and a shortage of

empirical data on the costs and benefits of codes.

Authority



### Model building codes are typically developed at the national or international level, adopted at

the state and/or local level, and implemented and enforced locally.

Obligated Parties



### Local parties, such as developers and property owners requiring building permits, are the most

common obligated parties.

Measurement and Verification



### Program implementation steps, including builder training, compliance assurance, and

enforcement, are typically the responsibility of state and local governments. These steps,

however, are often not fully or uniformly implemented for numerous reasons, including an

emphasis on health and safety issues over the proper functioning of mechanical equipment, a

lack of trained staff to review building plans and conduct onsite inspections, and limited

funding to carry out key implementation activities. As a result, most jurisdictions do not have

the capacity to analyze code compliance and to identify the measures and strategies that

should be targeted for improved implementation. For more information about measurement

and verification of energy efficiency, see earlier in the State Plan Considerations Technical

Support Document.

Penalties for Noncompliance



### In order to get building permits approved, the relevant developer or property owners must

show they are in compliance with standards. Since permitting is done at the local level, the use

of penalties and the ability to enforce standards vary significantly by region. DOE has been

working with states and localities to improve compliance practice

Implementation Status

To date, 28 states have adopted IECC 2009 while four states have gone further by adopting the IECC

2012. In the commercial sector, 33 states have adopted ASHRAE 90.1

‐ 2007 and five states have adopted Formatted: Normal

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‐ 2010. Currently, 11 states have outdated or no state

‐ wide residential energy code, and 9

states have outdated or no state

‐ wide energy codes for commercial construction. 90 The current status of

state residential and commercial energy codes are shown below in Figure 6 and

Figure 

### 7

, respectively. The State of Oregon, which has adopted residential and commercial codes based

on the IECC 2009, estimated total savings in 2009 from building energy codes of 1.17 GWh and 2.3 GWh

in the residential and commercial sectors, respectively. 91 This was equivalent to more than 7 percent of

total retail electricity sales in Oregon in 2009. 92

90 “Code Status”, Online Code Environment & Advocacy Network

, Accessed on March 19, 2014,

http://energycodesocean.org/code

‐ status

.

91 Oregon Department of Energy, 2011

‐ 2013 State of Oregon Energy Plan

, Accessed on March 19, 2014,

http://www.oregon.gov/energy/docs/repo rts/legislature/2011/energy_plan_2011

‐ 13.pdf

.

92 “ State Electricity Profiles : Oregon Electricity Profile 2012”, E nergy I nformation A dministration, Accessed on

March 19, 2014, http://www.eia.gov/electricity/state/oregon/

. Deleted:

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### Figure 6: Residential State Energy Code Status

Figure 7: Commercial State Energy Code Status Formatted: Complex Script Font: +Body

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### Appliance and E quipment E fficiency S tandards

Description



### State appliance standards establish minimum energy

‐ efficiency levels for those appliances and

other energy

‐ consuming products that are not already covered by the federal government.

These standards typically prohibit the sale of less efficient models within a state. States are

finding that appliance standards offer a cost

‐ effective strategy for improving energy efficiency

and lowering energy costs for businesses and consumers, though these standards are

superseded when Federal standards are enacted for new product categories,.

While state appliance standards can be useful in testing and exploring the effectiveness of

standards for new products, states cannot preempt or supersede existing Federal standards.

States may apply to DOE for a waiver to implement more stringent standards. This is

sometimes granted if a certain period of time has passed since the federal standard has been

updated.

Policy Mechanics

Design



### When states implement appliance and equipment standards, they are establishing a minimum

efficiency for products, such as refrigerators or air conditioners, thereby reducing the energy

associated with using the product. Standards prohibit the production and sale of products less

efficient than the minimum requirements, encouraging manufacturers to focus on how to

incorporate energy

‐ efficient technologies into their products at the least cost and hastening the

development of innovations that bring improved performance.

Authority



### State energy offices, which typically administer the federal state energy program funds, have

generally acted as the administrative lead for standards implementation. In contrast, inspection

and enforcement of appliance standards regulations has typically involved self

‐ policing.

Industry competition is such that competitive manufacturers usually report violations.

Obligated Parties



### Manufacturers of products being sold in a given state are typically obligated to ensure their

appliances meet the appropriate energy efficiency standards. Deleted: ¶

Deleted: e

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### Evaluating the benefits and costs of the standards is important during the standards

‐ setting

process. Once enacted, however, little field evaluation is performed. For more information

about measurement and verification of energy efficiency, see earlier in the State Plan

Considerations Technical Support Document.

Penalties for Noncompliance



### Appliances and equipment found in violation of the minimum energy performance standards

are not allowed to be sold or manufactured in the state.

Implementation Status



### Currently, fifteen states and the District of Columbia have enacted appliance efficiency

standards. However, most of these standards have been superseded by federal standards. Still,

nine states (AZ, CA, CT, MD, NV, NY, OR, RI, WA) and the District of Columbia have either

enacted standards for equipment not covered federally or obtained waivers to enact tougher

appliance standards where the federal regulations have become outdated. California currently

leads all states in active state standards, covering 13 products, including consumer audio and

video products, pool pumps and hot tubs, vending machines, televisions, battery chargers, and

various lighting applications. 93



### v. Incentives and Finance Mechanisms for Energy Efficiency

Description



### States offer a diverse portfolio of financing and incentive approaches that are designed to

address specific financing challenges and barriers and incentivize specific markets and customer

groups to invest in energy efficiency. These programs include revolving loan funds, energy

performance contracting, tax incentives, rebates, grants, and other incentives.

Policy Mechanics

Design



### Revolving loan funds provide low

‐ interest loans for energy efficiency improvements. The funds

are designed to be self

‐ supporting. States create a pool of capital that “revolves” over a multi

‐

93 “Appliance Efficiency Regulations: California”, D atabase of S tate I ncentives for R enewables & E fficiency,

Accessed on March 19, 2014,

http://www.dsireusa.org/inc entives/incentive.cfm?Ince ntive_Code=CA48R&re=0&ee=0

. Deleted: help Comment [A24]: Suggest “encourage” or

“incentivize”. Instead

Comment [A25]: Edited text accordingly.

Deleted:

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### year period, as payments from borrowers are returned to the capital pool and are subsequently

lent to other borrowers. Revolving loan funds can be created from several sources, including

public benefits funds (PBFs), 94 

### utility program funds, general state revenues, or federal funding

sources. Revolving funds can grow in size over time, depending on repayment interest rates and

program administrative costs.

Energy performance contracting allows the public sector to contract with private energy service

companies (ESCOs) to provide building owners with energy

‐ related efficiency improvements

that are guaranteed to save more than they cost over the course of the contracting period.

ESCOs provide energy auditing, engineering design, general contracting, and installation

services, and help arrange project financing. 95 

### The contracts are privately funded and do not

involve state funding or financial incentives.

State tax incentives for energy efficiency are available as personal or corporate income tax

credits, tax exemptions (e.g., sales tax exemptions on energy

‐ efficient appliances), and tax

deductions (e.g., for construction programs). Tax incentives aim to spur private sector

innovation to develop more energy efficient technologies and practices and increase consumer

choice of energy

‐ efficient products. 96



### Rebates (also known as “buy

‐ downs”) are used to promote demand

‐ side energy efficiency

reductions by providing direct incentives to customers who purchase or make upgrades to

approved efficient appliances or retrofit their homes (e.g., a utility may refund part of the cost

for a homeowner to improve attic insulation or purchase a high

‐ efficiency furnace). Funding for

rebates may come from PBFs, direct grants, or utility program funds.

Grants from the federal government, state government, regional agency, or private source may

be used to start or finance energy efficiency programs. A grant may be used to provide funding

for a specific construction project (e.g., retrofit of a school), finance a rebate program, initiate a

revolving fund, conduct a behavior change campaign (e.g., educate public about the benefits of

off

‐ peak energy use), or any other type of program that meets the specific grant requirements.

94 Public benefit funds (PBFs) are dedicated funds used for supporting research and development of energy

efficiency and renewable energy projects. Funds are normally collected either through a small charge for every

electric customer or through specified contributions from utilities.

95 EPA, Integrating State and Local Environmental and Energy Goals: Energy Performance Contracting. Fact Sheet

( EPA, September 2004).

96 E lizabeth Brown, H arvey Sachs, P atrick Quinlan, and D aniel Williams. “ Tax Credits for Energy Efficiency and

Green Buildings: Opportunities for State Action. ” A merican C ouncil for an E nergy E fficient E conomy (2002). Deleted: generation incentives Comment [A26]: Recommend changing

“generation incentive” to “program funds” for

consistency with the rest of the document.

Comment [A27]: Edited text accordingly.

Deleted:

.

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### Authority

Financial mechanisms and incentives for energy efficiency are run by utilities and state and local

governments. Utilities primarily offer rebates, grants, and loans. Personal, corporate, sales, and

property tax incentives are mainly offered by state and local governments. 97

Implementation Status



### Financial mechanisms and incentives for energy efficiency exist in all 50 states, with the most

prevalent financial mechanisms and incentives for energy efficiency are rebates and loan

programs. There are 43 tax incentives and over one

‐ thousand rebate, grant, and loan

programs. 98 

### In the first 3 years of Alaska’s Home Energy Rebate Program, the State provided an

estimated $110 million to help finance energy efficiency retrofits for 16,500 homeowners.

Retrofitted housed are currently saving an estimated 1.6 trillion BTUs of energy annually, or 5

percent of the Alaska’s total annual energy demand for residential space heating. 99



### c. Renewable Energy Policies and Programs

States have adopted a range of requirements and programs to advance the deployment of

renewable energy technologies, including renewable portfolio standards, performance

‐ based

incentives and public benefit funds. 100 

### These renewable energy policies and programs reduce

GHG emissions by increasing the use of renewable energy and altering the mix of energy supply.



### i. Renewable Portfolio Standards

Description



### A renewable portfolio standard (RPS), also known as a renewable electricity standard (RES), is a

mandatory requirement for retail electricity suppliers to supply a minimum percentage or

amount of their retail electricity load with electricity generated from eligible sources of

renewable energy. An RPS indirectly affects EGU CO 2 

### emissions by reducing the utilization of

97 “Financial Incentives for Energy Efficiency” Database of State Incentives for Renewables & Efficiency, accessed

on March 19, 2014, http://www.dsireusa.org/s ummarytables/finee.cfm

.

98 Ibid.

99 Scott Goldsmith, Sohrab Pathan, and Nathan Wiltse, Snapshot: The Home Energy Rebate Program

, ( Cold Climate

Housing Research Center

, May 2012), Accessed on March 19, 2014,

http://cchrc.org/docs/snapshots/HERP_snapshot.pdf

.

100 Feed

‐ in tariffs

, a performance

‐ based incentive, offer long

‐ term purchase agreements to renewable energy

electricity generators. Public benefit funds are typically created by levying a small fee as a part of retail electricity

rates and are used to support rebate, loan, and other programs that support renewable energy deployment. For

more information, see Database of State Incentives for Renewables and Efficiency

, available at

http://www.dsireusa.org/

. Comment [A28]:

Recommend changing the opening sentence for this

footnote to “Feed

‐ in tariffs, a performance

‐ based

incentive, offer...” This footnote is before the

reader is told that feed

‐ in tariffs are a performance

‐

based incentive.

Comment [A29]: Made edit requested.

Deleted:

Formatted: Font: (Default) Calibri, Complex

Script Font: Times New Roman

Deleted: http://www.dsireusa.org/summarytables

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### fossil

‐ fuel

‐ fired EGUs. As of June 2013, 29 states and Washington, DC have adopted a

mandatory RPS (see Figure 8)

, although designs vary (e.g., applicability, targets and timetables,

geographic and resource eligibility, alternative compliance payments) and an additional nine

have voluntary renewable goals. 101



### Figure 8: States with Renewable Portfolio Standards

Policy Mechanics

Design



### RPS requirements typically start at modest levels and ramp up over a period of several years.

An RPS relies on market mechanisms to increase electricity generation from eligible sources of

renewable energy.

101 Ibid. 

## Renewable Portfolio Standards

Mandatory -

Voluntary - 29

9

Last updated 8/8/2013 Deleted: ¶

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### Retail electricity suppliers can comply with RPS requirements through several mechanisms,

which vary by state, including:

 Ownership of a qualifying renewable energy facility and its electric generation output,

 Purchasing electricity bundled with renewable energy certificates (RECs) 102 

### from a

qualifying renewable energy facility, and

 Purchasing RECs separately from electricity generators. Unlike bundled renewable

energy, which is dependent on physical delivery via the power grid, renewable energy

certificates (RECs) can be traded between any two parties, regardless of their location.

However, state RPS rules typically condition the use of RECs based on either location of

the associated generation facility or whether it sells power into the state or to the

regional grid.

Authority

Most state RPS are established through legislation and administered by state PUCs.

Obligated Parties



### RPS applicability varies by state. All state RPS apply to investor

‐ owned utilities, while some

state RPS obligate municipal utilities, rural cooperatives, and/or other retail providers, often

depending on a minimum number of customers served.

Measurement and Verification



### Some state RPS include an alternative compliance payment (ACP) option, where a retail

electricity supplier may purchase compliance credits from the state at a known price, which

acts as a de facto price cap, if it has not procured sufficient electricity from renewable energy

sources or RECs to meet the RPS compliance requirement. State PUCs typically require annual

compliance reports from retail electricity suppliers subject to a RPS. Most states use regional

tracking systems (e.g., Western Renewable Energy Generation Information System, PJM

Generation Attribute Tracking System) to issue, track, and retire RECs for RPS compliance

purposes. 103 

### For more information about measurement and verification of renewable energy,

see earlier in the State Plan Considerations Technical Support Document.

102 RECs represent the non

‐ energy attributes, including all the environmental attributes, of electricity generation

from renewable energy sources. RECs are typically issued in single MWh increments.

103 For a summary of REC tracking systems, see : U.S. Department of Energy Renewable Energy Certificates (RECs)

National REC Tracking Systems. http://apps3.eere.energy.g ov/greenpower/markets/ce rtificates.shtml?page=3

( Deleted: “Renewable Power & Energy Efficiency

Market: Renewable Certificate Tracking

Formatted: Font: Italic

Deleted: ”

Deleted: Federal Energy Regulatory Commission,

2010); available at http://www.ferc.gov/market

‐ oversight/othr

‐ mkts/renew/othr

‐ rnw

‐ rec

‐ trk.pdf

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### States have developed a range of compliance enforcement and flexibility mechanisms. As of

2007, despite the fact that several states had not achieved the RPS targets, only Connecticut

and Texas had levied fines. A $5.6 million penalty was incurred in Connecticut in 2006. In 2003

and 2005, two competitive electricity service providers in Texas were penalized a total of

$4,000 and $28,000 respectively. Flexible enforcement and opportunities to “make

‐ up”

shortfalls in subsequent years or ACPs that are recycled to support other renewable and

efficiency measures have helped other states avoid penalties for noncompliance. 104

Implementation Status



### States with RPS policies have demonstrated higher levels of renewable energy capacity

development. From 1998

‐ 2012, 67 percent (46 GW) of all non

‐ hydro renewable capacity

additions occurred in states with active or impending RPS requirements, although other factors

may contribute to the growth in renewable capacity. 105



### ii. Performance

‐ Based Incentives and Finance Mechanisms f or Renewable

Energy

Description



### States offer a diverse portfolio of financing

, performance based incentive and state utility

ratemaking approaches that are designed to address specific financial challenges and barriers

and help specific markets and customer groups produce clean energy.

Policy Mechanics

Design



### States support the advancement of clean generation technologies through performance

‐ based

incentives, including feed

‐ in tariffs and other payments, or tax incentives. Performance

‐ based

incentives are paid based on the actual energy production of a system. Feed

‐ in tariffs establish

temporarily elevated price per kWh in order to encourage renewable energy innovation using

high cost technologies. Tax incentives are used to lower financial barriers to renewable energy

production.

104 Ryan Wiser and Galen Barbose, Renewables Portfolio Standards in the United States

– A Status Report with Data

Through 2007

, ( Lawrence Berkeley National Laboratory, 2008)

, Accessed March 19, 2014

http://emp.lbl.gov/sites /all/files/REPORT%20lbnl

‐ 154e

‐ revised.pdf

. 105 Ibid. Deleted: F

Deleted: and

Deleted:

Deleted:

Formatted: Font: Not Italic

Deleted: April

Formatted: Font: Not Italic Deleted:

.

Deleted:

Deleted: Barbose, Galen. (2013). Renewables

Portfolio Standards in the United States: A Status

Update. Lawrence Berkeley National Laboratory.

http://www.renewableenergymarkets.com/docs/pr

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### A major source of funding for renewable energy activities comes from PBFs, but states also

fund these activities through alternative sources including direct grants, rebates and generation

incentives provided by utilities.

State tax incentives for renewable energy and Combined Heat and Power (CHP) take the form

of personal or corporate income tax credits and tax exemptions. State tax incentives for

renewable energy are a common policy tool, mainly using credits on personal or corporate

income tax and exemptions from sales tax, excise tax, and property tax.

Authority



### Financial mechanisms and incentives for renewables are run by utilities, non

‐ profits, and state

and local government. Personal, corporate, sales, and property tax incentives are mainly

offered by state and local government. 106

Implementation Status



### Financial mechanisms and incentives for renewable energy of some form exist in most states.

According to the Database of State Incentives for Renewable Energy (DSIRE), there are over 200

tax incentives. In addition, nearly a hundred performance based incentives are offered from

state and local governments, as well as utilities and non

‐ profits. 107



### There are currently 18 states that have state

‐ wide performance

‐ based policies, and in several

other states utilities have adopted programs based on performance

‐ based incentives, including

feed

‐ in tariffs, standard offer payments, and payments in exchange for RECs. 108 

### In many cases,

however, PBI is limited to customer

‐ sited projects or limited by size eligibility.

Financial incentives, working in concert with a strong RPS and net metering policies, have

contributed to the rapid growth in solar power deployment in New Jersey. The state’s RPS

includes a minimum carve

‐ out for solar sources, and allows solar energy generators to earn

Solar Renewable Energy Certificates (SRECs) that can then be sold to electricity suppliers trying

to meet the minimum solar production and/or purchase requirement. As a result of these

interdependent policies, solar photovoltaic facilities are increasing, with installations more than

106 “Financial Incentives for Renewable Energy”, Database of State Incentives for Renewable Energy, accessed

March 19, 2014, http://www.dsireusa.org/ summarytables/finre.cfm

. 107 Ibid.

108 Ibid. Deleted:

Deleted: http://www.dsireusa.org/summarytables

/finre.cfm

Formatted: Default Paragraph Font, Font:

(Default) Times New Roman, Complex Script

Font: Times New Roman

Deleted: http://www.dsireusa.org/summarytables

/finre.cfm

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### doubling from 2010 through 2011. 109 

### New Jersey ranks second only to California in terms of

total installed capacity. 110



### d. Utility Planning Approaches and Requirements

Description



### Some public utility commissions require utilities to conduct portfolio management or

integrated resource planning (IRP) to ensure the supply of least cost and stable electric service

to customers over the long term. Portfolio management refers to energy resource planning

that incorporates a variety of energy resources, including supply

‐ side (e.g., traditional and

renewable energy sources) and demand

‐ side (e.g., energy efficiency) options. The term

"portfolio management" typically describes resource planning and procurement in states that

have restructured their electric industry and may be required for default service providers (the

backup electric service provider in areas open to competition)

. IRP is generally used by

vertically integrated utilities and is a long

‐ range planning process to meet forecasted demand

for energy within a defined geographic area through a combination of supply

‐ side resources

and demand

‐ side resources and considering a broad range of perspectives. The goal of an IRP is

to identify the mix of resources that will minimize future energy system costs while ensuring

safe and reliable operation of the system.

In addition to energy resource planning, two states have policies or requirements for utilities to

specifically factor pollution reduction requirements into their planning. In Colorado, the Clean

Air Clean Jobs Act (CACJA), signed into law on April 19, 2010, required utilities to submit a plan

to the PUC showing how they would meet EPA standards for a variety of pollutants. 111 

### The law

was passed because the state was out of compliance with the national Ambient Air Quality

Standard for Ozone, and the EPA threatened to propose more stringent standards for the state.

109 Solar

‐ New Jersey .org

, “Why has New Jersey become a Leader in Solar in the U.S.?”, Solar

‐ New Jersey.org

, August 15, 2012, Accessed March 19, 2014, http://www.solar

‐ new

‐ jersey.org/2012/08/15/why

‐ has

‐ new

‐ jersey

‐

become

‐ a

‐ leader

‐ in

‐ solar

‐ in

‐ the

‐ us/

.

110 “ Open PV State Rankings”, N ational R enewable E nergy L aboratory, Accessed March 19, 2014,

https://openpv.nrel.gov/rankings

.

111 RAP, Addressing the Effects of Environmental Regulations: Market Factors, Integrated Analyses, and

Administrative Processes ( Regulatory Assistance Project

, 2013 ), Accessed March 19, 2014,

www.raponline.org/docume nt/download/id/6455. Comment [A30]: This section (section d)

confuses portfolio management with IRP.

Recommend EPA review the SEE Action Using

Integrated Resource Planning to Encourage

Investment in Cost

‐ Effective Energy Efficiency

Measures Guide

[http://www1.eere.energy.gov/seeaction/pdfs/rate

payer_efficiency_irpportfoliomanagement.pdf].

Comment [A31]: Thank you for connecting us

with the resource.

Deleted: provide

Comment [A32]: This sentence equates

portfolio management with resource planning for all

energy resources. Portfolio management is used

generally for default service only, so it is not the

same thing as IRP. This should be clarified. For

example, this sentence could read: “The term

‘portfolio management’ typically describes resource

planning and procurement in states that have

restructured their electric industry and applies to

default services.”

Comment [A33]: Clarified text

Deleted: However, it can also include the more

traditional integrated resource planning (IRP)

approaches applied to vertically integrated utilities.

Comment [A34]: Commenters recommend

changing this sentence to “Related is integrated

resource planning (IRP) which is generally used by

vertically

‐ integrated utilities and is a long

‐ range

utility plan for meeting the forecasted demand for

energy within a defined geographic area through a

combination of supply side resources and demand

side resources.” (SEE Action IRP Guide)

They also recommend adding a sentence that

describes the goal of IRP. For example: “Generally

speaking, the goal of an IRP is to identify the mix of

resources that will minimize future energy system ... [1]

Comment [A35]: Updated text based on

comments.

Deleted:

Deleted:

Deleted: Project,

Deleted:

Deleted: The (RAP). (). “

Formatted: Font: Italic

Deleted: .”

Deleted: The

Deleted: Montpelier, VT.

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### n 2001, Minnesota enacted Minnesota Statute 216B.1692, which encourages utilities to make

voluntary emission reductions and provides them with a mechanism to recover the costs

through customer rate increases outside of the normal rate review cycle. 112

Policy Mechanics

Design



###  Portfolio Management and IRP

‐ Portfolio management emphasizes diversity in fuels,

technologies, and power supply contract durations. Portfolio management includes

energy efficiency and renewable generation as key strategic components. Portfolio

management typically involves a multi

‐ step process of forecasting, resource

identification, scenario analysis, and resource procurement.

Several states and vertically integrated utilities rely on an IRP process for long

‐ term

planning. Since these utilities own generation assets, they use their IRPs to evaluate a

broad range of options for meeting electricity demand over a 20

‐ or 30

‐ year time frame.

The IRP considers new supply

‐ side options (including renewable resources) and

demand

‐ side options, and purchased power (including transmission considerations). A

broad range of plans are considered, reflecting a range of objectives and capturing key

uncertainties. Plans are evaluated against established criteria (e.g., costs, rate impacts,

emissions, diversity, etc.) and are ranked. The IRPs detail fuel and electricity price

information, customer demand forecasts, existing plant performance, other plant

additions in the region, and legislative decisions. The following examples show how

various states have designed their programs:

o Montana is a deregulated state that has established least cost planning rules and

policy guidelines for default electricity suppliers. These rules and guidelines

target long

‐ term electricity supply and are slightly different for vertically

integrated utilities and restructured utilities. Vertically integrated utilities are

required to submit electric supply resource plans every two years with the aim of

providing a balanced, environmentally responsible electricity portfolio.

Meanwhile, restructured utilities must file updates to their portfolio action plans

every three years. 113 

### These plans must include supply

‐ side and demand

‐ side

resources, and they must address the need to supply power in a way that

112 Minnesota PUC

, Report To The Legislature On Emissions Reduction Projects Under Minnesota Statutes

216B.1692 (Minnesota Public Utilities Commission, 2008), Accessed March 19, 2014,

http://www.puc.state.mn.us/portal/groups/public/documents/pdf_files/000661.pdf

. 113 Wilson, Rachel and Bruce Biewald, Best Practices in Electric Utility Integrated Resource Planning (Regulatory

Assistance Project, 2013 ), Accessed March 19, 2014, http://www.raponline.org/document/download/id/6608. Comment [A36]: This bullet should also include

IRP.

Comment [A37]: Edited accordingly.

Deleted: involves deliberately choosing among

a variety of electricity products and contracts.

The approach emphasizes diversity—fuels,

technologies, and power supply contract

durations. In its fullest form,

Deleted: are

Formatted: Indent: Before: 0.5", No bullets

or numbering

Deleted:

Deleted: as well as

Deleted: use

Deleted:

, a specialized form of portfolio

management.

Deleted: their own

Deleted: weigh the benefits of building their

own generation plants against procuring energy

from other entities. The plans also evaluate how

best to balance peak versus off

‐ peak electric load

requirements. In addition, they compare various

supply

‐ and demand

‐ side options and contract

and financial hedging options. Companies

achieve these goals simultaneously by analyzing

different scenarios. Comment [A38]: This set of sentences isn’t

necessary and isn’t entirely accurate as written,

since IRP and PM are not the same thing. Also,

deciding between building generation or buying

from others isn’t the primary focus of IRP. Rather,

the purpose is to analyze resource options over a

20

‐ 30 yr timeframe, including supply and demand

side resources. This paragraph should be adjusted

or deleted, since it is not essential.

Comment [A39]: Modified text per comment.

Comment [A40]: Is this the most current

information on Montana? See the Synapse report ... [2]

Comment [A41]: Modified language to clarify;

used reference provided.

Deleted: and require

Deleted:

Deleted:

‐

Formatted: Font: Italic

Formatted: Font: +Body (Calibri)

Formatted: Font: Italic

Deleted:

,

Deleted: :

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### minimizes the environmental cost by estimating the cost to the environment of

alternatives. In addition, utilities must account for the costs of complying with

existing and future environmental regulations. When considering various

resource options, Montana requires a competitive solicitation process, allowing

resource operators and developers to submit their proposals to the default

electricity supplier for consideration. Montana also requires the portfolio

management plans to be subject to an advisory committee review and a public

review. 114



### o Oregon electric utilities submit IRPs every two years, covering a 20

‐ year

timeframe. The goal of these plans is to consider the acquisition of resources at

least cost while keeping the public interest in mind. Potential risk factors must be

considered, including price volatility, weather, and the cost of meeting existing

and future federal environmental regulations. Quantifiable environmental

externalities are included, as are less quantifiable developments such as changes

in market structure and the establishment of a renewable portfolio standard. As

for energy efficiency requirements during the planning process, Oregon

determines these on a utility

‐ by

‐ utility basis. 115



###  Multi

‐ Pollutant Utility Planning

– Two states, Minnesota and Colorado, have worked

collaboratively with their investor

‐ owned utilities to develop multi

‐ pollutant emission

reduction plans on a utility

‐ wide basis. This multi

‐ pollutant, collaborative approach

enables utilities to determine the least cost way to meet long

‐ term and comprehensive

energy and environmental goals.

o The Colorado CACJA require s investor

‐ owned utilities (IOUs) with coal plants to

submit a multi

‐ pollutant plan to the PUC to meet the EPA standards for NO

x 

### , SO 2 

### , particulates, mercury, and CO 2



### . Utilities were not required to adopt a specific

plan set by the state, but had to meet with Colorado Department of Public

Health and Environment (CDPHE) and PUC approval. Xcel Energy’s plan was

submitted and approved in 2010. 116

114 U.S. EPA

, Clean Energy

‐ Environment Guide to Action (U.S. Environmental Protection Agency, 2006), Accessed

March 19, 2014, http://epa.gov/statelocalclimate/resources/action

‐ guide.html

.

115 Ibid.

116 RAP, Addressing the Effects of Environmental Regulations: Market Factors, Integrated Analyses, and

Administrative Processes ( Regulatory Assistance Project, 2013 ), Accessed March 19, 2014,

www.raponline.org/docume nt/download/id/6455. Deleted:

,

Deleted:

Deleted:

Deleted:

Deleted:

Deleted: d

Formatted: Font: +Body (Calibri) Formatted: Font: +Body (Calibri)

Formatted: Font: +Body (Calibri)

Deleted:

–

Formatted: Font: Italic Formatted: Font: Italic

Deleted: :

Deleted: EPA

– Clean Energy Guide to Action:

http://epa.gov/statelocalclimate/resources/action

‐ guide.html

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### o The Minnesota Emission Reductions Rider allows utilities to submit plans for

projects that reduce emissions and go beyond federal requirements outside of a

general rate case. It allows them to recover the costs of those actions as an

incentive. 117 

### The specific design and process of the projects vary by utility, but

typically involve installing additional pollution control equipment at coal

‐ fired

power plants, or repowering them with natural gas.

Authority



### State utility commissioners oversee utilities’ and default service providers’ procurement

practices in their states. Typically, the commissions solicit comments and input as they develop

portfolio management practices from a wide variety of stakeholders. The utility regulator may

also play a role in reviewing and approving utilities’ planning procedures, selection criteria,

and/or their competition solicitation processes.

Obligated Parties



### Vertically integrated utilities are often obligated under integrated resource planning, while in

restructured markets, the default utility service provider may be obligated to conduct portfolio

management.

For multi

‐ pollutant planning, Colorado IOUs, Xcel Energy and Black Hills Energy were required

to file plans with the Department of Public Health and Environment and the PUC in order to be

compliant with the CACJA. Plans needed to meet the National Ambient Air Quality Standards

for a number of air pollutants.

As the Minnesota multi

‐ pollutant legislation is voluntary for state utilities, there is neither

compliance nor reporting requirements.

Measurement and Verification



### Regulatory oversight aims to ensure utilities are following through with their plans. Regulators

often require utilities to submit portfolio management plans and progress reports at regular

intervals. These plans and reports describe in detail the assumptions used, the opportunities

assessed, and the decisions made when developing resource portfolios. Regulators then

carefully review these plans and either approve them or reject them and recommend changes

needed for approval. California, for example, requires utilities to submit biennial IRPs and

quarterly reports on their plans.

117 Minn esota Office of Revisor of Statutes, 2013 Minnesota Stat utes, §216B,1692 Emissions Reduction Rider

, 2013

, Accessed March 19, 2014, https://www.revisor.mn.gov/statutes/?id=216B.1692. Deleted: is

Comment [A42]: Recommend changing this to

read “Vertically integrated utilities are often

obligated to conduct resource planning, while in

restructured markets the default utility service

provider may be obligated to conduct portfolio

management for default service.”

Comment [A43]: Edited text per comment.

Deleted:

,

Deleted: sometimes

Comment [A44]: Recommend changing to

“Regulators often require utilities to submit

resource plans and progress reports at regular

intervals.”

Comment [A45]: Updated with recommended

text.

Deleted: (

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### There are no penalties for noncompliance, however there is usually significant interaction with

the regulator during the planning and implementation process as is described above.

Implementation Status



### Currently more than half of the states have integrated resource or other long

‐ term planning

requirements, 118 

### while Minnesota and Colorado have multi

‐ pollutant planning policies or

requirements (see Figure 9)

.

In Montana, for example, the 2011 Electric Supply Resource Plan for NorthWestern Energy calls

for:

 Shortening the length of power supply contracts from seven years to a more

competitive, staged process of between three to five years.

 Diversifying Montana’s resource mix with the recent addition of a 150 MW gas

‐ fired

power plant.

 Improving the integration of intermittent power sources into the power supply as new

wind turbines play a larger role in the state’s resource mix.

 Meet state RPS requirements.

 Acquire cost

‐ effective demand side management resources, targeting 6 MW of

additional energy conservation per year.

 Monitor market, regulatory, and technology changes to better manage risks and

opportunities. 119

118 Wilson, Rachel and Bruce Biewald, Best Practices in Electric Utility Integrated Resource Planning (Regulatory

Assistance Project, 2013), Accessed March 19, 2014, http://www.raponline.org /document/download/id/6608.

119 North W estern Energy

, Electric Supply Resource Plan (NorthWestern Energy, 2011), accessed March 19, 2014,

https://www.northwesternenergy.com/our

‐ company/tariffs

‐ and

‐ rates/montana

‐ tariffs

‐ and

‐ rates/electric

‐ supply

‐

resource

‐ procurement

‐ plan

. Deleted:

Field Code Changed Formatted: Font: +Body (Calibri), 12 pt, Font

color: Auto, Complex Script Font: 12 pt

Formatted: Font: +Body (Calibri)

Formatted: Font: +Body (Calibri)

Formatted: Font: +Body (Calibri)

Deleted:

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10 pt

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10 pt

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10 pt

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10 pt

Deleted:

, June

Deleted: w

Deleted:

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### Figure 9: States with Integrated Resource Planning or Similar Processes

In Oregon, PacifiCorp has filed its 2013 integrated resource plan. Key highlights from the report

include:

 Demand

‐ side energy efficiency efforts are expected to meet 67 percent of electricity

load growth from 2013 to 2022

 Market analyses for integrating w ind resources into the grid, and pursuing opportunities

for combined heat and power resources.

 Goals to obtain 1,425

‐ 1,876 GWh of energy efficiency resources by 2015 and 2,034

‐

3,180 GWh by 2017.

 Permitting and development efforts to convert a unit of the Naughton power plant from

coal to gas. 121



### To meet Colorado’s multi

‐ pollutant planning requirement, Xcel Energy submitted a plan that

was approved by the Colorado PUC on December 9, 2010. Implementation of the plan will

reduce NO

x 

### levels 88% and CO 2 

### levels 28% relative to 2008 levels by 2018. 122 

### Black Hills Energy

has also filed its electric resource plan (ERP). This plan includes the retirement of a coal

‐ fired

121 PacifiCorp, Integrated Resource Plan ( PacifiCorp, 2013 ), Accessed March 19, 2014,

http://www.pacificorp.com/content/dam/pacificorp/do c/Energy_Sources/Integrated_Resource_Plan/2013IRP/Pac

ifiCorp

‐ 2013IRP_Vol1

‐ Main_4

‐ 30

‐ 13.pdf

. 122 Xcel Energy, Xcel Energy

‐ Emissions Reduction Plan

, (Xcel Energy, 2011), accessed March 19, 2014,

https://www.xcelenergy.com/staticf iles/xe/Corporate/Environment/10

‐ 12

‐ 303_CACJ

‐ 6E_FS.pdf

. Formatted: Font: 12 pt, Complex Script Font:

12 pt

Formatted: Caption

Deleted: 120 ¶

Formatted: Font: 11 pt, Complex Script Font:

11 pt

Deleted: W

Deleted:

Formatted: Font: Italic Deleted: :

Deleted: Public Service Company of Colorado, an

Company

Deleted:

,

Formatted: Font: Italic

Deleted: 11

‐ 06

‐ 325_6E_FS, 2011

Deleted:

.

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### power plant and two older natural gas

‐ fired gas units, as well as a proposal to build a 40 MW

natural gas turbine. It plans to add 100 MW of capacity by 2017, Black Hills Energy will use

competitive bidding to meet the remaining 60 MW. 123



### In Minnesota, projects currently implemented under the multi

‐ pollutant legislation include the

Minnesota Power’s Arrowhead Regional Emissions Abatement (AREA) Project, Minnesota

Power’s Boswell 3 Emissions Reduction Plan, Xcel Energy’s Mercury Reduction Plan, and Xcel

Energy’s Metropolitan Emissions Reduction Proposal (MERP). MERP, authorized in 2002, has

shown a 93% reduction in SO 2 

### , 91% reduction in NO

x 

### , 81% reduction in mercury, 55% reduction

in particulates, and 21% reduction in CO 2 

### from 2007

‐ 2009. 124

123 Black Hills Energy, “ Black Hill s Energy F iles P lan for O ngoing R eliable, C ost

‐ effective E nergy for Y ears to C ome in

Colorado ” accessed on March 19, 2014, http://www.blackhillsenergy.com/node/34671#.UzHkuIXYhIt

. 124 Xcel Energy, “Minnesota Metro Emissions Reduction Project

– Environmental Benefits,” accessed March 19,

2014, https://www.xcelenergy.com/Env ironment/Doing_Our_Part/Cl ean_Air_Projects/MN_MERP. Deleted:

Deleted: f

Deleted: p

Deleted: o

Deleted: r

Deleted: c

Deleted: e

Deleted: y

Deleted: c

Deleted: :

Deleted:

Deleted: Update/add citation

Formatted: Highlight

Deleted: :Page 42: [1] Comment [A34] Author



## Commenters recommend changing this sentence to “Related is integrated resource planning (IRP) which

is generally used by vertically

‐ integrated utilities and is a long

‐ range utility plan for meeting the

forecasted demand for energy within a defined geographic area through a combination of supply side

resources and demand side resources.” (SEE Action IRP Guide)

They also recommend adding a sentence that describes the goal of IRP. For example: “Generally

speaking, the goal of an IRP is to identify the mix of resources that will minimize future energy system

costs while ensuring safe and reliable operation of the system.” (SEE Action IRP Guide)

Page 43: [2] Comment [A40] Author



## Is this the most current information on Montana? See the Synapse report

[http://www.cleanskies.org/wp

‐ content/uploads/2011/05/ACSF_IRP

‐ Survey_Final_2011

‐ 04

‐ 28.pdf]

for more nuances on how Montana IRP is accomplished, including that Montana is unique in that it has

separate rules for vertically

‐ integrated utilities and restructured utilities. For vertically

‐ integrated

utilities, the focus is on “long

‐ term” planning, which some consider to mean 20

‐ 25 years. For

restructured utilities, the planning horizon is “the longer of: 1) the longest remaining contract term in a

utility’s supply resource portfolio; 2) the period of the longest lived electricity supply resource being

considered for acquisition; or 3) ten years.”

## DRAFT May 16, 2014

1 

## Technical Memorandum

Consideration of Heat Rate Improvement (HRI) Potential at Existing Oil/Gas

‐ fired Steam,

Natural Gas Combined Cycle, and Combustion Turbine EGUs for Inclusion in Building Block 1

As described in the GHG Abatement Measures TSD, the EPA identified four categories

of demonstrated measures, or “building blocks ,” that are technically viable and broadly

applicable, and can provide co st-effective reductions in CO 2 

## emissions from individual existing

EGUs. These building blocks include:

Building Block 1 - Reducing the carbon intensity of generation at individual affected

EGUs through heat rate improvements;

Building Block 2 - Reducing emissions of the most carbon-intensive affected EGUs in

the amount that results from substituting gene ration at those EGUs with generation from

less carbon-intensive affected EGU s 

## (including NGCC units under construction);

Building Block 3 - Reducing emissions from affected EGUs in the amount that results

from substituting generation at those E GUs with expanded low- or zero-carbon

generation; and,

Building Block 4 - Reducing emissions from affected EGUs in the amount that results

from the use of demand-side energy efficien cy that reduces the amount of generation

required.

Coal-fired Steam EGUs

For Building Block 1, the EPA evaluated the fl eet-wide potential fo r lowering the carbon

intensity of generation at indivi dual affected coal-fired steam EGUs by improving heat rates at

these EGUs (see the GHG Abatement Measures TSD). The EPA analyzed 11 years of historical

heat rate data and the literature on HRI methods to estimate that the U.S. coal-steam EGU fleet

might reasonably be expected to reduce its a nnual average gross heat rate by about 6%.

Furthermore, the EPA understood that any HRI met hod that reduces gross heat rate will also

reduce net heat rate, and that some HRI methods reduce net heat rate with out reducing gross heat

rate. As such, the EPA expects that the HRI pot ential on a net output basis is somewhat greater

## DRAFT May 16, 2014

2 

## than on a gross output basis, primarily through upgr ades that result in re ductions in auxiliary

loads. Therefore, the EPA conservatively assumed th at the coal-steam fleet average net heat rate

can be reduced by 6% and included th is finding in its Building Block 1.

As discussed in the preamble, for purposes of developing the alte rnate set of goals on

which we are taking comment, the EPA used an es timate of a 4% HRI from affected coal-fired

steam EGUs on average. The EPA views the 4% estimate as a reasonable minimum estimate of

the technical potential for HRI on average across affected coal-fired EGUs.

Oil/Gas Steam EGUs

As summarized above, the EPA made a detaile d assessment of the fleet-wide potential for

HRI at existing affected coal-fired steam EGUs in Building Block 1. However, we decided to not

make a detailed assessment of this potential for existing affected oil and gas steam units for the

three main reasons described below.

First, oil and gas contain signi ficantly less carbon pe r unit of heating value than coal. Oil

and gas therefore produce significantly less CO 2 

## than coal for the same amount of heat. (This is

discussed further under NGCCs, below.)

Second, coal-fired steam EGUs are utilized at much higher le vels compared to oil/gas

steam EGUs. Therefore the amount of CO2 reducti on that can be achieved via HRI at oil/gas

EGUs is significantly smaller. For example, EPA modeling 1 

## projects that in 2020 coal-steam

units will provide 59% of all fossil-fired electrical generation, while oil/gas steam units will

provide only 2%. Even if CO 2 

## emissions from all oil/gas steam units could be reduced by 6% on

average using HRI methods (as assumed on coal-steam units) th at reduction would amount to

only a fraction of 1% of the HRI reduction th at might be obtained from coal-steam units. 2



## Third, oil/gas steam EGUs employ less extensive systems and equipment compared to

coal steam EGUs and therefore, in general, ha ve a lesser range of opportunities for implementing

HRI. For example, oil/gas steam units do not typically use flue gas SO 2 

## scrubbers, particulate

1 

### IPM Base Case v5.13 modeling results as presented in RIA Chapter 3.

2 

### The EPA is not suggesting that relatively small CO 2 

### reductions from fossil-fired so urces other than coal-steam

EGUs are not important. Such reduction might be significant in a few situations, and states are free to make use of

these reductions in meeting their goals.

## DRAFT May 16, 2014

3 

## collection devices, coal mills, coal conveyors, ash handling systems, sootblowers, etc.

Consequently, some of the HRI methods discus sed in the GHG Abatement Measures TSD are

not applicable for oil/gas steam EGUs.

The above factors taken together explain why the potential for CO 2 

## reduction achieved

via HRI at oil and gas steam EGUs would be quite small compared to that from the existing fleet

of coal-fired EGUs. Therefore the EPA conserva tively decided to not se parately itemize and

include this potential in Building Block 1.

Natural Gas Combined Cycle (NGCC) EGUs

EPA modeling also projects that in 2020 na tural gas-fired NGCCs will provide about

39% of the U.S. electrical generation from fossil fuels, compared to 59% from coal-steam EGUs.

Also, as explained below, NGCCs in 2020 would emit only about 20% of the total CO 2



## emissions from fossil fuels us ed in electrical generation.

The significantly lower amount of CO 2 

## produced by combustion of natural gas compared

to coal (about 40% less for the same amount of heat input) is due pr imarily to the higher

hydrogen content and lower carbon c ontent in natural gas compared to coal. Also, because a

NGCC is typically more efficient than a coal-steam EGU, thus using less h eat input from fuel to

make an equal electrical output, a very efficient NGCC can further reduce the CO 2 

## emission rate

per MWh to about 60% less than that from coal-s team EGUs. Thus, natural gas, particularly as

used in NGCCs, inherently reduces CO 2 

## emissions by more than one-half. Existing NGCC EGUs

are therefore already significantly reducing CO 2 

## emissions compared to existing coal EGUs, per

MWh of output, before considering whether NGCCs might be able to further reduce their CO2

emissions via HRI methods.

The EPA has preliminarily considered that there may be some potential for a further

reduction in the CO 2 

## emissions of NGCC EGUs via HRI. Ho wever, as with coal-steam EGUs,

we do not have the unit-specifi c detailed design information on existing individual NGCCs that

would be needed to make a detailed assessment of the HRI potential vi a best practices and

upgrades for each NGCC unit. While it would be possible for EPA to make a “variability

analysis” of NGCC historical hourly heat rate data (as was done for coal-steam EGUs), we are

aware that the various NGCC configurations in us e and the historically lo wer capacity factors of

## DRAFT May 16, 2014

4 

## the NGCC fleet (less run time per start, and more part load operation) would require a NGCC

analysis that includes more complexity and likely more uncertainty than in the coal-steam

analysis. In addition, the analysis would be limited by the fact th at only one-th ird of the NGCC

fleet has historically reported complete (com bustion turbine and steam turbine generator) load

data to EPA.

To preliminarily gauge the HRI potential for NGCCs, EPA engineering staff familiar

with NGCC design and operation informally di scussed the NGCC HRI potential with power

sector engineering firms and NGCC suppliers. Our preliminary conclusion is that the fleet-wide

HRI potential for existing NGCC EGUs may be onl y about 2-3% at most, on a sustained basis,

for the following two reasons.

First, as a “combined” combustion turbine and steam turbine power cycle, some of the

available HRI methods would be applicable only to the steam turbine porti on of the power cycle:

the HRSG (heat recovery steam generator), the st eam turbine-generator, and the heat rejection

system (water or air-cooled condenser system s). The HRI potential asso ciated with the steam

portion of the NGCC is significan tly less than in a coal-steam unit because the NGCC steam

system is much simpler (gaseous fuel, no back-e nd scrubbers, less parasitic power, no air heater

leakage, no feedwater heaters, etc) and its flue gas exit temperature is typically already much

lower than in a coal-steam unit.

Second, the HRI methods applicable to th e combustion turbine portion of the NGCC

relate primarily to critic al components in the hot expansion side of the un it - components that are

exposed to the products of co mbustion of fuel and air th at contain small amounts of

corrosive/erosive contaminants at very hi gh temperatures. These critical components

(combustors, nozzles/vanes, seals, rotating blades ) therefore require regul ar periodic removal and

refurbishment or replacement to maintain high NGCC efficiency levels, and indeed to avoid

potentially catastrophic mechanical failures. The gr eatest loss in the perfor mance (increased heat

rate) of a NGCC is this physical degradation that occurs in proportion to its hours of operation

and number of starts. Consequently, it has l ong been an accepted practice by NGCC owners to

closely follow the NGCC manufacturer’s main tenance recommendations, a practice that

regularly restores the NGCCs efficiency and re liability. This close adherence to manufacturer

recommendations is financially motivated in pa rt by the fact that many NGCC owners have long-

## DRAFT May 16, 2014

5 

## term maintenance contracts with the manufacturers, wherein th e manufacturer guarantees the

service life and replacement costs of expensiv e critical components - pr ovided that the regular

preventive/restorative maintenance schedule is followed. Regularly scheduled maintenance

practices are the most effective HRI methods that can be applied on NGCCs, and the EPA

concludes that they are likely already be ing applied across most of the NGCC fleet.

With NGCCs projected to produce 20% of fossil CO 2 

## emissions in 2020, and with a max

sustained HRI potential for existing NGCCs of 2-3%, as mentioned earlier, the CO 2 

## reduction

potential for NGCCs would amount to only a fraction of 1% of total fossil emissions in 2020,

which would be only about 10% of the potential CO 2 

## reductions expected from coal-steam EGUs

via HRI. Because of this limited potential a nd the uncertainty associated with it, EPA

conservatively decided to not separately itemi ze and include this NGCC potential in Building

Block 1.

Simple-cycle Combustion Turbine (CT) EGUs

Natural gas-fired CTs provide peaking ge neration, typically operating at very low

capacity factors. This is prim arily because of their relatively low efficiency, which is

economically only partially offset by their relati vely low capital cost. As peaking capacity, any

CT may have many starts/shutdowns in the course of a year. It may also “load follow,” with an

average electric power output that may be well be low its most efficient load point. CTs have an

operational flexibility well suited to their role as peakers, but this role requires them to be

inherently less efficient than they could be if it were economic to operate them at higher capacity

factors.

EPA modeling projects that the power sector CT capacity in 2020 (Base Case) will be

about 21% of total fossil-fired capacity (GW), and that it will provide on ly about 1% of total

fossil-fired electrical generation (GWh). Whether gas or oil-fired, CT capacity can therefore only

contribute CO 2 

## emissions amounting about 1% of total fossil CO 2 

## emissions, or perhaps 2% of

total coal-steam CO 2 

## emissions. Any single-digit percentage reduction in CT heat rates, can

therefore only provide much less than a 1% reduction in total fossil-fired CO 2 

## emissions.

Most CTs likely benefit from the same regul ar preventive/restorative maintenance as the

combustion turbine portion of a NGCC, as discu ssed above, and for the same reasons, Thus, the

## DRAFT May 16, 2014

6 

## heat rates of most CTs are already periodi cally (even if not regularly, depending on their

irregular operating hour s and starts) restored to a level that allows them to be both reliable and as

efficient as reasonably possible. Therefore the EPA decided to not include HRI for CTs as an

additional potential in Building Block 1.

Conclusion

This technical memorandum outlines the EPA’s reasons for not including CO 2 

## reduction

potentials via HRI on oil/gas steam, NGCC, and CT EGUs as part of the CO 2 

## reduction target of

Building Block 1. For each non-coal technology the EPA concludes that the total additional

potential reduction is small compar ed to the potential coal-steam CO 2 

## reduction. Furthermore,

we do not have the detailed site-specific inform ation that would be needed to make a more

precise engineering evaluation of the HRI poten tial for any individual EGU, including coalsteam units; only the owners/ope rators of these EGUs would have that information.

The EPA notes, however, that although we did not include an HRI potential for these

non-coal classes of existing fossil-fired EGUs in Building Block 1, we do expect that some

amount of CO 2 

## reduction via HRI is available from th ese EGUs. States and sources would be

free to use HRI at these EGUs to help reach the state CO 2 

## reduction goals.

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## Chapter 5: Demand-side Energy Efficiency (EE)

Introduction

This chapter provides information on dema nd-side energy efficiency (EE) as an

abatement measure for reducing carbon dioxide (CO 2 

### ) emissions from fossil fuel-fired electric

generating units (EGUs). Specifically, this chapte r addresses EE as a component of both the

“best system of emission reduction” (BSER) and state goals, and the inclusion of EE within the

impacts assessment. Support is provided in this chapter for the discussion of the EE abatement

measure throughout the preamble (most extensively in these sections: Building Blocks for

Setting State Goals and Considerations, State Goal s, State Plans, and Impacts of the Proposed

Rule) and its representation within the RIA. Resu lts from this chapter feed into the technical

support document (TSD) on Goal Computation. EE is also addressed in TSDs on state plan

considerations and projecting emissions performance.

This chapter is organized as follows:

 Background

– EE Technologies and Practices

– Barriers to EE Investment

– EE Policies

– EE Programs

 The EE Opportunity

– Rapid Growth in EE

– EE Program Impacts

– EE Potential

– Costs and Cost-Effectiveness of State EE Policies

– EE as an Abatement Measure

 State Goal Setting

– Approach

– Inputs

– Calculations

– Results

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 Impacts Assessment

– Approach

– Inputs

– Calculations

– Results

 Analysis Considerations

 Appendices

 References

Background

As discussed in the State Plan Consideratio ns TSD (Appendix: “Survey of Existing State

Policies and Programs that Reduce Power Sector CO 2 

### Emissions”), demand-side energy

efficiency policies and programmatic efforts have existed for decades and are now used in all 50

states. These strategies are intended to help states achieve energy savings goals, reduce the

environmental impacts (including carbon dioxide (CO 2 

### ) emissions) of meeting energy service

needs, save energy and money for consumers, and provide a significant resource for meeting

power system capacity requirements. EE policies currently in place are considered by states to be

cost-effective strategies for contributing to these policy objectives 1 

### . Moreover, states – through

their utilities, primarily – have been rapidly in creasing their funding of EE programs in recent

years, more than tripling budgets in the five years from 2006 to 2011, from $1.6 billion to $5.9

billion i 

### . In 2012, the cumulative impacts of these programs represented a 3.7% reduction in

national electricity demand ii 

### . And, EE spending is projected to continue to grow at a substantial

rate. A recent study by Lawrence Berkeley National Laboratory (LBNL) projects EE program

spending to reach $8.1 billion to $12.2 billion (“Medium Case” an d “High Case,” respectively)

in 2025 even “without considering possible major new policy developments,” such as

requirements under Clean Air Act, Section 111(d) 2iii



### .

1 See below for discussion of cost-effectiveness and relate d cost tests used by states to evaluate EE programs.

2 Specifically, the LBNL study states: “By virtue of limiting the analysis to current energy efficiency policies, we do

not consider the potential impact of major new federal (or state) policy initiatives (e.g., a national energy efficiency

resource standard, clean energy standard, or carbon policy) that could result in customer-funded energy efficiency

program spending and savings that ex ceed the values in our High Case.” Comment [A1]: Suggest including citation.

Comment [A2]: EPA RESPONSE: Citation is at

end of the sentence (iii).

Comment [A3]: Calls into question the need for

BB 4??

Comment [A4]: EPA RESPONSE: This building

block will provide incentive to reach these forecast

levels and beyond. State EE policies are being

reconsidered in many states and sometimes rolled

back (e.g., OH). At the same time, we clearly are

building upon the ongoing work of leading states,

integrating their best ideas and successes into the

basis of this building block.

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This section provides relevant background for the subsequent sections that address the EE

opportunity, EE as a component of BSER, EE within state goal setting, and the integration of EE

within the benefit, cost, and impacts assessments as reported in the RIA and elsewhere. This

section begins with a discussion of EE technol ogies and practices, and then describes the market

failures that limit cost-effective EE investment s. We then summarize EE policy objectives and

discuss policy types, their relative impacts, and discuss in more detail the key strategy of

employing EE programs.

EE Technologies and Practices

Energy efficiency is using less energy to provide the same or greater level of service.

Demand-side energy efficiency refers to an extensive array of technologies, practices and

measures that are applied throughout all sectors of the economy to reduce energy demand while

providing the same, and sometimes better, level and quality of service. Utilities employ a large

array of strategies in implementing energy efficiency programs, these include financial

incentives such as rebates and loans, technical services such as audits and retrofits, and

educational campaigns about the benefits of energy efficiency improvements. The purpose of

these EE programs is to induce EE investments and practices that would not otherwise occur in

the presence of market failures and behavioral impediments . In the residential sector, examples

of EE activities include the purchase of more efficient products and equipment (e.g., ENERGY

STAR labeled), the upgrading of insulation in attics and walls, sealing of air leaks, and

undertaking home energy audits leading to cust omized whole home retrofits. Opportunities for

cost-effective EE in commercial buildings includ e optimization of heating, ventilation, and air

conditioning (HVAC) systems, upgrades of windows , and use of more efficient office equipment

at replacement. In the industrial sector ke y EE strategies include motor upgrades and

maintenance programs, recovery of waste heat streams, and optimization of processes through

modern instrumentation and controls systems.

The opportunity presented for economic invest ment in EE is dynamic, growing over time

as technologies and practices advance, as popul ations grow, and as investment occurs in the

construction of new homes, buildings, and industrial facilities. As new policies are enacted,

leading to the acceleration of investment in EE, an additional portion of the expanding Deleted: ’

Comment [A5]: Can EPA explain why the EE

intervention is needed?

Comment [A6]: EPA RESPONSE: As discussed in

the following section “Barriers to EE Investment,” a

number of market failures and behavioral

impediments hinder the realization of energy

efficiency level that is socially optimal. The diversity

of EE programs is designed and devised to address

these market failures and behavioral impediments.

Also, made edits to the text to clarify.

Comment [A7]: Although Figure 5.1 some pages

ahead does some of this, this would be a good

opportunity to bulletize real world examples of EE

actions, like windows, light bulbs, etc., with their

cost and efficiency and gains, even maybe a graph

showing how EE are the lowest cost GHG actions.

Otherwise, the next few pages read like a

philosophical discussion of an abstract concept.

Comment [A8]: EPA REPONSE: Thanks for this

observation. Real world examples are provided in

this paragraph. As you point out, Fig. 5.1 also

provides a sense of the varied nature of the

opportunities and strategies.

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opportunity is realized. After decades of experience implementing policies to accelerate

investment in cost-effective energy efficiency, states are finding renewed opportunities as they

develop more sophisticated and effective strate gies, evolving from a focus on individual enduses and products to whole-building and systems-based strategies that account for the

interactions between the many energy end-uses in buildings and industry iv 

### . As will be discussed,

the experience in the U.S. has been that on balance, a persistent and large potential for

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

have accumulated.

Barriers to EE Investment

Despite the persistent and large potential for electricity savings through investment in EE

technologies and practices, market failures, as well as non-market failures, limit the realization of

the many benefits of these invest ments. Several market failures that lead to inefficiencies in

energy use are well recognized by analysts and practitioners, and are discussed extensively in the

economic literature. v 

### Some of the most common examples of these market failures include:

 Pollution externalities. Energy consumption is associated with negative externalities, such as

emissions of CO 2 

### , SO 2 

### , and NO

x 

### that cause human health and environmental damages.

Energy prices that do not correctly reflect th ese externalities lead to investments in energy

efficiency below the socially optimal levels.

 Imperfect information. Energy users often lack accurate information about energy savings

and other attributes of energy efficient pro ducts or practices to understand the costs and

benefits of EE investments. Market failure due to information imperfection leads to

underinvestment in energy efficiency by consumers.

 Split incentives (or the “pri ncipal-agent problem”). Incentives of individuals who make EE

investment decisions are not always aligned w ith incentives of those who use and pay for

energy. Examples include misalignment between landlords and tenants, and between builders

and homeowners. Split incentives also persist with in organizations and institutions that lead

to underinvestment in EE in both public and the private entities. vi

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 Credit constraints . Limited access to credit may prevent some consumers, especially lowincome consumers, from making cost-effective EE improvement decisions due to the higher

upfront cost of energy efficient products or practices.

 Under-provision of research and development (R&D). Because of the public good nature of

knowledge, technology innovation invested by one firm likely spills over to other firms. As a

result, firms involved in technology developmen t may be less willing to invest in R&D,

leading to sub-optimal levels of EE investments from a social perspective. vii



###  Supply market imperfections. Market for energy efficient products is incomplete.

Manufacturers do not have perfect information about consumer preferences and may supply

limited menu of products to consumers. High start-up costs and the existence of patents may

create barriers to entry in markets and result in oligopolistic or monopolistic behavior.

Supply chains of EE products is fragmented, leading to underinvestment in innovation and

energy efficiency by suppliers. In addition, supply chain fragmentation may also add

complexity to the purchase an d installation of otherwise econo mically rational investments,

thereby slowing the adoption of EE technologies.

 Behavioral impediments . Behavioral economics and psychology have identified potential

behavioral phenomena that lead to consumers to deviate from the standard theory of welfare

maximizing in consumption and other decisions, including energy efficiency investments.

Behavioral economics posits possible explanations, including bounded rationality, heuristic

decision-making, and non-standard preference and belief. viii



### In the presence of market failures, users of electricity, or those making energy efficiency

investments, face prices or incentives that prevent them from weighing the social benefits and

costs of their investments and thus under-invest in approaches to reduce electricity consumption.

The behavioral impediments discussed above ex plain why individuals do not always make

energy efficiency investments that are seemingly in their own best interest to reduce their total

expenditure, given prevailing electricity prices.

In addition to market failures and behavioral impediments, other factors, such as hidden

costs, risk and uncertainty experienced by both consumers and suppliers of energy efficient

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products, and heterogeneity among consumers, producers and markets, also influence EE

investment decisions 3 

### . Examples of such factors include:

 Risk and uncertainty . Adopting an unfamiliar, typically more expensive EE technology can

be an uncertain undertaking given the lack of credible information on product performance

and future energy prices, and the irreversibility of the investment. Imperfect or asymmetric

information can exacerbate the perceived risk of energy efficiency investments and help

explain why consumers and firms do not always invest in EE measures. Suppliers also face

risk and uncertainty, without perfect inform ation of consumer preferences for energy

efficiency. In the presence of risk and uncertainties, consumers and suppliers alike will

underinvest in EE.

 Transaction costs . Consumers face transaction costs in searching, assessing and acquiring

energy efficient technologies and services . It can be time-consuming and difficult for

consumers to estimate lifetime operating costs of a product. The complexity of the search

process puts many efficient products at a disadvan tage relative to less-efficient products with

lower upfront costs.

 Capital market barriers . Consumers sometimes face higher interest rates to finance EE

investments compared to other investments. Le nders can be reluctant to invest in EE loan

portfolios in part because energy efficiency loans may lack standardization and financial

markets have difficulty ascertaining the likely payoff from such investments.

EE policies and programs can play an important role in correcting market failures and

addressing the barriers to the investment and adoption of socially beneficial energy efficiency

opportunities. Examples of effective EE policie s and programs include public funding of R&D,

information programs (such as energy labeling, the voluntary ENERGY STAR Program, and

3 It has been recognized that there is a difference between cost-effective energy efficiency investment levels, based

on cost-minimizing consideration, and observed levels of energy efficiency. This phenom enon, also termed ‘energy

paradox,’ or ‘energy efficiency gap,’ has been studied extens ively in the literature. See, for example, Jaffe, AB, and

RN Stavins. 1994. “The Energy Paradox and the Diffusion of Conservation Technology.” Resource and Energy

Economics 16(2): 91–122; Sanstad, A. H. and R. B. Howarth. 1994. ‘Normal’ markets, market imperfections and

energy efficiency, Energy Policy , 22: 811-818; DeCanio 1998; DeCanio, SJ and WE Watkins. 2008. Investment in

Energy Efficiency: Do the Char acteristics of Firms Matter? The Review of Economics and Statistics , 80: 95-107;

Allcott, H, and M. Greenstone. 2012. Is There an Energy Efficiency Gap? Journal of Economic Perspectives 26

(1):3-28.

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consumer education), rebates for high-efficiency products, product energy performance

standards, financing and loan programs, and technical assistance.

EE Policies 4



### Objectives and Role in Reducing CO 2 

### Emissions from the Power Sector

EE policies are implemented by states to meet a number of closely related policy goals ix



### including:

- Reducing costs to electricity customers

,

- Providing a significant resource for meeting power system capacity needs

,

- Meeting energy savings goals

,

- Stimulating local economic development and new jobs , and

- Reducing the environmental impacts of meeting electricity service needs

.

EE policies currently in place are considered by states to be cost-effective strategies for

contributing to each of these policy objectives x 

### . While each of these objectives, and others,

contribute to the motivation of state policymakers to pursue EE policies, reducing energy costs

over the long term is the leading objective in pur suing these policies. In addition, EE policies are

central to meeting state objectives for reducing CO 2 

### emissions from the power sector. As noted

in the State Plan Considerations TSD, EE policies are a leading tool for achieving CO 2



### reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in

ten states 5 

### with statutory requirements for greenhouse gas reductions.

Economy-wide studies of climate mitigation scenar ios confirm that energy efficiency plays a

critical role in reducing the costs and enhancin g the flexibility of meeting long-term climate

stabilization targets xi 

### . Analysis by the International Energy Agency (IEA) suggested that in order

to stabilize carbon concentration in the atmosphere at 450 ppm, as much as 44% of the estimated

global abatement potential in 2035 derives fro m greater energy efficiency in the world

economy xii 

### . Several recent Energy Modeling Forum (EMF) studies have investigated the role of

technology in achieving climate policy objectives in the U.S. (“EMF 24” and “EMF 25” studies)

4 Existing state EE policies are described extensively in the State Plan Considerations TSD.

5 States with GHG reduction laws include: California, Connecticut, Hawaii, Maine, Maryland, Massachusetts,

Minnesota, New Jersey, Oregon, and Washington. Comment [A10]: EPA RESPONSE: The rebound

effect is referenced in “Cost of Saved Energy”

section, below and a fuller discussion has been

added to that section. Comment [A9]: Rebound effect??

Formatted: Font: (Default) Times New Roman,

Complex Script Font: Times New Roman

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and globally (“EMF 27” study). 6 

### These studies concluded that compared to business-as-usual

energy efficiency, improvements in energy effi ciency in various economic sectors would slow

the increases of GHG emissions in the short run, substantially reduce the costs of GHG

mitigation (on average, by about 50% 7 

### ), and ease the technology transformation pathways to

achieve long-term carbon reduction goals xiii,xiv



### .

Several economic studies (including EMF25 studies) examined the role of energy efficiency

policies (such as energy efficiency standards and subsidies) in relation to other climate policy

instruments (such as carbon taxes). These studies found that when energy efficiency policies

address market failures, they are welfare improving and can complement climate policy. xv 

### In

addition, EE policies are recognized to be an appropriate response to demonstrated market

failures and behavioral impediments, particularly in contexts where these failures have broader

societal implications such as environmental externalities. xvi



### In addition to providing cost-effective opport unities for reducing GHG emissions, energy

efficiency is recognized to provide other co-benefits, including air quality and public health

benefits, waste reduction from energy generation, energy security, energy system reliability,

community economic and social development, and consumer amenities. xvii 

### Energy efficiency

investments and policies are also found to spur productivity growth, technology learning and

innovation. xviiixix 

### Recently, more attention has been paid to developing methods for recognizing

these co-benefits and integrating them into the cost-benefit analysis framework used by state

utility commissions and administrators of EE programs xx 

### . These co-benefits have not been fully

accounted for in the EPA analysis.

Policy Types

6 Energy Modeling Forum (EMF) is a consortium of energy economists and energy economic modeling teams that

was established in 1976. Through ad hoc working gro ups, the EMF has focused on a series of energy and

environmental topics that are of interest to policy d ecisions. In recent years, the EMF is recognized for its

contribution to the advancement of economics of climate change and the reports of the Intergovernmental Panel on

Climate Change (IPCC).

7 It should be noted that these energy-economy modeling studies do not typically include the costs of implementing

energy efficiency measures or w ould treat such costs as exogenous

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EE policies come in many forms. The most prominent and impactful EE policies in most

states include those that drive development and funding of EE programs 8 

### , and building energy

codes. Other policies that are leading to si gnificant impacts include state appliance and

equipment standards, building energy disclosure requirements, innovative financing strategies

(e.g., Property Assessed Clean Energy or “PACE”), state tax policies, and “lead by example”

strategies targeting energy use in state operations. Comparing the relative impact (achieved or

potential) of the different policy types is challenging, particularly to do so comprehensively,

across all states, and at the national level. EE programs are the only state EE approach that has

comprehensive and detailed reporting of impacts, costs, and other characteristics from all 50

states 9 

### . This information is generally based upon measurement and verification studies submitted

annually, most commonly to state utility commis sions, and reported to the Energy Information

Administration (EIA) for all program administrato r types (all utility types, third-parties, and

government agencies). EE program data reported to EIA includes incremental and cumulative

energy and peak demand savings, program costs broken down by component, and composition

by end-use sector (residential, commercial, industrial). In 201 2, utilities and other program

administrators in 48 states reported savings fr om EE programs to EIA through form EIA-861. At

a national level, the EPA is not aware of a comprehensive dataset reported by states of the

achieved impacts of strategies other than those that lead to investment in EE programs. However,

state and regional-level information does ex ist. For example, the Northwest Power and

Conservation Council (NPCC) has been compiling the impacts of EE policies (including utility

and third-party EE programs, state building energy codes, and federal appliance standards)

across their member states (ID, MT, OR, WA) for more than three decades. For the past decade,

EE programs have accounted for more than 75% of the cumulative energy savings from state EE

policies for NPCC, with building energy co des accounting for th e remaining savings xxi



### .

8 EE programs are described in more detail in the follo wing section of this chapter and in the State Plan

Considerations TSD.

9 This is a reference to data collected by the Energy Information Administration (EIA) through Form EIA-861. In

2011, EIA began collecting data from third-party administ rators of programs. Prior to 2011, this was a significant

shortcoming in the breadth of the data collected. The br eadth and quality of information collected through Form

EIA-861 has improved over time, however, outside entities (e .g., ACEEE) have found that the data can be improved

through expert review and supplementation with other data sources. While now fairly comprehensive, the EIA data

can be improved further with regard s to data quality and consistency. See “Analysis Limitations” section for further

discussion.

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Another representation of the relative opp ortunity provided by different state EE

strategies is presented by evaluations of EE achievable potential or projections of the impacts of

EE policies. The results from two recent evaluations at a national level are presented in Table 5-

1. EE programs account for 77% and 82% of achievable savings in ACEEE xxii 

### and Georgia

Tech xxiii 

### studies, respectively. These studies indicate that the substantial majority of potential

savings from state EE efforts are available through EE programs, and that state and local

building energy codes can make a significant additional contribution. Massachusetts provides a

state example of the impacts of EE programs relative to other state EE policies. The

Massachusetts Global Warming Solutions Ac t of 2008 established statewide limits on

greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. To achieve this

target, Massachusetts is relying upon an integrated portfolio of clean energy policies. State EE

policies are expected to provide the largest cont ribution to meeting the 25 percent target with

utility sponsored EE programs and state build ing energy codes accounting for 76% and 17%,

respectively, of those policies xxiv 

### . In their 2013 progress report, Massachusetts indicates that they

are generally on track for meeting or exceeding these projections. xxv



### TABLE 5-1

Relative Opportunities Provided by Key EE Programs and Building Codes

Study Year EE Programs Building Codes Other

ACEEE 2030 77% 13% 10%

Georgia Tech 2035 82% 18% 0%

The full range of EE policies are addressed in greater detail (including designs, authority,

obligated parties, measurement and verification (M&V), penalties for non-compliance, and

implementation status) in the State Plan C onsiderations TSD. Because EE programs have

provided the majority of state EE-policy electrici ty savings to-date and offer the majority of

potential savings going forward, we next summarize key characteristics of this strategy.

EE Programs Deleted: ¶

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EE programs (actually portfolios of programs) are comprised of numerous measures and

measure types that are applied across all sectors of electricity end-users. Figure 5-1 xxvi 

### illustrates

the multi-level composition and breadth of EE program portfolios. The diversity represented by a

typical portfolio of EE programs implemented by a utility (or other program administrator) is an

important characteristic relevant to analysis of EE policies. Every detailed program type (as

illustrated in the lower half of the figure) repr esents a unique set of ch aracteristics including

costs of energy saved, ratio of program to pa rticipant costs, investment life, scale, M&V

approach, etc. 10

10 See following sections for discussion of these factors.

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FIGURE 5-1 11



### Energy Efficiency Program Portfolio

Administrators

EE programs are admini stered by a variety of entities (“program administrators”)

including utilities of all ownership types (investor-owned, municipals, and cooperatives), nonprofit and for-profit third-parties (e.g., Vermon t Energy Investment Corporation), and state and

local government agencies (e.g., NYSERDA). Most EE programs (including all investor-owned

utilities which account for more th an 75% of reported savings xxvii 

### ) are overseen by state utility

commissions, which review and approve program plans, projected impacts, and associated

budgets; and establish annual reporting and M&V requirements.

11 The “EM&V” box is not comparable to the other program types and is not relevant to this discussion. It was

included in the referenced source to indicate that EM&V is a key activity within a program portfolio. Comment [A11]: The placement of EM&V in

Figure 5

‐ 1 as separate from the actual programs is

misleading. Each program has EM&V associated

with it, so showing it as a separate activity is

inaccurate and possibly confusing.

Comment [A12]: EPA RESPONSE: See added

footnote 11. We cannot easily edit this graphic.

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Policy Drivers

EE programs result from a number of different policy approaches or “drivers.” 12 

### These

include energy efficiency resource standards (EERS) (26 states) xxviii 

### , system benefit charges (14

states), integrated resource planning (IRP) requ irements (34 states), demand-side management

plan or multi-year energy efficiency budget (28 states), and statutory re quirement to acquire “allcost-effective EE” (6 states) xxix13 

### . EERS is a more recently developed strategy and has quickly

become the leading driver of the rapid growth in EE programs due to their clear goals and proven

success as a policy tool xxx 

### . These policy drivers lead to the evaluation, planning, and adoption of

EE programs and associated budgets, which are supported through different funding

mechanisms.

Funding Sources

Funding sources for EE programs are varied but for most states are dominated by revenues

collected from ratepayers through electricity surcharges, typically ranging from $1 to $4 per

megawatt-hour xxxi 

### . More recently adopted funding sources include proceeds from the auction of

allowances in the Regional Greenhouse Gas Initiative (RGGI) states and from EE resources bid

into the forward capacity market operated by the New England Independent System Operator

(NE-ISO). Ratepayer-funding accounts for more than 90% of total EE program support

nationally.

The EE Opportunity

As discussed, states are employing a number of EE strategies with EE programs yielding the

most significant impacts both historically as well as in terms of future potential. Furthermore, EE

programs are unique among state EE strategies in the comprehensiveness and transparency of

their reported impacts, funding, and other charact eristics. In this section we address the rapid

12 These policies are discussed in depth in State Plan Considerations TSD.

13 The number of EERS states is from ACEEE (see endnote) and includes states with explicit EERS, those with

long-term energy savings targets for individual program ad ministrators, and those with EE incorporated as an

eligible resource in a renewable portfolio standard. The numbers for the other policy approaches are from LBNL

(see endnote).

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growth in EE programs, estimated impacts of EE programs to-date and projections of the impacts

of existing EE programs and trends, and the el ectricity savings potential achievable through

expanded use of EE policies and programs. Finally, we will discuss the costs and costeffectiveness of EE programs, specifically.

Rapid Growth in EE

Funding for EE programs has increased rapidly in recent years driven by recent policy

innovations and increasing evidence of the eff ectiveness of these new strategies. Table 5-2

presents levels of EE program funding in the U.S. since 2006 xxxii 

### . In the previous five years,

funding increased by more than 200%, from $1.6 billion in 2006 to $5.9 billion in 2011.

TABLE 5-2

U.S. Electric Utility EE Program Funding (2006-2011)

Year

2006 2007 2008 2009 2010 2011 2012

Electric Efficiency

Program Budgets

(billions of $s, nominal) 1.6 2.2 2.6 3.4 4.6 5.9 5.9

Key new state policies that have helped to drive these rapid increases in EE program

funding include EERS, electricity savings goals, and “all cost-effective energy efficiency”

requirements. The adoption of EERS, in particular, increased through this period and clearly has

been the primary driving force behind the increasing success of and investment in EE programs.

Table 5-3 shows the number of states adopting EERS by year xxxiii



### .

TABLE 5-3

U.S. State Adoption of Energy Efficiency Resource Standards

Year States Adopting an EERS Total

1997-2004 California, Hawaii, Texas, Vermont 4 Comment [A13]: Please update this table to

include the most recent numbers

Comment [A14]: EPA RESPONSE: Done.

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1.6

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2005 Nevada, Pennsylvania 2

2006 Rhode Island, Washington 2

2007 Colorado, Connecticut, Illinois, Minnesota, North Carolina 5

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

2009 Arizona, Indiana, Iowa, Maine, Massachusetts 5

2010 Arkansas, Oregon 2

2011 Wisconsin 1

1997-2011 26

EE Program Impacts

Impacts to-date

The primary sources for EE program information (including costs and impacts) are

annual EE program reports required by utility co mmissions, or cooperative or municipal utility

boards of directors. These reports are based on M&V studies of individual EE programs within

the program portfolio. The Energy Information Admi nistration (EIA) has been collecting data on

EE programs through Form 861, “Annual Electric Power Industry Report,” for more than three

decades

. 14 

### The data collection reflects an increasing degree of breadth and detail over time. For

example, third-party-administered programs were not initially required to report but were added

beginning in 2011. Data fields have been added over the years to reflect industry trends (e.g., EE

programs are now reported separately from load management programs). Outside organizations

have taken the EIA data, supplemented it with additional sources including surveys of utility

commissions and program administrators, and pub lished their own annual reports that capture

EE program impacts xxxiv

, xxxv



### .

The EPA has relied on the EIA Form 861 dataset for identifying historic impacts of EE

programs by state. Specifically, the reported sales data, and incremental and cumulative

electricity savings in the 2012 EIA 861 dataset are used to estimate electricity EE impacts by

14 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/. Deleted: with

Formatted: Superscript

Comment [A15]: As noted elsewhere in the

comments, this dataset can be problematic.

Comment [A16]: EPA RESPONSE: EPA is aware

of issues associated with consistency and quality of

reported DSM program data in Form EIA

‐ 861. Our

goal setting analysis utilizes two variables in the EIA

‐

861 data. Specifically, we use the 2012 electricity

sales data and reported incremental annual

electricity savings of demand

‐ side management

programs to estimate electric EE program impacts

for each state. We recognize that different utilities

and states may have different approaches to

estimate incremental savings, but over time there

has been increased standardization in data

reporting. The instructions for the form direct the

filer to report net energy savings and most

states/utilities estimate net savings. We rely on this

dataset because it remains the most comprehensive

one publicly available that we are aware of. In the

preamble, we are soliciting comments on better or

more appropriate dataset that can be used for our

analysis. In addition, a FN is added to discuss the

data and caveats and the dataset is addressed

further in Analysis Limitations section. We would

welcome support from DOE on improving this

dataset prior to Final.

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state. 15 

### EIA data is reported by program administrator (e.g., utility, third-party, or state agency)

and requires the disaggregation of reported data by state for administrators with programs in

multiple states (e.g., multi-state investor-owned utilities). Program administrators in 48 states

reported savings in 2012. The EPA has compiled th is information and aggreg ated key data to the

state level. Table 5-4 provides a summary of this data by state for the 2012 reporting year, the

most recent available. At the national level, incremental electricity savings 16 

### in 2012 was 0.58%

of retail sales with individual state values ra nging from 0.00% to 2.19%. Cumulative electricity

savings 17 

### (representing the remaining impacts of pr ograms from all prior years) reported at the

national level for 2012 represent 3.74% of retail sales with individual state values ranging from

0.0% to 15.44%.

TABLE 5-4

2012 Reported Electricity Savings by State

State Incremental Savings as a %

of Retail Sales (2012) Cumulative Savings as a %

of Retail Sales (2012)

Alabama 0.07% 0.78%

Arizona 1.61% 5.39%

Arkansas 0.11% 0.39%

California 1.24% 13.67%

Colorado 0.84% 4.67%

Connecticut 1.05% 13.37%

Delaware 0.00% 0.00%

District of Columbia 0.00% 0.57%

15 EPA recognizes concerns associated with consistency and quality of 861 data that different reporting entities may

have used different methodologies to estimate savings and the EIA 861 data are self-reported. Over time, there has

been increased standardization in data reporting. We believe his dataset remains to be the most comprehensive

publically available dataset. See “Analysis Limitations” section below for further discussion.

16 Incremental savings (also known as first-year savings) represent the reduction in electricity use in a given year

associated with new EE activities in that same year , either new participants in DSM programs that already existed in

the previous years, or new DSM programs that existed for the first time in the current year

. 17 Cumulative savings (also known as annual savings) represen t the reduction in electricity use in a given year from

EE activities in that year and all preceding years, ta king into account the lifetim es of installed measures. Comment [A17]: EPA needs to provide much

more detail about how this data was derived.

Comment [A18]: EPA RESPONSE: See response

to immediately prior and following comments. The

data is from 861 as is now noted. And discussion has

been added addressing limitations of the data.

Comment [A19]: EPA used EIA 861 data

Comment [A20]: EPA RESPONSE: That is correct.

The estimates are based on the 2012 EIA 861 data.

A note is added at the end of the table. The text in

the previous paragraph is also modified to provide

more explanation of the use of EIA data.

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Florida 0.27% 3.60%

Georgia 0.18% 0.67%

Idaho 0.79% 6.20%

Iowa 1.05% 7.80%

Illinois 0.93% 2.15%

Indiana 0.58% 1.72%

Kansas 0.02% 0.24%

Kentucky 0.23% 1.04%

Louisiana 0.00% 0.00%

Maine 1.96% 5.42%

Maryland 0.89% 2.47%

Massachusetts 0.94% 6.27%

Michigan 1.01% 2.77%

Minnesota 1.12% 13.10%

Mississippi 0.08% 0.50%

Missouri 0.12% 0.55%

Montana 0.66% 5.85%

Nebraska 0.30% 0.99%

Nevada 0.54% 6.19%

New Hampshire 0.48% 4.90%

New Jersey 0.03% 1.04%

New Mexico 0.60% 1.86%

New York 0.93% 6.89%

North Carolina 0.37% 1.26%

North Dakota 0.07% 0.22%

Ohio 0.87% 3.20%

Oklahoma 0.21% 0.70%

Oregon 1.09% 7.72%

Pennsylvania 1.06% 3.08%

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Rhode Island 0.78% 11.22%

South Carolina 0.35% 1.12%

South Dakota 0.13% 0.33%

Tennessee 0.31% 1.76%

Texas 0.19% 1.54%

Utah 0.74% 6.59%

Vermont 2.19% 15.44%

Virginia 0.03% 0.30%

Washington 0.93% 7.37%

West Virginia 0.18% 0.20%

Wisconsin 1.05% 6.61%

Wyoming 0.14% 0.71%

Continental U.S. Total 0.58% 3.75%

Alaska 0.02% 0.10%

Hawaii 0.04% 0.25%

U.S. Total 0.58% 3.74%

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



### Projected Spending and Savings from EE Programs

In 2013, Lawrence Berkeley National Laboratory (LBNL) published an update to a 2009

analysis and projected future spending levels and savings through 2025 from energy efficiency

programs funded by electric and gas utility custom ers in the United States under three scenarios

(high, medium, and low cases) xxxvi 

### . The scenarios represent “a range of potential outcomes under

the current policy environment” and were based on detailed, bottom-up analysis of existing state

energy efficiency policies. Significantly, the study presumes no new major policy developments

such as a “national energy efficiency standard , clean energy standard, or carbon policy” and

specifies that such policy changes could “result in customer-funded energy efficiency program

spending and savings that exceed the values in our High Case.”

The study concludes that efficiency programs are “poised for dramatic growth over the

course of the next 10 to 15 years” with the most significant increases occurring in regions with Formatted: Font: 10 pt, Not Bold, Not Italic,

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lower levels of program spending, historically, including the Midwest and South. For example,

under the medium scenario total U.S. spending on electric efficiency programs increase by 40%

to $8.1 billion in 2025 from 2012 levels. Under the high scenario, spending more than doubles

from 2012 levels to $12.2 billion in 2025. Incremental savings levels grow commensurately, to

0.8% and 1.1% of sales under the medium and high scenarios, respectively. The study results

indicate that under the high scenario 20 states would be achieving 1.5% or higher levels of

incremental savings, with 11 of those reaching or exceeding 2.0% 18 

### . Table 5-5 summarizes the

results of the LBNL analysis.

18 LBNL provided these unpublished results from their analysis. Comment [A21]: So what does this imply about

BB #4? Why does EPA put into the baseline?

Comment [A22]: EPA RESPONSE: This building

block will provide incentive to reach these forecast

levels and beyond. State EE policies are being

reconsidered in many states and sometimes rolled

back (e.g., OH). At the same time, EPA is clearly

building upon the ongoing work of leading states,

integrating their best ideas and successes into the

basis of this building block.

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Table 5-5

Summary of Impacts:

Scenarios of Future Utility Customer-Fu nded Energy Efficiency Programs

Case 2025

Incremental Savings

(% of Sales) Program Costs

(billions of $, nominal) Programs Costs

(% of Revenues)

Low 0.5% 5.5 1.1%

Medium 0.8% 8.1 1.7%

High 1.1% 12.2 2.7%

EE Potential

Evaluations of EE Potential

Energy efficiency potential studies are a common tool for informing the development of EE

program plans and budgets, as well as supporting th e development of electricity savings targets,

required savings levels under an EERS, or “all cost-effective” EE requirement. In conducting

these studies, states and utilities have developed a standardized methodology that is often

described as a “bottom-up, engineering-based” approach xxxvii 

### . EE potential studies are conducted

at various geographic scopes (national, regional, st ate, and utility service territory level) and at

different degrees of aggregation (e.g., economy-wi de, sectoral, and program), and can be broadly

grouped into a few types: technical, economic, market, and program. 19



###  Technical potential represents the theoretical maximum amount of energy use that could

be displaced by efficiency, without regard to non-engineering constraints such as costs

and the willingness of energy consumers to adopt the efficiency measures. It often

assumes immediate implementation of all technologically feasible energy saving

measures, with additional efficiency oppo rtunities assumed as they arise.

19 The definitions discussed below largely follow that outlined in the Guide for Conducting Energy Efficiency

Potential Studies (NAPEE 2007) but the variations in definition are also discussed (e.g., Sathaye and Murtishaw

2004; Huntington 2011).

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 Economic potential refers to the subset of the technical potential that is economically

cost-effective. Definition of “economic potenti al” can vary to some degree by study.

Some estimate economic potential by evaluatin g technology upfront cost, operating costs

that considers energy prices, product lifetime and discount rate, compared to a

conventional alternative or the supply-side energy resources. Others incorporate

consideration of consumer preferences in addition to consumers’ out-of-pocket

expenditure when evaluating the economic potential. Both technical and economic

potential estimates assume immediate implementation of efficiency measures without

regard to technology adoption process or r eal-life program implemen tation. In addition,

these estimates do not always reflect market failures or barriers that impede energy

efficiency and often fail to capture transaction costs (e.g., administration, marketing,

analysis, etc.) beyond the costs of efficiency measures.

 Market potential (or “achievable” potential ) refers to the subset of economic potential

that reflects the estimated amount of energy savings that can realistically be achieved,

taking into account factors such as technology adoption process, market failures or

barriers that inhibit technology adoption, transaction costs, consumer preferences, social

and institutional constraints, and possibly the capability of programs and administrators

to ramp up program activity over time.

 Program potential refers to the subset of market potential that can be realized given

specific program funding levels and designs. Program potential studies can consider

scenarios ranging from a single program to a full portfolio of programs 20



### .

As mentioned, the EE industry standard for potential studies is the bottom-up,

engineering evaluation of energy efficiency potential of individual end-use technologies and

measures xxxviii 

### . Bottom-up analyses all employ a similar methodology but can vary significantly

in key assumptions (e.g., breadth of sectors and end-uses considered, study period, discount rate,

pattern of technology penetration, whether economically justified early replacement of

20 Each subsequent potential estimate described above is a subs et of the previous potential estimate, e.g., the market

potential is a subset of the economic potential, and the economic potential is a subset of the technical potential.

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technologies is allowed for, and whether continue d improvement in efficiency of technology is

provided for). As a result, estimated efficiency potential can vary significantly among studies 21



### .

Overview of Results

Studies of energy efficiency potential are numerous. In recent years, dozens of studies

have been conducted at regional, state, and util ity levels. This section reviews recent studies and

presents a summary of findings. We first address meta-analyses that summarize results from

multiple utility, state, and regional studies, and th en we address the few national studies that have

been conducted. To normalize results of analyses addressing different study periods, we present

average annual achievable potential by dividing cumulative percentage savings in the last year of

the study by the duration (in years) of the study period. This is a common method of

normalization for energy efficiency potential studies.

At the regional and state level, two meta-analyses, Sreedharan (2013) xxxix 

### and Eldridge et

al. (2008) xl 

### , captured numerous studies conducted between 2001 and 2009. The meta-analysis

conducted by Sreedharan (2013) presents average annual values of 4.1% per year in technical

potential, 2.7% per year in economic potential, and 1.2% per year in maximum achievable

potential. In comparison, Eldridge et al. (2008) estimated average annual values of 2.3% per year

in technical potential, 1.8% per year in economic potential, and 1.5% per year in achievable

potential. To supplement these studies with more recent data, the EPA has conducted a metaanalysis of twelve studies conducted between 2010 and 2014 at the utility, state or regional level

(see Appendix 5-1). The EPA review indicates an average annual achievable potential of 1.5%

per year across the reviewed studies. See Appendix 5-2 (Summary of Recent (2010-2014)

Electric Energy Efficiency Potential Studies) for complete results from the EPA research.

21 Because of the complex consumer behavior, energy mark et and macroeconomic drivers of energy use and energy

efficiency, and in some cases due to the lack of consiste nt data, quantifying energy efficiency potential and energy

savings from policies and programs remains a challenging analytical task. Assumptions about consumer technology

adoption behavior, market barriers and failures, and how t echnology diffusion occurs can also affect estimated

potential. Comment [A23]: Font is different in footnote

Comment [A24]: EPA RESPONSE: FN font

corrected.

Comment [A25]: Assuming these studies are

correct, what, if any, policies are needed to realized

the achievable potential of 1.2%

‐ 1.5%?

Comment [A26]: EPA RESPONSE: See p. 49,

section “Results in Context,” and Table 5

‐ 22. We

compare our best practice scenario and alternative

(options 1 & 2) results with results from these

studies of achievable potential and find that our

scenarios are either within or below ranges that

result from these scenarios (below the

utility/state/regional results and within the national

results). As discussed, EERS is one policy type that

could be used to realize these levels of impacts,

however, other policies could be used instead of or

in addition to EERS.

Comment [A27]: Make sure to check this

carefully!!

Comment [A28]: EPA RESPONSE: We have re

‐

checked.

Formatted: Font: (Default) Times New Roman,

Complex Script Font: Times New Roman

Formatted: Font: (Default) Times New Roman,

Complex Script Font: Times New Roman

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Table 5-6 presents a summary of these three meta-analyses of EE potential.

TABLE 5-6

Summary of Meta-Analyses of EE Potential at Utility, State, and Regional Levels

Study Dates of Studies Number of

Studies Average Annual Achievable

Potential

Sreedharan (2013) 2001-2009 10 1.2%/year

Eldridge (2008) 2001-2007 20 1.5%/year

EPA (2014) 20010-2014 12 1.5%/year

In addition to the numerous studies conducted at the utility, state, or regional levels since

2001, a number of studies have evaluated efficiency potential at the national level, applying a

generally consistent methodology and employing a common data set, across all regions of the

country. Sreedharan (2013) evaluated four major energy efficiency potential studies at the

national level, namely, McKinsey and Co. (2007), McKinsey and Co. (2009), EPRI (2009), and

AEO (2008) Energy Efficiency Side Case. All f our studies used the AEO 2008 reference case as

the baseline but differed in other key respects (e.g., breadth of end-uses, assumed technology

improvement over time, and definition of cost test for economic potential screening). These

studies suggest technical electricity savings potential in the range of 25-40% and economic

potential in the range of 10-25%, as a percentage of total demand in 2020. Of these studies, only

EPRI provided an estimate of achievable potential . On a normalized basis, the EPRI 2009 study

provides an achievable annualized potential range of 0.2-0.4% per year (realistically achievable

and maximum achievable potentia l, respectively) through 2030 at the national level.

Two more recent studies also provide national estimates of achievable EE potential:

EPRI (2014) xli 

### updates their 2009 analysis, using a conventional bottom-up engineering

approach, and ACEEE (2014) xlii 

### , using a top-down, policy-based approach derived from state

experience and their evaluated results. EPRI (2014) results show an average annual achievable

potential range of 0.5% to 0.6% per year (achievable and high achievable potential,

respectively). ACEEE found average annual achievable potential of 1.5% per year. The results of

the EPRI and ACEEE studies are summarized in Table 5-7. Comment [A29]: Seems like too few studies to

do a valid meta

‐ analysis.

Comment [A30]: EPA RESPONSE: EPA is

interested in any supplementary references that are

available and relevant to the evaluation of EE

potential at a regional or sub

‐ regional level.

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TABLE 5-7

Summary of National EE Potential Studies

Study Study Type Average Annual Achievable

Potential

EPRI (2009) Bottom-up, engineering 0.2%-0.4%/year

(realistic to maximum achievable)

EPRI (2014) Bottom-up, engineering 0.5%-0.6%/year

(achievable to high achievable)

ACEEE (2014) Top-down, policy-based 1.6%/year

Notably, each of these national potential st udies show significant potential in every

region of the country including regions with lowe r electricity prices like the southeast, regions

with historically high levels of EE program budge ts like the northeast and west coast, and across

regions with varied sectoral composition (e.g ., higher manufacturing regions like the midwest

and south, as well as higher service industry regions like the northeast and California). Both

EPRI studies illustrate the substantial and si milar scale opportunity across all regions. For

instance, EPRI (2014) shows achievable potential ra nging from 8% to 14% relative to baseline in

2035 across the thirteen regions of their analys is as well as significant opportunity in the

residential, commercial and industrial sectors in every region. The ACEEE (2014) study also

shows consistently large potential across all states and regions through 2030, with an average

potential of 24% and a range of 20% to 36% across 50 states.

Costs and Cost-Effectiveness of State EE Policies

EE Cost-Effectiveness

States enact EE policies to meet multiple policy objectives including reduction of customer

electricity bills, lower costs of meeting electrici ty supply needs, energy reduction, environment Comment [A31]: This raises the question of peer

review. Have these been independently peer

reviewed? If so, state it. If not, is EPA planning to do

so?

Comment [A34]: EPA RESPONSE: Note that this

metric is not the same as the 1.5% incremental

savings as a % of sales that is used as a basis for the

best practices scenario. As presented on p. 49,

section “Results in Context,” and Table 5

‐ 22, the

best practices scenarios result in average annual

pace of <1%, significantly lower than this value from

one study. Also, note that this particular study is

developed based upon the use of multiple EE policy

types rather than the single type (EE programs) that

serves as the basis for EPA’s BB4 and, thus, results in

higher levels of savings. If EPA had included, for

example, the potential additional impacts of

building codes, appliance standards, and other state

policies, our BB4 savings levels would be higher.

ACEEE presents a credible case indicating that these

levels are achievable. Comment [A33]: If these are the average

achievables, is it really feasible to go for 1.5% for all

states in each year? Comment [A32]: EPA RESPONSE: EPA is not

aware of a formal peer review of these studies. EPA

is not planning to conduct a peer review of these

studies prior to proposal.

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and health benefits, and local economic development benefits xliii 

### . Most states evaluate their EE

policy options through the application of cost te sts, weighing the projected benefits with the

costs of the energy efficiency technologies and practices xliv xlv 

### . Each state determines their own

policies for the specific costs and benefits to include in these tests. The costs and benefits are

compared on an equal footing by using present value analysis. This is necessary because EE

typically requires primarily upfront expenditure s (e.g., a whole home retrofit) while the

economic benefits (e.g., electricity bill savings) accrue over the life of the investment (“measure

life”) which can range from a few to twenty or more years. As such, the choice of discount rate

and the estimation of measure life are significant determinants of the cost-effectiveness results.

Most states employ multiple tests, adjustin g cost and benefit categories depending upon the

economic perspective of interest (e.g., utility, ratepayer, program participant, society), and

consider the results from each one, usually with an identified primary test type xlvi 

### . Policies that

are selected are those that are found to be cost-e ffective, with benefits greater than costs, as

determined by the utility applying methods de fined by their state utility commission.

There are five primary cost-effective tests used in the U.S.:

(1) Participant cost test from the perspective of the customer installing the measure. Costs

may include incremental equipment and installation costs; benefits include incentive payments,

bill savings, and applicable tax credits or incentives.

(2) Utility/program administrator cost test from the perspective of utility, government

agency or third-party implementing the program. Costs may include program incentive,

installation, and overhead costs; benefits may include avoided energy and capacity costs -

including generation, transmission a nd distribution - by the utility.

(3) Ratepayer impact measure test from the perspective of utilit y ratepayers not participating

in available energy efficiency programs. This text includes the costs and benefits that will affect

utility rates, including program and administration costs, as well as “lost revenues” to the utility;

benefits include avoided energy and capacity costs, and additional resource savings.

(4) Total resource cost test from the perspective of all utility customers in the service area.

Costs may include the full incremental cost of the measure, program installation and overhead

costs; benefits may include avoided energy and capacity costs, and additional resource savings.

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(5 ) Societal cost test from the social perspective. In addition to benefits considered in total

resource cost test, may also include non-monetized benefits such as environmental and health

benefits.

While many states consider more than one cost test in evaluating EE programs, the most

commonly used (29 states) primary test is the total resources cost test. This test is considered to

be the best measure of the interests of all utilit y customers. The utility and societal cost tests are

the next most commonly used primary tests, used by five states each. The utility cost test is

considered to be the most comparable metric to compare with supply-side resource investments

from a utility resource planning perspective.

Economic and modeling analyses of climate change policy suggests that energy

efficiency presents a large potential in reducing greenhouse gas emissions and plays a critical

role in offsetting the costs and enhancing the flexibility to achieve long-term GHG reduction

targets xlvii 

### . Consistently, evaluations of the economic potential for carbon dioxide reductions

from the United States’ power sector identify demand-side energy efficiency as the lowest cost

strategy (typically, as noted above, with positive net present value) as well as the strategy having

the greatest reduction potential xlviii 

### . For example, McKinsey (2007) xlix 

### found that EE accounted

for more than 60% of their mid-range potential for greenhouse gas reductions from the U.S.

power sector and that it was available at positiv e net present value if “persistent barriers to

market efficiency” could be addressed.

Costs of Saved Energy

A common metric for comparing alternative el ectricity resource options within utility

resource plans is the levelized cost of energy (LCOE) or, for EE resources, the levelized cost of

saved energy (LCSE) l 

### . LCSE EE is often compared favora bly with LCOE of alternative new

generation sources such as fossil-fueled or nucl ear power plants, or renewable energy resources

like wind or solar-power generation. In these comparisons, typically only utility (or program)

costs are considered, not the total costs of saved energy that are discussed later in this chapter.

The energy efficiency analysis literature reports average LCSE in the range of 1-6 cents/kWh Comment [A35]: These LCSE numbers by most

metrics would be considered very competitive and

sometimes even lowest

‐ cost. The methodology

presented later, based on program costs, ends up

providing numbers that seem high. Please consider

revisiting the methodology if appropriate.

Comment [A36]: EPA RESPONSE: Comment is

unclear to EPA. The LCSE values here do represent

program/utility costs. The later cost discussion

considers both program/utility and participant

costs. The later values are estimates of the total

costs of realizing EE savings at BB4 levels and are

include several conservative judgments leading to

relatively high values.

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based on program administrator cost. 22 

### A recent review by ACEEE (2014) examined studies

across 20 states between 2009 and 2012, and esti mated LCSE for electricity energy efficiency

programs in the range of 1.3-5.6 cents/kWh, with a mean value of 2.8 cents/kWh. li 

### Earlier

reviews of utility EE programs identified a similar range of LCSE. Friedrich et al. (2009)

reviewed 14 utility studies of LCSE and found a range from 1.6 to 3.3 cents/kWh, with a mean

value of 2.5 cents/kWh. lii 

### An earlier ACEEE study (2004) reviewed cost-effectiveness analysis

results in nine states and suggested that reported utility LCSE ranged between 2.3-4.4

cents/kWh, with a mean value of 3 cents/kWh. liii



### The economic literature also evaluates the LCSE from EE measures using other

techniques (e.g., econometric s , top-down modeling) , although this body of studies is much

smaller compared to the bottom-up, engineering-based analysis

. The economic literature has

varying treatment of the free ridership, EE program endogeneity, and the rebound effect. The

different assumptions used in these analyses ma ke direct comparison challenging, but overall

these empirical analyses present a wider range of estimates of cost of saved energy. For example,

a recent study by Auffhammer et al. (2008) examining utility DSM programs estimated the

average utility cost of saved energy in the range of 5.1 to 14.6 cents per kWh. 23 

### Some other

studies in the economic literature suggest estimated LCSE in a similar range as from the bottomup analys e s. Gillingham et al. (2004) estimated an average cost of 3.4 cents per kWh saved from

utility EE programs. liv 

### In a recent econometric analysis of utility rate-payer funded demand-side

management and energy efficiency programs between 1992 and 2006, Arimura et al. (2009)

found that the estimated energy savings in electricity consumption were achieved at an expected

average cost to utilities of approximately 5 cents/kWh. lvi 

### Using a top-down approach that

evaluates the savings potential of EE investments using state- and region-specific price elasticity,

Paul et al. (2011) estimated that electricity savings of 1 to 3 percent were available at a marginal

cost of 5 cents/kWh and a corresponding average cost of 2.5-3.5 cents/kWh. lvii

22 Unless otherwise noted, estimates of LCSE discussed in this section refer to program administrator cost (also

known as utility cost). The discount rates, average measur e lives, and other assumptions affecting the calculation of

LCSE were not always consistent or reported in all studies.

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

Energy Journal 29(3): 91-104. It is noted that the econometri c analyses in the literature addresses Deleted: ¶

Deleted: various

Deleted: empirical

Deleted: al

Deleted:

,

Deleted: and

Deleted: s

Deleted: foun

Deleted: $

Deleted: $

Deleted: recent analyses

Deleted: in

Deleted: energy efficiency

Deleted: i

Deleted: literature

Moved down [1]: Using a top-down approach

that evaluates the savings potential of EE

investments using state- and region-specific price

elasticity, Paul et al. (2011) estimated that electricity

savings were available at an average utility LCSE of

2.5-3.5 cents/kWh. lv Comment [A37]: “ Under the scenarios modeled,

electricity savings of between 1 and 3 percent are

achievable at a marginal cost of $50 per megawatt

hour (MWh) and a corresponding average cost of

$25–$35/MWh. ”

Comment [A38]: EPA RESPONSE: See revised

text below including

The marginal cost estimate from the paper.

Deleted: n

Comment [A40]: EPA RESPONSE: Your

calculation omits the large and critical cost savings

from avoided power system operations, etc. (fuel, ... [1] Comment [A39]: Implies $50/MWh and coal

emits appox. 1 metric ton/MWh which is valued at

an SCC of approximately $40. Am I missing

something? I guess the co

‐ benefits make it

economically viable?

Deleted: , using a 5% discount rate

Moved (insertion) [1]

Deleted: an

Deleted: utility LCSE Comment [A41]: “ Under the scenarios modeled, ... [2]

Comment [A42]: EPA RESPONSE: See revised ... [3]

Deleted: not

Formatted ... [4]

Formatted ... [5]

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A number of analytical and data considera tions related to LCSE estimation are also

discussed in the literature, including the issue of “free riders” in EE programs lviii 

### , and the

accuracy of utility reported costs and energy savings

. lix 

### Energy efficiency practitioners also

recognize the need to consider “free rider” an d “spillover” effects in program evaluation. A

slight majority of states adjust for free riders hip in energy savings estimates, leading to higher

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

effects which reduce LCSE values when addressed. lx



### Another consideration related to LCSE esti mation is the rebound effect. The economic

literature has extensive discussion of the poten tial rebound effect, market interactions and

economy-wide response of energy efficiency policies and investments. An improvement in

energy efficiency would effectively reduce the co st of a service or production input, potentially

boosting its demand or production output thus increasing energy use (“direct” rebound). In

addition, money saved from energy efficiency can be used for consumption or investment that

can increase energy consumption in other markets of the economy and lower energy prices as a

result of energy efficiency improvement may increase energy consumption (two forms of

“indirect” rebound). Reviews suggest that both direct and indirect rebound effects exist and the

size of such effects varies among different st udies, technologies, sect ors and income groups. lxi



### Overall, however, rebound effects are found to be relatively modest compared to the importance

of energy efficiency as an effective way of reducing energy consumption and carbon emissions

(Greening et al. 2000 lxii 

### ; Sorrell 2007 lxiii 

### ; Davis 2008 lxiv 

### ; Gillingham et al. 2013 lxv 

### ).

EE as an Abatement Measure

Demand-side energy efficiency is a technically viable and broadly applicable measure for

achieving significant reductions in the amount of generation required and associated emissions

from affected EGUs. Moreover, this measure has b een adopted by every state and most utilities

across the country, typically through multiple po licy approaches. Increased use of, and impacts

from, state energy efficiency policies is a leading industry trend over recent years and the trend

of increasing investment in EE programs is proj ected to continue for the next decade, at least.

These findings support the inclusion of demand-side energy efficiency as an abatement measure

for reducing carbon dioxide (CO 2 

### ) emissions from fossil fuel-fired electric generating units Deleted:

.

Deleted: ¶

Deleted: ;

Deleted: ¶

Comment [A43]: Suggest making a table

comparing the estimated LCSE for these studies.

Comment [A44]: EPA RESPONSE: EPA believes

that the discussion presents this information well.

We will consider alternative presentation

approaches for Final.

Deleted:

,

Deleted: , and generally cost-effective

Deleted: utilization of Comment [A45]: EPA needs to provide data to

show how DSM is cost

‐ effective relative to other

abatement techniques (e.g. increased NGCC

capacity and generation)

Comment [A46]: EPA RESPONSE: This section

summarizes the appropriateness of EE as an

abatement measure. In later sections we discuss

cost analysis of the EE best practices scenario,

specifically, and the reasonableness of those costs

including a conservative estimation of costs per ton

of CO2 reduced.

Deleted: nearly

Comment [A47]: 1) If it is projected to

increase then why is it not in the baseline? And 2)

If it does increase then the cost of achieving

incremental energy savings will be increasing.

Comment [A48]: EPA RESPONSE: The issue of

baseline is discussed in Analysis Limitations section

below. In short, the EE

‐ based state goals are not

additive to existing EE policies, but inclusive of those

policies. The EE goals generally represent both

continuation and expansion of existing policies. The

full costs and benefits of the goals are accounted for

without attempting to forecast BAU EE

policies/impacts through 2030.

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(EGUs). In the next section, we address the setti ng of state-specific goals for electricity savings

levels resulting from state demand-side energy efficiency efforts. In the final section, the

integration of these goals into the impacts a ssessment is presented and we consider the

reasonableness of the costs of this building block.

State Goal Setting

Approach

To estimate the potential CO 2 

### reductions at affected EGUs that could be achieved

through implementation of demand-side energy efficiency policies as a part of state goals, the

EPA developed a “best practices” demand-side energy efficiency scenario. This scenario

provides an estimate of the potential for states to implement policies that increase investment in

cost-effective demand-side energy efficiency tec hnologies and practices, and projects the annual

impacts of the scenario for each state. The scenario does not distingu ish between policies that are

currently in place and additional policies that in most states would be required to be implemented

to realize the goals established. It does not represent an EPA forecast of business-as-usual

impacts of state energy efficiency policies or an EPA estimate of the full potential of end-use

energy efficiency available to the power system, but rather is intended to represent a feasible

policy scenario showing the reductions of CO 2 

### emissions from fossil fuel-fired EGUs resulting

from accelerated use of energy efficiency policies in all states, generally consistent with ongoing

industry trends. The scenario uses : 1) a level of performance that has already been demonstrated

or required by policies (e.g., energy efficiency resource standards) of many leading states ; 2)

considers each state’s unique existing level of performance ; and 3) allows appropriate time for

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

level.

The best practices scenario is derived from state experience with, and reliance on,

policies that drive investment in energy efficiency programs, and the energy savings that result

from those efforts. We focus on energy efficiency programs for several reasons:

 EE programs have achieved significant levels of savings and are being used in almost

every state, Deleted: cost

Deleted: -effectiveness

Deleted: within the context of the broader

proposal

Comment [A49]: Does it include, though, BAU

for states that should be in the baseline?

Comment [A50]: EPA RESPONSE: See added

sentence. Also see Analysis Limitations section

below for additional discussion.

Deleted:

,

Deleted:

,

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 EE program spending and savings levels are reported by utility or other program

administrator, by state, and compiled nationally, using standardized elements and

definitions, and

 EE program savings are projected and evaluated under requirements established and

overseen by state utility commissions, and by municipal and cooperative utility boards of

directors.

While the approach is derived from information about energy efficiency programs

overseen by state utility commissions, other stat e energy efficiency policies are available to

realize a state’s goals 24 

### such as building energy codes, appliance standards, and building energy

benchmarking requirements. All policies included in a state plan will need to meet established

requirements or guidance for EM&V 25



### .

The following steps were taken to establish the inputs for development of the best

practices scenario for each state:

 Step 1: Determine current level of performance

 Step 2: Determine best prac tices level of performance

 Step 3: Determine start year for state efforts

 Step 4: Determine start year level of performance

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

performance

 Step 6: Determine average portfolio measur e life and distribution of measure lives

 Step 7: Determine sustainability of be st practices level of performance

Inputs

Step 1: Determine Current Level of Performance

A fundamental indicator of the level of energy efficiency program performance is

incremental annual savings as a percent of retail sales. This is a common metric defining savings

levels for energy efficiency resource standards and is readily calculated from EIA Form 861 data

24 See State Plan Considerations TSD.

25 See State Plan Considerations TSD.

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for each state. Incremental annual savings are also more directly estimated and evaluated than

are cumulative savings 26 

### . For the best practices scenario, we aggregated the most recent year of

EIA Form 861 data to the state level to establish each state’s current level of performance. These

results were presented previously in Table 5-4.

Step 2: Determine Best Practices Level of Performance

As discussed previously, achievable demand-si de energy efficiency potential exists at

significant and comparable levels (on the basis of total cumulative potential over a period of ten

to twenty years) in all regions of the country. While varied regional characteristics (e.g., avoided

power system costs, economic growth, sectoral mix, climate, and level of past energy efficiency

efforts) affect estimates of achievable poten tial, ongoing improvements in energy-efficient

technologies and practices, economic growth, po pulation increases, and continually improving

strategies for program delivery have resulted in persistent and substantial levels of achievable

potential regardless of specific regional characteristics.

A direct indicator of the achievable incremental levels of energy savings performance is

provided by past performance at the state and utili ty levels, and by requirements states have put

in place for levels of savings to be achieved by 2020. As discussed, these requirements are

typically in the form of energy efficiency res ource standards or similar savings goals that are

applied to utilities in the state 27



### .

Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA

Form 861 (2012) data, aggregated to the state level, and categorized into four ranges of savings

levels ( < 0.5%, 0.5% to 0.99%, 1.0% to 1.49%, and >= 1.5%). As shown, three states achieved

the highest level of performance (> 1.5%) and an additional eight states achieved the second

highest level of performance (1.0% to 1.49%).

TABLE 5-8

2012 Reported State Levels of Incremental Annual Savings

26 Estimates of cumulative savings impacts in a given year are derived from incremental savings values and

information on measure lives. Information on measure lives is less consistently gathered than is information on

incremental savings values.

27 See State Plan Considerations TSD for more information. Deleted: ¶

Formatted: Space After: 0 pt, Line spacing:

single

Deleted: Page Break

¶

Deleted: lxvi Comment [A51]: What and where is this

superscript?

Comment [A52]: EPA RESPONSE: This is an

endnote that provides reference to the EIA 861

data. It is deleted to avoid confusion and the data is

referenced elsewhere in the chapter.

Deleted: > Comment [A53]: Should this be < 0.5?

Comment [A54]: EPA RESPONSE: Thanks for

pointing out the typo. It is corrected in the text.

Comment [A55]: What evidence does EPA have

that 1) these states achieved this level of EE due to

the DSM policies they have in place; 2) these

policies will continue to have this level of effect in

the future; and 3) that demonstrates that these

DSM will be able to push the remaining states to

achieve the same level of success with these

policies?

Comment [A56]: EPA RESPONSE: 1) This is

discussed throughout this TSD. These are the values

that administrators of EE programs report to EIA as

representing the impacts due to their EE programs.

The large majority of savings reported are also

reported to state utility commissions using

methodologies required by the commissions. The

commissions statutory role is to ensure that rates

are “just and reasonable” and EE program costs are

a component of rates. 2) In this section we are not

addressing are asserting this point. 3) the ability of

other states to meet these levels is supported in

several sections of this chapter including the

following table, the discussion of studies of EE

achievable potential, and the results in context

section below.

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Incremental Savings as % of

Retail Sales # of States States

>= 1.5% 3 AZ, ME, VT

1.0% to 1.49% 8 CA, CT, IA, MI, MN, OR, PA, WI

0.5% to 0.99% 14

< 0.5% 25

Table 5-9 summarizes incremental savings levels required by state policy on or before

2020 and categorized into the same four ranges lxvii 

### . Eleven states are required to achieve the

highest level of performance (> 1.5%) and an additional five states are required to achieve the

next highest level of performance (1.0% to 1.49%).

TABLE 5-9

Levels of Incremental Savings Required by State Policy on or before 2020

Incremental Savings as %

of Retail Sales # of States States

>= 1.5% 11 AZ, CO, IL, IN,

MA, MN, NY, OH, RI, VT, WA

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

0.5% to 0.99% 3 AR, CA, WI

< 0.5% 1 TX

For the best practices level of performance for Option 1 28 

### , the EPA has chosen 1.5%

incremental savings as a percentage of retail sale s. This level was achieved by three states (AZ,

ME, and VT) in 2012 and an additional nine states (CO, IL, IN, MA, MN, NY, OH, RI, and

WA), accounting for overlap, are expected to achieve this level by 2020. Thus, twelve states

have either achieved or are required to achieve this level of performance by 2020.

28 See Preamble and Goal Computation TSD for description of Option 1. Comment [A57]: Indicates that all these states

should be in the baseline and not the policy

scenariors.

Comment [A58]: EPA RESPONSE: See sentence

added above in Approach section of this, the Goal

setting section. Also see discussion below in Analysis

Limitations. EPA establishes these goals to be

inclusive of, not in addition to, existing state efforts

including statutory/regulatory requirements for

future years.

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For Option 2 29 

### , the EPA has chosen 1.0% incremental savings as a percentage of retail

sales as the best practices level of performance for this alternate approach. This level was

achieved by eleven states in 2012 and an additiona l twelve states are expected to achieve this

level by 2020. In total, twenty states (accounti ng for duplication between th e two sets of states)

have either achieved or are required to achieve this level of performance by 2020.

Step 3: Determine Start Year for State Efforts

For construction of the best practices scenario, the EPA has used 2017, the year following

the required state plan submittal 30 

### , as the first year for state efforts.

Step 4: Determine Start Year Level of Performance

For construction of the best practices scenario, the EPA has set each state’s level of

performance (incremental savings) in the start year (2017) to its current level of performance

(aggregated to the state-level fr om reported EIA Form 861 data). This approach reflects neither

improvement nor decline in performance between 2012 and 2017. Any improvement in EE

savings performance between 2012 and 2017 will benefit a state in meeting its state EE goals for

the 2020-2029 interim compliance period 31



### .

Step 5: Determine Pace at Which States Improve from Start Year to Best Practices Level of

Performance

To determine a trajectory of incremental savi ngs levels from the 2017 level to the best

practices level, the EPA considered past perf ormance of individual program administrators 32 

### as

well as requirements of existing state energy efficiency resource standards. For the past

performance of individual program administrators, we first screened the data and divided them

into moderate and high performi ng sub-groups. The moderate group (47 entities) was defined as

programs that achieved from 0.8% to 1.5% maximum incremental savings levels and the high

group (26 entities) was defined as programs that achieved greater than 1.5% maximum

29 See Preamble and Goal Computation TSD for description of Option 2.

30 See Preamble and State Plan Considerations TSD for descriptions of the schedule for state plan submittals.

31 See Preamble for description of interim and final compliance periods.

32 EIA 861 was the primary data source; however, we supplem ented EIA 861 data with data for third-party program

administrators because prior to 2011 EIA did not co llect data from third-party program administrators. Comment [A59]: In the preamble, it is noted

that states can have an additional year to submit a

final state plan, or two additional years if submitting

a multi

‐ state plan. How is this reflected in terms of

determining the start year for state efforts?

Comment [A60]: EPA RESPONSE: The start year

used for this analysis is 2017. This is the year

following state plan submittal if not requesting

delay for one or two years. However, that does not

preclude a state from taking steps on EE programs

consistent with this goal setting approach and they

will benefit in EE contributing to their compliance by

doing so.

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incremental savings levels. We then calculated the rate at which each entity had increased

savings over time and calculated average values for each sub-group. For the moderate group, the

average rate of improvement of incremental annual savings rate was 0.30% per year. For the

high group, the average rate of improvement of incremental annual savings rate was 0.38% per

year. See Appendix 5-3 for supporting data and analysis.

The EPA also considered requirements of ex isting state EERS and evaluated the rate at

which their incremental savings levels increase ove r time. For several EERS , we were unable to

clearly identify ramp-up schedules. We identified ten states with clear schedules and calculated

the average rate of improvement for each. The average rate of improvement of incremental

annual savings rate required for these ten states is 0.21% per year. See Appendix 5-3 for

supporting data and analysis.

Based on these results, for the best practices rate of improvement the EPA has chosen

0.2% per year and 0.15% per year for Options 1 and 2, respectively. These values are

conservative by comparison with our analysis of past state performance and future state

requirements.

Step 6: Determine Average Portfolio Measur e Life and Distribution of Measure Lives

The next step in defining the best practices scenario requires projecting the cumulative

future impacts of the annual incremental savings levels for each state. The incremental savings

impacts reflect the savings from EE measures put in place in that year, driven by EE program

activities in that year. The cumulative annual savings represent the total impacts of all EE

measures put in place in that year and all prior years, due to EE program activities. The

cumulative savings account for the continuing impacts of energy efficiency measures that remain

in place for a period of time (the “measure life”) before being replaced. For example, the

purchase of a high-efficiency refrigerator may lead to savings for twelve years, before being

replaced with a new model. To estimate cumulative impacts of a series of years of incremental

savings, the industry uses the concept of an average measure life for the entire portfolio of EE

programs. Rather than use a single, average measure life to represent a diverse portfolio of

programs, that range in measure lives from as little as a few years (e.g., certain lighting

technologies and applications) to as long as fifteen or twenty years (e.g., adding insulation to an Deleted: s

Comment [A61]: Again if the improvements are

not driven by 111(d) then they should be in the

baseline.

Comment [A62]: EPA RESPONSE: See other

responses to this comment, above. This sentence is

simply defining the term incremental savings.

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attic), the EPA is assuming a distribution of measure lives around the average to account for

future impacts of incremental savings levels lxviii



### .

In 2014, ACEEE updated their 2004 and 2009 national reviews of EE program costs and

related program characteristics, including measur e lives. They reviewed electricity EE program

data from 20 states and summarized average measure lifetimes by state and customer class.

Table 5-10 summarizes the results from the ACEEE study and shows an average across all

sectors for these states of 10.6 years.

TABLE 5-10

Average Electricity Measure lifetimes by state and customer class

Sector

All Sectors Residential Commercial/Business Industrial

Average 8.1 12.5 9.5 10.6

Other studies have found slightly higher valu es for average measure life for EE portfolios,

ranging from 10 to 13 years lxix 

### . Our assumption of 10 years is conservative by comparison and

leads to lower cumulative impacts over time and correspondingly lower state goals.

To approximate a distribution of measure lives across an EE portfolio, consistent with an

average measure life of ten years, we have assumed an even distribution from one year in length

to two times the average measure life (twenty years) in length. Our approach is generally

supported by the substantial range in measure lives reviewed and summarized in a 2014 study by

LBNL which shows an interquartile range fro m five to 25 years across twelve program

categories (e.g., low income, resi dential new construction, commer cial/industrial custom, etc.).

Our approach represents a first-order approximati on of the distribution of measure lives across a

diverse portfolio of programs. The more common approach in other studies is to assume a

portfolio with no diversity of measure lives whatso ever, with the entirety of incremental savings

being realized in each year from the first through the full average measure life and then dropping

to zero in the following year. Our approach is a conservative one, leading to the same quantity of

total energy savings, but with a greater portion of the savings occurring in later years than occurs Comment [A63]: What are the costs?

Comment [A64]: EPA RESPONSE: EE program

costs include costs associated with all activities of

operating a comprehensive EE program portfolio.

These include costs such as: customer incentives for

investing in more efficient technologies and

practices, development and implementation of

information programs, subsidized energy audits,

etc., as well as the administrative costs associated

with planning, managing and implementing the full

portfolio.

Deleted: ¶

Deleted: ¶

Page Break

Comment [A65]: Consider referencing other

sources in addition to ACEEE data, including a

forthcoming DOE technical memo and Northwest

Regional Forum documents.

Comment [A66]: EPA RESPONSE: EPA is

interested in reviewing the DOE technical memo

cited. We do believe the values cited in this table

are a reasonable basis for the measure life

assumption for this analysis, particularly since we

are erring on the conservative side of lower average

measure life which leads to lower EE goals and

higher LCSE.

Comment [A67]: The distribution of measure

lives is not uniform, but if you do want to use a

uniform distribution, it should be longer, since LBNL

has shown that some measures last 25 years. (Based

on data, the best estimate of the top end of a

uniform distribution is the maximum of the

observations.) If you use a uniform distribution from

0 to 25 years, and take the midpoint as your

estimate, moving from 10 to 12.5 years would have

a significant impact.

Comment [A68]: EPA RESPONSE: EPA recognizes

that there are measures that have longer lives than

20 years and we are familiar with the LBNL analysis

referred to. We believe our approach is a

reasonable first order approximation that is an

improvement over the industry standard approach

of assuming no variation whatsoever across the

portfolio. If we took the approach suggested, we

would increase our average measure life to 12.5

solely on the basis that some measures have a life

longer than 20 years and we do not agree with this

approach as the 12.5 avg measure life is not

supported by known data sources.

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with the more common, and simpler, approach. This results in lower cumulative impacts in

earlier years and correspondingly lower state goals through 2030.

Step 7: Determine Sustainability of Best Practices Level of Performance

For construction of the best practices scenar io, once a state achieves the best practices

level of performance, the EPA has kept the level of performance constant through 2030. For

states with lower levels of current performance (and, hence, later achievement of the best

practices level of performance – as late as 2025 in some instances), this requires sustaining the

target level for as little as five years. For states currently at or above the best practices level of

performance, this reflects an ability to sustain the target level for thirteen years (2017 through

2030).

Limited empirical data suggests the reasonableness of this approach; however,

comprehensive data, across all regions and stat es, does not exist because these levels of

performance have not been achieved and sustained nationwide previously. The Northwest Power

Conservation Council (NPCC) provides one such example. NPCC has been conducting the most

consistent and long-running series of evaluations of achievable cost-effective potential in the

country, updated every five years, as part of their five-state 33 

### regional energy resource plans lxx



### .

These analyses have become more detailed, reliable, and purposeful over time. Since 1998,

NPCC’s estimates of achievable potential have mo re than tripled even as evaluated electricity

savings from energy efficiency programs have increased rapidly, more than quadrupling between

1998 and 2010 (while levelized costs of saved energy achieved have remained flat), and

exceeding plan targets every year since 2005. A study of the NPCC results concludes: “our

research shows that when programs invest in hi gher levels of efficiency, this helps drive

measurement improvements and technical innovation, resulting in larger and more reliable

conservation supply estimates.” lxxi 

### Table 5-11 summarizes the NPCC’s achievable potential

estimates and evaluated savings since 1998 lxxii



### .

TABLE 5-11

33 NPCC’s resource plans cover Idaho, Oregon, and Washingt on in their entirety, and western regions of Montana

and Wyoming. Comment [A69]: Does this mean that they no

longer make any improvements after the reach the

best practices level of performance

Comment [A70]: EPA RESPONSE: This means

that a state continues to achieve 1.5% (or 1.0%)

incremental savings each year. This requires

continuing implementation of effective EE programs

(or other policies). Without this ongoing effort, the

effects of EE programs wane over time. This ongoing

effort is a significant aspect of the state EE goal

setting approach.

Comment [A71]: EPA could reference the 12

‐ 13

year average measure life. This matches very well

with the sustained period from 2017 to 2030.

Comment [A72]: EPA RESPONSE: The measure

life represents the ongoing impact of a single year’s

portfolio of EE investments. It does not support the

ability of a state to sustain a best practice level of

incremental/first

‐ year performance for 10 or more

years.

Comment [A73]: Not peer

‐ reviewed. It would

be good for EPA to provide peer

‐ reviewed evidence

on the efficacy of these types of DSM programs.

Comment [A74]: EPA Response: EPA is

interested in reviewing any relevant peer

‐ reviewed

analysis on this issue that we have not referenced.

EPA believes that NPCC’s experience and analysis is

very significant and relevant. They are one of a small

number of leading entities that has been developing

resource plans that include EE policies, tracking the

impacts, and revising plans for several decades.

Their work is reviewed through extensive public

processes and provides a critical basis for energy

planning in the northwest. As such, their study has

significant relevance. In the next paragraph we

point to further support for the assumption being

addressed in this section.

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NPCC Achievable EE Potential and Achieved Incremental Savings (1998-2010)

Year

1998 2005 2010

Achievable Potential over 20 Years

(GWh) 13,447 24,651 51,684

Achieved Incremental Savings from

EE Programs (GWh) 547 1,184 2,248

Additional substantiation of this approach is provided by average annual achievable rates

from reviewed studies, as discussed previously, and comparison of those w ith the rates resulting

from the best practices scenario. We address this in a later section, Results in Context , after

presenting those results.

Summary of Best Practices Scenarios Construction

Table 5-12 provides a summary of inputs for the best practices scenarios for Options 1

and 2. The pace of improvements, average measure life, and distribution of measure lives are

each conservative and, therefore, contribute lower state goals than would otherwise result.

Similarly, the best practices level of performance, being based solely on results from and

requirements of EE programs, is less stringent than a level would be that accounted for potential

impacts of other state EE policies such as building energy codes, building energy benchmarking

requirements, and state appliance standards. The use of 2012 level of performance for the 2017

start year, allows states that increase their use of effective EE policies prior to submitting their

implementation plan to benefit.

Table 5-12

Summary of EE Best Practices Scenario Inputs

Input Option 1 Option 2

Current Level of Performance

(incremental savings as % of Data from 2012 EIA 861

(2012) Data from 2012 EIA 861

(2012) Formatted: Not Highlight

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retail sales)

Best Practices Level of

Performance

(incremental savings as % of

retails sales) 1.5% 1.0%

Start Year 2017 2017

Start Year Level of

Performance Data from 2012 EIA 861

(2012) Data from 2012 EIA 861

(2012)

Pace of Improvement

(increase in incremental

savings rate per year) 0.20% per year 0.15% per year

Average Measure Life and

Distribution of Measure Lives 10 years; evenly distributed

across 20 years 10 years; evenly distributed

across 20 years

Continued Performance Once achieved, best

practices level sustained

through 2030 Once achieved, best practices

level sustained through 2025

Calculations

This section addresses the calculations fo r determining cumulative savings levels

(cumulative savings as a percentage of baseline sales) for each state, for each year of the interim

and final compliance periods for Options 1 and 2. The cumulative savings levels are derived

based upon the key inputs summarized in Table 5-12. These levels represent the demand-side EE

component of the state goals for each state. See the Goal Computation TSD for a detailed

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

calculation of interim and final state emission rate goals.

Calculating the net cumulative savings as a perc ent of electricity sales for each state involves

six steps. For each state, for each year (2017-2030 for Option 1 and 2017-2025 for Option 2) the

following steps are taken:

1. Determine annual business as usual (BAU) sales

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2. Determine annual incremental EE savings as a percentage of sales

3. Determine annual incremental EE savings (GWh) and sales after net EE (GWh)

4. Determine annual expiring EE savings (GWh)

5. Determine net cumulative EE savings (GWh)

6. Determine net cumulative EE saving s as a percentage of BAU sales

To illustrate these calculations, each step is described and results provided for one

state (using South Carolina as an example) for 2017 through 2025 for Option 1. We

truncate the results at 2025 for simplicity, but full results are presented for all states in the

section.

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

BAU sales are derived by taking 2012 sales from EIA Form 861 data for the state

and increasing them for each subsequent year by the average annual growth rate from the

AEO 2013 Reference Case for the region corresponding to the state. For South Carolina

the corresponding region is SERC and the average annual growth rate from 2012 to 2040

is 1.10% per year. The resulting values are summarized in Table 5-13 for South Carolina.

TABLE 5-13

BAU Sales for South Carolina

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

BAU Sales

(GWh) 82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005

Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales

As discussed, the 2017 value for annual incremental EE savings as a percentage

of sales is set at the 2012 value based upon EIA-861 reported data. For South Carolina

that value is 0.34%. This value is then increased by the pace of improvement of 0.2% per

year (for Option 1) until the goal level of 1.50% (for Option 1) is reached and then held

constant. The resulting values are summarized in Table 5-14 for South Carolina. Deleted: ¶

Deleted: Page Break

¶

Formatted: After: 0", Line spacing: single

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TABLE 5-14

Annual Incremental EE Savings as a Percentage of Sales for South Carolina

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Annual

Incremental

EE Savings

(% sales) 0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50%

Step 3: Determine Annual Incremental EE Savings (GWh) and Sales after net EE

Annual incremental EE savings are calculated by multiplying the annual

incremental savings as a percentage of sales times the prior year sales after net EE. Sales

after net EE are calculated by subtracting ne t cumulative savings from BAU sales. The

resulting values are summarized in Table 5-15 for South Carolina.

TABLE 5-15

Annual Incremental EE Savings and Sales after Net EE Savings for South Carolina

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Annual

Incremental

EE Savings

(GWh) 274 440 608 777 945 1,113 1,250 1,249 1,249

Sales after

Net EE

(GWh) 82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346

Step 4: Determine Annual Expiring EE Savings (GWh)

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Expiring EE savings are calculated as th e sum of all expired savings in a given

year from all prior years’ incremental (first-year) savings based upon an average measure

life of 10 years and a linear decline in first-year savings over twenty years. As an

example, Figure 2 illustrates the decline in first-year savings from EE measures installed

in 2017. The resulting values for expiring EE savings are summarized in Table 5-16 for

South Carolina.

FIGURE 1

Generalized Distribution of First-Year Savings over Time.

TABLE 5-16

Expiring EE Savings for South Carolina

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Expiring

EE Savings

(GWh) 0 14 38 70 110 160 219 285 350

Step 5: Determine the Net Cumulative EE Savings (GWh)

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Net cumulative EE savings in a given year are equal to annual incremental

savings for that year minus total expiring savings for that year plus net cumulative

savings for the prior year. The resulting values are summarized for South Carolina in

Table 5-17. Formatted: Indent: First line: 0.5"

Deleted: ¶

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TABLE 5-17

Net Cumulative EE Savings for South Carolina

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Net

Cumulative

EE Savings

(GWh) 274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659

Step 6: Determine the Net Cumulative EE Savings as a Percentage of BAU Sales

Net cumulative EE savings as a percenta ge of BAU sales are equal to net

cumulative savings divided by BAU sales. Th e resulting values are summarized for South

Carolina in Table 5-18.

TABLE 5-18

Net Cumulative EE Savings as a Percentage of BAU Sales for South Carolina

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Net

Cumulative

EE Savings

as % of

BAU Sales 0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40%

Summary of General Formulas and Re sults by Step for South Carolina

Tables 5-19 and 5-20 provide summaries of the generic formulas and results for

South Carolina for each step.

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TABLE 5-19

Summary of Calculation Formulas by Step

Step Result Formula

1 BAU Sales (GWh) BAU Sales year i 

### = BAU Sale year i-1 

### * Annual Average Sales Growth Rate

2 Annual Incremental

EE Savings (% of

Sales) Annual Incremental EE Savings 2017 

### = Annual Incremental EE Savings 2012 

### ;

Annual Incremental EE Savings year i 

### = Annual Incremental EE Savings

year

i-1 

### + annual pace of improvement (until goal level is reached)

3 Annual Incremental

EE Savings (GWh) Annual Incremental Savings year i 

### = Annual Incremental Savings as a

Percent of Sales year i 

### * Sales After Net EE year i-1



### 3 Sales after Net EE

(GWh) Sales After Net EE year i 

### = BAU Sales year i 

### – Net Cumulative Savings year i



### 4 Expiring EE

Savings (GWh) Expiring Savings

year i 

### = Σ Expiring measures from all prior program years

(10-year average measure life with linearly decline over 20 years)

5 Net Cumulative

Savings (GWh) Net Cumulative Savings year i 

### = Σ Annual Incremental Savings YTD 

### –

Expiring Savings year i



### 6 Net Cumulative

Savings (% of Sales) Net Cumulative Savings year i 

### = Net Cumulative Savings year i 

### / BAU Sales

year i



### TABLE 5-20

Summary of Results by Step for South Carolina.

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

BAU Sales

(GWh) 82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005

Annual

Incremental 0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50%

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EE Savings

(% sales)

Annual

Incremental

EE Savings

(GWh) 274 440 608 777 945 1,113 1,250 1,249 1,249

Sales after

Net EE

(GWh) 82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346

Expiring EE

Savings

(GWh) 0 14 38 70 110 160 219 285 350

Net

Cumulative

EE Savings

(GWh) 274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659

Net

Cumulative

EE Savings

as % of BAU

Sales 0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40%

Results

Summary of Results

As discussed, the EE goals for each state are represented as cumulative savings as a

percentage of retail sales by year for each option. Table 5-21 summarizes these values for the

first and last year of the interim compliance period for Options 1 and 2. See Appendix 5-4 for

comprehensive results by state, for years 2017 through 2030, including both annual incremental

and cumulative savings as a percentage of retail sales, for each options.

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TABLE 5-21

Summary of State EE Goals for Options 1 and 2

State EE State Goal

Cumulative Savings as a % of Retail Sales

Option 1 Option 2

2020 2029 2020 2024

Alabama 1.36% 9.48% 1.07% 3.86%



### Arizona 1.52% 9.71% 1.24% 4.10%



### Arkansas 5.24% 11.42% 3.52% 5.98%



### California 4.95% 11.56% 3.55% 6.08%



### Colorado 3.92% 11.01% 3.32% 5.87%



### Connecticut 4.71% 11.88% 3.61% 6.25%



### Delaware 1.14% 9.47% 0.86% 3.59%



### District of Columbia 1.14% 9.47% 0.86% 3.59%



### Florida 2.03% 9.98% 1.75% 4.65%



### Georgia 1.76% 9.83% 1.48% 4.36%



### Idaho 4.36% 11.63% 3.52% 6.15%



### Iowa 3.80% 11.10% 3.28% 5.88%



### Illinois 3.20% 11.11% 2.89% 5.70%



### Indiana 4.65% 11.66% 3.58% 6.17%



### Kansas 1.22% 9.52% 0.94% 3.70%



### Kentucky 1.91% 10.02% 1.63% 4.55%



### Louisiana 1.14% 9.33% 0.85% 3.56%



### Maine 4.43% 11.77% 3.55% 6.21%



### Maryland 4.21% 11.51% 3.47% 6.10%



### Massachusetts 5.37% 12.13% 3.61% 6.25%



### Michigan 4.59% 11.77% 3.59% 6.22%

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Minnesota 4.80% 11.72% 3.58% 6.17%



### Mississippi 1.58% 9.92% 1.29% 4.20%



### Missouri 1.40% 9.59% 1.12% 3.93%



### Montana 3.36% 10.90% 3.01% 5.69%



### Nebraska 2.84% 11.00% 2.56% 5.49%



### Nevada 2.37% 10.26% 2.09% 4.98%



### New Hampshire 1.25% 9.58% 0.96% 3.74%



### New Jersey 3.10% 10.60% 2.81% 5.50%



### New Mexico 4.42% 11.76% 3.54% 6.20%



### New York 1.39% 9.71% 1.11% 3.95%



### North Carolina 2.20% 10.40% 1.91% 4.89%



### North Dakota 2.95% 10.69% 2.67% 5.45%



### Ohio 4.17% 11.56% 3.47% 6.12%



### Oklahoma 1.86% 9.97% 1.57% 4.49%



### Oregon 4.66% 11.41% 3.55% 6.06%



### Pennsylvania 4.67% 11.69% 3.58% 6.18%



### Rhode Island 3.90% 11.56% 3.35% 6.06%



### South Carolina 2.32% 10.23% 2.04% 4.94%



### South Dakota 1.60% 9.91% 1.32% 4.22%



### Tennessee 2.21% 10.26% 1.93% 4.86%



### Texas 1.78% 9.91% 1.50% 4.40%



### Utah 3.62% 11.03% 3.19% 5.82%



### Vermont 1.23% 9.33% 0.95% 3.67%



### Virginia 5.37% 12.13% 3.61% 6.25%



### Washington 4.24% 11.26% 3.45% 6.00%



### West Virginia 4.68% 11.79% 3.60% 6.22%



### Wisconsin 1.77% 10.11% 1.49% 4.44%



### Wyoming 1.61% 9.73% 1.33% 4.19%



### Continental U.S. 3.05% 10.66% 2.44% 5.18%

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Alaska 1.22% 9.45% 0.94% 3.69%



### Hawaii 1.29% 9.52% 1.01% 3.79%



### U.S. Total 3.04% 10.65% 2.43% 5.17%



### Results in Context

To provide context for state cumulative savings results presented in Table 5-21 and

Appendix 5-4, the average annual savings were calculated for each state through 2025 and 2030,

starting from 2017. Table 5-22 summarizes the results.

TABLE 5-22

Summary of Average Annual Savings Rates of Best Practices Scenario

Option Years Number

of Years Range of Cumulative

Savings (% of Sales)

across States in Last

Year (2025/2030) Range of Average

Annual Savings

Rates across States National

Average Annual

Savings Rate

1 2017-

2030 13 9.9% to 12.5% 0.76%/year to

0.96%/year 0.86% per year

2 2017-

2025 8 4.3% to 6.8% 0.54%/year to

0.85%/year 0.72% per year

The state range and national values for the average annual savings rate represented in the

EE best practices scenario are below the range of values found in recent utility, state, and

regional studies (1.2% to 1.5% per year) as summarized in Table 5-6, and within the range of

values found in the 2014 national studies from EPRI and ACEEE (0.5%/0.6% to 1.6% per year)

as summarized in Table 5-7. These results provide additional support for the feasibility of the EE

best practices scenario and asso ciated state-specific EE goals.

Impacts Assessment

Approach

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In the Goal Computation TSD, state-specific EE goals from the previous section are

integrated with the other building blocks and used to set state-specific emission rate goals for the

interim and final compliance periods. These state emission rate goals are then represented as

requirements within the power sector modeling for the Regulatory Impacts Analysis (RIA). In

addition, the EE state goals, resulting from the EE best practices scenario, are used to adjust

electricity demand levels used as exogenous i nputs to power sector modeling for the illustrative

compliance scenarios. In other words, the degree to which EE is employed as an abatement

resource is not determined endogenously within the power sector modeling based upon

optimization of costs but, rather, “hard wired” into the illustrative compliance scenarios. This

approach is taken because the EPA has determined, as discussed previously, that EE is costeffective at the established EE goal levels. The EE goal levels were constrained by practical

considerations of state EE policy implementati on, specifically, the current levels of EE

performance and the pace at which states can feasibly improve their levels of performance over

time.

The EE goals represented in the illustrative compliance scenarios lead to substantial

reductions in power system costs due to the reductions in specified electricity demand. Since EE

is not represented endogenously as an abatement measure within the power sector modeling, the

costs associated with the EE best practice scenario must be estimated outside of the power sector

modeling and integrated with the results from that modeling. These EE cost estimates, their

basis, and calculations are addressed in the following sections.

Inputs

The following steps were taken to establish the inputs for development of the EE cost

estimates for each state.

 Step 1: Determine state-specifi c electricity savings by year

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

 Step 3: Determine the ratio of program to participant costs

 Step 4: Determine the escalation rate of EE costs

Step 1: Determine State-Specific Electricity Savings by Year Comment [A75]: This section creates inputs that

feed directly through to end costs. These costs are

external to the IPM modeling and are simply added

to the “rest of system” costs produced by IPM. The

ultimate program costs are therefore sensitive to

these input parameters. Highlighted below are

several places where it seems EPA may have been

overly conservative in its choice of parameters.

Using other numbers that are cited in this report it

is possible to deduce input costs that are only half

as high as what EPA uses. Even a more moderate

calculation reduces input costs by roughly 20

‐ 30%.

Since this rule BSER relies on high EE, even a few

percentage points of reduction in these EE inputs

may lead to significant changes in the RIA cost

calculations. Please consider revisiting EE cost

inputs.

Comment [A76]: EPA RESPONSE: EPA

acknowledges that our determination of these

inputs have generally resulted in conservative inputs

(leading to higher costs) and, in combination, these

have a cumulative impact leading to costs that are

higher than would have resulted from less

conservative determinations.

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Results from the previous section, State Goal Setting, provide the starting point for

estimation of EE costs. From those results, st ate-specific annual incremental savings (MWh) and

yearly distribution of associated continuing savings (MWh) in future years are used as inputs to

the cost estimation calculations.

Step 2: Determine First-Year Program Costs of Saved Energy

First-year program costs refer to the full costs (e.g., administration, incentive payments,

marketing, information to consumers, etc.) incu rred by a utility or other administrator of EE

programs in a given year that lead to EE measures (technologies and practices) put in place in

that year and resulting in reductions in electricity demand in that and future years (driven by the

mix of measure lives across the portfolio of EE programs employed). Unlike participant costs,

program costs are readily known by the administrator of EE programs and are, therefore, an

appropriate starting point for EE program cost analysis. In 2009, ACEEE conducted a national

review of data on EE program costs from program annual reports, evaluation reports, and

information compiled from contacts at program administers in 14 states. Compiled data was

sourced from multiple EE program administrators in each state and over multiple years of data

for each administrator. ACEEE found average first-year net 34 

### costs of $275/MWh (2011$). The

EPA has used this value for our analysis.

Two recent national analyses have found lower program costs than the 2009 ACEEE

study. In 2014, ACEEE updated their analysis from 2009 , expanding the number of states to 20,

and including a greater number of program admini strators and years. In this analysis ACEEE

found average first-year net costs of $230/MWh (2011$). In 2014, an LBNL study presented

results from a uniquely comprehensive study of EE program costs. The LBNL analysis reviewed

program-level data from over 100 program administrators in 31 states. Data w ere collected from

over 1,700 individual programs for up to three years (2009-2011), covering more than 4,000

individual program years of data points. Because of the broad scope of their study and the lack of

net savings information for many programs, LBNL focused on gross 35 

### , rather than net, savings

34 “Net costs” refers to costs per electricity saved after accounting for effects of free-ridership on those savings.

Depending upon the state, spillover effects may also be accounted for in net costs.

35 “Gross savings” refers to electricity savings before an y accounting for effects of free-ridership or spillover. Comment [A77]: Some commenters

recommend using either the most recent ACEEE

analysis, with a first

‐ year net cost of $230/MWh or

the recent LBNL study, with a calculated first

‐ year

net cost of $175/MWh. If used, these numbers

would lead to a cost of saved energy more in line

with the available literature than what EPA is

currently proposing.

Comment [A78]: EPA Response: EPA agrees that

use of those sources would lower our cost values.

The LBNL value cited, however, is not directly

applicable as it represents costs per GROSS MWh

saved and we are seeking to represent costs per

NET MWh saved. LBNL study does not offer any data

to suggest how to derive costs per NET Mwh from

their study. EPA believes that our inputs are in line

with reasonable ranges from available literature but

our conservatism does lead to cost estimates

towards the higher end of available bottom

‐ up

engineering based estimates.

Deleted: slightly substantially (16-36%)

Comment [A82]: EPA RESPONSE: Already

responded to. See above. Comment [A81]: Suggest EPA analysis use these

more recent and more robust numbers. Comment [A79]: The RFF discussion paper

“Supply Curves for Conserved Electricity” at

http://www.rff.org/documents/RFF

‐ DP

‐ 11

‐ 11.pdf

has cost estimates that could be included: “Under

the scenarios modeled, electricity savings of

between 1 and 3 percent are achievable at a

marginal cost of $50 per megawatt hour (MWh) and

a corresponding average cost of $25–$35/MWh.”

Comment [A80]: EPA RESPONSE: This RFF paper

is referenced in the earlier discussion on “costs of

saved energy,” where estimates of levelized cost of

saved energy in the literature are discussed. This

section discusses the first

‐ year program costs, thus

is not the most appropriate place to reference this

paper

Deleted: as

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values. LBNL found national average first-year cost of gross savings of $162/MWh (2012$).

Applying an average net-to-gross ratio of 0.9 and deflating costs at 3%, results in an estimated

national average first-year cost of net savings of $175 (2011$). The up-to-date, more

comprehensive results from the ACEEE and LBNL studies, indicate that the value of $275/MWh

used for this analysis is conser vative, resulting in comparatively higher total costs than would be

the case based upon the newer studies.

Step 3: Determine the Ratio of Program to Participant Costs

As noted above, while program costs are readily known and consistently reported by the

program administrator, participant costs require significant effort to estimate, and are less

consistently estimated and reported. The ratio between program and participant costs will vary

significantly from one program to the next with in a utility’s portfolio. The EPA has used a

generic approach to estimate the ratio of program to participant costs across an entire portfolio,

thus providing for the estimation of total costs once program costs are determined. To derive the

ratio, the EPA reviewed 2012 EE annual reports from program administrators in 22 states

identified as leaders in EE programs 36 

### based upon their magnitude of savings or their savings as

a percentage of retail sales. Complete inform ation on full portfolio participant costs were

available for nine of the 22 states. Across the nine states, the average program and participant

costs as a percentage of total costs were 53% and 47%, respectively. See Appendix 5-3 for the

data and analysis documenting this review. Based on this review, the EPA has taken a slightly

conservative approach 37 

### and used a ratio of 1:1 between program and participant costs. We use

this ratio to derive participant and total costs based upon program costs. Starting from program

CSE of $275/MWh and applying the 1:1 ratio, we estimate participant CSE of $275/MWh and

total CSE of $550/MWh (all values 2011$).

Step 4: Determine the escalation rate of EE costs

36 Leaders were identified using re sults from the 2013 ACEEE State Energy E fficiency Scorecard based on energy

savings as a percentage of retail sales or total savings.

37 If we had used the 53% and 47% values, starting from prog ram costs, total costs would have been slightly lower

than calculated with the 1:1 split used. Comment [A83]: It may not be necessary to

take the most conservative assumption particularly

when there have been subsequent refinements to

the study that may create more reliable numbers. In

addition, it may not be appropriate to use this old

number as a single point estimate to calculate EE

program costs for this rule. Please consider

revisiting if appropriate.

Comment [A84]: EPA RESPONSE: EPA

acknowledges repeatedly the conservatism of the

cost estimates but also believe they are supported

by the sources provided with appropriate expert

judgement and recognition of context in which

estimates are being made.

Comment [A85]: A commenters suggest DOE

may have updated participant cost data.

Comment [A86]: EPA RESPONSE: EPA is

interested in reviewing any data or analysis DOE has

to provide.

Comment [A87]: If possible, please provide

further support for using a number different than

EPA’s calculations. If the derived number is used

(53/47), the total program costs would drop by

about 6%. This again inflates EE costs relative to

what the empirical evidence suggests they would

be.

Comment [A88]: EPA RESPONSE: Given the

survey on which these values are based, EPA finds it

reasonable to err towards the higher value for our

analysis. At higher levels of savings, national

breadth, etc., it is reasonable to anticipate the

historic results might underestimate portion of costs

borne by participants in future expanded program

efforts.

Comment [A89]: EPA seems to have taken a

conservative approach to costs that may not be

justified by the empirical literature. Please consider

revisiting if appropriate.

Comment [A90]: EPA RESPONSE: EPA

acknowledges repeatedly the conservatism of the

cost estimates but also believe they are supported

by the sources provided with appropriate expert

judgement and recognition of context in which

estimates are being made.

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The level of EE program impacts represented in the state EE goals are substantial and

represent a scenario that has not previously been achieved and sustained at a national level in the

U.S. Thus, even though the EPA has taken a conservative approach (i.e., leading to higher

estimates of costs) to the development of the EE st ate goals as well as to other factors that affect

the EE cost estimates, we have also chosen to take a cautious approach to the escalation of EE

costs at higher levels of performance (i.e., as states improve from their historic levels of

incremental savings to the best practices level of 1.5% of retails sales). Economic theory

suggests two mechanisms that would change EE costs as higher levels of performance are

achieved. Economies of scale in the operation of larger EE programs and larger portfolios of EE

programs, and learning and expertise gained over time from the continued implementation of

programs, are two factors that would lower costs as programs scale up and expand to realize

higher levels of performance. However, the limited supply of EE abatement measures and the

need to employ higher cost measures, over time, to reach higher levels of performance, and to

sustain high levels of performance, are factors that would increase costs as higher levels of

performance are achieved. Analysis based upon limited empirical data does provide support for

significant economies of scale and/or cost reduc tions over time as learning and expertise are

gained lxxiii 

### . “Supply curves” of EE as an energy resource, as well as EE as a measure represented

within a GHG abatement curve, provide support fo r escalating costs as higher levels of savings

are realized lxxiv 

### . In a recent analysis, Lawrence Berkeley National Laboratory (LBNL) adopted

an approach that generically represented both effects discussed above lxxv 

### . LBNL changed EE

costs (first-year program costs) as a function of EE savings levels, decreasing costs at savings

levels up to 0.5%, leaving costs constant at the base level at savings levels from 0.5% to 1.5%,

and increasing costs at savings levels above 1.5 %. Another recent analysis, by ACEEE, provides

weak statistical support for a cost increase of 20% when going from 0.5% to 1.0% savings rate

and an additional cost increase of 20% when going from 1.0% to 1.5% savings rate lxxvi



### .

In consideration of the above discussion, th e EPA has chosen to escalate EE costs over

three steps as a function of incremental savings (as a percentage of electricity sales) at the state

level. Until a state reaches a 0.5% savings level, their costs are set at the base level; for savings

levels between 0.5% and 1.0%, state costs are escalated to 120% of the base level; and for

savings levels over 1.0%, state costs are escalated to 140% of the base level. This approach leads Comment [A91]: On the timescales of this rule

(16 years) we can expect a reasonable amount of

technological change to be occurring over that

period as well, which would offset some of the cost

escalation from extracting new supply. Please

consider incorporating or accounting for this.

Comment [A92]: EPA RESPONSE: EPA is

interested in reviewing any data or analysis on this

topic that may be relevant to this discussion.

Comment [A93]: Please consider incorporating

literature on learning and cost reductions over time

across many technologies, and particularly in many

energy technologies.

Comment [A94]: EPA RESPONSE: EPA is

interested in reviewing any data or analysis on this

topic that may be relevant to this discussion.

Comment [A95]: EPA methodology seems to

select only the supply curve dimension and not the

cost improvement dimension. DOE now uses cost

improvements as part of their efficiency rulemaking

so it seems appropriate to apply that principle here.

Please consider revisiting if appropriate.

Comment [A96]: EPA RESPONSE: EPA is

interested in reviewing any relevant data or analysis

on cost improvements and would like to incorporate

them into the final rule.

Comment [A97]: This is quite different from the

approach EPA chose. The LBNL study takes into

consideration more programs and more driving

factors and seems to be a robust study. Please

consider revisiting EPA’s approach if appropriate.

Comment [A98]: EPA RESPONSE: EPA agrees

this is a significantly different approach, however,

LBNL was applying expert judgement in a context of

EE investments and savings at a substantially lower

level than EPA’s Best Practices scenario.

Comment [A99]: Also came in lower than the

approach EPA chose. Please consider revisiting

approach.

Comment [A100]: EPA RESPONSE: EPA agrees

this is a conservative assumption but we believe it is

reasonable and appropriate.

Comment [A101]: Here is a third example of

possibly overly conservative assumptions.

Reasonable alternates are to use 100% to base level

and then an adder, say 120%, above 1.5. LBNL

actually discounts from 0 to 0.5 level. If a cost

escalation to 140% is justified, please consider doing

it after 1.25 of base.

Comment [A102]: EPA RESPONSE: EPA agrees

this is a conservative assumption but we believe it is

reasonable and appropriate.

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to higher costs relative to the one used by LBNL when applied to EPA’s EE best practices

scenario.

Summary of Inputs for EE Cost Analysis

Table 5-23 provides a summary of inputs for the EE cost analysis including first-year

costs of saved energy, ratio of program to participant costs, and escalation of costs as a function

of the rate of incremental savings. Each of these factors incorporates some level of conservatism,

leading to higher costs than would otherwise result.

Table 5-23

Summary of EE Cost Analysis Inputs

Input Source or Assumption

State-Specific Electricity Savings by Year Results from state goal setting

First-Year Program Cost of Saved Energy $275/MWh (2011$)

Ratio of Program to Participant Costs 1:1

First-Year Participant Cost of Saved Energy $275/MWh (2011$)

First-Year Total Cost of Saved Energy $550/MWh (2011$)

Escalation of Costs Incremental savings rate

0.5% - 1.0% > 1.0%

120% of base costs:

$660/MWh (2011$) 140% of base costs:

$770/MWh (2011$)

Calculations

This section addresses the calculations for esti mating the costs associated with the statespecific EE goals discussed above. The results of these calculations are then used within the RIA

and preamble. Specifically, three values are calculated (annual first-year costs, levelized cost of

saved energy (LCSE), and annualized costs); for each, program and participant components are

then calculated using the 1:1 ratio (i.e., 50% of total for each) derived above. Specific results

from prior sections on state goal setting and im pacts assessment inputs are used as inputs for Comment [A103]: Shown below that the EE cost

inputs can, under defensible and reasonable

alternate assumptions, range from 34% to 50%

lower; and are very likely to be at least 10

‐ 15%

lower, than the numbers in this original Table 5

‐ 23.

Suggest that EPA revisit the set of assumptions and

modify accordingly.

Comment [A104]: EPA RESPONSE: EPA

understands your alternative approach and

justification. We believe our approach is defensible,

reasonable, and more conservative. Given the

unique nature of the attaining the EE Best Practices

Scenario at a national level and the aggregate

impacts, EPA believes that this conservatism is

appropriate.

Deleted: ¶

Summary of Alternate EE Cost Analysis Inputs

based on ACEEE 2014¶ ... [6] Comment [A113]: To clarify, we are not

suggesting these are redline additions, but instead ... [7] Comment [A106]: EPA RESPONSE: OK Comment [A105]: Using only information

provided in the preceding paragraphs about cost

inputs, derived below are two alternate price inputs,

one based on the LBNL study and one based on the

updated ACEEE numbers.

Comment [A107]: As stated above this

escalation can be treated in a couple of ways based

on the cited literature. Using the following:

0 To 1 = 100%

1 To 1.5 = 120%

Alternate suggestion (or some variation thereon):

0 to 0.5 = 80%

0.5 to 1 = 100%

1 to 1.25 = 120%

1.25 to 1.5 = 140%

Comment [A108]: EPA RESPONSE: OK

Comment [A109]: This uses LBNL seed data plus

100/120 escalation.

Comment [A110]: EPA RESPONSE: OK

Comment [A112]: EPA RESPONSE: OK Comment [A111]: Please compare these final

numbers to the original calculation of 660/770

$/MWh and consider revisiting approach.

Comment [A113]: To clarify, we are not

suggesting these are redline additions, but instead

are providing the information for illustrative

purposes. Comment [A112]: EPA RESPONSE: OK

Comment [A115]: Comment [A114]: EPA RESPONSE: OK

Deleted: ¶

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54

these calculations. For each state, the following steps are taken for each year (2017-2030) and for

each option. Calculations for steps 2 and 3 are done using real discount rates of 3% and 7%.

The steps are:

1. Calculate annual first-year costs

2. Calculate levelized cost of saved energy (LCSE)

3. Calculate annualized costs

To illustrate these calculations, each step is described and results are provided for one

state (using South Carolina as an example) for 2017 through 2025 for Option 1. The

results are truncated at 2025 for simplicity, but full national results (through 2030) are

presented below.

Step 1: Calculate Annual First-Year Costs

Annual total first-year costs are calculate d by multiplying annual total incremental

savings (MWh) (from Table 5-15) by the firs t-year total CSE (from Table 5-23 with

escalation based upon results from Table 5-14). Program and participant portions of the

first-year costs are then calculated as 50% of total first-year costs for each per Table 5-23.

The resulting values are summarized for South Carolina in Table 5-24.

TABLE 5-24

Calculation of Annual First-Year Costs for South Carolina

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Annual

Incremental

Savings

(GWh) 274 440 608 777 945 1,113 1,250 1,249 1,249

First-Year

Total Cost of

Saved Energy

(2011$/MWh) $550 $660 $660 $660 $770 $770 $770 $770 $770

First-Year 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 Deleted: ¶

Formatted: After: 0", Line spacing: single

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Total Cost

(millions

2011$)

First-Year

Program

(millions of

2011$) 75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9

First-Year

Participant

(millions of

2011$) 75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9

Step 2: Calculate Levelized Cost of Saved Energy

Levelized costs of saved energy (LCSE) are based on levelization of all savings

(first and future years) resulting from EE activities in a given year. The levelization

algorithm is based on the 2002 California Standard Practice Manual lxxvii 

### . The net present

value of all savings from a single year’s EE act ivities (i.e., over the entire distribution of

program lifetimes) is calculated using the real discount rate. The levelized cost of saved

energy is then calculated by dividing the annu al first-year costs (from Table 5-24) by the

levelized savings. The resulting values are summarized for South Carolina in Table 5-25.

TABLE 5-25

Levelized Cost of Saved Energy for South Carolina (at 3% discount rate)

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Levelized

Savings

(GWh) 2,313 3,720 5,139 6,563 7,987 9,405 10,562 10,553 10,554

First-Year

Total Cost 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7 Comment [A116]: There seems to be an error in

the calculation of LCSE in Table 5

‐ 25 below (factor

of 10 difference). Please check.

Comment [A117]: EPA RESPONSE: Calculation

has been re

‐ checked.

Formatted Table

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(millions

2011$)

Total LCSE

(cents/kWh) 6.51 7.81 7.81 7.81 9.11 9.11 9.11 9.11 9.11

Program

LCSE

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

Participant

LCSE

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

Step 3: Calculate Annualized Costs

The costs of the EE program can also be represented as annualized costs in a

given year. Annualized costs are calculated by multiplying the LCSE for each year by the

estimated savings in each year through the full distribution of measure lifetimes. For each

year in the analysis, the annualized costs resulting from all current and past investments

are summed to calculate the total annualized costs in that year . The resulting values are

summarized for South Carolina in Table 5-26.

TABLE 5-26

Annualized Costs for South Carolina

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Annualized

Total Costs

(millions

2011$) 17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2

Annualized

Program

Costs 8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1 Comment [A118]: Might want to check

calculation

– seems there is a factor of 10

difference?

Comment [A119]: EPA RESPONSE: Calculation

has been checked again.

Comment [A121]: EPA RESPONSE: Thanks.

Cents/kWh, added to the table. Comment [A120]: Unit?.

Deleted: remaining

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(millions

2011$)

Annualized

Participant

Costs

(millions of

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

Summary of General Formulas and Re sults by Step for South Carolina

Tables 5-27 and 5-28 provide summaries of the generic formulas and results for

South Carolina for each step.

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TABLE 5-27

Summary of Calculation Formulas by Step

Step Result Formula

1 Annual FirstYear Costs

(2011$) Annual First-Year Costs year i 

### = Annual Incremental Savings year i 

### x First-Year CSE year i



### First-Year CSE year i 

### = f(incremental savings rate) per Table 5-23

2 Levelized

Savings

(GWh) Levelized Savings year i 

### = ∑ ஺௡௡௨௔௟ ூ௡௖௥௘௠௘௡௧௔௟ ௌ௔௩௜௡௚௦ ೤೐ೌೝ	೔

ሺଵା௥ሻ ೔ ்

௜ୀ଴ 

### ,

where T = measure life, r = discount rate.

2 LCSE

(2011$/MWh) Levelized Cost of Saved Energy year i 

### = Annual First-Year Cost year i 

### / Levelized

Savings year i



### 3 Annualized

Costs (2011$) Annualized Cost of Saved Energy year i 

### =

∑ ܧܵܥܮሺ ௧ି௘௔௥	௜௬ 

### ݅ ݎ݁ܽݕ ݅݊ ݏ݅݊݃ݒܽݏ ݈ܽݐ݁݉݁݊ݎ݅݊ܿ ݈ܽݑܽ݊݊ ൈ ௧ି௘௔௥	௜௬ 

### ሻ ௡

௧ୀ଴ 

### ,

where LCSE year i-t 

### is the LCSE of EE progr ams implemented in year i-t

,

݈ܽݐ݁݉݁݊ݎ݅݊ܿ	݈ܽݑܽ݊݊ ݅	ݎ݁ܽݕ	݅݊	ݏ݅݊݃ݒܽݏ ௧ି௘௔௥	௜௬ 

### estimated represents

incremental savings in year i from EE programs implemented in year i-t

.

TABLE 5-28

Summary of Results by Step for South Carolina.

Year

2017 2018 2019 2020 2021 2022 2023 2024 2025

Total FirstYear Costs

(millions 2011$) 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7

Total LCSE

(2011$/MWh) 65.1 78.1 78.1 78.1 91.1 91.1 91.1 91.1 91.1

Annualized

Total Costs

(millions 2011$) 17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2 Formatted Table

Formatted: Subscript

Deleted: Levelized Savings i = NPV [Lifetime

Savings from EE Investments

y ear i at Discount Rate]

Deleted: Incremental

Deleted: Annualized Cost of Saved Energy year i = (LCSE year i x Remaining Savings year i )¶

+ (Levelized Cost of Saved Energy year i-1 x

Remaining Savings year i-1 ) + ...¶

+ (Levelized Cost of Saved Energy year i-n x Remaining Savings year i-n )¶

Annualized Cost of Saved Energy year i = ∑

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Results

Summary of National Results

Tables 5-29 and 5-31 summarize the national first-year and annualized EE costs for

Option 1 for 2018, 2020, 2025, and 2030. Table 5-30 summarizes national LCSE for Option 1

for the same years. Each of the three tables in cludes values for program , participant, and total

costs.

TABLE 5-29

First-Year EE Costs (3% discount rate, billions 2011$)

(Continental U.S.)

Year

2018 2020 2025 2030

Program 10.2 15.4 21.8 21.8

Participant 10.2 15.4 21.8 21.8

Total 20.5 30.7 43.6 43.5

TABLE 5-30

Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh)

(Continental U.S.)

Year

2018 2020 2025 2030

Program 42 43 45 45

Participant 42 43 45 45

Total 83 85 89 90 Deleted: ¶

¶

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TABLE 5-31

Annualized EE Costs (3% discount rate, billions 2011$)

(Continental U.S.)

Year

2018 2020 2025 2030

Program 2.0 5.1 14.4 21.4

Participant 2.0 5.1 14.4 21.4

Total 4.1 10.2 28.9 42.7

See Appendix 5-4 for comprehensive data sheet s of EE cost results at the national level

by year for Options 1 and 2, and at discount rates of 3% and 7%. These data sheets provide

results of LCSE (total, program and participant), first-year costs (total, program and participant),

and annualized costs (total, program and participant).

Results in Context

To provide context for the pace of increase in EE program spending levels represented by

Option 1, we consider the compound annual growth rate (CAGR) of the recent rapid increase in

historic investment (2006 to 2011) and the CAGR from 2011 through 2018, 2020, and 2025

represented by Option 1 program costs. Historic data is from Table 5-2 and Option 1 data is from

Table 5-29. Table 5-32 provides a summary of the results. The CAGRs represented by Option 1

through 2018, 2020, and 2025 vary from 8% to 11%. The historic growth rate reflecting the rapid

recent growth in EE program spending is 30%, roughly three times the Option 1 values.

TABLE 5-32

Comparison of Historic and Projected (Option 1) Annual Growth Rates

Time Period (Years) Compound Average Growth Rate

Historic (2006-2011) 29.8%

Option 1 (2011-2018) 8.1%

Option 1 (2011-2020) 11.3%

Option 1 (2011-2025) 9.8%

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Costs per Tonne CO 2 

### Reduced

To estimate the reductions in power system costs and CO 2 

### emissions associated with this

building block, EPA analyzed a scenario incorporating the resulting reduction in electricity

demand (the “energy efficiency scenario”) and compared the results with the base case scenario.

Both analyses were conducted using the Integrated Planning Model (IPM) described in earlier

chapters. Combining the resulting power system co st reductions with the energy efficiency cost

estimates associated with the energy efficiency scenario, EPA derived net cost impacts for 2020,

2025, and 2030. Dividing these net cost impacts by the associated CO 2 

### reductions for each year,

EPA found that the average cost of the CO 2 

### reductions achieved ranged from $16 to $24 per

metric tonne of CO 2 

### . Although EPA considers this estimated range of average $/tonne to be

reasonable, we expect the $/tonne would be lower in combination with the other building blocks

because, in that context, power system costs w ould be somewhat higher and, thus, avoided power

system costs due to this building block would be higher as well, leading to lower $/tonne CO 2



### avoided.

Analysis Considerations

Two considerations are worth noting in regards to the analysis described in the previous

two sections, “Goal Setting” and “Impacts Assessme nt:” 1) state energy efficiency policies

implicitly represented in the baseline electricity demand and 2) Form EIA-861 as a data source.

State Energy Efficiency Policies in the Baseline Electricity Demand

The baseline electricity demand forecast used for the state goal setting approach

represented in this chapter, as well as for the power sector modeling discussed in the Regulatory

Impacts Assessment, is based upon the AEO 2013 reference case scenario. AEO 2013 does not

explicitly represent existing utility energy efficiency programs or future requirements (e.g.,

EERS) to achieve savings goals through such programs. For example, existing state EERS are

not evaluated and represented in the AEO 2013 reference case. However, to some degree, AEO

2013 does implicitly reflect a continuation of th e effects of existing state energy efficiency

programs in the electricity demand projections represented in the reference case. This implicit Formatted: Subscript

Formatted: Subscript

Deleted:

Deleted: ¶

¶

Formatted: Line spacing: 1.5 lines

Formatted: Font: Not Bold

Formatted: Underline Deleted: ¶

¶

¶

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Deleted: , is calibrated

Deleted: to

Deleted: X

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62

representation is captured in part through a calibrati on process that is affected by several historic

factors including reported electricity sales and sectoral energy consumption surveys.

As noted, EPA’s state goal setting approach for demand-side energy efficiency is built

upon the AEO 2013 forecast of electricity demand. However, because the goal setting approach

uses percentage incremental savings by year to derive percentage cumulative savings by year

(for each state), the resulting state goals (expressed in percentage cumulative savings by year, by

state) are not affected by the underlying electricity demand forecast. The impacts assessment of

the demand-side energy efficiency building block is affected, to some degree, by the implicit

representation of a continuation of existing energy efficiency programs because the assessment is

built partly from absolute energy savings values that are partly derived from the business-asusual (BAU) demand forecast. If the BAU forecast did not implicitly represent a continuation of

existing energy efficiency programs, the forecast would indicate higher electricity demand, at

least in the near term. However, the direction (h igher or lower) of the net cost impacts (energy

efficiency program costs as well as power system cost reductions) is not clear as it is possible

that program costs could increase while avoided power system costs also increase.

Energy Information Administration Form EIA-861 as Data Source

Comprehensive data on energy efficiency programs’ spending and energy savings are

limited for evaluating and comparing energy efficiency programs and their effectiveness at the

utility, state, and national scale. Issues relate d to the lack of standardized definitions and

reporting, and data quality are noted to limit evaluation of energy efficiency programs. lxxviii 

### The

Energy Information Administration (EIA) Form 861, “Annual Electric Power Industry Report,”

remains the most comprehensive effort that co llects data annually on utility demand-side

management (DSM) programs, including their spending and energy savings impacts,

nationally. lxxix 

### The form is requested for electric utilities, electric power producers, energy

service providers, wholesale power marketers, and all DSM program managers and entities

responsible to estimate the DSM activity for the reporting year using their best available data,

including costs and incremental and cumulative energy savings from energy efficiency programs

and load management programs. Deleted: ¶

Deleted: ¶

Formatted: Font: Not Bold, No underline

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This analysis uses only two EIA-861 data variables. Specifically, we use the 2012 sales

data and reported incremental annual energy savings of energy efficiency programs to estimate

the current performance of energy efficiency programs to inform setting best practices

performance level for the state EE goal setting.

EPA notes potential concerns associated w ith consistency and quality of reported DSM

program data in Form EIA-861. Specifically, the data are se lf-reported by utilities and DSM

program administrators

. The definition and data categories may not be consistently applied

across utility, state, and data year. Over time, however, the data quality has improved

significantly and there is increased standardiza tion in data reporting and more detailed data

categories are being reported. For instance, in 2011, EIA began collecting data from third-party

administrators of programs. While now comprehensive, outside entities have found that the EIA861 data can be improve through supplementation with publicly available annual energy

efficiency program reports. lxxx Formatted: Indent: First line: 0.5", Line

spacing: 1.5 lines Deleted: tiliz

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## Appendices

Appendix 5-1: Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies

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

Appendix 5-3: Review of the Ratio of Program to Participant Costs

Appendix 5-4: Comprehensive Results: State Goal Setting and Impacts Assessment Deleted: ¶

¶

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## Appendix 5-1

Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies

The following table summarizes estimates of economic and achievable energy efficiency

potential from a number of recent studies (2010-2014) for states, utilities, and other agencies

across the U.S. Study periods ranged from five to twenty-one years in length. As Table 1 shows,

across the eleven studies that reported achievable potential, results for average annual achievable

potential range from 0.8% per year to 2.9% per year (of baseline sales) with an average of 1.5%

per year.

TABLE 1

Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies

State Client Analyst Study

Year Study

Period End-year Projected

Potential as % of

Baseline Sales Average Annual Projected

Potential as % of Baseline

Sales

Economic Achievable Economic Achievable

Arizona Salt River

Project Cadmus Group 2010 2012-

2020 29% 20% 3.2% 2.2%

California California

Energy

Commission California

Energy

Commission 2013 2014-

2024 Not

reported 9.6% N/A 0.9%

Colorado Xcel Energy Kema, Inc. 2010 2010-

2020 20% 15% 1.8% 1.4%

Delaware Delaware

DNR/DEC Optimal Energy,

Inc. 2013 2014-

2025 26.3% Not reported 2.2% N/A

Illinois ComEd ICF International 2013 2013-

2018 32% 10% 5.3% 1.7%

Michigan Michigan PSC GDS Associates 2013 2013-

2023 33.8% 15% 3.1% 1.4%

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New Jersey Rutgers

University EnerNOC Utility

Solutions 2012 2010-

2016 12.8% 5.90% 1.8% 0.8%

New Mexico State of New

Mexico Global Energy

Partners 2011 2012-

2025 14.7% 11.1% 1.1% 0.8%

New York ConEd Global Energy

Partners 2010 2010-

2018 26% 15% 2.9% 1.7%

Pacific

Northwest

(Idaho,

Montana,

Oregon,

Washington) US

Department of

Energy Lawrence

Berkeley

National

Laboratory 2014 2011-

2021 11% Not reported 1.9% Not reported

Pennsylvania Pennsylvania

PUC GDS Associates

and Nexant 2012 2013-

2018 27.2% 17.3% 4.5% 2.9%

Tennessee Tennessee

Valley

Authority Global Energy

Partners 2011 2009-

2030 24.8% 19.8% 1.1% 0.9%

Range 0.8% - 2.9%

per year

Average 1.5%

Per year



### References

Arizona: The Cadmus Group, Inc. 2010. Salt Ri ver Project 2012-2017 Energy Efficiency Plan –

Final Report. Prepared for Salt River Project, April 16.

Colorado: Kema, Inc. 2010. Colorado DSM Ma rket Potential Assessment – Final Report.

Prepared for Xcel Energy, March 12.

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Delaware: Optimal Energy, Inc. 2013. Delawa re Economic Energy E fficiency Potential.

Prepared for the Delaware Department of Natural Resources and Environmental Control, May

24.

Illinois: ICF International. 2013. ComEd Energy Efficiency Potential Study Report, 2013-2018.

August 20.

Michigan: GDS Associates, Inc. 2013. Michigan Electric and Natural Gas Energy Efficiency

Potential Study – Final Report. Prepared for the Michigan Public Service Commission,

November 1.

New Jersey: EnerNOC Utility Solutions Consulting. 2012. New Jersey Energy Efficiency

Market Potential Assessment, Volume 1: Executive Summary. Report Number 1401, Prepared

for Rutgers, The State University of New Jersey, October 17, 2012.

New Mexico: Global Energy Partners. 2011. Energy Efficiency Potential Study for the State of

New Mexico, Volume 2: Electric Energy Efficiency Analysis. June 30.

New York: Global Energy Partners, LLC. 2010. Energy Efficiency Potential Study for

Consolidated Edison Company of New York, Inc., Volume 2: Electric Potential Report. March.

Pacific Northwest states (combined): Barbose, Galen, and Alan Sanstad, Charles Goldman,

Stuart McMenamin, Andy Sukenik. 2014. Incorporating Energy Efficiency into Western

Interconnection Transmission Planning. Draft report, Lawrence Berkeley National Laboratory,

January.

Pennsylvania: GDS Associates, Inc., and Nexant. 2012. Electric Energy Efficiency Potential for

Pennsylvania – Final Report. Prepared for the Pennsylvania Public U tilities Commission, May

10.

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Tennessee: Global Energy Partners. 2011. Tennessee Valley Authority Potential Study, Final

Report, Volume 1: Executive Summary. Report Number 1360, December 21.

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## Appendix 5-2

Incremental Electricity Savings Pace of Improvement Analysis

This appendix summarizes and analyzes data to characterize the pace of improvement of

incremental (or first-year) savings as a percentage of retail sales for electricity energy efficiency

(EE) programs. We considered two different pers pectives: 1) historical data reflecting achieved

savings of EE programs and 2) requirements of existing state energy efficiency resource

standards (EERS). For the historical perspective, we reviewed data from the Energy Information

Administration’s Form EIA-861 on EE program el ectricity savings (supplemented as needed

with program administrator reports) and identi fied the pace at which entities reaching higher

savings levels have historically increased energy savings over time. 38 

### Specifically, we reviewed

the historical savings data in the following two groups of energy efficiency program

administrators.

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

of 0.8% to 1.5%.

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

of 1.5% to 3.0%. 39



### For the existing state requirements perspective, we reviewed energy savings ramp-up

schedules established under EERS for states that provide clear ramp-up schedules. According to

ACEEE’s 2013 State Energy Efficiency Scorecard lxxxi 

### , there are a total of 26 states that have

38 The EIA 861 was the main data source. However, we ha ve supplemented the EIA 861 with third-party program

administrator data because the EIA 861 just started to collect third-party administrator data in 2011. The third-party

entities included in our analysis are Efficiency Vermont, En ergy Trust of Oregon, Efficiency Maine Trust, and Cape

Light Compact. In addition, we supplemented the EIA 861 da tabase with additional data for two utilities that we

found achieved high energy savings, but did not report savings data in the EIA 861 data for one or two years. These

entities are Burlington Electric and Massachusetts Electric Company (now part of National Grid).

39 In addition to these maximum first-year savings threshol ds, we screened program administrators for the following

conditions: (a) the maximum savings levels occurs after the minimum savings levels; (b) sufficient amounts of

increase in first-year savings are provided to evaluate reas onable ramp-up schedules to gain an incremental 1% firstyear savings; and (c) savings data series are continuous between the years for the minimum and maximum savings

levels. Formatted: Not Highlight

Comment [A122]: Some commenters

recommend referencing DSIRE as the data source on

which states have EERSs rather than ACEEE

scorecard.

Comment [A123]: EPA RESPONSE: ACEEE is the

best quality and up

‐ to

‐ date source EPA has found.

All summary sources like ACEEE or DSIRE are based

upon state

‐ by

‐ state research. We are soliciting

comment in the preamble on all aspects of the

method. EPA will consider switching to DSIRE as a

source (or EPA or DOE research/analysis) for final

rule.

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mandatory EERS policies. 40 

### Our analysis contains 10 states for which clear ramp-up schedules

were identifiable.

Our research findings on historical savings performance are:

 The “Top Saver 1%” group (savings between 0.8% and 1.5%) exhibits a trend that these

entities took or would take about 3.4 years on average to increase first-year savings by

1% (with a range of 1.6 years to 10 years) (see Table 1 ). The entities in this group have

increased the level of first-year savings by 0.30% per year on average from their

minimum to their maximum first-year savings levels (with a range of 0.10% per year to

0.63% per year). 41



###  The “Top Saver 2%” group (savings between 1.5% and 3%) exhibits a trend that took or

would take about 2.6 years on average to increase savings by 1% (with a range of 0.8

years to 7.3 years) (see Table 1 ). The entities in this group have increased the level of

first-year savings by 0.38% per year on average from the minimum to the maximum firstyear savings levels (with a range of 0.14% per year to 1.28% per year). 42



### Table 1. Energy savings ramp-up trends in first-year savings for “Top Saver 1%” and

“Top Saver 2%” groups 43



### Top Saver 1% Top Saver 2%

40 ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013,

41 This is a simple average estimate of the annual averag e increase in first-year savi ngs from each entity in this

group.

42 This is the simple average estimate of the annual average increase in firs t-year savings from each entity in this

group.

43 Data sources: EIA-861; ISO New England, “Compilation of raw PA data for the 2013 EE Forecast,” Jun 21, 2013;

energy efficiency program administrators’ 2012 energy efficiency annual reports for the following entities:

Burlington Electric Department, Efficiency Vermont, Effi ciency Maine, Energy Trust of Oregon, Cape light

Compact, and Wisconsin Focus on Energy. Formatted: Font:

Deleted: Table 1

Formatted: Font:

Deleted: Table 1

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Average

Annual

First-Year

Savings

Increase Estimated

Years to

Gain

Incremental

1% Average

Annual

First-Year

Savings

Increase Estimated

Years to

Gain

Incremental

1%

Average 0.30% 3.4 0.38% 2.6

Median 0.29% 3.4 0.34% 3.0

Max 0.63% 1.6 1.28% 0.8

Min 0.10% 10 0.14% 7.3

# of sample entities 47 26

Our research findings on incremental electrici ty savings ramp-up based on existing state

EERS policies are:

 The states with EERS policies which exhi bit savings ramp-up schedules are requiring

increases in first-year energy savings at a pace that ranges from 0.11% (Colorado and

Oregon) to 0.40% (Rhode Island) as shown in Table 2.

 The first-year savings pace of increase averag es 0.21% per year across the 10 states. This

savings level translates to about 4.7 years to achieve an incremental 1% first-year savings

increase.

Table 2. First-Year Energy Savings Ramp-up Review of State EERS Policies 44



### State Minimum

Target Maximum

Target Climb

Time

(years) Annual

Average %

Increase Years to

Achieve 1%

Increase Min Year Max Year

a b c d e=d-b f=(c-a)/e g=1/f

Arizona 1.25% 2011 2.5% 2016 5 0.25% 4.0

44 Data sources: ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, Arkansas Public

Service Commission, Docket Nos. 13-002-U. Order No. 7, September 9, 2013.

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Arkansas 0.25% 2011 0.9% 2015 4 0.16% 6.2

Colorado 0.80% 2011 1.7% 2019 8 0.11% 9.3

Illinois 0.20% 2008 2.0% 2015 7 0.26% 3.9

Indiana 0.30% 2010 2.0% 2019 9 0.19% 5.3

Massachusetts 1.4% 2010 2.6% 2015 5 0.24% 4.2

Michigan 0.3% 2009 1.0% 2012 3 0.23% 4.3

Ohio 0.3% 2009 1.2% 2019 10 0.17% 5.9

Oregon 0.8% 2010 1.0% 2013 3 0.07% 15.0

Rhode Island 1.7% 2011 2.5% 2013 2 0.40% 2.5

Average 0.21% 4.8

References

MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual

Report to the Iowa Utilities Boar d," May 1, 2013, available at:

http://www.state.ia.us/government/com/util/energy/energy_efficiency/ee_plans_reports.html

Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30,

2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012-

Annual-Report.pdf

National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas

Energy Efficiency Plan," Docket No. 12-109, November 2, 2012, available at:

http://www.env.state.ma.us/dpu/docs /electric/13-120/8113ngptapb.xlsx

PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission,

for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215,

November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf

Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at:

http://www.efficiencyvermont.com/docs/about_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf

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## Appendix 5-3

Review of the Ratio of Program to Participant Costs

Introduction and Summary

This appendix summarizes and analyzes data on EE costs (program and participant) to

develop a ratio to enable the estimation of participant costs from known program costs. We

reviewed cost data from leading EE program admi nistrators in 22 states. Our research findings

are as follows:

 A 1:1 ratio between program and participant costs is a reasonable and slightly

conservative (i.e., slightly higher total costs) basis for estimating participant costs from

known program costs.

 Reported data was reviewed from 22 states; however, program administrator reports from

only nine states contained sufficient inform ation (participant costs across entire portfolio

of EE programs) to inform the analysis.

 Participant cost data from ten program admini strators in nine states indicate that the

weighted average and simple average participant costs were 47 percent of total costs.

Participant Cost Analysis

We first identified states having high incremental electricity savings rates or high

absolute savings levels based upon 2013 ACEEE State Energy Efficiency Scorecard. 45 

### These

states represent a large portion of total EE savings in the U.S. We identified 22 states meeting

these criteria and collected publicly available EE program reports for major program

administrators within each state. From thes e program reports we identified 10 program

administrators across nine states where we were able to identify both program administrator and

participant costs across their full portfolio of EE programs. The table below provides the 2012

portfolio-level program administrator and par ticipant costs for the nine states. Program

45 For the purpose of this research, we have defined leadi ng or high impact states as the top 15 states in the 2013

ACEEE State Energy Efficiency Scorecard in terms of incremen tal savings as a percentage of retail sales or absolute

annual energy savings in terms of total annual MWh savings. These criteria resulted in a total of 22 states which

include Arizona, California, Connecticut, Florida, Hawaii, Illinois, Indiana, Iowa, Maine, Massachusetts, Michigan,

Minnesota, New Jersey, New York, North Carolina, Ohio, Oregon, Pennsylvania, Rhode Island, Texas, Vermont,

and Washington.

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administrator costs represent the program administrator’s program development and

implementation costs, and the participant costs represent the customer costs to partake in the

program. Total program costs are the sum of both costs. Each state’s program administrator and

participant costs are presented as a percentage of total program costs in Table 3. The weighted

and simple average program and participant cost s across all nine states are presented as a

percentage of total program costs. The weighted average cost shares were based on each

program’s spending by administrator and participants.

Table 1

2012 Participant and Program Cost Information from Reported Entities

2012 Portfolio Costs

State Program

Administrator Program

Costs

(Million $s) Participant

Costs

(Million $s) Total Costs

(Million $s) Program

Costs as

Percent of

Total Cost

(%) Participant

Costs as

Percent of

Total Cost

(%)

A b c = a + b a / c b / c

California Southern

California

Edison $ 316 $ 269 $ 585 54.0% 46.0%

Hawaii Hawaii Energy $ 31 $ 37 $ 68 45.6% 54.4%

Iowa MidAmerican

Energy

Company $ 50 $ 70 $ 120 41.5% 58.5%

Maine Efficiency

Maine Trust $ 24 $ 36 $ 60 39.8% 60.2%

Massachusetts National Grid $ 173 $ 54 $ 227 76.3% 23.7%

Minnesota Xcel Energy $ 53 $ 98 $ 151 35.1% 64.9%

Pennsylvania PECO $ 68 $ 109 $ 178 38.5% 61.5%

Rhode Island National Grid $ 63 $ 13 $ 75 83.2% 16.8%

Vermont Efficiency

Vermont;

Burlington

Electric

Department $ 34 $ 23 $ 57 59.3% 40.7%

Weighted Average 53.4% 46.6%

Simple Average 52.6% 47.4%

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In our analysis, the weighted average program and participant costs are 53.4% and

46.6%, respectively, of total costs. On a simple average basis, program and participant costs are

52.6% and 47.4%, respectively, of total costs. Participant cost results range from a low of 17% of

total costs (National Grid in Rhode Island) to a high of 65% (Xcel Energy in Minnesota). When

deriving participant costs from program costs, using a ratio of 1:1 is consistent with these results

and slightly conservative, leading to slightly hi gher total costs than the precise average values

would provide.

References

MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual

Report to the Iowa Utilities Boar d," May 1, 2013, available at:

http://www.state.ia.us/government/com/util/energy/energy_efficiency/ee_plans_reports.html

Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30,

2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012-

Annual-Report.pdf

National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas

Energy Efficiency Plan," Docket No. 12-109, November 2, 2012, available at:

http://www.env.state.ma.us/dpu/docs /electric/13-120/8113ngptapb.xlsx

PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission,

for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215,

November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf

Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at:

http://www.efficiencyvermont.com/docs/about_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf

Burlington Electric Department, " 2012 Energy Efficiency Annual Report," 2012, available at:

https://www.burlingtonelectric.com/ELBO/a ssets/2012%20DSM%20Annual%20Report%20Mas

ter.pdf

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

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

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Xcel Energy, "2012 Status Report & Associated Compliance Filings Minnesota Electric and

Natural Gas Conservation Improvement Program Docket No. E,G002/CIP-09-198, " April 1,

2013, available at

http://www.xcelenergy.com/staticfiles/xe/Regulatory/Regulatory%20PDFs/MN-DSM-CIP-2012-

Status-Report.pdf

National Grid, "2012 Energy Efficiency Year -End Report," May 31, 2013, available at

http://www.nationalgridus.com/non_html /eer/ri/4295-YearEnd%20Rept%20(PUC%205-31-

13).pdf

Southern California Edison, "2013 Energy Efficiency Annual Report," June 2013, available at

http://eega.cpuc.ca.gov/Documents.aspx http://eega.cpuc.ca.gov/Documents.aspx

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## Appendix 5-4

Comprehensive Results: State Goal Setting and Impacts Assessment

See attached file, “Abatement Measures TSD Appendix 5-5.xlsx,” containing the

following:

Goal Setting Sheets

 Option 1 – Incremental Savings as % of Sales by State (2017-2030)

 Option 1 – Cumulative Savings as % of Sales by State (2017-2030)

 Option 2 – Incremental Savings as % of Sales by State (2017-2030)

 Option 2 – Cumulative Savings as % of Sales by State (2017-2030)

Impacts Assessment Sheets

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

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

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

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

 Option 2 – National Costs at 3% Discount Rate (2017-2030)

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

 Option 2 – National Costs at 7% Discount Rate (2017-2030)

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

Formatted: Not Highlight

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. xliv

U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs

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Gillingham, K., R. Newell, K. Palmer. 2004. Retrosp ective Examination of Demand-Side Energy Efficiency

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Joskow, P., and D. Marron. 1992. What Does a Negawatt Really Cost? Evidence from Utility Conservation

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.

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81

lxi Sorrell, S. 2007. “The Rebound Effect: an assessment of the evidence for economy

‐ wide energy savings from

improved energy efficiency.” A report produced by the Sussex Energy Group for the Technology and Policy

Assessment function of the UK Energy Research Centre. ISBN 1

‐ 903144

‐ 0

‐ 35.

lxii Greening, LA, DL Greene, and C Difiglio. 2000. Energy efficiency and consumption

— the rebound effect

— a

survey, Energy Policy

, 28: 389

‐ 401.

lxiii Sorrell, S. 2007. “The Rebound Effect: an assessment of the evidence for economy

‐ wide energy savings from

improved energy efficiency.” A report produced by the Sussex Energy Group for the Technology and Policy

Assessment function of the UK Energy Research Centre. ISBN 1

‐ 903144

‐ 0

‐ 35

lxiv Davis, LW 2008. Durable goods and residential demand for energy and water: evidence from a field trial. The

RAND Journal of Economics

, 39: 530–546.

lxv Gillingham K, MJ Kotchen, DS Rapson, G Wagner. 2013. Energy policy: the rebound effect is overplayed. Nature

493: 475

‐ 476.

lxvii American Council Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency Resource

Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/polic y-brief/eers-02-2014.pdf

. lxviii Billingsley, Megan A., I. M. Hoffma n, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence

Berkeley National Laboratory. March 201 4. The Program Administrator Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf

. lxix

Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence

Berkeley National Laboratory. March 201 4. The Program Administrator Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf

.

lxx

Northwest Power & Conservation Council (NPCC). Febr uary 1, 2010. “Sixth Northwest Conservation and

Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/

. lxxi Gordon, Fred, Lakin Garth, Tom Eckman, and Charle s Grist, “Beyond Supply Curves,” Proceedings of 2008

ACEEE Summer Study on Energy Efficiency in Buildings, August 17, 2008 available at:

http://aceee.org/files/proceedings /2008/data/pape rs/8_419.pdf

. lxxii Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and

Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/

. lxxiii Kenji Takahashi and David Nichols, Synapse Energy Econ omics, Inc. 2008. The Sustainability and Costs of

Increasing Efficiency Impacts: Eviden ce from Experience to Date. ACEEE Summer Study on Energy Efficiency in

Buildings. http://www.aceee.org/files/proceedin gs/2008/data/p apers/8_434.pdf

. lxxiv For example: Northwest Power & Conservation Coun cil (NPCC). February 1, 2010. “Sixth Northwest

Conservation and Electric Power Plan,” Council Document 2010-09. Available at

www.nwcouncil.org/energy/powerplan/6/plan/ ; and McKinsey & Company. December 2007. Reducing U.S.

Greenhouse Gas Emissions: How Much at What Cost? Available at

http://www.mckinsey.com/client_service/sustainability/l atest_thinking/reducing_us _greenhouse_gas_emissions

. lxxv Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded

Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility -customer-funded-energy-efficiency-programsunited-states-projected-spend

. lxxvi Molina, M. 2014. The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility

Energy Efficiency Programs. ACEEE Report No. U1402. Available at http://www.aceee.org/research-report/u1402

. lxxvii State of California Governor’s Office of Planning and Research. July 2002. California Standard Practice

Manual: Economic Analysis of Demand-Side Programs and Projects. Available at

http://www.calmac.org/events/SPM_9_20_02.pdf

. lxxviii MJ Bradley & Associates, LLC. 2011. Benchmarking Electric Utility Energy Efficiency Portfolios in the U.S.

lxxix More information on EIA Form 861 can be found at http://www.eia.gov/elec tricity/data/eia861/

lxxx Add citation

lxxxi American Council for an Ener gy-Efficient Economy (ACEEE). Nove mber 2013. The 2013 State Energy

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

.Page 27: [1] Comment [A40] Author



### EPA RESPONSE: Your calculation omits the large and critical cost savings from avoided power system operations,

etc. (fuel, variable O&M, and avoided/delayed new generation and T&D, etc.). EPA believes that those savings

need to be included in estimating the net cost per ton CO2 reduced. See added discussion later in this chapter with

results of EPAs EE costs per ton CO2 reduced showing a conservative range of $16

‐ $24/ton.

Page 27: [2] Comment [A41] Author



## “ Under the scenarios modeled,

electricity savings of between 1 and 3 percent are ac hievable at a marginal cost of $50 per megawatt hour

(MWh) and a corresponding average cost of $25–$35/MWh. ”

Page 27: [3] Comment [A42] Author



## EPA RESPONSE: See revised text including

The marginal cost estimate from the paper.

Page 27: [4] Formatted Author



## Font: (Default) Times New Roman, 10 pt, Complex Script Font: Times New Roman, 10 pt

Page 27: [5] Formatted Author



## Font: (Default) Times New Roman, 10 pt, Complex Script Font: Times New Roman, 10 pt

Page 53: [6] Deleted Author



## Summary of Alternate EE Cost Analysis Inputs based on ACEEE 2014 [A1][A2]



## Input Source or Assumption

State-Specific El ectricity Savings by Year Results from state goal setting

First-Year Program Cost of Saved Energy $230/MWh (2011$)

Ratio of Program to Participant Costs 53:47

First-Year Participant Cost of Saved Energy $204/MWh (2011$)

First-Year Total Cost of Saved Energy $434/MWh (2011$)

Escalation of Costs Incremental savings rate

0.5% - 1.0% > 1.0%

100% of base costs:

$434/MWh (2011$) 120% of base

costs [A3][A4] 

## :

$520/MWh (2011$)

Summary of EE Cost Analys is Inputs based on LBNL [A5][A6]



## Input Source or Assumption

State-Specific El ectricity Savings by Year Results from state goal setting

## First-Year Program Cost of Saved Energy $175/MWh (2011$)

Ratio of Program to Participant Costs 53:47

First-Year Participant Cost of Saved Energy $155/MWh (2011$)

First-Year Total Cost of Saved Energy $330/MWh (2011$)

Escalation of Costs Incremental savings rate

0.5% - 1.0% > 1.0%

100% of base costs:

$330/MWh (2011$) 120% of base costs:

$396/MWh

(2011$) [A7][A8]

Page 53: [7] Comment [A113] Author



### To clarify, we are not suggesting these are redline additions, but instead are providing the information for

illustrative purposes.