HomeMy WebLinkAbout5 Legislative Regulatory Compliance Agenda Item # 5
DONNEi AI F
Public Utility District
Memorandum
To: Board of Directors
From: Alan Harry
Date: August 7, 2007
Subject: Legislative and Regulatory Compliance; AB 2021
Why is this item before the Board?
Existing law, SB 1037, currently requires publicly-owned utilities (POUs) to invest in all
cost-effective, reliable and feasible energy efficiency as the first source of power in
their energy portfolio. California Assembly Bill 2021 restates that requirement and
adds additional independent reporting requirements for POUs.
The Truckee Donner PUD, working with the Northern California Power Agency, has
prepared the attached "Establishing Energy Efficiency Targets: A Public Power
Response to AB 2021" to meet the requirements of the statute. As part of this
reporting the District, as well as all participating California POU's, must formally adopt
their energy efficiency targets on a triennial basis. Scott Tomashefsky from the
Northern California Power Agency is scheduled to present this report to the Board
August 15, 2006.
Background / Summary
In September 2006 California Assembly Bill 2021 (Levine) was signed into law
expanding upon several of the energy efficiency policies adopted following the
passage of Senate Bill 1037 in 2005. This statute requires all publicly-owned utilities
(POU) to "identify all potentially achievable cost-effective electricity efficiency savings
and shall establish annual targets for energy efficiency savings and demand reduction
for the next 10-year period".
• Page 1 AB2021
New Information
Using an approach similar to that prepared last year in the development of public
power's energy efficiency status report to meet the requirements of Senate Bill 1037,
the California Municipal Utilities Association (CMUA), in partnership with the Northern
California Power Agency (NCPA) and the Southern California Public Power Authority
(SCPPA), have joined together to collaborate on the development of individual utility
energy efficiency and demand reduction targets. A total of thirty-five POU's
participated in the attached report.
As reported, the principal findings and conclusions of the analysis are:
➢ Energy efficiency programs among the 35 participating utilities target a
reduction in consumption of approximately 2,089 gigawatt hours and a peak
demand decline of 274 megawatts during the ten-year period ending in 2016.
This represents more than an eight percent reduction in consumption of the
period, and accommodates nearly half of public power load growth.
➢ The District's energy efficiency target for the same period is 10,014 MWh, an
average annual energy reduction target of 0.59%. The estimated cost of the
energy efficiency programs to meet the 2008 target reduction is$150,500.
Recommendation
Pursuant to the statute, it is recommended that the Board adopt by Resolution Truckee
Donner Public Utility District's energy efficiency targets through 2016 as follows:
Energy— 10,014 megawatt hours
Demand— 1.1 megawatts
It is further recommended that the Board direct NCPA to submit these targets to the
California Energy Commission.
0 Page 2
TRUCKEE DONNER
IF
Public Utility District
RESOLUTION NO. 2007 - XX
RESOLUTION OF THE
TRUCKEE DONNER PUBLIC UTILITY DISTRICT
TO ESTABLISH ENERGY EFFICIENCY PROGRAM TARGETS
WHEREAS, California Assembly Bill 2021 (Section 25310 of the Public Resources Code) requires all publicly-
owned utilities to identify all potentially achievable cost effective electricity efficiency savings and establish
annual targets for energy efficiency savings and demand reduction for the next 10 year period,
WHEREAS, each publicly-owned utility is required to adopt those targets by September 301" , 2007 and to
report adopted targets to the California Energy Commission,
WHEREAS, it is important that there is broad-based public power compliance with Assembly Bill 2021 on a
statewide basis,
WHEREAS, Northern California Power Agency, California Municipal Utilities Association, and Southern
California Public Power Agency contracted with the Rocky Mountain Institute, an independent organization with
well accepted energy efficiency expertise in the energy industry,
WHEREAS, the Rocky Mountain Institute provided a modeling tool to help publicly-owned utilities identify
energy savings potential and establish energy efficiency program targets, and
WHEREAS, Truckee Donner Public Utility District utilized the modeling tool and established energy efficiency
and demand reduction targets through 2016 as follows:
Energy 10,014 megawatt hours
Demand 1.1 megawatts
NOW, THEREFORE BE IT RESOLVED, that the Board adopt Truckee Donner Public Utility District's electric
energy efficiency program targets for energy savings and demand reduction.
PASSED AND ADOPTED by the Board of Directors of the Truckee Donner Public Utility District in a meeting
duly called and held within said District on the 15th day of August, 2007.
AYES:
NOES:
ABSTAIN:
ABSENT:
TRUCKEE DONNER PUBLIC UTILITY DISTRICT
Tim F. Taylor, President
ATTEST:
Peter L. Holzmeister, District Clerk
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CALIFORNIA
MU!IN-ICII AL ;
UTILITIES
ASSOCIATION
Establishing Energy Efficiency Targets:
A Public Power Response to AB2021
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June 2007
Acknowledgements
The California Municipal Utilities Association (CMUA) wishes to acknowledge the
following individuals for their substantial contributions to completing this effort:
Project Managers:
Scott Tomashefsky, Northern California Power Agency (NCPA)
Manny Robledo, Southern California Public Power Authority (SCPPA)
David Reynolds, Northern California Power Agency
Gary Ambach, Imperial Irrigation District Gerald Katz, Colton Electric Utility
Mike Bacich, Riverside Public Utilities Craig Kuennen, Glendale Water& Power
Jim Brands, Efficiency Services Group Anthony Kekulawela, Port of Oakland
Theresa Boucher, Lassen MUD Joyce Kinnear, Silicon Valley Power
Rick Coleman, Trinity PUD David Kolk, City of Needles
Veronica Craghead, City of Banning Rob Lechner, Lodi Electric Utility
Kurt Duvall, Corona Electric Utility Mariann Long, Anaheim Public Utilities
Paul Eichenberger, City of Shasta Lake Willie Manuel, Turlock Irrigation District
Phil Falkenstein, Imperial Irrigation District Fred Mason, City of Banning
Nancy Folly, Turlock Irrigation District Marlee Mattos, City of Biggs
Isaias Franco, Merced Irrigation District Jeanette Meyers, Burbank Water& Power
Peter Govea, Modesto Irrigation District Patrick Morrison, Roseville Electric
George Hanson, Moreno Valley Utilities Jessica Nelson, Plumas-Sierra REC
Alan Harry, Truckee Donner PUD Meredith Owens,Alameda Power&Telecom
Ted Haskell, Efficiency Services Group Paul Reid, Azusa Light&Water
John Hoffner, Pasadena Water& Power Glen Reddick, City of Hercules
Bob Hondeville, Modesto Irrigation District Jan Roosevelt, Anaheim Public Utilities
Carla Johannesen, Roseville Electric Mike Ten Eyck, City of Rancho Cucamonga
Bob Jones, Turlock Irrigation District Vanessa Xie, Pittsburg Power/Island Energy
Mary Kammer, City of Lompoc
Project Consultants (Rocky Mountain Institute):
John Anderson
Lena Hanson
Aaron Silverman
Kitty Wang
CMUA would like to thank our entire group of utility directors, general managers,
elected officials, as well as our partner joint power agencies, NCPA and SCPPA,
for dedicating resources and financial support for this effort. We would also like
to thank the staff of the California Energy Commission (CEC), in particular Sylvia
Bender, Cynthia Rogers, and Kae Lewis for their guidance and understanding in
addressing the needs of the public power community in the development of this
report.
2
TABLE OF CONTENTS
Executive Summary.............. ........""""""4
..................................................................................
I. Introduction .........................................................................................................................6
II. RMI Model and methodology 8
III. Energy Efficiency and Demand Reduction Targets.......................................................26
IV: Conclusion................ ..............28
............................................................................................
Appendix A: ......................................................29
.........................................................................
AlamedaPower &Telecom.........................................................................................................30
AnaheimPublic Utilities...............................................................................................................31
AzusaLight&Water ...................................................................................................................32
Banning Electric Utility .......33
.........................................................................................................
Cityof Biggs................................................................................................................................34
BurbankWater& Power..............................................................................................................35
Cityof Colton...............................................................................................................................36
Cityof Corona.............................................................................................................................37
GlendaleWater& Power.............................................................................................................38
Cityof Gridley ..........................................................39
Cityof Healdsburg.............................................................
Cityof Hercules...........................................................................................................................41
ImperialIrrigation District............................................................................................................42
Cityof Industry............................................................................................................................43
LassenMunicipal Utility District...................................................................................................44
LodiElectric Utility.......................................................................................................................45
City of Lompoc........................ ...................................46
.................................................................
MercedIrrigation District.............................................................................................................47
ModestoIrrigation District...........................................................................................................48
Cityof Moreno Valley..................................................................................................................49
Cityof Needles............................................................................................................................50
PasadenaWater& Power...........................................................................................................51
Pittsburg Power Company/Island Energy ...................................................................................52
PlumasSierra REC.....................................................................................................................53
Portof Oakland ...........................................................................................................................54
Cityof Rancho Cucamonga........................................................................................................55
RiversidePublic Utilities..............................................................................................................56
RosevilleElectric.........................................................................................................................57
Silicon Valley Power(City of Santa Clara)..................................................................................58
Cityof Shasta Lake.....................................................................................................................59
TrinityPUD..................................................................................................................................60
TruckeeDonner PUD..................................................................................................................61
TurlockIrrigation District.............................................................................................................62
Cityof Ukiah................................................................................................................................63
Cityof Vernon .............................................................................................................................64
3
Executive Summary
California Assembly Bill 2021 (Levine), signed into law in September 2006,
expanded upon several of the energy efficiency policies adopted via the passage
of Senate Bill 1037 in 2005. This report complies with Section 3 of the statute,
requiring each publicly-owned utility (POU) to:
"identify all potentially achievable cost-effective electricity efficiency
savings and shall establish annual targets for energy efficiency savings
and demand reduction for the next 10-year period."
Similar to the approach taken to develop public power's energy efficiency status
report last year pursuant to SB 1037, the California Municipal Utilities Association
(CMUA), in partnership with the Northern California Power Agency (NCPA) and
the Southern California Public Power Authority (SCPPA), have joined together to
collaborate on the development of individual utility energy efficiency and demand
reduction targets. A total of 35 POUs are participating in this report (Table 1).
We note that the Los Angeles Department of Water and Power, the Sacramento
Municipal Utility District, City of Palo Alto Utilities, and Redding Electric Utility are
submitting data separate from this report.
The principal findings and conclusions of this analysis are as follows:
With the exception of Silicon Valley Power, which adopted its
efficiency target on June 5, the estimates contained in this report are
preliminary in nature and have not yet been approved by any other
local governing board. With the concurrence of California Energy
Commission (CEC) staff and commissioners, as well as general
agreement from the office of Assemblyman Lloyd Levine (the
principal author of AB2021), POUs submitting information in this
report have until September 30, 2007 to submit formally-adopted
estimates to the CEC.
• The results from this analysis are based on a methodology developed by
the Rocky Mountain Institute, an independent organization with well-
accepted energy efficiency expertise in the energy industry.
• Energy efficiency programs among the 35 utilities participating in this
analysis target a reduction in consumption of approximately 2,089
gigawatt hours and a peak demand decline of 274 megawatts during the
ten-year period ending in 2016. This represents slightly more than an
eight percent reduction in consumption over the period, and
accommodates nearly half of public power load growth.
4
• Individual savings targets vary by utility for a variety of reasons, including
but not limited to climate zone, community demographics, and load growth
patterns. Achievable potential ranges for some utilities move well beyond
the state's goal of 10 percent reduction in consumption.
• In total, energy program targets are more than double the historical annual
energy savings achievements.
CMUA, NCPA, and SCPPA look forward to working with the CEC on energy
efficiency issues, and are committed to balancing statewide energy policy
direction with the needs and diverse interests of local communities. An updated
report with targets adopted by each utility's respective governing boards will be
sL:omitted to the CEC in the first week of October.
5
I. Introduction
On September 26, 2006, Governor Schwarzenegger signed Assembly Bill 2021
(Levine) into law, expanding upon several of the energy efficiency policies
adopted via the passage of Senate Bill 1037 in 2005. This report complies with
Section 3 of the statute, requiring each publicly-owned utility to:
"identify all potentially cost-effective electricity efficiency savings and shall
establish annual targets for energy efficiency savings and demand
reduction for the next 10-year period."
Similar to the approach taken to develop public power's energy efficiency status
report last year, the California Municipal Utilities Association (CMUA), in
partnership with the Northern California Power Agency (NCPA) and the Southern
California Public Power Authority (SCPPA), have joined together to collaborate
on the development of individual utility energy efficiency targets. A total of 35
POUs are participating in this report (Table 1).
Table 1
Publicly-owned Utilities Participating in Report
Alameda Imperial Irrigation District Plumas Sierra
Anaheim Industry Port of Oakland
Azusa Lassen MUD Rancho Cucamonga
Banning Lodi Riverside
Biggs Lompoc Roseville
Burbank Merced Shasta Lake
Colton Modesto Irrigation District Silicon Valley Power
Corona Moreno Valley (Santa Clara)
Glendale Needles Trinity PUD
Gridley Pasadena Truckee Donner PUD
Healdsburg Pittsburg Power Company/ Turlock Irrigation District
Hercules Island Energy Ukiah
Vernon
A considerable amount of time and resources have been put into this effort.
Rocky Mountain Institute (RMI) was retained to develop an Excel-based tool that
can be used to establish energy efficiency targets for each utility. Approximately
$150,000 of contract dollars was dedicated to this effort. The total cost in time
and money associated with this project, however, is considerably greater, when
utility staff time, workshop participation, and CMUA/NCPA/SCPPA coordination
is taken into consideration.
6
The following report contains three additional sections. Section II addresses the
RMI model and the methodology surrounding the calculation of energy efficiency
potential targets. As described in this report, it is assumed by the POUs
participating in this project that Section II addresses the requirement in AB2021
that calls for utilities to describe the basis for establishing individual targets.
Critical to this section is an explanation of the distinction between theoretical
cost-effective potential, and the utility-specified feasible number. A list of caveats
and considerations related to the numbers being provided is also included in this
section.
Section III provides each utility's energy efficiency and demand reduction targets
followed by some concluding thoughts for future consideration. Individual
program summaries are contained in the Appendix. Section IV describes some
of the lessons learned from the current study and provides thoughts for
consideration by the POUs when they update their energy efficiency
potentials again within the next three years.
With the exception of Silicon Valley Power, which adopted its efficiency
target on June 5, the estimates contained in this report are preliminary in
nature and have not yet been approved by any other local governing board.
With the concurrence of CEC staff and commissioners, as well as general
agreement from the office of Assemblyman Lloyd Levine (the principal
author of AB2021), POUs submitting information in this report have until
September 30, 2007 to submit formally-adopted estimates to the CEC.
Recognizing the timing of the data needed for the CEC to complete its
Integrated Energy Policy Report cycle without delay, the CEC has agreed to
accept preliminary data from the POUs in this regard.
7
II. RMI Model and Methodology
RMI's energy efficiency potential model is designed to calculate technical, cost-
effective, and feasible energy efficiency potential for a utility's service area. The
model forecasts energy savings and demand reduction potential in existing
buildings and new construction for the residential, commercial, and industrial
sectors for the years 2007-2016. Though flexible enough to be applied to any
utility, customized versions of the model have been created to reflect the specific
characteristics of each POU participating in the AB2021 project. In particular, the
model allows specific adjustments for:
• Forecasted energy load and demand growth,
• Climate (using Title 24 climate zones),
• Customer mix (by building type and industry),
• End use characteristics,
• Forecasted avoided costs and customer rates,
• POU and ratepayer discount rates,
• Non-capital program costs, including POU incentives and
marketing/EM&V/admin, and
• POU-specified feasible quantities for each measure
The model is based on the California Energy Efficiency Potential Study (CEEPS),
prepared by Itron in 2006 for California's investor-owned utilities (IOUs) - Pacific
Gas & Electric (PG&E), Southern California Edison (SCE), and San Diego Gas &
Electric (SDG&E). Cost and potential efficiency savings of individual measures
considered in that study were imported into the RMI model. Baseline results for
an IOU (baseline IOU), such as technical energy and demand reduction
potential, were converted into relative potentials that were then adjusted, and
finally applied to each participating POU. The cost test methodology used in the
model to calculate cost-effective efficiency potential is adapted from that
developed by Energy and Environmental Economics, Inc. (H).
For practical reasons primarily related to file size constraints, the model has been
created as a set of three complementary Excel files. The first file calculates
technical efficiency potential. The results of this model are then used to
determine cost-effective efficiency potential. The cost test model includes tables
and charts illustrating the technical and cost-effective potential for each sector.
In the last step, the cost-effective results are used as a basis for estimating
feasible potential. The graphs in the feasible model show the combined
technical, cost-effective, and feasible results. The summary table in the feasible
model contains the results each POU will report in accordance with AB2021
obligations.
8
A. Data Sources
RMI relied on a number of data sources for the development of the model, as
shown in the following list and Table 2.
• Itron, California Energy Efficiency Potential Study(San Diego, CA: Itron, 2006).
• Itron, California Commercial End-Use Survey(San Diego, CA: Itron, 2006).
• KEMA-XENERGY, California Statewide Residential Appliance Saturation Study
(Oakland, CA: KEMA-XENERGY, 2004).
• KEMA-XENERGY, California Statewide Residential Sector Energy Efficiency Potential
Study(Oakland, CA: KEMA-XENERGY, 2003).
Table 2. Summary of Specific Data and Metrics Used to Determine Efficiency
Potential for Each Sector'
• s - �Wrls l • s
Residential Existing CEEPS Appendix F Measure level • kWh/unit potential
• [IOU]Res.xls • Total kWh potential
• Incremental measure
cost
• Measure lifetime
New CEEPS Appendix I • Packages of • Therms/unit potential
[IOU]ResApp measures • kWh/unit potential
endix.xls • Only HVAC and • Total kWh potential
water heating 0 Incremental package
addressed cost
• Packages result • Package lifetime
in both electricity
and natural gas
savings
• Packages defined
to exceed 2005
T24 building
codes by 15%2
Commercial Existing CEEPS Appendix G • Measure level • kWh/unit potential
• [IOU]HVAC.xIs • Total kWh potential
• [IOU]Lighting. • Incremental measure
As cost
Many of the Excel files listed in the table have separate versions for each of the three investor-
owned utilities (IOUs). In these instances, "[IOU]"has been substituted for the actual utility name
in this table. The data source for a given participating POU was based upon the default IOU and
climate zone specified by that POU.
2 Itron developed a number of packages defined by the amount by which it exceeds either 2001
or 2005 Title 24 building standards. For this analysis, RMI used only those packages based on
2005 standards. Furthermore,the data set consists of packages for several types of single family
and multi family homes, such as single family one-story, single family two-story, single family
attached, multi family two-story, and multi family three-story. To simplify our analysis,the savings
and cost data were averaged into one set of values for single family homes and one set of values
for multi family homes.
9
[IOU]Misc.xls • Measure lifetime
[IOU]Refriger
ation.xls
New CEEPS Appendix J • Packages of • Therms/unit potential
[IOU]ComApp measures • kWh/unit potential
endix.xls • Only HVAC and . Total kWh potential
water heating . Incremental package
addressed cost
Packages result • Package lifetime
in both electricity
and natural gas
savings
• Packages defined
to exceed 2005
T24 building
codes by 15%
Industrial Conventional CEEPS Appendix H . End use leve13 • Total kWh potential
Industries— IndustrialOutp • Levelized costs of
Existing uts.xls individual measures4
Facilities
Conventional CEEPS Appendix K • Measure level, • kWh potential per
Industries— • [IOU]IndAppe except packages baseline MWh
New ndix.xls for lighting/HVAC consumed
Construction • Incremental
measure/package
cost
Measure/package
lifetime
High-Tech Public Reports, • Measure level • Savings as% of
Industries Personal Interviews, baseline consumption
RMI Estimates5 for targeted end use
B. Customization
A number of customization options have been built into the model to ensure that
the results reflect the unique characteristics of each POU's service area. Though
each POU's results are based upon the same modeling framework, these
customization options ensure that the potential results accurately reflect each
POU's size, growth rate, climate zone, and customer base. The model also
allows each POU to specify various financial parameters, including customer
rates, energy costs, discount rates, customer rebate levels, and overhead.
3 Reporting end use level data for industrial rather than measure level data captures the additive
effects of combining measures.
4 RMI averaged levelized costs of each measure to develop levelized costs for each end use.
5 A full list of sources consulted is included in the discussion of high-tech industries.
10
Forecasted Sales and Demand Growth
The RMI model forecasts energy savings on a relative basis, as a function of
forecasted sales. Though the actual efficiency potential is calculated based upon
sales to various customer sectors, each POU also provided its baseline system
total sales forecast so that it could be compared to the system total sales
forecast after implementation of efficiency programs. The model also requires
each POU to provide a 10-year forecast of system peak demand. Both values
represent total sales, rather than energy or power at the city gate.
Though all utilities were able to provide sales forecasts, some utilities did not
provide peak demand forecasts. In these instances, peak demand was grown at
the same rate as total system consumption. Like total system consumption, the
system peak values were used only as outputs. Peak demand reduction
potential was estimated as a function of energy savings potential (more detailed
explanation provided in subsequent sections of this appendix).
The calculations of efficiency potential were based upon the sector-level sales.
The model thus requires each POU to also break down system total sales into
the three primary sectors: residential, commercial6, and industrial. Other sectors,
such as agriculture, were included in the system total but were not evaluated for
efficiency potential, as the CEEPS report did not include applicable measures.
It is important to note that the model requires commercial and industrial sector
sales forecasts to be based on the type of business, rather than on the
customer's size. As such, it is highly recommended that POUs distinguish
between commercial and industrial customers within the same size category (for
example between 200kW and 1000 kW) when inputting data into the model. The
efficiency measures in the model apply to specific building types and industries.
The commercial and industrial sectors are defined in the following section on
customer mix.
Customer Mix (By Building Type/Industry) and End Use Characteristics
At the outset of the study, each POU was asked to provide building type and end
use proportions for their service territory. The full lists of building types and end
use types used in the model are provided in Table 3 and 4, respectively. If these
proportions were unavailable, RMI substituted the attributes of the IOU that each
participant felt was most similar to their own POU.
6 Municipal loads were included in the commercial sector.
11
Table 3. Enumeration of Building Types in Model, by Sector
CommercialResidential
Mobile Homes College Chemicals
Multi-Family Grocery Electronics
Single-Family Health Fab. Metals
Lodging Food
Large Office Industrial Machines
Miscellaneous Instruments
Refrigerated Warehouse Lumber, Furniture
Retail Miscellaneous
Restaurant Paper
School Petroleum
Small Office Primary Metals
Warehouse Printing
Rubber, Plastics
Stone, Clay, Glass
Textiles, Apparel
Transportation Equipment
Data Center
Semiconductor Manufacturer
Lab
Table 4. Enumeration of End Use Types in Model, by Sector
IndustrialResidential Commercial
HVAC Cooking Compressed Air
Lighting HVAC Cooling
Miscellaneous Lighting Drives
Refrigeration Miscellaneous Fans
Water Heating Refrigeration Heating
Water Heating Lighting
Other
Pumps
Refrigeration
Climate
To account for the impact of climate on equipment usage patterns, the technical
potential for each measure was calculated based upon typical usage patterns
specified for the Title 24 Climate Zone applicable to each POU. Additional
details concerning this adjustment are included in the technical potential
discussion.
12
Rates and Avoided Costs
Each POU's current and forecasted rate schedule was used in the calculation of
the Participant Cost test and the Rate Impact Measure test. If a forecast was
unavailable, RMI grew each POU's current rates by 3% each year. Utilities also
provided their forecasted avoided energy costs for use in performing the Total
Resource Cost test, Rate Impact Measure test, and Program Administrator Cost
test. If this information was unavailable, RMI substituted the avoided costs of the
IOU that each participant felt was most similar to their own POU.
POU and Ratepayer Real Discount Rates
The cost test calculations are based upon the net present value (NPV) of all
future costs and benefits associated with each measure. To discount the future
stream of avoided costs and customer rates, a separate discount rate was
needed for the POU and for the customer. For instance, when calculating the
total resource cost (TRC) test, the future avoided costs were discounted
according to the POU's discount rate. When calculating the participant cost test
(PCT), the future rates were discounted according to the customer's discount
rate.
Each POU had the option of providing their own discount rate and specifying
their customers' discount rate. If this data was not provided, RMI substituted a
real utility discount rate of 5% and a real customer discount rate of 10%. The
10% customer discount rate reflects the fact that customers often require a faster
payback than do utilities.
Non-Capital Program Costs Including POU Incentives and
Marketing/EM&V/Administrative,
Though the CEEPS report provided capital costs for each measure, each utility
must specify their overhead costs. These costs are considered when
determining cost-effective potential, as they are part of the TRC test. The RMI
model calculates overhead as a function of the lifecycle energy savings for each
measure. The lifecycle cost per kWh was initially determined based on each
POU's historical performance, as was provided in the SB 1037 report. However,
each POU has the option of choosing a different cost per lifecycle kWh if
preferred. This topic is discussed further in section D, which covers the cost-test
calculations.
Each utility can also specify to the degree to which they will provide rebates on
efficiency measures. This incentive level is input as a percentage of the capital
cost.
13
C. Technical Potential
The term technical potential is typically used to describe the full extent of
efficiency potential, without regard to practicality or costs. In theory, the technical
efficiency potential could reach 100% of baseline consumption, as it is technically
possible to create buildings that do not use any electricity. The RMI technical
potential model is based upon the technical potential calculated in the CEEPS
report for the IOUs. The CEEPS measures represent the subset of measures
that Itron deemed to be reasonable to include at the time of the study. The
technical potential results therefore do not represent the maximum technical
potential that is theoretically possible. It is also important to note that the
technical, cost-effective, and feasible efficiency potential reported by the RMI
models are net, based on the net-to-gross ratio reported by Itron for each
measure.7
The structure of the technical potential outputs in the CEEPS report was
somewhat different for each sector. Since each data set contained different data
elements, the RMI model used a combination of methodologies to calculate
technical potential for the various sectors. The potential for existing buildings
and industrial new construction was modeled as a function of baseline sales.
The potential for residential and commercial new construction was modeled as a
function of forecasted new building space. RMI also developed a "high-tech"
industrial module, which modeled efficiency as a function of baseline sales.
The following discussion is organized based upon the methodology employed.
The residential and commercial sectors are described together, as the same
methodology was used for both sectors. The industrial sector is described last,
as a separate methodology was used for each portion—existing conventional,
new conventional, and high-tech—of this sector.
I. Residential and Commercial
Existing Buildings
Technical Potential: Energy
The CEEPS report provided technical efficiency potential for individual efficiency
measures for the PG&E service territory. This data set was used to develop a
total generic, baseline technical potential. It is referred to as the Itron Study
Baseline within the RMI technical potential model.
7 Importantly, some of the assumptions built into the CEEPS data may overstate the technical
potential. For example, some data sources are assumed to be front loaded (all installed in the
first year),which add considerably to the year 10 cumulative total. In this case, re-adoption of the
measure appeared to be presumed for short-lived measures along with continued counting of
energy savings after the first life cycle.
14
The baseline technical potential was converted to a relative measure so that it
could be applied to each POU's unique system. For each building type, RMI
divided the baseline technical potential by the baseline sales for the
corresponding building type to determine savings as a percentage of
consumption. This baseline percentage potential was then adjusted for climate
and end use differences between the baseline utility's customers and those of
each participating POU. The climate adjustment was achieved by comparing
per-unit energy savings for each POU's specific climate zone to the per-unit
savings for the baseline utility, for each measure.8
For instance, due to climate differences that affect technology usage patterns,
the per-unit savings for an air conditioner in a particularly hot climate zone may
occasionally differ from the per-unit average savings for the baseline utility. The
end use adjustment was achieved by comparing the relative end use composition
for each POU to that of the baseline utility-9 Once these adjustments were
made, the percentage savings was multiplied by each POU's forecasted sales to
the relevant building type to determine its technical potential. These steps are
summarized in Figure 1.
Figure 1. Overview of Methodology for Technical Energy Savings Potential
Convert IOU sted
Adjust for Climate Adjust for End UJC
es to
Efficiency Potential Based on 1
Estimates to Percent rofiles as PerceentialAppropriate T24 Buiddr`r� T e POUSavings fey Measure CZ Savings � ypand Building Type
Technical Potential: Demand
As in the energy potential analysis, the baseline technical demand potential was
converted to a relative measure so that it could be applied to each POU's
system. First, for each measure, the average kW saved per unit was divided by
the kWh saved per unit.10 The resultant kW per kWh saved was then multiplied
8 A"unit" refers to a unit of a given efficiency measure (such as one light bulb or one square foot
of attic insulation). The adjustment factor was calculated by dividing the per-unit savings for the
appropriate climate zone by that of the baseline utility.
9 Baseline residential characteristics are derived from Appendix H of the California Statewide
Residential Sector Energy Efficiency Potential Study. Baseline commercial characteristics are
derived from the California Commercial End-Use Survey,Table 9-2.
10 Average kW saved per unit is calculated by dividing annual kWh savings per unit by 8760.
15
by each POU's technical energy savings potential to determine the average kW
savings for each measure. The average kW savings was then multiplied by a
peak factor" to determine peak reduction potential.
New Construction
Technical Potential: Energy
For new construction, the CEEPS report provided technical potential for
packages of measures, rather than for individual technologies. The electricity
savings potential per home (residential) or per square foot (commercial) was
multiplied by the number of new homes or square feet forecasted, respectively,
to be built in a given year. RMI derived this forecast of new homes and new
commercial space by dividing the portion of annual load attributable to new
construction by the average annual electricity consumption per home or square
foot.12 When the portion of load that is new construction was not provided
specifically by the POU, RMI used a default assumption of 50 percent. The
resultant annual electricity savings were then adjusted by comparing the relative
annual energy consumed by HVAC and water heating for each POU to that of
the baseline.
Technical Potential: Demand
To determine peak demand reduction potential, the average kW saved per home
or per square foot of commercial space was first multiplied by the number of new
homes or square feet forecasted, respectively, to be built in a given year. The
resultant annual average kW savings were then adjusted by comparing the
relative annual energy consumed by HVAC and water heating for each POU to
that of the baseline. In the final step, the adjusted average kW savings were
multiplied by a peak factor (provided in the CEEPS appendices for each building
type) to determine peak reduction potential.
II. Industrial
The CEEPS report provided outputs for conventional industries. For existing
facilities, the technical potential was reported at the end use level, rather than at
the measure level. However, new construction results were provided at the
measure level.
RMI also developed a separate module to forecast efficiency potential for both
existing and new "high-tech" facilities, such as data centers, semiconductor
" Peak factors were determined by Itron in the CEEPS appendix as a ratio of peak demand
impact to average demand impact. Peak factors varied by region.
12 New construction is defined as all buildings constructed after 2006.
16
manufacturers, and labs. These facilities were not covered in the CEEPS report.
This module was based upon a variety of sources, which are discussed in further
detail later in this section.
The methodology for each portion of the industrial sector varied based upon the
nature of the data available. The following discussion is therefore organized with
a different section for each of the three modules in the technical potential model:
existing conventional facilities, new conventional facilities, and high-tech facilities.
Conventional Industries: Existing Facilities
Technical Potential; Energy
For existing facilities, the CEEPS study only allowed for modeling of savings
potential at the end use level, rather than at the measure level. Furthermore,
climate adjustments were not possible, as savings potential was not available by
climate zone. Otherwise, the method for estimating energy efficiency potential
was the same as for residential and commercial existing construction. The
baseline technical potential was converted to a relative measure so that it could
be applied to each POU's system. This was done by dividing the baseline
technical potential by baseline sales for the applicable industry to determine
savings as a percentage of consumption for that industry. The model then
adjusts for potential differences in end use consumption within each industry
between the baseline utility's customers and those of each participating POU.13
In the final step, the percentage savings is multiplied by each POU's forecasted
sales to the relevant industry to determine its technical potential.
Technical Potential: Demand
As in the energy potential analysis, the baseline technical demand potential was
converted to a relative measure so that it could be applied to each POU's
system. First, the total average kW reduction potential was divided by the total
kWh savings potential. The resultant kW per kWh saved was then multiplied by
each POU's technical energy savings potential to determine average kW
savings. Given the relatively constant usage patterns inherent in most industrial
processes, the peak reduction was assumed to be the same as the average kW
savings.
Conventional Industries: New Construction
The CEEPS report limited the scope of its new facilities analysis based on
expected new construction patterns for the IOUs. For this study, only
refrigerated warehouses and electronics facilities were modeled. Though new
refrigerated warehouses were included in the CEEPS data set for industrial new
construction, existing refrigerated warehouses were part of the CEEPS data set
13 Baseline industrial characteristics were derived from the CEEPS file Industrial outputs-As.
17
for existing commercial buildings. For the sake of consistency, RMI grouped all
of the refrigerated warehouse results (both existing and new facilities) in the
commercial model outputs. Though it was necessary to model technical potential
for new refrigerated warehouses together with new electronics facilities, new
refrigerated warehouses were moved to the commercial calculations in the cost-
effectiveness model.
Technical Potential: Energy
The CEEPS report provided energy savings potential on a relative basis (kW
savings per MWh consumed) for each measure, thereby eliminating the need for
the RMI model to calculate a relative savings potential. The model multiplied this
value by the forecasted new construction energy consumption for each facility to
determine technical energy savings potential for each measure.
Technical Potential: Demand
First, the average kW saved per MWh was divided by the kWh saved per MWh.
The resultant kW per kWh saved was then multiplied by each POU's technical
energy savings potential to determine average kW savings. The average kW
savings was then multiplied by a peak factor (provided in the CEEPS appendices
for each end use) to determine peak reduction potential.
High-Tech Industries: Existing and New Facilities
For the purposes of this analysis, high-tech industries include data centers,
semiconductor manufacturers, and laboratories. The CEEPS report did not
specifically address data centers or labs, and only specifically addressed
semiconductor manufacturers for new construction. RMI therefore conducted
supplemental analysis on high-tech efficiency measures and potential. This
section summarizes that analysis.
Technical Potential. Energy
RMI's estimate of technical potential for high-tech industries was based on a
number of sources, summarized in Table 5:
18
Table 5. Sources Used to Develop Potential Estimates for the High Tech Sector
Source Data
Pacific Gas&Electric(PG&E) Design Data Center efficiency measures
Guidelines Sourcebook(Rumsey Engineers)
RMI personal conversation with Carl Data Center efficiency measures
McDonnell at Silicon Valley Power
CEEPS Industrial New Construction Semiconductor Manufacturer, Lab
Methodology&Asset Inputs, Appendix Q efficiency measures
Silicon Valley Power-commissioned energy Semiconductor Manufacturer
audits efficiency measures
EPA's 2003"Laboratories for the 21s` Lab baseline energy consumption
Century: Energy Analysis" breakdown
Semiconductor baseline energy
consumption breakdown
Lawrence Berkeley Lab's"Data Center Data Center baseline energy
Energy End Use Breakdown" consumption breakdown
Due to the lack of detailed and consistent source data regarding high-tech
efficiency potential, RMI attempted to identify the subset of measures that: 1)
affect the largest end-uses, or 2) are applicable to any type of industry (i.e.,
lighting retrofits), rather than developing a comprehensive list of measures.
For each measure identified, RMI used the sources listed in Table 5 to estimate
the percent savings over baseline for each type of high-tech industry, for the
particular end use affected by the measure. An estimate was then made of the
applicability of each measure to the high-tech industry in question. That is, can
the particular measure be installed at all customer sites within each category, or
only a portion? Finally, additive potential was calculated for each end use. That
is, care was taken to avoid double counting the impacts of partially redundant
measures. These metrics were combined with the baseline energy consumption
breakdown by end use to determine the total technical potential of each measure
as a percent of total system consumption.
Finally, each measure was defined as retrofit, replace-on-burnout, or new
construction measure. This determination was made based on the above source
documents as well as RMI's past experience with high-tech industries.
Technical Potential: Demand
The source documents used to develop the estimates of energy efficiency
potential do not, by and large, contain estimates of peak demand reductions in
addition to energy reductions. Given the relatively flat usage patterns inherent in
19
industrial processes, the peak reduction was assumed to be the same as the
average kW savings.
D. Cost-Effective Potential
Utility analysts use a variety of tests to judge the effects of any particular utility
program. Each of them is designed to identify the relative costs and benefits to a
set of players involved in the transaction. For example, the participant cost test
(PCT) is used to examine cost effectiveness from the perspective of utility
efficiency program participants, while the rate impact measure (RIM) test
examines the impact for all utility customers or ratepayers. RMI's efficiency
model performs four cost tests for each measure under consideration. These
tests are summarized in Table 6. The total resource cost (TRC) test was used to
determine total cost-effective potential.
For the residential and commercial sectors, all measures were evaluated based
on the ability of each measure to pass the TRC test. These calculations
evaluated the total benefits and the total costs for the full life of each measure.
The methodology for the industrial sector was altered slightly based upon the
need to evaluate efficiency potential at the end-use level rather than the measure
level. This is addressed in further detail in the Cost of Technology section. A
discussion of the various components included in the four cost tests.
Table 6. Description of Cost Tests Used in the Cost Effectiveness Potential Model
CostsAeasures
Participant Cost Are expenditures Cost of technology, Bill savings
(PCT) lowered for program after incentives
participants? (rebates)
Program Administrator Are utility revenue Incentive paid to Avoided energy and
(Utility)Cost requirements customer; marketing, capacity costs
(PAC) lowered? EMW admin costs
Rate Impact Measure Are utility rates Incentive paid to Avoided energy and
(RIM) lowered? customer; lost capacity costs
revenues; marketing,
EMW admin costs
Total Resource Cost Are total customer Cost of technology; Avoided energy and
(TRC) expenditures lowered? marketing, EMW capacity costs
admin costs
Avoided Energy Costs
Each POU had the opportunity to use its own avoided energy costs. If this data
was unavailable, RMI substituted the forecasted avoided energy costs for the
IOU specified by each participant. The annual forecasted avoided costs from
20
2007-202614 were required for each time-of-use JOU) period (e.g., summer
peak, summer off-peak, summer partial peak, winter partial peak, winter off-
peak). A weighted average avoided cost was developed for each year based
upon the TOU load shape associated with the end use targeted by each
measure.15 After calculating the annual avoided cost associated with each
measure, this stream of future costs was converted into a single "lifecycle"
avoided cost over the life of the measure, based upon its net present value. This
lifecycle avoided cost was then multiplied by the total kWh saved over the life of
the measure to determine the total avoided energy costs over the life of the
measure.
Avoided Capacity Costs
Each POU also had the opportunity to use its own avoided capacity costs. If
POUs did not provide this information, the avoided capacity cost was entered as
zero. Avoided capacity costs were embedded in the avoided energy costs for
each proxy IOU.
The stream of future avoided capacity costs was converted into a single
"lifecycle" avoided capacity cost, based upon its net present value. This lifecycle
avoided cost was then multiplied by the measure's peak demand reduction
potential to determine the total avoided capacity costs over the life of the
measure.
Bill Reduction
The participating customer's bill reduction was determined using the forecasted
rates for each of the three major customer classes — residential, commercial, and
industrial. The calculations were used in the PCT and RIM. Residential rates
were used to evaluate all residential measures, commercial rates were used to
evaluate all commercial measures, and industrial rates were used to evaluate all
industrial measures. The stream of future rates for the relevant customer class
was converted into a single "lifecycle" rate, based upon its net present value.
The customer discount rate was applied to PCT calculations, and the POU
discount rate was applied to RIM calculations. This lifecycle rate was then
multiplied by the total kWh saved over the life of the measure to determine the
total bill reduction over the life of the measure. This bill reduction is not a
component of the TRC test and therefore does not affect the cost-effective
efficiency potential
14 The model uses avoided costs and customer rates for the next 20 years rather than just the 10-
year study period. This is because each measure's cost-effectiveness if evaluated over the full
life of the measure. The maximum measure life in this study is 20 years.
15 A TOU load shape provides the percentage of annual energy consumption that occurs during
each TOU period.
21
Measure Cost or Cost of Technology
Residential and Commercial
The cost of the technology or measure being considered, also known as the
gross participant cost, represents the incremental capital cost of one unit of a
given measure (i.e., a light bulb or a square foot of attic insulation).16 These
costs were included in the CEEPS appendices. New construction measures
were bundled together as packages that save both electricity and natural gas. In
these instances, entire packages — rather than individual measures —were
evaluated using the cost test. To enable a fair evaluation of the cost to save
electricity, the cost of the package was adjusted based upon the proportion of
total BTUs saved that represents electricity savings.
Industrial
For industrial efficiency, additive technical potential was only available for entire
end uses, rather than for specific measures within each end use.17 The cost-
effective potential was therefore also evaluated at an end-use level, rather than
at the measure level. Furthermore, cost data for the industrial sector was only
available on a levelized ($ per kWh saved) basis.18 To determine the total
incremental capital costs for each end use, the levelized costs were unlevelized.
In other words, they were converted into net present value, assuming a 5%
discount rate and a measure life of 20 years.
Incentive Paid to Customer
The incentive paid to the customer represents the rebate that the POU will
provide to offset the cost of the technology. This value was applied to the PCT,
PAC, and RIM calculations. RMI calculated the incentive as a percentage of the
total technology cost. The default percentage was assumed to be 50% of the
incremental technology cost, though the model allows users to alter this
16 The cost of technology does not include direct installation costs.
17 Non-additive potential reflects the potential savings of a measure when implemented in
isolation. Given that measures are usually implemented in combination with several other
measures, it is more accurate to evaluate the additive potential,which adjusts for interaction
effects. While the CEEPS data provided additive potential at the measure level for the residential
and commercial sectors,the industrial measure-level data was non-additive. However, the
CEEPS data did provide additive potential for each industrial end use, and this data set was
chosen to more accurately reflect the actual energy and demand savings potential.
18 Levelized cost data was provided for each measure in the non-additive data set. A weighted
average of the levelized costs of measures associated with each end use was calculated to
determine a levelized cost for each industrial end use.
22
percentage if desired. This rebate is not a component of the TRC test and
therefore does not affect the cost-effective efficiency potential.
Marketing EM&V and Administration Costs
Overhead devoted to efficiency programs can vary considerably by utility. For
this model, the costs were calculated as a function of the total kWh saved over
the life of the measure. The cost per lifecycle kWh was estimated initially by RMI
based upon the total marketing, EM&V, and administration costs and the total
lifecycle kWh saved as reported by each POU in the SB 1037 report.19 The
model allows users to alter this cost per lifecycle kWh if desired.
E. Feasible Potential
AB 2021 requires all POUs to acquire "all available energy efficiency and
demand reduction resources that are cost-effective, reliable, and feasible." Given
the diversity of the POU electric systems (including but not limited to: local
demographics, age and condition of building stock, saturation of previously
installed energy efficiency measures, economic growth, rate of expansion and
new construction, and customer payback expectations), the implementation of all
cost-effective measures identified in the RMI study would not be feasible or
achievable. Therefore, each POU established feasible energy efficiency and
demand reduction targets based on the results of the RMI study and local
knowledge of their respective service areas.
Feasible Scenarios
To help utilities set feasible energy efficiency and demand reduction targets, RMI
modeled the following scenarios:
• Scenario 1—assumed that the historical incremental percent per year
reduction in load is maintained over the study period. This scenario is
considered to be the lower bound of feasible potential. This scenario is
based upon the annual spending and savings reported for the fiscal year
2005-06 in the SB 1037 report. To determine future energy savings, the
reported 2005-06 savings were first divided by the baseline annual
consumption to determine the incremental percentage of total
consumption saved per year. This percentage was then applied to the
forecasted system consumption to determine annual energy savings.
19 If this data was unavailable in the SB 1037 report, RMI applied the all-POU average of
$0.01/lifecycle kWh. RMI capped values at$0.03. However, the final determination for this value
was left to each POU.
23
• Scenario 2—suggested a utility feasible percentage assuming that each
POU could implement 50% of the total cost-effective measures identified
in the cost-effective model.
• Scenario 3—suggested a utility feasible percentage assuming that each
POU could implement 80% of the total cost-effective measures identified
in the cost-effective model.
POU Specified Targets
POUs were provided a number of options in setting annual energy efficiency
targets, these included:
• Option 1 — accept one of the targets developed by the RMI model
described in Scenarios 1, 2, and 3 above.
• Option 2 — adjust the unit inputs in the RMI model to arrive at a per
measure potential, taking into consideration local market conditions
(including known measure penetration levels). POUs following this option
specified number of units of each measure that passed the TRC in the
RMI model to arrive at a feasible quantity of measures installed per year.
The POU-specified annual energy and demand savings are calculated by
multiplying the feasible units by the per-unit energy or demand savings.
• Option 3 — Set an annual target based on a combination of factors,
including the RMI Scenario 1, 2, and 3 results, existing State energy
efficiency goals, and POU knowledge of local markets and conditions.
POUs following this option had concerns that while the RMI model may do
a good job of applying the CEEPS market potential data to POU
territories, the data and methodology is limited in its use for determining
program-specific energy efficiency goals on the local level20. With this in
mind, POUs following this option set reasonable, but aggressive program
targets. These targets are based on local market knowledge and take into
consideration existing cost-effective program offerings and previous
program year successes, and how these offerings could be expanded
and/or supplemented to meet targets.
The following list is illustrative of the types of adjustments that were made by
POUs setting feasible targets using Option 2 or 3 above:
20 We will not attempt to revisit all the methodology issues as they are well documented in Itron's
presentations on April 20, 2007 to the CEC and on May 4, 2007 to the CPUC. However to
summarize, market potential studies can only directly imply the relative market potential for
energy efficiency. Identifying an energy efficiency program's potential requires further analysis to
determine what savings potential can realistically be attributed to the program.
24
— The model favors the wide distribution of compact fluorescent lamps
(CFLs) because of their cost-effectiveness and ease of installation;
however, many POUs have already deployed a substantial number of
CFLs within their service territories, and the number of additional CFLs
recommended in the RMI model would not be feasible in some areas.
— Non-summer peaking utilities (generally along the coastal areas)
needed to adjust for model bias towards reducing summer peak.
Typically this involved adjusting the potential downward for air
conditioning measures.
— Measure potential was assessed against recent program performance
and adjusted to accurately reflect the potential that has already been
realized.
— The model favors pool pump measures and needed some adjustments
to accurately reflect the territory baseline stock and potential.
— Certain measures identified in the cost effective potential have
relatively poor chance of being installed due to regional-specific
barriers to implementation. Adjustments were made to accurately
reflect each measure's true potential, based on the expertise of utility
program staff.
— Lack of industrial diversity (or relatively few industrial customers)for
many POUs creates significant barriers to further penetrating the
industrial market beyond what has already been accomplished.
— Cost-effective potential includes measures with high local market
penetration rates. Some POUs assert that these measures should not
be subsidized through utility program interventions. While the measure
potential exists within the utility service territory, ultimate savings are
not necessarily attributed to the program and therefore removed from
some utility-specified program potential estimates.
— Economic considerations (recession, expansion, homogeneousness,
etc) were taken into account and adjusted for as needed.
25
III. Energy Efficiency and Demand Reduction Targets
This section provides energy efficiency and demand reduction targets by specific utility.
As shown in Table 7, the 35 POUs in this study expect to reduce their annual
consumption of electricity by approximately 2,089 gigawatt hours over the ten-year
period ending in 2016. This represents a savings of nearly eight percent over the period.
Table 7. Energy Efficiency Targets by POU 2007-2016
Cumulative Energy Reduction Targets(MWh) 10-yr 10-yr Average
Total Total Annual
Publicly Energy Forecasted Energy
Owned Reduction Electrical Reduction
utility 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Target Consumption Target
(MWh) (MWh) (%/yr)
Alameda 660 1,521 2,281 3,042 3,802 4,563 5,323 6,084 6,844 7,605 7,605 4,440,700 0.17%
Anaheim 15,897 32,014 48,247 64,839 81,514 98,470 115,562 132,722 149,952 167,682 167,682 27,814,949 0.60
Azusa 2,084 41168 6,252 8,336 10,420 12,504 14,588 1fi,672 18,75fi 20,840 20,840 2,729,194 0.76%
Banning 873 1,747 2,620 3,494 4,367
9 5,240 6,114 6,987 7,861 8,734 8,734 1,810,995 0.48%
Biggs 106 213 31 425 532 638 744 850 957 1,063 1,063 180,385 0.59%
Burbank 11,307 22,615 33,922 45,229 56,536 67,844 79,151 90,458 101,765 113,073 113,073 11,862,716 0.95%
Colton 2,625 5,251 7,876 10,501 13,127 15,752 18,378 21,003 23,628 26,254 26,254 4,293,194 0.61%
Corona 467 934 1,401 1,867 2,334 2,801 3,268 3,735 4,202 4,669 4,669 783,530 0.60%
Glendale 11,362 22,724 34,086 45,446 56,810 68,172 79,534 90,896 102,258 113,620 113,620 11,380,875 1.00%
Gridley 92 183 275 367 459 550 642 734 825 917 917 436,246 0.21%
Healds lung 198 397 595 794 992 1,190 1,389 11587 1,786 1,984 1,984 817,691 0.24%
Hercules 136 273 409 546 682 818 955 1,091 1,228 1,364 1,364 173,632 0.79%
❑D- 45,067 90,133 135,200 180,266 2251333 270,400 315,466 360,533 405,600 450,666 450,666 41,869,219 1.06%
Industry n/a n/a n/a n/a n/a n/a n/a n/a
LMUD 733 1,467 2,200 2,933 3,666 4,400 5,133 5,866 6,600 7,333 7,333 1,562,046 0.47%
Lodi 2,000 4,000 6,000 8,001 10,001 12,001 14,001 16,001 18,001 20,001 20,001 5,162,129 0.39%
Lompoc 1.121 2,242 3,363 4,484 5,605 6,726 7,847 8,968 10,089 11,210 11,210 1,485,125 0.75%
Merced 19 239 0, 8 4 13224,,577W5 138,111 13368,,5557 340,,943,4388 00..4753%%6 4 , 69,279 83,134 96,990 1101846
MID 1 77,7 5 423
Moreno Valley 822 1,644 2,466 3,289 4,111 41933 5,755 6,577 7,399 8,221 81221 741,070 1.11%
Needles 817 ,_ 2,452 3,269 4,086 4,904 5,721 6,538 7,356 8,173 8,173 726,509 1.12%
Pasadena 5,000 15,000 28,500 45,500 68,127 90,753 113,380 136,006 158,633 1811260 181,260 13,661,510 1.33%
Pittsburgh Power/ 178 355 133 711 888 1,066 1,144 1,421 1,119 1,777 1,777 195,394 0.91%
Island Ener
Port of Oakland 884 11767 2,651 3,535 4,418 5,302 6,186 7,070 7,953 8,837 B,B37 946,210 O.
Plumas Sierra 621 1,242 1,863 2,483 3,104 3,725 4,346 4,967 5,588 6,209 61209 11871,636 0.33%
Rancho Cucamonga 448 896 1,343 1,791 2,239 2,687 3,135 3,582 4,030 4,478 4,478 751,700 0.60%
"M,d, 2.2,2e 22,210 44,850 67,910 91,320 115,170 139,420 164,040 189,060 214,510 240,380 240,380 24,038,000 1.00%
le .2,2 17,432 26,149 34,865 43,581 52,297 61,014 69,730Valley Power 25,762 51,524 77,286 103,048 128,810 154,572180,334 206,09fi Lake 129 258 388 517 646775 905 1,034 1,163 1,292 1,292 787,736 0.16%
e Donner I,001 2,003 3,004 4,005 5,007 6,006 7,009 8,011 9,012 30,0147,824 15,095 2fi287 53,177 80,686 102,028 116458 124,2061399900 0 0198 396 594 792 990 1,188 1,386 1,584 n/a n/a n/a n/a n/a n/a n/a na186 916 378 928 578 698 798 775 3 025 420 1 246 579 1 461 332 1 669 872 3 879 003 2,089,159
2,089,159
267,453,831 0.78%
imPeoai agores are mr zoos mrough 2017
Using a slightly different metric for evaluation, the savings noted in Table 7 account for a
significant reduction in load growth among the utilities participating in this analysis.
Roughly one half of load over the next ten years is expected to be offset through via
implementation of energy efficiency measures. In some instances, it is anticipated that
all load growth will be met via energy efficiency.
Table 8 takes a slightly different perspective, analyzing the extent to which peak demand
can be reduced via utility energy efficiency programs. From this analysis, the 35 POUs
participating in this project estimate a peak demand savings of 274 megawatts over the
ten-year period, a reduction of roughly four percent, compared to peak demand in the
absence of such programs.
26
Table 8. Energy Efficiency Demand Reduction Targets by POU 2007-2016
Cumulative Demand Reduction Targets(MW) Avenge
Total peerage Annual
Demand Forecasted
Publicly Reduction
Demand Reduction
OwnedTarget
(Mw) Tarqet
utility 2007 2008 2009 2010 2011 j2012 2013 2014 2015 2016 (Mw)
Alameda 0.1 0.2 0,3 0.4 0.5 0.6 0.7 D.8 0.9 0.9 76 0.12%
Anaheim 3.3 6.7 10.1 13.6 17.1 24.3 27.931.5 3Azusa 0.2 0.5 0.7 1.0 12 0.1 0.2 0.4 0.5 0.6 Banning0.0 0,0 0.0 0.1Big sBurbank 2.4 4.7 7.1 9. .Colon 0.3 0.61 2.0 2.2 2.5 2.8 2.8 103 0.27%
Corona 0.3 0.1 0.2 0.2 0,3 0.3 0.4 0.5 0.5 0.6 0.6 16 0.36%
Glendale 1.3 2.6 3.9 5.2 6.5 7.8 9.1 10.4 11.7 13.0 13.0 336 0.39%
Gritlle 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 l4 O.OB%
Healdsburg 0.0 0.0 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0,2 22 0.10%
Hercules 0.0 0.0 0.1 0.1 0.1 0.1 0.1 1 0.1
1ID` 6.1 12.2 18.3 24.4 30.5 36.6 42.7 48.8 55.0 61.1 61.1 1,207 0.51%
Industry n/a n/a n/a n/a n/a
n/a n/a n/a n/a n/a n/a n!a Na
LMUD 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.7 0.8 0.9 0,9 28 0.33%
Lodi 0.2 0.5 0.7 1.0 1.2 1.5 1,7 2.0 2.2 2.5 2,5 146 0.17%
Lompoc 0.1 0.3 0.4 0.5 0.6 0.8 D.9 1.0 1.1 1.3 1.3 27 0.47%
q 5 4.5 99 0.45%
Merced 0.4 0.9
MID 1.6 3,2 4,8 6.3 7.5 9.5 11.1 12.7 14.3 15.9 15.9 797 0.20%
Moreno Valley 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.1 1.1 26 0.40%
Needles 0.1 0.2 0.4 0.5 0.6 0.7 0.8 1.0 1.1 1.2 1.2 23 0.52%
Pasadena 0.6 1.8 3.4 5.5 8,2 10.9 13.6 16.3 19.0 21.7 21.7 321 0.68%
Pittsburgh Power/ 0.0 0.1 0,1 0.1 0.1 0.2
Island Ene
Port of Oakland 0.1 0.2 0.3 0.4 0.5 0.7 0.8 0.9 1.0 1.1 1,1 35
Plumas Sierra 0.1 D.1 0.2 0.3 0.4 0.4 D.5 0.6 0.6 0.7 0.7 33 0.22%
Rancho Cucamonga 0.1 0.1 03 0.3 0.4 0.4 0.5 0.6 0.6 16 0.36%
Riverside 2.2 4.5 6.7 8.9 11.1 13.4 15.6 17,8 20.0 22.3 22.3 609 0.37%
Roseville 1.1 2.1 3.2 4.2 5.3 6.3 7.4 6.4 9.5 10.5 10.5 371 0.28%
Silicon Valley Power 3.1 6.0 8.9 11.9 14.9 76 1 20.9 23.9 26.8 29.8 29.8 509 0.59
Shasta Lake 3.0 6.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 19 O.OB%
Truckee Donner 0.0 0.0 0.3 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.1 41 0.28%
Tru 0.1 0.2 0.0 6.0 9.0 12.0 14.0 15.0 15.0 16.0 16.0 523 0.31%
TID Lo 2.0 3.0 0.0 0.0 0.0 0.0 D.0 0.0 0.0 0.0 18 0.00%
Ukiah 0.0 0.0 0.1 0.1 0.1 0.1 0,2 0.2 0.2 0.2 0,2 39 0.05%
Vernon
n/a n/a n/a n/a n/a n/a 1n/a n/a n/a n/a n/a n/a n/a
Total 25.0 50.6 77.
6.6 105.2 134.5 163.9 192.3 219.8 246.3 273.8 273.8 6458 0.42
*Imnerial figures are for 20cS 11-11 2017
27
IV: Conclusion
CMUA views this report as the beginning of an important dialogue to assist state
policymakers in developing a reliable assessment of energy efficiency throughout the
state. With this report, CMUA and its public power partners believe a realistic
assessment of energy efficiency potential moves the debate in the appropriate direction.
That being said, there are numerous issues that are currently being addressed not only
within the public power community, but also at the CPUC and the CEC that deserve
more discussion.
Many of these considerations will have a critical impact on the development of targets
beyond 2007. From a public power perspective, state policymakers may want to
consider developing additional tools that will enhance the reliability of future forecasts.
These tools should take into account a variety of factors:
Previous market potential estimates (for upper boundary limits),
Actual performance of programs,
• Lessons learned while utilities ramp up programs to reach targets,
Lessons learned from IOU efforts,
Potential for new and emerging technologies not previously identified,
• Code changes and their effects on program targets.
As vertically integrated utilities, consideration should be given to the overall energy
efficiency performance of POUs. When operational improvements on the distribution
side are considered, the energy savings potential is greatly increased. We continue to
recommend that all energy efficiency savings, both demand and supply, be reported and
tracked toward meeting statewide goals for energy efficiency.
As noted earlier, with the exception of Silicon Valley Power, which has already adopted
its efficiency targets, the estimates provided by each utility are preliminary in nature and
will be finalized by each utility's local governing board during the next three months.
We look forward to discussing these results in more detail at CEC workshops scheduled
for August 9 and 27, respectively.
28
Appendix A:
Individual Utility Data Sets
29
6130107 Preliminary Target: Pending Approval of Governing Board
Alameda Power & Telecom
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 51,885 59,042 63,077 67,370 72,62B 76,626 79,694 62,574 85,549 69,404
E Residential 26,882 28,853 29,990 30,878 31,787 32,777 33,790 34,783 35,841 37,888
I Commerdal 25,004 30,189 33,087 36,491 41,041 43,8511 45,9044 47,7911 49,8080 51,516
Conventional Industrial 0 0 0 0 0 0 0 0 0
® Data Centers 0 0 0 0 0 0 0
Technical Semiconductor Manufacturers 0 0 0 0 0 0 0
W Labs 0 0 0 0 0 0 0 0 0 0
Energy g 9 10 10 10 11
Efficiency System Total 6 7 B 8 3 3 3 4 4
Potential ; Residential 3 3 3 3 3 6 7 7
Commercial 3 4 5 5 6 6 6
g 0 0 0 0 0 0
Conventional Industrial 0 0 D 0 0 0 0 0 0
e Data Centers D 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0
p Labs 0 0 0 0 0 0 0 0 0 0
System Total 41,312 46,187 49,026 S2,046 55,733 58,470 60,774 62,964 65,176 68,379
Residential 21,067 22,683 23,560 24,247 24,935 25,682 26,443 27,187 27,908 29,773
39 i Commercial 20,245 23,504 25,466 27,799 30,798 32,788 34,331 35,770 37,26o 38,6000
Conventional Industrial 0 0 0 0 0 0 0
a Data Centers 0 0 0 0 0 0 0 0 0 0
Cost-Effective Isem Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
C Labs 0 0 0 0 0 0 0 0 0 0
Energy 6 7 7 7 7 86
Efficiency System Total 5 5 6 2 2 3 3 3 3
3 Residential 2 2 2 2 4 5 5 5
Potential Commercial 3 3 3 4 4 4
o Conventional industrial 0 0 0 0 0 0
0 0 0 0
Data Centers 0 0 0 0 0 0 0 0 0 0
E 0 0 0 0 0 0 0
Semiconductor Manufacturers 0 0 o 0 0 0 0 0 0 0
p Labs 0 0
� ;...
��r 4 �' " ffi2J7 a
£ 3 78 7,
Average :. Average Annual Technical Potential 2.01%
Annual W Average Annual Cos?-Effective Potential 1.54%
verage Annual Feasible Targets 0,17%
Impact on A
Forecasted
Consumption Average Annual Technical Potential 1.42%
and S Average.Annual Cost-Effective Potentlat 1.04%
Demand Average Annual Feasible Targets 0.12%
30
6/30/07 Preliminary Target: Pending Approval of Governing Board
Anaheim Public Utilities
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 291,105 313,142 329,442 350,330 360,540 378,757 389,534 399,444 409,992 430,783
Residential 86,544 95,519 101,028 106054 110,147 114,048 119,042 122,137 126,091 136,078
= Commercial 138,065 150,814 160,728 176:348 181,005 193,887 198,437 202,975 208,292 212,573
Conventional Industrial 66,496 66,809 67,686 68,129 69,389 70,822 73,055 74,332 75,609 82,132
LN Data Centers 0 0 0 0 0 0 0 0 0 0
Technical Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
Energy
W Labs 0 0 0 0 0 0 0 0 0 0
Efficiency System Total 36 40 42 45 48 49 50 52 33 56
Potential Fi Residential 9 10 11 12 12 13 13 14 14 15
Commercial 20 22 23 25 26 28 29 29 30 31
Conventional Industrial 8 8 8 8 8 8 8 8 9 9
n Data Centers 0 0 0 0 0 0 0 0 0 0
EE Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
O Labs 0 0 0 0 0 0 0 0 0 0
System Total 224,808 238,366 248,088 261,499 268,389 279,912 287,385 294,095 300,810 317,446
3 Residential 68,513 74852 78483 81,791 84,410 86,B52 89,323 91,826 94,230 102,195
Commercial 97,729 104:672 109:991 119,704 122,864 130,689 133,764 136,845 140,031 143,159
> Conventional Industrial 58,566 58,842 59,614 60,005 61,115 62,371 64,298 65,423 66,548 72,092
a Data Centers 0 0 0 0 0 0 0 0 0 0
Cost-Effective Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
Energy m Labs 0 0 0 0 0 0 0 0
Sys 0 0
tem Total 26 28 29 31 32 33 34 35 36 38
Efficiency ; Residential 6 7 7 8 8 8 9 9 9 10
Potential S Commercial 13 14 15 16 17 18 18 18 19 19
Conventional Industrial 7 7 7 7 7 7 7 7 8 8
a Data Centers 0 0 0 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
Labs 0 0 0 0 0 0 0 0 0 0
511
`:Z� � � � s '� "`•�.'\a ` a ��� �`��'ae� "q x..' � '��
WOW XMV
T r 6� ; x F iS,689 1�55 �'
3 # StF? 1it� 7u 2ie5
7,77
46
a 1_
z 364 7 572 S13
f _ ''btticel. ` 552 ASS
t
Average Average Annual Technical Potential
I Annual Average Annual Cost-Effective Potential 1.14%
Impact On yr Average Annual Feasible Targets 0.6D%
Forecasted
Consumption $',,., Average Annual Technical Potential 0.95%
and = Average Annual Cost-Effective Potential 0.65go
r Demand Average Annual Feasible Targets 0.60%
ii
t
kt€
S
31
6130107 Preliminary Target: Pending Approval of Governing Board
Azusa Light & Water
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 33,262 35,703 37,736 39,408 41,092 42,805 44,547 46,323 48,145 50,561
= Residential 14,446 15,851 16,806 17,587 18,337 19,095 19,871 20,666 21,456 22,913
S
Commercial 7,040 7,735 8,468 9,008 9,585 10,178 10,776 11,384 12,037 12,612
Conventional Industrial 11.,776 12,117 12,463 12,83 13,170 13,530 13,890 14,270 14,650 15,0
36
0 0
Data Centers 0 0 0 0 U o p 0 0
Technical Semiconductor Manufacturers 0 0 0 0 0
Energy W Labs 0 0 0 0 0 0 0 0 0
Efficiency System Total 4 4 5 S 5 5 6 6 6 7
Residential 2 2 2 2 2 2 3 3 3 3
Potential E Commercial 1 1 1 1 1 1 2 2 2 2
Conventional Industrial 1 1 1 1 2 2 2 2 2 2
a Data Centers 0 0 0 0 0 0 0 0 0 0
Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
Labs 0 0 0 0 0 0 0 0 0 0
System Total 28,011 29,887 31,372 32,656 33,936 35,227 36,538 37,870 39,212 41,198
t Residential 11,696 12,806 13,492 14,050 14,580 15,110 15,649 16,198 16,738 17,942
Z
Commercial 5,602 6,059 6,545 6,953 7,379 7,814 8,252 8,698 9,156 9,590
} Conventional Industrial 10,713 11,022 11,335 11,653 11,976 12,304 12,6360 12,9740 13,3180 13, 0
a Data Centers 0 0 0 0 0 0
Cost-Effective 5 Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
W 0 0 0 0 0 0 0
Energy Labs 0 0 0
System Total 3 3 4 4 4 4 4 4 4 5
Efficiency ; Residential 1 1 1 1 2 2 2 2 2 2
Potential S Commercial 1 1 1 1 1 1 1 1 1 1
o Conventional Industrial 1 1 1 1 1 1 1 1 2 2
a Data Centers 0 0 0 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
O Labs 0 0 0 0 0 0 0 0 0 0
I 1!
a66 2 .4997lk�72
a �R aT 68 68'
66 6
Average °pp'i Average Annual Technical Potential 1.85%
Annual C Z Average Annual QU;gffl&l;jyc Potential 1.51%
Impart on W Average Annual Fear,ibis Targets 0.76%
'i Forecasted
k Consumption Average Annual Technical Potential 0.96%
and _ Average Annual t^^�cLive Potential 0.69%
I
Demand. Average Annual Feasible Targets 0.35%
6
32
6130107 Preliminary Target: Pending Approval of Governing Board
Banning Electric Utility
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 24,S33 27,300 30,671 33,729 35,364 37,037 38,743 40,492 a2,300 44,810
Residential 17,160 19,075 21,069 22,898 24,005 25,134 26,298 27,495 26,701 30,682
Commercial 6,138 6,928 8,198 9,314 9,792 10,286 10,777 11,277 11,827 12,302
s Conventional Industrial 1,234 1,298 1,404 1,518 1,5677 1,6177 1,6688 1,7200 1,772 1,8255
Data Centers 0 0 0 0 0
Technical LP Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0
yCj Labs 0 0 0 0 0 0 0 0 0 0
Energy 4 5 5 5 5 6 6 6
Efficiency System Total 3 4 3 3 3 4 4 4
Residential 2 2 3 3 2 2 2 2
Potential I Commercial 1 i 1 1 1 2
9 Conventional Industrial 0 0 0 0 0 0 0 0 0 0
e Data Centers 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 p 0
p Labs 0 0
System Total 19,953 22,021 24,398 26,507 27,764 29,052 30,356 31,694 33,085 35,129
L Residential 13,871 15,312 16,706 17,945 18,795 19,664 20,563 21,489 22,433 24,066
3 Commercial 5,005 5,584 6,486 7,271 7,638 8,017 8,381 8,752 9,157 9,526
I
Conventional Industrial 1,076 1,126 1,206 1,29 1,33 1,37 1,41 1,4500 1,4900 1,
538
0 0 00 00 0
Data Centers 0 0 0 0 0 0 0
cost-Effective Semiconductor Manufacturers 0 0 0 0 0 0 0 0
W Labs 0 0 0 0 0 O O O O 0
Energy 3 3 4 4 4 a 4 5
System Total 3 3 3 3 3 3
Efficiency ; Residential 2 2 2 2 2 2 1 1
Potential I Commercial 1 1 1 1 1 1 1 1
o Conventional Industrial 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 Data Centers 0 0 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0
p Labs 0 0 0 0 0 0 0 0
low
r 44
Average Average Annual IWJMJCAI potential 2.47%
Annual Average Annual Oust-Effective Potential 1.94%
Impact on Average Annual Feasible Targets O:48%
Farersstetl
Consumption Average Annual Technical Potential 1.09%
and Average Annual Cost-Effective Potential 0.82%
Demand Average Annual Feasible Targets 0.22%
i
I
33
6130107 Preliminary Target: Pending Approval of Governing Board
City of Biggs
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 2,434 2,609 2,705 2,792 2,878 2,970 3,052 3,140 3,227 3,3SS
s Residential 941 1,045 1,102 1,150 1,195 1,242 1,286 1,333 1,378 1,463
3 Commercial 211 246 259 271 284 300 312 325 339 353
a
Conventional Industrial 1,281 1,318 1,344 1,371 1,399 1,428 1,455 1,482 1,510 1,539
Data Centers 0 0 0 0 0 0 0 0 0 0
Technical a Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
Energy s' Labs 0 0 0 0 0 0 0 0 0 0
System tem Total 0 0 0 0 0 0 0 0 0 0
Potential I Residential 0 0 0 0 0 0 0 0 0 0
Commercial 0 0 0 0 0 0 0 0 0 0
e Conventional Industrial 0 0 0 0 0 0 0 0 0 0
EA Data Centers 0 0 0 0 0 0 0 0 0 0
g Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
6 Labs 0 0 0 0 0 0 0 0 0 0
System Total 2,174 2,318 2,394 2,466 2,536 2,612 2,680 2,752 2,623 2,936
Residential 759 843 884 919 953 987 1,020 1,054 1,087 1,161
Commercial 176 200 209 219 230 243 253 264 275 286
s Conventional Industrial 1,240 1,275 1,300 1,327 1,354 1,382 1,407 1,434 1,461 1,489
a Data Centers 0 0 0 0 0 0 0 0 0 0
Cost-Effective Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
Energy a' Labs 0 0 0 0 0 0 0 0 0 0
Efficiency System Total 0 0 0 0 0 0 0 0 0 0
Potential
Commercial 0 0 0 0 0 0 0 0 0 0
v Conventional Industrial 0 0 0 0 0 0 0 0 0 0
A Data Centers 0 0 0 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
f Labs 0 0 0 0 0 0 0IN
i
i a yv Ck avG\v' m iv v\ v� v A, a`.
es ��
a
7,561 t7 630
SAW,
�m s` �e`w :itTi��il�.►f Fr � � �',� 1�`- i'�� ��'233t4
4
9 - tF
1 Average P,e Average Annual 1g>bn"Potential 1.86%
Annual Average Annual g;gst Effective Potential 1.63%
Impact on Average Annual Feasih1C Targets 0.59%
a Forecasted
Consumption ,. Average Annual Technical Potential ;0,90%
and g= Average Annual cos<=F(ferrive Potential 0.75%
Demand Ci Average Annual Feasible Targets 0.36%
34
6130107 Preliminary Target: Pending Approval of Governing Board
Burbank Water & Power
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total. 154,540 163,513 170,992 17"11S 183,233 190,256 196,509 202,837 200,266 217,783
.0 Residential 51,954 56,656 59,731 62,180 64,492 67,012 69,389 71,811 74,177 78,931
= Commercial 26,182 28,465 30,864 32,519 34,290 36,481 38,287 40,107 42,069 43,716
Conventional Industrial 76,404 78,392 80,397 82,416 84,452 $6,763 88,833 90,919 93,020 95,136
Data Centers 0 0 0 0 0 0 0 0 0 0
Technical a Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
Energy W tabs o 0 0 0 0 ° o 0 6 0
Efficiency system Total 19 20 21 22 22 23 24 25 26 20
Potential Residential 6 7 7 8 8 8 9 6 6 6
Commercial 4 4 4 5 5 5 5
g Conventionallndustria1 9 9 9 9 10 10 10 10 l0 10
EData Centers 0 0 0 0 0 0 0 0 0
e Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
as Labs 0 0 0 0 0 0
System Total 132,466 139,394 144,928 249,700 154,444 159,794 164,599 269,450 174,285 181,393
i Residential 42,067 45,763 47,959 49,694 51,319 53,087 54,731 56,397 58,006 61,977
I
Commercial 20,888 22,390 23,985 25,270 26,611 28,210 29,567 30,936 32,330 33,620
r Conventional Industrial 69,511 71,241 72,984 74,742 76,514 78,497 80,300 82,116 83,9400 85,7900
a Data Centers 0 0 0 0 0 0 0 0 0
Cost-Effective Semiconductor Manufacturers 0 0 0 0 0 0 0 Q 0
Energy "' labs 0 0 0 0 0 0 0
Efficiency System Total 15 16 16 17 17 18 19 19 26 26
Residential 4 5 5 5 5 5 6 4 4 4
Potential z Commercial 3 3 3 3 3 4 4
v Conventional Industrial 8 8 8 9 9 9 9 9 10 10
a Data Centers 0 0 0 0 0 0 0 0 0 0
0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0
g Labs 0 0 0 0 0 0 0 0 0 0
J
�w 1g32$r7 _
�1 i��fNi #t4Y1 a1iS � ,fy3�fi #i n9JY 1 A0. 42,.
�. -max _ � � � � ! 1iV�1192' �r�t'�55 �"11�0�'6 1dax24?la+•t_,
x« �
�,.Average Average Annual Technical Potential 1.84%
Annual Average Annual cost-Effective Potential 1.53%
Impact On E Average Annual Feasible Targets 0.95%
Forecasted
Consumption Average Annual Technical Potential 0.90%
and 3 Average Annual Cost-Effective Potential 0.68%
Demand Average Annual Feasible Targets 0.80%
4
i
35
6130107 Preliminary Target: Pending Approval of Governing Board
City of Colton
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
c .System Total 52,029 56,226 59,957 63,285 66,657 70,164 73,789 77,541 51,434 66,298
512
Resldential 20,037 22,142 23,682 25,006 26,314 16,007 17,300 18,635 32,060 21,405
lC Commercial 9,727 11,023 12,394 13,534 14,746 16,007 27,421 28,377 29,363 30,381
Conventional Industrial 22,265 23,060 23,880 24,725 25,596 26,495 27,421 28,377tl 29,363tl 30,3810
Data Centers 0 0 0 0 0 0 0
Technical d D 0 0 0 tl D D 0 0
Semiconductor Manufacturers 0 0
Energy W tabs o o D o 0 0 0 0 0
Efficiency system Total 6 7 7 8 8 9 9 10 10 14
Residential 2 3 3 3 3 3 3 4 4 4
Potential Commercial 1 2 2 2 2 2 2 3 3 3
v Conventional Industrial 3 3 3 3 3 3 3 3 3 3
Data Centers 0 0 0 0 0 D 0 tl 0 0
Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
g tabs 0 0 0 0 0 0 0 tl 0 0
System Total 43,711 46,954 49,746 52,329 54,967 57,687 60,502 63,414 66,407 70,410
= Residential 15,843 17,518 18,665 19,667 20,659 21,680 22,745 23,854 24,978 27,098
3' 79
Commercial 7,609 8,455 9,355 10,168 11,023 11,906 24,944 25,812 26,708 27,633
I
> Conventional Industrial 20,258 20,981 21,726 22,494 23,285tl 24,100 24,9400 25,810 26,7000 Z7, 0
Data Centers 0 0 0 0 0 0 0 0 0 0
Cost-Effective T Semiconductor Manufacturers 0 0 0 0 p 0
6
Energy W labs o 0 0 0 0 0 0
System Total 5 5 6 6 3
6 7 7 7 6 3
Efficiency ; Residential 2 2 2 2 2 2 2 3 3
Potential I Commercial 1 1 1 1 1 2 2 2 2 2
v Conventional Industrial 2 2 2 3 3 3 3 3 3 3
a Data Centers 0 0 0 0 0 0 0 tl 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
O labs 0 0 0 0 0 0 0 0 0 0
c \ s�.y �a\�o `�' ys c \w:>ab "" •r r "1 �"�' \ .1 y �. s
m ,� ^-�N _
a
+�V110t3'►�1 � 4 1tx
� 1 � g501 13 1
Average >s Average Annual Technical Potential .2.01%
Annual §ppp Average Annual cost-Effective Potentiat 1.64%
i G Average Annual Fe�iGle.Targets 0.61%
Impact on W
( Forecasted
consumption V Average Annual Technical Potential -1.04%
end Average Annual I;&U-affjajyA Potential 0,79
De ma Aveerage Annual Feasible Targets 0.27%
l
t
36
6130107 Preliminary Target: Pending Approval of Governing Board
City of Corona
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 7,061 1,640 B,Z28 8,743 9,278 9,829 10,392 10,969 11,575 12,186
= Residential 383 424 453 478 502 526 551 577 602 646
YCommercial 2,854 3,284 3,732 4,111 4,509 4,919 5,339 5,770 6,227 6,665
> Conventional Industrial 3,824 3,932 4,042 4,154 4,268 4,3844 4,5022 4,6222 4,7455 4,8711
Data centers 0 0 0 0 0 0
Technical g Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0
ra Labs 0 0 0 0 0 0 0 0 0 0
Energy 1 1 1 1 1 1 2
System Total 1 1 1 p p p p 0
Efficiency Residential 0 0 0 0 0 1 1 1
Potential I Commercial 0 0 1 1 1 1 1
v Conventional Industrial 0 0 0 0 0
e 0 0 0 p 0
g Data Centers 0 0 0 0 0 p p 0 0
Ep Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0
p Labs 0 0 0 0 0 0 0
System Total 5,454 5,820 6,190 6,540 6,900 7,267 7,642 8,026 8,420 8,835
Residential 304 332 349 363 376 389 402 416 429 459
= Commercial 1,851 2,099 2,359 2,602 2,853 3,109 3,371 3,641 4,073 4,178
a Conventional Industrial 3,299 3,390 3,482 3,576 3,672 3,7699 3,8688 3,9700 4,0730 4,1780
a Data Centers 0 0 0 0 0 0 0
Cost-Effective 1 Semiconductor Manufacturers 0 0 0 0 0 0 0 0
ra Labs 0 0 0 0 0 0 0 0 0 0
Energy 1 1 1 1 1 1 1 1
Efficiency system Totai 1 1 0 p p 0 0
Residential 0 0 0 0 0 0 1
Potential Z 0 0 0 0 0 0
_ Commercial 0 0 0 0 0 0 0
v Conventional Industrial 0 0 0 0 0 0 0
a Data Centers 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0
Semiconductor Manufacturers 0 0 p 0 0
p Labs 0 0 0 r 0:. x 0 0 �
4
x`
x si
3 yY
n6T" 7i91 80 34,
T
s
7
y
KIM OR
77
I
Average t Average Annual Technical Potential 1.55%
Annual ; Average Annual Cost-Effective Potential 1.13:/a
ImpeGt On Z. Average Annual Feasibie Targets Mo%
j Foreeaated
Consumption 9' Average Annual Technical Potential 0.98%
and ; Average Annual LQg&ffjQtLVj Potential 0.67%
s = A
Demand verage Annual Feasible Targets 0.36%.
1
37
6130107 Preliminary Target: Pending Approval of Governing Board
Glendale Water & Power
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 160,784 170,OS3 177,011 181,131 187,104 192,134 17 135 1 98 890 200 685 2100 ,430602 110439
L Residential 77,523 83,851 271
87,746 90,699 93,
_ Commercial 32,037 33,960 36,007 37,137 38,395 39,689 40,963 42,240 59,366 60,385
a Conventional Industrial 51,225 52,241 53:2588 54,2755 55,2922 56,3100 57,3288 58,3470 59,3660 60,3605
Data Centers 0 0 0 0 0 0 0
Technical � Semiconductor Manufacturers 0 0 0 0 0 0 0 0
W Labs 0 0 0 0 0 0 0
Energy 26 27
Efficiency system row 19 23 22 Z2 23 24 24 25 13 14
Potential y Residential 9 10 10 1� 16 16 16 16 6 6
_ Commercial 4 5 5 6 7 7 7 7
v Conventional Industrial 6 6 6 6 6 0 0
e 0 0 0 0 0
Data Centers 0 0 0 0 0 0 0 0 0
E o
semiconductor Manufacturers 0 0 0
$ 0 0 0 0 0 0 0 0 0 0
Labs
System Total 135,814 143,185 248,438 152,467 356,390 160,3Z0 164,Z23 168,146 172,027 179,028
Residential 5,814 67,388 70,182 72,286 74,210 76,119 78,041 79,980 81,814 86,941
2 Commercial 26,833 28,268 29,802 30,802 31,876 32,972 34,026 35,084 36,205 37,151
> Conventional Industrial 46,604 47,528 48,453 49,378 50,3044 51,2299 52,1555 53,082 54,0099 54,9366
E' Data Centers 0 0 a 0 0 0
Cost-Effective o semiconductor Manufacturers 0 0 0 0 0
c 0 0 0 0 0 0 0 0
Energy W Labs 0 0 19 20
System Total 15 16 17 17 17 Is 18 1g 8 9
Efficiency 7 7 7
Residential 6 4 4 5 5 5 5
Potential S Commercial 3 4 4 4 6 6
6 6 6 6 6 6
s� Conventional Industrial 5 5 0 0 0 0 0 0 0 0
s Data Centers 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0
p Labs
0 0 0 0 0 0 La 0 0
v
MR
i ems# 193 1,04
#r7F g fia43 9097
,
� i412t11i1...
Oto
Average plc Average Annual JCdI Milli Potential - 1.89%
Annual Average Annual Cost-Effective Potential 1.57%
Impact On Average Annual F_sibi Targets IM%
Forecasted
Consumption ; Average Annual JAdja al Potential 0.80%
and Average Annual r^=t-Effective Potential 0.60%
Demand Average Annual Feasible Targets 0.399A
z
38
6130107 Preliminary Target: Pending Approval of Governing Board
City of Gridley
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
,066
System Total 5,183 6,123 6,575 6,964 7,281 7,598 7,886 8,201 4,451 8,743
C 831 3,251 3,471 3,658 3,818 3,977 4,129 4,292 4,451 4,743
3 Residential 2, 215 3,341 3,483 3,630 3,774
= Commercial 2,021 2,514 2,733 2,922 3,069 3,
} Conventional Industrial 332 358 371 384 3955 4066 4166 4266 4377 449
Data Centers 0 0 0 0 0 0 0 0
Technical L Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0
C Labs 0 0 0 0 0 0 0
Energy 1 1 1 1 1 1 1 i 1
Efficiency System Total 1 0 0 0 1 1 1
0
Residential 0 0 0 0 0 1
Potential S Commercial 0 0 0 0 0 0 p 0 0 0
Conventional Industrial 0 0 0 0 0 0 0 0 0 0
■ Data Centers 0 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0
p bs 0 0
0 0 0 0 0 0
La 0 0
System Total 3,91 4,516 4,800 5,057 5,267 5,475 5,665 5,870 6,073 6,410
Residential 2,129 2,382 2,509 2,615 2,708 2,293 2,393 2,500 3,605 2,716
S Commercial 1,474 1,795 1,940 2,078 2,188 2,297 2,393 2,404 2,414 2,425
Conventional Industrial 314 339 351 3633 3744 3844 3930 4040 4100 4200
Data Centers 0 0 0 0 p 0 0 0
Cost-Effective Semiconductor Manufacturers 0 0 0 0 0 0 0
Energy
W Labs 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1
Efficiency system Total 0 1 0 0 0 0 0 0 0 0
Residential 0 0 0 0
Potential F_ Commercial 0 0 0 0 0 0 0 0
0 0 0 0 0
0 0 0
v Conventional Industrial 0 0 0 0 0 0 0 0
0 Data Centers 0 0 0 0 0
R
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
p Labs 0 0 0 0 0
R
I€ a1 x 1 3 �4 14 la
Average " Average AnnualTgjpical:Potential 2.06%
La
Effctive Potential 1.47%
Annual Average
Annual l:gsCe
W
S Average Annual feasible Targets 0.21%
Impact on
Forecasted
Consumption f1., Average Annual Technical Potential 0.82%
and. Average Annual Cost-Hfective'Potential 0.52%.
Demand Average Annual Feasible Targets 0.08%
f
39
6/30/07 PreliminaryTarget: Pending Approval of Governing Board
City of Healdsburg
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
447 10,767 11,343 13,833 12,321 12,851 13,286 13,767 14,252 14,953
586 7,004
System Total 9, 5,351 5,574 5,779 5,992 6,184 7,037 7,31E 7,593
L Residential 4,525 5,085 349 356
5,946 6,222 6,532 6,
5,383 5,686 320 328 334 342
S Commercial 4,636 299 306 313 0 0 0 0
Conventional Industrial 286 0 0 0 0 0 0
g> Data Centers 0 0 0 0 0 ° 0
Technical Semiconductor Manufacturers 0 0 p 0 2 2 2
c Labs 0 1 2 2 2 1 1
Energy 1 3 1 I 1 1
System TOUI 0 1 I 1 3 I 1 1 3
Efficiency Residential 1 I 1 1 1 0 0 0
Potential Z Commercial 3 0 0 0 0 0 0
0 0 0 0 0
e Conventional Industrial 0 0 0 p 0 0 0 0 0
Data Centers 0 0 0 0 0 0 0
s p 0 p 0 0
Y semiconductor Manufacturers 0 0 0 0 86g 11,227 11,827
O Labs ° 5,777
System Total 7,673 8,607 9,023 9,399 9,773 1 4 86 10,505 5,264 5,403
4,330 4,524 4,684 4,833 4,904 5,097 5,311 5,524 5,744
L Residential ,121
3,694 300 306
Commercial 3,533 4,020 4,236 4,446 41275 282 287 294 0 D
I 246 257 263 269 0 0 0
Conventional Industrial 0 0 p 0 0 0 0
Data Centers 0 p 0 0 0 p 0
0 0 0 p 0 0
Cost-Effective r Semiconductor Manufacturers 0 0 0 p 1 1 1 1
Labs 1 3 1 1 1
Energy m System Total 1 1 0 0 0 1 1 1 1
Efficiency 3 Residential ° I 1 1 1 o p o
Potential Z Commercial 0 0 0 0 0
0 0 0 0 0 p 0
o Conventional Industrial 0 0 p 0 0 0 p 0
e Data Centers 0 0 p 0 0 0 0 0
a 0 0 p 0
m Semiconductor Manufacturers 0 0 p 0
Labs o
� C w
RM
r4 i y3, a7 'i4 x4i 73A
rn
P23..€
It-
4,4
Average Annual JQdZliZ Potential 1.83%
Average 13 Average Annual CQU:tffeative Potential 1.45%
Annual o S Average Annual&ASAft Targets 0.24%
Impact on At
Forecasted
e Potential 0.85%
Consumption c Average Annual
and E Average Annual r'nct-Fffective Potential 0.61%
g,,, Average Annual.Feasible Targets 0.10%
Demand
l
40
6/30/07 preliminary Target: Pending Approval of Governing Board
City of Hercules
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
130 2,272 2,392 2,485 2,579 2,671 2,766 2,861 2,961 3,086
962 993 1,056
System Total 2, 808 840 870 900 930 1,900 1,969 2,029
i Residential 699 766 1,584 1,645 1,709 1,772 1,835 0 0 0
3= Commercial 1,431 1,506 0 0 0 0 q 0
I 0 a 0 0 0 0 0
Conventional industrial 0 0 0 p 0 0 0 0
> Data Centers 0 p 0 0 0
0 0 0 0 0 0
Technical m Semiconductor Manufacturers 0 0 0 p 0 0 0 0
to Labs 0 0 0 0 p 0
Energy System Total 0 0 0 0 0 0 0 0 0 0 0 0 0
Efficiency ; Residential 0 0 0 p 0 0 0 0 p 0
Potential Z Commercial 0 0 0 0
0 0 0 0 0 0 0 0
Conventional Industrial 0 0 0 0 q 0 0 0 0
mData Centers 0 0 p 0 0 0 0 p 0
EE Semiconductor Manufacturers 0 0 0 0 0 2,513
Labs 0 0 2,342 2,413
1,998 2,067 2,135 2,204 2,272 770 794 850
System Total 1,816 1,916 656 673 702 724 747 1,619 1,664
Residential 576 626 1,480 1,525 1,572 0
= Commercial 1,240 1,290 1,342 1,388 1,430 0 0 0 0
2 0 0 0 0 0 0 0
Conventional Industrial 0 p 0 0 0 0
CA Data Centers 0 U p p 0 0 p p 0
Semiconductor Manufacturers 0 0 0 0 0 0
Cost-Effective 1 Labs p 0 e
Energy W 0 0 0 0 O 0 p 0
System Total 0 0 0 0 0 p 0 p 0
EftlClenCY ; Residental 0 0 0 0 0 0 p 0 0 0 0
Potential Z Commercial 0 0 0 0 0 0 0 0 0
0
c Conventional Industrial 0 0 0 p 0 0 0 0
A Data Centers q 0 0 p 0 0 0 0 p 0
E Semiconductor Manufacturers 0 0 0 0 0 0
G Labs
*Uri
wor
17
3 3 3 9
"pow* 7777777,
3
3 3 3
NOO
- �asp Average Annuals a hnl Potential 1.78%
Ayer a" Average Annual QA-Effective Potential 1.4S%.
Annual Average Annual.ESasibig Targets 0.79%
Impact on p`.
Forecasted
Consumption Average Annual IWAWCa1 Potential
end E T Average Annual cost-Effective Potential
{Demand E Average Annual Feasible Targets
41
i
1
6130107 Preliminary Target: Pending Approval of Governing Board
Imperial Irrigation District
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
System Total 570,367 632,374 692,612 742,065 792,570 844,277 896,955 950,870 1,006,526 1,078,126
3 Residential 390,550 43,f74 469,037 500,036 531,124 562,959 595,822 629,690 663,987 716,216
Commercial 178,982 198,813 222,672 241,091 260,471 280,305 300,082 320,091 341,129 360,741
Conventional Industrial 836 866 902 938 9750 3,0130 1,0501 1,0900 1,1200 1,1600
> Data Centers 0 0 0 0 0 0 0
Technical Semiconductor Manufacturers 0 0 0 U. 0 0 0 p 0 0
W Labs 0 0 0 p
Energy 101 108 116 124 132 1a0 196
89
Efficiency System Total 76 56 94 65 70 74 79 84 51
Residential 50 56 63 51 53
Potential Commercial 26 29 33 35 39 41 40 400 0 0
0
o Conventional Industrial 0 0 0 0 0 0 0 0 0 0
m Data Centers 0 0 0 0 0 0 0 0 0 p 0 0
E 0
& Semiconductor 0 0 r Manufacturers 0 0 0 0 0 0 0
p Labs 0
System Total 476,810 529,789 580,379 621,003 641
662,779 705,633 749,009 793,480 839,496 899,
Residential 326,729 363,905 395,684 8
i 476,750 505,228 534,598 554,44. 609,816
422,284 449,186
3 Commercial 149,322 165,097 183,876 197,867 212,708 227,965 242,828 257,894 271,024 288,988
t 0 0
T Conventional Industrial 759 7870 8190 8500 8800 9100 9500 980 1,024 1,06
Data Centers 0 0 0 p 0 0 0 0
Cost-Effective C Semiconductor Manufacturers 0 0 0 p 0 0 0 0
Labs 0 0 a 0 0
Energy W 82 as 94 101 107 114 122
Efficiency System Total 61 69 76 57 61 65 69 74 80
Residential 40 45 49 53 31 33 36 38 40 42
Potential z Commercial 21 24 27 29 0 0 0 0
0 0
o Conventional Industrial 0 0 0 0 p p 0 0 0 0
C Data Centers 0 0 0 0 p 0
a 0 0 0
d Semiconductor Manufacturers 0 0 0 0 0 p 0 0 0 0
p Labs p 0
a
ROW
7 ��3 xeed."1 #j ' r 4r' c7
_ r 1 1a�1 x 151 14 T,2fi1 1 S 32
ti
Average _ Average Annual ni al Potential 2.57%
Annual litAverage Annual So:E gctl=Potential 7.35%
Impact On Average Annual Feasible Targets
Forecasted
Consumption Average Annual Technical Potential 1.24%
and Average Annual Cost-Effective Potential 1.01%
Demand Average Annual Feasible Targets 0.51
42
City of Industry
Data Still Pending
I
6
43
6/30/07 preliminaryTarget: Pending Approval of Governing Board
Lassen Municipal Utility District
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
263
18,239 19,466
System Total 19,744 21,671 23,192 24,373 25,554 26,754 27,967 29,203 30,240 311,775
i Residential 12,375 13,617 i�g32 18 429 18 965 694 19,526,3 10 10,070 10,631 1,023
Commercial 6,570 7.231 920 945 971 997 0
£ 823 847 871 895 0 0 0 0
Conventional industrial 8000 a 0 0 0 0 0 p 0
r Data Centers 0 0 0 0 0 p 0
0 0 0 p 0 0
Technical Lp Semiconductor Manufacturers p 0 0 3 4 4 4
labs 3 3 3 2 2 2
Energy W System Total 2 3 3 2 2 2 2 2
Efficiency Residential 1 1 2 2 2 2
1 1 1 1 0 0 0 0
Potential 2 Commercial 1 0 0 0 0 0 0
Conventional industrial 0 p 0 0 0 0 0
0 0 0 0
p 0 p 0
Data Centers 0 0 p 0 0 p 0
& Semiconductor Manufacturers 0 0 p 0 0
O
Labs 0 0 0 23,826 25,338
706 13,165 13,621 14,076 14,476 17,888 18,279
System Total 16,094 17,488 18,549 19,420 20,284 21,154 22,033 22,
t Resid6,115
ential 10,667 11,625 12,220 12, 7,476
6,850 7,873 896 920 944
3 Commercial 4,741 5,76 5,783 5,909
6 827 850 873 0 0
E 740 760 783 0 0 0 0
Conventional Industrial 0 0 0 0 0 0 p 0
> Data Centers 0 p 0 0 0 0 0
a p 0 p 0
Cost-Effective Semiconductor Manufacturers 0 0 0 0 3 3 3
Labs 0 2 2 2 2 1 2
Energy W System Total 2 2 2 1 1 1 1 1 1 1
Efficiency 3 Residential 1 1 1 1 1 1 1 0 0 0
Potential S Commercial 1 0 0 0 p 0
0 0 0 0 0 0 0
o Conventional Industrial p 0 0 p 0 0 0 0
e Data Centers 0 p 0 0 0 0 0
a p 0 p 0
b Semiconductor Manufacturers 0 0 0 p 0m• ,7..
Ldbs
zet, It
m
19 ipTrSi
a �ri� e� 12i3it1 13"Fi4$3 13S
1 t � pl
s- u � � 29
Z rR7 7,7
�� • �� a.+� Average Annual j9,ChllL6l1 Potential 2.07%
Averages @' Average Annual ^ -Effective Potential 1.62%
Annual S; Average Annual Eea,41b14 Targets
I an 1�
Forecasted
Consumption a 3_.
Average Annual ZY,G(]DlilAl Potentlal
and �' Average Annual C^<t-EHective Potential
C Average Annual Peastble Targets
Demand
k
44
6130
107 Preliminary Target: Pending Approval of Governing Board
Lodi Electric Utility
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 57,327 64,010 68,584 73,050 77,846 82,957 87,621 92,635 97,$34 104,120
CResidential ommercial 26,562 2%925 32 018 34,8189 36,592 39,1770 41,354 43,730 46,2306 48,7013p
0 0 0 0 0
I 0 0 0 0 0 0 0
Conventional industrial U 0 0 p 0 0 p 0
Data Centers 0 0 p 0 0 0 p 0
LA Semiconductor Manufacturers 0 0 0 0 0 0
Technical Labs 0 0 0 13 14 1s 16 17
Energy W it 12 13 9 10 10
g system Total 9 16 6 7 7 8 a 6 7 7
Efficiency # Residential 5 4 5 5 5 6 6 6 0 0
Potential I Commercial 4 0 0 0 0
0 0 0 0 0 0 0 0
Conventional Industrial 0 0 0 0 0 0 0 0
s Data Centers 0 0 0 0 0 0 0
0 0 0 0 0
Semiconductor Manufacturers 0 0 p p
C labs
757 28,032 29,310 36,447
System Total 39,265 43,323 45,850 46,658 51,629 54,759 57,570 60,581 63,61
.0 Residential 18,849 20,726 21,874 23,062 27,351 29,205 30, p 0
Commercial 20,416 22,596 23,976 25,5960 27,3501 29,2050 30,8100 32,5400 34,300 36,140
Z 0 0 0 0 0 0 0
Conventional Industrial 0 0 0 0 0 0
? Data Centers 0 0 0 0 0 0
a 0 0 0 0 0 0 p 0
COSt Effective Semiconductor Manufacturers 0 0 p 0 0 9 10
Labs 0 7 7 $ $ 9
Energy m 5 6 6 4 4 4 5
System Total 3 3 4 4 4 5 5
Efficiency Residential 3 3 3 g 4 4 p 0 p 0
Potential Z Commercial 3 0 0 0 0 0
Conventional Industrial 0 0 0 0 0 0 0
0 0 0
AData Centers 0 0 0 p 0 0
E Semiconductor Manufacturers 0 p 0 0
o
a �'F Average Annual 7ssilaitnl Potential 2.02%
Average Average Annual CgIL fAWn potential 1,31%
Annual _ Average Annual Fg;lSi4i8Targets 0.16%
Impact on
Foretaste-
Consumption g Average Annual IWJM"Potentiall 1.18%
'i and i 3 Average Annual felt-t�fectWe Potential '0.65%.
ri= Average Annual feasible Targets 0.07%
Demand G
9
t
z
I
45
P
6130107 Preliminary Target: Pending Approval of Governing Board
City of Lompoc
2007 20, 2009 2010 2011 2012 2013 2014 2015 2016
system Total 16,634 18,320 19,118 19,851 20,667 21,297 21,960 22,664 23,348 24,494
Residential 10,271 11,493 12,067 12,548 12,989 13,443 13,861 14,302 34,722 15,615
= Commercial 6,363 6,826 71051 7,3033 7,5680 7,8540 6,1000 8,362q 8,62� 8,870
Conventional industrial 0 0 q 0 0 0 0 0
p Data Centers 0 0 0 0 0 0 0 0
q q D 0 0 0 0 0 0
Technical Semiconductor Manufacturers 0 0 0 q q 0 0
Energy W Labs 0 2 2 2 2 2 3 3
System Total 2 2 2 1 1 1 1 1 1 2
Efficiency Residential 1 1 1 1 1 1 1 1
Potential S Commercial 1 1 1 1 1 0 0 0
0 0 0 0 0 0
v Conventional Industrial 0 0 0 q p 0 0 0 0
°Eg Data Centers 0 0 0 q 0 0
G 0 0 0 0 p
Semiconductor Manufacturers 0 0 q p 0 0 0 0
Labs 0 _- 2
System Total 14,797 16,227 16,692 17,498 18,089 18,706 19,258 19,842 20,409 21,48
Residential 9,019 10,055 10,508 10,875 16,875 11,562 11,878 17,630 17,879 18,110
S Commercial 5,778 6,172 6,384 6,623 6,875 7,1455 7,3811 7,6300 7,6700 8,110
Conventional Industrial 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0
Data Centers 0 0 q 0 0 0 0
Cost-Effective 40 Semiconductor Manufacturers 0 0 0 0 0 0 0 0
Labs 0 0 0 0 0 2 2
Energy W 2 2 2 2 1 1 2
System Total 2 1 1 1
Efficiency ; 1 1 1 1 1 1 1 1 I
Residential 1
Potential Z Commercial 1 1 1 1 1 O 0 0 q 0
e Conventional Industrial 0 0 q 0 0 0 0
e 0 q q p 0 0 0
a Data Centers 0 0 0 0 0 0
- E Semiconductor Manufacturers 0 0 0 0 0 p 0 0 0 0
g 0 0 0 0
Labs
•,. � .� `tom,,� g ��� ,ice, �t 1'"�,Taft-=' �2?,'97,
2
t zs5
An
verage Average Annual Technical Potential 1.65%
AAnnual 3; Average Annual SOL-IffACU=Potential 1.45%
e E Average Annual mil&Targets 0.75%
impact on w
Forecasted
E Consumption c.. Average Annual Technical Potential 0.99%
- Average Annual Cost-Effective Potential 0.86%
and .
f Average Annual Feasible Targets 0.47%
Demand
i
t
L
t
46
E
6130107 Preliminary Target: Pending Approval of Governing Board
Merced Irrigation District
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 48,419 53,172 57,877 62,102 66,330 70,713 74,957 79,108 63,425 86,019
Residential 7,157 8,024 8,690 9,271 9,836 10,420 11,008 11,600 12,199 13,172
= Commercial 18,388 21,122 23,980 26,455 28,953 31,546 29,921 31,075 32,260 43,378
Conventional Industrial 22,874 24,025 25,2D6 26,3766 27,5411 28,7460 29,9210 31,0750 32,260 33,470
T Data Centers 0 0 D 0 0 0 0 0
Technical a Semiconductor Manufacturers 0 0 D 0 0 0 p 0 0
W Labs 0 D 0 8 8 9 10 - SO it 11
Energy system Total 6 7 J 1 1 1 2 2
Efficiency 3 Residential 1 1 1 4 4 4 5 5 5 6
Potential S commercial 3 3 3 3 3 3 3 4 4 4
Conventional Industrial 3 3 3 0 0 0 0 0
E Data Centers 0 0 0 0 0 0 D 0 0 0
0 0 0 0
0
Semiconductor Manufacturers 0 0 0 D 0 p 0 0
p Labs 0 D 0
System Total 41,504 45,029 48,508 51,779 55,052 58,439 61,736 8,645 64,974 9,094 6 9,555 7 0,391
Residential 5,728 6,388 6,881 7,321 7,751 8,804 5,591 7,336 9,135 30,902
E Commercial 14,629 16,458 18,381 20,157 21,949 23,804 27,500 28,543 29,615 30,717
Conventional Industrial 21,147 22,183 23,246 24,3011 25,3511 26,4388 27,5000 28,5400 29,6100 30,7100
Data Centers 0 0 0 0 0 0
Cost-Effective Semiconductor Manufacturers O 0 0 0 0 0 0 0 0
0 0
Energy m 6 7 7 a 8 6 9
System Total 5 5 6 1 1 1 1 1
Efficiency ; Residential 1 1 1 1 1 4 4
3 4
Potential Z Commercial 2 2 2 3 3 3 3 3 3 4
o Conventional Industrial 2 3 U 0 D 0 0 0 0
Data Centers 0 0 0 0
0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
p
Z�
\ q ,
Average s Average Annual jaOnLW Potential 1.78%
Annual Average Annual LUgkCffSO=Potential 1.46%
a S Average Annual Pyaslbls Targets 0.73%
Impact on
Forecasted
Consumption v Average Annual Technical Potential 1.15%
and Average Annual C c- ective Potential 0.90%
Demand Average Annual Feasible Targets 0.45%
t
47
6130107 Preliminary Target: Pending Approval of Governing Board
Modesto Irrigation District
2007 2008 2009 2011 2011 2012 2013 2014 2015 2016
System Total 379,677 406,165 431,572 452,146 472,760 493,883 51 9,31 S37,619 60,278 559,954 71,100 5 89,92
t Residential 178,447 196,092 208,322 218,650 228,653 238,848 249,381 260,278 259,760 167,804
Commercial 93,680 101,955 110,535 118,133 126,069 134,285 142,564 151,064 159,094 131,934
Conventional Industrial 193,680 100,119 112,715 115,364 118,038 120,7500 123,4933 126,2777 129,090 131,930
Data Centers 0 0 0 0 0 0 0 D
Technical Semiconductor Manufacturers 0 0 0 p 0 0 0 0 0
W Labs 0 0 0 62 65 68 71 75
Energy System Total 47 50 54 56 59 33 34 37
Efficiency Residential 22 24 25 27 18 19 2p 21 22 23
Potential E Commercial 13 14 is 16 15 15
13 33 13 13 14 14 14 0 0
o Conventional Industrial 12 0 0 0 0 0 0 0 0
Data Centers 0 p 0 0 0 D
w 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0
Labs 0 0
System Total 181,530 197,201 208,495 218,470 228,267 236,248 248,371 258,7080,538 268,952 276,li
s Residential 113,414 125,124 132,267 138,120 143,641 149,157 154,788 160,538 166,187 169,828
= Commercial 44,819 48,223 51,813 55,361 59,058 62,936 66,834 70,817 74,802 78'S78
Conventional Industrial 23,296 23,853 24,410 24,980 25,5600 26,1500 26,7500 27,350 27,9600 28,5700
Data Centers 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
Cost-Effective Semiconductor Manufacturers 0 0 0 0 0 D
W Labs 0 0 0 27 28 29 31 32
Energy 22 23 24 26 17 18 18
System Total 20 14 15 15 16
Efficiency Residential I1 13 13 8 8 9 9 10 10 3
Potential y Commercial 6 6 7 7 3 3 3
3 3 3 3 3 D D
C0 Conventional Industrial 3 0 0 0 0 0 D 0 0
w Data Centers 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 D p 0 0 0 0 0
e Labs 0 0 r
E.
4
1191
Average t Average Annual IfOX901 potential
3: Average Annual CQgzW.ff aLW Potential
Annual ! Average Annual bible Targets 0.45%
Impact on
Forecasted
Consumption Average Annual T�Is4l POtentrdl 0.94%
and R�Ge Average Annual,Cost-EffecVye Potential 0,40%
- Demand 8 w.. Average Annual Feasible Targets 0.20%
7
48
6/30/07 preliminary Target: Pending Approval of Governing Board
City of Moreno Valley
2009 2010 2011 2012 2013 2014 2007 2016
2007 2008 17,120 19,0' 21,447
,009 10,863 12,225 13,712 1 8 348 4,047 9,810 10,897
System Total 7,024 8'g� 6,037 6,557 7,105 7,696 6 796 7,863 9,044 10,316
= Residential 4,111 5, 4,132 4,938 5.825 221 233
3 Commercial 2,043 2,690 3,167 191 200 210
Z 160 167 174 182 0 0 0 0
Convention al Industrial 153 0 0 0 0 0 p 0
ps Data Centers 0 0 0 0 0 0 0
& 0 0 0 0 0 0 0
Technical Semiconductor Manufacturers 0 0 p 0 2 3 3
c labs 0 3 3 2 2 1 1 1 1
Energy m System Total 1 1 1 1 1 1 1 1 1 1
Efficiency Residential 1 1 1 1 1 0
0 0 0 0 p 0 0
0
Potential Z Commercial 0 p p 0 0 p 0
Conventional Industrial 0 0 0 0 0 0 0
� p 0 p 0
0
eE Data Centers 0 0 p 0 0 0 p 0
& 941
Semiconductor Manufacturers 0 0 p 0 0
Labs 0 0
G 6,450 7,345 8,254 9,236 10,304 31,475 12 484 1 8 129 4,111 1 9,091
System Total 5,487 6,345 6,B /01
89 6,662
4,952 5,387 5,847
L Residential 3,947 4,505 4,424 5,103 5,849 188
1,816 2,258 2,726 3,241 3�854 161 170 178
Commercial 1,424 129 134 140 147 0 0 0 0
Conventional Industrial 124 0 0 0 0 0 0
> Data Centers 0 0 0 0 0 0 0
0 0 0 0 0 0 p 0
Cost-Effective Semiconductor Manufacturers 0 0 p 0 2 2 1
c Labs 0 1 1 1 1 1 1 1
Energy m 1 1 3 1 1 1
System Total 0 1 1 1 1 1 1
Efficiency ; Residential 0 0 0 p 0 0 0
Z Commercial 0 0
Potential 0 0 0 p 0 0 0 0 0 0 0 0
p
o Conventional Industrial 0 0 p 0 0 0 0 0
s Data Centers 0 0 0 0 0 0 0 0 p 0
e Semiconductor Manufacturers 0 0 0 p 0
Labs
x
_ $� �` � a se � � �75 �t9�► .
347.
m
ett
In
57
vp
- � Average Annual j�tGNsat Potennal 2.8gsti
Average Average Annual Gs-ffertive Potential ' 2.15%
Annual ! Average Annual Feas,Me'targets 1.11^h
Ilnpacton
Forecasted
Consumption Average Annual jechnlcal Potential 1.07%
and 3 Average Annual=I Potential 0.40%
vemand s Average Annual Feasible Targets
k
49
6/30/07 Preliminary Target:et: Pending Approval of Governing Board
City of Needles
2013 2014 2015 2016
2007 2008 2009 2010 2011 2012 1a,390 1.9,318 20,500
14,991 15,804 16,844 17,504 13,522 14,4441
System Total 11,975 13,151 14,206 711 12,30Y 12,908 6,055
30,027 10,583 11,140 14,933 5,203 5,482 5,796 0
i Restdential 6,487 9,332 4179 4,408 4,664 0 0 0
Commercial 3,488 3,819 0 0 0 0 0 0 0
= 0 0 0 0 0 0 0 0
Conventional industrial 0 0 0 0 0 0
Data Centers 0 0 0 0 0 0 0
Semiconductor Manufacturers 0 0 0 0 0 3 3
Technical �bs o 0 2 2 3 3 21
2 2 2 2 2
Energy System total 2 ? 1 2 2 i 1 1 1 1
Efficiency Residential 1 1 0
1 0 p 0 0 O p 0
Potential z Commercial 0 0
Conventional Industrial 0 0 0 0 0 0 0
g Data Centers 0 0 p 0 0 0 0 0
e 0 0 0 0 0 0 0
F Semiconductor Manufacturers 0 0 0 0 017 15,745 16,694
C Labs 12,986 13,650 3.4,323 1S,197 10,660 11,376
System Total 9,690 10,664 11,729 1 8 454 8,871 9,298 9,741 14,819 5,085 5,318
Residential 6,811 7,495 8,043 4,115 4,352 4,582 0 0
i 3,686 3,889 0 0
Commercial 3,079 3,369 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
Conventional Industrial 0 0 0 0 0 0
> Data Centers 0 0 0 0 0 0 0
a 0 0 0 0 2
cost.-EffeCtive Semiconductor Manufacturers 0 0 0 0 2 2 2
c Labs 2 2 2 2 1 1 2 2
Energy m system Total i ? 1 1 1 1 1 1
Efficiency ; Residential 0 1 1 1 p o 0 0 0
Potential S Commercial 0 0 0 0 0 0
0 0 0 0 0 0 p 0
y Conventional Industrial 0 0 0 0 0 0
a Data Centers 0 0 0 0 0 0 0 0 0
Semiconductor Manufacturers 0 0 0 0
a a
PRE,
� v a
JAW-
00
4arp .� liYF +�sxa
+� R`
3lTAtl \ s � ARK � ,^� +nW# tra"tSi4 ! 429
5&
`
77
g ,
>�+ Average Annual 7gplllSAl
Potential 2. 0%%
Average P Average Annual go kf920ve Potential 2.3
Annual �. Average Annual EeaStbWTarQtt5 1.12%
Impact on
FOtaeasted - . 1:34%
consumption .+ Average Annual j1;G[Il11Gdl potential
end 4� Average Annual c r_E{{ectnre Potential
S-= Average Annual Feastbie Targets
Demand 333
4
50
6/30/07 Preliminary Target: Pending Approval of Governing Board
Pasadena Water & Power
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
618 186,557 196,937 208,134 217,905 2299i 7�9 2395 O51 ?O1 124
System Total 149,475 163,254 176, 77 892 81,265 84,776 86,105
Residential 63,956 7Q095 74,412
0 0 0 0
Commercial 85,519 93,160 102,20fi0 108,6644 115,6720 123,350 129,800 137,374 144,1560 149, 0
I 0 0 0 0 0 0
Conventional industrial 0 0 0 0 0 0 0
Data Centers 0 0 p 0 0 p p p
0 0 0 0 0 0
Technical Semiconductor Manufacturers 0 0 0 30 31 33 34
labs 25 27 28 12 13
Energy W system Total 20 22 24 10 10 11 11 19 20 21
Efficiency ; Residential 8 13 14 15 16 17 10 0 0
Potential S Commercial 12 0 0 0 0
0 0 0 0 0 0 0 p
Conventional Industrial 0 0 p 0 0 0 0 0
Data Centers 0 p 0 0 p 0
0 0 0 0 p 0
Semiconductor Manufacturers 0 0 p 0
Labs 0
65,136 67,250 69,558 71,629 76,491
System Total 113,230 122,260 13 8 319 13 60,631 144,575 152,051 158,662 166,143 172,725 181,
L Residential 51,066 55, 0 0 0
�. Commercial 62,144 66,809 72,351 76,9010 81,7270 86,9100 91,4100 96,585 101,0960 304,760
E D 0 0 0 0 0 0
Conventional Industrial 0 0 0 0 0 0 0 0
a Data Centers 0 0 0 0 0 0 0 0 0
li Semiconductor Manufacturers 0 0 p 0 0 22
Cost-Effective c Labs 0 0 18 19 20 21
Energy W 14 15 16 17 7 7 8 8
System Total 7 14
13
Efficiency 3 Residential 5 9 9 1p 11 11 10 10 1p 0
Potential S Commercial 8 0 0 p 0 0 0 0 0 0
Conventional Industrial 0 p 0 0 0
go Data Centers 0 0 0 0 0 0 0 0 p 0
Semiconductor Manufacturers 0 0 p 0 0
o Labs 0 0 � e y, .'A
,'•. ... ',yam �., c �� t'�a$ � .�,,��� �a.�
4 a,. a ' 7 Tr27
p� 3Y2 AI
�Mg5 T+Y* tv'PMj2 7a a yx014 c ziw
zi l L747
'`..��, j ,l '@y ""tk:�'31ts1' { 7 1Oi•
T34
111
x 1 :`3T1T $1312 A 333
77,
" Potential 1.83%
p "Average Average annual IsshWsal
[ Average Annual r^�rive Potential 1.33%
g Average Annual Feasible Targets 1.33%
Annual ea
I impact on
` Forecasted
Consumption Average Annual J=bii"Potential
Average Annual r rn 9 akl2
and Pgtenbal
Z Average Annual Feasible Targets O.fiB°k
Demand
r
51
i
i
6130107 Preliminary Target: Pending Approval of Governing Board
Pittsburg Power Company/Island Energy
2007 2008 2009 2030 2011 2012 2013 2014
2,590 22,26
System Total 1,610 1,741 1,872 1,979 2,093 288 300 2,311 323 344
tEfficien�cyj
Residential 218 240 255 21 816 0
3 Commercial 1,392 1,501 1,617 1,713 1,810 1,920 2,030 2,14D 2,260 2,370
I 0 0 0 00 0 0Conventional industrial 0 0 00 0 0 000aData Centers 0 0 00 0 0 p p 000l Semiconductor Manufatturers o 0 0 0 p 00 0
a� ts o 0 D D0 p o 00 0
y ; Residential 0 0 O O 0 p 0 00Ul i Commercial 0 0 0 0 00 0 0 0 0 00 0
p Conventional industrial 0 0 0 p 0 0 0
a Data Centers 0 0 0 0 000 0 0 0 0
Semiconductor Manufacturers 0 0 0G 828 1,929 2,033 2,141 2,252System Total 1,340 1,441 1,542 1,633 1,729 1. 264 2732209 219 229 237 246 255sResidential 190 0
3Commercial 1,150 1,233 1,322 1,404 1,492 1,582 1,6700 1,7600 1,8600I 0 0 0 Conventional Industrial 0 0 0 p 0 0rData Centers 0 0 p 0 0 0
a 0 0 0 0 0 0tive Semiconductor Manufacturers 0 0 0 0 0 0
00 W D D DD 0 0System Total 0 p 0 0 0 00 0
cy ; Residential 0 0 0 0 0 00 0 p
al S Commercial 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0
y Conventional Industrial 0 0 p 0 0 0 0
a Data Centers 0 0 0 p 0 0
Semiconductor Manufacturers 0 0 0 0 0 , p 0 0
0 0 0 0
�. s z
�"� ��.¢ a �:. ,t� ,,,w :,,. - c T- `3
.,
a� P $ � k 1 f
�,n n- ..
g s g
+k
r
Average Average Annual f�hDjsal Potential 1-39%
Annual Average Annual foci-Effect ve POtentfal 1.15%
Average Annual f: Targets 0.91%
Impact on
Forecasted
v D.86Po6
Consumption n.-, Average Annual 7gghpil�!Potential
and Average Annual r 5LEffe 9a Potential b.6fi%
Demand Average Annual ITargets
f
f
52
6130107 Preliminary Target: Pending Approval of Governing Board
Plumas Sierra REC
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 21,825 24,126 25,361 26,635 27,829 29,092 30,196 31,401 32,534 34,104
Residental 11,541 12,866 13,683 14,403 15,046 15,700 16,354 17,026 17,642 18,650
I Commercial 1,723 2,205 2,381 2,603 2,824 3,074 3,235 3,438 3,fi3a 3,844
Conventional industrial 8,561 9,055 9,2977 9,6288 9,9580 10,3188 10,6066 10,93Q 11,2560 11,6100
Data Centers 0 0 0 0 0 0 0 0 0 0
Technical Semiconductor Manufacturers 0 0 0 p 0 0 0
W Labs 0 0 0 0 3 ; 3 4 4
Energy 3 3 3 3
System Total 2 2 2 2 2 2
EfEfficiencyResidential 1 1 1 1 1 1 1
potential 2 Commercial 0 0 0 0 1 0 0 1 1
1 1 1 1 1 1 1 1
s� Conventional lndustrial 1 I 0 0 0 0 0 0 0 0
Data Centers 0 0 0 0 p 0 0
Semiconductor Manufacturers 0 0 0 0 0 p 0
° Labs 0 0 0 0 0 0 0
0
System Total 17,893 19,501 20,322 21,157 21,961 22,867 23,554 23,715 24,098 26,4
34
Residential 10,124 11,067 11,589 12,031 12,445 12,867 13,285 13,715 14,098 15,O1fi
= Commercial 1,150 1,433 1,544 1,681 1,817 1,967 2,068 2,193 2,311 2,441
Conventional Industrial 6,619 7,001 7,1888 7,4444 7,7000 7.9788 8,2000 8,4500 8,700 8970
a Data Centers 0 0 0 0 0 0 p 0 0 0
Cost-Effective a Semiconductor Manufacturers 0 0 0 p p 0 0 0
Energy W Ldbs 0 0 2 2 2 2 3 3 3 3
Efficiency System Total 2 2 1 1 1 1 1 1 1 1
Residential 1 1 0 0 0
1
Potential E Commercial 0 0 0 0 0 0 0
1 1 1 1
o Conventional Industrial 1 1 1 1 1 0 0
0 0 0 0 0 p
E Data Centers 0 0 0 p 0 0
Semiconductor Manufacturers 0 0 0 0 0 0
p° Labs 0 0 0 0 U` r U we t0 �. 0
�I� c 3
Average. °' Average Annual Technical Potential 1.02%
Annual Average Annual osk9ff-ective Potential 1,41%
Impact on Average Annual Feasible Targets 0.33%
Forecasted
Consumption g Average Annual Technical Potential 1,16%
and Average Annual CosG- ffective Potential 0.88%
Demand Average Annual feasible Targets 0.22%
53
6130107 Preliminary Target: Pending Approval of Governing Board
Port of Oakland
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
1b,714
System Total 8,404 9,2$7 9,710 10,174 10,628 13,366 15,3020 15,7270 16,1620 0
0 0 0 0 0
tfficlency
Residential p 710 10,174 10,628 13,366 15,302 15,727 16,162 16,714
Commercial 8,404 9,287 9, 0 0 0 0 0 p
= 0 0 0 0 0 0 0 0
Conventional Industrial 0 00. 0 0 0 0
> Data Centers 0 p00000 0 11 00 p 0
i Semiconductor Manufacturers 0 0 WLabs 0 22 20
system Total 1 11 11 000 0 0; Residential 0 0 1 1 22 2 pl Z Commercial 1 0 0 0 00
000 0 0 0 0Conventional Industrial 0 000 0 00
EaData Centers 0 0 0 00S 0 0 0 0 0 0 0 Semiconductor Manufacturers 0 0 00GLabs 0
System Total 6,687 7,280 7,560 7,920 8,252 10,0 O 11,3170 11,620 11,920 12,0 0 0 0Residential 0 7,580 7,920 6,252 10,044 11 317 11,62011,923 12,3250Commercial 6,687 7,280 0 0 0 0 00 000 0 00 0
Conventional Industrial 00 0 0 0 0
> Data Centers 0 0 0 0 0
w 00 0 0 0 0 0
LiVe Semiconductor Manufacturers 0 0 0 00 0Labs 0 0 1 1 1 12 m 1 1 0 0
systemTotal 1 0 p 0 0 1 2
y Residential 0 1 U 1 01l Z Commercial
0 0 0 0 0 0 p 0
CConventional Industrial 0 0 0 0 0 0 0 0
I Data Centers 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0
0 0 0
C Labs p
x
� a
ei � b � �a8 74I 44G�
y+ yip r yY�a 7y3y4y �Y> w,
io
y�gg
�a3i 1S 'r
�� Average Annual Issh01!Al Potential 1.77%
Average 3
Annual Average Annual os-ffe ive Potential L30:lo
W
Z Average Annual Feasible Targets 0.93%
Impact on
Forecasted
Consumption Average Annual B ni al Potential 1.43%
and ' Average Annual COSt-Effective Potential 102%
Z Average Annual Feasible Targets 0.73
Demand "
54
i
6130107 Preliminary Target: Pending Approval of Governing Board
City of Rancho Cucamonga
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 7,336 10,220 11,569 12,664 12,795 12,9350 13,0670 13,199 13,3700 13,460
3 Residential 0 0 0 0 0
Commercial 7,336 10,220 11,569 12,664 12,795 12,935 13,067 13,1990 13,3700 13,460
Conventionallndustrial 0 0 0 ° ° 0 0
> 0 0 0 0 0
Data Centers 0 0 0 0 0 0 0 0 0
Technical Semiconductor Manufacturers 0 0 0 ° ° °
Energy W Labs D 0 0 0 0 0 0 0 0 °
System Total 1 1 1 2 2 2 2 2 2 2
Efficiency Syential 0 0 0 0 ° ° ° 0 0 0
Resid
Potential Commercial 1 1 1 2 2 2 2 2 2 2
e 0 0 0
Conventional Industrial 0 0 0 0 0 0 0
o Data Centers 0 0 0 0 0
p 0 0
5& Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
Labs D 0 0
System Total 4,718 6,495 7,322 8,035 8,140 8,240 8,340 8,4470 8,5490 6,6400
3 Residential 0 0 0 0 0 0 0
E Commercial 4,718 6,495 7,322 8,035 8,141 8,Z41 6,344 8,4477 8,5490 8,6410
Conventional Industrial 0 0 0 0 ° ° °
�> Data Centers 0 0 0 0 0 0 0 0 0 0
Cost-Effective C Semiconductor Manufacturers 0 0 0 ° °
Energy m Labs o 0 0 ° ° °
System Total 1 1 i 1 1 1
Efficiency Residential 0 0 0 0 0 0 0 0 0 0
1 1 1
Potential = 1 1 1 1 0 0 °
Commercial 1 1 1 0
a Conventional Industrial 0 0 0 0 0 0 ° p
s Data Centers 0 0 0 0 0 0 0 0
E o 0 0 0 ° ° ° °
Semiconductor Manufacturers 0 0 0 0
p Labs 0 0 0 0 0 0 0 0
c
NQ
L� w�
r T41S �70 722:
Aill
.1Aw-
A" 14
m.r
Average 'i Average Annual Technical Potential 1.79%
Annual z Average Annual Cost-EffO ive Potential 1.15%
Average Annual Feasitile Targets 0.60%
Impact on
Forecasted
Cons:mptlon Average Annual�pplcat Potential 1.12%
and _ Average Annual Cost-Hfectwe Potential 0.68%
Demand Average Annual Feasible Targets 0.36%
55
6130107 Preliminary Target: Pending Approval of Governing Board
Riverside Public Utilities
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Totsl 317,619 340,931 359,707 374,614 390,679 406,418 421,965 438,211 455,089 478,402
c Residential 150,477 165,055 175,130 183,069 191,134 199,104 207,109 Z72,227 276,460 280 293
141
Commercial 44,361 48,994 53,782 57,044 60,969 64,764 68,400 72,227 76,460 8039,141
? Conventional Industrial 122,981 126,682 130,794 134,5022 138,5766 142,5510 146,450 150,5170 154,7360 158,9600
Data Centers 0 0 0 p 0 0 0 0
Technical 2' Semiconductor Manufacturers 0 0 0 0 0 0 0 0
e 0 0 0 0 0
Energy m Labs o 0 53 Bs 58
System Total 31 40 43 45 47 49 51 26 27 29
Efflclency ; Residential 17 19 20 21 22 23 24 10 11 11
Potential Z Commercial 6 8 9 9 10
J e 18 18
16 16 17 170 0 0
a Conventional Industrial 14 15 10 10 0 0 0
Data Centers 0 0 0 0 0
0 p 0 0 0 0
Semiconductor Manufacturers 0 0 0 0 0 p 0 0
G Labs 0 0 0
System Total 266,408 284,563 298,706 310,446 323,054 335,377 347,562 360,295 373,3890 31188 504
Residential 66,4 2 13,563 138,787 144,712 150,774 156,742 162,727 168,989 175,
Commercial 19,772 31,355 38,787 43,453 46,312 49,068 51,731 54,524 57,476 60,235
122,280 125,9600 129,5600 133,1000 136,7800 140,60a 144,432
Conventional Industrial 111,852 115,382 118,923 0
T Data Centers 0 0 0 0 0 0 0 0 0
p 0 0 0 0 0 0
Cost-Effective Semiconductor Manufacturers 0 0 0 0 0 0 0
Energy W Labs 0 34 38 39 41 42 45
System Total 29 31 33 16 16 17 18 19 20
Efficiency # Residential 12 13 14 15 6 6 7 7 7 8
Potential Z Commercial 4 5 5 6 16 16 17
e Conventional Industrial 13 13 14 14 14 15 15 0 0 0
0 0 0 0 0
m Data Centers 0 0 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
e Labs 0 0 0
f
0,15
r
... ..,
Average Average Annual jUdIl Potential 1.99%
Annual Average Annual v Potential 1.64%
Average Annual Feasible Targets 1.00%
Impact on
Forecasted
Consumption v Average Annual IftC40 rlt Potential 0.95%
and E Average Annual r1+st-EPfective Potential 0.73%
Demand Average Annual Feasible Targets '0.37%
S
F
56
6130107 Preliminary Target: Pending Approval of Governing Board
Roseville Electric
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 162,772 179,6$9 194,761 207,161 219,863 233,454 246,205 258,668 271,784 286,337
s Residential 81,923 90,395 97,821 104,121 110,518 117,569 191,901 132,228 339,742 149,532
Z
Commercial 58,714 65,841 72,308 77,236 82,358 87,610 91,901 95,885 100,324 103,954
Conventional Industrial 22,135 23,453 24,630 25,800 26,98D 28,270 29,439 30,550 31,710 3 0
Data Centers 0 0 0 0 0 0 0 0 0 0
Technical 0 Semiconductor Manufacturers 0 0 p 0 0 0
Energy W Labs 0 0 0 0 0 0
Efficiency System Total 23 26 28 30 32 34 37 39 23 43
Potential 3 Residential 12 34 15 16 17 19 20 21 23 25
_ Commercial 8 9 10 11 12 13 13 14 14 14
3 3 3
e Conventional industrial 3 3 3 3 3 0 0 0 0 0
Data Centers 0 0 0 0 0 0 0
Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
p Labs 0 0 0 0 0 0 0 0
System Total 107,079 116,397 123,990 130,713 137,439 144,280 150,388 156,243 162,220 169,699
# Residential 42,430 45,812 48,061 49,974 51,813 53,629 55,461 57,296 59,096 62,647
Commercial 45,455 50,248 54,570 58,363 62,224 66,135 69,400 72,453 75,620 78,548
Conventional Industrial 19,194 20,336 21,3599 22,376
0 0 23,4022 24,5177 25,5277 26,4944 27,503 28,5033
Data Centers 0 0 0 0
Cost-Effective C Semiconductor Manufacturers 0 0 0 0 0 0 0 0
Energy
W Labs 0 0 0 0 0 0 0
Efficiency System Total 13 14 15 16 16 16 '6 19 27 7
Potential Residential 5 5 6 6 9 9 9 10 10
_ Commercial 6 6 7 8 8 3 3 3 3
v Conventional Industrial 2 2 2 3 3 3
0 0 0 0 0 0 0 0
Data Centers 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0
p Labs 0 0 0 0 0 0 0 0
OW
12
1Z#1 {f! 10r
1 2 xr
401
e a
Average Average Annual Technical Potential 2.02%
Annual I Average Annual CQU&fective Potential 1.20%
impact on W
5 Average Annual Feasible Targets 0.61%
^-'
Forecasted
Consumption -0 Average Annual Technical Potential 1.16%
and Average Annual C_,i+'-Effecti Potential 0.56%
Demand Average Annual Feasible Targets 0.28°h
€ 57
I
Silicon Valley Power (City of Santa Clara)
2007 2009 2009 2010 2011 2012 2013 2014 2,15 2016
System Total 346,547 387,633 425,925 462,550 499,264 536,044 564,935 67,263 609,660 672123411�6
76 686,465
ICResidential ommercial 507,012 61,105 64,710 67,194 69,870 72,569 75,281 78,041 8163,598
P ConventionalData Centers 148 817 353,514 158,210 162,907 167,604 172,3001 176,998 181,695 186191,089
Technical Semiconductor Manufacturers 774 12,345 23,906 35,466 47,027 87401 01,328 81,710 2914313
Energy W Labs 17 601 31 737 45 646 59 560 73 459 67 463 101,3g� 115 2�Z 124143 183i
27 1
System Total 41 46 50 54 7 7 7 8
Efficiency 6 6 6 6 12 12
Residential 5 9 10 10 10 11 11
Potential 2 Commercial B 10 10
8 8 q 9 4 9 4 22Conventional Industrial 18 19 19 20 20 21 Data Centers 17 18 7 g g 12 13 4 5 16Semiconductor Manufacturers 0 4 5 7 8 10 12 1304-
Labs 2System Total 318,614 342,897 346806 3 48 589 404,914
6,280 421994 4453,7
8,8655 6 470,041 49 75 61 640t Residential 40,716 44,613 243 61,391 63,578 667,9802.
Commercial 47,318 50,228 52,833 54,906 57,085 59,ConventionalDatanCenters 148,817 153,514 158210 162907 167,6041723072,940 1 176998 181,695 18 1901 089Cost-Effective 78
E' 0 5,940 11,869 17,798 23,728 29,657 35,587 41,517 4 53,3m Semiconductor ManufacturersEnergy ' Labs 14944 20,160 25,149 30,143 35,147 401449 4515250154 557 60160
System Total 37 39 42 45 5 5 6 6
Efficiency ; 4 4 5 5 5 9 9 9
Residential 4 g 9
Potential 2 Commercial 6 7 7 8 8 q 9 9 9
8 9
8 8 8 21 21 226
C Conventional Industrial 18 19 19 20 20
a Data Centers 17 18 3 3 4 5 5
E. Semiconductor Manufacturers 0 1 3 3 4 5 5 6 6 7
LabsIT�'
� x � k �` :tea � ,R •� �. ��. 7
�� �, `"�` , a �:Q � �g � ➢�'- � etc �,
T3tN7Me
VOW 010"
27 $7C8-�#9 #•.
TF lip-
r 4Q5 53.0
w 4ii4 i80 48
4
ill S : !lti 03 3S7 480 A
a.` eK. 1
-
Average Annual Technisa1 Potential 2.19%
Average
3 Average Annual C2g_Lff4 rive Potential 1.64%
Annual
2 Average Annual flASAft Targets -0.82%
Impact On
Forecasted
Consumption Average Annual'Tr hm a1 Potential 1.58%
' .and. �' Average Annual LgjL ffertive Potential %
0.59%
.
Demand Z Average Annual feasible Targets
1
58
6130107 Preliminary Target: Pending Approval of Governing Board
City of Shasta Lake
2007 2108 2009 2010 2011 2012 2013 2014 2015 20
System Total 11,765 12,681 13,345 13,896 14,436 14,480 15,535 16,102 16,670 17,557
i Residential 7,665 8,400 8,900 9,308 9,700 10,093 10,497 10,910 11,318 12,057
= Commercial 1,244 1,332 1,425 1,494 1,568 1,644 3,317 3,394 3,470 3,548
Conventional Industrial 2,877 2,949 3,021 3,094 3,168 3,2422 3,3100 3,3900 3,4700 3,5400
Data Centers 0 0 0 0 0 0 0
0 0 0 0 0
Technical m Semiconductor Manufacturers p °
W tabs 0 0 0 O 0
2 2 2 2 2 2 2
Energy system Total 1 1 2 1 1 1 1 1 1
Efficiency 3 Residential 1 1 1 1 0 0 0
0 0
Potential z Commercial 0 0 0 0 ° 0 0 p 0 0
v Conventional Industrial 0 0 ° 0 ° 0 0 0 0
c Data Centers 0 0 ° ° 0 0 0 D
s ° p 0 ° ° p °
v Semiconductor Manufacturers 0 p p 0 0 0 °
C Labs 0 0 0 0
System Total 9,318 9,943 10,375 10,741 11,095 11,448 13,806 12,167 12,526 13,213
138 7,367 7,599 7,833 8,063 8,625
3 Residential 5,852 fi,355 6,661 1,907 0776 1,134 1,192 1,251 1,311 1,371 1,427
Commercial 903 960 1,021 1,
Conventional Industrial 2,564 2,627 2,692 2,757 2,8230 2,H890 2,95fi0 3,0200 3,0900 3,1600
Data Centers 0 0 0 ° 0 0 °
0 0 0 0 0 0
Cost-Effective o Semiconductor Manufacturers 0 0 0 0 0 0 0 °
W Lads 0 0 ° 1 1 1
1 1
Energy system Total 1 1 1 1 1 1 1 1 1 1
Efficiency Residential 1 1 1 1 1 0 p 0 0 0
Potential f Commercial 0 0 0 0 0 0 0 p p 0
v Conventional Industrial 0 0 0 0 0 0 0 0 0
c 0 0 ° 0 0
s Data Centers 0 0 0 0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 0 0 0 0 0 0
e p 0 0
v '
a '
"
r Sys1,Sa
AMR
Average s Average Annual je nr ai Potential 2,23%
Annual ; Average Annual r'�-Effective Potential 1.68%
W
Z Average Annual Feasible Targets 0.16%
Impact on
Forecasted
Consumption .+ Average Annual Technical Potential 1.08%
and pp Average Annual Sr^ffectM Potential 0.73%
Demand S Average Annual Feasible Targets 0.08%
f
59
6130
107 Preliminary Target: Pending Approval of Governing Board
Trinity PUD
2007 2008 2009 2030 2011 2012 2013 2014 2015 2016
956,
631 13,069 13,496 14,354
Systmn Total 13,792 15,005 15,820 16,500 1.7,163 17,630 18,502 i9,i69 19,871 20 888
tEfficiency
Residential 9,434 10,347 10,886 11,346 11,773 14,065 198 14,268 4,473228 3,474 3,658 3,858 1,640 1,677 1,714
Commercial 2,963 3, 1,532 1,567 1,604 0 0Conventional Industrial 1,396 1,430 1,461 1,496 0 ° 0 0> 0 0 ° p 0 ° ° 0 lData Centers 00p 0 p 0
Semiconductor Manufacturers 0 p D 0 2
l a Labs 0 ° 2 2 2 2z 1
Wsystem Total 2 2 21 2 1 1 11 Residential 1 1 11 1 1 1 0p 0
l I Commercial 0 1 0 0 0 0 0 0
p 0 0 ° 0c Conventional Industrial ° 0 0 0 0
4EOata Centers 0 00 p 0 0 p 0 0
& Semiconductor Manufacturers 0 0 p 030CLabs 0 ° 7 12 21 20 p 0 0
System Total1 0 0 ° 40
,� Residential 0 50116 20 255 3030
YCommercial 00 0 0 0
Conventional Industrial 0 0 0 0 0 00 0 0 0is Data Centers 0 0 0 0 a 00 p 0 p 0
tive semiconductorManufacturers 0 0p 0 0 00
Labs 0 0 0 p O 0 m0 0 0 0 0 0
system total ° 00 0 0 p 0
y 3Residential 0 p 0 0 0 ° 0
0al S commercial 0 0 ° 0 0 0
0 0 0 ° ° 0 ° 0
o Conventional Industrial 0 0 0 0 0 0 0
Data Centers 0 0 0 0 0 °
0 0 0 ° 0
E semiconductor Manufacturers 0 0 p 0 0 x^
RO
S
S 71l 18 1$ 1
C .}
r P=
f N �
�� � �� Average Annual ISShOltsl Potential 2.08%
Average t r.,��_r-ffe.r,ve Potential 0.00°k
Average Annual... .-
Annual S Average Annual Fealbie targets
Impact On W
Forecasted
i Consumption Average Annual 1gi]01Gdil
Potential 1.35%
$ Average Annual cyst-Ef&a=Potenbal 0.00%
and S Average Annual Feasible Targets 0.00%
g Demand
60
6130107 Preliminary Target: Pending Approval of Governing Board
Truckee Donner PUD
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 17,037 1 008'100 20,943 22,533 24,067 25,664 27,347 29,074 30,778 32,303
s Residential 9,075 10,358 11,456 12,523 13,494 14,513 15,622 16,762 17,823 18,762
Commercial 5,659 6,365 7,037 7,490 7,960 8,464 6,982 9,491 12,901 12,982
0
Conventional Industrial 2,303 2,370 2,440 2,SZ00 2,590 2,660 2,744 2,820 2,900 2,98
Data Centers 0 0 0 0 0 0 p 0 0
Technical a Semiconductor Manufacturers 0 0 p 0 0 0 0 0
Labs 0 0 0 0 4 4
Energy "' 2 3 3 3 3 4 4 2
Efficiency System Total 2 1 1 1 2 2 2 2 Z
Residential 1 1 1 i 2 2 2 2
Potential E Commercial 1 1 1 0 0 0 0 0 0
Conventional Industrial 0 0 0 0 0 0 0 0
p 0 0 0 0 0
a Data Centers 0 0 p p 0 0 0
Semiconductor Manufacturers 0 0 0 p 0 0 0 0
Labs 0 0 0 0 0
System Total 12,332 13,415 14,317 15,108 15,886 1 9 363 1 9 906 1 0,461 1 0 921 211 392
Residential 6,882 7,520 7,998 4,331 8,
Commercial 3,316 3,691 4,050 2,335 2,403 2,472 2,543 2,615 2,689 2,766
I 0 p 0
> Conventional industrial 2,135 2,2040 2,26� 2,3350 2,4030 2,4720 2,5430 2,615 2,689 2,7
Data Centers 0 0 0 0 0 0 0
p p 0 0 0 p p 0
Cost-Effective C Semiconductor Manufacturers o p o p p o
Energy W 2 2 2 2 2 2 2
System Total 1 2 2 1 1 1 1 1
Efficiency 3 1 1 1 1 1 1
Residential 1 1 1 1 1 1
Potential f Commercial 0 1 1 0 0 0 0 0
p 0 0 0 0 0 0
C Conventional Industrial 0 0 0 0 0 p 0
Data Centers 0 0 0 0 0 p p 0 0
a 0 0 0 p 0 0
E Semiconductor Manufacturers 0
s 0
0 0
G v
Labs �r
u _
k A
t f F � 7F13{f ,88.
4�l 42423.
40 41 42.
[Average �'c Average Annual jg1811W Potential 1.91%
nnYel 3 AverageAnnual Lq;lL&ffrctive Potential 1.20%nnu l Average Annual Feas,bleTargets 0.59%umption Average Annual TSGhnical Potential 1.04%
and _ Average Annual Cost-Effective Potential 0.61%
emand Average Annual Feasible Targets 0.28%
`r
tf
k
61
6130107 Preliminary Target: Pending Approval of Governing Board
Turlock Irrigation District
2007 2008 2009 2010 2011 2012 2013 2014 2015
2016
System Total 258,291 276,103 293,839 312,797 327,212 342,625 358,864 374,401 390,549 a�84 546
c Residential 307,689 118,502 127,323 135,331 142,587 150,082 157,887 165,705 779,
Commercial 38,055 43,847 49,535 56,590 60,767 65,508 70,614 75,537 36,790 84,1 6
Conventional Industrial 110,487 113,754 116,981 120,8699 123,8599 127,0366 130,3622 133,530 136,7900 140,12p
Data Centers O Q 0 p 0 0 0 ° 0 0
dSemiconductor Manufacturers 0 p 0 0
F
p o 0 0 ° ° s5 58
iu Labs 45 48 50 53
System Total 3S 38 40 43 26 27 29 30
# Residential 17 19 20 28 28 29 101016 S Commercial 5 6
13 14 14 15 15 15 °
g Conventional Industrial 13 13 ° p 0 0 0
Data Centers 0 0 ° 0 0 0 p 0
■ 0 0 p 0 0 0 0
Semiconductor Manufacturers p 0 0 ° 0
O
Labs 0 0 0 0
System Total 197,195 210,019 220,895 233,262 242,530 252,325 262,5959,487 272,299 262,080 294,
3 Residential 63,986 70,192 74,504 78,699 82,203 85,782 89,487 93,076 96,430 103,027
Commercial 63,986 32,468 35,992 40,503 43,429 46,672 50,103 53,225 56,430 32,513p
Conventional Industrial 104,282 107,359 110,399 114,060 116,8777 119,8711 123,0055 125,9970 129,064p 132,207
> Data Centers 0 0 ° ° 0 0 0 0
0 0 p 0 0 ° p 0 0
Cost-Effective C Semiconductor Manufacturers p 0 0 0
Labs 0 0 0 30 31 32 34 35
Energy W 24 28 27 28 11 12 lz
System Total 23 8 B y q 10 16 7 7 8
Efficiency Residential 7 6 6
Potential 2 Commercial 4 4 5
5 15
13 13 14 14 14 15
y Conventional Industrial 12 12 13 0 0 0 0 0 0
Data Centers 0 0 ° 0 0 0 ° ° 0
E Semiconductor Manufacturers 0 ° 0 00 p 0 0 0 0 °
ae s
LAU. i 8a551
WOW
r
Average Average Annual 1=1111"Potential 1.89%
Annual ; Average Annual Wit-Effective Potential 1.36%
a i Average Annual Fmibls~Targets 0,65%
Impact on °/"
Forecasted
COnsY seed Average Annual T W al Potential 1.11%
and _ Average Annual Mgt-Effective Potennal 0.67%
pemand Average Annual Feasible Targets 0.31%
6
62
6130107 Preliminary Target: Pending Approval of Governing Board
City of Ukiah
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
System Total 14,266 16,031 16,828 17,513 18,201 18,979 19,617 20,328 21,052 22,088
t Residential 6,646 7,422 7,794 8,108 8,398 8,702 8,979 9,274 9,559 10,167
Commercial 7,279 8,254 8,671 9,035 9,431 9,889 10,243 30,650 11,082 11,500
i 0
Ij
Conventional Industrial 341 355 3620 370 370 3800 3905 400 410 4
LM Data Centers 0 0 0 0 0 0 0 0
Technical Semiconductor Manufacturers 0 0 0 0 0 0 0 p 0
Labs 0 0 0 0 0
Energy 2 2 2 3 3
W 2 3 3
System Total 2 2 1 1 1 1 1 1
Efficiency Residential 1 i 1 1 1 1 2 2 2 2
Potential E_ Commercial 1 1 1 1 0 0 0 0
p 0 0 0 0 p 0
9 Conventional industrial 0 0 0 0 0 0 0
c Data Centers 0 0 0 0 0
q 0 0 0 0 0 0
Semiconductor Manufacturers 0 0 0 0 0 p 0 0
O Labs 0 0 0
System Total 11,638 12,900 13,479 14,014 1 7,026 15,144 1 7,37 16,144 16,720 7,845 17,599
8,38
i Residential 5,719 6,322 6,592 6,817 7,026 J,244 7,860 8,188 8,517 8,386
Commercial 5,631 6,278 6,580 6,885 7,207 7,567 7,334 8,341 8,348 8,356
Conventional Industrial 289 300 306 3133 320 3270 3340 3401 340 3550
Data Centers 0 0 0 0 0 0
0 0 0 0
Cost-Effective m Semiconductor Manufacturers 0 0 0 0 p p 0 0 0 0
Energy 1O Labs 0 0 02 2 2 z 2 2
Efficiency 2
System Total 1 1 1 1 1
# 1 1 1 i 1
Residential 1 I1 1
Potential f Commercial 1 1 i 0 0 0 0 p 0
p Conventional Indust 0 rial 0 0 0 0 0 0 0
Data C 0
c 0 0 0 0 0 0 0 0
q e nters
0 0 0 0
E Semiconductor Manufacturers 0 0 0 0 p 0 0
e Labs 0 0 0 0
0 0
27s4 ���r�62 1��
Y" e ,. �� a311Y13 : 117 t13�R 113 35# 3j69
41
4 N
e,
Average s Average Annual Technical Potential 1.74%
Annual Average Annual Cost-Effective Potential 1.39%
c t: Average Annual Feasible Targets 0.16%
Impact on e
Forecasted
Consumption a Average Annual Technical Potential 0.720A
and E Average Annual Cost-Effective Potential 0.52%
Demand S.„ Average Annual Feasible Targets 0.05%
63
City of Vernon
Data Still Pending
b
I
64
I
i
i
i
I
CPA
Establishing Energy Efficiency Targets:
Meeting the Mandates of Assembly Bill 2021
Presented to Truckee Donner PUD
Scott Tomashefsky
Regulatory Affairs Manager
August 15, 2007
i
I
�a
a
Energy Efficiency Energy Efficiency Status
Targets - Triennial 9/08
Report — Annual 3/08
e�r�p. 'a
act
vv\
""A"CPA
Legal Requirements of Assembly
Bill 2021
■ Identify all potentially achievable, cost-
effective electricity efficiency savings
■ Establish annual targets for energy
efficiency savings s and demand Yy
,ry
reduction over 10 years
as
■ Report targets, programs, w
' Its and cost- a y
expenditures, results, v 4;
r
effectiveness to members and to the
{
Energy Commission
• Includes methodologies and independent
evaluation 3
NC PA
_- - -
Energy
Efficiency
Effort -
Basic Methodology
mok
MEOW
ni
,F b
fi
R
a
�. _�� ....a�. ,.. � .. . .. .. . ... 4
/ N" c Pal
Snapshot of TDPUD Analysis
2007 2009 2000 2010 201i 2012 20I3 201f 2015 201e�'
sY tt�et Total 1',037 19.,i.0 2Si,9:; 22,93 24, ,' 4ssf� 27�3�i? 39.d7 30. '7 12. s9
5 '�49a 14,513 S eZ.2 &a,762 1'T 623 i&,72
Sirsiatl '. :'s 1�3,3. kE, 13,_s. 1., .:d+d 3 � 9, 1 ln,�ngg i .55
'SF fi�'n'r'rac ai 5,659 6:365 7,037 7,490 -'"0 �?�� 2,621 901
ir~`u�u�ixiaV .,.3ti3 Z, Sz
2,I18 2,40 2,5<0 2,5<)3 2.667 0
%r�rrvaarirunal � G 0 0 p
Data Ctatem 0 5
k a 0 0
44 K 0
Technical stm1 �Wr atanutact�.m$ 00 b 0 �, v=
La€,� 0 4 a d 4
Energyywt*m Total 3 3 3
2
Efficiency Raxfeatiat + 2 i 1 .1 2 t 3 Z Y
Potential $ c nar iat 1 s x 1 0
v Casao ticanal l a:tial 0 ; 0 0 10 0 0
Ceti Cattets 0 0 0 0
at�itxrti artC€rr Alanu$ai.tular C, Cr 0
p of
&1`trt+"rn T�xWi 12,732 13,415 14,317 Y5,:1t1$ iS,'iY3 16,756,3�3 17,872 111,fi07 19,45r1 2i}.3a
7,999 8,+41 &,"6 '�90t fi0.461 10,9.2 i1, 2
Ra€ antiaf 6,56Z 7.saZ SW
z
Co mm'efu*1 +.3i6 3.c111. Cow 4,331 4.623 �421 5.5gf 2.63.1- 2,689
.c�rete�fzClanal l+*+'yea^iat .:,135 2. ai Z,263 z.335 2,413 2, ` 0 w,
0 0 0
Data clmtefs 0 0 n 0 0
0
Cost-Effectiv ; 5�rrsi cC r sari #atkuaar5 0 _
y Laha 0 C' 3 2 3 g 2 2 2
Energy System Total 1 2 2 1
Efficiency 1 1 1 1 1 1 1 1 1
Rad+tFt:tia) 1 ,� g 1 1
Potential Cc ini 0 1 1 e o
0 r, ,�,' '% tl
Carv�r,+ianat L�.tSt�aciai � t1 � ,u '_
Data txrters 0 i 0
Q
8r1n�3€tc u�..eUr Aladiafa t&e t'9 0MM
Wgq mg ,�,�M
- M,
�-
' ..,:.
a �,_ 1 i72.1t 129
€ � � k 11„ 1es4
11
z . . f 1k 24Y f� -'ta7 k#Ita ie+l ,1 ,I1if 1
...ACV44
r 39
41
..
cl?
k
tt
{ A4a tai 1..frf
t +p*"I sm 1,2
Impact on �It A lla Aaa+�ual Tatgtt# 11..3
Goxatatnpft amara 7+Huai s to aE
aWW vat a. z.'
k
A4CPA
• Recommendations for
Findings and
Adoption
Recommended energy efficiency targets
. - 10 014 megawatt hours through 2016 period
Energy g
- 0.59% of electricity consumption per year
• Demand — 1 . 1 megawatts
- 0.28% per forecasted demand per year
i uolilll
1W
ip[
i
f
6
NC PA
Is This Really Feasible?
SB 1037 established a loading in order that requires
■
unmet resource
needs to first be met through energy
efficiency, g utilizin procurement funds.
■ AB2021 requiresenergy efficiency targets be
established every three years.
nt for continuing evaluation of
■Built in requirement
g
programs
■ Implementation sho
uld be an ongoing partnership
Committee, TDPUD staff, and
between , Conservation
the Board
7
CPA
Board Approval is Needed
oRecommended for Adoption (through 2016)
Energy - 10,014 megawatt hours
Demand - 1 .1 megawatts
Targets must be
adopted by September
30 and reported to the
California Energy
Commission
s