# PUBLIC DISCLOSURE

Achievable Energy-Efficiency Potentials Assessment Submitted to Georgia Power Company

Submitted By Nexant In partnership with: The Cadmus Group, Inc. January 31, 2012

### SECTION 1 

## PUBLIC DISCLOSURE Executive Summary

Achievable Energy-Efficiency Potentials Assessment 2 

# C ONTENTS

1 EXECUTIVE SUMMARY .................................

................................................... ....................3

1.1 BACKGROUND

.................................................. ................................................... ...................................... 3
1.2 A SSESSMENT A PPROACH

.................................................. ................................................... ........................ 3
1.3 D EFINITIONS OF E NERGY -E FFICIENCY P OTENTIALS

.................................................. .......................................... 4
1.4 S UMMARY R ESULTS

.................................................. ................................................... .............................. 5
1.4.1 Achievable Potentials by Sector ...................

................................................... ........................ 7
1.4.2 Achievable Potentials by End Use ..................

................................................... ...................... 8
1.5 A CHIEVABLE P OTENTIAL C OST -E FFECTIVENESS

.................................................. .............................................. 9
1.6 C ONCLUSIONS

.................................................. ................................................... .................................... 12
1.7 C AVEATS

.................................................. ................................................... ........................................... 13
1.7.1 Uncertainties .....................................

................................................... ................................ 13
1.7.2 Potential Reliability Impacts .....................

................................................... ......................... 13
1.7.3 Rate Impacts ......................................

................................................... ................................ 13



### 2 STUDY APPROACH ....................................

................................................... ..................... 15

2.1 I NTRODUCTION

.................................................. ................................................... .................................. 15
2.1.1 Background ........................................

................................................... ............................... 15
2.1.2 Objectives ........................................

................................................... .................................. 15
2.2 A PPROACH

.................................................. ................................................... ........................................ 15



### 3 ESTIMATES OF ENERGY-EFFICIENCY POTENTIALS..........

................................................... ... 20

3.1 I NTRODUCTION

.................................................. ................................................... .................................. 20
3.2 S UMMARY OF E NERGY -E FFICIENCY P OTENTIALS

................................................... .......................................... 20
3.2.1 Technical and Economic Potentials .................

................................................... .................. 21
3.3 T HEORETICALLY A CHIEVABLE P OTENTIALS

.................................................. ................................................... 23
3.3.1 By Sector .........................................

................................................... ................................... 24
3.3.2 By End Use ........................................

................................................... ................................. 26
3.3.3 Cost-Effectiveness ................................

................................................... .............................. 27
3.3.4 Detailed Analysis .................................

................................................... .............................. 37

3.3.4.1 Residential Sector ................................

................................................... ............................. 38
3.3.4.2 Commercial Sector .................................

................................................... .......................... 44
3.3.4.3 Industrial Sector .................................

................................................... .............................. 49



### 4 CONCLUSIONS .......................................

................................................... ........................ 55

APPENDIX A DETAILED METHODOLOGY .............................. .............................................. A-1

A.1 D EVELOPING A B ASELINE F ORECAST

.................................................. ................................................... ..... A-2
A.1.1 Data Sources ......................................

................................................... .............................. A-3
A.2 C OMPILING E NERGY -E FFICIENCY T ECHNOLOGY D ATA

.................................................. ................................. A-3
A.3 E STIMATING T ECHNICAL P OTENTIAL

.................................................. ................................................... ..... A-5
A.3.1 Non-Equipment Measures ............................

................................................... ................... A-6
A.4 E STIMATING E CONOMIC P OTENTIAL

.................................................. ................................................... ..... A-7
A.4.1 Benefit Components ................................

................................................... ........................ A-8
A.4.2 Measure Cost Components............................

................................................... .................. A-8
A.4.3 Caveats ...........................................

................................................... ................................. A-8
A.5 E STIMATING A CHIEVABLE P OTENTIAL

.................................................. ................................................... ... A-9



### APPENDIX B BENEFIT-COST ANALYSIS .............................

.................................................. B-1  

 

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Achievable Energy-Efficiency Potentials Assessment 3 

# 1 E XECUTIVE 

# S UMMARY

1.1 BACKGROUND
As part of the 2013 Integrated Resource Plan (“IRP” ) process, Georgia Power Company (“Georgia Power”) must complete an updated energy-efficiency technical, economic, and achievable potentials study, as described in the 2010 IRP Fina l Order (Dockets #31081 and #31082), per the Georgia Public Service Commission. This report deta ils the results of the potentials study to be used as part of Georgia Power’s 2013 IRP filing.

Georgia Power retained Nexant, Inc, subcontracting to The Cadmus Group, Inc., (“Cadmus”), to conduct an assessment of energy-efficiency potentia ls in the Georgia Power service area, building on results from the energy-efficiency measure scree ning and technical analyses, which Nexant had supported over the preceding months. Southern Compa ny Services provided additional support with the study’s modeling analyses.



### 1.2 ASSESSMENT APPROACH
The achievable energy-efficiency potentials assessm ent used the following three sequential steps:

• Organize input data: Nexant and Cadmus compiled final energy-efficiency measure screening results, effective useful life, increment al cost, and collected service area sales forecast data. Inputs included confidential data, s uch as: forecast of customer counts and floor space, end-use saturations, end-use unit ener gy consumption, and energy intensity.

• Estimate energy-efficiency potentials impacts at th e end-use level: Southern Company Services modeled individual energy-efficiency measu res in EnerSim 1 

### to create loadshapes, calculate electricity revenues, and evaluate any ot her utility impacts. The Profitability Reliability Incremental Cost Evaluation Model (“PRI CEM”) used the EnerSim loadshapes to calculate the electric economics of energy-efficien cy measures. Using energy savings and associated economic impacts (electric, gas, and wat er) for each of the 332 discrete measures, Cadmus analyzed groups of measures by fac ility type and end use.

• Estimate theoretically achievable impacts: Following a review of energy-efficiency market potentials studies, reported Energy Information Age ncy (“EIA”) data, and observed program results, Cadmus applied plausible market penetratio n curves to each end use, estimating

1 EnerSim—an hourly building energy simulation model , used to predict energy consumption in buildings b ased on construction characteristics, insulation, occupancy , orientation, local weather, and other attributes— was used to generate all energy usage profiles for weather-sens itive end uses examined for both residential and no nresidential measures. The U.S. Department of Energy, having cer tified and approved EnerSim, lists the model as a “ Qualified Software” on its Website.  

 

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Achievable Energy-Efficiency Potentials Assessment 4 

### technically feasible, economically feasible, and po tentially achievable energy-efficiency impacts.



### 1.3 DEFINITIONS OF ENERGY-EFFICIENCY POTENTIALS
Figure 1 shows types of potentials available in a u tility’s territory, defined as follows:

• Technical potential: The quantification of savings that can be realized if energy-efficiency measures passing the qualitative screening are appl ied in all feasible instances, regardless of

cost.

• Economic potential: A subset of technical potential, where measures ar e cost-effective from the Total Resource Cost (“TRC”) perspective, withou t regard to cross-subsidies.

• Achievable potential: Energy savings that can feasibly be achieved throu gh program and policy interventions. This study estimated theoreti cally achievable potential for three policy intervention scenarios corresponding to varying inc entive levels provided to end-use

consumers:

o Low incentives: Monetary incentives to customers, e qualing 25% of incremental costs of energy-efficiency improvements.

o Moderate incentives: Monetary incentives to custome rs, equaling 50% of incremental costs of energy-efficiency improvements.

o High incentives: Monetary incentives to customers, equaling 100% of incremental costs of energy-efficiency improvements.

Figure 1. Illustration of Energy-Efficiency Potenti als

Note: Figure 1 is from the Environmental Protection Agency National Action Plan for Energy Efficiency (“EPANAPEE”) Guide for Conducting Energy Efficiency Pote ntial Studies; for illustrative purpose only.  

 

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### Each scenario included significant expenditures on incentives subsidizing energy-efficiency measure purchases. Note that this study has been based on a number of estimates, including penetration rate projections that attempt to describe human beh aviors associated with program structures. One cannot know with certainty whether results can be o btained under the scenarios studied.



### 1.4 SUMMARY RESULTS
This section provides high-level results of achieva ble potential; the following section provides detailed results. The assessment period covers 2012 –2023, with cumulative results shown below. Achievable potential ranged from 6.0% to 15.1% of 2 023 electricity sales and 6.4% to 16.1% of 2023 peak demand 2 

### , depending on incentive scenarios. Table 1 present s the 2023 potential and percent of load for the three incentive scenarios. Figure 2 and Figure 3 provide forecasts (electricity sales and peak demand, respectively) relative to Georgia Power’s baseline forecast 3 

### , based on the achievable potential scenarios over the study horiz on.

Table 1. Theoretically Achievable Potential Savings (Cumulative through 2023)



### Load Type Low Incentive Moderate Incentive High Incentive

Total Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Reduction in Electricity Sales (MWh) REDACTED 6.0% REDACTED 9.1% REDACTED 15.1%

Reduction in Peak Demand (MW) REDACTED 6.4% REDACTED 9.7% REDACTED 16.1%

2 Electricity sales are defined as electric energy a t the customer’s meter. Peak Demand is defined as c oincident peak at the customer’s meter.

3 Baseline forecast is Georgia Power’s total forecas t, excluding territorial wholesale customers.  

 

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Achievable Energy-Efficiency Potentials Assessment 6 Figure 2. Energy Forecasts for Theoretically Achiev able Potential (Electricity Sales)

Figure 3. Demand Forecasts for Theoretically Achiev able Potential (Peak Demand)

0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000

2012 2014 2016 2018 2020 2022Electricty Sales (MWh)

Base Forecast

High Incentive

Moderate Incentive

Low Incentive

0 5,000 10,000 15,000 20,000 25,000

2012 2014 2016 2018 2020 2022Peak Demand (MW)

Base Forecast

High Incentive

Moderate Incentive

Low IncentiveREDACTEDREDACTED  

 

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### 1.4.1 Achievable Potentials by Sector

Figure 4 and Figure 5 provide theoretically achieva ble energy and peak demand potential for 2023 under the high incentive scenario by sector.

Figure 4. 2023 Theoretically Achievable Potential b y Sector (Electricity Sales)

Figure 5. 2023 Theoretically Achievable Potential b y Sector (Peak Demand) 

### Commercial,
60% Industrial, 19% Residential,
21%



### Commercial,

67% Industrial,
13% Residential,
20%  

 

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### 1.4.2 Achievable Potentials by End Use

Figure 6 and Figure 7 provide energy and peak deman d potential for 2023 under the high incentive scenario by end use (all sectors).

Figure 6. 2023 Theoretically Achievable Potential b y End Use (Electricity Sales)

Com-HVAC Equipment, 2%

Com-HVAC Shell, 19%

Com-Lighting, 29%

Com-Miscellaneous,
2% Com-Office Equipment, 6% Com-Refrigeration, 1% Com-Water Heating, 1% Ind-HVAC Equipment, 2% Ind-Lighting, 2% Ind-Other, 1%

Ind-Motors,
5% Ind-Process, 9% Res-Appliances, 7% Res-HVAC Equipment, 4% Res-HVAC Shell, 5% Res-Lighting, 2% Res-Water Heating, 4% Res-Plug Load, 0.3% Res-Pool Pump, 1%  

 

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Achievable Energy-Efficiency Potentials Assessment 9 Figure 7. 2023 Theoretically Achievable Potential b y End Use (Peak Demand)



### 1.5 ACHIEVABLE POTENTIAL COST-EFFECTIVENESS
The following tables present cost-effectiveness ind icators from Total Resource Cost (“TRC”), Ratepayer Impact Measure (“RIM”), and Participant C ost Test (“PCT”) perspectives, respectively, for achievable energy-efficiency impacts from 2012 thro ugh 2023. The tables present economic indicators for each scenario of theoretically achie vable potential. Refer to Appendix B for a detailed description of the methods used for calculating TRC , RIM, and PCT. Theoretically, TRC net benefits of between $2.6 and $6.4 billion can be achieved, at a RIM net cost of between $1.7 and $7.2 billion to electricity ratepayers. The RIM net cost indicates the amount electricity rates would have to Com-HVAC Equipment, 5%

Com-HVAC Shell, 19%

Com-Lighting, 32%

ComMiscellaneous, 2% Com-Office Equipment, 7% Com-Refrigeration, 1% Com-Water Heating, 1% Ind-HVAC Equipment, 1% Ind-Lighting, 1% Ind-Other, 1% Ind-Motors, 3%

Ind-Process, 6% Res-Appliances, 2%

Res-HVAC Equipment, 4% Res-HVAC Shell, 11% Res-Lighting, 1% Res-Water

Heating, 0.4% Res-Plug Load,

0.04% Res-Pool Pump, 0.7%  

 

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### increase due to energy efficiency—over and above ra te increases ordinarily expected. 4 

### Theoretically, PCT net benefits of between $4.3 and $13.6 billion are available to participants.

Table 2. Net Benefits and Benefit-to-Cost Ratio for TRC, RIM, and PCT Perspectives
(Cumulative NPV through 2023)



### TRC RIM PCT

Achievable Scenario Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost
Low Incentive $ 2,550 2. 4

- $ 1,732 0.7 $ 4,283 3

. 9 Moderate Incentive $3,862 2.4

- $3,188 0.6 $7,050 4.1 High Incentive $6,409 2.4

- $7,163 0.5 $13,572 4.6



### Figures 8, 9, and 10 illustrate the cost breakdowns for TRC, RIM, and PCT perspectives for each of
the three scenarios.

Figure 8. TRC Benefits and Costs by Scenario (Cumul ative through 2023)

4 This study does not estimate RIM benefits and cost s from a gas and water utility perspective, when ap plicable. To the extent gas or water utility lost revenues excee d avoided gas or water supply costs, as would norma lly be expected, gas or water rates would be adversely aff ected, and would rise above otherwise anticipated i ncreases. This assessment provides estimated net present valu e data for 2012 through 2023.

$0 $2 $4 $6 $8 $10 $12 $14 $16 $18

Low Mod HighNPV ($ Billions) Program Admin & Marketing

Participant Cost

Benefits (Other Utility Avoided Cost)

Benefits (Elec Avoided Cost)  

 

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## PUBLIC DISCLOSURE Executive Summary

Achievable Energy-Efficiency Potentials Assessment 11 Figure 9. RIM Benefits and Costs by Scenario (Cumul ative through 2023)

Figure 10. PCT Benefits and Costs by Scenario (Cumu lative through 2023) $0 $2 $4 $6 $8 $10 $12 $14 $16 $18

Low Mod HighNPV ($ Billions) Program Incentives

Program Admin & Marketing

Electric Utility Lost
Revenue
Benefits (Elec Avoided Cost)

$0 $2 $4 $6 $8 $10 $12 $14 $16 $18

Low Mod HighNPV ($ Billions) Program Incentives

Participant Cost

Participant Bill Savings (Other Utility)

Participant Bill Savings (Electric)  

 

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### Table 3 illustrates program incentive and administr ative costs for the three scenarios.

Table 3. Energy Efficiency Program Expenditures (Cu mulative NPV through 2023)



### Achievable Scenario Incentives ($MM) Administrative Costs ($MM) Total Costs ($MM)
Low Incentive $ 373 $ 374 $ 747 Moderate Incentive $1,129 $566 $1,695 High Incentive $3,748 $939 $4,687



### 1.6 CONCLUSIONS
This assessment identifies the potential for increa sed energy-efficiency investments by Georgia
Power customers above the levels that would natural ly occur but would require the significant incentives assumed in this analysis. These results will be useful in targeting energy-efficiency program planning efforts to areas with the highest market potential.

Customers can reduce their energy consumption and p eak power requirements by implementing energy-efficiency measures or actions, receiving ec onomic benefits directly through reductions in their energy bills. Customers who participate could also benefit from financial incentives offered by programs intended to accelerate markets for purchas e and installation of high-efficiency measures. At the same time, rates will rise for all customers . Non-participating customers will pay higher bills

, in effect, subsidizing the costs of incentives and other program costs to the benefit of program participants.

As in a similar 2007 assessment, this study examine s scenarios of theoretically achievable energyefficiency potential associated with low incentives , moderate incentives, and high incentives.

Economic benefits include:



### • Potential electric energy savings ranging from 6.0% of year 2023 forecast sales under a low incentive scenario to 15.1% under a high incentive scenario.



### • Incentives ranging from $25 million per year to mor e than $250 million per year in 2012, the study’s first year.



### • TRC benefits from energy-efficiency improvements co uld range as high as $2.6 billion to $6.4 billion.



### • Not including incentives, net participant cost bene fits range from $3.9 billion to $9.8 billion.  

 

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Achievable Energy-Efficiency Potentials Assessment 13 

### However, these benefits come at substantial costs t o ratepayers:



### • Net costs to electric utility ratepayers could rang e from $1.7 billion to $7.2 billion, over and above those associated with supply-side costs.



### • Program costs alone could increase electric ratepay er burden by a total of $750 million to $4.7 billion.



### 1.7 CAVEATS
In interpreting this study’s results (and, generall y, all studies of this nature), readers should cons ider the following, important caveats.



### 1.7.1 Uncertainties
Market acceptance rate serves as a key determinant of the potential for achievable energyefficiency savings. However, market acceptance depe nds on behavioral factors that are hard to predict and involve a high level of uncertainty. Th e 2005 Georgia Environmental Facilities Authority (“GEFA”) study noted a similar caveat: “The greates t source of uncertainty in our projections of achievable potential are the estimates of market sh are growth under each policy scenario.”

The estimated impacts of efficient technologies on energy consumption provide another key determinant of savings potential; yet, these inputs also present uncertainty. While efficient technology options can be reasonably well defined i n the near term, customer behaviors and electricity usage patterns vary widely, and can dif fer significantly from assumptions made to model “typical” usage profiles. Future years may experien ce greater uncertainties due to insufficient information about future technology choices and fut ure codes and standards improvement. Consequently, the availability and magnitude of fut ure impacts must be considered inherently speculative.



### 1.7.2 Potential Reliability Impacts

The above-noted uncertainties could result in a det erioration of system reliability, if estimates of achievable energy savings are used to justify cance llation or deferral of Power Purchase Agreements (“PPAs”) and generation capacity construction progr ams. If expected energy and capacity savings do not materialize or sustain, planned PPAs and genera tion resource options—many of which require long lead times—might be insufficient to maintain s ystem reliability until additional resource options can be secured.



### 1.7.3 Rate Impacts
Energy-efficiency programs could cause electricity rates to rise faster than they would ordinarily. The noted uncertainties could result in lower energ y savings, without corresponding reductions in fixed program costs and, hence, adversely impact ra tes. Market acceptance rates failing to materialize, for example, reduce incentives and reb ate processing costs, but do not reduce  

 

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Achievable Energy-Efficiency Potentials Assessment 14 

### marketing or other fixed program management and rep orting costs. Moreover, if realized technology impacts prove less than estimated, impac ts of all estimated costs for rebates, processing, marketing, and administration would remain, but wit h diminished supply cost savings. Thus, rate impacts could be more severe than those estimated i n this study.

## PUBLIC DISCLOSURE

Achievable Energy-Efficiency Potentials Assessment 15 

# 2 S TUDY 

# A PPROACH

2.1 INTRODUCTION
This report presents findings from the electric ene rgy-efficiency technical, economic, and achievable potentials study, supporting Georgia Power’s 2013 I RP filing. The study’s horizon covers 2012–2023, encompassing the residential, commercial, and indus trial sectors.



### 2.1.1 Background

As part of the 2013 IRP process, Georgia Power must complete an updated energy-efficiency technical, economic, and achievable potentials stud y, as described in the 2010 IRP Final Order (Dockets #31081 and #31082), per the Georgia Public Service Commission. This report details the results of the potentials study to be used as part of Georgia Power’s 2013 IRP filing.

Georgia Power retained Nexant, Inc, subcontracting to Cadmus, to conduct an assessment of energy-efficiency potentials in the Georgia Power s ervice area, building on results from the energy efficiency measure screening and technical analyses , which Nexant had supported over the preceding months. Southern Company Services provide d additional support with the study’s modeling analyses.



### 2.1.2 Objectives

This study includes the following key objectives:

• Expanding the scope of energy efficiency measure sc reening and economic analyses to quantify theoretical technical and economic energy- efficiency potentials.

• Assessing theoretically achievable energy-efficienc y potential through scenario analyses, corresponding to potential policy settings, in whic h customers would be offered low, moderate, and high incentives.



### 2.2 APPROACH
The general methodology used can best be described as a hybrid “top-down/bottom-up” approach. As illustrated in Figure 11, it began by examining the current energy forecast, and then decomposing
this into its constituent customer-class and end-us e components. Effects for a range of energyefficiency approaches and practices for each end us e could then be examined, while accounting for fuel shares, current market saturations, technical feasibility, and costs. These unique impacts were aggregated to produce resource potentials estimates at the end-use, customer-class, and system
levels.  

 

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Achievable Energy-Efficiency Potentials Assessment 16 Figure 11. Potentials Assessment Methodology



### The following, detailed methodology for estimating energy-efficiency potentials remained consistent
for all three sectors:

• 

### Develop a baseline forecast: The study created a baseline electric energy forec ast, based on end-use consumption estimates, and calibrated to Ge orgia Power’s official electric energy forecast. This provided accurate consumption estima tes by fuel, sector, customer segment, end use, and year.

• 

### Compile measure lists: All measures applicable to Georgia Power’s climate and customers were analyzed to best depict energy-efficiency pote ntials over a 12-year planning horizon (2012–2023). A qualitative screen was applied to th e measure list using the following criteria
for removal:  

 

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Achievable Energy-Efficiency Potentials Assessment 17 

### o Difficult to quantify savings



### o Current practice



### o Better measure available



### o Immature or unproven technology



### o Limited applicability



### o Poor customer acceptance



### o Health and environmental concerns



### o End-use service degradation

• 

### Collect cost and impact data for measures: For all measures passing the qualitative screening, market research and models were used to estimate costs, energy, and demand savings.



### o Nexant conducted market research to estimate measur e costs and effective useful life. The cost data differentiated between the type of co st (capital, installation labor, maintenance, etc.) to separately evaluate different implementation modes: retrofit (capital plus installation labor plus incremental m aintenance); new construction (incremental capital and maintenance); and burnout costs (incremental capital and maintenance).



### o Georgia Power and Southern Company Services conduct ed the economic analysis for energy-efficiency measures. All residential and com mercial measures were analyzed using the EnerSim model, which simulated energy usa ge in homes and buildings using weather data specific to Georgia Power’s service ar ea. Home and building prototypes were modeled for existing and new dwellings and fac ilities. The resulting EnerSim data included loadshapes, calculation of electricity imp acts (revenues, energy, and demand), and gas and water impacts. Loadshapes and electrici ty revenue impacts provided inputs for PRICEM in calculating total electric utility av oided costs and electric utility revenue impacts.

Nexant determined industrial energy-efficiency meas ure savings using the Industrial Assessment Centers (“IAC”) Database, and other sour ces Nexant had available. Georgia
Power used BillGen 5 

### for calculating electricity revenue impacts of ind ustrial measures, with loadshapes either created using EnerSim or sel ected from an end-use database. As

5 BillGen® is a data analysis and billing applicatio n system used to calculate utility bills and perfor m rate comparisons for residential, commercial, and indust rial customers. BillGen is a registered trademark o f ABB.  

 

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Achievable Energy-Efficiency Potentials Assessment 18 

### with residential and commercial, the loadshapes and electricity revenue impacts were then input into PRICEM for calculating total electr ic utility avoided costs and electric utility revenue impacts.

• 

### Estimate Potentials:

o Naturally occurring conservation refers to energy-efficiency gains occurring due to normal market forces, such as technological changes , energy prices, market transformation efforts, and improved energy codes a nd standards. This analysis accounted for market effect components resulting fr om naturally occurring conservation by explicitly incorporating changes to codes and standards, and marginal efficiency shares in developing base-case forecasts

.



### o Technical potential assumes all resource opportunities may be captured , regardless of their costs or market barriers. For energy-efficien cy, technical potential is divided into two classes: retrofits and lost-opportunity resourc es.



### o Economic potential can be defined as technical potential measure provi ng costeffective, using the TRC test as the basis for an e conomic screen. Measures with a TRC benefit-cost ratio greater than one are included in the economic potential.



### o Achievable potential can be defined as the portion of economic potentia l that might be assumed achievable in the course of the planning ho rizon, given market barriers may impede customer participation in energy-efficiency programs; these achievable potential levels serve principally as planning guid elines. Ultimately, actual achievable opportunity levels depend on: customers’ willingnes s and ability to participate in energy efficiency programs; administrative constraints; an d availability of an effective delivery infrastructure. Customers’ willingness to participa te in energy efficiency programs also depends on the size of an incentive offered. As suc h, the study analyzed three incentive scenarios relative to their incremental cost: low ( 25% incentive); moderate (50% incentive); and high (100% incentive).

The study classified measures used to assess potent ials into the following four categories:

• 

### Existing retrofit represents retrofit opportunities in existing cons truction. An example is shell improvements (e.g., insulation, weather-strip ping). This potential can be considered a “retrofit” as it would occur in existing building s tock, and, theoretically, would be available any time during the study.

• 

### Existing equipment replacement refers to efficiency upgrades conducted during nor mal replacements of equipment in existing buildings. Th ese include efficient end-use equipment,
such as central air conditioners and ENERGY STAR® a ppliances. Equipment burnout rates  

 

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Achievable Energy-Efficiency Potentials Assessment 19 

### drive the availability of these resources: if an op portunity to upgrade is missed, it does not become available until the new equipment burns out (hence, is a lost opportunity).

• 

### New construction improvements represent the potential specific to retrofit measu res in new construction. For some retrofit measures, costs and savings differ from existing construction due to an array of baseline conditions (building codes vs. existing conditions). Georgia’s new construction forecast drives this pot ential’s availability, and missed efficiency upgrades typically must wait until installed techno logies are replaced (hence, a lost opportunity).

• 

### New construction equipment efficiency refers to efficiency equipment upgrades in new construction. These include efficient end-use equip ment above existing efficiency standards for new construction. Similarly to new construction retrofit opportunities, the new construction forecast drives this potential, and ef ficient equipment must be installed as part of the construction process. Missed efficiency upgr ades must be considered lost opportunities.

The methodology used for estimating energy-efficien cy potentials was based on standard industry practices. Appendix A provides a detailed discussion of this methodology.

## PUBLIC DISCLOSURE

Achievable Energy-Efficiency Potentials Assessment 20 

# 3 E STIMATES OF 

# E NERGY 

# -E FFICIENCY 

# P OTENTIALS

3.1 INTRODUCTION
This assessment primarily sought to develop estimat es of available energy-efficiency potentials to assist in targeting energy-efficiency programs in a reas where the highest market potentials exist. To support these efforts, Cadmus performed an in-depth assessment of technical, economic, and achievable potentials on energy-efficiency measures in the residential, commercial, and industrial

sectors.

Data on measure costs, savings, and market size wer e collected at the most granular level possible. Within each sector, the study distinguished between customer segments or facility types, and their respective applicable end uses. Six residential seg ments (existing and new construction for singlefamily, multifamily, and manufactured homes), 24 co mmercial segments (12 building types within existing and new construction vintages), and 15 ind ustrial segments were analyzed.

The study included a comprehensive set of energy-ef ficiency measures, applicable to climate and customer characteristics in Georgia Power’s service territory, and drawn from measures used in a previous assessment, as well as new measures made c ommercially available since the last study. As shown in Table 4, analysis began by assessing techn ical potential for 332 unique energy-efficiency measures. In considering all permutations of these measures across all customer sectors and segments, customized data had to be compiled and an alyzed for over 8,000 measures.

Table 4. Energy-Efficiency Measure Counts by Sector



### Sector Unique Measures Permutations Residential 118 1,703 Commercial 144 5,485
Industrial 70 822



### 3.2 SUMMARY OF ENERGY-EFFICIENCY POTENTIALS
This section presents detailed results of technical , economic, and achievable potential by sector. Table 5 and Table 6 provide the three potentials, b y sector, for energy and demand savings, respectively, in 2023 (the study’s final year). Pot entials are provided in absolute terms and as a percent of forecasted load. The achievable potentia l described in the summary represents the high scenario, which assumes incentives of 100% of incre mental costs.  

 

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## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 21 Table 5. Summary of Energy Potentials (Cumulative t hrough 2023) by Sector



### Sector Technical Potential Economic Potential Achievable Potential

MWh Percent of

2023 Load MWh Percent of 2023 Load MWh Percent of 2023 Load Residential REDACTED 22.6% REDACTED 14.7% REDACTED 9.5% Commercial REDACTED 34.8% REDACTED 31.9% REDACTED 22.1% Industrial REDACTED 16.2% REDACTED 15.8% REDACTED 11.4% TOTAL REDACTED 25.8% REDACTED 21.9% REDACTED 15.1%

Table 6. Summary of Peak Demand Potentials (Cumulat ive through 2023) by Sector



### Sector Technical Potential Economic Potential Achievable Potential

MW Percent of 2023 Load MW Percent of 2023 Load MW Percent of 2023 Load Residential REDACTED 23.5% REDACTED 11.7% REDACTED 7.7% Commercial REDACTED 44.5% REDACTED 39.9% REDACTED 26.7% Industrial REDACTED 17.4% REDACTED 16.9% REDACTED 12.2% TOTAL REDACTED 30.9% REDACTED 23.9% REDACTED 16.1%



### 3.2.1 Technical and Economic Potentials

The technical and economic potential concepts, used by energy-efficiency practitioners since the late 1980s, often have identified sectors and end u ses associated with the largest amounts of energy-savings potential. The concepts have not bee n used to represent how much potential could be achieved, but, rather, simply to focus research efforts on the sectors and end uses with higher savings potentials.

To derive technical and economic potential estimate s, technically and economically feasible measures are applied to all sectors and end uses. E conomically feasible measures have a TRC benefit-to-cost ratio greater than or equal to 1.0. Appendix A provides further details on the methodology.

Figure 12 and Figure 13 illustrate the residential and commercial sectors have the largest, technicall y feasible energy savings potential, followed by the industrial sector. The majority of technically feasible potential in each sector also proves econo mically feasible.  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 22 Figure 12. Technically and Economically Feasible Po tential by Sector (2023 Electricity Sales)

Figure 13. Technically and Economically Feasible Po tential by Sector (2023 Peak Demand)

0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000

Sales Technical Potential Economic

PotentialElectricity Sales (MWh)

Residential

Industrial

Commercial

0 5,000 10,000 15,000 20,000 25,000

Sales Technical Potential Economic

PotentialPeak Demand (MW)

Residential

Industrial

CommercialREDACTEDREDACTED  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 23 

### 3.3 THEORETICALLY ACHIEVABLE POTENTIALS
Table 7 provides the achievable potential in 2023 b y incentive scenario.

Table 7. Theoretically Achievable Potential Savings (Cumulative through 2023)



### Load Type Low Incentive Moderate Incentive High Incentive

Total Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Reduction in Electricity Sales (MWh) REDACTED 6.0% REDACTED 9.1% REDACTED 15.1%

Reduction in Peak Demand (MW) REDACTED 6.4% REDACTED 9.7% REDACTED 16.1%



### Figure 14 and Figure 15 present energy forecasts if all theoretically achievable potential was realized, in energy sales and peak demand, by scena rio over the planning horizon. These forecasts are produced by Cadmus using the methodology descri bed in Appendix A.

Figure 14. Energy Forecasts for Theoretically Achi evable Potential (Electricity Sales)

0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000

2012 2014 2016 2018 2020 2022Electricty Sales (MWh)

Base Forecast

High Incentive

Moderate Incentive

Low IncentiveREDACTED  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 24 Figure 15. Demand Forecasts for Theoretically Achie vable Potential (Peak Demand)



### 3.3.1 By Sector
Figure 16 and Figure 17 show theoretically achievab le energy and peak demand savings potential by sector for the high incentive scenario. The commerc ial sector accounts for more than half of the energy-savings potential, and almost two-thirds of the peak reduction potential. The residential and industrial sectors account for nearly equal portion s of the remaining potential for electricity sales, while the residential sector accounts for the major ity of the remaining peak demand reduction.

0 5,000 10,000 15,000 20,000 25,000

2012 2014 2016 2018 2020 2022Peak Demand (MW)

Base Forecast

High Incentive

Moderate Incentive

Low IncentiveREDACTED  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 25 Figure 16. 2023 Theoretically Achievable Potential by Sector (Electricity Sales)

Figure 17. 2023 Theoretically Achievable Potential by Sector (Peak Demand) 

### Commercial,
60% Industrial, 19% Residential,
21%



### Commercial,

67% Industrial, 13% Residential,
20%  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 26 

### 3.3.2 By End Use

A further understanding of theoretically achievable potential is aided by consideration of contributing end uses. Figure 18 and Figure 19 illu strate—in terms of potential savings under the high incentive scenario—commercial lighting (29% of potential energy savings) and commercial HVAC shell (19%) are the most significant end uses.

Figure 18. 2023 Theoretically Achievable Potential by End Use (Electricity Sales)

Com-HVAC Equipment, 2%

Com-HVAC Shell, 19%

Com-Lighting, 29%

Com-Miscellaneous,
2% Com-Office Equipment, 6% Com-Refrigeration, 1% Com-Water Heating, 1% Ind-HVAC Equipment, 2% Ind-Lighting, 2% Ind-Other, 1%

Ind-Motors,
5% Ind-Process, 9% Res-Appliances, 7% Res-HVAC Equipment, 4% Res-HVAC Shell, 5% Res-Lighting, 2% Res-Water Heating, 4% Res-Plug Load, 0.3% Res-Pool Pump, 1%  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 27 Figure 19. 2023 Theoretically Achievable Potential by End Use (Peak Demand)



### 3.3.3 Cost-Effectiveness

Theoretically achievable estimated energy savings p otential has significant economic benefits to the general economy and many customers served by the Ge orgia Power. These economic benefits, however, require significant investment, which woul d cost all ratepayers.

Participant and program costs associated with theor etically achievable potential scenarios include the following:

• 

### Net participant costs: Incremental costs to purchase, install, and mainta in energy-efficiency measures less any incentive received.

• 

### Program incentives: Financial incentives paid by energy-efficiency pro grams to subsidize purchases of energy-efficiency measures. Com-HVAC Equipment, 5%

Com-HVAC Shell, 19%

Com-Lighting, 32%

ComMiscellaneous, 2% Com-Office Equipment, 7% Com-Refrigeration, 1% Com-Water Heating, 1% Ind-HVAC Equipment, 1% Ind-Lighting, 1% Ind-Other, 1% Ind-Motors, 3%

Ind-Process, 6% Res-Appliances, 2%

Res-HVAC Equipment, 4% Res-HVAC Shell, 11% Res-Lighting, 1% Res-Water

Heating, 0.4% Res-Plug Load,

0.04% Res-Pool Pump, 0.7%  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 28 

### • 

### Program administration: Administrative, marketing, promotional, and other costs associated with managing programs designed to achie ve energy-efficiency savings.

Table 8 lists estimated participant and program cos ts associated with the theoretically achievable
scenarios.

Table 8. Participant and Program Costs by Achievabl e Potential Scenario
(NPV Thousands, Cumulative through 2023)



### Achievable Scenario Net Participant Costs Program Incentives Program Administration
Low Incentive $ 1,117,725 $ 372,575 $ 374, 3 75 Moderate Incentive $1,129,016 $1,129,016 $566,308 High Incentive $0 $3,748,332 $938,883



### Benefits and costs can be expressed from three uniq ue perspectives: TRC, RIM, and PCT. Further details on these perspectives can be found in Appen dix B.

Potentially achievable benefits, from programmatic efforts from 2012 through 2023, could range as high as $2.6 billion to $6.4 billion from a TRC per spective. Associated costs to electricity ratepayer s could range from $1.7 billion to $7.2 billion from the RIM perspective. The RIM net cost indicates the amount electricity rates would have to increase due to energy-efficiency—over and above rate increases ordinarily expected. 6 

### Figure 20, Figure 21, and Figure 22 show benefits and costs from TRC, RIM, and PCT perspectives for the theoreticall y achievable scenarios.

6 This study does not estimate RIM benefits and cost s from a gas and water utility perspective, when ap plicable. To the extent gas or water utility lost revenues excee d avoided gas or water supply costs, as would norma lly be expected, gas or water rates would be adversely aff ected, and would rise above otherwise anticipated i ncreases. This assessment provides estimated net present valu e data for 2012 through 2023.  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 29 Figure 20. TRC Benefits and Costs by Scenario (Cumu lative through 2023)

Figure 21. RIM Benefits and Costs by Scenario (Cumu lative through 2023) $0 $2 $4 $6 $8 $10 $12 $14 $16 $18

Low Mod HighNPV ($ Billions) Program Admin & Marketing

Participant Cost

Benefits (Other Utility Avoided Cost)

Benefits (Elec Avoided Cost)

$0 $2 $4 $6 $8 $10 $12 $14 $16 $18

Low Mod HighNPV ($ Billions) Program Incentives

Program Admin & Marketing

Electric Utility Lost
Revenue
Benefits (Elec Avoided Cost)  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 30 Figure 22. PCT Benefits and Costs by Scenario (Cumu lative through 2023)



### From each perspective, Table 9 provides net benefit s and benefit-to-cost ratios for each achievable potential scenario.

Table 9. Net Benefits and Benefit-to-Cost Ratio fro m TRC, RIM, and PCT Perspectives
(Cumulative NPV through 2023)



### TRC RIM PCT

Achievable Scenario Net

Benefits (Millions) Benefit/

Cost Net

Benefits (Millions) Benefit/

Cost Net

Benefits (Millions) Benefit/

Cost
Low Incentive $ 2,550 2. 4

- $ 1,732 0.7 $ 4,283 3.9 Moderate Incentive $3,862 2.4

- $3,188 0.6 $7,050 4.1 High Incentive $6,409 2.4

- $7,163 0.5 $13,572 4.6

$0 $2 $4 $6 $8 $10 $12 $14 $16 $18

Low Mod HighNPV ($ Billions) Program Incentives

Participant Cost

Participant Bill Savings (Other Utility)

Participant Bill Savings (Electric)  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 31 

### Table 10 through Table 15 list similar economic ind icators by sector and end use for each scenario. The PCT is given both with and without incentives.

Table 10. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for All Sectors

(Low Incentive Scenario, Cumulative NPV through 202 3)



### TRC RIM

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Res - Appliances $148 2.8 -$168 0.5 Res - HVAC Equipment $72 1.7 -$116 0.5 Res - HVAC Shell $426 1.7 -$286 0.5 Res - Lighting $27 2.4 -$56 0.5 Res - Plug Load $6 2.9 -$9 0.5 Res - Pool Pump $13 3.6 -$9 0.7 Res - Water Heating $51 1.4 -$111 0.5 Com - HVAC Equipment $95 4.9 -$19 0.9 Com - HVAC Shell $338 1.8 -$341 0.7 Com - Lighting $755 3.8 -$368 0.7 Com - Miscellaneous $165 3.8 -$56 0.6 Com - Office Equipment $67 3.1 -$53 0.7 Com - Refrigeration $23 7.8 -$10 0.7 Com - Water Heating $28 3.2 -$20 0.6 Ind - HVAC Equipment $46 7.9 -$11 0.8 Ind - Lighting $47 4.3 -$7 0.9 Ind - Motors $81 2.6 -$43 0.8 Ind - Other $21 3.4 -$7 0.8 Ind - Process $140 4.7 -$42 0.8  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 32 Table 11. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for All Sectors

(Moderate Incentive Scenario, Cumulative NPV throug h 2023)



### TRC RIM

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Res - Appliances $225 2.8 -$280 0.5 Res - HVAC Equipment $108 1.6 -$208 0.5 Res - HVAC Shell $646 1.7 -$614 0.4 Res - Lighting $40 2.4 -$90 0.5 Res - Plug Load $9 2.9 -$15 0.5 Res - Pool Pump $20 3.6 -$16 0.6 Res - Water Heating $77 1.4 -$203 0.5 Com - HVAC Equipment $144 4.9 -$37 0.8 Com - HVAC Shell $512 1.8 -$641 0.6 Com - Lighting $1,143 3.8 -$638 0.7 Com - Miscellaneous $250 3.8 -$103 0.5 Com - Office Equipment $101 3.1 -$90 0.6 Com - Refrigeration $35 7.8 -$16 0.7 Com - Water Heating $43 3.2 -$34 0.6 Ind - HVAC Equipment $70 7.9 -$19 0.8 Ind - Lighting $72 4.3 -$15 0.9 Ind - Motors $123 2.6 -$81 0.7 Ind - Other $32 3.4 -$13 0.8 Ind - Process $213 4.7 -$75 0.8  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 33 Table 12. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for All Sectors

(High Incentive Scenario, Cumulative NPV through 20 23)



### TRC RIM

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Res - Appliances $373 2.8 -$548 0.4 Res - HVAC Equipment $177 1.6 -$453 0.4 Res - HVAC Shell $1,072 1.7 -$1,620 0.3 Res - Lighting $67 2.4 -$170 0.4 Res - Plug Load $15 2.9 -$28 0.5 Res - Pool Pump $33 3.6 -$31 0.6 Res - Water Heating $128 1.4 -$455 0.4 Com - HVAC Equipment $240 4.9 -$85 0.8 Com - HVAC Shell $849 1.8 -$1,476 0.5 Com - Lighting $1,898 3.8 -$1,329 0.7 Com - Miscellaneous $415 3.8 -$230 0.5 Com - Office Equipment $168 3.1 -$182 0.6 Com - Refrigeration $59 7.8 -$29 0.7 Com - Water Heating $71 3.2 -$70 0.5 Ind - HVAC Equipment $116 7.9 -$39 0.8 Ind - Lighting $119 4.3 -$40 0.8 Ind - Motors $204 2.6 -$186 0.6 Ind - Other $53 3.4 -$31 0.7 Ind - Process $353 4.7 -$162 0.7  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 34 Table 13. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for All Sectors

(Low Incentive Scenario, Cumulative NPV through 202 3)



### PCT (with incentives) PCT (no incentives)

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Res - Appliances $316 5.7 $299 5.5 Res - HVAC Equipment $188 3.1 $166 2.9 Res - HVAC Shell $712 2.5 $592 2.2 Res - Lighting $82 6.2 $78 6.0 Res - Plug Load $15 7.0 $15 6.8 Res - Pool Pump $23 6.6 $22 6.3 Res - Water Heating $162 2.7 $138 2.5 Com - HVAC Equipment $114 6.8 $110 6.6 Com - HVAC Shell $679 3.1 $597 2.8 Com - Lighting $1,122 6.2 $1,069 6.0 Com - Miscellaneous $221 5.7 $209 5.5 Com - Office Equipment $120 5.8 $114 5.5 Com - Refrigeration $33 12.9 $32 12.7 Com - Water Heating $48 5.6 $46 5.4 Ind - HVAC Equipment $58 11.7 $56 11.5 Ind - Lighting $54 5.7 $52 5.5 Ind - Motors $124 4.0 $114 3.7 Ind - Other $28 5.0 $26 4.8 Ind - Process $182 7.0 $175 6.8  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 35 Table 14. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for All Sectors (Moderate Incentive Scenario, Cumulative NPV throug h 2023)



### PCT (with incentives) PCT (no incentives)

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Res - Appliances $504 6.0 $453 5.5 Res - HVAC Equipment $316 3.4 $249 2.9 Res - HVAC Shell $1,260 2.7 $898 2.2 Res - Lighting $131 6.5 $119 6.0 Res - Plug Load $24 7.3 $22 6.8 Res - Pool Pump $36 6.8 $33 6.3 Res - Water Heating $280 3.0 $209 2.5 Com - HVAC Equipment $181 7.1 $166 6.6 Com - HVAC Shell $1,153 3.3 $905 2.8 Com - Lighting $1,781 6.5 $1,619 6.0 Com - Miscellaneous $353 6.0 $317 5.5 Com - Office Equipment $192 6.0 $173 5.5 Com - Refrigeration $51 13.2 $49 12.7 Com - Water Heating $77 5.9 $69 5.4 Ind - HVAC Equipment $89 12.0 $85 11.5 Ind - Lighting $87 6.0 $78 5.5 Ind - Motors $204 4.2 $172 3.7 Ind - Other $45 5.3 $40 4.8 Ind - Process $287 7.3 $264 6.8  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 36 Table 15. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for All Sectors

(High Incentive Scenario, Cumulative NPV through 20 23)



### PCT (with incentives) PCT (no incentives)

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Res - Appliances $921 6.5 $753 5.5 Res - HVAC Equipment $630 3.9 $410 2.9 Res - HVAC Shell $2,692 3.2 $1,490 2.2 Res - Lighting $237 7.0 $197 6.0 Res - Plug Load $43 7.8 $37 6.8 Res - Pool Pump $65 7.3 $55 6.3 Res - Water Heating $583 3.5 $346 2.5 Com - HVAC Equipment $325 7.6 $275 6.6 Com - HVAC Shell $2,325 3.8 $1,502 2.8 Com - Lighting $3,226 7.0 $2,688 6.0 Com - Miscellaneous $645 6.5 $527 5.5 Com - Office Equipment $350 6.5 $286 5.5 Com - Refrigeration $88 13.7 $81 12.7 Com - Water Heating $141 6.4 $115 5.4 Ind - HVAC Equipment $155 12.5 $141 11.5 Ind - Lighting $159 6.5 $130 5.5 Ind - Motors $390 4.7 $286 3.7 Ind - Other $84 5.8 $66 4.8 Ind - Process $515 7.8 $439 6.8  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 37 

### 3.3.4 Detailed Analysis

Congruent with Georgia Power’s 2007 study and with the 2005 GEFA study’s scope and detail, this section presents analytical results of estimated sa vings associated with each theoretically achievable scenario, by sector.

Table 16 lists theoretically achievable potential, in absolute terms and as a share of 2023 load, by

sector.

Table 16. Theoretically Achievable Potential Saving s by Sector (Cumulative through 2023)



### Low Incentive Achievable Potential Moderate Incentive Achievable Potential High Incentive Achievable Potential

Load Type Total

Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Residential Reduction in Electricity Sales (MWh) REDACTED 3.9% REDACTED 5.8% REDACTED 9.5%

Reduction in Peak Demand (MW) REDACTED 3.1% REDACTED 4.6% REDACTED 7.7%

Commercial Reduction in Electricity Sales (MWh) REDACTED 8.8% REDACTED 13.3% REDACTED 22.1%

Reduction in Peak Demand (MW) REDACTED 10.6% REDACTED 16.1% REDACTED 26.7%

Industrial Reduction in Electricity Sales (MWh) REDACTED 4.5% REDACTED 6.9% REDACTED 11.4%

Reduction in Peak Demand (MW) REDACTED 4.9% REDACTED 7.4% REDACTED 12.2%

Total Reduction in Electricity Sales (MWh) REDACTED 6.0% REDACTED 9.1% REDACTED 15.1%

Reduction in Peak Demand (MW) REDACTED 6.4% REDACTED 9.7% REDACTED 16.1%  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 38 

### 3.3.4.1 Residential Sector
Table 17 presents the residential sector’s achievab le potential for the three incentive scenarios.

Table 17. Theoretically Achievable Potential Saving s for Residential Sector

(Cumulative through 2023)



### Low Incentive Achievable Potential Moderate Incentive Achievable Potential High Incentive Achievable Potential

Load Type Total

Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Reduction in Electricity Sales (MWh) REDACTED 3.9% REDACTED 5.8% REDACTED 9.5%

Reduction in Peak Demand (MW) REDACTED 3.1% REDACTED 4.6% REDACTED 7.7%



### Figure 23 and Figure 24 show the residential sector ’s baseline forecast and forecasts by achievable potential scenario, for energy sales and peak deman d forecast, respectively.

Figure 23. Energy Forecasts for Theoretically Achie vable Potential in Residential Sector (Electricity Sales)

0 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 30,000,000 35,000,000 40,000,000

2012 2014 2016 2018 2020 2022Electricity Sales (MWh)

Base Forecast
High Incentive

Moderate Incentive

Low IncentiveREDACTED  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 39 Figure 24. Demand Forecasts for Theoretically Achie vable Potential in Residential Sector (Peak Demand)



### Figure 25 and Figure 26 illustrate theoretically ac hievable residential savings under the high incentive scenario by end use for energy and peak d emand, respectively. The potential electric energy savings concentrate in four end-use categori es: appliances (30% of potential); HVAC shell (22%); HVAC equipment (17%); and water heating (17% ). Lighting, which historically has been a large portion of the potential, has dropped in significan ce, in large part due to the new federal efficiency
standards.

Figure 25. Residential - 2023 Theoretically Achieva ble Potential by End Use (Electricity Sales)

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

2012 2014 2016 2018 2020 2022Peak Demand (MW)

Base Forecast
High Incentive

Moderate Incentive

Low Incentive

Appliances,
30%

HVAC Equipment, 17% HVAC Shell,
22% Lighting, 10% Water Heating, 17% Plug Load, 2% Pool Pump, 2%REDACTED  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 40 Figure 26. Residential - 2023 Theoretically Achieva ble Potential by End Use (Peak Demand)



### The relative increase in the share for HVAC shell a nd equipment measures under the peak demand potential reflects the cooling savings from these m easures.

Potentially achievable residential sector benefits, from programmatic efforts from 2012 through 2023, could range from $0.7 billion to $1.9 billion from a TRC perspective. Associated costs to ratepayers could range from $0.8 billion to $3.3 bi llion from the RIM perspective.

Table 18 lists the net present value of participant and program costs associated with theoretically achievable potential scenarios.

Table 19 lists benefits and costs from TRC, RIM, an d PCT perspectives of theoretically achievable
scenarios.

Table 20, Table 21, and Table 22 list benefits and costs from TRC and RIM perspectives, by end use,
for the scenarios.

Table 23, Table 24, and Table 25 list benefits and costs from the PCT perspective by end use, with and without incentives, illustrating energy-efficie ncy measures’ economic attractiveness, even without utility incentives to subsidize initial mea sure costs. Appliances,
12%

HVAC Equipment, 23%

HVAC Shell,
54% Lighting, 5% Water

Heating, 2% Plug Load, 0.2% Pool Pump,
4%  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 41 Table 18. Participant and Program Costs by Achievab le Potential Scenario for Residential Sector (NPV Thousands, Cumulative through 2023)



### Net Participant Costs Program Incentives Program Administration Achievable Scenario
Low Incentive $561,553 $187,184 $188,984 Moderate Incentive $567,225 $567,225 $285,412 High Incentive $0 $1,883,187 $472,597

Table 19. Net Benefits and Benefit-to-Cost Ratio fo r TRC, RIM, and PCT Perspectives for Residential Se ctor (Cumulative NPV through 2023)



### TRC RIM PCT

Achievable Scenario Net

Benefits (Millions) Benefit/

Cost Net

Benefits (Millions) Benefit/

Cost Net

Benefits (Millions) Benefit/

Cost
Low Incentive $744 1.8

- $755 0.5 $1,499 3.0 Moderate Incentive $1,125 1.8

- $1,425 0.5 $2,550 3.2 High Incentive $1,865 1.8

- $3,305 0.4 $5,171 3.7

Table 20. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for Residential Sector

(Low Incentive Scenario, Cumulative NPV through 202 3)



### TRC RIM

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Appliances $148 2.8

- $168 0.5 HVAC Equipment $72 1.7

- $116 0.5 HVAC Shell $426 1.7

- $286 0.5 Lighting $27 2.4

- $56 0.5 Plug Load $6 2.9

- $9 0.5 Pool Pump $13 3.6

- $9 0.7 Water Heating $51 1.4

- $111 0.5  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 42 Table 21. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for Residential Sector

(Moderate Incentive Scenario, Cumulative NPV throug h 2023)



### TRC RIM

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Appliances $225 2.8

- $280 0.5 HVAC Equipment $108 1.6

- $208 0.5 HVAC Shell $646 1.7

- $614 0.4 Lighting $40 2.4

- $90 0.5 Plug Load $9 2.9

- $15 0.5 Pool Pump $20 3.6

- $16 0.6 Water Heating $77 1.4

- $203 0.5

Table 22. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for Residential Sector

(High Incentive Scenario, Cumulative NPV through 20 23)



### TRC RIM

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Appliances $373 2.8

- $548 0.4 HVAC Equipment $177 1.6

- $453 0.4 HVAC Shell $1,072 1.7

- $1,620 0.3 Lighting $67 2.4

- $170 0.4 Plug Load $15 2.9

- $28 0.5 Pool Pump $33 3.6

- $31 0.6 Water Heat $128 1.4

- $455 0.4

Table 23. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for Residential Sector

(Low Incentive Scenario, Cumulative NPV through 202 3)



### PCT with Incentives PCT without Incentives

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Appliances $316 5.7 $299 5.5 HVAC Equipment $188 3.1 $166 2.9 HVAC Shell $712 2.5 $592 2.2 Lighting $82 6.2 $78 6.0 Plug Load $15 7.0 $15 6.8 Pool Pump $23 6.6 $22 6.3 Water Heat $162 2.7 $138 2.5  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 43 Table 24. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for Residential Sector

(Moderate Incentive Scenario, Cumulative NPV throug h 2023)



### PCT with Incentives PCT without Incentives

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Appliances $504 6.0 $453 5.5 HVAC Equipment $316 3.4 $249 2.9 HVAC Shell $1,260 2.7 $898 2.2 Lighting $131 6.5 $119 6.0 Plug Load $24 7.3 $22 6.8 Pool Pump $36 6.8 $33 6.3 Water Heat $280 3.0 $209 2.5

Table 25. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for Residential Sector

(High Incentive Scenario, Cumulative NPV through 20 23)



### PCT with Incentives PCT without Incentives

End Use Net

Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost Appliances $921 6.5 $753 5.5 HVAC Equipment $630 3.9 $410 2.9 HVAC Shell $2,692 3.2 $1,490 2.2 Lighting $237 7.0 $197 6.0 Plug Load $43 7.8 $37 6.8 Pool Pump $65 7.3 $55 6.3 Water Heat $583 3.5 $346 2.5  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 44 

### 3.3.4.2 Commercial Sector
Table 26 presents commercial sector achievable pote ntial for the three incentive scenarios.

Table 26. Theoretically Achievable Potential Saving s for Commercial Sector (Cumulative through 2023)



### Low Incentive Achievable Potential Moderate Incentive Achievable Potential High Incentive Achievable Potential

Load Type Total

Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Reduction in Electricity Sales (MWh) REDACTED 8.8% REDACTED 13.3% REDACTED 22.1%

Reduction in Peak Demand (MW) REDACTED 10.6% REDACTED 16.1% REDACTED 26.7%



### Figure 27 and Figure 28 show commercial sector base line and forecasts by achievable potential scenario, for energy sales and peak demand forecast , respectively.

Figure 27. Energy Forecasts for Theoretically Achie vable Potential in Commercial Sector (Electricity S ales)

0 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 30,000,000 35,000,000 40,000,000 45,000,000 50,000,000

2012 2014 2016 2018 2020 2022Electricity Sales (MWh)

Base Forecast
High Incentive

Moderate Incentive

Low IncentiveREDACTED  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 45 Figure 28. Demand Forecasts for Theoretically Achie vable Potential in Commercial Sector (Peak Demand)



### Figure 29 and Figure 30 illustrates theoretically a chievable commercial savings under the high incentive scenario by end use. Potential savings of electric energy concentrate in four end-use categories: lighting (49%); HVAC (shell and equipme nt at 35% of potential); and office equipment (10%).

Figure 29. Commercial - 2023 Theoretically Achievab le Potential by End Use (Electricity Sales)

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

2012 2014 2016 2018 2020 2022Peak Demand (MW)

Base Forecast
High Incentive

Moderate Incentive

Low Incentive

HVAC

Equipment, 3%

HVAC Shell,
32% Lighting, 49% Miscellaneous,
3% Office

Equipment, 10% Refrigeration,
2% Water Heating, 2%REDACTED  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 46 

### The potential peak demand savings concentrate much more in lighting (48% of potential) and HVAC (shell and equipment at 37%), with lesser amounts i n office equipment (10%) and other end uses.

Figure 30. Commercial - 2023 Theoretically Achievab le Potential by End Use (Peak Demand)



### Potentially achievable benefits in the commercial s ector, from programmatic efforts from 2012 through 2023 could range from $1.5 billion to $3.7 billion from a TRC perspective. Associated costs to ratepayers could range from $0.9 billion to $3.4 billion from the RIM perspective.

Table 27 lists the net present value of participant and programs costs associated with theoretically achievable potential scenarios.

Table 28 lists the benefits and costs from TRC, RIM , and PCT perspectives of theoretically achievable
scenarios.

Table 29, Table 30, and Table 31 list benefits and costs from TRC and RIM perspectives, by end use,
for the scenarios.

Table 32, Table 33, and Table 34 list benefits and costs from the PCT perspective by end use, with and without incentives, illustrating energy-efficie ncy measures’ economic attractiveness, even without utility incentives to subsidize initial mea sure costs. HVAC Equipment, 8% HVAC Shell,
29% Lighting, 48% Miscellaneous,
3% Office Equipment, 10% Refrigeration,
1% Water Heating, 2%  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 47 Table 27. Participant and Program Costs by Achievab le Potential Scenario for Commercial Sector (NPV Thousands, Cumulative through 2023)



### Achievable Scenario Net Participant Costs Program Incentives Program Administration
Low Incentive $484,370 $161,457 $161,457 Moderate Incentive $489,263 $489,263 $244,631 High Incentive $0 $1,624,352 $406,088

Table 28. Net Benefits and Benefit-to-Cost Ratio fo r TRC, RIM, and PCT Perspectives for Commercial Sec tor (Cumulative NPV through 2023)



### Achievable Scenario TRC RIM PCT

Net Benefits (Millions) Benefit/

Cost Net

Benefits (Millions) Benefit/

Cost Net

Benefits (Millions) Benefit/

Cost
Low Incentive $1,471 2.8

- $867 0.7 $2,338 4.6 Moderate Incentive $2,228 2.8

- $1,559 0.7 $3,787 4.9 High Incentive $3,699 2.8

- $3,400 0.6 $7,099 5.4

Table 29. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for Commercial Sector

(Low Incentive Scenario, Cumulative NPV through 202 3)



### End Use TRC RIM

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $95 4.9

- $19 0.9 HVAC Shell $338 1.8

- $341 0.7 Lighting $755 3.8

- $368 0.7 Miscellaneous $165 3.8

- $56 0.6 Office Equipment $67 3.1

- $53 0.7 Refrigeration $23 7.8

- $10 0.7 Water Heat $28 3.2

- $20 0.6  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 48 Table 30. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for Commercial Sector

(Moderate Incentive Scenario, Cumulative NPV throug h 2023)



### End Use TRC RIM

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $144 4.9

- $37 0.8 HVAC Shell $512 1.8

- $641 0.6 Lighting $1,143 3.8

- $638 0.7 Miscellaneous $250 3.8

- $103 0.5 Office Equipment $101 3.1

- $90 0.6 Refrigeration $35 7.8

- $16 0.7 Water Heat $43 3.2

- $34 0.6

Table 31. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for Commercial Sector

(High Incentive Scenario, Cumulative NPV through 20 23)



### End Use TRC RIM

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $240 4.9

- $85 0.8 HVAC Shell $849 1.8

- $1,476 0.5 Lighting $1,898 3.8

- $1,329 0.7 Miscellaneous $415 3.8

- $230 0.5 Office Equipment $168 3.1

- $182 0.6 Refrigeration $59 7.8

- $29 0.7 Water Heat $71 3.2

- $70 0.5

Table 32. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for Commercial Sector

(Low Incentive Scenario, Cumulative NPV through 202 3)



### End Use PCT with Incentives PCT without Incentives

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $114 6.8 $110 6.6 HVAC Shell $679 3.1 $597 2.8 Lighting $1,122 6.2 $1,069 6.0 Miscellaneous $221 5.7 $209 5.5 Office Equipment $120 5.8 $114 5.5 Refrigeration $33 12.9 $32 12.7 Water Heat $48 5.6 $46 5.4  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 49 Table 33. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for Commercial Sector

(Moderate Incentive Scenario, Cumulative NPV throug h 2023)



### End Use PCT with Incentives PCT without Incentives

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $181 7.1 $166 6.6 HVAC Shell $1,153 3.3 $905 2.8 Lighting $1,781 6.5 $1,619 6.0 Miscellaneous $353 6.0 $317 5.5 Office Equipment $192 6.0 $173 5.5 Refrigeration $51 13.2 $49 12.7 Water Heat $77 5.9 $69 5.4

Table 34. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for Commercial Sector

(High Incentive Scenario, Cumulative NPV through 20 23)



### End Use PCT with Incentives PCT without Incentives

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $325 7.6 $275 6.6 HVAC Shell $2,325 3.8 $1,502 2.8 Lighting $3,226 7.0 $2,688 6.0 Miscellaneous $645 6.5 $527 5.5 Office Equipment $350 6.5 $286 5.5 Refrigeration $88 13.7 $81 12.7 Water Heat $141 6.4 $115 5.4



### 3.3.4.3 Industrial Sector
Table 35 presents the industrial sector’s achievabl e potential for the three incentive scenarios.

Table 35. Theoretically Achievable Potential Saving s for Industrial Sector

(Cumulative through 2023)



### Load Type Low Incentive

Achievable Potential Moderate Incentive Achievable Potential High Incentive Achievable Potential

Total Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Total

Potential Percent of

2023 Load Reduction in Electricity Sales (MWh) REDACTED 4.5% REDACTED 6.9% REDACTED 11.4%

Reduction in Peak Demand (MW) REDACTED 4.9% REDACTED 7.4% REDACTED 12.2%  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 50 

### Figure 31 and Figure 32 show industrial sector base line and forecasts by achievable potential scenario, for energy sales and peak demand forecast , respectively.

Figure 31. Energy Forecasts for Theoretically Achie vable Potential in Industrial Sector (Electricity S ales)

Figure 32. Demand Forecasts for Theoretically Achie vable Potential in Industrial Sector (Peak Demand)



### Figure 33 illustrates theoretically achievable indu strial energy savings under the high incentive scenario by end use. Potential electric energy savi ngs concentrate in four end-use categories: process (46% of potential); motors (includes pumps and fans, 27%); HVAC (10%); and lighting (12%). Other end uses constitute the remaining savings. 0 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 30,000,000

2012 2014 2016 2018 2020 2022Electricity Sales (MWh)

Base Forecast
High Incentive

Moderate Incentive

Low Incentive

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000

2012 2014 2016 2018 2020 2022Peak Demand (MW)

Base Forecast
High Incentive

Moderate Incentive

Low IncentiveREDACTEDREDACTED  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 51 Figure 33. Industrial - 2023 Theoretically Achievab le Potential by End Use (Electricity Savings)



### Figure 34 illustrates theoretically achievable indu strial peak demand savings under the high incentive scenario by end use. The potential peak d emand savings concentrate much more in the process (48% of potential) and motor (27%) end-use categories, with lesser amounts in other end

uses.

Figure 34. Industrial - 2023 Theoretically Achievab le Potential by End Use (Peak Demand) HVAC Equipment, 10%

Lighting, 12%

Other, 5%

Motors, 27% Process, 46%

HVAC Equipment, 11%

Lighting, 9%

Other, 5%

Motors, 27% Process, 48%  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 52 

### Potentially achievable industrial sector benefits, from programmatic efforts from 2012 through 2023, could range from $0.3 billion to $0.8 billion from a TRC perspective. Associated costs to ratepayers could range from $0.1 billion to $0.5 bi llion from the RIM perspective.

Table 36 lists the net present value of participant and programs costs associated with theoretically achievable potential scenarios.

Table 37 lists benefits and costs from TRC, RIM, an d PCT perspectives of theoretically achievable
scenarios.

Table 38, Table 39, and Table 40 list benefits and costs from TRC and RIM perspectives, by end use,
for the scenarios.

Table 41, Table 42, and Table 43 lists benefits and costs from the PCT perspective by end use, with and without incentives, illustrating energy-efficie ncy measures’ economic attractiveness, even without utility incentives to subsidize initial mea sure costs.

Table 36. Participant and Program Costs by Achievab le Potential Scenario for Industrial Sector (NPV thousands, Cumulative through 2023)



### Achievable Scenario Net Participant Costs Program Incentives Program Administration
Low Incentive $71,803 $23,934 $23,934 Moderate Incentive $72,528 $72,528 $36,264 High Incentive $0 $240,793 $60,198

Table 37. Net Benefits and Benefit-to-Cost Ratio fo r TRC, RIM, and PCT Perspectives for Industrial Sec tor (Cumulative NPV through 2023)



### Achievable Scenario TRC RIM PCT

Net Benefits (Millions) Benefit/

Cost Net

Benefits (Millions) Benefit/

Cost Net

Benefits (Millions) Benefit/

Cost
Low Incentive $336 3.8

- $110 0.8 $446 5.7 Moderate Incentive $509 3.8

- $203 0.8 $712 5.9 High Incentive $845 3.8

- $458 0.7 $1,303 6.4  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 53 Table 38. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for Industrial Sector

(Low Incentive Scenario, Cumulative NPV through 202 3)



### End Use TRC RIM

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $46 7.9

- $11 0.8 Lighting $47 4.3

- $7 0.9 Motors $81 2.6

- $43 0.8 Other $21 3.4

- $7 0.8 Process $140 4.7

- $42 0.8

Table 39. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for Industrial Sector

(Moderate Incentive Scenario, Cumulative NPV throug h 2023)



### End Use TRC RIM

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $70 7.9

- $19 0.8 Lighting $72 4.3

- $15 0.9 Motors $123 2.6

- $81 0.7 Other $32 3.4

- $13 0.8 Process $213 4.7

- $75 0.8

Table 40. TRC and RIM Net Benefits and Benefit-to-C ost Ratio by End Use for Industrial Sector

(High Incentive Scenario, Cumulative NPV through 20 23)



### End Use TRC RIM

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $116 7.9

- $39 0.8 Lighting $119 4.3

- $40 0.8 Motors $204 2.6

- $186 0.6 Other $53 3.4

- $31 0.7 Process $353 4.7

- $162 0.7  

 

### 3 

## PUBLIC DISCLOSURE Estimates of Energy-Efficiency Potentials

Achievable Energy-Efficiency Potentials Assessment 54 Table 41. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for Industrial Sector

(Low Incentive Scenario, Cumulative NPV through 202 3)



### End Use PCT with Incentives PCT without Incentives

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $58 11.7 $56 11.5 Lighting $54 5.7 $52 5.5 Motors $124 4.0 $114 3.7 Other $28 5.0 $26 4.8 Process $182 7.0 $175 6.8

Table 42. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for Industrial Sector

(Moderate Incentive Scenario, Cumulative NPV throug h 2023)



### End Use PCT with Incentives PCT without Incentives

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $89 12.0 $85 11.5 Lighting $87 6.0 $78 5.5 Motors $204 4.2 $172 3.7 Other $45 5.3 $40 4.8 Process $287 7.3 $264 6.8

Table 43. PCT Net Benefits and Benefit-to-Cost Rati o by End Use for Industrial Sector

(High Incentive Scenario, Cumulative NPV through 20 23)



### End Use PCT with Incentives PCT without Incentives

Net Benefits (Millions) Benefit/Cost Net Benefits (Millions) Benefit/Cost HVAC Equipment $155 12.5 $141 11.5 Lighting $159 6.5 $130 5.5 Motors $390 4.7 $286 3.7 Other $84 5.8 $66 4.8 Process $515 7.8 $439 6.8

## PUBLIC DISCLOSURE

Achievable Energy-Efficiency Potentials Assessment 55 

# 4 C ONCLUSIONS

This assessment identifies the potential for increa sed energy-efficiency investments by Georgia
Power customers above the levels that would natural ly occur but would require the significant incentives assumed in this analysis. These results will be useful in targeting energy-efficiency program planning efforts to areas with the highest market potential.

Customers can reduce their energy consumption and p eak power requirements by implementing energy-efficiency measures or actions, receiving ec onomic benefits directly through reductions in their energy bills. Customers who participate could also benefit from financial incentives offered by programs intended to accelerate markets for purchas e and installation of high-efficiency measures. At the same time, rates will rise for all customers . Non-participating customers will pay higher bills

, in effect, subsidizing costs of incentives and othe r program costs, to the benefit of program participants.

As in a similar 2007 assessment, this study examine s scenarios of theoretically achievable energyefficiency potential associated with low incentives , moderate incentives, and high incentives. Each scenario in the study involves substantial expendit ures on incentives, ranging from $25 million per year to more than $250 million per year in 2012, th e study’s first year.

Economic benefits from energy-efficiency improvemen ts made by customers in Georgia Power’s service territory, as calculated by the TRC Test, c ould range as high as $2.6 billion to $6.4 billion. If implemented through electric utility programs, pote ntial electric energy savings would range from 6.0% of year 2023 forecast sales under a low incent ive scenario to 15.1% under a high incentive scenario. However, these savings result in substant ial costs to ratepayers. Net costs to electric util ity ratepayers could range from $1.7 billion to $7.2 bi llion, over and above those costs associated with meeting customers’ energy and demand needs using su pply-side options. Program costs alone could increase rates from $750 million to $4.7 billion. T his study does not estimate costs to gas and water utility ratepayers, who could also experience adver se rate impacts.

The study also demonstrates that customers can real ize substantial benefits from increased energy efficiency, even without financial subsidies. Not i ncluding incentives, net participant benefits range from $3.9 billion to $9.8 billion. For a variety of financial and structural reasons, however, custome rs may not make these energy-efficiency investments on their own.

## PUBLIC DISCLOSURE

Achievable Energy-Efficiency Potentials Assessment A-1 

# Appendix A D ETAILED 

# M ETHODOLOGY

Energy-efficiency potentials can be determined thro ugh a sequential analysis of various energyefficiency measures in terms of technical feasibili ty (technical potential) and economic viability, based on standard cost-effectiveness criteria (econ omic potential) and market adoption criteria (achievable potential). This assessment follows two main steps:

• Baseline forecasts determine future energy consumpt ion by segment and end use, calibrated to the utility’s energy sales forecasts. Baseline forecasts reflect efficiency characteristics of current and pending codes and st andards.

• Estimation of forecasts based on technical, economi c, and theoretically achievable potentials, reflect technical impacts of specific e nergy-efficiency measures, measures’ costeffectiveness, and market constraints, respectively . The difference in energy consumption between the baseline and individual, alternative fo recasts represents the energy-efficiency potential.

Figure A-1 illustrates these steps conceptually, sh owing a hypothetical baseline forecast, along with alternative forecasts associated with technical, ec onomic, and achievable potential. 7 

### These alternative forecasts represent consumption under d ifferent sets of assumptions, and the difference
between the baseline and each alternative forecasts represents respective potential savings. For example, the technical potential forecast represent s total consumption following incorporation of all measures, with total consumption for the technical potential forecast much lower than the baseline (which also indicates the greatest amount of potent ial). As respective benefit-cost and market acceptance constraints are added, forecasts for eco nomic and achievable scenarios approach the baseline and the resulting potential savings decrea se.

This approach offers two advantages. First, it driv es savings estimates by a baseline, calibrated to each utility’s energy sales forecasts and thus cons istent with filings. The energy sales forecast serv es as a “reality check,” helping control for possible errors. Other approaches simply may generate the total potential by summing estimated impacts of ind ividual measures, resulting in total savings estimates representing unrealistically high percent ages of baseline sales.

Second, the approach maintains consistency among al l assumptions underlying the baseline and
theoretical forecasts. In the theoretical forecasts , relevant end-use level inputs change to reflect energy-efficiency measures’ impacts. As estimated s avings represent the difference between the baseline and alternative forecasts, they can be dir ectly attributed to specific changes made to analysis inputs.

7 Figure A-1’s baseline and alternative forecasts se rve purely as examples, and do not represent actual data underlying this assessment. 





  

### A 

## PUBLIC DISCLOSURE Detailed Methodology

Achievable Energy-Efficiency Potentials Assessment A-2 Figure A-1. Representation of an Alternative Foreca st Approach for Estimating Energy-Efficiency Potent ials



### A.1 DEVELOPING A BASELINE FORECAST
Cadmus created the baseline forecast by combining a ll baseline data to obtain average consumption estimates by fuel, customer segment, construction v intage, and end use; this breakdown is summed up to the sector level. This method was then calibr ated to Georgia Power’s B11 end-use forecast.

This approach offered several key advantages:

• Savings estimates were driven by a baseline, calibr ated to official energy sales forecasts. This calibration required a great deal of scrutiny to ensure underlying inputs and assumptions remained consistent with known customer population characteristics. Other potentials study approaches could generate the tota l potentials by summing individual measures’ estimated impacts, resulting in total sav ings estimates representing unrealistically high baseline sales percentages.

• The forecast incorporates the effects of known chan ges to equipment standards and naturally occurring efficiency improvements. Equipm ent standard changes and naturally occurring savings reduce energy use as customers re tire lower-efficiency units and install higher-efficiency systems. Including these effects in the baseline forecast prevents from inflating potentials estimates. 





  

### A 

## PUBLIC DISCLOSURE Detailed Methodology

Achievable Energy-Efficiency Potentials Assessment A-3 

### • The same assumptions underlying baseline forecasts were used to develop energy-efficiency measure inputs, along with technical, economic, and achievable potential estimates, ensuring consistency across the study.



### A.1.1 Data Sources
Creating a baseline forecast accurately reflecting Georgia Power’s customers’ consumption characteristics required many data inputs (Table A- 1), including:

• Energy sales and customer forecasts;

• Customer counts by major customer segments (e.g., b uilding type or industry);

• End-use and equipment saturations, including fuel s hares;

• Efficiency shares (the percent of equipment below, at, and above code); and

• Annual end-use consumption estimates, by efficiency level.

Table A-1. Key Data Sources



### Data Source Key Variables Georgia Power B11 end-use consumption forecast Total electric sales by customer segment and end us e, excluding energy-efficiency program activity. Georgia Power B11 customer forecast Number of customers by rate class, commercial squar e footage estimate. Georgia Power B11 saturations Residential and comme rcial equipment saturations.
Residential and commercial saturation surveys Supplemental saturations and fuel share information

.
Manufacturing Energy Consumption Survey End-use breakout for industrial facilities

EnerSim building simulations End-use equipment ener gy consumption estimate.



### A.2 COMPILING ENERGY-EFFICIENCY TECHNOLOGY DATA
The Nexant/Cadmus team, along with input from membe rs of the Demand Side Management Working Group (“DSMWG”), created a comprehensive ca talog of energy-efficiency measures applicable to Georgia Power’s service territory, co ntaining more than 332 unique measures. Expanding the measures to account for all appropria te combinations of segments, end uses, and construction types resulted in over 8,000 measure p ermutations, as shown in Table A-2. 





  

### A 

## PUBLIC DISCLOSURE Detailed Methodology

Achievable Energy-Efficiency Potentials Assessment A-4 Table A-2. Energy-Efficiency Measure Counts by Sect or


### Sector Unique Measures Permutations Residential 118 1,703 Commercial 144 5,485
Industrial 70 822



### These measures required a number of inputs to accur ately assess their potential and costeffectiveness, primarily using secondary local, reg ional, and national data, where appropriate. For example, the 2010 Georgia Power Residential Saturat ion Survey (“RSS”) provided inputs for the residential sector, and EIA’s Commercial Building E nergy Consumption Survey (“CBECS”) provided inputs for the commercial sector. The industrial se ctor relied on EIA’s Manufacturing Energy Consumption Survey (“MECS”). Relevant inputs for ea ch type of measure included the following:

• 

### End-use savings : Energy savings associated with a measure as a per centage of total end-use consumption, based primarily on EnerSim building si mulation models for the residential and
commercial sectors and the IAC database for the ind ustrial sector. Calculating some building types’ measure energy savings by scaling simulated energy savings from a similar, proxy building type (e.g., manufactured home energy savin gs derived from a single-family home simulation) helped manage the number of building si mulations required to estimate energy savings for each measure.

• 

### Measure costs : Per-unit costs (full or incremental, depending on the application) associated
with measure installations. Sources included: RS Me ans, merchant Websites, and other secondary sources. As with energy savings, scaling costs from similar, proxy building types
allowed measure costs to be calculated for some bui lding types.

• 

### Measure life : A measure’s expected useful lifetime. Sources inc luded the Database for Energy Efficient Resources (“DEER”), the American S ociety of Heating, Refrigerating and Air Conditioning Engineers (“ASHRAE”) Handbook, and oth er regional and national measure databases and energy-efficiency program evaluations

.

• 

### Measure applicability : A general term encompassing an array of factors, including: fuel saturation, end-use saturation, technical feasibili ty of installation, and the measure’s current saturation as well as factors to allocate savings a ssociated with competing measures. Information used primarily derived from data in cur rent regional and national databases.
These factors are described in Table A-3. 





  

### A 

## PUBLIC DISCLOSURE Detailed Methodology

Achievable Energy-Efficiency Potentials Assessment A-5 Table A-3. Measure Applicability Factors



### Measure Impact Explanation Sources
End-Use Saturation The percentage of customers with the specific end u se. (If not all

commercial customers had a chiller unit, for exampl e, end-use saturation would be less than 100%). RSS; CBECS; MECS; various secondary

sources. Fuel Saturation Of customers with a given end use, the percentage u sing

electricity to fuel a specific end use (e.g., water heat, space heat). RSS; CBECS; MECS;

various secondary

sources. Measure Share Used to distribute the percentage of market shares for competing

measures (e.g., Cool Roofs and Green Roofs each hav e their own shares of the market), and to account for measure r eplacement at burnout or early replacement. Various secondary sources and engineering experience.

Technical Feasibility Accounts for the percentage of buildings that can h ave the

measure physically installed. Various factors may a ffect this percentage, including whether the building already has the baseline measure (e.g., dishwasher), and limitation s on installation (e.g., size of unit and space available to install the unit). Various secondary sources and engineering experience.

Measure Incomplete Factor Represents the percentage of buildings without the specific

measure currently installed. RSS; CBECS; MECS; ENERGY STAR sales figures; and engineering experience.
Measure Interaction Adjustment to measure savings due to interactions w ith other end

uses. For example, increased lighting efficiency co uld increase space heating requirements. EnerSim modeling.



### A.3 ESTIMATING TECHNICAL POTENTIAL
Once the measure database had been fully populated, Cadmus used measure-level inputs to estimate technical potential over the planning hori zon. This required creating an alternate forecast, reducing consumption by installation of all technic ally feasible measures, and then subtracting this
forecast from the baseline forecast to estimate tec hnical potential by customer segment, vintage, end use, year, and measure.

As noted, this approach ensured consistency with Ge orgia Power’s forecast, and ensured the technical potential accurately reflected Georgia Po wer’s customer characteristics, and represented a reasonable amount of projected sales. Further, the approach accounted for the following three
interactive effects:

• 

### Equipment and non-equipment measures: Installing high-efficiency equipment, such as heat pumps, could reduce energy savings in absolute terms (kWh) associated with nonequipment measures, such as wall insulation. For ex ample, installing a high-efficiency heat pump reducing heating and cooling consumption by 20 % would reduce the baseline against which insulation would be applied, thus reducing sa vings associated with installing
insulation. 





  

### A 

## PUBLIC DISCLOSURE Detailed Methodology

Achievable Energy-Efficiency Potentials Assessment A-6 

### • 

### Non-equipment measure interactions: The “measure share”—as defined above— accounted for competing measures, ensuring savings were not double-counted. This
interaction occurred when two or more measures “com peted” for the same end use. For example, a T-12 lamp could be replaced with a T-8 o r T-5 lamp.

• 

### Inter-end-use interactions: Some measures could indirectly affect an end use. For example, installing more efficient lighting could increase h eating loads. Where appropriate, the EnerSim simulation analysis captured these impacts.

Technical potential for equipment and non-equipment measures required different estimating techniques. For equipment measures, the study assum ed consumers would install the most efficient unit available upon equipment burnout, generating s avings over standard equipment.



### A.3.1 Non-Equipment Measures
Estimating the potential for non-equipment (or “ret rofit”) measures required assessing the collective impacts of many measures with interactiv e effects. For each segment and end-use combination, the analysis objective sought to estim ate cumulative effects of the group of eligible measures, incorporating those impacts into the end- use model as a percentage adjustment to baseline end-use consumption. In other words, the a pproach estimated the percentage reduction in end-use consumption that could be saved in a “typic al” structure (e.g., office buildings, retail building) by installing all available measures. Thi s approach began by characterizing individual measure savings in terms of their percentage of end -use consumption, rather than their absolute energy savings. The following basic relationship es timated savings for each individual, nonequipment measure:



### SAVE ijm 

### = EUI ije 

### * PCTSAV

ijem 

### * APP ijem 

### where:



### SAVE ijm 

### = annual energy savings for measure 

### m for end use 

### j in customer segment 

### i.

EUI ije 

### = calibrated annual end-use energy consumption for the equipment 

### e for end use 

### j and customer segment 

### i.

PCTSAV

ijem 

### = the percentage savings of measure 

### m relative to base usage for the equipment configuration 

### ije , taking into account interactions among measures.



### APP ijem 

### = measure applicability, a fraction representing a combination of technical feasibility, existing measure saturation, end-use interaction, a nd adjustments to account for competing

measures.

A measure’s savings can be appropriately viewed in terms of its savings as a percentage of baseline end-use consumption, given its overall applicabilit y. For example, if wall insulation had overall applicability of only 50%, 10% savings in space hea ting consumption would yield a final 5% savings 





  

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## PUBLIC DISCLOSURE Detailed Methodology

Achievable Energy-Efficiency Potentials Assessment A-7 

### for this end use. This value would represent the pe rcentage of baseline consumption the measure would save in an average commercial building.

However, capturing all applicable measures required examining many instances where multiple measures affected a single end use. To avoid overes timating total savings, assessment of cumulative impacts accounted for interaction among various mea sures, a treatment known as “measure stacking.” Stacking effects can be accounted for pr imarily by establishing a rolling, reduced baseline

, applied sequentially to measures in the stack, as s hown in the equations below, which apply measures 1, 2, and 3 to the same end use:



### SAVE ij1 

### = EUI ije 

### * PCTSAV ije1 

### *APP ije1 

### SAVE ij2 

### = (EUI ije 

### - SAVE ij1 

### ) * PCTSAV ije2 

### * APP ije2 

### SAVE ij3 

### = (EUI ije 

### - SAVE ij1 

### - SAVE ij2 

### ) * PCTSAV ije3 

### * APP ije3



### Measures can be stacked using different criteria, s uch as total savings or cost-effectiveness. For thi s study, the TRC ratio determined the stacking order for measures within each end use.



### A.4 ESTIMATING ECONOMIC POTENTIAL
Once the technical potential had been established, the economic (cost-effective) potential had to be
determined.

This study determined measure cost-effectiveness ac cording to the TRC test, treating a measure’s benefits as the net present value of avoided utilit y costs, and costs incurred by the customer for installing the measure. If a measure had non-negati ve, net benefits, it could be considered costeffective. Expressed as an equation:



### 1 Costs TRC Benefits TRC ≥

Where:



###              
× = 

# ∑ ∑

= = e measurelif

year i

i i i 

### t avoided impact NPV

1 8760 

### ) cos ( Benefits TRC

And:

TRC Costs = Customer Cost

TRC was calculated using estimated utility savings benefits (electric, gas, and water) and incremental cost collected by Nexant/Cadmus. Hourly (electric) and monthly (gas and water) end-use loadshapes account for seasonal differences and system peak co incidence of different end uses. That is, a cooling measure using a greater amount of electric energy during peak periods would be more costeffective than a lighting measure with the same amo unt of savings, but lower peak coincidence. 





  

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## PUBLIC DISCLOSURE Detailed Methodology

Achievable Energy-Efficiency Potentials Assessment A-8 

### A.4.1 Benefit Components

Benefits used in the TRC calculation included the v alue of time-differentiated and seasonally differentiated electric avoided energy and capacity costs.



### A.4.2 Measure Cost Components
The cost-benefit analysis’ cost component consisted of total installed measure costs and applicable operation and maintenance costs (or savings) associ ated with ensuring the measure is properly functioning over its expected life. Total measure b enefits present values were calculated by discounting future streams at the designated discou nt rate.

Upon screening all measures, impacts of measures de emed cost-effective to baseline consumption estimates could be applied, creating a separate for ecast. Subtracting this new forecast from the baseline forecast provided the economic potential.



### A.4.3 Caveats
Three important considerations should be noted in i nterpreting economic screening results, as they relate to assessment of energy-efficiency potential s:

• As the analysis has been based on a societal perspe ctive, assumptions have not been made regarding how measure costs split between utilities and participants in energy-efficiency programs. Achievable potential included that analys is level. This pure economic screen at the measure level did not assume program administra tion costs.

• Screening procedure outcomes, described above, depe nded on assumptions that will likely change over time. Measure costs, for example, will likely decline over time, as demand for energy-efficient technologies increases. At the sam e time, costs of reaching each successive participant can often increase. Forecasted avoided costs will also likely change over time, and, as they change, so will the value of savings r esulting from installation of energyefficient technologies (i.e., a measure failing the economic screen in earlier planning period years may become cost-effective in later years, if decrement values increase over the course of the planning horizon).

• The economic analysis relied on assumptions intende d to reflect an “average” or “typical” customer. Thus, while a measure may have passed the economic screen within the study’s context, the measure could fail the cost-effectiven ess test in other instances. For example, a premium central air conditioner may be cost-effecti ve in an average, single-family home, but, in a smaller home with less occupants, it coul d fail the economic screen due to decreased savings.

Despite these caveats, the study’s underlying input s, having been thoroughly reviewed, represent the best information available about specific condi tions (regarding technical measure details and customer attributes) in Georgia at the time of the study. As with any study of this nature, as it 





  

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## PUBLIC DISCLOSURE Detailed Methodology

Achievable Energy-Efficiency Potentials Assessment A-9 

### becomes available, current information can be used to update key drivers, but the current study results sufficiently inform the resource planning a nd program delivery process.



### A.5 ESTIMATING ACHIEVABLE POTENTIAL
In estimating long-run achievable potential, this s tudy relied on secondary data to determine customers’ willingness to adopt energy-efficiency m easures at a range of incentive levels, adopting a two-step approach:

• Determining elasticity between incentive levels and savings from analysis of EIA Form 861
data. These data included historical information on expenditures and savings from energyefficiency programs by utilities around the country

.

• Determining the base value by reviewing utility rep orts with available information on incentive levels and achievable percentages.

Performing a regression analysis on EIA data allowe d determination of the relative change in savings, based on differing incentives. Per these d ata, a 100% increase in the total utility incentive equated to roughly a 65% increase in savings. Combi ned with findings from other publicly available assessments, this indicated roughly 50% of the econ omic potential can be achieved at an incentive
level of 50% of incremental cost. This allows calcu lating the achievable percentage under the low and high scenarios, resulting in 34% of economic po tential achievable at a 25% incentive (a low incentive scenario), and 83% of economic potential achievable at a 100% incentive (a high incentive scenario). Benchmarking these values against other potentials assessments ensured these estimates aligned with other jurisdictions.

The above analysis provided an estimate for achieva ble potential at the end of the planning horizon. Resource acquisition, however, is measure specific, depending on product maturity. Wellestablished energy-efficiency measures (such as CFL s) experience fast ramp rates, while relatively new technologies (e.g., a variable refrigerant flow heat pump) will have a slow ramp rate.

Annual utility and customer economic impacts were t hen estimated by applying the appropriate ramp rates for each measure, coupled with specific escalation rates on costs and discount rates. A single escalation rate was used to preserve the rel ative cost-effectiveness for both options, commonly done when conducting assessments of achiev able potential.

## PUBLIC DISCLOSURE

Achievable Energy-Efficiency Potentials Assessment B-1 

# Appendix B B ENEFIT 

# -C OST 

# A NALYSIS

When analyzing energy-efficiency measures, cost-eff ectiveness indicators are considered to account for perspectives of different stakeholders. The Par ticipant Cost Test (“PCT”) addresses a participant perspective, which considers net benefits to those participating in an energy-efficiency program. The Total Resource Cost (“TRC”) addresses a societal pe rspective, which considers costs of an energyefficiency measure or program relative to the benef its of avoided utility supply costs. The Ratepayer Impact Measure (“RIM”) addresses an electric utilit y perspective, which considers the net impact on electric utility rates associated with a measure or energy-efficiency program.

Descriptions follow of methods for allocating costs and benefits within each cost-effectiveness indicator (PCT, TRC, and RIM); the calculations rem ain consistent with the California Standard
Practice Manual. 8

Table B-1. Components of Cost-Effectiveness Calcula tions

Customer Incremental Costs All incremental costs incurred by the customer to p urchase, install, and maintain an energy-efficiency measure Program Costs Administrative and marketing costs re quired to implement energy-efficiency programs Incentives Costs from the utility to the participan t to encourage the purchase, installation, and maintenance of energy efficiency measures

Table B-2. Participant Cost Test (PCT)

Bene fit Decrease in electric bill

Decrease in gas bill Decrease in water bill Customer incentives Cost Increase in electric bill

Increase in gas bill Increase in water bill Customer incremental costs (less any tax incentives )

Table B-3. Total Resource Cost (TRC)

Benefit Decrease in avoided electric utility supply costs

Decrease in avoided gas utility supply costs Decrease in avoided water utility supply costs Cost Increase in avoided electric utility supply costs

Increase in avoided gas utility supply costs Increase in avoided water utility supply costs Customer incremental costs (less any tax incentives ) Program costs (excluding incentives)

8 California Standard Practice Manual: Economic Anal ysis of Demand-Side Program and Projects. Californi a Public Utilities Commission. San Francisco, CA. October 20 01. 





  

### B 

## PUBLIC DISCLOSURE Benefit-Cost Analysis

Achievable Energy-Efficiency Potentials Assessment B-2 Table B-4. Ratepayer Impact Measure (RIM)

Benefit Increase in electric revenues

Decrease in avoided electric utility supply costs Cost Decrease in electric revenues

Increase in avoided electric utility supply costs Program costs (including incentives)The Power of Experience



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