EPRi

lectric Power

esearch Institute

Topics:

Residential energy use

End-use consumption

Appliances

Load research

EPRI CU-6952

Project 2863-3

Interim Report

October 1990

~t70, e:

Residential Energy Usage

Comparison Project: An Overview

Prepared by

Quantum Consulting Inc.

Berkeley, California

EE-RM-94-230 COMMENT 61E
R'E P O R T S U M M A R

Demand-side planning / Market assessment / Lighting and appliances

Residential energy use Appliances

End-use consumption Load research

DSM and marketing managers / Market and load researchers

Residential Energy Usage Comparison

Project: An Overview

An integrated load and market research project has compared the

load-shape and energy consumption patterns of major residential

electric and gas appliances and has yielded new insights into

customer preferences for fuel and appliance types. The metered

energy consumption data provide an important benchmark for

comparing the costs of alternative end-use technologies.

BACKGROUND

OBJECTIVES

APPROACH

Traditional studies of the relative energy consumption of electric and gas

household appliances have relied on laboratory analyses and computer

simulations. EPRI and the Southern California Edison Company (SCE)

jointly sponsored the residential energy usage comparison project to study

the appliance energy consumption patterns of actual households.

To measure and compare typical energy consumption patterns of comparably efficient residential electric and gas appliances; to determine customer

satisfaction with electrical technologies and other factors determining customer appliance choices.

The project team identified. 92 households in Orange County, California,

southeast of Los Angeles, to serve as the scientifically designed study sample. The households were owner-occupied, single-family homes in a highgrowth area, thus representing typical newer homes in the SCE service

area. New space-conditioning, water-heating, cooking, and clothes-drying

appliances were installed in customers' homes. Half of the households

received electric equipment and the other half, gas equipment. All of the

appliances were metered, and data were collected regularly. The team surveyed the participating households to obtain information on their energy

needs and the acceptance of the appliances.

RESULTS Appliance energy data gathered from February 1988 to January 1989 revealed the following:

* Energy requirements for the electric heat pumps were less than 40% of

the energy required by the gas space-conditioning systems during the winter months. Electric heat pump water heaters consumed only 40% of the

energy used by the gas water heaters. Electric ranges consumed 38% of

SUBJECTS

TOPICS

AUDIENCE

Y

EPRI CU-6952s

-

Electric Power Research Institute

EE-RM-94-230 COMMENT 61E
the energy of gas ranges, and electric clothes dryers used 80% of thr

energy of gas dryers.

The winter-weekday space-conditioning peak load was much smaller

for households with an electric heat pump: 1 kW (about 3 kBtulh), cot

pared with about 11 kBtu/h for gas furnaces.

In addition, analysis of the customer survey data indicated that many

customers have a very short required payback period for appliance pu

chases. About 40%/ require payback on energy efficiency investments

in less than two years (through operating cost savings), and 30% require payback in two to five years.

EPRI PERSPECTIVE The energy-usage and consumer behavior data developed in this proj

ect provide important new information for utility market planners. As a

result of the project, SCE and the utility industry have gained new insights into the relative energy consumption patterns of comparable

electric and gas household appliances as they are operated in consumers' homes. Utilities can use this information to compare the

cost-effectiveness of these options for their customers as part of a

demand-side management or market-planning process. In addition, the

project clarified consumer fuel-type images and attitudes toward the

new appliances. Finally, the project provides an example of calculating

load-shape data.for alternative market segments, including energyneeds-based segments determined by EPRI's CLASSIFY computer

code (report EM-5908), which can help utilities assess the likely load

impacts of attracting particular types of customers through targeted

marketing programs.

PROJECT RP2863-3

EPRI Project Manager: Steven D. Braithwait

Customer Systems Division

For further information on EPRI research programs, call

EPRI Technical Information Specialists (415) 855-2411.

EE-RM-94-230 COMMENT 61E
)f the

.aller

. cot

Residential Energy Usage Comparison Project: An

any Overview . PU_

?nts

reCU-6952

Research Project 2863-3 ~roj.

s a .Interim Report, October 1990 in1-

the Prepared by

ting QUANTUM CONSULTING INC.

2030 Addison Street

Berkeley, California 94704

id

Principal Investigators

B. A. Smith

R. T. Uhlaner

T. N. Cason

Prepared for

Southern California Edison Company

Project Manager

P. Delaney

and

Electric Power Research Institute

3412 Hillview Avenue

Palo Alto, California 94304

EPRI Project Manager

S. D. Braithwait

Market Assessment Program

Customer Systems Division

EE-RM-94-230 COMMENT 61E
ORDERING INFORMATION

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Electric Power Research Institute and EPRI are registered service marks of Electric Power Research Institute. Inc

Copyright © 1990 Electric Power Research Institute, Inc All rights reserved.

NOTICE

This report was prepared by the organization(s) named below as an account of work sponsored in pan by the

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nor any person acting on behalf of any of them: (a) makes any warranty, express or implied, with respect to the

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use of. any information, apparatus, method, or process disclosed in this report.

Prepared by

Quantum Consulting Inc.

Berkeley. California

EE-RM-94-230 COMMENT 61E
ABSTRACT

This report provides an overview of the residential energy usage comparison project, an

integrated load and market research project sponsored by EPRI and the Southern California

Edison Company. Traditional studies of the relative energy consumption of electric and gas

household appliances have relied on laboratory analyses and computer simulations. This project

was designed to study the appliance energy consumption patterns of actual households.

Ninety-two households in Orange County, California, southeast of Los Angeles, served as the

study sample. Half of the households received new electric space-conditioning, water-heating,

cooking, and clothes-drying equipment; the other half received gas equipment. The electric

space-conditioning and water-heating appliances were heat pump technologies. All of the

appliances were metered to collect load-shape and energy consumption data. The households

were also surveyed periodically to obtain information on their energy needs and their acceptance

of the appliances.

The metered energy consumption data provide an important benchmark for comparing the

energy consumption and costs of alternative end-use technologies. The customer research results

provide new insights into customer preferences for fuel and appliance types.

iii

U

EE-RM-94-230 COMMENT 61E
 

iv 
EPRI FOREWORD

The residential energy usage comparison (REUC) project has been a joint effort by EPRI and

the Souther California Edison Company to measure and compare typical energy consumption

patterns of comparable efficient residential electric and gas appliances. This innovative demandside project has produced valuable information about how actual households use electricity

and gas for space conditioning, water heating, cooking, and clothes drying. This report provides

a synopsis of the research activities and results from the REUC project. A more comprehensive

presentation of the project results will be provided in the forthcoming final project report.

Steven D. Braithwait, Project Manager

Customer Systems Division

C .

v

EE-RM-94-230 COMMENT 61E
 

vi 
 EE-RM-94-230 COMMENT 61E

 
 

viii 
CONTENTS

Section Page

1 INTRODUCTION -1-1

Overview of the. REUC Project 1-1

EPRI Involvement in the REUC Project 1-5

Report Organization 1-6

2 REUC PROJECT DESIGN 2-1

Introduction 2-1

REUC Project Scope 2-1

History of REUC Data Collection 2-4

REUC Sample Design 2-5

Demographic Description of the REUC Sample 2-6

3 REUC APPLIANCE ENERGY USAGE 3-1

Introduction 3-1

Load Data Collection and Analysis 3-1

Average Annual and Seasonal Usage 3-2

4 REUC APPLIANCE LOAD PROFILES 4-1

Introduction 4-1

Load Profiles 4-1

5 REUC INTEGRATED MARKET RESEARCH 5-1

Introduction 5-1

Participants' Image of Electricity and Gas 5-1

Load Profiles by Customer Segment 5-2

Appliance Life-Cycle Costs 5-7

ix

EE-RM-94-230 COMMENT 61E
x 

 
FIGURES

Figure Page

1-1 REUC Project Milestones 1-3

2-1 REUC Orange County Survey and Load Data Collection 2-4

History

3-1 Annual Average Energy Usage - Space Conditioning and 3-3

Water Heating

3-2 Annual Average Energy Usage - Clothes Drying and Cooking 3-4

3-3 Difference in Annual Energy Consumption between Gas 3-6

Appliances and Electric Appliances

4-1 SCE REUC: Average Load Profiles - Winter Weekday Space 4-2

Conditioning

4-2 SCE REUC: Average Load Profiles - Winter Weekday Water 4-2

Heating

4-3 SCE. REUC: Average Load Profiles - Winter Weekday Cooking 4-3

5-1 REUC Participant Images of Electric vs. Gas 5-2

5-2 SCE REUC: CP&B Needs-Based Segments - Summer Weekday 5-4

Electric Space Conditioning

5-3 SCE REUC: Value Factor Segments - Summer Weekday Electric 5-5

Space Conditioning

5-4 SCE REUC: Quality Factor Segments - Winter Weekday Heat 5-6

Pump Water Heater

5-5 SCE REUC: Income-by-Square-Feet Segments - Winter 5-7

Weekday Heat Pump Water Heater

5-6 REUC Participants' Required Payback Periods 5-9

5-7 REUC Space Conditioning Fuel Choice Preference Regions 5-10

xi

EE-RM-94-230 COMMENT 61E
 

xii 

 
TABLES

Table Page

2-1 REUC Participant Demographics - Sample Design 2-7

Stratification Variables

2-2 FEUC Participant Demographics - Highest Completed 2-8

Education Level Among Key Household Members

3-1 Average Annual and Seasonal Energy Usage for Orange 3-5

County Sample Households

xiii

EE-RM-94-230 COMMENT 61E
SUMMARY

This report presents a synopsis of the research activities and results from the

Residential Energy Usage Comparison (REUC) project, a joint effort by Southern

California Edison Company (SCE) and the Electric Power Research Institute (EPRI).

The REUC project is an innovative demand-side project designed to measure and

compare typical energy consumption patterns of comparably efficient residential

electric: and gas appliances.

The results presented in this report pertain to a sample of 92 households in

Orange County, southeast of Los Angeles. The REUC Orange County participant

sample was designed to represent an important segment of SCE's residential customers, namely owner-occupied, new single-family homes. Over the past decade,

Orange County has experienced significant residential and commercial growth.

REUC PROJECT OVERVIEW

The REUC project has been designed to assess the energy performance and

customer acceptance of new electric and gas appliances. Traditionally, studies

that have examined the relative energy efficiencies of electric and gas end-use

technologies have relied on laboratory settings or computer simulation models.

The nature of the REUC project is to move away from the laboratory setting by

relying on the energy demands and customer behavior of actual households.

instead of simulated "test bench" usage patterns. The REUC project results have

thus accounted for many of the subtle but important determinants of appliance

usage, including household characteristics, that are not captured in engineering

models.

The results presented in this report illustrate the value of two unique facets of the

project's design: first, end-use (appliance) load data have been collected for both

electric and gas appliances; and, second, both customer survey-data and end-use

data have been collected from the participants. End-use data collection began in

late 1986 for a portion of the Orange County participants; survey research data

collection began in mid-1987. Both end-use data and survey research data

S-1

EE-RM-94-230 COMMENT 61E
collection concluded in December 1989. The REUC project Final Report will

present analysis results using all the available data.

A summary of the results presented in this report is provided below.

ELECTRIC AND GAS APPLIANCE ENERGY CONSUMPTION

A principal objective of the REUC project has been to measure electric and gas

appliance energy consumption, at point of use. Southern California Edison

identified four residential energy uses to be considered in the project: space

conditioning, water heating, cooking and clothes drying. These four end-uses

were selected because of the direct competition between electricity and natural

gas and the development of new, high-efficiency electric technologies.

Figure S-1 illustrates the point of use annual average energy consumption (in

millions of BTUs forcomparison) for the REUC space conditioning appliances

(electric heat pumps relative to gas furnaces/electric air conditioners) and water

heating appliances (electric heat pump water heaters and gas water heaters).

These results are based on appliance energy data gathered over a one-year

period, from February 1988 through January 1989, for the Orange County

households. The figure shows that on an annual basis the electric heat pumps

consume less than 45 percent as much energy as the gas furnace/electric air

conditioner space conditioning systems. As reported in Section 3, electric heat

pumps consume less than 40 percent of the energy of the gas systems during the

winter season. In addition, electric heat pump water heaters consume only 40

percent as much energy as gas water heaters.

Additional results for the REUC project electric and gas appliances' energy

consumption are presented in Section 3.

S-2

EE-RM-94-230 COMMENT 61E
Annual Average Energy Usage

Space Conditioning and Water Heating

22.6

Space Conditioning

10.1

23.7

Water Heating

9.4

MBTU

0 10 20 30

a At Point of Use

El GasSample . | Electric Sample |

Figure S-1

ELECTRIC AND GAS APPLIANCE LOAD PROFILES

An uncommon facet of the REUC project has been the collection of both electric

and gas appliance load data. The unique pairing of electric and gas end-uses

affords an opportunity to carefully compare the daily usage patterns of appliances

with different energy sources.

Figure S-2 illustrates the average winter weekday load profiles for both gas and

electric space conditioning appliances (electric heat pumps and gas furnaces).

Again, these load profiles are based on appliance energy data gathered over a

one-year period, from February 1988 through Jariuary 1989, ror the 92 household

Orange County sample. As the figure indicates, gas furnaces have a much higher

S-3

EE-RM-94-230 COMMENT 61E
morning peak (about 11 kBTU per hour) than electric heat pumps. Electric heat

pumps, on the other hand, have a lower diversified load (about 3 kBTU per hour

or 1 kW) and draw smaller amounts of energy over longer periods of time during

the day.

SCE REUC: Average Load Profiles

Winter Weekday Space Conditioning

6 9 12 15 18 21 24

ime of Day (in Hours PST)

Figure S-2

Additional REUC appliance load

presented in Section 4.

profiles developed from end-use data are

CUSTOMER ACCEPTANCE OF APPLIANCES

Measuring customer acceptance of the appliances was another objective of the

project. The REUC project included the administration of surveys designed to

measure the level of new appliance acceptance among REUC participants.

Acceptance was measured by allowing the participants to rate their new

S-4

kBTU 12

per

Hour

.10-

4

0

0 3

Electric Heat Pump

I

I -- .... Gas Furnace

I '

I

I

II I

I

I

I I

I

I

I

I

- - *< ^I

I

.3 -

I

Ar

EE-RM-94-230 COMMENT 61E
:Oppliances among various attributes including reliability, operating cost, response

ime and maintenance.

-INTEGRATED MARKET RESEARCH

The collection of both end-use load data and participant survey data is yet

another distinctive feature of the REUC project. Integrated market research is the

creative synthesis of load research information with market research/survey information to provide insight about customers' energy usage behavior patterns.

The REUC project surveys included questions designed to estimate participants'

appliance investment payback requirements based on their implied discount

rates. The higher an individual customer's discount rate for an appliance investment, the shorter the required payback period for such investments. As illustrated by Figure S-3, the REUC participants' required payback periods are

generally quite short, with the vast majority clustered between 0 and 5 years.

REUC Participants' Required Payback Periods

Percent of 50

Participants

.40 -

30-

20 -

10

0

<2 2-5 6-12 13-20

Payback Period (Yrs.)

Figure S-3

S-5

EE-RM-94-230 COMMENT 61E
The REUC project surveys also included questions designed to elicit participants'

images of electricity and gas. Figure S-4 portrays the REUC participants' fuel

images for electricity and gas. Fuel images were found to be useful for defining

customer segments. Factor analysis of the REUC participants' fuel images resulted

in the derivation of a Quality factor (a composite of the Safety, Convenience,

Service and Reliability images) and a Value factor (a composite of the Appliance

Cost, General Expense and Monthly Utility Bill images).

REUC Participant Images of Electric vs. Gas

Dirty

Dangerous

Hard to Hookup

Inconvenient

Poor Service

from Utility

Unreliable

Sluggish Heat

High Cost

Appliances

Expensive

High Monthly

Utility Bills

Electricity

Image ,

Gas

Image.

Clean T

Safe

Easy to Hookup

Convenient

Good Service

from Utility

Reliable

Fast HeatLow Cost

Appliances

Inexpensive

Low Monthly

Utility BillsFigure S-4

S-6

0'

La

Y- -

'

.

_-

EE-RM-94-230 COMMENT 61E
Figure S-5 illustrates the use of the Quality factor for customer segmentation. The fig

presents average winter weekday load profiles for participants' heat pumps segmen

with the electric and gas Quality factors. This figure shows the distinct load shapes 

these customer segments.

Figure S-5

The EPRI Customer Preference and Behavior (CP&B) CLASSIFYTM survey was

administered to the REUC participants and was also found to be a useful instrument for customer segmentation. Figure S-6 presents average winter weekday

load profiles for heat pumps segmented by Appearance Conscious and Value

Seekers needs-based segments.

S-7

SCE REUC: Quality Factor Segments

Winter Weekday Heat Pump

Load

in

kW

0 3 6 9 12 15 18 21 24

Time of Day (in Hours PST)

EE-RM-94-230 COMMENT 61E
Additional integrated market research results of the REUC project are presented

in Section 5.

Figure S-6

S-8

SCE REUC: CP&B Needs-Based Seements

Winter Weekday Heat PumD

Load 1.2

in

kW

0 .3 6 9 12 15 18 21 24

Time of Day (in Hours PST)

v .

Itt - J - T I

__ ·
Section 1

INTRODUCTION

This report presents a synopsis of the research activities and results from the

Residential Energy Usage Comparison (REUC) project, a joint effort by Southern

California Edison Company (SCE) and the Electric Power Research Institute (EPRI).

The REUC project is an innovative demand-side project designed to measure and

compare typical energy consumption patterns of comparably efficient residential

electric and gas appliances.

OVERVIEW OF THE REUC PROJECT

The REUC project has been designed to provide information necessary for

assessing the energy performance, the relative cost-effectiveness and the customer acceptance of new electric and gas appliances. Southern California Edison

has recognized that evaluating key residential end-uses such as space conditioning, water heating, cooking and clothes drying can provide much needed insight

into the energy efficiencies of these technologies for the benefit of its customers.

Traditionally, studies that have examined the relative energy efficiencies of gas

and electric technologies have relied on laboratory settings or computer simulation approaches in order to assess these end-use technologies' performance. The

nature of the REUC project is to move away from the laboratory setting by substituting the energy demands of actual households in place of simulated usage patterns. The advantage of this approach is that it accounts for many of the subtle

but important determinants of appliance usage not captured in engineering

models, including unobservable household characteristics. The advantages of

conducting a "field" experiment are reflected in the value of more comprehensive, realistic assessments of actual customers' usage and response to new electrotechnologies. These advantages offset the reduction in "control" afforded by a

laboratory simulation study.

1-1

- I II·· · ·
SCE Needs

The REUC project has been undertaken in part to meet several of SCE's needs for

assessing new electrotechnologies available to residential customers. These

needs include:

* real-world comparison of electric and gas appliance energy

usage;

* development of engineering and customer data for energy

efficiency assessment analyses; and

* evaluation of customer acceptance of electric and gas enduse technologies.

Proiiect Objectives

The REUC project was designed to meet these needs by satisfying several

objectives:

* measure energy usage of electric and gas residential

appliances;

* compare usage patterns of similar electric and gas appliances;

·* assess customer acceptance of new end-use technologies;

and

· generalize results to other residential customers.

Project Milestones

Since 1985, when the project was first suggested by SCE and the California Public

Utilities Commission, the REUC has involved three phases: Phase I, from early

1986 through late 1987, included the experimental design, analysis plan and

instrumentation installations; Phase II, which consisted of initial data collection

and analysis, ran through early 1989. Phase III has consisted of subsequent data

collection and analysis and is on-going. The major REUC project milestones are

illustrated in Figure 1-1 .

1-2

EE-RM-94-230 COMMENT 61E
REUC Project Milestones

1985 1986 1987 1988 1989 1990

1985 - Idea Suggested by CPUC and SCE

Early 1986 - Discussions with EPRI

Summer 1986 - Customer Sample Design;

REUC-RSS Survey; Installations Begun

- Winter 1986 - Irvine Installations Complete

Fall 1987 - Project Analysis Plan

- I Winter 1987 - Mission Viejo

Installations Complete

Q/ Spring 1988 - Second REUC Survey

Figure 1-1

EE-RM-94-230 COMMENT 61E
REUC Project Milestones

1986

!

1987

I

1988

I

1989

I

1990

I

I ,I -I I I

IQQ "41 44'

Summer 1989 - Third REUC Survey;

Winter 1989 - Final REUC Survey; Equipment Removal

Figure 1-1 (cont)

.... _~* * * * * . , ' ta , ,_ 

198

I.

it

-A 41

41

.,r&iiuiha--- -1 --- - ___ --

.. . I :.X.. .. . .

15

I
EPRI has contributed to the' REUC project in three principal areas:

project design and planning;

4, project analysis; and

it project management.

Proiect Design

EPRI first became involved with the REUC project during its experimental design

and planning activities. EPRI project staff emphasized the need to focus on a

clear set of objectives of the project, and then to carefully and systematically

design the REUC household sample accordingly. The participants would need to

accurately represent important segments of SCE's residential customer population. In addition, the REUC experimental design needed to determine how

much and what type of customer data should be collected in order to ensure the

precision of subsequent REUC analyses. Finally, the design needed to include a

comprehensive REUC data analysis plan, so that the collected data would be

applied to best effect.

The REUC experimental design activities took advantage of previous EPRIsponsored research results, including the design'of the Athens Automation and

Control Experiment (EPRI EM-4628), and the model-based sample design

techniques in EPRI EA-3286.

Project Analysis

The REUC project has pioneered the integration of load research/load shape

analysis with market' research/customer behavior analysis. The project team has

developed "integrated market research" techniques and "behavioral engineering

statistical techniques (BEST)" to meet the project objectives. These techniques

have leveraged upon research results from several other EPRI projects, including

the Customer Preference and Behavior (CP&B) Project (RP2671) and the Load

Data Analysis Workstation Project (RP2342).

1-5
Proiect Management

EPRI has assisted SCE staff by offering primary management of the REUC project

design and data analyses tasks.

SCE staff provided effective' management of the customer contact, equipment

installation, and data collection efforts. Members of the project team, from EPRI,

SCE and the Contractor, have presented preliminary results from the REUC

project at numerous industry conferences and meetings.

REPORT ORGANIZATION

The remainder of this report is organized as. follows: Section 2, REUC Project

Design, describes the design of the REUC project. Section 3, REUC Appliance

Energy Usage, presents results from the analysis of twelve months of appliance

energy data. Section 4, REUC Appliance Load Profiles, provides examples of

appliance load profiles for the REUC electric and gas appliances. 'Section 5, REUC

Integrated Market Research, presents results of integrated market research

analyses, such as participants' fuel images and load profiles by customer segment.

1-6

II

EE-RM-94-230 COMMENT 61E
Section 2

REUC PROJECT DESIGN

INTRODUCTION

The REUC customer sample consists of 100 households. These 100 households

are comprised of two distinct groupings: eight homes where load monitoring

equipment was initially tested, the "equipment test sample"; and 92 homes

located in Irvine and Mission Viejo, the "Orange County sample."

This report focuses only on data from the REUC's Orange County sample. Irvine

and Mission Viejo are located in Orange County along a high-growth corridor

between San Diego and Los Angeles. Over the past 20 years, this area has

undergone a transition from a lightly populated agriculturally based economy to a

metropolitan area consisting of high-technology research and manufacturing

industries. The population is largely new to the area and represents a diverse

cross-section of other parts of the SCE service area and of the country.

We first describe the design of the Orange County portion of the REUC project

and then provide an overview of the project. The history of the REUC data

collection follows, and then the sample design procedures and criteria are

discussed. Finally, we present the resulting demographic characteristics of the

REUC Orange County sample.

REUC PROJECT SCOPE

Southern California Edison identified four residential energy uses to be considered

in the REUC project - space conditioning, water heating, cooking, and clothes

drying. They were selected because these end-uses represent a majority of

residential energy consumption and the development of new, efficient electric

technologies. Because the project has been designed to provide direct comparisons between similar gas and electric appliances that might be installed in new

homes, new space conditioning, water heating, and clothes drying appliances

were installed in both gas and electric homes. In the Electric Sample, households

were retrofitted with heat pumps for both space conditioning and water heating.

2-1

0I, I
:~The retrofed Gas'~Samplewas with efficient gas furn~aces,-electric ait conditionThe Gas Sample was retrofitted with efficient gas furnaces, electric air conditioners, and gas water heaters.. In addition, these gas and electric homes were

selected from the same housing tracts, in order to reduce variation induced by

building envelope characteristics.

The heat pump space conditioning systems have connected loads of

approximately 6 kW, or 20.5 kBTU/hour. The heat pump systems have a resistance backup system designed to provide supplemental heat on cold days; this

system can draw loads up to 16 kW. The gas homes are equipped with high efficiency gas heating systems that have capacities of approximately 60 to 72 kBTU/hr

and electric fans that have loads under 800 watts. As mentioned above, the electric homes received new electric heat pumps for space'conditioning (typical

Seasonal Energy Efficiency Ratio [SEER] of 9, or Heating Season Performance

Factor 'HSPF] of 7) and heat pump water heating (Coefficient of Performance

[COP] of 3).- The gas homes received new gas furnaces (80 percent efficiency),

electric air conditioners (typical SEER of 9) and gas water heaters (58 percent

efficiency).

The cooking appliance experiment relies on the existing gas and electric resistance ranges, except in the Mission Viejo electric test sites, where new electric

induction ranges were installed. The induction cooktop uses a high-frequency,

electronically controlled electromagnet to produce. heat in ferromagnetic cookware. Induction stoves feature the same quick responsiveness as gas ranges

because of their low thermal capacity. Also, they are safer than either electric

resistance or gas cooking because the cooking surface remains relatively cool.

Though no new technologies have been applied to clothes drying, new gas and

electric clothes dryers were installed in the test homes to evaluate customer

attitudes and collect end-use consumption information.

In addition to energy usage comparison, the REUC project data can support

assessment of qualitative differences between appliance technologies and fuels. In

attempting to measure these differences, the REUC project differs from most

other customer load monitoring projects in two key ways. First, all participants

have had their homes retrofitted with new appliances, half with gas appliances and

half with electric or electric heat pump appliances. Second, the participants have

been repeatedly surveyed in order to assess their experiences with their new

2-2

EE-RM-94-230 COMMENT 61E
appliances and the influence these appliances may have had on their image of gas

and electricity. Since in the selected test areas almost all homes rely on gas for

space and water heating, the REUC experiment in these cases consists of a sample

of fuel-switched former gas customers matched with a "control group" of comparably equipped gas customers.

The REUC project analyses cover three main topic areas: realized appliance

energy usage comparisons; load profile analysis; and the integration of these topics

with market research data. Each of these areas is summarized below.

Energy Usage Comparisons

Comparisons of the appliances' energy usage have been made by customer strata

that reflect the initial sample design, as well as by appropriate alternative stratifications or segmentations of the sample derived from the analysis of the market

research data. These energy usage comparisons account for seasonal and weather

effects.

Load Profiles

The REUC load profiles can aid utilities in assessing the cost-effectiveness of

promoting customer adoption of particular appliances. The estimated REUC'load

profiles also have been combined with the project's market research/attitudinal

data in an attempt to understand the factors influencing the load shapes. The

unique pairing of gas and electric homes affords an opportunity to carefully

compare the usage patterns of appliances with different energy sources, since gas

end-use data are rarely collected.

Market Research Analysis

The REUC market research was undertaken to better understand customer

attitudes and behavior toward the electric and gas appliances. Analysis of the

market research data has concentrated on three basic issues. First, these data

were used to determine which aspects or features of gas and electricity affect customers' images of those energy sources. Are these features limited to cost, or do

they include other qualities associated with the two fuels? Second, analysis of customer reaction to the performance of their new appliances allows qualitative

assessment of non-pecuniary benefits, not just the dollar value of the savings from

operating and capital costs. Finally, the integration of this expanded set of

2-3

I I I
customer data with the traditionally collected set of household characteristics

enhanced the capability to model, or explain, customer energy usage by different

market segments.

HISTORY OF REUC DATA COLLECTION

The REUC Orange County data collection activities have consisted of load and

survey data collected from two samples: 42 homes in Mission Viejo and 50 homes

in Irvine. Figure 2-1 summarizes the survey and load data collection activities from

September 1986 through December 1989 of the REUC project.

Figure 2-1

Four different survey instruments have been administered to each sample. The

first survey, REUC-RSS, was designed to gather specific appliance saturation data in

order to select prospective participants. The REUC-RSS survey instrument is based

on the SCE 1985 RSS survey but augmented by several attitudinal and demographic questions. The second survey, administered in June 1987, contained all

new questions which focus on customer attitudes towards attributes of fuel types

and end-uses as well as the customer's propensity to "invest" in energy-efficient

appliances. The third survey instrument included refined versions of the second

2-4

REUC Orange Couity Survey and

Load Data Collection History

9/86 6/87 3/88 6/8.9 12/89

Mission I I

Viejo R M1 M 2 M3 M4

Irvine I I

R M M 2 : M3 M4

I- '= Load data collection

R = REUC-RSS survey

M i = REUC Market Research survey

I·IIL

I Y __ _ __

EE-RM-94-230 COMMENT 61E
new appliances, and the EPRI "20 Question" CLASSIFYTM questionnaire. In order

to perform longitudinal analysis of the participants' acceptance of the new appliances, the third survey was administered twice (March 1988 and June 1989). The

fourth and final survey was a shorter version of the third survey and contained portions of SCE's'residential attitude survey instrument. In this way, the final survey

measures the long-term acceptance of the REUC appliances and provides a link to

similar surveys administered to other SCE customers.

Appliance load data collection has continued through December 1989. As is

shown, the data collection history for the Irvine sample differs from the Mission

Viejo sample. In Phase I, the REUC project was limited to the Mission Viejo

sample and was primarily envisioned as an end-use load monitoring project. Once

the Mission Viejo sample was selected and retrofitted with new appliances, the

scope of the project broadened in Phase II to include a second customer sample

in Irvine and to undertake a comprehensive market research study. As a result of

adding Irvine, "before" (before the new appliances were installed) and "after"

market research data were collected for these 50 homes.

REUC SAMPLE DESIGN

In order to identify a potential sample of Orange County residential customers,

SCE administered an initial survey, called the REUC-RSS, to 2,380 potential

participants. This survey was based on SCE's 1985 Residential Saturation Survey.

From the 756 survey responses, a subset of potential participants was visited by SCE

project staff to gather additional information on specific appliances in the

household, household characteristics, and occupancy patterns.

Extensive experimental design analysis was undertaken to identify a sample of

customers that could provide the most pertinent and valuable information for the

study, namely end-use load shapes and customer characteristics, within the

constraint imposed by a budget limitation to a sample of 92 households. Analysis

was performed on key demographic variables used as stratification variables in

load research projects. Ultimate selection of the stratification variables reflected

the project objectives of selecting a sample that was statistically supportable and

that represented key customer segments of interest to SCE. In particular, this

2-5
sample represents the owner-occupied, single-family new home market in SCE's

large Southern Division.

A related design goal was to determine the optimal allocation of the available 92

sets of appliances and energy' monitors so that the difference in energy usage

between electric and gas appliances could be measured as accurately as possible.

This allocation was achieved by identifying observable household characteristics

that best predict overall household electric load shape and then stratifying the

sample according to those characteristics. Required sample sizes were calculated

using estimated variances determined in the experimental design analyses and the

criterion that a 95 percent confidence interval around the estimated difference in

energy usage between gas and electric appliances be no wider than 5 percent of

total load for each appliance.

Household income and square feet were selected as the key stratification

variables. In order to control for the effects of other household characteristics, the

sample is limited to'single-family owner-occupied homes built between 1975 and

1982. In addition, an attempt was made to select households with between two

and five members. Age and education level of the head of household were not

controlled for in the sample design.

DEMOGRAPHIC DESCRIPTION OF THE REUC SAMPLE

Analysis of utility demand-side programs typically can involve relating customer

energy usage, appliance choice, and program acceptance to key demographic

variables, such as household income, age of head of household, and family size,

and dwelling characteristics such as square feet, number of bedrooms, and type.

These data describing household characteristics and appliance inventories were

collected for the REUC by surveying the REUC Orange County participants.

Table 2-1 tabulates the key demographic data for the REUC sample based on the

stratification variables. The determination of household characteristics by which

the sample was drawn was based on the REUC project's objective of studying the

customer segments that best represent the characteristics of persons in the new

home market. Table 2-2 illustrates the distribution of the highest education level

completed among the heads of household. Education was identified as an

2-6

EE-RM-94-230 COMMENT 61E
important potential stratification variable. Unfortunately, the sample size 

ta

tions could not support level of education as a separate stratification variable.

Table 2-1

REUC Participant Demographics

Sample Design Stratification Variables

Electric Sample

House Size

SMALL

Less than' 1750 sq ft

Income

< $50K

50-80K

> 80K

Number of

Irvine

12

2

Households

Mission

Vieio

3

5

1

MEDIUM

1750 to 2000 sq ft

LARGE

Greater than 2000 sq ft

Gas Sample

House Size

SMALL

Less than 1750 sq ft

Income

< $50K

50-80K

> 80K

Number of

Irvine

2

12

Households

Mission

Vieio

3

5

1

MEDIUM

1750 to 2000 sq ft

LARGE

Greater than 2000 sq ft

< 50K

50-80K

> 80K

< 50K

50-80K

> 80K

4

6

2

21

2

8

1

25

< 50K

50-80K

> 80K

< 50K

50-80K

> 80K

4

6

2

21

1

8

2

25

2-7
Table 2-2

REUC Participant Demographics

Highest Completed Education Level Among Key Household Members

Level Completed

High School

2-Year Associate

Bachelor

Masters

Doctorate

Electric Sample Number of Households

Irvine Mission Vieio

2 3

3 4

14 3

5 6

1 2

Gas Sample

Number of Households

Irvine Mission Vieio

1 2

Level Completed

High School

2-Year Associate

Bachelor

Masters

Doctorate

5

8

10

3

8

2

2

2-8

EE-RM-94-230 COMMENT 61E
Section 3

REUC APPLIANCE ENERGY USAGE

I NTRODUCTION

This section presents a synopsis of results from the analysis of twelve months of

energy data collected from the REUC participants, followed by a description of

how the end-uses are monitored, how the data are collected, and a description

of the analysis methodology employed. We then present estimates, for each

end-use and fuel type, of average seasonal and annual energy usage.

LOAD DATA COLLECTION AND ANALYSIS

End-use load data were gathered from households for the REUC project with load

monitoring devices, each equipped with load measurement channels that record

usage readings in five-minute intervals. Each household provides data on from four

to twelve channels. This section summarizes results from the edited energy data

from the REUC's Orange County customer sample.

Total household electric usage is collected from each site, providing an important

cross-check for validating the end-use data. Each load monitoring device

downloads its data via modem to the central station microcomputer approximately once per week.

Simultaneously monitoring both' gas and electric appliances from a single

household including heat pump technologies posed new data collection and validation challenges. Moreover, this is the first SCE load monitoring project conducted entirely with microcomputers rather than mainframe computers -- from

initial data collection to final data analysis. As noted earlier, the load data were

downloaded from the recorders to a microcomputer via modem. The data were

then written to cartridge tapes and mailed to the EPRI contractor for analysis. The

REUC data analyses have been performed on the EPRI Load Data Analysis Workstation (LDAW), a microcomputer-based customer data analysis system. This has

allowed rapid validation of monitored load data so that malfunctioning monitors

3-1

I I_ a I~I
and appliances were quickly identified. Before consumption estimates and load

shapes were developed, the data were extensively screened for validity.

The twelve months of usage data presented in this report span the period

February 1988 through January 1989 for both the Irvine and the Mission Viejo

samples. Therefore, the complete 1988 summer season and a complete winter

season, beginning and ending in the month of February, is analyzed for both

cities. The final project report will include analysis of nearly one additional year of

data resulting in coverage of two complete summer and nearly two complete

winter seasons. To assure a consistent weighting of weekdays and weekends, a

"month" contains either a complete four- or five-week period that closely, though

not exactly, corresponds to the true calendar month.

AVERAGE ANNUAL AND SEASONAL USAGE

The average annual total usage for space conditioning, water heating, cooking

and clothes drying are presented in Figures 3-1 and 3-2.1 Energy consumption is

given in MBTUs for direct comparison between the gas and electric samples.

These average usage figures are also presented in their natural units (kWh and

therms) and broken down by season in Table 3-1.2 During the observation period

in Irvine, there were 206 cooling degree-days (base 75) during SCE's summer

season and 607 heating degree-days (base 65) during SCE's winter season.

The types of appliances monitored differ by end-use category.' The space heating

and water heating appliances are heat pumps for the electric sample,' which,

given their expected engineering performance, should consume correspondingly

less energy at point of use than the gas furnaces and water heaters. The electric

and gas clothes dryers differ only by fuel type; although the total energy

consumption of the gas dryer includes the electricity consumed by its motor.' The

electric and gas ranges also differ principally by fuel type; however, half of the

electric ranges in the Irvine sample were induction ranges, as described earlier.

1

All the energy comparisons made in this report are at the point of use; energy losses from generation and transmission are not reflected in these numbers.

2Summer is defined by SCE as the months of June, July, August and September.

These are the four months always used in generating summer usage and load

profiles. The remaining eight months define the "winter" season.

3-2

EE-RM-94-230 COMMENT 61E
Figure 3-1

Space Conditioning

As seen in Figure 3-1, the average annual household energy consumption for the

electric heat pumps amounts to 45 percent of the gas furnace/electric air

conditioner consumption. For the four-month summer period, electricity consumption for air conditioning is nearly identical for the gas and electric samples -

at about 800 kWh. During the eight winter months, however, energy consumption for space conditioning for the electric sample amounted to less than 40

percent of that for the gas sample (winter usage could not be readily separated

into space heating and air conditioning).

3-3

- III I u·
Annual Average Energy Usage

Clothes Drvinv and CnnMna

4.7

3.4

L . I al I

0 1 2

At point of use. Gas clothes drying

includes 0.39 MBTU of electricity

consumption for motor drive.

U iTI

I I I

3 4 5

| Gas Sample |

| Electric Sample

Figure 3-2

Water Heating

The electric sample's heat pump water heating consumption represents 40

percent of the consumption of gas for the gas sample (a ratio of gas to electric of

2.7 to 1). Seasonally, the ratio of energy consumption for the gas homes to the

electric homes varies from 2.5 in the winter to 2.7 in the summer.

Clothes Drying

Clothes dryer energy consumption for the electric sample averages 80 percent of

that for gas dryers. Note that the electric motor amounts to nearly 10 percent of

the energy consumption of gas dryers.

3-4

Clothe; Drying

Cooking

I'

6

W . , Ae**I

.~~~~~~~~~~.

d I

R~~- - - - - . - . R . , ^ . . p

. ^^^^^^^^^ _ |^^mE^flm^^ 13.

I

---

I

a _ I..W. i

EE-RM-94-230 COMMENT 61E
Average Annual and Seasonal Energy Usage for

Orange County Sample Households

Electric Sample

kWh MBTU

Gas Sample

(Gas Components)

MBTU

Gas Sample

(Electric Components)

kWh MBTU

Appliancea

Whole Houseb

Space Cond.C

Water Heating

Clothes Dryer

Oven/Range

Annual Average Usage (12 months)

14100 48.20

2970

2750

1100

390

10.10

9.38

3.76

1.33

NA

18.40

23.70

4.33

3.44

Winter Average Usage (8 months)

Whole House

Space Cond.

Water Heating

Clothes Dryer

Oven/Range

9900

2160

2020

770

270

33.80

7.35

6.90

2.63

0.93

NA

18.30

17.00

2.93

2.37

5030

430

NA

76

NA

17.20

1.47

NA

0.26

NA

Summer Average Usage (4 months)

Whole House

Space Cond.

Water Heating

Clothes Dryer

Oven/Range

4200

810

730

330

120

14.40

2.75

2.48

1.13

0.40

NA

0.10

6.60

1.40

1.07

3090

790

NA

37

NA

10.60

2.70

NA

0.13

NA

aClothes Dryer, Oven/Range and Water Heating are electric and gas appliances in

the electric and gas samples, respectively.

bWhole House is the electricity usage for the entire home.

cSpace Conditioning is a heat pump in the electric sample and a gas furnace and

conventional electric air conditioner in the gas sample.

3-5

8120

1220

NA

113

NA

27.80

4.17

NA

0.39

NA
Range

Somewhat surprisingly, the energy consumption for gas ranges is 2.6 times that of

the electric ranges.

Finally, as shown in Figure 3-3, total household energy consumption for the four

end-uses averages nearly 30 MBTU less for the electric sample than the gas

sample, with the largest savings accruing to heat pump water heaters.

Figure 3-3

3-6

Difference in Annual Energy Consumption

between Gas Appliances and Electric Appliancesa

Space Conditioning

14.3

Water Heating

2.1

Cooking

Clothes Drying

O 5 10 15

At Point of Use At Point of'Use

20

·I "--

I I-- - u

EE-RM-94-230 COMMENT 61E
Section 4

REUC APPLIANCE LOAD PROFILES

INTRODUCTION

This section provides illustrative examples of the appliance load profiles

developed from the REUC Orange County load data collected during the 12-

month period between February 1988 through February 1989.

LOAD PROFILES

Once again, the REUC appliance load data are unique in that daily energy usage

patterns of both electric and gas residential appliances have been calculated and

compared from field data. The data were collected at five-minute intervals and

subsequently averaged over half-hour or hour periods for purposes of analysis and

graphical presentation.

The data also have been aggregated over several day types in order to display the

load profile results shown here. Figures 4-1 through 4-3 illustrate selected average

load profiles for the Orange County sample, for winter weekday space

conditioning, water heating; and cooking, respectively. These profiles indicate the

relative energy usage of the two fuel types, as well as the different daily usage

patterns.

In general, the load profile results mirror the'seasonal and annual energy usage

presented in the previous section. The load shapes for electric and gas appliances

appear quite similar, differing primarily in the amount of energy under the

respective's curves. Electric ranges also appear to have both lower peak usage as

well as lower average energy usage than comparable gas ranges, as shown in Figure

4-3.

4-1

I r
Figure 4-1

SCE REUC: Average Load Profiles

Winter Weekday Water Heating

kBTU 10

per

Hour - Electric HP Water Heater

- Gas Water Heater

8

A

6 %

4 . I , I \

I . i

0 3 6 9 12 15 18 21 24

Time of Day (in Hours. PST)

Figure 4-2

4-2

SCE REUC: Average Load Profiles

Winter Weekday Space Conditioning

kITU 12

per

Hour

10

8

6

4

2

0

0 3 6 9 12 15 18 21 24

Time of Day (in Hours PST)

-- · II
Figure 4-3

4-3

SCE REUC: Average Load Profiles

Winter Weekday Cooking

kBTU 2.0

per

Hour Electric Range

---- Gas Range

I/ I\A

I '

A

1.0

I

;

\ \I %

I 'I

A\ I

0.0 ,, ·,

0 3 6 9 12 15 18 21 24

Time of Day (in Hours PST)

II I--

II I--

_ 1- aI
Section 5

REUC INTEGRATED MARKET RESEARCH

INTRODUCTION

Integrated market research is the creative synthesis of load research information

with market research/survey information to provide insights about customers'

energy usage and behavior patterns. This section provides a summary of several of

these results from the REUC project.

PARTICIPANTS' IMAGE OF ELECTRICITY AND GAS

The reliance on electricity and gas for particular end-uses in different regions of

the country varies due to regional differences in prices and supplies of various

fuels, climatic influences on the efficiency of appliances (e.g., heat pumps in very

cold climates), and "other" factors. The results presented in this section begin to

explore the "other" factors from the perspective of treating electricity and gas as

products that have certain distinguishing qualities, or images. Here, the REUC

Orange County participants' images of electricity and gas, as: heating fuels, are

analyzed.

Customers' images of electricity and gas have been derived by analyzing responses

to the series of fuel image questions contained in the REUC surveys. Figure 5-1

displays REUC participants' assessment of a range of qualities or characteristics of

natural gas and electricity as fuel sources. Electricity appears to dominate gas on

cleanliness and safety but suffers from an image of being more costly. The results

for the list of qualities were reduced through factor analysis to a pair of factors -

"Quality," a composite of the Safety, Convenience, Service and Reliability qualities; and "Value," representing elements of Expense, Appliance Cost, and Monthly

Utility Bill qualities. These factors were used subsequently in various segmentations of customer energy usage patterns.

5-1
REUC Participant Images of Electric vs. Gas

Dirty

Dangerous

Hard to Hookup

Inconvenient

Poor Service

from Utility

Unreliable

Sluggish Heat

High Cost

Appliances

Expensive

High Monthly

Utility Bills

Electricity

Image

Clean

Safe

Easy to Hookup

Convenient

Good Service

from Utility

Reliable

Fast Heat--'-

Low Cost

Appliances

Inexpensive

Low Monthly

Utility Billst

Z-J

Gas

Image

Figure 5-1

LOAD PROFILES BY CUSTOMER SEGMENT

This section discusses the integration of the REUC's market research based

customer data with data about the REUC participants' end-use energy consumption, using behavioral engineering statistical techniques (BEST).

These results provide information on appliance load profiles for different market

research-based customer segments, giving utility market planners the capability to

assess the value of targeting demand-side programs to,that segment.

5-2

A.I

I I___

I ''

- III - .' I -------- ;i------------
pg 

g

databases for demand-side programs consisting of both behavioral and engineering data. These techniques use existing utility customer data as a foundation to

systematically address both "traditional" as well as new demand-side planning

issues, producing enriched characterizations of customer segments.

In this section, average load shapes are calculated for several alternative customer

subgroups of the sample. Three different stratification schemes are employed:

the Customer Preference and Behavior (CP&B) CLASSIFYTM scheme, the factor

analysis described in the previous section, and the traditional income/square foot

stratification often used to define residential customer segments. For brevity, four

of the many possible subgroup stratifications are presented here for two electric

end-uses.

Summer weekday average electric space conditioning load profiles are shown in

Figures 5-2 and 5-3. Load shapes for "Appearance Conscious" and "Value

Seekers," two of the six CP&B needs-based segments, are presented in Figure 5-2.

The Value factor score stratification from the factor analysis is presented in Figure

5-3. As noted in the previous section, factor analysis of the participants' responses

to the image questions from the REUC surveys indicate that these customers

possess distinct images of electricity and gas (see Figure 5-1). For each REUC

respondent, a different factor score for the Quality and Value measures is

calculated. In the stratification shown in Figure 5-3, the customers are grouped

by their electric and gas Value scores (high versus low). The. figure shows four

stratifications for summer weekday electric space conditioning. Interestingly, the

space conditioning loads for those customers with a low value score for electricity

appear here to have a higher peak (and usage) than those with a high value score.

A low Value score indicates that the customer considers electricity an expensive

fuel. Therefore, Figure 5-3 shows that customers with higher electricity usage

have a stronger image of electricity as an expensive energy source. This

conclusion is consistent with studies at other utilities.

5-3

I· _. UI 
Figure 5-2

5-4

SCE REUC: CP&B Needs-Based Segments

Summer Weekday Electric Space Conditioning

Load

in

kW

0 '3 6 9 12 15 18 21 24

.Time of Day (in Hours PST)
Figure 5-3

Winter weekday average electric heat pump water heating load shapes are

.shown iin Figures 5-4 and 5-5. Two Quality factor score stratifications are presented in Figure 5-4 - customers whose quality scores are high for electric and

low for gas and customers whose quality scores are high for gas and low for electric. This dimension of fuel image also groups customers into distinct load shapes,

with higher loads for those having a relatively lower electric quality score. A more

traditional stratification scheme, income-by-square-feet, is presented in Figure 5-

5. The load shapes for these customer subgroups have somewhat different

profiles, but appear far less distinct than those in Figure 5-4.

5-5

Summer Weekday Electric Space Conditioning

Load 1.2-

in

kW kW Lo Electric/Lo Gas Value

1.0 - .------- Lo Electric/Hi Gas Value

.-.-- - . Hi Electric/Lo Gas Value

1.0- Hi Electric/Hi Gas Value

0.8

0.6

1 AA

0.4-

Time Time of of Day Day (in (in Hours Hours PST) PST)

U I, , 
SCE REUC: Quality Factor Segments

Winter Weekday Heat Pump Water Heater

Lced 1.0-

in

k - to Electric/Hi Gas Quality

8 ----Hi Electric/Lo Gas Quality

0.8

0.2-

0.0 .

0 3 6 9 12 15. 18 21 24

Time of Day (in Hours PST)

Figure 54

These REUC load profile results illustrate the potential value of developing

integrated databases for demand-side programs consisting of both behavioral and

engineering data. Segmenting customers on the basis of energy needs and fuel

image will prove valuable both for attracting customers to specific demand-side

programs and for targeting those customers with more attractive load shapes and

usage patterns.

0.0 · · , , - - ,'- ' ,- , '

0 3 6 912 15. 8 21 2

~~~~~~im ofDy(nHusPT

mmmmm~~~~~~mlmm ~ ~ ~

usage patterns. ,

5-6

'II~~~~~~~~~~~~~~~~~~
Figure 5-5

APPLIANCE LIFE-CYCLE COSTS

One of the objectives of the REUC project was to collect sufficient information to

permit, a comparison of the range of overall customer costs of operating

comparable sets of electric and gas appliances.' Another application of the

integrated market research data has been comparison of electric and gas

appliance life-cycle costs. The following discussion summarizes several aspects of

these analyses. The cost comparison approach developed for these analyses,

termed the Cost Payback Approach, combines appliance purchase price, energy

consumption, fuel price and customer survey data collected on the project to

compare the life-cycle costs of the monitored appliances. The results for space

conditioning appliances are summarized here. Water heating, drying, cooking

and whole house life-cycle costs also were analyzed.

5-7

SCE REUC: Income-by-Square-Feet Segments

Winter Weekday Heat Pump Water Heater

Load 1.0

in

kW

0.8

0.6

0.4

0.2

0.0

0 3 6 9 12 15 18 21 24

Time of Day (in -Hours PST)

_ _LI, I ma~~~~~~~~~
The cost comparison method is termed the Cost Payback Approach because it is

based on calculating a range of life-cycle costs for electric and gas appliances

based on a range of customer payback periods. The approach accounts for the

three cost components of a customer's appliance investment decision: (1)

appliance operating costs; (2) the appliance's capital costs; and (3) the customer's

required payback period. The cost information has been derived from the REUC

end-use load data and other sources. The customer required payback periods

have been derived from the REUC survey responses.

As used here, customer payback periods represent the amount of time needed

before an initial energy investment is recovered through reduced operation costs.

Required payback period represents the maximum length of payback period that

a customer will accept in deciding upon an energy investment. Required

payback period is closely related to the concept of implicit discount rate.1 Higher

consumer implicit discount rates imply shorter required payback periods.

Analyses of survey responses to a series of questions on possible energy investment

decisions indicate that the required payback period on an energy investment for

almost three-quarters of the REUC customers is'less than five years. The distribution of these customers' required payback periods is illustrated in Figure 5-6,

ranging from less than two years to 20 years.

In many areas, natural gas for residential customers is currently priced more

favorably on a BTU basis than is electricity. Thus, gas systems (gas furnace/electric

air conditioner) generally cost less to operate than standard electric (resistance

heating/air conditioning) systems. However, the improved energy efficiency of

heat pumps over standard electric systems closes the gas-electric cost gap

considerably. For customers with a short payback period (implying less importance on future operating cost savings), a small capital cost advantage for heat

pumps over a gas system will result in lower life-cycle costs for the heat pump

system. That is, the somewhat lower operating cost of the gas system can be offset

by a lower cost heat pump system to result in lower life-cycle costs.

1See "Implicit Discount Rates in Residential Customer Choices," February 1988,

EPRI EM-5587, for a survey of estimates.

5-8

.. ~~~~~~~ ,v ~~ ~~~~~
Figure 5-6

The Cost Payback Approach results illustrate this finding. Figure 5-7 indicates the

combinations of appliance capital cost differences and payback periods for which

electric heat pumps have a lower life-cycle cost than comparable gas systems.

Each point on the curve has been derived by combining the REUC appliance

energy consumption averages and payback information.. It should be noted that

the estimates of electricity. and gas consumption exhibit a range around an

average value. The curve shown in Figure 5-7 represents the fuel choice

"indifference curve" based only on these average values. The shaded region on

the figure indicates the combinations of capital cost differences and payback

periods for which the electric heat pump has a lower life-cycle cost than the gas

system.

5-9

p

40

30

20

10

0

<2 2-5 6-12 1-3-20

Payback Period (Yrs.)

'
ypp

y

y

eecc

appliances relative to gas appliances. This approach combines information

measured directly by the REUC project - energy usage and required paybacks -

and leaves assumptions about relative capita! costs as an unknown that can be

filled in by the reader when examining Figure 5-7.

It is important to remember that underlying a life cycle cost analysis are various

assumptions about customer appliance utilization, required paybacks, and the

capital and installation costs of the appliances themselves. The results of this

approach for individual customers will vary. Each customer utilizes appliances

differently and has a different forecast of energy prices and overall assessment of

future uncertainty that affects its required payback.

Furthermore, life cycle costs are only one of many appliance attributes that may

be considered by a customer selecting an appliance. The fuel image results

support the conclusion that customers value non-cost characteristics of fuels that

are the energy source for appliances. These non-cost attributes can be given a

dollar value and used to adjust the "Required Capital Cost Difference." For

example, if electricity is perceived to be cleaner and more reliable than gas, a

dollar premium for this attribute can be used to lower the required capital cost

difference to equalize the life cycle costs since customers would be willing to pay

more for electric technologies.

5-11