&EPA
Roadmap for Incorporating Energy
Efficiency/Renewable Energy
Policies and Programs into State and Tribal
Implementation Plans
Appendix J: Draft Methodology for EPA's Analysis of
Existing State Energy Efficiency/Renewable Energy
Policies
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EPA-456/D-12-001k
July 2012
Roadmap for Incorporating Energy Efficiency/Renewable Energy
Policies and Programs into State and Tribal Implementation Plans
Appendix J: Draft Methodology for EPA's Analysis of Existing State Energy Efficiency/Renewable
Energy Policies
By:
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Outreach and Information Division
Research Triangle Park, North Carolina
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Outreach and Information Division
Research Triangle Park, North Carolina
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ACKNOWLEDGMENTS
We would like to acknowledge substantial contributions from members of an inter-office EPA
team that included the Office of Atmospheric Programs, the Office of Policy Analysis and
Review, the Office of General Counsel and Regions 1 and 6. This document also reflects
comments received from a number of stakeholders, including state and local air quality
agencies.
J-2
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Contents
TABLES J-4
SECTION J.I: INTRODUCTION J-5
SECTION J.2: STEPS OF THE ANALYSIS J-5
Step 1: Understand Energy Efficiency/Renewable Energy Policy Assumptions in the Current Reference Case
Forecast J-5
Step 2: Identify and Review "On the Books" Energy Efficiency/Renewable Energy Policies not in the Reference
Case J-6
Step 3: Develop Methods to Estimate Incremental Impacts of Energy Efficiency/Renewable Energy Policies
Relative to the Reference Case J-7
SECTION J.3: OVERVIEW OF THE DRAFT METHODOLOGY AND ANALYTICAL STEPS J-7
Draft Methodology for Generating a Baseline Forecast of State Electricity Sales to Represent Annual Energy
Outlook 2010 Regional Forecasts J-8
Draft Methodology for Estimating Energy Savings of State Energy Efficiency Policies Embedded In Annual Energy
Outlook 2010 J-9
Draft Methodology for Estimating Projected Energy Efficiency Savings from Energy Efficiency Policies J-12
Energy Efficiency Resource Standards J-13
Public Benefit Funded Energy Efficiency Programs J-16
Energy Efficiency Programs Funded by the Regional Greenhouse Gas Initiative J-18
Draft Methodology for Generating State-Adjusted Forecast that Reflects Incremental Energy Savings J-19
Important Sources of Uncertainty in the Analysis J-20
SECTION J.4: DRAFT METHODOLOGY FOR ESTIMATING PROJECTED PEAK DEMAND SAVINGS OF ENERGY
EFFICIENCY POLICIES J-21
Draft Methodology for Generating Load Impact Curves of Energy Efficiency Policies J-22
SECTION J.5: DRAFT METHODOLOGY FOR ESTIMATING RENEWABLE ENERGY SALES FROM RENEWABLE PORTFOLIO
STANDARDS BEYOND WHAT IS CAPTURED IN ANNUAL ENERGY OUTLOOK 2010 J-25
References J-27
J-3
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TABLES
Table 1: Electricity Market Module Region and Annual Energy Outlook 2010 Sales Growth Rates by States J-8
Table 2: Energy Efficiency Savings Estimated to be Embedded in Annual Energy Outlook 2010 J-ll
Table 3: Measure Lifetime by State J-12
Table 4: Levelized Cost by State J-17
Table 5: EPA Base Case Regional Mapping for Integrated Planning Model J-21
Table 6: Shares of Savings by Sector J-23
Table?: Renewable Portfolio Standard Assumptions Made in This Analysis J-26
J-4
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SECTION J.I: INTRODUCTION
To help state, tribal and local agencies examine the role of energy efficiency/renewable energy
(EE/RE) policies and programs in their State Implementation Plans/Tribal Implementation Plans
(SIPs/TIPs), the U.S. Environmental Protection Agency (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) 2010
electricity projections. The EPA's draft methodology and associated analysis covers several
state-level "on the books" EE/RE policies that are adopted in law and/or codified in rule or
order. The EPA anticipates that the draft methodology may be useful to state, tribal and local
agencies preparing SIP/TIP submittals to meet the National Ambient Air Quality Standards for
ozone and other pollutants. (The EPA used this methodology to develop estimates of the
energy impacts of EE/RE policies not accounted for in AE02010.1)
In conducting this analysis, EPA benefited from feedback from other federal and state agencies.
The EPA plans to revisit its methods as new information becomes available and anticipates
benefiting from the experience of other efforts aimed at accounting for the energy impacts of
EE/RE policies in energy and environmental planning. In recognition of this opportunity to learn
from new information, this version of the methodology is labeled draft.
SECTION J.2: STEPS OF THE ANALYSIS
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., AE02010):
Step 1: Understand policy assumptions in the current reference case forecast (e.g.,
AE02010 Reference Case Forecast2 (AE02010)).
Step 2: Identify key state-level EE/RE policies not explicitly included in the current
reference case forecast (e.g., AE02010) and collect relevant design details.
Step 3: Develop analytical methods to estimate incremental3 impacts of state-level
EE/RE policies relative to the current reference case forecast (e.g., AE02010).
These steps serve as an example for air agencies interested in revising an energy forecast (e.g.,
AE02010) to reflect the EE/RE policies of interest.
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 AE02010 forecast, EPA reviewed
the ElA's documentation for the AE02010 reference case forecast and consulted with EIA staff.
1 For more information, go to: http://www.epa.gov/statelocalclimate/state/statepolicies.html.
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 AEO2010 refers to the impacts not captured within AEO2010,
taking into account any embedded impacts reflected in the forecast.
J-5
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From the review, it is clear that AE02010 explicitly includes the impacts of a number of existing
EE/RE policies, including:
Federal Appliance Standards4
o Ten residential and ten commercial appliance categories
Federal Funding for EE and related programs (e.g., through the American Recovery and
Reinvestment Act)5
o State Energy Program and Energy Efficiency Community Block Grant
o Weatherization Program
o Green Schools
o Smart Grid expenditures
Building Energy Codes6
o All states adopt and enforce:
International Energy Conservation Code (IECC) 2006 (Residential Building
Code) by 2011
IECC 2009 by 2018
American Society of Heating, Refrigerating and Air-Conditioning
Engineers 90.1-2007 (Commercial Building Code) by 2018
Renewable Portfolio Standards (RPS)7
o 30 states and Washington, D.C. effective as of September 2009
Step 2: Identify and Review "On the Books" Energy Efficiency/Renewable Energy
Policies not in the Reference Case
Based on its review, EPA identified four key "on the books" state-level EE/RE polices not
explicitly included in the reference case forecast. The EPA focused its analysis on EE/RE policies
that are currently in regulations, statutes, or state public utility commission (PUC) orders that
require parties to acquire EE and/or RE or commit to funding levels for programs aimed at
acquiring EE. 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)
EE programs financed by Public Benefits Funds
EE programs financed by the Regional Greenhouse Gas Initiative (RGGI)8
State RE policies:
RPS policies adopted or updated between September 2009 and December 2010
4 EIA (2010d), Appendix A, pp. 170-185.
5EIA(2010d), pp. 8-10.
6 Ibid p. 8.
7EIA(2010a), pp. 14-17
8 For more information, go to: http://www.rggi.org/.
J-6
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After identifying the applicable EE/RE policies, EPA scanned the EE/RE policies of all 50 states to
determine which states had adopted policies as of December 31, 2010. Once EPA identified
which states had policies, EPA reviewed the relevant design details for each state EE/RE policy
using publically available information, such as state legislation, state rules and regulations, PUC
orders, and summary reports from the American Council for an Energy-Efficient Economy
(ACEEE)9, Lawrence Berkeley National Laboratory (LBNL)10 and the Consortium for Energy
Efficiency (CEE).11
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 impacts estimates: annual energy savings and generation for 2014-
2030 and peak impacts and hourly load impact curves for 2015 and 2020.
SECTION J.3: OVERVIEW OF THE DRAFT METHODOLOGY AND ANALYTICAL
STEPS
The EPA applied the following analytical steps to estimate the projected annual energy savings
of EE Policies:
Step 1: Generate a baseline (i.e., business as usual (BAD)) forecast of state electricity
sales consistent with AE02010 regional forecasts.
Step 2: Estimate projected impacts of key state "on the books" EE policies already
embedded in AE02010 forecast of electricity sales.
Step 3: Estimate projected EE savings from key state "on the books" EE policies
o EERS (25 states)
o EE programs financed by Public Benefits Funds (3 states)
o RGGI allowance auction revenue for EE Programs (3 states)
Step 4: Generate state-adjusted national energy forecast that reflects the energy
savings not captured in (i.e., incremental to) the baseline forecast.
The EPA applied the following analytical steps to estimate peak demand savings for EE Policies
(see Section J.4):
Step 1: Estimate projected peak demand savings for the years 2010, 2012, 2015 and
2020.
Step 2: Generate load impact curves that represent typical hourly changes in load from
EE programs under consideration.
9 ACEEE (2010).
10 LBNL (2009).
11 Consortium for Energy Efficiency (2010).
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The EPA applied the following key analytical steps to estimate the projected annual energy
impacts of RE policies (see Section J.5):
Step 1: Estimate RE generation from RPS policies adopted or revised between
September 2009 and December 2010.
Step 2: Generate state-adjusted forecast and aggregate state-adjusted forecast to
facilitate the energy modeling of regional RPS impacts.
Draft Methodology for Generating a Baseline Forecast of State Electricity Sales to
Represent Annual Energy Outlook 2010 Regional Forecasts
State-level baseline sales12 data were developed by first using 2010 historical state sales data
from the EIA13 and then applying the electricity sales growth rates from AE02010. Annual
Energy Outlook 2010-based "annual average growth rates" (AAGR) were calculated for each
Electricity Market Module (EMM) region across the 2009-2035 forecast period. These regional
growth rates were then applied to the 2010 historical sales for each state lying predominantly
within the EMM region.14 The 2009-2035 AAGR was used to forecast sales for 2011-2035.
Table 1 shows the EMM region and the AAGRs used to forecast sales for each state.
Table 1: Electricity Market Module Region and Annual Energy Outlook 2010 Sales Growth Rates by
States
State Electricity Market Average Annual
Module Region Growth Rates
(2009-2035)
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Hawaii
Illinois
Indiana
Iowa
Maine
Maryland
Massachusetts
Michigan
Minnesota
Montana
RA
SERC
CA
RA
NE
MAAC
FL
HI
MAIN
ECAR
MAPP
NE
MAAC
NE
ECAR
MAPP
NWP
1.4%
1.0%
1.0%
1.4%
1.3%
0.9%
1.2%
1.0%
1.0%
1.0%
1.1%
1.3%
0.9%
1.3%
1.0%
1.1%
1.1%
Note that AEO2010 does not include state-level forecasts, so incremental impacts are calculated against the
BALI electricity sales forecast developed as described in Section J.3.
13 EIA (2011).
14 EIA maps states to EMM regions for regional modeling of Renewable Portfolio Standards. The EPA followed this
mapping approach where possible and assigned the remaining states to EMM regions based on population
distributions.
J-8
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State Electricity Market Average Annual
Module Region Growth Rates
(2009-2035)
Nebraska
New Hampshire
New Jersey
New Mexico
New York
Ohio
Oregon
Pennsylvania
Rhode Island
Texas
Vermont
Washington
Wisconsin
MAPP
NE
MAAC
RA
NY
ECAR
NWP
MAAC
NE
ERCOT
NE
NWP
MAPP
1.1%
1.3%
0.9%
1.4%
0.7%
1.0%
1.1%
0.9%
1.3%
0.9%
1.3%
1.1%
1.1%
Draft Methodology for Estimating Energy Savings of State Energy Efficiency Policies
Embedded In Annual Energy Outlook 2010
The AE02010 does not explicitly include the impacts of state EE policies such as EERSs, public
benefit funded EE programs and RGGI-funded EE programs. However, AE02010 results could
implicitly reflect these programs to the extent that the forecast is based on historical data that
was affected by state EE programs. The AE02010 also accounts for future EE improvements,
which could be partly attributed to these key state EE policies. Some portion of the savings
from EE policies may, therefore, be embedded in the AE02010 forecast and the AE02010-
based state-level BAD forecast. The EPA estimated the embedded savings for each state. The
EPA then subtracted the savings from the state's total EE policy savings and estimated the
impacts that are incremental to AE02010. The EPA only applied embedded savings for years in
which states see savings from EE policies. To the extent possible, EPA only calculated savings
for entities that are required to implement the EE policies under consideration.
The EPA estimated embedded savings using a methodology similar to the method LBNL used in
its analysis of ratepayer-funded EE,15 which, lacking better information, assumes that the
growth rates derived from the AEO forecast implicitly account for a continuation of 50 percent
of historical levels of reported energy savings. The EPA quantified embedded energy savings
for each state using the following three steps:
_1: Estimate historical savings for entities that implement key state EE policies
16
17
Total first-year electricity savings from existing and new programs in 2007, 2008 and
2009 were obtained from the ACEEE.
18
: LBNL (2009).
1 Key state policies consist of: EERS, public benefit funded EE programs and RGGI-funded EE programs.
J-9
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For states that have EERSs with a total sales basis, or have only ratepayer- or RGGI-
funded EE programs, savings from EE policies were taken to be equal to total
incremental savings for each historical year.
For states that have EERSs with a basis other than total sales, savings from EE policies
were estimated as follows:
o The EPA used EIA-861 utility-level data to identify utilities not affected by an EE
policy in each state and their savings for 2007, 2008 and 2009.19
o If these utilities had service areas in only one state, EPA assumed all the savings
would occur in that state.
o If these utilities had service areas in multiple states and they were either (a) affected
by EE policies in all states, or (b) not affected by EE policies in any state in which they
had a service area, EPA apportioned the savings to states based on 2009 utility sales
in each state.
o If the identified utilities had service areas in multiple states and they were affected
by these policies in some but not all states in which they had a service area, EPA
assumed all savings would occur in the states with EE policies. The EPA apportioned
savings to these states based on 2009 utility sales in each state.20
o The EPA estimated savings for entities that implement EE policies as the total first-
year electricity savings for the state minus any other savings apportioned to the
state.
Step 2: Estimating the weighted average of historical savings as a share of sales for 2007-
2009
The EPA divided historical savings by historical sales to estimate a weighted average
savings rate. Annual electricity sales data for 2007-2009 for each state were obtained
from EIA-861 state-level datasets.21 The weighted average (m) of historical savings for
entities that implement EE policies as a share of state sales was calculated as:
m = ZX(t)/Z/(t)
Where:
t goes from 2007 to 2009,
X is the savings for entities that implement EE policies, and
17 "First-year savings" is a common metric for characterizing savings associated with energy-efficient initiatives.
For example, if a piece of highly efficient equipment is expected to save 10 MWh per year (as compared to one of
average efficiency), and have a lifetime of 10 years, the first-year savings are 10 MWh, and the cumulative savings
are 100 MWh (10 MWh/year * 10 years).
18 ACEEE estimates state-level EE savings using utility-level data from EIA-861 and information from a state-by-
state survey conducted by ACEEE. ACEEE (2009b, 2010, 2011b).
19 Some utilities not affected by a state EE policy nonetheless may offer EE programs to their customers. EIA
(2008a, 2009a, 2010c).
20 EIA (2010e).
21 EIA (2008b, 2009b, 2010e).
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Y is the annual electricity sales.
Step 3: Estimating embedded savings for each future year
The weighted average of historical savings as a share of sales for 2007-2009 (m)
is multiplied by 50 percent to yield embedded savings as a share (n) of baseline
sales for each future year:
n = m * 50%
Table 2 presents the estimated embedded savings as shares of baseline sales.
Embedded savings were calculated as:
F(t) = n * fl(t)
E (t) = F(t) + F(t-l) + ... + F(t-/.+l)
Where:
F is the annual first-year embedded energy savings,
6 is the baseline total sales, L is the measure lifetime, and
E is the cumulative embedded energy savings.
Table 2: Energy Efficiency Savings Estimated to be Embedded in Annual Energy Outlook 201022
State Savings Estimated to be Embedded in AEO2010
(percent of BALI Sales in Each Year)
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Hawaii
Illinois
Indiana
Iowa
Maine
Maryland
Massachusetts
Michigan
Minnesota
Montana
Nebraska
0.20
0.04
0.50
0.18
0.51
0.00
0.07
0.71
0.06
0.01
0.38
0.40
0.09
0.40
0.06
0.41
0.17
0.05
22 ACEEE (2009b), Table 6; ACEEE (2010), Table 8; ACEEE (2010), Table 8; EIA (2008a), FileS; EIA (2009a), FileS; EIA
(2009a), FileS; EIA (2008b), Table 2; EIA (2009b), Table 2; EIA (2010e), Table 2.
Ml
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State Savings Estimated to be Embedded in AEO2010
(oercent of BAD Sales in Each Year)
New Hampshire
New Jersey
New Mexico
New York
Ohio
Oregon
Pennsylvania
Rhode Island
Texas
Vermont
Washington
Wisconsin
0.33
0.24
0.10
0.23
0.07
0.32
0.03
0.44
0.07
1.01
0.35
0.38
Draft Methodology for Estimating Projected Energy Efficiency Savings from Energy
Efficiency Policies
The EPA estimated state-level EE savings from EERSs, public benefit funded EE programs, and
RGGI-funded EE programs. Because these categories are not mutually exclusive, EPA took steps
to avoid double-counting of energy savings for states with EERSs by treating EERS targets as
overall goals that include savings from individual public benefit funded and RGGI-funded
programs. 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 ratepayer- and/or RGGI-
funded savings.23
For each policy category EPA estimated first-year electricity savings for each 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. So, therefore, the forecast
reverts to the AE02010, which includes improved technology and efficiency in the long term.
Table 3: Measure Lifetime by State
24
State Measure Lifetime
Connecticut
Iowa
Massachusetts
Minnesota
Nevada
New Jersey
New Mexico
New York
(Yrs)
13
15
13
13
13
14
9
15
For more information, go to: http://www.epa.gov/statelocalclimate/state/statepolicies.html.
lACEEE(2009a), Table 1.
J-12
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State Measure Lifetime (Yrs)
Oregon
Rhode Island
Texas
Vermont
Wisconsin
Default
12
11
13
13
12
13
Energy Efficiency Resource Standards
An energy efficiency resource standard (EERS) is a policy mechanism that sets targets for energy
savings over a specified time frame from end-use EE programs operated by utilities or other
program administrators. State-level screening revealed that 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 mandated targets based on total sales or some
other 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 2009 utility-level sales data from EIA25 and AE02010-based growth
rates to develop baseline forecasts of sales of affected utilities (see Table 1). Because 2010
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 2009 to estimate the affected utility sales as a
share of total sales for 2010. For most states, EPA assumes full achievement of EERS targets for
all years in the compliance period. For some states, EPA does not assume full achievement of
EERS targets in all years because of the way the programs are designed. One example of such a
program is EERSs that have cost/rate caps or other design features (e.g., permitting counting of
savings from building energy codes or historical EE programs) that may not lead to incremental
energy savings consistent with the EERS targets.26 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)Mt)*Z(t-l)
C(t) = A(i] + A(t-l) + ... + A(t-L+l)
/(t) = C(t)-f(t)
Z(t) =
Where:
25EIA(2010c).
26 For more information, go to: http://www.epa.gov/statelocalclimate/state/statepolicies.html.
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r is the annual first-year percent savings target,
A is the annual first-year energy savings,
L is the measure lifetime,
6 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 AE02010 forecast,
/ is the cumulative savings incremental to AE02010 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
C(t) = A(t) + A(t-l) + ... + A(t-L+l)
/(t) = C(t)-f(t)
Z(t) =
Where:
A is the annual first-year energy savings target,
L is the measure lifetime,
6 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 AE02010 forecast,
/ is the cumulative savings incremental to AE02010 forecast, and
Z is the adjusted sales after application of cumulative incremental savings.
3) EERS with Cumulative EE Savings Targets Specified in Percent Terms
= C(t)-C(t-l)+A(t-L)
If r(t) available,
C(t) = r(t) * fl(t)
/(t) = C(t)-f(t)
Z(t) =
If r(t) not available,
Z(t) calculated by interpolation
/(t) = fl(t)-Z(t)
C(t) = /(t) + E (t)
Where:
r is the cumulative percent savings target,
A is the annual first-year energy savings,
L is the measure lifetime,
6 is the baseline sales of utilities affected by these specific policies,
C is the cumulative energy savings,
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E is the cumulative savings embedded in the AE02010 forecast,
/ is the cumulative savings incremental to AE02010 forecast, and
Z is the adjusted sales after application of cumulative incremental savings.
4) EERS with Cumulative EE Savings Targets Specified in Absolute Terms
= C(t)-C(t-l)+A(t-L)
If C(t) available,
/(t) = C(t)-f(t)
Z(t) =
If C(t) not available,
Z(t) calculated by interpolation
/(t) = fl(t)-Z(t)
C(t) = /(t) + E (t)
Where:
C is the cumulative energy savings target,
A is the annual first-year energy savings,
L is the measure lifetime,
6 is the baseline sales of utilities affected by these specific policies,
E is the cumulative savings embedded in the AE02010 forecast,
/ is the cumulative savings incremental to AE02010 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 AE02010, no energy savings were estimated and RPS
compliance is modeled through RE resources.
2) Compliance Type and Cost/Rate Caps: Several states have EERSs that use cost-
containment provisions or other design features (e.g., allowing counting of energy
savings driven by building energy codes) that may constrain the ability of EE program
administrators to meet the EERS targets with incremental savings. The EPA identified
seven states with such design features - Arizona, Illinois, Maryland, Michigan,
Minnesota, Ohio, and Texas - and relied on available, state-specific academic reports27,
27Satchwell(2011).
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integrated resource plans28, and other studies29 to make downward adjustments to the
nominal EERS targets to reflect these design features.30
3) "All Cost-effective EE" Targets: Seven states-Connecticut, Maine, Massachusetts, New
Mexico, 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. In states with an "all cost
effective EE" target without an EERS target through 2020, EPA estimated savings based
on utility plans31 and EE resource potential studies32.
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.33
Public Benefit Funded Energy Efficiency Programs
The EPA estimated EE savings for public benefit funded EE programs. Data for these EE
programs are mainly available in terms of program expenditures, 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 for 2010 from state
publications34 or utility surveys,35 and projected funding for each future year as equal to the
funding for 2010.36 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 *(l+d)L)/((l+d)L -1)
28 AEP TCC (2010), AEP TNC (2010), Ameren Illinois (2010), CenterPoint (2010), ComEd (2010), EPE (2010), Entergy
(2010), Oncor (2010), SWEPCO (2010), TNMP (2010), Xcel (2010).
29 Good Company Associates (2010).
30 For more information, go to: http://www.epa.gov/statelocalclimate/state/statepolicies.html.
31 CT Utilities (2010), MDPU (2010), National Grid (2008), EERMC (2010), VEIC (2009).
32 KEMA (2010), NWPCC (2010)
33 For more information, go to: http://www.epa.gov/statelocalclimate/state/statepolicies.html
34 NHEU (2009), NJ BPU (2009).
35 CEE (2010).
36 In the case of New Jersey, total funding data for the NJ Clean Energy Program were available for 2010, 2011
and 2012. Though the share of total program funding projected to be spent on EE ranged from about 77 percent
to 85 percent in these three years (NJ BPU 2008), EPA made a conservative assumption that only 50 percent of
total funding will be allocated to EE programs. The EPA projected energy efficiency funding for each future year as
equal to the funding for 2012.
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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
(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.37 The
average LCSE of $0.025/kWh was used as the default LCSE where state-specific estimates were
not available. 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
38
State Levelized Cost of Saved Energy39
California
Connecticut
Iowa
Massachusetts
Minnesota
Nevada
New Jersey
New Mexico
New York
Oregon
Rhode Island
Texas
Vermont
Wisconsin
Default (simple average)
$0.029
$0.028
$0.017
$0.031
$0.021
$0.019
$0.026
$0.033
$0.019
$0.016
$0.030
$0.017
$0.027
$0.033
$0.025
The EPA estimated energy savings from ratepayer-funded programs in each year (t) using the
following formulas:
CRF=(d*(l+d)L)/((l+d)L-l)
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.
38 Source: ACEEE (2009a), Table 1.
39 LCSE is based on program administrator costs, not on total resource costs.
J-17
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= (F(t) * CRF)/LCSE(t)
C(t) = A(t) + A(t-l) + ... + A(t-L+l)
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, and
C is the cumulative energy savings.
Energy Efficiency Programs Funded by the Regional Greenhouse Gas Initiative
The EPA estimated savings from Regional Greenhouse Gas Initiative (RGGI)-funded EE programs
for the states of Delaware, New Hampshire and New Jersey.41 The seven other RGGI states
have EERSs and RGGI-funded EE improvements count towards their EERS goals.
The EPA also estimated RGGI-funded savings using state-level estimates of program funding
and costs of saved energy. The EPA estimated total RGGI proceeds available to each state in
each year during the policy period by using forecasted allowance prices and carbon dioxide
emissions.42 RGGI states have agreed to allocate at least 25 percent of their shares of RGGI
auction proceeds to a consumer benefit or a strategic energy purpose.43 To date, states have
allocated 52 percent of proceeds to improve EE.44 Proceeds are allocated according to state
laws, and the States of Delaware, New Hampshire and New Jersey have explicitly adjustable
allocations45 or have recently diverted RGGI proceeds for purposes other than RE, EE and direct
consumer assistance.46
The EPA made a conservative assumption that 25 percent of each state's proceeds in each year
are used to fund EE programs. B;
$0.026/kWh for the New Jersey.
are used to fund EE programs. Based on information from ACEEE,47 EPA used an LCSE of
Consistent with the assumptions used to estimate savings from ratepayer-funded programs,
EPA used a default LCSE of $0.025/kWh for the States of Delaware and New Hampshire, and a
40 In the case of New Hampshire, NHEU (2009) makes lifetime savings estimates, so EPA did not estimate them
using this formula.
41 The New Jersey Department of Environmental Protection notified RGGI Inc. in May 2011 that NJ would withdraw
from RGGI effective December 31, 2011 and not participate in allowance auctions post 2011. As a result, annual
incremental savings associated with NJ's participation in RGGI allowance auctions terminate after 2011. See
http://www.rggi.org/docs/New Jersey Letter.pdf.
42ICF (2010).
43 RGGI (2005).
44 RGGI (2011).
45 Delaware State Senate (2008).
46 Nashua Telegraph (2010).
47 ACEEE (2009a).
J-18
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discount rate of five percent for all states. The EPA did not assume decay of savings during
measure life, so savings for each year are equal to the lifetime savings averaged over the
measure lifetime.
The EPA estimated energy savings from RGGI-funded programs in each year (t) using the
following formulas:
CRF = (d *(l+d)L)/((l+d)L -1)
X\(t) = (F(t) * CRF)/LCSE(l)
C(t) = A(t) + A(t-l) + ... + A(t-L+l)
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, and
C is the cumulative energy savings.
Draft Methodology for Generating State -Adjusted Forecast that Reflects Incremental
Energy Savings
The EPA estimated energy savings that are incremental to the reference case (AE02010) by
subtracting cumulative savings embedded in AE02010 from total savings from EERSs,
ratepayer-funded programs and RGGI-funded programs:
/(t) = C(t)-f(t)
Where:
C is the cumulative energy savings,
E is the cumulative savings embedded in the AE02010 forecast and
/ is the cumulative savings incremental to AE02010 forecast.
The state-adjusted electricity sales forecast includes the impact of EE savings
that are incremental to the BAD reference case. State-level adjusted sales (Z)
are calculated as:
Z(t) = fl(t)-/(t)
Where:
6 is the baseline total sales and
/ is the cumulative savings incremental to AE02010 forecast.
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Important Sources of Uncertainty in the Analysis
In conducting this analysis, EPA used the best available information and generally adopted
conservative assumptions in order to reduce the likelihood of overstating the impacts of the
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.
At this point, EPA would like to highlight two sources of uncertainty that are important to keep
in mind when utilizing these estimates and employing similar methods:
The impacts of state EE policies embedded in the AEO reference case, and
PUC approval of EE program budgets necessary to meet the EERS targets.
As discussed in Section J.3, the AEO reference case likely includes the impacts of some
programs that are not explicitly listed. In this analysis, EPA assumes embedded impacts of state
programs are approximately half of the historical impacts reported to EIA over the latest 3
years for which data are available. In order to understand how this assumption influenced the
results, EPA conducted a sensitivity analysis in which the Agency varied the assumption about
what percentage of historical reported results are embedded in the AE02010 reference case.
The core analysis, which assumed 50 percent of historical savings were embedded, estimated
cumulative, national, incremental energy savings of 2.8 percent in 2020. Under the sensitivity
analysis, EPA utilized the following alternative assumptions: 0 percent, 25 percent, 75 percent,
100 percent of historical savings were embedded. Under these alternative assumptions,
cumulative, national, incremental savings in 2020 are 4.0 percent, 3.4 percent, 2.2 percent, and
1.7 percent, respectively. At the state level, this assumption is influential for states with a
history of reporting significant EE program savings and less influential in other states.
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 J.3, 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 budgets48
and generally good progress with states reporting achievement of EERS targets,49 this will be an
issue EPA tracks in the future as EERS targets increase.
48 CEE (2010).
49ACEEE(2011).
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SECTION J.4: DRAFT METHODOLOGY FOR ESTIMATING PROJECTED PEAK
DEMAND SAVINGS OF ENERGY EFFICIENCY POLICIES
The EPA estimated state-level peak savings as the hourly load impact of EE programs during the
hour of a state's peak energy use.50 In the absence of state-specific information on the timing
of the peak, EPA used the peak hour for each state that was assumed to be the same as the
peak hour from the Integrated Planning Model (IPM) region51 in which a state is located
(based on population) in EPA's Base Case.
Table 5 presents the state-to-IPM-region mapping that was used. Since the load shape data
used in EPA's Base Case were available for 2007, 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 Load Impact Curves of Energy Efficiency Policies" below. For each state, EPA
identified the peak hour for each year on the load impact curve for that year, and took the
corresponding hourly impact as the peak savings.
Table 5: EPA Base Case Regional Mapping for Integrated Planning Model52
State IPM Region
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Hawaii
Illinois
Indiana
Iowa
Maine
Maryland
Massachusetts
Michigan
Minnesota
Montana
Nebraska
New Hampshire
AZNM
ENTG
CA-S
RMPA
NENG
MACE
FRCC
HAWI
COMD
RFCO
MRO
NENG
MACS
NENG
MECS
MRO
NWPE
MRO
NENG
The EPA assumed that EE programs do not shift the peak. The EPA did not perform a dynamic analysis of peak
demand.
51 "Model region" refers to the geographic regions defined for the "EPA Base Case using IPMฎ v.4.10," 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 2010. The peak hour is taken from load shapes used in EPA's Base Case using IPMฎ, which are compiled by
aggregating FERC-714 data to the model region level.
52 US EPA (2010), Introduction.
J-21
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State IPM Region
New Jersey
New Mexico
New York
Ohio
Oregon
Pennsylvania
Rhode Island
Texas
Vermont
Washington
Wisconsin
MACE
AZNM
NYC
RFCO
PNW
MACE
NENG
ERCT
NENG
PNW
WUMS
Draft Methodology for Generating Load Impact Curves of Energy Efficiency Policies
The EPA developed regional load impact shapes by sector to represent typical hourly load
impacts from EE programs. The EPA estimated residential sector and commercial sector impact
shapes for each of the nine U.S. Census Divisions and industrial sector impact shapes for each of
the four U.S. Census Regions. The EPA based the shapes of the impacts on region- and sector-
specific EE program mixes that were developed independently.53 These program mixes were
not intended to represent any particular set of programs in place, but were generic, driven by
considerations including cost-effectiveness to the consumer, which varied mainly due to
regional building population and climate.
54
The EPA scaled the regional EE load impact shapes by sector previously developed based on
state savings shares and total incremental savings by sector in order to develop load impact
curves for this analysis. The implicit assumption was that the EE measures being modeled in
aggregate mirror the bundled measures underlying the original load shapes. The EPA
developed load impact curves for each state for 2010, 2012, 2015 and 2020 using the following
steps.
1) Estimating Shares by Sector of EE Savings
The EPA calculated the average (0) of national savings by sector55 (X) as a share
of national sales by sector56 (Y) for 2007-2009 for the residential (r), commercial
(c) and industrial (i) sectors.
= ((-^c,n,2007/Vc,n,2007)
Load shapes were developed using the Building Energy Analysis Console (Beacon), ICF's proprietary model for
simulating energy consumption by buildings.
54 For more information, go to: http://www.epa.gov/statelocalclimate/state/statepolicies.html.
55 EIA (2008a, 2009a, 2010c).
56 EIA (2008b, 2009b, 2010e).
J-22
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The EPA calculated sales by sector (Y) in 2009 as a share (P) of total residential,
commercial and industrial sales for each state (s).
Prs - V
r,s
r,s,2009
/( VV,s,
2009
Vc,s,2009 + ^1,5,2009)
PC,S = V'c,s,2009/( VV,s,2009 + ^,5,2009 + ^,5,2009)
P\,s = Y\,s,2009/( Vr,s,2009 + ^,5,2009 + ^,5,2009)
The EPA calculated shares 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 * 0|,n)
Qc,s = (Pc,s * Oc,n)/(Pr,s * Or,n + Pc,s * Oc,n + Pi(S * 0|,n)
n - IP * n \HP * n + P * n + P * n ]
(~i\,s ln,s ^\,nll\ir,s uf,n T "c,s uc,n T "i,s u\,nl
Table 6 shows savings shares for each state.
Table 6: Shares of Savings by Sector57
State Share of Savings ( percent)
Residential Commercial Industrial
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Hawaii
Illinois
Indiana
Iowa
Maine
Maryland
Massachusetts
Michigan
Minnesota
Montana
Nebraska
New Hampshire
New Jersey
New Mexico
New York
Ohio
Oregon
Pennsylvania
50.6
51.2
40.4
42.1
47.4
46.6
55.1
40.2
41.4
46.2
43.7
47.1
47.0
45.9
41.7
43.8
43.3
44.0
47.5
40.7
38.0
38.6
45.8
50.5
46.9
43.3
33.0
52.0
46.2
47.7
43.0
42.0
42.6
44.9
32.1
35.6
42.0
49.7
40.0
45.9
42.3
41.4
40.6
45.5
55.0
48.7
57.6
38.6
38.9
39.3
6.1
15.8
7.6
11.7
4.9
10.5
2.9
17.2
13.8
21.7
20.6
10.9
3.3
14.0
12.4
13.9
15.4
15.4
7.0
4.3
13.3
3.8
15.7
10.7
13.7
EIA (2008a), FileS; EIA (2009a), FileS; EIA (2010c), FileS; EIA (2008b), Table 2; EIA (2009b), Table 2; EIA (2010e),
Table 2.
J-23
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State Share of Savings ( percent)
Residential Commercial Industrial
Rhode Island
Texas
Vermont
Washington
Wisconsin
43.1
46.8
47.0
49.8
42.1
51.7
40.8
42.1
38.9
42.2
5.2
12.4
10.9
11.3
15.6
2) Scale Based on Savings Shares by Sector for Each State
The EPA selected the regional residential and commercial hourly EE 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 load impact shapes by sector using the appropriate
shares of EE savings by sector (Q) estimated in Step 1 to develop scaled 8,760
hourly load impacts by sector for each state.
The EPA summed the scaled residential, commercial and industrial 8,760-hour
load impacts by hour to get the total hourly load impact shape of 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 load impact shapes were developed for a year that began on a
Sunday.
The EPA identified the first day of each year of interest, and reconciled the load
impact shapes by determining the least number of days between that day and
Sunday.
o For example, the year 2010 begins on a Friday, and Friday is two days
before Sunday. The year 2020 begins on a Wednesday, and Wednesday
is three days after Sunday.
For each year of interest, EPA shifted the total hourly load impact shape for a
state ahead or behind by the least number of days to ensure that the first day of
the load impact shape corresponded with the first day of the year.
Two years of interest, 2012 and 2020, are leap years. The EPA did not include
the last day of each of these years in the analysis to ensure consistency across
years.
4) Scale based on Total Incremental Savings for Each State
For each year, EPA scaled the shifted and scaled hourly load impacts once more
by multiplying them by the total cumulative incremental savings estimated for
that year. The resulting 8,760 hourly load impacts sum to the total cumulative
incremental savings and represent the load impact shape for the year.
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SECTION J.5: DRAFT METHODOLOGY FOR ESTIMATING RENEWABLE ENERGY
SALES FROM RENEWABLE PORTFOLIO STANDARDS BEYOND WHAT IS
CAPTURED IN ANNUAL ENERGY OUTLOOK 2010
The AE02010 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
(e.g., California, New York), AEO assumes utilities comply with RPS requirements only to the
extent that state funding allows, as described in both the AE02010 and AE02011 assumptions
documents.58
This analysis maintains consistency with these limiting assumptions. The EPA included the RPS
policies for five states (California, Colorado, Delaware, Hawaii, and New York) in this analysis
because they were known to have been excluded from AE02010 (e.g., Hawaii) or revised since
the time of AE02010 forecast development. In this analysis, EPA assumes the incremental RPS
requirements set by these five states are fully achieved, with the noted exception of California
and New York. This analysis captures those limits by adopting the AEO forecast of renewable
generation for the corresponding regions instead of the RPS policies themselves. Specifically,
EPA compared the AE02010 RPS target for California (which was not subject to limiting
assumptions) and AE02010 renewable generation for New York (used in place of the RPS policy
target because of limiting assumptions) to the corresponding regional renewable generation in
AE0201159 (where both states were subject to limiting assumptions). The EIA did not identify
funding limitations for Colorado or Delaware, and EPA assumed their full RPS targets would be
achieved.60 Table 7 presents final RPS targets used in this analysis for the five states for which
EPA identified updated RPS requirements.
The RPS targets as a percent of total sales were available for each year in the policy period for
the States of Colorado and Delaware. The EPA applied these to the State-Adjusted Electricity
Sales Forecasts for the respective states to estimate required RE sales. In the case of Hawaii,
where RPS targets were only available for 2010, 2015, 2020 and 2030, EPA estimated sales in
intervening years by interpolation.
Because RPS targets for California and New York were limited by EIA assumptions as described
above, the table below reflects their targets as equal to the corresponding regional renewable
generation from AE02011. For all states, RPS requirements were frozen in percent terms for
the years after the RPS policy period.
58 'The California and New York programs require state funding, and these programs are assumed to be complied
with only to the extent that state funding allows." EIA 2010d, page 169.
59 AEO2011 was used instead of AEO2010 because the state RPS policies that were revised after AEO2010's
completion were subsequently captured in AEO2011, meaning that the AEO2011 forecast will have taken into
account both the higher RPS targets and the state funding limitations.
60 While Delaware is situated in the RFC-East region, which did not have sufficient renewable generation to meet
its combined RPS target in AEO2011, EIA considers the RPS target to have been satisfied via interregional trading.
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Table 7: Renewable Portfolio Standard Assumptions Made in This Analysis
State RPS General
California
Colorado
Delaware
Hawaii61
New York
28.37
6.94
1.56
1.43
4.83
ion (1,000 GWh)
34.85
10.75
2.49
2.28
4.93
AEO2010 provides a forecast for the continental U.S. only, so impacts of Hawaii's RPS are not included in
AEO2010.
J-26
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