20191018IRP Exhibit 4.pdf

lntermountain Gas Company
Integrated Resoil.:i#l
INTE RMOUNTAI N'
CAs C OMPANY
A Subsidiary 0f MDU Resources Group, lnc.
ln the Community fo Serve@
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Fall 2019
Book 2 of 2
Exhibits 4 - 1O
lntermountain Gas Company
Exhibit 4:
Exhibit 5:
Exhibit 6 Section A:
Exhibit 6 Section B:
Exhibit 6 Section C:
Exhibit 6 Section D:
Exhibit 5 Section E:
Exhibit 5 Section F:
Exhibit 7:
Exhibit 8:
Exhibit 9:
Exhibit I0:
Table of Contents
Table of Contents
Book 2 oJ 2
Conservation Potential Assessment
Avoided Cost Model
Total Company - Design Weather, Base Case Load Demand Curye
ldaho Falls Lateral- Design Weather, Base Case Load Demand Curwe
Canyon County Area - Design Weather, Base Case Load Demand
Curye
Sun Yalley Lateral - Design Weather, Base case Load Demand Curwe
State Street Lateral- Design Weather, Base Case Load Demand
Curue
Central Ada Lateral- Design Weather, Base Case Load Demand
Curve
Model Input Tables for All Scenarios
Design Weather Load Demand Curue
Design Base Output Tables
Input and Output Tables for Other Scenarios
lntegrated Resource Plan 20L9 - 2A73
INTERMOUNTAI N'
GAs COMPANY
lntermountain Gas Company
Conservation Potential Assessment
lntegrated Resource Plan 20 I 9 - 2023
A Subsidiary of MDU Resources Group, lnc,
ln the Community to Serve@
Exhibit No.4
Fall 2019
lntermountoin Gos Compony
CONSERVATION POTENTIAL ASSESMENT
FINAL REPORT
Submitted to: INTERMOUNTAIN GAS COMPANY
Prepored by: DUNSKY ENERGY CONSUTTING
with Gos Technology lnstitute ond Frontier Energy
Contoct: Froncois Boulonger, Senior Reseorch Leod
dunsk
July 2019 @ f NERGY CONSUTTING Y
CONSERVATION POTENTIAL ASSESSMENT
FINAL REPORT
SUBMITTED TO:
lntermountain Gas Company
Amm**'
PREPARED BY:
DUNSKY ENERGY CONSUTTING
with Gas Technology lnstitute and Frontier Energy
Contact:
Francois Boulanger
Senior Research Lead
Dunsky Energy Consulting
50 Ste-Catherine St. West, suite 420
Montreal, Canada H2X 3V4
T: 514 504 9030 ex t. 28
E: [e-mail]
dunsky gti,FRSNTIERenergy
Cover photo: Ann Morrison Pork, Boise, ldaho @ Creotive Commons,
--I
www.dunsky.com
/^O \
I . CONTENTS
1. INTRODUCT]ON ...1
CoNTExT 7
Potenrnl StuDY ScoPE 1
2
PoTENTTAL MoDEL 3
M e a s u re Ch a ra cte rizot i on 6
Measure Types and Replocement Schedules.
3. CUMULAT]VE CONSERVATION POTENTIAT.
9
10
TECHNICAI, EcoNoMIc, AND ACHIEVABLE CONSERVATION POTENTIAL...... ..,....,. 10
IMPACT oN Gas VolurraEs L4
15
16
18
20
21
23
26
4. ENERGY EFFIC!ENCY PROGRAM SAVINGS POTENTIAI..
AVERAGE ANNUAL SAVINGS AND BUDGETS
ENERGY SAVINGS BY SECToR AND SEGMENT
END-USE BREAKDoWN ANDToP SAVINGS MEASURES....
Residential Sector..
Commerciol Sector.
5. PROGRAM AND SCENARIO ANATYSIS .........27
BENcHMARKING IGC AcHIrvagLE PORTFOLIOS TO OTHER JUR]SDICTIONS 33
APPENDIX A. MARKET BASETINE AND CHARACTERIZATION A-1
APPENDTX B. DETATLED MODET METHODOIOGY............... ................ B-1
APPENDlX C. MEASURE CHARACTERIZATION DETAITS c-1
APPENDIX D. CTIMATE ZONE MAP... ........... D.1
APPENDIX E. DSM PROGRAM CHARACTERIZATION DETAITS AND MODET INPUTS........ ..............E-1
APPENDIX F. UCT RESULTS BY MEASURE ........................F-1
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Irrsr oF FTGURES
Figure 1. Alternative Scenario Assumptions for Achievable Potential Applied in this Study. .................... 1
Figure 2. Cumulative Natural Gas Potential (2020-2039) ..........2
Figure 3. Cumulative Natural Gas Potential: Base Scenario Impact on Natural Gas Volumes........4
Figure 4. Annual Program Savings (Therms): Low and Base Scenarios, Savings and Budget.. ..............5
Figure 5. Savings as a Percent of Natural Gas Volumes: Low and Base Scenari0s......................5
Figure 6. Key Steps and lnputs in Study Methodology 5
Figure 7. Key lnclusions of the Study ...........6
Figure 8. Cumulative Natural Gas Potential (2020-2039)
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure 21: Cumulative Savings by Sector and Climate Zone, Base Scenario
Figure 22. Alternative Scen ario Assum ptions for Ach ievable Potential
Figure 23. Comparison of Residential Program Savings: Low, Base and Max Scenarios (2020-2024)..
Figure 24. Comparison of Commercial Program Savings: Low, Base and Max Scenarios (2020-2024).
Figure 25: Scenario Analysis - Gas Savings and Budget
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9. Cumulative Conservation Potential: Base Scenario Savings by Sector and Time Period ............................ 13
10, Cumulative Conservation Potential:Base Scenario Savings...,. .......................13
11. Cumulative Natural Gas Potential: Base Scenario lmpact on Natural Gas Volumes .............14
12. Annual Program Savings (Therms): Low and Base Scenarios, Savings and Budget.. .......... 16
13. Savings as a Percent of Natural Gas Volumes: Low and Base Scenari0s................ ............17
14. Annual Program Conservation Potential (Therms): Base Savings by Sector and Time Peri0d.................. 18
15. Natural Gas Achievable Savings by Segment (Therms): Base Scenario, Annual Average (2020-2024) ...19
16. Natural Gas Achievable Savings by Segment (Therms): Base Scenario, AnnualAverage (2025-2039) ...20
17. ResidentialAverage Annual Savings by End-Use (Therms): Base Scenario,2020-2024 (left)and 2020-2039
.......................... 10
...................... 26
,.,,,,.'..,.,,,.,..',,,'. 27
(risht)..........,.......22
Figure 18, ResidentialAverage Lifetime Savings by End-Use (Therms): Base Scenario,2020-2024 (left)and 2025-2039
(right)..................22
29
30
32
Figure 19. CommercialGas Savings by End-Use (Therms): Base Scenario,2020-2024 (left)and 2025-2039 (right)2a
Figure 20. CommercialAverage Lifetime Gas Savings by End-Use (Therms): Base Scenario,2020-2024 (left) and 2025-
2039 (risht).. ......25
Figure 26: Comparison of Gas Portfolio Savings and Costs
Figure 27: Key steps and inputs in study methodology
Figure 28. Bottom-up combinations in the DEEP M0de1.............
Figure 29. Bottom-up combinations in the DEEP M0de1.............
33
..8-1
.. B-3
..B-4
Figure 30: Adoption Curves Used in the Study...... ........,.......8-7
Figure 31. Competing Measures Overview B-9
Figure 32: Chaining lmpact on Savings ... .,...........B-10
Figure 33: DEEP Model Structure B-13
Figure 34: DEEP Model- Dashboard View.................................8-14
Figure 35 : IGC Service Territory and Climate Map D-1
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Ittsr oF TABLES
Table 1. Residential Measures lncluded in the IGC PotentialStudy Organized by End-Use.....
Table 2. Commercial Measures lncluded in the IGC Potential Study Organized by End-Use....
Table 3. Measure Types and Schedules Applied in the IGC Conservation Potential Assessment Model..
Table 4. Residential Top 10 Measures: Base Scenario,2020-2024 and2025-2039
Table 5. Commercial Top 10 Measures: Base Scenario,2020-2024 and2025-2039....,..........
Table 6. Comparison of Residential Program Cost-Effectiveness, Savings, and Budgets by Scenario .
Table 7. Comparison of Commercial Program Cost-Effectiveness, Savings, and Budgets by Scenario
Table 8: Scenario Analysis - Portfolio CostEffectiveness, Budget, and Unit C0st.............
Table 9: Residential Market Baseline Results
Table 10: Size Classification by Therms Consumption in the Commercial Sector
Table 1't: Gas Consumption by Segment and Climate 20ne.............
Iable 12 Size classification in the ResidentialSector
Table 13: Residential Market Baseline Data................
Table 14: C&l Equipment Market Baseline Data
Table 15: Costs and Benefits that May Be Applied for Cost-Effectiveness Screening
Table'1 6: Residential Measure S0urce..................
Table 17. Residential Emerging Technologies lncluded in PotentialStudy Model....
Table 18: Commercial Measure Sources
Table 19. Commercial Measures lncluded in PotentialStudy Model.
Table 20: Average HDD and CDD per Climate Zone (2011-2017).........
7
7
I
,,,21
23
29
............31
,,32
.A-2
.A-3
.A-3
.A-4
.A-5
A-9
B-6
c-1
.. c-5
.. c-6
c-13
..D-2
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EXECUTIVE SUMMARY
Dunsky Energy Consulting, in collaboration with the Gas Technology lnstitute (GTl) and Frontier Energy,
conducted a conservation potential assessment for the lntermountain Gas Company (lGC) over the 2020-2039
timeframe. Emphasis was placed on the initial 5-year period (2020-20241The assessment is intended to support
both short-term energy efficiency planning and long-term resource planning activities. To this end, the study
quantifies energy and demand savings from gas efficiency measures as well as fuel switching from electric
heating accounting considering the two climate zones within IGC service territory.
The study relies on interviews with key market actors and subject matter experts, as well as and the most up-
to-date market data available for both the residential and commercial sectors. This research provided IGC-
specific saturation and baseline efficiencies of energy-using equipment in homes and businesses across the
service territory.
Three levels of savings potential were assessed: Technical, Economic, and Achievable. Within the Achievable
potential, three scenarios were modeled to examine how Demand-Side Management (DSM) program design
factors such as incentive levels and investments in enabling activities can impact potential savings. The
achievable potential scenarios are defined at the Low, Base, and Maximum, as described in the figure below.
Figure 1. Alternative Scenario Assumptions for Achievable Potential Applied in this Study
Applies increased
incentive levels to
match
"mid-class" programs
from other
jurisdictions; includes
investments in
enabling activities
starting in the sixth
year following the
initial ramp-up
period.
IJ.J
l,r1
co
Applies
incentive levels
similar to
Northwest Power
and Conservation
Council
assumptions, and
includes further
investments in
enabling activities
to address
customer barriers
to adoption.
x
Applies low
incentive levels;
includes the full
range of cost-
effective
technologies and
disregards any
budget constraints.
=oJ
ES-1
lcuuuLATIvE sAVTNGS PoTENTTAL
The achievable potential results are presented on both a Cumulative Savings and Program Savings basis, as
described below:
Cumulative savings capture a rolling sum of all new savings that will affect energy sales, excluding
measure re-participation. Cumulative savings express the long-term energy consumption and demand
impacts to inform resource planning for the energy generation and delivery systems.
Program savings capture annual savings from incentivized measures and are not adjusted to remove
the impacts of re-participation or mid-life baseline adjustment impacts. Program savings help to
understand the expected annual DSM portfolio savings and budgets, to inform DSM program planning.
This section focuses on the cumulative savings results from each technology stream, while the next section
provides further details on the program savings.
Below, the technical, economic, and achievable savings are presented side-by-side for natural gas savings (Figure
8).
Figure 2. Cumulative Natural Gas Potential (2020-2039)
Natural Gas Savings - Cumulative 2020- 2039
400,000
350,000
300,000
250,000
200,000
150,000
L00,000
s0,000
0 r
Achv. Low
I Commercial
Technical Economic
I Residential
Achv. Base Achv. Max
From these results, the following observations can be made:
Economic Potential is t7% lower than the technical potential under the least expensive scenario.
Additional opportunities exist in IGC's service territory that are not considered cost-effective under the
current cost-effectiveness framework. This is a consequence of two factors:
ES-2
a
o
6-O
!-(o
I;F _.8f*
a
o Using the Utility Cost Test (UCT) to screen cost-effective measures with low incentives levels
results in higher economic potential than other commonly used cost effectiveness tests, such as
the Total Resources Cost (TRC) test or the Societal Cost Test (SCT).
o Measures that are currently not commercially viable, and not expected to become viable within
the first L5 years, were excluded from the measure list.1 This reduces the technical potential but
has no impact on other types of potential.
o Under a UCT screen, the economic potential is dependent on the level of incentives provided
(see text box, below, for further information).
a Achievable potential is significantly lower than economic potential for all three scenarios, which is
largely attributed to customer bills savings being relatively low compared to efficiency measure costs,
and market barriers such as perceived higher cost of energy efficient equipment and uncertainty about
the savings from efficiency improvements.
a lnvesting in barrier reductions can increase achievable potential over and above raising incentives
alone. The combination of "best-in-class" incentive levels and barrier reduction strategies applied in the
Max Scenario more than triple the incremental savings over the Low Scenario.
IMPACT ON GAS VOTUMES AND SATES
The graph below contextualizes potential savings from conservation for the Base Scenario, as well as load growth
resulting from fuel-switching, through a comparison to the base volume forecast to demonstrate anticipated
network-level effects (Figure 11).
1 The commercial viability of measures is based on available research, technical and economic analysis, as well as
professional judgment of the Dunsky team.
ES.3
Figure 3. Cumulative Natural Gas Potential: Base Scenario lmpact on Natural Gas Volumes
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=o
F
E
c)
F
700,000
500,0m
500,000
400,000
300,000
200,000
100,000
Fuel switching
Base Forecast
Resulting Volume
Forecast
Energy
$,"SlS?$?$y"S$,"tr+$,"trep,"$itr+^,eg"trtr$,""$S"+4i Resulting Volumes I Energy Efficiency
-[61p6351
w/o fuel srvitching
From these results, the following observations can be made
Fuel-switching leads to growth in natural gas volumes and number of customers, as some customers
heated with electricity are expected to switch to natural gas furnaces. lf no efficiency programs were
implemented, gas consumption could be expected to rise by 50% by 2039 due to customer growth (new
construction) a nd fuel-switching.
o Efficiency savings has the potentia! to reduce natural gas consumption by L2% by 2039, after
accounting for the impact of fuel-switching. While fuel-switching could increase consumption by close
to 46,000,000 therms in 2039, efficiency savings may generate 70,000,000 therms of savings. Close to
50% of these savings are attributable to HVAC measures.
lrrrrcrENCy pRocRAM SAVTNGS eoTENTTAL
The program savings provides further details related to the projected annual savings arising from IGC's
portfolio of efficiency programs. These results below present the annual savings and budget for each program
stream, and unlike the cumulative savings, they are not adjusted to remove re-participation impacts or mid-life
baseline adjustments. Specifically, forecasted annual program savings and their corresponding budgets are
presented for the Low and Base Scenarios in Figure L2; savings are also presented as a percent offorecasted
natural gas volumes in Figure L3.
a
ES-4
25
20
15q C
.9
=
10
Figure 4. Annual Program Savings (Therms): Low and Base Scenarios, Savings and Budget
I LOW Achievable Potential r BASE Achievable Potential
-LOW
Budget
-BASE
Budget
Figure 5. Savings as a Percent of Natural Gas Volumes: Low and Base Scenarios
Low and Base Scenarios
0.45%
0.40%
0.350/o
0.30%
0.25%
o.20%
o.L5%
0.1,0%
o.o5%
o.oo%
7,000
6,000
5,000
4,000
3,000
2,OOO
1,000
o-OECtsoOoio,I
F
5
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I Low Achievable Savings as % of usage I Base Achievable Savings as % of usage
Based on the above results, the following observations can be made:
Savings in both scenarios exhibit strong growth in the first five years, followed by a relatively modest
growth for the rest of the study period. The rapid growth in the first period of the study reflects the
expansion of current initiatives in the residential sector and the introduction of new initiatives in the
commercial sector. New initiatives and measures have been ramped up over a period of three to six
years. The later period growth in savings represents a 2Yo year-over-year increase, following new
construction activity and fuel switching to natural gas heating.
Savings under the Base Scenario are 4O/o higher than under the Low Scenario in the first five years,
with further increases in the remaining portion of the study period, notably due to forecasted
investments towards a reduction of market barriers. Starting in the sixth year of the study, the Base
Scenario's budget is more than double the Low Scenario budget (from an average ratio in the first five
years of L.5:1), as higher incentive levels increases the cost of all savings, not only the incremental
portion. This increase in program savings is reflected in the cumulative savings, which shows a similar
increase (43%) between the Low and Base Scenarios, due to very similar mixes of measures in each
scenario. Despite the higher average cost per therm of savings in the Base Scenario, all of the savings
are cost-effective from a UCT perspective.
Efficiency measures provide a stable flow of natural gas savings. Gas savings as a percent of forecasted
volumes remain close to O.5%for the Low Scenario and around t.O%for the Base Scenario, following
the initial ramp-up period assumed in the analysis. Changes in codes and standards or technology do
not disrupt natural gas savings potential in a significant manner; however, customer fuel-switching to
natural gas heating has a significant impact on the overall gas consumption trend, counter-balancing
efficiency savings and leading to an overall net increase in gas consumption.
a
a
o
a Under the Base Scenario, conservation budgets need to increase significantly, first as programs are
introduced to the market and customers participate in a greater number of such programs, and second
as participation further grow due to sustained strategies to address market barriers and further increase
program pa rticipation.
ES-6
1. INTRODUCTION
Dunsky Energy Consulting, in collaboration with its subcontractors, Gas Technology lnstitute (GTl) and Frontier
Energy, conducted a conservation potential assessment for the lntermountain Gas Company (lGC) over the
2020-2039 timeframe. The assessment is intended to support both short-term energyefficiency planningand
long-term resource planning activities. To this end, the study quantifies energy and demand savings from gas
efficiency measures as well as fuel switching from electric heating accounting considering the two climate zones
within IGC service territory. ln addition to providing an assessment of IGC's combined conservation potential,
this report also presents a high-level explanation of our study methods and modelling approach.
CONTEXT
IGC is the sole distributor of natural gas in Southern ldaho, with a service area that extends across the entire
breadth of Southern ldaho-covering an area of 50,000 square miles with a population of approximately
1,260,000. During the fiscal year of 2017,lGC served an average of 349,000 customers in 74 communities
through a system of over 12,000 miles of transmission, distribution, and service lines.
Beginning October L,2077 , the ldaho Public Utilities Commission (PUC) granted IGC authority to offer an Energy
Efficiency program to residential customers and to collect a per therm charge to fund the program. IGC's initial
program offering includes rebates for seven measures in the residential sector-for furnaces, water heaters,
fireplaces, as well as a whole home Energy Star verified homes. While this initial program focuses on the most
cost-effective demand-side management (DSM) measures, the PUC advised all utilities to investigate all cost-
effective DSM.
ln response, IGC issued a Request for Proposals for an "Energy Efficiency (Conservation) Potential Assessment
and Modeling Software Tool" in May 2018. This study is the final product resulting from that process. lt is the
first of its kind completed on behalf of lGC. IGC also intends to use this study to explore new commercial DSM
programs in addition to its current residential programs.
leorrNrAL sTUDY scoPE
This study assesses the conservation potential of gas measures in both the residential and commercial sectors
over the 2O2O-2O39 timeframe. In addition to efficiency measures, it assesses the total energy impacts of
replacing electric heating equipment with high-efficiency natural gas equipment where economically beneficial.
The study considers the two climate zones within IGC service territory.
REPORT STRUCTURE
This report presents the methods, findings and the conservation potential study results from several
perspectives, including cumulative savings by scenario, sector, segment, and end-use. A brief outline of the
report structure is provided below.
1
Section 1 - lntroduction
Section 2 - Methodology: this section provides an overview of the potential study model and energy-saving
measures.
Section 3 - Cumulative Conservation Potential: This section outlines cumulative savings over the study period,
presenting technical, economic, and achievable potential, as well as savings by sector and impacts on gas
volumes.
Section 4 - Energy Efficiency Program Savings Potential: This section provides detailed results for program
savings, including average annual savings and budgets and energy savings by sector and segment. Top-10
contributing measures are presented for each sector with corresponding savings in therms.
Section 5 - Programs and Scenario Analysis: This section provides a comparison of the three scenarios program
savings, budgets, and cost-effectiveness.
2
2. METHODOLOGY
The Dunsky Energy Efficiency Potential (DEEP) model employs a multi-step process to develop a bottom-up
assessment of the Technical, Economic and Achievable Potentials.
Technical potential: The theoretical maximum conservation
potential, ignoring constraints such as cost-effectiveness and
market barriers.
Economic potential: The savings opportunities available
should customers adopt all cost-effective savings, as
established by screening measures against the Utility Cost Test
(ucr).
Achievable potential: The savings from cost-effective
opportunities once market barriers have been applied,
resulting in an estimate of savings that can be achieved
through demand-side management programs. Three
achievable potentialscenarios were modeled to examine how
varying factors such as incentive levels and market barrier
reductions impact uptake:
o Low: Applies low incentive levels (3O% of incremental costs), with an unconstrained budget and a broad
set of cost-effective measures.
o Base: lncentive levels are increased to cover 50% of the measure incremental cost.
o Maximum (Max): lncentives are set to 65% of incremental costs, a funding level similar to the
assumptions behind the Northwest Power and Conservation Council ramp-rate used for electric
conservation potential study in the northwest. This scenario also includes program investments towards
reducing market barriers through innovative program delivery.
This remainder of this section provides a high-level overview of the Dunsky potential model. Additional
information on the baseline research is provided in Appendix A, with the detailed modeling methodology
provided in Appendix B.
leorrNrAL MoDEL
The key steps conducted in the energy efficiency potential study were as follows:
Characterize Measures and their Applicable Markets A comprehensive list of energy saving measures
is characterized by applying jurisdiction-specific data and assumptions to each measure and market
segment. Primary and secondary data are compiled (as available) to establish an assessment of the
market baseline, detailing the current saturation of energy using equipment in each market sector and
o
3
Economic
Technical
Achievable
II
,III
II
Scenario
Analysis
a
a
a
a
a
segment. Markets for energy measures are then assessed by combining utility customer counts with
market growth factors, equipment turnover rates, and the market baseline results.
Economic lnputs: The model harnesses key economic inputs to assess the measure cost-effectiveness
and benefits. Utility avoided costs, customer discount rates, gas rates, and the utility cost of capital are
captured and entered into the model in real dollars based on the study period start yea12. The cost-
effectiveness test that will be applied for economic screening is selected, as well as the other test that
will be calculated to benchmark program performance.
Adoption Parameters: For each measure-market combination we assign adoption curves based on
customer barrier level assessments. Customer economics inputs such as measure savings, marginal
rates and other secondary energy sources) are applied to calculate the participant cost test (PCT), the
key driver of adoption levels in each adoption curve. Finally, program characterizations are entered into
the model by defining the fixed and variable program costs, incentive levels, and enabling activity
impacts on customer barriers.
Potential Assessment: The model assesses the technical potential by combining the measure
characterization with the market baseline inputs to determine the theoretical maximum amount of
savings possible for each measure-market combination, in each year, over the study period. Measures-
market combinations that pass the cost-effectiveness threshold are counted in the economic potential.
Achievable potential scenarios are applied by calculating the customer economics, under various
incentive program scenarios, and applying the adoption curves. At each level, the model applies chaining
factors to account for interactive effects among measures and assigns the appropriate market portion
in places where multiple measure may compete for the same market (e.9., Tier 1 and Tier 2 boilers).
Reporting: Reporting is conducted in four steps, from the presentation of the initial Draft Results to the
Final Report, each with an increasing level of precision and detail. Each report is vetted by the relevant
parties, and all feedback is considered and incorporated into the model and reporting before proceeding
to the next step.
Quality Assurance / Quality Control (aA/qC): Throughout the modeling process, a rigorous aA/aC
process is applied to ensure the inputs reflect the energy using equipment in the studied jurisdiction,
and that the results provide an accurate assessment of the energy savings potential. The model is
calibrated to past DSM program performance and benchmarked to the baseline sales projections and
individual end-uses, to ensure that the technical, economic and market factors align with the local
reality.
These steps are shown graphically in the figure below
2 The model conducts several different economic analyses, notably from the utility's perspective, used for cost-effectiveness
tests and screening (based on the UCT), but also from the participants' perspective, to forecast adoption to individual
measures.
4
Costs
Savings
Utility customer
consumption data
Equipment
satu rations
Applicable
markets
Fffprtive rrqofrrl
Avoided costs
Marginalenergy
rates
Discount rates
Screening tests
Define program
& incentive types
Participant
barriers
Adoption curves
Ramp-up periods
Technical
potential
Measure-level
cost-effective ness
Economic
potential
Pa rticipa nt
economics
Competition &
By segment
By sector
By source
By program type
By measure type
Cumulative
Savings
Program Savings
t. MEASURE & MARKET Choroclerizotion
2. ECONOMIC lnputs
3. ADOPTION Porometers
4. POTENTIAL Assessment
5. REPORTING
Figure 6. Key Steps and lnputs in Study Methodology
Qua lity Assura nce/Qua I ity Control
The model conducts a bottom-up analysis of the conservation potential based on the existing building stock and
equipment saturation. The assessment is developed at the measure level through individual characterizations
which are then combined into measure types, end-uses, climate zones, programs, segments, and sectors, which
allowed our Team to assess IGC's potential at a variety of levels, as highlighted in the figure below. In total, more
than 900 individual combinations were modeled.
5
2 SECTORS
Figure 7. Key lnclusions of the Study
Reside ntial, non-residential
Si , office, warehouse...
Zone 5, Zone 5 6 pnooRAMS
e.g. Replace on burnout, early replacement.
e.g. Furnaces, spray valves, controlt lnsulation...
Please see Appendix A for the methodology used to conduct the market baseline research and market
characterization, and Appendix B for the detailed modeling methodology.
EN ERGY-SAVI NG MEASU RES
MEASU RE CHARACTERIZAT!ON
Forty-two residential measures and 56 commercial measures were included in this study, as summarized in Table
1 and Table 2, respectively. All climate-dependent measures (i.e., those related to heating or cooling) were
characterized separately for both climate zones. Please refer to Appendix C for details on measure
characterization, and Appendix D for a climate zone map.
t
!
6
900 MoDELED
COMBINAIIONS
TO SEGMENTS
2 CLIMA;TE ZONES
5 END-USES
3 MEASURE TYPES
130 MEASURES
TECHNICAL, ECONOMIC,
AND ACHIEVABLE
POTENTIAL ASSESSM ENT
F
Table 1. Residential Measures lncluded in the lGC Potential Study Organized by End-Use
Table 2. Commercial Measures lncluded in the IGC Potential Study Organized by End-Use
Appliance HVACClothes Dryer ENERGY STAR Boiler post 202L standard
Appliance Clothes Washer ENERGY STAR HVAC Boiler Condensing
Behavioral Home Energy Report HVAC Boiler Reset Control
Envelope Air Sealing HVAC Boiler Tune Up
Envelope Attic lnsulation HVAC Combo Boiler (Heatine/HE) post 202L
sta n da rd
Envelope Basement lnsulation HVAC Combo Boiler (Heating/HE)
Envelope Efficient Windows HVAC Duct lnsulation
Envelope ENERGY STAR Doors HVAC Duct Sealing
Envelope New Home Construction Built Green
Home HVAC Fireplace < 40 kBtu/h
Envelope New Home Construction ENERGY
STAR Certified Home HVAC Fireplace >= 40 kBtu/h
Envelope Wall lnsulation HVAC Furnace
Hot Water Faucet Aerator HVAC Furnace
Hot Water Gas Heat Pump Water Heater HVAC Furnace Tune Up
Hot Water Low Flow Shower Head HVAC Heat Recovery Ventilator ENERGY
STAR
Hot Water Pipe Wrap (Hot Water)HVAC Natural Gas Heat Pump
Storage Water Heater Energy Star HVAC Thermostat Programma ble
Hot Water Tankless Water Heater HVAC Thermostat Wi-Fi
Hot Water Tankless Water Heater Energy Star HVAC Through-the-Wa ll Condensing
Furnace/AC
Hot Water Thermostatic Restrictor Shower Valve Other Pool Heater
End-Use Measure End-Use Measure
Appliance Modulating Dryer Retrofit HVAC
HVAC
Building Operator Certification O&M
OnlyBehavioral
Energy Recovery Ventilator (ERV)
Furnace Shut Off Damper, Space
Heating
Behavioral
Envelope
Building Operator Certification O&M
plus Capital Upgrades
Attic/Roof lnsulation Flat Roof
High Efficiency Unit HeatersHVAC
HVAC lnfrared Heater
HVAC Kitchen Demand Control VentilationEnvelope
Envelope
Building Shell Air Sealing
Green Roof HVAC Natural Gas AC and Heat Pump
Measure
Type
Measure
TypeMeasure Measure
7
Hot Water
Envelope Wall lnsulation HVAC Programma ble Thermostat
Hot Water Condensing Water Heater 2020 HVAC Steam Boiler Stack Economizer
Hot Water HVAC Steam Trap HVACHot Water Pipe lnsulation
HVACHot Water lndirect Water Heater Ventilation Hoods
Hot Water Low Flow Faucet Aerator HVAC Water Boiler Stack Economizer
Hot Water Low Flow Shower Head Kitchen Dishwasher
Hot Water
Natural Gas Engine Heat Pump Water
Heater Kitchen Efficient Cookware
Hot Water Pre-Rinse Spray Valve Kitchen Fryer
KitchenHot Water
Recirculation Pump with Demand
Controls Griddle
Hot Water Tankless Water Heater Kitchen lnfrared Broiler
HVAC Kitchen Oven Combination
Advanced Thermostat (Wi-Fi
Thermostat)
HVAC Air Curtains Kitchen Oven Convection - ENERGY STAR
HVAC Boiler < 300 kBtu/h - Tier I Kitchen Oven Convection - High Efficiency
HVAC Boiler >= 300 kBtu/h Kitchen Oven
HVAC Kitchen Steamer High EfficiencyBoiler < 300 kBtu/h- Tier 2
HVAC Boiler >= 300 kBtu/hPost 2024 Laundry ENERGY STAR Clothes Dryer
HVAC Boiler Blowdown Heat Recovery Laundry ENERGY STAR Clothes Washer
New
Construction LEED CertifiedHVACBoiler Reset Control
HVAC Boiler Shut Off Damper, Space Heating Other Biodigester
HVAC Other
Drain Water Heat Recovery (DWHR)
Medium
Combo Condensing Boiler/Water
Heater 90% AFUE
HVAC
Combo Condensing Boiler/Water
Heater 95% AFUE Other Duct lnsulation and Sealing
HVAC Other Pool Cover
Condensing Make Up Air Unit with 2
Speed Motor
HVAC Condensing RTU Other Pool Heater
HVAC Condensing Unit Heater Process Process Boiler - Steam
HVAC Demand Control Ventilation (DCV)Process Process Boiler - Water
HVAC Process Process Boiler Tune UpDestratification Fan - High Efficiency
HVAC Energy Management System (EMS)Windows Efficient Windows
Measure
Type
Measure
Type MeasureMeasure
8
MEASURE TYPES AND REPTACEMENT SCHEDUTES
The model considers four types of efficiency measures:
o Replace on Burnout (ROB)
o Early Replacement (ER)
o Addition (ADD)
r NewConstruction/lnstallation (NEW)
Each of these measure types requires a different approach for determining the maximum yearly units available
for potential calculations. Table 3 provides a guide as to how each measure type is defined and how the
replacement or installation schedule is applied within the study to assess the phase-in potentials, year by year.
Table 3. Measure Types and Schedules Applied in the tGC Conservation Potential Assessment Model
Measure Type Description Market Base Yearly Units Calculation
Replace on Burnout (ROB) Existing units are replaced
by efficient units after
they fail
Exam ple : Re ploci ng foi led
boiler with a condensing
boiler
Market"/Effective Useful Life
(EUL)
The EUL is set ot o minimum of 3
yeorsb to spreod installations
over the potentiol study period.
Alternative EULs were used to
calculote yearly units if baseline
units hove o different EUL than
efficient units.
Current building
code/equipment
standard or
industry
standard
practice
Addition (ADD)The eligible market is distributed
over the estimated useful life of
the measure using an S-curve
function.Exomple: Adding controls
to existing lighting
systems, odding
insulotion to existing
buildings
New Construction/ Measures not related to Building code, Market
lnstallation existing equipment equipment Morket bose is meosure-specific
(NEW) Example: New building standard or and defined os new units per
buitt to LEED standords industry yeor
sta nda rd
practice
a For the purpose of this table, market is defined as the number of units to which a specific measure applies.
b The Home Energy Report is a special case with an EUL of one year.
An EE measure is applied Existing units
to existing equipment or
structu res
9
3. CUMULATIVE CONSERVATION POTENTIAL
This section presents IGC's cumulative conservation potential. Specifically, it first presents technical, economic,
and achievable potential; then savings by sector; and then impact on gas volumes. ln reviewing the results and
analysis, the reader should be aware of the following:
Achievable potentia! is presented under the Base Scenario (i.e., incentive levels cover 50% of the
measure incremental cost), except where otherwise specified.
All savings are expressed in at-the-meter terms. The savings results have therefore not accounted for
line-losses in the transportation and distribution network.
All financial metrics are expressed in 2020 dollars. The applied analysis accounts for inflation and the
time value of money, when assessing all benefit and cost assumptions, including program costs, measure
costs, avoided energy costs, and marginal rates.
ItrcHNrcAL, ECoNoMrc, AND ACHTEVABLE coNSERVATToN eoTENTTAL
Below, technical, economic, and achievable savings are presented side-by-side for natural gas savings (Figure 8).
Figure 8. Cumulative Natural Gas Potentia! (2020-2039)
Natural Gas Savings - Cumulative 2020- 2039
a
a
a
E
OJ-cF
o 400,000
E
E :so,ooo
€ Eoo.oooF
2s0,000
200,000
150,000
100,000
50,000
0
Technical Economic
I Residential
I
Achv. Low
I Commercial
Achv. Base Achv. Max
10
From these results, the following observations can be made:
Economic Potential is 17% lower than the technical potential under the least expensive scenario.
Additional opportunities exist in IGC's service territory that are not considered cost-effective under the
current cost-effectiveness framework. This is a consequence of two factors:
o Using the Utility Cost Test (UCT) to screen cost-effective measures with low incentives levels
results in higher economic potential than other commonly used cost effectiveness tests, such as
the Total Resources Cost (TRC) test or the Societal Cost Test (SCT).
o Measures that are currently not commercially viable, and not expected to become viable within
the first 15 years, were excluded from the measure list.3 This reduces the technical potential but
has no impact on other types of potential.
o Under a UCT screen, the economic potential is dependent on the level of incentives provided
(see text box, below, for further information).
a lnvesting in barrier reductions can increase achievable potentia! over and above raising incentives
alone. The combination of "best-in-class" incentive levels and barrier reduction strategies applied in the
Max Scenario more than triple the incremental savings over the Low Scenario.
3 The commercial viability of measures is based on available research, technical and economic analysis, as well as
professional judgment of the Dunsky team.
1,1,
a
o Achievable potential is significantly lower than economic potential for all three scenarios, which is
largely attributed to customer bills savings being relatively low compared to efficiency measure costs,
and market barriers such as perceived higher cost of energy efficient equipment and uncertainty about
the savings from efficiency improvements.
lmpact of the Utility Cost Test (UCI) on Economic and Achievable Potential
Throughout this study, the UCT was used as the cost-effectiveness test to assess which measures are
included in the economic potential. The UCT examines the costs and the benefits from the Utility's
perspective. Benefits are derived from avoided costs attributed to energy savings, while costs are
incentive payments made to program participants (program administration costs are included in the
program-level UCI calculations). Screening the economic potential by the UTC, as approved by the
ldaho PUC, has several implications on results worth noting:
The maximum achievable potential under a UCT is not obtained with 100% incentive levels, but
rather with a mix of various incentive levels that depend on each measure's benefits and
incremental costs. Maximum achievable potential shown in this study is based on "best-in-class"
incentive levels, as it was found to be very close to the theoretical maximum potential.
a
a Economic potential shown in this study is assessed using lower program incentive levels (Low
Scenario), but using Base scenario's incentive levels reduces the economic potential by nearly 10%.
Testing a scenario with 100% incentive levels for all programs, which is common practice in
potential studies, was found to further reduce economic potential. As a result, increasing incentives
beyond a certain levelwill actually reduce the achievable potential, as measures will be screened
out (i.e., prevented from contributing to the achievable potential).
As the name suggests, measures screened using the UCT are, by definition, cost-effective to reduce the
overall revenue requirements to deliver energy supply.
lsnvrNGS BY sECToR
Cumulative gas savings by sector are shown in the figures below by sector
1.2
Figure 9. Cumulative Conservation Potential: Base Scenario Savings by Sector and Time Period
2039
2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2037 2032 2033 2034 203s 2036 2037 2038 2039
r Residential I Commercial
Natural Gas Savings - Cumulative
2034
Base Scenario- Cumulative Savings
.r,rrlllllll
I
2029
I Residential I Commercial
Figure 10. Cumulative Conservation Potential: Base Scenario Savings
I
2024
60,000
50,000
40,000
30,000
20,000
10,000
o
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
0
o-O
ilo
-.CF
-9ccL6(U6
-c=FO
F
13
Cumulative natural gas savings under the Base Scenario are presented by sector and year/period in Cumulative
gas savings by sector are shown in the figures below by sector.
Figure 9 and Figure 10, above. The following remarks can be made on these results:
Cumulative savings grow at a slower pace during the initial years of the conservation potential study.
Considering that IGC's customer currently only have access to conservation programs for residential new
construction and some residential space and water heating appliances, the study includes a ramp-up
period for new measures and initiatives to take into consideration the time required to introduce new
programs and measures to the market, and for programs to attain maturity and achieve their full market
potential. This period varies based on the complexity involved with the design, launch of the program
and effort to achieve maturity.
o Savings are concentrated in the residentaal sectors. By the end of the study period, nearly two-thirds
(65%l of the naturalgas savings are found in the residentialsector.
lruencr oN GAs voLUMES
The graph below contextualizes potential savings from conservation forthe Base Scenario, as well as load growth
resulting from fuel-switching, through a comparison to the base volume forecast to demonstrate anticipated
network-level effects (Figure 11).
Figure 11. Cumulative Natural Gas Potential: Base Scenario lmpact on Natural Gas Volumes
Fuel switchi
Base Forecast Energy
Resulting Volume
Forecast
a
700,000
600,000
500,000
40o,o0o
300,000
200,000
100,000
(!o
o
F
E
OJcF
tr"F "6P$?S?$FSf "^,"SC$F+9S,"S|tr+"Sdy"S$,"""4S"^,fI Resulting Volumes
-
Energy Efficiency
-[e1s6s5[
w/o fuel srvitching
t4
From these results, the following observations can be made:
Fuel-switching leads to growth in natural gas volumes and number of customers, as some customers
heated with electricity are expected to switch to natural gas furnaces. lf no efficiency programs were
implemented, gas consumption could be expected to rise by 50% by 2039 due to customer growth (new
construction) a nd fuel-switching.
a Efficiency savings has the potential to reduce natural gas consumption by L2% by 2039, after
accounting for the impact of fuel-switching. While fuel-switching could increase consumption by close
to 46,000,000 therms in 2039, efficiency savings may generate 70,000,000 therms of savings. Close to
50% of these savings are attributable to HVAC measures.
a
Fuel Switching Electric Heating Equipment to Natural Gas
The study assessed the impact of residential customers switching their electric heating equipment to
high-efficiency natural gas equipment due to the considerable rate advantage of natural gas compared
to electricity (given that electricity rates are more than five time higher than natural gas rates, on a per
energy unit basis). This analysis did not include any direct incentives for participants and assumed there
are no additional costs to homeowners for the natural gas connection.
The cumulative energy impacts from residential homes switching from electric heating to natural gas is
presented in the table below.
The model assumed that only homeowners with an existing heat distribution system would be interested
in fuel switching to natural gas. The baseline condition is an electric heat pump with standard efficiency
level. The total energy impacts of fuel switching do not account for power plant generation efficiencies.
Additional analysis would be required to include this in the total energy impacts reported.
NaturalGas (therms)25,666,955 37,479,176 44,774,316 46,129,317
Electricity (kWh)-443,344,442 -647,376,552 -773,385,255 -796,790,145
Total Energy lmpact (MMBtu) 1,053,579 1,538,447 1,837,899 1,893,519
2024 2029 2034 2039
15
4. ENERGY EFFICIENCY PROGRAM SAVINGS POTENTIAL
The following graphs and tables present the natural gas conservation potential within IGC's service territory.
Program savings refer to the savings from measures that are incentivized through programs in a given year,
including savings from measure re-participation.4 They are most representative of annual program savings and
can be used to inform DSM program planning to help meet savings objectives, and to determine which sectors,
end-uses, and measures hold the most potential.
All results in this section are achievable potential savings under the Base Scenario, except for average annual
savings and budgets results below, which include the Low Scenario to allow for comparison of high-level savings
and budgets.
AVERAGE ANNUAL SAVINGS AND BUDGETS
Forecasted annual program savings and their corresponding budgets are presented for the Low and Base
Scenarios below (Figure 12). Savings are also presented as a percent of forecasted naturalgas volumes (Figure
13).
Figure 12. Annual Program Savings (Therms): Low and Base Scenarios, Savings and Budget
I
7,000
5,000
5,000
4,000
3,000
2,000
1,000
20
25
5
1"5 qc
=
10
o-O
i(ua9
=o F
"&" "d)"dP"S "of "of ".sp" "$ ".p"
d
"&" "di"dP"dP "&" "d "&" "^d "e. "&"
I LOW Achievable Potential I BASE Achievable Potential
-LOW
Budget
-BASE
Budget
aMeasure re-porticipotionreferstotherenewal of pastyearssavingshavingreachedtheendof theiruseful life,butwould
require new incentives to maintain the savings previously achieved.
1,6
Figure 13. Savings as a Percent of Natural Gas Volumes: Low and Base Scenarios
Low and Base Scenarios
1.20%
7.OO%
0.80%
0.60%
0.40%
0.20%
o.oo%
r Low Achievable Savings as % of usage I Base Achievable Savings as % of usage
Based on the above results, the following observations can be made:
Savings in both scenarios exhibit strong growth in the first five years, followed by a relatively modest
growth for the rest of the study period. The rapid growth in the first period of the study reflects the
expansion of current initiatives in the residential sector and the introduction of new initiatives in the
commercial sector. New initiatives and measures have been ramped up over a period of three to six
years. The later period growth in savings represents a 2Yo year-over-year increase, following new
construction activity and fuel switching to natural gas heating.
a Savings under the Base Scenario are 4OYo higher than under the Low Scenario in the first five years,
with further increases in the remaining portion of the study period, notably due to forecasted
investments towards a reduction of market barriers. Starting in the sixth year of the study, the Base
Scenario's budget is more than double the Low Scenario budget (from an average ratio in the first five
years of 1.5:1), as higher incentive levels increases the cost of all savings, not only the incremental
portion. This increase in program savings is reflected in the cumulative savings, which shows a similar
increase (43%) between the Low and Base Scenarios, due to very similar mixes of measures in each
scenario. Despite the higher average cost per therm of savings in the Base Scenario, all of the savings
are cost-effective from a UCT perspective.
a Efficiency measures provide a stable flow of natural gas savings. Gas savings as a percent of forecasted
volumes remain close to O.5%for the Low Scenario and around t.0%for the Base Scenario, following
the initial ramp-up period assumed in the analysis. Changes in codes and standards or technology do
not disrupt natural gas savings potential in a significant manner; however, customer fuel-switching to
17
orONMRRooNON
dNcflstu)(Ol'.NNNNNO.INooooooo
oNoN
o.)bor!
:)
(o(,
E
l
roz
o
}R
an
N
c'lcnON
+r)(ol-.mm6mmmRRRRRcoao
N
N
R
a
natural gas heating has a significant impact on the overall gas consumption trend, counter-balancing
efficiency savings and leading to an overall net increase in gas consumption.
a Under the Base Scenario, conservation budgets need to increase significantly, first as programs are
introduced to the market and customers participate in a greater number of such programs, and second
as participation further grow due to sustained strategies to address market barriers and further increase
progra m participation.
lrNrncv sAVTNGS By sECToR AND sEGMENT
The distribution of combustible savings by fuel type and sector are presented below for years five, 10, and 15 of
the study.
Figure 14. Annual Program Conservation Potential (Therms): Base Savings by Sector and Time Period
4,500
4,000
3,500
3,000
2,500
2,OOO
1,500
1,000
500
0
o!CLLG
qJf
E^F-;F
,*"'ou c"'d
^g
. i,o'^,
1,Q,
^&"
,eo'
^u ^s ^r" ^&"^o'o' -"o'1,o' 1,o' 49o' ,&o'
Residential Commercial
Based on the above results, the following observations can be made
a
New initiatives in the residential and commercial sectors are required to achieve the mid- and long-
term conservation potential. There is significant growth in the residential and commercial sectors due
to the ramp-up period of new initiatives in the initialyears of the study. The residential energy savings
could grow by a factor of tSO% between the first and second five-year periods, while the commercial
energy savings could grow by LLS% during the same timeframe.
The achievement of the forecasted savings trajectory is contingent on the successful introduction of
those new initiatives. Both internal and external factors may have significant impacts on the trajectory
of achieved savings.
a
The average annual savings by segment are presented below forthe first five years (Figure 15)and the next 15
years (Figure 16). Residentialsegments are shown in yellow, and commercial segments are shown in blue.
18
Figure 15. Natura! Gas Achievable Savings by Segment (Therms): Base Scenario, Annual Average |2O2O-2O241
!c6
:,osF
1,600
1,400
1,200
1,000
800
500
400
200
too%
90%
80%
70o/o
50%
40%
30%
20%
LO%
0%
60%
E
(J-cF
*a*'bs
-rDY.b"
4-a
ob
koI
$u d*"'-d .'*.-".'\s ""-- xru""v *"*,"n",;\
{w
e@
19
Figure 16. Natural Gas Achievable Savings by Segment (Therms): Base Scenario, Annual Average (2025-2039)
1'c6
ao
F
4,000.00 700%
90%
80%
70%
60%
s0%
40%
30%
20%
LO%
o%
3,500.00
3,000.00
2,s00.00
E
(,
F
2,000.00
1,500.00
1,000.00
500.00
*us J $u .,9* d*"'
.**.'
E -.0
'rd ^"s."& $
s""$
".--""-'.-"*",q,
"f.
a Single Family is the segment with the greatest savings potential. Savings in this segment account for
90% of the total potential. The Single Family segment offers significant savings potentialfor allend-uses,
notably HVAC control (connected thermostats), insulation, and water savings fixtures.
o ln the commercial sector, the Education segment has the greatest savings potential, closely followed
by Office and Retail & Food Sales. High-efficiency boilers provide the majority @a%l of savings in these
segments.
lrNo-usr BREAKDowN AND Top sAVTNGS MEASUREs
This section presents a breakdown of savings opportunities by end-use and lists the top measures for both the
residential and commercial sectors. Both the end-use breakdown and the summary of top measures are
20
quantified using averages of annual program savings for the first S-year and the last L5-year periods. Lifetime
savings are also presented for the top measures and by end-use to provide information about the persistence
of savings.
RESIDENTIAT SECTOR
Table 4 below presents the top measure categories ranked by average annual savings. The lifetime savings are
also provided to provide an indication on the persistence of savings by measure.
Table 4. Residential Top 10 Measures: Base Scenario,2O2O-2O24 and 2025-2039
A breakdown of residential savings by end-use is presented below for the first five years (Figure 17, left) and the
next L5 years (Figure 17, right) of the study. Savings shown are averaged annual program savings over the time
period. Figure 18 presents lifetime savings by end-use for the same time periods.
Measure
Average
Annua! Savings
('000 Therms)
Lifetime
Savings
('000 Therms)
Measure
Average
Annual Savings
('000 Therms)
Lifetime
Savings
{'000 Therms}
Thermostats 458 3,667 lnsulation 7,853 779,42L
7,499lnsu lation 332 Thermostats 3,989 31,913
2,480Low Flow
Shower Head 248 Low Flow
Shower Head 1,824 78,237
782Faucet Aerators 78 Duct lnsulation L,298 32,442
2,1.67New
Construction 76 New
Construction 1,150 31,455
7,697Duct lnsulation 68 Fa ucet
Aerators 663 6,625
320
Thermostatic
Restrictor
Shower Valve
658 6,578
Thermostatic
Restrictor
Shower Valve
32
lnsulated Door 20 493 Air Sealing 270 4,044
Boilers 15 376 lnsulated Door 139 3,479
Fireplace 3 70 Boilers 7L4 2,853
2020-2024
21,
2025-2039
Figure 17. ResidentialAverage AnnualSavings by End-Use (Therms): Base Scenario,2O2O-202a (left) and 2020-
2039 (right)
2020-2024 2025-2039
HVAC,
548,860
Envelope,
427,975
HVAC,
L,'J.37,940
Envelope,
1,882,350
Hot Water,
358,849
Hot Water,
643,893
Figure 18. Residentia! Average Lifetime Savings by End-Use (Therms): Base Scenario,2O2O-2O24 (left) and
2o2s-2o39 (right)
2020-2024 202s-2039
HVAC,
5,874,374
HVAC,
1,4,454,358
Envelope,
10,158,635 Hot Water,
6,63r,322
Envelope,
Hot Water,
3,591,,877
43,679,676
Based on the above results, the following observations can be made:
a Envelope measures offer by far the most potential savings. Envelope improvements can provide 51%
of annual savings and 67% of lifetime savings. Insulation, new construction and air sealing are the top
three measures in both the short and long-term, and provide the highest annual and lifetime savings,
suggesting that they should be the priority for conservation programs.
a HVAC measures represent the second largest potential savings. Connected thermostats, duct
insulation and efficient boilers generate the majority of savings associated with the HVAC end-use. As
noted above, while these measures offer significant savings and are often the easiest retrofit
opportunity, they are a second priority when pursuing substantial long-term savings in IGC's service
territory.
22
Lifetime Savings
('000 Therms)Measure
Average Annual
Savings
('000 Therms)
Lifetime Savings
('000 Therms)Measure
Average Annual
Savings
('000 Therms)
Boilers 246 6,1-46 Demand Control
Ventilation 21,4 !0,715
Demand Control
Ventilation 85 858 Attic/Roof
lnsulation 201 34,!52
861
Energy
Recovery
Ventilator (ERV)
186 13,013Boiler Reset
Control 57
s98 Boilers 148 !8,473Fryer50
2020-2024
Hot water measures can also generate significant savings, contributing an estimate d 18% of savings in
the first five years, mostly through efficient water fixtures, such as shower head and faucet aerators. We
note that under the Base Scenario, none of the water heater measures are cost-effective, although they
represent an interesting technical opportunity. Lower incentives could be provided for these measures
to generate additional savings.
Residential Furnace - Proposed DOE Standard
ln 2015, the U.S. Department of Energy (DOE) issued a Notice of Proposed Rulemaking to increase the
Residential Furnaces standard from the Annual Fuel Utilization Efficiency (AFUE) of 80% to 92%. This
standard was originally expected to come into effect in 2O2L. The DOE however did not proceed to issue
a final rule with regards to a new efficiency standard, and there are proceedings underway which could
prevent the DOE from going ahead with the original proposed standard. While the base scenario assumes
that new furnaces available on the market will meet the standard proposed in 2015 starting in 2021,
additional analysis was conducted to assess the impact on the conservation potential study if the
proposed standard is delayed until 2028.
Assuming that the baseline performance of residentialfurnaces remains at80% would lead to additional
average annual savings of 725,474 Therms during the 2020-2024 period, and would require an average
annual budget increase of 5930,000.
COMMERCIAL SECTOR
Table 5 below presents the top measures ranked by average annual savings. The lifetime savings are also
provided, the ranking of which is largely consistent with the annual savings; a result of the fact that most
measures have similarly long EULs.
Ta ble 5. Com m ercia I Top 10 Measu res: Base Scena rlo, 2O2O-2O24 and 2O25-2039
a
23
2025-2439
Energy
Recovery
Ventilator (ERV)
49 680 Efficient
Cookware 108 7,62L
Attic/Roof
lnsulation 45 1,533 Boiler Reset
Control 106 7,942
250Low Flow
Faucet Aerator 26 High Efficiency
Unit Heaters 79 4,769
Kitchen Demand
Control
Ventilation
20 296
Natural Gas
Engine Heat
Pump Water
Heater
78 3,922
56Efficient
Cookware 19 Fryer 72 4,3O3
198High Efficiency
Unit Heaters 77
Water Boiler
Stack
Economizer
65 4,879
2020-2024
Commercial gas savings are broken down by end-use and are presented below for the first five years (left) and
last 15 years (right) of the study. Figure 19 presents an average of annual program savings by end-use while
Figure 20 presents lifetime savings by end-use.
Figure 19. Commercial Average Annual Savings by End-Use (Therms): Base Scenario,2O2O-2O24 (!eft) and
2o2s-2o39 (right)
Kitchen,
Kitchen,
236,94593,490
HVAC,
614,950 Other,
73,372
Hot Water,
61,655
HVAC,
t,366,461_
Other,
35,900
Hot Water,
206,609
24
2025-2439
Figure 20. CommercialAverage Lifetime Gas Savings by End-Use (Therms): Base Scenario,202O-2O24 (left)and
2025-2039 (richt)
Kitchen,
1,812,385HVAC,
r0,226,297
Kitchen,
925,668 Other,
Other,
79,659
262,018
Hot Water,
777,870
Hot Water,
2,573,802
Based on the above results, the following observations can be made:
HVAC measures provideT5% of potential savings. Equipment-based measures-notably condensing
boilers and energy recovery ventilators-represent a significant share of the potential in the first five
years of the study. lncentives for equipment measures can be introduced rapidly in the market and
generate most of the savings in the initial period.
a Share of savings by end-use remains similar for both periods considered. However, some measures
requiring different program strategies represent a higher percentage of achievable savings in the second
period. HVAC control and attic insulation notably represent additional opportunities for longer-term
savings.
Commercial kitchen appliances are a typically untapped savings opportunity. Representing 74% and
9% of the first and last period savings potential, respectively.
Boiler savings decrease in the later years of the study due to new codes and standards. DOE is
considering applying the new ANSI/AHRI standards to gas and oil-fired commercial boilers manufactured
on or afterJanuary 1,2023. This new standard will require new boilers to be more efficient and will
reduce potential savings that can be counted toward DSM programs.
a Hot water savings potential can grow through the study period, notably as natural gas engine heat
pump water heaters become cost-effective in the second period of the study. Close to two-thirds of the
savings for the hot water end-use come from high-efficiency water heaters.
s ANSI/AHRI Standard 1500-2015 Standard for Performance Rating of Commercial Space Heating Boilers. Available at
http://www.ahrinet.orelApp Content/ahri/files/standards%20pdfs/ANSI%20standards%20pdfs/ANSI.AHRI Standard 15
00-2015.pdf.
HVAC,
1,6,525,922
a
a
a
25
SAVINGS BY CTIMATE ZONE
Figure 21 below presents the achievable savings for the Base Scenario for IGC's clients located in the two climate
zones in ldaho (Zone 5 and Zone 5).6 Cumulative savings at the end of each S-year period are presented.
Figure 21: Cumulative Savings by Sector and Climate Zone, Base Scenario
30,000
25,000
20,000
15,000
10,000
5,000
Zone 5 Zone 5
Residential
.2024 .2A29 .2034 f2039
Zone 5 Zone 6
Commercial
Of note, for residential customers, the proportion of savings occurring in Zone 6 (approximately 40%) is higher
than the proportion of customers located in that zone (approximately 25%1. This is notably due to the higher
space heating requirement in that zone, leading to improved economic benefit for participants in that zone,
which in turn leads to increased adoption of energy efficiency measures.
ln the commercial sector, savings are generally well aligned with customer locations in the two climate zones,
with 65% of savings in Zone 5 for 67% of customers. This is notably because businesses in Zone 6 have on average
a 7Yo lower annual consumption than those in Zone 5, and because a higher share of the achievable savings are
not climate-dependent (notably, water heating and kitchen end-uses).
o-O-CEgoJ :iI;F_C
F
,rll
6The climate zone map used forthe potential study is presented in Appendix D.
26
5. PROGRAM AND SCENARIO ANALYSIS
The analysis up to this point has focused primarily on the Base Scenario. This section provides a comparison of
the three scenarios program savings, budgets, and cost-effectiveness.
As described in Section 2, three achievable potential scenarios were assessed in this study: Low, Base, and
Maximum. By varying factors such as incentive levelsT and barrier reduction strategies (see text box on the
following page) between these scenarios, we can develop insights into their respective impacts on program
savings, budgets, and cost-effectiveness. A summary of the assumptions associated with each scenario is
presented below.
Figure 22. Alternative Scenario Assumptions for Achievable Potential
The Low Scenario is based on what is typically considered as a low incentive level with simple delivery
mechanisms. lt covers a broad set of measures and does not consider budget constraints. lt provides an
assessment of the maximum level of savings that could be expected from a simple Conservation Portfolio.
To understand how higher incentives are expected to increase savings, the Base Scenario increases incentive
levels to those found in "mid-class" efficiency programs. As with the Base Scenario, typical delivery mechanisms
are used in the initial period of the study, a broad set of measures are considered, and no budget constraints
are applied. Following the initial ramp-up period, investments to reduce market barriers are included, leading
to higher adoption.
7 lncentive levels refer to the portion of a measure's incremental cost covered by a program incentive.
27
Applies increased
incentive levels to
match
"mid-class" programs
from other
jurisdictions; includes
investments in
enabling activities
starting in the sixth
year following the
initial ramp-up
period.
LUa
d)
Applies
incentive levels
similar to
Northwest Power
and Conservation
Council
assumptions, and
includes further
investments in
enabling activities
to address
customer barriers
to adoption.
x
Applies low
incentive levels;
includes the full
range of cost-
effective
technologies and
disregards any
budget constraints.
Bo
-J
Finally, to quantify the Maximum achievable savings, we applied further enabling strategies with higher
incentive levels, similar to those used in DSM planning by the Northwest Power and Conservation Council. Again,
a broad set of measures are considered, and no budget constraints are applied.
The results that follow highlight the achievable potential savings under each scenario, budgetary impacts, and
an analysis by segment and end-use to identify markets or end-uses for which incentive levels can have a higher
influence.
Enabling Strategies: Options for Reducing Customer Barriers
To reach the maximum achievable potential savings, programs must go beyond incentives to address
other barriers to customers participating in programs. Barrier reductions can be achieved through
activities generally categorized as enabling strategies. Examples include consumer education, contractor
training and support, market research, program design and enhancements, marketing strategies,
program evaluation (which can identify barriers to participation), and others. Enabling strategies can
assist IGC in reducing barriers to program uptake by:
lncreasing IGC's understanding of its markets and sectors (and the barriers they face) through
evaluation and market research;
Applying evaluation and market research results to inform program design and enhancements
for the purposes of reducing, bypassing, or addressing identified barriers to participation;
ln partnership with other utilities in the region, consider mid-stream and up-stream incentives to
bring market actors in the energy efficiency supply chain to promote energy efficient
technologies and measures;
Expanding awareness of conservation opportunities and benefits through:
o Consumer education through initiatives like website resources, energy manager
programs, commercialworkplace engagement initiatives, school programs, etc.;
o Contractor training and support, such as workshops on best practices, certification
courses, providing tools and calculators, organizing conferences, technology
demonstrations, etc.;
o Marketing strategies, such as attendance at industry-focused trade shows and other
forms of outreach;
Promoting building and home energy labelling requirements to make energy performance visible
to owners and renters;
Transforming the market by increasing the demand for and availability of energy-efficient options
through the deployment of emerging-technology pilots and/or behavior-based initiatives; and
Offering financing alongside incentives to address access to capital related barriers.
28
a
a
a
a
a
a
a
lnrsrDENTIAL PRocRAMS ANALYSTs
Below, modeled savings from the three achievable potential scenarios are presented (Figure 23). Cost-
effectiveness, budgets, and dollar per therm savings are also provided (Table 6). Scenario metrics are averaged
over the first years of modeled forecasts.
Figure 23. Comparison of Residential Program Savings: Low, Base and Max Scenarios |2O2O-2O241
I
E
OJ
F
2,500
2,000
1,500
1,000
500
c(D
lo.CF
0
Efficient New Home
I Low I Base I Max
Existing Homes lncentives
Table 5. Comparison of Residential Program Cost-Effectiveness, Savings, and Budgets by Scenario
Efficient New Home 1,.23 1.34 1.18 1.10 1.33 1,.40
1.80 1..52 1.37 1..37 r.29
Existing Homes
lncentives 1.85
L.74 1.46 1.33 1.35 1.31Total Residential L.78
UCT
Base
TRC
Base
Program
Low Max Low Max
292 s20 1,515 7.23 5.83 7.73Efficient New Home
Existing Homes
lncentives 2,457 3,575 7,965 2.61 2.84 3.72
4,O95 3.O7 4.05Total Residential 2,749 9,480 2.80
Budget ('0005)
Base
S/therm
Base
Program
Low Max MaxLow
29
Based on the above results, the following observations can be made:
a The Existing Homes Incentives program can provide most of the savings in the residential sector. These
savings are also achievable at a much lower unit cost than those required for the Efficient New Home
Program.
a The Base Scenario savings level can be achieved at a marginally higher unit cost than the Low Scenario.
There are significant fixed program administration costs required to deliver whole-house programs
targeting the envelope, and higher participation dilutes these costs, thereby lowering the impact on unit
cost of savings.
a Units costs of the Max Scenario are considerably higher than for the other scenarios. Under this
scenario, incentives represent a higher share of the total program costs compared to the other
scenarios.
a Program cost-effectiveness are similar under the Low and Base Scenarios. Overall, increasing incentive
levels to "mid-class" levels under the Base Scenario has a limited impact on program cost-effectiveness.
Program are cost-effective under both scenarios.
COMMERCIAL PROGRAMS ANALYSIS
Below, modeled savings from the three scenarios are presented (Figure 24). Cost-effectiveness, budgets, and
dollar per therm savings are also provided (Table 7). Scenario metrics are averaged over the first five years of
modeled forecasts.
Figure 24. Comparison of Commercial Program Savings: Low, Base and Max Scenarios 12020-20241
-E-(o
UJ-.c oF!F
900
800
700
600
500
400
300
200
100
0
Commercial Equipment Program
I Low I Base I Max
Commercial Retrofit
30
Table 7. Comparison of Commercial Program Cost-Effectiveness, Savings, and Budgets by Scenario
UCT
Base
TRC
Base
Program
Low Max MaxLow
Commercial Equipment Progra m
Commercial Retrofit
TotalCommercial
Commercial Equipment Program
Commercial Retrofit
TotalCommercial
a
a
3.56
1,.12
2.40
628
569
1,L98
3.11
1.26
2.2L
7,72O
1,048
2,L68
2.s2
1,.17
1.81
2,067
2,298
4,365
1.99
o.94
1.53
L.79
4.s0
2.51
1.94
0.98
L.49
2.04
4.45
2.77
1.85
0.95
1.38
2.44
s.09
3.35
Based on the program results above, the following observations can be made
Under the Base Scenario, the Commercial Retrofit Program can provide higher incentives than the Low
Scenario at a similar unit cost. lncreased participation will dilute the fixed administration cost required
to deliver this type of program.
The programs are cost-effective under all scenarios based on the UCT. However, the commercial
retrofit cost-effectiveness results are low under all scenarios. Careful consideration should be given to
the design of this initiative to ensure cost-effectiveness of the program, either through incentive-setting
strategies or by seeking efficiency in the program delivery strategies.
a
The Commercial Equipment Program can provide robust savings at a low unit cost. ln order to achieve
the highest level of savings, IGC could consider maximizing the incentive for this program.
lscrNnnro ANALYSTs - AGGREGATE
Portfolio-wide cost-effectiveness, budget, and cost-effectiveness are presented in Table 8 below. The
relationship between budget and forecasted savings is further illustrated in Figure 25. Results are presented for
the first 5-year period. Please see Appendix F for measure-level cost effectiveness results.
Budget ('0005)
Base
S/therm
Base
Program
Low Max Low Max
31
Table 8: Scenario Analysis - Portfolio Cost-Effectiveness, Budget, and Unit Cost
Figure 25: Scenario Analysis - Gas Savings and Budget
Average DSM Budget by Scenario - 2020-2024
20
15
...4
c
.9
=10
ll
5 J
1,000 2,000 3,000
Thousands
Therms
4,000 5,000 6,000
Based on the above scenario results, the following key insights can be gleaned
Savings under the Base Scenario can be achieved at a similar unit cost as the Low Scenario. While
budget increases significantly between the Low and Base Scenarios, this budget increase is
commensurate with the higher savings projected under the Base Scenario. Unit costs do not increase
materially between the two scenarios.
The portfolio as a whole is cost-effective under the UCT and TRC, for all scenarios.
a
1.78 L.74 1,.46 1.33 1.36 1.31Residential
2.40 2.21 1.81 1.53 1,.49 1.38Commercial
Total L.97 1.90 1.33 L.40 L.4L 1.33
Residential 2.75 4.10 9.48 2.80 3.O7 4.05
Commercial 1,.20 2.17 4.36 2.57 2.77 3.36
Total 3.95 6.26 13.8s 2.70 2.96 3.69
Sector
Max MaxLow
Program
Max MaxLowLow
UCT
Base
TRC
Base
Budeet (SM)
Base
S/therm
Base
a
32
Low
larNcurraARKrNG rGC ACHTEVABLE poRTFoLros To oTHER JURrsDrcTroNS
Figure 26 below compares the IGC conservation potential costs and savings to results from portfolios in other
states. The charts show the plot of portfolio costs per unit savings and annual savings as a portion of sales for
2017 prograrn years (converted lo 2O2O dollars for comparison) for a range of jurisdictions.s Results for IGC Low
and Base Scenarios are presented separately for the first 5-year period and the last 15 years.
Figure 25: Comparison of Gas Portfolio Savings and Costs
8.00
7.O0 aoH.PA OMA.tA
aRt
6.00 .NW
.CT.WA .CA
otD
.NYooK ooROMT IGC Base
2025-2039IGC Base a
E
os 5.00F
ofc^Ctr.
b 3 +.ooqF
64o-U
Eob 3.00o
L
.SD IGC low
2020-2024
2020-2024a .AR
lleoo o..
co tttt
DCa OUT.MS a lcc low
2025-2039
2.00
.NC a tvll OMNtJto6 *,
1.00 . ND .VT
OKY .ME
0.00
o.00%o.20%0A0%0.60% 0.80% L.00%
Annual Savi ngs as Portion of Vol umes
L.70%1-.40%1..60%
Key insights based on this comparison include
ln the first five-years of the study, the Low and Base Scenarios savings and unit costs would place IGC
among average utilities, with savings ranging between O.4% and 0.6% of annual volumes, at a unit costs
around $g/therm.
8ACEEE, "The 2018 State Energy Efficiency Scorecard", Weston Berg, Seth Nowak, Grace Reif, Shruti Vaidyanathan, Erica
Junga, Marianne DiMascio, and Emma Cooper, October 2018.
a
33
a Under the Base Scenario, IGC could evolve into one of the leading utilities, while maintaining its unit
costs at a reasonable level. ln order to accomplish this, investments and sustained growth in the
residential home retrofit market will be critical.
Note: most of the jurisdictions depicted in this chart use the TRC to screen measures and programs. ln
several jurisdictions, natural gas conservation program achievements have been significantly reduced in
recent years. Using the UCT to screen measures unlocks additional opportunities to achieve higher saving
levels.
34
This Appendix presents the Dunsky Team's approach for conducting market baseline research and market
characterizations in the residential and commercial sectors.
IV1ARKET BASELI NE RESEARCH
The Dunsky Team calculated the customer average energy consumption and total customer counts to formulate
the residential and commercial baseline. This data was then used to calculate the potential market size for
measures and to provide a metric for evaluating total savings. To formulate the baseline, the Dunsky Team used
customer data provided by lGC. Additional information on our approach used to define the baseline residential
and commercial sectors is provided below.
RESIDENTIAT SECTOR
The residential sector was split into the following categories:
1) Two segments:
a. Single-Family
b. Multi-family
2) Two U.S. Department of Energy climate zones
a. Zone 5
b. Zone 6
3) Two gas heating scopes:
a. Space heating
b. Hot water and space heating
To calculate the customer average energy consumption and total customer counts by category, IGC provided
The Dunsky Team with monthly residential customer data from January 2016 to December 2017. Dunsky then
rolled-up consumption data to the premise lD and integrated climate zone data by matching the county data to
its climate zone using the U.S. Department of Energy database.e Next, customers were tagged as single-family
or multi-family using a dataset provided by lGC. Lastly, the Dunsky Team determined gas heating scope based
on the customer rate classes (for space heating and hot water vs. space heating only).
The results for the residential market baseline are detailed in Table 9:.
s Volume 7.3: Guide to Determining Climate Regions by County, in Building Americo Best Practices Series. August 2015
Prepared by Pacific Northwest National Laboratory. Available at
https://www.energy.govlsites/prod/files/20151L01f27 /ba_climate_region_guide_7.3.pdf
A-1
APPENDlX A. MARKET BASETINE AND CHARACTERIZATION
Table 9: Residential Market Baseline Results
COMMERCIAT SECTOR
The commercial sector baseline was calculated and split into the following categories:
1) Eight segments:
a. Education
b. Food Services
c. Retail and Food Sales
d. Healthcare
e. Lodging
f. Manufacturing/lndustrial
g. Office
h. other
2) Three sizes:
a. Small
b. Medium
c. Large
To calculate the split, IGC first provided The Dunsky Team with monthly commercial customer data from January
2016 to December 2OL7.The Dunsky Team then rolled-up consumption data to the premise lD and integrated
climate zone data by matching the county data to its climate zone using a U.S. Department of Energy database.l0
Next, the Team assigned segments based on the customer SIC code and dropped; customers that appeared in
the dataset for less than 24 months or which changed SIC codes. The Dunsky Team determined sizes based on
total annual consumption, as shown in Table 10.
5 Single-Family Space Heating 45,828 47.3
Space & Water Heating 185,995 64.75Single-Family
5 Multi-Family Space Heating 9 37.0
5 Multi-Family Space & Water Heating 183 17.9
Space Heating 20,785 45.76Single-Family
Single-Family Space & Water Heating 59,078 70.86
6 Multi-Family Space Heating 159 19.0
5 Multi-Family Space & Water Heating 92 40.9
Average
Consumption
(Therms)
Heating Scope CountsClimate Zone Housing Type
10 lbid.
A-2
I
Table 10: Size Classification by Therms Consumption in the Commercial Sector
Small
Medium
Large
<250
2s0 - s000
>5000
Annual Gas Consumption (Therms)Size
Climate Zone Client Count Average Consumption Per Client
(Therms/year)
Segment
Education
Ed ucation
Food Services
Food Services
Healthcare
Healthcare
Lodging
Lodging
Manufacturing / I ndustrial
Manufacturing / I ndustrial
Office
Office
Other
Other
Retail & Food Sales
Retail & Food Sales
5
6
5
6
5
6
5
6
5
6
5
6
5
6
5
6
7295
613
1,1,45
449
1169
610
125
1,17
7s2
334
5560
2914
3500
7701,
4358
1992
7,693
6,960
6,640
6,397
3,488
2,264
10,255
13,230
5,902
7,204
3,048
2,944
3,664
3,320
3,497
3,056
MARKET CHARACTERIZATI ON
To develop estimates of baseline saturation and measure characteristics, the Dunsky Team relied on secondary
data from multiple sources and discussions with market actors. Because the team relied on existing data instead
of conducting new primary data collection activities, these baseline market characteristics are limited by the
source studies' design and results. ln order to validate the information used for the Conservation Potential
Assessment, the team compared the results from the source material to penetration and saturation data
available from other Dunsky potential studies.
A-3
The results for the commercial market baseline are detailed in Table 11, below.
Table 11: Gas Consumption by Segment and Climate Zone
RESIDENIIAI. SECTOR
METHODOLOGY
The Dunsky Team used the Residential Building Stock Assessment conducted by the Northwest Energy Efficiency
Alliance (NEEA) in 2OL6-77t1to calculate the measure saturation. This database is a representative sample of
single-family, multi-family and manufactured homes gathered across the Northwest region (i.e., Montana,
ldaho, Oregon and Washington).
To calculate the saturation of different measures, the Dunsky Team filtered for homes that used gas as their
primary heating source. The table below lists the number of homes in the four-state region, in ldaho, and as IGC
customers that use gas as their primary heating source.
Table 12: Size classification in the Residential Sector
CALCULATED METRICS
For the baseline, we estimated the following metrics to develop saturation calculations per home of measures
for the single-family and multi-family subsectors.
o Mean Number of Clothes Dryers
o Mean Number of Clothes Washers
o Mean Number of Kitchen Faucets
o Mean Number of Bathroom Faucets
o Mean Number of Showerheads
o Mean Number of Low-Flow Kitchen
Faucets
o Mean Number of Low-Flow Bathroom
Faucets
o Mean Number of Low-Flow Showerheads
11 See https://neea.oreldata/residentia l-bui ldi ns-stock-assessment.
Mean Number of Gas Storage Water
Heaters
Mean Number of Gas lnstant Water
Heaters
Mean Number of Gas Storage Water
Heaters with Pipe lnsulation
Mean Number of Gas lnstant Water
Heaters with Pipe lnsulation
Mean Number of Furnaces
a
a
a
a
a
A-4
Northwest Region 682
ldaho 240
rGc 96
Heating Customer CountScope
I
a
a
a
Mean Number of Furnaces with Smart
Thermostats
Mean Number of Furnaces with
P rogra mma ble Thermostats
Mean Number of Furnaces with Manual
Thermostats
o Mean Number of Furnaces with Unknown
Thermostats
o Mean Number of Boilers
o Mean Number of Doors
o Mean People per Household
o Mean Number of Fireplaces
The Dunsky Team calculated some additional averages to bolster market characterization:
o Average WallArea (ft2)
. Average Ceiling Area (ft2)
o Average High lnput Furnace Capacity (Btus)
o Average High lnput Furnace Efficiency (%)
o Averages High lnput Boiler Capacity (Btus)
o Average High lnput Boiler Efficiency (%l
o Average Conditioned Area per Home (ft2)
o Average Window Area (ft2)
The saturation values of these measures and metrics by single family and multi-family subsectors are presented
in the table below.
Table 13: Residential Market Baseline Data
HVAC Heating
Mean Number of Gas Furnaces 0.910.98
Mean Number of Gas Boilers (in Region)0.130.0s
Average High lnput Gas Furnace Capacity (Btus)4100078,566
Average High lnput Gas Furnace Efficiency (%)86%78%
Average High lnput Gas Boiler Capacity (Btus) (in Region)L34,751 68,010
Average High lnput Gas Boiler Efficiency (%) (in Region)84%79%
HVAC Other
Mean Number of Gas Furnaces with Smart Thermostats 0.04 0
Mean Number of Gas Furnaces with Programmable Thermostats 0.54 0.18
Mean Number of Gas Furnaces with Manual Thermostats 0.34 0.64
Mean Number of Gas Furnaces with Unknown Thermostats 0.06 0.09
Mean Number of Gas Fireplaces 0.46 0
Domestic Hot Water
Mean Number of Kitchen Faucets t.02 1.09
Saturation
SingleFamily Multi-Family
Metric
A-5
Mean Number of Bathroom Faucets 2.52 7.27
Mean Number of Showerheads 1.361.85
Mean Number of Low-Flow Kitchen Faucets 0.55 0.55
Mean Number of Low-Flow Bathroom Faucets 0.38 0.36
Mean Number of Low-Flow Showerheads 0.730.88
Mean Number of Gas Storage Water Heaters 0.74 0.91
Mean Number of Gas lnstant Water Heaters 0.02 0
Mean Number of Gas Storage Water Heaters with Pipe lnsulation 0.06 0.09
Mean Number of Gas lnstant Water Heaters with Pipe lnsulation 0 0
Miscel laneous/Other Appliances
Mean Number of Clothes Dryers 0.96 0.82
Mean Number of Clothes Washers 0.98 0.82
Building Characteristics
Mean Number of Doors 1.092.24
Average Wall Area (ft2)1,166 NA
Average Ceiling Area (ft2)935 795
Average Conditioned Area per Home (ft2)L,947 795
Average Window Area (ft2)75L.7 79.5
Mean People per Household 2.97 3
Saturation
SingleFamily Multi-Family
Metric
CAVEATS
Due to data limitations, we were unable to calculate the saturation in multi-family homes for the following
metrics:
1)
2)
3)
4)
average wall area
average ceiling area
average conditioned area
annual gas usage
A-5
ln addition, the saturation data were all based on IGC customers except for in the High lnput Boiler metrics (due
to a lackof data availability). Forthese metrics, we used the four-state regionaldata from NEEA.
COMMERCIAT SECTOR
PRIMARY RESEARCH METHODOLOGY
The Dunsky Team contacted key market actors throughout ldaho, as identified by lGC, in order to develop
commercial market saturation and building characteristics baselines for the service territory. lndividuals
contacted included:
o IGC account managers
o Licensed building and specialty contractors
o Building safety and code experts
o Energy auditors and consultants
Contacts were asked for their knowledge of the saturation and building characteristics data by segment.
However, none of the contacts were able to provide quantitative estimates to be included in the baseline data.
IGC account managers, one plumbing contractor, and one energy auditor provided anecdotal evidence to
confirm the general accuracy of a small number of data points identified via secondary research. Anecdotal
evidence about the rates of code compliance from the plumbing contractors served as an input to the calculation
of low flow showerheads per business.
For data points in the Food Services segment, the Dunsky Team relied on interviews with subject matter experts
in commercial food service who staff Frontier Energy's Food Service Technology Center. Staff provided
saturation rates for food service measures that were otherwise not available by IGC territory or the wider region.
SECO N DARY R ESEARC H M ET H O DO LOGY
Commercial measure saturation data was developed based on several sources to provide the most accurate
baseline characterization of commercial buildings within IGC territory. The primary data source was Northwest
Energy Efficiency Alliance's (NEEA's) Commercial Building Stock Assessment (CBSA).12 This 2014 assessment is a
comprehensive research study of energy efficiency in Northwest commercial buildings (i.e., Montana, ldaho,
Oregon, and Washington). The team used secondary data from several other sources to develop a more
com prehensive baseline among commercial segments, namely:
o Pacific Northwest National Laboratory (PNNL) Commercial Building Prototypes
r Oak Ridge National Laboratory (ORNL)- Characterization of the U.S. lndustriaUCommercial Boiler
Population
e Seventhwave's Small Commercial Characterization for the Minnesota Department of Commerce
12 Available at https://neea.orsldata/commercial-building-stock-assessments.
A-7
. 2017 Cascade Natural Gas Conservation Potential Assessment
o NEEA's Building Commissioning Long-Term Monitoring and Tracking
CALCULATED METRICS
For the baseline, the team calculated the saturation per business (building) of various measures for education,
food services, healthcare, lodging, manufacturing/industrial, office, retail and other building segments.
o Mean Number of Gas Unit Heaters per Business
o Mean Number of Gas Steam Boilers per Business
o Mean Number of Gas Hot Water Boilers per Business
o Mean Number of low-flow Showerheads Per Business
o Percent of Commercial Kitchen Hoods with a Dedicated Conditioned Makeup Air Unit
r Percent of Commercial Kitchen Hoods with a Dedicated Unconditioned Makeup Air Unit
To calculate saturation figures, the team applied installation data from secondary sources to commercial
customer counts by segment. Some data points were extrapolated from related data points within a secondary
resource. Low-flow showerhead saturation was calculated using the code compliance rate ascertained by
primary research.
ln addition to saturation calculations, the team calculated the following data points to support market
cha racterization.
o Average Year Built
o Mean Number of Floors per Business
r Total Roof Area
o Mean number of dock door per business
o Percent of Businesses that were Commissioned when Built
. Average Capacity of Gas Steam Boilers (kBtuh)
o Average Capacity of Gas Hot Water Boilers (kBtuh)
The saturation values of these measures and metrics by segment are provided in the table below.
A-8
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M ETH O DO LO G I CAL CAV EATS
Due to the limited commercial data availability from both primary and secondary resources within the IGC
service territory, it should be noted that baseline conditions apply generally beyond IGC territory or have been
extrapolated from a broader region. Results are limited by the source studies' design and results.
A-L2
APPENDIX B. DETAILED MODEL METHODOTOGY
The Dunsky Energy Efficiency Potential (DEEP) model employs a multi-step process to develop a bottom-up
assessment of the Technical, Economic, and Achievable Potentials. The process begins by establishing a
comprehensive set of inputs related to energy savings measures, markets, equipment saturations, and economic
factors, which are then applied in the model to assess energy savings potential. This appendix outlines the key
features of our modelling technique, including the calculation methodologies employed, and the steps taken to
ensure the accuracy and quality of the final results and reporting. The figure below provides a high-level
overview of the key assessment steps and inputs, followed by more details throughout this appendix.
Figure 27:Key steps and inputs in study methodology
Costs
Savings
Utility customer
consumption data
Equipment
saturations
Applicable
markets
Effective useful
Avoided costs
Marginalenergy
rates
Discount rates
Screening tests
Define program
& incentive types
Participant
ba rrie rs
Adoption curves
Ramp-up periods
Technical
potential
Measure-level
cost-effectiveness
Economic
potential
Pa rticipant
economics
Competition &
rhainins rr rlpc
By segment
By sector
By source
By program type
By measure type
Cumulative
Savings
Program Savings
l. MEASURE & MARKET Chorocterizotion
2. ECONOMIC lnputs
3. ADOPTION Poromelers
4. POTENTIAL Assessmenl
5. REPORTING
Qua lity Assura nce/Qua lity Control
The key steps in the modelling process are:
Characterize Measures and their Applicable Markets A comprehensive list of energy saving measures
is characterized by applying jurisdiction-specific data and assumptions to each measure and market
segment. Primary and secondary data are compiled (as available) to establish an assessment of the
market baseline, detailing the current saturation of energy using equipment in each market sector and
a
B-1
a
segment. Markets for energy measures are then assessed by combining utility customer counts with
market growth factors, equipment turnover rates, and the market baseline results.
Economic lnputs: The model harnesses key economic inputs to assess the measure cost-effectiveness
and benefits. Utility avoided costs, customer discount rates, gas rates, and the utility cost of capital are
captured and entered into the model in real dollars based on the study period start year. The cost-
effectiveness test that will be applied for economic screening is selected, as well as the other test that
will be calculated to benchmark program performance.
Adoption Parameters: For each measure-market combination we assign adoption curves based on
customer barrier level assessments. Customer economics inputs such as measure savings, marginal
rates and other secondary energy sources) are applied to calculate the participant cost test (PCT), the
key driver of adoption levels in each adoption curve. Finally, program characterizations are entered into
the model by defining the fixed and variable program costs, incentive levels, and enabling activity
impacts on customer barriers.
Potential Assessment: The model assesses the technical potential by combining the measure
characterization with the market baseline inputs to determine the theoretical maximum amount of
savings possible for each measure-market combination, in each year, over the study period. Measures-
market combinations that pass the cost-effectiveness threshold are counted in the economic potential.
Achievable potential scenarios are applied by calculating the customer economics, under various
incentive program scenarios, and applying the adoption curves. At each level, the model applies chaining
factors to account for interactive effects among measures and assigns the appropriate market portion
in places where multiple measure may compete for the same market (e.g., Tier 1 and Tier 2 boilers).
Reporting: Reporting is conducted in four steps, from the presentation of the initial Draft Results to the
Final Report, each with an increasing level of precision and detail. Each report is vetted by the relevant
parties, and all feedback is considered and incorporated into the model and reporting before proceeding
to the next step.
Quality Assurance / Quality Control (aA/qC): Throughout the modeling process, a rigorous aA/aC
process is applied to ensure the inputs reflect the energy using equipment in the studied jurisdiction,
and that the results provide an accurate assessment of the energy savings potential. The model is
calibrated to past DSM program performance and benchmarked to the baseline sales projections and
individual end-uses, to ensure that the technical, economic and market factors align with the local
reality.
a
a
a
a
B-2
BOTTOM-UP ASSESSMENT OF POTENTIAL
DEEP's bottom-up modelling approach assesses each measure-market segment combination, applying incentive
programs to arrive at a fulsome assessment of the energy savings potentials. Rather than estimating potentials
based on the portion of each end-use that can be reduced by energy saving measures and strategies (often
referred to as a top-down analysis), the DEEP model's bottom-up approach applies a highly granular calculation
methodology to assess the energy savings opportunity for each measure-market segment opportunity in each
year. Key features of this assessment include:
a Measure-Market Combinations: Equipment saturations, utility customer counts, and demographic data
are applied to create "markets" for each individual measure. The savings per year, and the market size
are unique for each measure-market segment combination, thereby increasing the accuracy of the
results.
Phase-in Potential: The DEEP model applies the equipment expected useful life (EUL) and market
growth factors to determine the number of savings opportunities for each measure-market combination
in a given year. This provides an important time series for each gas savings measure, upon which
accurate and realistic annual achievable program volumes (measure counts and savings) can be
calculated in the model, as well as phase-in technical and economic potentials.
Annual and Lifetime Savings: For each measure-market combination in each year, DEEP calculates the
annual savings as well as the lifetime savings, accounting for mid-life baseline adjustments. This provides
both an accurate read on the cumulative savings (above and beyond natural uptake), as well as a clear
read on the annual savings that will pass through DSM portfolios.
a
a
Figure 28. Bottom-up combinations in the DEEP Model
Residentia l, Commercial, lndustrial
e.g. Single family, office, warehouse.
-e.g. Hot water, HVAC, appliances Apply DSM PROGRAMS
e.g. Replace on burnout, building additions..,
SECTORS
SEGMENTS
THOUSANDS OF MoDELED
CON4BINATIONS to ASSESS:
TECHNICAL AND
ECONONAIC POTENTIAL
18 END-USES
3 MEASURE TYPES
HUNDREDS Of MEASURES ACHIEVABLE POTENTIAL
SCENARIOSe.g. Furnaces, spray valves, controls,
B-3
OVERVIEW OF MODELLING CALCULATIONS
The DEEP model assesses three levels of energy savings potential:technical, economic, and achievable. ln each
case, these levels are defined based on the governing regulations and practice in the modeled jurisdiction, such
as applying the appropriate cost-effectiveness tests, and applying the relevant benefit streams to ensure
consistency with evaluated past program performance.
o Technical Potential: The technical potential accounts for all theoretically possible energy savings
stemming from the applied measures. ln markets where multiple measures may compete,13 the
measure procuring the most energy savings per unit is selected.
o Economic Potential: The economic potential includes all measures that pass the cost-effectiveness test
screen. Economic screening is performed at the measure level, and only accounts for direct costs related
to the measure (i.e., incentives in the case of the UCT, incremental costs in the case of the TRC), not
including general DSM program costs.
o Achievable Potential: The achievable potential considers customer barriers and economics to assess the
annual adoption of measures within DSM programs. Achievable potential scenarios are applied based
on DSM program design variations (incentives and enabling activities).
Figure 29. Bottom-up combinations in the DEEP Model
Screen
No Barriers
ILOO% lnclusion)
Winner
takes all
Cost-Effectiveness
(ucr)
Cost-Effectiveness
(UCT and PCT)
No
Chaining
No Barriers
ILOOYo lnclusion)
Winner
takes all
Chaining
Adjustment
Market Barriers
(Adoption Curves)
Competition
Groups Applied
Chaining
AdjustmentAdjustment
13 We use the words "market" or "market size" to describe the number of baseline equipment or buildings in a given
segment that capture the opportunity for specific energy-efficient measures. For example, the number of shower heads in
the single-family residential sector would be an example of a "market" for low-flow shower heads.
B-4
ECONOMIC
POTENT!AL
ACHIEVABTE
POTENT!AL
1. ECONOMIC SCREENING
3. COMPETING MEASURES
2. MARKET BARRIERS
4. MEASURES
INTERACT!ONS
TECHNICAL
POTENTIAL
APPL!ED
CALCUTATION
CATCUTATION OF IECHNICAT AND ECONOMIC POIENTIAT
Various calculation methods are applied at different levels of potential, whether
technical, economic, or achievable. These are based on each measure's specific
characterization (cost-effectiveness, market applicability), as well as
interactive and competition effects among measures.
The calculations applied at the technical and economic levels of potential
assessment are outlined below. We note that calculations are conducted
independently at each level to account for shifting and dynamic measure
mixes and interactive effects at each level.
TECHNICAL POTENTIAL
Technical potential is the theoretical maximum savings opportunity, disregarding
constraints such as cost-effectiveness and market barriers. This excludes early replacement and retirement
opportunities, which are to be addressed in the subsequent ochievoble potential analysis.
Phase-in Technical Potential: The technical potential, and all other potential levels are calculated on an annual
phase-in basis to determine the size of the available market in each year. For each measure for each year, the
calculation applies the market size and growth factors, measure type, early and natural replacement rates of
existing equipment, and the maximum number of units that could be replaced or installed.
ECONOMIC POTENTIAL
Economic potential is determined by screening technical potential measures - or bundles of measures - against
the applicable standard cost-effectiveness tests. lt disregards market barriers to adoption.
The model can apply any standard cost-effectiveness test, and adaptations are made to follow localjurisdiction
cost-effectiveness testing requirements. The threshold for screening is set at 1-.0 (i.e., measures that achieve a
higher cost-effectiveness test result are counted in the economic potential) but can be adjusted in the model to
test various screening regimes. Tests included in the model are:
o Utility Cost Test (UCT)
o Total Resource Cost (TRC) Test
o Societal CostTest (SCT)
. Utility Cost Test (PAC[)
o Participant Cost Test (PCT)
TECHNICAL
ECONOMTC
B-5
The measure procuring the most energy savings per unit for each sub-sector and end-use is selected, which
maximizes overall energy savings. The focus of the technical potential is on energy savings (e.g., the measures
selected are based on energy savings, although demand savings are also calculated). The measures applied in
the modelare outlined in the approved study measure list.
. Utility avoided costs (TRC, SCT, UCT)
o Customer avoided energy costs (PCT)
o Non-energy benefits (SCT, PCT)
o GHG Costs (SCT, PACT)
lncremental measure costs (TRC, SCT,
PCT)
lncentive Costs (UCT)
o
a
Benefits Costs
Table 15: Costs and Benefits that May Be Applied for Cost-Effectiveness Screening
When calculating the inputs above, and indeed throughout the DEEP model, we apply the following:
o Lifetime Benefits: All benefits applied in the cost-effectiveness test are multiplied by their
corresponding cumulative discounted avoided costs to get a present value (S) of lifetime benefits.
o Real Dollar Accounting: All benefits and costs are adjusted to real dollars, expressed in the first year of
the study (unless otherwise requested).
ACHIEVABTE POTENTIAL SCENARIO ASSESSMENT
The achievable potential is the amount of energy and demand savings that can
be achieved by the portfolio of DSM programs applied to the market. Market
adoption is assessed by applying the PCT along with the market adoption
curve associated with the assigned market barrier level for each measure. ,
Various scenarios are applied by modifying the DSM program inputs, l
specifically the incentive levels and barrier reductions from enabling :
activities. Achievable potential scenarios are defined according to the study
requirements.
TECHNICAL
ECONOMIC
DSM PROGRAM ARCHETYPES
The achievable potential scenarios are assessed by applying DSM program archetypes that are developed based
on an analysis of local DSM program evaluation reports, best practices from other jurisdictions, and through
discussion with the DSM program administration team(s). Characterization of each program includes translating
enabling strategies into customer barrier reduction impacts, incentive levels, cost structure, and applicable
measures; those measures were mapped into the potential model. The model's bottom-up calculation approach
is used to obtain costs, savings and average persistence of energy savings at the program level by aggregating
measures by program archetypes and using program assumptions such as incentive levels and administration
costs.
ACHIEVABTE
atr!laaar
aaa
B-6
DEEP'S REFINED ADOPTION RATE METHODOLOGY
Rooted in the United States' Department of Energy (U.S. DOE) adoption curves,la the model methodology sets
adoption rates based on a combination of customer cost-effectiveness - applied differently for each sector -
and levels of market barriers. Figure 30 presents a schematic view of resulting adoption curves. Five levels of
barriers, to which measure categories are assigned based on market research or professional experience, define
the maximum adoption curves. Different end-uses and segments exhibit different barriers.
The DEEP model applies five steps to determine the achievable potential:
l-. Barriers: Assign each measure category, within
each segment to one of five adoption curves
based on its assumed market barrier level (these
can change over time if market transformation
effects are anticipated).
2. Drivers: Assign cost-effectiveness metrics to
each sector based on market research into
economic drivers or professional experience.
3. lncentives: Assign assumed incentive levels.
4. Economics: Calculate customer cost
effectiveness expressed by the PCT.
5. Adoption: Calculate resulting adoption rates
and adjust as needed based on other external
Figure 30: Adoption Curves Used in the Study
influences such as the ramp-up period (see Refinement #2 in text box, below).
While this methodology is rooted in the U.S. DOE's extensive work on adoption curves, it applies two important
refinements, as described in the text box below.
14 The U.S. DOE uses this model in several regulatory impact analyses. An example can be found in
http://www.resu lations.eov/contentStreamer?obiectld=090000648106c003&disposition=attach ment&contentTvpe=pdf ,
section 17-A.4.
1.O
o.9
o.8
No Barrie.s
Low garriers
o.?
EX o.a
.sE o.s
& o.a
S
o.3
High Barriers
BarrieB
o.2
o.l
o.0
o 20 40 50 80 100
bqqt-@st ratio
B-7
Refinements to U.S. DOE Adoption Curves
Relinement #7: Choice of the cost-benefit uiteria. The DOE model assumes that participants
make their decisions based on a benefit-cost ratio calculated using discounted values. While this
may be true for a select number of large, more sophisticated customers, experience shows that
most consumers use simpler estimates, including payback periods. This has implications for the
choice and adoption of measures, since payback period ignores the time value of money as well
as savings after the break-even point. The model converts DOE's discount rate-driven curves to
equivalent curves for payback periods.
Relinement #2: Ramp-up. Two key factors - measure awareness and program delivery structure
- can in theory limit program participation, especially during the first few years after a program's
launch, and result in lower participation than DOE's achievable rates would suggest. For example,
a new home retrofit program that requires the enrollment and training of skilled auditors and
contractors by program vendors could take some time to achieve the uptake assumed using
DOE's curves. ln this study, we have therefore applied an adjustment to select programs on a
case-by-case basis.
COMPETING MEASURES
Competing measures share the same market opportunity but are mutually exclusive. Examples include
ENERGY STAR storage water heaters vs. tankless water heaters. ln these cases, the model assesses the market
for each depending on the potential level as follows:
o TECHNICAL POTENTIAL: 100% of the market is applied to the measure with the highest savings.
o ECONOMICPOTENTTAL:700% of themarketisappliedtothe cost-effective measurewiththehighest
savings.
o ACHIEVABLE POTENTIAL: All cost-effective measures compete for the same market. Assuming that all
measures are cost-effective, each adoption rate will be a pro-rated value based on the maximum
adoption rate and each of the measures' respective adoption rates.
Below we present an example where two measures compete. First, the adoption rate is calculated for each
measure independent of any competing measures, as outlined in the figure below.
B-8
t
LOO%o%
Measu re A
Adoption Rate:7O%o
Measu re A
Adoption: 70%
"Winner Tokes All"
Figure 31. Competing Measures Overview
From this example, the maximum adoption rate is 70Yo, corresponding to the measure with the highest potential
adoption. From this, measures adoptions are pro-rated based on their relative independent adoption rates, to
arrive at each measure's share of the 70%tolal adoption rate. As a result, the totaladoption rate is stillTO%,
but it is shared by two different measures.
Measure A
Adoption Rate: 70%
II
!
64%
(70%/
Lto%l
iIt tt
II
!
36%
(40%l
L1,O%l
Measure A
Adoption:45%
Competition Groups
B-9
Measure B
Adoption Rate: 40%
Measure B
Adoption: 0%
MEASURE INIERACTIONS . CHAINING
Chained measures are subject to adjustment when other measures are also installed in the same segment.
Chaining is applied at all potential levels (technical, economic and achievable), and these interactive effects are
automatically calculated according to measure screening and uptake at each potential level. Figure 32 highlights
the calculations used when incorporating adoption rates to calculate chaining effects.
Measure B
Adoption Rate: 40%
Measure B
Adoption: 25%
Figure 32: Chaining !mpact on Savings
Unchoined Choined
Measure A
Savings: 25%o x \0=
2.5 MMBTU
Measure B
Savings: 20%xt0=
2 MMBTU
Measure C
Savings:30%xLO=
3 MMBTU
Measure A
Savings: 25Yo x 70 =
2.5 MMBTU
Measure B
Savings: 20o/o x7.5 =
1.5 MMBTU
Measure C
Savings: 30%xG =
1.8 MMBTU
An example with Measure A
(50% adoption rate) ond Meosure B
(40% odoption rote)
A alone 3Oo/o 2.5 MMBTU
B alone 20% 2 MMBTU
AandB 20% 4 MMBTU
None 30o/o 0 MMBTU
The DEEP model applies a hierarchy of measures in the chain, reducing the savings from each measure that is
lower down the chain. The model adjusts the chained measures'savings for each individual measure, with the
final adjustment calculated based on the likelihood that measures will be chained together (determined by their
respective adoption rates), and the collective interactive effects of all measures higher in the chain.
CUMUTATIVE SAVINGS AND AGGREGATE RESUTTS
To calculate the cumulative savings, and report aggregate savings by measure, end-use, segment and sector, the
following approaches are applied to roll up and adjust annual measure savings.
Cumulative Annual Savings: Cumulative savings are calculated for each potential type and each year,
using incremental savings potentials. Savings from individual measures are removed from the
cumulative savings at the end of their effective useful life (EUL). For instance, a measure installed in Year
1 and with a EUL of two years would not be recounted in the cumulative potential starting in Year 3.
a
a
a
Mid-tife Baseline Adjustments: Where a new standard may alter the baseline of a measure before the
end of its EUL, the model removes a portion of the savings for previously installed measures from the
cumulative savings for that measure. The amount removed is equivalent to the difference between the
baselines, which may represent all or just a portion of the previously installed measure's cumulative
savings.
Aggregate Results and Reporting: Measure-level consumption and demand savings-related costs, and
benefits are aggregated by sector, segment, end-use, measure-type, or program. Costs are reported
from both the program administrator's (program spending) and the service territory's perspectives.
B-10
Meosure SovingsMorket
Shore
End-use - 10 MMBTU
The program administrato/s costs do not include the participants'share of costs (i.e., costs that are
not covered by incentives), nor do they include any adjustments for early retirement measure costs.
IrrrnnrrvE eA/ec AND REFTNEMENTS
To ensure that the DEEP model provides valid results for assessing the potential at all levels, we apply a rigorous
aA/aC process throughout all steps in the study. This includes industry best-practices including:
. QA/QC checklists for all modelling processes
o lssue identification and trackers to ensure all items are addressed
o Data cleaning and input benchmarking to ensure all inputs
. Automated input compiling to avoid human error when loading model with study data
o Vetting with internal senior research leads, and relevant client/utility experts
e Model calibration to past program performance
o Feedback QA assessments, wherein model outputs are benchmarked to baseline sales data, and inputs
are reviewed where anomalous outputs are observed
o Vetting of model with client/utility via sharing of DEEPs transparent input and calculation sheets
The DEEP model draws it inputs from a detailed measure, market, program and economic databases that are
developed using jurisdiction specific data, as follows:
a
a
a
a
Measure !nputs: Each measure is characterized for the specific market being studies (i.e., all parameters
are updated to reflect local climate, equipment availability and costs). We then benchmark measure
costs, savings, EULs and market applicability against our internal database of over 15 past potential study
inputs to ensure that no values fall outside of the expected ranges, and that the inputs.
Market lnputs: Detailed saturation tables are created for each measure-segment combination
(refereed to as markets in DEEP's modeling process). These are then benchmarked against recognized
building energy thresholds (lighting densities, energy use intensities, cooling and heating capacity per
unit condition floor area, average floor area per business, etc.) Finally, the individual equipment
saturations are benchmarked against Dunsky's internal database of equipment saturation tables, to
identify any inputs that may be out of acceptable ranges or anomalous.
Economic lnputs: All economic inputs are converted to real dollar terms based on the study start year
and adapted to fit the model input table formats. These are vetted internally and with the client who
provided the sales projections and local economic settings to ensure consistency with internal planning
values.
Program lnputs: Program characterizations are developed based on a detailed study of current DSM
programs in the jurisdiction, and recent evaluation reports. These are then vetted internally against our
internal program characterization database and provided to utility DSM program administration
representatives to ensure consistency with current program approaches, costs and incentive levels.
Once the inputs have been prepared and quality checked, a characterization database employs an automated
script to assemble the input sheets and avoid any human transfer errors.
B-11
MODET CATIBRATION
Model calibration ensures that the overall estimated energy and demand savings levels are in line with utility
forecasts. Because the bottom-up potential methodology is based on baseline equipment saturation data, the
focus of the study calibration is on the validation of the market adoption forecast model, and to ensure that the
collective inputs provide valid ranges for measure savings, costs, and markets.
The study is refined using the most recently completed year of program activity, as available, using energy
savings, demand savings, and costs. This step is more of a "sanity check" on results than an actual model
calibration, as there might be good reasons for the potential to be materially different from the last annual DSM
results. For instance, some programs may be underperforming what is possible for such programs to achieve, or
some other anomaly may impact achieved savings.
To account for these factors, calibration is performed at two levels: the overall program by program comparison,
as well as at the measure level for a handful of "bell-weathe/'technologies that are typically not impacted by
differences in program scope or program underperformance.
The calibration exercise identifies the extent to which our assessment of adoption rates - based on a
combination of economic drivers and assumed market barrier levels - appears consistent with recent
achievements. Large discrepancies are then reviewed and classified with one (or a combination) of four findings:
(1) The model is consistent with expected results;
(2) The market adoption algorithm needs to be revisited;
(3) Barrier levels for market adoption need to be revisited; or
(a) An anomaly likely explains an inconsistency, so no change is required.
These findings then inform iterative adjustments to the model inputs and settings before draft and final results
are generated and shared with the client and/or stakeholders.
B-72
MODEL ARCHITECTURE
The figure below presents an overview of the DEEP model's computational structure, including inputs,
calculations, and aggregation. The methodology uses a bottom-up approach, beginning at the measure level
with individual measure characterization (the top-most row in the figure below). The measures are then
screened and adoption rates are calculated based on cost-effectiveness results (middle row below). Measure
results are then rolled-up by program, segment, sector, energy source, and end-use.
Figure 33: DEEP ModelStructure
INPUTS
DASHBOARD INPUTS MEASURE INPUTS
(Savings, EUL, costs,
barrier levels, etc.)
AVOIDED
(krergy&capacity
costs, customer energy
MARKET INPUTS
(Customer counts, baseline
saturations, demographics)
SCREENING AND
AGGREGATIO
OVERRIDES
(Manual selection by
measure)ADOPTION
(Chai ni ng; competition
groups, adoption rate)
COST-EFFECTIVENESS
(UCT, TRC, PCT)
MEASURE
SCREENINGSelection of measures)
B-13
AGGREGATION
(Results rolled up by
program, segment, etc.)
MARIGT INPUTS
(Potential and growth
by measure)DASHBOARD/
fDashboard results,
graphs, tables)
TADI NC
FORECASTS
(Forecasted sales
without EE by sector)
PROGRAM
The user also has access to measure and program input and output tables. Core input assumptions in the model
are clearly defined and can be easily changed to conduct sensitivity analysis and adjust to changing market
conditions (e.g., energy prices, economic growth) as well as recent program and evaluation results.
The figure below shows a snapshot of the DEEP dashboard, which is the main entry point to use the model's
features, run sensitivity analyses, and get high-level results.
Figure 34: DEEP Model- Dashboard View
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B-L4
SCENARIO ANATYSIS DASHBOARD
The DEEP model can be delivered for use by the client to run further what-if scenarios. To facilitate this, DEEP
is equipped with dashboard that provides a summary of the model outputs (results), and a range of user-input
fields to adjust the model settings to test further scenarios. The model comes equipped with all input data and
can be run on a PC equipped with MS Excel 2013 or later.
E
II
@dv.mKy
APPENDIX C. MEASURE CHARACTERIZATION DETAILS
This section presents the measure characterization for both the residential and commercial measures. For each
sector, characterizations are presented first for available measures and then for emerging technologies. Finally,
this appendix summarizes how future codes and standards were considered.
T RESIDENTIAL MEASURES
AVAII.ABTE MEASURES
Table 16 lists the residential measures included in the potentialstudy and the source(s)of the inputs. The table
includes the end-use category, measure, applicable TRM or other sources, and any adjustments made.
Table 16: Residential Measure Source
Appliance Clothes Dryer ENERGY STAR Mid Atlantic TRM Version 8. Clothes Dryer, p.239. Vented
Gas, Standard size (8. 5lbs)
Appliance Clothes Washer ENERGY STAR lowa Statewide Technical Reference Manual Version 2.0;
2.1.1 Clothes Washer p.5.
Behaviora! Home Energy Report Michigan 2019 Behavior Resource Manual (BRM) --> for
therms. Took average over 6 years of percentage
reduction for homes with between 900 and 1200 therms
annual usage a year.
Envelope Air Sealing
Envelope Attic lnsulation
Envelope Basement lnsulation
Envelope Efficient Windows
Envelope ENERGY STAR Doors
lowa Statewide Technical Reference Manual Version 2.0;
2.6.1 lnfiltration Control (conservative deemed approach),
p.260.
2019 lllinois TRM Version 7.0, Volume 3. 5.6.5 Ceiling/Attic
insulation p.327.
2019 lllinois TRM Version 7.0, Volume 3. 5.5.2 Basement
Sidewall lnsulation p. 306.
lowa TRM, Version 2.0, Volume 2.2.6.8 Efficient Windows,
p. 308.
lowa TRM Version 2.0, Volume 2: Residential Measures,
2.6.5 lnsulated Doors, p.287.
c-1
Measure
Type
SourceMeasure Description
Measure
Type
Measure Description Source
Envelope New Home Construction Built
Green Home
Envelope New Home Construction
ENERGY STAR Certified Home
Envelope Wall lnsulation
Hot Water Faucet Aerator
Hot Water Gas Heat Pump Water Heater
Hot Water Low Flow Shower Head
Hot Water Pipe Wrap (Hot Water)
Hot Water Storage Water Heater Energy
Star
Hot Water Tankless Water Heater
Hot Water Tankless Water Heater Energy
Star
Cascade natural gas potential study - ratio of 'Built Green
Homes' and Energy Star homes to adjust results from
Energy Star homes
Energy Star Certified Homes, Version 3 (Rev. 08), Cost &
Saving Estimates.
https://www.energysta r.gov/ia/pa rtners/bld rs_lenders_ra
te rs/down loads/EstimatedCosta ndSavi ngs. pdf
Assumed that 80% of saving in Energy star homes is
heating related (gas)and 20%is not (electricity).
Energy Star Certified Homes, Version 3 (Rev. 08), Cost &
Saving Estimates.
https ://www.energysta r. gov /ia/ partners/bldrs_lenders_ra
ters/down loads/EstimatedCostandSavings. pdf
Attribution of savings between electricity and gas is
currently done based on professionaljudgement - assume
that 80% of energy saving are heating related and 20% are
non-heating related.
2019 lllinois TRM Version 7.0, Volume 3. 5.5.4 Wall
insulation p. 320.
Mid Atlantic TRM V8 - NEEP; Faucet aerators (p. L74 of
529). Used average of kitchen and bathroom values.
2019 lllinois TRM Version 7.0, Residential Measures. 5.3.7
Gas High Efficiency Furnace p.103
Mid Atlantic TRM V8 - NEEP; Low flow showerhead (p. 170
of 529).
NBP TRM, DSM Plan 2Ot9 - 2021Technical Reference
Manual. Hot Water Pipe lnsulation p. 23.
Mid Atlantic TRM V8 - NEEP; High Efficiency Gas Water
Heater (p. 187 of 529). Used values for Gas Condensing.
Efficie ncy Ma i ne Reta i l/Residentia I TRM Versio n 2078.3,
Effective Jan. 1, 2018. On-Demand Natural Gas Water
Heater, p.97.
Efficiency Maine Retai l/Residentia I TRM Version 2078.3,
Effective Jan. L, 2018. On-Demand Natural Gas Water
Heater, p.97;EF of 0.91from Union Gas/Enbridge Gas.
Mid Atlantic TRM V8 - NEEP; Thermostatic Restrictor
Shower Valve (p .199 of 529).
2019 lllinois TRM Version 7.0, Residential Measures. 5.3.6
Gas High Efficiency Boiler p.99.
2019 lllinois TRM Version 7.0, Residential Measures. 5.3.6
Gas High Efficiency Boiler p.99.
Hot Water
HVAC
Thermostatic Restrictor Shower
Valve
Boiler post 2021 standard
HVAC Boiler Condensing
c-2
Measure
Type
Measure Description Source
HVAC
HVAC Combo Boiler (Heating/HE)
Duct lnsulation
HVAC Boiler Reset Control
HVAC Boiler Tune Up
Mid-Atlantic Technical Reference Manual Version 8.0, May
2018. Boiler Reset Controls, p.152.
Wisconsin Focus on Energy 20L8 TRM, Boiler Tune-Up,
Single Family p.757. Algorithm edited to be consistent
with other residential measures.
2019 lllinois TRM Version 7.0, Residential Measures. 5.3.17
Gas High Efficiency Combination Boiler p.767. Hot water
inputs from Maine TRM to be consistent with other hot
water measures.
2019 lllinois TRM Version 7.0, Residential Measures. 5.3.17
Gas High Efficiency Combination Boiler p.767. Hot water
inputs from Maine TRM to be consistent with other hot
water measures.
Efficiency M aine Retai l/Residentia I Tech nica I Reference
ManualVersion 2018.3; Duct lnsulation (Component of
LUB), p.79.
lowa Statewide Technical Reference Manual Version 2.0;
2.4.76 Duct Sealing. Deemed method.
Union Gas /Enbridge Gas Distribution - Updated DSM
Measures and TRM; EB-2076-0246. HE Fireplace with
pilotless ignition, zero clearance. 40 kBtu/h input rating or
freestanding fireplace, 0.7 Eff; Base: 0.65 Eff. p.7.
Union Gas /Enbridge Gas Distribution - Updated DSM
Measures and TRM; EB-2016-0246. p.7.
2019 lllinois TRM Version 7.0, Residential Measures. 5.3.7
Gas High Efficiency Furnace p.103.
2019 lllinois TRM Version 7.0, Residential Measures. 5.3.7
Gas High Efficiency Furnace p.103.
Wisconsin Focus on Energy 2018 TRM, BoilerTune-Up, p.
77.
lowa Statewide Technical Reference Manual Version 2.0;
2.4.8 Energy Recovery Ventilator, p.156.
Mid-Atlantic Technical Reference Manual Version 8.0, May
2018. Smart Thermostat, p.133.
Home Energy Services lmpact Evaluation (Res 34), August
2018. Navigant and cadeo for % heating saving, and ratio
of cooling saving relative to with a wifi thermostat.
Combo Boiler (Heating/HE) post
2021 standard
Heat Recovery Ventilator
ENERGY STAR
Thermostat Programmable
HVAC
HVAC Duct Sealing
HVAC Fireplace < 40 kBtu/h
HVAC Fireplace >= 40 kBtu/h
HVAC Furnace
HVAC Furnace
HVAC Furnace Tune Up
HVAC
HVAC
c-3
Measure
Type
Measure Description Source
HVAC Thermostat Wi-Fi
Other Pool Heater
Mid-Atlantic Technical Reference Manual Version 8.0, May
2018. Smart Thermostat, p.133.
https ://www.e n ergy.gov/energysaver/gas-swi m m i ng-poo l-
heaters
EMERGING TECHNOTOGIES
The table below summarizes the key assumptions used for the three (3) emerging technology measures
considered in the residential sector: gas heat pump water heaters, through-the-wall condensing furnaces/air-
conditioners (ACs), and naturalgas heat pumps.
c-4
MEASURE KEY ASSUMPTIONS
Gas Heat
Pump Water
Heaters
Through-the-
Wall
Condensing
Furnaces/ACs
Measure definition: Gas-fired heat pump water heater designed for residential applications with
a 1.30 Uniform Energy Factor (UEF).
Baseline definition: 0.62 UEF, minimum efficiency gas storage water heater
Key assumptions: For the multifamily market segment, there are a few important considerations.
Firstly, it is critical that there be sufficient space around the GHPWH to allow for the needed
quantities and movement of air to ensure rated performance of the heat pump. ln multifamily
circumstances where there were dedicated utility closets used for water heaters, an assurance of
adequate amounts of space would be necessary. Secondly, these units are currently designed to
be taller than conventional storage tank water heaters. Before a ROB replacement was installed,
sufficient vertical clearance will need to be confirmed. lf the multifamily site were to use a
centralized system, it would likely be more of a hybrid-type arrangement, similar to the integrated
GHPWH and A/C technology modeled in the commercial segment.
Assumes 84 gallons of hot water use per day
Assumes an effective useful life of 10 years
Measure definition: Through-the-wall (TTW) condensing system with code minimum 9.0 EER
cooling system (minimum code schedule to increase to 11.0 EER on September 23, 2019) and a
high-efficiency gas furnace with an AFUE of 90% or greater.
Baseline definition: TTW unit with a cooling system that meets the current minimum 9.0 EER
efficienry rating and a heating unit with an AFUE of 80% or less.
Key assumptions: Through-the-wall condensing furnace/AC packages have been designed for
cold-climate multifamily applications, and most multifamily residences do not have large
individual cooling loads, meaning high efficiency cooling has not been a priority. Therefore, high
efficiency cooling was not modeled in this measure. As such, the baseline and upgrade AC EER
would be the same and the electric savings is assumed to be zero.
Assumes L,576.2 effective full load hours (EFLH) for multifamily applications.
Assumes 40,000 Btu/hr capacity
Assumes effective useful life of 15.5 years.
c-5
Table 17. Residential Emerging Technologies lncluded in Potential Study Model
MEASURE KEY ASSUMPTIONS
Natural Gas
Heat Pumps
Measure definition: Residential gas-fired, absorption heat pump system with a UEF of 1.30.
Key assumptions: Measure performance is based on a prototype system currently in development
and expected for commercialization in 3-5 years. GTI's Source Energy and Emissions Analysis Tool
(SEEAT) was used to model performance of the measure and baseline systems in a residential
detached, 2-story home with 3 occupants located in Boise, ldaho.
COMMERCIAL MEASURES
AVAITABLE MEASURES
Table 18: Commercial Measure Sources
Measure Type Measure Description Source
Behavioral
Behavioral
Envelope
Envelope
Building Operator Certification
O&M Only
Bui lding Operator Certifi cation
O&M plus Capital Upgrades
Attic/Roof lnsulation Flat Roof
Building Shell Air Sealing
MA TRM, October 2015. p.368 of 436
MA TRM, October 2015. p.368 of 435.
NB Power TRM - September 2017.* Used for kWh heating savings, adapted
for GJ Savings
lowa TRM - July L2,2OL7.3.7.I.
Infiltration Control
c-6
Baseline definition: Standard efficiency, 80% AFUE natural gas-fired furnace and a minimum
efficiency, 0.62 EF natural gas storage water heater.
Measure Type Measure Description Source
Envelope
Envelope
Hot Water
Hot Water
Hot Water
Hot Water
Hot Water
Hot Water
Green Roof
Wall lnsulation
Hot Water Pipe lnsulation
lndirect Water Heater
Low Flow Faucet Aerator
Low Flow Shower Head
Pre-Rinse Spray Valve
Energy intensity from the Commercial
Buildings Energy Consumption Survey
(cBECS):
htt ps : //www.e ia.sov/co nsu m ptio n/co m
mercia l/data/2012li ndex. ph p?view=cons
umption,
https ://www.eia.gov/consu motion/com
mer cial I data I 2Ot2 / c&e lxls / eT .xlsx;
Green roof planning study:
http ://www. mass.sov/eea/docs/doe r/er
ee n-com m u n ities/l i bra rvlgree n-roof-
boston-st2009.pdf:
Assumes incremental costs of
S13.50/sq.ft. (mid way between the
range of 12S - 15$ per sq.ft.);
Green roof calculator:
https://sustaina bilitv.asu.edu/urba n-
cl i mate/gree n-roof-ca lcu lator/ ;
Discussion of leaf area index:
http ://e ne rsv-m ode ls.com/foru m /leaf-
a rea-i ndex-va I ues-roof-vegetation.
NB Power TRM - September 2OL7.
* Used for kWh heating savings, adapted
for GJ Savings
Measure based on2O2O upcoming
regulation for condensing water heaters
(https ://www. n rca n. gc.calene rgv/regu lat
ions-codes-sta ndards/19835) and U nion
Gas /Enbridge Gas Distribution - Updated
DSM Measures and TRM; EB-2016-0246,
p.94.
MATRM, GL9C2aO24.
MA TRM, October 30, 2015. p. 353 of
435.
NY TRM, Version 5, July L7,2017
Faucet - Low Flow Aerator, p. 198.
lA TRM - September 2017. p.50 of 376.
NY TRM, Version 5, July 17,2Ot7.
Low-Flow Pre-Rinse Spray Valve , p.206
Standards:
https ://www.enersv.eov/sites/prod/fi les
I 2015 / t2 /f27 / CPSY%ZOFinal%21Rule.odf
c-7
Condensing Water Heater 2020
Measure Type Measure Description Source
Hot Water Recirculation Pump with Demand
Controls
lowa TRM - Volume 3 Non-residential
Measures, JulT L2, 2017, FINAL, 3.2.4
Controls for Domestic Hot Water, p.60.
Union Gas /Enbridge Gas Distribution -
Updated DSM Measures and TRM; EB-
2OL6-O246.p.54.
MA TRM - 2016-2OLg Program Years;
October 2015, HVAC - Programmable
Thermostats, p. 250 and Mid-Atlantic
TRM v8.0, Final, May 2018, Smart
Thermostat, p.452.
IITRM v5.0, vol.2, Feb. 11, 2016. Section
4.4.33. p.300 of 493.
Measure from Union Gas /Enbridge Gas
Distribution - Updated DSM Measures
and TRM; EB-2016-O246. p.10.
Algorithm from Efficiency Maine C/l &
Multifamily TRM, Version 2018.3,
Effective Jan. 1, 2018, p.57.
Measure from Union Gas /enbridge Gas
Distribution - Updated DSM Measures
and TRM; EB-2OL6-O245. p.10.
Algorithm from Efficiency Maine C/l
&M ultifamily TRM, Version 2OL8.3,
Effective Jan. 1, 2OL8, p. 67.
Measure and algorithm from Efficiency
Maine C/l & Multifamily TRM, Version
2078.3, Effective Jan. L, 20L8, p.67.
Measure from Union Gas /f nbridge Gas
Distribution - Updated DSM Measures
and TRM; EB-2016-O245. p.10.
Algorithm from Efficiency Maine C/l
&Multifamily TRM, Version 2018.3,
Effective Jan. 1, 2018, p. 67.
Derived from lA potentialstudy -
calculated from DOE tip sheet #10,
7l2006.
MATRM
lowa TRM, vol.3, July 12,20L7. p. 149 of
376.
MA TRM, October 30, 2015. p.113 of 435
Hot Water Tankless Water Heater
HVAC Advanced Thermostat (Wi-Fi
Thermostat)
HVAC Air Curtains
HVAC Boiler < 300 kBtu/h _ Tier I
HVAC Boiler >= 300 kBtu/h
HVAC Boiler < 300 kBtu/h- Tier 2
HVAC Boiler >= 300 kBtu/h_Post2024
HVAC Boiler Blowdown Heat Recovery
HVAC
HVAC
Boiler Reset Control
Boiler Shut Off Damper, Space
Heating
Combo Condensing Boiler/Water
Heater 90% AFUE
Combo Condensing Boiler/Water
Heater 95% AFUE
HVAC
HVAC MA TRM, October 30, 2075, p.113 of 435.
c-8
Measure Type Measure Description Source
HVAC
HVAC
HVAC
HVAC
HVAC
HVAC
HVAC
HVAC
HVAC
HVAC Destratification Fan - High
Efficiency
HVAC Energy Management System
(EMs)
HVAC Energy Recovery Ventilator (ERV)
HVAC
Condensing Make Up Air Unit with
2 Speed Motor
Condensing Unit Heater
Demand Control Ventilation (DCV)
Furnace Shut Off Damper, Space
Heating
lnfrared Heater
Kitchen Demand Control
Ventilation
Program ma ble Thermostat
Steam Boiler Stack Economizer
Steam Trap HVAC
Ventilation Hoods
OEB TRM v3,2OL8/L2/03 p. 101
MA TRM, October 2015. p. 337.
lL TRM - v.5.0 Vol. 2 - February 9th 2OL7,
4.4.79 Dema nd Controlled Ventilation
(p.226) (algorithm, cost, EUL).
NB Power DSM Plan 2Ol9-2O21.
Appendix AC - TRM, September 2017
(EDR).
Commercial Destratification Fans, HVLS
OEB TRM (not sure if it is published yet);
lL TRM v7.
lowa PS measure characterization by
Micheals Energy using data from the
Michigan Energy Measures Database
(MEMD).
EULfrom MATRM.
OEB TRM v3,2OL8|L2/03, Commercial -
I ncremental energy recovery ventilation
(ERV) (no ERV baseline) - New
construction/retrofit, p. 168 of 320.
lowa TRM, vol.3, July L2,2017. p. 149 of
376.
REVISED - Gazifdre lnc, PGEE 2019-2020
lL TRM - v.6.0 Vol. 2 - February 8th 20L7,
4.4.12 lnfrared Heaters (all sizes), Low
lntensity (p.182).
lL TRM v5.0, Februa ry gth 2oL7, 4.2.76
Kitchen Demand Ventilation Controls, p.
72.
MA TRM - 201,6-2018 Program Years;
October 20L5, HVAC - Programmable
Thermostats, p. 250.
lL TRM v5.0. Feb.8, 2OL7 . p. 263 of 508.
Wisconsin Focus on Energy 2018 TRM.
Online LBNL calculator:httprl/@
EUL:
https ://www.mountsi na i.on.caled ucatio
n/staff-
professiona ls/m icrobioloev/micro bioloev
-la boratorv-ma n ua l/q ua I itv-
ma nua l/equipment/equipment-life-
expecta ncv-qeq m i02004
c-9
Measure Type Measure Description Source
HVAC
Kitchen
Kitchen
Kitchen
Kitchen
Kitchen
Kitchen
Kitchen
Kitchen
Laundry
Laundry
New Construction
Water Boiler Stack Economizer
Dishwasher
Fryer
Griddle
lnfrared Broiler
Oven Combination
Oven Convection - ENERGY STAR
Oven Convection - High Efficiency
Steamer High Efficiency
ENERGY STAR Clothes Dryer
ENERGY STAR Clothes Washer
LEED Certified
ILTRM v5.0. Feb.8,2077.p.263 of 508.
IOWA TRM - July 12,2017.
MA TRM 2015. p. 327 of 436.
MN TRM 2017, p.430 of 543.
lA TRM, July L2, 2Ot7.p. 27 4 of 37 6.
Ml measure database - Rack Oven Single.
MN TRM 2017, p. 424 of 643.
Mi Measure Database - Rack Oven Single.
MA TRM, October 2015. p. 328.
New York Standard Approach for
Estimating Energy Savings from Energy
Efficiency Programs - Residential, Multi-
Family and Commercial/lndustrial
Measures, Version 6. 2019. p. L7L.
lA TRM, July 12,2077. p.237 of 376.
Various / Professional Judgement (NBP
Measure).
* Used for kWh heating savings, adapted
for GJ Savings.
c-10
Measure Type Measure Description Source
Other Biodigester Marcus Lauer et all. July 2018. Making
money from waste: The economic
viability of producing biogas and
biomethane in the ldaho dairy industry
Kearney TE, Larkin MJ, Levett PN. The
effect of slurry storage and anaerobic
digestion on survival of pathogenic
bacteria. J Appl Bacteriol 1-993;74(1):86-
93.
Klavon KH, Lansing SA, Mulbry W, Moss
AR, Felton G. Economic analysis of small-
scale agricultural digesters in the United
States. Biomass Bioenergy 2OL3; 54: 36-
45.
ICF lnternational. Greenhouse gas
mitigation options and costs for
agricultural land and animal production
within the United States; 2013.
Option for sustainable heat use of biogas
pla nts. http ://www. biosasheat.org/wp-
content/uploads/20L3/06/4 WIP Option
s Sust Heat Use-.pdf.
IDENTIFYING BARRIERS AND POTENTIAL
SOLUTIONS TO FACILITATE ANAEROBIC
DIGESTER PROJECTS IN IDAHO:
ROUNDTABLE REPORT. April 2012,
https://www. resea rchgate.net/pu blicatio
n/256064493 ldentifying Barriers and
Potential Solutions to Facilitate Anaero
Other Drain Water Heat Recovery
(DWHR)Medium
bic Digester Proiects in ldaho.
https ://a rticles.exte nsio n.org/pa ges/194
61/eco nom ics-of-a naerobic-d igesters-
for-processing-ani mal-ma nu re .
lA TRM, September 2L,2OL7.
3.2.5 Drainwater Heat Recovery, p. 65.
c-11
Measure Type Measure Description Source
Other
Other
Other
Process
Process
Process
Duct lnsulation and Sealing
Pool Cover
Pool Heater
Process Boiler - Steam
Process Boiler - Water
Process Boiler Tune Up
Efficient Windows
lA TRM, September 2'J.,2017
3.3.15 Duct lnsulation.
Temperature Data for ISLIP LONG ISL
MACARTHUR AP:
http://rredc.nrel.eov/sola r/old data/nsr
db/1991.-
2005/tmy3/bv state and citv.html
MA Measure Characterization.
lowa Energy Efficiency Statewide
Technical Reference Manual. Volume 3:
Non-residential Measures, July L2, 2077.
Effective Jan 1, 2018. p.52.
DTE Energy, NG Efficiency Potential
Study, July 29,2OL6.
https ://energv.gov/e nergvsave r/gas-
swimmilg ppqlheaters
https://www.michiga n.gov/documents/
mpsc/DTE 2016 NG ee potential studv
w appendices vFINAL 554360 7.pdf
Capacity range of commercial pool
heaters https://www. ravpa k.com/pool-
a nd-spa/commercial-pool-heaters/
http://www.engi neeri ngtool box.com/swi
mm ins-pool-heatins-d 878.html
2017 Michigan energy measures
database (excelfile).
2017 Michigan energy measures
database (excelfile).
2017 Michigan energy measures
database (excel file).
lA TRM 20t7,3.7.5 Efficient Windows,
p.331 of 375.
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Windows
EMERGING TECHNOTOGIES
The table below summarizes the key assumptions used for the L0 emerging technology measures considered in
the commercial sector.
Table 19. Commercial Measures lncluded in PotentialStudy Model
High-Efficiency Unit
Heaters
Measure definition: Condensing, gas-fired unit heater with a thermal efficiency of 0.93.
Baseline definition: Non-condensing, gas-fired unit heater with a standard thermal efficiency
of 0.80.
Key assumptions: There is a wide range in runtimes for unit heaters - with those conditioning
interior zones typically having shorter runtimes (250 annual hours of runtime or less) than
those conditioning building perimeter zones (2,500 annual hours of runtime or more).
This measure is ideally suited for factories, warehouses, service shops, and some limited "big
box" retail stores, etc. These buildings typically have high ceilings and open floor plans. Wider
temperature variations are tolerated than in office or school spaces; frequently there is no air
conditioning, and sometimes there is only enough heating service to prevent freezing.
Assumes effective useful life of 12 years.
Modulating Dryer
Retrofits
Measure definition: Gas-fired clothes dryer fitted with a post-OEM modulating retrofit.
Baseline definition: Non-modulating, gas-fired clothes dryer.
Key assumptions: Targets dryers with capacities of 30-250 pounds. Primary markets that could
benefit from this technology are commercial on-premise laundry (OPL) such as laundromats,
hospitality, healthcare facilities (nursing homes, hospitals, etc.), prisons, and commercial
laundry services. This technology could be used in multifamily buildings as well.
Assumes effective useful life of 14 years.
Combination Ovens Measure definition: Combination oven/steamer unit operatingat35% efficiency in oven mode
and 20% efficiency in steam mode.
Baseline definition: Standard 30% efficient, gas-fired, full-size convection oven
Key assumptions: Assumes a combination oven/steamer with a 20-pan capacity for this model.
Assumes 12 hours of use per day with 50% of the time in steam mode operation. Assumes 250
pounds of food cooked per day.
MEASURE KEY ASSUMPTIONS
Assumes effective useful life of 12 years.
c-13
MEASURE [rev nssurvrPTroNs
Low-OilVolume
Fryers
Condensing RTUs
Measure definition: Standard-sized open deep-fat, gas-fired low oil volume fryer (30 pounds
of oil) operating at 56% efficiency.
Baseline definition: Standard open deep-fat, gas-fired fryer (50 pounds of oil) used in
commercial foodservice establishments operating at 40% efficiency.
Key assumptions: lt is important to note that the cost savings of reduced oil usage aren't
captured in the energy savings analysis but are expected to be significant. This cost reduction
can be even greater if the restaurant is seeking to use (and promote their use of) trans-fat free
oils, which are usually more expensive but have become increasingly popular in the past 5
years. The oil savings are based on the average oil savings amounts at six restaurant sites that
were monitored as part of a confidential GTI project.
Assumes fryer is operating 14 hours/day and 150 pounds of food is cooked daily. Assumes the
owning establishment operates 360 days/year.
Assumes effective useful life of 12 years.
Measure definition: Condensing, warm air furnace with a natural gas thermal efficiency (TE)
rating of 90% or higher, or alternatively, the unitary package must have equipment nameplate
information for natural gas that identifies a heating output and heating input rating that has
an output over input ration of 0.90 or higher. The furnace must be vented and condensate
disposed of in accordance with the equipment manufacturer installation instructions and
applicable codes.
Baseline definition: Non-condensing, warm air furnace with a natural gas TE rating of 80% or
alternatively, the unitary package will have equipment nameplate information for natural gas
that identifies a heating output and heating input rating that has an output over input ratio of
0.80.
Key assumptions: Condensing RTU system may also require a neutralization system for
condensate drainage. The requirement for this is often left up to the local jurisdiction. The
annual cost of such a system tends to be small (-S6S/yr) and was not included in the costs used
in this analysis.
Assumes effective useful life of 15 years.
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MEASURE KEY ASSUMPTIONS
Natural Gas AC and
Heat Pumps
Natural Gas Engine
Heat Pump Water
Heaters
Measure definition: lntegrated gas heat pump water heater with space cooling operating with
L4O% AFUE.
Baseline definition: A standard 80% AFUE/thermal efficiency commercial gas-fired water
heating system as well as 14 SEER space cooling system.
Key assumptions: The analysis focuses on the application of this technology to the commercial
foodservice market, where there are sizable hot water loads as well as the need for some
limited, concurrent space cooling. This is an excellent first market for this technology.
Measure is based on a prototype unit still under active development and is subject to change
based on future refinements in design and features.
Assumes 2,500 gallons of hot water used per day. Assumes water heating operates 14.7 hours
per day for 360 days per year. Assumes space cooling operates 10 hours per day for 151.5 days
per year. Assumptions of measure performance are based on laboratory and field research to
date.
Assumes effective useful life of 14 years.
Measure definition: Natural gas, engine-driven, air-source heat pump water heater that
captures and repurposes waste heat with a L.2 to 1.8 coefficient of performance (COP), with
an overall COP of 1.34.
Baseline definition: Conventional gas-fired boiler with an efficiency of 0.671 DHW/.731 HHW
Key assumptions: This analysis assumes application in one of the primary target markets with
sufficient high hot water usage to ensure economic attractiveness, such as pool facilities,
gymnasiums, inpatient healthcare, etc. The assumptions are based on the technology as
designed in the Tecogen llios gas engine heat pump water heater.
It is very important to note that energy savings for this technology are highly customized to the
specific application. Hot water usage profiles, storage capacity, ambient temperatures, and a
number of other factors affect performance and savings. For this analysis, a simple hybrid
installation arrangement is assumed, with a conventional gas boiler providing back-up at times
of especially high usage. lt assumes limited need for adjustment or customization and that the
companion boiler is already on-site and in working condition, i.e., no new equipment or
installation costs associated with the conventional boiler. The heat pump is sized with a
capacity closer to base load than peak load, which maximizes its use and efficiency. lt assumes
the technology is used for a multi-story residential-type living facility with approximately
180,000 square feet located in ASHRAE climate zone 4C.
Assumes effective useful life of 20,000 hours per engine.
c-L5
MEASURE f rrv nssuwrPTroNs
,
Efficient Cookware Measure definition: Fin-bottomed stock pot.
Baseline definition: Standard 12-inch diameter aluminum stock pot with a 24-quart capacity
Key assumptions: Assumes the improved heat transfer to the measure unit provides 50%
cooking-energy efficiency as compared tothe275% cooking-energy efficiency for the standard
pot. Performance based on laboratory testing completed at the Pacific Gas & Electric
Foodservice Technology Center in 2008.
Assumes effective useful life of 3 years.
Destratification Fans Measure definition: This measure is for the installation of large diameter High Volume Low
Speed (HVLS) destratification fans in warehouse-type spaces with high ceilings (retrofit only).
Baseline definition: The HVLS fans are installed in buildings affected by destratification and
where no other mechanisms that combat destratification are present.
Key assumptions: The HVLS fans are designed to destratify the whole space. lt is assumed that
the building is heated by natural gas forced air space heating system including unit heaters
operating without night setbacks.
Assumes a useful life of 15 years.
Assumes 4,880 heating hours per year on a 55'F basis, a heat transfer coefficient of 0.05
Btu/'F.h.ft2 for the roof and 0.062 Btu/"F.h.ft2 for the walls. Assumes a heating system
efficiency of 8O%.
Biodigesters Measure definition: This measure consists of installing a biodigester in ldaho dairy farms with
more than 3,000 cows.
Baseline definition: Dairy farm without a biodigester on site
Key assumptions: Assumes installation of a biodigester (on-site use) combined with a CHP
system to convert the biogas. The measure does not account for operation and maintenance
costs and for any additional benefits for the farm such as selling of supplemental heat or
electricity.
Assumes a useful life of 15 years.
Assumes the following efficiencies: 74%for the digester, 48%for heat conversion and 37%for
electricity conversion.
Assumes the production of 2m3 of biogas per cow per day and 8,000 operating hours per farm
c-16
APPENDIX D. CTIMATE ZONE MAP
The IGC Potential Model will takes into considerations the two climate zones where IGC's customers are located.
Specifically, the customer database was segmented into the respective climate zones, based on the following
climate zone map.
35 : IGC Service and Climate Ma
For weather-dependent measures (heating system upgrades, insulations, etc.), each measure is distinctly
included in the model to capture different saving levels for participants in each climate zone. Several of our
measure characterizations are algorithm based, and explicitly take into considerations the heating degree days
(HDD)to calculate savings; these measures will use the relevant HDDs for each zone, as presented in Table 20
below. Cooling Degree Days were also used for measures with secondary cooling impacts.
Intermountain Gas Company
Seruice Area by Climate Zone
Legend
-
IGC Service LaErals
- NW Pipeline
Idaho Munisr Town/City Sewed by IGC
T-l Clirnate Zone 5
I Oirnate zone 6
D-1
FrrHffif,oqP
|
.ltalra
5,561 1,4L6Boise (Zone 5)
ldaho Falls (Zone 6)7,737 799
Zone HDDs CDDs
Table 20: Average HDD and CDD per Climate Zone (2011-20t7l
For other climate dependent measures not explicitly using HDDs as part of their algorithm to calculate savings,
state-wide averages were corrected based on the ratio of HDD in the target zone to the statewide average.
D-2
APPENDIX E. DSM PROGRAM CHARACTERIZATION DETAILS AND
MODEL INPUTS
Table E-1: IGC Program Model lnputs Low Scenario
Efficient New Home
Existing Homes lncentives
Home Energy Report
Commercial Equipment Program
Commercial Retrofit
Table E-2: IGC Program Model lnputs Base Scenario
90,000
1,000,000
235,077
247,387
325,000
0.42
0.73
0.01
0.26
0.46
35o/o
35%
700%
3s%
35%
L
1
L
L
1
Percent
lncentive
CE
Threshold
Variable CostsFixed Costs
Variable Costs
(S/therm)
Percent
!ncentive
CE
Thresholdlnitiative Name
Efficient New Home
Existing Homes lncentives
Home Energy Report
Commercial Equipment Program
Commercial Retrofit
Table E-3: lGC Program Model Inputs Max Scenario
90,000
1,000,000
235,0tL
247,381
325,000
0.42
o.73
0.01
0.26
0.46
so%
s0%
100%
50%
so%
L
L
1
1
7
Variable Costs
($/therm)
Percent
lncentive
CE
ThresholdFixed Costslnitiative Name
Efficient New Home
Existing Homes lncentives
Home Energy Report
Commercial Equipment Program
Commercial Retrofit
99,000
1,100,000
258,5t2
272,LLg
370,000
0.46
0.80
0.012
0.29
0.51
6s%
6s%
TOOo/o
6s%
65%
1
1
1
1
L
E-1
!nitiative Name
Fixed Costs
APPENDIX F. UCT RESULTS BY MEASURE
E
IGC_CPA_App F.xlsx
F-1
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