Connecticut Electric Residential,
Commercial, and Industrial
Energy Efficiency Potential Study
Final Report
Prepared for the Connecticut Energy Conservation Management Board (ECMB)
April 2010 (revised)
Experience you can trust.
Copyright © 2010, KEMA, Inc.
The information contained in this document is the exclusive, confidential and proprietary property of
KEMA, Inc. and is protected under the trade secret and copyright laws of the U.S. and other
international laws, treaties and conventions. No part of this work may be disclosed to any third party or
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herein are proprietary to KEMA, Inc.
Experience you can trust.
Table of Contents
1.
2.
3.
4.
5.
Executive Summary ..........................................................................................................1-1
1.1 Scope and Approach ...............................................................................................1-1
1.2 Limitations of the Study............................................................................................1-3
1.3 Results.....................................................................................................................1-4
1.3.1 Aggregate Results ........................................................................................1-4
1.3.2 Results by Sector .........................................................................................1-9
Introduction .......................................................................................................................2-1
2.1 Overview..................................................................................................................2-1
2.2 Study Approach .......................................................................................................2-1
2.3 Layout of the Report ................................................................................................2-2
Methods and Scenarios ....................................................................................................3-1
3.1 Characterizing the Electric Energy-Efficiency Resource ...........................................3-1
3.1.1 Defining Electric Energy-Efficiency Potential ................................................3-1
3.2 Summary of Analytical Steps Used in this Study ......................................................3-3
3.2.1 DSM ASSYST Analytical Steps ....................................................................3-4
3.2.2 Total Economic, Total Achievable and Instantaneous Program
Achievable Analytical Steps ..............................................................................3-6
Baseline Data and Results ................................................................................................4-1
4.1 Overview..................................................................................................................4-1
4.2 Residential ...............................................................................................................4-2
4.3 Commercial .............................................................................................................4-4
DSM Potential Results ......................................................................................................5-8
5.1 Technical and Economic Potential ...........................................................................5-8
5.1.2 Energy-Efficiency Supply Curves ...............................................................5-13
5.1.3 Key Measures ............................................................................................5-18
5.2 Total Economic, Achievable and Instantaneous Program Achievable Potential .....5-21
5.3 Program Funding Scenarios ..................................................................................5-23
5.4 Comparison of Potential Results ............................................................................5-41
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Table of Contents
List of Exhibits:
Table 1-0 Summary of Results………………………………………………………………………..1-4
Table 1-1 Summary of Energy Savings Potentials and Program Funding Scenarios –
Instantaneous and in 2018 ............................................................................................... 1-6
Table 1-2 Comparison Between 2009 Program Plans and Program Funding Scenario Results 19
Table 5-1 UI Residential Existing Top Twenty Measures by Economic Potential (GWh) ....... 5-19
Table 5-2 CL&P Residential Existing Top Twenty Measures by Economic Potential (GWh).. 5-19
Table 5-3 Commercial Existing Top Twenty Measures by Economic Potential (GWh) .......... 5-20
Table 5-4 Industrial Top Twenty Measures by Economic Potential (GWh) ........................... 5-21
Table 5-5 Values for Adjustment Factors Applied to Economic Potential.............................. 5-22
Table 5-6 Comparison between 2009 Program Plan and Model Derived Scenarios ............. 5-26
Table 5-7 Summary of Program Funding Scenario Potential Results—2009–2018 ............... 5-30
Table 5-8 Base Funding Scenario- Energy Savings .............................................................. 5-30
Table 5-9 Base Funding Scenario- Demand Savings ............................................................ 5-31
Table 5-10 Current Funding Scenario- Energy Savings ........................................................ 5-31
Table 5-11 Current Funding Scenario- Demand Savings ...................................................... 5-32
Table 5-12 Expanded Funding Scenario- Energy Savings ................................................... 5-32
Table 5-13 Expanded Funding Scenario- Demand Savings .................................................. 5-33
Table 5-14 Energy Savings Potential – Annual GWh ............................................................ 5-43
Table 5-15 Peak Demand Savings – MW.............................................................................. 5-45
Figure 1-1 Cumulative Energy Savings Potentials and Program Funding Scenario Savings in
2018 – GWh per year ....................................................................................................... 1-5
Figure 1-2 Achievable Energy Savings: All Sectors ................................................................ 1-7
Figure 1-3 Benefits and Costs of Energy-Efficiency Savings – 2009-2018* ............................ 1-8
Figure 1-4 Net Achievable Energy Savings by 2018 by Sector—GWh per Year................... 1-10
Figure 1-5a United Illuminating Electric Energy Savings Potential by End Use (2018) — Current
Funding Scenario ........................................................................................................... 1-11
Figure 3-1 Conceptual Framework for Estimating Resources.................................................. 3-2
Figure 3-2 Conceptual Relationship among Energy-Efficiency Potential Definitions ................ 3-3
Figure 3-3 Conceptual Overview of Study Process ................................................................. 3-4
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Table of Contents
Figure 4-1 Sector Base Electricity Usage Breakdown – Connecticut ...................................... 4-2
Figure 5-1 Technical and Economic Potential by Sector (2018)–Cumulative GWh per year . 5-10
Figure 5-2 Cumulative Technical and Economic Potential by Sector (2018)–Percentage of
Current Funding Scenario Energy Use ........................................................................... 5-10
Figure 5-3 UI Residential Economic Electric Savings Potential by End Use (2018) ............... 5-11
Figure 5-4 CL&P Residential Economic Electric Savings Potential by End Use (2018) ......... 5-12
Figure 5-5 Commercial Economic Electric Savings Potential by End Use (2018) .................. 5-12
Figure 5-6 Industrial Economic Electric Savings Potential by End Use (2018) ...................... 5-13
Figure 5-7 United Illuminating Residential Energy Supply Curve ........................................... 5-14
Figure 5-8 Connecticut Light and Power Residential Energy Supply Curve ........................... 5-15
Figure 5-9 United Illuminating Residential Capacity Supply Curve ........................................ 5-15
Figure 5-10 Connecticut Light and Power Residential Capacity Supply Curve ...................... 5-16
Figure 5-11 Commercial Energy Supply Curve ..................................................................... 5-16
Figure 5-12 Commercial Capacity Supply Curve ................................................................... 5-17
Figure 5-13 Industrial Energy Supply Curve .......................................................................... 5-17
Figure 5-14 Industrial Capacity Supply Curve ....................................................................... 5-18
Figure 5-15 Comparison of Instantaneous Savings Potentials .............................................. 5-23
Figure 5-16 Program Funding Scenario Energy Savings in GWh: All Sectors ...................... 5-27
Figure 5-17 Benefits and Costs of Energy-Efficiency Savings – 2009-2018* ......................... 5-28
Figure 5-18 United Illuminating Current Funding Achievable Potential by End Use ............... 5-34
Figure 5-19 Connecticut Light and Power Current Funding Achievable Potential by End Use .. 534
Figure 5-20 United Illuminating Base Funding Achievable Potential by End Use................... 5-35
Figure 5-21 Connecticut Light and Power Base Funding Achievable Potential by End Use . 5-35
Figure 5-22 United Illuminating Expanded Funding Achievable Potential by End Use ........... 5-36
Figure 5-23 Connecticut Light and Power Expanded Funding Achievable Potential by End Use536
Figure 5-24 Commercial Net Energy Savings Potential - End Use Shares (2018) – Current
Funding Scenario ........................................................................................................... 5-38
Figure 5-25 Commercial Net Energy Savings Potential - End Use Shares (2018) – Base
Funding Scenario ........................................................................................................... 5-38
Figure 5-26 Commercial Net Energy Savings Potential - End Use Shares (2018) – Expanded
Funding Scenario ........................................................................................................... 5-39
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Table of Contents
Figure 5-27 Industrial Net Energy Savings Potential - End Use Shares (2018) – Current Funding
Scenario ........................................................................................................................ 5-40
Figure 5-28 Industrial Net Energy Savings Potential – End Use Shares (2018) –Base Funding
Scenario ........................................................................................................................ 5-40
Figure 5-29 Industrial Net Energy Savings Potential – End Use Shares (2018) – Expanded
Funding Scenario ........................................................................................................... 5-41
Figure 5-30 Instantaneous Savings Potential—Annual GWh................................................. 5-42
Figure 5-31 ........................................................................................................................... 5-44
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1.
Executive Summary
This study assesses the electric energy-efficiency potential for the residential, commercial, and
industrial sectors in Connecticut, served by Connecticut Light and Power and United
Illuminating. The major objective of this study was to identify and characterize the remaining
cost-effective electric energy-efficiency potential in Connecticut and to estimate the amount of
savings achievable through energy efficiency programs.
1.1
Scope and Approach
In the study, five levels of energy-efficiency potential and three levels of savings under program
funding scenarios are estimated:

Technical Potential, defined as the complete penetration of all measures analyzed in
applications where they were deemed technically feasible;

Initial Economic Potential, defined as the technical potential of those energy-efficiency
measures that are cost-effective when compared to supply-side alternatives, given
current technologies and costs;

Total Economic Potential is an estimate of the technical potential of energy-efficiency
measures that are expected to be cost-effective taking into account emerging
technologies and reductions in measure costs that occur as new technologies become
more common and mainstream;

Total Achievable Potential, which is an estimate of total achievable energy efficiency
savings from all sources, including from programs, building energy codes, equipment
standards, and outside-of-program savings;

Program Achievable Potential, which is an estimate of how much energy efficiency
programs can save, not including the simultaneous effects of building codes, equipment
standards, and outside-of-program savings.

Three levels of Program Funding Scenario Savings, the amount of savings that would
occur in response to specific program funding and measure incentive levels, based on
the results of the KEMA model. Program interventions include end user awareness and
education activities and various types of funding to reduce the cost of energy efficiency
measures in order to encourage investment in these efficient equipment and practices.
We estimate program scenario savings for:

A Current Program Funding Scenario that approximates the 2009 Program
Plan budget in its first year;
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
A Base Case Program Funding Scenario which approximates the 2009
Program Plan budget in the above scenario, minus any expected RGGI
funding, in its first year; and

an Expanded Program Funding Scenario based on expanded or
accelerated funding, which approximates and comes as close as possible,
subject to the limitations of stock turnover and the absence of emerging
technologies, to the instantaneous program achievable potential.
In addition, we calculate the Naturally Occurring Potential, which refers to the amount of
savings estimated to occur as a result of normal market forces. That is, in the absence of any
utility or governmental intervention. Achievable potentials and program scenario savings are
presented net of naturally occurring potential, which we refer to as Net Savings. We explicitly
present naturally occurring potential only when presenting gross savings potential.
The study estimates both energy savings and peak demand savings for each of these potential
scenarios. Peak Demand Savings is defined as the maximum hourly amount of electricity
delivered to customers within the system peak hours.
The scope of this study includes new and existing residential and commercial buildings and
existing industrial buildings. The focus of the study was on the ten-year period, 2009–2018.
Given the near to mid-term focus, the study was restricted to energy-efficiency measures that
are presently commercially available.
The method used for estimating potential is a “bottom-up” approach in which energy efficiency
costs and savings are assessed at the customer segment and energy-efficiency measure level.
Cost effectiveness is based on the Total Resource Cost Test (TRC test), a benefit-cost test
that compares the value of avoided energy costs to the costs of energy-efficiency measures.
For cost-effective measures, program savings potential is estimated as a function of measure
economics, incentive levels, and program marketing and education efforts. The modeling
approach was implemented using KEMA’s DSM ASSYSTTM model. This model allows for
efficient integration of large quantities of measure, building, and economic data in the
determination of energy efficiency potential.
In order to conduct the energy efficiency potential study many different types of data are
required, including: measure data (such as costs, savings, and current saturation levels),
building/market data (such as building stocks and end use saturation and consumption levels),
and economic data (such as avoided costs, inflation rates, and discount rates). These data
were developed from a number of different secondary sources, including electric usage and
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avoided cost data provided by the two Connecticut electric utilities and a 2007 Synapse study,
program data from Connecticut, the U.S. DOE Commercial Building Energy Consumption
Survey (CBECS), the Connecticut Department of Economic Development and Department of
Labor and various technology-specific internet sources.
1.2
Limitations of the Study
This report is not a program development or program implementation plan. It does not contain a
marketing plan, training and outreach plan, evaluation plan, staffing estimates, or detailed
program budgets. The focus of the program scenarios is not on next year (2010), but on the full
ten-year forecast period. The results represent a good initial estimate of the savings that can be
achieved at different budget levels, but the report does not lay out how to achieve those
savings. The program funding scenarios are neither a prediction of nor a recommendation for
future program budgets. However, the scenarios can serve as a starting point for a detailed
analysis by program planners in the process of developing a program implementation plan.
While the DSM ASSYST model simulates program interventions to derive the estimated
potential savings and program funding scenarios, it is still a model. That is, it is a simplified
mathematical representation of the real world, based on the best available data. While it is
designed to provide a general, aggregate forecast of potential savings, and to highlight
measures with high savings potentials for possible inclusion in a program, it should not be taken
to be an infallible prediction of the conditions that implementers will encounter under actual
program conditions. Among other factors:




Measure costs may be higher or lower than modeled due to regional cost variations or
unidentified hidden costs
Market barriers may be higher or lower than modeled
Implementers may target their marketing dollars differently than the model does (e.g. by
targeting specific types of contractors)
Evaluations may find that the actual savings for promising measures are more or less
than estimated (e.g. savings could be lower due to takeback). 1
1
One example of takeback is if demand-controlled ventilation (DCV) were installed in an existing building
where the existing ventilation system did not meet code for air changes per hour (ACH). With DCV, the
ventilation rate would be increased to meet code requirements, thereby reducing savings and possibly
even increasing total energy use (while improving air quality). An example of higher potential DCV
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1.3
Results
Table 1.0 summarizes the results of the study by showing the electric energy savings potential
over a ten year period (2009-2018) and the savings potential as a percent of the base energy
use. The study found that technical and total economic potential is 36% of base energy use,
total achievable potential (the achievable potential from all energy efficiency policies) is 31% of
base energy use, and program achievable potential is 23% of base energy use.
Table 1-0 Summary of Results: Potential Energy Savings Over a Ten Year Period
Technical Potential
(Technically feasible)
Total Economic
(Cost effective)
Total Achievable
(Achievable from all policies)
Program Achievable
(Achievable from programs)
1.3.1
Electric (GWh)
10,714
% of Base Energy Use
36%
10,722
36%
9,114
31%
6,616
23%
Aggregate Results
Technical potential is estimated at 10,714 GWh. Ninety percent of this potential, 9,748 GWh, is
estimated to be economically viable. Total achievable potential is estimated to be 9,114 GWh
and program achievable potential is estimated to be 6,616 GWh. Cumulative net savings for the
current program funding scenario are 3,333 GWh, representing the estimated results of program
activity for the entire 2009-2018 period. Under the base funding program scenario, cumulative
net savings in 2018 are 2,946 GWh and in the expanded program funding scenario, cumulative
net savings in 2018 reach 5,910 GWh.
Figure 1-1 compares the estimates of efficiency potential by sector created for this report,
including technical, initial and total economic, total achievable, instantaneous program
savings is if more DCV applications in the field become cost-effective due to higher gas prices, lower
measure costs, or higher performance.
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achievable, and three alternate program funding scenarios.2 Comparisons are made based on
the mix of existing and new construction in the tenth year of the program. Table 1-1 shows both
the unweighted (2009 instantaneous) and the weighted 2018 savings. The program funding
scenario results are included with the weighted 2018 results.
Figure 1-1 Cumulative Energy Savings Potentials and Program Funding Scenario
Savings in 2018 – GWh per year
30,000
3,965
25,000
GWh
20,000
12,845
Industrial
15,000
Commercial
1,184
1,302
4,901
5,391
4,294
3,663
4,029
3,425
Technical
Initial
Economic
Total
Economic
Total
Achievable
1,296
Residential
1,107
10,000
5,124
5,000
12,587
910
Base
908
4,582
3,485
629
698
2,221
1,440
878
1,657
978
Program
Achievable
Net Base
Funding
Achievable
Net Current
Funding
Achievable
3,684
1,318
Net
Accelerated
Funding
Achievable
Note: The expanded (accelerated) funding scenario and current funding scenario program savings are savings
potential in 2018 due to program activity from 2009-2018. Other potentials are instantaneous potentials. Technical
saving refers to the complete penetration of all measures analyzed in applications where they were deemed
technically feasible from an engineering perspective. Initial economic savings includes savings for all measures found
to be cost effective in the application analyzed. Total economic savings includes initial economic savings plus 10
percent, to account for emerging technologies and measure cost reductions not captured in the model. Total
achievable savings reduces total economic savings by 15 percent to account for savings that are not achievable.
Program achievable savings excludes savings due to building codes (35% in new construction), lighting standards
(half of CFL savings in commercial and residential) and outside-of-program savings. The expanded funding scenario
(accelerated funding) sets incentives to 100 percent of incremental measure costs with a first-year budget of $193
million. The first year of the current funding scenario approximates the Connecticut 2009 Program Plan as explained
in section 5.3. The base funding scenario uses the budget given in the 2009 Program Plan, but reduces it by 18%,
thus removing RGGI funding.
2
Because the achievable and program potentials are shown net of naturally occurring potential, naturally
occurring potential is not explicitly included in the chart.
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Table 1-1 Summary of Energy Savings Potentials and Program Funding Scenarios – Instantaneous and in 2018
GWH
Initial
Total
Total
Base
Technical Economic Economic Achievable
Sector
Energy Use Savings
Savings
Savings
Savings
Residential Existing
12,398
4,263
3,642
4,007
Residential New
189
30
21
23
Subtotal
12,587
4,294
3,663
4,029
3,425
Savings % of Base
34%
29%
32%
27%
10 Year Average Program Budget
Residential Existing- UI
Residential New - UI
Program
Achievable
Savings
2,221
18%
2,217
34
2,251
898
4
902
40%
770
3
773
34%
847
3
850
38%
Residential Existing - CL&P
Residential New - CL&P
Subtotal
Savings % of Base
10 Year Average Program Budget
10,181
155
10,336
3,365
27
3,391
33%
2,872
18
2,890
28%
3,159
20
3,179
31%
Commercial Existing
Commercial New
12,652
193
12,845
5,038
86
5,124
40%
4,815
86
4,901
38%
5,296
95
5,391
42%
4,582
36%
3,485
27%
3,965
1,296
33%
1,184
30%
1,302
33%
1,107
28%
910
23%
29,397
10,714
36%
9,748
33%
10,722
36%
9,114
31%
6,616
23%
Subtotal
Savings % of Base
10 Year Average Program Budget
Subtotal
Savings % of Base
10 Year Average Program Budget
Industrial
Savings % of Base
10 Year Average Program Budget
Total
Savings % of Base
10 Year Average Program Budget
723
32%
2,702
26%
470
21%
1,751
17%
Program Achievable Savings per KEMA Model
Net Base
Net Current
Net Accelerated
Funding
Funding
Funding
Achievable
Achievable
Achievable
Savings
Savings
Savings
836
937
1,274
41
42
45
878
978
1,318
7%
8%
10%
$41,341,688
$49,030,459
$72,087,077
131
5
137
6%
$5,841,322
151
5
156
7%
$6,841,059
277
6
283
13%
$16,226,935
705
36
741
7%
$35,500,366
786
36
822
8%
$42,189,400
996
39
1,035
10%
$55,860,142
1,018
422
1,440
11%
$30,512,795
629
16%
$16,230,450
2,946
10%
$88,084,933
1,188
469
1,657
13%
$37,626,308
698
18%
$19,084,256
3,333
11%
$105,741,022
3,208
476
3,684
29%
$94,276,326
908
23%
$39,185,843
5,910
20%
$205,549,247
a
Technical saving refers to the complete penetration of all measures analyzed in applications where they were deemed technically feasible from an engineering perspective.
Initial economic savings includes savings for all measures found to be cost effective in the application analyzed.
c
Total economic savings includes initial economic savings plus 10 percent, to account for emerging technologies and measure cost reductions not captured in the model.
d
Total achievable savings reduces total economic savings by 15 percent to account for savings that are not achievable.
e
Program achievable savings excludes savings due to building codes (35% in new construction), lighting standards (half of CFL savings in commercial and residential) and
outside-of-program savings.
f
Columns may not total due to rounding.
b
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Figure 1-2 Achievable Energy Savings: All Sectors
7,000
6,000
IRP (Accelerated) Funding
Current Funding
GWh
5,000
Base Funding
4,000
3,000
2,000
1,000
0
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Figure 1-2 shows our estimates of savings for the program funding scenarios over time. This
figure shows the incremental savings from each of the three program scenarios that were
developed for the study. As shown, savings potential tends to increase at a decreasing rate,
over time. In the early years, programs can target the most cost-effective and easy-to-achieve
measures and markets. Over time, the supply of these opportunities is expected to decline (in
the absence of significant new technologies), and the programs must penetrate harder-to-reach
markets and influence end users to adopt less attractive measures.
Figure 1-3 depicts costs and benefits for the three program funding scenarios for the 2009-2018
period. For the current program funding scenario, the present value of program costs (including
administration, marketing, and incentives) is $981 million. The present value of total avoidedcost benefits is $5,722 million. The present value of net avoided-cost benefits, i.e., the
difference between total avoided-cost benefits and total costs (which include participant costs in
addition to program costs), is $3,515 million. For the base funding scenario, the present value of
program costs is $802 million. The present value of total avoided-cost benefits is $4,716 million,
and the present value of net-avoided costs is $2,939 million. For the expanded (accelerated)
funding scenario, the present value of program costs is $1,939 million, the present value of total
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avoided cost benefits is $9,935 million, and the present value of net avoided-cost benefits is
$6,185million (over $6 billion in benefits, meaning benefits that exceed costs).
The current funding scenario has a TRC benefit/cost ratio of 2.6, the base funding scenario has
a TRC benefit/cost ratio of 2.7, and the expanded (accelerated) funding scenario has a TRC
benefit/cost ratio of 2.6 all of which are cost effective under the test used in this study to
determine program cost effectiveness. Program cost-effectiveness declines somewhat with
increasing program effort, reflecting penetration of more measures with lower cost-effectiveness
levels. This result reflects the assumption that the most cost-effective measures are targeted
first, both by the programs and by end users who are seeking to lower their electric utility bills in
the most cost-effective manner.
Key results of our energy efficiency program funding scenario forecasts are summarized in
Table 1-2. The table compares 2009 budget and savings from the current funding scenario with
the 2009 Integrated Resource Plan (the scenario is designed to approximate program savings in
its first year). Average annual budget and cumulative energy and demand savings for the full 10
year analysis period are presented for all scenarios and are compared against the
corresponding plans.
Figure 1-3 Benefits and Costs of Energy-Efficiency Savings – 2009-2018*
$12,000
$9,935
Present Value in $ Millions
$10,000
$8,000
$6,185
$5,722
$6,000
$4,716
$3,515
$3,750
$2,939
$4,000
Program Admin&
Marketing Costs
Program Incentive
Costs
Non-Incentive
Participant Costs
Total Benefits
$2,207
$1,777
$2,000
$0
Low
Funding
Current
Funding
Accelerated
Funding
* Present value of benefits and costs over normalized 20-year measure lives; nominal discount rate is 7.09
percent, inflation rate is 2.3 percent.
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Table 1-2 Comparison of 2009 Program Plans and Program Funding Scenario Results
UI Residential
CL&P Residential
Commercial
Industrial
C&I Total
Total
1.3.2
Model
Plan
% Difference
Model
Plan
% Difference
Model
Model
Model
Plan
% Difference
Model
Plan
% Difference
Base Funding Case
Current Funding Case
Accelerated Funding Case
Budget
Net kWh
Net kW
Budget
Net kWh
Net kW Budget
Net kWh
Net kW
$5,841,322
136,677,602
22,640
$6,841,059
156,026,155 26,228 $16,226,935
282,860,937
52,627
$5,980,418
128,225,108
20,132
$6,886,421
154,934,766 24,181 $16,704,120
288,619,073
48,697
-2.33%
6.59%
12.45%
-0.66%
0.70% 8.47%
-2.86%
-2.00%
8.07%
$35,500,366
740,853,188 126,230 $42,189,400
822,310,506 134,622 $55,860,142 1,035,281,478 174,143
$35,127,280
692,777,755 116,987 $42,683,107
842,044,984 142,167 $57,935,411 1,012,424,416 173,854
1.06%
6.94%
7.90%
-1.16%
-2.34% -5.31%
-3.58%
2.26%
0.17%
$30,512,795 1,439,722,531 271,728 $37,626,308 1,657,014,423 312,552 $94,276,326 3,683,926,186 705,787
$16,230,450
628,648,670 110,273 $19,084,256
697,526,536 123,536 $39,185,843
908,140,202 162,286
$46,743,245 2,068,371,201 382,002 $56,710,564 2,354,540,959 436,088 $133,462,170 4,592,066,388 868,073
$46,472,561 1,981,287,015 325,024 $56,357,574 2,399,566,464 393,917 $126,472,499 4,471,919,833 733,861
0.58%
4.40%
17.53%
0.63%
-1.88% 10.71%
5.53%
2.69%
18.29%
$88,084,933 2,945,901,990 530,872 $105,741,022 3,332,877,620 596,938 $205,549,247 5,910,208,803 1,094,843
$87,580,260 2,802,289,878 462,143 $105,927,102 3,396,546,214 560,265 201,112,030 5,772,963,322 956,413
0.58%
5.12%
14.87%
-0.18%
-1.87% 6.55%
2.21%
2.38%
14.47%
Results by Sector
Cumulative net achievable potential estimates sectors for the period 2009–2018 are presented
in Figure 1-4 for the three sectors, residential, commercial, and industrial. This figure shows
results for each funding scenario. Under the program assumptions developed for this study,
achievable energy savings are highest for the commercial sector under all scenarios, reflecting
the fact that the commercial sector consumes the larger share of electricity use and also has the
largest economic potential (4,901 GWh for commercial versus 3,663 GWh for residential and
1,184 GWh for industrial by 2018).
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Figure 1-4 Net Achievable Energy Savings by 2018 by Sector—GWh per Year
Cumulative Savings by 2018- GWh
4,000
3,500
3,000
Net Base
Funding
Achievable
2,500
2,000
Net Current
Funding
Achievable
1,500
Net Accelerated
Funding
Achievable
1,000
500
Residential
Commercial
Industrial
Figure 1-5a and I-5b show the end-use distribution for residential energy savings potential by
2018 under the current funding scenario. For both United Illuminating and Connecticut Light
and Power, lighting contributes the most to energy savings potential, even after removing all
savings beyond 2013 to account for changes from the federal lighting standards. In United
Illuminating’s service territory, space heating contributes the next amount of savings, and in
Connecticut Light and Power, appliances and other contribute the second most amount to the
energy savings potential.
Figure 1-6 shows the end-use distribution of commercial energy savings potential by 2018
under the current funding scenario. Lighting again contributes the most to the energy savings
potential, resulting from measures such as fluorescent lamps and fixtures, and high pressure
sodium lamps. Space cooling improvements have the next largest potential, reflecting savings
from measures like tune up and advanced diagnostics for cooling systems, and cooling system
upgrades.
Figure 1-7 shows the end-use distribution of industrial energy savings potential by 2018 under
the current funding scenario. Pumps have the highest contribution towards energy savings.
Savings in pumps result from operation and maintenance, and system optimization in the
pumping systems, as well as compressed air, which has the second largest savings opportunity.
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Figure 1-5a
United Illuminating Electric Energy Savings Potential by End Use (2018) — Current
Funding Scenario
Ten Year Cumulative UI Residential Current Funding
Achievable Potential by End Use (GWh)
Appliances &
Other
17%
Lighting
33%
Water Heating
19%
Cooling
11%
Heating
20%
Figure 1-5b
Connecticut Light and Power Electric Energy Savings Potential by End Use (2018) —
Current Funding Scenario
Ten Year Cumulative CL&P Residential Current Funding
Achievable Potential by End Use (GWh)
Appliances &
Other
19%
Lighting
38%
Water Heating
16%
Heating
17%
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Cooling
10%
April 29, 2010
Figure 1-6
Commercial Electric Energy Savings Potential by End Use (2018)—Current Funding
Scenario
Ten Year Cumulative Commercial Current Funding Achievable
Potential (GWh)
Other
13%
Refrigeration
12%
Heating
0%
Lighting
58%
Cooling
17%
Figure 1-7
Industrial Electric Energy Savings Potential by End Use (2018)—Current Funding
Scenario
Ten Year Cumulative Industrial Current Funding
Achievable Potential by End Use (GWh)
Other
0%
Compressed
Air
17%
Fans
14%
Lighting
12%
Pumping
19%
Cooling
9%
Other Process
2%
Drives
14%
Refrigeration
9%
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2.
Introduction
2.1
Overview
For this project, we used KEMA DSM Assyst Model to develop technical potential, initial
economic potential, and program funding scenario savings estimates for energy efficiency
measures and programs in Connecticut. We also calculate a total economic, a total achievable
and a total program achievable outside the model using the same initial data on technical
potential and economic potential. These terms are defined in detail in the executive summary,
section 1.1.
Key questions addressed in the study included:

How much cost-effective electric efficiency resource is available in Connecticut?

What levels of program savings are available?
Data for the study come from a number of different sources, including electric usage and
avoided cost data provided by the two Connecticut electric utilities (Avoided Energy Supply
Costs in New England: 2007 Final Report, prepared by Synapse Energy Economics, Inc.),
program data for Connecticut, the 2005 Connecticut Residential Appliance Saturation Survey,
the U.S. DOE Residential Energy Consumption Survey (RECS), Commercial Building Energy
Consumption Survey (CBECS), the California Commercial End Use Survey (CEUS), the
California Database for Energy Efficiency Resources (DEER), the Connecticut Department of
Labor, the Economic Census, and various technology-specific internet sources such as the
DOE’s Energy Star website.
2.2
Study Approach
This study involved identification and development of baseline end-use and measure data and
development of estimates of future energy-efficiency impacts under varying levels of program
effort. The baseline characterization allowed KEMA to identify the types and approximate sizes
of the various market segments that are the most likely sources of energy-efficiency potential in
Connecticut. These characteristics then served as inputs to a modeling process that
incorporated energy cost parameters and specific energy-efficiency measure characteristics
(such as costs, savings, and existing penetration estimates) to provide more detailed potential
estimates.
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To aid in the analysis, KEMA utilized the KEMA DSM ASSYST model. This model provides a
thorough, clear, and transparent documentation database, as well as an extremely efficient data
processing system for estimating technical, economic, and achievable potential. We estimated
technical potential, economic potential, achievable potential, program achievable potential and
program funding scenario savings for the commercial and industrial sectors, with a focus on
energy-efficiency impacts over the next 10 years.
2.3
Layout of the Report
The remainder of this report is organized as follows:

Section 3 discusses the methodology and concepts used to develop the technical and
economic potential estimates.

Section 4 provides baseline data and results developed for the study.

Section 5 discusses the results of the DSM potential analysis by sector and over time.
In addition, the report contains the following appendices:

Appendix A: Economic Inputs. Provides avoided cost, discount rate, and inflation rate
assumptions used for the study.

Appendix B: Top Twenty Measures. Given for each modelled potential, for each sector.

Appendix C: Economic Potential Output. Shows all measures with their measure TRC
and economic GWh and MW savings for each sector.

Appendix D: Supply Curves for energy and demand for each sector.

Appendix E: Program Funding Scenario Results. Provides the forecasts for the three
program funding scenarios.
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3.
Methods and Scenarios
This section provides a brief overview of the concepts and methods used to conduct this study.
3.1
Characterizing the Electric Energy-Efficiency Resource
Energy-efficiency has been characterized for some time now as an alternative to energy supply
options, such as conventional power plants that produce electricity from fossil or nuclear fuels.
In the early 1980s, researchers developed and popularized the use of a conservation supply
curve paradigm to characterize the potential costs and benefits of energy conservation and
efficiency. Under this framework, technologies or practices that reduced energy use through
efficiency were characterized as “liberating ‘supply’ for other energy demands” and therefore
could be thought of as a resource and plotted on an energy supply curve. The energy-efficiency
resource paradigm argued simply that the more energy-efficiency or “nega-watts” produced, the
fewer new plants would be needed to meet end users’ power demands.
3.1.1
Defining Electric Energy-Efficiency Potential
Like any resource, there are a number of ways in which the energy-efficiency resource can be
estimated and characterized. Definitions of energy-efficiency potential are similar to definitions
of potential developed for finite fossil fuel resources, like coal, oil, and natural gas. For example,
fossil fuel resources are typically characterized along two primary dimensions: the degree of
geological certainty with which resources may be found and the likelihood that extraction of the
resource will be economic. This relationship is shown conceptually in Figure 3-1.
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Decreasing Certainty of Existence
Figure 3-1
Conceptual Framework for Estimating Resources
Possible
and
Economically Feasible
Possible
but not
Economically Feasible
Known
and
Economically Feasible
Known
but not
Economically Feasible
Decreasing Economic Feasibility
Somewhat analogously, this energy-efficiency potential study defines several different types of
energy-efficiency potential, namely, technical, initial and total economic, total achievable,
program achievable, program funding scenarios, and naturally occurring, as defined in Section
1.1. These potentials are shown conceptually in Figure 3-2. The chart splits the potentials into
two groupings. The first includes technical potential, initial economic potential, the base funding
scenario, the current program funding scenario, the expanded program funding scenario, and
naturally occurring potential. For this study, these potentials were estimated using the DSM
ASSYST model and are based on currently available technologies and current measure costs.
The second grouping includes total economic potential, total achievable potential, and
instantaneous program achievable potential. Total economic potential is greater than initial
economic potential: it includes the effects of emerging technologies and future cost reductions
for current energy efficiency measures. Total achievable potential and instantaneous program
achievable potential are calculated from total economic potential and therefore also include
these effects.
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Figure 3-2
Conceptual Relationship among Energy-Efficiency Potential Definitions
Technical
Initial economic
Expanded program funding case
Current program funding case
Naturally occurring
Growth due to
emerging
technologies and
measure cost
reductions
Total economic
Total achievable
Instantaneous program achievable
3.2
Summary of Analytical Steps Used in this Study
Two parallel sets of analyses were used to generate the results in this report. The technical
potential, initial economic potential, and program funding scenario savings were estimated using
KEMA’s DSM ASSYST model, which was developed for conducting energy-efficiency potential
studies. The total economic potential, total achievable potential and instantaneous program
achievable potential were estimated outside the ASSYST model using an approach that allowed
us to take into account explicitly the impacts of projected building codes and standards, new
technologies and changes in measure cost. Figure 3-3 illustrates the combined steps in the
study and how the two approaches relate to one another.
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The DSM ASSYST approach and outside-of-model approach are described below.
Figure 3-3 Conceptual Overview of Study Process
Economic Data
Measure Data
Building Data
New Technologies
Technical Potential
Total Economic
Potential
Initial Economic
Potential
Building Codes
Program Data
Costs and Savings
Measure Cost
Reduction
Not Achievable
Standards
Total Achievable
Potential
Outside of
Program
Modeled
Program
Scenarios
3.2.1
Program
Achievable
DSM ASSYST Analytical Steps
The crux of the DSM ASSYST approach involves carrying out a number of basic analytical
steps to produce estimates of the energy-efficiency potentials introduced above. The bulk of the
analytical process for this was carried out within the DSM ASSYST model. The model used,
DSM ASSYST, is a Microsoft Excel®-based model that integrates technology-specific
engineering and customer behavior data with utility market saturation data, load shapes, rate
projections, and marginal costs into an easily updated data management system.
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The key steps implemented in this approach were:
Step 1: Develop Initial Input Data

Develop a list of energy-efficiency measure opportunities to include in scope. In this
step, an initial draft measure list was developed and circulated among utilities and
stakeholders. The final measure list was developed after incorporating comments.

Gather and develop technical data (costs and savings) on efficient measure
opportunities. Data on measures was gathered from a variety of sources

Gather, analyze, and develop information on building characteristics, including total
square footage or total number of households, natural gas consumption and intensity by
end use, market shares of key electric consuming equipment, and market shares of
energy-efficiency technologies and practices. Section 3 of this report describes the
baseline data developed for this study.

Collect data on economic parameters: avoided costs, electricity rates, discount rates,
and inflation rate. These inputs are provided in Appendix A of this report.
Step 2: Estimate Technical Potential and Develop Supply Curves

Match and integrate data on efficient measures to data on existing building
characteristics to produce estimates of technical potential and energy-efficiency supply
curves.
Step 3: Estimate Economic Potential

Match and integrate measure and building data with economic assumptions to produce
indicators of costs from different viewpoints (e.g., societal and consumer).

Estimate total economic potential.
Step 4: Estimate Program and Naturally Occurring Potentials

Screen initial measures for inclusion in the program analysis. This screening may take
into account factors such as cost effectiveness, potential market size, non-energy
benefits, market barriers, and potentially adverse effects associated with a measure. For
this study measures were screened using the total resource cost (TRC) test, while
considering only electricity avoided-cost benefits.

Gather and develop estimates of program costs (e.g., for administration and marketing)
and historic program savings.
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
Develop estimates of customer adoption of energy-efficiency measures as a function of
the economic attractiveness of the measures, barriers to their adoption, and the effects
of program intervention.

Estimate achievable program and naturally occurring potentials.
3.2.2
Total Economic, Total Achievable and Instantaneous Program
Achievable Analytical Steps
For this study we modified our typical approach to attempt to reflect more carefully the changes
in the market that are not captured in the Demand Side Assyst model as typically implemented.
We wanted an approach that allowed us to take into account explicitly:

new technologies;

reductions in measure cost;

the impacts of projected building codes;

the impact of standards.
To this end, this approach created new categories of potential, including

Total economic potential, which is an estimate of the potential of cost-effective energy
efficiency measures, taking into account the effects of new technologies on technical
potential and reductions to measure costs on cost effectiveness;

Achievable potential, which is an estimate of maximum energy efficiency savings from all
sources; and

Instantaneous program achievable potential, which is an estimate of how much energy
efficiency programs can save, taking into account the simultaneous effects of building
codes, standards, and outside-of-program savings.
These three potentials are calculated from the initial economic potential output, which is an
output of the DSM ASSYST model.
The key steps implemented in this approach were:
Step 1: Develop economic savings adjustment factors

Estimate a factor to estimate economic potential that will develop due to the
development of emerging technologies or cost reductions in technologies that are not
currently cost effective.
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
Estimate the not achievable portion of the economic potential; that is, what portion will
not adopt energy efficiency measures, regardless of program intervention

Estimate the effect of changes to building codes on energy efficiency in new construction

Estimate the impacts of federal minimum efficiency standards for equipment.

Estimate additional outside of program effects, including non-free-rider naturally
occurring savings, as well as the effects of any interventions not captured by the building
codes and standards (such as a federal tax credit for high efficiency equipment).
Step 2: Estimate Total Economic Potential

The economic potential growth factor is applied to the initial economic potential
produced by the DSM ASSYST model to estimate total economic potential, taking into
account innovation and measure cost reductions.
Total Economic Potential = Initial Economic Potential x (1 + Economic Potential Growth Factor)
Step 3: Estimate Achievable Potential

The not achievable portion of total economic savings is calculated and netted out to yield
achievable potential. Achievable savings includes savings from all sources, including
naturally occurring, building codes, standards, and efficiency programs.
Total Achievable Potential = Total Economic Potential x (1 - Not Achievable Factor)
Step 3: Instantaneous Program Achievable Potential

The effects of building codes, standards, and other outside-of-program effects are netted
out to yield the instantaneous achievable program potential. We refer to this potential as
instantaneous because it is not associated with a specific program time frame, and
therefore not limited by the turnover of replace-on-burnout measures. The program
funding scenario savings we developed, in contrast, are based on a 10-year program
and limited by the natural turnover of long-lived measures, such as residential central air
conditioners (18 years).
Instantaneous Program Achievable Potential =
Total Achievable Potential x (1 - Outside of Program Factor - Standards Factor)
- Total Achievable PotentialNC x (1 - Building Codes Factor)
where Total Achievable PotentialNC is the total achievable potential for new construction.
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4.
Baseline Data and Results
4.1
Overview
Estimating the potential for energy-efficiency improvements requires a comparison of the energy
impacts of standard-efficiency technologies with those of alternative high-efficiency equipment.
This, in turn, dictates a relatively detailed understanding of the energy characteristics of the
marketplace. Baseline data that are ideally required include:

Total count of energy-consuming units (residential homes, numbers of establishments
and floor space of commercial buildings, and industrial base energy consumption)

Annual energy consumption for each end use studied (both in terms of total consumption
in GWh and normalized for intensity on a per-unit basis, e.g., GWh/ft2)

The saturation of electric end uses (for example, the fraction of residential homes with
electric water heating)

The market share of each base equipment type (for example, the fraction of total
commercial floor space served by electric traditional water heaters)

Market share for each energy-efficiency measure in scope (for example, the fraction of
total commercial floor space already served by a high efficiency water heater).
Data for the baseline analysis comes from a number of sources, including U.S. Department of
Energy studies, a utility-provided breakdown of electric sales by industry, and other secondary
sources. Baseline data sources vary by sector and are described further below.
Figure 4-1 shows the overall breakdown of electricity usage by sector for Connecticut. The
Commercial sector accounts for the largest share of energy usage, followed by the residential
and industrial sectors. This report addresses energy efficiency for all three of those sectors.
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Figure 4-1
Sector Base Electricity Usage Breakdown – Connecticut
Base GWh Usage by Sector
Industrial
Residential
Commercial
Source: 2008 historical usage data
4.2
Residential
In the residential analysis, primary sources of data for Connecticut came from the Connecticut
Residential Appliance Saturation Survey, the Connecticut Program Savings Document, billed
consumption data for the two Connecticut electric utilities, and RECS.
The Connecticut Residential Appliance Saturation Survey was used to develop end use
saturations for various appliances. Data from the survey allowed KEMA to calculate end use
and measure saturations that were Connecticut specific.
End-use energy intensities were somewhat more problematic. KEMA used equations and
assumptions from the Connecticut Program Savings Document as well as data from various
internet sources like the Department of Energy’s Energy Star website. Data from past KEMA
potential studies was also utilized and calibrated it to fit weather or usage conditions (mostly for,
pools, space heating and cooling) typical in Connecticut. Using these various sources and
review from the Connecticut utilities and the ECMB, we calculated end-use energy intensities to
reflect Connecticut’s current base load.
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Figure 4-2a and b show residential energy consumption by end use for each utility. The largest
end use is appliances and other, followed by lighting. The “other” category includes items such
as pool pumps, televisions, and computers, and other miscellaneous consumer electronics.
Figure 4-2a
UI Residential Electric Usage by End Use
UI Residential Electric: Base Usage by End Use (GWh)
Lighting
18%
Appliances &
Other
44%
Cooling
16%
Water Heating
11%
Heating
11%
Figure 4-2b
CL&P Residential Electric Usage by End Use
CL&P Residential Electric: Base Usage by End Use (GWh)
Lighting
18%
Appliances &
Other
44%
Cooling
16%
Water Heating
11%
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Heating
11%
April 29, 2010
4.3
Commercial
The primary sources of commercial data for Connecticut were the U.S. DOE Commercial
Building Energy Consumption Survey (CBECS), Connecticut Department of Labor data on the
number of establishments, and billed consumption data from UI and CL&P.
CBECS data were used to develop end-use saturations. In order to get regionally applicable
saturations (the fraction of commercial floorspace that has a particular end use), KEMA looked
at buildings in the Northeast census region.
We developed Energy Use by Building type from the number of Connecticut Department of
Labor number of establishments by type, average use per square foot and average square
footage from CBECS. We did not have utility data by building type.
Figure 4-3 shows commercial energy consumption by building type. The building types with the
largest estimated usage are offices, restaurants, health care, schools, and other (which includes
facilities such as laundries, health clubs, churches, and auditoriums).
Figure 4-4 shows commercial energy consumption by end use. The largest end use is lighting,
followed by cooling. The “other” category includes items such as computers, vending machines,
and water heating.
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Figure 4-3
Commercial Electric Usage by Building Type
Commercial End Use (GWh) by Building Type
8%
2%
Office
9%
Restaurant
33%
Grocery
Retail
7%
Warehouse
Education
10%
Health Care
5%
Lodging
8%
Other
18%
Figure 4-4
Commercial Electric Usage by End Use
Commercial Electric: Base Usage by End Use (GWh)
Other
18%
Refrigeration
10%
Lighting
46%
Heating
0%
Cooling
26%
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CBECS provided data on saturations of equipment.
Industrial
For the industrial analysis, we relied on the Department of Energy’s Manufacturing Energy
Consumption Survey (MECS) and the Department of Labor Data on number of establishments.
The MCES survey provided, at the U.S. level, energy consumption by industry classification and
end use. These data were used to develop initial industrial end use saturations, which were
modified to reflect Connecticut’s usage.
We developed Energy Use by Building type from the number of Connecticut Department of
Labor number of establishments by type, average use per square foot and average square
footage from MECS. We did not have utility data by building type. Figure 4-5 presents the
baseline industrial usage as calculated by Building type. Figure 4-6 presents the usage by end
use as calculated.
Figure 4-5 Industrial Energy Use by Building Type
Industrial Energy Use (GWh) by Building Type
Food
5%
6%
2%
Textiles-Apparel
3%
Lumber-Furniture
9%
23%
Paper
Chemicals
Petroleum
7%
7%
Rubber-Plastics
0%
Non-Metallic Minerals
6%
Primary Metals
Fabricated Metals
3%
6%
7%
Ind. Machinery
Electronics
16%
Transportation Eqp.
Miscellaneous Manufacturing
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Figure 4-6 Industrial Energy Use by End Use
Industrial Electric: Base Usage by End Use (GWh)
Lighting
10%
Other
6%
Cooling
13%
Compressed
Air
10%
Fans
7%
Pumping
12%
Other Process
4%
Refrigeration
6%
Drives
20%
Heating
12%
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5.
DSM Potential Results
In this section, we present estimates of electricity energy-efficiency potential. First, we detail
technical and economic potential results for all measures considered in the study. Second, we
present the results of the out-of-model calculations for total economic potential (including the
effects of innovation and measure cost reductions), total achievable potential (in and out of
program) and instantaneous program potential (independent of stock turnover). Third, we
present estimates of program savings under the current funding scenario and the expanded
funding scenario. Finally, we present a summary and comparison of the various potentials
developed in this study. All results are associated with an avoided electricity price that starts are
$0.1497 per kWh in 2009.
5.1
Technical and Economic Potential
Technical potential represents the sum of all savings from all the measures deemed applicable
and technically feasible. Economic potential is based on efficiency measures that are costeffective as defined by the TRC test—a benefit-cost test that compares the value of avoided
energy costs to the costs of energy-efficiency measures.
Technical potential for Connecticut through the year 2018 is estimated to be 10,714 GWh. This
amount is approximately 36% of base energy use for all sectors. About 91% of this technical
potential, 9,748 GWh, is estimated to be cost effective. This economic potential is about 33% of
the base residential, commercial, and industrial energy use.
Figure 5-1 shows estimates of technical and economic electric savings potential by sector in
2018 due to program activities from 2009-2018. Figure 5-2 shows the same potentials as a
percentage of 2008 electric use. The commercial sector provides the largest contribution to
both technical and economic potential for energy savings, accounting for 5,124 GWh of
technical and 4,901 GWh of economic potential. The residential sector provides about 40% of
the technical potential at 4,294 GWh and 38% of economic potential at 3,663 GWh. The
industrial sector provides about 12% of the technical potential at 1,296 GWh, and 12% of the
economic potential at 1,184 GWh.
In the residential sector, technical peak demand savings are 1,035 MW, but economic savings
are 808 MW. Technical peak demand savings for the commercial sector are slightly lower than
residential sector, at 1,011 MW, while economic peak potential is slightly higher, at 940 MW. For
the industrial sector, technical peak demand savings are 230 MW, and economic peak potential
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is only slightly less at 212 MW. Overall between all three sectors the technical demand savings
are 2,257 MW and economic savings are estimated at 1,960 MW.
It is important to note that the savings potential for the industrial sector may be underestimated.
While we analyzed residential and commercial new construction, we did not analyze industrial
new construction due to slow turnover of floorspace in the industrial sector. The industrial
savings and share of savings in 2018 would be slightly higher if new construction were included.
KEMA did however add generic measures to some of the larger end uses; compressed air,
fans, and pumps, in order to better address some emerging technologies and overall generic
improvements that could be done at a specific site.
The commercial sector also shows the highest potentials relative to base energy use. Technical
potential is estimated to be about 40% of base commercial electric use, and economic potential
is estimated to be about 38% of base commercial electric use. For the industrial sector,
technical potential is estimated to be about 33% of base industrial electric use with economic
potential only slightly lower at 30%. Residential technical savings are 34% of the 2008 base
usage, and economic potential is 29%.
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Figure 5-1
Technical and Economic Potential by Sector (2018)–Cumulative GWh per year
12,000
10,714
9,748
10,000
8,000
GWh
Residential
6,000
4,294
Commercial
4,901
5,124
Industrial
3,663
4,000
Total
1,184
1,296
2,000
Technical
Initial Economic
Figure 5-2
Cumulative Technical and Economic Potential by Sector (2018)–Percentage of Current
Funding Scenario Energy Use
Technical and Initial Economic Potential as Percent of Base Usage (GWh)
45%
38%
40%
40%
36%
35%
33%
34%
33%
29%
30%
30%
Residential
25%
Commercial
Industrial
20%
Total
15%
10%
5%
0%
Technical as % of base
5.1.1.1
Economic as % of Base
Potentials by End-Use
Figure 5-3 and 5-4 show the end-use breakdown for economic potential in the residential sector.
Most of the energy savings potential in the residential sector came from lighting, followed by
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appliances such as refrigerators and clothes washers. Figure 5-5 shows the end-use break
down for the commercial sector where the majority of the potential comes from lighting as well
as other measures such as improved office equipment and behaviors. In the industrial sector,
process pumps savings dominate, followed by compressed air process savings. Other motor
driven processes such as process drives and fans also have a large amount of potential savings
in the industrial sector. This end use breakdown for industrial can be seen in Figure 5-6.
Figure 5-3
UI Residential Economic Electric Savings Potential by End Use (2018)
UI Residential Electric: Economic Potential by End Use (GWh)
Appliances &
Other
23%
Lighting
37%
Water Heating
15%
Heating
9%
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Figure 5-4
CL&P Residential Economic Electric Savings Potential by End Use (2018)
CL&P Residential Electric: Economic Potential by End Use
(GWh)
Appliances &
Other
23%
Lighting
36%
Water Heating
15%
Heating
9%
Cooling
17%
Figure 5-5
Commercial Economic Electric Savings Potential by End Use (2018)
Commercial Electric: Economic Potential by End Use (GWh)
Other
23%
Lighting
48%
Refrigeration
13%
Heating
0%
Cooling
16%
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Figure 5-6
Industrial Economic Electric Savings Potential by End Use (2018)
Industrial Electric: Economic Potential by End Use
(GWh)
Lighting
13%
Other
0%
Cooling
7%
Compressed
Air
18%
Other Process
1%
Refrigeration
8%
Fans
15%
Heating
4%
Pumping
19%
Drives
15%
5.1.2
Energy-Efficiency Supply Curves
A common way to illustrate the amount of energy savings per dollar spent is to construct an
energy-efficiency supply curve. A supply curve typically is depicted on two axes—one captures
the cost per unit of saved energy (e.g. levelized $/kWh saved) and the other shows energy
savings as a percent of the base use at each level of cost. Measures are sorted on a least-cost
basis, and total savings are calculated incrementally with respect to measures that precede
them. The costs of the measures are levelized over the life of the savings achieved.
Figures 5-7 through 5-12 present the energy and capacity supply curves for each sector in
Connecticut. The curves represent savings as a percentage of total sector electricity
consumption or peak demand. The residential supply curves show that energy savings of about
30 percent are available at or below $1 per kwh and that the maximum savings of about 33
percent are available at or below $20 per kwh. For commercial, the curve shows that about 37
percent savings are available for less than $0.50 per kwh, and in order to achieve the maximum
savings, there is a marginal cost of roughly $3.25 per kwh. Likewise, in industrial, a savings of
28 percent can be achieved for $0.20 per kwh, and the maximum amount of savings would
require a marginal cost near $1.40 per kwh. The capacity supply curves show Savings
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potentials and levelized costs for the individual measures that comprise the supply curves are
provided in Appendix D.
Figure 5-7
United Illuminating Residential Energy Supply Curve
25.00
Levelized $/kWH Saved
20.00
15.00
10.00
5.00
0.00
0%
5%
10%
15%
20%
25%
30%
35%
Savings Potential
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Figure 5-8
Connecticut Light and Power Residential Energy Supply Curve
25.00
Levelized $/kWH Saved
20.00
15.00
10.00
5.00
0.00
0%
5%
10%
15%
20%
25%
30%
35%
Savings Potential
Figure 5-9
United Illuminating Residential Capacity Supply Curve
25,000
Levelized $/kW Saved
20,000
15,000
10,000
5,000
0
0%
5%
10%
15%
20%
25%
30%
35%
Savings Potential
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Figure 5-10
Connecticut Light and Power Residential Capacity Supply Curve
30,000
25,000
Levelized $/kW Saved
20,000
15,000
10,000
5,000
0
0%
5%
10%
15%
20%
25%
30%
35%
Savings Potential
Figure 5-11
Commercial Energy Supply Curve
3.50
3.00
Levelized $/kWH Saved
2.50
2.00
1.50
1.00
0.50
0.00
0%
5%
10%
15%
20%
25%
30%
35%
40%
Savings Potential
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Figure 5-12
Commercial Capacity Supply Curve
10,000
9,000
8,000
Levelized $/kW Saved
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
0%
10%
5%
35%
30%
25%
20%
15%
40%
Savings Potential
Figure 5-13
Industrial Energy Supply Curve
1.60
1.40
Levelized $/kWH Saved
1.20
1.00
0.80
0.60
0.40
0.20
0.00
0%
5%
10%
15%
20%
25%
30%
35%
Savings Potential
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Figure 5-14
Industrial Capacity Supply Curve
35,000
30,000
Levelized $/kW Saved
25,000
20,000
15,000
10,000
5,000
0
0%
5%
10%
15%
20%
25%
Savings Potential
5.1.3
Key Measures
Table 5-1 and 5-2 present the top economic measures for residential existing buildings for both
utilities’ service areas. Measures that passed the TRC test were then ranked by total GWh
savings across the service territory. CFL’s provide the most savings potential, followed by heat
pump water heaters and CEE Tier 3 clothes washers for both utilities. Consistent with the
economic energy savings by end use pie chart in Figures 5-3a and 5-3b, many of the top twenty
measures are appliances or lighting related measures.
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Table 5-1
UI Residential Existing Top Twenty Measures by Economic Potential (GWh)
UI Residential Existing Top Twenty by Economic Potential (GWh)
Base
210
210
500
500
600
220
100
100
180
300
300
220
600
300
190
100
300
910
100
100
Measure Number
211
211
501
501
602
222
102
118
181
302
301
221
602
302
193
114
301
912
118
102
Measure Name
CFL (18-Watt integral ballast), 3.0 hr/day
CFL (18-Watt integral ballast), 3.0 hr/day
Heat Pump Water Heater (EF=2.5)
Heat Pump Water Heater (EF=2.5)
Tier 3 CW (MEF=2.20)
CFL Downlight
15 SEER Split-System Air Conditioner
2008 Energy Star Windows to Energy Star Phase-2 Windows
Variable Speed Furnace Fan
HE Refrigerator - CEE tier 2 (Top Mount)
HE Refrigerator - Energy Star version of above (Top Mount)
Downlights - LED Replacement
Tier 3 CW (MEF=2.20)
HE Refrigerator - CEE tier 2 (Top Mount)
Ceiling R-11 to R-38 Insulaton
Duct Repair (0.32)
HE Refrigerator - Energy Star version of above (Top Mount)
Energy Star TV
2008 Energy Star Windows to Energy Star Phase-2 Windows
15 SEER Split-System Air Conditioner
Technical GWh
Single Family
Multi-family
Single Family
Multi-family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Multi-family
Multi-family
Single Family
Single Family
Multi-family
Single Family
Multi-family
Multi-family
Technical GWh
143.67
79.19
50.15
28.95
25.16
19.59
18.65
17.21
14.89
13.98
13.10
12.41
11.55
10.39
10.31
9.76
9.74
9.62
9.42
9.25
TRC
17.55
17.55
1.33
1.03
1.41
4.35
1.37
1.35
2.50
1.36
2.89
2.20
1.24
1.36
1.41
6.04
2.89
9.74
4.21
1.36
Economic GWh
143.67
79.19
50.15
28.95
25.16
19.59
18.65
17.21
14.89
13.98
13.10
12.41
11.55
10.39
10.31
9.76
9.74
9.62
9.42
9.25
Table 5-2
CL&P Residential Existing Top Twenty Measures by Economic Potential (GWh)
CL&P Residential Existing Top Twenty by Economic Potential (GWh)
Base
210
210
500
600
220
100
500
100
180
300
300
220
190
100
910
500
230
100
310
400
Measure Number
Measure Name
211
CFL (18-Watt integral ballast), 3.0 hr/day
211
CFL (18-Watt integral ballast), 3.0 hr/day
501
Heat Pump Water Heater (EF=2.5)
602
Tier 3 CW (MEF=2.20)
222
CFL Downlight
102
15 SEER Split-System Air Conditioner
501
Heat Pump Water Heater (EF=2.5)
118
2008 Energy Star Windows to Energy Star Phase-2 Windows
181
Variable Speed Furnace Fan
302
HE Refrigerator - CEE tier 2 (Top Mount)
301
HE Refrigerator - Energy Star version of above (Top Mount)
221
Downlights - LED Replacement
193
Ceiling R-11 to R-38 Insulaton
114
Duct Repair (0.32)
912
Energy Star TV
508
Water Heater Blanket
231
ROB 2L4'T8, 1EB
113
Proper Refrigerant Charging and Air Flow
311
Refrigerator - Early Replacement Top Mount to 2008 Energy Star
401
HE Freezer
Building Type
Single Family
Multi-family
Single Family
Single Family
Single Family
Single Family
Multi-family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Single Family
Technical GWh
614.30
216.41
214.43
107.57
83.75
79.75
79.11
73.57
63.68
59.76
56.02
53.08
44.07
41.74
41.15
36.54
34.47
33.02
32.88
31.88
TRC
17.55
17.55
1.33
1.41
4.35
1.37
1.03
1.35
2.50
1.36
2.89
2.20
1.47
6.04
9.74
10.14
9.14
4.40
2.08
4.08
Economic GWh
614.30
216.41
214.43
107.57
83.75
79.75
79.11
73.57
63.68
59.76
56.02
53.08
44.07
41.74
41.15
36.54
34.47
33.02
32.88
31.88
Table 5-3 shows the top twenty economic measures for the commercial sector across the state
of Connecticut. Better practices with plug loads (i.e. turning off computers outside of business
hours) and CFLs have the highest savings potential throughout the commercial sector. Other
lighting measures, refrigeration improvements, and cooling measures account for the majority of
the top twenty measures.
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Table 5-3
Commercial Existing Top Twenty Measures by Economic Potential (GWh)
Commercial Existing Top Twenty by Economic Potential (GWh)
Base
610
160
500
220
320
130
110
165
320
190
110
180
320
175
180
500
500
500
600
320
Measure Number
611
161
513
221
321
133
114
166
328
191
115
181
322
176
182
510
508
512
602
323
Measure Name
Plug Loads Efficient Equipment & Practices
CFL Screw-in 18W
Refrigeration 30% More Efficient Design
High Pressure Sodium 250W Lamp
DX Tune Up/ Advanced Diagnostics
RET 2L4' Premium T8, 1EB
RET 4L4' Premium T8, 1EB
CFL Hardwired, Modular 18W
High Performance HVAC R/R - 30%
ROB 2L4' Premium T8, 1EB
RET 2L4' Premium T8, 1EB, Reflector
ROB 4L4' Premium T8, 1EB
DX Packaged System, EER=10.9, 10 tons
High Bay T5
Occupancy Sensor, 4L4' Fluorescent Fixtures
Demand Defrost Electric
Refrigeration Commissioning
Refrigeration 15% More Efficient Design
Data Center Best Practices
Window Film (Standard)
Technical GWh
879.81
469.94
263.14
263.84
199.19
197.46
168.71
156.65
149.72
144.04
131.63
129.91
143.00
99.16
73.09
72.71
67.91
66.73
59.78
60.42
TRC
201.15
41.14
24.48
4.16
4.31
24.07
45.05
25.87
5.54
13.58
25.24
16.07
1.24
15.28
3.18
73.05
4.52
11.79
66.16
3.05
Economic GWh
879.81
469.94
263.14
255.93
199.19
197.46
168.71
153.79
149.72
143.29
131.63
129.87
112.82
97.99
73.07
72.71
67.91
66.73
59.78
58.84
Notes: The TRCs presented in this table are averages weighted by economic savings for cost effective measures/building types combinations only
(that is, for measures that are cost effective in some building types, only the TRCs for cost effective building types have been averaged). It is possible
for some measures to be cost effective in one building type and not in others. Examples of this include occupancy sensors for fluorescent lights, which
are often cost effective in offices, grocery, health care, lodging, and miscellaneous, but not restaurants, retail, warehouses and education. Likewise,
the cost effectiveness of cooling measures like window film, cool roofs, and programmable thermostats varies across end uses as well.
Table 5-4 presents the top industrial measures. Continuous dimming in fluorescent fixtures and
system optimization in pumping and compressed air process systems provide the most savings
potential. Many of the top twenty measures are system improvements for motor related
processes like compressed air, pumps, fans, and drive processes. Adding controls to these
processes, optimizing processes, and doing O&M can provide a large amount of savings for the
industrial sector.
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Table 5-4
Industrial Top Twenty Measures by Economic Potential (GWh)
Industrial Top Twenty by Economic Potential (GWh)
Base Measure Number
810
814
100
105
300
305
300
303
400
434
200
203
200
208
300
301
400
429
100
101
100
108
550
553
550
551
820
824
200
205
200
202
100
106
550
554
710
711
500
511
Measure Name
Continuous Dimming, Fluorescent Fixtures
Compressed Air - System Optimization - 30% energy savings
Pumps - System Optimization - 30% savings
Pumps - Controls - 40% savings
Generic Drives Improvements 20%
Fans - Controls - 40% savings
Fans- Improve components - 20% savings
Pumps - O&M - 10% savings
Drives - Optimization process (M&T) - 20% savings
Compressed Air-O&M - 15% energy savings
Compressed Air- Sizing - 20% energy savings
Optimization Refrigeration - 25% savings
Efficient Refrigeration - Operations - 15% savings
Continuous Dimming, Fluorescent Fixtures
Fans - System Optimization - 25% savings
Fans - O&M - 10% savings
Compressed Air - System Optimization - 50% energy savings
Optimization Refrigeration - 50% savings
DX Tune Up/ Advanced Diagnostics
Heating - Optimization process (M&T) - 10% savings
Technical GWh
80.28
79.54
71.99
63.83
61.64
55.50
49.27
48.29
45.15
41.48
37.97
37.74
35.08
34.11
30.41
30.11
28.88
21.87
19.72
18.21
TRC Economic GWh
1.81
80.28
17.36
79.54
3.07
71.99
9.32
63.83
1.41
61.64
2.41
55.50
17.90
49.27
24.44
48.29
22.95
45.15
13.02
41.48
18.02
37.97
3.00
37.74
22.41
35.08
1.89
34.11
2.13
30.41
80.51
30.11
4.03
28.88
1.30
21.87
4.17
19.72
8.41
18.21
Notes: The TRCs presented in this table are averages weighted by economic savings for cost effective measures/industry combinations only (that is,
for measures that are cost effective in some industries, only the TRCs for cost effective building types have been averaged).
5.2
Total Economic, Achievable and Instantaneous Program
Achievable Potential
As discussed in Section 3, total economic potential, total achievable potential and instantaneous
program achievable potential were developed outside the DSM ASSYST model using
adjustment factors for a variety of factors not addressed by the model. The methodology was
developed for the Connecticut Electric Potential Study and adjustment factors were developed
using a consensus approach. The values used for this study are shown in Table 5-5.
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Table 5-5 Values for Adjustment Factors Applied to Economic Potential
Factor
% Change from
Initial Economic
Potential
Economic Potential Growth
+10%
Not Achievable
-15%
Building Codes (applies to new construction only)
-35%
Standards
-50% of
economic CFL
savings
Outside of Program
-15%
These factors were applied as follows:

Total economic potential was calculated to be 10 percent higher than initial economic
potential (includes new technologies and additional cost effective measures due to
measure cost reductions)

Total achievable potential was estimated to be 15 percent below total economic
potential, to account for cost effective potential that cannot be achieved through energy
efficiency programs, codes, standards, or naturally occurring efficiency (Not Achievable).

Instantaneous program achievable potential takes into account a 35 percent reduction in
total achievable potential for new construction. In addition, for all building types, the
effect of standards (here assumed to be 50 percent of economic CFL savings) and
outside-of-program effects (a 15% reduction) are taken into account.
The standards factor and building code factor take into account the effect of anticipated
changes to building codes and standards. The effect of current codes is already taken into
consideration in the model through the measures considered and the level of savings for new
construction compared to existing construction. Of course, in an actual program context,
building codes do not happen instantaneously. Measures may be covered by a program for
years before they are incorporated into building codes. In fact, energy-efficiency programs
influence the development of building codes and standards, by creating familiarity, acceptance,
and more competitive delivery for energy-efficient technologies.
Figure 5-15 shows the resulting total economic, total achievable and instantaneous program
achievable savings. Total achievable includes all economic savings except those not
achievable; instantaneous program achievable excludes savings that occur by other
mechanisms than energy efficiency programs (standards, building codes, and outside of
program savings). Because these savings are instantaneous (that is, they do not take into
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account stock turnover), building codes have little impact, since the calculation includes only a
single year’s new construction. Also, even equipment with long lifetimes (up to 20 years for
many of the measures modeled) is assumed to be completely replaced with energy-efficient
equipment in these calculations.
Figure 5-15
Comparison of Instantaneous Savings Potentials
Maximum Achievable Energy Savings (GWh)
12,000
10,000
1,608
889
889
1,608
1,608
8,000
Not Achievable
Building Codes and Standards
6,000
Outside of Program
Program Achievable
4,000
6,616
6,616
6,616
Total Economic
Total Achievable
Program Achievable
2,000
-
5.3
Program Funding Scenarios
In contrast with the previous estimates, program funding scenario savings estimates take into
account equipment turnover, market barriers and other factors that affect adoption of efficiency
measures. Our method of estimating measure adoption explicitly takes into account market
barriers and reflects actual consumer- and business-implicit discount rates. This section
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presents results for the three program funding scenarios, first at the summary level and then by
sector. More detail on program funding scenarios is shown in Appendix E.
Program funding scenario savings refers to the amount of savings that would occur in response
to one or more specific program interventions. Net savings associated with program scenarios
are savings that are projected beyond those that would occur naturally in the absence of any
market intervention. Program scenario savings depends on the type and degree of intervention
applied, so different incentive levels will result in different level of savings. The focus of these
scenarios is not on next year (2010), but on the full ten-year forecast period. The scenarios can
be fleshed out through a detailed analysis by program planners, but by themselves do not
constitute a program implementation plan. They do, however, represent a good initial estimate
of the savings that can be achieved at different budget levels.
We modeled program scenarios at three funding levels. The first is the current program funding
scenario, which in its first year mirrors the 2009 Program Plan incentive levels and budgets. In
years 2 through 10 of the scenario, the marketing budget is maintained at the year 1 level, while
the incentive budget increases as more customers become aware of the program (due to
ongoing marketing) and choose to participate. A portion of the administrative budget also
increases in proportion to energy savings.3 The second program scenario, the expanded
program funding scenario, has higher incentives and a larger marketing budget compared to the
current funding scenario, and represents an attempt to match as closely as possible the values
in Connecticut’s Integrated Resource Plan’s Expanded Energy Efficiency budget, subject to
stock turnover and static costs and technologies. The third program scenario, the base funding
scenario, uses the current 2009 budget for the first year, but removes expected RGGI funding
from the budget. As a result, the budget was roughly 18% below that in the current case.
Table 5-5 shows how KEMA’s modeling efforts compare to the cost and savings outlined by the
2009 Program Plan and Connecticut’s Integrated Resource Plan. We calibrated the current
funding scenario budget to be roughly equivalent to the 2009 Program Plan budget. Net energy
savings for the modeled current funding scenario are almost on target with the Program Plan,
3
Therefore, the current funding scenario mirrors the 2009 Program Plan only in its first year. The program
modeled in the current funding scenario continues to grow with increased awareness based on the
underlying model parameters. The authors of this study have no knowledge of what actual program
budgets will be in future years. The modeled budget is neither a prediction of nor a recommendation for
future program budgets.
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with each sector and the total savings coming in around two percent less than the plan’s
projected savings.
The low budget scenario is an 18% decrease from the current funding scenario. Overall, the
base funding scenario energy savings were 2,802 GWh, whereas the savings from the current
funding scenario average to 3,397 GWh in the Program Plan. The year one budget given in the
base funding scenario was $87.6, million compared to $105 million in the current funding
scenario. While it was difficult to calibrate the model to reach an 18% decrease from the current
funding results, both the budget and savings in the model derived results show roughly this
decrease. The DSM ASSYST Model is based off of non-linear penetration curves, so it was a
challenge to reduce both the budget and savings by the same percentage. The model derived
budget was almost equal to the Plan’s budget, at $88 million, while the overall energy savings
were five percent higher than predicted in the base funding scenario, as seen in Figure 5-16.
The expanded funding scenario almost doubles the savings over the current funding scenario,
at the cost of a doubling of the program budget. The expanded funding scenario has an annual
budget of almost $ 205 million, compared to $105 million for the current funding scenario (10year annual budget) in the Integrated Resource Plan. Annual net savings for the expanded
funding scenario average 5,773 GWh, compared to 3,397 for the current funding scenario. The
budget estimated in the model for this scenario was only two percent more than that given in the
Integrated Resource Plan, and savings were also two percent higher.
Demand savings for the each scenario did not calibrate as well as the budget and energy
savings however, and are often higher than the Program Plan and Integrated Resource Plan
values. This discrepancy in demand savings estimates is likely due to the measure mix that
was assumed and shown to be cost effective and easily adopted through program intervention.
Many measures with a large amount of demand savings potential like CFLs and cooling
measures were estimated to be cost effective, and therefore the model estimates of demand
savings were higher than the Integrated Resource Plan and Program Plan savings in every
scenario.
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Table 5-6 Comparison between 2009 Program Plan and Model Derived Scenarios
Model
UI Residential Plan
% Difference
CL&P
Model
Residential
Plan
% Difference
Commercial Model
Industrial
Model
C&I Total
Model
Plan
% Difference
Total
Model
Plan
% Difference
Base Funding Case
Current Funding Case
Accelerated Funding Case
Budget
Net kWh
Net kW
Budget
Net kWh
Net kW Budget
Net kWh
Net kW
$5,841,322
136,677,602
22,640
$6,841,059
156,026,155 26,228 $16,226,935
282,860,937
52,627
$5,980,418
128,225,108
20,132
$6,886,421
154,934,766 24,181 $16,704,120
288,619,073
48,697
-2.33%
6.59%
12.45%
-0.66%
0.70% 8.47%
-2.86%
-2.00%
8.07%
$35,500,366
740,853,188 126,230 $42,189,400
822,310,506 134,622 $55,860,142 1,035,281,478 174,143
$35,127,280
692,777,755 116,987 $42,683,107
842,044,984 142,167 $57,935,411 1,012,424,416 173,854
1.06%
6.94%
7.90%
-1.16%
-2.34% -5.31%
-3.58%
2.26%
0.17%
$30,512,795 1,439,722,531 271,728 $37,626,308 1,657,014,423 312,552 $94,276,326 3,683,926,186 705,787
$16,230,450
628,648,670 110,273 $19,084,256
697,526,536 123,536 $39,185,843
908,140,202 162,286
$46,743,245 2,068,371,201 382,002 $56,710,564 2,354,540,959 436,088 $133,462,170 4,592,066,388 868,073
$46,472,561 1,981,287,015 325,024 $56,357,574 2,399,566,464 393,917 $126,472,499 4,471,919,833 733,861
0.58%
4.40%
17.53%
0.63%
-1.88% 10.71%
5.53%
2.69%
18.29%
$88,084,933 2,945,901,990 530,872 $105,741,022 3,332,877,620 596,938 $205,549,247 5,910,208,803 1,094,843
$87,580,260 2,802,289,878 462,143 $105,927,102 3,396,546,214 560,265 201,112,030 5,772,963,322 956,413
0.58%
5.12%
14.87%
-0.18%
-1.87% 6.55%
2.21%
2.38%
14.47%
Figure 5-16 shows our estimates of program scenario savings over time. This figure shows the
three program scenarios that were developed for the study. As shown, savings potential tends
to increase at a decreasing rate, over time. In the early years, programs can target the most
cost-effective and easy-to-achieve measures and markets. Over time, the supply of these
opportunities is expected to decline (in the absence of significant new technologies), and the
programs must penetrate harder-to-reach markets and influence end users to adopt less
attractive measures. In the figure, the cumulative net savings for the program scenarios are
shown as the difference between the net savings for each scenario and the lower budgeted
scenarios.
Connecticut Electric EE Potential Study
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April 29, 2010
Figure 5-16
Program Funding Scenario Energy Savings in GWh: All Sectors
7,000
6,000
IRP (Accelerated) Funding
Current Funding
GWh
5,000
Base Funding
4,000
3,000
2,000
1,000
0
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Figure 5-17 depicts cumulative costs and benefits for the 2009-2018 period for the three
program scenarios. The current funding scenario has a TRC of 2.6, the base funding scenario
has a TRC of 2.7, and the expanded funding scenario has a TRC of 2.6. All three are cost
effective under the total resource cost test. Program cost-effectiveness declines somewhat with
increasing program effort, reflecting penetration of more measures with lower cost-effectiveness
levels. This result reflects the assumption that the most cost-effective measures are targeted
first, both by the programs and by end users who are seeking to lower their electric utility bills in
the most cost-effective manner.
Connecticut Electric EE Potential Study
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April 29, 2010
Figure 5-17
Benefits and Costs of Energy-Efficiency Savings – 2009-2018*
$12,000
$9,935
Present Value in $ Millions
$10,000
$8,000
$6,185
$5,722
$6,000
$4,716
$3,515
$3,750
$2,939
$4,000
Program Admin&
Marketing Costs
Program Incentive
Costs
Non-Incentive
Participant Costs
Total Benefits
$2,207
$1,777
$2,000
$0
Base
Funding
Current
Funding
Accelerated
Funding
* Present value of benefits and costs over normalized 20-year measure lives; nominal discount rate is 7.09
percent, inflation rate is 2.3 percent.
In Figure 5-17, the program costs are the costs to the Connecticut utilities for the program.
These include administration, marketing/education and financial incentives to the customer.
Non-incentive participants costs are also included in the overall program costs of the program.
The present value of the overall program costs is $2,207 million in the current funding scenario,
$1,777 million in the base funding scenario and $3,750 in the expanded funding scenario.4 The
present value of program administration and marketing/education costs (considered program
overhead costs) is $508 million in the current funding scenario, $381 million in the base funding
scenario and $1,006 million in the expanded funding scenario. The remainder of the utility
10
4
The present value of program costs is calculated as
1
 (1  d )
y 1
y 1
C y , where d is the utility discount rate
and Cy is the cost in year y.
Connecticut Electric EE Potential Study
5-28
April 29, 2010
program costs is the incentive costs paid to program participants to encourage investment in
energy efficiency measures. These incentive costs are transfer payments between the utility
and the customer and do not affect the program cost effectiveness calculations.
Participant costs are the costs borne by the program participants. They are calculated as the
incremental costs associated with the high efficiency equipment minus the incentives paid by
the program. These are added to the program costs to capture all the costs associated with the
installation of program-induced measures. Cumulative, ten year participant costs are $1,266
million in the current program case, $975 million in the base funding scenario, and $1,813
million in the expanded funding scenario.
The difference between the total avoided cost benefits and the program and participant costs
are the net benefits to society provided by the program. The net benefits are calculated based
on the avoided cost of electricity. The net benefits of the programs are substantial over the 10year time frame; $5,722 million for the current funding case, $4,716 million for the base funding
case, and $9,935 million for the expanded funding scenario.
Participation is modeled to increase with greater program awareness, which increases both
incentives paid and net savings. The DSM ASSYST model scales a portion of the admin budget
in proportion to energy savings (the bigger the program, the higher the administrative costs),
resulting in higher administrative costs over time, as well.
Key results are further summarized in Table 5-7. The table shows the total budgets and savings
for the 10-year program period as modeled for each of the three program scenarios. Cumulative
net energy savings by 2018 are projected to be 5,910 GWh per year for the expanded funding
scenario (based on the cumulative equipment installations over the entire program period),
compared to 3,333 GWh for the current funding scenario and 2,946 GWh for the base funding
scenario. The table also presents the present value of net avoided costs and present value of
program costs that are used to calculate the TRC ratio.
Connecticut Electric EE Potential Study
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April 29, 2010
Table 5-7
Summary of Program Funding Scenario Potential Results—2009–2018
Net Energy Savings – Total GWh
Net Energy Savings – % of base use
Net Peak Demand Savings (MW)
Net Peak Day Demand Savings -- % of base demand
Program Costs - Real
Administration and Marketing- $mil.
Incentives - $ mil.
Total Program Costs- $ mil.
PV Net Avoided Costs - $ mil.
PV Annual Marketing and Admin Costs - $ mil.
PV Net Measure Costs - $ mil.
TRC
Base Funding
Scenario
2,946
10%
531
8%
$381
$421
$802
$4,716
$381
$975
2.65
Current Funding Expanded Funding
Scenario
Scenario
3,333
5,910
11%
20%
597
1,095
9%
16%
$508
$473
$981
$5,722
$508
$1,226
2.59
$1,006
$931
$1,937
$9,935
$1,006
$1,813
2.65
PV (present value) of benefits and costs is calculated over a 20-year normalized measure life for 2009–2018 program
years, nominal discount rate = 7.09 percent, inflation rate = 2.3 percent; savings are cumulative through 2018.
Tables 5-8 through 5-10 show the net, naturally occurring and gross savings for each sector
under each program scenario. These tables also give the first year budget and average annual
budget for each sector under each funding scenario. A table is given for each energy savings
and demand savings. A further breakdown of savings by sector will follow in the subsequent
section, but these tables are presented to provide a deeper view into the expected budget
breakdown and associated savings by sector for each funding scenario.
Table 5-8
Base Funding Scenario- Energy Savings
Connecticut Electric EE Potential Study
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April 29, 2010
Table 5-9
Base Funding Scenario- Demand Savings
Table 5-10
Current Funding Scenario- Energy Savings
Current
Budget
Residential
Commercial
Industrial
Total
Existing
Existing CFLs
New
Total
% of total load
Existing
CFLs
New
Total
% of total load
Existing
% of total load
% of total Load
kWh
Naturally Occuring
Outside of Program Free Riders
649,330,139
25,582,712
16,537,778
287,317,385
9,889,355
23,075,162
41,689,137
973,621
2,256,407
978,336,661
36,445,688
41,869,347
8%
1,112,100,086
73,529,623
399,425,654
75,950,995
48,284,250
235,740,750
468,963,342
4,126,108
23,381,280
1,657,014,423
125,939,981
658,547,684
13%
697,526,536
31,968,328
140,833,447
18%
3,332,877,620
194,353,998
841,250,478
11%
Connecticut Electric EE Potential Study
Net
5-31
Total
691,450,629
320,281,903
44,919,165
1,056,651,697
8%
1,585,055,363
359,975,995
496,470,730
2,441,502,087
19%
870,328,311
22%
4,368,482,096
15%
Year 1
Budget
$29,757,583
$10,623,017
$1,884,179
$42,264,779
Average
10 Year
Budget
$28,811,991
$13,599,677
$6,618,790
$49,030,459
$17,317,439
$1,795,399
$12,033,818
$31,146,656
$23,013,131
$1,619,858
$12,993,318
$37,626,308
$16,102,943
$19,084,256
$89,514,378
$105,741,022
April 29, 2010
Table 5-11
Current Funding Scenario- Demand Savings
Table 5-12
Expanded Funding Scenario- Energy Savings
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April 29, 2010
Table 5-13
Expanded Funding Scenario- Demand Savings
5.3.1.1
Residential
Cumulative net program savings in the residential existing construction sector can reach 937
GWh by 2018 under the current funding scenario, 836 GWh for the base funding scenario and
1,274GWh under expanded funding scenario. Unlike existing buildings, savings for new
construction increase at an increasing rate as the population of new buildings expands over the
program period. Cumulative net program savings in the residential new construction sector can
reach 42 GWh by 2018 under the current funding scenario, 41 GWh under the base funding
scenario and 45 GWh under the expanded funding scenario.
Figures 5-18 through 5-23 show the end-use distribution of residential energy savings potential
by 2018 for the each funding scenario. Lighting contributes most to the energy savings potential
for both utilities under each scenario, resulting mainly from CFL installations. KEMA modeled
CFLs separate from the other measures and removed all savings attributed with CFLs after
2013, due to the new federal lighting standard that will take place between 2012 and 2014.
Appliances and other shows the next largest potential, reflecting savings from measures like
clothes washers and energy star televisions and computers. Water heating is also significant,
reflecting savings from measures like tank insulation and low flow showerheads.
Connecticut Electric EE Potential Study
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April 29, 2010
Figure 5-18
United Illuminating Current Funding Achievable Potential by End Use
Ten Year Cumulative UI Residential Current Funding
Achievable Potential by End Use (GWh)
Appliances &
Other
17%
Lighting
33%
Water Heating
19%
Cooling
11%
Heating
20%
Figure 5-19
Connecticut Light and Power Current Funding Achievable Potential by End Use
Ten Year Cumulative CL&P Residential Current Funding
Achievable Potential by End Use (GWh)
Appliances &
Other
19%
Lighting
38%
Water Heating
16%
Heating
17%
Connecticut Electric EE Potential Study
5-34
Cooling
10%
April 29, 2010
Figure 5-20
United Illuminating Base Funding Achievable Potential by End Use
Ten Year Cumulative UI Existing Residential Low Funding
Achievable Potential by End Use (GWh)
Appliances &
Other
15%
Lighting
33%
Water Heating
20%
Cooling
11%
Heating
21%
Figure 5-21
Connecticut Light and Power Base Funding Achievable Potential by End Use
Ten Year Cumulative CL&P Residential Low Funding
Achievable Potential by End Use (GWh)
Lighting
34%
Appliances &
Other
21%
Water Heating
18%
Cooling
10%
Heating
17%
Connecticut Electric EE Potential Study
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April 29, 2010
Figure 5-22
United Illuminating Expanded Funding Achievable Potential by End Use
Ten Year Cumulative UI Existing Residential Accelerated
Funding Achievable Potential by End Use (GWh)
Appliances &
Other
22%
Lighting
37%
Water Heating
14%
Cooling
12%
Heating
15%
Figure 5-23
Connecticut Light and Power Expanded Funding Achievable Potential by End Use
Ten Year Cumulative CL&P Residential Acclerated Funding
Achievable Potential by End Use (GWh)
Appliances &
Other
21%
Lighting
41%
Water Heating
14%
Cooling
10%
Heating
14%
Connecticut Electric EE Potential Study
5-36
April 29, 2010
5.3.1.2
Commercial
Cumulative net program savings in the commercial sector can reach 1,188 GWh per year by
2018 under the current funding scenario, 1,018 GWh per year under the base funding scenario
and 3,208 GWh per year under expanded funding scenario. A substantial amount of extra
savings can be obtained in the advanced funding scenario over the current and base funding
scenarios. For commercial new construction, cumulative net program savings in the
commercial sector can reach 469 GWh per year by 2018 under the current funding scenario,
422 GWh per year under the base funding scenario and 476 GWh per year under expanded
funding scenario.
Figure 5-24 shows the end-use distribution of commercial energy savings potential by 2018 for
the current funding scenario. Lighting again contributes the most to the energy savings
potential, resulting from measures such as CFL bulbs and high pressure sodium lamps. Like
residential, CFLs were modeled separately, and all savings attributed to CFLs were stopped
after 2013 to account for the changes in the federal lighting standards. Space cooling
improvements have the next largest potential, reflecting savings from measures like tune up and
advanced diagnostics for cooling systems, and cooling system upgrades. Figures 5-13 and 514 show the savings by end use from the base and expanded funding scenarios. The
breakdown is similar to the current funding scenario.
Connecticut Electric EE Potential Study
5-37
April 29, 2010
Figure 5-24
Commercial Net Energy Savings Potential - End Use Shares (2018) – Current Funding
Scenario
Ten Year Cumulative Commercial Current Funding Achievable
Potential (GWh)
Other
13%
Refrigeration
12%
Heating
0%
Lighting
58%
Cooling
17%
Figure 5-25
Commercial Net Energy Savings Potential - End Use Shares (2018) – Base Funding
Scenario
Ten Year Cumulative Commercial Low Funding Achievable
Potential (GWh)
Other
23%
Lighting
48%
Refrigeration
13%
Heating
0%
Cooling
16%
Connecticut Electric EE Potential Study
5-38
April 29, 2010
Figure 5-26
Commercial Net Energy Savings Potential - End Use Shares (2018) – Expanded Funding
Scenario
Ten Year Cumulative Commercial Expanded Funding Achievable
Potential (GWh)
Other
19%
Refrigeration
15%
Lighting
48%
Heating
0%
Cooling
18%
5.3.1.3
Industrial
Cumulative net program savings in the industrial sector can reach 698 GWh by 2018 under the
current funding scenario, 629 GWh under the base funding scenario, and 908 GWh under the
expanded funding scenario.
Figure 5-27 shows the end-use distribution of industrial energy savings potential by 2018 under
the current funding scenario. Pumps have the highest contribution towards energy savings.
Savings in pumping systems result from better operation and maintenance practices, and
system optimization in the systems. Various compressed air system improvements have a
large amount of potential and represent the second largest energy savings opportunity. The
other motor driven end uses we model, fans and drives, also offer a large amount of savings
beyond those in pumps and compressed air.
Connecticut Electric EE Potential Study
5-39
April 29, 2010
Figure 5-27
Industrial Net Energy Savings Potential - End Use Shares (2018) – Current Funding
Scenario
Ten Year Cumulative Industrial Current Funding
Achievable Potential by End Use (GWh)
Other
0%
Compressed
Air
17%
Fans
14%
Lighting
12%
Pumping
19%
Cooling
9%
Other Process
2%
Drives
14%
Refrigeration
9%
Heating
4%
Figure 5-28
Industrial Net Energy Savings Potential – End Use Shares (2018) –Base Funding Scenario
Ten Year Cumulative Industrial Current Funding
Achievable Potential by End Use (GWh)
Other
0%
Compressed
Air
17%
Fans
14%
Lighting
12%
Pumping
19%
Cooling
9%
Other Process
2%
Drives
14%
Refrigeration
9%
Connecticut Electric EE Potential Study
Heating
4%
5-40
April 29, 2010
Figure 5-29
Industrial Net Energy Savings Potential – End Use Shares (2018) – Expanded Funding
Scenario
Ten Year Cumulative Industrial Accelerated Funding
Achievable Potential by End Use (GWh)
Other
0%
Compressed
Air
19%
Fans
14%
Lighting
12%
Cooling
8%
Pumping
18%
Other Process
1%
Refrigeration
8%
5.4
Drives
16%
Heating
4%
Comparison of Potential Results
Figure 5-30 compares the various potential estimates included in this study. These include
technical potential, initial economic potential (output of the DSM ASSYST model), total
economic potential (initial economic potential adjusted for emerging technologies and measure
cost reduction over time), total achievable potential (inside and outside of the efficiency
programs), instantaneous program achievable potential (independent of stock turnover) and the
three program scenarios’ potential. The instantaneous results in Table 5-14 are based on the
first year program mix of existing and new construction, and therefore include only a single
year’s new construction.
Table 5-14 shows the same instantaneous results and program savings in annual GWh along
with annual base GWh. Each potential is presented as a percent of base GWh. Initial economic
Connecticut Electric EE Potential Study
5-41
April 29, 2010
potential is 91 percent of technical potential. The 9 percent decrease is due to measures that
were not cost effective in one or more building types or industries, most significantly DX
packaged systems in commercial installations, continuous dimming for fluorescent light fixtures
in both commercial and industrial applications, Motor processes system optimization at a fifty
percent savings level, and 17 SEER central air conditioners and high efficiency clothes dryers
and water heaters in the residential sector.
Figure 5-30
Instantaneous Savings Potential—GWh
30,000
3,965
25,000
GWh
20,000
12,845
Industrial
15,000
Commercial
1,184
1,302
4,901
5,391
4,294
3,663
4,029
3,425
Technical
Initial
Economic
Total
Economic
Total
Achievable
1,296
Residential
1,107
10,000
5,124
5,000
12,587
910
3,485
629
698
2,221
1,440
878
1,657
978
Program
Achievable
Net Base
Funding
Achievable
Net Current
Funding
Achievable
Base
Connecticut Electric EE Potential Study
908
4,582
5-42
3,684
1,318
Net
Accelerated
Funding
Achievable
April 29, 2010
Table 5-14 Energy Savings Potential – Annual GWh
GWH
Initial
Total
Total
Base
Technical Economic Economic Achievable
Energy Use Savings
Savings
Savings
Savings
12,398
4,263
3,642
4,007
189
30
21
23
Subtotal
12,587
4,294
3,663
4,029
3,425
Savings % of Base
34%
29%
32%
27%
10 Year Average Program Budget
Sector
Residential Existing
Residential New
Residential Existing- UI
Residential New - UI
Program
Achievable
Savings
2,221
18%
2,217
34
2,251
898
4
902
40%
770
3
773
34%
847
3
850
38%
Residential Existing - CL&P
Residential New - CL&P
Subtotal
Savings % of Base
10 Year Average Program Budget
10,181
155
10,336
3,365
27
3,391
33%
2,872
18
2,890
28%
3,159
20
3,179
31%
Commercial Existing
Commercial New
12,652
193
12,845
5,038
86
5,124
40%
4,815
86
4,901
38%
5,296
95
5,391
42%
4,582
36%
3,485
27%
3,965
1,296
33%
1,184
30%
1,302
33%
1,107
28%
910
23%
29,397
10,714
36%
9,748
33%
10,722
36%
9,114
31%
6,616
23%
Subtotal
Savings % of Base
10 Year Average Program Budget
Subtotal
Savings % of Base
10 Year Average Program Budget
Industrial
Savings % of Base
10 Year Average Program Budget
Total
Savings % of Base
10 Year Average Program Budget
723
32%
2,702
26%
470
21%
1,751
17%
Program Achievable Savings per KEMA Model
Net Base
Net Current
Net Accelerated
Funding
Funding
Funding
Achievable
Achievable
Achievable
Savings
Savings
Savings
836
937
1,274
41
42
45
878
978
1,318
7%
8%
10%
$41,341,688
$49,030,459
$72,087,077
131
5
137
6%
$5,841,322
151
5
156
7%
$6,841,059
277
6
283
13%
$16,226,935
705
36
741
7%
$35,500,366
786
36
822
8%
$42,189,400
996
39
1,035
10%
$55,860,142
1,018
422
1,440
11%
$30,512,795
629
16%
$16,230,450
2,946
10%
$88,084,933
1,188
469
1,657
13%
$37,626,308
698
18%
$19,084,256
3,333
11%
$105,741,022
3,208
476
3,684
29%
$94,276,326
908
23%
$39,185,843
5,910
20%
$205,549,247
aTechnical
saving refers to the complete penetration of all measures analyzed in applications where they were
deemed technically feasible from an engineering perspective.
bInitial economic savings includes savings for all measures found to be cost effective in the application analyzed.
cTotal economic savings includes initial economic savings plus 10 percent, to account for emerging technologies and
measure cost reductions not captured in the model.
dTotal achievable savings reduces total economic savings by 15 percent to account for savings that are not
achievable.
eProgram achievable savings excludes savings due to building codes (35% in new construction), lighting standards
(half of CFL savings in commercial and residential) and outside-of-program savings.
fColumns may not total due to rounding.
Connecticut Electric EE Potential Study
5-43
April 29, 2010
Figure 5-31 and Table 5-15 presents’ peak demand savings for the various potential estimates.
The percent savings vary slightly from energy savings because the specific mix of measures
varies between the different potentials.
Figure 5-31
Instantaneous Savings Potential—MW
Connecticut Base Demand and Achievable Potential (MW)
7,000
919
6,000
5,000
2,982
Industrial
4,000
Commercial
Residential
3,000
198
2,000
163
2,917
162
879
1,000
652
124
706
755
565
272
149
313
161
227
Total Achievable
Program Achievable
Net Base Funding
Achievable
Net Current Funding
Achievable
Net Accelerated
Funding Achievable
Base
110
Connecticut Electric EE Potential Study
5-44
April 29, 2010
Table 5-15
Peak Demand Savings – MW
MW
Base
Technical
Energy Use Savings
2,873
1,023
44
12
Subtotal
2,917
1,035
Savings % of Base
35%
10 Year Average Program Budget
Residential Existing- UI
538
215
Residential New - UI
8
1
Subtotal
546
217
Savings % of Base
40%
10 Year Average Program Budget
Residential Existing - CL&P
2,335
808
Residential New - CL&P
36
11
Subtotal
2,371
818
Savings % of Base
35%
10 Year Average Program Budget
Commercial Existing
2,937
994
Commercial New
45
17
Subtotal
2,982
1,011
Savings % of Base
34%
10 Year Average Program Budget
Industrial
919
230
Savings % of Base
25%
10 Year Average Program Budget
Total
6,818
2,257
Savings % of Base
33%
10 Year Average Program Budget
Sector
Residential Existing
Residential New
Initial
Total
Total
Economic Economic Achievable
Savings
Savings
Savings
802
882
6
7
808
889
755
28%
30%
26%
Program
Achievable
Savings
566
19%
169
1
170
31%
186
1
187
34%
159
29%
119
22%
633
5
638
27%
696
6
702
30%
597
25%
446
19%
923
17
940
32%
1,016
19
1,034
35%
879
29%
652
22%
212
23%
233
25%
198
22%
163
18%
1,960
29%
2,156
32%
1,832
27%
1,380
20%
Program Achievable Savings per KEMA Model
Net Base
Net Current
Net Accelerated
Funding
Funding
Funding
Achievable
Achievable
Achievable
Savings
Savings
Savings
132
144
208
17
17
19
149
161
227
5%
6%
8%
$41,341,688
$49,030,459
$72,087,077
20
24
50
2
2
3
23
26
53
4%
5%
10%
$5,841,322
$6,841,059
$16,226,935
111
120
158
15
15
16
126
135
174
5%
6%
7%
$35,500,366
$42,189,400
$55,860,142
189
221
613
82
92
93
272
313
706
9%
10%
24%
$30,512,795
$37,626,308
$94,276,326
110
124
162
12%
13%
18%
$16,230,450
$19,084,256
$39,185,843
531
597
1,095
8%
9%
16%
$88,084,933
$105,741,022
$205,549,247
aTechnical
saving refers to the complete penetration of all measures analyzed in applications where they were
deemed technically feasible from an engineering perspective.
bInitial economic savings includes savings for all measures found to be cost effective in the application analyzed.
cTotal economic savings includes initial economic savings plus 10 percent, to account for emerging technologies and
measure cost reductions not captured in the model.
dTotal achievable savings reduces total economic savings by 15 percent to account for savings that are not
achievable.
eProgram achievable savings excludes savings due to building codes (35% in new construction), lighting standards
(half of CFL savings in commercial and residential) and outside-of-program savings.
fColumns may not total due to rounding.
Note: The expanded funding scenario and current funding scenario program savings are savings potential in 2018
due to program activity from 2009-2018. Other potentials are instantaneous potentials. Technical saving refers to the
complete penetration of all measures analyzed in applications where they were deemed technically feasible from an
engineering perspective. Initial economic savings includes savings for all measures found to be cost effective in the
application analyzed. Total economic savings includes initial economic savings plus 10 percent, to account for
emerging technologies and measure cost reductions not captured in the model. Total achievable savings reduces
total economic savings by 15 percent to account for savings that are not achievable. Program achievable savings
excludes savings due to building codes (35% in new construction), lighting standards (half of CFL savings in
commercial and residential) and outside-of-program savings. The expanded funding scenario sets incentives to 100
percent of incremental measure costs with a first-year budget of $193 million. The first year of the current funding
scenario approximates the Connecticut 2009 Program Plan as explained in section 5.3. The base funding scenario
uses the budget given in the 2009 Program Plan, but reduces it by 18%, thus removing RGGI funding.
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Connecticut Electric EE Potential Study
5-45
April 29, 2010