Resolution No. 2010-063RESOLUTION NO. 2010-63
A RESOLUTION OF THE CITY COUNCIL OF THE CITY OF
VERNON ESTABLISHING ANNUAL TARGETS FOR ENERGY
EFFICIENCY SAVINGS AND DEMAND REDUCTION FOR THE
NEXT TEN YEARS AND AUTHORIZING THE SUBMISSION OF
REPORTS RELATING TO ENERGY EFFICIENCY AND DEMAND
REDUCTION IN ACCORDANCE WITH ASSEMBLY BILL 2021
WHEREAS, the City of Vernon ("City") is a chartered municipal
corporation of the State of California that owns and operates a system
for the generation, purchase, transmission, distribution and sale of
electric capacity and energy; and
WHEREAS, Assembly Bill 2021 (Levine) ("AB 2021") was signed
by the Governor and thus enacted as law on September 26, 2006; and
WHEREAS, AB 2021 requires all local publicly owned utilities
to acquire all available energy efficiency and demand reduction
resources that are cost effective, reliable, and feasible; and
WHEREAS, AB 2021, codified in relevant part as California
Public Utilities Code Section 9615, requires each local publicly owned
utility, every three years, to identify all potentially achievable cost
effective electricity efficiency savings and to establish annual
targets for energy efficiency savings and demand reduction for the next
ten year period; and
WHEREAS, Section 9615(c) of the California Public Utilities
Code requires each local publicly owned utility, within sixty days of
establishing annual targets for energy efficiency savings and demand
reduction, to report such annual targets to the State Energy Resources
Conservation and Development Commission and the basis for establishing
those targets; and
WHEREAS, Section 9615(d) of the California Public Utilities
Code requires each local publicly owned utility to submit annually to
its customers and to the State Energy Resources Conservation and
Development Commission a report related to energy efficiency and demand
reduction; and
WHEREAS, Section 9615(e) of the California Public Utilities
Code also requires each local publicly owned utility to submit annually
to the State Energy Resources Conservation and Development Commission a
report describing the sources of funding for its investments in energy
efficiency and demand reduction program investments, the methodologies
and input assumptions used to determine cost effectiveness, and the
results of an independent evaluation that measures and verifies the
energy efficiency savings and reduction in energy demand achieved by
its efficiency and demand reduction programs; and
WHEREAS, the Light & Power Department retained the assistance
of independent energy consultant Navigant Consulting ("Navigant") to
develop an energy efficiency resource model for estimating efficiency
savings potential; and
WHEREAS, Navigant developed the California Publicly Owned
Utility Efficiency Resource Assessment Model ("CalEERAM"), an energy
efficiency potential model designed to estimate technical, economic and
market energy efficiency potential; and
WHEREAS, the model forecasts energy savings and demand
reduction potential within the residential, commercial and industrial
sectors for years 2011-2020; and
WHEREAS, the Light & Power Department evaluated the City's
electric distribution system and identified potential realistic,
achievable energy savings as a result of system modifications and
upgrades; and
WHEREAS, using the data from the report by Navigant and from
-2-
the evaluation of the City's electric distribution system, the Light &
Power Department was able to determine realistic, achievable energy
savings targets for each of the next ten years, which are summarized in
the chart attached as Exhibit A; and
WHEREAS, the City Council has considered the report of the
Light & Power Department staff regarding whether adoption of the energy
savings targets for each of the next ten years presented by the Light &
Power Department is in the best interest of the City; and
WHEREAS, the City Council has concluded that it would be in
the best interests of the City to adopt the energy savings targets for
each of the next ten years presented by the Light & Power Department.
NOW, THEREFORE, BE IT RESOLVED BY THE CITY COUNCIL OF THE
CITY OF VERNON AS FOLLOWS:
SECTION 1: The City Council of the City of Vernon hereby
finds and determines that the recitals contained hereinabove are true
and correct.
SECTION 2: The City Council of the City of Vernon hereby
further finds and determines as follows:
A. That adoption of the annual energy efficiency savings and
demand reduction targets for the next tens years is necessary to allow
the City to fully comply with California law as enacted by AB 2021; and
B. The proposed annual energy efficiency savings and demand
reduction targets for the next ten years will allow the City to improve
its energy efficiency and reduce demand for energy on the City's electric
system.
SECTION 3: The City Council of the City of Vernon hereby
adopts the annual energy efficiency savings and demand reduction
targets for the next ten year period as presented by the Light & Power
-3-
Department in the chart attached as Exhibit A.
SECTION 4: The City Council of the City of Vernon hereby
authorizes the Light L Power Department to report the annual energy
efficiency and demand reduction targets adopted hereby to the State
Energy Resources Conservation and Development Commission, as required
by AB 2021, and to prepare and distribute annually the reports required
pursuant to Section 9615 of the California Public Utilities Code.
SECTION 5: The City Council of the City of Vernon hereby
authorizes the City Administrator, or his designee, to take whatever
actions are deemed necessary or desirable for the purpose of
implementing and carrying out the purposes of this Resolution and the
actions herein approved or authorized.
SECTION 6: The City Clerk of the City of Vernon shall
certify to the passage, approval and adoption of this resolution, and
the City Clerk of the City of Vernon shall cause this resolution and
the City Clerk's certification to be entered in the File of Resolutions
of the Council of this City.
APPROVED AND ADOPTED this 24th day of May, 2010.
Name: Hilario Gonzales
Title: Mayor
ATT T:
Willard G. Y gu hi ity Clerk
STATE OF CALIFORNIA )
) ss
COUNTY OF LOS ANGELES )
I, Willard G. Yamaguchi, City Clerk of the City of Vernon, do
hereby certify that the foregoing Resolution, being Resolution
No. 2010-63, was duly passed, approved and adopted by the City Council
of the City of Vernon at a regular meeting of the City Council duly
held on Monday, May 24, 2010, and thereafter was duly signed by the
Mayor or Mayor Pro-Tem of the City of Vernon.
Executed this e?6 day of May, 2010, at Vernon, California.
C
llard ma u hi, City Clerk
(SEAL)
-5-
EXHIBIT A
City of Vernon
Exhibit A
Energy EtWency Prapram TBrg@"
uos6
eeue
4Aoo
2611 2.12 H1. 2414 201a xale 2017 2030 2619 2020A
MWII 9"
7,063 7,932 6,455 . 9,766 10,716 9,4H BA73 6,967 6,007
%a11,0e4FOrewll 0.64%
0.61% 0.61%. OAG% 0.74% 0.80% 0.70% 0.59--A OM% 0.43%.
Technical Potential
Eaerp Fok.W (Mwb)
-m11
2012 2013. i-. 2114` ; 1015- ; *919 ' - 2117 2019 .. -
Deddladal
49
- 42 43 43 44 44 45 46 46 47
No.-DWd1atN1
298,4B6
30D,784 303" MAN 309,777 312,823 317,2T/ 322,036 326,8% 331,769
71ds1ARD.11,11mv
798035
3MA27 303" 3051l57 3UM21 312,867 317,322 322A81 3M932 331011
1p a tolU0lty Fw Lo
23.69%
23.49% 2333% 23.29% 23.42% 23.33-% 23.45% 2347-- 23.47Y. 23A7%
Da"Od► les"(M)
ill}
2032 2013 T+114 2015 2116 2017 ]918 -2119. - 2020
11"Madol
18
17 !8 18 18 18 )a 19 19 19
Nm-Rcddmslil
56,313
56AM 57,207 57,642 58,389 5,,963 59l01 00,700 61,610 6U35
7bhl AY D.nbap
56.331
56,712 57,224 57AN 58,407 58,982 59,111 $0,712 61,039 42AW
hlcend Udlky F.Mmw
27.60%
27Z7%. 26.99% 26.94% 27.17% 27.06% 27.19% 2723% 27.15% 27.20%
Economic Potential
D.e.lp Pokadal WWI)
2011
2612 2023: 2014 : 2615. - 2616 : 2017 -. 201t An 2020
Relidladd
42
35 35 36 36 37 37 38 38 39
11e.-Reslde.d.l
291,529
293,776 296,430 298,688 302.$58 305,533 3M.U4 314,332 319,250 324.039
TWO All D611dlaw
291,631
2931811 2%466 293,723 30,095 305,78 3119,921 M4,469 319,= 314,M
Tase.torRddty FOUM
23.14%
22.95% 22.78Y. 22,75% 22,97% 2210% 229D% 2292% 2297Y. 22.92%
Dem"wl Poksd.l (kY.�
2111.
2612 2013 2014 1615 2016 2017 ton 2019 2628 -
ALW11.111iN
9
8 8 8 a 8 8 9 9 9
NO. RVddead.l
54,868
55,226 55.725 56,149 56.rn 37,436 58.254 59.129 60,015 60,915
7.1.1 An "MO
54,877
55,2M 39,733 56,158 56,885 57,445 56,263 59,136 WJW 60,924
PeleralerDtwty I,—cw
26.09%
20,55% 26.29Y. 26,74% 26.46% 2635% 26.48% 26.52% 26.44% 2649%
Market Potential -Recommended Target for Council Approval
Seely Foleadal (MtV►)
- .Srepy
2011
2012 2013 2024 2013 U16 2017 2018 2019 MW
Dr21de tial
0
d, 0 0 0 0 0 0 0 0
No6-D4.W-w
8.020
7,863 7,992 9,655 9,766 1%716 9,468 8,073 6,962 6A87
7bul AN D.H4hw
8,020
7.163 7,992 61655 9,766 16,716 9t10 e,073 6.%2 f.007
pmal or valy Fs1e1m1
064%
0.61% 0.61% 066% 034% 0.80% 0.70% 0.59% OSO% 0.43%
Delse6d FOkadl (IA1)
Beefier
2a11 -..
2012 2013 2014 2015 2914 2017 2018 20U 2670
Reddeodal
0
0 0 -0 0 0 0 0 0 0
N00-Reddeetlal
973
958 981 1,070 1,205 1,317 1,178 2,023 900 804
Thal All%Oldl.p
973
958 "1 1,070 1,705 1,317 1,178 1,%3 9w 864
Yqr torllddq• FOree617
0.48%
0.46% 0.46% 0.50% 0.56% 0.60% 454% 0A61A 0.40%035%
Market Energy Potential
0
1011 2e12 2013 1014 201t 2010 2017 2016 2010 2M0
r Residenin.1 aN0n•RwWmgal t 70tal All Bundlnpt
Ha►ket Demand Potential
SAW T—
S.w
1,600
- 1
soe
z
,ON
i
y
O03e 2012 2017 W14 .016 =16 2017 L16 20to
Residan1,161 c Non-ResideMlai ft Ulal All Buildings
CITY CLERK'S OFFICE
INTEROFFICE MEMORANDUM
DATE: May 25, 2010
TO: Donal O'Callaghan, City Administrator/Director of Light & Power
FROM: Willard Yamaguchi, City Clerk 4T
RE: Resolution No. 2010-63 — A Resolution of the City Council of the City of Vernon
Establishing Annual Targets for Energy Efficiency Savings and Demand Reduction for the
Next Ten Years and Authorizing the Submission of Reports Relating to Energy Efficiency
and Demand Reduction in Accordance with Assembly Bill 2021
Transmitted herewith is a copy of Resolution No. 2010-63 referenced above, which was approved by City
Council on May 24, 2010.
Thank you.
WY:dj
c: Resolution No. 2010-63
RECEIVED
u��� „ ,•4a flJ7U /3
J-.
r
STAFF REPORT
LIGHT' & POWER
DATE: May 17, 2010 (�R 4;;1
TO: Honorable Mayor and City Council
MAY 2 0 2010
CITY CLERK'S OFFICE
MAY t 7 2010
FROM: Donal O'Callaghan, Director of Light & Power
RE: Electric Energy and Demand Savings First Triennial Update in
Accordance with Assembly Dill AB-2021 for the 10-Year Period of
2011-2020
Purpose:
Assembly Bill 2021 requires each publicly owned utility ("POU") to identify all potentially
cost-effective energy efficiency saving and establish annual target for energy efficiency
savings and demand reduction for the next 10-year period. The statue also requires
POU's to initially report the adopted targets to the California Energy Commission by
June 1st, 2007 and by June 1st of every third year thereafter.
In compliance with AB 2021, in order to meet the triennial requirement for updating
targets, California Municipal Utility Agency (CMUA), in partnership with Northern
California Power Authority (NCPA) and Southern California Public Power Authority
(SCPPA), initiated a collaborative framework for 36 publicly owned utilities including the
City of Vernon to analyze market potential and update individual utility program targets.
A consultant, Navigant Consulting (formerly Summit Blue Consulting), was hired to
develop an energy, efficiency resource model for estimating efficiency saving potential.
The consultant developed the California Publicly Owned Utility Efficiency Resource
Assessment Model (CalEERAM), an energy efficiency potential model designed to
estimate technical, economic and market energy efficiency potential. The model
forecasts energy savings and demand reduction potential within the residential,
commercial and industrial sectors for years 2011.2020. Customized versions of the
model were created for each of the 36 publicly owned utilities.
The analysis performed for the City of Vernon focused on the Market Potential energy
efficiency savings which determined the annual electricity and demand reduction saving
targets based on specific statistics mentioned in the forecast model. The recommended
targets for the City of Vernon are attached to this staff report as Exhibit A.
The Market Potential is an estimate of the portion of the economic energy efficiency
potential that could be attributed to a utility energy efficiency program, recognizing the
Date:
To:
From:
INTEROFFICE����
Light & Power Department
May 17, 2010
Donal O'Callaghan
Director of Light and Power
MEMORANDUM
Abraham Alemu A -A—
Electric Resources Planning & Development Manager
SUBJECT: Electric Energy and Demand Savings First Triennial Update in
Accordance with Assembly Bill AB-2021 for the 10-Year Period of
2011-2020
Assembly Bill 2021 requires each publicly owned utility ("POU") to identify all potentially
cost-effective energy efficiency saving and establish an annual target for energy efficiency
savings and demand reduction for the next 10-year period. The statue also requires
POU's to initially report the adopted targets to the California Energy Commission by
June 15t, 2007 and by June Vt of .every third year thereafter. Attached for City Council
approval is Electric Energy and Demand Savings First Triennial Update in Accordance
with Assembly Bill AB-2021 for the 10-Year Period of 2011-2020.
The initial ten-year annual target was prepared by Marketable Engineered Projects, LLC
("MEP,LLC") in 2008 and approved by Resolution No. 9546. Staff requests a similar
resolution to be prepared by the City Attorney's office.
DO:AA
Attachments
c: Abraham Alemu
Document Control
ACI IVED
MAY 1 7 2010
BY: 3:.�
Staff Report- May 17, 2010
Mayor and City Council
Page 2
effect of a limited set of market barriers. Market energy efficiency potential is modeled
to vary with specific parameters, such as the magnitude of measure incentives and
customer awareness and willingness to adopt energy efficient measures.
Compared to the initial ten-year annual target prepared by Marketable Engineered
Projects, LLC ("MEP, LLC") in 2008 and approved by Resolution No. 9546, the current
analysis recommends higher energy and demand saving targets per year. The initial
target prepared by Marketable Engineered Projects, LLC ("MEP, LLC') was based on
historic energy audits conducted for businesses within the City limit. However, the
analysis performed by Navigant Consulting is a comprehensive study employing
updated statistics which resulted in higher energy & demand savings goals.
Attached for references purposes is a description of the model developed by Navigant
Consulting to estimate energy efficiency program targets. The results for the City of
Vernon are labeled Exhibit A and are attached to the document.
Recommendation:
Staff recommends the following:
a The City Council establish the recommended Electric Energy and. Demand
Savings First Triennial Update prepared in accordance with Assembly Bill AB-
2021 for the 10-Year Period of 2011-2020
• The City Council authorize the L&P Department to submit to the CEC the
resolution, the Staff Report, and Exhibit A to the Staff Report to the California
Energy Commission
Fiscal Impact:
The program is funded as part of the Public Benefits Program.
DO:AA:eo
DRAFT
RESOLUTION NO.
A RESOLUTION OF THE CITY COUNCIL OF THE CITY OF
VERNON ESTABLISHING ANNUAL TARGETS FOR ENERGY
EFFICIENCY SAVINGS AND DEMAND REDUCTION FOR THE
NEXT TEN YEARS AND AUTHORIZING THE SUBMISSION OF
REPORTS RELATING TO ENERGY EFFICIENCY AND DEMAND
REDUCTION IN ACCORDANCE WITH ASSEMBLY BILL 2021.
WHEREAS, the City of Vernon ("City") is a chartered municipal
corporation of the State of California that owns and operates a system
for the generation, purchase, transmission, distribution and sale of
electric capacity and energy; and
WHEREAS, Assembly Bill 2021 (Levine) ("AB 2021") was signed
by the Governor and thus enacted as law on September 26, 2006; and
WHEREAS, AB 2021 requires all local publicly owned utilities
to acquire all available energy efficiency and demand reduction
resources that are cost effective, reliable, and feasible; and
WHEREAS, AB 2021, codified in relevant part as California
Public Utilities Code Section 9615, requires each local publicly owned
utility, every three years, to identify all potentially achievable cost
effective electricity efficiency savings and to establish annual
targets for energy efficiency savings and demand reduction for the next
ten year period; and
WHEREAS, Section 9615(c) of the California Public Utilities
Code requires each local publicly owned utility, within sixty days of
establishing annual targets for energy efficiency savings and demand
reduction, to report such annual targets to the State Energy Resources
Conservation and Development Commission and the basis for establishing
those targets; and
WHEREAS, Section 9615(d) of the California Public Utilities
Code requires each local publicly owned utility to submit annually to
its customers and to the State Energy Resources Conservation and
Development Commission a report related to energy efficiency and demand
reduction; and
WHEREAS, Section 9615(e) of the California Public Utilities
Code also requires each local publicly owned utility to submit annually
to the State Energy Resources Conservation and Development Commission a
report describing the sources of funding for its investments in energy
efficiency and demand reduction program investments, the methodologies
and input assumptions used to determine cost effectiveness, and the
results of an independent evaluation that measures and verifies the
energy efficiency savings and reduction in energy demand achieved by
its efficiency and demand reduction programs; and
WHEREAS, the Light & Power Department retained the assistance
of independent energy consultant Navigant Consulting ("Navigant") to
develop an energy efficiency resource model for estimating efficiency
savings potential; and
WHEREAS, Navigant developed the California Publicly Owned
Utility Efficiency Resource Assessment Model ("CalEERAM"), an energy
efficiency potential model designed to estimate technical, economic and
market energy efficiency potential; and
WHEREAS, the model forecasts energy savings and demand
reduction potential within the residential, commercial and industrial
sectors for years 2011-2020; and
WHEREAS, the Light &Power Department evaluated the City's
electric distribution system and identified potential realistic,
achievable energy savings as a result of system modifications and
upgrades; and
-2-
WHEREAS, using the data from the report by Navigant and from
the evaluation of the City's electric distribution system, the Light &
Power Department was able to determine realistic, achievable energy
savings targets for each of the next ten years, which are summarized in
the chart attached as Exhibit A; and
WHEREAS, the City Council has considered the report of the
Light & Power Department staff regarding whether adoption of the energy
savings targets for each of the next ten years presented by the Light &
Power Department is in the best interest of the City; and
WHEREAS, the. City Council has concluded that it would be in
the best interests of the City to adopt the energy savings targets for
each of the next ten years presented by the Light & Power Department.
NOW, THEREFORE, BE IT RESOLVED BY THE CITY COUNCIL OF THE
CITY OF VERNON AS FOLLOWS:
SECTION 1: The City Council of the City of Vernon hereby
finds -and determines that the recitals contained hereinabove are true
and correct.
SECTION 2:' The City Council of the City of Vernon hereby
further finds and determines as follows:
A. That adoption of the annual energy efficiency savings and
demand reduction targets for the next tens years is necessary to allow
the City to fully comply with California law as enacted by AB 2021; and
B. The proposed annual energy efficiency savings and demand
reduction targets for the next ten years will allow the City to improve
its energy efficiency and reduce demand for energy on the City's electric
system.
SECTION 3: The City Council of the City of Vernon hereby
adopts the annual energy efficiency savings and demand reduction
-3-
targets for the next ten year period as presented by the Light & Power
Department in the chart attached as Exhibit A.
SECTION 4: The City Council of the City of Vernon hereby
authorizes the Light & Power Department to report the annual energy
efficiency and demand reduction targets adopted hereby to the State
Energy Resources Conservation and Development Commission, as required
by AB 2021, and to prepare and distribute annually the reports
required pursuant to Section 9615(d) and (e) of the California Public
Utilities Code.
SECTION 5: The City Council of the City of Vernon hereby
authorizes the City Administrator, or his designee, to take whatever
actions are deemed necessary or desirable for the purpose of
implementing and carrying out the purposes of this Resolution and the
actions herein approved or authorized.
SECTION 6: The City Clerk of the City of Vernon shall certify to
the passage, approval and adoption of this resolution, and the City
Clerk of the City of Vernon shall cause this resolution and the City
Clerks certification to be entered in the File of Resolutions of the
Council of this City.
APPROVED AND ADOPTED this 24th day of May, 2010.
Name:
Title: Mayor / Mayor Pro-Tem
ATTEST:
Willard G. Yamaguchi, City Clerk
STATE OF CALIFORNIA )
) as
COUNTY OF LOS ANGELES )
I, Willard G. Yamaguchi, City Clerk of the City of Vernon, do
hereby certify that the foregoing Resolution, being Resolution
No. was duly passed, approved and adopted by the City Council
of the City of Vernon at a regular meeting of the City Council duly
held on Monday, May 24, 2010, and thereafter was duly signed by the
Mayor or Mayor Pro-Tem of the City of Vernon.
Executed this day of May, 2010, at Vernon, California.
(SEAL)
Willard G. Yamaguchi, City Clerk
16'1
Exhibit A
City of Vernon
Energy Efflclency Program Targets
12,000
6,000
—rsrm�-
4,000.
..
2.000
_.. `
2011 2012 2013 2014 =IS 2010 2017 2018 Wig 2020
MWR
9,020
i
7^3 7,992 9,655 0,766 10,716 9,468 9A73 6,962 6,087
%of Land FOreeasl
0.64%
0.61% 0.61% 0.66% 0.74% 0.8054 0.70% 0,59% OSO% 0:43%
Technical Potential
Energy PaRngal(1NWh) "
SeciW I(
2011
2612 2013 ".._2014 i 2015.. 2016 32017 1 2018 2019 1:.2020 -;
Residential
49
42 43 43 44 44 45 46 46 41
Non-Residendd
298,486
300,794 303.502 305.814 309,777 112,823 317,277 322,036 3AS66 331.769
Total All Buildings
298PS
300,827 303,545 305JU7 309,821 312,867 317,322 322.081 326912 331,816
Peraentuf U$lity Fa.ccast
23.69%
23.49% 23.33% 23.29% 23A2% 23.35% 23.45% 23.47% 23.47% 23,47%
Demand Potential (M)
" Sector .`
1,011
. 2012` 2813 1 '4114 2015 2016 2011 2018 2019 2610 '
Residential
18
17 Is 18 IS 18 18 1') 19 19
Non -Residential
36,313
56,694 57,207 57,642 58,389 58.963 59,803 60,700 61,610 62,535
Total All Baildinp
56,331
56,712 57,224 51,660 S8,407 59,982 59,821 60,719 61,629 62,553
Percentof Uglily Foreeast
21.60%
2727% 26.99% 26.94% 27.17% 27.06% 27.19% 27.23% 27.15% 27.WS
Economic Potential
Energy Potential 1MW id
... Seel or
,2011
2012+ 2013 :; 2014 2015`` 2016 2017" 2MB. 201l, 2020 -.'.
Reddeatid
42
35 35 36 36 37 37 38 38 39
Non -Residential
291,589
293,776 296.430 298,698 302,SSS 305.533 309,084 314,532 519.250 324,039
Total All Buildinga
291,631
293All 296,466 298,723 302'995 305,570 389,921 314,569 319,788 324,077
Percent of Utility Fereeasl
23.14%
22.95% 22.78% 22,75% 22.97% 22.80% 22.90% 22.92% 2292% 22925,
Demand Potential (Bid)
Stator.
2011
2012 2013 2014 201S-2016 2017 2610 - 2019 (202D'
Beddentiol
9
8 8 8 8 8 8 9 9 9
Non-Resldeadal
54,963
55,226 55725 56,149 56.877 57,436 18.254 59.129 60,015 60,915
Tout All Building,.
541077
55,234 S5,733 56,159 56AS3 51,445 59,263 59,136 60,023 40,924
Ferceatefulilily Forecast
26.89%
26.5P°/. 26.29% 26,24%. 26.461/6 26.35% 26,18% 26.52% 26,44% 26,49%
Market Potential
-Recommended
Target for Council Approval
Energy Potential (MWb)
' Sector =
-.: -2011
2012 201J :2014 2013 2016 '2017 101& 2019 `_ 2020
Residential
0
0 0 0 0 0 0 0 0 0
Non-It"wengal
8.020
7,863 7,992 3,655 9,766 10,716 9,468 9,073 6,962 6.097
Told All Building.
8,020
7,863 7992 8.655 9,766 10,716 9068 8,073 6,962 6,087
Percentof Uglily Forecast
0.64%
0.61% "1% 0.66% 0.74% 090% 0.70% 0.59% 0.50°/a 043%
Demand Potential (kW)
Sour -
2011
2012'. 2013 `. 2014 201S-- 2ry16 -. t1017 2018 2019 2020-'
Residential
0
0 0 0 0 0 0 0 0 0
Non-Residen1131
973
958 981 1.070 1,205 1,317 1,179 1,023 900 804
Total All Oulldings
973
959 991 1,070 1,305 I,)17 1,179 1,023 900 804
Percentof Utlllty Forecast
0.48%
0.46% 0.46-A 0.5036 0.56% 0.60% 0.54% 0,46% 0A0% 0.35%
Market Energy Potential
l7 000
10,000
6,000
1 6Aoa
4,000
2,000
o
2011 2012 2013 2014 2015 2018 2017.1 20Fe 2019 2020
$Residential ®NOn•Reeldentlal 0 Total All Buildings
Energy Efficiency Program Targets
CalEERAM is an Excel spreadsheet model based on the integration of energy efficiency measure impacts and
costs, utility customer characteristics, utility load forecasts, and utility avoided costs and rate schedules.
Excel is used as the modeling platform to provide transparency to the estimation process. Using Excel also
allows the model to be customized to each client's unique characteristics and can accommodate their ability
to provide specific model input data. The model utilizes a "bottoms -up" approach in that the starting points
are the study area building stocks and equipment saturation estimates, forecasts of building stock decay and
new construction, energy efficiency technology data, past energy efficiency program accomplishments, and
decision maker variables that help drive the market scenarios. CalEERAM does not estimate annual market
energy efficiency potential based on a diffusion curve, instead the model calculates market potential based
on a decision maker adoption rate algorithm.
CalEERAM estimates energy efficiency resource potential for three perspectives. Each perspective provides
"net" estimates of resource potential:
• Technical energy efficiency potential represents the amount of energy efficiency savings that could
be achieved when not considering economic and market barriers to customers' installing energy
efficiency measures. Technical potential is calculated as the product of the energy efficiency
measures' savings per unit, the quantity of applicable equipment in each facility, the number of
facilities in a utility's service area, and the measure's current market saturation. Technical potential
estimates include energy efficiency measures that may not be cost effective, and technical potential
does not consider market barriers such as customers' lack of awareness of or willingness to
implement energy efficiency measures. Technical energy efficiency potential estimates, while not
realistically obtainable, are used to establish the outer boundary of what can be achieved through
energy efficiency programs.
• Economic energy efficiency potential represents the portion of the technical energy efficiency
potential that is "cost-effective," from a societal perspective, as defined by the Total Resource Cost
(TRQ test. Economic potential does not consider market barriers that limit a voluntary utility
efficiency program's success in encouraging customers to install energy efficiency measures.
• Achievable energy efficiency (Market) potential is an estimate of the,portion of the economic
energy efficiency potential that could be attributed to a utility energy efficiency program,
recognizing the effect of a limited set of market barriers. Market energy efficiency potential is
modeled to vary with specific parameters, such as the magnitude of measure incentives and
customer awareness and willingness to adopt energy efficient measures.
Within the achievable energy efficiency potential assessment, the individual measures are modeled by
expected type of energy efficiency program design. Three program design options are included in
CalEERAM: Replace on Burnout, Retrofit, and New Construction. Replace on Burnout (ROB) means that an
energy efficiency measure is implemented only after the existing equipment fails. An example would be
purchasing an energy efficient clothes washer after the existing clothes washer fails. Retrofit (RET) means
that an energy efficiency measure could be implemented immediately. For instance, installing a low flow
showerhead is usually implemented before the existing showerhead fails. Replacing incandescent lamps
may be an RET but can be treated as a ROB because of the relatively short lifetime for incandescent bulbs.
New Construction (New) means that a measure is installed when the building is first constructed. Baseline
technologies for new construction measures may be different than those for RET, with different energy
impacts and incremental technology costs. Additionally, the incremental implementation costs for new
construction measures are often lower than RET because, in new construction, a technology is being
installed, regardless of efficiency, and the incremental cost to install the efficient version is typically small.
Within CaIEERAM, several financial tests and calculations are performed. The primary test is the Total
Resource Cost (TRC) test. The present value of avoided costs (the benefits) is divided by the technology cost
and the program administrative costs. A TRC value greater than or equal to 1.0 indicates that the resource is
cost effective. CalEERAM utilizes the TRC test to identify which of the technically achievable measures are
economically achievable. Measures with a TRC of 1.0 or higher are included in economic potential. The
model allows for, under limited conditions, certain measures to be included (or excluded) in the economic
potential regardless of its cost effectiveness. The model also calculates several additional cost effectiveness
parameters: the Utility Cost Test, the Participant Cost Test, simple customer payback, and levelized measure
costs. Simple customer payback is used in the model's decision maker adoption rate algorithm. The
payback calculation takes measure cost less the incentive received and divides it by first year energy bill
savings. The energy efficiency supply curves are based on the energy efficiency measure cost per kWh
levelized over the lifetime of the measure. It is calculated by multiplying energy efficiency measure costs by
the Capital Recovery Factor (CRF) then dividing by the first year kWh savings.
CalEERAM Model Inputs and Outputs
Figure 1 illustrates the flow of information in and out of CaIEERAM. The model can be segregated into three
sections.
• Utility Service Area Inputs:
— Utility specific information on rates, avoided costs, load and building stock forecasts, and
historical levels of DSM achievement.
— Customer data including building/equipment characteristics, decision maker awareness of
conservation measures and if aware, willingness to install.
— Technology data including measure level impacts and costs, measure life, incentive levels,
administrative costs, and net -to -gross estimates.
• Model Calculations:
— Develop Technical Potential based on the inputs above.
2
— Develop Economic Potential by screening Technical Potential with the TRC test.
— Develop Market Potential based on available economic potential, calibration targets, and
the decision adoption methodology, detailed in the sections below.
• Model Outputs:
— Tables and graphs on Technical, Economic, and Market Potentials.
-- Both cumulative and incremental market potential estimates by planning year. The
incremental values are used to define annual goals.
— Both cumulative and incremental administrative and incentive cost estimates by measure
and planning year
— Market Potential supply curves.
3
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Utility Service Area Inputs
Navigant relied on a number of data sources for model inputs. Input data are grouped as follows:
e Rate schedules by sector — Rate schedules were provided by each utility.
• Avoided costs — For most utilities, the avoided costs were taken from the 2010 version of the E3
Reporting Tool used by publicly owned utilities for reporting their energy efficiency programs. A
few utilities provided their own estimates of avoided costs.
• Energy and demand forecasts - Energy and demand forecasts were provided by each utility.
Discount Rate Discount rates were provided by each utility. In general, the discount rates used
are the same as used in the E3 Reporting Tool.
• Inflation Rate —The average inflation rate from January 2000 to September 2009.as reported by
the US Bureau of Labor Statistics.
s Residential Housing Stock— Baseline estimates of residential building stocks were generally
provided by each utility. For a few small utilities, estimates were based on an average use per
dwelling from similar utilities in the same climate zone. Splitting housing stock between single
family and multi -family varied by utility. Some utilities had baseline estimates, but most utilities
did not. For those who did not, census data was used.
• Non -Residential Building Stock — Only one utility had specific information of building square
footage. For the remainder, the following technique was used:
-- Shares of non-residential sales by building type were developed from the utility "Quarterly
Fuels and Electricity Report" submittals to the California Energy Commission (CFC). These
submittals had sales identified by the North American Industry Classification System
(NAICS) code.
— Average use per thousand square feet of floor space by building type and climate zone
was obtained from the California Commercial End -Use Survey' (CEUS).
— Dividing the sales per NAICS by the average use per thousand square feet created the
baseline estimates of non-residential building floor space.
e Residential Sector HVAC and Water Fuel Shares - Several utilities provided fuel share estimates.
For the remaining utilities, the fuel share estimates were derived from the California Residential
Appliance Saturation Survey.
`The California Commercial End -Use Survey, prepared by Itron, Inc., prepared for the California Energy
Commission, March 2006.
2 'California Statewide Residential Appliance Saturation Study', prepared by KEMA-Xenergy, prepared for
the California Energy Commission, June 2004.
5
Estimates of Administrative Costs/kWh = A small number of utilities provided their own
estimates. For the remaining, the estimates represent an average of the administrative casts/kWh
as reported in the 2009 CMUA report3.
• Utility Program Accomplishments in FY 2006, FY 2007, and FY 2008 — Based on the three previous
versions of the CMUA report. This data was used for two purposes. First, the sum of FY 2006
through FY 2008 accomplishments was used to update the baseline densities of the efficient
technologies. Second, the annual accomplishments provided guidance on appropriate 2011 target
values.
• Energy Efficiency Measure Impacts, Load Shapes, and Costs — Extracted from the 2010 version of
the E3 Reporting Tool
Baseline Estimates of Technology Density— For both the base technology and the energy efficient
technology, the primary source were the input files used to develop the California Energy
Efficiency Potential Study conducted by Itron for the State of California's Investor Owned Utilities
(IOUs) in 20084. ,For the City of Alameda, some of these baseline densities were modified based
on a combination of a telephone survey of customers, conducted by Navigant, and a detailed
discussion regarding saturation estimates between Navigant and City of Alameda staff.
e Baseline estimates of Decision Maker Awareness and Willingness -The primary source was the
input files used to develop the California Energy Efficiency Potential Study conducted by Itron for
the State of California's Investor Owned Utilities (IOUs) in 2008.
• Net -to -Gross Values —The model accepts net -to -gross value inputs at the measure and building
type level However, Navigant relied on the values for net -to -gross included in the E3 Reporting
Tool as its source for net -to -gross values. Each utility has the option to modify this input if
desired.
• Calibration Targets — Navigant collected energy efficiency program results for 54 publicly owned
utilities: 27 municipal utilities from California and 27 municipal and cooperatively owned utilities from
Connecticut, Iowa, Minnesota, and Vermont. The analysis used publicly available data from primary
utility DSM annual regulatory reports and EIA FERC Form 861 baseline sales Batas. We categorized
incremental DSM program results and baseline data by major customer sector: residential and
commercial and industrial (C&I).
Incremental DSM results and expenditures were normalized overall and for the two major customer
sectors by using baseline sales data to determine expenditures as a percentage of revenue, energy
savings as percentage of sales, and peak demand savings as a percentage of peak demand. Navigant
3 'Energy Efficiency in California's Public Power Sector: A 2009 Status Report', prepared by the California
Municipal Utility Association, March 2009.
4 `California Energy Efficiency Potential Study', by Itron, Inc. and Kema-Xenergy, for Pacific Gas and
Electric, September 10, 2008.
s Baseline and DSM data were used from 2007, as E:IA 2008 baseline data were not available.
6
also calculated costs of conserved energy ($/kWh) and demand ($/kW) on a first year basis.
The median of normalized spending, savings, and cost of savings were identified. The median is used
to identify best practice organizations —those with above median savings at median or below median
cost of savings. To calibrate CalEERAM, Navigant used as a guideline the median energy savings, as a
percentage of sales, of the best practice organizations of the reviewed POUs that are outside
California, 0.96% for C&I and 0.93% for residential.
Non -Residential Building Types Assessed
The Navigant Energy Efficiency Potential Model (on which CalEERAM is based) has the capability of
modeling a large number of different building types. For CalEERAM, the building types assessed included
three residential building types, four commercial building types and,one industrial category type. The
building types and categories include:
• Residential new construction
• Residential single family existing
• Residential multi -family existing
• The largest commercial sector building type by sales volume
+ The second largest commercial sector building type by sales volume
+ The third largest commercial sector building type by sales volume
• The largest industrial category by sales volume
+ The remaining balance of sales include in the miscellaneous building type
The three commercial sector building types and the single NAILS industrial category for each utility vary.
The determination of which to include in CalEERAM was based on the shares of non-residential sales by
building type as identified from the utility "Quarterly Fuels and Electricity Report" submittals to the CEC.
These submittals have sales identified by NAICS code. The non-residential sales were sorted and the three
largest commercial building types, along with the largest NAICs industrial type, were identified and
specifically included in the model. The balance of non-residential sales was categorized into the
miscellaneous building type.
Table 3 identifies the building types and shares of non-residential sales for each of the utilities. Some of
the smaller utilities don't have information available from the CEC because either they did not make the
submittal (very small utilities are not required to) or some of the sales data cannot be sufficiently masked
to prevent identification of specific customers.
For most utilities, the largest sales were to commercial sector buildings with the most common being office
buildings. Retail non-food and retail food stores were the next most common. For the industrial sector, the
food manufacturing industrial type was the most common followed by computer and electronics manufacturing.
7
Table XXX: The Largest Non -Residential Building Types by Sales Volume by Utility
Utility
Bldg type #1
Sales % #1
Bldg Type #2
Sales %#2
Bldg Type #3
Sales %#3
Largest Industrial
Largest
Industrial
sales %
Miscellaneous
Sales %
Alameda
Office
49.8%
Retail - Non Food
5.9%
Restaurant
5.3%
Computers &
Electronics
1.51%
37.6%
Anaheim
Office
12.01%
Retail - Non Food
8.4%
Hotels
6.1%
Computers &
Electronics
22 6%
50.8%
Azusa
School
13.4%
Office
10.9%
Retail - Non Food
9.5%
Food
11.6%
54.6%
Banning
Retail - Food
7.7%
Restaurant
7.5%
Office
6.9%
Computers &
Electronic
13.8'9A
64.2%
Biggs
Retail -Non Food
2.0%
Retail - Food
1.5%
Office
1.0%
Food
95.0%
0.5%
Burbank
Office
18.2%
Medical Care
9.81%
Retail - Non Food
9.0%
Motion Picture &
Broadcasting
34.996
28 0,9
Colton
Office 1
17.4%
Retail - Non Food
10.V 1
Retail - Food
5.69/6
Food
24.4%
41.7%
Corona
Office
19.2%
Restaurant
7.9%
Retail - Non Food
7.3%
None
0.0%
65.6%
Glendale
Office
39.0%
Retail - Non Food
14.2%
Medical Care
8.4%
Food
1.5%
36.9%
Gridley
Retail - Food
21,5%
Office
21.11%
Retail - Non Food
15.41
Food
14.7%
27.4%
Healdsburg
Office
21.8%
Retail - Food
16.8%
Restaurant
10.2%
Food
8.11/6
43.2%
Hercules
Notavailable
Notavailable
Notavailable
Notavailable
100.0%
Imperial
Office
21.5%
Retail - Non Food
10.2%
School
6.9%
Food
3.4%
58.0%
Lassen
School
10.0%
Office
8.0'1
Retail - Food
8,G1%
None
0.0•�
74.0%
Lad!
Office
8.7%
Retail - Non Food
7.9%
Retail - Food
5.8%
Food
20.3%
57.3%
Lompoc
Retail - Non Food
31.4%
Office
16.1%
Restaurant
9.3%
Food
0.7%
42.5%
Merced
Office
10.5%
School
9.6%
Retail - Non Food
8.1%
Food
46.491
25.4%
Modesto
Office
11.0%
Retail -Non Food
9.191.
Restaurant
3.9%
Food
34.4%
41.651.
MorenoValley
Office
50.4%
Restaurant
37.5%
Retail - Non Food
4.0•O6
None
0.0'1
8.1916
Needles
Office
32.2%
Hotels
18.93A
Restaurant
18.0%
None
0.0%
30.9%
Palo Alto
Office
43.0'�
Medical Care
10.8%
Retail -Food
4.2%
Computers &
Electronics
16.8%
25.2%
Pasadena
Office
36.2%
School
13.3%
Medical Care
8.5%
Information
5.5%
1 36.5%
Plumas-Sierra
Office
60.1%
Hotels
2,7%
Restaurant
1.9%
Machinery
0.91.
1 34.491.
Portof Oakland
None
0.0%
None
0.0%
None
0.0%
Transportation Support
93.0°/u
7.0%
Rancho Cucamon a
Retail - Non Food
42.69/6
Restaurant
10.7%
Office
4.2%
None
0.0%
42.5%
Redding
Retail - Non Food
36.9%
Office
24.176
Restaurant
8.9%
Telecommunications
2.6%
27.5%
Riverside
Office
27.5%
School
14.61/6
Retail - Non Food
11.7%
Plastics & Rubber
10.5%
35.7%
Roseville
Office
22,9%
Retail - Non Food
16.1%
Medical Care
7.9%
Computers &
Electronics
26.0%
27.1%
Shasta Lake
Not available
Not available
Not available
Not available
100.01Y.
Silicon Valley Power
Office
37.551.
Retail - Non Food
2.1%
School
2,0%
Computers &
Electronics
46.3%
12.1%
Trinity
Notavailable
Notavallable
Notavailable
Notavallable
100.0%
Truckee -Donner
Notavailable
Notavailable
Notavailable
Notavailable
100.0•OA
Turlock
Retail -Non Food
6.9%
Office
5.0%
School
4.6%
food
33.2%
50.4%
Ukiah
Office
32.3%
Retail - Non Food
18.4%
Reta!I - Food
11.1%
None
0.0•�
88.2%
Vernon
Office
1.6%
Restaurant
0.3%
Retail - Non Food
0.2%
Food
76.7%
1 21.2%
Model Calculations
CalEERAM's "bottom's up" approach uses the input data listed in the previous section to calculate
Technical, Economic, and Market Potentials. Calculating the estimates of Technical and Economic
Potential is relatively straightforward: the estimates are the product of available building stocks,
technology densities, and measure impacts. For Technical Potential, it is assumed that all measures can
be implemented in all available applications at the same time. Technical potential changes by small
amounts over time to reflect changes in the amount of building stocks over time caused by new
construction. Economic Potential is the subset of Technical Potential that includes only the efficient
technologies that pass the TRC screen.
Calculating Market Potential is unlike calculating Technical and Economic Potential. Calculating Market
Potential relies on a calibrated decision adoption methodology and an accounting system that adjusts for
F]
potential double counting and for recurring participation of efficient technologies once measure life is
passed.
Decision Adoption Methodology
One of the key features of CalEERAM is use of a decision maker based energy efficiency measure adoption
rate algorithm (DEEMARA). DEEMARA simulates consumer choice based on simple measure payback and
other decision components. For each measure, by building type and by year, the algorithm estimates the
number of measures implemented. The algorithm has two parts with the overall formula having the
following form:
Number of measures implemented = total available measure units * binary logit function
* market factor * decision maker measure awareness and willingness to install the measure
The first part of the algorithm includes the first three of the four variables identified in the formula above.
The "total available measure units" is a variable that changes with each forecast year and has the form:
Total available measure units = Available building stock * (maximum density for the competing
technologies — base year efficient technology density) — running sum of previous years of efficient
technology units installed
The "binary logit function" identifies the share of the efficiency measures implemented each year. The
logit function has the form:
Share of Efficiency Measures Implemented = Exp (0.0 -- Beta Constant * Measure Payback)
Where:
• The Beta constant represents the average influence of all excluded (non -payback)
factors.
0 The Beta constant is allowed to be modified at the end use level (within bounds):
— Larger number representing influences that speed up adoption.
-- Lower number representing influences slowing down adoption (such as a
recession).
• Measure payback is simple measure payback and is calculated for each measure,
each forecast year.
The "market factor" is a calibration constant that is computed in the first simulation year to adjust
computed participation shares to equal the calibration targets. The calibration target needs to be a value
that can be reasonably expected to occur given incentive levels, the cost effectiveness of the measure,
and the available resource. Navigant estimates calibration targets at the measure level based on a
combination of the estimates of economic potential by measure, past program accomplishments by the
utility in providing this or a similar measure, and a review of other similar type utilities to see what level of
accomplishments they are achieving.
The second part of the algorithm includes the "decision maker measure awareness and willingness to
install the measure" function. It is an exponential curve function based on the forecast year and the two
input variables of decision maker awareness of a measure and corresponding willingness to purchase.
Awareness is the percentage of decision makers who are aware that a specific energy efficient
technology exists.
Willingness is the subset of the aware group who are willing to install the energy efficient measure.
The initial values for decision maker Awareness and Willingness by measure were taken from the
California Energy Efficiency Potential Study conducted by Itron for the State of California's Investor Owned
Utilities (IOUs) in 20086. These Itron estimates of decision maker Awareness and Willingness are based on
a combination of consumer research performed for Northern States Power in Minnesota in the mid to late
1990's by Itron's predecessor,company, Regional Economic Research and more recent consumer research
performed by California utilities. CalEERAM assumes that the initial estimates of awareness and
willingness are not static, but improve over time as consumers become both more aware of energy
efficiency and more willing to purchase as technology improves. The speed by which these variables
approach 100% is determined by the starting values for Awareness and Willingness and a decision maker
curve function. The decision maker curve function takes the form:
The Decision Maker Curve = Min(1, Awareness * Willingness + (1tEXP(curve midpoint in
years -years into the forecast))^ -I)
Where:
o Decision Maker Awareness = The baseline percent of the population of eligible consumers
who are aware of the technology
o Decision Maker Willingness = The baseline percent of the population of eligible
consumers who are both aware of the technology and willing to purchase it
o Program year = The number of years after the start of the forecast
o Adoption curve tipping point year = Within a measure's lifetime, the point of time on an
"S" curve where the curve is at its midpoint.
The "S" curve diffusion portion of the algorithm is based on changing consumer awareness and willingness
over time. Where a measure is along the curve depends on its baseline estimates of consumer awareness
and willingness. If a measure is well known and with a high level of willingness to install, then the starting
point is very high on the curve with little change overtime expected from this portion of the decision
maker algorithm. However, if both awareness and willingness are low, then this portion of the decision
maker algorithm will experience change over time. The current assumption is that over time, every
6 'California Energy Efficiency Potential Study', by Itron, Inc. and Kema-Xenergy, for Pacific Gas and
Electric, September 10, 2008.
10
measure will reach 100% consumer awareness and willingness. It is possible to modify at the measure
level the maximum value for consumer awareness and willingness.
The change over time and the speed of that change depend on the initial baseline estimates and the curve
midpoint year. For new technologies, both awareness and willingness are typically low, simply because
the technology is new. A program can be designed not only to provide incentives but also to increase
awareness and promote the technology's reliability and superiority. Such a program typically has low
initial participation that ramps up over time before leveling out. In contrast, a mature technology typically
ha's high initial willingness and awareness, and, thus, participation that follows a flatter trend over time.
Figure 2 illustrates the shape of the "S" curve over the ten year forecast period using different curve
midpoint years. The Figure 2 illustration shows the curves for midpoint years of 2, 5, and 8 years. Note in
this example that the curve with the earliest midpoint year achieves saturation near year 8 where the
curves with later midpoint years do not achieve saturation by year 10.
11
Figure 2. Decision Maker "S" Curve for Midpoint Years 2, 5, and 8
The following example illustrates the year to year impacts of the decision maker measure awareness and
willingness factors on DEEMARA:
o Baseline awareness =50%
• Baseline willingness = 80%
• Base year adjustment due to awareness and willingness = 50% * 80% = 40%
• The five year midpoint "S" curve has the following values in its first four years:
— Year 1= 0.7%
— Year 2 =1,8%
— Year 3=4.7%
— Year 4 =11.9%
• Each forecast year adjustment due to awareness and willingness is the previous year's awareness
and willingness value plus the "S" curve value.
— Base Year = 40%
-- Year 1= 40% + 0.7% = 40.7%
— Year 2 = 40.7 % + 1.8% = 42.5%
— Year 3 = 42.5% + 4.7% = 47.2%
— Year 4 = 47.2% + 11.9% = 59.1%
12
The function has a maximum value of 100%, when the measure achieves total saturation.
Interactive Effects and Mutual Exclusivity
Double counting savings is often an unintended consequence in modeling potential estimates. This
section describes the nature of this problem and CalEERAM's two approaches to avoid this error.
Double counting savings can occur two ways within a modeling structure. The first is through failing to
account for interactive affects between measures; here the sum of the individual measure savings may be
more or less than the effective savings of the interactive measures. The second is not accounting for
mutual exclusivity between measures.
The first issue between two or more non -mutually exclusive measures has been a long standing point of
discussion in the development of the DEER database. An example of this kind of double counting involves
lighting measures. For cooled homes, efficient lighting reduces air-conditioning load because of the
efficient lighting's reduced waste heat discharge. For the same reason, efficient lighting increases electric
space heating loads.
The energy impacts reported in the 2005 DEER did not account for interactive effects. However, the 2008
DEER did account for interactive effects. Reviewing the 2010 version of the E3 Reporting Tool indicates
that measure impacts are not differentiated by heating fuel type and cooling applicability. However, the
reporting tool's energy impacts represent a reduced form of values included in DEER. That is, many
represent weighted averages of the different combinations of vintages and building types within a climate
zone. It is uncertain if the weighting also included end -use fuel type. The energy impacts used in
CaIEERAM are from the 2010 E3 Reporting Tool and are representative of the weighting method used to
develop the reporting tool values. As much as reporting tool accommodates these effects, CaIEERAM
accommodates these effects.
The second issue for double counting savings involves failing to account for mutually exclusive measures.
An example would be different SEER levels for energy efficient air conditioning. There are at least two
methods to address this issue.
The first method is to distribute the shares of a competition group total (a set of mutually exclusive
measures) among the competing technologies so that the sum equals the maximum applicability of the
total. For example, consider a competition group consisting of a SEER 14, a SEER 15, and a SEER16+
technology options for central air conditioning. For this example, assume the total applicability of central
air conditioning (the competition group as a whole) is 90% (i.e., 90% of all customers have central AC).
Then each technology within the competition group would have a share of the 90% applicability, for
example, 60% SEER 14, 25% SEER 15, and 5% SEER 16+.
Another method is to model only the most efficient of the competing technologies; in the example above,
only SEER16+. From a conservation potential perspective, this method identifies a larger, but still realistic,
potential. CaIEERAM uses this approach to prevent this kind of double counting.
Either of these approaches could have been used within the model, but the second method was selected
to identify the true level of market potential. Distributing among an array of mutually exclusive measures
13
with widely varying energy savings could have misrepresented the potential as being lower than actual
potential
Recurring Participation
Each measure included in the analysis has an expected measure life. Some of these measure life
estimates extend beyond the ten years of the planning horizon while others end within the ten year
period. The model assumes that each measure implemented will be replaced at the end of its measure
life by another technology at least as efficient as the originally installed efficiency measure. Given that the
replacement technologies are not known, it is assumed that the replacement technology is the same as
the current efficient technology.
These assumptions result in accounting for the continuation of the originally installed energy efficient
impacts throughout the ten year planning horizon. This is unlike other models that assume that, at the
end of measure life, all or a portion of all installations return to the original baseline technology.
The impact of this assumption affects Market Potential results in two ways. First, the cumulative Market
Potential does not fall at the end of measure life. Second, future year incentive and administrative costs
are affected significantly. At the end of measure life, the model assumes that original participants re -
participate. Thus cumulate energy and demand impacts are sustained with no increase in incremental
impacts. Re -participation, however, incurs incentive and administrative costs. Therefore, for measures
with measure life less than ten years, such as CFLs, the total incentive and administrative costs will rise
more quickly than the incremental energy impacts.
Appliance re -cycling is handled in a special manner. At the end of an appliance recycling measure life, the
energy savings does disappear from cumulative potential and there are no re -Curing incentive and
administrative costs. The appliance cannot be re -cycled more than once.
Model Output
For compliance reporting purposes, the primary model outputs are found in the "Report to the CEC" tab of
the spreadsheet. Data is provided on Technical and Economic Potential for'both energy and peak demand
as well as incremental annual energy and demand Market Potential estimates for the ten year planning
horizon.
Other tables and graphs are also provided by the model, for use by utility program planners. Figure 3
identifies the various output data that is available. This information can also be found in the
"Introduction" tab of the model
Figure 3: CaIEERAM Output Information Years
14
Caveats and Limitations
Energy efficiency potential models are invaluable tools for utility program planners to use when
establishing efficiency program targets. They provide a credible and technically rigorous approach to
estimating the potential energy efficiency savings attributable to a utility's energy efficiency program.
However, it is understood that there are many limitations to utilizing a technical model to forecast real
world results'. In particular, customer willingness and awareness assumptions in potential models do not
sufficiently explain consumer behavior, lifestyle, or decision -making styles that ultimately drive the
success of voluntary efficiency programs. Such limitations create uncertainty that utility program planners
must consider when setting realistic yet aggressive goals for efficiency programs tailored to the
communities they serve. In addition to behavioral barriers, the following issues are also worth noting:
• Emerging technologies. The potential models do not include the incremental potential associated
with emerging or yet to be discovered energy efficient technologies. In particular, utility program
planners are very interested in the potential for solid state lighting. However, the development of
uniform product specifications and affordable pricing will take time, and so it is not expected that
solid state lighting will significantly Contribute to utility program potential in the near future. Even
so, utility planners wishing to aggressively promote emerging technologies may account for the
market potential by adopting a more aggressive potential scenario, as allowed for by the model.
• Policy -driven changes. The potential model does not include future changes to codes and
standards for buildings and appliances (with one exception: 2012 national standards for
incandescent lighting). As policy makers continue to raise the bar for minimum levels of energy
Behavioral Assumptions in Energy Efficiency Potential Studies, California Institute for Energy and
Environment, May, 2009
15
efficiency through the adoption of codes and standards, they correspondingly reduce the
efficiency potential attributable to utility programs, It is certain that some of the market potential
currently identified by the potential models will not be realized by the utility, but instead will be
attributed to future changes in codes and standards.
• Slow economic recovery. The economic recession creates a significant hurdle for the continued
success of utility efficiency programs. Potential models do not directly attempt to gauge the
impact of economic recession. Utility planners must assess the impact of the economy on their
community, and adjust their program expectations accordingly.
• Erosion of energy savings potential due to intrusion of investor owned utility programs. Retail
point of sale and product give-away programs (e.g. CFLs, consumer electronics) offered by
investor owned utilities often spill over into publicly owned utility service territories. Chain stores
often distribute IOU -subsidized products in stores located within public power communities. POU
customers often shop at big box retailer locations outside of the POU utility service territory. The
energy efficient products that are purchased and installed end up being attributed to IOU
programs. For example, the new IOU statewide upstream program, the Business Consumer
Electronics Program, targets energy efficient computers, televisions, and computer monitors at
the retail and distributer level. Many of these products will be sold to POU customers, yet the
savings will be attributed to IOU programs. Although IOU statewide and upstream programs can
be a very effective means for capturing energy savings, they effectively remove a portion of the
energy savings potential from POU programs.
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