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BARTON L. KLINE ISB #1526
MONICA B. MOEN ISB #5734
Idaho Power Company
O. Box 70
Boise, Idaho 83707
Telephone: (208) 388-2682
FAX Telephone: (208) 388-6936
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Attorney for Idaho Power Company
Street Address for Express Mail
1221 West Idaho Street
Boise, Idaho 83702
BEFORE THE IDAHO PUBLIC UTILITIES COMMISSION
IN THE MATTER OF THE FILING BY
IDAHO POWER COMPANY OF ITS
2004 ELECTRIC INTEGRATED
RESOURCE PLAN (IRP)
SUPPLEMENT TO APPLICATION
CASE NO. IPC-04-
In consultation with Idaho Power s Energy Efficiency Advisory Group, in
2004 Idaho Power Company retained Quantum Consulting, Inc. to perform a study of
the potential for additional Demand Side Management in the Idaho Power service area
Quantum Study
During the development of the 2004 Integrated Resource Plan, which is
the subject of this proceeding, the Integrated Resource Plan Advisory Council (IRPAC)
requested that in addition to the energy efficiency and demand response programs
considered in the 2004 IRP (which were principally focused on achieving summer peak-
hour load reductions), Idaho Power should conduct a study to identify other cost
effective energy efficiency and DSM opportunities.
SUPPLEMENT TO APPLICATION , Page
Idaho Power retained Quantum Consulting to perform this study, and
agreed to file a copy of the Quantum Study as an addendum to its 2004 Integrated
Resource Plan.
Because the final Quantum Study was not scheduled for release prior to
the December 3, 2004 deadline for filing comments in this case, Idaho Power provided
a draft final version of the Quantum Study to members of the IRPAC and the EEAG in
October so they could include the results of the Quantum Study in their comments. The
Northwest Energy Coalition , Natural Resources Defense Council, Renewable Northwest
Project and Advocates for the West attached a copy of the draft final Quantum Study to
their comments filed in this proceeding.
Enclosed for filing with the Commission as a supplement to the
Company s pending filing is the final Quantum Study. Because Idaho Power distributed
draft versions of the Quantum Study to interested parties prior to the comment deadline
Idaho Power does not believe that the filing of this final Quantum Study as a
supplement to the Company s pending filing provides any basis for delaying the
schedule in this proceeding or for modifying the Commission s determination that this
case should be processed under modified procedure.
Respectfully submitted this 15th day of December, 2004.
SUPPLEMENT TO APPLICATION, Page 2
QUANTUM
CONSULTING
IDAHO POWER DEMAND-SIDE MANAGEMENT
POTENTIAL STUDY
FINAL
Prepared for
Darlene Nemnich
Project Leader
Customer Relations and Research Department
Idaho Power
1221 West Idaho Street
Boise, Idaho 83702
Prepared by
QUANTUM CONSULTING INc.
2001 Addison Street, Suite 300
Berkeley, CA 94704
510-540- 7200
with assistance from
KEMA-XENERGY, Inc.
P1992
November 2004
Section
TABLE OF CONTENTS
EXECUTIVE SUMMARY
INTRODUCTION
Page
ES-
ENERGY EFFICIENCY METHODS
Characterizing the Energy-Efficiency Resource
Overview of Energy Efficiency Forecasting Method
2.3 Baseline and Measure Data Development
2.4 Estimation of Technical Potential and Development Energy-
Efficiency Supply Curves
Estimation of Economic Potential
Estimation of Maximum Achievable, Program, and Naturally
Occurring Potentials
DEMAND RESPONSE POTENTIAL METHODS
Overview of Demand Response Forecasting Methods
DR Data Development
Estimation of "Economic" Potential for Demand Response
3.4 Forecasting Program Impacts
ENERGY EFFICIENCY PEAK DEMAND AND ENERGY SAVINGS
POTENTIAL RESULTS
Technical and Economic Potential
Energy Efficiency Supply Curves
Forecasts of Achievable Program Potential Scenarios
DEMAND RESPONSE POTENTIAL RESULTS
Economic Potential
Forecast Scenarios
DISCUSSION OF UNCERTAINTY
Quantum Consulting Inc.Table of Contents
APPENDICES
ENERGY EFFICIENCY MEASURE DESCRIPTIONS
MEASURE INPUTS
ECONOMIC INPUTS
BUILDING STOCK & LOAD SHAPES
NON-ADDITIVE MEASURE RESULTS
ACHIEVABLE POTENTIAL SCENARIOS
ENERGY EFFICIENCY POTENTIAL RESULTS - FIGURES FOR PHASE
Quantum Consulting Inc.Table of Contents
EXECUTIVE SUMMARY
The Idaho Public Utilities Commission (IPUC) directed the Idaho Power Company (IPCo) to
consult with their Energy Efficiency Advisory Group regarding the need to initiate a
comprehensive DSM study of the Idaho Power service territory. In July 2002, the Energy
Efficiency Group at Idaho Power received recommendations from the Idaho Power Energy
Efficiency Advisory Group and from Idaho Power management to proceed with a study
DSM opportunities. This study characterizes the potential for DSM resources through 2013 for
the commercial and residential sectors.
This study was carried out in two phases. In the study s initial phase, the focus was on the
potential for summer capacity reduction from demand-response (DR) programs and energy-
efficiency (EE) opportunities based on assessment of measures that maximize peak reduction.
For a second phase of the study, additional measures were added to the original EE portion of
the analysis to produce estimates of DSM potential that include an emphasis on overall energy
savings. Based on IPCo s resource planning needs, the potential for capacity reduction was the
most important component of the study. As such, the results from the initial phase of the study
were provided to IPCo s resource-planning department in late 2003 and early 2004 for
incorporation into its 2004 Integrated Resource Plan (IRP).
The scope of this study also includes review and analysis of Idaho Power s summer peak load
characteristics and identification of residential and commercial end-uses that have potential for
demand reduction during the summer peak time. In addition, significant effort went into the
development of baselines for residential and commercial customers in Idaho Power s service
territory. This included estimation of end use energy and peak demand contribution;
development of parameters such as electric equipment saturation, current efficiency measure
saturation; incorporation of the impact of current codes and standards; analysis of Idaho Power
forecasts and rate schedules; and review of Idaho Power s current DSM programs.
Inherent differences between EE and DR - with respect to both technologies and program types
- called for distinct methodologies in assessing their respective potentials. The analysis of
potential followed a measure-based methodology in which technology and market
characteristics were combined to produce an estimate of the total technical potential of all
measures under consideration. Using a forecast of avoided costs to remove all measures that
were not cost effective from a total resource cost (TRC) perspective, the technical potential was
reduced to produce an estimate of economic potentiaL Finally, the influence of market
constraints given different program funding levels was modeled to reduce the economic
potential to various estimates of achievable potentiaL A detailed description of these concepts
and methodologies is presented in Chapter 2 of this report.
The DR portion of the study was based on an approach that merged professional judgment
about DR participation levels with available Idaho Power data to assess potential peak demand
reduction for a specific set of program offerings. Following an approach similar to that of the
analysis of EE measures, the DR analysis first assessed the maximum amount of load to which
DR programs could feasibly apply. This "applicable load" was then partitioned into "low,
partial " and "high" capability segments, which reflected the extent to which load
Quantum Consulting Inc.ES-Executive Summary
automated and/ or centrally controlled. From this initial breakout of applicable load, achievable
potential was estimated by modeling shifts in capability based on IPCo s program efforts and
customer motivation given different incentive levels. The end result is a set of potential
estimates by program concept and funding levels. Chapter 3 provides a comprehensive
description of the methodology.
Finally, the results of the two analyses must stand on their own. Although EE and DR programs
are not mutually exclusive, without accounting for the complex interactivity of the two, the
individual results cannot be added to each other to produce a figure for the combined potential
of both types of programs.
BASELINE ESTIMATES
In Exhibit ES-we show estimated summer peak demand and actual energy sales for Idaho
Power for 2002. The residential sector is the largest contributor to both summer peak demand
and annual energy representing roughly 30 percent of each. The commercial sector is relatively
small, representing roughly 20 percent of energy and 18 percent of peak demand. Seasonal
irrigation contributes a very large and disproportionate amount to summer peak demand
(representing 24 percent of summer peak demand but only 12 percent of annual energy). The
industrial sector makes up 18 percent of annual energy usages but only 13 percent of summer
peak, due to its higher than average load factor.
Exhibit ES-
Estimated Breakdown of Summer Peak Demand by Sector for Idaho Power, 2002
Residential
28%
Losses Commercial
18%
Off System Sales
Other
Irrigation
24%
Quantum Consulting Inc.ES-Executive Summary
ENERGY EFFICIENCY POTENTIAL
The study resulted in a total economic potential of 384 MW of peak demand reduction and
107 GWh of annual energy savings. These are displayed in Exhibit ES-, broken out into
residential and commercial sectors. This peak demand reduction represents nearly 23 percent of
the combined residential and commercial peak demand forecast in 2013. For annual energy
savings, the economic potential is about 12 percent of IPCo s 2013 energy forecast.
Comprehensive results of technical and economic potential by sector, home or building type,
and end use are presented in Chapter 4.
Exhibit ES-
Economic Potential (2013)
Peak Demand (MW) and Energy (GWh) Savings
200
--------------------
000 . Commercial
. Residential
800
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -------
600
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -------
400
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -------
200
---------------------------
GWH
Economic potential, which represents the savings possible if all cost-effective measures were
installed in every application deemed physically feasible, is the point of departure from which
more realistic assessments of the value of energy-efficiency programs are derived. To develop
the estimates of achievable potential, the study modeled market penetration based on four
different funding scenarios. These scenarios consisted of the following:
A Low efficiency funding scenario with rebates covering 33% of incremental measure
costs and base marketing funding levels;
Moderate efficiency funding scenario with rebates covering 500/0 of incremental
measure costs and slightly higher marketing expenditures;
A High efficiency funding scenario with rebates ramping up over time to 75% of
incremental measure costs and significantly increased marketing expenditures; and
Quantum Consulting Inc.ES-Executive Summary
A Maximum Achievable scenario with rebates ramping up over time to cover 100% of
incremental measure costs and marketing expenditures sufficient to create maximum
market awareness.
The achievable potential peak demand for the four scenarios as well as the estimated naturally
occurring energy efficiency (which represent efficiency adoption in the absence of any
programs) is displayed in Exhibit ES-3. For year 10 of the analysis, peak demand reductions
range from 190 MW (around 11 percent of 2013 peak demand) for the maximum achievable
scenario to 42 MW (less than 30/0 of 2013 peak demand) for the low funding scenario.
As shown in Exhibit ES-, the achievable potential energy savings in 2013 were 681 GWh for the
maximum achievable scenario, roughly 7.5 percent of IPCo s energy forecast for that year. The
low-funding scenario showed 195 GWh for the same year, just over 2 percent of the forecast.
Based on the methodology used for this study, all of the measures that go into the assessment of
achievable potential are estimated to be cost effective based on their incremental costs and
incremental savings. For the achievable potential, however, marketing and administrative costs
are added into the equation. After incorporating these costs, all four scenarios were still cost
effective from the TRC perspective. In Exhibit ES-5, the present value of benefits is presented
along with a breakout of the various costs components included in the TRC for all four
scenarIOs.
Exhibit ES-
Peak Demand Reduction Potential by Funding Scenario, 10-Year Forecast
200
180
160
140
120
100
0 Nat. Occurring
0 Low
0 Moderate
.High
. Max. Achievable
- - - -- - -- - -- - - -- - -- -
Year
Quantum Consulting Inc.ES-Executive Summary
Exhibit ES-
Energy Savings Potential by Funding Scenario, 10-Year Forecast
700 0 Nat. Occurring
0 Low
600 0 Moderate
.High
500 . Max. Achievable
400
- - -
GWH
300
- -
200
100
Year
Exhibit ES-
Present Value Costs and Benefits Achievable Potential Scenarios
$450
$400
iI!II Net Benefits
0 Total Benefits
. Program Incentives
. Non-Incentive Participant Costs
0 Marketing
. Administration
$350
c::
$300
:?:
s $250
$200
- - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - -
c::
~ $150
...---------------
$100
$50
Low Moderate
------------------------------------.----
$104
High Max. Achievable
Quantum Consulting Inc.ES-Executive Summary
DEMAND RESPONSE POTENTIAL
As displayed in Exhibit ES-6, of IPCo s total peak demand in 2004, 469 MW (32 percent) were
deemed to be applicable for peak demand reduction programs. Of this applicable load, 105 MW
of potential savings were estimated to be economic. Of the total economic potential, AC load
control programs for the residential sector accounted for nearly 60 MW, around 57 percent. The
next largest contributors were the small- and large-commercial back-up generation programs,
which combined for around 43 percent of the total economic potential.
Exhibit ES-
Economic Potential for Residential and Commercial DR Programs
% Of Total Peak
MW in 2004 Demand
Estimated Applicable Demand for DR 469 32%
Economic Potential for DR 105
The assessment of achievable DR potential was based on analysis of four program concepts -
AC Load Control (DLC), Critical Peak Pricing (CPP), Voluntary Demand Response Incentives
(DRP), and Back-up Generator Incentives (BUG) - bundled into four program strategies:
DLC and BUG - Low Incentive Levels
All 4 Concepts - Low Incentive Levels
All 4 Concepts - High Incentive Levels
Maximum Achievable
The forecast of annual estimated MW reduction that would occur during system peak
conditions is shown in Exhibit ES-7 for each of the four strategies. The growth in the various
scenarios represents a forecasted successful effort of IPCo to shift applicable load into higher
capability segments as well as customer response to incentives. The maximum achievable
scenario s 129 MW in 2013 amounts to more than 7.5 percent of the peak demand. The lowest
potential is associated with the DLC and BUG program concepts with low funding, which has a
potential of 25 MW in 2013, approximately 1.5 percent of peak demand. Chapter 5 presents
complete results for the assessment of DR potential.
Quantum Consulting Inc.ES-Executive Summary
Exhibit ES-
Comparison of Load Reduction Forecasts Residential and Commercial Sectors
140
120
100
0 DLC & BUG - Low $
.4 Concepts - Low $
.4 Concepts - High $
0 Max. Achievable
Year
Quantum Consulting Inc.ES-Executive Summary
1. INTRODUCTION
The Idaho Public Utilities Commission (IPUC) directed the Idaho Power Company (IPCo) to
consult with their Energy Efficiency Advisory Group regarding the need to initiate a
comprehensive DSM study of the Idaho Power service territory. In July 2002, the Energy
Efficiency Group at Idaho Power received recommendations from the Idaho Power Energy
Efficiency Advisory Group and from Idaho Power management to proceed with a study of
DSM opportunities with the primary focus being peak demand reduction opportunities in its
service territory. The Energy Efficiency Advisory Group noted that since the information was
to be used primarily as an Idaho Power management tool for DSM, that the focus of the study
should be driven by the needs of Idaho Power DSM resource planning. In August 2003, Idaho
Power selected the team of Quantum Consulting Inc. and KEMA-XENERGY Inc. to conduct this
DSM potential study.
The information needed most by Idaho Power for future DSM planning is summer peak end-
use information and summer demand reduction and demand response program research.
Because Idaho Power s original focus in this study was on summer peak demand reduction
potential, the consultant team originally focused on those end uses within the residential and
commercial sectors that would contribute most to summer peak demand savings. The results of
this initial project scope - or, phase - were provided to IPCo by the consultant team in
December 2003 and January 2004 for use by IPCo in its 2004 Integrated Resource Plan. These
initial results were also presented to the Energy Efficiency Advisory Group in January 2004.
In late spring 2004 IPCo requested an expansion of the study scope to address additional
measures and end uses that may produce cost effective energy efficiency savings even though
they may not contribute significantly to summer peak demand reductions.
Specific tasks included in this study were:
Review of Idaho Power s summer peak load characteristics and identification of
residential and commercial end-uses that have potential for demand reduction during
the summer peak time.
Development of a DSM measures database for end-uses identified above. Assessment of
the measures, technologies and equipment practices that could reduce peak demand and
annual energy consumption. Identification of savings, costs, measure lives, load shapes,
non-energy benefits and other factors influencing cost-effectiveness.
Establishment of current baselines for residential and commercial customers in Idaho
Power service territory. Development of parameters such as equipment type,saturation, building size, fuel type, efficiency and age. Collection of existing data on
current customers in residential and commercial sectors. Incorporation of the impact of
current codes and standards. Examination of Idaho Power forecasts and rate schedules.
Review of current DSM programs in Idaho Power service territory.
Quantum Consulting Inc.Introduction
Development of estimates of technical, economic, and achievable potential and
performance of cost-effective analysis on programs options. Review of anomalies in
Idaho markets that may affect program success as well as Idaho specific issues, trends,
barriers and opportunities. Incorporation of potential barriers to the adoption of
suggested technologies or practices.
In the 1980s and early 1990s, DSM potential studies were conducted routinely by many utilities
and other organizations throughout the United States. These studies were largely abandoned,
however, with the advent of electric restructuring. Recently, a number of factors-western U.
supply shortages and price increases related to the California energy crisis, future price and
supply uncertainty, and the environmental impacts of traditional power plants-have
combined to warrant a detailed analysis of DSM potential.
This study estimates potential electricity and peak demand savings from DSM measures in the
Idaho Power territory. Analyses were carried out separately for demand response (DR) and
energy efficiency program options.
For DR, four program concepts were modeled with some slight variations either over time or
across segments. The four concepts included:
AC Load Control (DLC):these programs provide lower energy rates for customers who
are willing to have cycling equipment installed that can be directly controlled by the
utility.
Qitical Peak Pricing (CPP):this program offers dynamic rates that change based on
demand versus supply available. This program generally provides consistently lower
off-peak rates. However, during a CPP event, rates may increase dramatically (e.g. 5
times the average for that period).
Voluntary Demand Response Incentive (DRP):this program offers a credit to customers
over a certain demand, who voluntarily commit to reduce their electricity usage by a
significant percentage (such as 100/0) during a DRP event.
Back-up Genera or Incentives (BUG):this program offers financial incentives to
customers who run their back-up generation during program events.
In contrast to energy conservation, which often involves short-term behavioral changes, energy-
efficiency opportunities are typically physical, long-lasting changes to buildings and equipment
that result in decreased energy use while maintaining constant levels of energy service.
Examples of energy efficiency include:
Compact fluorescent lighting systems that deliver equivalent light using 70 percent less
electricity than incandescent light bulbs.
New variable-speed drive chillers that deliver cooling to buildings using 40 percent less
energy than typical systems in today s buildings.
Energy management control systems that eliminate energy waste and optimize building
operation.
Quantum Consulting Inc.Introduction
Identification and repair of leaks in industrial compressed air systems that otherwise
result in wasteful increases in product costs.
These types of improvements, and hundreds of others, reduce electricity consumption without
affecting the end-use services (e.g., light, heat
, "
coolth," drivepower, and the like) that
consumers and businesses require for comfort, productivity, and leisure.
This report provides both detailed and aggregated estimates of the costs and savings potential
of DSM measures in Idaho. In addition, forecasts are developed of savings and costs associated
with different levels of program funding over a 10-year period. Consistent with our 10-year
focus, the study is restricted to DSM measures and practices that are presently commercially
available. These are the measures that are of most immediate interest to DSM program and
resource planners.
Quantum Consulting Inc.Introduction
2. BASELINE ESTIMA TES AND ENERGY EFFICIENCY ASSESSMENT METHODOLOGY
In this chapter, we give a brief overview of the concepts, methods, and scenarios used to
develop the baseline and energy efficiency estimates for this study. Methods used to develop
our estimates of demand response potential are presented in Section
CHARACTERIZING THE ENERGY-EFFICIENCY RESOURCE
Energy efficiency has been characterized for some time now as an alternative to energy supply
options such as conventional power plants that produce electricity from fossil or nuclear fuels.
In the early 1980s, researchers developed and popularized the use of a conservation supply
curve paradigm to characterize the potential costs and benefits of energy conservation and
efficiency. Under this framework, technologies or practices that reduced energy use through
efficiency were characterized as "liberating 'supply' for other energy demands" and could
therefore be thought of as a resource and plotted on an energy supply curve. The energy-
efficiency resource paradigm argued simply that the more energy efficiency, or "mega-watts
produced, the fewer new plants would be needed to meet end users' power demands.
Defining Energy-Efficiency Potential
Energy-efficiency potential studies were popular throughout the utility industry from the late
1980s through the mid-1990s. This period coincided with the advent of what was called least-
cost or integrated resource planning (IRP). Energy-efficiency potential studies became one of
the primary means of characterizing the resource availability and value of energy efficiency
within the overall resource planning process.
Like any resource, there are a number of ways in which the energy-efficiency resource can be
estimated and characterized. Definitions of energy-efficiency potential are similar to definitions
of potential developed for finite fossil fuel resources like coal, oil, and natural gas. For example,
fossil fuel resources are typically characterized along two primary dimensions: the degree of
geologic certainty with which resources may be found and the likelihood that extraction of the
resource will be economic. This relationship is shown conceptually in Exhibit 2-
Somewhat analogously, this energy-efficiency potential study defines several different types
energy-efficiency potential namely: technical, economic, achievable, program, and naturally
occurring. These potentials are shown conceptually in Exhibit 2-2 and described below.
Technical potential is defined in this study as the complete penetration of all measures analyzed
in applications where they were deemed technically feasible from an engineering perspective.
Economic potential refers to the technical potential of those energy conservation measures that
are cost-effective when compared to supply-side alternatives. Maximum achievable potential
is defined as the amount of economic potential that could be achieved over time under the most
aggressive program scenario possible. Achievable program potential refers to the amount of
savings that would occur in response to specific program funding and measure incentive levels.
Savings associated with program potential are savings that are projected beyond those that
would occur naturally in the absence of any market intervention. Naturally occurring potential
Quantum Consulting Inc.Baseline Estimates and EE Methods
refers to the amount of savings estimated to occur as a result of normal market forces, that is, in
the absence of any utility or governmental intervention.
Exhibit
Conceptual Framework for Estimates of Fossil Fuel Resources
Possible Possible
and but not
Economically Feasible Economically Feasible
Known Known
and but not
Economically Feasible Economically Feasible
(I)
(I)
111
....
::0.
(I)
(I)
Decreasing Economic Feasibility
Exhibit
Conceptual Relationship Among Energy-Efficiency Potential Definitions
Technical
Economic
Maximum Achievable
Program
Naturally Occurring
Quantum Consulting Inc.Baseline Estimates and EE Methods
OVERVIEW OF ENERGY EFFICIENCY FORECASTING METHOD
The crux of any forecasting process involves carrying out a number of systematic analytical
steps that are necessary to produce accurate estimates of energy efficiency (EE) effects on
system load. A simplified overview of these basic analytical steps used in this study is shown
in Exhibit 2-
Exhibit
Simplified Conceptual Overview of Modeling Process
ECONOMIC DATA MEASURE DATA BUILDING DATA
~ iF
MODEL
INPUTS
~Ir
TECHNICAL
POTENTIAL
ECONOMIC
POTENTIAL
~..
MAXIMUM
ACHIEVABLE
..........
POTENTIAL
NATURALLY PROGRAM DATAOCCURRING
.--
i--ANDEFFICIENCYADOPTION INPUTS
PROGRAM
--.
POTENTIAL
(Inputs to IRP model
::;a
Quantum Consulting Inc.Baseline Estimates and EE Methods
The approach to developing an energy efficiency forecast used for this study involves a five-
step process. The steps include:
Step 1: Develop Initial Input Data
Develop list of energy efficiency measure opportunities to include.
Gather and develop technical data (costs and savings) on efficient measure
opportunities.
Gather, analyze, and develop information on building characteristics, including total
square footage and households, electricity consumption and intensity by end use, end-
use consumption load patterns by time of day and year (i.e., load shapes), market shares
of key electric consuming equipment, and market shares of energy efficiency
technologies and practices.
Gather economic input data such as current and forecasted retail electric prices and
current and forecasted costs of electricity generation, along with estimates of other
potential benefits of reducing supply, such as the value of reducing environmental
impacts associated with electricity production.
Step 2: Estimate Technical Potential and Develop Supply Curves
Match and integrate data on efficient measures to data on existing building
characteristics to produce estimates of technical potential and energy efficiency supply
curves.
Step 3: Estimate Economic Potential
Match and integrate measure and building data with economic assumptions to produce
indicators of costs from different viewpoints (e.g., utility, societal, and consumer).
Estimate total economic potential using supply curve approach.
Step 4: Estimate Maximum Achievable, Program, and Naturally Occurring Potentials
Gather and develop estimates of program costs (e.g., for administration and marketing)
and historic program savings.
Develop estimates of customer adoption of energy efficiency measures as a function of
the economic attractiveness of the measures, barriers to their adoption, and the effects of
program intervention.
Estimate maximum achievable, program, and naturally occurring potentials; calibrate
achievable and naturally occurring potential to recent program and market data.
Quantum Consulting Inc.Baseline Estimates and EE Methods
Develop alternative economic estimates associated with alternative future scenarios.
Step 5: Scenario Analyses and Resource Planning Inputs
Recalculate potentials under alternate economic scenarios and deliver data in format
required for resource planning.
Provided below is additional discussion of data development and the modeling approaches for
technical, economic, and achievable DSM forecasts. The analysis was carried using KEMA-
XENERGY's DSM ASSYSTTM (Demand-Side Management Technology Assessment System).
BASELINE AND MEASURE DATA DEVELOPMENT
Measure Data
Measure level data was developed and obtained from a variety of sources for this study. The
study authors had previously developed much of the measure information on recent previous
studies, including the following:
Northwest Power Planning Council The Fifth Plan Draft Conservation Resources
Assessment April 8, 2004 (Presentation on NWPPC web site and associated
spreadsheets)
Regional Technical Forum
Energy Trust of Oregon Energy Efficiency and Conservation Measure Resource Assessment,
January 2003
Puget Sound Energy Least Cost Plan, 2003
Pacific Northwest Energy Star New Construction Specification for Site-built, Single-Family
Dwellings, 20043
The California Statewide Commercial Sector Energy Efficiency Potential Study, 2002 (covering
the commercial existing construction market)
The California Statewide Residential Sector Energy Efficiency Potential Study, 2003 (covering
the residential existing construction market)
California s Secret Energy Surplus: The Potential for Energy Efficiency, 2002 (covering the
industrial and new construction markets)
2001 Database on Energy-Efficient Resources (DEER) Update4
http:/ /www.nwppc.org/energy/rtf/presentations/ResourceAssess2003 0408/and personal communication
with Tom Eckman.
http:/ /www.nwppc.org/energy/rtf/about.htm
3 Prepared by Ecotope Inc.
http:/ /www.energy.ca.gov/deer/
Quantum Consulting Inc.Baseline Estimates and EE Methods
Following is a description of the measure data used in the study. Refer to the above-referenced
reports for a more complete discussion.
Much of the measure cost and savings data for this study were developed as part of the DEER
2001 Update study. Part of that study involved collection and analysis of residential and
commercial measure cost data. A second part of the study focused on development of savings
fractions for residential measures. Regional sources, in particular the NWPPC'Fifth
Conservation Assessment, were used to compare to cost and savings estimates developed on
previous studies. In several cases, adjustments were made based on this comparison and
discussions with the NWPPC's primary author.
In order to assess the amount of energy efficiency savings available, estimates of the current
saturation of energy efficient measures were developed from available data sources. Key
sources for this study include:
Baseline Characteristics of the Residential Sector (Idaho, Montana, Oregon, and
Washington), 20015
Baseline Characteristics of the Non-Residential Sector (Idaho, Montana, Oregon, and
Washington), 20016
Assessment of the Commercial Building Stock in the Pacific Northwest 20047
Development of Building and Base Energy Forecast Data
Key building data necessary for this study include: units of consumption (number of
households and square feet of building space), end use energy consumption (kWh/unit),
electric end use saturations, and load shapes. The primary sources for these data were obtained
and developed from Idaho Power internal data and models. Idaho Power currently utilizes
econometric rather than end-use forecasting models. In the mid-1990s, however, Idaho Power
implemented residential and commercial end-use forecasts using the REEPS and COMMEND
models. These model inputs were developed from residential and commercial saturation
surveys (mail based) carried out in the late 1980s and mid-1990s. Although dated, these model
runs represented the only sources of Idaho Power specific end use data available. QC staff
working with Idaho Power staff, re-ran these models to obtain an initial set of estimates of
residential and commercial end use consumption, saturation, and units (households and square
feet). These estimates were then compared to Idaho Power s latest system energy consumption
and peak load data and adjusted so that the bottom-up end use estimates were reconciled with
the known system totals. This process is described below.
Initial Energy End Use Breakdowns and Calibration to Idaho Power Sales. Idaho Power
provided QC staff with REEPS and COMMEND files from the mid-1990s, the last time the
models were run for Idaho Power. Idaho Power reviewed what was necessary to rerun the
output to produce the type of detailed end use and building type data needed for this study.
5 Prepared by Ecotope Inc. for the Northwest Energy Efficiency Alliance.
6 Prepared by Ecotope Inc. for the Northwest Energy Efficiency Alliance.
7 Prepared by Kema-Xenergy Inc. for the Northwest Energy Efficiency Alliance.
Quantum Consulting Inc.Baseline Estimates and EE Methods
QC re-ran the models, generated numerous individual output files, and re-aggregated the files
into more useful summaries. As it turned out, both the REEPS and COMMEND forecasts were
quite good forecasts out to 2002 from a total sales perspective. The REEPS estimates of
households, end use UECs (kWh/household) and electric end use saturations were also found
to be reasonable starting points for this study. However, the COMMEND estimates of square
footage, end use EUIs (kWh/square foot), and electric end use saturations could not be
reconciled with the 2002 sales data. In particular, the EUIs (kWh/ft2) by building type did not
appear to be reasonable in many cases. As a result, we used whole-building EUIs by building
type from the recent Pacific Northwest Building Stock Assessment, with adjustments for Idaho
Power electric end use saturation levels, to back into estimates of square footage. We also
adjusted end use EUIs to ensure that they were consistent with reasonable estimates of installed
capacity (kW /square foot) and full load hours of operation.
Peak Load Development and Calibration. The peak calibration process was driven by the
whole-building load research and census data provided by Idaho Power as well as end use load
shapes from secondary sources. The Idaho Power load research data proved invaluable to the
process. In particular, the breakout of true commercial from true industrial business types in
the load research sample and census data was extremely useful. For the residential sector, we
calculated a peak-to-energy ratio from the load research data and then adjusted our end-use
peak-to-energy factors slightly to get close to the overall ratio of 0.21 MW per GWh (i.e., a load
factor of 55 percent). For the commercial sector, we multiplied the calibrated energy by
building type by the building type-specific peak-to-energy factors obtained from the Idaho
Power load research data. This produced building type-specific estimates of peak demand. then calibrated the end use peak demand estimates to sum to these control totals within each
building type.
To investigate the reasonableness of the estimates developed from the bottom up baseline peak
demand estimates described above for the residential and commercial, estimates of peak
demand were developed for the remaining Idaho Power customers (e.g., industrial, irrigation
and special customers). Peak demand estimates for these sectors were based on load factors
from Idaho Power load research data. The combined results were very close to actual total
Idaho Power peak demand in 2002.
Housing and Building Stock Forecasts. After calibrating the baseline end use data to Idaho
Power s 2002 sales and peak load, QC used Idaho Power s forecasts of residential and
commercial load growth to develop baseline data for the 10-year period used for this study.
Existing and new construction loads were developed by decaying the existing stock and taking
the difference between the forecasted loads and decayed existing stock loads as new
cons truction. 8
Baseline Results. The results of our baseline development work are presented in Exhibits 2-
through 2-10. In Exhibit 2-4 and 2-5 we show the distribution by sector (including losses and
off-system sales) of the estimated summer peak demand and actual energy sales for Idaho
Power for 2002. In Exhibits 2-6 through 2-11 we present our estimates of residential and
8 Residential stock was decayed at a rate of 1 percent per year, commercial stock was decayed at a rate of 2
percent per year.
Quantum Consulting Inc.Baseline Estimates and EE Methods
commercial loads by end use and building type for only Idaho (Le., excluding Idaho Power
non-Idaho loads).
Key characteristics of Idaho Power s customer base relevant to the findings in this study include
the following:
Total summer peak load in 2002, including line losses, was approximately 2 900 MW.
Total energy consumption, including losses, was roughly 14 500 GWh.
Seasonal irrigation contributes a very large and disproportionate amount to summer
peak demand (representing 24 percent of summer peak demand but only 12 percent
of annual energy).
The industrial sector makes up 18 percent of annual energy usages but only 13
percent of summer peak, due to its higher than average load factor.
The residential sector is the largest contributor to both summer peak demand and
annual energy representing roughly 30 percent of each.
Summer peak demand is dominated by air conditioning loads, which represent 57
percent of residential peak demand.
- A much wider variety of loads contribute significantly to annual energy
consumption, particularly electric heating, water heating (including water loads for
clothes and dish washers), air conditioning, and lighting.
Single family homes dominate the residential sector, multi-family and mobile homes
are relatively small contributors to peak demand.
The commercial sector is relatively small, representing roughly 20 percent of energy and
18 percent of peak demand.
Summer peak demand is dominated by air conditioning and lighting loads, which
represent 34 percent and 28 percent, respectively of commercial peak demand.
With respect to annual energy usage, lighting is the largest contributor followed by
electric heating, cooling, miscellaneous loads, refrigeration, and ventilation.
Small Offices and Non-Food Retail are both individually at least twice as large as
any other building type. Cooling and lighting dominate peak demand for both of
these segments.
Refrigeration loads are relatively small overall but are significant in both the grocery
and warehouse segments.
Quantum Consulting Inc.Baseline Estimates and EE Methods
Exhibit
Estimated Breakdown of Summer Peak Demand by Sector for Idaho Power, 2002
Residential
28%
Losses Commercial
18%
Off System Sales
Other
Industrial
13%
Irrigation
24%
Exhibit
Estimated Breakdown of Energy Sales by Sector for Idaho Power, 2002
Residential
30%
Other
Commercial
20%
Losses
Off System Sales
Irrigation
12%Industrial
18%
Quantum Consulting Inc.Baseline Estimates and EE Methods
Exhibit
Estimated Residential Summer Peak Demand by End Use, 2002
Estimated Peak MW for Idaho = 800
Freezer 2% e ngera Ion
Lighting
Water Heating
Clothes Washer
Exhibit
Estimated Residential GWh by End Use, 2002
Estimated GWh for Idaho = 4 300
Other
20%
Clothes Washer
Ig tmg 1019% Heating
21%
Heating (Sec.
Cooling
10%
Water Heating
15%
Refrigeration
Quantum Consulting Inc.Baseline Estimates and EE Methods
Exhibit
Estimated Residential Summer Peak Demand by Home Type, 2002
Mobile
Home
Large
Multi-Family
Small
Multi-Family
Single-Family
100 200 300 400 500 600
Estimated Peak MW
700 800
Exhibit
Estimated Commercial Summer Peak Demand by End Use, 2002
Lighting
28%
Cooking
Water Heating
Miscellaneous
11%
Lighting (Ext.
Refrigeration
Office Equipment
Ventilation
Cooling
34%
Estimated Peak Demand for Idaho = 490 MW
Quantum Consulting Inc.Baseline Estimates and EE Methods
Exhibit 2-
Estimated Commercial GWh by End Use, 2002
Miscellaneous
11%
Office Equipment
Heating
20%
Cooling
12%
Ventilation
Exhibit
Estimated Commercial Summer Peak Demand by Business Type and End Use, 2002
Miscellaneous
Lodging
Health
College
School
Warehouse
Grocery
Retail
Restaurant
Large Office
Small Office
Lighting
28%
Refrigeration
Estimated GWh for Idaho = 2 700
Peak MW
. Heating
. Cooling
0 Ventilation
0 Water Heating
. Cooking
. Refrigeration
. Lighting (Ext.
0 Lighting
. Office Equipment
. Miscellaneous
100 120
Quantum Consulting Inc.Baseline Estimates and EE Methods
Economic Data
The key economic inputs utilized in the forecasting process are avoided costs, electricity rates,
discount rates and inflation rates. Electricity rates were obtained from Idaho Power tariffs.
Idaho Power rates are very low, roughly 4 cents per kWh for commercial customers and 6 cents
per kWh for residential customers. A voided cost forecasts were developed by Idaho Power as
part of the current Integrated Resource Plan. The avoided costs used for this potential study
ranged from 3 cents per kWh for off-peak periods to 5 cents per kWh for the summer on-peak
period. In addition, a capacity avoided cost value of $50 per kW -year was also included in the
calculation of total avoided costs. A nominal utility discount rate of 8 percent was used in the
analysis. The inflation rate used was 3 percent per annum.
2.4 ESTIMATION OF TECHNICAL POTENTIAL AND DEVELOPMENT ENERGY-EFFICIENCY
SUPPL Y CURVES
Technical potential refers to the amount of energy savings or peak demand reduction that
would occur with the complete penetration of all measures analyzed in applications where they
were deemed technically feasible from an engineering perspective. Total technical potential is
developed from estimates of the technical potential of individual measures as they are applied
to discrete market segments (commercial building types, residential dwelling types, etc.
Core Equation
The core equation used to calculate the energy technical potential for each individual efficiency
measure, by market segment, is shown below (using a commercial example):
Technical
Potential of
Efficient
Measu re
Total
Square
Feet
Base Case
Equipment
EUI
(kWh/fe)
Applicability
Factor
Not
Complete
Factor
Feasibility
Factor
Savi ngs
Factor
where:
Square feet is the total floor space for all buildings in the market segment. For the
residential analysis, the number of dwelling units is substituted for square feet.
Base-case equipment EDI is the energy used per square foot by each base-case
technology in each market segment. This is the consumption of the energy-using
equipment that the efficient technology replaces or affects. For example, if the efficient
measure were a CFL, the base EUI would be the annual kWh per square foot of an
equivalent incandescent lamp. For the residential analysis, unit energy consumption
(UECs), energy used per dwelling, are substituted for EUIs.
9 Note that stock turnover is not accounted for in our estimates of technical and economic potential, stock
turnover is accounted for in our estimates of achievable potential. Our definition of technical potential assumes
instantaneous replacement of standard-efficiency with high-efficiency measures.
Quantum Consulting Inc.Baseline Estimates and EE Methods
Applicability factor is the fraction of the floor space (or dwelling units) that is
applicable for the efficient technology in a given market segment, for the example above,
the percentage of floor space lit by incandescent bulbs.
Not complete factor is the fraction of applicable floor space (or dwelling units) that has
not yet been converted to the efficient measure; that is, (1 minus the fraction of floor
space that already has the ENERGY EFFICIENCY measure installed).
Feasibility factor is the fraction of the applicable floor space (or dwelling units) that is
technically feasible for conversion to the efficient technology from an engineering
perspective.
Savings factor is the reduction in energy consumption resulting from application of the
efficient technology.
Technical potential for peak demand reduction is calculated analogously substituting kW for
kWh per household or square foot of commercial floorspace.
An example of the core equation is shown in Exhibit 2-12 for the case of a perimeter-based
daylight dimming system.
Exhibit
Example of Technical Potential Calculation-Peak Period Commercial Perimeter Zone
Dimming (Generic Data for Example Purposes Only)
Technical Total Base Case Not
Potential of =Square x Equipment x Complete x Feasibility x Savings
Measure Feet Demand Applicability Factor Factor Factor
(kW/ff)Factor
20.13 MW 214 1.5 0.2 0.4
million
Technical potential is calculated in two steps. In the first step, all measures are treated
independently; that is, the savings of each measure are not adjusted for overlap between
competing or interactive measures. By treating measures independently, their relative cost-
effectiveness is analyzed without making assumptions about the order or combinations in
which they might be implemented in customer buildings. However, the total technical potential
across measures cannot be estimated by summing the individual measure potentials directly.
The cumulative savings cannot be estimated by adding the savings from the individual savings
estimates because some savings would be double counted. For example, the savings from a
measure that reduces heat gain into a building, such as window film, are partially dependent on
other measures that affect the efficiency of the system being used to cool the building, such as a
high-efficiency chiller - the more efficient the chiller, the less energy saved from the application
of the window film.
Quantum Consulting Inc.Baseline Estimates and EE Methods
Use of Supply Curves
In the second step, cumulative technical potential is estimated using an energy efficiency supply
curve approach. This method eliminates the double-counting problem. A supply curve
typically consists of two axes-one that captures the cost per unit of saving a resource or
mitigating an impact (e.g., $/kWh saved or $/ton of carbon avoided) and the other that shows
the amount of savings or mitigation that could be achieved at each level of cost. The curve is
typically built up across individual measures that are applied to specific base-case practices or
technologies by market segment. Savings or mitigation measures are sorted on a least-cost basis,
and total savings or impacts mitigated are calculated incrementally with respect to measures
that precede them. Supply curves typically, but not always, end up reflecting diminishing
returns, i.e., as costs increase rapidly and savings decrease significantly at the end of the curve.
The cost dimension of most energy efficiency supply curves is usually represented in dollars
per unit of energy savings. Costs are usually annualized (often referred to as "levelized") in
supply curves. For example, energy efficiency supply curves usually present levelized costs per
kWh or kW saved by multiplying the initial investment in an efficient technology or program
by the "capital recovery rate" (CRR):
CRR =
1 - (l + d)"n
where is the real discount rate and is the number of years over which the investment is
written off (i.e., amortized).
Thus,
Levelized Cost per kWh Saved = Initial Cost x CRR/ Annual Energy Savings
Levelized Cost per kW Saved = Initial Cost x eRR/Peak Demand Savings
The levelized cost per kWh and kW saved are useful because they allow simple comparison of
the characteristics of energy efficiency with the characteristics of energy supply technologies.
However, the levelized cost per kW or kWh saved are biased indicators of cost-effectiveness
because all of the efficiency measure costs are allocated to either peak savings or annual energy
savings. As a result, energy efficiency supply curves do not reflect the integrated value of both
peak and energy savings. The integrated value of both peak and energy savings is captured in
the methodology used in this study by calculation of the total resource cost test for each
measure as described in the section on Economic Potential below.
Exhibit 2-13 shows a simplified numeric example of a supply curve calculation for several
energy efficiency measures applied to commercial lighting for a hypothetical population of
buildings. What is important to note is that in an energy efficiency supply curve, the measures
are sorted by relative cost-from least to most expensive. In addition, the energy consumption
of the system being affected by the efficiency measures goes down as each measure is applied.
As a result, the savings attributable to each subsequent measure decrease if the measures are
interactive. For example, the occupancy sensor measure shown in Exhibit 2-13 would save
more at less cost per unit saved if it were applied to the base-case consumption before the
lamp and electronic ballast combination. Because the T8 electronic ballast combination is more
cost-effective, however, it is applied first, reducing the energy savings potential for the
Quantum Consulting Inc.Baseline Estimates and EE Methods
occupancy sensor. Thus, in a typical energy efficiency supply curve, the base-case end-use
consumption is reduced with each unit of energy efficiency that is acquired. Notice in Exhibit 2-
13 that the total end-use GWh consumption is recalculated after each measure is implemented,
thus reducing the base energy available to be saved by the next measure.
Exhibit 2-13 shows an example that would represent measures for one base-case technology in
one market segment. These calculations are performed for all of the base-case technologies,
market segments, and measure combinations in the scope of a study. The results are then
ordered by levelized cost and the individual measure savings are summed to produce the
energy efficiency potential for the entire sector.
In the next subsection, we discuss how economic potential is estimated as a subset of the
technical potential.
Exhibit
Sample Technical Potential Supply Curve Calculation for Commercial Lighting
(Note: Data are illustrative only)
Total End Use Applicable, Not
Consumption Complete and Average levelized
of Population Feasible kWh/fe of Savings GWh Cost ($/kWh
Measure (GWh)(1 OOOs of fe)population Savin2s saved)
Base Case: T12 lamps with 425 100 000 4.3 N/A N/A N/AMagnetic Ballast
1. T8 w. Elec. Ballast 425 100 000 4.3 21%$0.
2. Occupancy Sensors 336 000 3.4 10%$0.
3. Perimeter Dimming 322 10,000 45%$0.25
With all measures 309 27%116
ESTIMA TION OF ECONOMIC POTENTIAL
Economic potential is typically used to refer to the technical potential of those energy
conservation measures that are cost effective when compared to either supply-side alternatives
or the price of energy. Economic potential takes into account the fact that many energy
efficiency measures cost more to purchase initially than do their standard-efficiency
counterparts. The incremental costs of each efficiency measure are compared to the savings
delivered by the measure to produce estimates of energy savings per unit of additional cost.
These estimates of energy efficiency resource costs can then be compared to estimates of other
resources such as building and operating new power plants.
Cost Effectiveness Tests
To estimate economic potential, it is necessary to develop a method by which it can be
determined that a measure or program is economic. We used the total resource cost (TRC) test to
assess cost effectiveness. The TRC is a form of societal benefit-cost test. Other tests that are
Quantum Consulting Inc.Baseline Estimates and EE Methods
sometimes used in analyses of program cost-effectiveness include the utility cost, ratepayer
impact measure (RIM), and participant tests. Before discussing the TRC test and how it is often
used in our DSM forecasts, we present below a brief introduction to the common tests:
Total Resource Cost Test-The TRC test measures the net costs of a demand-side
management program as a resource option based on the total costs of the program
including both the participants' and the utility'costs. The test is applicable to
conservation, load management, and fuel substitution programs. For fuel substitution
programs, the test measures the net effect of the impacts from the fuel not chosen versus
the impacts from the fuel that is chosen as a result of the program. TRC test results for
fuel substitution programs should be viewed as a measure of the economic efficiency
implications of the total energy supply system (gas and electric). A variant on the TRC
test is the societal test. The societal test differs from the TRC test in that it includes the
effects of externalities (e.g. environmental, national security), excludes tax credit
benefits, and uses a different (societal) discount rate.
Participant Test-The participant test is the measure of the quantifiable benefits and
costs to the customer due to participation in a program. Since many customers do not
base their decision to participate in a program entirely on quantifiable variables, this test
cannot be a complete measure of the benefits and costs of a program to a customer.
Utility (Program Administrator) Test-The program administrator cost test measures
the net costs of a demand-side management program as a resource option based on the
costs incurred by the program administrator (including incentive costs) and excluding
any net costs incurred by the participant. The benefits are similar to the TRC benefits.
Costs are defined more narrowly.
Ratepayer Impact Measure Test-The ratepayer impact measure (RIM) test measures
what happens to customer bills or rates due to changes in utility revenues and operating
costs caused by the program. Rates will go down if the change in revenues from the
program is greater than the change in utility costs. Conversely, rates or bills will go up if
revenues collected after program implementation are less than the total costs incurred
by the utility in implementing the program. This test indicates the direction and
magnitude of the expected change in customer bills or rate levels.
The key benefits and costs of the various cost-effectiveness tests are summarized below in
Exhibit 2-14.
10 California Standard Practice Manual, October 2001.
Quantum Consulting Inc.Baseline Estimates and EE Methods
Exhibit
Summary of Benefits and Costs of Common Benefit-Cost Tests
Test Benefits Costs
TRC Test . Generation, transmission and . Generation costs
distribution savings . Program costs paid by the
Participants avoided equipment costs administrator
(fuel switching only)
. Participant measure costs
Participant Test Bill reductions Bill increases
. Incentives . Participant measure costs
Participants avoided equipment costs
(fuel switching only)
Utility (Program . Generation, transmission and . Generation costs
Administrator) Test distribution savings
. Program costs paid by the
administrator
. Incentives
Ratepayer Impact . Generation, transmission and . Generation costs
Measu re Test distribution savings . Revenue loss
. Revenue gain
. Program costs paid by the
administrator
. Incentives
Generation, transmission and distribution savings (hereafter, energy benefits) are defined as the
economic value of the energy and demand savings stimulated by the interventions being
assessed. These benefits are typically measured as induced changes in energy consumption
valued using some mix of avoided costs. Electricity benefits are valued using three types of
avoided electricity costs: avoided distribution costs, avoided transmission costs, and avoided
electricity generation costs.
Participant costs are comprised primarily of incremental measure costs. Incremental measure
costs are essentially the costs of obtaining energy efficiency. In the case of an add-on device
(say, an adjustable-speed drive or ceiling insulation), the incremental cost is simply the installed
cost of the measure itself. In the case of equipment that is available in various levels of
efficiency (e.g., a central air conditioner), the incremental cost is the excess of the cost of the
high-efficiency unit over the cost of the base (reference) unit.
Administrative costs encompass the real resource costs of program administration, including
the costs of administrative personnel, program promotions, overhead, measurement and
evaluation, and shareholder incentives. In this context, administrative costs are not defined to
include the costs of various incentives (e.g., customer rebates and salesperson incentives) that
may be offered to encourage certain types of behavior. The exclusion of these incentive costs
reflects the fact that they are essentially transfer payments. That is, from a societal perspective
they involve offsetting costs (to the program administrator) and benefits (to the recipient).
Quantum Consulting Inc.Baseline Estimates and EE Methods
Use of the Total Resource Cost to Estimate Economic Potential
The TRC test is used in two ways in this study. First, we develop an estimate of economic
potential by calculating the TRC of individual measures and applying the methodology
described below. Second, we develop estimates of whether different program scenarios are cost
effective.
Economic potential can be defined either inclusively or exclusively of the costs of programs that
are designed to increase the adoption rate of energy efficiency measures. At this stage of the
analysis, we define economic potential to exclude program costs. We do so primarily because
program costs are dependent on a number of factors that vary significantly as a function of
program delivery strategy. There is no single estimate of program costs that would accurately
represent such costs across the wide range of program types and funding levels possible. Once
an assumption is made about program costs, one must also link those assumptions to
expectations about market response to the types of interventions assumed. Because of this, we
believe it is more appropriate to factor program costs into our analysis of maximum achievable
and program potential. Thus, our definition of economic potential is that portion of the technical
potential that passes our economic screening test (using the TRC test) exclusive of program
costs. Economic potential, like technical potential, is a theoretical quantity that will exceed the
amount of potential we estimate to be achievable through even the most aggressive voluntary
program activities.
As discussed previously, the TRC focuses on resource savings and counts benefits as utility-
avoided supply costs and costs as participant costs and utility program costs. It ignores any
impact on rates. It also treats financial incentives and rebates as transfer payments; i., the TRC
is not affected by incentives. The somewhat simplified benefit and cost formulas for the TRC
are presented in Equations 2-1 and 2-2 below.
Avoided Costs of Supply p t
Benefits = L....J t=l (l + d)
Eqn.
Program Cost t + Participant Cost
o~s = L....J t=l (l + d)
Eqn. 2-
where:
d = the discount rate
p =
the costing period
t = time (in years)
n = 20 years
Quantum Consulting Inc.Baseline Estimates and EE Methods
A nominal discount rate of 8 percent is used.11 We use a normalized measure life of 20 years to
capture the benefit of long-lived measures. Measures with measure lives shorter than 20 years
are lire-installed" in our analysis as many times as necessary to reach the normalized 20-year
life of the analysis. This assumption is reasonable given that most measures are eventually
replaced with more, not less, efficient alternatives.
The avoided costs of supply are calculated by multiplying measure energy savings and peak
demand impacts by per-unit avoided costs by costing period. Energy savings are allocated to
costing periods and peak impacts estimated using load shape factors.
As noted previously, in the measure-level TRC calculation used to estimate economic potential,
program costs are excluded from Equation 2-2. Using the supply curve methodology discussed
previously, measures are ordered by TRC (highest to lowest) and then the economic potential is
calculated by summing the energy savings for all of the technologies for which the marginal
TRC test is greater than 1.0. In the example Exhibit 2-15, the economic potential would include
the savings for measures 1 and 2, but exclude savings for measure 3 because the TRC is less
than 1.0 for measure 3. The supply curve methodology, when combined with estimates of the
TRC for individual measures, produces estimates of the economic potential of efficiency
improvements. Again, by definition and intent, this estimate of economic potential is a
theoretical quantity that will exceed the amount of potential we estimate to be achievable
through program activities in the final steps of our analyses.
Exhibit
Sample Use of Supply Curve Framework to Estimate Economic Potential
(Note: Data are illustrative only)
Total End Use Applicable, Not Savings
Consumption Complete and Average Total Included in
of Population Feasible kWh/fe of Savings GWh Resource Economic
Measure (GWh)Sq. Feet (OOOs)population Savin2s Cost Test Potential?
Base Case: T12 lamps 425 100 000 4.3 N/A N/A N/A N/A
with Magnetic Ballast
1. T8 w. Elec. Ballast 425 100,000 21%Yes
2. Occupancy Sensors 336 000 3.4 10%1.1 Yes
3. Perimeter Dimming 322 10,000 3.2 45%
Technical Potential w. all measures 27%116
Economic Potential w. measures for which TRC :;:. 1.24%102
11 We recognize that the 8-percent discount is much lower than the implicit discount rates at which customers
are observed to adopt efficiency improvements. This is by intent since we seek at this stage of the analysis to estimate
the potential that is cost-effective from primarily a societal perspective. The effect of implicit discount rates is
incorporated into our estimates of program and naturally occurring potential.
Quantum Consulting Inc.Baseline Estimates and EE Methods
ESTIMATION OF MAXIMUM
OCCURRING POTENTIALS
ACHIEVABLE PROGRAM AND NA TURALL Y
In this section we present the method we employ to estimate the fraction of the market that
adopts each energy efficiency measure in the presence and absence of energy efficiency
programs. We define:
Maximum achievable potential is a forecast of the amount of economic potential that
could be achieved over time under the most aggressive program scenario possible
Program potential is a forecast of the amount of savings that would occur in response to
one or more specific market interventions
Naturally occurring potential is a forecast of the amount of savings estimated to occur
as a result of normal market forces, that is, in the absence of any utility or governmental
intervention.
Forecasts of program potential are the most important results of the modeling process.
Estimating technical, economic, and maximum achievable potentials are necessary steps in the
process from which important information can be obtained; however, the end goal of the
process is better understanding how much of the remaining potential can be captured in
programs, whether it would be cost-effective to increase program spending, and how program
costs may be expected to change in response to measure adoption over time.
According to our definitions and the method described in this section, the maximum achievable
potential forecast is really a type of program potential forecast that defines the upper limit of
savings from market interventions. Therefore, in the remainder of this section, we will often
discuss our general method using the term "program potential" to represent both program andmaximum achievable potentiaL
Adoption Method Overview
We use a method of estimating adoption of energy efficiency measures that applies equally
be our program and naturally occurring analyses. Whether as a result of natural market forces
or aided by a program intervention, the rate at which measures are adopted is modeled in our
method as a function of the following factors:
The availability of the adoption opportunity as a function of capital equipment turnover
rates and changes in building stock over time
Customer awareness of the efficiency measure
The cost-effectiveness of the efficiency measure
Market barriers associated with the efficiency measure.
The method employed is executed in the measure penetration module of KEMA-XENERGY'
DSM ASSYST model. Only measures that pass the measure-level TRC test are put into the
penetration module for estimation of customer adoption.
Quantum Consulting Inc.Baseline Estimates and EE Methods
Availability
In most cases, the model uses a stock accounting algorithm that handles capital turnover and
stock decay over a period of up to 20 years. In the first step of our achievable potential method
we calculate the number of customers for whom each measure will apply. The input to this
calculation is the total floor space available for the measure from the technical potential
analysis, i., the total floor space multiplied by the applicability, not complete, and feasibility
factors described previously. We call this the eligible stock. The stock algorithm keeps track
the amount of floor space available for each efficiency measure in each year based on the total
eligible stock and whether the application is new construction, retrofit, or replace-on-burnout.
Retrofit measures are available for implementation by the entire eligible stock. The eligible stock
is reduced over time as a function of adoptions13 and building decay.14 Replace-on-bumout
measures are available only on an annual basis, approximated as equal to the inverse of the
service life.15 The annual portion of the eligible market that does not accept the replace-on-
burnout measure does not have an opportunity again until the end of the service life.
New construction applications are available for implementation in the first year. Those
customers that do not accept the measure are given subsequent opportunities corresponding to
whether the measure is a replacement or retrofit-type measure.
Awareness
In our modeling framework, customers cannot adopt an efficient measure merely because there
is stock available for conversion. Before they can make the adoption choice, they must be aware
and informed about the efficiency measure. Thus, in the second stage of the process, the model
calculates the portion of the available market that is informed. An initial user-specified
parameter sets the initial level of awareness for all measures. Incremental awareness occurs in
the model as a function of the amount of money spent on awareness / information building and
how well those information-building resources are directed to target markets. User-defined
program characteristics determine how well information-building money is targeted. Well-
targeted programs are those for which most of the money is spent informing only those
customers that are in a position to implement a particular group of measures. Untargeted
12 Replace-on-burr1out measures are defined as the efficiency opportunities that are available only when the base
equipment turns over at the end of its service life. For example, a high-efficiency chiller measure is usually only
considered at the end of the life of an existing chiller. By contrast, retrofit measures are defined to be constantly
available, for example, application of a window film to existing glazing.
13 That is, each square foot that adopts the retrofit measure is removed from the eligible stock for retrofit in the
subsequent year.
14 An input to the model is the rate of decay of the existing floor space. Floor space typically decays at a very
slow rate.
15 For example, a base-case technology with a service life of 15 years is only available for replacement to a high-
efficiency alternative each year at the rate of 1/15 times the total eligible stock. For example, the fraction of the
market that does not adopt the high-efficiency measure in year will not be available to adopt the efficient alternative
again until year + 15.
Quantum Consulting Inc.Baseline Estimates and EE Methods
programs are those in which advertising cannot be well focused on the portion of the market
that is available to implement particular measures. The penetration module in DSM ASSYST
has a target effectiveness parameter that is used to adjust for differences in program advertising
efficiency associated with alternative program types.
The model also controls for information retention. An information decay parameter in the
model is used to control for the percentage of customers that will retain program information
from one year to the next. Information retention is based on the characteristics of the target
audience and the temporal effectiveness of the marketing techniques employed.
Adoption
The portion of the total market that is available and informed can now face the choice of
whether or not to adopt a particular measure. Only those customers for whom a measure is
available for implementation (stage 1) and, of those customers, only those who have been
informed about the program/measure (stage 2), are in a position to make the implementation
decision.
In the third stage of our penetration process, the model calculates the fraction of the market that
adopts each efficiency measure as a function of the participant test. The participant test is a
benefit-cost ratio that is generally calculated as follows:
Customer Bill Savings ($) ene Its = t=l (l + d)
Eqn. 2-
Participant Costs ($) o~s=t=l (l + dr-
Eqn. 2-
where:
= the discount rate
= time (in years)
= 20 years
As noted previously, we use a normalized measure life of 20 years in order to capture the
benefits associated with long-lived measures. Measures with lives shorter than 20 years are "re-
installed" in our analysis as many times as necessary to reach the normalized 20-year life of the
analysis.
The bill reductions are calculated by multiplying measure energy savings and customer peak
demand impacts by retail energy and demand rates.
The model uses measure implementation curves to estimate the percentage of the informed
market that will accept each measure based on the participant's benefit-cost ratio. The model
provides enough flexibility so that each measure in each market segment can have a separate
implementation rate curve. The functional form used for the implementation curves is:
Quantum Consulting Inc.Baseline Estimates and EE Methods
( 1 +
In
J x (
1 +
cln( bx)
Eqn. 2-
where:
the fraction of the market that installs a measure in a given year from the pool of
informed applicable customers;
the customer s benefit-cost ratio for the measure;
the maximum annual acceptance rate for the technology;
the inflection point of the curve. It is generally lover the benefit-cost ratio that will
give a value of 1/2 the maximum value; and
the parameter that determines the general shape (slope) of the curve.
The primary curves utilized in our model are shown in Exhibit 2-16. These curves produce base
year program results that are calibrated to actual measure implementation results associated
with major IOU commercial efficiency programs over the past several years. Different curves
are used to reflect different levels of market barriers for different efficiency measures. A list
market barriers is shown in Exhibit 2-18. It is the existence of these barriers that necessitates
program interventions to increase the adoption of energy efficiency measures. (For more
information on market barriers see Eto, Prahl, Schlegel 1997, Golove and Eto 1996, DeCanio
2000, DeCanio 1998.
Note that for the moderate, high, and extremely high barrier curves, the participant benefit-cost
ratios have to be very high before significant adoption occurs. This is because the participant
benefit-cost ratios are based on a IS-percent discount rate. This discount rate reflects likely
adoption if there were no market barriers or market failures, as reflected in the no-barriers
curve in the figure. Experience has shown, however, that actual adoption behavior correlates
with implicit discount rates several times those that would be expected in a perfect market.16
The model estimates adoption under both naturally occurring and program intervention
situations. There are only two differences between the naturally occurring and program
analyses. First, in any program intervention case in which measure incentives are provided, the
participant benefit-cost ratios are adjusted based on the incentives. Thus, if an incentive that
pays 50 percent of the incremental measure cost is applied in the program analysis, the
participant benefit-cost ratio for that measure will double (since the costs have been halved).
The effect on the amount of adoption estimated depends on where the pre- and post-incentive
benefit-cost ratios fall on the curve. This effect is illustrated in Exhibit 2-17.
16 For some, it is easier to consider adoption as a function of simple payback. However, the relationship between
payback and the participant benefit-cost ratio varies depending on measure life and discount rate. For a long-lived
measure of 15 years with a 15-percent discount rate, the equivalent payback at which half of the market would adopt
a measure is roughly 6 months, based on the high barrier curve in Exhibit 2-7. At a 1-year payback, one-quarter of
the market would adopt the measure. Adoption reaches near its maximum at a 3-month payback. The curves reflect
the real-world observation that implicit discount rates can well over 100 percent.
Quantum Consulting Inc.Baseline Estimates and EE Methods
Achievable potential energy efficiency forecasts are developed for several scenarios, from low
levels of program intervention, through moderately increased levels, up to an aggressive energy
efficiency acquisition scenario. The final results produced are forecasts of annual streams of
achievable program impacts (energy and demand by time-of-use period) and all societal and
participant costs (program costs plus end-user costs).
Exhibit
Primary Measure Implementation Curves Used in Adoption Model
100%
90%
80%
70%0:::
60%:;:J
50%
a..
40%::J
30%
20%
10%
No Barriers
- - - - - - - - - - ~~- - - - - - - - - - - - - - - - - - - - - - - - - - - - \.'
Moderate Barriers
- - - - - ~~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - -
I~
- - - - - - - - - - - - - - -
Participant Benefit-Cost Ratio
Exhibit
Illustration of Effect of Incentives on Adoption Level
as Characterized in Implementation Curves
80%
70%
.m 60%
0:::
5 50%:;:J
~ 40%
a..
::J 30%
~ 20%
.'-"_.'_.'-,'-'._.._n_,,_n
! B-C Ratio: With 50% incentive
Initial B-C Ratio: No incentive
Net increase
In adnotine
10%
Participant Benefit-Cost Ratio
Quantum Consulting Inc.Baseline Estimates and EE Methods
Exhibit
Summary Description of Market Barriers from Eto, Prahl, Schlegel 1997
Barrier Description
Information or The costs of identifying energy-efficient products or services or of learning about energy-efficient
Search Costs practices, including the value of time spent finding out about or locating a product or service or hiring
someone else to do so.
Performance The difficulties consumers face in evaluating claims about future benefits. Closely related to high search
U ncertai nties costs, in that acquiring the information needed to evaluate claims regarding future performance is rarely
costless.
Asymmetric The tendency of sellers of energy-efficient products or services to have more and better information
Information and about their offerings than do consumers, which, combined with potential incentives to mislead, can lead
Opportunism to sub-optimal purchasing behavior.
Hassle or The indirect costs of acquiring energy efficiency, including the time, materials and labor involved in
Transaction obtaining or contracting for an energy-efficient product or service. (Distinct from search costs in that it
Costs refers to what happens once a product has been located.
Hidden Costs Unexpected costs associated with reliance on or operation of energy-efficient products or services - for
example, extra operating and maintenance costs.
Access to The difficulties associated with the lending industry s historic inability to account for the unique features
Financing of loans for energy savings products (i.e., that future reductions in utility bills increase the borrower
ability to repay a loan) in underwriting procedures.
Bounded The behavior of an individual during the decision-making process that either seems or actually is
Rationality inconsistent with the individual's goals.
Organ ization Organizational behavior or systems of practice that discourage or inhibit cost-effective energy efficiency
Practices or decisions, for example, procurement rules that make it difficult to act on energy efficiency decisions
Customs based on economic merit.
Misplaced or Cases in which the incentives of an agent charged with purchasing energy efficiency are not aligned with
Split incentives those of the persons who would benefit from the purchase.
Product or The failure of manufacturers, distributors or vendors to make a product or service available in a given
Service area or market. May result from collusion, bounded rationality, or supply constraints.
Unavailability
External ities Costs that are associated with transactions, but which are not reflected in the price paid in the
transaction.
Non-external ity Factors other than externalities that move prices away from marginal cost. An example arises when utility
Pricing commodity prices are set using ratemaking practices based on average (rather than marginal) costs.
Inseparability of The difficulties consumers sometimes face in acquiring desirable energy efficiency features in products
Product Features without also acquiring (and paying for) additional undesired features that increase the total cost of the
product beyond what the consumer is willing to pay.
Irreversibility The difficulty of reversing a purchase decision in light of new information that may become available
which may deter the initial purchase, for example, if energy prices decline, one cannot resell insulation
that has been blown into a wall.
Quantum Consulting Inc.Baseline Estimates and EE Methods
3. DEMAND RESPONSE POTENTIAL METHODS
OVERVIEW OF DEMAND RESPONSE FORECASTING METHODS
Similar to the energy efficiency forecast, the carrying out of a number of systematic analytical
steps was necessary to produce accurate estimates of demand response effects on system load.
To conduct this analysis we utilized a model to forecast demand reduction from demand
response (DR) programs.
The supply curve method used to forecast DR impact is a simpler process than the measure-
based models used to forecast energy efficiency. Information on the characteristics and
penetration of potential DR measures does not exist in sufficient fashion to justify a measure-
based modeling approach. We therefore relied on the professional judgment of a panel
experts to reach a consensus on key inputs to the supply curve models based on their
experience in designing, managing, and evaluating DR programs.
The forecast of demand reduction from potential demand response programs was produced
using a series of DR supply curves that varied by program type and market segment. An
overview of the DR modeling framework used is shown in Exhibit 3-
DR DA T A DEVELOPMENT
This section describes the data used for the DR Forecasting Model.
Although the DR forecasts produced for Idaho Power are largely the outcome of professional
judgment, they rely on a modeling framework that provides the ability maximize the use of the
limited amount of data available. The framework accounts for both the capability" and
motivational" aspects of DR programs. Capability is a somewhat abstract concept that reflects
a combination of awareness, experience, and technology. Increases in DR capability will occur
over time due to external market forces and possibly due to capability-building activities
pursued by Idaho Power.
In addition to capability, motivation is other key factor that determines the amount of load
achievable from a DR program. A customer must have sufficient motivation to reduce electric
demand for a period of time. Motivation usually takes the form of a financial incentive,
although the ability to avoid a blackout can also be significant motivator to reduce a portion of
load. Incentives can take the form of reduced rates or a performance payment. For modeling
purposes, the motivation for all programs was expressed in terms of dollars per kWh reduced.
The $/kWh concept allowed us to take into consideration that customers required additional
motivation for each hour that they are asked to reduce their demand.
Quantum Consulting Inc.DR Methods
Exhibit
DR Forecasting Model Framework
ELIGIBLE LOAD
by Sector and
End Use
APPLICABLE LOAD
LOW DR
CAPABILITY
Motivation Programs
PARTIAL DR
CAPABILITY
RESPONSE CURVE
Capability Segment
& Program Type
PROJECTED
I MP ACTS & COST
ESTIMATE
HIGH DR
CAPABILITY
Quantum Consulting Inc.DR Methods
Load Forecast Shares
The first step of the DR modeling framework is to define market segments and the demand
produced by each segment during the system peak. We elected to segment load using a
combination of market sector, end use, and customer size based on maximum demand. The
eight market segments were defined as shown in Exhibit 3-2. The industrial and irrigation
sectors were excluded from the analysis.
Exhibit
DR Market Segment
Sector End-use Size
Residential Other All
Residential Cooling All
Small Commercial HVAC 0( 1 000 kW
Small Commercial
Lighting 0( 1 000 kW
Small Commercial Other 0( 1 000 kW
:;:. 1 000 kWLarge Commercial HVAC
:;:. 1 000 kW
Large Commercial Lighting
Large Commercial Other :;:. 1 000 kW
Back-up Generation All All
The system peak load forecast by market segment was developed by market sector from Idaho
Power s 2003 demand forecast. A table of Idaho Power electricity sales by market segment and
customer demand group was available and was used to split the forecast into the various size
categories. Segmenting the load by end use was based on data for the Idaho Power end use
forecast database.
Applicability Factors
The issue of technical potential for DR is not as straightforward a concept. One could argue that
the technical potential for DR is 100 percent of load. However, our expert panel felt that there
was a significant portion of peak demand that would be unresponsive to standard DR
programs at any reasonable level of motivation. We elected to apply an applicability factor to
the load of each segment, reflecting that portion of load where response was feasible.
The Eligible Load for each market segment is equal to the total peak period load for that market
segment. The Applicable Load (or the technical potential) is a portion of Eligible Load where
customers are willing and able to reduce demand at the highest conceivable motivation level.
Quantum Consulting Inc.DR Methods
The applicability factors were set using Delphi estimation. These factors were held constant
throughout the time period addressed in the forecast. Exhibit 3-3 shows the estimates of
applicable load by market segment.
Exhibit
2004 Peak Load and DR Applicable Load
600
500
0 Other Load
. Tech Potential
400
3: 300
:!E
100
200
Res-oth Res-ac Small-hvac Small-oth Small-Igt Large-hvac Large-oth Large-Igt Back-up
Gen
Market Segments
Capability Shares
Once the applicable load was determined, this load was split into three capability segments for
the base year: Low, Partial, and High. There are two primary reasons for splitting load into
capability segment. This first reason is based on the theory that the portion of load that will
respond at a given motivation level will vary by capability segment. Stated differently, each
segment has a different motivation response curve. The second reason for segmentation
involves the ability to assess the impact of DR programs that are designed to build capability in
addition to providing motivation. We estimate the portion of the load that moves from one
segment to another resulting from capability-building activities, such as an incentive program
for enhanced au toma tion.
Low Capability is characterized as the loads that lack variable control and cannot be easily
controlled from a centralized location. DR activities in the low capability segment would be
achieved through a labor-intensive process and often will have high transaction costs. The
Partial Capability segment contains the load that has either variable control or centralized
control but not both. The High Capability segment includes loads that involve an automated
response process or centralized control of variable loads. This High Capability segment can
implement DR actions with little or no transaction costs while minimizing the impact on
Quantum Consulting Inc.DR Methods
productivity and building comfort. Exhibit 3-4 provides examples of lighting loads for each of
the three segments.
Exhibit
Lighting Examples of Capability Segments
Se~ment Load Description DR Option
Low Building with individual wall switches in each Manually turn off lights selected
area or floor. No bi-Ievellighting.areas.
Partial or Lighting circuits are controlled by a central EMS Use EMS to turn off lights in selected
Medium system. No bi-Ievellighting areas
Partial or Building with individual bi-Ievel wall switches Manually turn off portion of lamps in all
Medium in each area or floor.areas.
High Bi-Ievel or dimmable ballast lighting is Use EMS to reduce lighting levels in all
controlled by EMS.areas.
Our panel of experts concluded that the majority of load would currently fall into the Low
Capability segment because most customers have very little experience with DR programs and
the penetration of DR-friendly technologies such as dimmable lighting ballasts is very low.
Certain segments such as ::::-1 000 kW customers and residential HV AC were felt to have a
moderate portion of the market in the Partial or Medium Capability segment, based on their
experience with existing cycling and interruptible rate programs. It was the conclusion of the
panel that a very small portion of all markets would fall into the High Capability segment at
this time, given the very limited experience with dynamic rates, demand bidding, real-time
energy information systems, and DR automation technologies.
The estimated peak load, DR applicable load, and the assumed portion of applicable load by
capability segment for each market segment are shown in Exhibit 3-
Exhibit
2004 Load Statistics by Market Segment
Applicability Applicable Low Medium High
Sector End-use Size Peak MW Factor Capability Capability Capability
Residential Other All 38C 10%100%
Residential Cooling All 504 50%252 100%
Small Commercial HVAC -( 1 000 kW 198 40%100%
Small Commercial Lighting -( 1 000 kW 141 10%100%
Small Commercial Other -( 1 000 kW 132 20%100%
Large Commercial HVAC )- 1 000 kW 50%98%
Large Commercial Lighting )- 1 000 kW 15%95%
Large Commercial Other )- 1 000 kW 30%100%
Back-up Generation All All 75%80%20%
Quantum Consulting Inc.DR Methods
The portion of load in each capability segment was forecast to change over time based on two
factors: Idaho Power capability-building activities, such as customer education, and external
market forces. The effect from external market forces was addressed by assuming a very small
portion of the load would move each year from the low to medium segment and from the
medium to high segment. The effect from Idaho Power capability building was modeled by
estimating, using the Delphi process, the cost per kW to increase the capability level and by
specifying the amount of capability-building budget spent by market segment in each year. The
cost to increase capability was set between $5/kW and $30/kW, depending on the market
segment.
Exhibit
Capability Building Cost Assumptions
per k (Shift from Low to Medium Capability)
Segment All Others Max. Ach.
Res-$15 $20
Small-HV AC $10 $15
Small-Other $20 $30
Small-Lighting $15 $20
Large-HV AC $10
Large-Other $15 $20
Large-Lighting $10 $15
Back-up Gen
Program Definitions
Once the amount of load in each capability segment was estimated, we developed a set of
motivation-response curves for various types of DR programs. It is our theory that the
motivation response curve for an emergency program is different than that for an economic or
rate program. Customers tend to be more willing to take actions when a rotating blackout is
possible. The motivation response curve relates the portion of applicable load that will be
reduced at a given $/kW of motivation.
Given that the goal of this forecast was the support of resource planning and that the forecast
was largely developed based on expert opinion, it was not feasible to forecast every possible DR
program. Instead, four program concepts were modeled with some slight variations either over
time or across segments. The four concepts included:
AC Load Control (DLC):these programs provide lower energy rates for customers who
are willing to have cycling equipment installed that can be directly controlled by the
Quantum Consulting Inc.DR Methods
utility. There are usually a maximum number of events and/ or hours that may be called
In a year.
Critical Peak Pricing (CPP):this program offers dynamic rates that change based on
demand versus supply available. This program generally provides consistently lower
off-peak rates. However, during a CPP event, rates may increase dramatically (e.g. 5
times the average for that period). Customers may choose to voluntarily reduce load
during a CPP event or pay the substantially higher charges for maintaining their peak
load. There are usually a maximum number of events and/or hours that may be called
ill a year.
Voluntary Demand Response Incentive (DRP):this program offers a credit to customers
over a certain demand, who voluntarily commit to reduce their electricity usage by a
significant percentage (such as 10%) during a DRP event. Customers can generally chose
whether to participate when an event is called, as long as they meet the program
minimum requirements.
Back-up Generator Incentives (BUG):this program offers financial incentives to
customers who run their back-up generation during program events.
Since, in many cases, two DR programs will compete for the same load, it was necessary to
account for this competition in the forecast model. An overlap factor was specified for each
program that reflected the amount of load that a program would lose to the other programs that
were offered to the same segment.
We recognize that program types listed above may not represent every possible DR program;
however, they provide reasonable program prototypes for the purposes of IRP. There is little
justification for specifying a large number of well-defined DR programs given there is
considerable uncertainty in the response and impacts of anyone DR program. The forecasts
produced in this project are designed to support strategic resource planning rather than tactical
program design. Thus, the program concepts for the DR forecasts only need to be representative
of the program activities that could be pursued.
Where feasible, we based our assumptions on information provided directly by Idaho Power
regarding their current or intended future offerings. For example, we varied the programs
addressed in each scenario by market segment, as indicated by Idaho Power tariff structures.
ESTIMA TION OF #ECONOM/C" POTENTIAL FOR DEMAND RESPONSE
The concept of economic potential for a DR program is not as straight forward as the economic
potential for energy efficiency measures. The economic potential for an energy efficiency
measure involves the comparison of the measure cost to the avoided supply cost that is
obtained from installing the measure. Most DR programs involve encouraging customers to
make behavioral changes on the use of appliances or equipment and do not often involve the
purchase of a measure. Thus, the standard concept of economic potential of energy efficiency
measures does not readily apply.
Quantum Consulting Inc.DR Methods
An estimate of economic potential is useful because it provides a measure of the maximum
amount of load reduction that could be obtained within some economic constraint. In order to
achieve this information need, a definition of economic potential was developed for both DR
and TaU programs.
Economic potential for DR programs was defined as the load reduction that could be obtained if
the entire applicable market was in the high capability segment and if a minimum of 50 cents
per kWh was offered for all programs.
The economic potential results provided in Section 5 are based on the peak demand load in
2004. The economic potential for future years would increase in proportion to the increase in
total peak demand.
FORECASTING PROGRAM IMPACTS
A supply curve or response curve was developed for each program concept, market segment,
and capability segment using the Delphi process. The response curve provides an estimate of
the portion of applicable load in each capability segment that will be reduced at a given $/kWh
of motivation. Although the ability existed in the model to specify a different curve for each
program and market segment, the experts felt that their collective knowledge and experience
did not justify the development of a large number of unique curves. Curves were developed
that vary significantly across capability segment but tended to vary only slightly across
programs and market segments. Exhibit 3-7 shows an example of the curves used for CPP
program and market/ capability segments.
Exhibit 3- 7
CPP Supply Curves
60%
50%
-tr- High
Medium
-.- Low
10%
"C 40%
...J
'I-
30%
a. 20%
100
Cents per kWh
Quantum Consulting Inc.DR Methods
Scenario Definitions
Achievable potential forecasts can be developed for multiple scenarios. For example.! program
savings can be modeled under low levels of program intervention, through moderate levels, up
to an aggressive DSM acquisition scenario.
As discussed above, four program concepts were modeled: AC Load Control (DLC), Critical
Peak Pricing (CPP), Voluntary Demand Response Incentives (DRP), and Back-up Generator
Incentives (BUG). Using these concepts, four bundled program strategies were developed:
1. DLC and BUG - Low Incentive Levels
2. All 4 Concepts - Low Incentive Levels
3. All 4 Concepts - High Incentive Levels
4. I.1Maximum Achievable
The primary drivers between the lower intervention bundles and the more aggressive
intervention bundles are the amount of capability building or marketing that is pursued and the
amount of customer incentives offered for demand reductions. The Maximum Achievable
scenario is designed to forecast the maximum achievable DR that is obtainable by large-scale
capability building and high incentive payments. In the Maximum Achievable scenario,
incentive payments were set at 50 cents per kWh, the highest level that was determined to be
cost effective.
Exhibit 3-8 summarizes the incentive payments utilized for each program concept and program
bundling strategy. Exhibit 3-9 shows the assumed capability budget for each program strategy.
Exhibit
Customer Incentive Assumptions
Cents per kWh
Program Concepts
Program Bundle Strategy AC OlC Back-up Gen CPP ORP
OLC & BUG - Low $
4 Concept - Low $
4 Concepts - High $
Maximum Achievable
Quantum Consulting Inc.DR Methods
600
$1,400
$1 ,200
$1,000
$800'0('"'
$600
$400
$200
Exhibit
Capability Building Budgets
..."..."..."..."..."..."...".,
Max. Achievable
-.-4 Concepts - High $
4 Concept - Low $
-+-OLC & BUG - Low $
..........
2004 2005 2006 2007 2012 20132008200920102011
Year
Quantum Consulting Inc.DR Methods
4. ENERGY EFFICIENCY PEAK DEMAND AND ENERGY SAVINGS POTENTIAL RESULTS
In this section we present summary results of the Idaho Power energy efficiency potential
analysis for the residential and commercial sectors. First, economic and technical potential are
discussed. Next, we present summary energy efficiency supply curves, which are an alternative
method of presenting forecasted potentials. Finally, we present scenario forecasts for achievable
energy efficiency potential. Definitions of the different types of energy efficiency potential and
methods used to develop them are provided in Section 2 of this report. Section 2 also presents
the baseline estimates used in our analyses.
At the outset of this study, the primary focus was on peak demand reduction and the scope was
limited to measures with impacts on summer peak. In a later, second phase, the scope was
expanded to look at all measures with the potential to provide cost-effective energy savings.
Where possible, the figures in this section delineate the peak demand and energy savings
associated with the two phases. In cases where there is no distinction, the figures represent the
results of the second phase. Because the results of the first phase were provided to the resource-
planning group at IPCo, identical graphs based only on the results of the initial phase are
provided separately in Appendix G.
TECHNICAL AND ECONOMIC POTENTIAL
In Exhibits 4-1 and 4-2 we present our overall estimates of total technical and economic
potential for peak demand and electrical energy in the residential and commercial sectors in the
Idaho Power territory. Technical potential represents the sum of all savings achieved if all
measures analyzed in this study were implemented in applications where they are deemed
applicable and physically feasible. As described in Section 2 economic potential is based on
efficiency measures that are cost-effective based on the total resource cost (TRC) test, a benefit-
cost test used to compare the value of avoided energy production and power plant construction
to the costs of energy-efficiency measures and program activities necessary to deliver them. The
value of both energy savings and peak demand reductions are incorporated into the TRC test.
Overall and by Sector
If all measures analyzed in this study were implemented where technically feasible, we estimate
that overall technical demand savings would be roughly 551 MW, about 33 percent of projected
combined residential and commercial peak demand in 2013. If all measures that pass the TRC
test were implemented, economic potential savings would be 384 MW, about 23 percent of total
residential and commercial demand in 2013. Technical energy savings potential is estimated to
be roughly 1 917 GWh, about 21 percent of total residential and commercial energy usage
projected in 2013. Economic energy savings are estimated at 1 107 GWh, about 12 percent of
base residential and commercial usage. The technical and economic potential estimates are
shown by sector and vintage (existing stock versus new construction) in Exhibits 4-3 through 4-
5. The largest share of both technical and economic savings is in the residential existing stock.
Quantum Consulting Inc.Efficiency Potential Results
Exhibit
Technical and Economic Potential (2013)
Peak Demand Savings-
300
a..
600
500
Exhibit
Technical and Economic Potential (2013)
Energy Savings-G Wh per Year
500
. Phase
- - - - - - - - - - - -
. Phase 000
- - - - - - - - - - - - - - - - - - - - - - - -
. Phase
. Phase
400
200
100
Technical
- - - - - - - - - - - - - - - - - - - --------
500
-------------------------
1 ,500
1 ,000
--------------
Economic
------
Technical Economic
Exhibit
Technical and Economic Potential by Sector and Vintage, Peak Demand Savings (2013)
350
300
250
~ 200
150
100
. Phase
. Phase
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Tech. Econ.
Residential
Existing
Tech. Econ.
Residential
New
Tech. Econ.
Commercial
Existing
----------------
Tech. Econ.
Commercial
New
Quantum Consulting Inc.Efficiency Potential Results
Exhibit
Technical and Economic Potential by Sector and Vintage, Energy Savings (2013)
1200
1000
800
.c:
600
::::I
c:(
400
200
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
. Phase
. Phase
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -----------------
Tech. Econ.
Residential
Existing
Tech. Econ.
Residential
New
Tech. Econ.
Commercial
Existing
Tech. Econ.
Commercial
New
Exhibit 4-
Phase II Technical and Economic Potential Estimates
GWh
Sector and Vi ntage Technical Economic Technical Economic
Residential - Existing 299 201 102 554
Residential - New 139 102 373 235
Commercial - Existing 373 252
Commercial - New
Total 551 384 917 107
Quantum Consulting Inc.Efficiency Potential Results
Exhibi
Phase Technical and Economic Potential Estimates
GWh
Sector and Vintage Technical Economic Technical Economic
Residential - Existing 237 189 520 444
Residential - New 117 216 173
Commercial - Existing 265 179
Commercial - New
Total 442 337 060 851
End Use Potential
Residential economic potential is presented by key end use in Exhibit 4-6. Lighting, cooling,
and clothes washing dominate economic energy savings, while cooling makes up the vast
majority of peak demand impacts. Exhibit 4-7 shows commercial sector economic potential
estimates by end use. Lighting is the largest contributor in terms of both energy savings
potential and peak demand savings potential, cooling is the second largest contributor to
commercial economic peak demand savings.
Potential by Building Type
Exhibit 4-8 displays residential economic potential by building type. Single-family homes
account for the vast majority of potential. Commercial sector economic potential is displayed
by building type in Exhibit 4-9. The largest contributors to both GWh and peak MW potential
are small offices, food stores, retail establishments, hospital/health care facilities, and
miscellaneous" buildings.
ENERGY EFFICIENCY SUPPL Y CURVES
Energy efficiency supply curves for energy and peak demand savings are shown in Exhibits 4-
10 and 4-, respectively. The supply curves show the distribution of measure-level potentials
by relative cost. Energy supply curve summary data are presented Exhibits 4-12 through 4-
for the residential existing, residential new construction, commercial existing and commercial
new construction vintages. Note that these values are aggregated across market segments and
that individual segment results can vary significantly from the average values shown.
addition, it is important to recognize that cost-effectiveness, as defined by the TRC test, cannot
be determined exclusively from these curves because the value of both energy and demand
savings must be integrated when comparing to supply side alternatives. Measure-level TRC
estimates are provided in Appendix E.
Quantum Consulting Inc.Efficiency Potential Results
Exhibit
Residential Economic Potential by End Use (2013)
300
250
----------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
. Phase
. Phase
200
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
150
- - - - - - - - - - - - - - - - - - - - - --------------
100
- - - - - - - - - - - - - - - - - - - - - --------------
GWH MW
Space
Cooling
GWH MW GWH MW GWH MW
Water
Heating
GWH MW
Clothes
Washer
GWH MW
Dish
WasherLightingRefrigeration
Exhibit
Commercial Economic Potential by End Use (2013)
200
180
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
. Phase
. Phase
160
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
140
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
120
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
100
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ---------------
GWH MW
Lighting
GWH MW
Cooling
GWH MW
Ventilation
GWH MW
Refrigeration
GWH MW GWH MW
Heating Water Heat
Quantum Consulting Inc.Efficiency Potential Results
700
600
500
400
300
200
100
Exhibit
Residential Economic Potential by Building Type (2013)
. Phase
. Phase
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
GWH MW
Single-Family
GWH MW GWH MW
Small Multi-Family Large Multi-Family
GWH MW
Mobile Home
Exhibit
Commercial Economic Potential by Building Type (2013)
GWHRestaurant MW
GWHeal MW
Small Office G;JJJ
Off GWHarge Ice MW
GWHFood Store MW
GWHWarehouse MW
GWHSchool MW
College G;JJJ
Hospital G;JJJ
Hotel G;JJJ . Phase
Miscelianeous ;JJJ . Phase
Quantum Consulting Inc.Efficiency Potential Results
Exhibit 4-
Residential and Commercial Energy Efficiency Supply Curve Energy
$0.
$0.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
CIStJ)
3: $0.
I..
-g $0.
!::!
...J
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
$0.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
$0.
10%15%20%25%
Percent Savings
Exhibit
Residential and Commercial Energy Efficiency Supply Curve Peak Demand
000
$900
$800
$700
D::$600
I..$500
$400
!::!
$300
...J $200
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
$100
-------- - - - - - - - - - - - - - - - - - - - - - - - -
10%15%20%25%30%35%
Percent Savings
Quantum Consulting Inc.Efficiency Potential Results
Exhibit
Residential-Existing Energy Efficiency Supply Curve Data
Cumulative Levelized Cumulative LevelizOO
Measure GWH Savings GWH Savings Energy Cost Measure MW Savings MW Savings Capacity Cost
$/kWh $IkW
Low"Flow Showerheads $0.012 Double Pane, Moo Low-E Coating $35
Tank Wrap $0.016 Duct Insulation (,$51
Hot Wilter i"ipe Insulation ;i3 $0;019 Basic HVAC Diagnostic Testing And Repair $74
Double Pane, Med Low-E Coating $0.033 HE Room Air Conditioner - EER 10,$75
CFL 227 318 $0,040 Duct Repair (0,32)109 $94
SEHA CW Tier 2 (MEF=2.20)149 467 $0,044 10 to 12 SEER Split-System Air Conditioner 151 $98
EnergyStarDW (EF=0.76)
~?_
$0,053 : Wall2x4 R-O to Blow-In R-13 Insulation (0,14)162 $98
Duct Insulation (.526 $0.055 Direct Evaporative Cooler 184 $139
Heat PulT\pWater Heater (EF'=2.4) 402 !j?8 $9.059 L9~Flow'Showerheads 185
Basic HVAC Diagnostic Testing And Repair 950 $0,079 Till1~'W~P , .,
. .
186 $295
ENERGY STAR or better Freezer 965 $0.079 - SEHA CW Tier 2 (MEF=2.20)213 $240
HE Room Air Conditioner - EER 10,975 $0.080 Hoi' Water Pipe,lnsulation 214 $241
Duct Repair (0,32)989 $0,100 AttieVentil1g , $31810 to 12 SEER Split-System Air Conditioner 1028 $0,105 10 to 13 SEER Split-System Air Conditioner 237 $408
ENERGYST ARor ,be.tt!lrRefrigerCl~r 1053 $0.113 CFL 256 $452
Wall2x4 R-O to Blow-In R-13 Insulation (0.14)1062 $0,122 ENERGY STAR orbetter Freezer 259 $544Direct Evaporative Cooler 1083 $0,154 tieCl~.F'l!IT\P\"lllterHeater (1;1":;2.291
$?~
Attic V!lnting 1094 $0",285 Ceiling R-19 to R-38 Insulation (.27)294 $743
Whoie House Fans 1113 $0.419 i:nergYStarDW (1:1"=0.76)298 ~51
10 to 13 SEER Split-System Air Conditioner 1126 $0,436 Whole House Fans 308 $799
Solar Water Heater 1200 $0.461 J:;I\II;~~Y !)JJl,Ror b!lt(erRefrig~rator $8fi~,Ceiling R-19 to R-38 Insulation (.27)1204 $0.577 Infiltration Reduction (0.313 $870Ceiling Fans 1208 $0.758 Ceiling Fans 314 $1,445
Infiltration Reduction (0.4)1209 $1.094 10 to 14 SEER Spiit-System Air Conditioner 325 $1,648
10 to 14 SEER S lit-S stem Air Conditioner 1219 $1.759 Solar Water Heater 331 $5725
*Measures incremental to Phase II are highlighted.
Exhibit 13*
Residential-New Construction Energy Efficiency Supply Curve Data -10 Years
Cumulative Levelized Cumulative LevelizOO
Measure GWH Savings GWH Savings Energy Cost Measure MW Savings MW Savings Capacity Cost
$/kWh $IkW
Low-Flow ShQwerheads $0.012 Double Pane, Moo Low-E Coating $18
Double Pane, Med Low-E Coating $0,017 Basic HVAC Diagnostic Testing And Repair $70
Tank Wrap $0.019 Duct Repair (0.32)$92
Hot Water Pipe Insulation $0.020 , Direct Evaporative Cooler $98
CFL 130 $0,040 HE Room Air Conditioner - EER 10.$116
SEHA CW Tier 2 (MEF=2.20)177 $0.040 10 to 12 SEER Split-System Air Conditioner $117
Energy Star OW (EF~0)'6) ,198 $0.052 bow-,FlowShowerh~ds '$151
Heat Pump Water Heater (EF"2.$0.061 Wall2x4 R-13 to 2x6 R-13 Insulation (0,14)$167
Basic HVAC Diagnostic Testing And Repair 281 $0,074 SEI-IACW,ner2(MEF~.20j 101 $218
ENERGYSTARor better Freezer 285 $0;0"'TankWi'ap, 101 $233
Duct Repair (0.32)291 $0,098 HotVl!l1!er PipeJnsulatiQn 102 $249'
Direct Evaporative Cooler 300 $0,113 10 to 13 SEER Split-System Air Conditioner 109 $383
ENERGY STAR or better Refrige",tor ~08 $9.114 CFL 117 $455HE Room Air Conditioner - EER 10,309 $0.124 ENE~GYSTAR or better Freezer 117 $54410 to 12 SEER Split-System Air Conditioner 330 $0.125 Whole House Fans 122 $596Wall2x4 R-13 to 2x6 R-13 Insulation (0,14)333 $0.209 Energy SlatOvv (1:1'..0."'6) ,124 $741
Whole House Fans 342 $0,312 HE\BtpumpVl!aterHeater (EF'T2;4)129 $756
10 to 13 SEER Split-System Air Conditioner 350 $0,409 ENERGY STAR or better Refriglarator 130 $856,
Solar Water Heater 365 $0.477 . Ceiling Fans 132 $1,034
Ceiling Fans 367 $0.542 10 to 14 SEER Split-System Air Conditioner 138 $1,514
10 to 14 SEER S lit-S stem Air Conditioner 373 $1,616 SolarWaterHeateir 139 $5927
Measures incremental to Phase II are highlighted.
Quantum Consulting Inc.Efficiency Potential Results
Exhibit
Commercial-Existing Energy Efficiency Supply Curve Data
Measure GWH Savings Cumulative
GWH Savings
Outdoor Lighting Controls (Photocell/Timeclock)
Refrigeration ,
Prog. Thermostat - DX
T8/EB Replacement
CFL Screw-, Modular 18W
Ventilation
Occupancy Sensor
HighPressure Sodium 250WLamp
DX Packaged System, EER=10., 10 tons
DX Tune Upl Advanced Diagnostics
Heating
Window Film (Standard)
Water Heating
Continuous Dimming
Eva orative Pre-Cooler
34.
10.
56.
79.
16.
11.
19,
13.
69.
7.4
32.
39;3
49.
106.
185.
202.
213.
gzO
240.
253.2
323;0
330,
337:5
369.
376.
Levelized
Energy Cost
$/kWh
$0;017
$0:0211
$0.028
$0.031
$0.040
$0.047
$0.059
056
$0,057
$0.068
$0;068
$0.114
$0,117
$0.233
$0.290
Measure CumulativeMW Savings MW Savings
Levelized
Capacity Cost
$/kW
$88
$116
$136
$155
$180
$?03
$200
$312
$446
$481
$474
$691
$6,'733
N/A
N/A
DX Packaged System, EER-10.9, 10 tons
T8/EB Replacement
Prog. Thermostat - DX
CFL Screw-, Modular 18W
Window Film (Standard)
Ref~ge~ti9n , .
Occupancy Sensor
DX Tune Up/ Advanced Diagnostics
Evaporative Pre-Cooler
Ventilation
Continuous Dimming
Water Heating
HiQtiF'r~ur~SocliUIT1,?50W Lamp
Outdoor Lighting Cpnti'ols (PhotocelllTimeclock)Heatin '
. '
*Measures incremental to Phase II are highlighted.
Exhibit 15*
Commercial-New Construction Energy Efficiency Supply Curve Data -10 Years
Cumulative Levelized Cumulative Levelized
Measure GWH Savings GWH Savings Energy Cost Measure MW Savings MW Savings Capacity Cost
$/kWh $/kW
Low-e Windows $0.022 Low-e Windows 3.4 $33
10 % More Efficient Lighting Design 14.4 19.$0.023 10 % More Efficient Lighting Design $87
Refrigeration 11.4 30.$0.026 DX Packaged System 11.$92
20 % More Efficient Lighting Design 15.46.$0.034 20 % More Efficient Lightin~ Desi!:)n 16.$109
Ventilation 51.$0.047 R~frigerati9n 17$$219..Water Heating 52.058 DX Tune Up/ Advanced Diagnostics 20.$318
DX Packaged System 59.4 $0.060 vv~terHeating' . 20.$344
DX Tune U Advanced Dia nostics 69.$0.070 Ventilation 20.$481
*Measures incremental to Phase II are highlighted.
4.3 FORECASTS OF ACHIEVABLE PROGRAM POTENTIAL SCENARIOS
In this section we present our overall achievable potential forecasts. In contrast to technical and
economic potential estimates, achievable potential estimates take into account market and other
factors that affect adoption of efficiency measures. Our method of estimating measure adoption
takes into account market barriers and reflects actual consumer and business implicit discount
rates (see Section 2 for this methodology). Achievable potential refers to the amount of savings
that would occur in response to one or more specific program interventions. Net savings
associated with program potential are savings that are projected beyond those that would occur
naturally in the absence of any market intervention. Because achievable potential will vary
significantly as a function of the specific type and degree of intervention applied, we develop
estimates for multiple scenarios. Peak demand and energy savings forecasts were developed
for four possible program-funding scenarios. These scenarios were designed to address market
changes to increasing incentive levels (as a percent of incremental measure cost) and marketing
levels. The scenarios include:
A Low efficiency funding scenario with rebates covering 33% of incremental measure
costs and base marketing levels;
Quantum Consulting Inc.Efficiency Potential Results
2. A Moderate efficiency funding scenario with rebates covering 50% of incremental
measure costs and slightly higher marketing expenditures;
3. A High efficiency funding scenario with rebates ramping up over time to 75% of
incremental measure costs and significantly increased marketing expenditures; and
4. A Maximum Achievable scenario with rebates ramping up over time to cover 100% of
incremental measure costs and marketing expenditures sufficient to create maximum
market awareness. Maximum achievable efficiency potential is the amount of economic
potential that could be achieved over time under the most aggressive program scenario
possible.17
We forecasted program energy and peak demand savings under each achievable potential
scenario for a 10-year period beginning in 2004. Our estimates of achievable potentials and their
effect on forecasted demand and energy consumption are shown in Exhibits 4-15 through 4-
for both Phase II and Phase
As shown in Exhibit 4-15a, by 2013 net18 peak demand savings are projected to be roughly 42
MW under Low, 72 MW under Moderate, 116 MW under High, and 190 MW under Maximum
efficiency spending scenarios. In Exhibit 4-16a, we show projected net annual energy savings of
195 GWh under Low, 298 GWh under Moderate, 489 under High, and 681 GWh under
Maximum efficiency futures.
Exhibit 4-17 provides a breakdown of Year-l0 peak demand reduction potential by scenario,
sector and vintage for both Phase II and Phase I results. As shown, the residential and
commercial existing construction market segments account for most of the potential for the Low
and Moderate scenarios. The residential existing segment accounts for an increasing share of
potential impacts for the higher funding scenarios. Exhibits 4-18 and 4-19 summarize the total
ten-year results for all funding scenarios for both phases of results. Exhibit 4-18 juxtaposes the
total program benefits - based on the cumulative avoided costs associated with each scenario -
with a breakout of the various cost components. Exhibit 4-19 provides the total ten-year
program spending and forecasted achievable potential estimates by program scenario, sector
and vintage. All of the funding scenarios are cost effective based on the TRC test. The TRC
benefit-cost ratios are 1.7, 1.6, 1.5, and 1.4 for the Low, Moderate, High, Maximum Achievable
scenarios, respectively.
17 Experience with efficiency programs shows that maximum achievable potential for voluntary programs will
always be less than economic potential for two key reasons. First, even if 100 percent of the extra costs to customers of
purchasing an energy-efficient product are paid for through program financial incentives such as rebates, not all
customers will agree to install the efficient product. Second, delivering programs to customers requires additional
expenditures for administration and marketing beyond the costs of the measures themselves. These added program
costs reduce the amount of potential that it is economic to acquire. Policy makers should consider a combination of
standards that follow behind strong voluntary programs as a more optimal efficiency acquisition strategy than trying
to achieve maximum potential through voluntary programs only.
18 Again net refers throughout this chapter to savings beyond those estimated to be naturally occurring, that is,
from customer adoptions that would occur in the absence of any programs or new standards.
Quantum Consulting Inc.Efficiency Potential Results
Exhibit 4-15a
Phase II Net Peak Demand Reduction Potential by Funding Scenario, 10-Year Forecast
1-- Max Achievable -- High Moderate -- Low -- Nat. Occurring
140
120
"t:I
c:: 100
:!: 80
200
180
160
Year
Exhibit 4-15b
Phase Net Peak Demand Reduction Potential by Funding Scenario, 10- Year Forecast
140
120
"t:I
c:: 100
:E
rf. 60
1-- Max. Achievable -- High Moderate -- Low -- Nat. Occurring
200
180
160
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -------------
Year
Quantum Consulting Inc.Efficiency Potential Results
Exhibit 4-16a
Phase II Net Energy Savings Potential by Funding Scenario, 10-Year Forecast
1-- Max Achievable -- High Moderate -- Low -- Nat. Occurring
800
700
600
s; 500Ct1
400
300
c:(200
100
Year
Exhibit 4-16b
Phase Net Energy Savings Potential by Funding Scenario, 10-Year Forecast
700
600
~ 500
Ct1CJ) 400
:5:
(!) 300Ct1::I
c:( 200
100
1-- Max. Achievable -- High Moderate -- Low -- Nat. Occurring
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Year
Quantum Consulting Inc.Efficiency Potential Results
Exhibit 4-17a
Phase II Net Peak Demand Reduction Potential by Funding Scenario and Segment- Year
I- Res Exist - Res NC D Com Exist D Com NC
(/)
s 140
:;..
en 120
100
Q,)a.. 80
.....
en 60Q,)
200
180
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
160
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - -------------------------
Nat. Occurring Low Moderate
Scenario
High Max Achievable
Exhibit 4-17b
Phase Net Peak Demand Reduction Potential by Funding Scenario and Segment- Year
(/) 160
s: 140
120
100
Q,)a.. 80
.....
en 60Q,)
)- 40
I - Res Exist D Com Exist DCom NC - Res
200
180
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
Nat. Occurring Low Medium High Max.
Achievable
Scenario
Quantum Consulting Inc.Efficiency Potential Results
Exhibit 4-18a
Phase II Cumulative Ten-Year Program Costs and Benefits
$450
$400
$350
CI)
~ $300
(;f).
S: $250
~ $200
~ $150
....
a..
$100
$50
II Net Benefits
0 Total Benefits
. Program Incentives
. Non-Incentive Participant Costs
0 Marketing
. Administration
- - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - ----------------------------------------------------
High Max. Achievable
A voided cost benefits and program costs discounted at nominal rate of 8 percent per year.
Exhibit 4-18b
Phase Cumulative Ten-Year Program Costs and Benefits
$350
Low Moderate
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
$300
CI)
~ $250
(;f).
S: $200
~ $150
CI)
C: $100
$50
i!I!I Net Benefits
DTotal Benefits
. Program Incentives
. Non-Incentive Participant Costs
0 Marketing
. Administration
- - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - ----------------------------------------
High Max. Achievable
Avoided cost benefits and program costs discounted at nominal rate of 8 percent per year.
Quantum Consulting Inc.Efficiency Potential Results
Low Moderate
Exhibit 4-19a
Summary of Phase II Net Achievable Energy Efficiency Potential Forecasts
Year 10 (2013) Impacts
Cumulative 10-
Year Program Net MW Net Annual
Costs ($Reductions by GWh Savings by Total Resource
Sector/Vinta~e Scenario Millions)*2013 2013 Cost Ratio
Residential Low $16 1.6
Existing Moderate $31 126 1.5
High $78 249 1.4
Maximum $148 103 348 1.3
Residential Low
New Moderate $12 1.8
Construction High $21 1.7
Maximum $38 1.6
Commercial Low $15 1.7
Existing Moderate $24 126 1.7
High $37 159 1.6
Maximum $60 202 1.5
Commercial Low
New Moderate 1.7
Construction High $12 1.5
Maximum $28 1.2
Total Low $39 195 1.8
Moderate $73 298 1.7
High $149 116 488 1.6
Maximum $274 190 681 1.3
Program costs discounted for inflation at 3 percent per year.
Quantum Consulting Inc.Efficiency Potential Results
Exhibi 19b
Summary of Phase Net Achievable Energy Efficiency Potential Forecasts
Year 10 (2013) Impacts
Cumulative 10-
Year Program Net MW Net Annual
Costs ($Reductions by GWh Savings by Total Resource
Sector/Vinta2e Scenario Millions) * 2013 2013 Cost Ratio
Residential Low $12 1.3
Existing Moderate $25 1.3
High $68 200 1.3
Maximum $139 295 1.2
Residential Low 1.9
New Moderate 1.9
Construction High $14 1.7
Maximum $46 1.5
Commercial Low $12 1.4
Existing Moderate $18 1.4
High $36 144 1.4
Maximum $48 173 1.4
Commercial Low 1.7
New Moderate 1.6
Construction High $10 1.5
Maximum $21 1.4
h'" otal Low $31 131 1.4
Moderate $54 201 1.4
High $128 395 1.4
Maximum $255 183 584 1.3
Program costs discounted for inflation at 3 percent per year.
Quantum Consulting Inc.Efficiency Potential Results
5. DEMAND RESPONSE POTENTIAL RESUL
This section presents the economic potential and forecast results for Demand Response (DR)
programs. Economic potential estimates are provided first. The forecast impacts DR programs
are provided for three scenarios. The primary drivers in the scenarios are the effort directed at
DR capability building (i.e. marketing, education and the promotion of DR enabling
technologies) and the incentive levels provided to customers who reduce demand.
ECONOMIC POTENTIAL
As stated in Section 3, an estimate of economic potential is useful because it provides an
indication of the maximum amount of load reduction that could be obtained within an
economic constraint. The difficulty in determining economic potential for demand response
and rate programs is estimating the total resource cost associated with reducing load. Although
it may be possible in the future to develop an economic potential definition for DR that is
consistent with what is typically done with energy efficiency measures, it was decided to define
and calculate a simplified measure of economic potential for DR programs at this time.
The estimated economic potential for DR programs is shown in Exhibit 5-1. Economic potential
was defined as the amount of peak load reduction that would occur if all customers had a high
level of DR capability (i.e. awareness, experience, technology) and 50 cents per kWh was offered
as the incentive for all DR programs. Since our definition of economic potential is dependent on
the number and type of programs being offered, the economic potential estimates were based
on the forecast loads and programs that would be in place in 2004 since this is the first year
where the full set of potential programs are modeled to be offered to each market segment.
Exhibit
Economic Potential for Residential and Commercial DR Programs
% Of Total Peak
MW in 2004 Demand
Estimated Applicable Demand for DR 469 32%
Economic Potential for DR 105
The residential sector AC Load Control program component contributes over half of the
economic potential (57%). The economic potential is about 120/0 of the total residential AC load.
The small commercial segment and the large commercial/back-up generation segment provide
about 26% and 170/0 of the total economic potential, respectively. Overall cooling load
reductions account for about 55% of the commercial economic potential and just over 80% of the
total economic potential.
Quantum Consulting Inc.DR Results
FORECAST SCENARIOS
As discussed in Section 3, four program concepts - AC Load Control (DLC), Critical Peak
Pricing (CPP), Voluntary Demand Response Incentives (DRP), and Back-up Generator
Incentives (BUG) were bundled into four program strategies:
DLC and BUG - Low Incentive Levels
All 4 Concepts - Low Incentive Levels
All 4 Concepts - High Incentive Levels
Maximum Achievable
The forecast of annual estimated MW reduction that would occur during system peak
conditions is shown in Exhibit 5-2 for each of the four strategies.
Exhibit
Comparison of Load Reduction Forecasts Residential and Commercial Sectors
150
135
- ~
Max. Achievable
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -...-
OLC & BUG - Low $
120
....... 4 Concepts - High $
--- 4 Concepts - Low $
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
105
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
:s: 75
:2:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - -
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
DR potential is compared against system peak demand in Exhibit 5-3. It is expected that
Maximum Achievable" potential would approach economic potential after ten years of
significant investment in building DR capability in the residential and commercial sectors.
Quantum Consulting Inc.DR Results
Exhibi t 5-
Peak Demand Load and DR Potential- Residential and Commercial Sectors
-+- Total Peak Load --- Technical Potential -.- Economic Potential"""*""" Max Achievable
000
800
600
1,400
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
200
1 ,000
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
800
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
600
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
400
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
200
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
A comparison of the estimated total annual cost for the three scenarios is provided in Exhibit 4. These costs include program administration, capability building expenditures, and the
equipment costs associated with direct load control and metering for the voluntary TaU
program. The metering costs required for the dynamic rate programs were not included in
these cost estimates.
Quantum Consulting Inc.DR Results
Exhibit
Forecast of Estimated Costs by Scenario Residential and Commercial Sectors
-*"" Max. Achievable -+-4 Concepts - High $ -+-4 Concepts - Low $ -+- DLC & BUG - Low $
$12 000
000
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
$10 000
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
$8,000
a $6 000
..----------------
000
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
Exhibit 5-5 summarizes the net present value of 10-year program costs and benefits for each
program strategy.
Exhibit
Net Present Value of 10 Year Costs and Benefits
Avoided Costs Program Costs ($Utility Benefit-Cost
Pro~ram Strategy ($ Mil.)Mil.)Ratio Potential
AC OLC and Back-up Gen - Low $$4.$7.
All 4 Concepts - Low $$5.$9.
All 4 Concepts - High $$12.$21.
Maximum Achievable $19.$45.0.44 129
Quantum Consulting Inc.DR Results
Exhibit 5-6 provides the MW impact and program cost forecast results for the DLC and BUG -
Low Incentives scenario. The estimated load reductions grow from 3.5 MW in 2004 to 24.6 MW
in 2013. Program costs (including incentives) increase from $0.84 million in 2004 to $1.59
million in 2013.
Exhibit
Forecast Results: DLC and BUG Low Incentive Levels
Year 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Critical Peak Pricin~ (CPP)
MW Impact
Incentive Costs ($1 OOOs)
Demand Response Incentives (DRP)
MW Impact
Incentive Costs ($1 OOOs)
AC load Control (DlC)
MW Impact 2.3 10.12.14.3 16.18.20.
Incentive Costs ($1 OOOs)123 162 203 245 287 329 372 417
Back-Up Generator Incentives (BUG)
MW Impact 1.3 1.8 2.3 3.3 3.4
I ncentive Costs ($1O00s)
DR Total
MW Impact 8.4 10.13.15.17.20.22.3 24.
Incentive Costs ($1 OOOs)101 145 190 237 279 322 365 409 455
Admin, equipment, and marketing costs 785 767 815 864 917 941 983 028 074 134($1000s)
Quantum Consulting Inc.DR Results
Exhibit 5-6 provides the MW impact and program cost forecast results for the 4 Concept - Low
Incentives scenario. The DLC and BUG program concept results are similar to the DLC-BUG
scenario presented in the previous table. Increases in impacts result from the addition of the
CPP and DRP program concepts. The estimated load reductions grow from 4.2 MW in 2004 to
29.3 MW in 2013. Program costs (including incentives) increase from $1.02 million in 2004 to
$1.88 million in 2013.
Exhibit
Forecast Results: Concepts Moderate Incentive Levels
Year 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Critical Peak Pricin2 (CPP)
MW Impact 0.4 0.7 1.1 1.4 1.6 1.9 2.4
Incentive Costs ($1O00s)
Demand Response Incentives (ORP)
MW Impact 0.3 0.4 1.0 1.1 1.3
Incentive Costs ($1 OOOs)
AC Load Control (OLC)
MW Impact 4.3 8.4 10.12.14.17.19.21.6
Incentive Costs ($1 OOOs)127 168 210 253 297 341 385 432
Back-Up Generator Incentives (BUG)
MW Impact 1.3 1.8 3.4
Incentive Costs ($1O00s)
DR Total
MW Impact 12.15.18.21.1 23.26.29.
Incentive Costs ($1 OOOs)121 172 225 279 329 379 431 483 538
Admin, equipment, and marketing costs 952 938 991 044 102 130 177 227 278 345($1000s)
Quantum Consulting Inc.DR Results
Exhibit 5-7 provides the MW impact and program cost forecast results for the 4 Concept - High
Incentives scenario. Most of the increase over the 4 Concept - Low Incentives scenario are
attributable to the DLC program concept. The estimated load reductions grow from 8.4 MW in
2004 to 69.MW in 2013. Program costs (including incentives) increase from $2.12 million in
2004 to $4.86 million in 2013.
Exhibit
DR Forecast Results: Concepts High Incentive Levels
Year 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Critical Peak Pricin~ (CPP)
MW Impact 1.3 1.8 2.4
Incentive Costs ($10005)
Demand Response Incentives (DRP)
MW Impact 0.4 1.1 1.2 1.4
Incentive Costs ($10005)9 I
AC Load Control (DLC)
MW Impact 11.9 17.23.29.35.41.47.54.60.
Incentive Costs ($10005)172 332 496 663 834 002 171 342 515 698
Back-Up Generator Incentives (BUG)
MW Impact 1.6 3.4 4.4
Incentive Costs ($10005)
DR Total
MW Impact 8.4 15.22.29.36.42.49.55.62.69.
Incentive Costs ($10005)203 377 555 737 923 098 276 1,455 637 829
Admin, equipment, and marketing 916 960 097 235 385 465 589 721 855 033costs ($1000s)
Quantum Consulting Inc.DR Results
Finally, Exhibit 5-8 shows the maximum achievable forecast results. All four program concepts
show significant increases in impacts versus the 4 Concept - High Incentives scenario. The
estimated load reductions grow from 14.5 MW in 2004 to 128.9 MW in 2013. Program costs
(including incentives) increase from $4.08 million in 2004 to $10.52 million in 2013.
Exhibit
Forecast Results: Maximum Achievable
Year 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Critical Peak Pricin~ (CPP)
MW Impact 1.4 9.4 10.12.
Incentive Costs ($10005)128 168 210 252 296 340 386 435
Demand Response Incentives (DRP)
MW Impact 1.1 4.4
Incentive Costs ($10005)100 112
AC Load Control (DLe)
MW Impact 10.19.29.39.4 49.59.70.4 80.91.6 102.
Incentive Costs ($10005)406 788 178 576 984 398 814 236 664 113
Back-Up Generator Incentives (BUG)
MW Impact 8.4
Incentive Costs ($10005)110 124 138 152 167
DR Total
MW Impact 14.26.38.50.4 63.75.88.101.114.128.
Incentive Costs ($10005)515 958 1 ,409 871 344 825 310 802 301 828
Admin, equipment, and marketing costs 565 668 905 143 402 641 871 117 364 688($1000s)
Quantum Consulting Inc.DR Results
6. DISCUSSION OF UNCERTAINTY
There are two principal classes of uncertainty underlying the results presented in this study.
The first area is uncertainty associated with estimates of the current characteristics of end-use
electricity consumption and DSM measure data (hereafter
, "
current market" uncertainty). The
second area concerns estimates of the future potential for DSM, which is affected by the
uncertainty in the first area, as well as additional uncertainty in future energy prices and electric
load forecasts, changes in market and DSM measure characteristics over time, and forecasts of
customer adoption of measures as a function of program interventions, among other factors
(hereafter, "forecast" uncertainty). While there is considerable overlap in the underlying data
associated with both types of uncertainty, it is useful to separate these classes of uncertainty for
two reasons. First, the study attempts to reduce the effects of the two types of uncertainty
through different approaches. Second, although both types of uncertainty could be reduced
through further research, the types of research necessary are significantly different across the
two classes.
With respect to the first class of uncertainty noted above, current market uncertainty, readers and
users of this study should recognize that estimates of DSM potential involve a process of
modeling the substitution of DSM equipment and systems in place of existing energy
equipment and systems. As such, this process starts with estimates of current equipment
characteristics and energy use by end use and market segment. These data typically are
provided as inputs to DSM potential studies and are, in the best of cases, developed from up-to-
date and statistically accurate studies that involve detailed collection of technology market
shares and comprehensive modeling of end-use consumption and peak demand. When these
data are absent, outdated, or inaccurate, the uncertainty in estimates of current equipment
shares and associated consumption and peak demand directly impact estimates of DSM
potential because DSM potential varies by equipment type and market segment.
The principal sources of data used to develop estimates of current consumption by end use and
market segment were data from the late 1980s and mid-1990s (see Section 2). These end-use
data were then analyzed with respect to Idaho Power s latest (2003) forecast of consumption
the sector level. Note that the most recent Idaho Power forecast did not provide any updated
information for this potential study on the end use and market segment shares of energy
consumption or peak demand. In addition, other sources of equipment saturation data were
very limited for this study.
DSM measure data are the second type of data associated with current market uncertainty.
Examples of DSM measure data include the current incremental costs and savings of DSM
measures, the useful lives of those measures, their current market saturation levels, and
estimates of the fraction of the market for which DSM equipment and systems could substitute
for existing equipment and systems. Fortunately, considerable data on the costs and savings
associated with DSM measures were available for this study. This is attributable to the
considerable number and quality of energy savings measurement and evaluation studies that
have been conducted in the Pacific Northwest, as well as the rest of the United States.
Nonetheless, uncertainties exist to varying degrees in estimates of costs and savings by
individual technology. In general, new measures (e., those on the market for two years or
Quantum Consulting Inc.Discussion of Uncertainty
less) have somewhat greater uncertainty in costs and savings than measures that have been on
the market for longer periods (e.g., 3 years or more). The most significant uncertainty in the
measure-level data is also in the area of measure saturation. Measure-level saturation data
typically come from the same types of sources discussed above for baseline equipment
consumption and saturation data.
Turning now to the area of forecasting uncertainty, it should be somewhat obvious that forecasts
of DSM potential end electricity demand are also affected by current market uncertainty. In any
forecasting process, one wants to begin with as accurate an assessment of current conditions as
possible; errors in estimates of current conditions are otherwise carried forward and
exacerbated. However, even with perfect data on current market conditions, forecasts are
subject to their own uncertainties by their very nature. For this study, the key areas of forecast
uncertainty are future:
end use consumption levels and equipment shares;
incremental costs and savings for measures on the market today;
incremental costs and savings for measures not on the market today but likely to be
available over the ten-year forecast period (no such measures are included in this study);
DSM program funding levels;
customer adoption levels of DSM measures as a function of program intervention types
and levels; and
benefit-cost ratios for DSM measures, which, in addition to uncertainty in future
measure costs and savings, are a function of uncertainty in:
energy and capacity prices, both retail and wholesale, including those associated
with constrained areas,
the value of any environmental externalities, and
the level of the discount rate used in financial analyses of efficiency measures.
As noted above, there is also uncertainty with future forecasts for Idaho Power electricity sales
and peak demand. If the future demand for electricity turns out to be higher than currently
forecast, then there will be more potential for savings from DSM measures. Likewise, if the
future demand for electricity is lower than expected, the potential for savings from DSM
measures will be lower than the figures provided in this report.
Quantum Consulting Inc.Discussion of Uncertainty
APPENDICES
APPENDIX A
ENERGY EFFICIENCY MEASURE DESCRIPTIONS
A. ENERGY EFFCIENCY MEASURE DESCRIPTIONS
RESIDENTIAL MEASURES
This subsection provides brief descriptions of the residential measures included in this study.
HVAC
Central Air Conditioner Upgrade (Phase I and II): Air conditioner equipment includes a
compressor, an air-cooled or evaporatively-cooled condenser (located outdoors), an expansion
valve, and an evaporator coil (located in the supply air duct near the supply fan). Cooling
efficiencies vary based on the quality of the materials used, the size of equipment, the condenser
type, and the configuration of the system. Central air conditioners may be of the unitary variety
(all components housed in a factory-built assembly) or be a split system (an outdoor condenser
section and an indoor evaporator section connected by refrigerant lines and with the
compressor at either the outdoor or indoor location). Efficient air conditioner measures involve
the upgrade of a standard efficiency unit (10 SEER) to a higher efficiency unit (12, 13, or
SEER) .
Direct Evaporative Cooler (Phases I and II): Evaporative coolers use a fan to draw in outside
air and pass it through moistened pads before it enters the home, humidifying, filtering, and
reducing the temperature of the air by as much as 20 degrees. Energy savings are due to the low
voltage requirement of the fan, which also allows the units to be plugged into any standard
outlet. The units are most effective when the outside temperature is consistently near 100
degrees.
Ceiling Fans (Phase I and I): The convective heat transfer from the body depends on the
velocity of the air moving over it. Humans can remain comfortable in a warm humid
environment if the air movement is high. For this measure, propeller style fans are hung from
the ceiling to provide air motion directly to occupants. Energy savings are assumed to occur
because higher cooling temperature set points are facilitated by the rapid air motion provided
by the fans.
Whole House Fans (Phases I and II): Whole house fans keep a home cool during the cooling
months instead of running the air conditioner. These fans typically consume 0.22kW (1/3 hp),
about one-third the consumption of a central air conditioner. These fans pull cool air from the
outside, move air through the house, and/ or remove hot air through the attic.
Attic Venting (Phase II): Attic venting reduces heat gain in the summer and prevents
condensation (humidity) in the winter. This measure involves a motor-driven, thermostat-
controlled fan.
HV AC Diagnostic Testing And Repair (Phases I and II): This measure involves diagnostic and
repair services for existing central air conditioners to improve their efficiency. Inspection and
services of AC systems involves checking the refrigerant level, cleaning the coils, cleaning the
blower, and cleaning or replacing filters.
Quantum Consulting Inc.Measure Descriptions
High Efficiency Room Air Conditioner (Phases I and II): Window (or wall) mounted room air
conditioners are designed to cool individual rooms or spaces. This type of unit incorporates a
complete air-cooled refrigeration and air-handling system in an individual package. Cooled air
is discharged in response to thermostatic control to meet room requirements. Each unit has a
self-contained, air-cooled direct expansion (DX) cooling system and associated controls. The
efficient room air conditioner measure involves the upgrade of a standard efficiency unit (9
SEER) to a higher efficiency unit (10.5 SEER).
Building Envelope
Duct Repair (Phases I and II): An ideal duct system would be free of leaks, especially when the
ducts are outside the conditioned space. Leakage in unsealed ducts varies considerably with the
fabricating machinery used, the methods for assembly, installation workmanship, and age of
the ductwork. To seal ducts, a wide variety of sealing methods and products exist. Care should
be taken to tape or otherwise seal all joints to minimize leakage in all duct systems and the
sealing material should have a projected life of 20 to 30 years. Current duct sealing methods
include use of computer-controlled aerosol and pre- and post-sealing duct pressurization
testing.
Duct Insulation (Phases I and II): Insulation material inhibits the transfer of heat through the
air-supply duct. Several types of ducts and duct insulation are available, including flexible duct,
pre-insulated flexible duct, duct board, duct wrap, tacked or glued rigid insulation, and water
proof hard shell materials for exterior ducts. Duct insulation for existing construction involves
wrapping un-insulated ducts with an R-4 insulating material.
Window Film (Phases I and II): This measure involves application of a dark-colored film to the
existing windows of a home. The film lowers the shading coefficient of a window, reducing the
amount of solar heat gain of a building, and thus decreasing the cooling load for the building.
Double Pane Clear Windows to Double Pane Med Low-E Coating (Phases I and II):
Windows affect building energy use through thermal heat transfer (U-value), solar heat gains
(shading coefficient), daylighting (visible light transmittance), and air leakage. The performance
of a window is determined by the type of glass, the number of panes, the solar transmittance,
the thickness of, and the gas type used in the gap between panes (for multi-pane windows).
Ceiling and Floor Insulation (Phases I and II): Thermal insulation is material or combinations
of materials that are used to inhibit the flow of heat energy by conductive, convective, and
radiative transfer modes. By inhibiting the flow of heat energy, thermal insulation can conserve
energy by reducing heat loss or gain of a structure. An important characteristic of insulating
materials is the thermal resistivity, or R-value. The R-value of a material is the reciprocal of the
time rate of heat flow through a unit of this material in a direction perpendicular to two areas of
different temperatures.
Wall Insulation (Phases I and II): For existing construction, this measure involves adding R-
insulation to un-insulated walls. This is usually accomplished by drilling holes into the
building s siding and blowing in insulation material.
Infiltration Reduction (Phases I and II): Infiltration reduction measures include weather
stripping and caulking. These measures reduce energy consumption by improving the tightness
Quantum Consulting Inc.Measure Descriptions
of the building shell and limiting heat gain and loss. Home installation of these measures is
usually most effective at fixing easily found leaks. Professional installation of these measures
sometimes includes use of blower doors and is usually much more effective than home
installation methods. Measure costs for this study reflect professional weatherization.
Lighting
Compact Fluorescent Lighting (CFLs) (Phase II): Compact fluorescent lamps are designed to
replace standard incandescent lamps. They are approximately four times more efficient than
incandescent light sources. Screw-in modular lamps have reusable ballasts that typically last the
life of four lamps.
Appliances
Energy Star Efficiency Refrigerator (Phase II): ENERGY ST ARCS) refrigerators must exceed the
stringent new July 1 2001 minimum federal standards for refrigerator energy consumption by
at least 10 percent. As specified for this study, the average efficiency improvement is 15 percent.
An energy efficient refrigerator/freezer is designed by improving the various components of
the cabinet and refrigeration system. These component improvements include cabinet
insulation, compressor efficiency, evaporator fan efficiency, defrost controls, mullion heaters,
oversized condenser coils, and improved door seals.
High Efficiency Freezer (Phase II): Stand-alone freezers include either upright or chest models.
Efficient freezers should exceed standard efficiencies by 10 percent or more. As specified for
this study, the average efficiency improvement is 15 percent.
Water Heat
Heat Pump Water Heater (Phase II): Air-to-water heat pump water heaters extract low-grade
heat from the air then transfer this heat to the water by means of an immersion coil. This is the
most commonly utilized residential heat pump water heater. The air-to-water heat pump unit
includes a compressor, air-to-refrigerant evaporator coil, evaporator fan, water circulating
pump, refrigerant-to-water condenser coil, expansion valve, and controls. Residential heat
pump water heaters replace base electric units with the same tank capacities.
Water Heater Tank Wrap (Phase II): Much of water heater efficiency is related to the amount of
insulation surrounding the tank. For low-efficiency units, placing an additional layer of
insulation around the tank saves energy by reducing the amount of heat loss due to inadequate
insulation.
Solar Water Heater (Phase II): Heat transfer technology that uses the sun s energy to warm
water. Solar water heaters preheat water supplied to a conventional domestic hot water heating
system. The energy savings for the system depend on solar radiation, air temperatures, water
temperatures at the site, and the hot water use pattern.
Low-Flow Showerhead (Phase II): Many households are still equipped with showerheads
using 3+ gallons per minute. Low flow showerheads can significantly reduce water heating
energy for a nominal cost. Typical low-flow showerheads use 1.0-2.5 gallons per minute
compared to conventional flow rate of 3.5-0 gallons per minute. The reduction in shower
Quantum Consulting Inc.Measure Descriptions
water use can substantially lower water heating energy use since showering accounts for about
one-fourth of total domestic hot water energy use.
Pipe Wrap (Phase II): Thermal insulation is material or combinations of materials that are used
to inhibit the flow of heat energy by conductive, convective, and radiative transfer modes. By
inhibiting the flow of heat energy, thermal insulation can conserve energy by reducing heat loss
or gaIn.
Tankless Water Heater (Phase II): Also known as "instant" or "on-demand" water heaters,
tankless units function only when a hot water faucet is turned on. There is no energy required
to maintain the temperature of the water in a tank, which results in significant energy savings.
Energy Star and High Efficiency Clothes Washer (Phases I and II): A standard clothes washer
uses various temperatures, water levels, and cycle durations to wash clothes depending on the
clothing type and size of the laundry load.
Energy Star Dishwasher (Phase II): ENERGY STAR labeled dishwashers save by using both
improved technology for the primary wash cycle, and by using less hot water to clean. They
include more effective washing action, energy efficient motors and other advanced technology
such as sensors that determine the length of the wash cycle and the temperature of the water
necessary to clean the dishes.
COMMERCIAL MEASURES
This subsection provides brief descriptions of the commercial measures included in this study.
Lighting
Super T -8 Lamps with Electronic Ballast (Phase 11)19 T -8 lamps are a smaller diameter
fluorescent lamp than T -12 lamps. When paired with specially designed electronic ballasts, T-
lamps provide more lumens per watt, resulting in energy savings. Electronic ballasts replace the
standard core and coil technology in magnetic ballasts with solid-state components. This
technology allows for more consistent control over ballast output and converts power to higher
frequencies, causing the fluorescent lamps to operate more efficiently. For existing first
generation T -8 systems, this measure is specified as an upgrade to efficiency levels associated
with optimal Super T-8Iamp-ballast combinations on a replace-on-burnout basis.
Reflectors (Phases I and II): Optical reflectors are mirrored surfaces installed in fluorescent
fixtures to direct light toward a specific area or work surface. By installing optical reflectors,
four-lamp and three-lamp fluorescent fixtures can be reduced to two lamp fixtures and still
meet the needed lighting levels.
19 Second generation T-8lamps and ballasts were included in the Phase I work. The measure was upgraded to a
Super T -8 lamp-ballast combination for Phase II.
Quantum Consulting Inc.Measure Descriptions
Occupancy Sensors (Phases I and II): Occupancy sensors (infrared or ultrasonic motion
detection devices) turn lights on upon entry of a person into a room, and then turn the lights off
from 1h minute to 20 minutes after they have left. Occupancy sensors require proper installation
and calibration. Their savings depend on the mounting type.
Continuous Dimming (Phases I and II): Dimming electronic ballasts can be incorporated into a
daylighting strategy around the perimeter of office buildings or in areas under skylights. These
systems use photocells to reduce power consumption and light output when daylight is
available.
Compact Fluorescent Lighting (CFLs) (Phases I and II): Compact fluorescent lamps are
designed to replace standard incandescent lamps. They are approximately four times more
efficacious than incandescent light sources. Screw-in modular lamps have reusable ballasts that
typically last for four lamp lives.
High Pressure Sodium Lamps (Phase II): In many situations, 400 watt mercury vapor lamps
can be replaced by 250 watt high pressure sodium (BPS) lamps. BPS lamps are BID lighting
and emit a golden-white or yellow light. The color rendition for BPS lamps is worse than for
MV lamps, but the number of lumens per watt, although dependent on the size of the lamps, is
much improved over MV lamps.
Outdoor Lighting Controls (Photocells and Timeclocks) (Phase II): Photocells can be used to
automatically control both outdoor lamps and indoor lamps adjacent to skylights and windows.
When lights do not need to be on all night, a photocell in series with a time clock provides
maximum savings and eliminates the need for manual operation and seasonal time clock
adjustments. Time clocks enable users to turn on and off electrical equipment at specific times
during the day or week.
10 % More Efficient Design (Lighting) (Phases I and II): This scenario represents a 10 percent
reduction in lighting power densities and associated energy usage below current practice. This
decrease would be achieved through modest design changes that focus on better optimization
of fixture layout and product choices, but would not require aggressive use of controls and
daylighting.
20 More Efficient Design (Lighting) (Phases I and II): This scenario incorporates all of the
savings associated with the 100/0 Improvement case and adds savings associated with advanced
lighting controls and daylighting. This represents a 20 percent reduction in energy usage below
current practice. Note that summer peak demand savings would be higher under this scenario
due to the coincidence of available daylight with this period.
Space Cooling
DX Packaged System Efficiency Upgrade (Phases I and II): A single-package A/C unit
consists of a single package (or cabinet housing) containing a condensing unit, a compressor,
and an indoor fan/ coil. An additional benefit of package units is that there is no need for field-
installed refrigerant piping, thus minimizing labor costs and the possibility of contaminating
the system with dirt, metal, oxides or non-condensing gases. This measure involves installation
of a TIER 2 high-efficiency unit (EER=10.9) as compared to a base case unit with EER=10.
Quantum Consulting Inc.Measure Descriptions
Tune up/Advanced Diagnostics(Phases I and II): The assumed tune-up includes cleaning the
condenser and evaporator coils, establishing optimal refrigerant levels, and purging refrigerant
loops of entrained air. The qualifying relative performance range for a tune-up is between
and 85 percent of the rated efficiency of the unit. Includes fresh air economizer controls
providing demand control ventilation and consisting of a logic module, enthalpy sensor(s), and
CO2 sensors in appropriate applications.
Low-e Windows(Phases I and II): Low-e (short for low-emissivity) windows, have thin metal
coatings that permit the entry of short-wave radiation but block the exit of the majority of the
long-wave thermal energy. The energy savings from these measures are due to the reduced
load placed on the primary cooling equipment.
Direct Evaporative Cooler(Phases I and II): Evaporative coolers use a fan to draw in outside air
and pass it through moistened pads before it enters a building, humidifying, filtering, and
reducing the temperature of the air by as much as 20 degrees. Energy savings are due to the low
voltage requirement of the fan, which also allows the units to be plugged into any standard
outlet. The units are most effective when the outside temperature is consistently near 100
degrees.
Air Handler Optimization, 5 HP(Phase II): Optimization of abuilding s air-handling system is
concerned principally with the proper sizing and configuration of its HV AC units. Energy
savings can result from a variety of improvements, including reduced equipment loads and
better functionality of existing equipment.
Window Film(Phases I and II): Reflective window film is an effective way to reduce solar
energy gains, thus reducing mechanical cooling energy consumption. Windows affect building
energy use through thermal heat transfer (U-value), solar heat gains (shading coefficient),
daylighting (visible light transmittance), and air leakage.
Evaporative Pre-cooler (Phases I and II): Evaporative pre-cooler pre-cools outdoor air through
an air-to-water heat exchanger so that the outdoor supply air is sensibly cooled and humidity is
not raised. This process is designed to reduce the need for mechanical cooling by providing a
cooler than ambient source of supply outdoor air. The effectiveness of this measure is highly
dependent on the characteristics of the outdoor and the cooling requirements of the building.
Programmable Thermostat (Phases I and II): Setback programmable thermostats are
appropriate controls for BV AC equipment that serve spaces with regular occupied and
unoccupied periods, resulting in long periods of time when heating and cooling setpoints can
be adjusted.
Roof / Ceiling Insulation (Phases I and II): Thermal insulation is material or combinations of
materials that are used to inhibit the flow of heat energy by conductive, convective, and
radiative transfer modes. By inhibiting the flow of heat energy, thermal insulation can conserve
energy by reducing heat loss or gain of a structure. An important characteristic of insulating
materials is the thermal resistance, or R-value. The R-value of a material is the reciprocal of the
time rate of heat flow through a unit of this material in a direction perpendicular to two areas of
different temperatures.
Quantum Consulting Inc.Measure Descriptions
Installation of Air-Side Economizers (Phases I and II): Air-side economizers reduce the energy
consumption associated with cooling by providing access to outside air when temperatures
permit - in lieu of using mechanical cooling of recirculated indoor air.
Ventilation
Motor Efficiency Upgrade (Phases I and II): Premium-efficiency motors use additional copper
to reduce electrical losses and better magnetic materials to reduce core losses, and are generally
built to more precise tolerances. Consequently, such motors are more reliable, resulting in
reduced downtime and replacement costs. Premium-efficiency motors may also carry longer
manufacturer s warranties.
VFD on Motor Installation (Phases I and II): Energy usage in HV AC systems can be reduced
by installing electronic variable frequency drives (VFDs) on ventilation fans. VFDs are a far
more efficient method of regulating speed or torque than throttling valves, inlet vanes and fan
dampers. Energy required to operate a fan motor can be reduced as much as 85% during
reduced load conditions by installing a VFD.
Installation of Automated Building Ventilation Control (via Occupancy Sensors, CO2
Sensors, Etc.) (Phases I and II): Often, usage of a building s ventilation control goes beyond
what is necessary to maintain a healthy and comfortable environment. A variety of controls can
save energy by limiting the use of the ventilation system to minimum amount necessary.
Sensors that detect critical contaminants activate ventilations systems only when necessary.
Occupancy sensors limit the operation ventilation systems to periods when the building is
use.
Refrigeration
Motor Efficiency Upgrade for Fans and Compressors (Phase II): In addition to saving energy,
premium-efficiency motors are more reliable, resulting in reduced downtime and replacement
costs.
Strip Curtains (Phase II): Installing strip curtains on doorways to walk-in boxes and
refrigerated warehouses can produce energy savings due to decreased infiltration of outside air
into the refrigerated space. Although refrigerated spaces have doors, these doors are often left
open, for example during product delivery and store stocking activities.
Night Covers (Phase II): Installing film or blanket type night covers on display cases can
significantly reduce the infiltration of warm ambient air into the refrigerated space. This
reduction in display case loads in turn reduces the electric use of the central plant, including
compressors and condensers, thus saving energy. The target market for this measure is small,
independently owned grocery stores and other stores that are typically closed at night and
restock their shelves during the day. The target cases are vertical displays, with a single- or
double-air curtain, and tub (coffin) type cases.
Evaporator Fan Controller for Medium Temperature Walk-Ins (Phase II): In response to the
temperature setpoint being satisfied in a medium temperature walk-in cooler, evaporator fans
are cycled to maintain minimum necessary air flow, which prevents ice build-up on the
Quantum Consulting Inc.Measure Descriptions
evaporator coils. In conventional systems, fans run constantly whether the temperature setpoint
is satisfied or not.
Variable Speed Compressor Retrofit (Phase II): A variable speed compressor is a screw or
reciprocating compressor whose current is modulated by a frequency inverter. A controller
senses the compressor suction pressure and modulates the current and therefore the motor
speed in response to changes in this pressure. When low load conditions exist, the current to the
compressor motor is decreased, decreasing the compressor work done on the refrigerant.
Floating Head Pressure Controls (Phase II): Floating head pressure controls allow a
refrigeration system to operate under lower condensing temperature and pressure settings,
where compressor operation is most efficient, working against a relatively low head pressure.
The condensing temperature is allowed to float below the design setpoint of, say, 95 deg. F
under lower outdoor temperatures, which in-turn lowers the condensate pressure. In a
conventional system a higher fixed condensing temperature setpoint is used which results in a
lowered capacity for the system, requires extra power, and may overload the compressor motor.
Energy savings can be realized if the refrigeration system head pressure is allowed to float
during periods of low ambient temperature, when the condensing temperature can be
dramatically reduced.
Refrigeration Commissioning (Phase II): Refrigeration commissioning refers to a process
whereby refrigeration systems are subject to inspection on a variety of criteria to ensure
efficiency. The commissioning process can involve tests that cover a system s controls for
humidity and temperature, anti-condensation, and heat recovery, among others.
Demand Defrost (Phase II): Defrost of a refrigeration system is critical to its efficient operation.
Demand defrost uses a pressure-sensing device to activate the defrost cycle when it detects a
significant drop in pressure of the air across the refrigeration coil. Because load during defrost
can be three times that of normal operation, defrosting on demand only - not when an
individual operator deems it necessary - can save energy by minimizing the amount of time
spent on defrosting.
Humidistat Controls (Phase II): A humidistat control is a control device to turn refrigeration
display case anti-sweat heaters off when ambient relative humidity is low enough that sweating
will not occur. Anti-sweat heaters evaporate moisture by heating the door rails, case frame and
glass of display cases. Savings result from reducing the operating hours of the anti-sweat
heaters, which without a humidistat control generally run continuously. There are various types
of control strategies including cycling on a fixed schedule.
Water Heat
Water Heater Tank Wrap (Phase II): Much of water heater efficiency is related to the amount of
insulation surrounding the tank. For low-efficiency units, placing an additional layer of
insulation around the tank saves energy by reducing the amount of heat loss due to inadequate
insulation.
Pipe Insulation (Phase II): Thermal insulation is material or combinations of materials that are
used to inhibit the flow of heat energy by conductive, convective, and radiative transfer modes.
Quantum Consulting Inc.Measure Descriptions
By inhibiting the flow of heat energy, thermal insulation can conserve energy by reducing heat
loss or gain.
Demand-Controlled Circulating Systems (Phase II): Hot water cools quickly within pipes,
causing the initial flow of water to be cooler than desired. Circulating systems transfer hot
water from the pipes back to the heater so that hot water is immediately available upon
activation of the faucet. Recirculation pumps can be activated by a switch, thermostat, or
motion sensor, with the last typically producing more energy and water savings.
Quantum Consulting Inc.Measure Descriptions
APPENDIX B
MEASURE INPUTS
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84
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11
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18
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11
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9
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42
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84
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84
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18
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1
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11
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A
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20
1
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4
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21
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s
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g
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5
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r
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98
3
98
3
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A
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A
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21
1
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2
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hr
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d
a
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a
h
o
75
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75
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18
0
98
3
24
6
42
5
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1.
4
8
Ne
w
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22
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s
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t
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g
,
6
,
0
h
r
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d
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y
Id
a
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53
3
53
3
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A
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A
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A
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w
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22
1
CF
L
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6
.
hr
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d
a
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a
h
o
75
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75
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53
3
13
3
37
7
.
4
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01
6
APPENDIX G
ENERGY EFFICIENCY POTENTIAL RESULTS - FIGURES FOR PHASE
(Note: This appendix does not present the final results of the study. Rather, it is intended as a
supplement to show the equivalent results for the initial phase of the study that were provided
to Idaho Power for use in the IRP.
APPENDIX G: ENERGY EFFICIENCY POTENTIAL RESUL TS- FIGURES FOR PHASE I
Exhibit
Technical and Economic Potential (2013)
Peak Demand Savings-
Exhibit
Technical and Economic Potential (2013)
Energy Savings-GWh per Year
500
450 -------------------------------
400 - - - -
350 - - - -
:5: 300
- - - -:!:
~ 250 - - - -
a.. 200
- - - -
150 ----
100 - - - -
50 ----
200
1 ,000 - - -
--------
800 - - - -
---------------------------- - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - ----------
Economic
Exhibit
Technical and Economic Potential by Sector and Vintage, Peak Demand Savings (2013)
150
Cl.. 1
---------
600 - - - -
:5:
-----------------------------------
400 - - - -
--------
200 - - - -
---------
Technical Economic Technical
250
200
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
. Technical
. Economic
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - -
Residential-
Existing
Residential -
New
Commercial -
Existing
Commercial -
New
Quantum Consulting Inc.Energy Efficiency Potential Results
Exhibit
Technical and Economic Potential by Sector and Vintage, Energy Savings (2013)
600
500
8 Technical
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
8 Economic -
100
400
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
..c:
Ct!300
c:(
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
200
- - - - - - - - - - - - - - - - - - - - ------------------
Residential -
Existing
Residential - New Commercial -
Existing
Commercial -
New
Exhibi t 4-
Technical and Economic Potential Estimates
GWh
Sector and Vintaae Technical Economic Technical Economic
Residential - Existing 237 189 520 444
Residential - New 117 216 173
Commercial - Existing 265 179
Commercial - New
Total 437 332 052 843
Quantum Consulting Inc.Energy Efficiency Potential Results
Exhibit
Residential Economic Potential by End Use (2013)
300
250
--- -- - --- - - - --- -
j : ~:h
- - - - - - - - - - - - - - - - - - - -
200
------------------- - - - - - - - - - - - - - - - - - - - - - - - -
150
-------- - - - - - - - - - - - - - - - - - - - - - - - -
100
-------------------------
Space Cooling Lighting Clothes Washer
Exhibit 4- 7
Commercial Economic Potential by End Use (2013)
200
180
- - -- -- -- -- - -- -- - - -- -- -- -- - - -- -- -- -- -- -- - -- --:~;
160
------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
140
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
120
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
100
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ------------------------
Lighting Cooling
Quantum Consulting Inc.Energy Efficiency Potential Results
Exhibit
Residential Economic Potential by Building Type (2013)
600
500
- -- -- -- - -- - -- -- -- -- -- -- -- -- -- -- - -- -- :~;
100
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
400
300
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
200
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Single-Family Small Multi-Family Large Multi-Family Mobile Home
Exhibit
Commercial Economic Potential by Building Type (2013)
Small Office
Large Office
Restaurant
Retail
Food Store
Warehouse
School
College
Hospital
Hotel
Miscellaneous
Quantum Consulting Inc.Energy Efficiency Potential Results
Exhibit 4-
Residential and Commercial Energy Efficiency Supply Curve Energy
$0.
$0.
3: $0.
-g $0.
..oJ
$0.
$0.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
10%12%
Percent Savings
Exhibit
Residential and Commercial Energy Efficiency Supply Curve Peak Demand
$1,000
$900
$800
$700
$600
$500
$400
$300
...J
$200
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
$100
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -------------
10%15%20%25%
Percent Savings
Quantum Consulting Inc.Energy Efficiency Potential Results
Exhibit
Residential-Existing Energy Efficiency Supply Curve Data
Cumulative Levelized Cumulative LevelizedMeasureGWh Savings GWh Savings Energy Cost Measure MW Savings MW Savings Capacity Cost
$/kWh $/MW
Windows 55.55.Windows 53.53.35.
CFL 227.282.49 Duct Insulation (.4)58.51.
Duct Insulation (.4)286.41 HE Room Air Conditioner - EER 10.11.69.75.
SEHA CW Tier 2 (EF=3.25)132.419.40 Basic HVAC Diagnostic Testing And Repair 28.98.84.
HE Room Air Conditioner - EER 10.10.430.Duct Repair (0.32)14.112.92.
Basic HVAC Diagnostic Testing And Repair 27.457.Wall2x4 R-O to Blow-In R-13 Insulation (0.14)10.123.96.
Duct Repair (0.32)13.41 470.10 to 12 SEER Split-System Air Conditioner 42.165.98.10 to 12 SEER Split-System Air Conditioner 39.41 510.Direct Evaporative Cooler 51.217.165.
Wall2x4 R-O to Blow-In R-13 Insulation (0.14)518.47 SEHA CW Tier 2 (EF=3.25)24.241.312.Direct Evaoorative Cooler 56.575.CFL 19.261.452.
Exhibit
Residential-New Construction Energy Efficiency Supply Curve Data -10 Years
Cumulative Levelized Cumulative LevelizedMeasureGWh Savings GWh Savings Energy Cost Measure MW Savings MW Savings Capacity Cost
$/kWh $/MW
Windows Windows 17.
CFL 12.Basic HVAC Diagnostic Testing And Repair 74.
SEHA CW Tier 2 (EF=3.25)14.Duct Repair (0.32)91.42
Basic HVAC Diagnostic Testing And Repair 15.HE Room Air Conditioner - EER 10.114.
Duct Repair (0.32)16.41 10 to 12 SEER Split-System Air Conditioner 115.HE Room Air Conditioner - EER 10.16.49 Wall2x4 R-13 to 2x6 R-13 Insulation (0.14)166.10 to 12 SEER Split-System Air Conditioner 18.SEHA CW Tier 2 (EF=3.25)0.48 298.
Wall2x4 R-13 to 2x6 R-13 Insulation (0.14)18.10 to 13 SEER Split-System Air Conditioner 345.42Whole House Fans 19.CFL 10.455.
10 to 13 SEER Split-System Air Conditioner 20.Whole House Fans 10.549.
Ceiling Fans 21.Ceiling Fans 11.952.
10 to 14 SEER Split-Svstem Air Conditioner 21.1.48 10 to 14 SEER Solit-5vstem Air Conditioner 11.389.
Exhibit
Commercial-Existing Energy Efficiency Supply Curve Data
Cumulative Levelized Cumulative LevelizedMeasureGWh Savings GWh Savings Energy Cost Measure MW Savings MW Savings Capacity Cost
$/kWh $/MW
Prog. Thermostat - DX 11.11.DX Packaged System, EER=10.9, 10 tons 10.10.110.
T8/EB Replacement 94.106.Prog. Thermostat - DX 11.164.
CFL Screw-in, Modular 18W 106.213.Occupancy Sensor 17.49 212.
Occupancy Sensor 23.42 236.T8/ES Replacement 17.35.214.
DX Packaged System, EER=10.9, 10 tons 19.256.Window Film (Standard)38.232.DX Tune Upl Advanced Diagnostics 15.271.CFL Screw-, Modular 18W 18.47 57.257.
Window Film (Standard)278.DX Tune Upl Advanced Diagnostics 59.46 382.
Continous Dimming 37.316.Evaporative Pre-Cooler 63.562.
Evaporative Pre-Cooler 323.Continous Dimmino 15.79.565.
Exhibit
Commercial-New Construction Energy Efficiency Supply Curve Data -10 Years
Cumulative Levelized Cumulative Levelized
Measure GWh Savings GWh Savings Energy Cost Measure MW Savings MW Savings Capacity Cost
$/kWh $/MWLow-e Windows Low-e Windows 32.
10 % More Efficient Design (Lighting)1.44 DX Packaged System, EER=10.9, 10 tons 0.42 113.
20 % More Efficient Design (Lighting)10 % More Efficient Design (Lighting)87.
DX Packaged System, EER=10.9, 10 tons 20 % More Efficient Design (Lighting)0.49 108.
DX Tune Uol Advanced Diaonostics DX Tune Upl Advanced Diaonostics 337.
Quantum Consulting Inc.Energy Efficiency Potential Results
Exhibit
Net Peak Demand Reduction Potential by Funding Scenario, 10-Year Forecast
C/) 140
:g 120
1:1
0::: 100
::2: 80
a.. 60
C/)
400
.J:::.
(!)
co 300
-+- Max, Achievable -+-Hlgh Moderate ""*- Low --Nal. Occurring
200
180
160
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - --------------
Year
Exhibit
Net Energy Savings Potential by Funding Scenario, 10-Year Forecast
-+- Max, Achievable -+-High Moderate ""*- Low
--
Nal. Occurring
700
600
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
500
200
100
Year
Quantum Consulting Inc.Energy Efficiency Potential Results
Exhibit
Net Peak Demand Reduction Potential by Funding Scenario and Segment- Year
200 I . Res Exist D Cern Exist DCem NC . Res NC
180
160
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
IJ)
g' 140
C/) 120
::a:100
Il..0 ~ -----------------------------------
"""- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
40 - -
- - - - - - - - - - - - - - - - - - - - - --------------
Nat. Occurring Low Medium
Scenario
High Max. Achievable
Exhibit
Cumulative Ten-Year Program Costs and Benefits
$350
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
$300
III Net Benefits
0 Total Benefits
. Program Incentives
. Non-Incentive Participant Costs
0 Marketing
. Administration
Q $250
:2:
(;17
5 $200
- - - - - - - - - - - - - - - - - - - - - - - - - - - -
:;::. $150
c::
C: $100
- - - - - - - - - - - - - - - - - - - - - - - - - - - ----------------
$50
Low Moderate
------------------------
High Max. Achievable
Quantum Consulting Inc.Energy Efficiency Potential Results
Exhibit 4-
Summary of Net Achievable Energy Efficiency Potential Forecasts Year 10 (2013) Impacts
Cumulative 10-
Year Program Net MW Net Annual
Costs ($Reductions by GWh Savings Total Resource
SectorNi ntage Scenario Milions)*2013 by 2013 Cost Ratio
Residential Low $12
Existing Moderate $25
High $68 200
Maximum $139 295
Residential Low
New Moderate
Construction High $14
Maximum $46
Commercial Low $12
Existing Moderate $18 1.4
High $36 144 1.4
Maximum $48 173
Commercial Low
New Moderate
Construction HiQh $10
Maximum $21 1.4
Total Low $31 131
Moderate $54 201 1.4
HiQh $128 395
Maximum $255 183 584
*Program costs discounted for inflation at 3 percent per year.
Quantum Consulting Inc.Energy Efficiency Potential Results