February 2014

1 

# Background and Draft Methodology for Estimating Energy Impacts of EE/RE Policies

**SECTION I : INTRODUCTION**  States, tribal and local agencies with non -attainment areas for air pollutants regulated under the National Ambient Air Quality Standards (NAAQS) are required to submit State Implementation Plans or Tribal Implementation Plans (SIPs/TIPs) to the U.S. Environmental Protection Agency (EPA) that describ e how they will attain the NAAQS by a certain date. To help state, tribal , and loc al agencies examine the role of energy efficiency and renewable energy ( EE/RE ) policies and programs in SIPs/TIPs, EPA has developed a draft methodology for estimating the energy impacts of k ey EE/RE “on the b ooks ” policies that are not explicitly reflected in the Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) 201 3 electricity projections. These policies include energy efficiency resource standards (EERS)

, dedicated sources of EE program funding that are adopted in state law and /or codified in rule or order , and renewable portfolio standards

. EPA also conducted a detailed state -by-state policy review and, on this basis, applied the draft methodology to produce numeric estimates 1 of the energy impacts of state EE/RE policies not acc ounted for in AEO2013 forecast. The time period covered by this analysis i s 2013

- 2030.

These estimates are intended for use by state, tribal, and local agencies responsible for developing SIP s /TIP s for ozone and other criteria air pollutants . These agencies can use EPA’s EE/RE energy impact estimates to quantify the resulting emissions reductions, and then include these reductions in their SIP/TIP submittals. Alternatively, agencies can apply EPA’s draft methodology to develop their own estimates of EE/RE impacts and asso ciated emissions reductions. Jurisdictions not currently preparing a SIP/TIP, but interested in better understanding the energy and emissions impacts of EE/RE policies, can likewise use EPA’s methodology and numeric estimates to identify strategies for staying in attainment with the NAAQS.

1 For more information, go to http://www.epa.gov/statelocalclimate/state/statepolicies.html

. At the current time

– in order to finalize these energy impact estimates

– EPA is seeking feedback from state and other federal agencies, as well as energy efficiency and renewable energy policy experts . EPA plans to revi se its methods based on feedback during the public input period and as new information becomes available . In recognition of this opportunity to gain insights from experts during a public comment period, this version of the methodology is labeled draft

.2 

### SECTION **II: OVERVIEW OF METHODOLOGY & APPROACH** 
The EPA undertook three steps to analyze the “on the books” EE/RE policies that are not explicitly accounted for in the reference case forecast currently used by EPA (e.g., AEO201 3):

• Step One : Understand policy assumptions in the current reference case forecast (e.g.,
AEO201 3 Reference Case Forecast 2 ( AEO201 3)).

• Step Two : Identify key state

- level EE/RE policies not explicitly included in the current reference case forecast (e.g., AEO201 3 ) and collect relevant design details.

• Step Three : Develop analytical methods to estimate incremental 3 impacts of state- level EE/RE policies relative to the current reference case forecast (e.g., AEO201 3 ).

Whil e EPA has applied these steps to the AEO2013, state s can consider applying this methodology to other electricity forecasts.

**Step 1 :**  **Understand Energ y Efficiency/Renewable Energy**  **Policy Assumptions in the Current Reference Case Forecast**  To understand the EE/RE policy assumptions included in the AEO201 3 forecast, EPA reviewed the EIA ’s documentation for the AEO201 3 reference case forecast and consulted with EIA staff. From the review, it is clear that AEO201 3 explicitly includes the impacts of several existing EE/RE policies, 4 including:

• Federal appliance and equipment standards for residential and commercial ca tegories 5

• Lighting efficiency standards for various types of lighting technologies 6

• Tax credits for EE appliances and equipment, and investment tax credits for EE/RE technologies 7

• Federal EE programs and funding

o American Recovery and Reinvestment Act 8

o State Energy Program and Energy Efficiency Community Block Grant

o Weatherization Program

o Green Schools

o Smart Grid expenditures

• Building e nergy c ode s for residential and commercial new construction 9

2 The reference case is a business -as- usual projection that generally assumes that laws and regulations remain unchanged throughout the projection period. For more information, go to http://www.eia.gov/analysis/

. 3 Incremental impacts of EE/RE policies relative to AEO201 3 refers to the impacts not captured within AEO201 3

, taking into account any embedded impacts reflected in the forecast.

4 This discussion highlights several of the most important policies, but is not intended as a comprehensive review of AEO assumptions. See http://www.eia.gov/forecasts/aeo/assu mptions/pdf/appendix_a.pdf for further information.

5 EIA (2013c), Appendix A, pp. 187

- 204

6 EIA (2013c), Appendix A, pp. 187

- 204

7 Ibid.

8 EIA (2013c), pp. 32

9 Ibid.3 o E.g., a ll s tates adopt and enforce International Energy Conservation Code (IECC) 2006 (Residential Building Code) by 2017

• State Renewable Portfolio Standards (RPS) 10

o 30 s tates and Washington, D.C. effective as of October 2012

**Step 2 :**  **Identify and Review “On t**  **he Books” E nergy Efficiency/Renewable Energy**  **Policies n**  **ot in the Reference Case** Based on its review, EPA identified three key “on the books” state

- level EE/RE polices not explicitly included in the reference case forecast. EPA focused its analysis on EE/RE policies that are currently codified in regulation s , statute s, or state public utility commission (PUC) order s that require parties to achieve minimum levels of EE and/or RE

, or to fund program s . The EE/RE policies listed below are the set of “on the books” state EE/RE policies EPA identified for this analysis.

State EE p olicies

• Energy Efficiency Resource Standards (EERS) adopted or updated as of June 2013

• Funding for EE Programs adopted or updated as of June 2013

o EE programs funded by Public Benefits Funds (PBF s)

o EE programs funded by the Regional Greenhouse Gas Initiative (RGGI) 11

o E E programs funded by Forward Capacity Market (FCM) revenues 12

State RE p olicies:

• RPS policies adopted or updated between October 2012 and June 2013.

After identifying the applicable EE/RE policies, EPA scanned all 50 states to determine which had adopted one or more of these policies as of June 2013 . Once EPA identified which states had EE/RE policies , EPA reviewed the relevant design details for each state policy using publically available information, such as state legislation, state rules and regulations, PUC orders, summary reports from the American Council for an Energy

- Efficient Economy ( ACEEE ), 13 and the Database of State Incentives for Renewables & Efficiency (DSIRE)

. 14

**St ep 3**  **: Develop Methods t**  **o Estimate Incremental Impacts o f Energy**  **Efficiency/Renewable Energy Policies Relative**  **t o the Reference Case**  Once EPA understood the state

- level policy characteristics, EPA developed analytical methods to estimate the impacts of the “on the books” EE/RE policies. These analytical methods produced the following incremental impact estimates relative to the referen ce case : annual energy savings and generation for 2014

- 2030 and peak impacts and hourly load impact curves for 2020

, 202 5 , and 2030

.

10 EIA (2013a), pp. 14

- 17

11 For more information, go to: http://www.rggi.org/

. 12 For example, several states participating in ISO

- NE’s forward capacity market are using auction revenues to fund energy efficiency

13 ACEEE (201 2).

14 DSIRE (201 3).4 SECTION III : OVERVIEW OF THE DRAFT METHODOLOGY AND ANALYTICAL STEPS FOR ESTIMATING EE/RE POLICY IMPACTS EPA applied the following analytical steps to estimate the projected annual energy savings of EE Policies:

• Step One : Generate a baseline (i.e., business as usual (BAU)) forecast of state electricity sales consistent with AEO201 3 regional forecasts.

• Step Two : Estimate projected impacts of key state “on the books” EE policies already embedded in AEO201 3 forecast of electricity sales.

• Step Three : Estimate projected total EE savings from key state “on the books” EE policies

o EERS (25 states)

o Funding for EE Programs in non

- EERS states (e.g., dedicated funding from PBFs, RGGI and FCM revenues) (5 states)

 Step Four : Generate state

- adjusted national energy forecast that reflects the energy savings not captured in (i.e., incremental to) the baseline forecast.

For p eak demand savings, EPA applied the following analytical steps to estimate the hourly savings of state EE policies and for the single hour that corresponds to the peak demand within a given year (see Section IV ):

• Step 1 : Develop regional savings impact profiles 15 to represent typical hourly changes in load from representative EE programs.

• Step 2: Develop state

- specific savings impact profiles for select analysis years (2020, 2025 and 2030) by allocating the projected total energy savings in that year across all 8,760 hours, proportional to the representative regional savings profiles developed in Step 1.

• Step 3: Identify the peak generating hour applicable to each state using historical generation . The energy savings from that corresponding hour in the state

- specific savings impact profiles (from Step 2) is the projected peak savings for that year.

For R E policies, EPA applied the following key analytical steps to estimate the projected annual energy impacts ( see Section V ):

• Step 1 : Estimate RE generation from RPS policies adopted or revised between October 2012, when the AEO2013 RPS assumptions were formulated, and June 2013 , when this analysis was released in draft form

. ( Minnesota and Hawaii )

• Step 2 : Generate state

- adjusted forecast reflecting policy changes

.

15 A savings impact profile provi des the estimated load impacts (savings) associated with the relevant set of EE programs for hours 1 through 8760. See Peak Energy Savings Summaries for the profiles developed for this analysis.5 **Draft Methodology f**  **or Generating a**  **Baseline Forecast o**  **f State Electricity Sales t**  **o Represent A**  **nnual Energy Outlook 201**  **3 Regional Forecasts** 
State- level baseline sales 16 data were developed by first using 201 2 historical state sales data from the EIA 17 and then applying the electricity sales growth rates from AEO201 3

. Annual Energy Outlook 201 3- based “annual average growth rates” (AAGR) were calculated for each Electricity Market Module (EMM) region across the 20 12

- 20 40 forecast period. These regional growth rates were then applied to the 201 2 historical sales for each state

. For states whose boundaries cross EMM regions, state

- specific growth rates were derived by using a historical sales weighted average of EMM region growth rates

. 18 The 20 12

- 20 40 AAGR was used to forecast sales for 201 3- 20 40

. Table 1 shows the EMM region s and the AAGRs used to forecast sales for each state

.

Table 1 : Electricity Market Module Region and A nnual Energy Outlook 201 3 Sales Growth Ra tes by
States

State /Jurisdiction Electricity Market Module Region Average Annual Growth
Rates (2012

- 20 4 0 )
Arizona AZNM, NWPP 1.30%
Arkansas SPNO, SPSO, SRDA 0.87%
California AZNM, CAMX, NWPP 0.90%
Colorado AZNM, NWPP, RMPA, SPNO, SPSO 1.22%

Connecticut NEWE 0.22%

Delaware RFCE 0.51%
District of Columbia RFCE 0.51%
Florida FRCC, SRSE 1.18%

Hawaii HI 19 0.78%
Illinois MROW, RFCW, SRGW 0.43%
Indiana RFCW 0.41%

Iowa MROW, SRGW 0.54%

Maine NEWE 0.22% Maryland RFCE, RFCW 0.50%

16 Note that AEO201 3 does not include state

- level forecasts, so incremental impacts are calculated against the
BAU e lectricity s ales f orecast developed as described in Section III.

17 EIA (2013b).

18 Each state was first mapped to one or more EMM regions, depending on the geographical overlap. The share of each state’s electricity sales (from EIA

- 861) in a given EMM region was calculated as a percentage of total sales for that state. These shares repr esent the contribution of each EMM region’s growth rate to the state’s growth rate. The growth rate of each EMM region overlapping a state was then weighted by the share of each state’s sales within that EMM region.

19 Because the AEO2013 does not include H awaii, the US Average Annual Growth Rate was applied.6 Massachusetts NEWE 0.22% Michigan MROE, MROW, RFCM,
RFCW 0.33%

Minnesota MROW 0.54%

Montana MROW, NWPP, RMPA 0.94%
Nebraska MROW, RMPA 0.55%

Nevada AZNM, NWPP 1.20% New Hampshire NEWE 0.22%

New Mexico AZNM, NWPP, RMPA,
SPSO 1.18%

New Jersey NYUP, RFCE 0.50%
New York NEWE, NYCW, NYLI, NYUP, RFCE 0.20%

North Carolina SRCE, SRVC 1.10%
Ohio RFCW 0.41% Oregon NWPP 0.97% Pennsylvania NYUP, RFCE, RFCW 0.48%
Rhode Island NEWE 0.22%

Texas AZNM, ERCT, SRDA,
SPSO 0.89%

Washington NWPP 0.97%

Wisconsin MROE, MROW, RFCW 0.41%

Vermont NEWE 0.22%

**D raft Methodology f**  **or Estimating Energy Savings o f**  **State E nergy Efficiency**  **Policies Embedded In A**  **nnual Energy Outlook 201 3**  As explained in the introduction, the goal of this analysis is to produce numeric estimates of the energy impacts of state EE/RE policies not accounted for in AEO2013 forecast. In order to estimate the impacts not accounted for in the baseline electricity sales forecast, the analysis necessarily must define the impacts already accounted for in the baseline. The impacts already accounted for in the AEO 2013 Reference Case forecast are characterized in this analysis as embedded savings. Embedded savings are subtracted from estimates of total state EE/RE policy impacts to yield the incremental savings effects on the baseline, thus avoiding potential double counting.

The AEO201 3 does not explicitly include the impacts of state EE policies such as EERSs and dedicated sources of EE program funding

. However, the AEO forecasts are understood to implicitly represent the impacts of EE policies implemented at the state level. This implicit representation of energy efficiency occurs in two key ways: 1) The AEO forecast incorporates historical data that reflect energy consumption levels and trends influenced by state

- level EE policies in place at that time. The effects of these existing policies lower the sales level in the last historic year (e.g., if 2011 is the last7 historic al year of data in AEO2013, then the 2011 energy demand was lower than it would have been in the absence of existing EE policies) and may also affect AEO’s near

term growth rates partially derived from recent historic demand growth trends (which otherwise would have been expected to be higher in the absence of existing EE policies ). 2) The AEO forecast assumes an ongoing and persistent savings from energy efficiency policies and programs that expire after a defined period of time, or “ measure life time .” Typically, the impacts of EE programs are estimated in terms of first

- year savings, plus the persistent savings realized from that program (or EE measure ) over an assumed “measure lifetime ” ( a 1 3- year lifetime is used for this analysis ) . EPA’s assessment of the AEO forecast, however, does not identify the expected end of these persistent savings ( i.e., does not identify a consequent increase in energy intensity that should accompany the end of an EE savings stream), leading to the conclusion that the AEO forecast assumes an ongoing stream of savings beyond the lifetime of the efficiency measure

. 20

R ecognizing that AEO2013 is implicitly affected by these historic and persistent effects of state EE po licies and programs, EPA concludes that some portion of the total policy - and program

induced EE savings are embedded in the AEO2013 regional forecast and the AEO2013 -based

state- level BAU forecast. EPA the refore developed a methodology for estimating th ese embedded savings for each state

. This methodology involves two st eps : estimating national savings from energy efficiency, and then allocating these national savings to the states covered in the analysis.
For national savings, reported cumulative energy efficiency savings from programs implemented in prior years (reported as “annual effects” via EIA

- 861, and as aggregated in EIA’s Electric Power Annual for residential, commercial, and industrial sectors ) are divided by reported electricity sales (a lso reported via EIA

- 861, and as aggregated in EIA’s Electric Power Monthly, Retail Sales of Electricity by State by Sector by Provider ). This calculation yields national average energy efficiency savings as a percentage of sales within the given year.
B ecause the national average savings is calculated from the most recent year’s total cumulative savings, this value is divided by the average energy -efficiency “ measure lifetime ,” here assumed to be 13 years. This yields a percentage represent ing the ongoi ng annual effects of energy efficiency that are embedded in the AEO forecast ( 0.29 %). 21

Allo cating the national average embedded savings to the individual states in this analysis uses

state- specific data for first

- year energy efficiency savings reported by the American Council for an Energy

- Efficient Economy (ACEEE). Assuming that cumulative savings are generally proportional to first

- year savings, the national average embedded savings percentage (0.29 %) is divided by the ACEEE national first

- year average savings (0. 62 %) 22 to define the relationship between the embedded savings and first

- year savings data (0.4 7 ). 23 The resulting relationship
is then multiplied by the state’s first year savings percentage to calculate the embedded savings

20 Synapse Energy Economics (2012 ).

21 Synapse Energy Economics (2013 ).

22 ACEEE (201 3)

23 This is the “calibration value” discussed in Synapse Energy Economics (2013).8 for the state as a percentage of sales. These embedded savings estimates used in the EPA analysis are presented in Table 2.

Table 2 : Energy Efficiency Savings Estimated to be Embedded in Annual Energy Outlook 201 3 24

State Savings Estimated to be Embedded in AEO2013 (percent of BAU Sales in Each Year)

Alabama 0.04
Alaska 0.01

Arizona 0. 64
Arkansas 0.0 6
California 0. 63
Colorado 0. 30

Connecticut 0. 61
Delaware 0 .08
District of Columbia 0. 00
Florida 0. 12 Georgia 0. 05

Hawaii 0. 61
Idaho 0. 38
Illinois 0. 31
Indiana 0. 27

Iowa 0. 48

Kansas 0 .04 Kentucky 0. 12

Louisiana 0.01

Maine 0. 49 Maryland 0. 27
Massachusetts 0. 67 Michigan 0. 47

Minnesota 0. 56 Mississippi 0. 07

Missouri 0. 20

Montana 0. 27
Nebraska 0. 13
Nevada 0. 34 New Hampshire 0. 30

24 Synapse Energy Economics ( 2013), Exhibit 3

.9 State Savings Estimated to be Embedded in AEO2013 (percent of BAU Sales in Each Year) New Jersey 0. 32

New Mexico 0. 22
New York 0. 58
North Carolina 0. 18
North Dakota 0. 03
Ohio 0. 57
Oklahoma 0. 09 Oregon 0. 46 Pennsylvania 0. 48
Rhode Island 0. 58
South Carolina 0. 15
South Dakota 0. 08

Tennessee 0. 15

Texas 0. 09
Utah 0. 40

Vermont 0. 99 Virginia 0.05 Washington 0. 43 West Virginia 0.01

Wisconsin 0. 27 Wyoming 0. 04

E PA estimates embedded savings for each state by multiplying the percentages shown in Table
2 by the BAU sales for that state. For example, estimating the embedded savings for Arizona in 2013 involve s multiplying the percentage from Table 2 (0. 64 %) by the BAU sales for 2013 (75,898 GWh) to yield 486 GWh of embedded savings in that year, and then subtract ing the cumulative total of the state’s embedded savings from the state’s total EE policy savings to yield the impacts that are incremental to AEO201 3

. EPA only estimated embedded savings for years in which states achieve savings from EE policies and, to the extent possible, for the segments of state electricity load to which the EE/RE policies apply

. The next section of this paper includes discussion of how the cumulative total of the state’s embedded savings is subtracted from the state’s total EE policy savings to yield the impacts that are incremental to
AEO201 3.

**Draft Methodology f**  **or Estimating Projected Energy Efficiency Savings f**  **rom Energy Efficiency Policies**  The EPA estimated s tate- level EE savings from EERS policie s and dedicated sources of EE program funding that are adopted in state law and /or codified in rule or order . Because these categories are not mutually exclusive, EPA took steps to avoid double- counting of energy10 savings for states with EERSs by treating EE RS targets as overall goals that include savings from individual public benefit funded programs, RGGI

- funded programs, and FCM revenues (in the states that have them) . The EPA found that q ualifying individual programs were not incremental to the EERS targ et, so each state with reported savings has eithe r EERS savings, or dedicated sources of EE program funding

. 25

For e ach policy category

, EPA estimated annual f irst

- year electricity savings (i.e., savings achieved in a given year from programs implemented during that year), and cumulative savings from EE measures implemented in the current year and past years. The EPA calculated

c umulative savings using state

- specific measure lifetimes (see Table 3 below) and assuming no decay of savings over the life of t he measure s

. The EPA used a default lifetime of 13 years where state

- specific assumptions were not available. The EPA did not estimate first

- year savings beyond the requirements of each state’s policy period except for a limited set of states whose policy indicate d a continuation of savings beyond the policy period; these assumptions are documented within the 2013 Annual Energy Savings and Generation w orkbook

. For the majority of states, however, the forecast reverts to the AEO201 3 reference

- case

- based forecast after the EE policy period ends

.

Table 3 : Measure Lifetime by State 26

State Measure Lifetime (Yrs)
Connecticut 13
Iowa 15 Massachusetts 13
Minnesota 13
Nevada 13 New Jersey 14
New Mexico 9 New York 15 Oregon 12 Rhode Island 11
Texas 13
Vermont 13
Wisconsin 12 Default 13

**En ergy Efficiency Resource Standards** 
An energy efficiency resource standard ( EERS ) is a policy that sets targets for energy savings over a specified time frame from end

- use EE programs operated by utilities or other program administrators. S tates typically specify annual first

- year or cumulative targets as percentages of electricity sales or as absolute energy savings. They use different bases for specifying EERS goals: some states specify goals based on sales from investor- owned utilities, while others have

25 For more information, go to: http://www.epa.gov/statelocalclimate/state/statepolicies.html

. 26 ACEEE (2009a), Table 1.11 mandated savings (i.e., megawatt

- hours ( MWh) ) targets based on total sales or a subset of total sales.

The EPA estimated energy savings for each state using formulas specific to the state’s EERS, as shown below. The EPA identified t he appropriate sales basis for each state and, if the basis was not total sales, EPA used 20 11 utility- level sales data from EIA 27 and AEO201 3-bas ed growth

rates to develop baseline forecasts of sales of affected utilities (see Table 1) . Because 201 2 utility- level sales data were not available from EIA at the time of this analysis, EPA used the ratio of affected utility sales to total sales in 20 11 to estimate the affected utility sales as a share of total sales for 201 2

. For most states, EPA assumes f ull achievement of EERS targets for all years in the compliance period

. However, there are some states for which EPA does not assume full achievement of EERS targets in all years because of the way the programs are designed. One example is an EERS policy that includes cost/rate caps or other design features (e.g., counting savings from building energy codes or historical EE programs) that may not lea d

to incremental energy savings relative to AEO 2013 28 or are otherwise in consistent with the EERS targets

. 29 Additionally, s avings were not estimated for purely voluntary EERSs.

The general formulas used to estimate annual first

- year and cumulative energy savings for each year (t) were:

1) EERS with Annual First

- Year EE Savings Targets Specified in Percent Terms

A (t ) = r (t) * Z (t

- 1)
C (t) = A (t) + A (t

- 1) + ... + A (t

- L +1)
I (t) = C (t) - E (t)
Z (t) = B (t) - I (t)

Where:

r is the annual first

- year percent savings target,
A is the annual first

- year energy savings,
L is the measure lifetime,
B is the baseline sales of utilities affected by these specific policies,
C is the cumulative energy savings,
E is the cumulative savings embedded in the AEO201 3 forecast,
I is the cumulative savings incremental to AEO201 3 forecast, and
Z is the adjusted sales after application of cumulative incremental savings.

2) EERS with Annual First

- Year EE Savings Targets Specified in Absolute Terms

27 EIA ( 201 3b ).

28 Building energy codes are already incorporated in the AEO 2013 forecast, so any associated savings from those existing building codes already assumed in the AEO 2013 would not be incremental to the AEO forecast, and thus are removed from the applicable state’s EERS target.

29 For more information, go to individual state summary sheets at http://www.epa.gov/statelocalclimate/state/statepolicies.html

.12 C (t ) = A (t) + A (t

- 1) + ... + A (t

- L +1)
I (t) = C (t) - E (t)
Z (t) = B (t) - I (t)

Where:
A is the annual first

- year energy savings target,
L is the measure lifetime,
B is the baseline sales of utilities affected by these specific policies,
C is the cumulative energy savings,
E is the cumulative savings embedded in the AEO201 3 forecast,
I is the cumulative savings incremental to AEO201 3 forecast, and
Z is the adjusted sales after application of cumulative incremental savings.

3) EERS with Cumulative EE Savings Targets Specified in Percent Terms

A (t ) = C (t) – C (t

- 1) + A (t

- L )

If r (t) available,
C (t) = r (t) * B (t)
I (t) = C (t) - E (t)
Z (t) = B (t) - I (t)

If r (t) not available,
Z (t) calculated by interpolation
I (t) = B (t) - Z (t)
C (t) = I (t) + E (t)

Where:

r is the cumulative percent savings target,
A is the annual first

- year energy savings,
L is the measure lifetime,
B is the baseline sales of utilities affected by these specific policies,
C is the cumulative energy savings,
E is the cumulative savings embedded in the AEO201 3 forecast,
I is the cumulative savings incremental to AEO201 3 forecast, and
Z is the adjusted sales after application of cumulative incremental savings.

4) EERS with Cumulative EE Savings Targets Specified in Absolute T erms

A (t ) = C (t) – C (t

- 1) + A (t

- L )

If C (t) available,
I (t) = C (t) - E (t)13 Z (t) = B (t) - I (t)

If C (t) not available,
Z (t) calculated by interpolation
I (t) = B (t) - Z (t)
C (t) = I (t) + E (t)

Where:
C is the cumulative energy savings target,
A is the annual first

- year energy savings,
L is the measure lifetime,
B is the baseline sales of utilities affected by these specific policies,
E is the cumulative savings embedded in the AEO201 3 forecast,
I is the cumulative savings incremental to AEO2 01 3 forecast, and
Z is the adjusted sales after application of cumulative incremental savings.

Some special considerations that warranted adjustments to the general formulas were:

1) RPS that defines EE as a qualifying resource: The States of Nevada and North Carolina have RPSs that treat EE as a qualifying resource, subject to a quantitative limit. The National Energy Modeling System (NEMS), which is used to produce the AEO, does not currently have the capability to evaluate tradeoffs between EE and RE in cases where both are eligible RPS resources; so, it relies on RE to meet RPS requirements. For RPS policies explicitly included in AEO201 3 , no incremental energy savings were estimated.

2) Compliance Type and Cost/Rate Caps : Several states have EERSs th at use cost

containment provisions or other design features that may constrain the ability of EE program administrators to meet the EERS targets with incremental savings relative to the AEO

. The EPA identified six states with such design features – Arizon a, Illinois, Minnesota, Ohio, and Texas – and relied upon available, state

- specific academic reports, 30 integrated resource plans

, 31 and other studies 32 to make downward adjustments to the nominal EERS targets to reflect these design features. 33

3) “All Cost

- effective EE” Targets: S ix states – Connecticut, Maine, Massachusetts, Rhode Island, Vermont and Washington – require utilities (or other EE program administrators) to implement all cost

- effective EE. In states with an “all cost

- effective EE” requir ement and EERS targets

, EPA used the EERS targets to the policy sunset date and then assumed first

- year savings equivalent to the last policy year, going forward. In states with an “all

30 Satchwell (2011).

31 Ameren Illinois ( 2010 ), ComEd ( 2010 ).

32 Good Company Associates ( 2010 ).

33 For more information, go to http://www.epa.gov/statelocalclimate/state/statepolicies.html

.14 cost effective EE” target without an EERS target through 2020, EPA estimated savings based on utility plans 34 and EE resource potential studies

. 35

4) State L egislature or PUC D isapproval of EE Program Budgets Necessary to Meet EERS Targets : Two states – Florida and Wisconsin – did not approve requests for EE program budget increases necessary to meet growing EERS targets, opting instead to maintain current EE program offerings. In these states, EPA reduced the EERS nominal targets to levels achieved with approved EE program budgets. 36

**E n**  **ergy Efficiency**  **Program Funding**  In states without an EERS policy but with one or more sources of EE funding, t he EPA developed an approach for estimating the associated savings. The sources of funding evaluated by EPA include public benefit funds (PBFs), funding from the proceeds of Regional Greenhouse Gas Initiative (RGGI) allowance auctions, and funding from Forward Capacity Market (FCM) payments . Data for these EE programs are mainly available in terms of program administrator expenditures (i.e., costs to the utility of administering EE programs, exclusive of customer costs) , so EPA calculated savings based on estimates of energy savings per program dollar spent. For each state with qualifying programs, EPA obtained information on annual program fundin g from state 37 or utility publications, 38 and projected funding for each future year as equal to the funding for the year for which the latest information is available. The funding information consists of either actual or committed expenditures, depending on the data source. Estimates of levelized costs of saved energy (LCSE) were available for some states from ACEEE ( 2009a ). These are presented in Table 4. The ACEEE report presents costs of saved energy as reported by programs, except in cases where the methods used by program administrators to estimate the LCSE were different from ACEEE’s standard approach. In such cases, ACEEE calculates LCSE as:

LCSE = ( F * CRF )/ A
CRF = ( d *(1+ d ) L )/((1+ d ) L

- 1)

Where:
A is the annual first

- year energy savings,
F is the annual program funding,
CRF is the Capital Recovery Factor,
L is the measure lifetime, and d is the discount rate.

ACEEE uses a real discount rate of five percent to calculate the Capitol Recovery Factor, and estimates that the average LCSE across the states included in the report is $ 0.025/ kilo w att hour

34 DEEP

- BETP ( 2012 ), Mass Save ( 2012 ), EMT (2010), EMT (2012), National Grid ( 2008 ), EERMC ( 2010 ), VEIC ( 2009 ).

35 NWPCC ( 2010 )

36 For more information, go to http://www.epa.gov/statelocalclimate/state/statepolicies.html

37 DCSEU (2012),DSEU (2013), NHEU ( 2012 9 ), NJ CEP ( 2013 ).

38 MDU (2012), MECA (2011), NorthWestern Energy (2012).15 (kWh). To apply ACEEE’s LCSE estimates in a manner that is consistent with the methodology by which they were calculated, this analysis also used a discount rate of five percent. 39 The average LCSE of $0.025/kWh was used as the default LCSE where state

- specific estimates were not available. In order to adjust for the effects of inflation, the EPA converted the dollar values employed in the ACEEE analysis (reported in 2007 $ ) to 2011 $

, which is the price metric used throughout the AEO2013 analysis. Implicit p rice d eflators for Gross Domestic Product (GDP) were assumed as the measure for conversion

. 40 The EPA did not assume a decay of savings during the measure life, so savings for each year are equal to the lifetime savings averaged over the measure lifetime.
Table 4 : Levelized Cost by State 41

Th e EPA estimate d e nergy savings from ratepayer

- funded programs in each year (t) using the following formulas:

CRF = ( d *(1+ d ) L )/((1+ d ) L

- 1)
A (t) = ( F (t) * CRF )/ LCSE (t)
C (t) = A (t) + A (t

- 1) + ... + A (t

- L +1)

39 A five percent discount rate is also the average of the two rates (i.e., 3 percent and 7 percent) that EPA currently uses when performing economic analysis as a part of its rule develo pment; for more information, go to http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html

. 40 BEA (2013), Ta ble 1.1.9

41 Source: ACEEE ( 2009a ), Table 1.

42 LCSE is based on program administrator costs, not on total resource costs (that include costs to participating utility customers)

. State Levelized Cost of Saved Energy 42 (2007$/kWh) Levelized Cost of Saved Energy (2011$/kWh)
California $0.029 $0.031

Connecticut $0.028 $0.030

Iowa $0.017 $0.018
Massachusetts $0.031 $0.033

Minnesota $0.021 $0.022

Nevada $0.019 $0.020 New Jersey $0.026 $0.028

New Mexico $0.033 $0.035

New York $0.019 $0.020

Oregon $0.016 $0.017
Rhode Island $0.030 $0.032

Texas $0.017 $0.018

Vermont $0.027 $0.029

Wisconsin $0.033 $0.035 Default (simple average) $0.025 $0.02716 Where:
CRF is the Capital Recovery Factor,
L is the measure lifetime, d is the discount rate,
A is the annual first

- year energy savings,
F is the annual program funding,
LCSE is the levelized cost of saved energy in 2011 $ , and
C is the cumulative energy savings.

For this analysis

, the EPA did not estimate the magnitude of savings from EE programs funded by dedicated funding sources (i.e., RGGI and FCMs) separately, but instead incorporated t he ir funds in the EE Program Funding ca tegory

. This decision was motivated by the availability of

state- level program budget information data which aggregated the funding sources.

**Draft Methodology f**  **or Gene rating State-**  **Adjusted Forecast t hat Reflects**  **Incremental Energy Savings**  The EPA estimated e nergy savings that are incremental to the reference case (AEO201 3 ) by subtracting cumulative savings embedded in AEO201 3 from total savings from EERSs, programs funded by public benefit funds and other program funding sources (e.g., RGGI and FCM):
I (t) = C (t) - E (t)

Where:
C is the cumulative energy savings,
E is the cumulative savings embedded in the AEO201 3 forecast and
I is the cumulative savings incremental to AEO201 3 forecast.

The sta te-adjusted e lectricity s ales f orecast includes the impact of EE savings that are incremental to the BAU r eference c ase. State

- level adjusted sales ( Z ) are calculated as:

Z (t) = B (t) - I (t)

Where:
B is the baseline total sales and
I is the cumulative savings incremental to AEO201 3 forecast.

**Important Sources of Uncertainty in the Analysis** In conducting this analysis

, EPA used the best available information and adopted assumptions
in tended to reduce the likelihood of overstating the impacts of the states’ EE/RE policies. The EPA plans to revisit its methods as new information becomes available and anticipates benefiting from the experience of parallel efforts aimed at accounting for the impacts of EE/RE policies in energy and environmental planning.

For this analysis, EPA i s highlighting three sources of uncertainty to keep in mind when utilizing these estimates and employing similar methods:17 • T he impacts of state EE polic ies embedded in the AEO reference case

• PUC approval of EE program budgets necessary to meet the EERS ta rgets

• Variations in state approaches for evaluatin g and reporting EE savings

As discussed in Section III

, th e AEO reference c ase likely includes the impacts of some programs that are not explicitly identified in the AEO documentation. E stimat ing the impacts of existing energy efficiency policies at the national level, and then applying these national savings to individual states requires significant simplifying assumptions about the degree to which these impacts are embedded in electricity sal es projections and the associated magnitude of double counting. While some intrinsic uncertainty exists, EPA believe s its assumptions are reasonable in light of available data

.

Another source of uncertainty relates to PUC approval of EE program budgets necessary to meet the adopted targets. The EE policy that drives the core results of this analysis – EERS – depends on PUC approval of EE program budgets necessary to meet the targets. As discussed
in Section III , several states’ EERS legislation includes explicit cost or rate impact caps that may constrain the ability of EE program administrators to meet the nominal EERS targets , and EPA attempts to account for this design feature in its analysis. However, even in states without specific cost or rate impact caps, PUCs generally have authority over EE program budgets and

, as the EERS targets increase in stringency (necessitating larger EE program budgets), there is uncertainty over whether PUCs will continue to approve the budgets necessary to achieve the EERS targets. While recent reports have documented steadily increasing EE program budgets 43 and generally good progress with states reporting achievement of EERS targets

, 44 this will be an issue EPA tracks in the future as EERS targets increase.

A third source of uncertainty in EPA’s analysis is the energy

- savings definitions that states use when calculating and reporting program impacts

. In some states, e nergy savings are evaluated and reported to Public Utility Commissions (PUCs) as “ gross ” savi ngs – that is, savings attributed

to an efficiency program that would have occurred even in the absence of the program (i.e., program savings not attributable to a specific program intervention)

. Other states require the reporting of “net” savings , which adjust gross savings by accounting for so

- called “free

- riders ,” or customers who receive program rebates even though they would have invested in the efficient equipment without the program. This difference in how energy savings are defined and measured co mplicates efforts to make cross

- state comparisons. The degree of uncertainty this conveys to EPA’s analysis is not precisely known, but ACEEE uses a negative adjustment factor of 10% applied to gross savings to rec oncile the two values. Such an adjustment is not used in this analysis. A recent survey indicated that approximately 2/3 of states report gross savings and 1/3 report net. 45

43 I EE (201 2 ).

44 ACEEE (2011).

45 ACEEE (2012a)18 S ECTION IV : DRAFT METHODOLOGY FOR ESTIMATING PROJECTED PEAK DEMAND SAVINGS OF E NERGY EFFICIENCY POLICIES Energy efficiency programs are typically measured in terms of the electricity savings that they achieve over time , in term of MWh s . F or example, savings occur ring throughout the year or during the heating or cooling season. However, efficiency programs also sav e energy during specific “peak demand period s ” when power costs are high and supplies are constrained. EPA
estimate s these peak savings, measured in megawatts (MW), by determining the energy savings impact of a state’s EE programs during the single hour of the year that corresponds to the state’s peak energy use. 46

A s background, it is important to define several key terms. Recognizing that these terms are not always used consistently across the industry, the definitions below apply only to this analysis . They are not intended as a definitive statement of how terminology should be applied universally

.

• Savings impact shape: The distribution of savings (i.e., impacts on load ) of a set of EE programs for a defined geography, sequenced from hour 1 to hour 8760. The savings impact shape is unitless, representing only the fraction of savings attributable to each hour; the sum of the savings impact shape thus necessarily equals 1.

• Savings impact profile : The distribution of estimated total annual savings (i.e., impacts on load , in MWh ) from a set of EE programs for a defined geography, sequenced from hour 1 to hour 8760. The summation of the savings profile equals the total estim ated electricity savings (kWh) for the year.

• Load profile: Historical data representing the distribution of load (kW) across each hour of a year

, sequenced from hour 1 to hour 8760 . For purposes of this analysis, it reflects the system load for a select region or state, though it could be scaled to specific types of loads (e.g., customer class) for other purposes.

• Load duration curve: Load profile sorted from maximum to minimum hour values for a period of time (e.g., whole year, season, week, day).

**Draft Methodology f**  **or Generating**  **Savings Impact Profiles for**  **E nergy Efficiency**  **Policies**
T his analysis uses savings impact shapes previously developed for EPA 47 to represent the distribution of savings from EE programs for a defined geography within a given year (e.g., 8760 hours)

. These savings impact shapes are defined for the r esidential sector and commercial

sector for each of the nine U.S. Census Divisions ( New England

, Middle Atlantic

, East North Central

, West North Central

, South Atlantic

, East South Cent r al, West South Central

, Mountain

, and Pacific ) , and for the industrial sector for each of the four U.S. Census Regions (Northeast

, Midwest

, South , and West )

. The savings impact shapes are based on typical EE program mixes by region and sector . They do no t represent a particular set of programs currently in place, but

46 The EPA assumed that EE programs do not shift the peak. The EPA did not perform a dynamic analysis of peak demand.

47 Savings impact shapes were developed using the Building Energy Analysis Console (BeaconTM), ICF’s proprietary model for simulating energy consumption by buildings.19 instead aim to represent a typical program portfolio . The resulting savings impact shapes vary by region, depending upon considerations such as cost

- effectiveness, regional building population, and climate. 48

The EPA weighted and combined the sector -specific regional EE saving s impact shapes into a composite savings impact shape for each state, and then multiplied the shapes by the state’s total incremental savings projected by this analysis . EPA then used this information to develop savings profiles for each state for 20 20 , 20 2 5 and 20 3 0 using the following steps :

1) Estimating EE Savings Distribution by Sector

• The EPA calculated average ( O ) national savings by sector 49 ( X ) as a percentage of national sales by sector 50 ( Y ) for 20 09- 20 11 for the residential (r), commercial (c) and industrial (i) sectors.

O r,n = (( X r,n, 20 09 / Y r,n, 20 09 ) + ( X r,n, 2010 / Y r,n,2010 ) + ( X r,n, 20 11 / Y r,n, 20 11 ))/3
O c,n = (( X c,n, 2009 / Y c,n, 2009 ) + ( X c,n, 20 10 / Y c,n, 2010 ) + ( X c,n, 2011 / Y c,n, 2011 ))/3
O i,n = (( X i,n, 2009 / Y i,n, 2009 ) + ( X i,n, 20 10 / Y i,n, 2010 ) + ( X i,n, 20 11 / Y i,n, 2011 ))/3

• The EPA calculated s ales by sector ( Y ) in 20 11 as a percentage ( P ) of total residential, commercial and industrial sales for each state (s).

P r,s = Y r,s,2011 /( Y r,s, 2011 + Y c,s, 2011 + Y i,s, 2011 )
P c,s = Y c,s, 2011 /( Y r,s, 2011 + Y c,s, 2011 + Y i,s, 2011 )
P i,s = Y i,s, 2011 /( Y r,s, 2011 + Y c,s, 2011 + Y i,s, 2011 )

• The EPA calculated percentages by sector of EE savings ( Q ) in each state as:

Q r,s = ( P r,s * O r,n )/( P r,s * O r,n + P c,s * O c,n + P i,s * O i,n ) Q c,s = ( P c,s * O c,n )/( P r,s * O r,n + P c,s * O c,n + P i,s * O i,n ) Q i,s = ( P i,s * O i,n )/( P r,s * O r,n + P c,s * O c,n + P i,s * O i,n )

Table 5 shows s avings percentages for each state.

Table 5 : Distribution of Savings by Sector 51

State Distribution of Savings (percent)

Residential Commercial Industrial
Alabama 49.7% 30.7% 19.6%

48 For more information, go to http://www.epa.gov/statelocalclimate/state/statepolicies.html

. 49 EIA ( 2010

, 2011

, 2012 ).

50 EIA ( 2010

, 2011

, 2012 ).

51 EIA (2010), File3; EIA ( 2011 ), File3; EIA ( 2012 ), File3; EIA (201 0), File2; EIA ( 2011 ), File2; EIA ( 2012 ), File2.20 Arizona 50.9% 41.7% 7.3%
Arkansas 51.5% 30.5% 18.0%
California 40.1% 51.1% 8.7%
Colorado 43.1% 43.0% 13.9%
Connecticut 49.0% 45.6% 5.4%
Delaware 48.5% 41.0% 10.5%
District of Columbia 19.9% 79.3% 0.8%
Florida 56.2% 40.7% 3.1% Georgia 51.1% 38.1% 10.8%
Hawaii 39.4% 41.6% 19.0%
Idaho 48.5% 31.7% 19.9%
Illinois 42.5% 41.9% 15.6%
Indiana 45.5% 29.7% 24.8%

Iowa 43.6% 33.8% 22.6%

Kansas 43.7% 43.6% 12.7% Kentucky 44.4% 28.1% 27.5%
Louisiana 48.6% 33.8% 17.6%
Maine 47.5% 39.9% 12.6% Maryland 47.5% 49.1% 3.4%
Massachusetts 47.2% 37.6% 15.1% Michigan 42.2% 43.0% 14.8%
Minnesota 43.2% 39.4% 17.5% Mississippi 50.6% 33.0% 16.4%
Missouri 50.6% 40.0% 9.4%

Montana 44.9% 41.0% 14.0%
Mountain 44.8% 40.5% 14.7%
Nebraska 44.4% 37.4% 18.2%
Nevada 46.1% 33.1% 20.8% New Hampshire 47.8% 44.1% 8.0% New Jersey 43.0% 52.5% 4.5%
New Mexico 38.1% 47.1% 14.8%
New York 40.5% 55.4% 4.1%
North Carolina 52.3% 38.4% 9.2%
North Dakota 42.6% 41.8% 15.6%
Ohio 45.6% 36.7% 17.7%
Oklahoma 50.3% 37.1% 12.6% Oregon 50.5% 37.6% 12.0% Pennsylvania 48.1% 35.1% 16.8%
Rhode Island 45.7% 49.1% 5.2%
South Carolina 50.1% 32.2% 17.6%
South Dakota 47.8% 42.0% 10.3%

Tennessee 53.3% 33.0% 13.7%

Texas 48.1% 38.9% 13.0%21 Utah 40.3% 43.5% 16.2%

Vermont 47.1% 40.8% 12.1% Virginia 47.9% 45.2% 6.9% Washington 49.1% 36.4% 14.5% West Virginia 50.2% 30.5% 19.3%
Wisconsin 42.3% 40.4% 17.3% Wyoming 26.1% 37.1% 36.8%

2) Scale Based on Savings Percentages by Sector for Each State

• The EPA selected t he regional residential and commercial hourly EE savings impact shapes for the U.S. Census Division and the industrial shape for the U.S. Census Region in which the state lies.

• The EPA scaled the regional savings impact shapes by sector using the appropriate percentages of EE savings by sector ( Q ) estimated in Step 1 to develop scaled savings for each ho ur of the year ( 8,760 hour s) by sector for each

state.

• The EPA summed the scaled residential, commercial and industrial savings by hour ( 8,760 hour s) to get the total hourly shape of the energy savings for the state (this is still normalized to base 1).

3) Shift Based on First Day of the Year and Accounting for Leap Years

• The original savings impact shapes were developed for a year that began on a Friday

.

• For each year of interest, EPA shifted the total hourly savings impact shape for a state ahead or behind by the least number of days to ensure that the first day of the savings impact shape corresponded with the first day of the year. The 2020 leap year was also taken into account.

4) Scale B ased on Total Incremental Savings for Each State

• For each year, EPA multiplied the shifted and scaled hourly savings impact shapes by the total cumulative incremental savings estimated for that year, thus calculating the electricity savings in each hour . The resulting 8,760 hourly savings increments sum to the total annual cumulative incremental savings and represent the savings profile for the year.

**Identifying the Peak Generating Hour and Projected Peak Demand Savings** To identify the specific hour of the year in which each state’s electricity generation is at its

m aximum, EPA aggregated the load profiles of the relevant Integrated Planning ModelTM (IPM) region s 52 located within a state ( relying on state

- specific capacity

- weighting of IPM regions that

52 “Model region” refers to the geographic regions defined for the “EPA Base Case using IPM® v. 5.13 ,” a projection of electricity sector activity that takes into account only those federal and state air emission laws and regulations

w hose provisions were either in effect or enacted and clearly delineated at the time the base case was finalized in August 201 3 . The peak hour is taken from load profiles used in EPA’s Base Case using IPM®, which are compiled by22 cross state boundaries ) in EPA’s Base Case. Table 6 presents the sta te-to- IPM

- region mapping that was used. Since the load data used in EPA’s Base Case were available for 20 10

, the peak hour for each year of interest was also shifted based on the first day of the year in the same

manner as described in Step 3 (“ Shift Based on First Day of the Year and Accounting for Leap
Years ”) of the “Draft Methodology for Generating Savings Impact Profiles for Energy Efficiency Policies ” above . For each state, EPA identified th e peak hour for each year, and took the corresponding h ourly EE savings (i.e., impact ) from the EE policy or program as the peak savings.

Table 6 : EPA Base Case Regional Mapping for I ntegrated Planning Model 53

State IPM Region
Arizona WECC_AZ
Arkansas S_D_REST, S_D_N_AR, SPP_WEST
California WEC_CALN, WECC_SCE, WEC_LADW, WEC_SDGE, WECC_IID, WECC_PNW, WECC_AZ, WECC_SF, WECC_NNV Colorado WECC_CO

Connecticut NENG_CT
Delaware PJM_EMAC
District of Columbia PJM_SMAC
Florida FRCCS_SOU
Illinois PJM_COMD, MIS_IL, MIS_MIDA, MIS_MO
Indiana MIS_INKY, PJM_West

Iowa MIS_IA, MIS_MIDA, MIS_MO, MIS_IL, MIS_MNWI, MAP_WAUE
Maine NENG_ME Maryland PJM_SMAC, PJM_EMAC, PJM_PENE, PJM_AP
Massachusetts NENGREST Michigan MIS_LMI, MIS_WUMS, PJM_West, MIS_MNWI

Minnesota MIS_MNWI, MIS_IA, MIS_MAPP, MIS_LMI, MAP_WAUE

Montana WECC_MT, MIS_MAPP, MAP_WAUE New Hampshire NENGREST New Jersey PJM_EMAC, NY_Z_J

New Mexico SPP_SPS, WECC_NM
New York NY_Z_C&E, NY_Z_F, NY_Z_D, NY_Z_G

- I, NY_Z_J, NY_Z_K, NY_Z_A&B
Ohio PJM_West, PJM_ATSI, S_C_KY, MIS_INKY, PJM_EMAC Oregon WECC_PNW Pennsylvania PJM_PENE, PJM_West, PJM_WMAC, PJM_ATSI, PJM_EMAC, PJM_AP

aggregating FERC

- 714 data t o the model region level. See http://www.epa.gov/airmarkets/progsregs/epa

ipm/docs/v513/attachment2_1.xlsx for region

- specific l oad duration c urves used in EPA Base Case v.5.13.

53 http://www.epa.gov/airmarkets/progsregs/epa

- ipm/docs/v513/Documentation.pdf, Figure 3 -1

.23 State IPM Region
Rhode Island NENGREST

Texas ERC_REST, WECC_NM, SPP_SPS, ERC_WEST, SPP_WEST, S_D_WOTA
Vermont NENGREST Washington WECC_PNW

Wisconsin MIS_MNWI, MIS_WUMS

S ECTION V : DRAFT METHODOLOGY FOR ESTIMATING RENEWABLE ENERGY SALES FROM R ENEWABLE PORTFOLIO STANDARDS BEYOND WHAT IS CAPTURED IN ANNUAL ENERGY OUTLOOK 2013 The AEO201 3 Reference Case incorporates RPS policies or substantively similar laws in place at the time of forecast development. In general, the AEO assumes that utilities will meet the RPS targets; however, where states have explicitly limited state funding for RPS implementation, AEO assumes utilities comply with RPS requirements only to the extent that state funding allows, as described in the AEO assumptions documents

.

This analysis maintains consistency with these limiting assumptions. In this current version, the EPA included t he RPS policies for only two states, Hawaii and Minnesota. The RPS

- related energy production in Hawaii is considered incremental to the AEO forecast because the state is excluded from AEO201 3 modeling. 54 Minnesota was added becaus e its RPS target was changed 55 after the analysis underlying the AEO assumptions was performed. The expected increase of 1.5% for certain utility types after 2020 counts as incremental to AEO.

The EIA did not identify funding limitations for either state, and EPA assumed their full RPS targets would be achieved. Table 7 presents final RPS targets used in this analysis for the two states for which EPA identified updated RPS requirements

.

Since the RPS targets for Hawaii were only available for 2015, 2020 and 2030, EPA estimated sales in intervening years by interpolation. Similarly, the Minnesota targets for 2016, 2020 and 2025 were used to interpolate expected sales levels for all years.

RPS requirements were frozen in percent terms for the years after the RPS policy period.

54 “ NEMS provides electricity market projections for the contiguous lower 48 states only. ” EIA (2013), Page 13.

55 Law HF 279 was enacted on May 23, 2013. The 2020 target for Xcel Energy was increased from 30% to 31.5%. The target for Non

- Xce l Public Utilities was increased from 20% to 21.5% in 2020 and from 25% to 26.5% in 2025. DSIRE (2013a)24 Table 7 : R enewable Portfolio Standard Assumption s Made in This Analysis

56 NEMS provides electricity market projections for the contiguous lower 48 states only , so impacts of Hawaii’s RPS are not included in AEO201 3

. State State RPS Generation (1,000 GWh)
2015 2020 2030
Hawaii 56 1.39 2.16 3.43
Minnesota 0.00 0.64 0.6525 

### References

A merican Council for an Energy Efficient Economy (ACEEE ) (2009a). Saving Energy Cost

- Effectively: A National Review of the Cost of Energy Saved Through Utility

- Sector Energy Efficiency Programs

. September 2009. Available online at

< http://www.aceee.org/sites/default/files/publications/researchreports/U092.pdf >

A CEEE (2011). Energy Efficiency Resource St andards: A Progress Report on State Experience

. June 2011. Available online at: < http://www.aceee.org/research- report/u112 >

A CEEE (2012 )

. Th e 2012 State Energy Efficiency Scorecard. October 2012. Available online at

< http://aceee.org/files/pdf/fact -sheet/e12c- es.pdf >

A CEEE (2012a). A National Survey of State Policies and Practices for the Evaluation of Ratepayer -Funded Energy Efficiency Programs . February 2012. Available online at

< http://www.aceee.org/research

- report/u122 >

Am eren Illinois (2010). Electric and Gas Energy Efficiency and Demand

- Response Plan. September 2010.

U.S. Bureau of Economic Analysis (BEA) (2013). Implicit Price Deflators for Gross Domestic Product

. May 2013. Available online at

< http://www.bea.gov/iTable/iTable.cfm?ReqID=13&step=1#reqid=13&step=3&isuri=1&1305=2
012&1306=A&1310=X&1311=0&1301=I&1303=13&1304=2005 >

C ommonwealth Edison Company (ComEd) (2010). Commonwealth Edison Company’s 2011

- 2013 Energy Efficiency and Demand Response Plan. October 2010. Available online at

< http://webapps.cee1.org/sites/default/files/library/8779/CEE_Eval_ComEd2011

- 13EEDRPlan_1Oct2010_7.pdf >

Connecticut Department of Energy & Environmental Protection Bureau of Energy and Technology (DEEP

BETP) (2012)

. 2012 Integrated Resource Plan Final Approval

. June 2012. Available online at

< http://www.dpuc.state.ct.us/DEEPEnergy.nsf/c6c6d525f7cdd1168525797d0047c5bf/cb827b1f fa58b2fd85257a1d0060c374/$FILE/CTIRP%202012_Appendix%20C%20(Energy%20Efficiency).p
df >

D atabase of State Incentives for Renewables & Efficiency (DSIRE) (2013). Rules, Regulations & Policies for Energy Efficiency

. Accessed on June 2012

. < http://www.dsireusa.org/summarytables/rrpee.cfm >

D elaware Sustainable Energy Utility (DSEU) (2013). Monthly Budget Report . February 2013. Available online at < https://imageserv11.team

logic.com/mediaLibrary/191/Feb_2013_Monthly__Bond__SREC_Reports.pdf >26 D istrict of C olumbia Sustainable Energy Util ity (DCSEU) (2012). 2012 Annual Report

. Available online at

< http://www.dcseu.com/docs/DCSEU_2012Annual%20Report_opt.pdf >

Ef ficiency M aine Trust (EMT) (2010). Triennial Plan of the Efficiency Maine Trust 2011

- 2013 . April 2010. Available online at < http://www.efficiencymaine.com/docs/other/EMT_Final_Tri_Plan.pdf >

E fficiency Maine Trust (EMT) (2012). Triennial Plan of the Efficiency Maine Trust 2014

- 2016 . October 2012. Available online at < http://www.efficiencymaine.com/docs/TriPlan2

- 11

- 26

- 2012.pdf >

EIA (20 10 ). Annual Electric Power Industry Database ( Form EIA

- 861 Database) 200 9

. Available online at

< http://www.eia.doe.gov/cneaf/electricity/page/eia861.html >

E IA (20 11 ). Annual Electric Power Industry Database ( Form EIA

- 861 Database) 20 10

. Available online at

< http://www.eia.doe.gov/cneaf/electricity/page/eia861.html >

E IA (201 2 ). Annual Electric Power Industry Database ( Form EIA

- 861 Database) 20 11

. Available online at

< http://www.eia.doe.gov/cneaf/electricity/page/eia861.html >

E IA (2013a). Annual Energy Outlook 2013

. May 2013. Available online at

< http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf >

E IA (201 3b ). Detailed Sales and Revenue Data by State, Monthly Back to 1990 (Form EIA

- 826)

. Downloaded June 2013 . Available online at

< http://www.eia.gov/electricity/data/eia826/xls/sales_revenue.xls >

E IA (201 3c ). Assumptions to the Annual Energy Outlook 2013. May 2013. Available online at

< http://www.eia.gov/forecasts/aeo/assumptions/pdf/0554(2013).pdf >

U S Environmental Protection Agency (EPA) (2010). Documentation for EPA Base Case v.4.10 Using the Integrated Planning Model. August 2010. Available online at

< http://www.epa.gov/airmarkt/progsregs/epa

- ipm/docs/v410/Chapter1.pdf >

G ood Company Associates (2010). Substantive Changes Made to Rules Governing Texas Utility Run Energy Efficiency Programs. August 2010. Available online at

< http://www.goodcompanyassociates.com/files/m anager/Summary_PUCT_EE_Rule__8 -6- 10.pdf >

I nstitute for Electric Efficiency (IEE) (2012). Summary of Ratepayer

- Funded Electric Efficiency Impacts,Budgets, and Expenditures . January 2012. Available online at

< http://www.edisonfoundation.net/iee/Documents/IEE_CEE2011_FINAL_update.pdf >27 Mass Save (201 2 ). Massachusetts Joint Statewide Three

- Year Electric and Gas Energy Efficiency Plan. November 2012 . Available online at < http://www.ma

eeac.org/Docs/4.1_Three%20Year%20Plans/2013

- 2015%20Plan/Exhibit%20Compact

1%20Statewide%20Electric%20and%20Gas%20Three

- Year%20%20Plan%2011 -2- 12.pdf >

M ontana

- Dakota Utilities Co (MDU) (2012)

. 2011 Electric Universal System Benefits Report . February 2012. Available online at < http://leg.mt.gov/content/Committees/Interim/2011

- 2012/Energy

and

- Telecommunications/Meeting

- Documents/May

- 2012/201 1USBMDU.pdf >

M ontana Electric Cooperatives’ Association (MECA) (2011). 2011 Final Year

- End Universal System Benefits Pool Report. December 2011. Available online at

< http://leg.mt.gov/content/Committees/Interim/2011

- 2012/Energy

- and

Telecommunications/Meeting- Documents/May

- 2012/2011USBCoops.pdf >

N ational Grid (2008). 2009

- 2011 Energy Efficiency Procurement Plan. September 2008. Available online

at < http://www.ripuc.org/eventsactions/docket/3931

- NGrid -ComplianceProcurePlan(9-3- 08).pdf >

N ew Hampshire Electric Uti lities (NHEU) (20 12 ). 2013

- 2014 CORE Energy Efficiency Programs

. September 20 12 . Available online at < http://www.puc.nh.gov/Regulatory/Docketbk/2012/12

- 262/TRANSCRIPTS -OFFICIAL%20EXHIBITS- CLERKS%20REPORT/12

- 262%202012

- 12

- 21%20EXH%202%20PSNH%20MERGED%20ATTACHMENT%20A%20AND%20B%20TO%20SETTLE
MENT%20AND%20UPDATED%20TO%20INCLUDE%20LATEST%20CORRECTIONS.PDF >

N ew Jersey Clean Energy Program (NJ CEP ) (20 13 ). Monthly Report of Progress Toward Goal s. January
2013

. Available online at

< http://www.njcleanenergy.com/files/file/Library/NJCEP%20PTG%20Month%20Report%20

- %20Jan%202013.pdf >

No rthwest Power and Conservation Council (NW PCC) (2010). 6th Plan Conservation Target Calculator. January 2010. Available online at

< http://www.nwcouncil.org/energy/powerplan/6/supplycurv es/I937/UtilityTargetCalc_v2.0_6th
Plan.xls >

No rthWestern Energy (2012). Universal System Benefits Activities 2011 Annual Report . March 2012

. Available online at

< http://www.northwesternenergy.com/documents/E+Programs/E+USBDORReport

- 11.pdf >

R hode Island Energy Efficiency and Resource Management Council (EERMC) (2010). Energy Efficiency Savings Targets. September 2010. Available online at

< http://www.ripuc.org/eventsactions/docket/4202

- EERMC

- EST -Filing(9-1- 10).pdf >

Sa tchwell, Andrew, et al (2011). Carrots and Sticks: A Comprehensive Business Model for the Successful Achievement of Energy Efficiency Resource Standards . Lawrence Berkeley National Laboratory.28 LBNL

- 4399E. March 2011. Available online at < http://eetd.lbl.gov/ea/EMS/reports /lbnl

4399e.pdf >

Synapse Energy Economics, Inc. State Energy Efficiency in the AEO Electricity Forecasts

. October 31, 2012

. Available online at < http://www.synapse

- energy.com/Downloads/SynapseReport.2013

- 10.0.EE

- in

- AEO.12

- 094.pdf >

S ynapse Energy Economics, Inc. State Energy Efficiency Embedded in the AEO Electricity Forecasts, 2013 Update. November 15, 2013.

V ermont Energy Investment Corporation (VEIC) (2009). Efficiency Vermont: Annual Plan 2010

- 2011. November 2009. Available online at

< http://www.efficiencyvermont.com/stella/filelib/EVT_Annual_Plan_2010.pdf >