EPA-600/5-74-033
November 1974
                        Socioeconomic Environmental Studies Series
  The Economic and  Environmenta
  Benefits  from  Improving Electrical
  Rate Structures
                                   £

                                             LU
                                             (3
                               Office of Research and Development

                               U.S. Environmental Protection Agency

                               Washington, D.C. 20460

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               RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development,
Environmental Protection Agency, have been grouped into five
series.  These five broad categories were established to
facilitate further development and application of environmental
technology.  Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface
in related fields.  The five series are:

     1.  Environmental Health Effects Research
     2.  Environmental Protection Technology
     3.  Ecological Research
     4.  Environmental Monitoring
     5.  Socioeconomic Environmental Studies
This report has been assigned to the SOCIOECONOMIC ENVIRONMENTAL STUDIES
series.  This series describes research on the socioeconomic impact of
environmental problems.  This covers recycling and other recovery
operations with emphasis on monetary incentives.  The non-scientific
realms of legal systems, cultural values, and business systems are
also involved.  Because of their interdisciplinary scope, system
evaluations and environmental management reports are included in this
series.

This report has been reviewed by the Office of Research and
Development.  Approval does not signify that the contents
necessarily reflect the views and policies of the Environmental
Protection Agency, nor does mention of trade names or commercial
products constitute endorsement or recommendation for use.

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                                             EPA-600/5-7^-033
                                             November 197^
THE ECONOMIC AND ENVIRONMENTAL BENEFITS FROM
      IMPROVING ELECTRICAL RATE STRUCTURES
                            By

                      Mark Sharefkin
                 Contract No.  68-01-1850
                 Program Element 1HA093
                  ROAPNo. 21-AQL/03
                       Project Officer

                       Roger Don Shull
         Washington Environmental Research Center
           U.S. Environmental Protection Agency
                   Washington, D.C.  20460
                         Prepared for
            Office of Research and Development
           U.S. Environmental Protection Agency
                  Washington, D.C.  20460
 For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402 - Price $2.65

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                         ABSTRACT
Quantitative estimates of the internal cost savings to be
derived from changes in the pricing of electric power are
devised and evaluated.  The econometric literature on elec-
tricity demand is surveyed, and elasticity values are selec-
ted which are parameters for the overall benefit measures.
A method for using reported utility data to estimate the
cost of delivered power--at the system peak and off the sys-
tem peak, and for each customer class- is devised.  Data on
five electric utilities is used to make estimates of the
potential benefits from improvements in the pricing of elec
trie power, for each customer class in each system.  The
estimated potential benefits are sufficiently large to
merit load curve studies by block for residential customers,
Such studies are necessary preliminaries to a definitive
assessment of the proposals for so called rate inversion.
                                11

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                       CONTENTS
Abstract

List of Figures

List of Tables

Acknowledgements

Executive Summary


Sections
   I     Conclusions, Recommendations, and
           Introduction                            1
              Conclusions                          1
              Recommendations                      3
              Introduction                         4
              Selection of Sample Companies        7

   II    The Demand for Electric Power            10
              The Econometric Evidence   .         11
              Econometric Estimation of Elec-
                tricity Demand:  General
                Problems                          11
              Residential Demand Estimates        26
              Industrial Demand Estimates         40

  III    Some Relevant Features of the Internal
           Cost Structure of the Electric Power
           Industry                               47
              A Typology of Customers Based
                Upon "Information" Costs          47
              The Uses of the Typology:  A Pre-
                liminary Overview of Indicators
                to be Estimated, and Cost Analy-
                sis Required                      52
              The Reconstruction of Internal
                Cost Functions:  Short Run Mar-
                ginal Costs                       55
              Offpeak Versus Peak Costs:  An
                Explicit Allocation of Capacity
                Costs                             67
              Estimates of Peak Responsibility
                Capacity Cost Recovery            82

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                 CONTENTS (Continued)
Section
   IV    The Pricing of Electricity:  Indicators
           of Potential Improvement               98
              The Variety of Tariffs             100
              Category I Indicators of Poten-
                tial Pricing Improvement         107
              Category II Indicators of
                Potential Pricing Improvement    113
              Category III Indicators of
                Potential Pricing Improvement    144
              Category IV Indicators of
                Potential Pricing Improvement    146

   V     References                              157
                           IV

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                     LIST OF FIGURES
No.                                                Page

 1   Short Run Marginal Costs, Potomac Electric
        Power Company, 1972                         58

 2   Sample System Load Curves, Potomac Electric
        Power Company, 1972                         59

 3   Welfare Gains from Peak Load Pricing          116
                             v

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                     LIST OF TABLES


No.

 1   An Overview of the Central Economic Papers
        on Electricity Demand                      12

 2   Variables* Units, and Notations Employed In
        Econometric Studies of the Residential
        Demand for Electricity                     l3

 3   Selected Regression Results, Residential
        Demand Equations                           19

 4   Residential Elasticity Estimates              32

 5   Industry Regressions:  Two Digit Industries,
        1956                                       45

 6   "Commercial and Industrial Elasticity Esti-
        mates                                      46

 7   A Typology of Electricity Customers           48

 8   Short Run Marginal Costs of Generation,
        Potomac Electric Power Company, 1972       57

 9   Efficiency (in Fuel Terms) by Unit, Potomac
        Electric Power Company, 1972               60

10   Monthly Peaks; Trial Repair Schedule 1,
        Potomac Electric Power Coirtpany, 1972       63

11   System Load Peak by Month                     66

12   SRMC(2), Trial Repair Schedule 1, Repair
        Period I - January-February                67

13   Income Statement Data, Potomac Electric
        Power Company, 1972                        70

14   Functionalization of Operating and Mainten-
        ance Costs, Potomac Electric Power
        Company, 1972                              71

15   Generation and Transmission Nonfuel Operd-
        tion and Maintenance, Patomac Electric
        Power Company, 1972                        74

16   Cost of Capital:  Rate of Return on Ratet
        Base and Depreciation, Potomac Electric
        Power Company, 1972                        75
                            VI

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               LIST OF TABLES (Continue^
No,                                               Page

17   Electric Plant in Service, Potomac Electric
        Power Company, 1972                        77

18   Taxes Other Than Federal Income Taxes,
        Potomac Electric Power Company, 1972       79
19   Summary of Functionalized Capacity Costs,
        Potomac Electric Power Company, 1972       79

20   Crude Estimates of Allocation of Capacity
        Costs Among Customer Classes, Potomac
        Electric Power Company, 1972               81

21   Number of Hours in Peak Under Various
        Periodizations                             84

22A  Initial Cost Recovery Cpmparisons:  Genera-
        tion Only, Potomac Electric Power
        Company, 1972                              86

22B  Range of Total Peak Hours, and Corresponding
        Approximate Total KWH Sales, Potomac
        Electric Power Company, 1972               87

23   Transmission Capacity Cost Allocation,
        Potomac Electric Power Company, 1972       89

24   Distribution Cost Allocation, Potomac
        Electric Power Company, 1972               92

25   Summary of Allocation of Capacity Costs,
        Potomac Electric Power Company, 1972       93

26   Imputation of Customer Class Load Curves,
        Potomac Electric Power Company, 1972       96

27   Tariff Types and Cost Recovery Strategies    101

28   Deviation and Elasticity Ratios, Potomac
       'Electric Power Company, 1972              110

29   Policy Implications of Table 28              111

30   Bands of Suggested Prices for Peak Season,
        Potomac Electric Power Company, 1972      115

31   Illustrative Indicators of Potential Pricing
        Improvement, Potomac Electric Power
        Company, 1972                             117
                          vii

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                LIST OF TABLES (Continued)
No.                                               Page

32   Bands of Suggested Prices by Season: Potomac
        Electric Power Company, 1972               122
33   Bands of Suggested Prices by Season:  Common-
        wealth Edison Company, 1972                123
34   Bands of Suggested Prices by Season:  Duke
        Power Company, 1972                        125
35   Bands of Suggested Prices by Season:  New
        York State Electric and Gas Corp., 1972    128
36   Bands of Suggested Prices by Season:  Penn-
        sylvania Power § Light, 1972               130
37   Peak Benefits by Season:  Average Prices
        Compared With Peak Prices Which Decrease
        Peak KWH Ten Percent and With LRMC,
        Potomac Electric Power Company, 1972       135
38   Peak Benefits by Season:  Average Prices
        Compared With Peak Prices Which Decrease
        Peak KWH Ten Percent and With LRMC,
        Commonwealth Edison Company, 1972          136

39   Peak Benefits by Season:  Average Prices
        Compared With Peak Prices Which Decrease
        Peak KWH Ten Percent and With LRMC, Duke:
        Power Company, 1972                        137
40   Peak Benefits by Season:  Average Prices
        Compared With Peak Prices Which Decrease
        Peak KWH Ten Percent and With LRMC, New
        York State Electric and Gas,, 1972          138

41   Peak Benefits by Season:  Average Prices
        Compared With Peak Prices Which Decrease
        Peak KWH Ten Percent and With LRMC,
        Pennsylvania Power § Light Company, 1972   139
42   Net Peak Period Residential, Schedule Indi-
        cators of Improved Pricing                 143
43   Category III Indicators of Potential Pricing
        Improvement                                147
44   Potomac Electric Power Company, Demand Billed
        Accounts for District of Columbia, Select-
        ed Months of 1972                          150
                          Vlll

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                LIST OF TABLES (Continued)
No.                                                Pag<

45   Load .Curve for a Single Industrial Customer,
        Commonwealth Edison Company, 1972          151

46   Indicators of Potential Pricing Improvement,
       • Demand-Billed Schedules                    154
                             IX

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                 ACKNOWLEDGEMENTS
The assistance of Mr. Joseph Kowalski in the empiri-
cal work on this project was invaluable.  Mark Seidel
and Jon Goldstein, project officers at the Environ-
mental Protection Agency, contributed above and beyond
the call of duty.  Seidel, the original project officer,
helped shape the study with his probing questions; and
Goldstein's careful reading of the draft final report
immeasurably improved the end product.  Thanks are
also due to Lee Matthews, Katleen Weiss and Sylvie
Durand-Jansiac for the painstaking typing of the manu-
script and tables.

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                   EXECUTIVE SUMMARY
This is a study of the pricing practices of the electric
power industry, motivated by the importance of this industry
to any overall program of environmental management.  The
generation of electricity is a major source of air and thermal
pollution; the siting of new electric power plants has been
a major focus of the preservation versus development contro-
versy, and a harbinger of the growing importance of the
land use issue.  Both the level and pattern of utilization
of existing capacity, and the rate and composition of addi-
tional capacity, are therefore critical to environmental
policy.

Our laws and institutions are built around the presumption
that, unless there is good reason to believe otherwise,
markets and market-determined prices are the best arbiters
of both output and investment decisions.  The rationale for
that presumption is very simple:  under certain conditions,
market prices equal "social costs".  Under these conditions
each consumer, in deciding whether or not to take an addi-
tional unit of the good in question, knows that he must pay
tjie full costs that society will incur in producing that
additional unit of the commodity.  Markets and prices then
guide us to a situation in which each consumer (and there-
fore society) takes only as much of the commodity as he
(and therefore we) are willing to pay for.
                         ES-1

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Two kinds of "conditions" are necessary to this result.
First, economies of scale must be exhausted with firm sizes
much smaller than market demand:  otherwise one firm will
grow to dominate the entire market, and there will not be
any competition between firms.  Second, there must be no
externalities, so that the costs to the firm of producing a
unit of the commodity reflect the full costs thereby imposed
upon society.

Both of these conditions are violated in the case of electric
power.  This simultaneous violation has brought the issue
of electricity rates to the forefront of environmental con-
troversy.  The first condition is violated by economies of
scale in the generation and distribtuion of electric power:
it is cheaper per KWH to supply more KWHs up to and beyond
the number of KWHs taken in large markets.  Consequently, we
have devised the social institution of regulated monopoly:
electric power companies are given a monopoly of their ser-
vice areas, so that society may reap the benefits of scale
economies.  And they are regulated--their pricing and invest-
ment deicions are subject to the approval of public authori-
ties --in order to spare us the potential dangers of. monopoly
power.

The second condition is violated by the familiar "external
diseconomies" of power generation--air and thermal pollution.
Some associated costs, for example the health costs of air
pollution, are not seen as costs by power companies, and
therefore do not enter into the determination of prices.
                               ES-2

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The well-known solution to this second problem is to
"internalize" external costs:  in the last example, this
requires adding the health costs of air pollution to the
internal production costs of the polluting firm.   Health
costs will then be reflected in prices, thereby restoring
a rough equality between price and social costs.

The implementation of this simple prescription faces severe
difficulties of practice.  For, as we have emphasized above,
electric power prices are regulated monopoly prices, set in
order to guarantee a "fair" return on capital.  Consequently
it cannot be assumed that some simple adjustment of existing
prices will equate price and social cost.  And there is a
further serious difficulty:  the internal costs of power
production are rather complex.

A major source of that complexity is associated with the
"peak load" problem.  In the early hours of the day much
system capacity is sitting idle, so that the costs which
an additional user imposes upon society are essentially
only the cost of the fuel required to generate enough
electricity to meet that user's demand.  But at some hour
of the day the demands of residential, commercial and
industrial electricity customers will inevitably approach
system capacity.  All customers taking power at those peak
                                   %»
hours will, collectively, be imposing upon society the full
capital costs of system capacity.  The costs of serving
these users therefore include both fuel  (or operating) costs
and capital costs. .
                          ES-3

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Our purpose in this study is to take two essential steps in
the direction of a rationalization of the pricing of elec-
tricity:  first, an examination of the relationship between
existing prices and internal costs, and second, a quantifi-
cation of the potential benefits to be derived from the
redesign of rate structures.  In this Executive Summary
we will begin with a highly simplified conceptualization of
the problem.  Then, bit by bit, we will introduce the
complexities and data difficulties which have forced us to
imputation, approximation, or estimation.  Finally, we
shall discuss the results of our empirical work, and the
policy implications of those results.

CONCEPTUALIZATION OF THE PROBLEM

Consider Figure 3 of the report text, reproduced below.
That figure illustrates the distortions which arise from
failing to charge different offpeak and peak prices for a
commodity subject to a peak load problem.  A peak load prob-
lem arises whenever demand fluctuates much more rapidly than
the time in which capacity can be adjusted to demand.  (In
the case of electric power, demand varies sharply over the
working day, while capacity takes years to plan and build.)
At the single price P, offpeak customers take KWHQ£^   v
and peak customers take KWHp  , , with these quantities de-
fined by the intersections of the P line and the offpeak and
peak demand curves.

the problem with this method of pricing electricity is that
it is inefficient.  Economic efficiency requires that every
customer pay the full incremental resource costs his consump-
tion imposes upon society, no more and no less.  Depreciation
                             ES-4

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                             Peak Demand
   Peak
Offpeak
          sQffpeak Demand
           KWH
              Offpeak
ItWH
   Peak
      Figure  3.  Welfare  Gains  from  Peak Load Pricing


  is  a resource  cost,  and  the peak load pricing problem is
  essentially  a  problem  in assigning  responsibility for de-
  preciation or  the maintenance  of capacity*  A priori) it
  may appear that because  there  is excess  capacity during
  offpeak  hours, offpeak users impose no  incremental capac-
  ity gosts upon society.  More  generating capacity need not
  be  built in  order to serve these users:  in  fact, equip-
  ment could be  allowed  to deteriorate slightly, capacity
  could  be reduced, and  offpeak  demand could still be  met.
  Thus,  it may appear  that because capacity is not scarce
  during offpeak hours,  the price paid by  offpeak users
  should not include a charge for depreciation.  Further,
  it  may also  appear that  since  capacity  must  be maintained
  in  order to  meet the demands of peak hour users, it  is they
 who must pay a charge  sufficient to cover depreciation.
                            ES-5

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This  solution  is not  entirely correct.  Depreciation takes
two forms:  that associated with use and that which is in-
dependent of use.   Any  depreciation resulting from use
constitutes a  resource cost imposed upon society by that
user.   In the  case  of an electrical utility capacity is
scarce  during  peak  hours, and if depreciation occurs when
electricity is supplied  to offpeak users, then a scarce
resource has been used up, a resource cost has been imposed
upon  society,  and the price charged to offpeak users should
legitimately include  a charge for this depreciation.
Obviously the  same  holds  for any depreciation associated
with  use by peak hour users.

The situtation is quite  different for depreciation which
cannot  be attributed  to  use.  Since offpeak users are neither
contributing to such  depreciation nor demanding that capacity
be maintained, they are  not imposing a resource cost on
society, and the price which they pay should not reflect this
type  of depreciation.  However, if peak hour demands for
power are to be met,  capacity must be maintained.  Although
peak  hour users cannot be said to be causing non-use depre-
ciation, their demand for electricity implies the need to
maintain capacity and imposes a resource cost on society-
Hence the price charged  these peak users must be sufficient
to cover both  use and non-use depreciation, normal return
on investment,  and  incremental operating costs.

Since most depreciation  in the electrical utility industry
is not  attributable to use, the efficient prices are
P0ffpeak to o£fPealc users, where PQffpeak is ectual to the
incremental operating costs of serving these users, and
Pp  ,  to on peak users, where ?pea^ is the sum of incremental
operating costs and incremental capacity cost.
                              ES-6

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The shaded areas in Figure 3 represent the losses to society
from incorrectly pricing the commodity at P.  At price P,
offpeak users are being denied consumption which they value
more than the resource costs (poffpeak) that consumption
would impose upon society, and AWQp is the magnitude o£ those
losses.  Similarly, at price P peak users are being charged
less than the resource costs (Ppeak) they impose upon society
by their consumption, and the area AWp represents the social
gain available if current price P is raised to Pp^^* thereby
eliminating inefficient consumption.  Correct pricing will
give net social benefits equal to AW^p + AWp.

DIFFICULTIES OF IMPLEMENTATION
        «.

Implementation of this scheme runs up against many practi-
cal difficulties, and here we set out the most prominent,
together with some comments on their resolution.

Demand

In Figure 3, we have drawn two demand curves, one for the
offpeak hours of the day and one for the peak hours of the
day.  The demand for electric power fluctuates over the 24
hour daily cycle, and we have taken as "the" peak period of
every 24 hour day that eight hour period in which the larg-
est KWH total is generated.  (Electricity demand also ex-
hibits a seasonal peak, with average daily consumption
peaking in some month of the year.  This seasonal peaking
problem will concern us later; our focus here is on the
daily peak.)
                          ES-7

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In order to compute the potential welfare gains AWQp and
AWp, we need to know how much offpeak and peak demands
change as offpeak and peak prices change.  The technical
term for the required measure of price sensitivity is price
elasticity:  the information we require is offpeak and peak
price elasticities.  But existing studies of the price elas-
ticity of the demand for electricity generally estimate the
price elasticity of total demand--offpeak plus peak demand--
and do not try to estimate the price elasticities of off-
peak and peak demand separately.  We were therefore forced
to use the best of recent studies of overall demand elas-
ticity, and to assume that peak demand is independent of
offpeak price--atid vice versa.  The latter assumption is
uncomfortable, especially in the long run, since there
would almost certainly be some shifting in temporal .pat-
terns of electricity consumption in response to relative
price changes. ^Moreover, it is the long run—the time span
in which capacity can be adjusted--that interests us most.
The welfare gain AWD in Figure 3 arises in part because
                   i          *           *
society is spared the incurrence of the costs of provision
of some inefficient capacity, and that capacity adjustment
can only be made in the long run.  Note that were prices
off peak lowered so as to capture the welfare gain AWQp,
electricity constimptiSn offpeak would be increased--as
would be environmental degradation, the costs of which are
not counted in AW~p.  For these reasons, we have, in 6ur
welfare gain estimates, used AWp, which can be Used without
reservation as a lower bound -welfare gain estimate*  After
a survey of available econometric elasticity estiffiates we
                             ES-8

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adopted those of Chapman, et. al., because of the exceptional
quality of their econometric work and their estimation, on a
comparable basis, of elasticities by customer class (residen-
tial, commercial, and industrial) and by state.  Their long
run elasticity estimates are roughly equal to one.

Cost

In figure 3 we have drawn two horizontal lines at poffpeak
and Ppeaj,» and these represent the incremental cost of serv-
ing offpeak and peak users respectively.  That simple repre-
sentation covers a multitude of conceptual and empirical
difficulties in the estimation of these incremental costs.

The offpeak incremental costs of delivering an additional
KWH to a customer are relatively easy to estimate, since they
are essentially the fuel cost of generating an additional KWH.
Strictly speaking, that cost is different from hour to hour,
for every electric utility has a stock of generating units of
various ages and sizes.  Typically, the older and smaller
units are less efficient, and in order to minimize operating
cost, the units are brought on line in ascending cost order.
At any moment, the offpeak incremental cost of delivering an
additional KWH is therefore approximately equal to the genera-
tion costs of the least efficient unit operating at that
moment.  Further, it costs more to deliver a KWH to a residen-
tial customer than to an industrial customer, since there are
energy losses in the low voltage distribution system serving
residential customers.  But these differences are relatively
small, and we have taken average fuel cost as an approximate
measure of the offpeak cost of delivering a KWH.
                             ES-9

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The peak incremental costs of delivering an additional KWH
to a customer are much more difficult to estimate, since
that requires the allocation of capacity costs among cus-
tomer classes.  There is inevitably some arbitrariness in
these allocations, but our exploration of a range of reason-
able procedures led to little quantitative variation in re-
sults.

Pricing

Our purposes in making estimates of the costs incurred in
serving offpeak and peak customers of various types (residen-
tial, commercial and industrial) are two:  first, to allow
us to compare present prices charged for each of these kinds
of service with the costs incurred in providing that service;
and, second, given that comparison, to suggest improvements
in rates--methods of pricing electricity- -rwhich will better
approximate price cost.   We therefore turn to a summary of
our treatment of the pricing problem.

In Figure 3, a single horizontal line P represents the pres-
ent price of electricity.  The reality is more complex; elec-
tricity is generally priced at a quantity discount, in so-
called declining block rates.  Any customer taking a specified
amount of energy under a schedule is paying some definite mar-
ginal price and some definite average price, but he is not
paying any single price.   In order to quantify his sensitivity
to price changes, we need to know what kind of changes he is
sensitive to --marginal,  average, or both.

There is no firm basis for asserting that, e.g., residential
customers are responsive only to average prices o'r that indus-
trial customers will shift their time profile of electricity
                           ES-10

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consumption in response to price differentials between peak
and offpeak.  But a reasonable argument can be made for such
a typology of customers.

Assume that every consumer reacts optimally to the options
open to him.  Then any consumer of electricity will allocate
time to the electricity consumption decision to the point
where marginal benefits of such time--the reduction in elec-
tric bill resulting from the incremental minute spent in
making the electricity consumption decision--just equal the
incremental costs involved (in this, case, the value of the
incremental minute in its next most valuable use).  The out-
comes of this allocation decision process will be classified
in two dimensions:  time differentiating versus time-undif-
ferentiating consumption decisions, and average price respon-
sive versus marginal price responsive consumption decisions.
Table 7 of the text sets out this typology, and is reproduced
below.

       Table 7.  A TYPOLOGY OF ELECTRICITY CUSTOMERS
                                Time
                          Undifferentiating
                 Time
            Differentiating
Average Price Responsive
Marginal Price Responsive
 I
III
II
IV
Customers in Category I have found it optimal not to distin-
guish between average and marginal prices in their electric-
ity consumption decisions.  For these customers, the existence
of block rates is irrelevant, since they would make the same
                                                    ^
consumption^decision at a flat price equal to the average
revenue they are currently paying.  Customers in Category II
                            ES-11

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elect to pay the cost of differentiating between their con-
sumption on and offpeak by paying the additional costs of
metering peak and offpeak consumption separately.  By assump-
tion, they are insensitive to any differential between aver-
age and marginal prices on peak, and to any differential be-
tween average and marginal prices off peak.  They do distin-
guish between average peak period price and average offpeak
price.

Customers in Category III do not find it optimal to distin-
guish between peak and offpeak consumption, but they find it
optimal to distinguish between marginal and average price.
Finally, customers in Category IV find it optimal to distin-
guish between consumption in both dimensions:  between power
taken off peak and at peak, and between average and marginal
prices paid for electricity.

So much for typology:  which kinds of customers belong where?
There are no unambiguous guidelines.  Thus, it is not entirely
clear that all customers on a given rate schedule belong in a
single category.  Large residential users, for example, may
have some marginal price sensitivity and may therefore belong
in Category III, whereas very small residential users almost
certainly belong in Category I.

Our identifications of rate schedules with categories of the
above typology are as follows.

Category I

This category is the domain of small residential and commer-
                                                   i
cial users.  The relevant question regarding possible improve-
ment in rate structures is then restricted by the'.assumptions
                           ES-12

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that consumers in this category do not, for information cost
reasons, distinguish either marginal and average price or off-
peak and peak consumption.  The only remaining policy question
is then as follows:  how much "better" can we do by changing
the average KWH prices paid by customers on individual rate
schedules?  For example, how much better can we do, in terms
of our welfare measures, by slightly raising the average price
per KWH paid by commercial customers, and by simultaneously
slightly lowering the average price per KWH paid by industrial
customers?  To the extent that the derived quantitative mea-
sures are reliable, they indicate that available gains are neg-
ligibly small.

Category II

We will compute net benefit measures for all rate schedules of
the sample companies as if it were the case that customers are
average-price responsive--that they have found it optimal not
to distringuish between peak and offpeak consumption.  For
residential customers, presently metered on. a KWH monthly or
bimonthly basis, this will require netting of the additional
cost of double-rate registers required to charge differential
fates off peak and on peak.  A warning regarding the £ull spec-
trum of beiiefits and costs for double rate register metering
is in order:  there is a potentially serious drawback to double
ratfe register metering of 6lipeak and peak hours.  Should
service to a given area be interrupted and restored in any
time interval not a multiple of 24 hours, the correct setting
of the double rate register shall have been lost.  It would
be necessary to meter on a KWH basis, taking the simple  sum
of the offpeak and peak registers as the relevant number of
KWH, until the time at which the meter was read; at that time,
the reader could reset the device.  The evaluation of this
problem is beyond the scope of this report.
                             ES-13

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Category  III

The prime candidates  for  Category  III  are  large residential
users  if  it  is  assumed  that,  for some  reason, there is no
possibility  of  differentiating  between offpeak and peak usage
for these customers.  Again,  recall that all customers on a
given  rate schedule need  not  necessarily fall into the same
category  of  our typology.   Nevertheless, as we will see in
our analysis  of Category  I, there  is little to be gained
from pricing  changes  which  do not  discriminate between off-
peak and  onpeak consumption.  However, there is still the
possibility  of  "implicitly" differentiating between offpeak
and peak, and our major estimate corresponding to Category
III is  the estimation of  an upper  bound on the gains attain-
able from implicit differentiation.  How might this work?
Suppose that  some electric utility had a declining block
rate schedule with two  blocks,  with the tailblock lower than
the first block.  Suppose further  that tailblock customers
buy all their electricity on  peak, while first block cus-
tomers  buy all  their  electricity off peak.  Then we can in
some measure  simulate peak  load pricing by raising the tail-
block and lowering the  first  block.  Advocates of "rate in-
version"  often  argue  for  something like this, and we will
calculate a rough upper bound on the potential welfare gains
associated with one kind of rate inversion proposal.

Category  IV

In Category IV  we place our large commercial and industrial
users.   They  incur little incremental  expense in differen-
                                                .:  I :
tiating betwen  their  consumption off peak and on peak, since
utilities-generally know the  instantaneous load being pulled
                                              ...        ••*
by their  individual large customers, and those customers
                         ES-14

-------
generally know the loads they are pulling.  Some of these
customers also have that information.  Similarly, there is
little incremental expense to be incurred by a "switch" from
average price sensitivity to marginal price sensitivity:  so
long as someone is watching the electric bill, the additional
cost of watching it in a slightly different way is negligible.
For these customers, a relevant benefit/cost question is:
what is the magnitude of the gains likely to be had from time-
differentiated pricing, e.g., a better matching of peak period
(perceived) prices and costs?  Some technical problems make
this comparison less than straightforward.  But we shall see
that it can be made, and that the attainable gains are probably
substantial.

External Costs and Welfare Gain Measures

All of the costs we have described are strictly internal to
the firm.  The welfare gain measures depicted as the shaded
areas of Figure 3 are constructed on the assumption that the
horizontal lines PQ££ g^i, and Ppeai, reflect all the incremen-
tal costs of offpeak or peak consumption, and since lowering
the offpeak price will expand offpeak consumption and the
corresponding external costs, we cannot confidently assert
that we gain AWQp by such a change in price.  But raising the
price of peak electricity restrains peak consumption, and
spares us both AWp in welfare loss and the associated external
costs.  Consequently, the welfare gain measures we report are
our evaluations of AWp alone.
                            ES-15

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WELFARE GAIN ESTIMATES

Category  I

The evaluation of  several welfare gain measures subject to
the stringent assumptions defining this category--that cus-
tomers are average price responsive and do not distinguish
between offpeak and peak consumption--gave negligible bene-
fit estimates.  This line of work was pursued no further.

Category  II

Customers in this category were assumed to distinguish be-
tween offpeak and peak consumption, but not between average
and marginal price.  In terms of Figure 3, we need PQ££ _ ^
and Ppeaj. for each customer class, and we take for demand
elasticities the average price demand elasticities reported
in econometric studies.  For residential customers, we must
remember that additional metering costs will be imposed if
we distinguish off peak and peak, so that for this customer
class these costs must be netted from benefits.

For each electric utility and for each rate schedule, two
kinds1 of AWp were computed.  The first of these measures is
the gain to be derived from a peak period price increase
which diminishes peak consumption by 10 percent; the second
is the gain associated with peak prices equal to full peak
costs.

The numerical results obtained are fairly consistent across
our sample of electric utilities.  The estimate of AWp based
upon a 10 percent decrease in peak consumption was generally
a small dollar figure, of the order of hundreds of thousands
                         ES-16

-------
of dollars.  The estimate based upon full peak cost was typi-
cally a much larger dollar figure, of the order of millions
or tens of millions of dollars.  We believe that a reasonable
interpretation of this divergence is as follows.  The analyst's
determination of the "true" figure somehow must attach weights
to these two bounds, and those weights are unavoidably judge-
mental.  Our inclination, based upon our experience with the
cost data, is to favor the higher estimate:  that expected
social returns to the full cost pricing of peak power are sub-
stantial.

Category III

Customers in Category III are assumed not to distinguish be-
tween offpeak and peak consumption, but to be marginal rather
than average price responsive.  Large residential customers
are prototypical of this category.  The best hope of simulat-
ing an offpeak versus peak price differential to these cus-
tomers is to exploit whatever correlation there may be between
monthly consumption and load pattern.  It is widely suspected
that tailblock customers--customers with high monthly consump-
tion- -take a disproportionate amount of their electricity on
peak.  Studies to test this hypothesis are only now being done
by many major systems, and some private communications of pre-
liminary results lend support to the idea.

In order to estimate the potential social gains from a seri-
ous attempt to use the block rate structure to simulate off-
peak-peak differentials, we have made an extreme assumption
and computed benefits on the basis of that assumption.  We
assume that all tailblock consumption is on peak, and we esti-
mate the benefits associated with raising the tailblock price
to the level of the first block price.  The proposal has been
one frequently advanced by advocates of so called rate  inver-
sion.
                             ES-17

-------
For all electric utilities in the sample, the resulting wel-
fare gain estimates are of the order of millions of dollars.
The policy implications seem clear:  the expected social
gains from the use of residential rate block load curve in-
formation to simulate peak period pricing are substantial.
Nevertheless, this method must be inferior to direct peak
period pricing via double register metering.

Category IV

Recall that customers in Category IV are assumed to be both
marginal price responsive and to be able to distinguish be-
tween offpeak and peak consumption.  Estimates of the poten-
tial social gain AWp from correct pricing of peak electricity
can then be derived as follows.  From the existing rate struc-
tures filed by the individual companies, we can determine what
commercial and industrial customers actually pay for power
taken during peak hours:  this corresponds to a determination
of P in Figure 3 above.  From our estimates of the cost of
providing peak power to these customers, we- have an estimate
of Pp  ,  in Figure 3.  And finally, use of our econometric
estimates of average price demand elasticities together with
the relationship between average and marginal price elastici-
ties gives us an estimate', by state and customer class, of
marginal price elasticities.

The evaluation of AWp by system, season, and customer class
is then routine, and the results are compiled in Column 9 of
Table 46.  The dollar estimates of potential gain are large
for all systems.  The policy implication is again clear: there
are large benefits to be expected from movement towards a sys-
tem of peak pricing of large commercial and industrial con-
sumption.
                           ES-18

-------
We conclude this executive summary with a brief recapitula-
tion of our conclusions and recommendations.

CONCLUSIONS

The major discrepancy between cost to the power company and
price charged the user is associated with the large differ-
ence between the costs of serving offpeak and peak customers
and the failure of existing rate schedules to reflect that
cost differential in different prices.   Each customer class
(residential, commercial, industrial) has distinctive charac-
teristics which must be considered in evaluating proposals
for better reflecting the offpeak versus peak cost differen-
tial in prices.  For all customer classes, there are probably
large net benefits to be derived from doing so.

For residential and small commercial customers, there are two
ways in which the price differential between offpeak and peak
power can be communicated to the customer.  First, by double
register metering, the customer's actual consumption can be
metered separately off peak and on peak.

Second, customer load curve surveys can provide information
on the contribution of customers in the different blocks of
the system's block rate structure, and that information can
be used by the system to approximate an offpeak versus peak
price differential.  Estimates of the potential benefits to
  '•' •'  >i s
be derived indicate that both methods would be a substantial
        "^
improvement over current pricing practice; direct double reg-
ister metering, a "first-best" peak pricing method, is pref-
erable to "second-best" methods based upon rate block load
curves.
                              ES-19

-------
For large commercial and industrial customers, the change-
over to a pricing system reflecting the offpeak versus peak
cost differential would not require major changes in utility
practice, since companies generally monitor these customers'
loads individually and on a half hourly or hourly basis.
Estimates of the potential benefits to be derived from such
a changeover indicate that they are substantial.

RECOMMENDATIONS

Two kinds of recommendations follow from our work.  First,
there are policy recommendations which can be made based on
what can be learned from existing data.  Second, there are
recommendations for improving the data base upon which all
rate making rests.

Residential and small commercial customers can and should be
metered with double rate meters.  It is of particular impor-
tance that peak hour prices be brought into closer alignment
with peak hour costs.

Large commercial and industrial customers can and should be
charged rates which distinguish between peak hour and off-
peak hours.

For .all classes of customers, there are relatively simple
ways of quantifying the cost differential between offpeak
and peak power.  A quantification of this difference should
be required in rate proceedings before public utility com-
missions, and it should be incumbent upon a system applying
for a rate increase to demonstrate that there is no better
                                                 i
way to reflect the offpeak versus peak hour cost'differen-
tial in prices.
                          ES-20

-------
Public service commissions should require that companies do
the demand elasticity studies that can easily be done with
data every system accumulates in the course of time, i.e.,
customer bill histories.

Public service commissions should require that companies do
customer class load curve studies, in order to establish the
contribution each customer class makes to the system peak in
each season.

Public service commissions should require that, if the block
rate structure based upon monthly consumption is to be re-
tained for residential and small commercial customers, then
the company in question do customer surveys of customers in
individual blocks, so that the contribution of each block to
the system peak can be established.
                               ES-21

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                         SECTION I

       CONCLUSIONS,  RECOMMENDATIONS AND INTRODUCTION
CONCLUSIONS

Central to the evaluation of any industry is the relation-
ship between internal production cost and selling price:
price, the amount a potential consumer must sacrifice for
another unit of consumption, must equal the cost that pro-
duction of that last unit imposes upon society, otherwise
resources are being misallocated.

In the case of the electric power industry, there are two
special circumstances which make the comparison of price and
social cost somewhat difficult.  First, there are high exter-
nal costs associated with the thermal generation of electric
power: thus air pollutants impose health costs, but those
health costs are borne by individuals and not by the power
company.  Second, electric utilities are regulated monopolies
whose price and investment policies are publicly regulated,
so that even the relationship between price and internal cost
is not what it is in competitive sectors of the economy.

This study was motivated by the first of these two special'
circumstances, i.e., high external costs.  But our emphasis
is almost entirely upon the second--the fact of regulation--
and our objective is a better understanding of the relation-
ship between price and internal cost.  We believe that a clear
understanding of that relationship is an essential step to-
wards the rationalization of pricing and capacity decisions
in the industry.

-------
We find that the major discrepancy between internal cost and
price arises from the sharp cost differences between peak and
offpeak electric power and the failure of most existing elec-
tric rate schedules to reflect that cost differential.  Each
customer class--residential, commercial, and industrial--has
distinctive characteristics which must be considered in eva-
luating proposals for reflecting that cost differential in
prices.  For all customer classes, however, there are probably
large benefits to be derived from doing so.

For residential and small commercial customers, there are two
ways in which the price differential between offpeak and peak
power can be communicated to the customer.  First, by double
register metering in which the customer's actual consumption
is metered separately offpeak and on peak.  Second, customer
load curve surveys can provide information on the contribution
of customers in the different blocks of the system's block
rate structure, and that information can be used by the system
to approximate an offpeak versus peak price differential.
Estimates of the potential benefits to be derived indicate
that both methods would be a substantial improvement over
current pricing practice.

For large commercial and industrial customers, the changeover
to a pricing system reflecting the offpeak versus peak cost
differential would not require major changes in utility prac-
tice, since companies generally monitor these customers' loads
individually and on a half hourly or hourly basis.  Estimates
of the potential benefits to be derived from such a changeover
indicate that they are substantial.

-------
RECOMMENDATIONS

Two kinds of recommendations follow from our work.  First,
there are policy recommendations which can be made based
upon what can be learned from existing data.  Second, there
are recommendations for improving the data base upon which
all rate making rests.

Residential and small commercial customers can and should be
metered with double rate meters.  It is of particular impor-
tance that peak hour prices be brought into closer alingment
with peak hour costs.

Large commercial and industrial customers can and should be
charged rates which distinguish between peak hour and off
peak hours.

For all classes of customers, there are relatively simple
ways of quantifying the cost differential between off peak
and peak power.  A quantification of this difference should
be required in rate proceedings before public utility com-
missions, and it should be incumbent upon a system applying
for a rate increase to demonstrate that there is no better
way to reflect the peak hour cost price differential in
prices.

Public service commissions should require that companies do
the demand elasticity studies that can easily be done with
data every system accumulates in the course of time:  custo-
mer bill histories.

Public service commissions should require that companies do
customer class load curve studies, in order to establish the

-------
contribution each customer class makes to the system peak in
each season.

Public service commissions should require that, if the block
rate structure based upon monthly consumption is to be re-
tained for residential and small commercial customers, then
the company in question do customer surveys of customers in
individual blocks, so that the contribution of each block to
the system peak can be established.

INTRODUCTION

The Overall Framework

This study was undertaken in the hope of obtaining a more
dependable and quantitative grasp of a related set of prob-
lems critical to environmental management.  At the center of
that set of problems is the pricing "policy" of the electric
power industry.  It is no longer necessary to discuss the
importance of energy in general, and electricity in particu-
lar, in environmental management.  Our concern is with one
possible dimension of that set of problems:  the possibility
that they are either exacerbated or made more intractable or
both because of the way in which electric power is priced.

It is a well-known principle of welfare economics, now wide-
ly absorbed into the conventional wisdom, that perfectly com-
petitive markets guarantee a result--in terms of price, the
level of output, and the level of capacity in the industry--
which in some sense is the best' possible — the optimal — result.
Crudely, this means that no customer who values the particular
good or service at least as highly as the social opportunity
cost of satisfying his demand is left unsatisfied:  that, at
                                 4

-------
the margin, the last customer is paying exactly the costs he
imposes upon society for the incremental unit of output.   The
usefulness of the competitive model in public policy analysis
arises because, in those situations requiring measurement of
departures from optimum performance.  The model suggests those
policies most likely to nudge an imperfect market towards the
competitive outcome.

Turning to the electric power industry, which departures from
competitive industry structure  are most likely to  lead to
suboptimal performance?  Electric power is a regulated indus-
try,  and  the conventional rationale for regulation  rests upon
a  feature  of the  industry which rules out a competitive  indus-
try structure.  Usually referred to as long run decreasing
average costs, the  essence  of this problem is that  there are
economies  of scale  over the whole range of the market--that as
more  of the market  of  the typical electric utility  is served
by a  single utility, up to  the  extent of the market, larger
plants with lower unit costs can be used, and the market
served at  lower cost.  It would impose needlessly .high costs
of power production upon consumers of electricity  to allow
more  than  one producer of electricity to serve the  market.
Thus  our  resort to  regulated monopoly in the provision of
electric power.  Next, the market failure associated with
external  costs is of obvious relevance to the electric power
industry.  The best known of these is the emission  of parti-
culates and of noxious gases into the ambient  air  during the
process of combustion.  To  the  extent that final product
price--in this case, the price  of electricity  to the final
user--does not adequately reflect the full social  costs  of
production, actual  industry output  can  be expected to be
larger than the  social optimum.

-------
The solutions to the departures from competitive optimum
which arise from long run decreasing costs and from external
costs have become almost as well known as the problems them-
selves.  For the first, the welfare economist prescribes re-
gulated monopoly, with prices equal to marginal cost and the
resulting deficit covered by a subsidy or, if the enterprise
is constrained to balance its budget, so-called second-best
marginal cost pricing: prices which depart from marginal cost
so as to minimize the resulting distortion of consumption
patterns from optimum.  And for external costs, the well-known
prescription is internalization.  Through effluent fees or
equivalent devices, producers must be made to feel the full
social costs imposed by their production processes; prices,
communicated to consumers, become correct signals to those
consumers of the resource costs imposed upon society by their
consumption decisions.

It would seem that, in applied work, we need only examine
particular industries with these standards, and shape policy
recommendations in accord with these standardized correctives.
Sadly, things are infinitely more complicated, and especially
so in the case of the electric power industry.  As elsewhere,
we do not have an accurate measure of the social costs of"the
environmental impacts associated with the industry as a whole,
let alone with particular companies or with particular plants.
As elsewhere, we do not have certain but rather only hazy know
ledge of demand conditions; worse, demand varies rapidly over
time--there is a "peak load" problem--so that our crude mea-
sures of demand are even further removed than usual from the
underlying reality.

But the applied welfare economist is used to this sort of ad-
versity.   There is no excuse for defeatism.  There can be no

-------
precise determination of "the" optimum of welfare theory.
But intelligent conceptual and empirical work can guide us
in the identification of inefficient aspects of present
policies, and can establish where the main chances for im-
provement lie.

That conceptual and emprical work proceeds through the body
of the report.  In Section II, we review econometric work on
                                                 t.
electricity demand, with an eye less on a comprehensive re-
capitulation of this literature than on the selection from
that literature of a set of demand elasticities which, much
later in Section IV, enter directly into welfare estimates.
In Section III, we enter into the cost side of power produc-
tion, again with the same limited objective:  the derivation
of cost measures required for those welfare estimates.  Fi
nally, in Section IV, come the estimates themselves.   The
remainder of this Introduction treats a problem of relevance
to every portion of the report, the selection of a sample of
companies used in the empirical work done in later Sections.

SELECTION OF A SAMPLE OF COMPANIES

Our sample of systems should be representative in at  least the
foilowing senses:
Clearly  it should be representative of the  ownership  struc-
ture of  the  industry.   In 1970, the approximately 250  in-
vestor-owned systems generated roughly 80 percent of  total
continental  United  States net generation.   There are,  of
course,  publicly-owned  systems with,significant  generating
capacity, e.g., the  Tennessee Valley Authority.  But,  our
focus in this study  is  upon pricing practices  common  to
public and private  sectors  of the power  industry.   We have

-------
therefore restricted our sample to Class A investor-owned
utilities, utilities having annual electric revenues of
$2,500,000 or more.

Further, our sample should be representative of the variation
in cost structure found within the industry.  If we are to
measure the success or failure of the industry in tailoring
rates to cost, the full variation in cost conditions should
be represented.  Two of many determinants of the cost struc-
ture of electric service are location and load pattern.
There are sharp regional variations in cost structure associ-
ated with the availability or unavailability of cheap hydro-
electric or cheap competitive public power.  The nature of
the market--the mix of residential, commercial, and industrial
markets, and the specific time pattern exhibited by each of
these loads--varies between regions.  For example, Southern
systems have in recent years typically become summer peak
systems, with maximum system load tied to the growth of the
air conditioning load.

Thus much of the variation across systems is ultimately re-
gional in nature, and our selection process was designed
accordingly.  First, all Class A companies were assigned to
Federal Power Commission, in part, in order to divide the con-
tiguous United States into regions of roughly similar cost
and load characteristics.  Next, the systems within each
region were cross-classified with respect to capacity, by
timing and size of system peak, and as combination* or non-
combination utilities.  From this classification we selected
 Combination utilities sett both gas and electricity; non-
 combination electric utilities sell only electric energy.
                              8

-------
38 systems, distributed over the regions in rough conformity
with the distribution of system characteristics within each
region.  All of those 38 systems were contacted, and the 10
systems which seemed most disposed towards cooperation with
the study then became the study sample.

In this report, full results are presented for five systems.
Even this small sample embraces considerable geographic di-
versity and therefore considerable variation in cost and
load conditions.  This should be kept in mind through all
of what follows.  We feel that a good sign that our pro-
cedures are relatively robust against many of the inevitable
arbitrary assumptions and imputations employed along the way
is the uniformity--in order of magnitude terms--of results
across the sample.

-------
                         SECTION II

                THE DEMAND FOR ELECTRIC POWER
Any comparison or ranking of rate structures depends, ulti-
mately, upon knowledge of cost structure and of demand.
Implicit in every argument over rates is some disagreement
over either cost or demand or both.  We would suggest that
the electric utility  industry has, on the whole, better
explored the cost side than the demand side, and for obvious
reasons:  utility expenses are registered as tangible dollar
outflows, while the economically relevant measure of demand
must be reconstructed from a quantity measure, instantaneous
system load.

In our discussion of  rate making, we will necessarily resort
to a hedged dependence upon the results of econometric
studies of demand.  The hedging is required, in part, by
Henri Theil's dictum  that models are to be used, but not
necessarily believed.  More seriously, the elasticities crit-
ical to rate making--the elasticities of (daily) offpeak and
peak demand for electricity--have never been directly esti-
mated.  In view of these constraints, our purpose in this
chapter is not a comprehensive view of the econometric demand
literature but rather an assessment of the conceptual differ-
ences underlying the various, estimates, a defensible ration-
ale for our ultimate choice of elasticities, and a working
knowledge of their limitations.
                               10

-------
THE ECONOMETRIC EVIDENCE

In the course of our discussion of the econometric evidence
we refer to several tables summarizing the scope, method and
empirical results obtained in the major papers.  Table 1 is a
cross comparison of markets studied and the nature of the data
base.  Table 2 enumerates and defines the relevant variables,
and specifies the units in which they are measured.  Table 3
provides a comparison of regression results obtained by the
various authors in estimation of constant-elasticity equa-
tions for residential demand, so that all variables are to
be thought of as natural logarithms:  thus KWH (s,b;a) refers
to the natural logarithm of the number of thousands of KWH
sold, in period t, to customers in block b, of rate schedule
s, in region a.  We proceed to a general discussion of the
numerous places at which an econometric study of electricity
demand must make essentially judgmental choices.   Subsequent-
ly, in our discussion of the individual papers, we will
examine the choices made by some individual investigators.

ECONOMETRIC ESTIMATION OF ELECTRICITY DEMAND: GENERAL
PROBLEMS

To begin at the beginning, the theory of consumer behavior
tells us that demand for any commodity depends upon the
price of that commodity, upon income, and upon the prices of
all other commodities.  A glance at that formulation suggests
the difficulties of application to the electric power case.
In order of descending intractability these are:

     (a)  The definition of price:  electricity is charac-
          teristically sold at block rates, i.e., at a
          quantity discount, so that there is no one "price."
          Stated in another way, marginal price and average
          price differ, in contrast to the situation,  for
                              11

-------
           Table 1.  AN OVERVIEW OF THE CENTRAL ECONOMETRIC PAPERS ON ELECTRICITY  DEMAND'
to
Paper
(Fisher and Kay sen, 1962)
(Halvorsen, 1971)
(Wilson, 1971)-
(Baxter and Rees, 1968)
(Anderson, 1971)
(Chapman et. al., 1973)
(Smith et. al., 1973)
Model
Residential
Industrial

I;
pp. 11-13
ii;
pp. 13-16




Markets
Studied
Residential
X
X
X
X


X
X
Conraerical
1 	





X
X
Industrial
X


X
X
X
X
Data Base
a
o
•rt
«J
U
M
V)
VI
in
o
M
U
47 State data
48 Contiguous
state data for
all variables
except MJTliMP
77 Cities
83 SMSA's

48 Contiguous
state data for
SIC primary
metals indus-
try
48 Contiguous
state data
7 New York
State utilties

•rt
h
&
V
e
•M
H
1946-1957
1961-1969,
inclusive, for
each state

1954-1964
Quarterly data
on 16 British
industry groups
31 states in
1958; 29 states
in 1962
1946-1970
inclusive
1951-1970
i«
M
h
«

MJTEMP time series (for
each state) developed as:
average of MJTEMP for
three largest cities in
that state
Utility price, quantity
data based upon utility
service areas
-
A unified energy supply -
demand model
MJTEMP series (for each
state) taken as mean
January temperature for
largest city in each
state

          a
           References  are  compiled at the  end of the  report.

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  Table  2.   VARIABLES,  UNITS,  AND  NOTATIONS EMPLOYED IN  ECONOMETRIC STUDIES
                  OF THE  RESIDENTIAL DEMAND  FOR ELECTRICITY
                     Variable
                         Unit
                     Definition
Quantity and
 Other
 Independent
 Variables
KWHt[s,b;ot]
                    KWHt[s;ct]
                    KWHt[s,b;otl

                    Bt[s, b;a]



                    KWH/HHt[s;a]
                    KWH/Bt[s;a]
                    PCTAP3[t[ct]
103KWH per
period
KWH sales to customers  in
block b of rate  schedule s,
in the t^h period,  in
region a

KWH sales to customers  on
schedule s in period t, in
region a.

KWH sales per customer  in
block b of rate  schedule s,
in the t— period,  in rate
schedule a

KWH sales per household on
rate schedule s,  in the tHl
period, in region a

KWH sales per customer  on.,
rate schedule s,  in the t—
period, in region a

Percent of homes  in service
area (roughly coincident
with region a) with at  least
one unit of applicance
installed, in the tth period

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Table 2  (continued).  VARIABLES, UNITS, AND NOTATIONS EMPLOYED  IN ECONOMETRIC
                STUDIES OF THE  RESIDENTIAL DEMAND  FOR ELECTRICITY
                      Variable
                            Unit
                     Definition
 Dependent
  Variables:
  Own-Price
  NOMREVt[s,b;a]




  NOMREVt[s;a]



  NMQREVt[s,b;a]




  REREVt[s,b;a]




  REREVt[s;a]



;  RMQREVt[s,b;a]




  FPCt[s,500,a]
Cents per
KWH
Nominal revenue  per  KWH  for
customers in block b of
schedule s,  in the till
period, in region a

Nominal revenue  per  KWH  for
customers on schedule s, in
the till period,  in region a

Nominal marginal revenue
for customers in block b of
schedule s,  in the till
period, in region a

Real revenue per KWH for
customers in block b of
schedule s,  in the till
period, in region a

Real revenue per KWH for
customers on schedule s, in
the tth period,  in region a

Real marginal revenue for
customers in block b of
schedule s,  in the till
period, in region ci

Federal Power Commission
typical bill for, e.g.,
customers on schedule s, in
the till period,  in region
a, taking 500 KWH per month

-------
Table 2  (continued).   VARIABLES, UNITS, AND NOTATIONS EMPLOYED  IN ECONOMETRIC
                 STUDIES  OF THE  RESIDENTIAL  DEMAND  FOR ELECTRICITY
                    Variable
                          Unit
                      Definition
Dependent
 Variables:
 Prices of Close
 Substitutes
Dependent
 Variables:
 Income
Other
 Variables:
 Demographic
NOMNGt[r;a]
                    RENGJr;  ]
                    NOMDISt[a]
                    CPIEL,
                    CPING,
                    cpr
MFYt[o]
                    MHEMFGt[a]
                    DPIPCt[a]
POPt[a]


PCTURB [a]
Cents per
Therm
                         Dollars
                         per
                         Barrel
Dollars
per Year

Dollars
per Hour

Dollars
per Year
per Capita

Thousands
Nominal revenue  per therm for
natural gas customers,on rate
schedule r, in the t—
period, in region a
 \
Real revenue per therm for
natural gas customers

Nominal price of distillate
oil, in the till  period, in
region a

Consumer price index for
electricity in the t^- period

Consumer price index for
natural gas in the tth period

General consumer price index
in the t— period

Median family income, in the
tM. period, in region a

Average hourly earnings in
manufacturing

Disposable personal income
per capita
Population of region  a  in the
ttn period

Percent of alll region living in
in urban areas in the t*h
period

-------
Table  2   (continued).  VARIABLES, UNITS, AND  NOTATIONS EMPLOYED  IN ECONOMETRIC
 	STUDIES OF THE  RESIDENTIAL DEMAND FOR^ELECTRICITY	
                      Variable
                          Unit
     Definition
  Other
   Variables:
   Demographic
   (continued)
  Other
   Variables:
   Market
   Characteristic
   Variables
HS/HHt[a]
                     BPCt[s;a]
                     R/HSEt[a]
Bt[s,b;a]
Bt[s;ct]
                     PCTPVTt[a]
                     FUELSGt[a]
                     R/ISt[a]
                        Rooms per
                        House
                        Cents per
                        106 BTU
Number of houses  per house-
hold, in the tlE  period, in
region a

Number of customers per
capita on rate  schedule s,
in the tM. period, in
region a

Average size of housing
units

Number of bills in block b
of schedule s,  in the t£h
period, in region a

Number of bills in rate
schedule s, in  the t—
period, in region a

Percent of total  region a
generation by investor-
owned electric  utilities

Cost of fuel consumed, in
cents per 10s BTU, times the
percent of total  net genera-
tion (in the tth.  period) by
thermal plants

Ratio of total  residential
KWH sales to total indust-
trial KWH sales

-------
Table 2  (continued).  VARIABLES,  UNITS,  AND  NOTATIONS EMPLOYED IN  ECONOMETRIC
                STUDIES OF THE RESIDENTIAL DEMAND FOR ELECTRICITY
                    Variable
                          Unit
                    Definition
Other Variables:     TIME
 Market Charac-
 tertic
 Variables (continued)
Other
 Variables:
 Climate
 Elasticities
JATEMPt



JUTEMPt

DDAYSt[a]


S[s;P]



e[s,a;P]
                    Z[s;Y]



                    Z[s,a;Y]
Degrees F
                                            Degrees. F
                                       Time trend
Mean January temperature,
in the t— period,  in region
a

Mean July temperature

Degree Days


Elasticity of demand with
respect to average  price for
customers on rate schedule s

Elasticity of demand with
respect to average  price for
customers on rate schedule s
in region a (relevant where
the specification includes
shift variables  distinguish-
ing states)

Elasticity of demand with
respect to income for cus-
tomers on rate schedule s

Elasticity of demand with
respect to income for cus-
tomers on rate schedule s in
region a (relevant  where the
specification includes shift
variables distinguishing
states)

-------
              Table 2  (continued).   VARIABLES, UNITS, AND NOTATIONS EMPLOYED IN ECONOMETRIC
                              STUDIES OF THE RESIDENTIAL DEMAND FOR ELECTRICITY  •
                Elasticities
                 (continued)
oo
                                    Variable
e[s;NG]
                                   e[s,ot;NG]
                          Unit
       Definition
Cross elasticity of elec-
tricity demand with respect
to (average) price of
natural gas for customers on
(electricity)

Cross elasticity of elec-
tricity demand with respect
to (average) price of
natural gas for customers in
region a on rate schedule s

Lag parameter linking short
run and long run elasti-
cities

-------
            Table  3.   SELECTED REGRESSION RESULTS,

                 RESIDENTIAL DEMAND EQUATIONS
HALVORSEN






  /KWH  [s;a]\

An  P  rc-rvl   =  -1-238  -  1.138 AnREREV. [s;a]

  \  t *•  '  J  /



                 +  .0355 &nRENGt[s;a]  + .6113





                 -  .3474 JlnPCTURB [a]  = .9245 AnJUTEMP  [a]
                                 I*                     L-




                 -  .0151 AnTIME(t)





           R =  .9031







WILSON
   KWH  [s;a]

      ^   1   =  10.25  -  1.33 £nFPC500^ [s ;a]
   rurit L15 > UJ /                          t
                 +  .31  £nNOMNGt[s;a] - .46






                 +  .49  AnR/HSE [a]  - .04 £nDDAYS  [a]
                              L*                  L>
            2

           R  =  .566
                             19

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          most consumption goods, of equality between mar-
          ginal and average price.  Which "price" is appro-
          priate  for the specification of an econometric
          model of electricity,demand?

      (b)  The appropriate approximation to the universe of
          all other goods:  obviously all other goods cannot
          be considered, and  so  it is necessary to limit the
          goods considered to all other relevant goods, goods
          which are either close complements of or close sub-
          stitutes for  electricity.  This in turn devolves
          into the examination of the disaggregated compo-
          nents of residential consumption.

We turn  to a discussion of these and related difficulties.

The Relevant Price Variable

Which price is appropriate to the specification of an econo-
metric model of residential electricity demand?  The obvious
answer is:  whatever price consumers respond to in making
consumption decisions.  In asking what that price is, we
must be mindful that information is costly--that time spent
in the careful examination of a rate schedule has an oppor-
tunity cost.  Casual empiricism  suggests that few residential
consumers know the difference between the steps of their rate
schedules, and it has been suggested that utilities be com-
pelled to mail a  copy of their rate schedules to residential
customers at least once annually, as some phone companies
are required to do.  The situation is unlikely to change with
the advent of electricity-intensive housing styles, since--
as the evidence we shall review below makes clear--residen-
tial electricity demand is income inelastic and thus comes
to occupy a smaller portion of the family budget, while
                               20

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higher real  incomes  increase the opportunity cost  of  time
spent in making  consumption decisions.

Average real residential price thus appears to be  the appro-
priate price variable  in the specification of the  residential
demand for electricity.  This is the variable that has been
used in most econometric studies of residential  demand,  so
that we can  simply take over those estimates.  Further,  there
'is  a simple  relationship between average  and marginal price
elasticities of  demand for a commodity  sold at a quantity
discount, so that we can construct an estimate of  marginal
price elasticity from  an estimate of average price elasticity.
A quantity discount  relationship can be approximated  by
                       ar(q)  = pxq       -1<3<0,             (1)

where  q  is  KWH  purchased per month,  ar  average  revenue,  and
p  and  3  are constants.  Then the relationship between aver-
age  and  marginal  expenditure is derived as  follows:   equat-
ing  two  necessarily  equal  expressions for total expenditure
gives
                     q  ar(q)  = /   (dqjmr(q)                 (2)
where  mr  is  marginal  revenue.   Substituting  the above rela-
tionship  for average  price  as  a function of  quantity, we are
left with
                                                           (3)

 Differentiating with respect to   q  we have

                (l+3)pq3 = (l+3)ar(q) = mr(q)               (4)
                             21

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so that we may solve for marginal revenue in terms of average
                                                          •
revenue, obtaining
Now suppose that we have estimated the coefficients in an
average revenue demand equation by regressing the natural
logarithm of average KWH consumption upon average residential
revenue and other variables.  Then the resulting coefficients
in the equation

           An qt[s;a] = A + BAn  ar  [s;a] + . . .         (6)

can be related to the estimates which must be appropriate to
the marginal-price demand equation as follows.  Since

            An ar(q) = An mr(q) — An(1+3)                 (7)

substitution into the average price  equation gives

           An qt[s;a] =  (A.-BAn(l+3)J  + B An mr(qt) + • • -(8)

Thus, if -/B/ is the average price elasticity of residential
electricity demand, the "corresponding" marginal price elas-
ticity is -/B/: the two are equal.

Which Other  Goods Must be  Included?

Which goods  are appropriately-close  complements  and  substi-
tutes and therefore worthy  of  inclusion  in  the specification
of the demand function?  Consider the spectrum of  residential
uses of electricity:  lighting, space heating, space cooling,
and water heating.  With the exception of lighting,  there are
                               22

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non-electric alternatives for the other functional require-
ments, e.g., gas and oil for space and water heating.   But
the substitution of gas for electricity requires costly con-
version of consumer durable equipment.  Residential demand
for electricity and fuels is ultimately demand for service
flows produced by use of fuels and electricity in conjunction
with "appliances" or "white goods" (broadly defined so as to
include lighting fixtures).  This complementarity is the
novelty in the problem of electricity demand estimation, and
is ultimately responsible for the discrepancies between ear-
lier and later elasticity estimates.   Consider the complica-
tions introduced into the usual conceptual distinction between
short run and long run demand elasticities.  The short run is
that period in which consumer-owner capital, or appliance
stocks, cannot be varied in response to demand, so that short
run changes in demand in response to price changes are wholly
attributable to variations in the intensity of use of fixed
stocks of appliances.  The relevant "other goods" for an esti-
mate of short run demand elasticity are, therefore, severely
limited:  appliance stocks definitionally are fixed, and fuel/
electricity substitutions cannot proceed without changes in
appliance stocks.  The appropriate specification of short run
residential electricity demand would seemingly include only
electricity price, and perhaps income, as independent variables.

The long run is that period in which capital stocks of consu-
mer durables are subject to adjustment in response to relative
price changes.  A cost minimizing consumer would,  in long
run adjustment, be producing the desired bundle of service
flows with least cost fuel-appliance  combinations.  An
appropriate specification  of independent variables for  the
long run demand for electricity would,  therefore,  necessarily
include measures of relative appliance  prices,  or, more
                              23

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specifically, the annual price of capital services for
various appliance types.

Short Run Versus Long Run Elasticities

In which elasticities are we interested, short run or long
run?  Our interest  is in the probable response of demand
patterns to  changes  in rate levels and structures, and in
valuation of the associated benefits.  Short run elastici-
ties are, therefore, appropriate to the question of attain-
able benefits within a period where consumers cannot alter
appliance stocks and utilities cannot alter their capital
structure and the requirement of meeting the fixed costs
of that capital structure.  Long run elasticities are rele-
vant to the  evaluation of benefits attainable over the
"period" in  which both producer and consumer capital struc-
tures can be adjusted.  They are the benefits foregone by
inappropriate pricing policies.

Cross Section, Time  Series and Pooled Models:  Which
Elasticities do They Measure?
                       %
Demand studies have  been don^ in cross section, in time
series, and  with pooled time series and cross Section data.
Cross sectional studies employ data from a given year, with
the various  data points corresponding to different locations;
time series  data build upon the observations, for several
years, of data from  one location, and pooling of time series
and cross section data is just what the name implies.  Time
series data  from many locations are thrown together to give
a larger sample than either pure time series or pure cross
section data alone could provide and, hopefully, improved
estimates of model parameters.  Table 1 indicates that only
John Wilson's 1971 paper does an estimate in pure cross
                              24

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section, which lends a special significance to the results
of this paper.  All other reported results are based upon
pooled time series and cross section data bases.

To begin, then, with the pure cross section case, the elas-
ticity estimates derived from such a study are properly to
be considered long run.  For there is great heterogeneity of
cost conditions among the contiguous states, and state data
for any given year presumably reflect the adjustment to local
conditions which consumers have made over time.  Since state
cost differences are persistent--due to factors such as the
presence or absence of cheap hydroelectric and/or public
power--cross section coefficients are, therefore, reason-
ably interpreted as based upon data on consumers in long run
equilibrium.  The regional variation in cost is, as we shall
see, fortunate, for it enables us to get a significant esti-
mate of the price coefficient.

What of estimates based upon pooled data?  Clearly there is
the possibility of interpretations of such data which conflict
with the interpretation of cross section results offered above,
Each year's data cannot reflect the long term adjustment of
consumption to price and other determinants, for clearly there
must be some adjustment of consumption to changes in short run
determinants--prices and incomes--in a time span smaller than
that in which complementary consumer durables  (stocks and
appliances) can be adjusted.  In a reasonably long time se-
ries of cross sections--say ten years, a period in which the
stock of consumer durables is considerably changed by re-
placement and additions--both will be present, with  short
run adjustment of consumption to changes  in price and  in-
come accompanied by long run  adjustment  of  consumer  durable
stocks.  The pressing  problem in the  interpretation  of the
                             25

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results of cross section studies is therefore the disen-
tanglement of short and, long term effects.  This, in general,
requires that some specific assumption regarding the mechan-
ism by which consumers  adjust to disequilibrating changes in
independent variables be specified.  However unpalatable and
oversimplified the specific models employed seem, it is of
some comfort that the form of the lagged response assumed
usually has little effect upon the relevant paxame^ter esti-
mates.  Once a specific adjustment structure is assumed,
short run and long run  estimates are functionally related.

Having thus enumerated  the problems that beset all of the
efforts to date at econometric estimation, we turn to a dis-
cussion of the individual estimates of the residential demand
for electricity.  Industrial demand estimates are often very
different methodologically, and are therefore treated sepa-
rately later.

RESIDENTIAL DEMAND ESTIMATES

Fisher and Kaysen

This study merits attention greater than that usually accord-
ed an econometric study more than ten years old, and for a
very simple reason:   as a first and an exhaustive study of
the demand for electric power , it set the agenda for almost
all subsequent work in  the field.  Indeed, most of the im-
provements of later papers — and we believe these have been
substantial--are to be  found as throwaways in the Fisher-
Kaysen book, suggested  but never pursued.

The hallmark of the Fisher-Kaysen approach is the recognition,
at every turn, that residential electricity is used in the
                             26

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home in conjunction with consumer durables--"white  goods,"  or
appliances, with the definition of appliances  stretched to  in-
clude lighting fixtures--in order to  produce desired service
flows.   All behavioral models  exploit this  dependence in the
specification of the demand for electricity.

Fisher-Kaysen start from the behavioral  hypothesis  that, in
the short run, price and income are determinants  of the level
of utilization of the existing stock  of  white  goods, so that
demand may be written
            KWHt[s:a]  = C
 ,H
:t[-i]
 REREV1

DPIPC. [a]| *•*•*,?* YW  [a]         (9)
where we have transcribed the notation used in Fisher-Kaysen
into the unified notation introduced in Table 2;  additional
variables required here are  W.  [a], the average  stock of the
                              1 «-
i-1— white good possessed by the community during time period
t.  The "price" variable is what purports to be a real price
variable, i.e., nominal average revenue deflated by the con-
sumer price index.

This is not the equation estimated by Fisher and Kay sen; they
first take (natural) logarithms, obtaining

               [s;a] = CT + e[s;pj  JlnREREVt [s;a]
                 e[s;y] AnDPIPCt[a] + Jin J(Wu[a])       (10)
and then take first differences, which gives
                             27

-------
     JlnKWHt[s;a]-«,nKWHt_1[s;a] = C" + e[s;p](&nREREVt[s;a]

                - £nREREVt_1[s;a]) + e [s;y] (JlnDPIPCt [a]

                - JlnDPIPC   , [a]) + white goods term       (11)

Assuming that  changes  in the  stock of white goods follow  an
exponential  growth path at  a  constant growth  rate, first-
differencing "eliminates" the time dependence in the white
goods term,  since

                Jln(WoJlrt) -  £n(WoAr^t"1)) =  +r.            (12)

Then from  (11)  and  (12) we  have

      KWHt[s;a]
     KWH
t-l[s;0]
     = C
             REREVt[s;a]
            REREVt_1[s;a]
                          DPIPCt[a]
                                                 (13)
Note that this equation  could  almost  have  been written down
from scratch:  it  is  a variant of the simplest model  of  short
run demand adjustment, with demand dependent upon  own-price
and income.  The growth  of  white  goods is  thus subsumed  into
the constant term  of  the model of the above equation.

The short run elasticity estimates are thus estimates of a
fluctuation, assumed  due to short run fluctuations in prices
and income, about  a trend.   The growth trend is  deemed exo-
genous.  The problem  of  disentangling long run and short run
elasticities is therefore "solved" in this case  by assumption,
for price and income  are not determinants  of the long run de-
                              28

-------
demand for electricity.   That long run trend is determined
solely by exogenous growth.   This procedure makes us wary of
the Fisher-Kaysen short  run estimates.

The situation is even more serious for the Fisher-Kaysen long
run elasticity estimates.  Given the commitment of these au-
thors to the use of white good stock data--as opposed to some
indirect measure of consumer durable stock decisions, such as
appliance prices--the validity of the final estimate will de-
pend critically upon the quality of the stock data.  It is
therefore unfortunate that the time series data on white good
stocks employed in the Fisher-Kaysen study is questionable.
This much they recognize.  Worse, further examination of
their stock data indicates that it seems to be wrong in just
such a manner as to bias the price elasticity estimate down-
wards:  that is, appliance stocks in states in which electri-
city is expensive seem to be overestimated, and appliance
stocks in states in which electricity is cheap seem to be
underestimated.  For this reason it would seem unadvisable
to use Fisher-Kaysen elasticities in our subsequent work.
               3
Chapman et. al.

This recent addition to the literature, presented at the
February 1973 NSF-MIT conference and available in preliminary
form from Oak Ridge National Laboratory, has one notable ad-
vantage of conceptual simplicity:  the simplicity of the dy-
namic specification leads to a transparent and appealing re-
lationship between short and long run demand elasticity esti-
mates.^ The price paid for that simplicity is the somewhat
obscured relationship between the model specification and
behavioral assumptions.   The Chapman et. al. specification
is
                              29

-------
        KWHt[s;a]  = (KWI^^ [s;a]) A [tthperiod factors]      (14)

where  only  the  time dependence of the multiplicative  factors,
and not their precise interpretation, are  relevant.   Suppose
that there  is only one multiplicative factor specified in the
form  (F(t))£fs;Fl.   Then in logarithms

        £nKWHt[s;a] = A£nKWHt_ ^s ;a] + e[s;F] £nF(t)       (15)

Suppose that  in the first period there is  a once and  for all
(exogenous)  increase in the factor F; serviceable examples
include an  increase in the price  of a substitute fuel or an
increase in the price of complementary goods,  e.g., appli-
ances.   Then  the  specification above tells us  that the
corresponding first-period fractional change in consumption
is
                             = £s;-                    (16)

But this is the beginning  and not  the end of the story,  since
the sequential adjustment  specification leads to changes in
all future periods.  Thus  second-period consumption is  deter-
mined by the  two equations

        AnKWHitsja] = X£nKWHQ[s;a] +  e[s;F]£nF(y)         (17)

        JtnKWH2[s;a] = AJlnKWH! [s;a] +  e [s;F] JlnF(y)         (18)

so that the percentage change in second-period consumption
arising from  a small change  in F(l)  is,  after using tlie  first
equation to eliminate AnKWH^sja]  from the second and then
differentiating,
                              30

-------
                3&nKWH2[s;aJ
In general, the percentage change in n — period consumption
is
               il^	(1 + X + X2 +...+ Xn"1)e[s;F]
if 0 < X < 1.  The ultimate consumption change--the  change  as
n  is taken to be very large--is thus
                            W	l^T ^'^         C2l)

The conventional interpretation of  the parameters--or, more
precisely, of econometric estimates of these parameters--is
as follows.  e[s,f] is taken to be  the short run elasticity
of electricity consumption with respect to determinant F, and
  *l m^V T-TZ.~.   """Jr..
•i . \ £[s;F] the long run elasticity of electricity  consump-
tion with respect to this same determinant.  If  annual data
is used in the estimation--and all  time series estimates with
which we are familiar use annual data--the "short run" of
reference is the year.  The long run is,  strictly speaking,
infinity.  The fraction of adjustment completed  after  n
periods is, as computed above,

           -k_          -I
     -L + A •*- ... + A	 _  fl-A*)fl + X +     "*•  X   i
              1
            i - x
                        =  (1 - X) (V^!!\  =  1  -  Xn       C22)
                              31

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Thus, for X close to zero, adjustment is rapid, and for X
close to 1, adjustment of consumption to long run equilibrium
values is slow:  for X = .1, consumption has reached .99 of
its long run equilibrium value after five years, whereas for
X = .8, consumption has reached only .33 of its long run
equilibrium value after five years.  As we shall see, the
estimates of X are all approximately .9, indicating a pro-
tracted period of adjustment.

Because of the plausibility and conceptual appeal of the
Chapman et. al. dynamic specification--and the specificity,
to individual states, of their price elasticity estimates--
their long run elasticity estimates are the ones we have used
in our later numerical evaluations of pricing improvement in-
dicators.  We have compiled the Chapman et. al. estimates in
Table 4.
         Table  4.  RESIDENTIAL ELASTICITY ESTIMATES,
                       Chapman et.  al.
       System
    State
Long Run (Average)
 Price Elasticity
     of Demand
 Potomac Electric
   Power Company

 Commonwealth
   Edison Company
 Duke Power Company
 New York State
   Electric and Gas
 Pennsylvania Power
   and Light
District of
Columbia and
Maryland
Illinois
North Carolina
New York

Pennsylvania
       -1.22


       -1.22

       -1.18
       -1.24

       -1.22
                              32

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These are the numbers which we actually use; accordingly,
our remaining discussion of residential demand estimates fo-
cuses principally upon their conceptual innovations, with
little attention to the numerical estimates they actually
yield.

Wilson8

John Wilson's 1971 paper differs from almost all of the other
econometric demand estimates, and in several important dimen-
sions.  The data base is purely cross sectional, so that there
is not question of distinguishing short run and long run ad-
justment of consumers to local conditions; the regression
analysis ideally can isolate the long run effect of each of
the variables upon consumption.  How, we may ask, does this
square with the underlying reality assumed in the estimation
of the Chapman et. al. models?  Or, put another way, what com-
parability is there between a "long run" elasticity estimated
in pure cross section and the "long run" elasticity estimated
from a pooled sample of time series and cross sections with a
specific dynamic adjustment mechanism assumed?  In general,
the question is quite complex.  Here, it may help to think
along the following lines for specific equations which we
wish to compare.  The pure cross section and time series
studies might be contrasted as based, respectively, on the
following data:

                          Variables
                  Dependent _ Independent      Data Base
Pooled             &nQt[a]         ^nQt_1[a],        t = 1,  2,.
                                  AnF[t;a]          a = 1,  2,.

                              33

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Compare  the  equations  to  be  estimated.

Pure Cross
 Sectional   ~^t  [a] = C + Y *nFIVaJ + '•'    « = 1, 2,...
                 o

 Pooled      AnQt[a] = 6 + XAnQt_1[a] + wAnF[t;a]...

The comparison  indicates that,  if we consider only the t = t
cross  section from the pooled sample, then the lagged term,
its coefficient  and the constant term collapse into one over-
all constant.   Estimation of this cross section  alone is com-
pletely equivalent to estimation of the pure cross section
model.  What then  is the relationship--in magnitude and re-
liability- -between estimates of the all-important elasticity
parameters in the  two models?   Suppose, for  the  sake of expo-
sition, that the general "causal factor" F(t;a)  is taken to
be the average real price of electricity.  Then  the differ-
ence between the parameter estimates Y and --rrp  the respec-
tive "long run" elasticity measures, depends upon the corre-
lations between the lagged consumption variable  and the price
variable..  Since consumption has grown almost exponentially
over the postwar period, while  average real price has, de-
pending upon the measure used,  either declined or remained
constant, the correlation between lagged consumption and
average price variables is probably extremely small.  We
therefore might anticipate that price elasticity estimates--
Y and -J-TX" ~~should be of comparable magnitude.   However, we
know there are strong correlations between income and con-
sumption measures  over the relevant period, so that cross
sectional and pooled estimates'  of comparable income elasti-
city parameters might be expected to differ substantially.

 To be somewhat more precise about comparability, if consump-
 tion were dominated by trend growth at rate r,  then the com-
                              34

-------
parable long run elasticity parameters would be
                       Y  and   	—
                                A - 1 + r

Note that the latter differs from the Chapman et.  al.  "long
run elasticity" in that (1 - ^~^—) >  rather than (1 -  A),
alone appears in the denominator.  In the section in which
we discuss the empirical estimates obtained by the various
investigators, we shall see that these comments are fairly
well borne out.  For present purposes, an idea of the numer-
ical magnitudes may help.   Were GO = .2, X = .9, and r = .07,
all of which values are fairly realistic, then the expression
[^j     X  1  equals 1.258,  which is the value we might reason-
ably expect to emerge from a cross sectional study.

We must return, briefly, to the problem of the choice of
price variable.  For any direct comparisons of the Wilson
and Chapman et. al. results must take account of the differ-
ent price variables used in the two studies.  Chapman et.  al.
use average revenue, as do almost all other investigators.
Wilson, in this as in many other respects the exception, uses
FPC500.(s;a), the Federal Power Commission typical electrical
bill for 500 KWH consumption in region a (i.e., state a).
The typical electric bill is a widely-used construct, and
worth a few definitional and critical comments.  The typical
electric bill for a given KWH quantity in a given state is
for a given rate schedule--here, residential--constructed as
follows.  From utilities serving the state in question  the
Federal Power Commission (FPC) obtains rate schedules.  Next,
the FPC computes the bill, under each rate schedule, for  a
given consumption--in our case 500 KWH, which  is the computed
consumption closest to the national average residential con-
                              35

-------
sumption for the year studied by Wilson.   (Incidentally, that
year is never directly  identified.)  Since typically only one
utility serves a given  city, no further work is required.
For cases where a  city  or a Standard Metropolitan Statistical
Area (SMSA) is served by two or more utilities, the individ-
ual utility bills  are weighted by  the numbers of customers
served to give an  average typical  bill.   (Note that, since
Wilson works in cross section, there is no need to worry
about real versus  nominal price specifications.)

Which price variable--average price or typical bill — is to
be preferred, and  why?  The defects and virtues are distri-
buted over both candidates.  The use of statewide average
revenues as a price  variable undoubtedly,  as Wilson suggests,
blurs the often substantial variation of  average revenue
within a state.  Using  an example  of Wilson's, the city of
Buffalo in New York  State, which benefits  from cheap Saint
Lawrence River hydropower, is averaged with relatively expen-
sive New York City power.  Market  and State boundaries simply
do not coincide.   Furthermore, the use of  the typical elec-
tric bill provides a natural means of circumventing the diffi-
culty of estimation  imposed by the declining block rate sche-
dule.  For if the  estimation is to be a single-equation esti-
mate, then how can we face up to the fact  that quantity taken,
our dependent variable  in Wilson's first model, is in fact
simultaneously determined with "price" because of the declin-
ing block schedule?  Technically,  the problem is that of the
identification problem  of econometrics.   In words the diffi-
culty is that, if  we seek information on  the relationship
between price and  quantity taken from data reflecting con-
sumer purchases under declining block rate schedules--i.e.,
with true quantity discounts--then we cannot be certain of
the interpretation of our result.  In some measure it will
reflect the negative relation, arising from the rate schedule
                             36

-------
alone, between quantity taken and unit price;  in some mea-
sure it will also reflect the inverse relationship between
quantity taken and effective price,  basic to demand theory.
Wilson's use of the typical electric bill for  a given level
of consumption as the price variable is one way around the
difficulty, but its rationale is not easy to state precisely.
For KWH consumption per household is the independent variable
in the Wilson paper (cf.  Table 3),  so that higher and lower
per household consumption levels have been washed out, and all
are being explained by a "price" variable which corresponds,
and only approximately, to the total bill for  a KWH total
(500) approximating average consumption.  The  possibility of
attributing too much explanatory power to the  "price" variable
(i.e., of biasing upwards estimates  of "price" elasticity of
demand) thus arises as follows.  Since utilities typically
cover average costs of service for customer classes, there
may be considerable variation in the block height assigned
any one block.  If for some reason there was a systematic
downward bias of the average consumption block in low consump-
tion areas, and a similar upward bias of 'the average consump-
tion block upwards in high consumption areas,  the resulting
price elasticity estimate would be too high.  There is, how-
ever, little reason to expect such systematic effects.

Halvorsen
          E
The wrinkle in this paper is the effort to improve upon pre-
vious estimates by explicitly modeling both demand and supply
sides of the market.  The supply side is specified by an
equation in which average nominal supply price is explained
as a function of variables which may be classified as factor
cost variables, market structure variables, and  a time trend
variable.  Since this is a supply equation and not  a  demand
equation, it is the only residential-market equation  in  the
                               37

-------
papers discussed in this chapter which is not enetered in
Table 3; we therefore enter it here, with all variables as
defined in Table 2:
                  KWH. (s ;q )
 NOMREVt(s;a) = F(B ^.^  , PCTPVTt ( a ), R/ISt( a ),

                PCTURBt( a ); FUELSGt( a ), MHEMFGt(a);
                TIME(t))

The dependent variable  is the average nominal revenue earned
in residential  sales.  Demand is taken to be a function of
real price, so  that deflation is necessary in order to link
demand and supply parts of the Halvorsen model.  Since Hal-
vorsen chooses  to deflate by the Consumer Price  Index, the
relevant linking equation is

                 NOMREVt  =

Use of the Consumer Price Index as a deflator is common to
several papers, notably Chapman et.  al. and Halvorsen, and
we comment below on the implications of this procedure.  Re-
turning to the  Halvorsen supply equation, the factor  cost
variables are (13 the average price  of fuel used in steam
generation variable FUELSG.(oO --see  Table 2 for  the exact
definition--and (23 a labor cost variable MHEMFG..  However,
it is capital costs that bulk largest in the cost structure
of the electric power industry, as we will see,  and clearly
these costs must be important in explaining supply price.
Where, then, are these costs in Halvorsen's supply equation?
He suggests that the major determinant of capital cost is
"public versus  private ownership," so that the variable
PCTPVT , the percent of total electric utility generation
generated by investor-owned utilities in the state in ques-
tion in year t, is in effect a capital cost variable.  But
not the only one, for a major component of the cost of resi-
                             38

-------
dential service is the distribution cost,  which is almost pure
capital cost.  Distribution costs are in turn determined by
the density of customers and the intensity of use by those
customers.  To the latter factors correspond the variables
PCTURB  and KWH /B  respectively, the percent of the given
state's population in urban areas and KWH sales per customer.
Thus the all important capital cost determinants of the supply
schedule facing the individual residential customers are
spread over three independent variables.  The sole remaining
market structural variable R/IS.(a), the ratio of total resi-
dential to total industrial sales, is included as a measure
of possible cross subsidization of the residential market by
the industrial market.  For why, were there no such cross
subsidization, should the supply price of electricity to the
residential consumer depend upon the relative market shares
of residential and industrial customers?  Note that the var-
iable in question is a ratio, and thus scale effects cannot
be relevant.  Clearly a larger overall market allows the ex-
ploitation of economies of scale, so that both residential
and industrial supply prices may be lower than otherwise,
but—with one small quibble--there should be no dependence of
average supply price on the composition of the market.  The
quibble is as follows.  If residential sales are more sharply
peaked than industrial loads--this is typically the case--
then markets of equal total consumption will be higher cost
the higher the fraction of residential sales in total sales,
since capacity requirements are correspondingly higher.  This
argument would lead us to expect a positive coefficient for
the R/ISt(a) variable; the cross-subsidization argument,
in the form that residential customers, being more numerous
and correspondingly more vocal than large power customers,
are likely to get a subsidy from industrial customers, indi-
cates that a negative coefficient for this variable is prob-
able.  Since that latter expectation is borne  out  in  the
                               39

-------
estimates,  the first,  contrary argument may be dismissed.
Halvorsen's  specification  of  a supply side--remember this is
not "industry" supply, whatever  that might mean in the case
of electric  power, but the supply schedule faced by the indi-
vidual consumer--is his means of circumventing the problems
raised by the declining block schedule.  Note the difference
between his  and Wilson's approach:  Wilson chooses as price
variable the typical bill  for 500 KWH, hopefully a quantity
independent  measure of price within a small quantity range.
Halvorsen,  on behavioral grounds, uses an average price var-
iable, with  supply to  the  individual customer then considered
perfectly elastic at that  price, so that the various data
points given by  the time series  of cross sections used in the
estimates trace  out the demand curve.  Wilson's assumption
can be re-expressed as follows:  if most consumption occurs
in a relatively narrow band around residential consumption,
then the cross section used in estimation sketches out the
movement of  the particular block in which 500 KWH sits along
the demand  curve; if customers are responsive to marginal
price, this  traces out a small portion of the demand curve,
providing an estimate  of that curve.  The resulting estimate
is, of course, not clearly a marginal price elasticity or an
average price elasticity,  since  different data points differ
in both marginal price and average price:  an easy way to
think about  the different  cross  section data points is as
originating  from the motion of the intersection of the
marginal price graph and the demand curve as the former is
moved vertically-

INDUSTRIAL DEMAND ESTIMATES

We know less about industrial and commercial demand than
about residential demand.   The reasons center upon the
                             40

-------
different pricing schemes employed for the different rate
schedules.  Residential electricity is invariably priced at
some block rate, with block heights and lengths independent
of particular characteristics of the customer's load.   But
commercial and industrial schedules characteristically are
"demand billed," i.e. the customer's bill depends upon both
energy consumption and load characteristics, and upon the
latter in a way that can become quite complex.   Consequently,
the use of an average revenue figure as a price variable
distorts the actual operation of the rate structure even more
seriously than in the residential case.  We know of no study^
wherein this problem is faced even somewhat squarely.   What
is known, is summarized briefly below.  Brevity is dictated
not by the intrinsic unimportance of the subject--certainly
an allocation of time between residential and commercial and
industrial markets on the basis of any measure of intrinsic
importance would heavily favor the latter two categories--but
by the circumstance that, although the data base for estimation
and, of course, the resultant estimates are different, the
methods either yield little or are suspiciously similar to
those developed for the estimation of residential demand.

Roughly speaking, there are two sorts of estimates of indus-
trial demand:  those based upon specific industry data, and
those based upon data on sales to customers served under
industrial rate schedules in the individual states.  The
original industrial demand 'estimates of Fisher and Kaysen
and the subsequent work of Baxter and Rees and of Anderson
are in the first category, whereas the industrial estimates
presented by Chapman et. al. are in the second category.  For
reasons to be discussed below, the applicability of the Baxter
and Rees and the Anderson papers to a discussion of electricity
               r
alone is questionable.  The remaining menu  of  industrial  demand
studies is limited,  and  it  is  to a  comparison  of  those  approaches
                             41

-------
that we turn.  After the completion of that general compari-
son, we return to  the  individual papers and finally to their
numerical estimates.

Industrial Demand  Estimates:   Some General Comments

Very crudely, what is  likely  to be the difference between
econometric  estimates  of industrial electricity demand based
upon aggregative industry data and estimates based upon state
industrial rate schedule data? In the first category, for
example, we  might  have electricity consumption by two-digit
Standard Industrial Classification industry group, and value
of purchased electricity at that same level of aggregation.
(Self-generated electricity can, and typically is, adjusted
for in these studies by valuing such an input as the firm
"should," i.e., at the market average revenue "price" for
electricity.  The  adjustment  is added to purchased electric
power to give a market value  of electricity used, and it is
this latter  market value that enters the industry demand
studies.)  Thus there  is considerable aggregation over phys-
ical outputs, since the two-digit industry groups are already
aggregates of firms producing closely-related products.
Further, there may be  considerable geographic aggregation
since, for example, a  two-digit manufacturing industry may
subtend establishments spread over the entire country.  What
of the other kind  of industrial demand estimate?  If we use
state data on sales under industrial rates schedules, then we
disaggregate in one dimension while further aggregating in
another:  the aggregation over .products includes everything
produced by  firms  purchasing  electricity under industrial
rates schedules, while spatial aggregation is restrictedcto
areas no larger than the largest state.

To put the matter  in this way virtually dictates our choice
   *                                        - v-          "Zj
of elasticity estimate.  Our  work is to be based upon the
                             42

-------
study of individual utility costs and rates, and the customer
classes we study will be the customer classes served by indi-
vidual utilities under individual rate schedules.   Ideally,
we should like to have elasticity estimates specific to those
individual rate schedules of individual systems.  As a second
best choice, estimates based upon sales by rates schedule and
by state will probably not be too bad, since an individual
utility service area is often a good part of a state, and
there is at least some hope that industry mix is not too
nonhomogeneous across one state.  Thus, we must work with the
state-based estimates.  To work in the other direction--from
industry-specific estimates through some estimate of industry
mix in individual service areas to an imputed elasticity for
a specific utility service area--would be close to impossible.
Nevertheless, it is instructive to look at the magnitudes of
elasticity estimates obtained on the two types of studies,
and for this purpose we discuss the Fisher and Kaysen esti-
mates.  The estimates we actually use in our later work are
those of Chapman et. al. and are made in the same way as the
residential demand estimates given by those authors, so that
our above discussion of their method of estimation need not
be repeated.

Fisher and Kaysen

The industrial demand estimates of Fisher and Kaysen are a
relatively small portion of their book.  As in the case on
their residential demand estimates, there is an extensive and
not entirely persuasive effort, based upon the theory of
derived demand, to justify the final specification.  We con-
tent ourselves, as Fisher and Kaysen might have done, with the
following observation, which automatically yields the functional
form they finally estimate.  For industry  j,   suppose that
output  Y-(t)  in period  t  is produced with electricity  in-
                              43

-------
put  E..(t)  and other  inputs   Xk(j,t), k = 1,  ...m.  Then if
all firms  in the  industry  are  identical in size and production
technology, and the  technology is  Cobb-Douglas, the industry
production function  can be written as

               =  (Constant)x(E..(t))a^  (Xk(j,t))ak

If the price of electricity to the industry in period  t  is
 IQ                                                        1
PJ (t),  and the price  of each  other input in that period p.  (t),
then the Cobb-Douglas  production function has the pleasant
property of giving inverse demand  functions which are them-
selves products of powers  of  (industry) output and input
prices:

         D?(t) =  (Constant)x(Y.(t))P(P?(t))a (Prices of other
           J                    J       J       inputs to differ-
                                              ent powers.)

Because Fisher and Kaysen  have no  information on other inputs,
they drop  all other  factors, and proceed with estimation on
the assumption that  industry electricity demand may be repre-
sented as  the product  of industry  output to some power and
the price  of electricity to some other power, a sort of
truncated  Cobb-Douglas derived input demand function:

         D*(t) -  (Constant)X(Yj(t))3(P^(t))a.

This is the equation Fisher and Kaysen estimate.  The data
base for estimation, as indicated  in table 1, is derived from
Census of Manufactures 1956 data for selected states.  Since
the number of such states  differ across two-digit industries,
the degrees of freedom for each industry estimate (See Tables,
Industry Regressions:  Two-Digit Industries, 1956, repro-
duced from Fisher and  Kaysen)  differ between states.
                             44

-------
Table  5.   INDUSTRY REGRESSIONS:   TWO-DIGIT INDUSTRIES,  1956
Industry
20 Food and
Kindred
I'roducts
22 Textile
Mill
I'roducts
26 Pulp, Paper,
and
Products
28 Chemicals
and
Products
32 Stone. Clay,
and Glass
Products
33 Primary Met-
al Industries
34 Fabricated
Metal
Products


35 Machinery,
Except
Electrical
36 Electrical
Machinery
37 Transpor-
tation
Equipment

a.

—0.7841
(0.4065)

— 1.6167*"
(0.1117)

—0.9747*
(0.2077)

— 2.5976*»
(0.5234)

— 1.7386
(1.2231)
— 1.2829"»
(0.2117)

+ 0.5533
(0.4832)
—


— 1.3349"
(0.4286)
— 1.8209*
(0.4489)
+ 0.6877
(0.6445)
—

P

+ 0.6591***
(0.1324)

+ I.007IM*
(0.0877)

+ 0.7203
(0.4205)

+ 0.6150"
(0.2167)

+ 1.0273*
(0.3074)
+ 0.4937*"
(0.1188)

+ 1.1094*"
(0.1143)
+ 1.1 009*»»
(0.1175)

+ 0.9043s"
(0.0870)
+ 0.3797
(0.2191)
+ 1 .0526*"
(0.1174)
+ 0.9SD9aaa
(0.1005)
K

12.88


2.84


26.43


22.55


2.44

9.17


0.29

0.39


1.30

76.50

0.61

1.04

£2

.8323***


.9880»a»


.8822*


.6387***


.8429

.7428"*


.9593aaa

.9460s8*


.9742""

.8985*

.9521"*

.9412"*

Degress ot
Freedom

11


6


3


14


3

16


4

5


7

4

5

6

ll
zZ*
IP
ens

-------
Chapman et. al.
We have discussed the method employed in this paper above;
in Table 6 we compile the actual estimates from this paper
which we use in later calculations.  Remember that, although
Fisher and Kaysen do not discuss the commercial sector--and
for obvious reasons, since there is no data for the commer-
cial sector which would mesh with their estimation methods--
any unified estimation method constructed so as to mesh with
state data, such as the Chapman et. al. method, can distin-
guish a separate commercial sector.  Therefore we employ
this additional level of detail in our later calculations,
and in Table 6 we compile the estimates for the states in
which systems in our sample are located.

This completes our discussion of our selection of demand
elasticities, which enter parametrically into our later indi-
cator estimates.  We turn to the cost side of our problem.
  Table 6.   COMMERCIAL AND INDUSTRIAL ELASTICITY ESTIMATES
                       Chapman et.  al.
System
Potomac Electric
Power Company
Commonwealth
Edison Company
Duke Power
Company
New York State
Electric and
Gas
Pennsylvania
Power and Light
-State
District of
Columbia
and
Maryland
Illinois
North Carolina
New York
Pennsylvania
Long Run (Average) Price
Elasticity of Demand
Commercial
-1.46
-1.48
-1.13
-1.65
-1.46
Industrial
-1.93
-1.87
-1.65
-1.89
-1.93
                              46

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                         SECTION III
           SOME RELEVANT FEATURES OF THE INTERNAL
       COST STRUCTURE OF THE ELECTRIC POWER INDUSTRY
A cost-of-service study for an individual utility is likely
to be a one or two year or longer effort, often involving
much of the staff of the rate division.   The number of ques-
tions that can be raised is boundless.   But by careful selec-
tion of the portion of the cost structure to be explored, we
can guarantee that our analysis of the  cost structure is
exactly as detailed, and no more so, than required by our
objectives.  We therefore begin this chapter with the intro-
duction of a framework for classifying  and identifying those
dimensions of cost structure which we must quantify.  In a
sense, this discussion belongs in the discussion of rates in
Section IV; it has been located here because, without it, the
selection of focus in the cost discussion must seem arbitrary,

A TYPOLOGY OF CUSTOMERS BASED UPON "INFORMATION" COSTS

Assume that every consumer reacts optimally to the options
open to him.  Then any consumer of electricity will find it
efficient to allocate time to the electricity consumption
decision to the point where marginal benefits of such time--
the reduction in electric bill, for given consumption, for
the incremental minute spent in making the electricity con-
sumption decision--just equal the incremental costs involved,
in this case the value of the incremental minute in its next
most valuable use.  The outcomes of this allocation decision
     fro
                             47

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process will be classified  in  two dimensions:  time differen-
tiating versus time-undifferentiating consumption decisions,
and average price responsive versus marginal price responsive
consumption decisions.

       Table 7.  A  TYPOLOGY OF ELECTRICITY CUSTOMERS
                                 Time
                           Undifferentiating
                  Time
            Differentiating
Average  Price Responsive
Marginal  Price Responsive
 I
III
II
IV
Customers  in  Category  I  have  found  it  optimal not to distin-
guish between average  and marginal  prices  in their electri-
city consumption  decisions.   For  these customers, the exis-
tence of block rates is  irrelevant,  for they would make the
same consumption  decision at  a  flat price  equal to the aver-
age revenue they  are currently  paying.   Customers in Category
II by definition  find  it optimal  to pay the cost of differen-
tiating between their  consumption on and off peak--either by
paying the additional  costs of  metering peak and off peak
consumption separately,  or by taking a rate schedule option
under which the company  (nominally)  bears  the costs of such
metering,  or  by accepting such  devices as  deferable load
water heating.  Note that, by definition,  these customers
have not found it optimal to  distinguish between average and
marginal price so that,  once  again,  the question of block
structure  is  of no relevance  to them,  for  they would take
exactly as much electricity at  a  flat  average rate equal to
their current average  price as  they take presently.      ..

Customers  in  Category  III by  definition do not find it opti-
mal to distinguish between peak and off peak consumption,
                              48

-------
but they have found it optimal to distinguish between mar-
ginal and average price.  Finally, customers in Category IV
have found it optimal to distinguish between consumption in
both dimensions:  between power taken off peak and at peak,
and between average and marginal prices paid for electricity.

So much for typology.  The really important question is what,
if anything, belongs in the boxes:  which customers wind up
where?  There are no unambiguous guidelines. First, it is
not entirely clear that all customers on a given rate sche-
dule belong in a single category.  Large residential users,
for example, may have some marginal price sensitivity and
therefore belong in Category III, whereas very small residen-
tial users almost certainly belong in Category I.

Our identification of rate schedules with the categories of
the above typology, and the corresponding benefit-cost cal-
culations performed, are as follows.

Category I

This category is the domain of small residential and commer-
cial users.  The relevant question regarding possible improve-
ment in rate structures is then restricted by the assumptions
that consumers in this category do not, for information cost
reasons, distinguish either marginal and average price or
offpeak and peak consumption.  That relevant question is  in
fact restricted to the question of inter customer-class ad-
justments in average price.  How  large are  the efficiency
gains to be expected from improved average  pricing?  Our
methodology for the derivation of a quantitative measure  of
such available gains is based upon the work of Baumol and
Bradford.
                              49

-------
 The method and results  are  spelled  out  in  Section  IV below.
 To the  extent  that the  derived quantitative measures are re-
 liable,  they indicate that  available  gains are negligibly
 small.

 Category II

 Almost  all rate schedules are  potentially  fair game for this
 category,  and  we will compute  net benefit  measures for all
 rate  schedules of the sample companies  as  if  it were the case
 that  all rate  schedules are average-price  responsive--that
 they  have  found it optimal  not to distinguish between peak
 and offpeak consumption.  For  residential  customers presently
 metered on a KWH monthly or bimonthly basis,  this will re-
 quire netting  of the additional cost  of double-rate registers
 required to charge differential rates off  peak and on peak.
 A warning  regarding the full spectrum of benefits and costs
 for double rate register metering is  in order:  there is one
 potential  serious drawback  to  double  rate  register metering
 of offpeak and peak hours.  Should  service to a given area
 be interrupted and restored in any  time interval not a mul-
 tiple of 24 hours,  the  correct setting  of  the doube rate
 register shall have been lost.   It  would be necessary to meter
 on a  KWH basis,  taking  the  simple sum of the  offpeak and peak
 registers  as the relevant number of KWH, until the time at
 which the  meter was read, at that time  the reader could reset
 the device.  The evaluation of this problem is beyond the
 scope of this  report.

 Category III

The prime  candidates for Category III are  large residential
users if it is  assumed  that, for some reason, there is no
possibility of  differentiating between  offpeak and peak
                              50

-------
usage for these customers.   Again,  recall our observation
that all customers on a given rate  schedule need not neces-
sarily fall into the same category; for the return to an
additional minute spent in a consumption decision is higher
the higher the range of the contemplated purchase, so that
it may pay a large residential user to become familiar with
his or her rate schedule where it would not so profit a
small residential user.  Nevertheless, as we will see in our
analysis of Category I, there is little to be gained from
pricing changes which do not discriminate between offpeak
and onpeak consumption.  However, there is still the possi-
bility of "implicitly" differentiating between offpeak and
peak, and our major estimate corresponding to Category III
is the estimation of an upper bound on the gains attainable
from implicit differentiation.  How might this work?
Suppose that some system had a declining block rate schedule
with only two blocks, with the tailblock lower than the first
block.  Suppose further that tailblock customers buy all
their electricity on peak, while first block customers buy
all their electricity off peak.  Then we can in some measure
simulate peak load pricing by raising the tailblock and
lowering the first block.  Advocates of "rate inversion"
often argue for something like this, and we will calculate
a rough upper bound on the welfare  gains that implementation
of one kind of rate inversion proposal will confer.

Category IV

Finally, in Category IV, we place our large commercial and
industrial users.  They incur little incremental expense
in differentiating between their consumption off peak and
on peak, since many utilities know and must know what the
instantaneous load being pulled by their individual large
customers is.  Some of these customers also have that infor-

-------
mation.  Similarly, there is little incremental expense to be
incurred were such a  large customer to "switch" from average
price  sensitivity to  marginal price sensitivity, since so long
as someone is watching  the electric bill, the cost of watching
it in  a slightly different way  is negligible.  For these cus-
tomers, a relevant benefit/cost question is:  what is the mag-
nitude of the gains likely to be had  from time-differentiated
pricing, e.g. a better  matching of peak period (perceived)
prices and costs?  Some technical problems--the existence of
demand-billing--make  this comparison  awkward, but we shall see
that it can be made,  and that the attainable gains are probably
substantial.

THE USES OF THE TYPOLOGY:  A PRELIMINARY OVERVIEW OF
INDICATORS TO BE ESTIMATED, AND COST  ANALYSIS REQUIRED

Our purpose in constructing the above typology is the organi-
zation of our welfare gain calculations, and guidance of the
cost analysis necessary for those calculations.  In this
section we spell out  the first  linkage.  The discussion of
cost structure, which completes the work of this section,
follows.

It is  simplest to proceed seriatim through the four categories
of the typology.  In  each case  the question is the same:  what
welfare gain estimates  are apposite to the corresponding
typology category?

Category I

These  are customers who find it impossible--extremely costly--
to differentiate between peak and off peak consumption and
similarly costly to distinguish between average and marginal
prices.  Where, under these constraining conditions, could
                               52

-------
improvement reasonably be sought?  Only in adjustment of the
relative average prices paid by the various customer classes.
Suppose further that utility management chose to avoid the
problems of offpeak versus peak period cost allocation for
this class of customer, and attempted to follow naive se-
cond-best short run marginal cost pricing rules.  (Discussed
in detail below, and mentioned above, these rules suggest
that prices be deviated from short run marginal cost in or-
der to cover costs, with the deviations designed so as to
minimize the resulting distortion of consumption patterns.)
Then we can actually compute the welfare gains associated
with such improved pricing.  Obviously we will need for these
purposes a reconstruction of short run marginal costs.  That
reconstruction will prove useful in introducing us to the
difficulties inherent in utility cost data, and in the iden-
tification of marginal costs.  The indicator associated with
this calculation, call it indicator I, will be evaluated in
Section IV.

Category II

These are customers assumed to differentiate between offpeak
and peak usage, but not between average and marginal price.
The relevant question is:  how much is to be gained by charg-
ing differential flat average prices in offpeak and peak
periods?  We therefore cross into territory where a knowledge
of the differential costs of providing electric service off-
peak and on peak is necessary.  Consequently, we require an
extensive discussion of peak versus offpeak cost structures.
The welfare gain calculation relevant to this customer cate-
gory is, as suggested, efficiency gain available from a bet-
ter matching of price and cost in offpeak and on peak periods
                              53

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Category  III

These are customers who, because of  their information cost
structure, distinguish between marginal and average price but
not between peak  and offpeak periods;  large residential users
who cannot be metered in a way that  distinguishes between time
periods might reasonably be placed in  this category.  Then
some leverage over their consumption pattern is available from
changes in tailblock rates, i.e., from a form of what has come
to be known as rate inversion.  An upper bound to the efficien-
cy gains  from such inversion may then  be estimated as follows:
assume all tailblock consumption occurs during the peak, and
assume marginal elasticities are relevant.  By a "tailblock
customer" we mean a customer whose monthly consumption of elec-
tricity is sufficiently large to place him in the last block
of the rate structure:,  if, for example, all KWHs over 800KWH
per month are billed at 1.0
-------
demand schedules--the difference is explained below--for-
mulation of the corresponding indicator is not as straight-
forward as in the previous cases.   But the cost-structural
information required for this evaluation is the same:   an
explicit identification of offpeak and peak costs.

We have completed a sketchy survey of the cost information
we shall require, and we turn to the development of that
information.

THE RECONSTRUCTION OF INTERNAL COST FUNCTIONS:
SHORT RUN MARGINAL COSTS

Our objective in this subsection is a reconstruction of the
short run marginal cost of serving each customer class, and
an understanding of the limitations of the measure construc-
ted.  The incremental cost of service, at any particular
time, is almost purely generating cost, the cost of the fuel
required to generate an incremental KWH.  There are usually
larger line losses involved in "delivering" a KWH to a resi-
dential customer than in delivering the same amount of elec-
trical energy to a large industrial customer, since in the
former  case there are additional losses in passage through
the low-voltage distribution system.  But the major differ-
ence in incremental cost of serving different customer classes
turns upon the timing of the additional KWH, since the major
cost differential involved in serving various customers  at
various times arises from the capacity costs imposed by  peak
period users--no such costs are imposed by offpeak users.
Short run marginal cost is, strictly speaking, different  at
every moment, as demand fluctuates  in relation to capacity.
In this section we shall see that the variation  over time in
what can be explicitly identified as marginal  generation
cost is not extreme.  Later, in Section  IV, we will therefore
feel justified in using as an approximation a  time-independent
 _      "•  *           - ^ "•
and constant marginal cost of generation.
                               55

-------
Any  electric  utility has  in operation,  at  any  given  time,
plants  of varying  vintage and consequently of  varing econo-
mic  efficiency.  The trend to larger  capacity  units  which
exploit economies  of scale in generation has left  all  systems
with a  spectrum  of plant  from oldest  and least efficient to
newest  and most  efficient.   A cost-minimizing  management will
meet any given load on the system by  firing plants in  decreas-
ing- efficiency order.*  Thus, given a list of  all  plants owned
by a given system  and the unit production  costs of boiler-
turbine-generator  combination in each plant, we can  construct
a first and most naive estimate of marginal generation costs
which we refer to  as SRMC(l).  This function specifies the
marginal cost of a KWH, given any load, subject to the assump-
tion that all units at all plants are functioning.   Table 8
below lists what Federal  Power Commission  Form I calls "total
production cost  per KWH"  for individual plants, with those
plants  ranked from least  efficient to most efficient.  The
FPC  "total production cost" concept includes some  small fixed
costs,  such as the salaries of plant  personnel.  But because
these are negligible in comparison with the fuel cost  compo-
nent, "total  production cost" per KWH is a reasonable  measure
of fuel  cost  per KWH.  And, with some important qualifications
discussed below, fuel  cost per KWH is a reasonable measure of
short run marginal cost.   Figure 1 depicts SRMC(l).   (As the
table and figure captions indicate, 1972 Potomac Electric
Power Company data is  used here and elsewhere  in the report
in describing methodologies.)  Table  9, a  compilation  of fuel
efficiency by unit,  provides the basis for a stricter  measure
of marginal cost,  given fuel prices.   The  latter are currently
reported  to the  Federal Power Commission on a  monthly  basis.

How  useful  is  SRMC(l)?  Consider Figure 2,  the  system  load
curve for  three  representative days in three representative
*Under many current  -interchange and pooling agreements ,* the
 pool rather  than  the  utility itself  makes  the  operating de-
 cisions.

                              56  .

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Table ,8.  SHORT RUN MARGINAL COSTS OF GENERATION
    Potomac Electric Power Company, 1972
Pl^nt
Morgantown
Connemaugh
Dickerson
Chalk Point
Potomac River
Benning Station
Connemaugh Diesel
Buzzard Point
Chalk Point GT
Morgantown GT
Buzzard Point GT
Dickerson GT
Total Production
Cost <£/KWHR
.454
.516
.598
.674
.725
.971
1.301
1.3331
1.530
1.679
1.745
2.135
Cumulative
Capability 10*KW
1.114
1.273
1.823
2.533
3.019
3.713

4.019
4.041
4.076
4.344
4.367
                        57

-------
oo

       in
       o
       U


       'g
       •H
       +J
       U
O
M
(X.
          -I
                      toriint
                                   !   i
                                   Connmauth

                           la I.I
                           jj I
                                            Dlckerion
                                            . .*

                                            •'
                                                         Chilk Point  I
                                                •M
                                                           : J.J !
                                                                            TT
                                                                               i-r
                                                                                  . iennlm smion
                                                                                i I i I
                                                                      M.U
                                                                                   '
                                                                                                I  I 	L
                                                                                                       i • •
                                                                                                        '..•«J ft.nl c.l
                                                                                              Chilk Folnt CT
                                                                                                  1
                                                                                              Fu::ar«  nt
           Plant Load (106KW)
            Figure  1.   Short  Run  Marginal  Costs,  Potomac Electric  Power  Company,  1972

-------
en
                     August
                     Monday
                     April
                     Monday
                     December
                     Monday
                                     12 1234 56789 10 11 12 1234 56789 10 U 12
                                    Time
           Figure  2.   Sample System Load  Curves, .Potomac  Electric Power  Company,  1972

-------
                         Table  9.  EFFICIENCY  (IN  FUEL  TERMS)  BY UNIT
                             Potomac Electric  Power  Company,  1972
ON
O
Plant
Potomac River




Dickersoh


Dickerson GT
Chalk Point
Chalk Point GT
Morgantown
Morgantown GT
Unit
No,
1
2
3
4
5
1
2
3

1
2

1
2

Installation
Date
1949
1950
1954
1956
1957
1959
1960
1962

1964
1965

1970
1971

Fuel Type and Rate
Tnal Ct0nS
Coal TrD
38
38
37
37
37
55
55
55

115
115

200
200

Oil(^i\
mm.)











630
630

Net
Continuous
Plant
Capability


486.0



550.5
507.0

710.0

1114

Net
Peak
Demand
On
Plant


478.0



547.0

23. 0
654.0
22.0
1128.0
35.0
Gross
Capacity
103KWH
95
95
108
108
108
190
190
190
16.2
355
355

573
575

Efficiency
103BTU/KWH
11.0
11.0
9.0
9.0
9.0

	 8.7
M

— 8.5

- o a
o«O


-------
Table 9 (continued).  EFFICIENCY (IN FUEL TERMS) BY UNIT
          Potomac Electric Power Company, 1972
Plant
Connemaugh
Benning
Station




Buzzard Point





Buzzard Point
Combustion
Turbines
Unit
No.

10
11
12
13
14
15
16
1
;2
3
4
5
6
(16
Units
Installation
Date

1927
1929
1931
1947
1952
1968
1972
1933
1938
1940
1942
1943
1945
)
Fuel Type and Rate
-0 , (tons
Loal i \
hr.)

30 total
23
31









mm.)


74
100
340
340
58
58
70
70
70
70
500
Net
Continuous
Plant
Capability
1640

712



288





(Not.
applicable
since not
base load
plant)
Not
Peak
Demand
On
Plant
1732.0

720



205





251
Gross
Capacity
10'KWH
total plani
30.0
30.0
30.0
55.0
28.0
289.0
289.0
37.5
37.5
57.5
57.5
57.5
57.5
268.0
Efficiency
103BTU/KWH

WMi
~ combined
«M
11.0
11.0
13.0
13.0
11.0
11.0
11.0
11.0
15.0

-------
How useful  is  SRMC(l)?   Consider  Figure  2,  the  system  load
curve  for three  representative  days  in three  representative
months (August,  April,  and December).  The  comparison  with
Table  8  reveals  that, were all  units in  the system  function-
ing perfectly  with no downtime, the  system  peak load could
be  met with ample excess  generating  capacity  in August, the
peak month,  and  with superabundant excess capacity  during
the seasonal winter trough.   Somehow this scenario  does not
square with the  current fears of  brownout and blackout, and
the problem is one of equipment availability.   Every unit,
boiler and  generator, must be periodically  taken "down,"
inspected,  and perhaps  repaired or overhauled.   A common
rule of  thumb  concerning  such scheduled  outages is:  every
boiler must be scheduled  for  one  outage  per year, and  every
generator for  one outage  every  three years.   Unfortunately,
not all  outages  are scheduled.  "Unscheduled  outages," as
they are  called  in the  trade--breakdowns or takedowns  in an-
ticipation  of  trouble--are far  from  infrequent.   This  supply
side uncertainty is not the only  source  of  uncertainty for an
electric  utility:  on the demand  side the uncertainty  is
associated  with  the unpredictability of  load.   Trouble can
arise  from  either side,  and the problem  may be  stated  as:
what are  we  willing to  pay for  service of a given quality--
one component  of that quality index  being the guarantee that,
with certain probability,  all loads  will be served?  The
problem of  how much of  a  capacity margin is necessary  is
amenable  to  benefit-cost  analysis.   We are  not  aware of any
such analysis  in the literature on the electric power  indus-
try.

If  the utilities have based their capacity  requirement poli-
cies upon such analysis,  the  process has been implicit.
What one  finds repeatedly--in the trade-  literature  and in
                             62

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conversation with engineers in utility generating depart-
ments --is the citation of rules of thumb.  Two are cited more
frequently than others:  first, that a 20 percent margin of
capacity over expected load must be carried, and second, that
the system must be able to meet loads even if the largest
unit operating at any given point in time should fail.

Such rules of thumb should be replaced by a more explicit
benefit-cost calculus.  But our purpose is the reconstruc-
tion of short run cost functions "as they are," not as  we
think they should be.  We therefore accept the second rule
as binding and proceed with our reconstruction, now with the
knowledge that any such reconstruction turns upon availabili-
ty assumptions.  There are two possible sources of informa-
tion on availability:  individual company data on scheduled
and non-scheduled outages of individual units, and Edison
Electric Institute (EEI) data.  The latter is a compilation,
by unit size, of industry availability data, and is there-
fore closer to what we might call "expected availability"
than any one year record for an individual firm.  We there-
fore take the EEI overall availability measure, compute the
corresponding expected downtime, and proceed to a "by sight"
scheduling of downtime over the course of the year.  The ca-
pacity margin requirement we impose is, as discussed above,
that in any given month capacity on line to be able to meet
last year's demand during that month even if the largest on
line unit were to fail.  The scheduling problem thus defined
is, when formulated as a mathematical programming problem,
of forbidding complexity.  We therefore follow utility prac-
       g
tice in scheduling "by sight," guided by the rule:  repair
your most efficient capacity in the minimum demand months,
the next most efficient capacity in the next highest demand
months, and so on.
                              63

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Table  10 presents  the results of this exercise for one system
in one year.  By comparing Column  6 of this table, "Margin if
Largest Running Plant Fails," with Table 11, "System Peak
Loads by Month," we can verify that the suggested schedule
satisfies  the rule of thumb  discussed above.  Finally, given
this schedule, the linkage to system short run marginal costs
of generation--call this  schedule  SRMC(2), an improvement in
realism over SRMC(l) above--is a simple matter of construct-
ing the SRMC schedule in  each month, given the capacity
available  in that  month.  Table 12 compiles SRMC(2), for the
above  repair schedule, in repair period I.  Entries in the
column headed "SRMC of Generation" are fuel costs per KWH
for the least efficient unit that  must be operated (in order
to meet system load) when the major unit listed in the left-
hand column is down for repairs.

Thus we have, in any month,  a SRMC schedule reflecting ac-
tually available capacity.   When placed side by side with the
system load curve  for any day of that month, we have the cost
of generating the  marginal KWH during any hour that day or,
when averaged over peak hours (respectively Off peak hours),
the marginal generation cost during peak hours (respectively
off peak hours).

SRMC(2) is about the best that can be said about short run
marginal costs from Federal Power  Commission "total produc-
tion cost" data.   The limitations  of this measure have been
sufficiently belabored above.  Here we re-emphasize two
points.  First, note the  comparatively small variation of
SRMC(2) between peak and  offpealc periods.  From Table 11 note
that the January peak load was 1,975 MW.  From Table 12 we
know that, had availability  been as assumed in constructing
that table, peak hour short  run marginal costs would have
been roughly . 72<£.  Suppose  that January offpeak hour demand
                              64

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                       Table 10.  MONTHLY PEAKS; TRIAL REPAIR  SCHEDULE  1,
                             Potomac Electric Power Company, 1972
en
Month
January
February
March
April
May
June
July
August
September
October
November
December
System
Peak
Demand
106KW
1.98
1.99
1.87
1.94
2.33
2.73
3.48
3.29
3.03
2.04
2.06
2,11
If Repair
Morgantown
1 § 2

Chalk Point
x Ci £*
Dickerson
3
Dickerson
152

No Scheduled
Outages
No Scheduled
Outages

Benning Stati
15 5 16
Potomac River
3, 4, « 5

Remaining
Capacity
106KW
2.372

2.618

3.138

3.518

on
2.616

Largest
Plant
Running
106KW
.355

.573

.573

.573


.573

Margin if
Largest
Running
Plant Fails
106KW
2.017

2.045

2.565

Need
Peaking
2.945 Capacity


2.043


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            Table 11.   SYSTEM PEAK LOAD BY MONTH
                         Load Data
Month
January
February
March
April
May
June
July
Augus t
September
October
November
December
Annual Peak
Peak Demand
106KW
1.975
1.990
1.867
1.944
2.331
2.730
3.479
3.288
3.034
2.044
2.061
2.110
3.479
Peak Load
Date
17
7
14
20
31
19
21
25
14
6
30
18
7-21-72
was roughly 1,000 KW:  then the corresponding SRMC(2) esti-
mate is approximately . 47<£.

But it would be a mistake to accept even this improved short
run marginal cost measure as a reliable guide to "true" peak
period short run marginal cost.  For, at the peak, short run
marginal cost cannot be approximated by incremental fuel
costs for generation from baseline capacity.  If capacity
has been appropriately adjusted to peak demand, the short run
cost of serving the marginal peak customer must equal the
(long run) cost of serving that customer by expanding capacity
Thus, system long run marginal cost is a better measure of
                             66

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      Table 12.  SRMC(2), TRIAL REPAIR SCHEDULE 1
          Repair Period I   January-February
Plant and
Unit
Morgan town
1

2
Dickerson
1
2
3
Chalk Point
1
2
Potomac River
3
4
5
Potomac River
1
2
Benning Station
15

16
Net Continu-
ous Capabil
ity 106KW

.557

.557

.184
.184
.184

.355
.355

.108
.108
.108

.095
.095

.289

.289
Last
Unit

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Electric Utility Costs:  Some Nomenclature

Discussions of electric utility costs lean heavily upon four
cost "vocabularies."  Each will serve us in what follows.
For purposes of discussion, we distinguish these vocabularies
as the conventional utility, income statement, economic cost,
and functional vocabularies.  First, we introduce them seria-
tim; below, we make use of these classifications in apportion-
ing costs between subperiods and between customer classes.

The Conventional Utility Vocabulary--So named  (here) because
of its origin  in the utility literature, this  framework  clas-
sifies the  cost of  service into energy, capacity, customer
and residual costs.  Each category  specifies one dimension
of service, and the dimensions of service provided are pre-
sumably  independent.   Thus energy costs are those associated
with the provision  of  delivered KWHs,  all else held fixed.
Capacity costs are, similarly, costs  incurred  for the pro-
vision of capacity.  Customer costs are those  which vary when
the number  of  customers is varied.  Among the  latter are, un-
ambiguously, the  (annualized) installed cost of a meter,  and
the cost of meter reading.  Less unambiguous--it can make a
great deal  of  difference in the calculation of the minimum
charge to be recovered from every customer--is the status of
customer-related distribution plant.   Clearly  the wire run-
ning from a distribution line to an individual house repre-
sents a  pure customer  cost, a cost  incurred in the service
of an identifiable  customer.  But what of the  distribution
lines and poles?  Are  they to be subsumed under capacity cost
or customer cost?   Finally, residual  costs are all costs not
subsumed under energy, capacity or  customer cost categories:
for example some, but  not all, administrative  and general ex-
penses,  i.e. such regulatory commission expenses as are  in-
dependent of the other three "dimensions."
                            68

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There is much imprecision in this cost classification.   In
addition to the ambiguities cited above,  there is the ob-
viously unsatisfying fiction of independent dimensions  of
cost incurrence:  for example, the cost of providing an in-
cremental KWH depends upon the level of capacity in the sys-
tem in a complex way.  Nevertheless, the  persistence of the
conventional utility vocabulary is a tribute to the adequacy
of certain cost-function approximations implicit in that
vocabulary--in the above example, the approximate constancy
of energy costs over wide ranges--and to  the format in  which
data are collected and reported.  Again,  in the above example,
production cost is typically reported on  a per unit or  per
plant basis, whereas there is always some small variation of
unit efficiency between zero load and maximum load.

The Income Statement Vocabulary--The characteristic framework
in which cost data are summarized for the purposes of review
of the financial status of the company is a useful point of
departure in our later cost calculations, precisely because
the income statement categories, aggregative as they are,
have definite economic content suggestive of correct alloca-
tion procedures.  Thus, in 1972, the Potomac Electric Power
Company reported summary income statement data as compiled
in Table 13.  Of the broad cost categories--Operating Ex-
penses, Maintenance Expenses, Depreciation, Federal Income
Taxes, Taxes Other than Federal Income Taxes, Interest on
Long Term Debt, and Other Interest and Amortization--only
Operating Expenses and Federal  Income Taxes require further
scrutiny, the other categories are clearly assignable — in
"conventional utility" terms--to non-energy cost categories.
Table 14, obtained from Federal Power Commission Form  1  as
filed by the Potomac Electric Power Company for  1972,  sup-
plies the breakdown of electric operation expenses between
energy and non-energy related costs:  only the  fuel cost of
                              69

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            Table 13.  INCOME STATEMENT DATA,
          POTOMAC ELECTRIC POWER COMPANY, 1972
                  (thousands of dollars)
         Operating  Revenues
         Operating  Expenses
         Maintenance Expenses
         Total  Operating and
           Maintenance Expenses

         Depreciation
         Federal Income Tax
         Other  Tax
         Total  Operating Expenses
         Operating  Income, Gross
         Other  Income, Net
         Income Before Interest
           Charges
         Interest on Long-Term
           Debts
         Other  Interest and
           Amortization
         Total  Interest Charges
         Net Income
272,717
 94,493
 21,146

115,639

 35,516
 10,804
 31,844
193,888
 78,829
    449

 79,278

 32,704

  1,714
 34,418
 44,860
$105,170,553 represents true energy cost, the remainder of
total operations costs of $113,386,960 being incurred in ways
largely independent of the level of output--e.g., supervision
of generation.  Depreciation and Texes Other than Federal In-
come Taxes are subsumed as capacity charges: Depreciation
with little further ado, and Tax.es Other than Federal Income
Taxes because property taxes on assessed valuation should be
in rough proportion to value of electric plant in service.
There remain customer costs--reported separately for the
most part and, with qualifications discussed above arising
from ambiguities in the assignment of certain distribution
                             70

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Table  14:
FUNCTIONALIZATION  OF OPERATING AND MAINTENANCE COSTS,
Potomac Electric Power Company,  1972
                (dollars)
    GENERATION

    Operation,  Supervision and Engineering
    Fuel
    Steam Expenses
    Electric  Expenses
    Miscellaneous Steam Expenses
    Rents

    Total Operation
    Operation Overhead

    Total Maintenance

    OTHER POWER GENERATION

    Total Power Production Expenses - Other Power

    OTHER POWER SUPPLY EXPENSES

    Purchased (Sold) Power

    System Control and Load Dispatching
    Other Expenses

    TRANSMISSION

    Total Transmission Expenses

    DISTRIBUTION

    Meter Expenses
    Maintenance of Meters
    Total Distribution Expenses
    Total Nonmetering Distribution Expenses

    CUSTOMER  ACCOUNT EXPENSES

    Meter Reading Expenses
    Total Customer Accounts Expenses

    Total Metering Expenses

    Sales Expenses

    ADMINISTRATIVE AND GENERAL EXPENSES

    Total A § G Expenses
                                               484,739
                                           105,170,553
                                             3,723,141
                                             1,972,373
                                             2,033,635
                                                 2,519

                                           113,386,960
                                               487,258

                                            12,694,220
                                             2,055,885
                                           (56,349,939)

                                             1,194,892
                                               196,788
                                               320,739
                                               765,938
                                               151,815
                                            12,791,639
                                            12,025,701
                                               978,214
                                             5,244,393

                                             1,895,967

                                             2,444,162



                                            21,659,040
    TOTAL ELECTRIC  0  § M
                                           115,638,779
                                     71

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plant, readily  identifiable--and what might be called non-
depreciation  cost of  capital charges, the latter category
covering Interest, Net  Income and Federal Income Taxes.  A
simplifying device for  treating these cost categories, a de-
vice which does not violence to the facts, is discussed below
in the sample assignment of  capacity costs.

The Economic Vocabulary--The distinction between fixed and
variable costs  is related to, but less precise and useful
than, what we have called the conventional utility vocabu-
lary.  Fixed  costs, those not changing with the level of
output, embrace capacity, customer and residual expenses.
Variable costs, definitionally those which do vary with out-
put, are closest to energy costs.  Why bother to complicate
matters with  this additional and extremely thin "vocabulary"?
Only because  it is so familiar that we shall probably inad-
vertently use it in what follows.

The Functional Vocabulary--Costs are herein classified by the
stage of the production process in which they are incurred.
In sequence, those stages are generation, transmission and
distribution.

A Classification of Capacity Costs

The key first step is the selection of a workable classifica-
tion of capacity costs.  The classification we select, based
upon the discussion above, must be exhaustive of all capacity
costs identified in the income statement framework.  Such an
exhaustive classification is as follows:
     1.   Nonfuel Operation and Maintenance Expenses;
     2.   Cost of Capital:   Rate of Return on Rate Base
         and Depreciation; and
     3.   Taxes Other than Federal Income Taxes.
                                72

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Category 1 has been discussed above, and can be obtained di-
rectly from Federal Power Commission Form 1 by subtracting
Fuel Cost from Total Operation Cost to give the Total Non-
fuel Operation Cost.  To these must be added System Control,
Load Dispatching Expenses, and Other (nonfuel) Expenses; the
result, Total Nonfuel Operation and Maintenance Expenses, is
as compiled in the final column of Table 15.  The same pro-
cedure is applicable to transmission operation and mainte-
nance costs, which are almost wholly "fixed" costs of oper-
ating and maintaining the transmission system.  Distribution
nonfuel operation and maintenance expenses are given directly
in Form l--note the last line of the operation and maintenance
distribution category in Table 14--and therefore need not be
adjusted a la Table 15.  Note that in terms of our cost vo-
cabularies, Table 15 covers one component of capacity cost,
and decomposes that component by function.

Consider next Table 16, Cost of Capital:  Rate of Return on
Rate Base and Depreciation.  The title of this table includes
some utility jargon, and an explanation may be helpful. Econ-
omists customarily define the net cost of capital as equal to
the gross cost of capital minus depreciation.  When economists
study regulated utilities, they are often asked whether a
company is earning a "fair (net) return on capital."  In
practice, a fair return generally means a rate of return
sufficient to attract capital into the industry.  And in
practice, the net return on capital is computed as the prod-
uct of a "rate of return" times a "rate base."  This proce-
dure could not be faulted if the "rate of return" figure used
were the opportunity cost of capital, and if  the "rate base"
figure used were the company's net worth.  But how can a reg-
ulatory commission determine the opportunity  cost of capital?
What usually happens is that some very rough  approximation
to net worth (such as original cost of physical plant)  is
                             73

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   Table  15.
GENERATION AND TRANSMISSION NONFUEL OPERATION  AND MAINTENANCE
       Potomac Electric Power Company,  1972
                          (dollars)
Functional Component
of Plant in Service
GENERATION
Total Steam
Production
Plant
Total Other
Production
Plant
Total Production
Plant
TRANSMISSION
Total
Operation

113386960
1718671

155975
Fuel

.105170553
1714086


Total
Nonfuel
Operation

8216407
4585


Total
Maintenance

12694220
2055885

164764
System
Control
and Load
Dispatching"



1194892

Other
Expenses0



196788

Total
Nonfuel
0$M Plus

20910627
2060470
24362777
320729
In principle some of these expenses are allooable between modes of generation.  But there is no
data available with which to make the allocation, so that we must attribute these expenses to
overall generation.     '

-------
--J
1/1
           Table  16.   COST OF CAPITAL:  RATE OF RETURN ON RATE BASE AND DEPRECIATION,
                              Potomac Electric Power Company, 1972
                                            (dollars)
Functional Component
of Plant in Service
GENERATION
Total Steam
Production Plant
Total Other
Production Plant
Total Production
Plant
TRANSMISSION
Total Transmission
Plant
Plant in
Service :
Balance, at
End of Year


558,409,172

30,203,993

588,636,054


200,706,727
Cost of Capital
at 8 Percent
of Original
Cost


44,672,734

2,418,151

47,090,884


16,056,538
Depreciation
at Composite
Rate12


16,417,230

888,670

17,305,900


5,900,778
Gross Cost
of Capital


61,089,964

3,306,821,

64,396,785


21,957,316

-------
taken as the "rate base," and some rough estimate of the
opportunity cost of capital is taken as the "rate of return."
All that matters is the product of these two numbers, which
is the "target" net income allowed the company.

The purpose of Table  16 is the compilation, in a form conve-
nient for allocation  procedures, of the cost of capital in
terms of the income cost vocabulary.  The relevant categories
are (recall the income statement categories in Table 13)
Depreciation, Federal Income Taxes, Interest on Long Term
Debt, Other Interest  and Amortization Charges, and Net In-
come.  Treating these income statement categories seriatim,
we begin with Depreciation.  Conceptually the least ambiguous
of the cost of capital categories, our difficulties in the
treatment of depreciation arise from the wide variations in
economic lifetime of  the capital stock held by electric uti-
lities, and the practice of reporting only the total depre-
ciation category found in Form 1.  Thus generating plant may
have an economic life of twenty years--many older units are
still in service—whereas underground distribution plant may
function for fifty or more years.  Public Service Commissions
typically will assign allowed rates of depreciation for spe-
cific types of equipment.  A composite straight line rate
will then be computed by weighting equipment-specific rates
by some weights related to the division of plant in service
between various equipment types.

Our procedure in assembling depreciation estimates by func-
tion begins by computing an "effective" composite straight
line rate in force, that "effective" rate being defined as
the ratio of total depreciation charges to end-of-year elec-
tric plant in service.  (A minor ambiguity surrounds the use
of end-of-year electric plant since, for plant completed
during the year, something less than an annual depreciation
                             76

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charge at the composite straight line rate is appropriate.

The "effective" electric plant in service is somewhere be-
tween beginning-of-year and end-of-year plant in service.)

Table 17, derived from Federal Power Commission Form 1,
assembles electric plant in service by function.  Applica-

tion of the imputed composite straight line depreciation
rate to functionally identified plant in service gives the

column of Table 16 headed Depreciation at Composite Rate.
            Table 17.  ELECTRIC PLANT IN SERVICE,
            Potomac Electric Power Company, 1972
                           (dollars)
            Electric Plant  in  Service
 End-of-Year
   Total  Intangible Plant
   Total  Steam Production Plant
   Total  Other Production Plant
   Total  Production Plant

   Total  Transmission Plant

   Distribution Plant:
     Land and Land Rights
     Structures and Improvements
     Station Equipment

   Poles, Towers, Fixtures
   Overland Conductors and Devices   -
   Underground Conduits
   Underground Conductors and Devices
   Line Transformers
   Services
   Meters
   Installation on Customer  Premises
   Street Lights  and Signals
   Total  Distribution Plant

   Total  General  Plant

   Total  Electric Plant  in  Service
      75,578

 558,409,172

  30,203,993

 588,636,054

 200,706,721
   8,806,101
  18,439,647
  46,641,883

  25,775,660
  29,860,660
  89,960,956
  67,877,917
  86,938,999
  52,965,185

  21,300,501

   2,347,571

  26,092,906

  478,008,178

  27,160,981

1,284,587,512
                              77

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Turning next  to  the net cost of capital concept--the oppor-
tunity cost of capital which is present even in the absence
of economic depreciation--our method  is pegged to an eight
percent rate  of  return on original cost.  That computed fig-
ure appears in the column of Table 16 headed Cost of Capital
at 8 Percent  of  Original  Cost.  The sum of that pure cost of
capital and of the depreciation estimate leads to a Gross
Cost of Capital  estimate.   Since electric plant in service is
already broken out by function, the Gross Cost of Capital es-
timate is  likewise automatically broken out by function.  Fi-
nally, only the  third component of our simplified cost of
capital classification remains.  Table 18, Taxes Other than
Federal Income Taxes, allocates such  taxes among functionally
specified  components of electric plant in service in propor-
tion to electric plant in service.  The validity of that pro-
ration as  a reasonable measure of cost incurrence associated
with various  facilities depends upon  the assumption that in-
direct business  taxes are levied in proportion to assessed
valuation, with  the later assessment  assumed to reflect the
costs of services provided  by state and local governments.

In Table 19,  Summary of Functionalized Capacity Costs, the
three simplified capacity cost components--Nonfuel Operation
and Maintenance  Expenses, Cost of Capital, and Taxes Other
than Federal  Income Taxes--are summed for each function, with
the last column, the sum, giving total capacity cost respon-
sibility by function.  Note that this table includes, albeit
somewhat out  of  sequence, the full results for Nonmeter Dis-
tribution  costs.  Calculation of those costs requires that
metering costs be deducted  from total distribution costs,
and this is done below.
                               78

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  Table 18.   TAXES OTHER THAN FEDERAL INCOME TAXES
        Potomac Electric Power Company, 1972
                     (dollars)
Functional Component
of Plant in Service
Total Production
Plant
Total Transmission
Plant
Total Distribution
Plant
Total Electric
Plant in Service
Corresponding
Original Cost

559,288,714

200,706,721

456,707,678

1,294,587,512
Fraction of
Plant in
Service, by
Function

.432

.155

.353


Proration of
Tax Over
Plant

14,507,157

4,941,999

11,255,003


Table 19.  SUMMARY OF FUNCTIONALIZED CAPACITY COSTS,
         Potomac Electric Power Company, 1972
                      (dollars)
Function
GENERATION
TRANSMISSION
NONMETER
DISTRIBUTION
Total
Nonfuel
0 $ M
24,352,777
320,729
11,873,886
Cost of
Capital
64,396,785
21,957,316
49,963,820
Taxes Other
Than Federal
Income Taxes
14,507,157
4,941,999
11,255,003
Total
by Function
103,266,719
27,220,044
73,092,709
                          79

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Allocation of Capacity Costs Among Rate Schedules:
A Preliminary Example
                                   !
We repeat what we have said several times above:  that we
have neither the time nor the resources for a fine-grained
cost of service study, but that we can tolerate much less.
It will prove sufficient to have a fairly accurate compari-
son of actual versus appropriate patterns of cost recovery-
In moving towards that comparison we first sketch what it
might mean, and then turn to the actual allocation of the
capacity cost components listed in Table 19 among individual
customer classes.  By a customer class we mean all those
customers served on a given rate schedule.

For a guide to how fixed costs are actually recovered, the
simplest procedure is to use crude average revenue data.
Consider Table 20, Crude Estimates of the Allocation of
Capacity Costs Among Customer Classes, Potomac Electric Pow-
er Company, 1972; all data derive from Federal Power Commis-
sion Form 1 filed by that company in that year.  For present
purposes, it will suffice to take, from our previous work on
short run marginal generation costs, a flat, conservative
estimate, say . 7
-------
       Table 20.  CRUDE ESTIMATES OF ALLOCATION OF CAPACITY COSTS AMONG CUSTOMER CLASSES,
                              Potomac Electric Power Company,  1972
oo
Customer Class
Total
Residential
Total Low
Voltiigc
Commercial
Total Large
Power
Interchange
and Resale

KWH Sold
3,128,684,929
6,123,240,159
3,181,396,529
5,803,591,000
Revenue $
77,455,188
133,766,262
45,330,042
56,349,939
Average
Number of
Customers
391,046
47,596
239
--
KWHR Sales
per
Customers
8,001
128,650
194,515
--
Revenue
per
KWHR *
2.476
2.185
1.42S
.971
Marginal
Cost
.7
.7
.7
.7

Capacity
Costs
Recovered
per KWH
1.776
1.485
.725
.271
Capacity
Costs
Recovered
by Custo-
mer Class
55,565,444
90,930,116
23,065,125
15,727,732
Capacity
Costs
Recovered
per
Customer
142.1
1,910.5
189,685.8
--
Total Capacity
Costs Recovered $ 185,288,417

-------
As must be  true because  of  the heavy  distribution costs
associated  with residential service,  the highest capacity
cost per KWH recovery  figure is  the residential figure, with
remaining rate schedules in the  expected sequence:  commer-
cial,  large power,  and interchange and resale.  The very low
figure for  interchange and  resale is  remarkable.  Remember
that the . 271 
-------
definite prices and will change if those prices  change,  so
the question should be stated:   given the load curve obtained
under present prices, what is "the peak"?  As  in other places
above, we have a problem susceptible of formalization, but
a formalization of such complexity as to be nearly useless.
That formal problem is:  given a set of (independent or in-
terdependent) demands in several subperiods of a period over
which demand is periodic, and given the costs  of pricing
differentially between periods and of having additional
rates, what optimum switching times and rate levels will be
selected by a seller seeking to maximize the sum of consumer
and producer surpluses?  In practice, we might proceed as
follows:  from the known form of the system load curve (in
peak season and off peak season months) we select some band
of hours during the peak season as "the peak"  hours for the
year.  One measure of peak responsibility capacity costs to
be recovered is then obtained by dividing, for each customer
class, fixed costs of generation to be recovered by the num-
ber of hours in the peak under various definitions of the
peak.  Table 21, Number of Hours in Peak Under Various
Periodizations, compiles total peak hours (over the year)
under three definitions of the daily peak and two alterna
tive definitions of the division of the year between peak
and offpeak seasons.  The plausibility of these definitions
of the peak, has been based upon inspection of the system
load curve, and the location--both seasonal and time of day--
of peak hours will be different for different systems.  Nev-
ertheless, the range of "total peak hours" can be taken as
applicable to all systems:  for any given system, a reason-
able definition of the peak will fall within this total
hours range.  Our initial cost recovery range comparison  is
therefore based upon one total peak hours range exhibited
in Table 21, the four month peak season with an eight hour
daily peak period.

                              83

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            Table 21.   NUMBER OF HOURS IN PEAK UNDER
                    VARIOUS PERIODIZATIONS
     Seasonal Division
     Assumption
                               Daily Division Assumption
                                                        a
 Peak
 lpm-»-9pm
 = 8 hrs
Peak
9am-»-9pm
= 12 hrs
Peak
3pm-*-7pm
= 4 hrs
 Peak Season
   = 4 months
   s 96 days
 Peak Season
   = 6 months
   s 180 days
  768
1,152
 1,152
 1,728
 384
 576
  a
   Sundays  excluded*  4  x 6 = 24  days/months.
Having  adopted a preliminary definition of the  peak,  we  turn,
in Tables  22A and 22B,  to some initial cost recovery  compari-
sons.   (Remember that here,  in order  to have a  clear  illustra-
tive example,  we are  looking at generation costs  alone.)

Table 22B  is  a set  of calculations  of upper bounds  on the
number  of  KWH  taken during peak hours for  various definitions
of "the peak."  In  Column 1  of that table  we have entered
the number of  hours in  the peak period under various  period-
izations (see  Table 21).   The first row of Table  22B  is  com-
puted as follows.   In Column 4 of Table 22B we  list the  peak
season  months,  June through  September,  corresponding  to  the
choice  of  the  four  month  season.  In  Column 5 of  Table 22B
we enter,  for  each  of those  months, the maximum demand upon
the system as  reported  in Federal Power Commission  Form  12.
Assume  that monthly maximum  demand  is approximately equal to
actual  system  demand  during  all system peak hours.  Then KWH
                               84

-------
taken during peak hours in any one month is  approximately
equal to system peak demand times the number of peak hours
in a month.   By summing over months we get  the  final column of
of Table 22B, Upper Bound on Annual Peak KWH.

That column becomes the third column of Table  22A.   But from
Table 19 we have an estimate of total generation capacity
costs to be recovered,  i.e. $103,266,719.   Column 5 of Table
22A is computed by dividing this figure by  each upper bound
figure in Column 4.

Columns 6 through 9 of Table 22A compile the ratios of actual
fixed cost recovery per peak KWH to our Column  5 estimates of
advisable fixed cost recovery.  For example, the first row
entry in Column 6, 4.82£, is equal to the first row entry in
Column 5 divided by 1.78<£/KWH.  Column 5 is  therefore a first,
crude estimate of the capacity costs per KWH that "should"
have been recovered.

The implications of Table 22A should be stated  explicitly.
For all definitions of the peak period, presently recovered
fixed costs were far exceeded by peak responsibility assign-
ment of fixed costs.

Again, a reminder that Table 22A is an initial  comparison,
since transmission and distribution costs have  yet to be in-
cluded.  When that reckoning is made, it will be seen that
results for residential service are much closer to those for
commercial and industrial service than presently, so that for
all categories of service the conclusions are the same:  the
deviation of present cost recovery from any reasonable patr
tern of cost recovery which acknowledges peak responsibility
is significant.  The implication--that there are realizable
gains to be had from peak load pricing--is, in part, the
work of Section IV.
                              85

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                    Table  22A.  INITIAL COST  RECOVERY  COMPARISONS:   GENERATION ONLY,
                                     Potomac Electric Power Company,  1972
Total
Annual
Peak
Hours
384
576
768
1,152
1,152
1,729
Hours
in
Daily
Peak
4
4
8
12
8
12
Months
in
Seasonal
Peak
4
6
4
4
6
6
Upper
Bound
on
Peak
KWH
Sales
103KWH
1,202,976
1,622,976
2,405,952
3,608,928
3,245,952
4,868,928
Correspond-
ing Fixed
Generation
Cost to be
Recovered
per KWH
in {
8.58
6.36
4.29
2.86
3.18
2.12
Actual Recovery of All Fixed Costs per KWH
Actual
Residential
1.78 <£/KWH
Actual
Low Voltage
Commercial
1.49 */KWH
Actual
Large Power
.73 f/KWH
Actual
Interchange
and Resale
.27 */KWH
Ratios of Column 5. to Actual
4.82
3.57
2.41
1.61
1.79
1.19
5.76
4.27
2.88
1.92
2.13
1.42
11.75
8.71
5.88
3.92
4.36
2.90
31.78
23.56
15.89
10.59
11.78
7.85
00
        aBaaed upon total fixed generation ooat  to be recovered  = $103^266^719  (Table 19 above).

         Baaed upon Table 20,  Crude Estimates  of Allocation of Capacity Costa Among Customer Classes.

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       Table  22B.   RANGE OF TOTAL PEAK HOURS,  AND CORRESPONDING APPROXIMATE TOTAL KWH SALES,
                                Potomac Electric Power Company, 1972
(Total)
Annual
Peak
Hours

384



576





768
1,152
1,152
1,728
Hours
in
Daily
Peak

4



4





8
12
8
12
Months
in
Seasonal
Peak

4



6





4
4
6
6
Months

June
July
August
September
May
June
July
August.
September
October




System Peak
Demand in
Those
Months
103KW
2,730
3,479
3,288
3,034
2,331
2,730
3,479
3,288
3,034
2,044




I System
Peak Demands ,
4 Month and
6 Month Cases
103KW

(12,531)




(16,906)



(12,531)
(12,531)
(16,906)
(16,906)
Monthly
Peak
Hours


96


96





192
288
192
288
Upper Bound
on Annual
Peak KWH

1,202,976



1,622,976





2,405,952
3,608,928
3,245,952
4,868,928
00

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Extension  to Transmission and Distribution Costs

A  full  comparison  of  costs  and benefits associated with peak
responsibility pricing  obviously requires a full reckoning of
all  costs—not just the generation  costs discussed above — of
serving peak and offpeak users.  We have used generation
capacity costs in  our illustrative  example for, with the
obvious qualification regarding losses, every KW of demand
at the  system peak is equally responsible for the incurrence
of generation capacity  costs, and therefore must share co-
equally in that cost  burden.  But transmission and distribu-
tion capacity costs are, equally obviously, not so simply
interpretable.  Clearly the line of causal responsibility
for  the incurrence of these costs is nowhere as simple as in
the  case of generation.  To take only  the most obvious exam-
ple, any reasonable assignment of distribution capacity costs
must show  a highly disproportionate assignment of such costs
to residential customers, since there  are so many more of
them and since each requires a separate connection.  We be-
lieve the  crude allocation  introduced  below is adequate for
our  later  purposes, and we  proceed  to  illustrate that allo-
cation.

First,  an  allocation  of transmission capacity costs among
rate schedules.  Table  23,  Transmission Capacity Cost Allo-
cation, begins this process with an apportionment of total
transmission capacity costs between interchange and resale
and  all other customer  classes--in.  the case of our illustra-
tive system, the Potomac Electric Power Company, the other
categories  are Residential, Commercial, and Industrial.

Interchange and resale  agreements are  agreements between com-
panies  to  "interchange"  electric energy under certain speci-
fied conditions and at  certain specified times.  Such agree-
                              88

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                       Table 23:   TRANSMISSION CAPACITY COST ALLOCATION,
                             Potomac Electric Power Company, 1972

Total 'Fixed1
Transmission
Cost



$27,220,044

Interchange
and Resale
KWH


5,803,591


Total
Residential
KWH


3,128,685


Total Low
Voltage
Commercial
KWH


6,123,240


Total Large
Power KWH


3,181,397


Total Non-
Interchange
KWH


12,433,322


Interchange
and Non-
Interchange


18,236,913

Inter-
change
KWH as
Fraction
of Total
KWH

.318

Allocation
of Total
Fixed
Transmis-
sion Cost
to Inter-
change

$8,655,974
Non-
inter-
change
KWH as
Fraction
of Total
KWH
.682

Allocation
of Total
Fixed
Transmis-
sion Cost
to Nonin-
terchange

$18,564,070

Total Non-
interchange
'Fixed' Trans-
mission Costs

$18,564,070

Average
Number of
Residential
Customers

391,046
Average
Number
.of Low
Voltage
Commer-
cial
Customers
47,596

Average
Number
of Large
Power
Customers

239

Sum of
Averages

438,881

Residen-
tial
Customers
as
Fraction

.891

Allocation
of Trans-
mission to
Residential

$16,540,586

Commer-
cial
Customers
as
Fraction

.108

Allocation
of Trans-
mission to
Commercial

$2,004,919

Indus-
trial
Customers
as
Fraction

.001

Allocation
of Trans-
mission to
Industrial

$18,564
00

-------
ments can benefit both  companies:  e.g., by  (1) taking advan-
tage of differences  in  the  system  load curves so that total
capacity requirements are reduced, or by (2) allowing each
company to  expand its capacity  at  longer intervals and with
larger, more  efficient  plants.

An  interchange  or resale customer  of an electric utility is
thus another  electric utility.  We have therefore allocated
transmission  capacity costs between interchange and resale
and all other customers on  a KWH basis; Table 23 sets out the
numbers.

Our rationale for the above assignment is the obvious inappro-
priateness  of a number-of-customers based allocation  (as is
employed below  for.different purposes) for this first split:
clearly one large interchange connection may account for an
important portion of a  system's fixed transmission costs, but
may nevertheless represent  a negligible portion of the system's
customers.  Then the remaining  noninterchange and resale fixed
transmission  costs are  allocated among the usual customer
classes on  a  number-of-customers basis, which should be rough-
ly  appropriate.  For imagine residential, commercial, and in-
dustrial customers to be evenly interspersed over a circular
region surrounding the  generation plant a system operates.
Then where  individual transmission lines serve individual
squares of  a  grid covering  the  service area, the number-of-
customers allocation would  be exact.

For the allocation of distribution capacity  costs among cus-
tomer classes there  is  a strong case for allocation on a num-
ber-of-customers basis.  The reason is obvious:  distribution
costs are most  immediately  connected with service to individ-
ual customers.  Strictly speaking, only the  drop wire to the
house from the  distribution system--we have  isolated metering
                              90

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expenses--is unambiguously identifiable with service to an
individual customer.   Nevertheless,  the distribution plant
required to serve equal squares of grid with roughly equal
customer density should be roughly equal.   Customer densities
do, of course, differ from neighborhood to neighborhood,  and
in principle these differences could become the justification
for differences in rates between neighborhoods and, more  im-
portant, between localities.   But, the American practice
has been overwhelmingly opposed to accurate reflection of
such cost differentials in rates--in part  because a sub-
sidy is thus granted rural areas — and since our objective
is a careful comparison of each company's  rates with their
understanding of costs, we adhere to the number of customers
method of apportioning distribution costs  among customer
classes.  Table 24, Distribution Cost Allocation, compiles
these results.

The allocations of generation, transmission, and distribution
capacity costs among customer classes, and an estimate of the
cost recovery per KWH that would have reproduced that alloca-
tion, are compiled in Table 25, Summary of Allocation of
Capacity Costs.  The elements of this matrix give, for each
rate schedule and each function--generation, transmission,
and distribution--the associated allocation of capacity costs.
The numbers in parentheses below the elements of the matrix,
labelled as "Naive $/KWH Recovery," are obtained by dividing
each matrix element by the number of KWH in "the peak."  For
purposes of illustration we have taken, in this case, a 768
hour definition of the peak.   By a procedure to be described
momentarily, we estimate  (as an upper bound) that our illus-
trative system sold 2,405,000 KWH during these peak hours in
1972.  Thus the figures in parentheses have the following in-
terpretation:  had all fixed costs been recovered during
these peak hours in 1972, and had the pattern of consumption
                              91

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                           Table 24:  DISTRIBUTION COST ALLOCATION
                             Potomac Electric Power Company, 1972
N>
Nonmetering Distribution Operation and Maintenance

Total
Distribution
Operation
Expenses
$
5,690,999


Meter
Expenses
$
765,938

Nonmeter
Distribution
Operation
Expenses
4,925,061

Total
Distribution
Maintenance
Expenses
7,100,640



Meter
Maintenance
Expneses
151,815

Total
Nonmeter
Distribution
Maintenance
Expenses
6,948,825

Total Nonmeter
Distribution
Operation and
Maintenance
Expenses
11,873,886
Total Nonmeter
Distribution
Costs
$ 73,092,709
Fraction of
Residential
Customers
.891
Allocation o£
Nonmeter Dis-
tribution to
Residential
65,125,604
Fraction of
Low Voltage
Commercial
Customers
.108
Allocation of
Nonmeter Dis-
tribution to
Low Voltage
Commercial
7,894,012
Fraction of
Industrial
Customers
.001
Allocation of
Nonmeter Dis-
tribution to
Industrial
73,093

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                     Table  25.   SUMMARY  OF  ALLOCATION OF CAPACITY COSTS,
                            Potomac Electric  Power  Company,  1972
Function
GENERATION CAPACITY COSTS
Naive KWH Allocation:
KWHs to Schedules during peak
TRANSMISSION CAPACITY COSTS
Naive $/KWH Recovery:
NONMIiTER DISTRIBUTION CAPACITY
COSTS
Naive $/KWH Recovery:
Customer Class
Residential


647,588 x 103KWH
$16,540,586
(.0255)
65,125,604
(.1006)
Commercial


1,268,353 x 10'KWH
$ 2,004,919
(.0016)
7,894,012
(.0062.)
Industrial


279,009 x lO'KWH
$ 18,564
(.0000)
73,093
(.0000)
Interchange
and Resale


211,002 x 103KWH
$ 8,655,974
(.0410)


Total
$103,266,719
.0429$
KWH

$ 27,220,044

73,092,709

VD
t/J

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remained  the  same even with  such  cost recovery practice, fixed
costs of  generation would have been recovered at the rate of
$.0429/KWH, which figure is  obtained as  ($103,266,719/2,405,952
x  103)--the ratio of  total fixed  costs of generation to total
peak KWH.  But  only the total costs of generation are to be
 livided by total peak KWHs,  since only generation capacity
costs are commonly incurred.  Since we have already apportioned
transmission  and distribution costs among customer classes--
the results of  that apportionment are summarized in Table 25,
Summary of Allocation of Capacity Costs--those figures must be
divided by the  number of KWHs taken on peak by the correspond-
ing customer  class.   The line of  Table 25 labelled KWH to
Schedules During Peak presents our estimate of individual cus-
tomer class consumption on peak,  to be explained below; then,
for example,  the entry  (.0255) below the matrix element for
Transmission/Residential indicates that, had total fixed
transmission  costs allocable to residential service--
$15,540,586--been recovered  from  our estimated number of peak
KWH taken by  residential customers, i.e. 647,588 x 103KWH,
recovery  per  KWH would have  been  $.0255/KWH.  The other
bracketed figures are obtained similarly.

Our description of the procedures whereby Table 25 is obtain-
ed will therefore be  complete once we explain our method for
imputing  the  customer class  KWH consumption during peak hours.
In principle, it would, of course, be preferable to work from
directly  measured data--from data on customer class load
curves.   Some systems do some sampling of some rate classes,
and some  have a fairly.accurate knowledge of the load curves
of large  individual customers, but very  few try seriously to
decompose the system  load curve into its individual customer
class constituents.   Of the  systems in our sample, only
Pennsylvania Power and Light and  Commonwealth Edison Company
have a fairly accurate grasp of their customer class load
                               94

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curves.  Pennsylvania Power and Light, probably the most so-
phisticated system in the industry in this (and, we suspect,
not only in this) respect, actually decomposes the system
load curve into customer class load curves;  Commonwealth
Edison does something similar, but only for  the week in which
the system peak day occurs.

How serious a limitation is this?  We believe that the answer
is that it is serious for the systems but not so serious for
our purposes.  We mean by this peculiar turn of phrase that
intelligent rate making requires greater sensitivity to
changes in customer class load patterns than now exists; but
that for our purposes — the construction of indicators of po-
tential pricing improvement--the distortions are sufficiently
large that they survive the crude procedure  about to be de-
scribed.  That the procedure is not too crude is, we believe,
indicated by our comparison--for Pennsylvania Power and
Light--of actual and imputed customer class  load curves:
the two were found to differ by less than 5  percent in KWH
terms.

Table 26, Imputed Customer Class Load Curves, begins this
procedure.  Under the assumptions that both interchange and
resale and industrial loads are flat over the year, the con-
tribution of these loads is removed from total peak KWH.
Residential and commercial contributions to the residual
peak KWH are taken in proportion to residential and commer-
cial annual KWH consumption.   (A similar calculation gives
customer class contributions to KWH consumption in offpeak
hours during the peak months;  those figures will be required
in our indicator estimates and are, therefore,  also computed
in Table 26.)
                              95

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Table  26.   IMPUTATION OF  CUSTOMER CLASS  LOAD CURVES
         Potomac Electric Power Company, 1972
                                                       103KWH
  Total Peak
  Total Interchange, 1972
  Fractioll
768
  Peak  Interchange = (.0877) (2,405,952)  =
  Total Peak  - Peak Interchange =
  Total Industrial, 1972
  Peak  Industrial = (.0877) (Total Industrial)  =
  (Total Peak   Peak Interchange -\_
  I Peak Industrial                /"
  Total Residential, 1972
  Total Low Voltage Commercial, 1972
  Sum
  Fraction Residential
  Fraction Low Voltage Commercial
  Peak  Residential - (.338) (2,405,952)
  Peak  Low Voltage Commercial = (.662)(2,405,952)
       June
       July
       August
       September
  Total Peak  Season
  Peak  Hour in Peak Season
  Total Peak  Season Offpeak Hour
  Fraction of Total Year Hours in Hours
    in  Peak Season Offpeak Hours
  Interchange in Peak Season Offpeak =
    (.2466)(5,803,591) =
  Industrial  Sales in Peak Season Offpeak =
  Sum
  Total Peak  Season Offpeak Hour «
     3,291,360 - 2,219,291 =
  Fraction Residential
  Fraction Low Voltage Commercial
  Peak  Season Offpeak Hour Residential =
    (.338)(1,072,069) =
  Peak  Season Offpeak Hour Commercial =
    (.662)(1,072.069) =
2,405,952
5,803,591
     .0877
  211,002
2,194,950
3,181,397
  279,009
1,915,941
3,128,685
6,123,240
9,251,925
     .338
     .662
  647,588
1,268,353
1,244,243
1,614,291
1,548,762
1,290,016
5,697,312
2,405,952
3,291,360

     .2466

1,433,486
  785,805
2,219,291

1,072,069
     .338
     .662

  362,359

  709,710
                                      96

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Return momentarily to Table 25, Summary of Allocation of
Capacity Costs:  the above procedure is the one responsible
for the row specifying customer class consumption during
peak hours.  Table 25 thus summarizes the capacity cost di-
mensions of cost structure which we require in the construc-
tion of indicators in Section IV.  A similar table must be,
and has been, constructed for each system in the sample.
These constructions are, typically, much more tedious and
somewhat more judgmental than the one we have used as an
illustration of the general method, for the simple reason
that most system rate schedules are much more complicated--
there are many more rate classes--than the system used above.
Without further ado, we turn to the work of Section IV.
                              97
                                \

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                            SECTION IV
                    THE PRICING OF ELECTRICITY:
                INDICATORS OF POTENTIAL IMPROVEMENT
 The  purpose of this  chapter is  to  select  and  estimate  quanti-
 tative measures of the improvement possible in  the pricing of
 electricity.   Improvement  usually  can and should be  called by
 its  proper  name,  welfare gain or gain in  net  benefit.  But
 here we will  use  the term  "indicator" for two reasons.  First,
 our  very real ignorance of many crucial features of  demand
 and  cost structure suggests modesty.   We  believe that  the mea-
 sures  to be discussed are  good  order  of magnitude estimates
 and  good indicators  of where additional demand  and cost infor-
 mation might  usefully be "bought"--where  more fine-grained
 demand and  cost studies could reasonably  be expected to pay
 for  themselves  in pricing  improvements.   Second, there are
 large  and difficult  to measure  external effects associated
 with the electric power industry.   In industries where exter-
 nal  effects are small,  a total  surplus measure  of welfare is
 plausible and acceptable;  the difference  between what  some
 customer is willing  to  pay for  a unit of  the  commodity and
 the  opportunity cost of the resources used in producing the
 commodity is  an obviously  appropriate measure of the contri-
 bution  of that  unit  of  the  commodity  to overall welfare.
 The  difference  between  an  industry with only  minor external
 effects  and an  industry with  major external economies  is
 that in  the first  case, privately registered  costs of pro-
ducing  output are  a  relatively  good measure of  the social
opportunity costs  of producing  that output, while in the
case of  an industry  with large  external diseconomies,
                              98

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private costs understate social costs.  A proposed change in
pricing practices which in an internal efficiency sense de-
creases output and thereby adds $1 to surplus (as computed
from demand and private costs) is deserving of more careful
attention than a similar proposed change which increases out-
put by enough to add $1 to surplus.   In the first case there
are more than the $1 in measureable  gains, since the decrease
in external costs imposed by the industry is a net gain.  In
the second case, there are less than $1 in gains, since the
external costs imposed by the industry are thereby increased.

The direction of this line of argument can be dangerous, for
it seems to lead to an argument that computed welfare gains
can be aggregated judgementally when there are unmeasured ex-
ternal effects.  We draw the line far short of this in what
follows, but we find the argument persuasive for asking the
usualy questions of welfare economics--how can welfare be in-
creased by changes in pricing--in a  somewhat different way,
i.e., how can welfare be increased by selective price in-
creases.  Put another way, a naive version of the rules for
a welfare optimum might be stated as:  charge no customer
less than the incremental costs of service, nor any customer
more than the incremental costs- of service.  Our effective
restatement of that rule is then:  in an industry with large
external diseconomies, first insure  that no customer is be-
ing charged less than the full incremental costs of service.

The implementation of this rule we leave to later in the
section.  We turn to a brief overview of the variety of
electricity tariffs and their traditional rationale.  Fol-
lowing that is the construction of the indicators of poten-
tial pricing improvement.
                             99

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THE VARIETY OF TARIFFS

There are probably  several dozen electricity tariff types in
use throughout the  world, the precise number depending upon
the system of classification.  This diversity has its origin
in the great variety  of  electricity systems throughout the
world and in the way  in  which rate structures have evolved.
The earliest American electric systems served lighting loads
and often charged a flat subscription fee independent of
actual consumption--actual.consumption was not metered--but
presumably based, in  some way, upon expected consumption.
A particular utility's tariff structure is the product of
a long series of incremental changes and therefore reflective
of the distinctive  history and policies of that system.
Nevertheless, several distinctive tariff types are identifi-
able, and these have  been listed in Table 27.  The last
column of that table, headed Cost Recovery Strategy, summar-
izes the cost rationale  of the corresponding tariff.  Since
it is essential in  what  follows that we recognize the valid
and invalid content of each tariff rationale, some further
explanation is in order.

The decomposition of  costs listed is what we have called the
conventional utility  cost vocabulary.  Recall from our dis-
cussion of that vocabulary the underlying assumption that
the four dimensions of cost therein identified--energy,
capacity, customer  and residual costs — are, for purposes of
rate making, roughtly independent dimensions.  Suppose we
begin with the two-part  tariff entry in Table 27.  That
tariff is the simples to explain.  A customer whose
monthly bill is computed under such a tariff pays a minimum
bill, or meter rent.M independent of monthly consumption;
that is, the bill even if consumption is zero.  The obvious
cost rationale for  that  meter rent is the necessity of
                              100

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             Table  27.   TARIFF TYPES AND COST RECOVERY STRATEGIES'*
Tariff Type
Two-Part Tariff
[M;e]
Fixed Energy Block
Rates:
No meter rent and
no seasonal
differential
[B(j), e(j)]
Energy and Demand :
[B(j);e(j)l
[D(j);S(j)]
No meter rent and
no seasonal
differential
Second-Best Marginal
Cost Pricing
Peak Responsibility
[M;P.(D,P(2)]
Bill for ^V of£ Peak» Elasticity o(l)
Consumer *™ ?n **?'• Elasticity a(2)
Talc in* ^ In Toto
laKing ^ Maximum Demand
M +
qe
Zi^BCJDeCj) +
Cq-Z?"1B(j))e(S)
where
2?"1B(j)
-------
 covering customer costs--by definition those costs,  such  as
 billing  and general  and administrative expenses  and  the an-
 nualized cost  of the drop  line  connecting  the  individual  cus-
 tomer  to the distribution  system,  independent  of consumption.
This is perhaps  the  least controversial of all features of
utility rate making,  for the obvious reason that the cost
incurrence  involved  is unambiguously identifiable with an
individual  customer.  Next, the two-part tariff  customer
pays an energy charge  e  per unit of  consumption  q.  And
there, as indicated  in the final column of Table 27, the
difficulties and ambiguities begin.  For the energy charge
must recover both energy and capacity  costs imposed upon the
utility by  the two-part tariff customer.  Since  capacity
charges are being levied at a flat rate independent of the
timing of consumption, and since we have argued  that any
reasonable measure of peak versus offpeak costs  gives esti-
mates of peak costs many times higher  than offpeak costs,
the flat energy  charge of the two-part tariff provides
perverse incentives:  prices offpeak are too high, discour-
aging consumption unnecessarily, while prices at peak are too
low, inefficiently encouraging consumption.  This defect,
among others, has led to pressure for  the abandonment of the
two-part tariff, but  it should be noted that a two-part tariff
may, under  some  circumstances, be the  best possible tariff.
Suppose, for example, that all consumers take so little elec-
tricity that they will not, within the relevant  band of
possible peak versus  offpeak prices, distinguish between con-
sumption in those subperiods.  Then the question facing a
rational pricing authority would be that of the  best single
energy charge.
                              102

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Next, in Table 27, consider the characteristic type of resi-
dential rate, the fixed block rate.   In general that tariff
is specified by a block structure {B(jJ} and a structure of
intrablock charges e(j).  The first  block of KWH is (0,B(1)),
the second block (B(l) ,B(2)) , and so on.  Generally, there
will be a minimum bill associated with the first block, so
that the customer must pay e(l)q for consumption q in the
interval 0e(2)>...-- the marginal energy charge is below
the average energy charge.  That average charge can be
computed by dividing the total bill  by total consumption.

As with the two-part tariff, the interesting question here is
that of cost rationale.  And as with the two-part tariff,
the minimum bill can be identified with the customer compo-
nent of cost service.  But how can we then rationalize the
differential effective minimum bills paid by customers in
different blocks?  For a customer in the second block one
may think of the effective minimum charge as the entire
first block charge 6(1)B(1).  But for a customer in the
third block, whose marginal energy charge must be inter-
preted as e(3), that same interpretation of the first block
price as minimum bill and therefore as customer charge will
                             103

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no longer pass master.  For that third-block customer is paying
a per unit "excess" of  (e(2)-e(3)) above his marginal charge
for each second-block unit he takes.   In short, the identi-
fication of customer cost recovery and minimum bill is ob-
scured.  The difficulty mentioned above in connection with
the two-part tariff is also present here:  the line between
energy and capacity cost recovery is not finely drawn, so
that identical marginal prices obtain  off and on peak, with
the corresponding problem of perverse  incentives.

Consider next the typical tariff applicable to larger users,
often called a general service tariff, a category is some-
time disaggregated into commercial and industrial rate classes.
(Industrial rates are typically designed for larger users with
higher volumes and better load factors than commercial.rate
users.)  This tariff amounts to a doubling of the structure
of the energy-block rate tariff:  there are effectively two
block structures, one for the pricing  of energy consumption
and one for the pricing of maximum demand.  Thus this tariff
requires that total KWH and also maximum demand, or KW, be
metered.  As above let {B(j)> be the energy block structure
and let {D(k)} be the demand block structure.  Then the
third row third column entry of Table  27 gives an algebraic
expression for the bill paid by a customer who takes energy
q (which puts him in the N   energy block) and whose maximum
                                    + Ti
demand is u, which puts him in the Q   demand block.  Thus
his first block demand bill  is  the "length" of that demand
block, D(l), times the charge S(l) per KW in that block.
Summing the contributions to the demand charge from each of
                                                        i
the covered blocks and computing the remainder block charge
gives the total demand bill.  A similar calculation gives
the energy bill, and the customer's total bill is then the
sum of energy and demand bills.
                               104

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The critique of the cost rationale underlying this tariff
follows the lines of that given above for the energy block
structure alone, but must be extended to the way in which
capacity costs are recovered.   For the demand block struc-
ture is an attempt to explicitly price the capacity costs
imposed by the user.  Its major difficulty is the non-
coincident demand basis of the capacity charge.   User A
and user B may have the same maximum demand, say 1,000 KW.
But if user A's maximum demand comes offpeak, say at 1 a.m.,
there is no reason to bill him at the same rate  as user B,
whose maximum demand comes at the instant of the system peak.
User A is imposing no resource cost upon society for the pro-
vision of capacity to meet his demand (He is imposing a
resource cost in the sense of fuel used for generation).
User B is imposing the full costs of providing 1,000 KW of
capacity.  Thus the use of noncoincident demand charges can
lead to the same sort of perverse offpeak versus peak incen-
tives as the flat marginal charge tariff.

For completeness, and because several systems in our sample
do employ such tariffs, we what are sometimes called sliding
block tariffs--tariffs with a mixed structure in which the
length of the energy blocks may depend upon maximum demand.
Usually the demand block structure is defined by taking the
lengths of the various blocks to be proportional to maximum
demand u : if the basic demand block structure is {W(l) } then
for a customer with maximum demand ja the first demand block
is of length uW(l), the second of length uW(2), and so on.
The idea is to penalize customers with "poor" load factors--
with maximum demand much higher than average demand -- for
the capacity costs they impose.  But note that the scheme  is
based upon maximum customer demand, which may or may not be
coincident with the system peak demand.  The problem of
perverse incentives remains.
                             105

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The  last  two  row entries  of  Table  27  are not seen as tariffs
in the  United States--there  are  some  attempts to introduce
peak responsibility principles into bulk power pricing, one
of which  we refer to  below--but  are listed as guiding prin-
ciples  for rate  making, and  because of  their relevance to
the  discussion below.   In second-best marginal cost pricing,
each user is  charged  a  price which inevitably must differ
from the  short run marginal  cost of serving him--because,
since short run  marginal  cost is below  average cost, prices
equal to  marginal cost  would be  insufficient to cover cost.
But  the deviation is  arranged to cover  cost in a way that
least distorts the pattern of consumption that would arise
were prices equal to  the  short run marginal cost measures
we have discussed in  Section III.  The  appropriate second
best rule is  that prices  differ  from  short run marginal
costs of  service in inverse  proportion  to demand price elas-
ticities  of demand.

This normative rule for utility  pricing has been the subject
of a great deal  of theoretical discussion.  The correspond-
ing  difficulties of interpretation and  implementation have
not  been  so thoroughly  treated.  Our  interpretation and im-
plementation  of  this  rule, which corresponds to Category I
of our  customer  response  typology, may  be subject to some
objection.

Our  dicussion of Table  27  concludes with some remarks on
the  last  line of that table.  We used the term peak respon-
sibility  in the  very broad sense'of any tariff which attempts
to restrict recovery of capacity costs  to a charge billed at
the  system peak;  or,  in other works,  to any tariff the /
demand  charge component of which is a strictly coincident
demand  charge.   The coincidence  referred to is coincidence
with  the  system  peak.  We  have indicated that customer and
                              106

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residual costs can and should be recovered in a minimum bill
or meter rent M under this tariff;  and further that there will
be prices per KWH P(l) and P(2)  differentiating between off-
peak and peak.

So much for this necessary and preliminary overview of tariff
structure, which has served to introduce the tariffs and to
sketch the structure of the remainder of this Section.  For
an overview of that structure we must piece together our
scattered remarks concerning the perverse incentives provided
by the various tariffs with the typology of customer responses
set out above.  Indeed, it is only  now that the role of that
typology in guiding the construction of potential pricing
gains can be set out.

The remaining four sub-sections of  this Section complete
the task of constructing indicators of potential gain, with
each section treating one category  of the typology:  the
relevant customer classes associated with each category
(this subject has been broached above), the interpretation
of the corresponding indicator, and the evaluation of that
indicator for the companies in the  sample.

CATEGORY I INDICATORS OF POTENTIAL  PRICING IMPROVEMENT

Category I embraces customers who,  for information cost rea-
sons, will not distinguish between peak and offpeak nor be-
tween average and marginal price.  Very plausibly, residential
and small commercial customers belong in this category.  Under
our assumptions the only signal which registers for these
customers is average price, so that the only relevant poten-
tial pricing change is a change in average price.  Thus the
question to pose regarding these customers is as follows:
if the average prices charged the various customer classes
                           107

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are not the prices required by second best short run marginal
cost pricing, how large are the potential gains associated
with realigning these average prices as required by the
second best standard?  The answer shall prove to be very
small, so that average price changes are not prime candidates
as instruments of rate structure improvement.  A sample cal-
culation for one system should illustrate the orders of
magnitude involved.

First, a formal statement of the second-best efficiency
conditions which have been stated in words above:
          P.  -
            P.        B.
               - —  = TT"       i, j = all rate classes     (26)
Where P. and P. are the average prices charged rate classes
i and j respectively, y. and y. the short run marginal
costs of serving those rate classes, and E. and E. the
elasticities demand of those rate classes.  Before launching
into the empirical work, some further discussion of equation
(26) will probably be helpful.  Note first that the equations
are necessary conditions for a second best set of (relative)
average prices, but that these equations alone are insuffi-
cient to determine the second best solution- -for that deter-
mination we need another equation, the requirement that
total revenue equal total cost.  Next, in what sense is the
solution determined by this set of sufficient conditions
"second best"?  Remember that first best always means price
equal to short run marginal cost.  Because electric utilities
are required to recover their costs from their customers,
and because short run marginal costs are below short run
                              108

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average costs, first best pricing of electric power would
lead to deficits.  It is necessary to price above short
run marginal cost in order to cover costs,  and the second
best solution is the least distorting way of doing so:  it
leads to the smallest loss in total welfare (the sum of
consumers' plus producer's surpluses).   The reader trained
in economics may be troubled because this solution seems
identical with the pricing policy a discriminating monopolist
would pursue.  This is true, but there is a crucial difference
The discriminating monopolist is able to capture all of the
surplus, consumers' and producer's*, the public utility
pricing at second best marginal cost leaves consumers with
all realized consumer surpluses.

As a first guide to where pricing improvement of this kind
may be possible, we construct a comparison table, Table 28,
of existing values of "deviation ratios" and "elasticity
ratios".  The deviation ratio is the left side of equation
(26) and the elasticity ratio the right side of that same
condition when computed for present values of average price,
marginal cost and elasticity:  the equation defines second-
best prices, so that it only holds when prices have been
adjusted to a second-best optimum.

As elsewhere  in  the report, we use 1972 Potomac Electric
Power Company data for  illustrative purposes, and for  that
system we treat,  initially,  the three rate classes--Residen-
tial, Commercial, and Industrial.

For each pairwise combination of customer classes there  is  a
comparison between deviation and elasticity ratios.  Thus,
for our three customer  classes  case there are three  such com-
parisons.  Again, the efficiency condition  (26) holds  only
                             109

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         Table 28.  DEVIATION AND ELASTICITY RATIOS,
            POTOMAC ELECTRIC POWER COMPANY, 1972
^>Henominator
Numerator ^\_
Residential
Commercial
Industrial
Residential
Deviation

.953
.846
Elasticity

.737
.583
Commercial
Deviation
1.049

.888
Elasticity
1.357

.792
Industrial
Deviation
1.182
1.126

Elasticity
1.714
1.263

when prices are optimal, so that present values of deviation
ratios--i.e., values based upon present prices and associated
marginal costs--will not necessarily equal the corresponding
elasticity ratios, and in the case of our trial run utility,
for which deviation ratios have been computed and compiled
in Table 28, they do not.  The deviation ratios.computed in
Table 28 are based upon average prices associated with sales
under each rate schedule, and with a marginal cost figure
based upon the marginal unit in use during peak hours in
August  (cf.  our discussion of marginal costs above).  The
elasticity ratios are based upon elasticity estimates by
state and customer class published by Chapman, Tyrell and
Mount   and discussed in Section II.

A first question suggested by Table 28 is that of consistency:
are the (pricing) policy implications of the various compari-
sons afforded by Table 28 consistent with one another?  Since
the deviation ratio—for example, for the residential-inc^us-
trial comparison--is
                              110

-------
         R
           R
                                 (27)
and since the expression    -  is montonic increasing in p
so long as y>0 , a comparison of deviation and elasticity
ratios suggests the following pricing changes:  if the
present deviation ratio is greater than the corresponding
eleasticity ratio, either decrease the "numerator" price
or increase the "denominator" price or do both, in order
to bring the two ratios closer into line.  Conversely, if
the present deviation ratio is less than the elasticity
ratio, either increase the numerator price, or decrease
the denominator price, or both.

Carrying through the three possible palrwise comparisons
for the test case summarized in Table 28 leaves us with the
following policy implications, presented in Table 29.

          Table  29.  POLICY  IMPLICATIONS  OF TABLE  28
       Rate Schedule
       Residential

       Commercial

       Industrial
Direction of Implied Price Chage
                             111

-------
There is no inconsistency associated with the opposing arrows
in the commercial price column:  it simply happens that the
residential-commercial pairing comparison leads  to the
policy recommendation raise, or lower, or both; whereas the
commercial-industrial pairing leads to the policy implication
lower or raise or both.  We thus may choose residential and
industrial prices as "policy instruments" and proceed to a
determination of the required changes in their magnitudes,
and, following that, of the associated welfare gains.

Now  if the revenue  constraint is to be continued to be satis-
fied under the new  prices  (as it presumably has been under
the  old) then the changes  in residential and  industrial prices
are  not  independent, but must satisfy a condition derivable,
after some manipulation, from the  revenue  constraint.  That
condition is
       6Pn   qT 1 - ATET
         K - __±, 	•*• •*•                                  f?8l
             qR 1 - ARER                                  CZ8J
              IV      Jtx S\.
where    AT,AD     are the corresponding fractional departures
          IK                          p _p
from marginal cost:   A _ is defined as   I  _  , and similarly
                       I                 p  -
f or A D                                    *
     K..

The  efficiency condition requires that changes in residential
and  industrial prices be such as to equate deviation and elas
ticity  ratios
         R
           PR + ~^r- - ^                               (29)
           PI + 5
                               112

-------
Equations (28) and (29)  together determine the required price
changes.  Solution of a quadratic equation for  pR gives the
numerical value of the required change as roughly + .207
-------
 install  and  operate  than  a  double  register meter, so that we
 commit no  error  of overstatement in  our final indicator of
 feasible benefits for  these customers  if we assume no change
 in metering  costs under time differentiated average pricing.

 We therefore proceed to the estimation of indicators of po-
 tential  pricing  improvement for all  rate classes on a common
 basis.   When those estimates are completed, we net out the
 metering costs for residential customers.

 An Overview  of the Calculation

 It may be  helpful to look at a simplified version of the indi-
 cator estimate,  one  which exhibits the essentials of the prob-
 lem without  the  inessential problems associated with the
 numerous rate schedules that some  systems have.  We there-
 fore take  our Potomac  Electric Power Company cost information,
 the work of  Section  III,  and construct Table 30, captioned
 Bands of Suggested Prices for Peak Months.  In the columns
 headed Generation, Transmission, and Distribution, we have
 entered, from Table  25, our derived  costs to be recovered per
 KWH figures  for  the  individual functions, cross-classified by
 customer class.  By  summing the functional costs for each
 rate schedule we obtain,  for each  customer class, an "upper
 bound" on  capacity costs  to be recovered during peak season
 peak hours from  that customer class.   By further adding an
 estimate of  the marginal  costs of  generation during peak
 hours, obtained  from our  previous  analysis of short run mar-
 ginal cost,  we have  what  may be considered an upper bound on
 total costs  to be recovered from each  customer class at
peak hours.   In Column 3, we record that estimate of marginal
 generation costs is  $.007/KWH.  This is certainly an in prac-
tice lower bound on  costs to be recovered.  For purposes of
                               114

-------
                     Table 30.  BANDS OF SUGGESTED PRICES FOR PEAK SEASON,
                             POTOMAC ELECTRIC POWER COMPANY, 1972
tn
Rate 'Schedule'
Residential
Commercial
Industrial
Interchange
and Resale
Present
Average
Price
IWH
.02476
.02185
.01425
.00971
Lower
Bound
("SRMC")
$
KWH
.007
•.007
.007
.007
Generation
$
KWH
.0429
.0429
.0429
.0429
Transmission
$
KM
.0255
.0016
.0000
.0410
Distribution
$
IWH
.1006
.0062
.0000
.0000
Upper Bound
$
OT
.1760
.0577
.0499
.0909

-------
comparison we have tabulated, in Column 1, average revenue
for each customer class.  The striking, if unsurprising,
comparison is evident for all rate schedules:  marginal cost
is well below average revenue which, in turn, is far below
"peak responsibility" price.  Recalling our discussion of
peak responsibility pricing above, there will be substantial
welfare gains from peak responsibility pricing.
Consider next Figure 3, which with Table 31 presents a first
illustrative calculation of the welfare gains available from
improved pricing of electricity sold to the various customer
classes.
                                  Peak Demand
        Peak
     Offpeak
               sOffpeak Demand
                KWH
                   Offpeak
                                               KWH
Peak
           Figure  3.  Welfare  Gains  from  Peak Load  Pricing
                             116

-------
Table 31.   ILLUSTRATIVE  INDICATORS OF  POTENTIAL PRICING  IMPROVEMENT,
                 POTOMAC  ELECTRIC POWER COMPANY, 1972


Rate 'Schedule1
Residential
Commercial
Industrial
Interchange
and Resale

(1).
" /«/
.14
.19
.24
.24
(2)
Present
Average
Price
KwR..
.02476
.02185
.01425
.00971
(3)
'reposed
•>{{ Peak
lour, Peak
•lonth
Price
S/KKII
.014
.014
.014
• .007
(4)
Proposed
Peak Hour
Peak
Month
Price
$/KWII
..088
.029
.025
.045
Off Peak Hour, Peak Season Indicator
(S)
'Pop
2-3
.011
.008
.00025
.0027
(6)
2sa.ca
Pf (T)
.786
.571
.018
.386
(7)
""op
10'KWH
362,359
709,710
785,805
1,433,486
(8)
AWop-
lEApKWHop££
199,370
324,196
424
165,998
Peak Hour, Peak Season Indicator
(9)
*Ppk
4-2
.063
.007
.011
.035
(10)
*Ppk . 9
-PT'T
.716
.241
.440
.778
(")
KKIIpk
10'KIVII
647,588
1,268,353
279,009
211,002
(12)
«pk '
lEipKhHpk^
2,040,161
213,971
159,593
689,470
                                            E 689,988
                                                                    t 3.103.195
                                                       £1 3.793.183

-------
of demand between off peak and peak are taken to be zero.
Finally, note that the calculation refers to only those
months identified as peak season months in the discussion of
Section III.  The use of short run elasticities is for illus-
trative purposes, to indicate the orders of magnitude ob-
tained in such estimates.

We turn now to a more realistic indicator estimate in which
some of the restrictive assumptions which make the above
example simple are relaxed.
Indicators of Potential Pricing Improvement:
Seasonally Spread Peak Responsibility Rates
The above calculation is an instructive guide to the source
of the distortions inherent in average cost pricing of elec-
tric power, but is insufficient as a benchmark for further
analysis.  As we have argued in our discussion of short run
marginal costs, the notion of "the peak" is complex:  at
almost any given time the relationship between capacity and
demand is different, and in order to reduce that relation-
ship to something upon which rate making can be based, con-
siderable "averaging" over random elements in the relation-
ship- -especially the stochastic component of outages--is
necessary.  Even where the seasonal load curve of a given
system exhibits a pronounced peak, the month or season of
that peak cannot naively be identified with "the" peak,
since the necessity of scheduling downtime for maintenance
often means that there is no grea£ surfeit of capacity dur-
ing the offpeak seasons.  If the point of peak pricing is
to appropriately penalize those casually responsible for the
incurrence of capacity costs, then even peak hour off peak
season customers must be so penalized, since much nominally
"free" capacity is actually in maintenance during that time.
                        118

-------
Present average price P is too high off peak and too low on
peak, so that there are welfare losses.   The off peak wel-
fare losses AW«p arise because off peak customers are being
charged more than the marginal costs of serving them.  The
on peak losses AWp arise because on peak customers are being
charged less than the incremental costs of serving them, so
that capacity plus operating costs higher than the value of
the marginal peak KWH are incurred by the utility and imposed
upon society.  In terms of Table 30, Figure 3 refers to a
single customer class:  the appropriate off peak price Pgp
will be something close to the lower bound for that customer
class compiled in Column 3, and the appropriate peak price
will be something close to the upper bound compiled in Col-
umn 7 of that table.  The welfare loss triangles can be com-
puted in terms of e, the elasticity of the relevant demand
            p
schedule,  Af, the differential between correct and present
average price, and p and q, initial quantities and prices.
Those computations are summarized in Table 31, and the ex-
pressions for the welfare losses are entered at the heads of
Columns 8 and 12 of that table.

In Column 3 of Table 31 we have entered a conservative esti-
mate of proposed offpeak prices, namely twice marginal gene-
ration .cost, and in Column 4 a similarly conservative pro-
posed peak price, half of our Table 30 "upper bound" peak
responsibility price.  In Columns 8 and 12 benefits are
tabulated by rate schedule, having been computed with the
formula at the head of each column.  Summation of those
benefits gives our estimate of total benefits.  The elasti-
cities used in this calculation have been taken as short run
elasticities, and are the short run elasticities estimated
by Chapman et. al. in the paper discussion  in Section II.
We have tacitly assumed that these elasticites are identical
on peak and off peak, and that the cross price elasticities
                             119

-------
 and  therefore  they are  imposing  capacity  costs over and above
 those  required to  meet  the  demands  of  off peak hour, off peak
 season customers.

 But  how shall  capacity  costs  be  apportioned  among  seasons?
 There  is,  here as  elsewhere,  no  unambiguous  allocation, for
 the  underlying problem--akin  to  the scheduling problem men-
 tioned in  connection with short  run marginal costs--is a
 difficult  one.   The use  of  several  reasonable measures of
 the  relationship of capacity  to  demand during the  three sea-
 sons into  which we have  divided  the year--June through Sep-
 tember,  October through  January,  and February through May--
 gives  very comparable results, and  we  have therefore adopted
 the  simplest of procedures  in this  seasonal  allocation of
 capacity costs, an allocation based upon  the seasonal distri-
 bution of  total 'energy  sales.  This means that,  e.g., depre-
 ciation is apportioned  among  systems as if it were a pure
 user cost,  incurred only in proportion to output.  The ambi-
 guities  of the allocation of  capacity  costs  among  seasons do
 not, we  feel,  blur the basic  cost differential,  that between
 the  cost of peak hour and off peak  hour power during any day
 of any season.   Finally,  a  word  on  utility practice in doing
 what amounts to this  allocation.  Many summer peak systems
 do have  some rate  seasonal  differential,  but we  have found
 it impossible  to get, from  any one  system, a clear statement
 of the basis for that differential.  We have been  told pri-
 vately by  the  officials  of  several  systems that  the present
 differential is inadequate.   A conjecture which  seems to fit
 the  facts  is that  the interseasonal differential--e.g., the
 difference  between the heights of the  residential  tailblocks
 in peak  and offpeak seasons--is  often  taken  in a rule of
 thumb  way  as the short run marginal cost  differential between
 the most expensive  unit  in  the system  and base load plants.
The  latter  differential  is  typically of the  order of
                              120

-------
Given our allocation of capacity costs by system,  rate sched-
ule, and season, our steps in deriving upper and lower bounds
for prices can be retraced,  and the results are as tabulated
in Tables 32 through 36, Bands of Suggested Prices by Season;
there is one such table for  each system in the sample.  The
major differential, already  evident in our preliminary com-
parison of Table 30, holds:   average pricing substantially
underprices peak period power.  Also in line with what we
have come to expect is the relative size of the differential
among rate classes.  Thus the commercial load is typically
not "as underpriced" as residential and industrial loads.
Two explanations for this seem appropriate.  First, the
commercial load is typically right on peak--nowhere near as
flat as the industrial load, and not as spread as the resi-
dential load, since the latter has the lighting component
late into the evening and an early-morning component.  Sec-
ond, and not entirely fanciful, since it has been suggested
to us by personnel at several utilities, residential custo-
mers are more numerous, more vocal, and more likely to be
the source of complaints.  If not having to deal with irate
customers is a benefit valued by utility personnel, there
should be some bias of rates in favor of residential custo-
mers and against commercial  customers.

Having thus spread capacity costs "over seasons," we turn to
the calculation of indicators of potential pricing improve-
ment by rate schedule and season.  Recall Figure 3.  Both
off peak and peak welfare gains AWQp and AWp are based upon
internal cost measures, since all our cost estimates  (which
underlie our peak and off peak price estimates) are based
upon internal cost measures.  Further, correct pricing of
off peak power will result in increased off peak consump-
tion- -and increased external cost--while correct pricing of
peak power will result in decreased consumption and decreased
                             121

-------
                           Table  32.  BANDS OF SUGGESTED PRICES BY SEASON:
                                   Potomac  Electric  Power Company,  1972
Rate Schedule
by Season
CD
Residential :
June -Sept.
Oct. -January
Feb. -May
Commercial:
June-Sept.
Oct. -January
Feb. -May
Industrial:
June-Sept.
Oct. -January
Feb. -May
Interchange §
Resale:
June-Sept.
Oct. -January
Feb. -May
Lower Bound
"SRMC"
$/KWH
(2)

.007
.007
.007

.007
.007
.007

.007
.007
.007


.007
.007
.007
Generation
$/KWH
(3)

.0171
.0205
.0194

.0171
.0205
.0194

.0171
.0205
.0194


.0171
.0205
.0194
Transmission
$/KWH
(4)

.0125
.0122
.0110

.0008
.0008
.0007


--
""


.0137
.0137
.0137
Distribution
$/KWH
(5)

.0493
.0479
.0433

.0030
' .0030
.0027


--
""



--

Upper Bound
"LRMC"
$/KWH
(6)

.0796
.0813
.0744

.0216
.0250
.0235

.0178
.0212
.0201


.0315
.0349
.0338
Present
Av. Annual
Price
$/KWH
(7)


.02476



.02185



.01425




.00971

ts)
to
         SRMC a .Short-Run Marginal Cost.
         LRMC » Long-Run Marginal Cost.

-------
                        Table 33.  BANDS OF SUGGESTED PRICES BY SEASON:
                                  Commonwealth Edison Co.,  1972
Rate Schedule
by Season
(1)
Small Residential:
June -Sept ember
October- January
February-May
Large Residential:
June-September
October-January
February-May
Residential Space
Heating:
June-September
October- January
February-May
Small Commercial §
Industrial:
June-September
October- January
February-May
Large Commercial §
Industrial :
June-September
October-January
February-May
Lower Bound
"SRMC"
$/KWH
(2)

.0046
.0046
.0046

.0046
.0046
.0046

.0046
.0046
.0046

.0046
.0046
.0046

.0046
.0046
.0046
Generation
$/KWH
(3)

.0182
.0182
.0182

.0182
.0182
.0182

.0182
.0182
.0182

.0182
.0182
.0182

.0182
.0182
.0182
Transmission
$/KWH
(4)

.0469
.0469
.0469

.0117
.0117
.0117

.0114
.0114
.0114

.0017
.0017
.0017

--
Distribution
$/KWH
(5)

.0933
.0933
.0933

.0233
.0233
.0233

.0028
.0028
.0028

.0035
.0035
.0035

--
Upper Bound
"LRMC"
$/KWH
(6)

.0630
.0630
.0630

.0578
.0578
.0578

.0370
.0370
.0370

.0280
.0280
.0280

.0228
.0228
.0228
Present
Av. Annual
Price
$/KWH
(7)

.0353

.0302

.0170

.0249

.0132
to
Ol

-------
                   Table  33 (continued).   BANDS OF SUGGESTED PRICES BY SEASON:
                                  Commonwealth Edison Co., 1972
Rate Schedule
by Season
'CD
Street Light §
Signal System:
June-September
October- January
February-May
Water § Sewer
Pumping:
June - S ep t emb e r
October -January
February-May
Railroads § Rail-
ways :
June-September
October -January
February-May
Resale, Municipal!
ties:
June- September
October- January
February-May
Lower Bound
"SRMC"
$/KWH.-
-C2)


.0046
.0046
..0046


.0046
.0046
.0046


.0046
.0046
.0046


.0046
.0046
.0046
Generation
$/KWH
(3)



--



.0182
.0182
.0182


.0182
.0182
.0182


.0182
.0182
.0182
Transmission
$/KWH
(4)







.0001
.0001
.0001


.0094
.0094
.0094


.0067
.0067
.0067
Distribution
$/KWH
(5)


.0435
.0435
.0435


.0002
.0002
.0002


* —
w —


.*> —

Upper Bound
(6)-(2) + (3) + (4)*(5)
"LRMC"
$/KWH
(6)


.0481
.0481
.0481


.0231
.0231
.0231


.0322
.0322
.0322


.0295
.0295
.0295
Present
Av. Annual
Price
$/KWH
(7)



.0209




.0135



.0160



.0112

NJ

-------
                        Table 34.
BANDS OF SUGGESTED PRICES BY SEASON:
Duke Power Company, 1972
Rate Schedule
by Season
CD
Residential (R)
July-October
Nov. -February
March-June
Residential (RA) :
July-October
Nov. -February
March- June
Residential (RW) :
July-October
Nov. -February
March-June
Residential (WGS §
MISC.) :
July-October
Nov. -February
March-June
Commercial § Indus-
trial (G) :
July-October
Nov. -February
March-June
Commercial 5 Indus-
trial (GA):
July-October
Nov. -February
March-June
Lower Bound
"SRMC"
$/KWH
(2)

.0044
.0044
.0044

.0044
.0044
.0044

.0044
.0044
.0044

.0044
.0044
.0044

.0044
.0044
.0044

.0044
.0044
.0044
Generation
$/KWH
(3)

.0091
.0091
.0094

.0091
.0090
.0094

.0091
.0090
.0094

.0091
.0090
.0094

.0091
.0090
.0094

.0091
.0090
.0094
Transmission
$/KWH
(4)

.0174
.0169
.0174

.0046
.0045
.0046

.0093
.0090
.0093

.0059
.0058
.0059
.
.0024
.0023
.0024

.0003
.0003
.0003
Distribution
$/KWH
(S)

.0341
.0332
.0341

.0181
.0087
.0090

.0181
.0177
.0182

.0115
.0112
.0116

.0046
.0045
.0046

.0005
.0005
.0005
Upper Bound
"LRMC"
$/KWH
(6)

.0650
.0635
.0653

.0266
.0272

.0409
.0401
.0413

.0309
.0304
.0313

.0205
.0202
.0208

.0143
.0142
.0146
Present
Av. Annual
Price
$/KWH
(7)

.0265

.0167

.0201

.0155

.0168

.0112
N)
Cn

-------
Table 34 (continuedj.  BANDS OF SUGGESTED PRICES BY SEASON
                 Duke Power Company, 1974
Rate Schedule
by Season
(1)
Commercial § Indus-
trial (I)
July-October
Nov. -February
March-June
Commercial § Indus-
trial (IP-IS) :
July-October
Nov. -February
March-June
Commercial § Indus-
trial (All Other) :
July- October
Nov. -February
March-June
Street Lighting §
Signal System:
July-October
Nov. -February
March- June
Other Public
Authorities:
July-October
Nov. -February
March-June
Lower Bound
"SRMC"
$/KWH
(2)

.0044
.0044
.0044

.0044
.0044
.0044

.0044
.0044
.0044

.0044
.0044
.0044
i
.0044
.0044
.0044'
Generation
$/KWH
. (3)

.0091
.0090
.0094

.0091
.0090
.0094

.0091
.0090
.0094

.0091
.0090
.0094

.0091
.0090
.0094
Transmission
$/KWH
(4)

.0001
.0001
.0001

--

.0092
.0092
.0092



.0002
.0002.
.0002
Distribution
$/KWH
(5)

.0001
.0001
.0001

--

.0183
.0183
.0183

»

.0004
.0004
.0004
Upper Bound
(6)-(2) + (3) + (4) + (5)
"LRMC"
$/KWH
(6)

.0137
.0136
.0140

.0135
.0134
.0138

.0410
.0409
.0413

.0135
.0134
.0138

.0141
.0140
.0144
Present
Av. Annual
Price
$/KWH
(7)

.0089

.0079

.0278

.0322

.0105

-------
                   Table  34 (continued).   BANDS OF SUGGESTED PRICES BY SEASON
                                    Duke Power Company, 1974
Rate Schedule
by Season
CD
Sales for Resale:
July-October
Nov. -February
March-June
Interdepartmental :
July-October
Nov. -February
March-June
Lower Bound
"SRMC"
$/KWH
(2)

.0044
.0044
.0044

.0044
.0044
.0044
Generation
$/KWH
(3).

.0091
.0090
.0094

.0091
.0090
.0094
Transmission
$/KWH
(4)

.0061
.0061
.0061

- -

Distribution
$/KWH
(5)

--


"" ""
Upper Bound
(6)-(2) + (3) + (4) + (5)
"LRMC"
$/KWH
(6)

.0196
.0195
.0199

.0135
.0134
. Oloo
Present
Av. Annual
Price
$/KWH
(7)

.0089

.0144

ts)

-------
                        Table 35.  BANDS OF SUGGESTED PRICES BY SEASON:
                         New York State Electric and Gas  Corp., 1972
Rate Schedule
by Season
(1)
Residential:
Nov. -February
March-June
July-Octo'ber
General Service
(SC2 PSC 113):
Nov. -February
March-June
July-October
General Service
(SC2 PSC 108):
Nov. -February
March-June
July-October
Large Light § Power
(SC3 PSC 113) :
Nov. -February
March -June
July- October
Primary Light §
Power (SC3 PSC 108)
Nov. -February
March-June
July -October
Lower Bound
"SRMC"
$/KWH
(2)

.0047
.0047
.0047


.0047
.0047
.0047


.0047
.0047
.0047


.0047
.0047
.0047


.0047
.004.7
.0047
Generation
$/KWH
(3)

.0128
.0131
.0130


.0128
.0131
.0130


.0128
.0131
.0130


.0128
.0131
.0130


.0128
.0131
.0130
Transmission
$/KWH
(4)

.0148
.0146
.0147


.0049
.0048
.0048


.0016
.0016
.0016


.0001
.0001
.0001


::

Distribution
$/KWH
(5)

.0336
.0331
.0331


.0109
.0107
.0108


.0035
.0035
.0036


.0002
.0002
.0002


.0001
.0001
.0001
Upper Bound
C6) = (2) + (3) + (4) + CS)
"LRMC"
$/KWH
(6)

.0659
.0655
.0655


.0333
.0333
.0333


.0227
.0229
.0229


.0178
.0181
.0180


.0176
.0179
.0178
Present
Av. Annual
Price
$/KWH
(7)


.0272




.0273



.0175



.0138



.0103

to
oo

-------
                  Table 35 (continued).  BANDS OF SUGGESTED PRICES BY SEASON:
                         New York State Electric and Gas Corp., 1972
Rate Schedule
by Season
CD
Other Public
Authority:
Nov. -February
March-June
July-October
Street Lighting §
Signal Systems:
Nov. -February
March-June
July-October
Interchange §
Resale :
Nov. -February
March-June
July-October
Lower Bound
"SRMC"
$/KWH
.(2)


.0047
.0047
.0047


.0047
.0047
.0047


.0047
.0047
.0047
Generation
$/KWH
(3)


.0128
.0131
.0130


.0128
.0131
.0130


.0128
.0131
.0130
Transmission
$/KWH
(4)


.0013
.0013
.0013



--



.0115
.0115
.0115
Distribution
$/KWH
(5)


.0031
.0031
.0031




--


_ _
--

Upper Bound
(6) = (2) + (3) + (4) + (5)
"LRMC"
$/KWH
(6)


.0219
.0222
.0221


.0175
.0178
.0177


.0290
.0293
.0292
Present
Av. Annual
Price
$/KWH
(7)



.0169




.0486




.0080

Ni

-------
Table 36.  BANDS OF SUGGESTED PRICES BY SEASON:
        Pennsylvania Power $ Light, 1972
Rate Schedule
by Season
CD
Residential (RS) :
Nov. -February
March-June
July-October
Residential (RH) :
Nov. -February
March-June
July-October
Residential (SGS,
AL, d, CS) :
Nov. -February
March-June
July-October
Commercial § Indus-
trial (SGS) :
Nov. -February
March- June
July-October
Commercial § Indus-
trial (LP3) :
Nov. -February
March-June
July-October
Lower Bound
"SRMC"
$/KWH •
(2)

.0047
.0047
.0047

.0047
.0047
.0047


.0047
.0047
.0047


.0047
.0047
.0047


.0047
.0047
.0047
Generation
$/KWH
(3)

.0150
.0156
.0139

.0150
.0156
.0139


.0150
.0156
.0139


.0150
.0156
.0139


.0150
.0156
.0139
Transmission
$/KWH
(4)

.0115
.0119
.0115

.0024
.0025
.0019


.0015
.0015
.0011


.0084
.0087
.0066


.0002
.0003
.0002
Distribution
$/KWH
(5)

.0413
.0428
.0323

.0085
.0088
.0067


.0053
.0055
.0041


.0304
.0315
.0237


.0008
.0009
.0007
Upper Bound
(6)-(2) + (3) + (4) + (5)
"LRMC"
$/KWH
(6)

.0741
.0762
.0624

.0318
.0328
.0272


.0277
.0285
.0238


.0597
.0617
.0489


.0219
.0227
.0195
Present
Av. Annual
Price
$/KWH
(7)


.0271



.0171




.0673




.0426




.0231


-------
Table 36 (continued).  BANDS OF SUGGESTED PRICES BY SEASON:
             Pennsylvania Power § Light, 1972
Rate Schedule
by Season
(1)
Commercial § Indus-
trial (LP4):
Nov. -February
March-June
July-October
Commercial § Indus-
trial (LP5):
Nov. -February
March-June
July-October
Commercial 3 Indus-
trial (LP6):
Nov. -February
March-June
July-October
Commercial § Indus-
trial (LP) :
Nov. -February
March-June
July-October
Commercial § Indus-
trial (HS)
Nov. -February
March-June
July-October
Lower Bound
"SRMC"
$/KWH •
(2)


.0047
.0047
.0047


.0047
.0047
,0047


.0047
.0047
.0047


.0047
.0047
.0047


.0047
.0047
.0047
Generation
$/KWH
(3)

.
.0150
.0156
.0139


.0150
.0156
.0139


.0150
.0156
.0139


.0150
.0156
.0139


.0150
.0156
.0139
Transmission
$/KWH
(4)




—



- -


—
- —


.0002
.0002
.0002


.0003
.0003
.0003
Distribution
$/KWH
(5)


.0001
.0001
.0001


.0002
.0002
.0002


- -
"" ™"


.0008
.0008
.0008


.0010
.0010
.0010
Upper Bound
(6)-(2) + (3) + (4) + (5)
"LRMC"
$/KWH
(6)


.0210
.0216
.0187


.0211
.0217
.0188


.0209
.0215
.0186


.0219
.0225
.0196


.0222
.0228
.0199
Present
Av. Annual
Price
$/KWH
(7)



.0153



.0128



.0096



.0128



.0166


-------
                 Table 36  (continued).   BANDS  OF  SUGGESTED PRICES  BY SEASON:
                              Pennsylvania  Power  §  Light,  1972
Rate Schedule
by Season
CD
Commercial § Indus-
trial (BST) :
Nov. -February
March-June
July-October
Commercial § Indus-
trial (All Other) :
Nov. -February
March-June
July-October
Street Lighting and
Signal System:
Nov. -February
March- June
July-October
Other Public
Authorities :
Nov. -February
March- June
July-October
Railroads § Rail-
ways :
Nov. -February
March-June
July-October
Lower Bound
"SRMC"
$/KWH •
(2)


.0047
.0047
.0047


.0047
.0047
.0047


.0047
.0047
.0047


.0047
.0047
.0047


.0047
.0047
.0047
Generation
$/KWH
(?)


.0150
.0156
.0139


.0150
.0156
.0139


.0150
.0156
.0139


.0150
.0156
.0139
•

.0150
.0156
.0139
Transmission
$/KWH
C4)


--




.0027
.0027
.0027


—




—



~ ~
Distribution
$/KWH
(5)


--




.0096
.0096
.0096


.0036
.0036
.0036


— —
- -


_ _
_ «•
Upper Bound
"LRMC"
$/KWH
C6)


.0209
.0215
.0186


.0332
.0338
.0309


.0245
.0251
.0222


.0209
.0215
.0186


.0209
.0215
.0186
Present
Av. Annual
Price
$/KWH
(7)



.0092




.0243



.0691



.0223



.0111

Iri
NJ

-------
                  Table 36 (continued).   BANDS OF SUGGESTED PRICES BY SEASON
                               Pennsylvania Power § Light, 1972
Rate Schedule
by Season
CD
Interdepartmental :
Nov. -February
March- June
July-October
Interchange §
Resale:
Nov. -February
March-June
July-October
Lower Bound
"SRMC"
$/KWH
(2)

.0047
.0047
.0047


.0047
.0047
.0047
Generation
$/KWH
(3)

.0150
.0156
.0139


.0150
.0156
.0139
Transmission
I/KWH
(4)






.0062
.0062
.0062
Distribution
$/KWH
(5)






--


Upper Bound
"LRMC"
$/KWH
(6)

.0209
.0215
.0186


.0271
.0277
.0248
Present
Av. Annual
Price
$/KWH
(7)


.0175




.0110

CM

-------
external cost.  In what follows we will therefore take AW
                                                         P
alone, or some measure of AW  alone, as a conservative esti-
mate of potential pricing improvement.

There is inevitably some element of judgement in the selec-
tion of a procedure for making those conservative estimates.
Peak costs are much higher than average prices, and our econo
metric evidence on demand elasticities is based upon a rela-
tively much  smaller variation around average prices.  It
therefore would be improper to compute estimates of AW
based upon our full upper bounds--columns 6 of Tables 32
through 36--where those upper bounds are many times higher
than present  average prices.

In Tables 37  through 41 we have computed two appropriate
indicators of potential pricing improvement.  First, we
have calculated the welfare gain AW-.Q associated with a 10%
decrease in peak consumption.  This requires that we calcu-
late the peak price increase AP-.Q over present average price
Po-,r necessary to cut peak consumption by 10%, and then that
 av
we compute the corresponding welfare gain.  In columns 8 of
Tables 37 through 41, these welfare gain estimates are pre-
sented by system, by season, and by rate schedule.  Second,
we have computed an estimate of AW  based upon the full
upper bound estimates of peak correct peak prices--columns
6 of Tables 32 through 36.  As indicated in columns 9 of
Tables 37 through 41, we have used that full upper bound
directly when it implies less than a doubling of peak price.
When use of the full upper bound'would imply more than a
doubling of present average price, we have taken half the
upper bound as the revised peak price.  In this way we have
computed,  far each system, season and rate schedule, a sec-
ond estimate AW k of AW .  Columns 11 of Tables 37 through
41 summarize the results of this second calculation.
                              134

-------
             Table 37.   PEAK BENEFITS BY  SEASON:   AVERAGE PRICES COMPARED WITH PEAK
                    PRICES WHICH DECREASE PEAK KWH TEN PERCENT AND WITH  LRMC

                              Potomac Electric Power Company, 1972



. Rate Schedule by Season




Residential
June-September
October -February
February-May
Commercial
Junc-Scptcciber
October -February
February-May
Industrial
June-September
October-February
February-May
Interchange and Resale
June-September
October-February
February-May



Long Run
Average
Price
Elasticity
av


1

1.22
1.22
1.22

1.46
1.46
1.46

1.93
1.93
1.93
1.93
1.93
1.93



Present
Average
Price,
P.v
$/KWH



2

.0248
.0248
.0248

.0219
.0219
.0219

.0143
.0143
.0143
.0097
.0097
.0097


Price
Consls.
with a
10 i
Decrease
in Peak
KKII,
4PM
$/KbH

3

.0020
.0020
.0020

.0015
.0015
.0015

.0007
.0007
.0007
--


LRMC if
|XLRMC 25,367
- 138, 303

- 117,443
- 209,536
- 1S1.3S6
- 192,222
- 233,790
- 220,313
-2,128,700

Percentage
Charge in
1'cak Ki.il


1 2

- SO. 6
- 58.9
- 48.8

* 2.0
- 19.3
- 10.2

- 42.1
- 73.1
- 65.0
- 91.1
-110.8
-104.4
- 38.4
I-1
w
en
        "Full upper bound

-------
            Table 38.   PEAK BENEFITS BY SEASON:  AVERAGE PRICES  COMPARED WITH PEAK
                   PRICES  WHICH DECREASE PEAK KWH TEN PERCENT  AND  WITH LRMC

                                Commonwealth Edison Company,  1972


. Rate Schedule by Season




Snail Residential
June-September
• October-January
February -May
Large Residential
June-September
October-January
February-May
Residential Space lleatin
June-September
October-January
February-May
Small Commercial and Ind
June-Scptenber
October-January
February -May

Long Run
Average
Price
Elasticity
>°av'



i

1.22
1.22
1.22

1.22
1.22
1.22
g
1.22
1.22
• 1.22
Jstrial
1.48
1.48
1.48
'Large Commercial and Industrial
June-September
October- January
February-May
Katcr and Sewer Pumping
June-September
October- January
February-May
Railroads and Railways
June-September
October- January
February-May
Resale, Municipalities
June-September
October-January
February-May

1.87
1.87
1.87

1.87
1.87
1.87

1.87
1.87
1.87

1.87
1.87
1.87


Present
Average
Price,
Pav
j/Ktm



2

. .0353
.0353
.0353

.0302
.0302
.0302

.0170
.0170
.0170

.0249
.0249
.0249

.0132
.0132
.0132

.0231
.0231
.0231

.0160
.0160
.0160

.0112
.0112
.0112

'rice
Change
Consis.
nth a
10 I
iccrcas'e
in Peak
KWII,
APil
J/KKH

3

.0029
.0029
.0029

.0025-
.0025
.0025

.0014
.0014
.0014

.0017
.0017
.0017

.0007
.0007
.0007

.0012
.0012
.0012

.0008
.0008
.0008

.0006
.0006
.0006

LRMC if
|xLRMCkKIVHPK^
pav
1 0

3,558,591
3,473,975
3,218,879

21,217,272
20,332,770
19,191,828

7,427
7,250
6.257

610,972
596,446
552,648

9,520,830
9,297,070
8,614,378

1.413
1.379
1,278

21
:o
19

64,036
88,421
82,746
1100,445,946

Change in
Peak KKH
pk
10'KKH

1 1

256,903
250,799
232,383

- 1,529,569
- 1,465,800
- 1,385,549

9,856
9,622
8,304

393,81:
384,443
356,218

- 1,984,095
- 1,937,453
- 1,795,184

3,136
3,06:
2,837

410
400
371

36,599
35,376
33.105
1-12,113,294

"erccr.taje
:hanj;c in
>cak Xttl


1 2

-6B.8
- 68.8
-66.8

-76.1
-76.1
-76.1

•10.2
- 10.2
- 10.2

-17.3
- 17.3
• 17.3

- 99.7
- 99.7
• 93.7

- 7.1
- 7.1
- 7.1

- 1.1
• 1.1
- 1.1

- 50.5
- 50.5
-50.5
-61.1
ON
        "full upper bound

-------
OI
            Table  39.   PEAK  BENEFITS  BY  SEASON:  AVERAGE  PRICES  COMPARED WITH PEAK
                   PRICES WHICH  DECREASE PEAK  KWH  TEN  PERCENT AND WITH  LRMC
                                   Duke  Power  Company,  1972
Rate Schedule by Season

Residential (R)
July-October
Xovenber- February
March-June
Residential (RA)
July-October
Xovunber-Fcbruary
March-June
Residential (RW)
July-October
March-June
Residential (MiS ( Nile
July-October .
March-June
July-October
March-June
Comercial and Industri
July-October
Xoicnber- February
March-June
Coanerclol and Industrl
July-October
March -June
Cooncrcidl and Industrl
July-October
Novcnbor- February
March-June
CoETcrcial and Industrl
July-October
Xoveabcr-Fobruary
March-June
Other Public Authorltie
July-October
Noveaher -February
March-June
Sale! for Resale
Jul) -October
Nnvcnbcr - February
March-June
July-Uctober
Noveaber* February
March-June

"Full ufi'Jf bJund
long Run
Price
Elasticity
/«.„/
1
1.11
1.11
1.18
l.U
1.11
l.U
1.11
l.U
1.11
....
1.11
1.11
1 '?!„
1.13
1.1)
1 (CA)
1.13
1.13
1.13
1 CD
1.6S
1.65
1.65
1 (IRES)
I.6S
1.65
1.65
1 (All Oth
1.6S
1.65
1.6$
1.6S
1.6S
1.6S
1.6S
1.6S
1.6S .
1.6S
1.6S
1.6S

Present
Price,
P.»
J/IKII
2
.0265
.0265
.0265
.0167
.0167
.0167
.0201
.0201
.01111
.0155
.015!
.0155
.0161
.0161
.0169
.0112
.0112
.0112
.0089
.0019
.0049
.0079
.0079
.0079
")
.0271
.0271
.0271
.0105
.0105
.0105
.0019
.0019
.0019
.0144
.0144
.0144

Price
Change
Consist
with a
10 t
Decrease
in Peak
XMI.
4P,,
J/XKII
3
.0022
.0022
.0022
.0014
.0014
.0014
.0017
.0017
.0017
.0013
.0013
.0011
.0015
.0015
.0015
.0010
.0010
.0010
.0005
.0005
.0005
.0005
.0005
.0005
.0017
.0017
.0017
.0006
.0006
.0006
.0005
.0005
.0005
.0009
.0009
.0009

LR.MC if
£xl.KNC«S
146°
137°
136°
140°
135°
134°
131°
410°
409°
413°
141°
140°
144°
191°
>»*
199°
'»S
114°
131°

Peak XKH
in Season,
XMI
P«
10'XHII
S
279,346
270,234
225.059
571,645
559,769
466,193
1,010,01.6
977,911
114,499
13,302
12,161
10,717
191,246
162,173
711,045
541,715
530,116
442,010
1,402,112
1,402,112
1,402,112
73,1154
73,954
73,954
29,512
29,512
29,512
11,347
11,347
11,347
510,130
510,130
510,130
461
461
461
[15,327,744
Efficiency Calm Associated
Kith n Ten Percent Decrease
In I'cak XMI
Frac-
tional.
I'rlce
Change
i|;jj..
av
£"'
6
.0147
.01141
.0147
.0147
.0147
.0147
.0147
.11147
.0147
.0147
.0147
.0147
.0115
.01115
.0115
•.0115
.0115
.0115
.0606
.06U6
.0606
.0606
.0606
.0606
.0606
.0606
.0606
.0606
.0606
.0606
.0606
.0606
.0606
.0606
.0606
.0606

Efficiency Cains
«K|« •
Js^MWIVk^
7
30,711
29,710
24,743
40,413
39,103
30,307
15,116
13,084
69,195
ll>4
136
696
66,147
64,666
53,156
27,437
26,542
22,105
35,051
35,051
31,051
1,149
1,149
1,149
1,509
2,509
2,509
550
550
550
12,769
12,769
12,769
21
21
21
1155,371
Efficiency Gain Associated with
Upper Bound or One-Half Upper Bound
Price Chang*
at Peak, IP ^ •
LRMC-P., if
jLRHC«P.y.
• JlRHC-P.y
Otherwise
8
.0060
.11052
.0062
.0103
.0099
.0105
.0004
00
.0006
.0154
.0149
.0151
.0037
.0034
.0040
.0031
.0030
.0034
.0041
.0047
.0051
.0056
.0055
.0059
.0132
.0131
.0135
,0036
.0035
.0039
.0107
.0106
,0110
.0009
.0010
.0006

Ave age
Fra •
lio al
Pri e
Cha ge
9
.203
.179
.209
.471
.457
.471
.020
00
.0:9
.664
.649
.675
.!'.>«
.114
.213
.243
.236
.263
.425
.411
.445
.523
.516
.544
.314
.311
.391
.293
.216
..313
.751
.746
.764
.065
.071
.043

Efficiency Cains
•V
a*.^pk""w^t
rav
1 0
201,1:9
125, >67
172,329
1,656,894
1,493,489
1,3»0,33
340, 051
233.190
212,592
223,207
2,359,032
2,273,639
2,625,643
171,702
178,121
195,912
123,493
121,581
128,486
15,917
15,155
11.461
3,386,111
3,332,810
3,542,103
22
27
32
125,456,6.36
Chanue in
Peak XKH
iXHIpk
10'KtVH
1 1
67,043
- 41,372
55,590
- 3J1.72;
- 301,715
- 262,933
24,263
00
27,693
10,4:9
9,144
1,541
• 10S.747
• 113,643
• 1:3,04!)
- 150,197
- 141,721
• 131,298
- 902,930
• 967,506
•1,029,664
• 63,122
-. 62.93S
66,411
- 18,711
• 18,563
19,035
1,143
1,600
9,467
- 632,911
• 628,832
- 644,157
49
54
32
t-7,:io,ioi
Percentage
Change in
Peak Xiill
1 2
- 24.0
- 21.1
- 24.7
• 55.6
- 53.9
- 56.4
• 2.4
0
- 3.4
- 78.4
- 76.5
- 79.7
- 2J.3
• 2I.J
• 24.1
- 27.5
- 26.7
- 2S.7
- 70.1
- 69.0
- 73.4
- 86.3
- 15.1
- 19.1
- 63.4
• 62.9
- 64.5
• 41. J
- 47.2
• 51.6
-123.9
-l.'l.l
•126.1
- 10.7
• 11.7
• 1.1
• 47.5


-------
            Table 40.  PEAK  BENEFITS BY SEASON:  AVERAGE PRICES COMPARED  WITH PEAK
                   PRICES WHICH  DECREASE PEAK KWH TEN PERCENT AND WITH  LRMC
                              New York State Electric and Gas, 1972




Rita Schedule by Season




Residential
November-February
March-June
July-October
General Service (SC2 PSC
November- February
Ma rch- June
July-October
General Service ' (SC2 PSC
Novenbe r - February
M.I rch -June
July-October
Large Light and Power (S
November- February
March-June
July-October
Prinary Light and Power
Novcebcr -February
Ma rch -June
July-October
Other Public Authority
Novcnber- February
Ma rch -Juno
July-October
Interchange and Resale
November - February
March-June
July-October
.


Long Run
Average
Price
Elasticity



1

1.Z4
1.24
1.24
113)
1.65
1.65
1.65
108)
1.65
1.6S
1.65
C3 PSC 113)
1.89
1.89
1.89
(SC3 PSC 10
1.89
1.89
1.89
1.89
1.89
1.89
1.89
1.89
1.89



Present
Average
Price,
v.
$/KHH



2

.0272
.0272
.0272

.0273
.0273
.0273

.0175
.0175
.0175
.0138
.0138
.0138
8)
.0103
.0103
.0103
.0169
.0169
.0169
.0080
.0080
.0080


Price
Change
Consls ,
Kith a
10 1
Decrease
in Peak
KKII, •
APu
(/KWH

3

.0022
.0022
.0022

.0017
.0017
.0017

.0011
.0011
.0011
.0007
.0007
.0007
.0005
.0005
.0005
.0009
.0009
.0009
.0004
.0004
.0004



LRMC if
2*LRMC
-------
Table 41.  PEAK BENEFITS BY SEASON:  AVERAGE PRICES COMPARED WITH PEAK
       PRICES WHICH DECREASE PEAK KWH TEN PERCENT AND WITH LRMC
              Pennsylvania Power and Light Company, 1972
Rite Schedule by Season

Residential (RS)
Novenher- February
• March-June
July-October
Rc.'av'
1
1.22
1.22
1.22
1.22
1.22
1.22
d CS)
1,22
1.22
1.22
1 (SCS)
1.46
1.46
1.46
1 (LP3)
1.46
1.46
1.46
1 (LP4)
1.93
1.93
1.93
1 (LP5)
1.93
1.93
1.93
1 (LP6)
1.93
1.93
1.93
1 (LP)
1.93
1.93
1.93
1 (NS)
1.93
1.93
1.93
1 (BST)
1.93
1.93
1.93
Present
Average
Price,
Pav
J/KKH
2
.0271
.0271
.0271
.017!
.0171
.0171
.0673
.0673
.0673
.0426
.0426
.9426
.0231
.0231
.0231
.0153
.0153
.0153
.0128
.0128
.0128
.0128
.0128
.0128
.0128
.0128
.0128
.0166
.0166
.0166
.0166
.0166
.0166
Price
Change
Consis.
with a
10 1
Decrease
in Peak
KIVII,
oPi.
$/KKH
3
.0022
.0022
.0022
.0014
.0014
.0014
.0055
.0055.
.0055
.0029
.0029
.0029
.0016
.0016
.0016
.0008
.0008
.0008
.0007
.0007
.0007
.0007
.0007
.0007
.0007
.0007
.0007
.0009
.0009
.0009
.0009
.0009
.0009
LRMC if
£xLRMCpkKW11PK^
av
1 0
1,348,139
1,285.758
205,405
1,946,389
1,706,773
815,425
43,072
31,958
43,536
486,168
466,667
59,474
20.426
3,527
157,026
277,102
344,669
105,279
321,390
3:6, 909
180,174
684,918
803,541
389,884
203,856
228,079
122,098
109,824
132,530
40,533
40,894
52,627
9,469
Change in
Peak KWH
A1C™pk
'10 'Kim
1 1
- 272,351
- 233,774
- 100,197
- 264,814
- 217,423
- 161,470
* 2.175
» 1.647
» 2,001
56,861
48.865
13,830
* 34,043
« 17,636
» 37. 236
97, 228
- 105,589
61,929
77,468
81.5C-:
60,025
- 175,249
- 184,722
• 134,442
44,789
47,004
35.9:9
39,222
42,751
24,565
19,022
21,347
9,467
Percentage
Change in
Peak KKH
1 2
- 37.6
- 41.1
- 17.2
- 73.3
- 76.7
- 55.6
• 1.017
• 98.8
» 1.165
- 48.8
- 53.4
- 20.1
» 7.7
• 5.1
• :.s
• 60.6
- 65.8
- 38.6
- 94.6
- 9T.6
• 73.3
- 9Z.8
- 97.8
- 71.2
- 101.1
-106.1
- 81.1
- 55.8
- 60.8
- 34.9
- 44.2
- 49.6
- 22.0 .

-------
   Table 41  (Continued)   PEAK BENEFITS BY SEASON:  AVERAGE PRICES  COMPARED
      WITH PEAK  PRICES WHICH DECREASE PEAK KWH TEN PERCENT AND WITH  LRMC

                   Pennsylvania Power and Light Company, 1972



Rite Schedule by Season




Commercial and Industrie
November- February
March-June
July-October
Other Public Authorities
Xovenber- February
March-June
July-October
Railroads and Railways
Xovecber -February
March -June
Jilly-October
Intcrdepartnental
Xqvenber- February
March-June
July-October
Interchange and Resale
November -February
March-June
July-October



Long Run
Average
Price
Elasticity
/«„'




L (All Otha
1.93
1.93
1.93

1.93
1.93
1.93

1.93
1.93
1.93

1.93
1.93
1.93
1.93
1.93
1.93



Present
Average
Price,
P.v
J/KNH




r)
.0092
.0092
.0092

.0691
.0691
.0691

.0223
.0223
.0223

.0111
.0111
.0111
.0110
.0110
.0110


Price
Change
Cons Is.
with a
10 1
Decrease
in Peak
KKH,
aPit
J/KKI1



.0005
.0005
.0005

.0036
.0036
.0036

.0012
.0012
.0012

.0006 ',
.0006
.0006 ,
.0006
.0006
.0006


LRMC if
|xLRMC
-------
Turning to the task of estimating the incremental cost of
double register metering of residential customers,  an ex-
ample will serve to illustrate the procedure.   From the
Sangamo Electric Company we have obtained acquisition cost
figures for the ordinary, or single register,  KWH meter and
for the double register meter which would be necessary if
residential customers were to be charged different  prices
offpeak and on peak.   The simpler meter could be acquired
by utilities for $16.00 in 1972, and the double register
meter for $57.58.  But it would be incorrect to take these
as capital cost figures, for the capital cost of a meter
which is entered into a utility's rate base is the  installed
cost of the meter, and installation cost can be substantial
and varies between companies.  From Federal Power Commission
From 1 we can reconstruct each system's installation costs
by the simple expedient of deducting from the reported per
meter increase in the rate base our known acquisition cost
of $16.  For example, for the Potomac Electric Power Company,
1972 installation cost computed thus is $56.51.  Assuming
that installation costs for the double rate register are no
higher than those for the single rate register, we may add
this installation cost figure to the acquisition cost figure
for the double rate register, $57.58, in order to obtain a
capital cost- figure for double register metering, in this
case $114.09.  Of course, the single register figure, ob-
tained directly from Form 1, is $72.51.  By annualizing each
of these capital cost figures--as above, we assume  an  8 per-
cent rate of return on original cost--we have annual capital
cost figures for single and double rate registers.  For oper-
ating and maintenance cost estimates, we have available the
breakdown provided by Form 1 in which operating  costs  are  de-
composed  into meter reading costs, meter maintenance  costs,
and a miscellaneous meter expenses category.  The definition
of meter expenses given  in the  Federal  Power  Commission's
                              141

-------
 standard  accounts  is  the  obvious  one; while meter expenses
 "shall  include  the cost of  labor, materials and expenses
 used  and  incurred  in  the  operation of customer meters and
 associated  equipment," i.e.,  operating as opposed to main-
 tenance expenses associated with  metering, exclusive of
 meter reading expenses.

 Since we  have,  for each system, the number of meters, each
 of  these  figures can  be put on a  per meter basis.  For ex-
 ample,  in 1972  the Potomac  Electric Power Company reported
 per meter reading  expenses  of $2.11, per meter maintenance
 expenses  of $.33,  and per meter meter expenses of $1.65, or
 total per meter operating and maintenance expenses of $4.09.
 In  our  estimates of the corresponding figures for double
 register  metering,  we have  somewhat naively assumed, for
 each  system, the same numbers.  This is certainly defensible
 for meter reading:  the major expense is the labor and trans-
 portation cost  involved in  moving the reader between meters.
 For the remaining  components  of operating and maintenance
 cost, the assumption  is not as persuasive, but we have no
 alternative.  The  cost differencial between single register
 and double  register metering  is then equal to the difference
 between annualized capital  cost figures for the two modes of
 monitoring,  and it  is this  differential that is entered as
 the column  "Incremental Cost  of Metering per Customer" in
 Table 42, Net Peak  Period Residential Schedule Indicators of
 Improved  Pricing.   By multiplying that figure by the average
 number  of customers served  during 1972 under each residential
 rate  schedule for  each of our systems, and deducting the
product from our previous estimates for these schedules in
Tables  37 through  41--remember that there are two such fig-
                      /
ures, one for a price change  which depresses peak consump-
tion by 10 percent, and another for a price change in which
our upper bounds are  used as  prices--we obtain the net bene-
fit or indicator figures  of the final two columns of Table 42.
                              142

-------
Table 42.  NET PEAK PERIOD RESIDENTIAL, SCHEDULE INDICATORS OF IMPROVED PRICING


System Rate Schedule

POTOMAC ELECTRIC POWER
Residential
COMMONWEALTH EDISON COM
Small Residential
Large Residential
Residential Space
Heating
DUKE POWER COMPANY
Residential (R)
Residential (RS)
Residential (RW)
Residential (WGS § MI
NEW YORK ELECTRIC AND G
Residential
PENNSYLVANIA POWER AND
Residential (RS)
Residential (RH)
Residential (SGS,
AL $ CS)
1
Ten
Percent
Peak
Benefits
-Gross
Benefits
(*)
COMPANY
137,610
PANY
156,775
719,558

19,515

85,164
109,953
235,165
SC) 2,396
AS
169,635
LIGHT
206,429
65,457
1,518
2
Peak
Upper
Bound
-Gross
Benefits
(!)

5,548,191

10,251,715
60,741,870

20,934

499,225
4,530,781
13,161
221,115

1,027,309
2,839,302
4,468,587
118,566
3

Average
Number
of
Customers


391,046

1,003,359
1,348,632

62,894

253,559
138,189
488,754
3,657

525,616
674,736
69,486
232
4

Incre-
mental
Metering
Cost
per
Customer
m
4,48

4.84




4.56




4.65

4.59

5

Total
Incremental
Metering
Cost
($)

1,751,886

4,856,257
6,527,379

304,407

1,156,229
630,141
2,228,718
16,676

2,444,114
3,097,038
318,940
1,065
6

Net
Benefits
I
= 1-5
m

-1,614,276

-4,699,482
-5,807,821

- 284,892

-1,071,065
- 520,188
-1,993,553


-2,274,479
-2,890,609
- 253,453
453
7

Net
Benefits
II
= 2-5
m

3,796,305

3,830,542
54,214,491

-9,473

-657,004
-13,906,640
-2,215,557


-1,416,805
-257,736
4,149,647
117,501

-------
 CATEGORY  III  INDICATORS  OF  POTENTIAL  PRICING  IMPROVEMENT

 Recall  that  customers  in category  III  are  assumed to have
 decided,  on  information  cost  grounds,  to be marginal rather
 than  average  price  sensitive;  it is further assumed that
 they  do not,  or  do  not have the opportunity to, distinguish
 between offpeak  and peak consumption.   (The latter constraint
 might be  assumed to arise institutionally.)   This set of as-
 sumptions is,  as we have argued above,  probably most germane
 to  the  situation of large residential  users;  not because it
 is  not  potentially  relevant to large  commercial and indus-
 trial users,  but because these later  customers typically know
 their load curves,  so  that  the assumption  of  unwillingness to
 differentiate  between  offpeak and  peak consumption seems arti-
 ficial.

 A major difficulty  surrounds  the estimates of this section.
 For example,  no  company  with  which we  are  familiar knows the
 load  curve of  tailblock  residential customers, i.e., those
 residential  customers  whose monthly bills  put them in the
 final consumption block.  Under the curcumstances, we be-
 lieve that a  sensible  estimate of  the  potential benefits to
 be  derived from  futher investigation  of load  curves by block
 is  as follows.   Make the somewhat  drastic  assumption that
 all tailblock  consumption occurs during peak  hours.  This,
 we  hasten to point  out,  is  not much different from what many
 utility personnel suspect:  that much  of peak growth attri-
 butable to residential consumption has, in recent years,
 been  in the tailblock.   Then  an  indicator of potential im-
 provement can  be computed by  estimating the benefits accru-
 ing from  an upwards  adjustment of  the  tailboock rate towards
 the peak  prices  we  have  computed (and which are reported in
 columns 6  of Tables  32 through 36). For illustrative pur-
poses, we  have chosen  a  variety of "inversion" which many
                              144

-------
of the advocates of rate inversion have put forward,  an  in-
version in which the height of the tailblock is raised to be
equal to the height of the first block.  Where one half of
the derived upper bound is lower than the first block height,
we use the former figure in this calculation.

Table 43, Category III Indicators of Potential Pricing Im-
provement, presents the results of these estimates.   In
column 1 of Table 43, we have entered the fraction of resi-
dential sales assumed to be tailblock sales, .1996.   We have
taken the same fraction for all systems only because  we were
able to get data for only one system, the Potomac Electric
Power Company.  In column 2 of Table 43 we have compiled
estimates of peak KWH sales to residential customers  by sys-
tem and by season; these have been computed by the procedure
set out in Table 26.  Column 3 of Table 43, an estimate of
peak tailblock sales, is then the product of columns  1 and 2.
In column 4 of Table 43 we have compiled the relevant econo-
metric estimates of price elasticity, the Chapman  et. al.
long run elasticity estimates.  In column 5 of Table  43, we
have recorded the height of the first block of each residen-
tial rate schedule in 1972, and in column 7 of Table  43 we
have recorded the tailblock rate in effect, by system and
season; in column 6 we have entered our upper bound estimate
of appropriate peak price, from Tables 32 through 36.  Gen-
erally, but not always, the tailblock rate is lower than the
upper bound estimate of peak price and the first block rate
lies between the two.

Accordingly, we compute a welfare gain estimate based upon
whichever price is smaller, the difference between tailblock
and first block, or the difference between tailblock  and upper
bound prices:  that welfare estimate is what we could hope to
gain by raising tailblock price by the smaller differential,
                            145

-------
assuming all tailblock consumption to be on peak.  Column 10
of Table 43 is a compilation of those welfare estimates.  A
warning is appropriate in  the interpretation of these figures:
the reductions in peak consumption given by the usual elasti-
city formula are very large, sometimes amounting to total
peak consumption.  The source of this result is apparent:
the application of our long run elasticity estimates to peak
price changes often  amounting to more than 90 percent of in-
itial price.  Accordingly, the benefit estimates are to be
taken as order of magnitude estimates.

CATEGORY IV INDICATORS OF  POTENTIAL PRICING IMPROVEMENT

Finally, recall that customers in category IV are assumed to
be both marginal price responsive and to be able to distin-
guish, at no additional cost, between offpeak and peak con-
sumption:  this certainly would be the case for large commer-
cial and industrial  customers who already monitor their load
curves, and of these there are many.  Many of these customers
are billed under tariffs which have block structures for
both energy and demand charges, so that the customer's bill
is computed from both energy and maximum demand readings.
Thus, some additional procedures must be devised before pro-
ceeding to the estimation of indicators of potential pricing
gain for this customer category.

Net Benefit Indicators for Demand Billed Accounts

The procedures we have employed above in order to derive in-
                                  •
dicators of the net benefits available from improved pricing
cannot be directly applied to schedules with a demand charge
component.   The reason is somewhat obvious:  when the cori-
sumer's bill depends in some complex way upon not only con-
sumption but also upon maximum demand, the relationship be-
                              146

-------
Table 43.  CATEGORY III INDICATORS OF POTENTIAL PRICING IMPROVEMENT


System Rate Schedule. (Season)

POTOMAC ELECTRIC POWER COMPANY
Residential
June-September
October- January
February-May

COMMONWEALTH EDISON COMPANY
Large Residential
June-September
October-January
February-May

DUKE POKER COMPANY
Residential (R)
July-October
November -February
March-June

Residential (RA)
July-October
Novenber- February
March-June

Residential (RW)
July-October
November-February
March-June

Total All Residential
NEK YORK STATE ELECTRIC AND CA
Residential
November -February
March-June
July-October

PENNSYLVANIA POKER AND LIGHT
Residential
Novcobe r - February
July-October

1

Fraction
of Sales
Assumed
in Tail-
Block



.1996
.1996
.1996



.1720
.1720
.1720



.1996
.1996
.1996

.1U96
.1996
.1996


.1996
.1996
.1996


S
.1996
.1996
.1996


.1996
.1996
.1996

2

Peak KWII
in Season
10'KWH



647,588
365,872
362,110



2,383,353
1,290,684
2,155,833



279,346
270,234
225,059

578,645
559,769
466,193


1,010,966
977,988
814,499



574,163
471,407
497,435


724,801
568,600
582,447

3

Peak Tail-
Block
Sales
lO'JCWH



129,258
73,028
72,277
E 274,563


409,937
393,998
370,803
El, 174, 738


55,757
53,939
44,922
E 154,618
115,498
111,730
93,052
E 320,280

201,789
195,206
162,574
E 559,569
El, 034, 467

114,603
94,093
99,288
I 307,984

144,670
113,493
116,256
E 374,419
4

Estimate of
State Average
(and Marginal)
.Price Elasti-
cities



-1.22
-1.22
-1.22



-1.21
-1.21
-1.21



-1.18
-1.18
-1.18

-I'.IB
-1.18
-1.18


-1.18
-1.18
-1.18



-1.24
-1.24
-1.24


-1.22
-1.22
.-1.22

5

1972 First
Block Rate
by Season
KWT



.0375
.0375
.0375



.0386
.0386
.0386



.0390
.0390
.0390

.0400
.0400
.0400


.0390
.0390
.0390



.0501
.0501
.0501


.0500
.0500
.0500

6

Upper
Bound
$
KBR"



.0796
.0796
.0796



.0578
.0578
.0578



.0680
.0635
.0653

.0270
.0266
.0272


.0409
.0401
.0413



.0659
.0655
.0655


.0741
.0762
.0624

77

1972 Tail-
Block Rate
by Seasoa
KWR



.0205
.0135
.0135



.0226
.0226
.0226



.0140
.0140
.0140

.0100
.0100
.0100


.0140
.0140
.0140



.0164
.0164
.0164


.0130
.0130
.0130

8
Difference
Between
Tailblock
Rate and
Smaller of
6 or 7
$
KKH


.0170
.0240
.0240



.0160
.0160
.0160



.0250
.0250
.0250

.0170
.0166
.0172


.0250
.0250
.0250



.0337
.0337
.0337


.0370
.0370
.0370

9

Fractional
Price
Change



-.5862
-.9412
-.9412



-.5230
-.5230
-.5230



-.9434
-.9434
-.9434

-.919
-.907
-.925


-.9434
-.9434
-.9434



-1.0135
-1.0135
-1.0135


-1.175
-1.175
-1.175

10
Upper
Bound on
Efficiency
Gains
*V •
T^P^V P


785,755
1,006,265
995,917
E 2,787,926


2,075,320
1,994,628
i,877,:o:
E 5,947,150


775,852
75750,557
625,036
F 2,151,495
1,064,593
992,497
886,534
E 2,943,624

2,306,684
2,715,122
2,261,242
E 7,7S3,048
III, 378,167

2.426,798
1,992,435
2,102,553
E 6,521,841

3,822,236
2,998,523
3,071,526
E 9,892,290

-------
tween perceived price and average price is somewhat more
elusive.  For, with few exceptions, demand charges are based
upon noncoincident demand--upon the customer's maximum demand,
whenever that maximum demand may occur, and not upon coinci-
dent demand  (the customer's demand at the time of the system
peak).  Our  route around this  dilemma is, and must be, dif-
ferent  for the different utilities studies, largely because
the nature of the data we have been able to assemble varies
from company to company; valuable information would be need-
lessly  sacrificed with a uniform methodology.

We are  encouraged by the comparability of results between
systems.  The magnitude of the benefit measure indicator
does not seem to vary widely between systems.

There are three kinds of data  upon which an appraisal of the
performance  of demand billed rate structures can be based.
(1) From some systems we have  been able to obtain data which
summarize, on a monthly basis, total KWH and total KW for
demand  billed accounts:  for each rate schedule served under
a tariff with both demand and  energy charges, we therefore
have, on a monthly basis, total KWH, total KW, and, typically
the number of bills sent.  (2) For one system we have been
able to obtain something very  unusual:  for Commonwealth
Edison  of Illinois we have, for a large sample of major in-
dustrial users, individual customer load curves on an hourly
integrated demand basis for the whole of one week in August.
Since industrial loads exhibit relatively little seasonal
variation, this is valuable information.  (3) For most sys-
tems, we must work from our rough  constructed load curves
by customer  class for each season.
                             148

-------
Such is the variation in data availability across our sample.
We turn to a more explicit description of methodologies em-
ployed in each case, of checks on the adequacy of assumptions
and approximations, and finally to a discussion of the re-
sults.  A reminder of our objective:  our guiding question is
how well does the existing pattern of demand charges and
energy charges approximate cost at peak?  Of interest is not
only the absolute deviation of perceived price from (our
best estimate of) cost at peak, but also the importance of
that derivation--a measure of benefits to be had from nar-
rowing the discrepancy.  Because methods for treating the
demand billed accounts must necessarily differ between sys-
tems, whereas the methods for computing indicators of poten-
tial pricing improvement are identical, we reserve our dis-
cussion of those indicators until after the various methodo-
logies have been discussed.

Imputation of a MeanDemand Bill Where Aggregate Demand and
Energy Data are Available--Suppose we have, as we do for the
Potomac Electric Power Company, data on the total KWH, total
KW and number of bills, for each demand billed account, by
month for 1972.  Total KWH means the sum of the KWH for which
customers in each demand billed customer class are billed  in
each month; total KW means the sum  of  customer maximum de-
mands for the corresponding customer class  and month.  These
data are compiled in Table 44.  A representative bill may
then be imputed  as  follows:   take the  per  customer  average
KWH and KW, and, using the rate  schedule,  price  out  the  bill.
Imputation of Mean Demand Bill Where  Sample Data  on  Individ-
ual Demand-Billed Customers  is Available--Table 45,  Load
Curve for a  Single Industrial  Customer,  Commonwealth Edison
                             149

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         Table 44.  POTOMAC ELECTRIC POWER COMPANY,
      DEMAND BILLED ACCOUNTS FOR DISTRICT OF COLUMBIA,
                   SELECTED MONTHS OF 1972
Rate Schedule
Commercial


Industrial


Month
January
April
Augus t
January
April
August
Total
KWH
204,825,718
193,396,901
298,741,659
118,316,350
113,582,130
181,845,708
Total
KW
496,079.4
500,531.7
751,304.0
, 280,948.6
280,038.4
395,610.2
Number
of
Bills
5-, 241
5,329
5,391
129
130
131
Company, is included to show the type of data upon which this
section builds, and to emphasize what we have said before--
that it would cost almost nothing for many systems to begin
billing in a time-dependent way, since they necessarily know
the load curves of their major industrial customers.  By ex-
amining the hourly-integrated load figures, we can find the
hour and the day, during the week for which we have this in-
formation, of the individual customer's noncoincident peak.
Thus, for the customer occupying premise 47044, the peak
came at 8 p.m. of August 16.  We have the size of this cus-
tomer's noncoincident peak--21,816 KW--and, from Table 45,
this customer's energy consumption for the week.  By multi-
plying that latter figure by four, we obtain an estimate of
the customer's monthly consumption.  Thus we have, for each
individual industrial premise in the sample, an estimate of
energy taken and demand.  The calculation of the actual
energy and demand bills paid by the individual customers is
then a simple matter of looking at the relevant rate schedule
and pricing out the particular customer's energy and demand
charges.  (This amounts to evaluating the algebraic expres-
sions in the row 4, column 3 entry of Table 27.)  In summary,
                              150

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Table 45.  LOAD CURVE FOR A SINGLE INDUSTRIAL CUSTOMER,
          COMMONWEALTH EDISON COMPANY, 1972
              (Hourly Integrated Demand)
Hour
Ending
1 AM
2 AM
3 AM
4 AM
5 AM
6 AM
7 AM
8 AM
9 AM
10 AM
11 AM
12 AM
1 PM
2 PM
3 PM
4 PM
5 PM
6 PM
7 PM
8 PM
9 PM
10 PM
11 PM
12 PM
Total
Aug
13
702
702
756
702
702
702
702
702
756
810
865
756
648
702
702
648
648
648
648
648
702
1,026
1,836
3,240
20,953
Aug
14
14,094
18,090
11,556
9,990
18,684
9,666
10,692
16,686
16,470
8,316
19,872
19,440
13,824
19,278
18,522
9,990
15,822
18,954
12,582
13,338
18,630
17,064
19,656
17,766
368,982
Aug
15
9,882
15,552
16,362
12,042
15,714
16,578
11,826
20,682
16,578
13,878.
13,716
16,794
16,470
17,658
16,632
15,822
13,122
10,692
11,880
14,256
20,250
15,498
20,466
16,200
368,550
Aug
16
9,936
10,962
11,448
5,670
12,690
13,176
11,340
12,312
11,664
18,900
17,496
14,742
19,008
16,254
11,340
12,852
17,334
9,072
16,092
21,816
14,688
18,630
20,358
12,042
339,822
Aug
17
6,426
13,878
9,666
7,992
16,524
12,096
5,076
17,280
21,114
13,176
5,616
5,616
5,022
6,102
6,750
5,238
12,906
19,454
17,766
6,318
5,130
5,022
3,726
3,780
231,714
Aug
18
9,666
18,198
12,420
9,126
17,442
12,744
16,956
12,204
7,506
9,612
7,830
8,262
5,454
9,180
6,048
2,970
2,322
2,538
3,240
3,672
3,240
3,078
2,646
2,322
188,676
Aug
19
2,754
2,430
972
972
864
918
918
1,080
1,026
1,134
1,404
1,134
918
918
918
810
756
702
756
756
810
756
756
702
25,164
                          151

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for this case in which we have obtained individual customer
data, we can compute energy and demand charges for each cus-
tomer.

Imputation of a Mean Demand Bill Where Only Federal Power
Commission Data are Available--Finally, in the case where all
we have to go on are the reports all large systems must file
with the Federal Power Commission (FPC Froms 1 and 12), a
representative bill for demand billed schedules may be con-
structed as follows.  First, recall that we have imputed (in
the course of our reconstruction of cost structures) customer
class load curves subject to various assumptions.  We may, by
dividing the individual rate schedule contribution to the sys-
tem peak by the average number of customers and by the number
of hours during the system peak, derive an estimate of indi-
vidual customer demand.  Similarly, an average energy per cus-
tomer figure can be derived.  Taking the resulting energy and
demand combination as our representative bill for each rate
structure, we may price out this mean bill--again, this amounts
to evaluating the algebraic expression in the row 4, column 3
entry of Table 27--and proceed.

These representative bills have .been constructed as guides to
what might be called "perceived" prices at peak.  The central
fact about them is that, with few exceptions, all demand
charges are based upon noncoincident demand--upon the cus-
tomer's maximum demand, whenever it occurs.  This is in prin-
cipal unrelated to imposed capacity cost, and only makes sense
to the extent that individual customer and system peak demand
coincide.  Do they?  The question can only be answered by  ,
sample data on individual large use load curves.  But the only
such sample we have seen, the Commonwealth Edison data in
Table 45 above, is not supportive of this inference.  Another
                             152

-------
rationale for noncoincident demand billing is, of course, that
if industrial demand is approxiamtely flat then it matters not
where billing demand is measured, since maximum noncoincident
and coincident peak demands necessarily coincide.

How then to move from these representative bills to our bene-
fit assessments?  The crucial comparison is,  of course, be-
tween perceived price at system peak and our  reconstruction
of cost at system peak on a rate schedule basis.  The cost
estimate has already been done, and amounts to our upper bound
column of Tables 33 through 37.  The perceived price estimate
remains to be computed.  First, recall that in terms of our
customer typology, customers are here assumed to be both mar-
ginal price responsive and time differentiating, i.e., of
type IV.  Thus the price we want is the perceived marginal
price of a peak KWH.  Since the rate schedules we are con-
sidering in this section are demand-billed, the marginal
price must be the sum of an energy and a demand component.
For the energy component, the obvious candidate is the actual
marginal energy charge corresponding to the mean bill for
each rate schedule — in effect, the height of the energy block
in which the mean bill sits.  For the demand charge, things
are. not so clear cut, for here the charge is levied upon  a
noncoincident maximum demand basis.  We therefore  assume,  in
constructing a measure of the perceived demand  charge, that
customers subject to a noncoincident  demand  charge spread that
charge  evenly over  time:  they assume  that their monthly de-
mand charge  is incurred  at  a  constant  hourly rate.   Summation
of energy and, demand components  gives  us,  at last, the per-
ceived  peak period  marginal prices  compiled, for each system
and each demand billed rate  schedule,  in column 2 of Table 46.

Given both perceived price  and estimated marginal cost,  the
construction of new benefit  indicators  on a  rate schedule
                             153

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          Table  46.   INDICATORS  OF POTENTIAL  PRICING
              IMPROVEMENT,  DEMAND-BILLED SCHEDULES



System Rate Schedule (Season)


POTOMAC ELECTRIC POWER COMPANY
General Service (GS)
June-September
October- January
February-May
Large Power Service
June-September
October- January •
February -Hay

COMMONWEALTH EDISON COMPANY
Small Commcrical and Industri
June-September
October- January
February '•May
1


10'KWH




1,268,353
716,594
709,222

279,009
279.009
279,009
I 3.531,196

al
2,276,368
2,222,243
2,059,061
Large Commercial and Industrial
• June-September*1
October- January^
February-May"

DUKE POWER COMPANY
General Service (G)
July-October
November -February
March-June
General Service (GA)
July-October
November -February
March-June
General Service (I)
July-October
November- February
March-June

1,990,614
1,943,283
1,800,586
£12,292,155

891.246
862.173
718,0.45

548.715
530,816
442,080
1,402,182
1,402,182
1,402,182
£ 8,199,621
2
Perceived
KNH
Marginal .
Price
During
System
Peak •
TOT


.0151
.0145
.0145

.00859
.00844
.00844



.0148
.0148
.0148

.0094
.0094
.0094 .
•

.0121
.0121
.0121
.
.0081
.'0081
.0081
.0061
.0061.
.0061

3


Upper
Bound
$
TGHT
Mlfl



.0216
.0250
.0235

.0178
.0212
.0210



.0280
.0280
.0280

."0228
.0228
.0228


.0205
.0202
.0208

.0143
.0142
.0146
.0135
.0134
.0138

4


%k




.00650
.01050
.00900

.00921
.01276
.01166



.0132
.0132
.0132

.0135
.0135
.0135


.0084
.0081
.0087

.0062
.0061
.0065
.0074.
.0073
.0077

5


3* -4




.354
.532
.474

.698
.861
.817



.617
.617
.617

.841
.841
.841


.515
.502
.529

.554
.547-
.573
.755
.749
.774

6


Estimate of
State Average
(and Marginal)
Price Elasti-
cities



-1.46
-1.46
-1.46

-1.93
-1.93
-1.93



-1.48
-1.48
-1.48

-1.87
-1.87
-1.87


-1.13
-1.13
-1.13

-1.13
-1.13
-1.13
-1.65
-1.65
-1.65

7
Seasonal
Upper
Bound on
Efficiency
Gains -
P*
|eAPKWHpk^


2,131,621
2,919,815
1, 207,172

1,730,847
2,957,993
2,553,178
£14.500.626


13,718,828
13,392,638
12,409,199

21,422,454
20,913,235
19.582,367
£101,239,221

2,178.306
1.980,697
1,867,074

1,064,835
1. 000, 682
930,258
6,462,854
6,324,853
6,894,097
£28,703,656
"circled numbers art aoVumn numbert; unolroled number la the digit Z.

 Data are averages from aaleulationa from a eampla of premises.
                                  154

-------
Table 46 (Continued).   INDICATORS OF POTENTIAL
 PRICING IMPROVEMENT,  DEMAND-BILLED SCHEDULES




System Rate Schedule (Season)




NEW YORt STATE ELECTRIC AND GAS
General Service (PSC108SC2)
November-February
March-June
July-October
General Service (PSC113SC2)
November-February
March-June
July-October
1



^k
lO'Klffl





86,342
71,023
74,944

147,458
120,931
127,991
Large Light and Power (PSC113SC3)
November-February
March-June
July-October
191,910
191,910
191.910
Primary Light and Power (PSC108SC3)
November-February
March-June
July-October

•33,310
33,310
33,310
I 1.304,349
PENNSYLVANIA POKER AND LIGHT COMPANY
General Service (SGS)
November -February
March-June
July-October
Large General Service (LP-3)
November - February
March-June
July-October
Large General Service (LP)
November-February
March-June
July-October
Primary General Service (LP-4
November - February
March-June
July-October
High-Tension General Service
November - February
March-June
July-October
High-Tension General Service
November-February
March-June
July-October


116,606
91,447
93,709

439,437
345,134
353,557

44,302
44,302
44,502
160,438
160,438
160,438
81,890
81,890
81,890
(LP-6)
188,779
188,779
188,779
E 2,866,077
2
Perceived
KW1!
Marginal
Price
During
System
Peak
J
XHH


.0121
.0121
.0121

.0240
.0240
.0240

.0149
.0149
.0149

.0073
.0073
.0073



.0328
.0328
.0328

.0121
.0121
.0121

.0102
.0102
.0102
.0085
.0085
.0085
.0066
.0066
.0066
• .0057
.0057
.0057

3


linn A i*
upper
Bound
VLJU
Ann




.0227
.0229
.0229

.0333
.0333
.0333

.0178
.0181
.0180

.0176
.0179
.0178



.0597
.0617
.0489

.0219
.0227
.0195

.0219
.0225
.0196
.0210
.0216
.0187
.0211
.0217
.0188
.0209
.0215
.0186

4



Ap ,
p*





0106
0108
0106

0093
0093
0093

0029
0032
0031

0103
.0106
.0105



.0269
.0289
.0161

.0098
.0108
.0074

.0117
.0123
.0094
.0125
.0131
.0102
.0145
.0151
.0122
.0152
.0158
.0129

5



*Ppk _ J&,
P zd'





.6092
.6171
.6092

.3246
.3246
.3246

.1774
.1939
.1884

.8273
.8413
.8367



.582
.612
.394

.577
.621
.468

.729
.752
.631
.848
.870
.750
1.047
1.067
.961
1.143
1.162
1.062

6


istimate of
tate Average
and Marginal)
Price Elasti-
cities




-1.65
-1.65
-1.65

-1.65
-1.6S
-r.es

-1.89
-1.89
-1.89

-1.89
-1.89
-1.89



-1.46
-1.46
-1.46

-1.46
-1.46
-1.46

-1.93
-1.93
-1.93
-1.93
-1.93
-1.93
-1.93
-1.93
-1.93
-1.93
-1.93
-1.93

7
Seasonal
Jpper
iound on
Efficiency
Gains •
Ah' v "
P*
1 4l>
IeipKWHpk p


459,969
390,497
399,248

367,232
301,167
318,751

93,271
112,491
105,884

268,145
280,625
276,457
I 3,373,737


1,332,126
1,178,758
433,762

1,813,207
1,689,087
893,482

364,635
395,429
253,573
1,641,113
1,764,504
1,184,388
1,199,694
1.273,202
926,486
3.164.962
3.344,583
2,495,703
£25,348.694
                      155

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basis is straightforward, and is carried out in Table 46,
Indicators of Potential Pricing Improvement, Demand-Billed
Schedules.  Again, as in the case of the Category III bene-
fit estimates, a warning is appropriate in the interpretation
of these figures.  The reductions in peak consumption given
by the usual elasticity formula are very large, sometimes
amounting to total peak consumption.  Here, as before, the
source of this result is apparent:  the application of long
run elasticities to peak price changes often amounting to
more than 90 percent of perceived price.  Accordingly, the
benefit estimates are to be taken as order of magnitude esti-
mates.
                             156

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                          SECTION V


                         REFERENCES
1.   Anderson, K.,  Industrial Energy Demand (Rand Corporation,
    1971).

2.   Baumol, W.,  and Bradford, D.,  "Optimal Departures  from
    Marginal Cost  Pricing," American Economic Review (June,
    1970).

3.   Chapman, L.D., et.  al., "Electricity Demand in the
    United States:  An Econometric Analysis," Oak Ridge
    National Laboratory, Preliminary, June 1973.

4.   Federal Power  Commission, The 1970 National Power  Survey
    (Washington,  D.C.:   U.S. Government Printing Office,
    1971),  Volume  I, p. 1-1-11.

5.   Fisher, P.M.,  and Kaysen, C., A Study in Econometrics:
    The Demand for Electricity in the United States (Am-
    sterdam:  North-Holland Publishing Company, 1962).

6.   Halvorsen, R., "Residential Electricity:  Demand and
    Supply," Environmental Systems Program, Harvard Univer-
    sity,  preliminary mimeograph, 1971.

7.   Smith,  V.K. ,  et. al. , ".Econometric Estimation of Elec-
    tricity Demand," mimeograph,  1973.

8.   Wilson, J.W.,  "Residential Demand for Electricity,"
    Quarterly Review of Economics and Business  (Spring,
    1971) .
                          157

-------
BIBLIOGRAPHIC DATA
 HEET
1. Report No.
 Title and Subtitle

 The  Economic  and Environmental Benefits from
Improving Electrical Rate  Structures
                                         3.-Recipient's Accession No.
                                         3. Report Date
                                           November 1971*
                                         6.
 Author(s)
        Dr. Mark Sharefkin
                                         8. Perfocauix Or*«oiz«iion Kept.
                                          No.JACXFAU-101-74
  Performing Organization Nam* and Address
Jack  Faucett Associates
5454  Wisconsin Avenue
Chevy Chase,  Maryland   20015
                                         10. Proiect/Tcsk/Vork Unit No.

                                         PE THAOQ^   PI AQT._rn
                                         11. Contract/Grant No.

                                           68-01  1850
 2. Sponsor ing Organization Name and Address

 Environmental  Protection  Agency
 Implementation Research Division
Washington, D.C.   20460
                                         13. Type of Report 8t Period
                                           Coveted

                                            Final  Report
                                         14.
 5. Supplementary Notes
    •traces   Quantitative  estimates of  the internal cost savings to be  de-
rived from changes in the  pricing  of electric  power are  devised and  eva-
luated.   The  econometric  literature   on electricity demand is surveyed,
and  elasticity values are  selected which are parameters  for the overall
benefit measures.   A method for using reported utility data to estimate
the  cost of delivered power--at the  system peak and off  the system,  and
for  each customer class--is devised.   Data on  five electric utilities is
used to make  estimates  of  the potential benefits from improvements  in the
pricing of electric power, for each  customer class in each system.   The
estimated potential benefits are sufficiently  large to merit load  curve
studies by block f,or residential customers.  Such studies are necessary
preliminaries to a definitive assessment of the proposals for so called
inversion.
17. Key Voids and Document Analysis. 17*. Descriptor*


     Electric Power

     Rate  Structure

     Environmental  Benefits

     Load  Curves

     Peak-Load Pricing


ITb, Identifiers/Open-Eaded Terms
Ue. COSATI Field/Group
                                                   21. Mo. of Pagea
                                                       187
 18. Availability. Statement
^..Security Class (This
  Report)

H. Security Class li'Sis
  Page
                                                                    22. Price
FORM NTIS-39 (REV. 8-721
                                                                    USCOM4-DC 40US>I»71

-------