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 describe how they will attain the NAAQS by a certain date. To 

help state, tribal, and local 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 key EE/RE “on the books” policies that are not explicitly 

reflected in the Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) 2013 

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 estimates1

of the energy impacts of state EE/RE policies not accounted for in AEO2013 forecast. The time 

period covered by this analysis is 2013-2030. 

These estimates are intended for use by state, tribal, and local agencies responsible for 

developing SIPs/TIPs 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 associated 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 revise 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., AEO2013): 

• Step One: Understand policy assumptions in the current reference case forecast (e.g., 

AEO2013 Reference Case Forecast2 (AEO2013)). 

• Step Two: Identify key state-level EE/RE policies not explicitly included in the current 

reference case forecast (e.g., AEO2013) and collect relevant design details.

• Step Three: Develop analytical methods to estimate incremental3 impacts of state-level 

EE/RE policies relative to the current reference case forecast (e.g., AEO2013).

While EPA has applied these steps to the AEO2013, states can consider applying this 

methodology to other electricity forecasts. 

Step 1: Understand Energy Efficiency/Renewable Energy Policy Assumptions in the 

Current Reference Case Forecast

To understand the EE/RE policy assumptions included in the AEO2013 forecast, EPA reviewed 

the EIA’s documentation for the AEO2013 reference case forecast and consulted with EIA staff. 

From the review, it is clear that AEO2013 explicitly includes the impacts of several existing 

EE/RE policies,4 including: 

• Federal appliance and equipment standards for residential and commercial categories5

• Lighting efficiency standards for various types of lighting technologies6

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

technologies7

• Federal EE programs and funding 

o American Recovery and Reinvestment Act8

o State Energy Program and Energy Efficiency Community Block Grant

o Weatherization Program

o Green Schools 

o Smart Grid expenditures 

• Building energy codes for residential and commercial new construction9

 

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 AEO2013 refers to the impacts not captured within AEO2013, 

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/assumptions/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., all states adopt and enforce International Energy Conservation Code (IECC) 

2006 (Residential Building Code) by 2017

• State Renewable Portfolio Standards (RPS)10

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

Step 2: Identify and Review “On the Books” Energy Efficiency/Renewable Energy

Policies not 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 regulations, statutes, or state public utility commission (PUC) orders 

that require parties to achieve minimum levels of EE and/or RE, or to fund programs. The 

EE/RE policies listed below are the set of “on the books” state EE/RE policies EPA identified for 

this analysis. 

State EE policies

• 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 (PBFs) 

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

o EE programs funded by Forward Capacity Market (FCM) revenues12 

State RE policies:

• 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).

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Step 3: Develop Methods to Estimate Incremental Impacts of Energy 

Efficiency/Renewable Energy Policies Relative to 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 reference case: annual 

energy savings and generation for 2014-2030 and peak impacts and hourly load impact curves 

for 2020, 2025, 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 (2012). 14 DSIRE (2013). 
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 AEO2013 regional forecasts.

• Step Two: Estimate projected impacts of key state “on the books” EE policies already 

embedded in AEO2013 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 peak 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 profiles15 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 RE 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 provides 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 for Generating a Baseline Forecast of State Electricity Sales to 

Represent Annual Energy Outlook 2013 Regional Forecasts

State-level baseline sales16 data were developed by first using 2012 historical state sales data 

from the EIA17 and then applying the electricity sales growth rates from AEO2013. Annual 

Energy Outlook 2013-based “annual average growth rates” (AAGR) were calculated for each 

Electricity Market Module (EMM) region across the 2012-2040 forecast period. These regional 

growth rates were then applied to the 2012 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 2012-2040 AAGR was used to 

forecast sales for 2013-2040. Table 1 shows the EMM regions and the AAGRs used to forecast 

sales for each state. 

Table 1: Electricity Market Module Region and Annual Energy Outlook 2013 Sales Growth Rates by 

States

State/Jurisdiction Electricity Market 

Module Region

Average 

Annual Growth 

Rates

(2012-2040)

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 HI19 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 AEO2013 does not include state-level forecasts, so incremental impacts are calculated against the 

BAU electricity sales forecast 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 represent 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 Hawaii, 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%

Draft Methodology for Estimating Energy Savings of State Energy Efficiency Policies 

Embedded In Annual Energy Outlook 2013 

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 AEO2013 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 last 
7 

historical 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 nearterm 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 lifetime.” 

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 13-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

Recognizing that AEO2013 is implicitly affected by these historic and persistent effects of state 

EE policies and programs, EPA concludes that some portion of the total policy- and programinduced EE savings are embedded in the AEO2013 regional forecast and the AEO2013-based 

state-level BAU forecast. EPA therefore developed a methodology for estimating these 

embedded savings for each state. 

This methodology involves two steps: 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 (also 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. 

Because 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 representing the ongoing annual effects of energy 

efficiency that are embedded in the AEO forecast (0.29%).21

Allocating 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 (0.47).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 (2013) 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 201324

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 0.06

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

EPA 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 involves 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 subtracting 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 AEO2013. 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 

AEO2013. 

Draft Methodology for Estimating Projected Energy Efficiency Savings from Energy 

Efficiency Policies

The EPA estimated state-level EE savings from EERS policies 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 energy 
10

savings for states with EERSs by treating EERS 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 qualifying individual programs were not 

incremental to the EERS target, so each state with reported savings has either EERS savings, or 

dedicated sources of EE program funding.

25

For each policy category, EPA estimated annual first-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 

cumulative savings using state-specific measure lifetimes (see Table 3 below) and assuming no 

decay of savings over the life of the measures. 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 indicated a continuation of savings beyond the policy period; these assumptions 

are documented within the 2013 Annual Energy Savings and Generation workbook. For the 

majority of states, however, the forecast reverts to the AEO2013 reference-case-based forecast

after the EE policy period ends. 

Table 3: Measure Lifetime by State26

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

Energy 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. States 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 the appropriate sales basis for each state and, if the basis was 

not total sales, EPA used 2011 utility-level sales data from EIA27 and AEO2013-based growth 

rates to develop baseline forecasts of sales of affected utilities (see Table 1). Because 2012 

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 2011 to estimate the affected utility sales as a 

share of total sales for 2012. For most states, EPA assumes full 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 lead 

to incremental energy savings relative to AEO 201328 or are otherwise inconsistent with the 

EERS targets.

29 Additionally, savings 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 AEO2013 forecast, 

I is the cumulative savings incremental to AEO2013 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 (2013b). 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 AEO2013 forecast, 

I is the cumulative savings incremental to AEO2013 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 AEO2013 forecast, 

I is the cumulative savings incremental to AEO2013 forecast, and 

Z is the adjusted sales after application of cumulative incremental savings.

4) EERS with Cumulative EE Savings Targets Specified in Absolute Terms

A(t) = C(t) – C(t-1) + A(t-L) 

If C(t) available,

I(t) = C(t) - E(t) 
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 AEO2013 forecast, 

I is the cumulative savings incremental to AEO2013 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 AEO2013, no incremental energy savings were estimated.

2) Compliance Type and Cost/Rate Caps: Several states have EERSs that use costcontainment 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 – Arizona, Illinois, 

Minnesota, Ohio, and Texas – and relied upon available, state-specific academic 

reports,30 integrated resource plans,

31 and other studies32 to make downward 

adjustments to the nominal EERS targets to reflect these design features.33

3) “All Cost-effective EE” Targets: Six 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” requirement

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 plans34 and EE resource potential studies.

35

4) State Legislature or PUC Disapproval 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

Energy Efficiency Program Funding 

In states without an EERS policy but with one or more sources of EE funding, the 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 

funding 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/kilowatt 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 (20129), 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 price deflators 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 State41

The EPA estimated energy 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 development; for more information, go to 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html. 40 BEA (2013), Table 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 

Energy42

(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 $0.027 
16

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 their 

funds in the EE Program Funding category. This decision was motivated by the availability of 

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

Draft Methodology for Generating State-Adjusted Forecast that Reflects Incremental 

Energy Savings

The EPA estimated energy savings that are incremental to the reference case (AEO2013) by 

subtracting cumulative savings embedded in AEO2013 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 AEO2013 forecast and 

I is the cumulative savings incremental to AEO2013 forecast. 

The state-adjusted electricity sales forecast includes the impact of EE savings 

that are incremental to the BAU reference case. 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 AEO2013 forecast. 

Important Sources of Uncertainty in the Analysis

In conducting this analysis, EPA used the best available information and adopted assumptions 

intended 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 is highlighting three sources of uncertainty to keep in mind when utilizing 

these estimates and employing similar methods:
17

• The impacts of state EE policies embedded in the AEO reference case 

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

• Variations in state approaches for evaluating and reporting EE savings

As discussed in Section III, the AEO reference case likely includes the impacts of some programs 

that are not explicitly identified in the AEO documentation. Estimating 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 sales projections and the associated magnitude of double 

counting. While some intrinsic uncertainty exists, EPA believes 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 budgets43

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, energy savings are evaluated 

and reported to Public Utility Commissions (PUCs) as “gross” savings – 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 complicates 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 reconcile 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 IEE (2012). 44 ACEEE (2011). 45 ACEEE (2012a)
18

SECTION IV: DRAFT METHODOLOGY FOR ESTIMATING PROJECTED PEAK 

DEMAND SAVINGS OF ENERGY EFFICIENCY POLICIES

Energy efficiency programs are typically measured in terms of the electricity savings that they 

achieve over time, in term of MWhs. For example, savings occurring throughout the year or 

during the heating or cooling season. However, efficiency programs also save energy during 

specific “peak demand periods” when power costs are high and supplies are constrained. EPA 

estimates 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

As 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 estimated 

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 for Generating Savings Impact Profiles for Energy Efficiency

Policies

This analysis uses savings impact shapes previously developed for EPA47 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 residential 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 Central, 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 not 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 savings 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 2020, 2025 and 2030 using the following steps: 

1) Estimating EE Savings Distribution by Sector

• The EPA calculated average (O) national savings by sector49 (X) as a percentage 

of national sales by sector50 (Y) for 2009-2011 for the residential (r), commercial 

(c) and industrial (i) sectors.

Or,n = ((Xr,n,2009/Yr,n,2009) + (Xr,n,2010/Yr,n,2010) + (Xr,n,2011/Yr,n,2011))/3

Oc,n = ((Xc,n,2009/Yc,n,2009) + (Xc,n,2010/Yc,n,2010) + (Xc,n,2011/Yc,n,2011))/3

Oi,n = ((Xi,n,2009/Yi,n,2009) + (Xi,n,2010/Yi,n,2010) + (Xi,n,2011/Yi,n,2011))/3

• The EPA calculated sales by sector (Y) in 2011 as a percentage (P) of total 

residential, commercial and industrial sales for each state (s).

Pr,s = Yr,s,2011/( Yr,s,2011 + Yc,s,2011 + Yi,s,2011) 

Pc,s = Yc,s,2011/( Yr,s,2011 + Yc,s,2011 + Yi,s,2011) 

Pi,s = Yi,s,2011/( Yr,s,2011 + Yc,s,2011 + Yi,s,2011) 

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

Qr,s = (Pr,s * Or,n)/(Pr,s * Or,n + Pc,s * Oc,n + Pi,s * Oi,n) 

Qc,s = (Pc,s * Oc,n)/(Pr,s * Or,n + Pc,s * Oc,n + Pi,s * Oi,n) 

Qi,s = (Pi,s * Oi,n)/(Pr,s * Or,n + Pc,s * Oc,n + Pi,s * Oi,n) 

 Table 5 shows savings percentages for each state.

Table 5: Distribution of Savings by Sector51

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 (2010), 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 the 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 hour of the year (8,760 hours) by sector for each 

state. 

• The EPA summed the scaled residential, commercial and industrial savings by 

hour (8,760 hours) 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 Based 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 

maximum, EPA aggregated the load profiles of the relevant Integrated Planning ModelTM (IPM) 

regions

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 

whose provisions were either in effect or enacted and clearly delineated at the time the base case was finalized in 

August 2013. The peak hour is taken from load profiles used in EPA’s Base Case using IPM®, which are compiled by 
22

cross state boundaries) in EPA’s Base Case. Table 6 presents the state-to-IPM-region mapping 

that was used. Since the load data used in EPA’s Base Case were available for 2010, 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 the peak hour for each year, and took the 

corresponding hourly EE savings (i.e., impact) from the EE policy or program as the peak 

savings.

Table 6: EPA Base Case Regional Mapping for Integrated Planning Model53

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 to the model region level. See http://www.epa.gov/airmarkets/progsregs/epaipm/docs/v513/attachment2_1.xlsx for region-specific load duration curves 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

SECTION V: DRAFT METHODOLOGY FOR ESTIMATING RENEWABLE ENERGY 

SALES FROM RENEWABLE PORTFOLIO STANDARDS BEYOND WHAT IS 

CAPTURED IN ANNUAL ENERGY OUTLOOK 2013 

The AEO2013 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 the 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 AEO2013 modeling.54 Minnesota was added because its RPS target was 

changed55 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-Xcel Public Utilities was increased from 20% to 21.5% in 2020 and from 25% to 26.5% in 2025. 

DSIRE (2013a)
24

Table 7: Renewable Portfolio Standard Assumptions 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 AEO2013.

State State RPS Generation (1,000 GWh)

2015 2020 2030

Hawaii56 1.39 2.16 3.43

Minnesota 0.00 0.64 0.65
25

References

American 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> 

ACEEE (2011). Energy Efficiency Resource Standards: A Progress Report on State Experience. June 2011. 

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

ACEEE (2012). The 2012 State Energy Efficiency Scorecard. October 2012. Available online at 

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

ACEEE (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> 

Ameren 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> 

Commonwealth 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 (DEEPBETP) (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> 

Database 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> 

Delaware Sustainable Energy Utility (DSEU) (2013). Monthly Budget Report. February 2013.Available 

online at <https://imageserv11.teamlogic.com/mediaLibrary/191/Feb_2013_Monthly__Bond__SREC_Reports.pdf> 
26

District of Columbia Sustainable Energy Utility (DCSEU) (2012). 2012 Annual Report. Available online at 

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

Efficiency Maine 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> 

Efficiency 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 (2010). Annual Electric Power Industry Database (Form EIA-861 Database) 2009. Available online at 

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

EIA (2011). Annual Electric Power Industry Database (Form EIA-861 Database) 2010. Available online at 

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

EIA (2012). Annual Electric Power Industry Database (Form EIA-861 Database) 2011. Available online at 

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

EIA (2013a). Annual Energy Outlook 2013. May 2013. Available online at 

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

EIA (2013b). 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> 

EIA (2013c). Assumptions to the Annual Energy Outlook 2013. May 2013. Available online at 

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

US 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> 

Good 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/manager/Summary_PUCT_EE_Rule__8-6-

10.pdf> 

Institute 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 (2012). Massachusetts Joint Statewide Three-Year Electric and Gas Energy Efficiency 

Plan.November 2012. Available online at <http://www.maeeac.org/Docs/4.1_Three%20Year%20Plans/2013-2015%20Plan/Exhibit%20Compact1%20Statewide%20Electric%20and%20Gas%20Three-Year%20%20Plan%2011-2-12.pdf > 

Montana-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/Energyand-Telecommunications/Meeting-Documents/May-2012/2011USBMDU.pdf>

Montana 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-andTelecommunications/Meeting-Documents/May-2012/2011USBCoops.pdf> 

National 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> 

New Hampshire Electric Utilities (NHEU) (2012). 2013-2014 CORE Energy Efficiency Programs.

September 2012. 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> 

New Jersey Clean Energy Program (NJ CEP) (2013). Monthly Report of Progress Toward Goals. January 

2013. Available online at 

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

%20Jan%202013.pdf> 

Northwest Power and Conservation Council (NWPCC) (2010). 6th Plan Conservation Target Calculator.

January 2010. Available online at 

<http://www.nwcouncil.org/energy/powerplan/6/supplycurves/I937/UtilityTargetCalc_v2.0_6th

Plan.xls> 

NorthWestern 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> 

Rhode 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> 

Satchwell, 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/lbnl4399e.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>

Synapse Energy Economics, Inc. State Energy Efficiency Embedded in the AEO Electricity Forecasts, 2013 

Update. November 15, 2013.

Vermont 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>