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RED:The Railbelt Electricity
Demand Model Specification
Report
Volume VIII
Odober 1982
Prepared for the Office of the Governor
State of Alaska
Division of Policy Development and Planning
and the Governor's Policy Review Committee
under Contrad 2311204417
Dalelle
Pacific Northwest Laboratories
,
LEGAL NOTICE
This report was prepared by Battelle as an account of sponsored
research activities.Neither Sponsor nor Battelle nor any person acting
on behalf of either:
MAKES ANY WARRANTY OR REPRESENTATION,EXPRESS OR
IMPLIED,with respect to the accuracy,completeness,or usefulness of
the information contained in this report,or that the use of any informa-
tion,apparatus,process,or composition disclosed in this report may not
infringe privately owned rights;or
Assumes any liabilities with respect to the use of,or for damages result-
ing from the use of,any information,apparatus,process,or composition
disclosed in this report.
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RED:THE RAILBELT ELECTRICITY DEMAND MODEL
SPECIFICATION REPORT
Vo 1ume VII I
M.J.King
M.J.Scott
October 1982
Prepared for the Office of the Governor
State of Alaska
Division of Policy Development and Planning
and the Governor's Policy Review Committee
Under Contract 2311204417
Batte lle
Pacific Northwest Laboratories
Richland,Washington 99352
NOTE TO USERS
The Volume VIII documentation report covers the RED model as developed
prior to its use in producing the final forecasts for the Railbelt Electric
Power Alternatives Study.During the course of making the forecasts included
in Volume I of this series of reports,it appeared to the Battelle-Northwest
researchers that including both an assumed increase in the saturations of
residential electrical appliances and a residential price sensitivity
coefficient,as reported in Chapter 5,double-counted the effect of
electricity prices on appliance choice.Consequently,appliance saturations
were held constant at 1980 levels to do the initial residential forecast prior
to price adjustments;this was then modified by the Chapter 5 price adjustment
to incorporate the effect of price on appliance ownership and to produce the
residential forecast included in Volume I.
Battelle-Northwest advises researchers using the RED model to use the
increasing saturations reported in Chapter 5 or the residential price
elasticities,but not both.
iii
Volume I
PREFACE
The State of Alaska commissioned Battelle to investigate potential
strategies for future electric power development in Alaska's Railbelt region.
The results of the study will be used by the Office of the Governor to
formulate recommendations for electric power development in the Railbelt.
The primary objective of the study is to develop and analyze several
alternative long-range plans for electric energy development in the Railbelt
region (see Volume I).Each plan is based on a general energy development
strategy representing one or more policies that Alaska may wish to pursue.
The analyses of the plans will produce forecasts of electric energy demand,
schedules for developing generation and conservation alternatives,estimates
of the cost of power,and discussions of the environmental and socioeconomic
characteristics for each plan.
This report (Volume VIII of a series of seventeen reports,listed below),
presents the structure of the Railbelt Electricity Demand (RED)model.This
model,together with the AREEP model (Volume XI)and an extensive data base,
was used to produce electricity demand forecasts (reported in Volume I)for
the Railbelt.These demand forecasts provided the electric plans,also
presented in Volume I.
RAILBELT ELECTRIC POWER ALTERNATIVES STUDY
-Railbelt Electric Power Alternatives Study:Evaluation of
Railbelt Electric Energy Plans
Volume II -Selection of Electric Energy Generation Alternatives for
Consideration in Railbelt Electric Energy Plans
Volume III -Executive Summary -Candidate Electric Energy Technologies for
Future Application in the Railbelt Region of Alaska
Volume IV -Candidate Electric Energy Technologies for Future Application
in the Railbelt Region of Alaska
Volume V -Preliminary Railbelt Electric Energy Plans
v
p
Volume VI -Existing Generating Facilities and Planned Additions for the
Railbelt Region of Alaska
Volume VII -Fossil Fuel Availability and Price Forecasts for the Railbelt
Region of Alaska
Volume VIII -Railbelt Electricity Demand (RED)Model Specifications
Volume VIII -Appendix -Red Model User's Manual
Volume IX -Alaska Economic Projections for Estimating Electricity
Requirements for the Railbelt
Volume X -Community Meeting Public Input for the Railbelt Electric Power
Alternatives Study
Volume XI -Over/Under (AREEP Version)Model User's Manual
Volume XII -Coal-Fired Steam-Electric Power Plant Alternatives for the
Railbelt Region of Alaska
Volume XIII -Natural Gas-Fired Combined-Cycle Power Plant Alternative for
the Railbelt Region of Alaska
Volume XIV -Chakachamna Hydroelectric Alternative for the Railbelt Region
of Alaska
Volume XV -Browne Hydroelectric Alternative for the Railbelt Region of
Alaska
Volume XVI -Wind Energy Alternative for the Railbelt Region of Alaska
Volume XVII -Coal-Gasification Combined-Cycle Po'wer Plant Alternative for
the Railbelt Region of Alaska
vi
SUM'vlARY
The Alaska Railbelt Electric Power Alternatives Study is an electric
power planning study for the State of Alaska,Office of the Governor and the
Governor's Policy Review Committee.Begun in October 1980,and extending into
April 1982,the study's objectives are to forecast the demand for electric
power through the year 2010 for the Railbelt region of Alaska and to estimate
the monetary,socioeconomic,and environmental costs of all options (including
conservation)that could be used to supply this power.
This document,Volume VIII in the series,describes the Railbelt
Electricity Demand model (RED),which is a partial end-use/econometric
forecasting model.RED has several unique capabilities:a Monte Carlo
simulator for analysis of uncertainty in key parameter values,a fuel price
adjustment mechanism that incorporates the impacts of fuel prices on demand,
and an explicit consideration of subsidized investments in conservation
measures.
Volume VIII Appendix,Red Model User's Manual,describes how the model
can be used and provides a description of the computer code derived from this
report.The forecasts produced by RED for the study are documented in
Volume V,Railbelt Electric Power Alternatives Study:Evaluation of Railbelt
Electric Energy Plans.
vii
CONTENTS
NOTE TO USERS ··· ·· ·
·· ····iii
PREFACE .···· ··· ·····v
SUMMARY .·· ·
·· ··· · ·
vii
1.0 INTRODUCTION ····· ·····1.1
2.0 OVERVIEW ···· ·
·····2.1
UNCERTAINTY MODULE · ···· ···2.3
THE HOUSING MODULE ······ ···2.4
RESIDENTIAL CONSUMPTION MODULE ··· ····2.4
BUSINESS CONSUMPTION MODULE ····· ·
··2.5
CONSERVATION MODULE .·· ·······2.6
MISCELLANEOUS CONSUMPTION MODULE · ·
·····2.7
PEAK DEMAND MODULE ······ ···2.7
3.0 UNCERTAINTY MODULE ··· ···· · ·
·3.1
MECHANISM .··· ····· ···3.1
INPUTS AND OUTPUTS · · · · · · ·
···3.1
MODULE STRUCTURE · ···· ·
··· ·
3.2
PARAMETERS · ·
·········3.4
4.0 THE HOUSING MODULE ···· ·
·····4.1
MECHANISM .···· ·
· ·····4.1
INPUTS AND OUTPUTS · ·········4.1
MODULE STRUCTURE ·· ·
·· ·
···4.1
PARAMETERS · ··· ·· ·
··4.8
5.0 THE RESIDENTIAL CONSUMPTION MODULE .····5.1
MECHANISM .· ·
·· · · ·
···5.1
INPUTS AND OUTPUTS ·· ·
··· ··5.2
ix
.r....--------------------------------------.,.....,,,,,,,"-
MODULE STRUCTURE
PARAMETERS
Appliance Saturations
Fuel Mode Splits
Consumption of Electricity per Unit
E1ectr i ca 1 Cap ac ity Growth
Appliance Survival
Price Elasticities
6.0 THE BUSINESS CONSUMPTION MODULE
MECHANISM .
INPUTS AND OUTPUTS
MODU LE STRUC TURE
PARAMETERS
Floor Space Stock Equations
Business Electricity Usage Parameters
Business Price Elasticities
7.0 THE CONSERVATION MODULE
MECHANISM .
INPUTS AND OUTPUTS
MODEL STRUCTURE
Scenario Preparation (CONSER Program)
Residential Conservation
Business Conservation
Peak Correction Factors
PARAMETERS
8.0 THE MISCELLANEOUS MODULE .
x
5.2
5.11
5.11
5.25
5.25
5.28
5.30
5.30
6.1
6.1
6.1
6.1
6.5
6.5
6.6
6.6
7.1
7.1
7 .4
7.6
7.7
7.10
7.13
7.16
7.17
8.1
MECHANISM .·· ··· ·· ·
··8.1
IN PUTS AND OU TPUTS ····· ·
· ··8.1
MODULE STRUCTURE ·· · ·
· ····8.1
PARAMETERS · ·
··· ·
· ··· ·
8.3
9.0 THE PEAK DEMAND MODULE ·· ··· · ·
·9.1
MECHANISM.· · · · ·····9.1
INPUTS AND OUTPUTS ···· ···9.1
MODU LE STRUC TUR E · ··· ·
· ··· ·
9.1
PARAMETERS ·· ·
· ···· ·
· ·
9.5
10.0 RATE MODEL · · ·
·· · ·
···10.1
MECHANISM .· ···· · ·· ·
·10.1
INPUTS AND OUTPUTS ·· ··· ·· ·
10.1
MODEL STRl£TURE ··· ·· ·
· ··10.1
PARAMETERS ··· · ·· · ·
···10.5
REFERENCES .·· ·· · ·
·····R.1
BIBLIOGRAPHY .·· · ·· ·· · ·· ·
B.1
xi
FIGURES
2.1 Information Flows in the RED Mode 1 2.2
3.1 RED Uncertainty Module 3.3
4.1 RED Housing Module 4.3
5.1 RED Residential Consumption Module.5.4
6.1 RED Business Consumption Modu le 6.3
7.1 RED Conservation Module 7.2
8.1 RED Miscellaneous Module 8.2
9.1 RED Peak Demand Module 9.3
10.1 Structure of the Rate Model 10.3
xii
TABLES
3.1 Inputs and Outputs of the RED Uncerta inty Modu le.. . .3.2
3.2 Parameters Generated by the Uncertainty Module . . . .3.4
4.1 Inputs and Outputs of the RED Housing Modu le . . . .4.2
4.2 Average Household Size in Railbelt Load Centers,1980-2010..4.9
4.3 Number of Military Households Assumed to Reside
on Base in Railbelt Load Centers.. . . . . .4.9
4.4 Probability of Size of Household in Railbelt Load Centers..4.10
4.5 Regional Frequency of Age of Household Head
Divided by the State-Wide Frequency . . . . . .4.11
4.6 Housing Demand Equations:Parameters·Expected
Value,Range,and Variance
4.7 Assumed Normal and Maximum Vacancy Rates by
Type of Hou se .
4.8 Assumed Five-Year Housing Removal Rates in
Railbelt Region,1980-2010
4.9 Railbelt Housing Stock by Load Center and Housing Type,
1980
5.1 Inputs and Outputs of the RED Residential Module.
5.2 Percent of Households Served by Electric Utilities in
Railbelt Load Centers,1980-2010
5.3 Market Saturations of Large Appliances with Fuel
Substitution Possibilities in Single-Family
Homes,Railbelt Load Centers,1980-2010
5.4 Market Saturations of Large Appliances with Fuel
Substitution Possibilities in Mobile Homes,
Railbelt Load Centers,1980-2010
5.5 Market Saturations of Large Appliances with Fuel
Substitution Possibilities in Duplexes,
Railbelt Load Centers,1980-2010
5.6 Market Saturations of Large Appliances with Fuel
Substitution Possibilities in Multifamily Homes,
Railbelt Load Centers,1980-2010
xiii
4.12
4.13
4.13
4.14
5.2
5.12
5.13
5.14
5.15
5.16
5.7 Market Saturations of Large Electric Appliances in
Single-Family Homes,Railbelt Load Centers,1980-2010 .
5.8 Market Saturations of Large Electric Appliances in
Mobile Homes,Railbelt Load Centers,1980-2010
5.9 Market Saturations of Large Electric Appliances in
Duplexes,Railbelt Load Centers,1980-2010
5.10 Market Saturations of Large Electric Appliances
in Multifamily Homes,Railbelt Load Centers,1980-2010
5.11 Percentage of Appliances Using Electricity and
Average Annual Electricity Consumption,
Railbelt Load Centers,1980
5.12 Growth Rates in Electric Appliance Capacity and Initial
Annual Average Consumption for New Appliances
5.13 Percent of Appliances Remaining in Service Years
After Purchase,Railbelt Region
5.14 Price Elasticities for Residential Electricity Use
6.1 Inputs and Outputs of the Business Consumption Module.
6.2 Floor Space Equation Parameters
6.3 Business Consumption Equation Results
6.4 Price Elasticities for Business Electricity Consumption
7.1 Inputs and Outputs of the Conservation Module
7.2 Payback Periods and Assumed Market Saturation Rates for
Residential Conservation Options
8.1 Inputs and Outputs of the Miscellaneous Module
8.2 Parameters for the Miscellaneous Module
9.1 Inputs and Outputs of the Peak Demand Module
9.2 Assumed Load Factors for Railbelt Load Centers
10.1 Inputs and Outputs of the Rate Model
10.2 1980 Load Center Electricity Rates
10.3 Miscellaneous Rate Model Parameters
xiv
5.19
5.21
5.22
5.23
5.26
5.29
5.31
5.31
6.2
6.5
6.7
6.7
7.5
7.17
8.1
8.4
9.2
9.5
10.2
10.5
10.5
1.0 INTRODUCTION
The Railhelt Electricity Demand (RED)forecasting model documented in
this report is a partial end-use/econometric model.Initial estimates of
total residential demand are derived hy forecasting the number of energy-using
devices and aggregating their potential electricity demand into preliminary
end-use forecasts.The model then modifies these preliminary forecasts,using
econometric fuel price elasticities,to develop final forecasts of total
residential energy consumption.The model thus uses both technical knowledge
of end uses and econometrics to produce the residential forecast.The
industrial (basic)and commercial (government and support)sectors are treated
similarly,hut since little information is available on end uses in these
sectors in Alaska,preliminary demand is estimated on an aggregated basis
rather than by detailed end use.Miscellaneous demand is based on the demand
of the other three sectors.
Other important features of the model are a mechanism for handling
uncertainty in some of the model parameters,a method for explicitly including
government programs designed to subsidize conservation and consumer-installed
dispersed energy options (i.e.microhydro and small wind energy systems),and
the ability to forecast peak electric demand by load center.The model
recognizes three load centers:Anchorage and vicinity (including the
~atanuska-Susitna Borough and the Kenai Penninsula),Fairbanks and vicinity,
and Glennallen-Valdez.It produces annual energy and peak demand forecasts
every fifth year from 1980 to 2010.
To produce a forecast,the model user must supply the model with
region-specific estimates of total nonmilitary employment and total population
for each forecast period.A few statewide variables are also required:
forecasts of the age/sex distribution of the state1s population,the statewide
average annual wage rate,and the Anchorage Consumer Price Index.All of
these variables were produced by the University of Alaska Institute of Social
and Economic Research MAP econometric model;however,they can be derived from
other sources.The user must also supply estimates of prices for gas,oil,
1.1
IJ'---------------------
and electricity.Finally.the model user may select either ranges or default
values for the model's parameters and may run the model in either a
certainty-equivalent or uncertain (Monte Carlo)mode.The model then produces
the forecasts.
This report consists of 10 sections.In Section 2.0 an overview of the
RED model is presented.In Section 3.0 the Uncertainty Module.which provides
the model with Monte Carlo simulation capability.is described.In
Section 4.0 the Housinq Module.which forecasts the stock of residential
housing units.is described.These forecasts are used in the electricity
demand forecasts of the Residential Consumption Module.discussed in
Section 5.0.Forecasts of demand in the business sector are produced by the
Business Consumption Module.which is described in Section 6.0.The effects
of government market intervention to develop conservation and dispersed
generation options are covered by the Conservation Module.Section 7.0.In
Section 8.0 miscellaneous electricity demand (street lighting.second homes.
etc.)is discussed.The Peak Demand Module.Section 9.0.concerns the
relationship between annual electricity consumption and annual peak demand.
In Section 10.0 the Rate Model is discussed.
1.2
2.0 OVERVIEW
The Railbelt Electricity Demand (RED)Model is a simulation model
desiqned to forecast annual electricity consumption for the residential,
commercial-industrial-government,and miscellaneous end-use sectors of
Alaska's Railbelt region.The model also takes into account government
intervention in the energy markets in Alaska and produces forecasts of system
annual peak demand.The forecasts of consumption by sector and system peak
demand are produced in five-year steps for three Railbelt load centers:
•Anchorage and vicinity (including Anchorage,Matanuska-Susitna
Borough and Kenai Peninsula)
•Fairbanks and vicinity (including the Fairbanks-North Star Borough)
•Glennallen/Valdez (including settlements along the Richardson
Highwav).
When run in ~onte Carlo mode,the model produces a sample probability
distribution of forecasts of electricity consumption by end-use sector and
peak demand for each load center for each forecast year:1985,1990,1995,
2000,2005,2010.This distribution of forecasts can be used for planning
electric power generating capacity.The RED model is accordingly designed to
be run in tandem with a separate electric capacity planning and dispatching
model entitlen Alaska Railbelt Electric Energy Planning (AREEP)model.
Separate documentation of this model will be available in a report to be
issued in September 1982.
Figure 2.1 shows the basic relationships among the seven modules that
comprise the RED model.The model begins a simulation with the Uncertainty
Module,selecting a trial set of model parameters,which are sent to the other
modules.These parameters include price elasticities,appliance saturations,
and regional load factors.Exogenous forecasts of population,economic
activity,and retail prices for fuel oil,gas,and electricity are used with
the trial parameters to produce forecasts of electricity consumption in the
Residential Consumption and Business Consumption Modules. These forecasts,
along with additional trial parameters,are used in the Conservation Module to
model the effects on electricity sales of subsidized conservation and
2.1
ECONOMIC UNCERTAINTY
FORECAST MODULE
...HOUSING
./STOCK K
~IRESIDENTIALK
'--BUSINESS kA
r--
{l ...
K:::CONSERVATION
~
INDUSTRIAL I I MISC.
I
~ANNU::SALES 1d
LS1 K
PEAK DEMAND ~
K
FIGURE 2.1.Information Flows in the RED Model
dispersed generatinq options such as windmills or microhydro installations.
The revised consumption forecasts of residential and business (commercial,
small industrial,and government)consumption are used to estimate future
miscellaneous consumption and total sales of electricity.Finally,the
unrevised and revised consumption forecasts are used along with a trial system
load factor forecast to estimate peak demand.The model then returns to start
the next Monte Carlo trial.When the model is run in certainty-equivalent
mode,a specific "default"set of parameters is used,and only one trial is
run.
2.2
The RED model produces an output file of trial values for consumption by
sector and system peak demand by year and load center.This information can
be used bv the AREEP model to plan and dispatch electric generating capacity
for each load center and year.The AREEP model produces an estimate of the
cost of electricity,which is converted to electricity prices used to run the
RED model.If the demand level changes by more than 5%,the RED model can be
rerun in tandem with AREEP,using new prices until consistency is achieved.
Convergence (consistency or near-equality of two successive demand forecasts)
is usually achieved in two to three passes.
The remainder of this section presents brief descriptions of each
module.Detailed documentation of each of the modules is contained in
Sections 3.0 through 9.0 of this report.
UNCERTAINTY MODULE
The purpose of the Uncertainty Module is to randomly select values for
individual model parameters that are considered subject to forecasting
uncertainty.These parameters include the market saturations for major
appliances in the residential sector;the price elasticity and cross-price
elasticities of demand for electricity in the residential and business sector;
the market penetration of conservation and dispersed generating technologies;
the intensity of electricity use per square foot of floor space in the
business sector;and the electric system load factors for each load center.
These parameters are generated by a Monte Carlo routine,which uses
information on the distribution of each parameter (such as its expected value
and range)and the computer's random number generator to produce sets of
parameter values.Each set of generated parameters represents a IItrial.1I By
running each successive trial set of generated parameters through the rest of
the modules,the model builds distributions of annual electricity consumption
and peak demand.The end points of the distributions reflect the probable
range of annual electric consumption and peak demand,given the level of
uncertainty.
2.3
•
The Uncertainty Module need not be run every time RED is run.The
parameter file contains "default"values of the parameters that may be used to
conserve computation time.
THE HOUSING MODULE
The Housing Module calculates the number of households and the stock of
hou si nq by dwe 11 i ng type in each load center for each f oreca st year in wh i ch
the model is run.Using exogenous state-wide forecasts of the number of
~ouse~olds,~ousphold headship rates hy age,the age distribution of Alaska's
population,and regional forecasts of total population,the housing stock
module first derives a forecast of the number of households in each load
center.Next,it estimates the distribution of households by age of head and
size of household for each load center.Finally,it forecasts the demand for
four types of housing stock:single family,mobile homes,duplexes,and
multifamily units.
The supply of housing is calculated in two steps.First,the supply of
each type of housing from the previous period is adjusted for demolition and
compared to the demand.If demand exceeds supply,construction of additional
housing begins irrnnediate1y.If excess supply of a given type of housing
exists,the model examines the vacancy rate in all types of houses.Each type
is assumed to have a maximum vacancy rate.If this rate is exceeded,demand
is first reallocated from the closest substitute housing type,then from other
types.The end result is a forecast of occupied housing stock for each load
center for each housing type in each forecast year.This forecast is passed
to the Residential Consumption Module.
RESIDENTIAL rONSUMPTION MODULE
The Residential Consumption Module forecasts the annual consumption of
electricity in the residential sector for each load center in each forecast
year.It does not,in general,take into account explicit government
intervention to promote residential electric energy conservation or
self-sufficiency.Such intervention is covered in the Conservation Module.
The Residential Consumption Module employs an end-use approach that recognizes
2.4
-
nine major end uses of electricity,and a "small appliances"category that
encompasses a large group of other end uses.For a given forecast of occupied
housing,the Residential Consumption Module first adjusts the housing stock to
net out housing units not served by an electric utility for each type.It
then forecasts the residential appliance stock and the portion using
electricity,stratified by the type of dwelling and vintage of the appliance.
Appliance efficiency standards and average electric consumption rates are
applied to that portion of the stock of each appliance using electricity.The
stock of each electric appliance is then multiplied by its corresponding
consumption rate to derive a preliminary consumption forecast for the
residential sector.Finally,the Residential Consumption Module receives
exogenous forecasts of residential fuel oil,natural gas,and electricity
prices,along with "trial"values of price elasticities and cross-price
elasticities of demand from the Uncertainty Module.It adjusts the
preliminary consumption forecast for both short-and long-run price effects on
appliance use and fuel switching.The adjusted forecast is passed to the
Conservation and Peak Demand Modules.
BUSINESS CONSUMPTION MODULE
The Business Consumption Module forecasts the consumption of electricity
by load center in commerc ia 1,sma 11 i ndu str ia 1,and government uses for each
forecast year (1980,1985,1990,1995,2000,2005,2010).Direct promotion of
conservation in this sector is covered in the Conservation Module.Because
t~e end uses of electricity in the commercial,small industrial and government
sectors are more diverse and less known than in the residential sector,the
Business Consumption Module forecasts electrical use on an aggregate basis
rather than by end use.
REO uses a proxy (the stock of commercial and industrial floor space)for
the stock of capital equipment to forecast the derived demand for
electricity.Using exogenous forecasts of regional income,regional
population,the rate of inflation,and interest rates,the module forecasts
the regional stock of floor space.Next,econometric equations are used to
predict the intensity of electricity use for a given level of floor space in
2.5
the absence of any relative price changes.Finally,a price adjustment
similar to that in the Residential Consumption Module is applied to derive a
forecast of business electricity consumption (excluding large industrial
demand,which must be exogenously determined).The Business Consumption
Module forecasts are passed to the Conservation and Peak Demand Modules.
CONSERVATION MODULE
Because of the potential importance of government intervention in the
market place to encourage conservation of energy and substitution of ot~er
forms of energy for electricity,the RED model includes a module that permits
explicit treatment of technologies and programs that are designed to reduce
the demand for utility-generated electricity.The module structure is
designed to incorporate assumptions on the technical performance,costs,and
market penetration of electricity-saving innovations in each end use,load
center,and forecast year.The module forecasts the aggregate electricity
savinqs by end use,the costs associated with of these savings,and adjusted
consumption in the residential and business sectors.
The Conservation Module requires a set of off-line calculations by a
nested computer program called CONSER.These calculations are more complex in
the residential than the commercial sector,since more data are available on
residential sector conservation ootions.In the residential sector,the model
user supplies information to CONSER on the technical efficiency (electricity
savings),electricity price,and costs of installation.Government market
intervention in the form of capital subsidies or low-interest loans is
incorporated in lowered installed cost to the consumer.CONSER then
internally calculates the internal rate of return on the option to the
consumer.That rate of return must exceed the passbook savings interest rate
if the option is to gain assumed market acceptance.The Conservation Module
t~en calculates the option's pavback period for technologies considered
"acceptable"by the user,and a payback decision rule links the payback period
to a range of market saturations for the technologies.The savings per
installation and market saturation of each option are used to calculate
residential sector electricity savings and costs.In the business sector,the
2.6
..'
model user must specify the technical potential for new and retrofit
energy-savinq technologies.The user must also specify the range of
conservation saturation as a percent of total potential conservation.The
Conservation Module then calculates total electricity savings due to market
intervention in new and retrofit applications and adjusts residential and
business consumption for each load center and forecast year.
MISCELLANEOUS CONSUMPTION MODULE
The Miscellaneous Consumption Module forecasts total miscellaneous
consumption for second (recreation)homes,vacant houses,and other
miscellaneous uses such as street lighting.The module uses the forecast of
residential consumption (adjusted for conservation impacts)to predict
electricity demand in second homes and vacant housing units.The sum of
residential and business consumption is used to forecast street lighting
requirements.Finally,all three are summed together to estimate
miscellaneous demand.
PEAK DEMAND MODULE
The Peak Demand Module forecasts the annual peak hour demand for
electricity.A two-stage approach using load factors is used.The unadjusted
residential and husiness consumption,miscellaneous consumption,and load
center load factors generated by the Uncertainty Module are first used to
forecast preliminary peak demand.Next,displaced consumption (electricity
savings)calculated by the Conservation Module is multiplied by a peak
correction factor supplied by the Uncertainty Module to allocate a portion of
electricity savinqs from conservation to peak demand periods.The allocated
consumption savinqs are then multiplied by the load factor to forecast peak
demand savings,and the savings are subtracted from peak demand to forecast
revised peak demand.
The fo 11 owi ng secti ons descri be each modu 1e of the mode 1 in greater
deta il.
2.7
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3.0 THE UNCERTAINTY MODULE
RED's Uncertainty Module allows the forecaster to incorporate uncertainty
in key parameters of the RED Model forecast.In other words,the impact of
uncertain parameter values is reflected in the forecast values.
RED allows generation of key subsets of the full set of parameters.It
is not practical to allow all parameters to vary on all runs of the model,as
the total number of such parameter values required for a single pass through
the model is greater than 1000.For example,if the user wanted to generate
50 values for every uncertain parameter,over 50,000 values would have to be
produced.While this is within RED's capabilities,the cost of such an
exercise would be very high.Therefore,discretion is encouraged when using
this option.
MECHANISM
A Monte Carlo routine uses the host computer's pseudo random number
generator to translate user-supplied information on a parameter,such as its
expected value,its range,and its subjective probability distribution,into
random trial parameter values.By doing repeated simulations of the model,
using several such randomly generated values of the parameter,the model will
yield electricity consumption forecasts that incorporate each parameter's
uncertainty.
INPUTS AND OUTPUTS
The Uncertainty Module requires three basic inputs:
•the number of values to be generated
• a selection of parameters to vary
•the parameter file.
The parameter file contains the default values,ranges,and (if required)the
expected value and variance of each parameter.Table 3.1 provides a summary
of the inputs and outputs of the module.
3.1
•
TABLE 3.1.Inputs and Outputs of the RED Uncertainty Module
(a)Inputs
Symbo 1
N
(see Table 3.2)
(b)Outouts
Symbol
(See Table 3.2)
N
MODULE STRUCTURE
Var i ab 1e
Number of Values
to be generated
Parameter's Range,
Vari ance,and
Expected Values
Variable
Random Parameter
Values
Number of Times
Model is to be Run
Input From
User Interface
Parameter Fi 1e
Output To
Other Modules
Model Control Program
An overview of information flows within the Uncertainty Module is given
in Figure 3.1.First,the program asks whether the user would like to
generate a parameter.If the answer is no,then the default value (from the
parameter file)for each parameter is assigned.If a random parameter value
is to be generated,then the user is queried as to which parameters will be
a"owed to vary.
The next step is to choose the number of values to be generated for each
parameter.This is the number of times the remainder of the model will be
run,each time with a different generated value for each parameter.Next,an
arbitrarY seed for the ran~om number generator is entered.
Next,the computer generates a random number for each value to be
produced.This is accomplishe~by calling the computer's "pseudo"random
number generator,which generates a random number between 0 and 1.From the
parameter file,the information on the range of parameter,or (for parameters
with a normal distribution)the ranqe,expected value,and variance are used
3.2
START
SELECT PARAMETERS
TO BE
GENERATED
RANDOMLY
NO
SELECT NUMBER
OF VALUES TO
BE GENERATED
(N)
COMPUTER
GENERATES N
RANDOM
NUMBERS
ASSUMED RANGE
EXPECTED VALUE
TRANSFORM
RANDOM NUMBERS
TO
PARAMETER
VALUES
OUTPUT
PARAMETER
VALUES
ASSIGN DEFAULT
VALUE OF
UN SELECTED
PARAMETERS
.....
FIGURE 3.1.RED Uncertainty Module
to construct cumulative probability functions for each parameter.The random
values for each parameter are then generated by applying the random numbers to
these functions.
The Uncertainty Module then passes the generated values for each
parametpr to the remainder of RED.The mean and variance for each generated
parameter are also reoorted.
3.3
PARAMETERS
Table 3.2 provides a list of the parameters that can be generated by the
Uncertainty Module.
Svmbo 1
SAT
BBETA
CONSAT
LF
Name
Housing Demand Coefficients
Saturation of Residential Appliances
Residential,Industrial,and Combined
Support and Government Own-,Oil-Cross
and Gas-Cross Price Elasticities
Floor Space Consumption Parameter
Saturation of Conservation Technologies
Load Factor
3.4
Norma 1
Un if orm
Un iform
Norma 1
Un iform
Uniform
4.0 THE HOUSING MODULE
The consuming unit in the residential sector is the household,each of
which is assumed to occupy one housing unit.The Housing Module provides a
forecast of the number of households and the stock of housing by the type of
dwelling in each of the Railbelt's load centers.The type of dwelling is a
major determinant of energy use in residential space heating.Furthermore,
the type of dwelling is correlated with the stock of residential appliances.
This module,therefore,provides essential inputs for the Residential
Consumption Module.
MECHANISM
The Housinq Stock Module uses assumed average household sizes to
translate an exogenous forecast of regional population into a forecast of
regional households.This forecast is then stratified on the age of the head
of household and the number of household members.The housing demand
equations then use this distribution of households by size and age of head to
predict the initial demand for housing by type of dwelling.The initial
demand for each housinq type is compared with the remaining stock,and
adjustments in housing demand and construction occur until housing market
clearance is achieved.
INPUTS AND OUTPUTS
Table 4.1 oresents the data used and generated within this module.An
exogenous forecast of regional population and the state-wide distribution of
households by age of head is needed as input,while the module passes
information on the occupied and vacant housing stock to the remainder of RED.
MODULE STRUCTURE
The Housing Module's structure is shown in Figure 4.1.The module begins
each simulation with a user-supplied forecast of population for the load
center.Assumed household sizes for each load center are divided into the
population to obtain a forecast of total households,which is then adjusted
4.1
TABLE 4.1.Inputs and Outputs of the RED Housing Module
(a)Inputs
Symbol Variable
POP Population Forecast
HH Ata State Households by Age Group
b,c,d Housing Demand Coefficients
(h)Outputs
Symbol Variable
HD Ty Occupied Housing Stock by Type
IfH Vacant Housing
Variable Input From
Population Scenario File
Forecast File
Uncertainty Module
Variable Output From
Residential Module
Miscellaneous Module
for military demand,next stratified by age and size of household,and then
used to generate an estimate of demand for each type of housing (TV).Demand
is compared to the initial stock,resulting in new construction or
reallocation of demand as appropriate.The end result is a set of estimates
of occupied and unoccupied housing units by type.Finally,the housing stock
is reinitialized for the next forecast period.
The first step in the Housing Module is to find the number of households
in a given Railbelt load center,dividing the exogenous forecast of regional
Dopulation by the region's predicted average household size:
where
THH =
POP =
AHS =
BHH =
=
t =
(4.1)
total number of households
population (exogenous)
average household size (parameter)
military households residing on base (exogenous)
region subscript
forecast period subscript.
4.2
---------------------
REGIONAL
POPULATION
FORECAST
CALCULATE NUMBER
OF HOUSEHOLDS IN
LOAD CENTER
ASSUMED
HOUSEHOLD
SIZE
STRATI FY
HOUSEHOLDS BY
AGE OF HEAD
SIZE OF HOUSEHOLD
.AGE DISTRIBUTION
OF HOUSEHOLD
HEADS
.SIZE DISTRIBUTION
.QF HOUSEHOLDS
-.
I
I
I
I...--------
FILL VACANCIESYE~I TY WITH
COMPLEMENT ARY
DEMAND
.YES I NEW
~CONSTRUCTION
OF TYPE TY
FORECASTS OF
OCCUPIED,
UNOCCUPIED
HOUSING BY
TYPE
CALCULATE
DEMAND FOR
HOUSING UNITS
BY TYPE TY
I
REINITIALIZE
HOUSING
STOCKS
DEMAND
PARAMETERS
(UNCERTAINTY
MODULE)
INITIAL HOUSING
STOCK TY
FIGURE 4.].RED Housing Module
On-base military households are subtracted out because they do not
significantly affect off-base housing and,since the military supplies
electricity to them,on-base households have no impact on the residential
demand for utility-supplied electricity.
Once the total number of households in the region has been obtained,they
are stratified ry the size of the household and the age of the household
head.To obtain the distribution of households by size of household,the
4.3
total number of households is multiplied by the probabilities of four size
categories derived from information provided in a residential survey conducted
in the Railbelt hy Battelle-Northwest in March and April,1981.To estimate
the distribution of households by the age of head,the 1970 Census ratio
between the regional and state relative frequencies of age of head is assumed
to remain constant.The user supplies forecasts of the statewide age
distribution of heads of households.In the Railbelt Alternatives study,this
forecast was produced by the MAP model.Using the state relative frequency
distribution,therefore,and applying the constant ratios of regional to
statewide frequencies,the model obtains forecasts of the regional
distribution of households by age of head.
The joint distribution by size of household and age of head is obtained
by multiplying the two distributions:
(4.2)
where
HH =number of households in an age/size class
A =subscript denoting aggregate state variable
p =regional household size probability (parameter)
R =ratio of the regional to state relative frequency of age of
~ousehold head (parameter)
a =age of head subscript
s =household size subscript.
The demand for a particular type of housing -single family,
multi-familv,mo~ile home,or duplex -is hypothesized to be a function of the
size of the household and the age of the head (which serves as a proxy for
household wealth).Equations projecting demand for three of the types of
housing (single family,multi-family,mobile homes)were estimated by the
4.4
Institute of Social and Economic Research (ISER)from Anchorage data collected
by the University of Alaska's Urban Observatory (Goldsmith and Huskey 1980b).
The remaining category (duplex)is filled with the leftover households.
The demand for a particular type of housing is given by the following
equations:
HO SFit =THH it x bO +ba1 x Slit +ba2 x S2it +ba4 x S4it +
(4.3)
ba2s x A2it +b3s x A3it +b4s x A4it
HO MFit =THH it x Co +cal x Slit +c a2 x S2it +c a4 x S4it +c2s x
(4.4)
A2it +c3s x A3it +c4s x A4it
HO MHit =THH it x do +da1 x Slit +da2 x S2it +da4 x S4it +
(4.5)
d2s x A2it +d3s x A3it +das x A4it
HD OPit =THH it -HO SFit -HD MFit -HD MHit
It/here
HO =housing demand
SF =index for single family
(4.6)
4
\it =l:HH itas ;
a=l
4
Aait =~1 HH itas ;
s =1,2,4
a =2,3,4
MF =index for multifamily
MH =index for mobile home
DP =index for duplex
4.5
a =index denoting the age of household head
a :::1 <25
a =2 25-2g
a =3 30-54
a =11 55+
s =index denoting the size of household
s =1 ~2
s :::2 3
s :::3 4-5
s :::4 6+
b,c,and dare parameters from the Uncertainty Module.
1
The model then adjusts
housing market is cleared.
previous period's stock net
the housing stock and housing demand so that the
Initially,the housing stock is calculated as the
of demo 1it i on:
where
(4.7)
HS :::housinq stock
TV =index denoting the type of housing (SF,MF,MH,and DP)
r :::period specific removal rate (parameter).
Net demand for each type of dwelling is defined as the demand minus the
housing stock:
(4.8)
where
NO :::net demand.
If net demand for all types of housing is positive,then new construction
immediately occurs in sufficient quantity to meet the net demand plus an
equilibrium amount of vacancies required to ensure normal functioning of the
housing market:
4.6
where
NC TYit =ND TYit +VTy x (HS TYit +ND Tyit )
NC =new construction
V =normal vacancy rate (parameter).
(4.9)
The equilibrium vacant housing stock is the "normal"vacancy rate times the
stock of housing.
If the net demand for a particular type of housing is negative,however,
then the vacancy rate for that type of housing has to be calculated:
where
HD TYitAVTYit=1 -HS
TYit
AV =actual vacancy rate.
(4.10)
If the actual vacancy rate is greater than its assumed maximum,then the
excess supply of that particular type of housing is assumed to drive down the
price of that type of dwelling.Individuals residing in other dwellings could
be induced to move to reduce mortgage or rent payments.An adjustment to the
distribution of housing demands,therefore,is appropriate.
Substitution occurs,if possible,within groups of housing that are close
substitutes (single-family and mobile homes;duplexes and multifamily).If
not enough excess demand exists from the close substitutes to fill the
depressed market,then substitution occurs from all types.The procedure is
as follows:
1.The number of excess vacancies within a type is calculated by
subtracting the housing demand from one minus the maximum vacancy
rate,times the stock.
2.The number of substitute units available to fill the excess supply
is given by subtracting one minus the normal vacancy rate,times the
close substitute stock from the close substitute demand.
4.7
1
3.The minimum of 1 or 2 is subtracted from the complementary housing
demand and added to the depressed demand.
4.If excess supp ly pers i sts (the actua 1 vacancy rate is above its
assumed maximum),then the above procedure is repeated,only the
number of housing units available is now calculated using maximum
vacancy rates and all types of housing where the actual vacancy rate
is less than their assumed maximum.The available units are then
allocated based on normalization weights of the number available by
type.
The final outputs of this module are occupied housing by type (HD Tyit )
and unoccupied housing:
The number of on-base military households,presented in Table 4.3,is
assumed to remain constant over the forecast periods.The level of military
activity in Alaska has stabilized,and little indicates that a major shift
will occur in the future.
Table 4.2 presents the average household size parameters for the forecast
periods by load center.The values for 1980 were derived from 1980 Census of
Population (Bureau of Census 1980c).Average household size has declined
significantly in the past ten years.Continuity in this trend is anticipated,
but the rate of reduction is believed to be more moderate than in the past.
The future values of household size in the three Railbelt load centers,
therefore,were reduced from the 1980 levels to converge on the predicted
statewide household size in the year 2010 (which is 2.443).
PARAMETERS
(4.11)(HS -HD )
\TYit TYit=L:
TV
VH
it
VH =total vacant dwelling units.
where
4.8
TABLE 4.2.Average Household Size in Railbelt Load Centers,1980-2010
Year Anchorage Fairbanks Glennallen-Valdez
1980(a)2.834 2.812 2.843
1985(a)2.769 2.751 2.776
1990 la )2.704 2.689 2.710
1995(a)2.638 2.628 2.643
2000(a)2.573 2.566 2.576
2005(a)2.508 2.505 2.510
201O la )2.443 2.443 2.443
(a)Based on assumption that average household size
linearly falls to the predicted statewide household
size in 2010.
TABLE 4.3.Number of Military Households Assumed to Reside on Base
in Railbelt Load Centers
Anchorage
3,212
Fairbanks
3,062
Glennallen-Valdez
o
10.
Source:Supplied by the Institute of Social
and Economic Research,University
of Alaska.
Tables 4.4 and 4.5 present the parameters used to derive the joint
distribution of households by size and age of head.The baseline figures for
the probability of size parameters were derived from the Battelle-Northwest
end-use survey.Those parameters were adjusted to linearly approach the 1977
Western Regional average household size of 2.6 (Bureau of Census 1977)by the
year 1992,then continue to change at the same rate for the remainder of the
period.The ratio of regional to statewide frequency of age of head was
derived by ISER.These ratios are assumed to remain constant.
The housing demand parameters were estimated by ISER using a linear
probability model.(a)The expected values in Table 4.6 are the estimated
4.9
~-_;i_Wii."---••••••••--------------------1
TABLE 4.4.Probability of Size of Household in Railbelt Load Centers
Year Size Anchorage Fairbanks Glennallen-Valdez
1980(a)<2 0.476 0.455 0.426
3 0.190 0.210 0.190
4-5 0.291 0.287 0.341
6+0.042 0.048 0.043
1985(b)<2 0.515 0.515 0.463
3 0.182 0.152 0.185
4-5 0.262 0.262 0.309
6+0.041 0.041 0.042
1990 lb )<2 0.553 0.553 0.501
3 0.174 0.174 0.180
4-5 0.233 0.233 0.277
6+0.040 0.040 0.042
1995 lb )<2 0.591 0.591 0.538
3 0.165 0.165 0.176
4-5 0.205 0.205 0.245
6+0.039 0.039 0.041
2000 lb )<2 0.629 0.629 0.575
3 0.157 0.157 0.171
4-5 0.176 0.176 0.213
6+0.039 0.039 0.040
2005(b)<2 0.667 0.667 0.612
3 0.149 0.149 0.166
4-5 0.147 0.147 0.182
6+0.038 0.038 0.040
2010(b)<2 0.705 0.705 0.650
3 0.140 0.140 0.161
4-5 0.118 0.118 0.150
6+0.037 0.037 0.039
(a)Source:Battelle-Northwest End-Use Survey.
l b)The distribution is assumed to linearly approach the 1977 Western
Regional Distribution by 1992 (Bureau of Census 1977).
4.10
TABLE 4.5.Regional Frequency of Age of Household Head
Divided by the State-Wide Frequency
Age of Head Anchorage Fairbanks Glennallen-Valdez
<25 1.07 1.45 0.56
25-30 1.02 1.12 0.76
31-54 1.02 0.97 1.02
55+0.80 0.69 1.49
Source:Supplied by the Institute of Social and Economic
Research,University of Alaska.
coefficients reported by ISER.The ranges were calculated as the width of the
95%confidence intervals,whereas the variance was backed out of the reported
F statistics.
Table 4.7 presents the assumed normal and maximum vacancy rates by type
of house.ISER {Goldsmith and Huskey 1980b)derived the normal vacancy rates
by taking the ten-year U.S.averages of vacancy rates for owner and renter
units.Single-family and mobile homes have the owner rate;multifamily homes
have the renter rate;and duplexes are the average of owner and renter rates.
For the maximum vacancy rates,Anchorage multifamily rates were available.
The relationship between the normal rates for multifamily and all other types
was used to derive the maximum rates.
Housing demolition rates (Table 4.8)are a function of the age of the
housing stock and the demand for housing.ISER found that approximately one
percent of the housing stock was removed between 1975 and 1980 in Anchorage
and Fairbanks (Goldsmith and Huskey 1980b).As the existing stock ages,the
removal rate is assumed to grow towards the U.S.average,which has been
estimated to be between 2 and 4%per forecast period (5 years).
The housing stock figures displayed in Table 4.9 are those derived by
ISER for their end-use model.The numbers have been adjusted to reflect the
total stock,not only stock served by an electric utility.
4.11
......-----------------------------··1
TABLE 4.6.Housing Demand Equations:Parameters'Expected Value,
Range,and Variance
Parameter Expected Value Range Variance
b 0.461
0
ba1 -0.303 0.142 0.001
ba2 -0.175 0.152 0.001
ba4 0.080 0.230 0.003
b2s 0.182 0.205 0.003
b3s 0.317 0.182 0.002
b4s 0.380 0.226 0.003
c 0.383
0
cal 0.225 0.124 0.001
ca2 0.086 0.133 0.001
ca4 -0.090 0.202 0.003
c 2s -0.203 0.180 0.002
c 3s -0.280 0.159 0.002
c 4s -0.352 0.198 0.003
d 0.097
0
da1 0.068 0.101 0.001
da2 0.039 0.109 0.001
da4 0.014 0.159 0.002
d2s 0.008 0.152 0.001
d3s -0.020 0.130 0.001
d4s -0.016 0.162 0.002
Source:Goldsmith and Huskey 1980b,Table B.6.
4.12
TABLE 4.7.Assumed Normal and Maximum Vacancy Rates by Type of House
(Percent)
Norm(1 Maxiru)
T.Ye e Rate a)Rate b
Single Family 1.1 3.3
Mobil e Home 1.1 3.3
Dup 1ex 3.3 10.0
Mul tifamily 5.4 16.0
(a)Imputed by ISER from Bureau of
the Census (1980a).
(b)Imputed by ISER from Anchorage
Real Estate Research Committee
(1979).
TABLE 4.8.Assumed Five-Year Housing Removal Rates in Railbelt Region,
1980-2010 (Percent of Housing Stock at Beginning of Period
Removed During Period)
Years
1980-1985
1985-1990
1990-1995
1995-2000
2000-2005
2005-2010
Source:Author Assumption.
4.13
Removal
Rate (percent)
1.25
1.50
1.75
2.00
2.25
2.50
TABLE 4.9.Railbelt Housing Stock by Load Center and Housing Type,1980
(number of unitS)(a)
Type Anchorage Fairbanks Glennallen-Valdez
Single Family 37,422 9,900 665
Mob i1e Homes 9,239 2,475 904
Duplexes 5,871 1,397 270
Mu 1t ifam i1y 19,061 5,265 266
Total 71,593 19,037 2,105
(a)A unit is occupied by one household.Thus,a 4-plex is
considered four housing units.
Source:Goldsmith and Huskey 1980b,Table C.16,C.17,C.18.
These fiqures have been adjusted to reflect the total
stock,rather than units served by electricity.
4.14
...
r!
I 5.0 THE RESIDENTIAL CONSUMPTION MODULE
The purpose of the Residential Consumption Module is to provide forecasts
of residential consumption of electricity.Consumption of electricity is not
the instantaneous demand for electricity;rather,it is the quantity of
electricity (kWh)required to meet the customer1s needs over a period of
time.The forecasts of the needs of the residential sector do not include the
impacts of conservation produced by market intervention by government.The
potential for and impacts of such conservation activities are handled in the
Conservation Module.Furthermore,the module's forecast of residential
requirements is the amount of electricity that needs to be delivered to the
residential sector -it does not include allowances for line losses.
What this module does do,however,is estimate the amount of electricity
residential consumers use,with explicit consideration of the impacts of
electricity price changes and fuel switching between electricity,gas,and
oil.Impacts of fuel switching to and from other fuels (such as wood)are
handled in the Conservation Module.
MECHANISM
The Residential Consumption Module employs an end-use approach.In an
end-use analysis,the first step is to identify the major uses of
electricity.Future market saturations of the uses are forecasted so that the
future stock of electricity-consuming devices is defined.The next step is to
estimate the amount of electricity demanded to meet a future demand for the
services of the devices.The forecast of average consumption of the appliance
stock,therefore,reflects both the trend in the size of the device and its
utilization rate.Once the stock of major electricity-consuming devices and
their corresponding average annual per unit consumption of electricity are
forecast,the future consumption of electricity by device type is obtained by
multiplying the number of ~evices by their predicted annual average
consumption of electricity.Using the same procedure for miscellaneous
residential uses and summing over all end-uses yields an aggregate forecast of
electricity requirements.
5.1
One major problem of the end-use approach is that the impacts of changes
in fuel prices (both electricity and alternatives)and income on electricity
usage are usually treated directly through the forecaster's judgment.The RED
Residential Consumption Module addresses this problem differently.By
adjusting the aggregate residential consumption figure with price and
cross-price elasticities of demand derived econometrically from actual
consumption data,RED accounts for price change and fuel-switching impacts in
the residential sector.
INPUTS AND OUTPUTS
Table 5.1 presents the inputs and outputs of the module.The number of
households by dwelling type is the number of occupied dwelling units by type
predicted in the Housing Module.The short-run and long-run price and
cross-price elasticities,as well as the appliance saturations,are generated
in the Uncertainty Module.The output of the module is preliminary
residential sales of electricity.
TABLE 5.1.Inputs and Outputs of the RED Residential Module
(a)Inputs
Symbol Var i ab 1e From
HP Ty Households by Type of Dwelling Housing Stock Module
E,CE Price Elasticities Uncertainty Module
SAT Appliance Saturations Uncertainty Module
(b)Outputs
Symbol Variable To
RESCON Residential Electricity Miscellaneous,Peak Demand
Requirements and Conservation Modules
MODU LE STR UCTUR E
The Residential Consumption Module identifies the following major uses of
electricity in the residential sector:
5.2
1.Water Heating
2.Cooking
3.Refrigeration
4.Freezing
5.Clothes Washing (and additional water heating)
6.Clothes Drying
7.Dishwashinq (and additional water heating)
8.Saunas-Jacuzzis
9.Space Heating
In addition,several additional uses of electricity by households are captured
hy a small appliance category.Small appliances include televisions,radios,
lighting,head-bolt heaters,kitchen appliances,heating pads,etc.The basic
p~emise of this module is that the household is the primary consumer of
electricity,not the individual.However,the number of individuals in the
household significantly affects the consumption of energy for clothes washing,
clothes drying,and water heating.Therefore,there is an adjustment in the
model for changes in the average household size to recognize the impact of
such changes on the usage of these appliances.
For the nine major uses of electricity,the end-use approach is used (see
Figure 5.1).Figure 5.1 shows the calculations that take place in the
Residential Consumption Module.Beginning with a regional estimate of
occupied housing stock by type,the module uses appliance market saturation
parameters to estimate the stock of each of the major appliances recognized by
the model.The module then calculates the initial fuel mode split for
multifuel appliances,calculates preliminary electric consumption for each
appliance type (including small appliances),and then sums these estimates
together into a preliminary consumption estimate for the residential sector.
Price forecasts for gas,oil,and electricity and IItrial ll -specific own-price
and cross-price elasticities are used to adjust the preliminary forecast.
Results from the Battelle-Northwest (BNW)end-use survey show significant
differences in the saturations of these nine end uses by the type of dwelling
in which the household resides.The module,therefore,uses the number of
5.3
FORECAST OF
OCCUPIED HOUSING
STOCK BY TYPE
(HOUSING MODULE)
CALCULATE STOCK
OF LARGE
APPLIANCES
BY END USE,
DWELLING TYPE
APPLIANCE
SATURATIONS
BY HOUSING TYPE
(UNCERTAI NTY
MODULE)
CALCULATE INITIAL
SHARE OF EACH
APPLIANCE USING
ELECTRICITY
FUEL MODE
SPLIT
1980
CALCULATE AVERAGE
ELECTRICAL USE IN
LARGE APPLIANCES
BY APPLIANCE
EFFICIENCY
STANDARDS
CALCULATE TOTAL
PRELlM1 NARY LARGE
APPLIANCE USE
BY
APPLIANCE
CALCULATE
PRELIMINARY
SMALL APPLIANCE
USE OF
ELECTRICITY
SUM PRELIMINARY
CONSUMPTION FOR
ALL APPLIANCES
PRICE
ELASTICITIES,
RESIDENTIAL SECTOR
(UNCERTAINTY
MODULE)
PRICE AND
CROSS-PRICE
ADJUSTMENTS
RESIDENTIAL
CONSUMPTION
PRIOR TO
CONSERVATION
ADJUSTMENT
PRICE FORECASTS
(EXOGENOUS)
FIGURE 5.1.RED Residential Consumption Module
5.4
occupied housing units of each type of dwellinq (single family.multifamily.
mobile home.and duplex)as predicted by the Housing Module as one of the
inputs to estimate the stock of appliances.
The Housing Module.however.merely predicts the number of occupied
primary(a)residences by type in a given region.not the number of units
served by electric utilities.By multiplying the number of occupied housing
units by type ~y an assumed percentage served.the Residential Consumption
Module forecasts the number of primary occupied housing units served:
where
HHS =
TY =
SE =
HD =
i =
t =
HHS TYit =SE it x HD TYit
households served
denotes the type of dwelling
proportion of type served by an electric utility
stock of occupied dwellings from the Housing Module
region subscript
forecast period (t =1.2.3 •...•7).
(5.1)
Once the number of electrically-served households by type of dwelling is
known.the applicance stock can be estimated.The saturation rate for an
appliance is the percentage of households residing in a certain type of
dwelling and having the appliance in question.By multiplying the
housing-type-specific saturation rate by the number of households residing in
that type of housing and then summing across housing types.the model
forecasts appliance demand in each future forecast period t:
AD itk
4
=T~=l (SATTYitk x HHS Tyit )(5.2)
(a)Excluding second or recreation homes.
5.5
where
AD =appliance demand
SAT =saturation rate (parameter)
k =end-use appliance.
Next,the model calculates the number of additions to the stock in the
future.Assuming demand is fully met,the number of new appliances
in period t is found by calculating the stock of appliances surviving from all
previous periods and subtracting this surviving stock from appliance demand:
where
NA =number of new appliances
AS.k =initial stock of appliances (1980 )10
m vintage specific rate in period t;for vintage mdtk=scrap
(parameter)(m =1,2,3,...,7).
Equation 5.3 can be rearranged so that the stock equals the demand:
The future appliance stock,therefore,can be stratified by vintage.Next,
the model calculates the initial stock of electricity-consuming appliances by
multiplying the number of appliances in each vintage by the percentage using
electricity:
(5.4 )
(5.5)
5.6
EAD itk =FMS ik x AD itk
where
EAS =initial stock of electric appliances
FMS =fuel mode split
ENA =additions to the electric appliance stock
EAD =total electric appliance stock.
(5.6)
The Residential Consumption Module next calculates the average annual
electricity consumption of each major appliance.Different vintages of
appliances use different amounts of electricity,so the average consumption
must reflect the vintage composition of the stock.Furthermore,industry
energy efficiency standards for appliances could change in future years.The
future vintage specific consumption rate can be derived by multiplying the
current (1980)consumption rate by a growth factor and adjusting for any
changes in efficiency standards.By weighting these figures by the proportion
of the stock they represent,the average consumption of each appliance type in
a forecast year is derived:
AC itk
where
_EAS iok x (l-d~k)t ((m-l)x Z
-AC i ok x EAD!.L L +J;1 \AC i ok x (1 +gk ) x
(l-cs ) x ENA.k (l_d m ))mk 1m tk
EAD itk
(5.7)
AC itk =average consumption of appliance k in period t (parameter)
AC iok =average consumption of appliance k in the beginning period
(oarameter)
Z =length of forecast periods t and m in years (parameter)set
equal to 5 for this study.
5.7
•
9 =growth rate of appliance k consumption (parameter)
cs =conservation standards target consumption reduction
(parameter)
Finally.the preliminary consumption for each major appliance can be
calculated by multiolying the stock of each appliance by its calculated
average consumption:
•
where
CONS =preliminary consumption of electricity prior to price
atijustments.
(5.8)
The Residential Module makes no distinction among the various types of
appliances in the small aopliance category.The requirements for these units
are simply the product of the number of households in the region,the initial
consumption level,and a growth factor in consumption over time:
where
ACG =growth factor in small appliance consumption
sa =index denoting small appliances.
Total preliminary residential consumption is found by summing across end
uses:
where
RESPRE it
9
~CONSo tk +CONSo tk=l 1 1 sa (5.10)
RESPRE total preliminary residential consumption.
5.8
RESPRE it reflects mainly the physical characteristics of the stock of
electrical appliances.Consumers,however,can respond dramatically to
changes in the prices of electricity and alternative fuels.The own-and
cross-price elasticities of demand measure the responsiveness of consumers to
price changes.Specifically,the own-price elasticity of demand is the ratio
of the percentage of change in the quantity taken of a good to the percentage
of change in the price of the good relative to the prices of other goods.If
this ratio is less than -1 (for example,-2),then the demand for the good is
said to be elastic.In this case,consumers are fairly responsive to price,
as they reduce the amount they purchase by more than the relative increase in
price (in percentage terms).Similarly,the demand is said to be inelastic if
the elasticity is between -1 and zero,in which case the change in quantity is
less than the relative change in price (again in percentage terms).In simple
terms,the own-price elasticity measures the responsiveness of consumption of
a good to changes in the price of the good.
The demand for electricity is also a function of the prices of
alternative fuels.The cross-price elasticity of electricity measures the
responsiveness of the quantity of electricity taken with respect to change in
the price of another fuel.In other words,the cross-price elasticity
predicts the percentage change in the quantity of electricity taken for a
one-percentage change in the relative price of an alternative fuel.
If the cross-price elasticity is positive,then the fuels are said to be
substitutes.As the price of another fuel rises,the quantity taken of
electricity rises.For example,natural gas and electricity are substitutes.
If the price of gas rises enough relative to the price of electricity,then
some natural gas customers will switch to electricity.If the cross-price
elasticity is negative,the fuels are complements,and increases in the price
of the alternate fuel will cause reductions in the amount of the electricity
that is taken.
Distinguishing between short-run and long-run responses to price is
possible.In the short run,or the immediate future,consumers cannot alter
their usage as much as over longer periods of time,since their stock of
appliances is fixed.Over a longer period of time,they can replace elements
5.9
P tU,l
of their stock with devices that use less electricity,or perhaps use another
fuel source.Therefore,making the distinction between the short-run and the
long-run elasticities is important.
The elasticities generated in RED are aged over the forecast period from
t~eir short-run values to their lonq-run values,thus explicitly modeling con-
sumers'changing the pattern of use in the short run and fuel switching in the
lonq run.The Uncertainty Module generates both the short-run and long-run
values of the elasticities for specific trials.Since RED produces a forecast
only every five years in the current study,the transition from the short-run
to lnnq-run value is rather abrupt.Specifically,the transition time is
assumed to be seven years.If a price change occurs in the first forecast
year,in the next forecast period the elasticity is 5/7 of the way from the
short-run value.
The actual calculation is as follows:
RESCON it =RESPRE it x OPA it x CPA it
=~Pite )E SR (Pi(t-l)eaPAitPx""'P----'---'---
i(t-l)e i(t-2)e
)
ElR
x (Pi~~-2)e
10 e
(5.11)
(5.12)
k
"."
where
RESCON =consumption of electricity in the residential sector
OPA =own-price adjustment
CPA =cross-price adjustment
P =price
E =own-price elasticity
SR =denotes short-run value
LR =denotes long-run value
CE =cross-price elasticity
o =index denoting oil
G =index denoting gas
e =index denoting electricity.
RESCON is the predicted electricity consumption in the residential sector
before adjustments for subsidized conservation.This figure is passed to the
Peak Demand and Conservation Modules.
PARAMETERS
The percentage of households served by an electric utility (Table 5.2)is
an important parameter.ISER has estimated that only 91%and 71%of the
occupied housing in Fairbanks and Glenallen-Valdez were connected to an
electric utility (Goldsmith and Huskey 1980b).Due to the high emphasis the
state legislature and governor have placed on energy,the extension of
electrical service to all who would like service is highly probable.
Therefore,electrical services are assumed to be extended to the entire stock
of housing in the Fairbanks and Glennallen load centers by 1995.
Appliance Saturations
Because historical growth and comparison with the lower 48 provide weak
guidance on both current and future market saturations of major appliances,
somewhat arbitrary maximum penetration rates necessarily have been assumed,
for which wide bands of uncertainty have been specified.Market penetration
for most appliances is already outside the bounds of lower 48 experience and
shows no signs of slowing down.However,many of the major appliances most
5.11
TABLE 5.2.Percent of Households Served by Electric Utilities in
Railbelt Load Centers,1980-2010
Year Anchorage Fairbanks Glennallen-Valdez
1980(a)100 91 71
1985(b)100 93 80
1990(b)100 96 90
1995(b)100 100 100
2000 (b)100 100 100
2005 (b)100 100 100
2010 (b)100 100 100
(a)Source:Goldsmith and Huskey 1980b,Table C.13,
C.14,C.15.
(b)The state is assumed to extend electrical service to
all residents by 1995.
likely will never reach 100r.market saturation for a variety of reasons,such
as transient population,convenience of substitutes like laundromats,small
housing units,etc.The assumptions in this seeton reflect a compromise
between rapid historical growth in appliance stocks in Alaska and approaching
boundaries on market saturation.
Tables 5.3 through 5.0 show the default value and range for future market
saturations of major appliances that can use one of several fuels in normal
home installation.The table values are the expected percentage of housing
units of a given type that will have the appliance in a given year and market
area,and the subjective uncertain range that can be used instead of the
default value if the Monte Carlo option is used.The table title indicates
the type of house.The assumptions for each type of appliance are given
below.
Hot Water
Hot water was available in nearly 99%of single-family homes in the
Anchorage market area,according to the Battelle-Northwest end-use survey.It
5.12
TABLE 5.3.Market Saturations (Percent)of Large Appliances with Fuel Substitution
Possibilities in Single-Family Homes,Railbelt Load Centers,1980-2010
Water Heatel's Clothes Dryers ~e (cooking)Saunas -Jacuzzis
Load Center Year Def ault Range Default 'iang e Default Range_Def au lt Ran~---------
a.Anchorage 1980 98.6(a)--90.2 --99.5(a)--14.1
1985 98.8 95-100 91.2 88-94 100.0 99-100 17 .0 14-20
1990 99.0 98-100 92.5 89-95 100.0 99-100 21.0 15-25
1995 99.0 98-100 93.7 90-96 100.0 99-100 25.0 20-30
2000 99.0 98-100 95.0 92-98 100 99-100 30.0 20-40
2005 99.0 98-100 95.0 92-98 100 99-100 35.0 25-45
2010 99.0 98-100 95.0 92-98 100 99-100 40.0 30-50
b.Fairbanks 1980 86.9(a)--81.4 --100.0(a)99-100 7.9
1985 93.0 91-95 84.0 80-88 100.0 99-100 12.0 10-15
1990 99.0 98-100 87.5 82-92 100.0 99-100 21.0 15-25
<.Jl.1995 99.0 98-100 92.5 87-97 100.0 99-100 25.0 20-30........w 2000 99.0 98-100 95.0 92-98 100.0 99-100 30.0 20-40
2005 99.0 98-100 95.0 92-98 100.0 99-100 35.0 25-45
2010 99.0 98-100 95.0 92-98 100.0 99-100 40.0 30-50
c.Glennallen-1980 89.1(a)--87.1 --100.0(a)--13.6
Valdez 1985 90.0 88-92 89.0 87-93 100.0 99-100 17 .0 15-20
1990 95.0 92-98 91.0 87-95 100.0 99-100 21.0 15-25
1995 97.0 96-98 93.0 91-95 100.0 99-100 25.0 20-30
2000 99.0 98-100 95.0 92-98 100.0 99-100 30.0 20-40
2005 99.0 98-100 95.0 92-98 100.0 99-100 35.0 25-45
2010 99.0 98-100 95.0 92-98 100.0 99-100 40.0 30-50
(a)For hot water and cooking,missing values on the Battelle-Northwest survey were not counted.
TABLE 5.4.Market Saturations (Percent)of Large Appliances with Fuel Substitution
Possibilities in Mobile Homes,Railbelt Load Centers,1980-2010
Water Heaters Clothes Dryers Range (cooking)Saunas -Jacuzzis
Load Center Year Default Rang~Def ault Range Default Range Default Range
a.Anchorage 1980 98.2 (a)--79.0 --95.7(a)--6.1
1985 99.0 98-100 80.0 79-81 100.0 100-100 12.0 8-16
1990 99.0 98-100 82.0 80-84 100.0 100-100 21.0 16-25
1995 99.0 98-100 84.0 82-85 100.0 100-100 25.0 20-30
2000 99.0 98-100 85.0 83-87 100.0 100-100 30.0 20-40
2005 99.0 98-100 90.0 85-95 100.0 100-100 35.0 25-45
2010 99.0 98-100 95.0 91-99 100.0 100-100 40.0 30-50
b.Fairbanks 1980 99.0(a)--92.3 --98.6(a)--2.5
1985 99.0 98-100 94.0 91-97 100.0 100-100 10.0 5-15
c..n 1990 99.0 98-100 95.0 92-98 100.0 100-100 21.0 11-31
>-'1995 99.0 98-100 95.0 92-98 100.0 100-100 25.0 15-35.p,
2000 99.0 98-100 95.0 92-98 100.0 100-100 30.0 20-40
2005 99.0 98-100 95.0 92-98 100.0 100-100 35.0 25-45
2010 99.0 98-100 95.0 92-98 100.0 100-100 40.0 30-50
c.Gl enna 11 en-1980 99.0(b)--85.4 --100.0(a)--5.1
Va ldez 1985 99.0 98-100 90.0 88-92 100.0 100-100 12.0 8-15
1990 99.0 98-100 95.0 92-98 100.0 100-100 21.0 16-25
1995 99.0 98-100 95.0 92-98 100.0 100-100 25.0 20-30
2000 99.0 98-100 95.0 92-98 100.0 100-100 30.0 20-40
2005 99.0 98-100 95.0 92-98 100.0 100-100 35.0 25-45
2010 99.0 98-100 95.0 92-98 100.0 100-100 40.0 30-50
(a)For water heat and cooking,missing values on the Battelle-Northwest end-use survey were not counted.
(b)Glennallen-Valdez water heater percent for 1980 was adjusted downward to 99 to a low for some
customers without hot water.
•
TABLE 5.5.Market Saturations (Percent)of Large Appliances with Fuel Substitution
Possibilities in Duplexes,Railbelt Load Centers,1980-2010
Water Heaters Clothes Dryers Range (cooking)Saunas -Jacuzzis
Load Center Year Def ault Range Default Range Default Range Default ~~
a.Anchorage 1Q80 100.0(a)--90.0 --96.4 --16.9
1985 100.0 100-100 91.0 90-92 100.0 100-100 18.0 16-20
1990 100.0 100-100 92.5 90-95 100.0 100-100 21.0 18-24
1995 100.0 100-100 93.0 91-96 100.0 100-100 25.0 20-30
2000 100.0 100-100 95.0 92-98 100.0 100-100 30.0 20-40
2005 100.0 100-100 95.0 92-98 100.0 100-100 35.0 25-45
2010 100.0 100-100 95.0 92-98 100.0 100-100 40.0 30-50
b.Fairbanks 1980 100.0(a)--85.5(b)--100.0 --8.2
1985 100.0 100-100 91.0 90-92 100.0 100-100 12.0 8-16
1990 100.0 100-100 92.5 90-95 100.0 100-100 21.0 16-26
U1 1995 100.0 100-100 93.0 91-96 100.0 100-100 25.0 20-30
t--'
(Jl 2000 100.0 100-100 95.0 92-98 100.0 100-100 30.0 20-40
2005 100.0 100-100 95.0 92-98 100.0 100-100 35.0 25-45
2010 100.0 100-100 95.0 92-98 100.0 100-100 40.0 30-50
c.Gl enna 11 en-1980 100.0(a)--88.9 --100.0 --0.0
Valdez 1985 100.0 100-100 90.0 89-91 100.0 100-100 10.0 5-15
1990 100.0 100-100 92.0 91-93 100.0 100-100 21.0 11-31
1995 100.0 100-100 94.0 91-97 100.0 100-100 25.0 15-35
2000 100.0 100-100 95.0 92-98 100.0 100-100 30.0 15-45
2005 100.0 100-100 95.0 92-98 100.0 100-100 35.0 20-50
2010 100.0 100-100 95.0 92-98 100.0 100-100 40.0 30-50
(a)Values from Battelle-Northwest end-use survey were adjusted to 100 percent for water heaters in 1980.
For explanation,see text.
(bl 1QBO Clothes dryer penetration in Fairbanks for 1980 adjusted downward by one to match the number of
washers in duplexes.
"~-,..,,..._..~".==.-~",...,,_.------
TABLE 5.6.Market Saturations (Percent)of Large Appliances with Fuel Substitution
Possibilities in Multifamily Homes,Railbelt Load Centers,1980-2010
Water Heaters Clothes Dryers Range (cooking)Saunas -Jacuzzis
Load Center Year Def ault Range Def ault Range Default Range_Default Ran9.E!-
a.Anchorage 1980 100.0(a)--75.7 --98.2 --13.6
1985 100.0 100-100 83.0 82-84 100.0 100-100 17.0 14-20
1990 100.0 100-100 83.5 82-85 100.0 100-100 21.0 16-26
1995 100.0 100-100 84.0 82-86 100.0 100-100 25.0 20-30
2000 100.0 100-100 85.0 83-87 100.0 100-100 30.0 20-40
2005 100.0 100-100 90.0 85-95 100.0 100-100 35.0 25-45
2010 100.0 100-100 95.0 92-97 100.0 100-100 40.0 30-50
h.Fa irbanks 1980 100.0(a)--61.0 --100.0 --5.7
1985 100.0 100-100 65.0 61-69 100.0 100-100 12.0 8-16
1990 100.0 100-100 70 65-75 100 100-100 21.0 16-26
(Jl 1995 100.0 100-100 80 75-85 100.0 100-100 25.0 20-30
>-'
0"1 2000 100.0 100-'100 85.0 80-90 100.0 100-100 30.0 20-40
2005 100.0 100-100 90.0 85-95 100.0 100-100 35.0 25-45
2010 100.0 100-100 95.0 92-97 100.0 100-100 40.0 30-50
c.Gl enna 11 en-1980 100.0(a)--75.0(b)--100.0 --0.0
Valdez 1985 100.0 100-100 77 .5 75-80 100.0 100-100 10.0 0-20
1990 100.0 100-100 80.0 77-83 100.0 100-100 21.0 11-31
1995 100.0 100-100 82.5 80-85 100.0 100-100 25.0 15-35
2000 100.0 100-100 85.0 82-88 100.0 100-100 30.0 20-40
2005 100.0 100-100 90.0 85-95 100.0 100-100 35.0 25-45
2010 100.0 100-100 95.0 92-98 100.0 100-100 40.0 30-50
fa)Water heat survey numbers adjusted to 100 percent for 1980.For explanation,see text.
(h)Numher of dryers in Glennallen-Valdez adjusted to 75 percent from 100 percent because of small sample size
(7 un it s)•
~~~·'''''''''\'«~:;;';'-'!I'.'~~'''''~~'~'~''l'-''t'l'''''N''''!J'W''~~''''''''''''I:'1<~.,.",..t'''''"'""'·""?"''''"'''·~'''''~fi··''''-'·'~'''_'·'"·!'~""~'~',,,·r ....,~·,n \,_""."'~..•
is assumed that 99%is a maximum for two reasons:the market saturation of
hot water in the Western U.S.was 99%in the 1970 Census (Bureau of Census
1970);and Alaska can be expected to have rural cabin-like structures with
limited electric service for some time to come.In the Fairbanks and
Glennallen-Valdez market areas single-family saturations are projected to
increase to the Anchorage level by 1990 and 2000,respectively.The end-use
survey and 1970 Census both show saturations in the vicinity of 90%in these
areas.Increasing urbanization in these two areas and better electric service
should increase this percentage.Glennallen will remain sufficiently rural,
however,to keep the pace somewhat slower.
The other types of structures in the Battelle-Northwest survey showed
market saturations of nearly 100%in all market areas.The exception was
multifamily housing.However,the wording of the question in the survey upon
which this calculation is based may have been interpreted as asking whether
the respondent had a hot water tank in his unit rather than (as was intended)
whether he had hot water available.A 100%market penetration for hot water
in duplexes and multifamily buildings was assumed.Mobile homes were
considered the same as single-family units.
Clothes Drier
The Battelle-Northwest survey and 1970 Census (Bureau of Census 1970)
both show Railbelt market saturations for clothes driers far above the U.S.
average.Information available from the 1980 U.S.Statistical Abstract for
10 79 shows about 61.5%of electrically served housing units have an electric
or gas drier (up from 44.6%in 1970)(Bureau of Census 1980b).In contrast,
the Battelle survey showed market saturations ranging from 61%in Fairbanks
multifamiuly structures to 100%in Glennallen-Valdez multifamily
structures.(a)Single-family drier saturations ranged from 81%in Fairbanks
to 90%in Anchorage.Because Alaska already has such high saturations,the
forecast is outside the bounds of historical experience.A reasonable guess
(a)The 100%figure is believed to be an anomaly caused by the small group of
multifamily housing units sampled in the Cooper Valley Electric
Association (CVEA)market area.The true figure was assumed to be 75%.
5.17
iLL
is that no more than about 95%of single-family homes,mobile homes,and
duplexes will ever have driers because of the availability of laundromats and
because of the room taken up by washer-drier combinations in small housing
units.For multifamily units,penetration is assumed to be much slower
because of the space problem.Since washers and driers are now installed in
pairs in most new housing,market saturations for driers (which are now about
2%below those for washers in most areas)will approach that for washers as
old housing stock is replaced.In general,the lower the existing saturation,
the greater is the uncertainty concerning its future growth rate.The
uncertainty is reflected in wider ranges used in Table 5.7 for multifamily
unit clothes driers.
Cooking Ranges
The Battelle-Northwest end-use survey indicated that between 96 and 100%
of all households surveyed had a range available.The difference between 96
and 100%may be caused by the substitution of hot plates and broiler ovens
(for which estimated national saturation in 1979 was about 26%)and microwave
ovens (for which the estimated national 1979 saturation was 7.6%).Therefore,
100%of all units currently are assumed to have cooking facilities available
bV 1985.This percentage holds throughout the period.
Saunas,Jacuzzis,Etc.(a)
These units are a relatively new phenomenon in private homes,almost all
having been installed since lQ70.The Battelle-Northwest end-use survey found
market saturations ranging from zero to 17%,depending on market area and
housing type.Fourteen percent of Anchorage single-family households reported
having one of these units.Among single-family homes built since 1975,the
saturation was 21%.The 21%has been used as a target saturation on which
other groups of housing will converge by 1990 through new construction and
retrofit.The inflation-adjusted cost of saunas and jacuzzis,whirlpools,
etc.are expected to drop somewhat as it does with any new appliance type.
This could lead to much higher market saturations than currently exist.A
(a)Including hot tubs.whirlpool baths.and "total environment"units.
5.18
hi
TABLE 5.7.Market Saturations (Percent)of Large Electric Appliances in Single-Family Homes
Railbelt Load Centers,1980-2010 '
Refrigerators Freezers Dishwashers Clothes Washers
Load Center Year Default Range Default Range Default Range Default Range--
a.Anchorage 1980 100.0 --88.3 --78.2 --91.7
1985 100.0 98-102 90.0 85-95 85.0 80-90 92.0 90-94
1990 100.0 98-102 90.0 85-95 90.0 85-95 92.5 90-95
1995 100.0 98-102 90.0 85-95 90.0 85-95 93.7 91-96
2000 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2005 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2010 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
b.Fairbanks 1980 100.0 --84.9 --53.8 --84.9
U"1 1985 100.0 98-102 88.0 86-90 79.0 75-85 86.0 84-88
>-'
l..O 1990 100.0 98-102 90.0 85-95 90.0 85-95 87.5 85-90
1995 100.0 98-102 90.0 85-95 90.0 85-95 92.5 90-954
2000 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2005 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2010 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
c.Gl enna llen-1980 100.0 --95.7 --64.3 --87.1
Valdez 1985 100.0 98-102 93.0 91-95 85.0 80-90 90.0 88-92
1990 100.0 98-102 90.0 85-95 90.0 85-95 92.5 90-95
1995 100.0 98-102 90.0 85-95 90.0 85-95 93.7 91-96
2000 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2005 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2010 100.0 98-102 90 85-95 90.0 85-95 95.0 92-98
very wide range of uncertainty is indicated,whereas the default saturation
for saunas and jacuzzis is projected to be double the current level for new
single-family homes by the year 2010.
Tables 5.7 through 5.10 indicate default market saturations and ranges of
values for large household appliances that are almost always electric.These
include refrigerators,freezers,dishwashers,and clothes washers.The table
title indicates the housing type,and the table values show an expected market
saturation for each appliance by market area and year.The ranges shown in
the tables reflect the degree of uncertainty attached to the default value.
The wider the range,the greater is this subjective uncertainty.The
assumptions supporting the table values are given below by appliance.
Refrigerators
The Battelle-Northwest end-use survey found that virtually 100%of
households had a refrigerator.The California Energy Commission found in 1976
that enough housing units had second refrigerators to raise total California
market saturation to 113 to 116%.ISER,in their report to the Alaska State
Legislature (Goldsmith and Huskey 1980b)assumed that this high percentage
would likely not prevail in Alaska because of the cooler climate.In the RED
model the ISER assumption is modified to permit a range of values from 98 to
102%.
Freezers
The end-use survey found market area-wide saturations of freezers ranging
from about 80%in Fairbanks to about 90%in Glennallen-Valdez.These figures
are 10 to 20%higher than assumed by ISER for 1980 for these areas,about 40%
above 1970 Census values for the Railbelt,and 30 to 40%above the U.S.
average.In other words,area-to-area comparisons and historical experience
are not very helpful for predicting future saturations.For single-family
homes and mobile homes,the maximum saturation has been assumed to just about
have been reached because with better shopping facilities and increased
urbanization,fewer freezers will be necessary for long-term food storage
associated with bulk buying.
5.20
i~
TABLE 5.8.Market Saturations (Percent)of Large Electric Appliances in Mobile Homes,
Railbelt Load Centers,1980-2010
Refrigerators Freezers Dishwashers Clothes Washers
Load Center Yeilr Def ault Range Default Range Default Range Default Range
a.Anchorage 1980 100.0 --94.8 --43.9 --80.6
1985 100.0 98-102 92.0 90-96 67.6 62-72 85.0 80-90
1990 100.0 98-102 90.0 85-95 90.0 85-95 90.0 85-95
1995 100.0 98-102 90.0 85-95 90.0 85-95 90.0 85-95
2000 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2005 100.0 98-102 90.0 85-95 90.0 85-95 95 92-98
2010 100.0 98-102 90.0 856-95 90.0 85-95 95.0 92-98
b.Fairbanks 1980 100.0 --73.0 --48.6 --92.3
1985 100.0 98-102 82.0 75-89 71.4 66-76 93.0 91-95
1990 lOO.O 98-102 90.0 85-95 90.0 85-95 93.5 91-96
(Jl 1995 100.0 98-102 90.0 85-95 90.0 85-95 94.0 92-96
N 2000 100.0 98-102 85-95 95.0.......90.0 85-95 90.0 92-98
2005 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2010 100.0 98-]02 90.0 85-95 90.0 85-95 95.0 92-98
c.Glenna llen-1980 100.0 --86.4 --49.2 --88.1
Va 1dez 1985 100.0 98-102 88.0 85-91 72.3 67-77 90.0 88-92
1990 100.0 98-102 90.0 85-95 90.0 85-95 93.0 91-95
1995 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-96
2000 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2005 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
2010 100.0 98-102 90.0 85-95 90.0 85-95 95.0 92-98
---"••,~.<.o__.,_·_._~".,._..~..........~H -=._r~"'=-"~,"'''-'_"_""-~~,,,,,,_-:"__,_,_.
TA8LE 5.9.Market Saturations (Percent)of Large Electric Appliances 11.Duplexes,
Railbelt Load Centers,1980-2010
Refrigerators Freezers Dishwashers Clothes Washers
Load Center Year Def au 1t Range Default Range Default Range Default Range
a.Anchorage 1980 100.0 --66.5 --76.5 --92.5
1985 100.0 98-102 75.0 70-80 85.0 80-90 93.0 91-95
1990 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
1995 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
2000 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
2005 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
2010 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
b.Fairbanks 1980 100.0 --75.2 --57.4 --85.5
1985 100.0 98-102 80.0 75-85 85.0 80-90 91.0 90-92
Ul 1990 100.0 98-102 85.0 80-90 90.0 85-95 92.5 90-95
N 1995 100.0 98-102 85.0 80-90 90.0 85-95 93.0 91-96N
2000 100.0 98-102 85.0 80-90 90 85-95 95.0 92-98
2005 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
2010 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
c.Glennallen-1980 100.0 --77 .8 --55.6 --90.0(a)85-95
Valdez 1985 100.0 98-102 80.0 75-85 82.0 77-87 92.0 90-94
1990 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
1995 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
2000 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
2005 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
2010 100.0 98-102 85.0 80-90 90.0 85-95 95.0 92-98
(a)Clothes washer 1980 saturation adjusted to 90 percent from 100 percent in Glennallen-Valdez because of small
sample problem.
P'~'~'
~.
TABLE 5.10.Market Saturations (Percent)of Large Electric Appliances
in Multifamily Homes,Railbelt Load Centers,1980-2010
Refrigerators Freezers Dishwashers Clothes Washers
Load Center Year Def au 1t Range Default Range Default Range Default Range
a.Anchorage 1980 100.0 --62.5 --73.3 --76.5
1985 100.0 98-102 65.0 60-70 85.0 80-90 85.0 80-90
1990 100.0 98-102 70.0 65-75 90.0 85-95 90.0 85-95
1995 100.0 98-102 70.0 65-75 90.0 85-95 92.0 90-94
2000 100.0 98-102 70.0 65-75 90.0 85-95 95.0 92-98
2005 100.0 98-102 70.0 65-75 90.0 85-95 95.0 92-98
2010 100.0 98-102 70.0 65-75 90 85-95 95.0 92-98
b.Fairbanks 1980 100.0 --57.2 --23.3 --63.8
1985 100.0 98-102 65.0 60-70 34.0 30-39 68.0 63-72
(J1 1990 100.0 98-102 70.0 65-75 50.0 45-55 70.0 65-75.
N 1995 100.0 98-102 70.0 65-75 74.0 70-79 80.0 75-85LV
2000 100.0 98-102 70.0 65-75 90.0 85-95 85.0 80-90
2005 100.0 98-102 70.0 65-75 90.0 85-95 90.0 85-95
2010 100.0 98-102 70.0 65-75 90.0 85-95 95.0 92-98
c.Glennallen-1980 100.0 98-102 14.3 --28.6 --77 .0
Valdez 1985 100.0 98-102 20.0 15-25 42.0 37-47 78.0 73-83
1990 100.0 98-102 30.0 20-40 62.0 57-67 80.0 75-85
1995 100.0 98-102 35.0 30-40 90.0 85-95 82.0 77-87
2000 100.0 98-102 40.0 35-45 90.0 85-95 85.0 80-90
2005 100.0 98-102 45.0 40-50 90.0 85-95 90.0 85-95
2010 100.0 98-102 50.0 45-55 90.0 85-95 95.0 92-98
(a)Clothes washer 1980 saturation adjusted from 100 percent to 77.0 percent (2 percent above dryer saturation)
because of small sample problem.
~1IIII"'-----IIIIII'lIIlii:i_••••••••••••••••••,..
For duplexes and multifamily units,the percent of saturation should
remain significantly lower.The tenants in such units tend to be more
transient and are probably less involved in Alaskan hunting,fishing,and
gardening pursuits than most Alaskans.(onsequently,they would have less
demand for freezers.Second,rental units tend to be smaller.Consequently,
renters might tend to substitute rented commercial cold-storage locker space
for a freezer to conserve scarce living space in duplexes and multifamily
units.The range of uncertainty is shown to be quite broad,since market
penetration has been rapid in the last 10 years,but the maximum appears to
have been reached in some cases.
Dishwashers
The Battelle-Northwest end-use survey found market saturations for
dishwashers well above the existing U.S.average.In the U.S.as a whole,the
1979 saturation was about 41%of homes served by electricity (Bureau of Census
1980b),but this percentage ranged from 50%in Fairbanks to 75%in Anchorage
survey homes.Saturations have increased by about 50 percentage points in all
three Railbelt load centers since 1970,again outside the range of historical
experience.(Using this experience,ISER (Goldsmith and Huskey 1980b)
projected 1q78 market saturations of 50%in Anchorage and 36%in Fairbanks.)
Since the rate of increase in market saturation was very rapid in the 1970s,
but increases in saturation in Anchorage in particular may slow down soon,a
maximum saturation of 90%was assumed for all homes.The annual rates of
saturation growth for the 1970s were then projected for each region:9%per
year for Anchorage,8%per year for Fairbanks,and 23%per year for
Glennallen-Valdez.The latter rate was considered too high to be credible for
forecasting,so Fairbanks·rate was used in Glennallen-Valdez.In each table,
these rates of growth were assumed to prevail until the 90%maximum was
reached.The growth rate was then assumed to fall to zero.A wide range of
uncertainty is assumed for dishwasher saturations because of the tenuous
nature of the required assumptions.
Clothes Washers
The Battelle-Northwest end-use survey found that clothes washer
saturations ranged from about 84%in Fairbanks to 89%in Anchorage.These
5.24
figures are well above the 73%reported for the U.S.in 1979 in the 1980
Statistical Abstract (Bureau of Census 1980b).It also represents about 10 to
15 percentage points growth since the 1970 census.The rate of saturation
increase did not slow down appreciably in the 1970s compared to the 1960s;
consequently,market saturation may have not yet approached its maximum.For
forecasting,the maximum penetration is assumed to be 95%.Different types of
housing reach this maximum at different rates.In particular,since
single-family homes are already 85 to 90%saturated,they reach 95%slowly,
achieving this level by the year 2000.Some markets are closer to being
completely saturated.Even at low rates of growth they reach 95%somewhat
earlier.In no case is clothes-washer saturation allowed to be below that for
clothes driers.The Battelle-Northwest survey generally found that washer
saturation was one to two percentage points higher than that for dryers.
Where this was not the case (e.g.,duplexes in Fairbanks)the difference
appears to have occurred because of the small number of households in the
category.The market saturations for washers and driers gradually converge,
since they are now usually installed in pairs.Multifamily saturation of
washers and driers grows the slowest,reaching 95%by 2010 in Fairbanks and
Glennallen-Valdez.
Fuel Mode Splits
The fuel-mode splits presented in Table 5.11 were also derived from the
Battelle-Northwest end-use survey.These parameters are assumed to remain
fixed over the forecast period,~he cross-price ~gsticity adjustment
handles fuel switching.Several adjustments have been made to the
Glennallen-Valdez duplex and multifamily splits because the number of
respondents in these categories was limited.
Consumption of Electricity per Unit
The average kilowatt hour consumption figures are primarily based on the
Midwest Research Institute's findings on the subject (MRI 1979).Below is a
brief discussion of each parameter.
5.25
AL....\.SKA EF.;~~,OURCE~LTB~'A.RY
U.S.DEPT.OF INTEEle-R
.J.~_
TABLE 5.1l.Percentage of App 1i ances Using Electricity and Average Annual
Electricity Consumption,Ra ilbelt Load Centers,1980
Anchorage fairbanks --Glennallen-Valdez
Average Averaqe Avera~e
Percentage Using Electricity Annua 1 kWh Percentage Using Electricity Annua'kWh Percentage Using Electricity Annua I k\lh
App I iance ~Mil -~MF Consump t i on SF MH ~MF Consunp t ion SF .MH_DP ~_MF_.Consu'!'Q..L!Q!'---
Space Heat
Sing Ie Family 16.0 NA NA NA 32,850 9.7 NA NA NA 43,380 0.7 NA NA NA 29,970
Mob i Ie Home NA 0.7 NA NA 24,570 NA 0.0 NA NA 33,210 NA 0.7 NA NA 22,fl60
Duplex NA NA 22.8 NA 21,780 NA NA 11.7 NA 2B,710 NA NA 0.7 NA ]9,710
Multi Family NA NA NA 44.4 15,390 NA NA NA 14.8 19,OBO NA NA NA 0.7 13,140
Water Iteaters 36.5 50.4 44.0 60.9 3,475 55.1 71.3 71.8 47.0 3,475 17.1 25.9 10.0(a)16.7 3.47~
Clothes Dryers 1l4.3 88.1 BI.3 B6.6 1,032 96.2 94.6 94.4 100.0 1,032 59.B 62.0 87.5 B5.7 1,037
Cookin9 Ranges 75.B 23.2 85.2 88.2 1,200 79.0 48.2 95.0 97.1 1,200 39.1 15.5 66.7 71.4 1,200
Sauna-Jacuzzis 93.5 100.0 93.7 81.B 1,300 61.8 100.0 60.8 100.0 300 89.5 100.0 77.3(b)90.9(b)300
Refrigerators.100.0 100.0 100.0 100.0 1,250 100.0 100.0 100.0 100.0 1,250 100.0 100.0 100.0 100.0 1,250
Freezers 100.0 100.0 100.0 100.0 1,342 100.0 100.0 100.0 100.0 1,342 100.0 100.0 100.0 100.0 1,342
Dishwashers 100.0 100.0 100.0 100.0 230 100.0 100.0 100.0 100.0 230 100.0 100.0 100.0 100.0 230
LTI Additional.Water Heating 36.5 50.4 44.0 60.9 700 55.1 71.3 71.8 47.0 700 17.1 25.9 1O.0(a)16.7 700
N
())Clothes Washers 100.0 100.0 100.0 100.0 70 100.0 100.0 100.0 100.0 70 100.0 100.0 100.0 100.0 70
Addiliona 1
Water Healing 36.5 50.4 44.0 60.9 1,050 55.1 71.3 71.8 47.0 1,050 17.1 25.9 1O.0(a)16.7 1,050
Mi sce 11 aneou s 100.0 100.0 100.0 100.0 2,110 100.0 100.0 100.0 100.0 2,466 100.0 100.0 100.0 100.0 2,333
(a)The 8NW survey revealed this fuel mode split to be O.However,this result seems very implausible given the responses for other dwellinq types and the
limiled number of respondents.Therefore,10%was assumed to be a more descriptive number.
(b)Due to insufficient responses,these figures are the averages of the other two reqions.
-.
Space Heat
For space heating~the average annual consumption figures derived by ISER
are used (Goldsmith and Huskey 198Gb).These figures were derived based on
heating degree days~floor space~and average consumption of all electric
homes within the Railbelt region and were adjusted down by 10%to allow for
additional conservation in the building stock since ISER's study.
Waterheaters
The average consumption for waterheaters is based on the California
Energy Commission's estimates (CEC 1976)because the CEC separates out
consumption related to clothes washers and dishwashers.Unfortunately~the
CEC could not separate the consumption used for dishwashing without
dishwashers~so the figure was adjusted upwards by 15%to account for this for
the colder-water inlet temperature in Alaska.
Clothes Dryers
For clothes dryers~average consumption is the figure reported by MRI.
ISER (MRI 1979)picked a lower estimate based on household size~but the
colder climate in Alaska should also raise the estimated use of dryers.This
is reflected in high saturation values for this appliance.
Cooking-Ranges
This category is broadly interpreted as production of heat for cooking
purposes.The figure reported was derived by ISER with a similar definition.
Miscellaneous Appliances
For miscellaneous appliances~estimates of consumption were originally
prepared by ISER by subtracting estimated large appliance electricity
consumption for 1978 from total 1978 consumption/residential customer
(Goldsmith and Huskey 198Gb).Lighting was inferred from national statistics
and increased to 1000 kWh/year/customer.The remainder was charged to small
appliances.Research for the RED Model checked ISER's work by assuming:
1)televisions (rated at 400 kWh/year)are included in small appliances;
and 2)the ISER estimate of 480 kWh/year/customer for headbolt heaters is
replaced with load center-specific figures derived from load-center specific
5.27
usage data produced by the Battelle-Northwest end-use survey and National
Oceanic and Atmospheric Administration (NOAA)data on normal minimum
temperatures (NOAA 1979);and 3)1000 kWh/year lighting.The revised
estimates for block heaters are as follows:Anchorage,459 kWh/year/customer;
Fairbanks,1127 kWh/year/customer;Glennallen-Valdez 398 kWh/year/customer.
Because the results were broadly consistent with ISER's figures,ISER's totals
were used (Goldsmith and Huskev 1980b).
Saunas-Jacuzzis
The authors informally contacted several suppliers of saunas,jacuzzis,
and hot tubs and were told that the consumption of these devices ranged from
JOO to 3000 kWh annually.A figure of 300 kWh annually was used for the
existing stock (which probably contains several bathtub whirlpools)and about
1300 kWh (Hunt and Jurewitz 1981)for new additions to the stock.
Refrigerators
The ISER number was chosen because it is consistent with estimates by
Merchandising Week (1973)and because electricity consumption for
refrigeration should be lower in Alaska due to the lower ambient air
temperature.
Freezers
This figure showed little variation between Merchandising Week,MRI,and
ISER.The MRI fiqure was chosen.
Dishwashers
This figure is the mean of the Edison Electric Institute reported by MRI
and MRI fiqures.
Dishwasher and Clothes Washer Water
These values are from the CEC (1976).
Electrical Capacity Growth
The growth rates in electric capacity (Table 5.12)were derived by ISER
(Goldsmith and Huskey 1980b).The growth rate for capacity of saunas and
jacuzzis is assumed to be the same as refrigerators.The consumption of new
appliances was also derived by ISER.
5.28
1
l...
TABLE 5.12.Growth Rates in Electric Appliance Capacity and Initial Annual
Average Consumption for New Appliances
Average Annual Growth Rate in
kWh Consumption for New Appliances (1985)Electric Capacity
Appliance Anchorage Fairbanks Glennallen-Valdez Post 1985 (annual)
- --------
Space Heat
Single Family 40,100 53,000 36,600 0.01
Mobile Homes 30,000 40,600 27,900 0.01
Dup 1exes 26,600 35,100 24,100 0.01
Mu 1t if amily 18,800 23,300 16,100 0.01
Water Heaters 3,650 3,650 3,650 0.005
Clothes Dryers 1,032 1,032 1,032 0.0
Cooking Ranges 1,250 1,250 1,250 0.0
VI Sauna-Jacuzzi 1,309 1,309 1,309 0.0.
N
1.0 Refrigerators 1,560 1,560 1,560 0.01
Freezers 1,550 1,550 1,550 0.01
Dishwashers 230 230 230 0.005
Additional Water Heating 740 740 740 0.0
Clothes Washers 70 70 70 0.0
Additional Water Heating 1,050 1,050 1,050 0.0
Miscellaneous Appliances 2,160 2,536 2,403 (a)
(a)Incremental growth of 50 kWh per customer in Anchorage per 5-year period;70 kWh in
Fairbanks and Glennallen-Valdez.
r-------III::::IIII:R'••••••••••••••••••••_-------------.,....
Appliance Survival
Table 5.13 presents the percentage of appliances'remalnlng t forecasts
periods after their purchase.These figures were derived by ISER based on
Hausman's work (1979)with implicit discount rates for room air conditioners.
He found that the stock of a particular vintage of air conditioners was fairly
well approximated by a Weibull distribution.By substituting differing
lifetimes (EPRI 1979)for alternative appliances,ISER used his results to
derive the figures in Table 5.13.For saunas and jacuzzis RED assumes the
appliance lifetime was comparable to clothes washers.
Price Elasticities
The final parameters used in the Residential Module are the price
elasticities shown in Table 5.14.The ranges of elasticities were picked to
include the results of several studies (Taylor 1975;Maddala,Chern and Gill
1978;Baughman,Joskow and Kamat 1979;Halversen 1978;EPRI 1977a,1977b).
The short-run default values were shifted towards the inelastic range of the
distribution since it is difficult for the populace to switch fuels in the
short run,while the long-run default values were assumed to be approximately
the midpoints of the ranges.
5.30
'1
1
TABLE 5.13.Percent of Appliances Remaining in Service Years After Purchase,
Rail be 1t Reg i on
a.Old Appliances
Space Heat (A 11)
Water Heaters
Clothes Dryers
Ranges -Cook i ng
Saunas-Jacuzzis
Refri gerators
Freezers
Dishwashers
Clothes Washers
b.New Appliances
Space Heat (A 11 )
Water Heaters
Clothes Dryers
Ranges -Cook i ng
Saunas-J acuzz is
Refrigerators
Freezers
Dishwashers
Clothes Washers
5
0.90
0.6
0.8
0.6
0.5
0.8
0.9
0.6
0.6
0.75
1.00
0.75
1.00
1.00
1.00
0.75
0.75
10
0.80
0.3
0.6
0.3
0.3
0.6
0.8
0.3
0.3
0.35
0.75
0.35
0.75
0.75
1.00
0.35
0.35
15
0.6
0.1
0.3
0.1
0.1
0.3
0.6
0.1
0.1
0.1
0.35
0.1
0.35
0.35
0.75
0.1
0.1
20
0.3
0.0
0.1
0.0
0.0
0.1
0.3
0.0
0.0
0.0
0.1
0.0
0.1
0.1
0.35
0.0
0.0
25
0.1
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
30
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Source:ISER (Goldsmith and Huskey 1980b)except for saunas-jacuzzis,
which is author assumption.
TABLE 5.14.Price Elasticities for Residential Electricity Use
Own-Price Elasticity
Oil Cross-Price Elasticity
Gas Cross-Price Elasticity
DefaUlt
-0.15
0.01
0.5
Short Run
Range
(-0.08)-(-0.54)
0.01 -0.03
0.05 -0.10
5.31
Long Run
•
6.0 THE BUSINESS CONSUMPTION MODULE
The Business Module forecasts the requirements for electricity in the
business and government segments of the Railbelt economy.The figures
predicted here are before the impacts of explicit conservation policy are
considered.Conservation policy is handled in the Conservation Module.
MECHAN!SM
The Business Consumption Module uses a forecast of the stock of floor
space (a proxy for the stock of capital equipment)to predict the level of
business electricity consumption.The predicted consumption of electricity is
then adjusted for price impacts to yield the price-adjusted forecast of
business electricity sales.
INPUTS AND OUTPUTS
Table 6.1 presents the inputs and outputs of the Business Consumption
Module.Load-center-specific forecasts of employment~regional population~
the relative price level~and statewide wage rates are exogenous to RED.For
the Railbelt Alternative Energy Study~these come from forecasts of the ISER
Man in the Arctic Program (MAP)model.The use per square foot of building
space is assigned in the Uncertainty Module.The output of the Business
Consumption Module is the price-adjusted forecast of electricity requirements
of the business sector before the impacts of subsidized conservation are
considered.
MODEL STRUCTURE
Figure 6.1 presents a flowchart of the module.The first step is to
construct proxies for regional income (by multiplying regional employment and
the statewide wage rate)and the Anchorage Consumer Price Index (CPI).Next~
econometric coefficients are applied to estimate the percentage change in the
stock of floor space by year (rather than by five-year forecast period).By
first converting this forecast to predicted stocks and then picking out the
forecast years~each forecast year1s stock of floor space is found.
6.1j
!
I
!
I
.J."-----------------------71 Nr!t ...
ri
,,:::::"a
From
Forecast File (exogenous)
Forecast File (exogenous)
Forecast File (exogenous)
Forecast File (exogenous)
Uncertainty Module
(parameter)
WR99 Statewide Average Wage Rate
8BETA Electricity Consumption Floor
Space Elasticity
CPI Consumer Price Index,Anchorage
TABLE 6.1.Inpu ts and Outputs of the Busi ness Consumption Modu 1e
a)Inputs
Symbol Name
TEMP Total Regional Employment
POP Regional Population
Price-Adjusted Business
Consumption
b)Outputs
Symbol
BUSCON
Name From
Peak Oem and and
Conservation Modules
Multiplying the floor space stock by the econometrically-derived use per
square foot yields a preliminary forecast of business requirements,which is
then adjusted for price impacts.
To prepare a forecast of the floor space stock,RED uses preliminary
results from Staloff and Adams.(a)They developed a simultaneous demand and
supply model of commercial floor space using pooled cross-section/time-series
data for the 48 contiguous states.In their preliminary formulation,the
percentage change in the stock of floor space is a function of the changes
in:the annual change of the nominal interest rate,the annual percentage
changes of the Gross National Product deflator,the annual percentage change
in regional income,and the annual percentage change in regional population,
as well as some cross-product terms:
(a)Staloff,S.J.and R.C.Adams.1981 (Draft).liThe Development of BPA
Region Commercial Floor Space ModeL"Battelle,Pacific Northwest
Laboratories,Richland,Washington.
6.2
FORECAST
•AVERAGE WAGE
•EMPLOYMENT
•POPULATION
•CONSUMER PRICES
CALCULATE
REGIONAL INCOME
PROXY
CALCULATE
BUSINESS/
GOVERNMENT/
LIGHT INDUSTRIAL
FLOOR SPACE
CALCULATE
PRELIMINARY
BUSINESS
ELECTRICAL
CONSUMPTION
PRELIMINARY
BUSI NESS USE
COEFFICIENTS
(UNCERTAINTY
MODULE)
PRICE
ELASTICI TIES,
BUSINESS SECTOR
(UNCERTAINTY
MODULE)
PRICE AND
CROSS-PRI CE
ADJUSTMENTS
BUSINESS
CONSUMPTION PRIOR
TO
CONSERVATION
ADJ US TMENTS
PRICE
FORECASTS
(EXOGE NOUS)
FIGURE 6.1.RED Rusiness Consumption Module
6/Stock u l =61bllr +62 6/GNPDEF t l +6311/POP U I
+64 6/INC U I +26 5 llr£/GNPDEFtl +
26 6 llrt/POPitl +26 7 llr/INCitl +
26 8 /GNPDEF t ilINC iti +26g/POPitilINCiti
(6.1)
6.3
~d
i-t ;iiUJJiS
where
Stock =floor space stock
S,-S =parameters..9
~=symbol for the first difference (annual change)
GNPDEF =gross national product price deflator
POP =population
INC =income
=index for the region
£=index for the year
/ /=symbol for the annual percentage change
r =nominal interest
The Business Consumption Module uses the Anchorage CPI as a proxy
for the GNP price deflators.It is assumed (as historically revealed)
that the nomina'interest rate is approximately three percentage points
above the measure of inflation.A proxy for regional income is derived
by multiplying regional employment by the statewide average wage rate.
Once the forecast of the percentage change in the stock of floor space is
found,the level of the stock is easily constructed by using an "observed"
stock level as a reference point.The module then predicts the nonprice
adjusted requirements,based on a regression equation:
PRECON it exp [BETA i +BBETA i x ln (STOCK it )](6.2)
where
PRECON =nonprice adjusted business consumption
BETA parameter equal to regression equation intercept
BBETA percentage change in business consumption for a one percent
change in stock (floor space elasticity).
t =index for the forecast year (t =1980,1985,...,2010).
Finally,price adjustments are made with the price adjustment mechanism
(See equations 5.12 -5.13):
6.4
BUSCON it =PRECON it x OPA it x CPA it
where
BUSCON =price-adjusted business requirements (MWh)
OPA =own-price adjustment
CPA =cross-price adjustment.
The price-adjusted business requirements are then passed to the
Conservation and Peak Demand Modules.
PARAMETERS
(6.3)
Floor Space Stock Equations
The parameters used to forecast the floor space stock were extracted from
work in progress at Battelle-Northwest for the Bonneville Power
Administration.Staloff and Adams have developed a theoretical and empirical
formulation for the demand and supply of floor space.(a)Using three-stage
least squares multiple regression,they estimated their system of equations
using pooled cross-section/time-series data for the years 1971-1977 for the
48 contiguous states.Table 6.2 presents the estimated coefficients.
TABLE 6.2.Floor Space Equation Parameters
Parameter Coeffi ci ent Standard Error T-Statistic
SI -0.1291 0.00345 -3.75
62 1.2753 0.2566 -4.97
S3 0.3553 0.0302 11.76
64 -0.113 0.0037 -3.04
S5 0.1929 0.0355 5.43
66 -0.0947 0.0078 -12.09
67 -0.0078 0.0008 -9.92
88 -0.0116 0.0253 -0.46
69 -0.0412 0.0061 -6.68
(a)Staloff,S.J.and R.C.Adams.]981 (Draft).
6.5
IIIIIIIIIll"-....
."iiiA.;;ti"
Business Electricity Usage Parameters
These parameters were estimated with regression analysis.Using
historical data on regional income and population,the GNP price deflator,and
treasury bill rates,a historical backcast of the stock of floor space in each
load center was obtained using the supply equation presented in Equation 6.3.
Then,using historical large and small commercial consumption,the following
regression equations were estimated:
'P IF
In(CON·t );BETA.+BBETA.x In(STOCK·t )+€'t11111
where
CON;historical business section consumption (MWh)
BETA;intercept
BBETA ;regression coefficient
STOCK;estimated stock of floor space,hundreds of square feet
€;stochastic error term.
Table 6.3 presents the results of the regression analysis.The
parameters BBETA can be allowed to vary within a normal distribution,
truncafed at the 95%confidence intervals.
Business Price Elasticities
(6.4)
The elasticities used in the price adjustment mechanism are extremely
important.Table 6.4 presents the elasticities used in the Business
Consumption Module.The ranges of the elasticities were picked to incorporate
the results of several electricity demand studies (Taylor 1975;Maddala,Chern
and Gill 1978;Baughman et al.1979;Halvorsen 1978;EPRI 1977a and 1977b).
The default own-price elasticities (both short-run and long-run)were assumed
to have values towards the lower half of the distribution due to lack of close
substitutes for electricity in many applications,such as lighting.
Conversely,the default cross-price elasticities were assumed to lie at the
approximate midpoint of their range.
6.6
l
.i
TABLE 6.3.Business Consumption Equation Results
Gl enna 11 en-
Anchorage Fa irbanks Valdez
BETA -4.7963 1.4394 8.2046
standard error 0.6280 0.6981 0.1122
t-statistic -7.6368 2.0619 73 .1098
BBETA 1.4288 0.9536 0.1977
standard error 0.0491 0.0620 0.0138
t-statistic 29.1159 15.3762 12.8917
range(a)1.346 to 1.522 0.833 to 1.074 0.151 to 0.244
-2 0.9906 0.9716 0.9659R
(a)95%confidence interval.
TABLE 6.4.Price Elasticities for Business Electricity Consumption
Measure Def au 1t Range Default Range
Own-Price Elasticity -0.3 -0.2 to -0.54 -1.0 -0.87 to -1.36
Oil-Cross Elasticity 0.03 0.01 to 0.05 0.2 0.15 to 0.31
Gas-Cross Elasticity 0.05 0.01 to 0.1 0.3 0.18 to 0.41
6.7
lt;
7.0 THE CONSERVATION MODULE
The purpose of the Conservation Module is to account for the electricity
savings that can be obtained with a given set of conservation technologies and
government policies,together with the associated costs of these savings.The
peak demand or capacity savings of the technologies set are calculated in the
Peak Demand Module.
MECHANI SM
The fuel price adjustments i~the Residential Consumption and Business
Consumption Modules account for market-induced technology-related conservation
impacts,as well as reductions in the use of appliances and changes in the
manner in which they are used.The Conservation Module identifies the
technological portion of these impacts by estimating those savings and costs
associated with a given set of conservation technologies.Furthermore,if the
government attempts to intervene in the marketplace to induce conservation via
loan programs,grants,or other policy actions,then the Conservation Module
accounts for the effects of this policy-induced conservation on demands for
electric energy and generating capacity.
RED separates conserved energy into two parts:energy saved from the
actions of residential consumers and energy saved from reduced energy use in
the business and government sectors.Figure 7.1 provides a flow chart of the
process employed.
A separate interactive program (CONSER)is called outside of RED to
prepare a conservation data file.This file contains information on the
costs,energy savings,and the level of market acceptance of various
conservation options.For the residential sector,CONSER queries the user for
the technical parameters of each option (up to ten options may be included).
Based on a user-supplied forecast of electricity prices and the costs
associated with each option,CONSER calculates the internal rate-of-return on
each technology.The user compares this rate to a bank passbook savings
rate.If the user decides,based on this comparison,that the option should
be included in the analysis,CONSER calculates the payback period for each
7.1
111111111111116 •
•:hli'U"imliUM"
BUSINESS
REOUIRP.IE""J.TS
[BUSINESS
I,,'ODULE I
SUM OVER
USES
•SAVI~CS
•COSTS
ADJUST
REOU1RE'.1E""J.TS
FOR 5UB51D1 ZED
CONSERVATION
=I__
CALCULATE SALES]
TO
•NEW USES
•EXISTINC USES
CALCULATE
•SAVINGS
•COSTS
IN NEW AND EXISTING
USES
OUTPuTS
eEl rCrRICITY SAVED
.(os-r OF SAVINGS
\eplAK (ORRE,C nON
~TOR
SUM OVER
OPTIONS
•SAVINGS
•COSTS
RESIDENTIAL
REOUIRE"'~ENTS
(RESIDE,~TIAL
'IODULEJ
ADjUST
REOLJI RP.'.ENTS
FOR SUBSIDIZED
CONSERVATION
TECHNICAL INPUT
.PEAK CORRECTION
-FACTOR (PCF)
TECHNICAL INPUTS
•MAXIMuM
SATURATION
•PAYBACK RULE
GO TO NEXT
CONSERVATiON
OPTION
TECHNICAL INPUTS
_SUBSIDIZED
INSTALLED COST
•O~M COST
TECHNICAL INPUT
•UNSLBSIDIZED
INSTALLED COST
TECHNICAL INPUTS
-ELECTRICITY SAVED
-LIFETIME
_ELECTRICITY
PRICES
AUSlp..,ESS INPUTS
INEW 'EXISTING USES)
·POTENTIAL SAVINCS
epROPORTIOf-t SAVED
.PlAK CORRECTION
FACTOR
-COST OF SAVINGS'
MWH
START
CONSER
(ONSl:.RVATION
DATA FILE
SELECT
RESIDENTIAL
CONSERVATION
OPTION
FIGURE 7.1.RED Conservation Module
Th is market
of values for the
The user is then
default values and range
to an output data file.
option.CONSER then writes the
option's market saturation rate
queried for the market saturation of electricity in the use that the
conservation option offsets (e.g.,electric water heating).
saturation is also written to the output data file.
7 .2
.....l.
Government residential conservation programs primarily reduce the
effective purchase price of conservation options to the consumer.Therefore,
CONSER next requests the user's estimate of consumer purchase and installation
costs for each option with and without government subsidization.The
saturation of each technoloqy with and without subsidization is calculated and
is written to the output data file.
For the business sector,CONSER requests the potential proportion of
predicted electricity use that might be saved through conservation,the
estimated proportion of these potential conservation savings that are
realized,and the costs per kWh for conservation savings in existing and new
buildings.These values are also written to the output data file,which now
becomes an input data file for the Conservation Module.
RED uses the residential conservation information in the CONSER data file
to account for the impacts of the conservation technologies under
consideration.First,the amounts of conservation occurring in the
residential sector with and without government subsidization are calculated by
multiplying together the electric use saturation rate,the conservation
saturation rate,and the number of households.Next,the level of
policy-induced conservation is calculated by subtracting the nonsubsidized
conservation savings from the subsidized figure.Finally,this figure is
subtracted from the price-adjusted residential requirements to derive the
utilities'total residential sales.
The business conservation calculation separately addresses the sales to
new and existing uses,and two potential pools of electricity savings are
calculated.For simplicity,existing uses are defined as the previous
forecast periods'electricity requirements,whereas new uses are the
difference between the previous period1s requirements and the current period's
requirements.The two potential pools of savings are the sales to new uses
and retrofits times user-supplied potential savings rates (for new uses and
retrofits).The predicted level of savings in each case is found hy
multiplying the potential pools of savings times user-supplied conservation
saturations with and without government intervention.Finally,the total
policy-induced savings are derived by subtracting the savings without
7.3
government intervention from sales with government intervention for both new
and existing uses.Total price adjusted requirements,minus policy-induced
business conservation,equals utilities 'total sales to business.
The economic costs of the residential conservation technology package are
found by multiplying together the government subsidized conservation
saturation rate;the electric saturation rate;the number of households;and
the cost to consumers per installation without government intervention for
each conservation option;and summing over options.For the economic costs of
business conservation,the total megawatt hours saved by government subsidized
conservation is multiplied by the cost per megawatt hour saved.
Finally,the Conservation Module helps calculate the effect of
conservation on peak demand.Unfortunately,not all conservation technologies
can be given credit for displacing the demand for peak generating capacity.
Therefore,CONSER queries the user for a peak correction factor,a variable
that takes on a value between zero and one if the option receives credit for
producing some portion of its energy savings during the peak demand period;
otherwise the value is zero.These peak correction factors for each option
are aggregated in RED.First,they are weighted by the proportion of total
policy-induced electricity savings each option represents during a given
forecast period.Next,the weighted correction factors are summed together.
The resulting aggregated peak correction factor is sent to the peak demand
model to calculate the peak savings of the set of conservation technologies.
INPUTS AND OUTPUTS
The inputs and outputs of the Conservation Module are summarized in
Table 7.1.The potential market for the conservation option is defined by the
total number of households served (THHS)and the saturation of the electrical
devices (ESAT)whose use of electricity can be displaced by investment in a
particular conservation option.ESAT equals the total market saturation of
the applicance times the fuel mnrle split.The total number of households
served is calculated in the housing module,while ESAT is interactively
entered by the user.RCSAT,the penetration of the potential market by the
7.4
TA8LE 7.1.Inputs and Outputs of the Conservation Module
a)Inputs
~
THHS
TECH
COST!
COSTa
RCSAT
ESAT
PRES
RESCON
CF
PPES
BCSAT
COST
BUSCON
b)Outputs
~
TCONSAV
TCONCOST
ADRESCON
ADBUSCON
ACF
Name
Tota"households served
Technical energy savings
Installation and purchase cost
of the residential conservation
device
Operation and maintenance costs
of the residential conservation
device
Residential saturation of the
device (with and without govern-
ment intervention)
Residential electric use
saturati on
Expected residential electri-
city price
Price-adjusted residential
consumpti on
Peak correction factor
Potential proportion of elec-
tricity saved in business in
new and retrofit uses
Business conservation saturation
rate (with and without govern-
ment intervention)
Cost per megawatt hour saved
in business
Business price-adjusted
consumption
Name
Total electricity saved
(business plus residential)
Total cost of conservation
(business plus residential)
Adjusted residential consumption
Adjusted business consumption
Aggregate peak correcti on factor
7.5
From
Residential Module
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
Residential Module
CONSER,Interactive Input
CONSER,Interactive Input
CONSER,Interactive Input
Uncertainty Module
CONSER,Interactive Input
Business Module
To
Report
Report
Miscellaneous and Peak
Demand Modules
Miscellaneous and Peak
Demand Modules
Peak Demand Model
.~-----------------,-•
------------------------------p
conservation technology.is determined within the CONSER parameter routine.
The technical energy savings and the costs of residential conservation devices
(both installation and maintenance)are interactively specified within CONSER
by the user.
The business segments of CONSER also query the user for the potential and
actual saturations of electricity conservation in the business sector and the
costs per megawatt hour saved for business investments in conservation.
Finally.the correction factors are decimal fractions which are
interactively supplied by the user to CONSER and which reflect the extent to
which conservation options receive credit for peak savings.
The outputs of the Conservation Module are the final electricity sales to
the business and residential sectors.and the electricity savings of the
conservation technology set considered in a given run of the RED model.
MODEL STRUCTURE
The price adjustment mechanisms used in the Business and Residential
Consumption Modules employ price elasticities derived from studies that did
not distinguish among the impacts of conservation technologies and other
effects of energy price changes.Since conservation of electricity is argued
to be induced either by energy price changes or by market intervention to
encourage conservation.the treatment of conservation in RED was cautiously
developed to eliminate the possibility of double counting energy savings and
costs.
In RED's formulation.the Conservation Module serves primarily as an
accounting mechanism that tracks the impacts of a given set of technology
options in the residential sector and the aggregate level of conservation in
the business sector.However.since government policies and programs could
have a significant.direct impact upon the level of conservation adopted.and
since the incremental impacts of these actions are not incorporated in the
price adjustment process of the Residential and Business Consumption Modules.
the Conservation Module explicitly calculates these impacts and accordingly
adjusts the forecasted sales to consumers.
7.6
'""'"
1
Scenario Preparation (CONSER Program)
The calculations of the Conservation Module require scenarios of the
saturation of conservation options,the expected electricity savings,and
their associated costs.To reduce the amount of data entry in scenario
preparation,and to facilitate the use of a broad set of conservation
technologies and government policy options,a separate program (CONSER)
queries the user for information necessary to calculate the saturations,
savings,and costs.These parameters are then written to a data file where
they can be accessed by the remainder of the Conservation Module.Two steps
are required:1)determining if an option will achieve market acceptance;and
2)calculating market saturations for options gaining acceptance.
The first step is to determine whether a specific conservation option
will achieve market acceptance.For the residential sector,the way RED
identifies acceptable options is to compare them with other investments
available to the consumer.Conservation is an investment with a financial
yield that can be calculated and compared with other investment options.By
comparing the internal rate-of-return (IRR)of a conservation option with the
market rate of interest,one can determine whether conservation options'
return is sufficient to encourage market acceptance.
The market rate of interest to which RED compares the internal
rate-of-return is the standard commercial bank passbook interest rate.
Passbook accounts have several characteristics:
1.They are virtually risk free.
2.They are extremely liquid.
3.They have trivial requirements as to the size of the initial deposit.
4.They are readily available to everyone.
Investments in conservation technologies,however,are characterized by
the fo 11 owi ng:
1.risky
2.difficult to liquidate
3.(sometimes)require a large initial payment.
7.7
it,,'I
.r-
These factors would cause most homeowners-investors to require a higher
rate-of-return on conservation than those on passbook accounts to invest in
conservation.Therefore,a conservation option can pass the internal
rate-market interest test even though it might not be adopted.Such a
comparison insures that every option that could achieve market acceptance is
included in the portfolio of conservation technologies to be considered.
The IRR is calculated with the following formula:
r
where
=0 (7.1)
T =lifetime of the device (maximum of 30 years)
P =internal rate-of-return
~=subscript for the year.Takes on values 1 to 30
ES =value of electricity saved
C =total cost of the option in the year
=subscript f~the load center
k =subscript for the option.
The value of electricity savings is based on the energy prices the consumer
expects.It is calculated by querying user for price forecasts and the
electricity savings (in kWh)for each option and multipl~ing:
(7.2)
where
PRESi~=dollars per kWh in load center i at time ~
TECH ik =annual kWh savings in region i per installation of device k.
The cost (Ci~k)is the 1980 dollar installation and purchase cost in the
year the device is purchased and the annual maintenance and operating 1980
dollar costs in all remaining periods.
7.8
-,-
........tl
Recognizing that initial cost is a major barrier to conservation,the
Congress has provided incentives for individuals to install energy conserving
equipment.Furthermore,the State of Alaska has also instituted several
programs aimed to promote installation of conservation equipment.Because the
main impact of these programs is to reduce the initial cost of conservation,
CONSER uses the subsidized installation and purchase costs of the device to
forecast whether a device will achieve additional market acceptance over an
unsubsidized case.
As previously stated,CONSER requests the expected electricity price
forecast for each year,the operating and maintenance costs,the kWh savings
and the government subsidized purchase and installation costs of the device
for each region.CONSER calculates the internal rate-of-return of the option,
prints this information,and asks the user if the option is to be used.If it
is,then the unsubsidized costs of purchasing and installing the option are
also requested.
If the scenario to be considered does not include government
intervention,the installation and purchase costs entered for the subsidized
and unsubsidized cases should be the same (and equal to the unsubsidized
costs).
The next step of scenario preparation is to determine the market
saturation rate of each conservation option.RED employs a payback decision
rule to determine the default value and the range of the conservation
saturation rate.Since the expected value of electricity savings probably is
not constant across time,the payback period is calculated by dividing the
installation and purchase costs by the cumulative net value of electricity
savings (value of energy savings minus operating and maintenance costs),
starting with the first year and continuing until the ratio is less than one.
The number of years required to drive the ratio to less than one is the
payback period.
The payback period is calculated for both the subsidized and nonsubsi-
dized cases.Since the subsidized case usually will have lower installation
and purchase costs,the payback periods for the subsidized case will usually
be lower and the conservation saturation rates will usually be higher.
7.9
•If'"R,.\:!!iiA
CONSER also requests the name of the conservation option,a forecast of
the market saturation rates for electric devices from which the option
displaces consumption,and the peak correction factor for each conservation
option.The saturation of electric devices is used within the Conservation
Module to define the potential market of the conservation option,whereas the
peak correction factor indicates the extent to which the option displaces
electricity consumption at the peak.This information,as well as the costs
and saturation of the conservation option (for the unsubsidized and subsidized
cases),are written to a data file for later access by the remainder of the
Conservation Module.
Funding constraints in the Railbelt Alternatives Study prohibited the
development of detailed cost and performance data for business conservation
applications.CONSER,therefore,requires the user to provide the following
for both new and retrofit uses:the potential proportion of electricity that
conservation technology can displace and an estimate of the proportion of
those potential savings actually realized for subsidized and unsubsidized
cases.CONSER also requests the cost per megawatt hour saved for both cases
and the peak correction factor for new and retrofit uses.
This business sector information is also written to CONSER's output data
file.By running CONSER with several different technology packages and
government policy packages,conservation scenario files can be easily
constructed for later analysis within RED.
Residential Conservation
Using the information from the data file that CONSER creates,the
calculation of electricity saved by the set of technologies is
straightforward.By multiplying the electric device saturation and the
incremental number of households served,the total number of potential
applications of the conservation device is found.The incremental number of
households served in the first forecast period (1980)is zero,since the
current consumption rates already include the current level of conservation.
7.10
"I
By next multiplying the potential number of uses by the savings per
installation and the saturation of the conservation option~the amount of
electricity saved is derived:
CONSAV.tk .=RCSAT.k . x TECH'k x, J , J ,
(ESAT itk x THHS it -ESATi(t_l)k x THHSi(t_l))
where
CONSAV =electricity saved (kWh)
RCSAT =conservation saturation rate
TECH =electricity savings per installation (kWh)
ESAT =electric device saturation rates
THHS =total households served
t =denotes the forecast period (1~2~3~•••~7)
j =denotes subsidized (j=l)or nonsubsidized (j=o).
The total electricity displaced through the residential conservation set
considered is found by summing across the options (subscript k):
K
RCONSAV itl =f;l CONSAV itk1
where
RCONSAV =residential electricity conserved (kWh)
K =total number of residential options considered.
(7.3)
(7.4 )
Since the price adjustment mechanism does not account for government-
induced conservation~the model next adjusts residential sales by the
incrementa'conservation attributable to government programs:
......
ADRESCON it =RESCON it -(RCONSAV itl -RCONSAV ito )
7.11
(7.5)
where
ADRESCON =final electricity requirements of residential consumers
RESCON =price-adjusted residential consumption.
The electrical device saturation and the incremental number of households
define the number of potential applications.The cost of purchasing and
installing the option is calculated by multiplying the potential number of new
uses by COSTI (the installation and purchase costs per option).Next,by
multiplying COSTO (annual operations and maintenance costs per option)by the
cumulation of previous forecast periods 'potential uses,the operating and
maintenance costs are found.Finally,by summing all these components,the
total annual costs associated with conservation savings in a given forecast
period can be found.During any forecast year,the annual costs are equal to
one year's total installation costs,plus operating costs associated with all
previous additions to stock:
r
CONCOST itkj =[COSTI ikj x RCSAT itkj x (ESAT itk x THHS it -
ESATi(t_llk x THHS1(t_11Y!s +COSTO ik x ~l RCSAT ikj x
(ESAT ihkj x THHS ih -ESAT ihkj x THHS i (h_1 U
where
(7.6)
CONCOST =the option's total annual cost
COSTI =unit cost in 1980 dollars for purchasing and installing the
conservation option
COSTO =unit cost in 1980 dollars of operating and maintaining the
conservation option
h =forecast period subscript.Can take on values 1 to t.
By summing over the options,the total costs of the residential conservation
set is found.
7.12
where
RCONCOST itj
K
=L CONCOST itkj
k=l
(7.7)
--'IIi.
RCONCOST =present value of the total costs of the set of
residential conservation options.
The total costs of conservation are the unsubsidized total costs
(RCONCOST ito )'consumers pay the subsidized costs (RCONSAV it1 ).and
government pays the difference (RCONCOST ito -RCONCOST it1 ).
Business Conservation
For business conservation impacts,funding constraints prohibited
collection of detailed cost and performance data.Fortunately.a limited
number of studies have estimated the potential energy savings and associated
costs for aggregate conservation investments in new and existing buildings.
RED separates the conservation impacts for the business sector into two
parts:those arising from retrofitting existing buildings.and those arls1ng
from incorporating conservation technologies in new construction.As in the
residential segment of the Conservation Module.the potential pool of
electricity that can be displaced must be identified for both new construction
and retrofits.This II poo lll is determined by the state of conservation
technology and is supplied to the conservation module from the CONSER output
file.The actual amount of conservation that occurs depends upon the price of
electricity and competing fuels.and upon the cost and performance
characteristics of the options available.This is also supplied by CONSER.
In RED.the potential pool of displaced electricity for businesses is
derived by first separating business sales into sales to existing structures
and sales to new structures.For simplicity.the change from the previous
periods'business requirements as calculated by the Business Consumption
Module is assumed to be the sales to new buildings:
7.13
SALNB it =BUSCON it -BUSCONi(t_l)
where
SALNB =sales to new buildings
BUSCON =business consumption prior to conservation adjustments.
(7.8)
Therefore,the sales to existing buildings are the sales in the previous
period:
where
SALEX it =BUSCONi(t_l)(7.9)
SALEX =sales to existing buildings.
To find the potential pool of electricity use displaced through retrofits and
incorporation of conservation options in new buildings,the Conservation
Module multiplies the disaggregated sales figures times the potential
percentage of electricity saved in new and retrofit buildings:
where
POTEX it =SALEX it x PPES itE
(7.10a)
(7.10b)
POTNB =potential amount of displaced electricity in new buildings
PPES =proportion of electricity that technically can be displaced
via retrofit or incorporation of conservation options in new
buildings
POTEX =potential amount of displaced electricity in existing
buildings
E =subscript for existing buildings
N =subscript for new buildings.
7.14
T--
These figures,however,only provide the technically feasible amount of
electricity that could be displaced.Market forces determine what level of
the potential electricity savings will be achieved.
In the residential segment of the Conservation Module,RED used an
internal rate-of-return test and a pa~ack period decision rule to determine
first,whether an ootion would achieve market acceptance,and second,what
level of acceptance it would achieve.As mentioned above,the information
available for business conservation does not permit such an analysis.
Therefore,the model user is required to assume a level of potential market
saturation.The saturation rates (one for retrofits,one for new buildings)
must reflect the prices of fuels (including electricity),the costs of the
package of options employed,and the electricity savings expected for
subsidized and nonsubsidized cases.
The saturation rates are obtained from the data file CONSER creates.The
displaced electricity can be found by multiplying the total saturation rates
by the total potential pool of electricity savings:
where
BCONSAV itNj =BCSAT itN x POTNB itj
BCONSAV itEj =BCSAT itE x POTEX itj
BCONSAV =electricity savings
BCSAT =saturation rate for conservation options in business.
(7.lla)
(7.llb)
As in the residential sector,the business requirements must be adjusted
for the incremental impact of government programs:
where
ADBUSCON it =BUSCON it -(BCONSAV itN1 -BCONSAV itNO )
-(BCONSAV itE1 -BCONSAV itEo )
ADBUSCON =adiusted business consumption.
7 .15
(7.12)
il......_
The total cost of the conservation set in a given future forecast year is
given by multiplying the 1980 dollar cost per megawatt-hour saved by the
conservation savinqs in each use:
I3CONCOST itj =(BCONSAV itEj x COST'iEj +BCONSAV itN1 x COST iN1 )
where
BCONCOST =business conservation costs,future forecast year
COST =1980 dollar costs per megawatt hour saved.
(7.13 )
The total costs of the conservation in a future forecast year to "society"is
the nonsubsidized costs (BCONCOST ito )'whereas the value of the subsidy in
that year is (BCONCOST ito -BCONCOST it1 ),and businesses bear only the
subsidized costs (BCONCOST it1 ).
Peak Correction Factors
The last item to be calculated is the aggregate peak correction factor
for the incremental impact of government conservation programs on peak
demand.This factor is calculated by weighting each -option's peak correction
factor by the option's proportion of incremental conservation:
K (CONSAV itk1 -CONSAV itko )x CF k
ACF it =~1 IRCONSAV it1 -RCONSAVitoJ +(BCDNSAV it1 -BCONSAV ito )
(BCONSAV itE1 -BCONSAV itEo )x CF E +(BCONSAV itN1 -BCONSAV itNo )x CF N
+(RCONSAV it1 -RCONSAV ito )+(BCONSAV it1 -BCONSAV ito )(7.14)
where
ACF =aggregate peak correction factor
CF =option-specific peak correction factor,equal to the
proportion of the electrical demand of displaced appliances
that can be displaced at the peak demand period of the year
(e.g.,January).
7.16
PARAMETERS
One of the requirements of the Alaska state program whereby homeowners
request state money to install conservation measures is that the payback
period for the measure be less than seven years.Therefore,if a conservation
option's payback period is assumed to be greater than seven years,the options
market penetration will be very limited,effectively zero.However,if the
option pays for itself within the first year,then the option would penetrate
the entire potential market immediately.The relationship between payback
period and penetration rate for payback periods between zero and seven years
is assumed to be linear.A range of 15%on these values is arbitrarily
assumed.Table 7.2 presents these market penetration parameters.
TABLE 7.2.Payback Periods and Assumed Market Saturation
Rates for Residential Conservation Options
Payback
Peri ad
(years)
o
1
2
3
4
5
6
7
8
Assumed
Saturation
(percent)
100.0
87.5
75.0
62.5
50.0
37.5
25.0
12.5
o
Assumed
Range
(percent)
80-95
67.5-82.5
55-70
42.5-57.5
30-45
17.5-32.5
5-20
0-5
Source:Author Assumption
7.17
8.0 THE MISCELLANEOUS MODULE
MECHANISM
The Miscellaneous Module uses outputs from several other modules to
forecast electricity used but not accounted for in the other modules,namely,
street lighting,second homes,and vacant housing.
INPUTS AND OUTPUTS
This module uses the forecasts of electrical requirements of the
residential and business sectors and the vacant housing stock.The only
output is miscellaneous requirements.Table 8.1 provides a summary of the
inputs and outputs of this module.
TABLE 8.1.Inputs and Outputs of the Miscellaneous Module
a)Inputs
Symbol Name From
ADBUSCON Adjusted Business Requirements Conservation Module
ADRESCON Adjusted Residential Requirements Conservation Module
VACHG Vacant Hou si nq Housing Module
b)Outputs
Symbol Name To
MISCON Miscellaneous Requirements Peak Demand Module
MODULE STRUCTURE
Figure 8.1 provides a flowchart of this module.For street lighting,the
requirements are assumed to be a constant proportion of conservation adjusted
business and residential requirements:
where
SR it =sl x (ADBUSCON it +ADRESCON it )
8.1
(8.1)
(
RESIDENTIAL
PLUS IBUSINESS
CONSUMPTION,,
CALCULATE CALCULATE CALCULATE
SECOND HOME STREET LIGHTING VACANT HOUSING
CONSUMPTION REQUIREMENTS CONSUMPTION
•
SUM FOR
MISCELLANEOUS
CONSUMPTION
•
I MI SCE LLANEOUS
1CONSUMPTION
FIGURE 8.1.RED Miscellaneous Module
SR =street lighting requirements
ADBUSCON =business requirements after adjustment for the
incremental conservation investments
ADRESCON =final electricity requirements of residential consumers
=subscipt for load center
t =forecast period (1,2,3 •..,7)
sl =street lighting parameter.
For second-home consumption,RED calculates the number of second homes as
a fixed proportion of the total number of households.A fixed consumption
factor is then applied:
SHR it =sh x THH it x shkWh
8.2
(8.2)
where
SHR =second home requirements
THH =total number of households
sh =proportion of total households having a second home
shkWh =consumption factor.
Finally,the use of electricity by vacant housing is a fixed
consumption factor times the number of vacant houses:
VHR it =vh x VACHG it
where
VHR =vacant housing requirements
VACHG =number of vacant houses
vh =assumed consumption per vacant dwelling unit.
Total miscellaneous requirements are found by summing the three
components above:
MISCON it =SR it +SHR it +VHR it
where
MISCON =miscellaneous electricity consumption.
PARAMETERS
(8.3)
(8.4 )
Table 8.2 gives the parameter values used for the Miscellaneous Module.
These parameters are all based on the authors'assumption because no source of
information is available.
8.3
~_d
Symbol
Sl
sh
shkWh
Vh
TABLE 8.2.Parameters for the Miscellaneous Module
Name
Street lighting(a)
Proportion of households having a second home(b)
Per unit second-home consumption(b)
Consumption in vacant housing(c)
Value
0.01
0.025
500 kWh
300 kWh
(a)1980 ratio of street lighting to business plus residential sales.
(b)O.Scott Goldsmith,ISER,personal communication.
(c)Author assumption.Reflects reduced level of use of all appliances.
8.4
9.0 THE PEAK DEMAND MODULE
Up to this point,only the method to forecast the total amount of
electricity demanded in a year has been considered.However,for capacity
planning,the maximum amount of electricity demanded (or peak demand)is
probably more important.Peak demand identifies the amount of capacity that
must be available to meet electricity requirements at the time of maximum
demand.
MECHANISM
The Peak Demand Module uses regional load factors to forecast peak
demand.The load factor is the average demand for capacity throughout the
year divided by the peak demand for capacity in the year.RED first
calculates the peak demand without the peak savings of government-induced
conservation.Next,the peak savings of the incremental government-induced
conservation is calculated,taking into account the mix of conservation
technologies being considered.Finally,by netting out the peak savings,RED
calculates the peak demand the system must meet.
INPUTS AND OUTPUTS
Table 9.1 provides a summary of the inputs and outputs of the Peak Demand
Module.The load factors (LF)are generated by the Uncertainty Module,
whereas the aggregate peak correction factor (ACF)comes from the Conservation
Module.The business,residential,and miscellaneous requirements (BUSCON,
RESCON,and MISCON)come from the Business,Residential,and Miscellaneous
Modules,whereas the conservation adjusted requirements (ADRESCON and
ADBUSCON)come from the Conservation Module.The outputs of this module are:
1)the peak demand in each regional load center at the point of sale to final
users,and 2)the incremental peak savings of subsidized conservation.
MODULE STRUCTURE
Figure 9.1 provides a flowchart of this module.First,the peak demand
without subsidized conservation is calculated.This is done by dividing the
9.1
TABLE 9.1.Inputs and Outputs of the Peak Demand Module
a)Inputs
Symbol
LF
RESCON
BUSCON
ADRESCON
ADBUSCON
ACF
h)Outputs
Symbol
FPD
PS
Name
Regional load factor
Residential electricity sales before
adjustment for subsidized conservation
Business requirements prior to adjustment
for subsidized conservation
Subsidized conservation-adjusted residen-
tial requirements
Business requirements adjusted for sub-
sidized conservation
Aggregate peak correction factor
Name
Peak demand
Incremental peak savings
From
Uncertainty Module
Residential
Consumption Module
Business
Consumption Module
Conservation Module
Conservation Module
Conservation Module
To
Report
Report
total electricity requirements in each region by the product of the load
factor times the number of hours in the year.Next,the same operation is
performed on the increment to conservation due to subsidized conservation
investments.This yields the preliminary peak savings.RED then adjusts the
peak savings by multiplying the aqgregate peak correction factor times the
peak savings.The corrected peak savings are then subtracted from the peak
demand calculated in the first step to derive the regional peak demand at the
point of sale.
The first step is to calculate the total electricity requirements without
subsidized conservation by adding the residential,business,and miscellaneous
requirements:
TOTREQB it =BUS~ONit +RESCON it +MISCON it
9.2
(9.1)
LOAD
FACTORS
(FROM UNCERTAINTY
MOQULE)
ANNUAL ELECTRICITY
REQUI REMENTS
•RESIDENTIAL
•BUSINESS
•MISCELLANEOUS
CALCULATE
PRELIMINARY
PEAK DEMAND
CALCULATE
PEAK
SAVINGS
•ANNUAL SAVINGS
DUE TO SUBSIDY
•PEAK CORRECTION
FACTOR
(FROM CONSERVATION
MODULE)
where
LARGE
INDUSTRIAL
DEMAND
PEAK
DEMAND
FIGURE 9.1.RED Peak Demand Module
...
TOTREQB =total electricity requirements before conservation
adj ustment (MWh)
BUSCON =business requirements before conservation adjustment (MWh)
RESCON =residential requirements before conservation adjustment
(MWh)
MISCON =miscellaneous requirements (MWh)
=index for the load center
t =index for forecast period (t =1,2,•..,7).
9.3
Next,the Peak Demand Module calculates the peak demand without
accounting for the incremental conservation due to subsidized investments in
conservation by applying the load factor:
TOTREQB it=.,--;---""=,,,,:-::-LF it x 8760
where
PD =Peak Demand (MW)
LF =Load Factor
8760 =number of hours in a year
p =index denoting pre 1imi nary.
(9.2)
To calculate the peak savings due to subsidized conservation investments,
RED first must find the incremental number of megawatt hours saved:
TOTREQSit =tiUSCON it -ADBUSCON it +RESCON it -ADRESCON it
where
TOTREQS =incremental megawatt hours saved by subsidized
conservation investments
AD~USCON =business requirements after adjustment for the
incremental impact of subsidized conservation
ADRESCON =residential requirements after adjustment for the
incremental impact of subsidized conservation.
(9.3)
Next,peak savings is found by multiplying the incremental electricity
saved by the aggregate peak correction factor and applying the load factor:
TOTREQSit
=ACF it x LF it x 8760
9.4
(9.4)
where
PS =peak savings (MW)
ACF =aggregate peak correction factor.
Finally,by subtracting the peak savings from the preliminary peak
demand,the final peak demand for each region is derived:
FPO i t =PO Pit
where
FPD =index denoting final peak demand.
PARAMETERS
PS it (9.5)
The only parameters in the Peak Demand Module are the system'load factors
assumed for the three load centers:Anchorage,Fairbanks,and Glennallen-
Valdez.These load factors are shown in Table 9.2.The default values for
Anchorage and Fairbanks were derived by weighting the 10-year average load
factors reported by Woodward Clyde by the 1978 peak demands for each utility
(Smith and Kirkwood 1980).For Glennallen-valdez,the default value was
assumed to be the average of the Fairbanks and Anchorage values.
For the range of the parameter values,the minimum and maximum values
reported for the 1970-78 period by each major utility were used.
Unfortunately,no information was available for the range of the load factor
for Glennallen-Valdez,so the range was assumed to be t.he average of Fairbanks
and Anchorage.
TABLE 9.2.Assumed Load Factors for Railbelt Load Centers
Load Center
Load Factor (%)
Default Range
Anchorage
Fairbanks
Glennallen-Valdez
55.73
48.99
52.16
9.5
49.2-63.4
41.6-59.1
45.4-61.3
10.0 RATE MODEL
The Rate Model is a separate program used to interface REO and the Alaska
Railbelt Elective Energy Planning model (AREEP).This model employs
information on demand from a previous run of RED and the cost of power from
AREEP to generate a forecast of electricity rates in Anchorage and
Fairbanks.(a)
MECHANISM
The Rate Model uses the cost of power,regional electricity consumption,
and user-supplied cost allocation factors (costs all load centers)to derive
load center cost of power series.Next,the user can choose to specify the
rates for a customer class (business or residential)and allow the model to
solve for the other series,or can use a historical (default)or user-supplied
price differential weight to derive the rates for both customer classes.
INPUTS AND OUTPUTS
Table 10.1 presents the inputs and outputs of this model.The inputs are
electricity sales by customer class and the cost of power.The only outputs of
the model are the rates for the business and residential customer classes.
MODEL STRUCTURE
Figure 10.1 provides a flow chart of this model.The cost of power
obtained from AREEP contains a charge for the Anchorage-Fairbanks transmission
intertie.The Rate Model,however,differentiates between the rates for the
Anchorage and Fairbanks load centers by netting out the average intertie cost,
then adding back a charge for each load center's proportion of the cost of the
intertie.
Using the regionally differentiated cost of power series obtained above,
the next step is to construct rates that will exactly cover that series.The
user is offered two choices:either use price weight (the business rate is
10 .1
(a)Inputs
Symbol
ACP
TS
BS
RS
TABLE 10.1.Inputs and Outputs of the Rate Model
Var i ab 1e
AREEP cost of power
Total electricity sales (by load center)
Business Sales (by load center)
Residential Sales (by load center)
From
AREEP
RED
RED
RED
(b)Outputs
Symbol
BR
RR
Var i ab 1e
Business Rates
Residential Rates
To
RED
RED
(a)Because e1ectricity is very expensive in the Glennallen-Valdez area,it is
necessary to exercise extreme caution in forecasting rates for this load
center.Therefore,the Rate Model currently requires that rates remain at
their 1980 levels or be exogenously entered.
1.10 of the residential rate,for example)or specify the rates for one class,
and let the model solve for what the rates must be in the other.The result
is a rate forecast for the two load centers.
To obtain the average charge for the intertie embedded in the AREEP cost
of power,the cost of the intertie is divided by total electricity sales in
the two regi ons.
TS n1)(,r
where
TCIARIC=~--:;::----31 2
E L:.Q.=5 r =
(10.1 )
ARIC =average intertie cost
Tel =total intertie cost
TS.Q.r =total electricity sales in year .Q.in load center.
10.2
ALLOCA TlON FACTOR
COST OF POWER
FROM AREEP
ALLOCATE INTERTIE
COST TO LOAD CENTERS
CALCULATE LOAD
CENTER'S COST OF POWER
o
w
PRICE WEIGHT RATE FORECAST
PRICE WEIGHT CALCULATE RESIDENTIAL
AND BUSINESS RATES
CALCULATE RESIDENTIAL
AND BUSINESS RATES
RATE FORECAST
FOR BUSINESS
2R RESIDENTIA~
FIGURE 10.1.Structure of the Rate Model
--,-'..._,.....__.~
The load center cost of power is found by allocating the interties cost
to the two load centers.dividing by sales at each load center.then replacing
the average intertie charge with the new load center intertie charge:
AF *TC I_r ARICARSCrt=
where
Ar:P t +
31
Lt=5 TS 9"r
(10.2)
ARSC =average load center system cost
ACP =AREEP cost of power
AF r =user supplied allocation factor (02.-AF ~1;AF +(l-AF)~1)
at load center r
t =forecast period (1.2.3 •..•7).
If the user chooses to use price weights.the rates are found by solving
the following equations:
(10.3)
where
BR =business rate
RR =residential rate
wf =weighting factor (user supplied or default valve).
TSrQ,*ARSCr!J..=RRrt*RSn,+BRrt*BSrQ,(10.4 )
where
RS =residential sa 1es
BS =busi ness sa 1es.
10.4
To get the residential rate series,the right hand side of equation 10.3 is
substituted into equation 10.4.The business rates then drop out of equation
10.3.
If the user specifies the rates for one customer class,then the model
solves equation 10.4 for the missing rate series.
PARAMETERS
The only parameters in the Rate Model are the initial prices for 1980
(Table 10.2)the Anchorage-Fairbanks intertie cost and the default price
weights (Table 10.3).The intertie cost is from Gilbert Commonwealth
(Gilbert/Commonwealth 1981),and the default price weights were obtained from
the 1980 rates.
TABLE 10.2.1980 Load Center Electricity Rates ($/kWh)
Anchorage
Fairbanks
Glennallen-Valdez
Business
0.37
0.82
0.131
Res i denti a1
0.031
0.074
0.128
TABLE 10.3.Miscellaneous Rate Model Parameters
Intertie Cost
Weighting Factor
10.5
$130,800,000
0.108
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