Connecticut Electric Residential, Commercial, and Industrial
Energy Efficiency Potential Study
Final Report
Prepared for the Connecticut Energy Conservation Management Board (ECMB)
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 used, reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, without first receiving the express written permission of KEMA, Inc. Except as otherwise noted, all trademarks appearing herein are proprietary to KEMA, Inc.
Table of Contents
1. 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
2. Introduction 2-1
2.1 Overview 2-1
2.2 Study Approach 2-1
2.3 Layout of the Report 2-2
3. 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
4. Baseline Data and Results 4-1
4.1 Overview 4-1
4.2 Residential 4-2
4.3 Commercial 4-4
5. 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
List of Exhibits:
Table 1-0 Summary of Results………………………………………………………………………..1-4
Table 11 Summary of Energy Savings Potentials and Program Funding Scenarios – Instantaneous and in 2018 1-6
Table 12 Comparison Between 2009 Program Plans and Program Funding Scenario Results 1-9
Table 51 UI Residential Existing Top Twenty Measures by Economic Potential (GWh) 5-19
Table 52 CL&P Residential Existing Top Twenty Measures by Economic Potential (GWh) 5-19
Table 53 Commercial Existing Top Twenty Measures by Economic Potential (GWh) 5-20
Table 54 Industrial Top Twenty Measures by Economic Potential (GWh) 5-21
Table 55 Values for Adjustment Factors Applied to Economic Potential 5-22
Table 56 Comparison between 2009 Program Plan and Model Derived Scenarios 5-26
Table 57 Summary of Program Funding Scenario Potential Results—2009–2018 5-30
Table 58 Base Funding Scenario- Energy Savings 5-30
Table 59 Base Funding Scenario- Demand Savings 5-31
Table 510 Current Funding Scenario- Energy Savings 5-31
Table 511 Current Funding Scenario- Demand Savings 5-32
Table 512 Expanded Funding Scenario- Energy Savings 5-32
Table 513 Expanded Funding Scenario- Demand Savings 5-33
Table 514 Energy Savings Potential – Annual GWh 5-43
Table 515 Peak Demand Savings – MW 5-45
Figure 11 Cumulative Energy Savings Potentials and Program Funding Scenario Savings in 2018 – GWh per year 1-5
Figure 12 Achievable Energy Savings: All Sectors 1-7
Figure 13 Benefits and Costs of Energy-Efficiency Savings – 2009-2018* 1-8
Figure 14 Net Achievable Energy Savings by 2018 by Sector—GWh per Year 1-10
Figure 15a United Illuminating Electric Energy Savings Potential by End Use (2018) — Current Funding Scenario 1-11
Figure 31 Conceptual Framework for Estimating Resources 3-2
Figure 32 Conceptual Relationship among Energy-Efficiency Potential Definitions 3-3
Figure 33 Conceptual Overview of Study Process 3-4
Figure 41 Sector Base Electricity Usage Breakdown – Connecticut 4-2
Figure 51 Technical and Economic Potential by Sector (2018)–Cumulative GWh per year 5-10
Figure 52 Cumulative Technical and Economic Potential by Sector (2018)–Percentage of Current Funding Scenario Energy Use 5-10
Figure 53 UI Residential Economic Electric Savings Potential by End Use (2018) 5-11
Figure 54 CL&P Residential Economic Electric Savings Potential by End Use (2018) 5-12
Figure 55 Commercial Economic Electric Savings Potential by End Use (2018) 5-12
Figure 56 Industrial Economic Electric Savings Potential by End Use (2018) 5-13
Figure 57 United Illuminating Residential Energy Supply Curve 5-14
Figure 58 Connecticut Light and Power Residential Energy Supply Curve 5-15
Figure 59 United Illuminating Residential Capacity Supply Curve 5-15
Figure 510 Connecticut Light and Power Residential Capacity Supply Curve 5-16
Figure 511 Commercial Energy Supply Curve 5-16
Figure 512 Commercial Capacity Supply Curve 5-17
Figure 513 Industrial Energy Supply Curve 5-17
Figure 514 Industrial Capacity Supply Curve 5-18
Figure 515 Comparison of Instantaneous Savings Potentials 5-23
Figure 516 Program Funding Scenario Energy Savings in GWh: All Sectors 5-27
Figure 517 Benefits and Costs of Energy-Efficiency Savings – 2009-2018* 5-28
Figure 518 United Illuminating Current Funding Achievable Potential by End Use 5-34
Figure 519 Connecticut Light and Power Current Funding Achievable Potential by End Use 5-34
Figure 520 United Illuminating Base Funding Achievable Potential by End Use 5-35
Figure 521 Connecticut Light and Power Base Funding Achievable Potential by End Use 5-35
Figure 522 United Illuminating Expanded Funding Achievable Potential by End Use 5-36
Figure 523 Connecticut Light and Power Expanded Funding Achievable Potential by End Use 5-36
Figure 524 Commercial Net Energy Savings Potential - End Use Shares (2018) – Current Funding Scenario 5-38
Figure 525 Commercial Net Energy Savings Potential - End Use Shares (2018) – Base Funding Scenario 5-38
Figure 526 Commercial Net Energy Savings Potential - End Use Shares (2018) – Expanded Funding Scenario 5-39
Figure 527 Industrial Net Energy Savings Potential - End Use Shares (2018) – Current Funding Scenario 5-40
Figure 528 Industrial Net Energy Savings Potential – End Use Shares (2018) –Base Funding Scenario 5-40
Figure 529 Industrial Net Energy Savings Potential – End Use Shares (2018) – Expanded Funding Scenario 5-41
Figure 530 Instantaneous Savings Potential—Annual GWh 5-42
Figure 531 5-44
1-5
Connecticut Electric EE Study April 29, 2010
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;
· 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 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]
1.3 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
Electric (GWh) / % of Base Energy UseTechnical Potential
(Technically feasible) / 10,714 / 36%
Total Economic
(Cost effective) / 10,722 / 36%
Total Achievable
(Achievable from all policies) / 9,114 / 31%
Program Achievable
(Achievable from programs) / 6,616 / 23%
1.3.1 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 11 compares the estimates of efficiency potential by sector created for this report, including technical, initial and total economic, total achievable, instantaneous program 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 11 shows both the unweighted (2009 instantaneous) and the weighted 2018 savings. The program funding scenario results are included with the weighted 2018 results.