COGENERATION AND UTILITY PLANNING
FREDERICK H. PICKEL
July 1982
Energy Laboratory
Utility Systems Program
Massachusetts Institute of Technology
MIT Energy Laboratory Report
No. MIT EL82-0.i15.
7VVKl
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F.I.T.UiBRARIES
SEP ,1 9 1983
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ABSTRACT
COGENERATION AND UTILITY PLANNING
Frederick H. Pickel
.This research refines methods for forecast-rfg customer
cogeneration to assist electric utility planning for power supply and
tariff design. The study employs a physically-based, or
engineering-economic process, approach to cogeneration forecasting
that accounts for both cogeneration investment and operating choices
by the utility's customers. This approach reflects the long-term
uncertainties in the fuel and electric prices affecting those customer
choices and the short-run uncertainties influencing cogeneration plant
performance. The analysis devotes special attention to the effect of
utility tariffs on the customer's decision to build and operate a
cogeneration plant and the resulting impact of these plant investment
and operating choices by the customer on the utility.
To identify the important factors in the forecasting process,
detailed case studies have been carried out for 7 large commercial or
industrial sites in southern New England; an aggregate forecast is
then made using survey data from 123 sites with existing boiler
facilities.
Under the assumed economic conditions, coal-fired steam
turbine cogeneration is found to be economic at sites much smaller
than previously has been thought to be economic.
Since southern New England is a region with slow electric load
growth, utility planning of cogeneration tariff policy is the primary
use for the modeling techniques in the case study. Cogeneration has
an adverse impact on the utility's customers and stockholders when the
utility's marginal cost, which is assumed to be the price for
electricity purchases from cogenerators, drops below its average
tariff cost. The impact is diminished by designing tariffs so that
the reduction in a customer's tariff costs through cogeneration
matches the utility's marginal cost reduction as closely as possible.
When the utility can pay slightly less than the marginal cost, the
latitude for the tariff design increases.
0747-609
PREFACE AND ACKNOWLEDGMENTS
This report is a portion of the research efforts on utility
operation and planning being carried out within the Utility Systems
Program at the MIT Energy Laboratory. It is based on the doctoral
dissertation by Pickel (1982), with only minor revisions. Other major
research projects within the Utility Systems Program have developed
coordinated models for power system planning, analyses of the
integration of solar photovoltaics and other alternate energy sources
into the utility system, and new approaches for electricity pricing
that encourage customer responsiveness to power system conditions.
The study was supported, in part, by research funds provided under
contract to the MIT Energy Laboratory by New England Power Service
Company, a subsidiary of the New England Electric System. Of course,
neither New England Power Service Company nor any of its affiliates,
officers, directors, agents, or employees makes any warranty or
representation of the accuracy or completeness of this document.
Professor Fred C. Schweppe has provided extensive encouragement
and advice, which was required to coax this research to completion.
Richard Tabors, as co-supervisor, provided the Energy Laboratory base
from which I could obtain funding and accomplish the research.
Together they are responsible for the continuity of MIT's utility
planning research at the Energy Laboratory and the Electric Power
Systems Engineering Laboratory, of which this report is a component.
Professors David H. Marks, David C. White, and Henry D. Jacoby have
long assisted and encouraged me in my doctoral program and my pursuit
of this research.
The insights of my colleagues at New England Electric, in
particular Frederic E. Greenman, William G. Hayes, Michael S. Hirsch,
John L. Levett, and John W. Newsham, have enabled me to add special
touches of realism to the study. In addition, I am grateful to
numerous individuals in industrial firms and other utilities who have
helped educate me on the subject of cogeneration and have provided the
data required for this study.
Alice Sanderson typed and retyped this report, quietly prodding
the progress. Richard Desjardins graciously prepared the many figures.
I must especially thank Carol Chilk for her support, sacrifice,
and patience while this was being completed. The completion of this
research rewards countless prayers and enduring faith of my mother.
SUMMARY TABLE OF CONTENTS
PREFACE AND ACKNOWLEDGEMENTS
Chapter 1
Introduction
Chapter 2
A Customer's Perspective on the Adoption
of Cogeneration
Chapter 3
Estimating the Impact of Customer Cogeneration
on a Utility
151
Chapter 4
Conclusions
230
Appendix A
Symbols, Abbreviations, and Conversion Factors
244
Appendix B
Examples of Typical Industrial and Large
Commercial Electricity Tariffs
255
Appendix C
Site and Survey Data Assumptions
267
Appendix D
Cogeneration Technology and Cost Summary
280
Appendix D
Modeling Summary
316
REFERENCES
327
BIOGRAPHICAL NOTE
334
TABLE OF CONTENTS
Page
ABSTRACT
2
PREFACE AND ACKNOWLEDGEMENTS
3
SUMMARY TABLE OF CONTENTS
4
TABLE OF CONTENTS
5
LIST OF FIGURES
11
LIST OF TABLES
14
CHAPTER 1:
1.1
1.2
1.3
INTRODUCTION
An overview of cogeneration history and policy studies
18
20
1.1.1
The decline of cogeneration
20
1.1.2
Cogeneration studies
24
National level
24
Regional level
28
Utility level
30
Project level
31
Comments
31
Utility planning for customer cogeneration
33
1.2.1
Cogeneration-utility interactions
34
1.2.2
Electric load forecasting and customer
cogeneration
37
Electric load forecasting methods
38
Physically-based cogeneration forecasting
40
Organization of the report
41
CHAPTER 2: A CUSTOMER'S PERSPECTIVE ON THE ADOPTION OF COGENERATION 45
2.1
Site studies
46
1;:I eeF"ILI
a
TABLE OF CONTENTS (continued)
Page
2.2
The basic economics of cogeneration
49
2.2.1
50
Selecting and operating a cogeneration plant
2.2.1.1
Selection of utility tariffs under
cogeneration
52
Economic operation of a cogeneration
plant
53
2.2.1.3
Sizing the cogeneration plant
59
2.2.1.4
Selecting the minimum cost plant
68
2.2.1.2
One design versus an existing boiler
Comparison of multiple designs versus
an existing boiler
2.2.2
2.3
The complications imposed by realistic utility
tariffs
2.2.2.1
An introduction to utility ratemaking and
industrial tariffs
82
2.2.2;2
The difficulties associated with
typical utility tariffs for a
cogenerating customer
Modeling the optimal economic design and operation of a
cogeneration and boiler plant
2.3.1
Description of the cogeneration plant analysis
model
2.3.1.1
2.3.1.2
92
95
96
The minimization of plant operatinq cos ts 101
Plant performance modeling
102
Operating cost escalation
110
Selection of minimum cost operating
policy by year
i11
The investment decision analysis
112
TABLE OF CONTENTS (continued)
Page
2.3.1.3
2.4
112
Expected value calculation
112
Minimum present cost plant design
118
Sensitivity analysis of operating and
investment decisions
125
2.3.2
Overview of plant studies by technology and site
131
2.3.3
Simplification of the model
134
2.3.4
Comparison of analytical and modeling results
137
Summary
CHAPTER 3:
3.1
Financial analysis
148
ESTIMATING THE IMPACT OF CUSTOMER COGENERATION ON
A UTILITY
The impact on the utility from cogeneration by a
single customer
3.1.1
The basic economics of the impact upon a utility
from customer cogeneration
3.1.1.1
3.1.1.2
3.1.2
151
156
The influence on the utility from each
level of a customer's decision to
cogenerate
157
The levels of impact
159
Combined impact
160
Special complications for the utility
arising from typical tariffs
165
Modeling the impact from a customer cogeneration
system
3.1.2.1
151
An example of the impact from a single
customer
171
172
8
TABLE OF CONTENTS (continued)
3.1.2.2
3.2
Modeling utility policies to reduce
the impact from customer cogeneration
by limiting the tariff menu
177
3.1.2.3
Overview of the different site studies
182
3.1.2.4
Simplification of the modeling
185
3.1.2.5
Comparison of analytical and modeling
estimates of the utility impact
188
An aggregate forecast of the impact from cogeneration
190
3.2.1
3.2.3
3.3
199
A simple multi-technology forecast
204
Forecasting using the cogeneration plant
investment and operation model
211
227
230
Research results
230
Cogeneration economics from the utility's
perspective
231
4.1.2
Forecasting cogeneration at the utility level
234
4.1.3
Physically-based electric load forecasting
236
Directions for further research
4.2.1
4.3
A simple single technology forecast
CONCLUSIONS
4.1.1
4.2
198
Summary and conclusions
CHAPTER 4:
4.1
Exploratory forecasts of the maximum cogeneration
capacity development
237
Cogeneration economics and utility planning
research
238
4.2.2
Cogeneration forecasting research
239
4.2.3
Physically-based electric load forecasting
research
240
Closing
241
TABLE OF CONTENTS (continued)
Page
APPIENDIX A:
SYMBOLS, ABBREVIATIONS, AND CONVERSION FACTORS
244
AP PENDIX B:
EXAMPLES OF TYPICAL INDUSTRIAL AND LARGE COMMERCIAL
ELECTRICITY TARIFFS
255
B.1
A traditional industrial tariff
256
B.2
An energy-only industrial tariff
259
8.3
A time-of-use industrial tariff
260
B.4
A supplemental provision for customers who cogenerate
262
B.5
A special cogeneration tariff
263
AP PENDIX C:
C.1
C.2
D.2
267
Detailed modeling data
267
C..1
Steam loads
267
C.1,2
Electric loads
270
Modeling data for survey sources
270
C.2.1
Steam loads from survey sources
274
C.2.2
Electric loads from survey sources
274
COGENERATION TECHNOLOGY AND COST SUMMARY
280•
AP PENDIX D:
D.1
SITE AND SURVEY DATA ASSUMPTIONS
Components of cost and performance
281
D.,1
Fuel efficiency and output shares
283
D.1.2
Plant reliability
287
D.1.3
Environmental factors
290
D.1,.4
Capital costs
290
D.1.5
Operation and maintenance costs
299
Cogeneration and steam technologies
302
D.2.1
302
Diesel cogeneration systems
i:
10
TABLE OF CONTENTS (continued)
Page
D.3
D.2.2
Gas turbine cogeneration systems
305
D.2.3
Steam turbine cogeneration
308
D.2.4
Air conditioning chilled water systems combined
with cogeneration
311
Summary and.comments
313
MODELING SUMMARY
316
APPENDIX E:
REFERENCES
327
BIOGRAPHICAL NOTE
334
IST
OF
FIGURES
LIST OF FIGURES
Figure N(o.
1.1
Page
Estimated share of cogeneration in total U.S.
electric and industrial steam energy supply
21
Total U.S. electricity production and estimated
cogeneration with projections for 1985
23
1.3
Cogeneration-utility interactions
35
2.1
Economic operation of a cogeneration plant
58
2.2
Optimal sizing of cogeneration plant capacity
64
2.3
Cost components for diesel cogeneration per unit of steam
66
2.4
Components in the cost of cogeneration systems
per unit of steam
69
2.5
Steam loads and cogeneration operating savings
73
2.6
Sensitivity of the net present value of
cogeneration projects to unit cost reduction
and peak electricity price conditions
76
Sensitivity of the net present value of oil-fired
cogeneration to oil prices when electricity
credits are based on oil
79
Sensitivity of the net present value of oil-fired
cogeneration to oil prices when electricity
credits are based on coal
80
2.9
The incremental cost of demand or energy use charges
87
2.10
Comparative incremental value of additional
cogeneration on a net sale/internal usage basis
by the large manufacturer on the H rate
91
2.11
The site steam and electricity cost analysis model
98
2.12
Monthly average steam usage and the simulated
shift-by-shift steam load duration curve for the
large manufacturer
108
Results for the large manufacturer
119
1.2
2.7
2.8
2.13
_I___W___*il~fl__llII
LIST OF FIGURES (continued)
Page
Figure No.
2.14
The customer's cogeneration decision
121
2.15
The distribution of incremental discounted cost
savings for the alternative plant types
123
Comparison of modeled and simple analytical
plant sizing
147
Magnitude of the adverse impact on a utility from
a customer selling cogenerated electricity on a net
sale basis
164
Fixed time-of-supply pricing versus time-of-oil
pricing for electricity purchases by the utility
169
Frequency of peak month steam loads for the survey
data sites
201
Frequency of average steam loads for the survey
data sites
202
Frequency of base month steam loads for the survey
data sites
203
Cumulative peak month steam loads at survey
data sites
205
Cumulative average steam loads at survey
data sites
206
Cumulative base month steam loads at survey
data sites
207
Total cogeneration capacity projection using simple
multi-technology approach as a function of
average steam load for survey data sites
210
Forecast of cumulative cogeneration capacity
by technology: base case
218
Forecast of cumulative cogeneration capacity
by technology: higher cost of capital &
lower oil price case
219
Ten-year cogeneration forecast:
220
2.16
3.1
3.2
3.3a
3.3b
3.3c
3.4a
3.4b
3.4c
3.5
3.6a
3.6b
3.7a
capacity
LIST OF FIGURES (continued)
Figure No.
3.7b
Ten-year cogeneration forecast:
expected energy output
3.7c
Ten-year cogeneration forecast:
utility impact
expected
3.8
221
222
Distribution of utility impact for aggregate
forecast
226
Comparison of steam load duration data and
estimates for the hospital site
269
D.1
Capital cost of boilers
294
D.2
Net capital cost of cogeneration equipment
296
D.3
Gross capital cost per MBTU/hour of boiler
and cogeneration equipment
297
Gross capital cost per kilowatt of cogeneration
equipment
298
Variation of gas turbine efficiency with
unit capacity
307
C.1
D.4
D.5
e
5.
i
r~L~
14
LIST OF TABLES
Table No.
Page
1.1
Typology of cogeneration studies
25
2.1
Site survey
48
2.2
Cogenerated electricity sales choice
52
2.3
Examples of incremental fuel operating costs and
optimal economic operation for cogeneration systems
60
2.4
Overview of the model implementation
99
2.5
Plant operating modes
103
2.6
The menu of tariff options
104
2.7
Fuel and utility conversion scenarios
105
2.8
Site-specific data used in detailed plant analysis
107
2.9
Sample operating analysis for the large manufacturing
site
113
Cash flow analysis for the existing oil-fired boiler
versus a new coal-fired steam turbine cogeneration plant
115
Components in adjusted present value for a coal
cogeneration plant at the large manufacturing site
117
The impact of fuel and electricity price uncertainties
on the value of a cogeneration plant
120
The optimal operating policies by scenario for an
oil-fired steam turbine cogeneration plant at the
large manufacturing site in 1985
126
Sensitivity of the plant net present value because of
restrictions or economic changes
128
2.15
Net present value of new plants
132
2.16
Site-specific data used in aggregate plant analysis
136
2.17
Present value for the replacement of an oil boiler
138
2.18
The value of time-of-oil pricing for the firm relative
to all tariffs with fixed time-of-supply pricing
139
2.10
2.11
2.12
2.13
2.14
LIST OF TABLES (continued)
Page
Table No.
2.19
The expected value of perfect information
140
2.20
The optimal operating mode for 1985 by the analytical
approach
142
The optimal tariff choice for 1985 by the analytical
approach
144
Parallels in a customer's cogeneration decisions
and the utility impact
158
Impact on the utility from a customer's cogenerated
electricity sales choice
161
Impact on the utility from the choice of a
coal-fired steam turbine cogeneration plant by
the large manufacturer
173
Impact on the utility by scenario from the optimal
tariff and operating decisions for a coal-fired steam
turbine cogeneration plant at the large manufacturer
in 1985
174
Utility impact of the second choice plant, oil-fired
steam turbine cogeneration, at the large manufacturer
175
Losses for the utility under different fuel and
electricity price scenarios
178
Sensitivity of the impact from customer cogeneration
on the utility for the large manufacturer
180
2.21
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
Present value of the utility impact from the cogeneration
183
plants
3.9
Combined value of time-of-oil pricing for utility
and customer
184
3.10
Effect of modeling simplifications on the estimated.
impact on utility system
187
3.11
Influence of model simplifications on the value
of time-of-oil pricing for the utility
189
Impact on the utility from a coal-fired cogeneration
plant in 1985 as estimated by the analytical approach
191
3.12
Yi
_~-Lsll~a~L--~-Uru~ar;u~~--s~-~--.
-rr-
LIST OF TABLES (continued)
Table No.
Page
Classes of customer information employed in the
construction of a cogeneration forecast
193
Utility sales in 1978 and growth rates expected to
1990
196
Coverage of the survey relative to the total utility
sales
197
3.16
Sensitivity of the simple multi-technology forecast
212
3.17
Model forecast of cogeneration capacity, energy,
and utility impact
215
3.18
Sensitivity of forecast cogeneration capacity
and energy output in 1985
217
Sensitivity of the utility impact to economic and
environmental assumptions
224
3.20
Utility impact by scenario
225
4.1
Impact on the utility from a customer's cogenerated
electricity sales choice
232
A.1
Conversion factors and abbreviations
244
A.2
Subscripts and superscripts for symbols
245
A.3
Symbols
246
C.]
Information from utility survey of major industrial
and commercial customers
271
Steam usage pattern information derived from survey
data
275
Electrical usage pattern information derived from
survey data
278
D.1
Equipment performance and cost data sources
282
D.2
Plant output shares
285
D.3
Assumed plant reliabilities
288
3.13
3.14
3.15
3.19
C.2
C.3
17
LIST OF TABLES (continued)
Table No.
Page
D.4
Capital costs and tax credits
292
D.5
Time distribution of capital expenditures
300
D.6
Operating and maintenance costs
303
D.7
Comparative air conditioning chilled water costs
312
D.8
Cost and performance summary for cogeneration plants
serving a constant 50 MBTU/hr high pressure steam load
315
E.1
Summary of detailed modeling assumptions
317
E.2
Real fuel price escalation rates
322
E.3
Share of fuel types in average utility fuel mix
323
E.4
Share of coal as marginal cost utility fuel
324
E.5
Summary of aggregate modeling assumptions
325
I
Chapter 1
INTRODUCTION
Cogeneration, or the simultaneous production of heat and electric
or mechanical power, emerged as one of the main components of the
energy conservation strategies in the past decade. All the major of
national energy legislation in the past few years has adopted special
tax treatment, exemptions from fuel use restrictions, and electricity
and natural gas regulatory policy changes that are intended to
encourage cogeneration's more wide-spread adoption in anticipation of
its higher energy conversion efficiencies. l The most debated issue
has been the reform of rates between individual cogenerators and the
local electric utility. Many of the cogeneration studies in the late
1970's urged an analysis of the exact impact from current electric
utility rates upon cogeneration project economics. 2 The changes
influenced or mandated by the Public Utilities Regulatory Policies Act
1The
Powerplant and Industrial Fuel Use Act of 1978 (PL95-620),
the Public Utility Regulatory Policies Act of 1978 (PL95-617),
hereinafter PURPA, the Natural Gas Policy Act of 1978 (PL95-621), the
Energy Tax Act of 1978 (PL95-618), and the Windfall Profit Tax Act
(PL96-223). Both PURPA and regulations deriving from it are now under
several major court challenges, with the original decisions against
the law and against the regulations now on appeal. Mississippi v.
Federal Energy Regulatory Commission, No. J79-0212(c), slip op.
(U.S.C.S., S. Miss., February 19, 1982), argued No. 80-1749 (U.S.
January 19, 1982). American Electric Power Service Corp. v. Federal
Energy Resulatory Commission, No. 80-1789, slip op. (D.C. Cir.,
January 22, 1982), rehearing denied (D.C. Cir., April 9, 1982).
2 Resource
Planning Associates (1977), Pickel (1978), and TRW and
ThermoElectron (1979).
of 1978 have reached the implementation stage and the pilot
cogeneration projects of the mid-1970's are nearing completion.
The purpose of this research is the refinement of cogeneration
forecasting methods to supplement the information used in long-range,
electric utility planning for capacity expansion and for tariff
design.
It employs a physically-based, or engineering-economic
process, approach to cogeneration forecasting that accounts for both
investment and operating choices by the cogenerating customers and
reflects the long-term uncertainties in conditions affecting those
customer choices and the short-run uncertainties influencing
cogeneration plant performance.
To identify the important factors in
the forecasting process, detailed case studies have been carried out
for 7 large commercial or industrial sites in southern New England; an
aggregate forecast is then made using survey data from 123 sites with
existing boiler plants.
The analysis devotes special attention to the
influence of utility tariffs on the customer's decision to build and
operate a cogeneration plant and on the resulting economic impact of
these plant and operating choices by the customer on the utility.
This chapter provides a brief history of cogeneration's role in
U.S. steam and electricity supply along with a short survey of recent
cogeneration studies.
An introduction to utility planning for
customer cogeneration'and the application of physically-based electric
load forecasting techniques to cogeneration follows the survey.
The
introduction concludes with a discussion of the report's organization.
a
20
AN OVERVIEW OF COGENERATION HISTORY AND POLICY STUDIES
1.1
SThe
two products from cogeneration, steam and electricity, are
both important components in total U.S. fuel consumption.
Fuels for
the generation of industrial process steam constituted nearly 17
percent of the total U.S. fuel consumption in the late 1960's.3
Only transportation and combined residential and commercial space
heating exceeded this share for the end use of fuels.
Electricity
generation, which is not an end use, received about 21 percent of the
total fuel consumed.
Given these large shares for steam and
electricity in total fuel consumption, cogeneration has attracted the
attention of policy makers, who have questioned why such an apparently
fuel-efficient technology has seemingly diminished in importance since
the 1930's.
1.1.1
The Decline of Cogeneration
Cogeneration has long been a technique for supplying industrial
and large commercial steam needs while simultaneously producting
electricity. For example, many paper mills provided power for the
local towns earlier in this century.
The relative importance of
cogeneration in electricity supply has declined since the 1940's, as
illustrated by Figure 1.1.
Its share in electricity supply has
dropped from 18 percent in 1941 to less than 4 percent in 1979. 4
3Stanford
Research Institute (1972).
4 Electricity
data from Edison Electric Institute (annually), a
personal communication with Sam Ferraro, Federal Power Commission
(December 1975), and U.S. Energy Information Administration (1980).
ESTIMATED
SHARE OF COGENERATION
IN
U.S. ELECTRIC AND INDUSTRIAL STEAM
TOTAL
ENERGY SUPPLY
0I8
ST
M
6-
4-
0-ELE
64
,
4
..
....
0.
1935
NN_ N_
TRICITY
,
2... .....
1940
1945
1950
1955
1965
1960
YEAR
Figure
1
1970
1975
1980
--
1985
22
This continual drop in the relative share of electricity supply does
not tell the whole story.
Cogeneration also serves steam needs, but
industrial energy use has grown historically at a rate much slower
than total electricity consumption.
This growth rate differential can
explain a large part of the relative decline of cogeneration in
electricity supply.
Figure 1.1 also gives a crude estimate of
cogeneration's share in industrial steam supply;
in steam supply, the
role of cogeneration did not decline continually over the last 40
years but only during the last 15 years.
The total amount of
cogenerated electricity started to decline after the events of 1973,
as Figure 1.2 shows;
this is in contrast Lo t ie forecasts by a broad
range of studies and the intended purposes of a variety of government
,
programs. 5
5 These
statistics are derived from industrial generation
statistics (see note 4). Not all industrial generation is
cogeneration: in 1979, 4.3 percent was from hydro, 4.0 percent was
from gas turbines, 4.5 percent was from diesel, and 87.2 percent was
from steam turbines. In 1945, steam provided 85 percent of industrial
generation. This analysis assumed a fixed 87 percent of industrial
generation was cogeneration. The estimate of steam production from
cogeneration was made by assuming a constant ratio between electricity
and steam output from cogeneration over this period, taking the ratio
of industrial cogeneration to total fuel consumption from Chigioji
(1979, p. 15), and calibrating the ratio on an estimate of
cogeneration in steam supply for 1976 from Resource Planning
Associates (1977, Ex. 5). Since the Energy Information Administration
has altered and may abandon the gathering of statistics on industrial
generation (personal communication with Melvin Johnson, U.S. Energy
Information Administration, 1980), the industrial generation decrease
after 1977 may be due, in part, to tne collection procedure changes.
The projections for 1985 are derived from Dow et al (1975, pp. 88,
121), ThermoElectron (1976, table 6.32), Resource Planning Associates
(1977, exhibits l.d and 4), and Pickel (1978, pp. 160-173), and U.S.
General Accounting Office (1980).
TOTAL U.S. ELECTRICITY PRODUCTION AND
COGENERATION
ESTI MATED
WITH
10,000t
PROJECTIONS FOR
.
-
1985
COGENERATION PROJECTIONS
"
FOR
1985
I-
Ar
...
-
_-___J_
..
_
U
EThermo
Electron (1976)
Dow (1975)
_...
_
LL
0
_
INDUS RIAL COGENERATIC N
z
-J
-
_
Pickel (1978)
RPA (1977)
GAO (1980)
._
YEAR
Figure 1.2
_
_
_
o
1.1.2
24
Cogeneration Studies
Attracted to the substantially higher energy efficiencies
associated with cogeneration over separated steam and electricity
generation, long series of studies have offered reasons and proposed
programs for increased utilization of cogeneration by industry.
The
analyses have covered the national, regional, utility and individual
project levels from public policy, scholarly, utility, equipment
manufacturer, and industrial firm perspectives.
surveys a limited group of these studies.
This section briefly
Table 1.1 offers a typology
of these works.
National Level
At the national policy level, the study by Dow Chemical et al.
(1975) brought the subject of cogeneration to the attention of policy
makers, suggesting substantial energy and capital savings through
cogeneration by coal and nuclear facilities at very large industrial
sites.
The ThermoElectron report (1976) for the Federal Energy
Administration on three major steam using industries suggested greater
electricity production and energy savings at medium sized industrial
sites were possible through the use of gas turbine and diesel
engine-based cogeneration systems; they also recommended large federal
investment tax credits and regulatory changes to combat a perceived
hesitancy by industry and utilities to get involved in these expensive
projects.
Resource Planning Associates (1977, revised 1981), in a
study covering six industries,projected much more moderate energy
Table 1.1:
Typology of Cogeneration Studies
Perspective
Public
Policy
National
Level
Utility
Industrial/
Manufacturers
General
Analysis
Dow (1975)
Manuel et al.
(1980)
Schweizer &
Sieck (1978)
Williams
(1978)
ThermoElectron
(1976)
Synergistic
Resources
Pickel (1978)
(1981)
RPA (1977,1981)
Camm (1981)
GAO (1980)
Joskow (1981)
TRW/TE (1979)
NASA (1980)
Regional/
State Level
Utility/
Plant Level
Williams (1976)
Dittrich &
Allon (1977)
Wakefield
(1975)
Mass. Gov. Comm.
(1978)
RPA (1979)
Calif. PUC
(1978)
Helliwell &
Cox (1979)
Bright, Davitian TVA (1979)
Martorella
Charmichael
(1978)
(1980)
ConEd (1979)
RPA (1980b)
NEES (1978)
Gordon &
DeRienzo (1979)
RPA (1980)
I~--~-"^---
-~----
savings and a slower growth of cogeneration both with and without
federal incentives for industrial.
These policy studies heavily influenced the inclusion of special
provisions for cogeneration in the National Energy Act.
The Public
Utility Regulatory Policies Act of 1978 (PURPA) required the
development of rules governing the purchase and sale of electricity by
utilities from qualifying cogeneration facilities.
The Powerplant and
Industrial Fuel Use Act of 1978 allows special exemptions from coal
conversion for some industrial cogeneration plants.
The Energy Tax
Act of 1978 indirectly includes special investment tax credits for
portions of some copeneration technology capital costs and altersd
depreciation provisions for oil and gas-fired boilers in a way that
influences cogeneration economics.
The Windfall Profit Tax Act of
1980 specifically allows special investment tax credits for non-oil
and gas-fired cogeneration equipment.
1978
The Natural Gas Policy Act of
allows the Federal Energy Regulatory Commission to exempt
qualifying industrial cogeneration facilities using natural gas from
the incremental pricing provisions of that act;
FERC has exercised
this option.
Several national policy studies have followed this legislation.
The U.S. General Accounting Office (1980) completed a review of
cogeneration policy using the same general method as the Resource
Planning Associates (1977) study.
TRW and ThermoElectron (1979)
examined regional and industry differences in selecting the optimal
cogeneration plant designs under current economic conditions.
Two
27
studies coordinated by the National Aeronautics and Space
Administration (1980) have explored the potential for different
advanced technologies in cogeneration.
Several scholarly studies have also examined cogeneration at the
national level.
Williams (1978) provided a summary of policy reports
up to that time and perspectives on cogeneration as a complement to
other current and future electricity sources, especially as they
relate to national oil import policy.
Pickel (1978) explored the
economics of cogeneration from an industrial organization perspective,
simulating the perfect market performance expected for cogeneration in
the U.S. given historical and possible future cost conditions and
comparing this to historical market performance.
Camm (1981) providea
an international comparison of cogeneration's role by examining it in
the Swedish national power system, where many large industrial firms
own cogeneration and hydroelectric plants and interchange electricity
with the national electricity grid.
Joskow (1981) and Joskow and
Jones (1981) discussed the fundamental microeconomics of cogeneration
and its role in total U.S. electricity producton in the light of the
microeconomic discussion. 6
From the utility perspective, research by Synergic Resources Corp.
(1981) for the Electric Power Research Institute has developed
historical statistics on the cost and performance of cogeneration
systems.
6 The
In addition, a project by Manuel et al. (1980) has developed
plant sizing analysis in Joskow and Jones (1981) and Joskow
(1981) is similar to that in Section 2.2.1.3 of this report.
28
detailed mathematical models for characterizing the design and
economics of cogeneration systems;
results from these models are
summarized to make national level forecasts of cogeneration.
This
project makes a simple attempt to characterize utility tariffs, but it
lacks the treatment of long and short-term uncertainties needed for
the analysis of a specific utility's rates.
A number of equipment .manufacturers have entered or considered
entering the cogeneration equipment business.
Few of the studies in
support of these decisions, however, have been published.
An
exception is Schweizer and Sieck (1978), who used detailed
characterizations of cogeneration plants to develop their projections
for U.S. cogeneration.
Regional Level
At the state and regional level, New Jersey was one of the first
states to explore cogeneration as a means for reducing energy
consumption;
this evolved into a dispute between a public interest
group and one of the state's major utilities over the magnitude of
potential cogeneration in the state. 7
The California Energy,
Resources, Conservation, and Development Commission, through its
facility siting powers, and the California Public Utilities
Commission, through its powers of rate regulation and determination of
need, have explored the potential for cogeneration in California
7 Williams
(1976)
and Dittrich and Allon (1977).
within a number of siting and rate-setting hearings.
The California
PUC, in a landmark case, reduced the allowed rate-of-return for
Pacific Gas and Electric Company until PG&E had added specified
amounts of cogenerated power to their energy supply. 8
In
Massachusetts, a federally funded Governor's Commission examined the
state barriers to increased cogeneration and estimated possible
development for cogeneration in New England. 9 A commission in Texas
has completed a plan for expanding industrial cogeneration within that
state.10
From a methodological perspective, Resource Planning Associates
(1979) have developed a general approach for states to follow in
examining the potential development of cogeneration in their energy
plans.
A study by Wakefield (1975) evaluated the impact of a large
cogeneration project on the local utility system operations and
planning along with'its influence on regional interfuel competition.
Helliwell and Cox (1979) evaluated the influence of marginal versus
average cost industrial pricing upon cogeneration in the paper and
pulp industry and the resulting impact on regional electricity
planning for British Columbia, Canada.
8 California
Public Utilities Commission 011-26 (filed Sept. 6,
1978).
9
Massachusetts Governor's Commission on Cogeneration (1978).
10Erwin
and Stratton (1980).
Utility Level
A number of utilities have performed stuaies of cogeneration
within their individual service areas, although the studies have
usually been unpublicized unless they were required in the regulatory
or legislative process.
The two major classes of analysis are studies
of avoided costs for the determination of electricity purchase rates
under the state implementations of the Federal Energy Regulatory
Commission rules issued under PURPA and forecasts of industrial and
commercial cogeneration required for rate and capacity planning.
The
earliest publicized report was prepared by Dittrich and Allon (1977)
of Public Service Electric and Gas Co. of New Jersey, which was a part
of the New Jersey legislative study noted above.
All the California
utilities have prepared forecasts and cogeneration rate studies as
required under their PUC's resolutions.II
The TVA (1979), as a part
of its capacity and fuel planning, undertook a major study of
cogeneration at existing industrial sites, the development of
large-scale industrial energy centers, and the possibilities for the
conversion of existing regular generation plants to cogeneration.
Consolidated Edison has performed studies of cogeneration economics at
a surveyed group of customer sites and then calculated the impact of
these conversions.12
New England Electric, at the same time as tne
Massachusetts Governor's Commission on Cogeneration mentioned above,
11California
SSeptember
Public Utilities Commission, Docket OIR-2 (Filed
3, 1980).
12 Described
in Wagers at the EPRI Cogeneration Worksnop (1979).
31
made an extensive survey of its larger customers and calculated the
cost from the customer's perspective of converting to
13
cogeneration.
Resource Planning Associates (1980b) nas made
projections for the Pacific Gas and Electric territory using the same
basic method employed in the RPA(1977) national study.
Project Level
A large number of detailed analyses have been made of cogeneration
at individual plant sites;
they can be separated into studies for
large industrial plant sites and for heating-ventilatingair-conditioning systems in commercial complexes.
A typical process
industry case study is documented by Gordon and De Rienzo.14
Because of the continually changing mix of steam, cooling, and
electrical needs, HVAC-based systems require more elaborate studies,
as discussed in Channichael (1978) or Bright, Davitian, and Martorella
(1980).15
Comments
These studies span the globe from Sweden to San Diego, offering
13 Communications
with New England Electric personnel (1979 et
.seq).
14 From
Gordon and DeRienzo of Dravo Corp. at the EPRI
Cogeneration Workshop (1979).
15 Appendix
D discusses some of the problems with incorporating
absorbtion air conditioning, but this report does not examine in
detail the combined addition of cogeneration and absorbtion air
conditioning.
recommendations on everything from federal tax policy to the brand of
scrubber for coal systems;
several factors, however, characterize
their approach to analyzing cogeneration.
First, recognizing that an
industrial or commercial establishment must use the heat output from
the plant, most studies make some calculation of the economics of a
cogeneration plant from the firm's prospective. 16
Second, as the studies become more geographically localized in
their scale, cogeneration forecasts are more likely to be based on an
enumeration of potential sites rather than an estimate based on
aggregate fuel consumption with an allowance for the scale of the
individual sites in the population.
The forecasts base( on tne
enumeration of potential cogeneration sites commonly estimate a lower
potential for cogeneration than those based on aggregate energy data
for the same area.
Finally, nearly'every study has assumed a small number of
characteristic cogeneration plant types, one approach to the sizing of
the cogeneration relative to the plant's steam and electric loads, and
one principle set of fuel and electricity prices.
Within these
studies there has been no explicit consideration of the substantial
plant performance and electricity and fuel price uncertainties.
1GOne
attempt to apply econometric methods to cogeneration was
in an appendix to the U.S. General Accounting Office (1980) report.
1.2
UTILITY PLANNING FOR CUSTOMER COGENERATION
A utility may require the adaptation of its capacity, rate-making,
and operating plans in response to the potential development of new
cogeneration in its service territory.
Significant growth in customer
cogeneration might alter capacity requirements
generation capacity mix.
or shift the desired
The utility's profits may change adversely
because tariffs for cogenerating or potentially cogenerating customers
affect each firm's cogeneration plant design and operation and may
alter incentives for electric energy conservation and load management
for the inplant loads.
The operation of the cogeneration plants
influences the economic operation of the utility's plants.
Finally,
the dispersed cogeneration plants can affect the physical Dehavior of
the power system.
As noted at the start of the introduction, this research refines
cogeneration forecasting methods to improve the information provided
for long-range electric utility for planning capacity expansion and
tariff design.
Previous studies of cogeneration at the utility level
have been ad hoc and only rarely documented in a way that provides a
reproducible procedure for a similar situation. 17
Furthermore, the
forecasting of cogeneration must be coordinated with tne other aspects
of capacity and rate planning.
Specifically, it must be directly
compatible with other components of the electric load and revenue
17Exceptions
are Resource Planning Associates (1980b) and
Tennessee Valley Authority (1979), although they do not mention any
attempt to include actual tariffs in their analyses.
-
i 34
forecast.
In the process of attaining these goals, the project also
extends an aspect of the physically-based, or process, approach to
electric load forecasting.
This section, first, discusses the economic interactions that
influence the level of industrial cogeneration and, then, the
utilization of this economic understanding within physically-based
electric load forecasting approach to obtain the information needed
for utility planning.
1.2.1
Cogeneration-Utility Interactions
Figure 1.3 provides a scheoatic of the major econosmic interacLioris
between the utility and the industrial sector.
The industrial firms,
or even the large commercial customers, must select sources for their
heat and electricity needs, which are influenced by the costs of heat
and electricity.
In this conceptual perspective, the supply of heat
and steam is separated from the basic industrial process needs so the
technologies for providing heat and power can be determined
independently of the selection of heat and electricity use in the
basic industrial (or commercial) process needs. 18
Assuming that
steam is the principle of heat requirement need tnat will be under
investigation here, the supply of heat and electricity amounts to
18 This
separation of industrial energy demand into a main and
sub-production function (here, into basic processes and into
electricity and steam services) is an intellectual step-child of the
separation discussed in Berndt (1978) and Berndt and Wood (1979) in
their discussion of engineering versus economic approaches to
industrial energy conservation and demand.
COGENERATION- UTILITY
INDUSTRIAL
aI
THE UTILITY
SECTOR BY SITE
at
BASIC
INDUSTRIAL
PROCESSES
AT
INDIVIDUAL
SITES
INTERACTIONS
-I
41
STEAM & ELECTRICITY
ELECTRICITY SERVICES
8 STEAM
COSTS
ELECTRICITY
PURCHASE &
SALE COST
- OPERATIONS
- EXPANSION
-RATES
QUANTITIES
BOUGHT 8
SOLD
ELECTRICITY
& STEAM
USAGE
IQ
I'
I I I
I|
I
liii
l
-WEATHER
MACROECONOMIC
CONDITIONS
--
FUEL COSTS
ql
il~
IEQUIPMENT COSTS
-I
...i
COSTS
I CAPITAL COSTS
-
Figure 1. 3
4VV
raising steam in boilers or through cogeneration and producing
electricity or buying it from the utility.
The steam and electricity
services question will be the main point of study.
Influences upon
the underlying demands for steam and electricity by the basic
industrial processes will be discussed qualitatively.
The industrial firm's choice of steam and electrical services is
affected by fuel costs, equipment costs, capital market conditions,
taxes, and electricity tariffs, as illustrated by Figure 1.3.
Electricity tariffs are likewise influenced by fuel prices, similar
equipment costs, capital market conditions, taxes, and the overall
demand for electricity.
Tne overall demand for electricity is the
combined load from all sectors less any cogeneration.
The analysis
here accounts for the influences of fuel prices and equipment costs on
electricity rates;
the changes in net electric loads because of
cogeneration, however, are not directly included.
This allows full
consideration of fuel, equipment, and capital market changes that
affect the industrial choices directly and indirectly through
electricity tariffs without going through the elaborate procedures
needed to make a full equilibrium model of the local industrial and
utility electricity market.
This major simplification is valid as
long as the cogeneration forecast does not shift the nominal total
values used for developing the utility capacity and revenue plans,
i.e., a change in cogeneration from this forecast does not
I
substantially upset the whole utility plan, feeding back upon the
cogeneration forecast.
1.2.2 Electric Load Forecasting and Customer Cogeneration
Within the planning process, the utility must consider three
components within a cogeneration forecast for it to be useful for both
capacity and rate decisions:
loads will change;
first, a description of how electric
second, an estimate of the change in utility
revenues because of cogeneration or any rate schedule changes
I
influencing cogeneration;
third, the change in utility production
costs from any cogeneration.
The latter two components combine to
form an estimate of the final profit or net revenue requirements
impact on the utility because of the load change.19
Each component
needs to be further characterized by a time scale ranging from hours
For example, steam usage may be very high on a cold day, so
to years.
existing cogeneration plants would probably be running near full steam
output;
consequently, cogenerated electric output might be higher on
cold days.
At another extreme of the time scale, once a customer
decides to build a cogeneration plant, it will be several years before
it is in full operation, and it will have the potential for generating
electricity for decades.
Finally, the cogeneration forecast must be
19This report combines the ratepayer's (all electricity
customers of the utility) and the utility stockholder's interests
under the umbrella term "utility". The exact impact on each of these
two groups from a change in cogenerated electricity and its attendant
cost and revenue changes depends upon the regulatory process for
setting the utility's rates. Often fuel cost changes will be
reflected immediately in fuel adjustment charge changes for the
ratepayers; changes in the per kWh utilization of the total utility
ratebase will be bourne by the stockholders until a revision in tariff
levels under a formal rate hearing. See Section 3.1.1.1 for a more
complete discussion.
coordinated with the overall utility electric load forecast.
Electric Load Forecasting Methods
Since forecasts of electricity consumption are one of the
fundamental aspects of electric utility planning, many research
efforts have developed, discussed, and categorized approaches for
modeling and projecting changes in peak electric loads and electric
energy consumption.
Like the cogeneration studies, they have been
conducted at the national, regional, and utility level from both the
public and private perspectives.
This section discusses only the
methods designed primarily to assist a specific utility's planning.
Three general types of analysis have been employed for the medium
-and long-range of one to twenty years required for utility rate and
capacity planning.
Time series analysis provides only elaborate
extrapolation of historical data.
Econometric analysis links
historical information and hypothesized economic relationships to
estimate possible future electricity usage, but it cannot adequately
address the diversity of technological and rate design questions asked
concerning cogeneration policies.
Finally, engineering-economic
process or physically-based approaches to electric load forecasting
disaggregate the specific process components that make up the total
load, forecast changes in these components, and then recombine them to
make a full forecast.
The Energy Modeling Forum (1980), Mahmouo
(1980), and Woodard (1972), among others, have surveyed the utility
load forecasting subject area.
This report follows the phyically-based school of thought
discussed in Ruane, Manichaikul, Schweppe, and Woodard (1978).
This
perspective within the engineering-economic process method of analysis
decomposes the electric load into electrical devices or cohesive
groups of devices, called elemental demands or loads.
Each elemental
load is separated into two further factors, a representation of the
capital stock for each device and of the utilization for each device:
Elemental
Load
Device
Capital
Stock
=
x
Device
Utilization
Factor
1
1(1.1)
The elemental load can be categorized by device, usage class, and
customer.
For example, micro-wave ovens might represent an elemental
demand in the ovens device class, in the cooking usage class, and in
the residential user'group.
Woodward (1974) pioneered this general
approach, applying it to the residential sector.
The works by Galiana
()971) and by Mahmood and Schweppe (1980) are related through their
treatment of weather effects on the utilization factor in the
elemental demand.
Ruane (1980) extended this approach for the
residential sector.
Manichaikul (1978), examining the influence of
time-of-day pricing in the industrial sector, advanced the treatment
of the utilization factor by considering both the effect of the
underlying process usage and the economic operating choices made by
the industrial firm.
All these studies have devoted extensive
attention to the short-term uncertainties from weather or industrial
40
process fluctuations.
Physically-Based Cogeneration Forecasting
In applying this approach to cogeneration load forecasting, three
modifications and additions to the previous works must be made.
First, a customer generation source becomes a negative load.
Second,
the capital stock factor must now specifically treat an economic
decision by the customer to add cogeneration capacity.
Finally, the
utilization of the cogeneration capital stock becomes conditional on
long-term economic uncertainties as well as short-term weather and
cogeneration plant outage uncertainties.
As noted above, a useful cogeneration forecast requires estimates
for changes in three components:
electric load, revenues from the
customers, and utility production costs.
Taking a si.ngle customer of
the utility, these can be defined in the format of physically-based
load modeling--first, the electric loads can be described as:
instantaneousnet load
from a
customer
=
sum of
elemental
loads
-
instantaneous
site
cogeneration
(1.2)
where
[an
instantaneous]
elemental load
=
[device
]
capital stock
x
instantaneous
Idevice utilization
factor
(1.3)
and, more specifically,
instantaneous
site
cogeneration
=
cogeneration plant
plant electrical capacity
cogeneration plant
utilization factor
j(1.4)
and
cogeneration plant]
utilization factorJ =
rplant economic
utilization
sub-factor
plant availability
x and load following
sub-factor
(1.5)
Second, the costs are:
instantaneous
cost to serve
customer
=
instantaneous
power system
Ix
incremental cost
instantaneous
net load from
a customer
(1.6)
Third, the revenues for a billing period are:
revenue from a
customer over
a month
=
the tariff, selected by the customer,
computed on the relevant history of the
customer's loads and generation
(1.7)
The utility can directly influence the revenues and costs through
changes in the tariffs available and the production costs.
The
customer affects the loads and, ultimately, the revenues and costs
through capital stock, device utilization, and tariff type choices.
The tariff can be affected by past and present loads, so changes in
elemental loads can influence the economics of the cogeneration plant
and visa versa.
1.3
ORGANIZATION OF THE REPORT
To improve cogeneration forecasting for utility capacity and
tariff planning, efforts must be made in three areas.
First, a better
understanding is required concerning the major economic motivations
behind a firm's decision to build and operate a cogeneration plant and
the impact of these decisions on the local utility.
Second,
cogeneration forecasting techniques that project electric load,
revenue, and cost changes for a utility must be developed.
Third,
given the nature of cogeneration economics, extensions are necessary
to the general physically-based approach to electric load forecasting,
especially the inclusion of long-run uncertainties in fuel and
electricity prices and their influence on the individual cogeneration
plant's operating policies.
In the exploration of improvements to cogeneration electric load
forecasting, this report addresses itself to two questions:
*
How does customer cogeneration influence an electric
utility's loads, revenues, and costs?
*
What are the major influences on a customer's decisions to
build and operate a cogeneration plant?
Since the second question must be addressed to answer the first, the
next chapter examines the economics of cogeneration from an industrial
firm's perspective, taking into account the long and short-term
uncertainties that affect the firm's choice.
Returning to the utility
perspective, the third chapter uses the physically-based approach to
electric load forecasting to project cogeneration development in a
specific utility area, estimating total cogeneration capacity
development and its possible operation under various fuel and
electricity price scenarios.
The fourth chapter discusses the
implications of chapter three's results for utility planning,
suggesting approaches for limiting any adverse impacts of cogeneration
while cooperating in the public policy goal of promoting energy
conservaion.
The fourth chapter then summarizes the methodological
aspects of the forecasting research and suggests directions for
#
further inquiry.
43
Appendices describe the cogeneration moael used in
Chapters 2 and 3 and provide documentation of the economic and
technical assumptions used in the case studies.
An analysis of cogeneration requires attention to too many factors
to be done in the abstract.
To identify the key influences on the
utility and the local firms, this research had to be carried out for a
specific utility and on information from the local industrial and
large commercial establishments, all of which are sites with existing
boiler plants.
Detailed site information for 7 large commercial or
industrial facilities is used in case studies in Chapters 2 and 3 in
order to test the relevance of economic and modeling assumptions; the
aggregate forecast examples in Chapter 3 use survey data from 123
sites.
Since this study was performed in association wit'h a large New
England electric utility, many of the conditions and issues reflect
the situation in that region.
This analysis, however, provides a
general approach for examining the impact of cogeneration on utilities
in other regions as well. 20
Furthermore, a cogeneration forecast
must be coordinated with the overall electric load forecast; the
development of a physically-based load forecasting model for New
England, described in NEPOOL Load Forecasting Task Force and
2 0To
maintain confidentiality for the utility and the private
firms, the analysis in this report has altered some aspects of the
data and economic assumtions so that--while representative--they do
not precisely reflect the conditions at the specific firms or the
utility. In particular, the tariffs used in the calculations are the
same as those actually offered.
44
Battelle-Columbus (1977),
provides a similar type of overall
forecasting model with which this type of cogeneration forecast could
be coordinated after revisions.
45
Chapter 2
A CUSTOMER'S PERSPECTIVE ON THE ADOPTION OF COGENERATION
During the last decade, fimnns have witnessed extraordinary
increases in the energy components of their operating costs.
In real
terms nationwide, tne price of electricity for industrial uses has
risen by 100 percent while fuel oil for boilers has risen 300 percent
from 1973 through mid-1981.1
Tnese cost increases have forced tne
re-examination of opportunities for industrial energy conservation ana
substitution of energy sources.
Owing to the widespread use of Doth
steam and electricity in many industries, the cogeneration of heat and
mechanical or electrical power has been one of the most widely
discussed energy cost reduction opportunities for the industrial sector.
This chapter explores the economic motivations for the conversion
to cogeneration from the private firm's viewpoint.
To provide a
realistic perspective, the first section describes electricity and
-steam production and usage at seven industrial and large commercial
sites in southern New England.
Any specific plant site faces local
fuel prices and electricity tariffs; the detailed analytical work for
this chapter devoted extensive effort to careful inclusion of the
influences from the local electricity tariffs.
The complex nature of
these tariffs, however, often occludes the general understanding of the
economic forces underlying the tariffs.
Consequently, the second
1Based on statistics from the U.S. Department of Energy
Information Adminstration (Uecember 1981) and the GNP price deflator.
_I
46
section begins with a discussion of the economic incentives related to
cogeneration plant operation, sizing, and selection without regard to
specific utility tariffs.
At the end of that section, several
complications induced by realistic utility tariffs are described.
The
analysis of cogeneration requires too many details to be carried out
entirely in the abstract:
the third section, therefore, describes the
application of a detailed model of cogeneration project economics to
six of the seven industrial and commercial sites.
This model
calculates the present value of the expected cash flows from
alternative boiler and cogeneration plants at a site, allowing tne firm
to shift operating modes and tariffs over time; the model captures the
major fuel and electricity price uncertainties impinging upon the
2 by employing a decision analysis approach.
firm
Note that this chapter takes the perspective of the utility's
customer throughout the discussion of cogeneration economics.
Chapter
3 will provide a parallel analysis from the utility's perspective.
2.1
SITE STUDIES
To both reduce the number of details in the evaluation of
cogeneration systems and focus the analysis on realistic situations,
the study surveyed seven industrial and large commercial sites in the
-service territory of the utility.
2 Tnroughout
The selection of the sites was
the next two chapters, the term site will De used to
refer to the whole of an industrial or commerciaT-facility. The term
plant will refer only to the boiler or cogeneration equipment at the
facility's location.
a
47
intentionally diverse; they all, however, are major customers of the
utility, each with a peak electric load of more than 1 MW.
They ranged
from a multi-story office building to a large manufacturing facility
with over 120 separate buildings at a single location.
Two major factors affected the selection of potential sites for the
visits and. study:
1.
the importance of that particular site type to the utility's
load, either as an existing customer group or as a new group
through growth (altnough this chapter adopts the customer's
perspective, it is important to focus on the important
customers).
2.
the availability of data on steam and electricity use at the
given site.
Since the commercial sector of the specific utility .studied is a larger
share of the total load than the industrial sector, and the commercial
sector is expected to grow more quickly, the surveys included several
sites of that class.
In addition, to explore the differences between
sites of the same industry, the large and medium manufacturing sites
were selected since they produce similar product lines.
Table 2.1 summarizes the size, steam and electric loads, and tne
existing equipment at the seven sites.
The first site visited,
interestingly enough, could not be converted to cogeneration without
great difficulty because of the distributed nature of its cooling
equipment and its minimal heating needs.
The Large Manufacturer site
will be discussed the most since it is used as the example for
Table 2.1:
SITE SURVEY
Steam Loads
ElecUsing
(est.)
tric
Low
Existing Plant Equipment
Peak/
Loads Pressure Median/
Avg.
Boilers
Size of
Steam
GeneraBase
(Fuel)
Cooling
Faci Iity (KW) (Percent) (MBtu/hr) tion
approx.
Compu- 650,000 ft2
1500 employees 1200
ter
Assembly
Plant
Office
Build-
100%
15/0/0
20 MBtu/hr
roof45 KW
(#2 oil)
emergency top
electric
diesel
750,000 ft2
5 floors
2150
10%
23/5/0
2 @ 40
3000 KW 3000
MBtu/hr
emergency ton
(#2 oil)
elecdiesel
tric
chillers
165,000 ft2
600 employees
5070
15%
69/42/35
300 KW
60 MBtu/hr
steam
cogeneration
30 MBtu/hr
10 MBtu/hr
ing
Paper
Mill
Medium 700,000 ft2
1300 employees
Manufacturer
2410
0%
36/21/17
126 buildings
Large
Manu4000 employees
facturer
8280
0%
179/85/58 6000 KW
(#6 oil)
2 @ 25
900
485 KW
MBtu/hr
emergency tons
(#6 oil)
roofdiesel
top &
central
electric
1300
200 MBtu/hr
(#6 oil)
tons
steam
cogenera- electric
& 950
tion
tons absorption
.Hospital
College
296,000 ft2
1,000 employees
264 beds
480
19 buildings
2400 students
500 staff
700
67%
100%
17/7/4
20 MBtu/nr
1250 KW 125
13 MBtu/hr
emergency tons
diesel
electric (#6 oil)
& 600
tons ikbsorption
37/8/0
250 KW
steam cogeneration
20 MBtu/hr
500
tons
2 @ 15
roofM8tu/hr
(#6 oil)
top
electric
demonstrating each aspect of the cogeneration modeling for a single
site.
One unanticipated result of the site surveys was the wide variation
in steam loads from season to season because of plant heating and
cooling loads from space conaitioning needs or ambient temperature
effects on the process steam loads.
Even energy intensive paper and
manufacturing plants had winter/summer steam load ratios of 2:1 to
3:1.
Section 2.3.1 and Appendices C and E contain further information
on the site heat and electric loads.
2.2
THE BASIC ECONOMICS OF COGENERATION
Since any firm can obtain its steam and electricity needs from a
cheap package boiler and through electricity purchases from the local
utility, the decision to build a cogeneration plant involves making a
major capital investment with the intention of lowering future
operating costs.
Since the project must be justified by these future
operating cost reductions, the future operating decisions must be
considered at the time of the project's design and selection.
This
section explores the basic economics behind this trade-off between
capital expenditures and the possibility of lower future operating
costs.
Although the ultimate goal of this study is to explore the impact
of utility tariffs, the complications contained in them often obscure
the fundamental effects--the exposition of the basic economics in
Section 2.2.1 avoids the details of the realistic tariff schedules.
Section 2.2.2 introduces some- of the complexities associated with
realistic utility tariffs and cogeneration.
2.2.1
Selecting and Operating a Cogeneration Plant
Any industrial or large commercial site considering the
installation of a cogeneration plant must contemplate two levels of
decisions concerning the plant:
*
At the design or capital investment stage,
- the type of plant to be built and its fuel capabilities;
- the size of the plant in relation to the site's steam and
electric loads;
*
At each point over the operating life of the plant built,
- the disposition of the cogeneration plant's electric output;
it can be used to serve internal electric loads at the site,
thereby reducing the electricity bill for the firm, or it
can be sold directly to the utility.
- the level of operation of the plant at each instant; for
example, it could be operated all the time, meeting all
steam loads up to its capacity, or it could operate only
during peak hours, allowing a regular boiler to meet all the
steam loads during off-peak hours, or it could operate in
coordination with the utility.
This section explores in a simplified manner these alternatives faced
by the customer.
Three comments should help simplify this-exposition.
First, as
51
noted in the introduction unless stated otherwise in this report, this
customer is assumed to be an existing industrial or commercial facility
that has been receiving all its electricity from the utility ana owns
sufficient boiler capacity to meet its own peak steam loads.
Second, cogeneration economics can be viewed on a total cost, a
per-unit steam cost, or a per-unit electricity cost perspective because
of the dual product nature of cogeneration.
While the total cost view
is the most analytically correct, it often does not convey sufficient
insight into the problem.
This section will switch between these
perspectives in a way that, hopefully, gives the reader the best view
into the issue at hand.
Most previous expositions on cogeneration
economics have avoided viewing cogeneration on a per-unit steam cost
basis; this perspective is adopted often here because it Dest
illustrates the need for the coordination of the cogeneration plant
with the existing or back-up boilers.
Third, this section makes tne assumption that a utility will
3
purchase electric energy at the utility's marginal fuel costs.
This
simplifies the analysis substantially and concentrates attention on the
most uncertain aspects of the economics:
fuel prices and the
3 This
assumption also coincides with the Federal Energy
Regulatory Commission's interpretation of Sections 201 and 210 of PURPA
for utilities with excess capacity. Federal Energy Regulatory
Commission, Docket RM79-55, Order 69, 45, Fed. Reg. 12214 (February
Federal Energy Regulatory Commission, Docket RM79-54, Order 70,
190U).
45, Fed.
Reg.
17959 (March 1980).
Ins interpretation has been called
into question by a recent court opinion. American Electric Power
Service Corp. v. Federal Energy Regulatory Commission, No. 80-1789,
slip op. (D.C. Cir., January 22, 1982), reheari.ng denied (D.C. Cir.,
April 9, 1982).
Ak
possibility of the utility converting the lower-cost fuels at some times.
2.2.1.1
Selection of utility tariffs under cogeneration
First, throughout the operating life of a plant, the owner must
consider the short- and long-term disposition of power produced by the
generation plant.
choices.
This report assumes the customer nas two general
The customer may sell all the output of the cogeneration
plant directly to the interconnected utility while the customer
continues to purchase all its electric requirements from the utility,
as it did before the cogeneration plant existed (arbitrage).
Alternatively, the customer may sell the electricity in excess of its
loads to the utility (net sale).
Given the complexity of realistic
tariffs, no simplification can be made beyond noting that the customer
will select the option imposing the least costs.
In more specific terms, if the utility buys all customer generated
electricity at the utility's marginal fuel costs, the customers will
sell only the net output in excess of their own loads net when the
average value of electricity through a reduction in the tariff charges
(under net sales) is greater than the marginal fuel costs.
Table 2.2
summarizes this choice.
Table 2.2
COGENERATED ELECTRICITY SALES CHOICE
Reduction in Standard
Tariff Cost Exceeds
Reduction in Standard
Tariff Cost is Less Than
Utility's Marginal Cost Utility's Marginal Cost
The Customer's
Economic Choice
Sell cogenerated
electricity to the
utility net of site's
internal electric loads
(net sale).
Sell all cogenerated
electricity to the
utility; buy all
electricity for site's
internal electric loads
from the utility
(arbitrage).
, 53
2.2.1.2
Economic operation of a cogeneration plant
At the second stage of the operating level decisions, the customer
must consider how to operate the cogeneration plant in relation to the
site's electricity and steam loads.
The cogeneration plant together
with the back-up boilers will follow the site's steam loads unless the
incremental value of electricity output from the cogeneration system
would dictate an electric load following operating strategy.
Such an
electric load following strategy would imply unusually high cost
electricity from the utility or an expensive provision in the tariff
under a net sale arrangement.
Since the complexities of electricity
tariffs are neglected until Section 2.2.2, the simplified discussion
here assumes that all the electricity proauced from cogeneration is
sold to the local utility at the utility's marginal operating costs, as
with arbitrage sales or the case of a site without internal electric
loads.
Assuming that a site with a cogeneration plant has more than enough
boiler capacity to serve its total thenrmal loads even when the
cogeneration plant is not operating, the cogeneration plant operating
policy depends upon comparing the relative operating costs of running
the back-up boiler system versus running the cogeneration unit up to
its capacity and supplementing the cogenerated steam with the back-up
boiler as necessary'to meet thermal loads.
The simplest case is when
the boiler and the cogeneration plant all operate with fuel use linear
to their output levels.
If the combined boiler and cogeneration system is run on the basis
* 54
of minimizing operating costs, the cogeneration plant should operate if
its marginal operating costs minus its marginal electricity production
credit is less than the marginal cost of operating the back-up boiler
alone:4
cop CG < cop
(2.1)
where
cop
CG,
:
the net marginal operating cost of steam from the
cogeneration plant (in $/MBtu)
copB :
the marginal operating cost of steam from the back-up
boiler (in $/MBtu)
4 Tnis
can be seen as the result of a cost-minimizing mathematical
prog ram for steam costs with electricity credited to the cogeneration
plant unit steam costs:
CG CG
8 B
Min (cop G YST(t) + copB yST t))
CG
YST(t)
CG
YST ( t
)
CG
< XST
ST
yT(t)
ST(t)
For each point in time, t, where
y T(t):
site steam load at t,
YsT(t)
cogeneration plant steam output at t
yST(t):
boiler output at t
XsG
:
cogeneration plant maximum steam output capacity
For convenience, this chapter alters the standard (Yiik) load
superscript
forecasting notation so the device subscript becomes
(Yjk).
The marginal operating costs are given on a per-unit heat basis
from their constituent parts:
net
marginal
operating
cost for
cogeneration
marginal
fuel
cost,
+
$/MBtu
marginal]
O+M
[cost,
$/Mbtu
for elec-
-
tricity
per unit
Lof heat J
CG
CG
fuel
opCG
cop
marginal
credit
CG
COPOM
CG
ST
EL
L
(.0
CG PEL
ST
0 3412 )fST
(2.2)
and
marginal
fuel
cost,
$/MBtu
marginal 1
operating
cost
for i
boilers
+
marginal
O+M
cost,
$/MBtu
PB
cop8B
fuel
cop
S8
f ST
+
cop8 M
OM
(2.3)
where
PCG
Pfuel'
Puel
fuel:
the price of fuel in $/MBtu for the cogeneration
plant and the back-up boiler, respectively,
PEL:
the price for electricity in $/kWh paid by the
utility
fCG.
fCG
ST' EL
the fractional energy outputs from the cogeneration
plant in terms of fuel input (see Appendix 0),
fBST
the fractional energy output from the boiler in
terms of fuel input (see Appendix D),
8
CG
cop0M, coPOM:
the gross non-fuel marginal operating costs in
$/MBtu for cogeneration and the boilers,
respectively.
Further assuming that the price of electricity is given by a proxy fuel
price and heat rate for the utility's generation system,
price of
electricityl
sold to thel
utility,
$/kWh
marginal
utility
O+M costs
i$/kWh
marg inal
utility
fuel costs,
$/kWh
J
puti lity
fuel
PEL
futi lity /.003412
EL
+
utility
copoM
(2.4)
where
fuel
utility:
fEL
the price of fuel for the utility in $/MBtu,
the fractional energy output from the utility's
central generating plants,
utility.
cOPOM
the non-fuel marginal operating costs in $/kWh
for the utility.
Neglecting non-fuel operation and maintenance costs and assuming tnat
the back-up boiler and cogeneration system run on the same fuel,
equation (2.1) becomes:
PCG
fCG
EL
fCG
ST
CG
.003412 f
ST
fuel
.003412 Putility
fuel
futility
EL
PCG
f
(2.5)
ST
fST
for economic operation of the cogeneration plant.
This can be
rearranged so the costs are compared in terms of the incremental cost
of electricity from cogeneration versus the cost of utility generated
electricity:
the marginal
]the
fuel cost of
<
electricity from
cogeneration, $/kWh
CG
(IHR)
utility (UHR)
fuel
106
fuel
marginal fuel
cost of utility
generated electricity,
$/kWh
106(2.6)
where
3412(1/fsT -
CGTCG
f EL /f ST
IHR
/fT
(2.7)
the cogeneration/boiler system incremental heat rate in
Btu/kWh.
and
UHR =
:
3412
341(2.8)
futility
EL
the utility heat rate in Btu/kWh.
These results are illustrated by Figure 2.1 on the basis of rearranging
equation (2.6).
This shows .that the cogeneration plant is economic to
operate if:
IHR
UHR
putility
fuel
.
PCG
fuel
(2.9)
Since the typical incremental heat rates for cogeneration systems range
from 4000 Btu/kWh to nearly 7500 Btu/kWh and the typical utility heat
I
ECONOMIC
OPERATION
OF A
COGENERATION
Steam Turbine
Cogeneration
PLANT
Diesel & Gas Turbine
Cogeneration
I.0-
.8-
UTILITY FUEL PRICE
COGEN FUEL PRICE .6RATIO
.4-
.2
.0-
COGENERATION IHR/UTILITY HEAT RATE RATIO
Figure 2.1
° 59
rate is about 10,000 Btu/kWh, the utility/cogeneration fuel price ratio
must be greater than .4 to .75 for cogeneration systems to be economic
to operate.
See Table 2.3 for examples based on equation (2.6).
Note
that some diesel cogeneration systems could become uneconomic to
operate even if the utility is also using oil generation when the ratio
of the relative prices for the two oil types is unfavorable for the
cogenerator.
2.2.1.3
Sizing the cogeneration plant
One of the fundamental steps in designing a cogeneration system is
selecting the plant size in relationship to the site's steam loads.
This involves balancing the capital costs of the new boiler or
cogeneration plant capacity incrementS against the operating cost
saving for the new system in comparison to the older'boiler.
As a
simple example, consider the operating and capital costs for a single
year in a system supplying steam to meet changing steam loads over the
year.
Assume price conditions remain so that the new plant covers the
base heat load at the site with the older, higher running cost boiler
meeting peak heat loads beyond the capacity of the new plant.
The size
of the new plant should be increased until the marginal cost of
capacity for the new system equals the marginal operating cost savings
for the capacity addition; alternatively under constant economies of
scale for capital costs, the incremental levelized per unit steam costs
from the new plant must equal the levelized per unit steam costs for
the old.
~____I_
~I~
To make this calculation, three sources of data are
TaDle 2.3: EXAMPLES OF INCREMENTAL FUEL OPERATING COSTS AND
OPTIMAL ECONOMIC OPERATION FOR COGENERATION SYSTEMS
Cogen.
Plant,
Type of
Fuel*
Cogen.
Cogen.
Backup
IncreBoiler
mental
Fuel
Heat
Rate
Price
(Btu/kWh) ($/MBtu)
Example No. 1
Steam
4500
Turbine
w/low S
#6 oil
Example No. 2
Steam
4500
Turbine
w/high S
#6 oil
Example No. 3
Steam
4500
Turbine
w/coal
Example No. 4
Diesel w/ 6500
low S, #6
oil, no
low pressure
steam use
Example No. 5
Diesel w/ 7200
#6 oil,
no low
pressure
steam use
Example No. 6
Diesel w/ 6300
#2 oil,
low
pressure
steam
loads
Incremental
Cogen.
Fuel
Util.
Cost
Util. Heat
Fuel
(cents/ Fuel Rate
kWh)
Type (Btu/kWh) Price
Incre- Is
mental CoUtil1. generFuel
ation
Cost
Economic
to
(cents/
kWn)
__ _ Operate?
5.45
2.45
Coal
10,000
2.05
2.05
No
4.55
2.05
Coal
10,000
2.05
2.05
Break
even
2.05
.92
Coal
10,000
2.05
2.05
Yes
5.45
3.54
High
S #6
oil
10,000
4.55
4.55
Yes
6.37
4.58
High
S #6
oil
10,000
4.55
4.55
No
6.37
4.01
Low
S #6
oil
10,000
5.45
5.45
Yes
*Assuming the backup boiler uses the same fuel with the same boiler
efficiency.
the capital and per unit operating costs for the new plant;
required:
the per unit operating costs for the existing boiler; and the load
duration characteristics of the steam requirements.
Let:
cap CG
the annual per unit cost of new steam cogeneration
capacity ($/MBtu/hr per year) assuming constant
returns to scale.
cop
,
cop :
per unit operating costs for the new and old systems,
respectively ($/MBtu),
assuming constant returns to
scale.
CG*
XS
T
the capacity of the new system in terms of steam
(MBtu/hr) since a linear fixed proportions
technology is assumed, this also indirectly
specifies the electrical capacity.
fthe hours per year that the steam load is less than
H(x):
or equal to the load level x; this specifies the
steam load duration curve in Figure 2.2b, shown
later.
The total steam energy provided by the newer plant with capacity
XCG is:
ST
CG
ST
YST
ST
CG
CG
ST(XST):
=
H(x)dx,
(2.10)
annual steam energy input from the new unit (MBtu)
as a function of its capacity.
I
This assumes that the plant is available for 100 percent of the year.
Since the older boiler system is assumed to have more than enough
capacity.to meet even the peak loads without the new plant, the old
boiler system provides for the remaining steam load:
YST(
XS
G)
ST
H(x)dx
=
-
(2.11)
YS(XsT)C
ST
YST:
annual steam energy usage at the site
Y T(X G):
ST ST
annual steam energy served by the old boiler as a
function of the new cogeneration system's size, XST,
(MBtu) or the total annual steam load, YST minus
CG
CG
YsT(XsT ).
The total annual steam supply costs for the system are:
CCG
CG + cop
=CGcapCG XS
STT
C(X)
ST
CG
B
CG G + copB YT(XT)CG
YCG(XCG)
ST ST
ST ST
(2.12)
The cost minimizing capacity for the new plant can be found using the
calculus:
ST
CG
ST
ayB
aCG
ac(xCG
apCG +
=
cap
+ cop
CG
ST + copB
ST
CG
SCG
ST
ST
= 0
capCG + copCG H(XsT ) = copB (-H(XT )) = 0
(.13)
.13
(2.14)
or, the optimal capacity is the size for which the conditions hold that:
CG*
copCG H(ST
cap CG ++ cop
CG*
= cop B H(XST )
(.15)
Tnis means that the tota-l annual cost per unit for the old boiler and
for the new system must be equal at the last incremental addition of
new system capacity.
A second interpretation can be seen by rearranging Equation (2.15):
cap
CG
CG
B
CG*
= H(XST )(cop - cop )
(2.16)
So the annual operating cost savings from the extra increment of new
system capacity (on the right) must equal the annual capital cost for
the capacity increment.
A third interpretation is that the levelized costs must be equal:
CG
ctotCG
cap
CG = cop B
(2.17)
H(XST
CG is a monotonic function,
Since H(XST)
it has an inverse, ana the optimal
capacity can be found:
CG*
-1
(2.18)
ST= H (H*)
CG*
where XST : the cost minimizing cogeneration capacity for the new system.
Figures 2.2a and 2.2b illustrate tnis optimization process.
In
'Figure 2.2a, the first-order optimization condition is met at the point
where the new total system per unit marginal cost (ctotCG) equals the
operating cost for the old system.
At this point, an incremental
addition of new steam system capacity would operate H* hours per year,
retiring an increment of old boiler capacity which also would have
operated H* hours per year.
Moving vertically down to the steam load
duration curve in Figure 2.20, for the new increment of capacity to
operate H* hours, the total new system capacity would have to be XCG*
The
area
total
energy
supplied
STcross-hatched
by
the
new
system
is
the
The total energy supplied by the new system is the cross-hatched area
OPTIMAL SIZING OF COGENERATION PLANT CAPACITY
(a)
Levelized per Unit Steam Costs
LEVELIZED
COST PER
UNIT OF
STEAM
ENERGY
copB
ctot C G =
I
I
capCG+ copCG
-
H
IH
H (XCG
ST
I oad Duration C
(b)
Steam Load Duration Characteristics
L
ST
STEAM
LOAD
CG*
ST
CG
H (X T)
HOURS PER YEAR
Figure 2.2
ST
65
in Figure 2.2b under the load duration curve; the energy supplied by
B 5
the old boiler is that slashed area, YCG'
Simple comparisons between the optimal sizing of different
cogeneration technologies can be made by using the sample cogeneration
plant data in Table 0.8 and assuming fuel and electricity price
conditions of $5.45/MBtu for the low sulfur residual oil used in
cogeneration systems, $2.05/MBtu for coal used in either cogeneration
systems or by the utility, $5.00/MBtu for the residual oil used Dy the
utility, and a 10,000 Btu/kWh utility heat rate.
Figure 2.3 shows the
levelized unit cost of steam from a low speed diesel cogeneration
system (ctotCG) as a function of the hours per year that the unit is
operated.
Note that copCG from Equation (2.2) is separated into the fuel
cost plus the O+M cost less the power credit for the electric energy
produced along with the steam; the levelized capital cost (capCG/H(X))
is added to the unit operating costs (Equation 2.17).
For example, at
all capacity factor levels, using equations 2.2 and 2.4 and assuming no
marginal 0&M cost for the utility (i.e., electricity at $.05/kWh):
.diesel
capacity
costs,
$/MBtu J
5This
=
plant gross
average fuel
+
[credit for
plant gross]
- electricity
average O&M
cost, $/MBtu
cost, $/MBtu
output,
J$/MBtu
is similar to the method suggested in Joskow and Jones
(1981). Both approaches are derived from a similar simple model for
the optimal sizing of electrical generation for a power system given by
Turvey (1968, pp. 29-31) and described in greater detail by Baughman,
Joskow, and Kamat (1979). This model is more appropriate for
cogeneration plant sizing than for power system capacity mix selection
because the steam load duration curve is more liKely to be static in
shape over the years than a power system load duration curve.
COST COMPONENTS FOR DIESEL COGENERATION
PER UNIT OF STEAM
20-
New Diesel Cogeneration
copCG +
cap
H(X)
= ctotCG
COST
($/MBTU)
Existing Boiler
CAPITAL (+)
O0M
(+)
r
8760
H*
HOURS OF OPERATION
PER YEAR
FUEL
(+)
POWER CREDIT
(-)
---
Figure 2.3
"''~'~
T~~-rri~rm~nrarrc~lFiaR
n~ ;;
~
-- ~aar~
~Fr~-~"qIRaq~
-
c CG
cOP
2--
5.4535
(.008)(390))
.745 + (.255++ (.05(.
-
.
(.05)4 )1
27034
)
(2.17)
= 20.20 + 3.29 - 19.00
= $4.50/MBtu
At a 90% capacity factor, using a constant dollar capital charge (FC)
based on the same financial assumptions as the base case modeling
analysis (see Table E.1), the levelized cost is:
[diesel
diesel
Sdiesel
levelized
cost,
$/MBtu
=
operating
costs,
$/MBtu
+
levelized
capital
cost, $/MBtu
capCG
ctotCG = copCG + H(x)
CG (CAPCG/XCG)FC
=
copCG +
= $4.50 +
ST
H(x)
($8,190,000/50 MBtu/hr) (.0969)
(8760 hr)(.90)
= $4.50 + $2.01 = $6.51/MBtu
The comparative costs for operating the existing oil-fired boiler is
shown as the dotted line, cop B , from Equation 2.3:
cop B = (5.45/.83) + .25 = $6.82/MBtu
A small shift in the size of the power credit relative to the fuel cost
could suostantially increase copCG for such a diesel system,
increasing the hours of operation per year required to make the plant
economic--or making it uneconomic to build or even operate at all.
The comparative levelized unit steam costs necessary for this
optimal plant sizing for diesel, oil-fired steam turbine, and
, 68
coal-fired steam turbine cogeneration systems are given by Figure 2.4
in a format identical to Figure 2.3.
This demonstrates the comparative
sensitivity of the sizing to fuel prices, power credits, and capital
costs.
In this example, while diesel and oil-fired steam turbine
systems are likely to be sized for similar base load capacity factors
relative to the site's steam loads, the diesel unit is much more
sensitive to small shifts in fuel prices relative to the electricity
credit because of the size of its power credit relative to the other
components of its total levelized cost.
Furthermore, coal-fired steam
turbine systems break even with existing oil-fired boilers at a very
low capacity factor, implying that coal-fired systems can be sized to
meet nearly the peak steam loads.
This is in contrast to the base
steam load designs required for the oil-fired systems.
2.2.1.4
Selecting the minimum cost plant
The final step in the analysis of a new cogeneration or boiler
plant for a site is the selection of the plant and fuel type after the
consideration of the optimal sizing for each type and the future
operation of each plant type.
The question to be answered is whether
or not the additional capital expenditures for any new plant are
expected to reduce the steam and electricity costs for the site over
the anticipated life of the new facilities.
The approach adopted here compares the net present value of
expected cash flow for each of the designs over a fixed horizon, which
is approximately equal to the life of the new plants.
There are
COMPONENTS
IN THE COST OF COGENERATION SYSTEMS
PER UNIT OF STEAM
DIESEL
COAL- FIRED STEAM TURBINE
OIL-FIRED STEAM TURBINE
20
New Oil ST
'I
Existing
New Coal ST
/
Existing
Boiler
COST
($/MBTU)
:: . ...
L/
FUEL
, ,, ,, ,
,
,,
8760
POWER CREDIT
760
POWER CREDIT
POWER CREDIT
-201
HOURS OF OPERATION PER YEAR
Fioure 2.4
8760
, 70
several reasons for this approach.
First, in the previous section on
the sizing of different designs, the technologies were compared only
for marginal additions of capacity; the analysis of capital expenditure
decision must account for the fixed components of capital cost for the
individual technologies as well as the marginal capacity costs in
relation to the total steam load served by cogeneration.
Second, as
demonstrated by Section 2.2.1.2, the optimal operating policy for a
cogeneration system will change as electricity and fuel prices change,
influencing the operating policies and cost reduction of the
cogeneration system over the existing system.
Finally, the combination
of the effects from the operating decisions and the capital costs are
best expressed as a net present value of expected cash flows because
the uncertainties in future fuel and electricity prices and becuase of
the advantages of the net present value approach in properly expressing
the ranking of capital expenditure options. 6
One Design Versus an Existing Boiler
Consider, first, the comparison between a diesel cogeneration plant
and an existing boiler.
6 See
If.the diesel system is of steam output
Brealey and Myers (1981, Chapter 5) for an excellent
exposxtion of the superiority of net present value over other metrics
for the comparison of alternative investments. The internal rate of
return method can cause ambiguous or incorrect results when used in the
evaluation of cogeneration projects because of the radically different
mix of operating and capital costs between the different technologies.
Often a project that has the best net present value when evaluated at a
market rate of return will have a lower internal rate of return than a
less capital-intensive project (as the discount rate is increased a
more capital-intensive project may drop to zero net present value
before a less capital-intensive project which had a lower net present
value at lower market discount rates.
4..--
capacity X T, at a time in which it is economic to operate the diesel
system, the operating cost reduction for each unit of output from the
diesel system will be
crH = cop T,H -
(2.19)
STCGTH
While, at a time in which it is uneconomic to operate the cogeneration
system so the old boiler operates, the cost reduction will be:
- min (cop CG
crL = cop
ST,L'
ST
L
co
COPST,L
or
= cop
cr
L
ST,L
- cop
ST,L
(2.20)
= 0
where
crq:
the unit operating cost savings for a cogeneration system
at cost level q.
cop
: the unit incremental steam production operating costs
(including electricity generation credits for
cogeneration technology) for technology i during a time
with cost level q.
q:
indicates high electricity credits (H) or low credits (L).
The operating cost savings must be totaled to reflect the size of
the cogeneration system in relation to the steam loads.
The simplest
case is when the plant is economic to operate throughout the year, as
shown in Figure 2.5(a).
The cogeneration plant then operates first to
supply all base heat loads while the existing boilers cover peak steam
loads.
The total cost savings for a year within a unit cost reduction
CG CG
for steam energy for crH is YsT(XsT).
(crH
) . In
a year when it is
uneconomic to operate the system, there are no cost savings.
The net
present value of a system with constant unit cost operating savings
crH
over the life of the cogeneration unit is
CG(XCG,
NPVI =-CAPCG +
t=O
STST
(1 + d)
(2.21)
where
d:
the rate of return appropriate for this class of
investments,
n:
the life of the cogeneration plant in years
CAPCG:
the total capital cost of the cogeneration plant
NPV 1 :
the net present value of the plant.
More complicated cases arise when electricity prices vary within
each year.
First, assume that the electricity prices can be at either
of two levels.
At the high level, cogeneration is economic to operate,
and it results in the unit savings of crH > 0; at the low level,
cogeneration is uneconomic to operate, and the savings is crL = 0.
Second,.the timing of the high and low electricity prices can differ in
its correlation to the steam loads.
If the electricity price changes
are totally uncorrelated with the steam loads, the annual operating
cost savings will be reduced in direct proportion to the number of
hours that the system is at the higher electricity prices:
NPV 2 = CAPCG
n
crH
+E
CH
t=O
CG
ST
h (t)/8760
H
(1 + d)t
(2.22)
STEAM LOADS AND COGENERATION OPERATING SAVINGS
(a)
All Electricity Sales at High Prices
STEAM LOAD
ITotal Cogenerated
CG
Energy, YST
CG
IST
ST
Cogeneration'
Operates, crH> 0
HOURS PER YEAR
(b) Perfectly Correlated High Electricity Prices
High Steam Loads
Aand
STEAM LOAD'
High Electricity-Prices
--1 Low electricity
Prices
I
CG
Boiler
Total Cogenerated
ST
Energy,
Cogeneration'
Operates, crH> 0
,h
-ST,h
-Boiler Operates for
Economic Reasons,
cr = 0
hH
Figure 2.5
HOURS PER YEAR
where hH(t):
the number of hours in year t that the electricity is at
the high price level.
An alternative situation would be if the power system's higher
electricity price periods were perfectly correlated with the site's
steam loads.
higher levels for
This could be the case of a winter
peaking electric utility and an industrial site with steam loads that
increase with lower ambient temperatures--not an uncommon situation for
industrial fuel sites in the northern part of the U.S.
In this case, as
shown in Figure 2.5(b), the plant would cogenerate during the hH hours
of high electricity price and highest steam loads, producing cost
savings for the cogenerated steam.
For the remainder of the hours (8760
- hH), the cogeneration plant would be uneconomic to operate,
producing no cost savings (the cross-hatched area in Figure 2.5b).
Since the steam loads are lowest when the electricity price is lowest,
however, the reduction in cogenerated energy as a result of the optimal
economic operation can be less than proportional to the decrease in high
electricity price hours.
The total cogenerated energy for a year with
the steam loads
hH hours of high electricity prices correlated with
would be such that
CG
hH
ST( 8
CG
< YST,h
<
CG
(2.23)
ST
where
YST
CG
the maximum possible cogenerated steam at the site
CG
with the given cogeneration capacity XST (equivalent to
shaded area in Figure 2.5(a)).
Y
Th: the energy economically cogenerated during a year with the
high electricity price perfectly correlated with the steam
load levels for the hH peak hours (the shaded area in
Figures 2.5(b)).
Because of the relationship in Equation 2.23,
n
NPV 3 = CAPCG
t=O
3
cr
crH
CG
ST,h
(2.24)
(1 + d)t
and
NPV 1 > NPV 3 2> NPV
2
All three models (Equations 2.21, 2.22, and 2.24) could be easily
extended to allow for changes in hH and crH by year.
The three different models illustrate how uncertainty in the key
variables, fuel prices and changes in electricity prices through coal
conversion by the utility can affect the present value of a new project
versus an existing oil-fired boiler.
For example, assume that the
utility is generating electricity with oil for hH hours per year, and
the cost reduction for oil-fired cogeneration over the existing boiler
is the high cr H.
The level of crH is determined by oil prices and
the given cogeneration technology.
The utility has coal as its
marginal cost fuel for the other hours in the year; the coal prices are
assumed to be low enough in relation to oil prices so that crL
=
0.
Figure 2.6 shows the relationship between the models NPVI,
NPV 2 , and NPV 3 for different crH and demonstrates the impact of
lowering hH on NPV 2 . An increase in oil prices, ceteris paribus,
results in an increase in the value of these projects.
An increase in
IENSITIVITY OF THE NET PRESENT VALUE OF COGENERATION PROJECTS
TO UNIT COST REDUCTION AND PEAK ELECTRICITY PRICE CONDI T IONS
NPV
($)
1
NPV 1
NPV 3
NPV
htT
S8760
]
2
NPV 1
DcrH
crH
($/MBTU)
-CAPCr
Figure 2.6
the extent of coal conversion, causing a decrease in hH but no change
in crH, results in a proportional decrease in
the slope of NPV 2
with respect to NPV 1 since
aNPV 2
Dcr H
(hHh
H
(876)
NPV 1
cr
aNPV 2
aNPV 3
aNPV 1
(2.26)
H
and
acrH
crH
- acrH
(2.27)
As hH becomes smaller, any given change in crH will have a smaller
impact on the NPV but the value of the project could become negative
for a given crH.
In other words, as coal conversion by the utility
increase (hH becomes smaller), the project will benefit less from oil
price increases.
Comparison of Multiple Designs Versus an Existing Boiler
The previous section examined the value of a single new
cogeneration or boiler project versus an existing boiler system; this
section extends that analysis to the comparison of several projects and
their relative sensitivity to fuel prices and utility coal conversion.
In the previous section, the analysis was conducted with respect to tne
unit operating savings for the plant, crH or crL, depending on two
levels for electricity prices.
In this section, since the change in
crq depends upon the technology, a multi-plant analysis must De based
on tne fuel price conditions directly.
Two extreme cases for the electricity credits limit the analysis.
The first case assumes that the cogeneration system, tne old boiler,
and the utility all use oil, so the unit operating cost savings
(crH)
increases with an increase in oil prices although the rate of increase
with oil prices is different between technologies.
Figure 2.7
illustrates the impact on the present value of different plants.
The
second case assumes that the cogeneration system and the old Doiler
burn oil while the utility uses coal, so the unit operating cost
savinqs 7 decreases with an increase in oil prices under constant coal
prices.
Again, the slope of the change differs between technologies;
each technology reacnes a maximum oil price (PCG), above which it will
max
not operate, so the losses are limited to the capital cost of the
project.
Figure 2.8 presents this case.
The impact of increased utility conversion to coal as its marginal
cost fuel is not as easily demonstrated for the multiple plant examples
as in the single new plant case.
The net present value of a given
cogeneration plant selling electricity to a "marginal coal cost"
utility is a mixture of the two extreme cases in Figures 2.7 and 2.8,
assuming that steam loads are uncorrelated with the utility coal
burning.
Comparing the present value of two new plants against the
existing boiler system depends on the exact relationship of capital
costs and operating cost savings.
The plant with the higher share of
electricity output as a fraction of total cost savings, however, will
7 This
is crH or crL depending on the exact oil/coal price
conditions which indicate whether or not the cogeneration system snould
operate.
SENSITIVITY OF THE NET PRESENT VALUE OF OIL-FIRED COGENERATION
TO OIL PRICES WHEN ELECTRICITY CREDITS ARE BASED ON OIL
NPV
($)
NPvCG, 2
/
NPV
'
PPoil
($/IIBTU)
-CAPCG, 1-.
-CAPCG ,2
Figure 2.7
80
SENSITIVITY OF THE NET PRESENT VALUE OF OIL-FIRED COGENERATION
TO OIL PRICES WHEN ELECTRICITY CREDITS ARE BASED ON COAL
NPV
($)
0
Poil
($/MBTU )
NPVCG, 1
-CAPCG,
2
NPVCG, 2
(Coal Prices Constant)
Figure 2.8
° 81
decline faster in net present value with an increase in the fraction of
the year that the utility has coal as a marginal cost fuel, assuming
the price of coal cannot drop below the price of oil.
These simple present value models demonstrate several things.
The
analysis includes the total new plant capital cost; because of scale
economies associated with the capital equipment, marginal capital costs
alone are not sufficient to specify the total cost and the impact on
the project's value.
Changes in coal conversion and oil prices (as
reflected in crH down to a lower limit) interact in their impact on
the project's value.
The year-by-year analysis is important because of
the potential for changes in hH and crH over time.
Finally, the
complete shape of the steam load duration curve below the level of the
plant's capacity can affect the value of the project if the steam loads
and the level of crH are correlated.
2.2.2
The Complications Imposed by Realistic Utility Tariffs
In contrast to Section 2.2.1, in which all analytical discussions
assumed that the utility purchased all cogenerated electricity at the
utility's marginal fuel cost, this section explores the complications
created for the firm by the typical industrial electricity tariffs.
The effect is more significant for the firm's decisions at the
operating level, alth6ugh the resulting changes in operating cost must
be reflected in the anticipated cost savings for any capital
investments in new cogeneration equipment.
The section begins with a
brief introduction to utility rate-making and tariff design.
The
second sub-section discusses the major difficulties arising in the
adaptation of typical industrial tariffs for firms that cogenerate.
2.2.2.1
An Introduction to Utility Ratemaking and Industrial Tariffs
There are two stages to the setting of industrial electricity
tariffs in the United States for regulated utilities.
At the first
stage, an independent regulatory agency sets the intended revenue that
the utility will be allowed to collect from all its customers.
second stage is to design tariffs for each type of customer.
The
Kahn
(1970, 1971), Turvey and Anderson (1977), and Crew and Kleindorfer
(1979), among many others, offer discussions on utility regulation,
tariff design, and their associated public policy consequences.
This
section offers only a basic overview with an introduction to some of
the influences on cogeneration.
The overall level of anticipated revenue collection by a regulated
utility in the U.S. is based upon the utility's total operating
expenses plus an allowed return on its capital investments.
These
allowed revenues are allocated to the residential, commercial, and
industrial customer classes by an assessment of the utility's costs
incurred to serve each class.
The next stage is the design of tariffs to collect these allowed
revenues on the basis of the total costs to serve each class.
As
traditionally designed in the U.S., industrial tariffs separate the
charges for a customer into three components in order to more closely
follow the accounting components of the utility's total costs.
These
component charges to the firm are:
1.
A customer charge for costs that the utility would incur no
matter how much electricity the customer uses, for example,
metering and billing costs.
2.
An energy charge (or kilowatt-hour charge) for the utility's
running costs that vary directly with energy used by the
consumer, such as fuel and certain other operating costs.
3.
A demand charge (or kilowatt charge) for the utility's capital
equipment costs that are allocated to the industrial class,
such as a portion of the total generation, transmission, and
distribution equipment costs.
Appendix B contains the text of several tariffs wnich are employed in
examples within this section:
the "H Rate" is the most typical of a
traditional industrial tariff.
A number of features common to these tariffs make them
substantially more complex than a simple billing of a fixed customer
charge with single unit charges for each kilowatt-hour of energy usage
in a month and for each kilowatt of peaK usage over the month.
Following the mathematical approach suggested in Manichaikul (1978),
let:
R(t
)
= Rc + Rd (d(tn),...) + Re(YEL(tn),...)
(2.28)
with
t
YEL(tn)
and
n
Jtn-1
YEL(t)dt
(2.29)
d(t)
= max
(yEL(t))
(tn 1 ,tn)
(2.30)
where
YEL(t):
the continuous sale of electric power to the customer at
time t,
YEL(t n):
the total electric energy sold to the customer during
d(tn):
the monthly period tnl to tn,
the peak power demand by the customer during the monthly
period tnl to tn,
R(t ):
the total tariff charges for the month,
Rc:
the customer charge component,
Rd(.):
the demand charge function which can depend on several
variables, principally the current month's peak usage,
Re(.):
the energy charge component, which can also depend on
several variables, principally the current montn's total
electric energy sales.
First, the demand and energy charges in some tariffs change in
tncremental value as the montnly level of usage changes, so the charges
are not strictly linear with the demand, d(tn), or the energy,
YEL(tn).
For example, the energy charge in the C Rate is 7.151
i/kWh plus average fuel costs for an increment of energy when tne
monthly usage is 20 to 100 kilowatt-hours, but 4.241 O/kWh plus average
fuel costs when the usage is over 2000 kilowatt-hours.
This is known
as a declining block energy tariff.
Another feature which is becoming accepted as part of recent
* 85
rate-making reforms, is the changing of incremental energy charges at
each level of usage with season and time of day or week.
The X Rate is
an example of this tariff type.
Third, demand charges are often based not only on the maximum usage
in the current month, but also on the peak usage over many prior
months; this feature is known as a demand "ratchet."
Tne H Rate, for
example, bases the $1.57/kW unit charge for demand on the higher of the
peak usage in the current month or 80 percent of the peak in the 11
previous months.
In algebraic form,
Rd(d(tn), d(tn-1),...) = 1.57 D(t )
(2.31)
and
D(t ) = max(d(t ),
.8d(t_),...,.8d(tn-_1
) , 500 )
(2.32)
where
Rd:
the demand charge under the H-Rate in dollars for the
month, and
D(tn):
the "billing demand" for the month tn in kW with an 80%
ratchet for 11 prior months.
Finally, some industrial tariffs vary the energy charge on the
demand level as well as the energy usage level in the billing period.
The H-Rate bases its energy charge on this type of formulation.
The
energy charge decreases as the ratio of monthly energy use to the peak
demand (YEL/D) increases.
This is called a load factor or
"hours-use" energy discount.
Analyzing the economic incentives imbedded in these tariff formulae
becomes very complex because the instantaneous incremental cost of a
86
kilowatt-hour of electric energy to a customer can vary depending on
the specific customer's history of electricity consumption for the
current month and, possibly, many previous months.
This current
incremental cost can also reflect the customer's anticipated usage
patterns and consumption opportunities for months to come.
The general
formulation of the electricity usage problem as a part of indutrial
production becomes an infinite horizon dynamic programming problem if a
demand ratchet exists in the tariff the customer is using; a few
assumptions, however, can reduce the analytical task.
Assuming that the demand ratchet does not become important (d(tn) >
.8d(tm), tm = tnll'
...*
tn-1),
Figure 2.9 demonstrates how
the incremental costs for the control of demand and energy vary
depending upon the total anticipated level of demand and energy usage
in a month under the H Rate.
The incremental costs illustrated depend
on:
DR
RR
_
ad(t n)
d
+
ad(tn)
d,
YEL
BRe
aRe
(2.33)
ad(t)
d, YEL
and
R
YE L n
Re
EL
(2.34)
(2.34)
n
d, YEL
Note that, although the incremental demand cost in the demand charge
portion of the rate (Rd) is only $1.57/kW, the change in the
hours-use energy charge from the demand increase can more than double
THE INCREMENTAL COST OF DEMAND OR ENERGY USE CHANGES
ACTUAL
DEMAND
(KW)
(K
$1.57)
$1.802
$ 1.57
.57
t0
3.0020 2.7040
t
%
2,3970
$3.599
$3.191
2.2810
1.818it
-0
0
1.716t
1000
3 8
3.859
A
B
1.664e
co
C
aR
Dd
Key,
($/kW)
a RE
YEL
500
0
100
200
300
400
500
ELECTRIC ENERGY USAGE (thousand KWH)
Impact on H Rate monthly bill; excludes the average fuel cost,
which must be adred to the energy component.
Figure 2.9
(C/kWh)
the incremental impact.
For example, at Point A on the figure where
the energy usage is 350,000 kWh per month and the peak demand is 1000
kW, the incremental cost of a kilowatt in peak demand is $3.191/kW and
the incremental energy cost for the firm is 1.818 &/kWn,
neglecting the
average fuel cost adjustment; at Point B, with half the energy usage
but the same demand, the incremental cost of demand is $1.57/kW and the
incremental cost of energy is 2.397 0/kWh.
The tariffs become even more complex when the customer has the
option to generate electricity as well as purchase electricity from the
utility.
As briefly noted in Section 2.2.1.1, two general pricing
relations have evolved in the U.S. for customers owning their own
source of regular electric generation:
a) Arbitrage, or simultaneous purchase and sale, where the utility
purchases the entire electric output of the customer's generation
source while the utility sells the customer all of its electric
requirements; and
b) Net sale, where the customer uses the generation for internal
needs and the utility only purchases generation in excess of the
instantaneous site electric loads.
The arbitrage approach provides the industrial firm with the same
incentives for electricity consumption as would exist if the customer
owned no generation.
The tariffs or contracts for the purchase of
electricity by the utility can be designed without explicit regard to
the customer's usage patterns.
The net sale of electricity from a generation source such as
* 89
cogeneration, however, reduces both the demand and the energy charges
under the typical tariff.
The reduction in kilowatt-hour usage occurs
on a direct one-for-one basis with the energy output of the
cogeneration plant.
Often this does not translate into a proportional
reduction in the energy cost component of the tariff because of a
declining block structure in the energy charge; the reduction depends
on the specific tariff structure and the electricity consumption
patterns at the site.
The reduction in the kilowatt demand depends on
the difference between the load and the cogeneration plant output
throughout the month, so the coincidence of internal loads and
cogeneration plant output together determines the net peak demand.
The
net peak demand influences both the demand and energy charges in some
tariffs like the H rate.
Taking a firm with internal loads of 350,000 kWh/month and a peak
demand of 1000 kW at Point A in Figure 2.9, a cogeneration source that
produces 175,000 kWh in the month without ever exceeding the site's
peak load would result in a bill based on 1000 kW demand and 175,000
kWh energy usages (Point B).
A different cogeneration sources that
constantly produced 240 kW electrical generation for the month would
reduce the demand to 760 kW and the net energy usage to 175,000 kWh
(Point C).
Not only do the absolute levels of the monthly tariff
differ significantly but the incremental energy consumption,
generation, and peak load control incentives change.
At Point A, tne
incremental costs of energy and demand increases are 1.818 0/kWh and
$3.191/kW, respectively; at Point B, they are 2.397 0/kWh and down to
$1.57/kW; while at Point C, they are 2.281 i/kWh and $1.802/kW.
The incremental value of the internally used electricity varies
with the level of cogeneration output under a tariff like the H Rate.
Using electric load and cogeneration plant performance modeling
discussed in Section 2.3, Figure 2.10 illustrates the impact of
additional cogeneration capacity on the non-fuel tariff demand and
energy charges for the Large Manufacturer, whose typical monthly energy
usage is about 6,000,000 kWh with a peak load of about 13,000 kW.
Assuming the cogeneration system operates continuously to serve steam
loads, the savings are shown as a function of the average annual
cogeneration energy output.
A less reliable generation source
diminishes the reduction in the demand component and affects the energy
charge hours-use discount at lower levels of cogeneration capacity.
The lower reliability plant savings moves slightly above the
incremental savings from a high reliability cogeneration system at the
higner capacity levels because the demand savings are smaller and the
lower reliability system is thus saving higher energy costs in a lower
hours-use region of the tariff.
The total dollar savings for the
higher reliability cogeneration system, however, are higher at any
level.
This illustrates the need to examine total cost as well as
incremental savings under complex tariffs.
Additional tariff provisions, such as the auxiliary service rider
or the special cogeneration CG Rate in Appendix B, further complicate
the total cost and incremental cost incentives for electricity usage
and production by the generating customer beyond the basic difficulties
COMPARATIVE_ INCREMENTAL VALUE OF ADDITIONAL COGENERATION
A NET SALE /
INTERNAL
ON
USAGE BASIS BY THE LARGE MANUFACTURER
ON THE H RATE
2 4 0
.
T
2.30,
z0
H
:
U
2.204
lc,
t\
0
U
H
s,
I
P
I
2.1o-I4
F'
I-.l
F,
,<
E-4
zU
H
2.00-
Z
t.-
o = Savings for standard reliability
steam turbine (2840 hours MTBF)
1.904
at the large manufacturer
*
= Savings for low reliability
steam turbine (460 hours 1TBF)
at the large manufacturer
1.80
F-IJ
1
1000
2000
REDUCTION
I
4000
3000
IN ANNUAL ELECTRIC
PURCHASES BY CUSTOMER (KUi
Figure 2..10
ENERGY
AVERAGE)
I
5000
illustrated above.
2.2.2.2
The Difficulties Associated with Typical Utility Tariffs for a
Cogenerating Customer
Three fundamental tariff-related problems complicate the evaluation
of cogeneration operation and investment from a firm's perspective.
The first arises from the option of the cogenerating firm to arbitrage
the cogenerated electricity or sell it net of internal loads.
There
are two aspects to the difficulties posed by the first fundament
problem.
First, the utility's average costs, which will be reflected
in the general levels of the tariffs, will not generally be equal to
the utility's marginal costs of serving the industrial firm.
Because
of this, the industrial tariff levels will usually differ from the
marginal fuel cost-based price that the utility offers for the
electricity purchases from the plant.
The firm considering
cogeneration must weigh anticipated changes in the comparative levels
of the utility's average versus marginal costs since the firm will wish
to adapt the cogeneration plant's future operating and tariff policies
to these changing conditions.
For example, imagine a utility with a
fully depreciated capital stock, fixed operating costs of I/kWh
allocated to energy use, and average fuel costs that are half coal at
2.50/kWh and half oil at 5/kWh,
Assuming a simple average energy
tariff, the average tariff level and unit charge would be 4.75 /kWn.
If oil is tne marginal fuel for 80 percent of the time, the
time-average marginal cost is 4.5g/kWh.
A cogenerating customer would
be wise to sell net under these conditions.
If oil prices increase so
, 93
that oil generation costs 6/kWh, the average tariff becomes 5.25 V/kWh
but the marginal fuel costs jump to 5.30/kWh; the cogenerator would
wish to sell on an arbitrage basis.
The second aspect to the first fundamental problem arises for
customers using the cogenerated electricity output internally to reduce
the total tariff charges.
The incremental cost reduction in the tariff
through cogeneration often differs from both the utility's average
costs and its marginal costs, so the calculation of the rate choice
must reflect the average cost level, the aetailed structure of the
tariff, and the utility's marginal costs for the purchase of
electricity.
As the example for Figure 2.10 demonstrated, the
incremental savings in a tariff through cogeneration varies with the
level of cogeneration, its reliability, and the overall tariff level.
In the example, the incremental savings varied over a-.4 /kWh
range; in
contrast, recent savings in oil prices and changes in utility fuel mix
indicate that electric utility average versus marginal costs can have a
wider range.
The second fundamental problem occurs because changes in the net
electricity usage pattern from cogeneration can change the customer's
incremental costs for internal electric energy consumption and peak
demand control.
The changes in these incentives, as illustrated by the
examples in the section above, could significantly alter the firm's
incentives for the control of peak loads in coordination with the
utility's peak loads.
The third fundamental problem is that even a marginal cost-based
94
price for the cogenerated electricity under an arbitrage purchasing
arrangement can distort the project economics.
If the marginal cost is
averaged over a time period in which the actual marginal costs
fluctuate over a range that would allow the cogenerator to adapt its
operating policy, the cogeneration plant could improve its operating
cost savings without changing the utility's total costs.
Returning to
the example above, consider a utility with coal at 2.5/kWh as the
marginal fuel 20 percent of the time and with oil at 6w/kWh as the
marginal fuel 80 percent of the time.
for the utility is 5.3V/kWh.
The time-average marginal cost
If an industrial firm is selling the
utility all its output from its I kW cogeneration system which it runs
at a cost of 3 /kWh, then the firm receives 2.3C/kWh operating profit.
The utility pays more than its actual marginal cost when it is running
on coal and less than its marginal cost when it is running on oil, but
these gains and losses cancel out for the utility if the cogeneration
output is constant.
As an alternative, the industrial firm could coordinate its
cogeneration with the utility's actual marginal costs, so that the firm
received the 2.5W/kWh when the utility was on coal and 6C/kWh when it
was on oil.
Under this "time-of-oil" pricing scneme, following
economic operating rules the cogenerator should not operate when the
utility is on coal because it would lose .5V/kWh, but it should operate
the 80 percent of the time that the utility is on oil because it
profits 3V/kWh.
Assuming constant steam and thus cogeneration output,
the average profit is .8 (3V/kWh)
or 2.4V/hour per kilowatt for tne
industrial firm.
The firm gains 14 per hour per kilowatt of capacity
with no gain or loss by the utility at any instant. 8
2.3 MODELING THE OPTIMAL ECONOMIC DESIGN AND OPERATION OF A
COGENERATION AND BOILER PLANT
This section describes and presents results from a model designed
to capture the major capital and operating choices faced by a firm
considering changes in its boiler plant.
The model solves the
cost-minimization problem for the four levels of capital and operating
decisions outlined in Section 2.2.1, explicitly incorporating the
effects of realistic tariffs.
It incorporates explicit treatment of
the long-term uncertainties associated with fuel prices and electricity
price changes through utility coal conversion by a decision analysis
formulation of the current plant type and size decisions; after the
uncertainties in fuel and electricity prices are resolved, the model
determines the subsequent minimum cost operating decisions.
A plant
performance submodel captures short-run uncertainties in site
electricity and steam loads and in cogeneration plant availability by a
Monte Carlo simulation of each potential plant's operating performance
under all operating mode and tariff choices.
The plant performance
simulation includes the effects from the energy and ratcheted demand
tariff charges.
8 This
is one of the simplest forms of electricity "spot pricing"
as discussed in Schweppe et al. (1980). Bohn (1981, 1982) describe in
general fonn a broader range of opportunities that industrial firms
might use to reduce costs by altering operations in response to
electricity "spot prices."
Section 2.3.1 describes the structure of the model, using results
from the analysis of the large manufacturing site as an example.
Section 2.3.2 presents an overview of the results from all the sites.
Section 2.3.3 discusses the simplification of the model, particularly
modifications allowing an analysis of a site using less detailed data.
Finally, Section 2.3.4 compares aspects of the modeling results with
results from simple application of the analytical approach from Section
2.2.
2.3.1
Description of the Cogeneration Plant Analysis Model
As the earlier sections of this chapter have stressed, a firm
considering cogeneration faces decisions at two distinct stages:
the
operating decisions, with the resulting operating cost savings; and the
investment decisions, where capital expenditures must be weighed
against the potential for future operating cost savings.
At the
operating stage, short-term uncertainties in the cogeneration plant
outaqes and their coincidence with steam and electric loads influence
the anticipated operating cost savings.
At the investment stage,
long-term uncertainties in fuel and electricity price escalation
significantly affect the expected savings for different plant designs,
thus influencing the investment decision.
The first step in the modeling of a cogeneration plant's economics
is the calculation of the possible operating cost savings from each
potential plant design.
These savings for eacn design are determined
from the minimum cost operating mode and electricity tariff option in
~---------
each year.
The search for the minimum cost operating mode and tariff
combination is accomplished by examining all possible options.
The
cost of each operating mode and tariff alternative depends upon the
fuel, operation and maintenance, and utility tariff costs projected for
each year.
These costs can be calculated from each plant's technical
performance in terms of fuel use by operating mode, combined with the
impact on the demand charge and energy components of the available
utility tariffs.
To determine how the plant performs under each of
these mode/tariff combinations, each tariff and operating mode choice
must be simulated, accounting for the uncertainties in plant outages
and in steam and electric loads.
The following subsection describes
the development of the operating cost savings, working from the plant
performance simulation to the selection of the minimum operating cost
policy as illustrated in Figure 2.11.
In coordination with the figure,
Table 2.4 presents a summary of the extent of the modeling effort.
The second step in the analysis of the plant economics is the net
present value calculation of the expected operating costs under each
possible fuel and electricity price scenario, weighed against the
capital expenditures necessary to build the plant.
The present value
of each plant alternative, including the existing plant, can then be
compared to find the least cost system.
In the implementation of tnis
model, even with capital cost economies of scale, sensitivity analysis
showed that the optimal sizing of the different plant types in relation
to the site steam load could be effectively approximated in a
single-year, levelized-cost computation using the method discussed in
THE SITE STEAM AND ELECTRICITY COST ANALYSIS MODEL
Site Data
Minimization of Operating Costs
by Year for Mode and Tariff
Decisions
Plant Performance
Operating Cost Escalation
for All Mode and Tariff
Options
Selection of Minimum Cost Operating
Policy by Year for Each Fuel
and Electricity Price Scenario
Investment Decision Analysis
Financial Analysis for Each
Fuel and Electricity Scenario
Expected Value Calculation for
Each Plant Type
Minimum Present Cost Plant Design
Figure 2.11
Table 2.4:
An Overview of the Model Implementation
Modeling Level/Submodel
P.urpose
Implementation
Site Data Retrieval
Selection of plant types and sizes
for detailed modeling; electric
load data retrieved; synthetic
steam loads developed from montnly
steam use and ambient temperature
data
6 plant types selected
from 9 technology
options, depending on
existing boiler fuel
plant sizes preselected in single
year levelized cost vs
capacity factor
analysis
(approximately optimal)
Operating Cost Minimization
Plant
Performance
Operating
. Cost
Escalation
Selection of
Minimum Cost
-Operating
Pol icy
~II
A time series of plant performance
and outages (Dy an exponential
random process) is simulated in
response to electric and steam
loads to get average annual fuel
use, tariff energy and ratcheted
demand charges, and electricity and
steam output by mode and tariff
6 year simulation over
12 months/year, 30
days/month, 3 periods/
day in 2 time-of-use
classifications; 5
fundamental modes, 6
tariffs.
Tariff, fuel, and O+M-related costs
costs are escalated for each fuel
and electricity price scenario
using plant performance
statistics; electricity prices are
derived from electricity utility
production cost simulations
performed outside of this
modeling
15-year horizon; 5
fundamental singleyear modes are interpolated to 4 modes,
one of which
(time-of-oil
operation) varies in
performance by year
and scenario; b tariff
options.
Search made over all operating
options in each year for each
plant under each scenario to
find minimum operating costs
9 scenarios, a combination of 3 fuel
cases and 3 utility
coal conversion/
electricity price
cases, calculated over
a 15-year horizon.
~-~
~---~~P1IBPQ~
---
. 100
Table 2.4:
An Overview of the Model Implementation (cont.)
Modeling Level/Submodel
Purpose
Implementation
Investment Decision Analysis
Financial
Analysis
After-tax operating and investment
cost projections are combined into
discounted costs by plant and
scenario
All 6 plant types over
15 year horizon under
the 9 scenarios
Expected
Value
Calculation
Present value for each plant is
calculated from expected
discounted cost for each plant
type
Weighted Dy the 9
scenario subjective
probabilities
Minimum
Present Cost
Design
Selection
Least present cost alternative
selected by comparison of alternative plant types to the current
plant design
From Present Value of
6 plant types
101
Section 2.2.1.3; this allows confining the detailed operating analysis
and present value calculation to a single, pre-sized design for each
plant studied at a site. 9
The second subsection discusses the net
present value analysis of the expected operating cost savings for each
plant.
The third subsection demonstrates the sensitivity of the modeling
results to economic conditions, tariff restrictions, and further
information on fuel prices and utility coal conversion.
Appendix E
summarizes the detailed modeling assumptions, including fuel prices and
electric utility price conditions.
2.3.1.1
The Minimization of Plant Operating Costs by Year
This section describes the development of year-by-year minimum
operating cost estimates for each plant design being considered in the
modeling analysis.
As implemented, each plant at a site can operate in
4 different modes and have the utility purchase or sell it electricity
9 The
pre-selection of plant sizes is subject to a lower limit to
reflect restrictions on commercially available equipment for each
technology: coal-fired steam turbines, 1000 kW; oil-fired steam
turbines, 500 kW; #6 oil-fired diesels and gas turbines, about 1000 kW;
#2 oil-fired diesels, 250 kW; #2 oil-fired gas turbines, 400 kW; and
stand-along coal boilers, 15 MBtu/hr. Supplementary-fired boiler
capacity for diesel and gas turbine cogeneration was set at the lesser
of a capacity providing a 20% incremental capacity factor or the basic
cogeneration system's steam capacity times the
supplementary/cogenerated steam output ratio.
This heuristic approach for supplementary-fired capacity sizing was
developed in a simple sizing analysis using the levelized cost method
in Section 2.2.1.3, which assumes no capital cost economies of scale.
This does not reflect the exact first-order optimality conditions unaer
economies of scale, but it is sufficiently accurate for this analysis,
as will be demonstrated in Section 2.3.4.
102
under 6 different tariff combinations.
Tables 2.5 and 2.6 summarize
these modes and tariffs that make up the operating policy options.
The
operating modes all assume the cogeneration and boiler plants would
follow the site's thermal requirements; assuming otherwise
substantially increases the complexity of the modeling and is
unwarranted given the economic conditions in the utility used for this
case study.
As elaborated by Figure 2.11 and Table 2.4, the operating cost
estimation process requires three stages.
First, the plant performance
modeling takes data on electric and steam loads together with the
individual cogeneration and boiler system fuel use and outage rate
statistics and performs a multi-year Monte Carlo simulation, which
provides annual operating statistics on all plants operating under all
tariffs and modes.
Second, the cost escalation extrapolates the fuel,
operations and maintenance, and utility costs and revenues for the
anticipated fuel and electricity price trends.
Finally, selection of
the minimum cost operating policy for each plant and year takes place
under each fuel and electricity price scenario, which are described in
Table 2.7.
Plant Performance Modeling
The Monte Carlo plant performance model estimates each plant's
expected annual fuel use, electricity purchases and sales by the
utility, and non-fuel tariff charges for a base year through a
period-by-period simulation of the plant operation for a multi-year
period.
This elaborate Monte Carlo simulation is necessary for one
-- L
-~IIP~I~W---*JI~I2~--~srd(rOr
_~
_ _ _
___iyl
103
Table 2.5:
Plant Operating Modes
1. Operate Always: the cogeneration or main boiler ouput follows the
sites' steam loads up to the system's capacity at all times
that the system is available; the older boiler handles peak
steam loads or the entire load during an outage for the new
plant.
2. Never Operates: The cogeneration system does not operate; all
steam loads are met by the older boiler.
3. Operates On-Peak:
The coqeneration system follows the site's steam
loads up to the system's capacity during the utility's peak
electricity price periods and ceases operating in the off-peak
periods; the older boiler handles peak steam loads and the
entire steam load during outages for the new plant or during
the off-peak electricity price periods.
4. Operates on a Time-of-Oil Basis:
The cogeneration system follows
the site's steam loads, up to the system's capacity, whenever
oil is the utility's marginal cost fuel, and it ceases
operation whenever coal is the utility's marginal cost fuel;
the older boiler handles peak steam loads, and the entire
steam load during outages for the main plant or whenever coal
is the utility's marginal cost fuel.
Since the percentage of
the year that the utility is on oil as a marginal cost fuel
changes year by year for each scenario, the plant performance
in each year is interpolated using the given year's time on
oil and the mean annual operating statistics for three special
submodes, the operate always mode, a submode with constant
operation on peak and intermittent operation off-peak, ana a
submode with intermittent operation at all hours.
The
intermnittent operation is correlated with the hignest heating
and cooling degree hours.
104
Table 2.6:
Option
The Menu of Tariff Options
Sales by the Utility
Purchases of Cogenerated
Electricity by the Utility
H-Rate (a large industrial
for site loads in excess of
Purchase of electricity
generation in excess of
with the Auxiliary Service
internal site loads at any
Provision minimum billing
instant; this "net sale"
price is at the utility's
marginal fuel cost.
2
C-Rate (an energy-only rate)
for site loads in excess of
generation with the Auxiliary
Service Provision minimum billing.
3
X-Rate (a time-of-use industrial
rate for site loads in excess
of generation rate) with the
Auxiliary Service Provision
minimum billing.
4
CX-Rate (a time-of-use industrial
rate that is designed especially
for cogeneration) for loads in
excess of generation
5
H-Rate for all site loads
Purchase of all cogenerated
electricity; the "arbitrage"
purchase price is at the
utility's marginal fuel cost
6
X-Rate for all site loads
105
TaDle 2.7:
Fuel and Utility Conversion Scenarios
(a) Fuel price Escalation Scenarios.
1. High:
Oil prices escalating rapidly in constant dollars (about 5
percent/year at first); coal prices escalating quickly (about
3 percent/year), but not as fast as oil prices.
2. Middle: Oil prices escalate moderately (about 2 percent/year);
coal prices escalate more rapidly (about 4 percent) than oil
prices for 5 years and then taper off to a slightly slower
growth rate than oil.
3.
Low:
Oil prices drop (about -2 percent/year) in constant dollar
terms for a 5 year period and then stabilize; coal prices
escalate moderately for 5 years (about 4 percent/year) and
then taper off to a slow growth rate (about 1.4 percent/year).
(b) Electricity Price/Utility Coal Conversion Scenarios
1. Fast:
All coal capable plants are converted from oil to coal;
coal-oil mixtures are used at other major plants and electric
load growth stays low. Oil is reduced to about a sixth of the
total utility fuel energy usage; coal becomes the marginal
cost fuel for almost half the time, up to a third of the peak
hours and more than half of the off-peak hours.
2.
Medium: All the major coal-capable plants are converted from oil
to coal, and electric load growth stays low to moderate. Oil
is reduced to about a quarter of the total utility fuel energy
usage; coal becomes the marginal cost fuel for up to a quarter
of the time, up to about 10 percent of the peak hours and
slightly more than a third of the off-peak hours.
3. Slow: Only the iargest coal-capable plants are converted from oil
to coal, or utility electric load growth increases. Oil is
reduced to about a third of the total utility fuel energy
usage; oil is always the marginal cost fuel.
106
primary reason:
the complexity of tariffs, especially as they relate
to peak demand charges, necessitates a period-by-period analysis that
does not lend itself to more general treatment of changing steam and
electric loads together with the uncertain cogeneration plant
availability, such as Markov process analysis.
The key data requirements on this simulation are a time series for
the steam and electric loads, an outage pattern for the main
coqeneration or boiler system under study, an operating policy, and a
method for calculating each possible electricity tariff.
Table 2.8
lists site-specific information needed for this process.
Since the
modeling was conducted on an 8-hour shift-by-shift basis, electric load
data taken on a quarter-hourly basis for five of the sites was reduced
to shifts of appropriate length.
For the one other site, the college,
shift-by-shift'electric loads extrapolated using a simple
disaggregation assumption between the site's electric load factor and
the split of peak versus off-peak electric energy usage.
The steam loads were not available on a shift-by-shift basis for
any of the sites studied in a form that would allow their expeditious
usage.
By assuming that ambient temperatures are one of the primary
factors affecting site steam loads, a simple disaggregation
relationship between monthly heating and cooling degree information and
monthly boiler fuel use can be extrapolated to shift-oy-shift steam
loads using local shift-by-shift ambient temperature data.
Figure 2-12
provides a comparison of a site's steam load duration curve for the
monthly data and the resulting shift-by-shift simulation.
The site's
-107
Table 2.8:
1. Steam loads:
Site-Specific Data Used in Detailed Plant Analysis
simulated on a shift-by-shift basis as a function of
ambient temperature using a linear equation estimated from monthly
steam usage or boiler fuel consumption and heating/cooling degree
data.
2.
Electric loads:
a)
Shift-by-shift when hourly electric purchase data are
available, corrected for any existing on-site generation
b)
When hourly data are not availaule, monthly energy and peak
demand data are extrapolated to a peak and off-peak pattern
using the site's average electric load factor in relation to
the average electric load factor and peak/off-peak load for a
large sample of sites.
3. Percentage of steam usage at low pressure (< 15 psig) is used to
adjust the cogeneration thermal efficiencies.
4. Existing on-site cogeneration is included as a zero capital cost
plant option if it is significant in size.
5.
Current type of oil being used for steam production.
-
--- ~-
C -- ~r -
108
MONTHLY AVERAGE STEAM USAGE AND THE SIMULATED SHIFT-BY-SHIFT
STEAM
LOAD DUATIN
DURATION CURPVE
STEALOD
CURVE FOR
FOR THE
THE LARE
LAR%3E MANUFACTU
MANFACTURER ---
FRACTION
OF THE YEAR
Figure 2.12
~~4~
lii---~~---'
~-~~~~-~1_~_11II-1-
#
109
steam load temperatures, which affect plant fuel efficiency, are also
required.
. The outage pattern for each plant was set from an exponentially
distributed random process using the mean time to failure and repair
statistics for the different technologies from Appendix D. The fuel
use/steam and electric output relations, also in Appendix D, determine
the plant's impact when it is running; the backup boiler system and the
utility supply steam and electricity service during any outage.
The simulation calculation starts with each 8-hour snift's steam
load.
This determines the level of cogeneration steam output, up to
the plant's capacity; for loads above the main boiler or cogeneration
plant capacity, the backup boiler carries the steam load.
The level of
cogeneration steam output also specifies the electrical output, plant
fuel consumption, and in systems with waste heat recovery boilers, the
maximum permissible level of supplementary firing.
The electricity
output is combined with the shift-by-shift electric loads and
aggregated to yield monthly electricity purchases, sales, and tariff
costs.
The simulation is recomputed for each operating mode using the same
outage patterns, adjusted for mean failure rates between plant types.
The plant performance simulation bases its analysis on.5 different
operating patterns, which are later used to form 4 operating policies
in the cost extrapolation.
Since the fraction of the year that the
utility has oil as its marginal cost fuel changes annually, the
time-of-oil mode is constructed by a year-by-year linear interpolation
110
between the "always operating" mode and two special submodes that
depict plant operation at two fixed time-of-oil percentages.
The first
special time-of-oil submode has the plant operate during all peak hours
and during the off-peak hours that had the highest 70 percent of the
heating and cooling degree levels.
The extreme time-of-oil submoae has
the plant operate during the 65 percent of the peak and the 45 percent
of the off-peak periods that had the most extreme heating and cooling
degree levels.
The heating and cooling degree information uses tne
same ambient temperatures that drive the simulation of the steam loads.
Operating Cost Escalation
Using the performance mooel's statistics on average fuel use, steamn
and electricity output including average electricity purchases and
sales by the utility at the site, and the non-fuel utility tariff
charges, the annual operating costs can be computed for each plant
under each fuel and electricity price scenario in each operating mode
and tariff choice.
The fuel costs for the main plant, the backup boiler, and tne
supplementary-fired boiler are calculated directly from the fuel price
in each year for each tariff and operating mode choice under the given
scenario.
The fuel type differs between the types of equipment.
Operations and maintenance costs are based on the standard unit O+M
cost assumptions discussed in Appendix D. These unit costs are assumed
to escalate with inflation.
The transactions with the utility involve the non-fuel components
of the tariff, the additional fuel adjustment charges (average fuel
ni+
~a9prCCIB~IIRP~~.'MP~~~c~""s~n
r~-----
* 1ll
cost) on electricity sales by the utility, and the payment at the
utility's marginal fuel costs for the sale of electricity to the
utility.
The non-fuel portion of the tariffs was assumed to escalate
with inflation, but it could be escalated in any desired manner.
The
fuel adjustment and the marginal fuel costs depend upon both fuel
prices and the extent of the utility's coal conversion; this analysis
assumed a simple summary relationship between the snare of oil in the
average cost and the shares of oil and coal in the marginal cost.
This
allows consistency in the effect of fuel prices on the industrial firm
and, through the average and marginal shares relationships, on the fuel
adjustment for the tariffs and the marginal fuel cost for the purctiase
of electricity by the utility.
As discussed above, since the share of oil as a marginal cost fuel
changes over time, the time-of-oil operating mode cost must reflect
this changing share in the costs projected year-by-year.
First, the
plant performance for these modes is interpolated as described above.
Then the typical fuel and inflation escalators are applied.
Selection of Minimum Cost Operating Policy by Year
Within any restrictions set on the tariff and operating mode
combinations, the minimum cost operating mode and tariff are selected
for each plant in each year over the planning horizon under the price
conditions set by the scenario.
The minimization is accomplished by
exhaustive search of the alternatives.
This minimum cost operating
policy for each plant, year, and scenario is stored for the
determination of the impact on the utility, a process which Chapter 3
112
describes.
Table 2.9 presents a sample of the operating simulation for
a single year in the middle scenario,
2.3.1.2
The Investment Decision Analysis
This stage of the analysis combines all the minimum operating cost
cashflows for each project with the consequences of the initial capital
investment in order to determine the net present value of each
project.
The final plant design is selected on the basis of the least
net present cost.
Financial Analysis
First, for each given plant and fuel/electricity price scenario, a
discounted value is computed from the after-tax minimum operating costs
and the after-tax results of the initial plant investment.
Table 2.10
illustrates the cashflow calculation comparing the existing oil-fired
boiler with a new coal-fired cogeneration plant under the middle fuel
price escalation/medium coal conversion scenario.
This includes the
optimal operating policy for each year under the given scenario; note
how electricity revenues change from 1985 to 1990 as the steam turbine
plants switches from net sale to arbitrage of the electricity output.
The discounted value is calculated in Table 2.11 using the Adjusted
Present Value method (see Brealey and Myers, 1981).
The detailed
financial assumptions and tax computation methods are described in
Appendix E.
Expected Value Calculation
Second, the discounted values for each plant under all scenarios
' 113
Table 2.9:
Sample Operating Analysis for the Large Manufacturing Site
I
I
...
. .
..
CoalFired
Steam
Turbine
Oil
Boiler
Coal
(existing)
Boiler
Cogen.
104
104
Design Size
MBtu/nr*
179***
MW
-
-
Average Output
in Always Run
Mode
MBtu/hr*
87.7
81.8
MW
-
-
OilFired
Steam
Turbine
Cogen.
74
5.6
4.0
81.1
67.9
5.6
4.0
81.1
4.3
67.9
3.6
Diesel
(No.6)
Cogen.+
Suppl.
Firing
Gas
Turbine
Cogen.+
Suppl.
Firing
58(+55)
58(+55)
22.1
11.0
56.5(+25.8)
54.3+25.
21.5
10.3
45.1(+23.6)
17.1
43.6+23.2
8.3
1985 Conditions for Optimal Operation****
Average Output
M3tu/nr*
MW
Electricity Sold
by Utility
(MW avg)
87.7*
8.28**
81.8
-
8.28
Electricity
Purchased
by Utility
(MW avg)
Operating
Selection
Mode
Tariff
Operating Cost
Savings over
Existing Boiler
(million $ 1981)
4.01
.067
4.66
8.28
.006 17.1
8.28
8.3
Always
H-Rate
Always
H-Rate
Always
H/Net
Sale
Always
H/Net
Sale
Time of
Oil; H/
Arbitrage
Time of
Oil; H/
ArDitrage
0
1.155
1.830
.382
.584
.114
114
Taole 2.9 (continued)
Notes:
*Plus supplementary-fired steam output
**This is the size's average steam and electrical load
***The site's peak steam load
****Middle Fuel Escalation/Medium Coal Conversion scenario.
115
Table 2.10:
Cash Flow Analysis for the Existing Oil-Fired Boiler
Versus a New Coal-Fired Steam Turbine Cogeneration Plant
Year
1981
1982
1983
1984*
1985
1990
(Costs in thousand
for current year)
Existing Boiler
Operating Costs:
1 Fuel Cost
2 O+M Cost
Tariff Charges
3 Direct Tariff
4 Fuel Adjustment
5 Total Tariff (3+4)
Operate
Always
5552
232
H-Rate
2037
3233
5269
6205
255
6935
281
7751
309
8662
340
--
m--
-- i
15860
547
2240
2449
4689
2464
2686
5150
2711,
2950
5660
2982
3243
6225
4802
5412
10214
11054
11149
12366
13720
15227
26622
5748
5798
6430
7134
7918
13844
1381
35
6079
187
8358
395
0
507
0
483
0
308
0
276
0
1216
0
1672
1977
0
1845
0
1186
0
293
1305
1861
1192
1118
717
1123
4960
6892
-686
-635
-409
6 Total Operating Cost
(1 + 2 + 5)
7 After-Tax Operating
Cost (6 x (1-Tax
Rate))
New Coal-Fired Steam Turbine
Capital Related Costs:
8 Direct Expenditures
9 Property Taxes
Tax Shield
10 Depreciation
11 Tax Credits
12 Total Shield
(11 + (9 + 10) x
Tax Rate)
13 After-Tax Direct
Expense (8 + 9 - 12)
116
Table 2.10:
Cash Flow Analysis for the Existing Oil-Fired Boiler
Versus a New Loal-Fired Steam lurnine Cogeneration Plant
(continued)
Year
1981
1982
1983
1984*
--
New
plant
1985
1990
(Costs in thousand $
for current year)
New Steam Turbine (cont.):
Operating Costs:
Existing
plant
14 Fuel
15 O+M
5552
232
Tariff Charges
H-Rate
6265
255
6935
281
--
--
H-Rate
--
Arbitrage
1672
4802
1571
5412
43
5091
3942
1697
4491
1867
7853
3007
H-Rate
16 Direct Tariff
17 Fuel Adjustment
18 Elec. Revenues
2037
3233
0
2240
2249
0
2464
2686
0
Net
1520
1429
42
19 Net Utility Costs
(16+17+18)
5269
4689
5150
2907
3200
5123
11054
11149
12366
8547
9558
15483
Tax Rate)
5748
5798
6430
4444
4970
8311
22 Total After Tax
Cost (13 + 22)
6871
10757
13322
3759
4335
7902
-4960
-6892
3376
3583
5942
20 Total Operating Cost
(14 + 15 + 19)
21 After-Tax Operating
Cost (21 x (1 -
Incremental Saving
With Respect to
Existing Boiler (7-22)
-1123
*New plant starts operation (always) in 1984.
117
Table 2.11:
Components in Adjusted Present Value for a Coal Cogeneration
Plant at the Large Manufacturing Site
$55.410
Total Adjusted Present Cost of Running Old Plant
Capital Costs for New Plant
Present cost of capital expenditures
for plant less direct tax benefits
(pre-tax direct construction cost
of $12.599 without escalation or AFUDC)
Value of additional debt-related
tax shields from project
$ 7.540
(2.394)
Adjusted Net Present Cost for New
Capital Equipment
$ 5.056
Operation Costs for New Project
Present cost of steam and .electricity
operations
38.592
Total Adjusted Present Cost of Building and Running New Plant
43.648
Incremental Adjusted Net Present Value for New Plant
(old less new)
11.762
Values in million 1981 $
Fuel and Electricity Prices Based on
Middle Fuel/Medium Coal Conversion Scenario
118
are weighted according to the -subjective probabilities assigned to the
fuel and capacity price scenarios.10
The resulting net present value
for these expected cash flows determines the minimum cost plant.
Figure 2.13 illustrates the plant net present values for the expected
costs in comparison to an existing boiler for a stand-alone coal
boiler, a coal-fired steam turbine cogeneration plant, an oil-fired
steam turbine cogeneration plant, a gas turbine cogeneration plant, and
a diesel cogeneration plant; the figure shows both the adjusted present
value of the capital cost and the expected operating cost savings for
each plant type.
As Table 2.12 demonstrates, using the standard
scenarios in Table 2.7, the uncertainties in fuel and electricity
prices can substantially impact the value of the different projects.
Note that the diesel cogeneration system, wnich has a high
electricity share for its output, is more sensitive to the electricity
price scenario changes than the oil-fired steam turbine system.
Miminum Present Cost Plant Design
The combined set of investment and operating decisions can be
illustrated in the form of a decision tree, as shown in Figure 2.14 for
101n applying the systematic-risk adjusted discount in all years
to all scenarios, several assumptions are being made. First, the
systematic uncertainties with respect to the wnole economy are being
resolved for these decisions at a constant rate throughout the life of
the projects (Myers and Turnbull, 1977). Second, the fuel price and
utility coal conversion scenarios are not correlated with conditions in
the economy; this may be a relatively weak assumption with respect to
the fuel price uncertainties.
119
RESULTS
NET
PRESENT
VALUE OF
AFTER-TAX
OPERATING
COST
SAVINGS
LESS
CAPITAL
COSTS
VERSUS AN
EXISTING
OIL-FIRED
BOILER
FOR LARGE---MANUFACTURER
PRESENT
VALUE
Coal
Coal
Oil ST
Boiler
Cogen
Cogen
Gas
Turbine
Diesel
Cogen
10+
5+
(MILLION
1981$)
T
!I
r
ADJUSTED
PRESENT
VALUE OF
AFTER-TAX
CAPITAL
COSTS
(MILLION
Steam
Load
Peak
1981 $)
PLANT
50
STEAM
CAPACITY
(MBTU/HR) 50-
0-
SIZE
Avr
Base
aT
-
r
Avg -
10ELECTRIC
CAPACITY
(KI)
Peak
Electric
Load
201
Figure 2.13
120
Table 2.12:
The Impact of Fuel and Electricity Price Uncertainties
on the Value ot a Cogeneration Plant
(a) Diesel Cogeneration
Utility Coal Conversion Scenario
Fast
Fuel
Price
High
Middle
Escalation
Low
Medium
Slow
3.24
1.01
4.69
1.52
-.25
-.99
-1.90
Expected Value: .99
2.09
.06
(b) Oil-Fired Steam Turbine
Utility Coal Conversion Scenario
Fast
High
Fuel
Price
Middle
Escalation
Low
Medium
Slow
1.82
2.48
1.08
.79
1.63
1.09
1.05
.56
.63
Expected Value:
1.21
(c) Coal-Fired Steam Turbine
Utility Coal Conversion Scenario
Fuel
Price
Escalation
High
Middle
Low
Slow
Fast
Medium
16.03
11.19
17.18
11.76
18.34
6.23
6.41
6.75
Expected Value:
11.80
Net present value in millions 1981 $
12.59
THE CUSTOMIER'S COGENERATION DECISION
INVESTMENT DECISIONS
PRICE UNCERTAINTIES
ANNUAL OPERATING DECISIONS
Plant
Type
Fuel
Prices
Tariff
Choice
Plant
Sizing
oal
Peak Steam
Boiler*
Load Design
Coa I
Cogen
Utility Coal
Conversion
Hi h
1/3
Fast
.35
Operating
Mode
H-Rate
Run
Net Sale
Always
C-Rate
Net Sale
Don't Run
X-Rate
Oil St
Net Sale
Cogen
Avera e
Load Design
Middle
Middle
1/3
.35
Cogen.
Net Sale
Cogen
Run
On -Peak
GTH-Rte
1/3
Cogen
Existing Oil Boiler
Base Load
Design
Low
Arbitrage
.3
Slow
Figure 2.14
Figure 2.14
- Rate
Arbitrage
Run
Time -of -Oil
* 122
a limited set of alternatives.
The first choice in the tree is the
plant type selection, which is the decision discussed in this section.
At the next stage is the sizing of each plant type, which is done by
the simple heuristic method discussed in Section 2.3.1.1 prior to the
operating cost simulation and minimization.
At the next two stages,
the uncertainties in fuel and electricity prices are resolved on a
year-by-year basis.
Once given the fuel and electricity prices, the
electricity tariff for the site and the operating mode for the plant
are selected, as described in the sections above.
Figure 2.15 snows
the distribution of the scenario outcomes for an expanded tree in terms
of the discounted cost savings; this is the same information as in
Table 2.12, but with the subjective assessments for the scenarios
added. 12
11A
comprehensive decision analysis approach (Howard, 1966)
determines the key uncertainties in a decision tnrough sensitivity
studies, elicits probabilities from experts or the decision maker on
the key uncertainties, applies the modeling from the sensitivity
studies together with the probabilities, and suggests a course of
action with notation of the value of further information on aspects of
the decision. The analysis here determined the key uncertainties in
the preliminary modeling and has demonstrated them in the theoretical
discussions in Section 2.3. The probabilistic opinions are on factors
that affect all the sites; since this analysis is being conducted "by
proxy," similar expectations by all the decision makers at all sites
are assumed.
12Financial
theory questions the presentation of a probability
distribution for the discounted cashflows since the purpose of
discounting is to determine a present value, which is not an uncertain
quantity but a market appraisal of a current "price" for the project
(see Brealey and Myers, p. 203). The presentation, however, is
insightful to the analyst because it forces questions about the extreme
values in the distributon--such as: Would the market ever get that
good; would we ever let the project get that bad; what subsequent
decisions could be made to reduce the risks?
~_~~II
~_I
r-R
THE DISTRIBUTION OF INCREMENTAL DISCOUNTED COST SAVINGS
FOR THE ALTERNATIVE PLANT TYPES
1.00
CUMULATIVE
PROBABILITY*
OF
INCREMENTAL
DISCOUNTED
COST
SAVINGS
BEING LESS
THAN OR
4
EQUAL TO
THE GIVEN
LEVEL
INCREMENTAL DISCOUNTED COST SAVINGS
(MILLION L91 $)
Fiqure 2.15
' 124
Having information about the key uncertainties prior to the plant
selection decision can have value.
If it is environmentally acceptable
to build a coal system, noting Table 2.12 (c) versus (a) and (b),
further information on fuel and electricity price scenarios nas no
value becaue the best decision in all cases is to build a coal-fired
cogeneration plant.
If it is not possible to build a coal-fired
system. there can be some value to further information.
Assuming
perfect information is available, i.e., all uncertainty about a group
of prices is resolved prior to the decision, the expected value of
perfect information on fuel prices and utility coal coversion with
respect to a choice between an existing boiler, a new diesel
cogeneration system, or an oil-fired steam turbine cogeneration plant
is positive because the information would be valuable in selecting the
best alterative; in this example, the value is $440,000 for perfect
information on fuel prices, $165,000 for perfect information on the
extent of utility coal conversion, and $486,000 for both together.
The
expected value of perfect information changes substantially if
restric.tions are placed on the future operating and tariff choices for
the plants.
For example, if time-of-oil pricing is not availaole as an
operating mode, there is no value to information on fuel prices for a
choice among oil-fired cogeneration systems, while the expected value
of perfect information on utility coal conversion remains nearly the
same (see Table 2.19, later in this chapter).
•
......
:L.
. - -
125
2.3.1.3
Sensitivity Analysis of Operating and Investment Decisions
As the discussion in the previous section demonstrated, the value
of a new cogeneration system can change substantially with changes in
fuel and electricity prices.
This section will explore in further
detail the sensitivity of the operating and investment decisions to
these price risks and the sensitivity of the investment decision to the
cost of capital and future fuel and electricity prices.
In addition,
the impact of restrictions in the allowed operating modes or in the
menu of utility tariffs available to the customer is discussed.
At the operating level, as the theoretical discussions in Section
2.2 suggested, a site has a number of valuacle opportunities to adjust
the plant's operating policy and the tariff choice for changes in
electricity and fuel prices.
Table 2.13 provides the optimal operating
policies in 1985 for the oil-fired steam turbine cogeneration system; a
diesel system must operate in a similar fashion, but coal-fired
cogeneration operates continuously and only switches tariff choices.
If low fuel prices occur, the operator can keep savings high by
reducing the site's utility bill; if fuel prices are high, selling tne
plant's output on an arbitrage basis at marginal fuel cost is better.
At high fuel prices, as the changes between electricity price scenarios
show, the oil-fired steam turbine plant can advantageously adapt its
operating mode to th'e times that the utility uses oil as the marginal
cost fuel, running the back-up boiler for all steam loads when the
utility uses coal.
At low fuel prices, since the cogenerated
electricity output from the plant is almost always less than the site's
126
The Optimal Operating Policies by Scenario
For an Oil-Fired Steam Turbine Cogeneration Plant
at the Large Manufacturing Site in 1985
Table 2.13:
Optimal
Operating Mode;
Tariff Choices;
Operating
Cost Savings
High
Fuel
Price
Escalation
Scenario
Middle
Low
Utility Coal Conversion Scenario
Fast
Time-of-oil;
H-Rate and
Arbitrage
.352
Medium
Time-of-oil;
Run Always;
H-Rate and
Net Sale
.355
Run Always;
H-Rate and
Net Sale
Run Always;
H-Rate and
Net Sale
.377
Run Always;
H-Rate and
Net Sale
.398
H-Rate and
Arbitrage
.435
..382
Values in Millions of 1981 $
Slow
Run Always;
H-Rate and
Arbitrage
.522
Run Always;
H-Rate and
ArDitrage
.446
Run Always;
H-Rate and
Net Sale
.437
127
loads, the plant uses the electric output internally and runs
continuously no matter what level coal coversion by the utility takes
place because the savings from the tariff reduction are higher than the
utility's marginal fuel cost-based purchase price.
Restrictions on the possible operating mode or tariff menu
influence the value of the project.
For example, in Table 2.14, tne
base case for 4 new plant .projects is compared to several alternative
cases.
The first restricted case does not allow the plants to follow
"time-of-oil" pricing; as illustrated in Section 2.2.2, this poses no
problem for the coal-fired plant, a small economic penalty for the
oil-fired steam turbine system, and a significant disadvantage for the
high operating cost cogeneration systems such a the diesel and gas
turbine.
As the operating restrictions become more severe in Cases 3
and 4, the increased limitation on operating flexibility hurts only the
diesel and gas turbine systems more than the Case 2 results.
As Case 5
shows, special limitations on the tariffs availaole for cogeneration
systems that sell their net output also reduce the value of tne
project; but the limitation of these customers to time-of-day tariffs
(X or CG rates), when selling the net output or using it all at the
site, results in only a minimal reduction in the project's value.
If
the special cogeneration rate (CG) is eliminated and the utility only
pays 95 percent of its marginal fuel cost for electricity that it
purchases, rather than 100 percent, the cogeneration systems with a
high ratio of electricity to steam output (diesels and gas turbines)
drop in value significantly while the steam turbine-based systems are
128
Table 2.14:
Sensitivity of the Plant Net Present Value Because of
Restrictions or Economic changes
Case
Coal
Boiler
Coal
Steam
Turbine
Cogen.
Oil
Gas
Turbine
Cogen.
(No. 6)
Diesel
Steam
Cogen.
Turbine (No. 6)
Cogen.
1. Base case:
8.04
2.
11.80
1.21
.99
-1.99
11.80
1.08
-.88
-2.55
No Time-of-Oil Pricing:
8.04
3. Plant must run year-around or not run at all:
1.08
8.04
11.80
4.
Plant must always run:
8.04
-2.87
-1.60
11.80
1.08
-2.17
-3.69
ile" cogeneration:
5. Mandatory time-of-use type tariff fo r "net sa
.98
-1.99
8.04
11.51
1.01
6. No CG Rate; 95 percent marginal fuel cost as purchase rate:
1.01
.98
-1.99
8.04
11.51
7. Lower base year oil prices; higher coal prices:*
2.51
6.21
1.20
.85
8.
Higher cost of capital (18.9 percent vs.
15.1 percent
-1.63
)
-2.28
-. 41
9. Higher cost of capital; lower base year oil prices + higher coal price:*
-1.99
-. 49
.44
3.39
1.08
10. One year delay of construction start-up time:
-1.85
.78
1.02
10.59
7.29
5.13
11.
7.73
.41
An unexpected 1 percent higher fuel consumption rate by main plant
(supplementary firing and back-up have original fuel consumption rate):
-2.24
7.94
11.69
.99
.55
Net present value in millions 1981 $
*These cases have plant and supplementary firing capacity that differs
from the base case sizing; this is a result of the plant optimization
process.
ri-i
129
affected only slightly.
Changing the underlying economic assumptions for the fuel price
escalation basis or for the cost of capital fundamentally changes the
trade-off between operating cost savings tnrough fuel cost reduction
and capital expenditure.13
Case 7 shows the result wnen the fuel
price escalation startes with lower oil prices and higher coal prices
(low sulfur no. 6 oil down from $5.45/;,Btu to $4.56/MBtu, high sulfur
no. 6 oil down from $4.55/MBtu to $4.09/MBtu, coal up from $2.05/MiAtu
to $2.31/MBtu, and nuclar fuel down from $.68/MBtu $.64/MBtu at the
start of the planning horizon).
This reduces the operating benefits of
both the coal-fired and the oil-fired cogeneration systems since they
must both be compared to the original oil-fired boiler.
Case 8 demonstrates the importance of the cost of capital for
cogeneration plants:
an increase in the cost of capital from 15.1
percent/year to 18.9 percent/year decreases the value of all projects
substantially.
The base case for all sites studied in this chapter has
assumed that the appropriate cost of capital for a cogeneration project
is similar to that for an electric utility investment--an investment
that provides services which are required stably in economic ups and
downs.
This means that a textile mill will evaluate a cogeneration
plant using the same incremental cost of capital as a paper mill would
use since both would-perceive the plant as a project with relatively
stable returns.
13 The
Electric utility investments have their level of
optimal sizing for each plant type can change as a result
of a change in the general economic assumptions.
130
revenues guaranteed, in part, by rate revisions in the regulatory
process, while their costs depend upon fuel prices and the cost of
heavy equipment; the industry has a real return on assets of about half
the average for all U.S. industries, or about 15.1 percent/year
assuming a 10 percent/year inflation rate, .2 percent/year cost of
risk-free debt, and an asset Beta of .5 for the utility inaustry
(Brealey and Myers, p. 113, 167).
An industry with a higher average
return on assets, petroleum refining, depends upon oil prices for its
revenues while its costs depend also upon oil prices and the cost of
heavy equipment; the refining industry has a real return on assets of
about 90 percent the U.S. industrial average, or about 18.9
percent/year assuming an asset Beta of .9. Cogeneration plants have
"revenues" that depend upon fuel prices (embodied in the replacement of
the existing oil-fired boiler costs and utility oil-fired generation)
and, to the extent that the plant reduces the site's electric bill, the
regulated utility tariff levels while their costs depend upon fuel
prices and heavy equipment costs.
Thus, the return for the electric
utility industry and the petroleum refining industry might be good
lower and upper bounds for the expected asset return for cogeneration
plant investments.
Case 9 presents the combined impact of higher costs of capital and
lower initial oil prices.
The sensitivity Case 10 demonstrates the
importance of the new cogeneration system coming "on-line" to replace
the existing steam system as soon as possible.
This case assumes that
the construction start of the new plants is delayed for one year, so
i
i
i
j
B
V'2~m.--l*c*anur~o-soY-,
' 131
the distribution of capital expenses remains the same with respect to
the start of operation but the savings-start one year later.
The final case presents the impact if the new plants have an
unexpected I percent increase in fuel consumption in the main plant;
supplementary-fired boilers and back-up boilers retain their original
fuel consumption rates.
2.3.2
Overview of Plant Studies by Technology and Site
The previous section described the overall model using the Large
Manufacturing site as an example.
This section compares the modeling
results from six of the seven diff ereIt sites aescribed in
-ection 2.1
and comments on the conditions necessary for a viaole cogeneration
project.
These results have been calculated in detail similar to tnat
presented for the Large Manufacturer.
There are two classes of conditions influencing cogeneration
economics at these sites.
The first set affects all projects:
the
cost of capital for cogeneration and boiler systems; the fuel and
electricity price projections; capital costs for different plant types;
and restrictions upon the choice of utility tariffs and operating
modes.
The previous sections demonstrated the sensitivity of the
project economics to these factors.
changes from site to site:
The second class of conaitions
the usage patterns for steam and
electricity; the temperatures and pressures of the steam required; and
the original boiler and fuel type on the site.
A comparison of sites allows the exploration of the sensitivity of
132
Taole 2.15:
Net Present Value of New Plants
Steam
at
New
Existing MBtu/hr Low
Pres- Boiler
Peak/
Boiler
Minimum sure (fuel)
Fuel
Steam
Net Present Value
Oil
ST
Diesel
Coal
Cogen. Cogen.
ST
Cogen.
(#6) (fuel)
Gas
TurDine Best
Cogen. Plant
(fuel) Cnoice
Office
#2
oil
23/0
10%
69/35
15%
.15
(#6 oil)
.038
.14
-.23
#6 oil
-. 047
boiler
(#2 oil) (#2 oil)
3.83
5.72
.49
-1.54
Coal
.42
(#6 oil) (#6 oil) cogen.
2.44
.11
Coal
-.99
-.24
(#6 oil) (#6 oil) cogen.
Paper Mill
#6
(coal)
oil
Medium Manufacturer
36/17
#6
oil
0%
1.67
(coal)
Large Manufacturer
179/58
#6
oil
0%
8.04
11.80
1.21
(coal)
Coal
cogen.
(#6
oil)
(#6 oil)
.99
-1.99
Hospital
#6
oil
16/4
67%
.46
.73
.034
Coal
-.30
cogen.
(#6
oil)
(#6 oil)
.430
-.14
-.49
-.15
Coal
(#6 oil) (#6 oil) cogen.
(coal)
.036
College
#6
oil
Note:
mode.
36/0
100%
.209
(coal)
The sites were unrestricted in their choice of tariff and operating
Net present values in million 1981 $.
s133
Table 2.15:
(continued)
New Plant Size
(Steam load dectile;MW; MBtu/hr + Suppl. FiriIn.g)
Diesel
Gas Turb.
Coal
Oil ST
New
Cogen.
Cogen.
Boiler
Cogen.
Cogen.
Office
Percentile***
MW
MBtu/hr
40X
0
6. B
0%
1.0
18.2
30%
.50
9.0
70%
.26
.9+1.8
40
0
45
30%
2.7
48
70%
2.0
36
80%
12.7
35+16
0%vl
80%
50%
.42
3.1+7. 1
Paper Mill
Percentile
MW
MBtu/hr
80%
6.1
35+16
Medium Manufacturer
Percentile
MW
M8tu/hr
40%.
1.2
22.5
.89
16.6
6.3
16.6+9
2.7
16.6+9
30%
0
104
30%
5.6
104
60%
4
74
80%
22
58+55
80%
11
58+55
60%
0
6.4
50%
.45
6.8
80%
.36
5.4
80%
1.7
5.4+2.9
80%
.69
5.4+2.9
30%*
0
15
30%*
1.0
13.7
50%*
.5
6.8
0
22.5
Large Manufacturer
Percentile
MW
MBtu/nr
Hospital
Percenti le**
MW
MBtu/hnr
College
Percentile
MW
MBtu/hr
60%
1.5
5+10
50%*
1.1
8.3+8.4
*Minimum commercial size limit applied.
**The minimum commercial ly available technology limits were not applied for
this case study.
***Steam load duration percentile on which tne economic sizing of the plant
is based.
# 134
cogeneration or new boiler system choices to the second class of
conditions.
Table 2.15 provides this comparison for six of the seven
original sites visited.
Since oil-fired cogeneration systems must be
sized to serve the base, year-around steam loads, a good site must have
a large year-around steam load, as in the Large Manufacturing and Paper
Mill sites.
Coal-fired systems can be economically sized to meet
nearly the peak loads, but their benefits also increase with larger
year-around steam loads.
Note-the differences between the College and
the Medium Manufacturer, which have similar peak steam loads.
The projects also benefit when the steam needs are at lower
pressures.
For example, compare the diesel systems between the
Hospital and the Medium Manufacturer.
Finally, if the original boiler system is running on very expensive
distillate oil, even switching to residual oil is ecqnomic; the Office
Building demonstrates this case.
A system running on distillate oil,
however, may indicate local environmental restrictions, which are not
captured directly in this analysis.
2.3.3
Simplification of the Modeling
Since the model is intended for the analysis at many sites, steps
were taken to reduce the major computational sites:
the Monte Carlo
operating simulation; the optimization of operating costs under
different fuel and electricity price scenarios.
To be eeffective, the
simplifications must not alter the major conclusions of the modeling:
primarily, the plant choice and sizing.
Other factors of interest,
135
such as the impact of a restriction in operating policy or the expected
value of perfect information, may be interesting to measure the impact
of a simplification.
The simplifications were tested by comparing
three different site data sources and modeling combinations:
1.
The original detailed modeling based on extensive load site
data.
2.
The simplified modeling using the original site data
simplified to the "aggregate" data characteristics but
consistent with the original data, especially in total annual
steam consumption.
3.
Tne simplified modeling using aggregate data gatnered by a
different survey with substantially reduced data requirements.
Table 2.16 lists the simplified data needed for this "aggregate plant
analysis," which especially stresses less complex data for the steam
and electric load characteristics.
The operating simulation cost was reduced substantially by
shortening the simulation to a one-year period with only one week in
each month.
Electricity loads were characterized by only a peak and
off-peak level throughout the year; an adjustment factor was selected
to relate the peak load to the average peak and off-peak energy
electric use.
Steam loads were simulated on the oasis of an ambient
temperature distribution for each month.
Instead of generating a
random process for plant outages, an average number of plant outages
were evenly spaced throughout a one-year simulation; the correct
average energy output of the plant was maintained by r.educing the
ar~ra
-
136
Table 2.16:
1. Steam loads:
Site-Specific Data Used in Aggregate Plant Analysis
developed from--
a)
the average steam load for process use, heating, and cooling
b)
When the data are available on peak and lowest monthly steam
use, the steam load pattern implied
Dy
1(a) is adjusted to
coincide with the peak monthly and minimum monthly heat use.
2.
Electric loads:
Annual internal electricity loads (energy and peak demand) are
extrapolated to a peak and off-peak pattern using the site's
average electric load factor in comparison to the average load
factor and peak/off-peak load statistics for a different sample of
sites.
3. Percentage of steam usage at low pressure (15 psig or below) is
used to adjust the cogeneration thennal efficiencies.
4. Existing on-site cogeneration is included as a zero capital cost
plant option if it is significant in size (1000 KW or above).
5. Current type of oil being used for steam production.
~_ ____yyml__lll___ ~
137
effective capacity so the output reduction together with the
predetermined outages produced the design plant availability.
The only way to reduce the computation costs for optimal operating
costs is to reduce the number of scenarios studied.
By carefully
selecting the middle fuel and medium utility coal conversion scenarios
to be close to the mean of the nine combined fuel and electricity
scenarios, the present value of the cashflows from the middle scenario
was made to be close to the present value of the expected cashflows for
all nine scenarios.
As Tables 2.17 and 2.18 demonstrate, the adjusted
present value and the incremental value of the time-of-oil operating
mode does not chanoe significantly either for the modeliny changes Ocr
the switch in data sources with these simplifications.
This is an
important result because a key purpose of the modeling is the
forecasting of the most economic technology at each site and the impact
of tariff changes upon the site.
Unfortunately, the expected value of perfect information cannot be
computed without the multiple scenario decision tree calculation,
although the plant performance simulation simplification works
reasonably well for this purpose, as shown in Table 2.19.
Also, as
will De discussed in the next chapter, the calculation of the impact of
customer cogeneration on the utility cannot be calculated with the
scenario reduction si'lplification.
2.3.4
Comparison of Analytical and Modeling Results
This section compares the modeling results described above with the
138
Table 2.17:
Present Value for the Replacement of an Oil Boiler
Expected Value
for Plant Change
Site/
Plant
Detailed
Simulation
Middle Scenario Value
for Plant Change
Aggregate
SimulaDetailed
Aggregate tion
SimulaSimula- Survey
tion
tion
Dat a
Large Manufacturer
8.04
Coal boiler
.99
Diesel # 6 oil
8.04
1.01
Gas Turbine #6 -1.99
1.21
ST#6 (new)
-1.93
1.21
-1.87
5.93
4.74
11.85
10.89
ST#6 (old 6 MW)
ST coal
Hospital
Coal boiler
m--
11.80
5.27
.34
Aggregate
SimulaAggregate tion
Simula- Survey
ation
Data
8.16
1.01
8.17
5.36
1.00
-2.13
1.08
-2.09
1.09
.29
-1.97
11.76
5.79
4.67
11.82
7.87
.460
.189
.168
.473
.199
.178
.036
.029
.023
-.158
.023
.023
-.300
-.395
-.384
-.310
-.407
-.395
ST #6
.034
-.012
-.020
.013
-.031
-.038
ST coal
College
.730
.305
.267
.728
.302
.263
Coal boiler
.209
.200
.219
.220
.212
.230
Diesel # 6 oil
-.150
-.142
-.287
-.145
-. 139
-.297
Gas Turbine #6
ST #6
-.490
-.491
-.496
-.505
-.501
-.497
-.141
-.141
-.154
-.150
-.151
-.165
.430
.437
.209
.428
.433
.207
Diesel # 6 oil
Gas Turbine #6
ST coal
Paper Mill
3.83
Coal boiler
.42
Diesel # 6 oil
Gas Turbine #6 -1.54
ST #6
ST coal
.49
5.72
3.75
.39
-1.51
.47
5.61
3.17
-.63
3.89
.49
-1.63
-1.72
.25
4.72
.46
5.75
Values in million 1981 $
3.81
.439
-1.69
.442
5.64
3.22
-.68
-1.66
.19
4.70
139
Table 2.18:
The Value of Time-of-Oil Pricing for the Firm
Relative to All Tariffs with Fixed Time-of-Supply Pricing
Middle Scenario Value
Expected Value
Site/
Plant
Detai led
Simulation
Aggregate
SimulaAggregate tion
Detai led
Simula- Survey
Simulation
Data
tion
Aggregate
SimulaAggregate tion
Simula- Survey
ation
- Data-
Large Manufacturer
Diesel #6
1.870
2.18
1.484
2.229
2.539
1.787
.132
.138
.255
.149
.161
.321
Diesel #6
.168
.138
.173
.195
.153
.197
ST#6 Oil
.005
.015
.013
.057
.015
.012
Diesel #6 Oil
.105
.094
.133
.126
.112
.161
ST#6 Oil
.017
.015
.016
.022
.019
.019
ST#6 Oil
Hospital
College
PRaper Mill
Diesel #6 Oil
ST#6 Oil
1.081
1.24
.997
1.329
1.418
1.149
.077
.079
.064
.092
.095
.079
Net present value in million 1981
140
Table 2.19:
Site/
Perfect
Information
on
The Expected Value of Perfect Information
Choice of Any Plant
Choice of Only Oil-Fired Plants
Aggregate
Aggregate
SimulaSimu laDetailed Aggregate tion
Detailed Aggregate tion
SimulaSimula- Survey
SimulaSimula- Survey
tion
Data
ation
tion
Data
tion
Large Manufacturer
Fuel Prices
Coal Conversion
0(0)
0(0)
0(48)
0(0)
440(0)
0(0)
0(40)
486(267) 484(0)
Both
Hospital
Fuel Prices
0
0
55
64
Coal Conversion
Both
0
0
0
55
28
439(0)
0(0)
165(165) 176(0)
0(0)
52(0)
37
0
31(32)
27
49
34
64
64(39)
47
61
24
95
0(0)
0
0
0
28
0
24
19
101
0(0)
11(11)
0
10
0
0
Fuel Prices
0
0
0
289(0)
273
Coal Conversion
0
0
0
124(124) 120
68
Both
0
0
0
332(184) 308
204
College
Fuel Prices
Coal Conversion
Both
Paper Mill
186
Medium Manufacturer
Fuel Prices
0
34(0)
Coal Conversion
0
1(17)
Both
Office Building
0
60(54)
Fuel Prices
80
11(0)
Coal Conversion
0
4(4)
Both
80
12(7)
Figures in parentheses represent the Expected Value of Perfect Information
when the firm cannot operate the chosen cogeneration plant on a time-of-oil
basis but must select continuous, time-of-day, or no operation for any
given year. Present values in thousand 1981 $.
' 141
analytical approach of Section 2.2 applied under similar assumptions.
The discussion follows the modeling structure.
First, the model calculates the cogeneration plant
steam and
electrical output in a period-by-period time series simulation.
This
results in an average 81.1 Mbtu/hr and 4.3 MW output for a coal-fired
cogeneration plant of 5.6 MW and 104 MBTU/hr capacity at the Large
Manufacturer.
By taking the steam load duration curve in Figure 2.12,
which is interpolated from the dectiles of the actual load duration
curve, the output of the plant can be calculated indirectly:
after
adjusting for the plant's availability, the expected output by this
simple analytical metnod is 80.9 MBtu/hr and, with a 53 kWni/lti
electricity/steam ratio, 4.29 MW.
Second, the operating strategy must be determined.
The modeling
indicates that a coal-fired cogeneration system would'operate
continuously; since both the cogenerator and the utility buy coal at
the same price in this study and the cogeneration incremental heat rate
is 4450 Btu/kWh (Table D.2) while the utility's is assumed to be 10,000
Btu/kWh,. there is little question that the modeling and analytical
approaches agree when the utility is burning coal or higher priced
oil.
A more interesting case is the operating mode for an oil-fired
steam turbine cogeneration plant.
Table 2.20 lists the incremental
operating cost for the cogeneration plant in 1985 and the minimu cost
operating strategy if the plant must sell all of its output to the
utility.
Comparing this with Table 2.13, the modeling approach agrees
with the analytical method for the high fuel price escalation case when
142
Table 2.20:
The Optimal Operating Mode for 1985 by the Analytical
Approach
Incremental
Fuel
Escalation
Scenario
Cost of
Utility Mar ginal
Fuel Cost
r
(g/KWh)
Optimal
Cogen.
Operating
Mode
ST Cogen.
( /kWh)
On Oil
on Coal
High
2.96
6.1
2.38
Time-of-oil
Middle
2.59
5.3
2.38
Time-of-oil
Low
2.28
4.7
2.37
Always
Cogeneration uses low sulfur #6 oi1; utility uses a 50/50 mixture of
high arid low sulfur #6 oil or coal
conditions.
Values in
,3 1 $ for 1985
__ ~_II
* 143
.coal is a marginal cost fuel, as in the fast and medium coal conversion
cases.
For the middle and low fuel price escalation scenarios, the
site switches to net sale use of the cogenerated electricity, so it is
impossible to determine the result from Table 2.13.
The next stage of the operating strategy comparison is the choice
of tariff, primarily the choice Detween arbitrage and net sale of the
cogeneration plant electric output under the H Rate.
Since the
cogenerated output from the steam turbine systems is rarely in excess
of the Large Manufacturer's internal loads, this involves the
comparison of the utility's marginal fuel costs and the average cost
savings associated with the reduction in tne utility's tariff charges.
Table 2.21 lists the utility's average marginal fuel cost in 1985 and
an estimate of the Large Manufacturer's average tariff charge savings,
which is the utility's average fuel cost plus a non-f'uel charge savings
of about 2.2V/kWh for either steam turbine-type cogeneration system.
The optimal tariff choice derived from the analytical approach is
identical to the modeling results shown later in Table 3.4 for a
coal-fired cogeneration system, which would operate continuously and
thus receive an average price for its output approximately equal to the
utility's average marginal fuel cost.
The tariff choice results depend upon the non-fuel charge added to
the utility's average fuel cost in the estimation of the cogenerator's
potential tariff charge savings.
For tne Large Manufacturer, wno nas
an average electric load with a 442 nours-use load factor (kWh
load/peak kW) before cogeneration and a net 270 hours-use with the
144
Table 2.21:
The Optimal Tariff Choice for 1985 by the Analytical
Approach
(a) Utility's Average Marginal Fuel Cost
Utility Coal Conversion Scenario
Medium
Fast
Slow
Fuel
Price
High
Middle
4.7
4.2
5.4
4.7
6.1
5.3
Escalation
Low
3.8
4.2
4.7
(b) Large Manufacturer's Average H Rate Tariff Reduction Savings
(Avera'ge Utility Fuel Cost + 2.2V/kWh Non-Fuel Charge)
Utility Coal Conversion Scenario
Fuel
High
Medium
5.2
Fast
5.0
Slow
5.5
5.2
5.0
4.8
Middle
Price
5.1
4.8
4.7
Low
Escalation
(b) Large Manufacturer's Maximum Electricity "Revenue" Choice by
Analytical Approach
Utility Coal Conversion Scenario
Fast
....
Medium
Slow
Fuel
Price
High
Middle
Net
Net
Arbitrage
Arbitrage
Net
Arbitrage
Escalation
Low
Net
Net
Net
Prices are in i/kWh in 1981 $; the optimal operating choices are for a
coal-fired cogeneraion plant at the Large Manufacturer site; the plant
operates continuously in a thermal load following mode under all
scenarios.
, 145
coal-fired system selling net, the incremental unit of electric energy
has a non-fuel tariff charge between 1.716 v/kWh and 2.281 v/kWh under
the H rate (see Appendix Section B.1).
This customer's average total
non-fuel tariff charges are between 2.6V/kWh witnout any cogeneration
and 2.95V/kWh for the customer's purchases with net sale cogeneration.
If Table 2.21D was recomputed using an average non-fuel charge of
2.7/kWh, an upper bound reflecting the average total tariff cost, the
customer would always select net sale disposition of tne cogenerated
electricity.
If Table 2.21b was recomputed using an average non-fuel
charge of 1.8 /KWh,
a lower bound reflecting savings on the non-fuel
tariff energy charges only, the customer would select net sale
cogeneration for only three of the nine scenario combinations.
The
2.2g/kWh non-fuel cnarge used in Table 2.21b is the average non-fuel
tariff savings as computed from the plant performance model:
lNon-fuel tariff I
charge without
cogeneration
jAnnual coal cogen.
Tariff charge with net
net sale coal ST cogeneration
operating always
electrical outputt
These Analytical results demonstrate both the reasonable character of
the results from the modeling and the necessity of the modeling for
estimating the impact on the tariff charges.
This is an important
result because the utility impact calculations in Chapter 3 depend upon
the tariff choice by the customer and the difference between customer
tariff charge reductions and the utility's fuel cost savings.
The analysis of the operation and tariff choice becomes much more
cmplex for an oil-fired system, which would adapt its operating mode
° 146
and net/arbitrage tariff choice simultaneously.
This requires a
calculation of the cogeneration system's operating profit under the two
principal choices:
time-of-oil operation with arbitrage sale of the
electric output or continuous operation with net sale of the output.
This involves estimation of the electricity production under
time-of-oil operation as well as continuous operation.
The value of
the output is determined by how it is sold; the operating profit is
derived from the operating revenues less the marginal cogeneration
operating costs under the different levels of output.
were carried out for the high fuel escalation case:
The calculations
the modeling ana
analytical results agreed for the medium coal conversion case, but
disagreed for the fast coal conversion case, although the operating
profits were similar.
Third, at the investment stage, the model pre-selects the plant
sizes by type using the levelized cost formula described in Section
2.2.1.3 and in the introduction to 2.3.1.
This heuristic analytical
approach, which is not exact under capital cost economies of scale,
agrees closely with sensitivity studies performed using the detailed
model to calculate the net present value of the same plant for a range
of sizes.
Figure 2.16 illustrates tnese results in comparison to the
size selected by the simple method.
Note that the net present value of
the plants is relatively close for sizes near the optimal; since there
are much larger differences in the net present values of the different
plant types, this analytical simplification works well within the model.
Finally, the net present value of the different plant types could
--
147
COMPARISON OF MODELED AND SIMPLE ANALYTICAL
PLANT SIZING
Simple Sizing and
Actual Steam Turbine
Optimal Size
1.50
1.25
D G
I
1.00
Simple
PLANT
Actual Sizing for
Diesel Diesel
Optimal
Size
.75
NET
PRESENT
VALUE
(MILLION
.50
.25
I
20
40
a
I
60
I
80
STEAM LOAD DURATION
FOR PLANT SIZING
-.5 0
-.7 5
2
.0 0
2.25
2.50
o
Steam Turbine on #6 Oil
f
Diesel Coqen on f 6 Oil
Figure 2.16
i
I
100
PERCENTILE
148
be calculated using an analytical approach.
This would involve
operating cost optimization in every.year--like in the modeling.
Given
the similarity of analytical and modeling results at the previous
stages of plant operation and investment analysis, these detailed
analytical calculations are not carried out here.
2.4
SUMMARY
Since any firm can obtain its steam and electricity needs from a
package boiler and through electricity purchases from the local
electric utility, the decision to build a cogeneration plant involves
making a major capital expenditure with the intention of loweering
future operating costs.
The analysis of a new cogeneration or boiler
plant, therefore, must examine future plant operating decisions and
costs at the time of the plant's sizing and design.
This chapter
demonstrated how this process can be viewed in four steps:
1.
Plant Operation.
The day-to-day operation of the plant is
affected by the then current fuel prices and electricity
tariff levels.
Oil-fired cogeneration plants need to adopt
their operating policies to fuel and electricity price
conditions.
2.
Tariff Choice.
The electricity purchase and sales tariff
combination through which the site chooses to purchase and
sell electricity in any year depends primarily upon tne
relative level of the utility's average costs, reflected in
the level of the utility's tariffs, and the utility's marginal
* 149
costs, reflected in the price paid for electricity purchases
by the utility.
The exact provisions of the tariffs can be
important, but are usually secondary to the relationship of
the tariff levels and the utility's marginal costs.
3.
Plant Sizing.
The sizing of the cogeneration plant depends on
the comparative size of the operating cost savings- and
incremental capital cost for the new plant size increase in
relationship to the site's steam load duration.
Coal-fired
systems can often be designed for near peak steam loads, while
oil-fired cogeneration must be designed for base steam loads.
4.
Plant Type.
The resulting net present value of a plant desi-n
depends upon its projected total operating cost savings by
year for the expected fuel and electricity price conditions as
projected from the steps above in relation t'o the total
capital costs.
The first two sections of this chapter described, in a conceptual
analysis, these decision stages.
The third section presented an
overview of the complex modeling needed to calculate the actual savings
for multiple plant types over a long horizon with the choice of
different operating modes and tariffs in each year under different fuel
and electricity price conditions.
Fortunately, major simplifications
can be made in the analysis of the plant choice:
the operating
simulation can be shortened, and the investment analysis can oe made
using a median fuel and electricity price scenario.
The results of the
modeling coincide with the results anticipated by the conceptual
150
analysis, but the modeling is necessary to capture the influence of the
electricity tariffs, especially their effect upon a plant's tariff and
operating mode choices, which are important in the utility impact
analysis later.
As the next chapter demonstrates, however, not all the
modeling simplifications can be used when calculating the impact on the
utility from the cogeneration by a customer.
For the modeling case studies at the 6 different sites, coal-fired
cogeneration was the overwhelming favorite at the projected fuel and
cost of capital conditions.
This results from the substantial
operating cost savings for coal-fired steam versus the existing
oil-fired boilers at all these sites.
Note that the coal-firea
boilers--without cogeneration--had about two-tnirds of the Denefits of
the coal-fired cogeneration system, thus demonstrating the importance
of the coal-fired steam operating cost saving.
Oil-firea cogeneration
systems have substantially smaller benefits in comparison to tne coal
systems.
First, they require sites witn large, year-around steam loads
to support the substantial capital expenditure relative to the smaller
operating cost savings.
Second, high electric output cogeneration
systems, such as diesel cogeneration, are very risky owing to the risky
nature of the utility's marginal electricity costs; fuel prices can
change so that it is even uneconomic to operate some diesel and gas
turbine coqeneration plants.
151
Chapter 3
ESTIMATING THE IMPACT OF CUSTOMER COGENERATION ON A UTILITY
This chapter addresses the impact of cogeneration by a utility's
customers upon the utility.
As described in the introduction, this
report employs a physically-based, or process, approach to the
forecasting of cogeneratiQn development and its impact.
Using
insights from the previous chapter on the economic motives for a
utility's customer to select a cogeneration system, this chapter
describes, first, how these choices by a single customer will change
the forecast of the utility's revenues, production costs, and
electricity purchases and sales.
This analysis is then tested on the
six sizes studied in Chapter 2. Second, given information from a
broad survey of 123 of the utility's largest industrial and commercial
customers, the development of further cogeneration capacity and the
ultimate effect upon the utility is explored in successively complex
stages.
In parallel to the analysis from the customer's perspective in
Chapter 2, this chapter takes the perspective of the utility, tne
combination of its stockholders' and ratepayers' interests, througnout
the discussion of 'the cogeneration economics.
3.1
THE IMPACT ON THE UTILITY FROM COGENERATION BY A SINGLE CUSTUO4ER
As as described in the introduction, the utility requires three
components in any forecast of cogeneration development:
152
1.
A description of the change in electrical loads because of
the output from cogeneration systems;
2.
An estimate of the change in utility revenues, net of
purchases by the utility, because of cogeneration or a change
in tariff schedules;
3.
The change in utility production costs because of
cogeneration by the customer.
This section describes the impact on each of these components from a
single customer installing a cogeneration system.
The first part of
the section discusses the change in the three components from each
decision.-aking stage that a customer will go through in deciding to
build and operating a cogeneration system. The secona part of this
section employs the results of the customer's choices as modeled in
the previous chapter to calculate the effect on the utility from a
single customer.
Before beginning a description of the parallels between the
customer's choices and the result for the utility, the
notation, adapted to cogeneration
physically-based load forecasting
forecating must be introduced. Expanding on equations 1.2 to 1.7,
taking a single customer of the utility, the electric load for tni
customer at any instant is:
rsum of
instantaneous
net load
from a
=
customer, k
yk(t, m(t),
e(t)) =
instantaneous
elemental
-
instantaneoussite
cogeneration
Lloads
jk (tI m(t),
Y~
CG
e(t)) - yEL ,k (t,
m(t,
e(t))
(3.1)
153
or
yk(t, m(t),
=
e(t))
m(t),
y (t,
k
e(t)) -yCG
EL,k
(t,
m(t), e(t))
(3.2)
where
an instantaneous
I
elemental
electric load
Yijk (t,m(t), e(t))
device
= capital
stock
x
X' (t, e(t)) u(t
jk(t
jk
instantaneous
device
utilization factor
m(t),, e(t))
(3.3)
and, more specifically,
=
instantaneous
site
cogeneration
CG
EL,k(t, m(t), e(t)) =
cogeneration
cogeneration
plant electrical x plant utilization
factor
capacity
CG
CG
kXE(t, e(t)) uELk(t, m(t), e(t))
(3.4)
and
4
:
device class, e.g., CG for cogeneration or CGi for a
specific type of cogeneration,
3
:
usage or customer generation class, e.g., EL for
electricity generation at the site,
k
:billing utility, i.e., customer index,
t
continuous time,
m(t)
:
e(t)
CG
vector of meteorological data or device availability,
vector of economic conditions and anticipations,
:
the special device superscript for cogeneration; CGi
154
represents a specific cogeneration technology,
EL
the special "usage" subscript for electrical
generation at the site ,
L
:
the special combined load superscript for all
electrical device and usages except generation.
Tne cogeneration plant utilization factor can be further decomposea into
two separate subfactors distinguishing economic operating.policy from
plant responses to steam loads or plant outages.
And, more specifically,
cogeneration
plant utilizatio
factor
CG
(t
EL,k'
m(t), e(t))
=
=
plant economic
plant availaoility
utilization I x and steam load
subfactor
J
Lfollowing subfactor]
CG
(t, e(t))
EL,k
-
CG
EL,k
(t
m(t), e(t))
(3.5)
Here:
v
:
represents the choices made by a customer with
regard to the economic operation of the generation
w
:
represents the usage response, for example, the
automatic control of the system to changing steam
loads for a thermal-load following cogeneration
plant, and the availability of the device.
ICGi
Bi
Tne terms XT,k and X,k will be used to represent analogously the
cogeneration and ooiler steam output capacity for a customer.
Tne
subscript ST can similarly apply to the steam energy production, e.g.,
CG(t, m(t), e(t)).
ST,k(t
155
Second, the utility's instantaneous production costs for the given
customer are:
instantaneous
system
marginal operating
cost
instantaneousi
cost to serve
customer, k
ck(t, m(t), e(t))
S ower
x
instantaneousl
net load from
customer
X(t, m(t), e(t)) y k(t, m(t), e(t))
(3.6)
Let:
the total incremental cost to serve a
Ck(tn):
customer k over the period tn
Finally, the total revenues from a customer for a billing period are:
S Ftne tariff scIrecul,
seaecte Dy tIn,*
= customer, computed on the relevant
I
Lhistory of loads and generation
over a billing period
[e
ncnue fro0r
customer
Rk (tn, m(tl), e(tn) )
=
m(t )e(t )), YL(t
I-kL(t n , m
n
n
-k n-i,
Rs(t;
*CG (tn)
n m(tn
ykG
e
e(tn)),
(3.7)
2 This
analysis evaluates the cost reduction of the utility from
cogenerated electricity in terms of marginal fuel costs. There is a
miniscule "economic surplus" for the utility because the existence of new
capacity with a low marginal running cost in the total generation
"market" reduces the marginal operating costs slightly at higner system
load levels. For individual plant in the size range that is typical of
cogeneration systems, this is a minor, second-order effect in comparison
-to the cogeneration output evaluated at the unit marginal operating
cost. The aggregate value of several hundred megawatts of new generation
capacity will produce a small economic surplus through the lowering of
the previous marginal operating cost. This surplus must go to tne
utility (its stockholders or customers) because the utility would have to
create economic inefficiencies by pricing electric generation purchases
above marginal operating cost to distribute the surplus to generation
suppliers.
156
where:
tn
: represents the time period from tn.l to t n
s
: tariff type from a set (a "menu") of the available
tariffs
k n(t
: vector describing the loads (i = L) or cogeneration
(i = CG) over the period t .3
Rs(.;.;.):
the functional relationship describing tne given
tariff schedule.
The principal effort in this chapter is to forecast XECGk
resulting
3.1.1
CGk'
the
and the impact on ck(t) and Rk(t) over time.
The Basic Economics of the Impact Upon a Utility from Customer
Cogeneration
Paralleling Section 2.2, tnis section aescribes the influence upon
the utility from the investment and operating decisions made Dy a firm
that decides to build and operate a cogeneration system.
Section
3.1.1.1 discusses the influence of each stage in the customer's
decision-making process upon the utility.
Section 3.1.1.2 comments on
the complications resulting from typical industrial tariffs and
suggest possibilities for the reduction in the resulting problems for
the utility.
30ften the net payments by a customer in a net sale situation
will be based on the utility's sales to the customer (yt (t), or
yk(t) for Yk(t) > 0) under a standard tariff less the utility's
purchases (yk(t)- or -Yk(t) for Yk(t) < 0) at the utility's
"Duy-back" price. For simplicity, this discussion will assume the
"buy-back" price is X(t) or a time average of -(t), A(tn).
* 157
3.1.1.1
The Influence on the Utility from Each Level of a Customer's
Decision to Cogenerate
Each step in the customer's decision to build and operate a
cogeneration plant affects one aspect of the forecasting process.
These single factors combined to form the ultimate impact upon the
utility.
Accordingly this section first reviews the levels of the
decision to cogenerate and then parallels them with the stages of the
impact on the utility.
Second, the combined impact is discussed in
the light of its staged influence on the utility's stockholders and
other customers, or ratepayers.
Before examining the customer's
actions and the concurrent result for the utility, it is important to
question the underlying assumptions for the motivation to build and
operate such a plant.
Why will a firm decide to build and operate a cogeneration plant
at a site?
Chapter 2 assumes the motivation is primarily economic, an
opportunity to reduce future operating costs balanced against a
significant capital investment.
Other factors, such as environmental
restrictions, specific budgetary constraints on tne customer's
organization, and behavior resulting from non-economic considerations,
can alter the firm's choices. 4
This cnapter also however assumes
that firms will base the cogeneration plant operation and investment
choices on economic considerations, leaving the inclusion of the
4 Choffray
and Lilien (1980) discuss tnese factors in tne
industrial marketing process; their example of solar equipment offers
an apt comparison with cogeneration equipment investments.
158.
Table 3.1:
Parallels in a Customer's Cogeneration Decisions and the
Uti lity Impact
The Customer's Decision
Impact on the Utility Forecast
Forecasting
Component
Determined
Investment Level
1.
The choice of cogeneration, new
boiler, or existing
Set existence of cogeneration
at the site; indicates future
economic characteristics
The superscript,
The maximum electrical output
from the facility; indicates
xCGi
EL,k
(also XCGi k
the limit on future revenue
arnd XB i
CGi and/or Bi
plant type
2. Sizing of the plant
ST,K
and cost impacts as well
Operating Policy Level
3. The choice of tariff
and net or arbitrage
Sets the net revenue parameters
for the plant
Set the tariff
superscript, s
purchase of electricity by the utility
4. Selection of operating mode
The electric generation from
the plant, the net electric
purchases and sales by the
utility to the site, the
utility's production cost
and the change in revenues
CGi
EL,k;
yCGi
EL,k;
Yk; ck; RK
(also yCG
an
ST k
ST, k
* 159
non-economic behavior for further research on the combination of
ing
physically-based electric load forecasting and industrial marketing
ed
analysis of large capital investment decisions.
The Levels of Impact
rscript,
or Bi
Each economic operating and investment level decision that was
described in the previous chapter has a different impact on the
utility.
As summarized in Table 3.1, the investment decisions on the
cogeneration or new boiler plant type and sizing do not directly
affect the utility, but they set the maximum level for the future
,k
impact upon the utility of the firm's operating decisions.
It is the
combination of the tariff choice and the planL operatirg mosde
rolt
directly influence the electric loads and the uility's revenues and
costs.
The decision on the plant type determines the plant's later
ariff
economic operating characteristics.
pt, s
this decision sets the plant type superscript, CGi.
In the load forecasting notation,
The sizing
decision sets the electrical and steam output capacity
XCG i
EL,k
and XCGi
ST; k
At the operating level, the site's tariff decision, whicn will be
made less frequently in reality than changes in the plant's operating
mode, sets the limits for tne customer's revenue changes by selecting
the specific tariff option, R , from a menu of possible tariff
k
schedules available.
The incremental economic incentives for
electricity production and consumption at the site, influences which
are imbedded in the tariff selected, will affect the operating mode
160
decision.
The operating mode selection for the plant is the action tnat
ultimately sets
the cogeneration electrical output, vEk(t,
CGi
e(t)) and yELk(t,m(t),e(t))
over the relevant period.
This in
turn sets the net electric load from site and utility marginal
production costs for this customer, Yk(t, m(t), e(t)) and
ck(t,m(t),e(t)).
The modeling discussions for the remainder of this
chapter explore the financial effect from cogeneration in terms of the
change in the utility's operating profit.
This comparison is made in
each year between the operating profit (or loss) to serve the customer
without cogeneration and the operating profit (or less) to serve the
customer with the cogeneration system. 5
Combined Impact
The operation of the cogeneration plant together with the
customer's choice of tariff comDine to make the financial impact of
the customer's cogeneration on the utility, both its stockholders and
ratepayers.
As shown in Taole 3.2, an extension of Table 2.2, under
the assumption that the utility pays its marginal fuel cost for any
electricity receivea, the reduction in the utility's revenues will
never be less than the reduction in costs because the customer can
5The
difference in operating profits rather tne absolute level
of operating profits is employed in these calculations because
absolute level does not reflect all the dedicated costs for the
utility in serving the customer. For example, the capital investments
in distribution facilities made to provide electricity to the
cogeneration customer's site' are not included in the operating profits
estimate.
S161
Table 3.2:
Impact on the Utility from a Customer's
Cogenerated
-Electricity Sales Choice
Electricity Cost and Price Conditions
-Reduction in Standard
Tariff Cost Exceeds
Utility's
Marqinal Cost
I
Reduction in Standard
Tariff Cost is Less Tnan
Utility's Marginal Cost
I. The customer's
economic choice
Sell cogenerated electricity
to utility net of the site's
internal electric loads
(net sale)
Sell all cogenerated
electricity to the
utility; ouy all
electricity for the
site's internal
electric loads from
the utility
(arbitrage)
II. Impact on the
combined stockholder and rate
payer interests
with respect to
the cogeneration
Utility revenue is reduced
more than the decrease in
production costs; losses
No net effect from
cogeneration*
snared oy stocknolders and
ratepayers over time, as
discussed below.
II.A. Stockholder Impact
(before a revision
in regulated tariffs)
1. Utility revenues
Down by tariff reduction**
Unaffected*
2. Utility costs
Down by marginal cost times
cogenerated output
No net effect;
production costs down
but payments to
cogenerator are by an
identical amount
3. Change in Utility Down by (Marginal Cost
Profit
Output) less Tariff
No net effect from
cogeneration
Reduction
*Note the utility could be pricing its sales below marginal costs
in this situation, perhaps for all large customers.
**This exposition assumes only a reduction in tariff charges and
no excess electricity sales.
162
Table 3.2:
Impact on the Utility from a Customer's Cogenerated
Electricity Sales Choice (continued)
I.
_
_
_
_
_
I
II.B. Ratepayer Impact
(after a readjustment in regulated
tariffs
1. Total Utility
Revenue Requirements
Down by marginal cost times
output
2. The New Tariff
Level (Revenue
Requirement per
unit utility
sales)
The utility's fixea costs are No net effect from
concentraited across reuce,
sales, so tariffs are up
(the revenue requirements
are reduced by a smaller
fraction than the energy
sales)
No net effect from
cogeneration; production costs oy utility
are down but payments
to cogenerators are
up y an identical
amount in tne Revenue
Requi rements
calculation
coy tener at i on
163
always dispose of the cogenerated electricity at the utility's
marginal fuel costs or more.
Referring again to Table 3.2, the staged nature of tne utility
ratesetting process divides any negative aspects from the customer's
cogeneration between the utility's stocknolders ana the other rate
payers over time.
After tne cogenerator decides to nave the utility
purchase its output on a net sale basis because of prevailing fuel and
electricity price conditions, the utility's revenues are reduced by
more than its proauction cost decrease; this results in a drop in tne
profit stream to the utility until the next revision in tariff levels.
After tre reaiignment of tariff levels, tne utility's ratepayers oear
the burden of the prior level of the utility's fixed costs averaged
over reaucea utility sales, a Concentration of tne utility's rateodse.
The size of the loss for tne utility per kilowatt-hour of "net
sale" customer cogeneration proauction depenos upon tne gap between
the possiole reduction in tariff revenue, througn "net sale" reauction
in the utility's sales to the customer, less the utility's marginal
fuel cost savings resulting from the customer's total cogenerated
energy.
In the most simplistic form, Figure 3.1 illustrates tnis loss
per unit of cogeneration resulting through the customer's tariff
.reduction.
Fitnlly, uncertainties in future fuel prices and utility
-electricity marginal costs make the cnoices forecast for the
cogenerator very uncertain.
As the conceptual discussion
demonstrated, the choices by the customer, with the resulting impact
rll
164
MAGNITUDE OF THE ADVERSE
IMPACT ON A UTILITY
FROM A CUSTOMER SELLING COGENERATED ELECTRICITY
ON A NET SALE BASIS
IMPACT ON UTILITY
FROM COGENERATION
(O/KWH COGENERATED)
AVERAGE TARIFF COST REDUCTION
FOR COGENERATING CUSTOMER
MINUS THE UTILITY'S
COST (C/KWH)
0
Figure 3.1
LMARGINAL
165
on the utility, depend upon these price conditions; hence, any
forecast of the impact by cogeneration is conditioned upon the prices
anticipated.
3.1.1.2
Special Complications for the Utility Arising from Typical
Tariffs
The previous section demonstrated the general negative results for
the utility that arise when economic conditions encourage a
cogenerator to reduce its tariff charges by the net sale of the
electricity produced.
In parallel with Section 2.2.2.2, this section
describes several complications that can result from the typical
utility tariff, both from the ratesetting for the general tariff level
and the detailed structure of the tariff itself.
The first two result
from the net sale of electricity by the cogenerator.
The final
problem derives from the time-averaging of continually cnanging
utility marginal fuel costs to form a fixed-period time-of-supply
price for electric energy purchased by the utility from the
cogenerator.
First, as the last section discussed, a reduction in tariff
charges greater than the current marginal fuel cost saving associated
with the cogeneration results in a negative impact upon the utility.
Since the overall level of tariffs has typically been set on the basis
of historical average fuel, operating, and capital costs, the
utility's average tariff cost will seldom equal the marginal fuel cost.
Furthermore, the reduction in a single customer's tariff charges
does not have to be equal to the average tariff cost.
The typical
I
166
industrial tariff does not charge for electricity consumption solely
on an energy basis.
The revenue decrease results from the customer
reducing both the energy and peak demand components of the tariff, a
subject explored in Section 2.2.2.1.
Since the demand charge
reduction for a customer using its cogenerated electricity on a net
basis depends upon the correlation of the site's loads and
cogeneration plant output and availability, the revenue reduction for
identical cogeneration patterns can differ between customers because
of differences in the internal loads of the customers.
The tariffs can be designed so the incremental reduction in
charges through a net load decrease by the internal usage of the
cogeneration more closely matches the utility's production cost
reduction, but several factors limit the flexibility required to
accomplish this feat.
First, marginal cost can change much more
rapidly than the tariff levels, so the matching process is likely to
lag far behind the cost conditions.
Second, the reduction in the
tariff depends upon the site's internal steam and electric load
characteristics, so a completely general, fixed tariff design for all
customers is impossible.
Third, the cost of other generation sources
for the customer, such as stand-alone diesel generators for stand-by
and peaking electric loads at the customer's site, act as an upper
limit on the utility's ability to set the variable portion of tne
tariff equal to marginal production costs, with the remainder of the
average costs carried in the fixed portion of the tariff.
The second difficulty in the structure of the typical tariff is
that changes in the customer's charges by the net sale of cogenerated
, 167
electricity can alter the incentives for internal electricity
consumption at the customer's site.
Using the example from Section
2.2.2.1 for Figure 2.10, the incremental cost of electric energy for the
customer increases while the incremental cost of peak electric loads
drops solely because of the cogeneration plant output and the load
factor or hours-use energy discounts in the H Rate.
The modeling for
the Large Manufacturer in Section 2.3 produced similar results:
without
cogeneraton, the site's marginal tariff charges on the H rate were in
the 400-500 hours-use block of the schedule; with the coal-fired steam
turbine cogeneration system operating continuously with net sales, the
site's marginal tariff charges on the H Rate were in the 300-400
hours-use block for 37.5 percent of the months, tne 200-300 hours-use
block for 50 percent, and the 0-200 hours-use block for 12.5 percent.
This means that the incremental internal energy and peak load costs
varied over a .6 cent/kWh and a $2 per KW range respectively solely
because of the cogeneration system's performance under the net sale
arrangement.
While this may not be a problem if the change in
incentives was isolated to a few small customers, the customers who
might cogenerate will be a group of the largest industrial customers
served by the utility, a major percentage of the total utility load. 6
6 1n
the survey or 123 major industrial and commercial customers
described in the next section, 9 nave existing cogeneration facilities;
sales to these customers are about 13% of total sales to the survey
group. Under the base case forecast in Section 2.3, if all customers
that could economically install coal-fired cogeneration actually built
cogeneration systems, the current sales to tnose projected to convert
would be 58% of current sales to the survey group. Note that this figure
does not adjust for any decline in the new cogenerator's group on total
survey's sales.
'
168
Finally, the problems of time-averaged marginal cost prices for
electricity delivered to the utility and the advantages to the
cogenerator of a simple "spot pricing" scheme called time-of-oil
pricing were demonstrated in Section 2.2.2.2.
In that example, the
utility was ambivalent between the time-average price and the
time-of-oil pricing method.
A different example of the same general
problem results if the utility offers to purchase the electricity in
two, pre-set time-of-supply periods.
The difficulty arises in the
selection of the dividing point between the two fixed time periods.
Take the extreme case of a utility with a high marginal cost fuel,
such as oil, during the whole peak period and a lower-cost fuel, such
as coal, during the whole off-peak period.
Further assume the time of
transition from the off-peak to the peak fuel does not occur at
exactly the same time every day, but the transition iime is uniformly
distributed over a specific time interval, tO to t I in Figure
3.2.
For the purposes of this example, let the time-of-supply price
change from the lower level to the higher level at tf, the midpoint
of possible transition period.
An oil-fired cogenerator who is
assumed to operate in the peak period but not in the off-peak period
will receive, on average, the same revenues and incur the same
operating costs under either the fixed interval time-of-supply price
or a flexible interval time-of-oil price starting at the instant of
the utility's transition to tne peak period fuel costs. 7
7This
There are
assumes the cogenerator has no special costs associated
with the flexible start-up time.
169 '
FIXED TIME-OF-SUPPLY PRICING VERSUS
TIME-OF-OIL
PRICING
FOR ELECTRICITY PURCHASES BY THE UTILITY
UTILITY
MARGINAL
FUEL
COST
(I/KWH)
Peak
PH
0
O
0
O
O
S0
O
O
PL Off-Peak
The Region for the
Utility's Pote ntial
Cost Increase
0
n --q-
0
Fixed Time-ofSupply Price
000
Time Bounds for Off-Peak to
Peak Time-of-Oil Price Jump
tf
Fiqure 3.2
tl
TIME
170
advantages for the utility:
1.
From t0 to tf,
(a) Under time-of-supply pricing, tne customer does not
generate at all, so all generation is at coal prices if
transition has not occurred or at oil prices if
the
the
transition has occurred--no change in total cost (producton
plus payments to cogenerators) for the utility.
(b) Under time-of-oil pricing, the cogenerator will not
generate if the transition has not occurred, but will
generate at peak prices if the transition has
occurred--again, no change in cost for the utility.
2.
From tf to t
i,
(a) Under the time-of-supply pricing, the customer always
generates.
If the transition to the peak costs has not
occurred, the cogenerator gets paid peak prices for
generation that results in only off-peak level cost
reductions for the utility--a cost increase for the utility;
if the transition to peak costs has occurred, the cogenerator
gets paid peak prices for a peak level cost reduction--no
cost change for the utility.
(b) Under time-of-oil pricing, as in l(a), the customer gets
paid the value of its generation--no change in cost for the
utility.
Thus, the utility avoids the cost increase under case 2(a) by
switching from fixed time-of-supply pricing to time-of-oil pricing for
171
#
cogenerators that will alter their generation patterns because of the
pricing change.
This is the peak/off-peak price differential times
the expected time after tf that the transition to the peak cost will
occur, given that the transition occurs after tf.
In summary, there are a few changes to the tariffs that can
partially alleviate the problems caused for the utility by customer
cogeneration.
Within the limitations discussed above, the electricity
sales tariffs should be redesigned so that reductions through customer
cogeneration match utility marginal production cost savings as closely
as possible.
Given the uncertain and rapidly fluctuating nature of
the marginal costs versus the average costs employed in setting the
level of the tariffs, this may be an impossiDle task witnin the
context of the typical approach to ratemaking.
Second, attention must
be paid to the influence of the tariff sales reductions on the
incentives for electricity consumption at the plant deciding to
cogenerate on a net sale basis.
Finally, when customers choose high
operating cost cogeneration systems such as the typical oil-fired
plant, time-of-oil pricing for the purchase of electricity may benefit
both the customer and the utility.
3.1.2
Modeling the Impact from a Customer Cogeneration System
Using the model described in Chapter 2 to determine the customer's
least-cost opportunities for steam and electricity supply, this
section discusses the calculation of the utility impact resulting from
those choices.
The first subsection uses the Large Manufacturing Site
172
as an example of the choices made and the resulting impact on the
utility.
The second subsection discusses change in the menu of
tariffs offered by the utility and the resulting changes in the
forecast results for the utility.
The third subsection summarizes
similar calculations for all six case study sties and describes
results on the combined utility and customer value of time-of-oil
pricing.
The fourth subsection discusses simplification to the
modeling and why some of the simplifications that were effective for
the calculation of the customer's economic decision will cause
distortions in the calculation of the results from the utility
perspective.
The final subsection compares the modeling results with
simple analytical calculations.
3.1.2.1
An Example of the Impact from a Single Customer
As presented in Section 2.3, the Large Manufacturing site would
select a coal-fired steam turbine cogeneration system, sizing it to
meet the site's intermediate to peak steam loads over the average
year.
This system will always operate to meet the site's steam loads
since it has a lower operating cost than other other steam source for
the customer, at least within the limits of the fuel and electricity
price scenario considered in the modeling effort.
The choice of
tariff by customer however changes over time depending on the
prevailing price conditions.
Table 3.3 summarizes the cogeneration
plant decisions and the resulting impact on the utility in the
physically-based load forecasting form for the middle fuel price
173
Table 3.3:
Impact on the Utility from the Choice of a Coal-Fired
Steam Turbine Cogeneration Plant by the Large Mlanufacturer*
Utility
Site cogeneration
Capacity,
XCGi
Plant
Site
Tariff Operating
Choice, Mlode,
Plant
Annual
Production,
YCGi
EL,k
(tflH)
Annual
Sales
to Site,
y+
k
(MWH)
Utility
Change
Annual
in Utility
Operating
Profit for
Serving
Customer,
A(Rk - Ck)
Purchases
from
Site,
k(
(0WH)
Yr.
EL,k
(MW)
1981
0
H-Rate
--
0
72566
0
1982
0
H-Rate
--
0
72566
0
1983
0
H-Rate
--
0
72566
0
1984
5.6
H-Rate/ Always
Net Sale
37997
35153
583
1985
5.6
H-Rate/ Always
Net Sale
37997
35153
583
1989
5.6
H-Rate/ Always
Arbitrage
37997
72566
37997
s
vCGi(t
YkI
)
*This represents the results for the middle fuel escalation/medium
coal conversion scenario.
(}000 1981)
-82
174
Table 3.4: Impact on the Utility by Scenario from the Optimal Tariff
and Operating Decisions for a Coal-Fired Steam Turbine
Cogeneration Plant at the Large Manufacturer in 1985
Tariff Choice;
Net (Revenue
mpact
less Cost) Ii
High
Utility Coal Conversion Scenario
Fast
H-Rate/Net Sale
Meaium
um
eai
H-Rate/ArDitrage
Slow
H-Rate/Arbitrage
Fuel
-106
0
0
Price EsMiddle
H-Rate/Net Sale
H-Rate/Net Sale
H-Rate/Arbitrage
calation
-228
-82
0
Scenario
Low
H-Rate/Net Sale
-325
H-Rate/Net Sale
-210
H-Rate/Net Sale
-119
Values in thousand 1981 $
The optimal operating policy is continuous operation in a tnermal load
following mode for all scenarios.
175
Table 3.5: Utility Impact of the Second Choice Plant, Oil-Fired Steam
Turbine Cogeneration, at the Large Manufacturer
(a) The Middle Fuel/Medium Coal Conversion Case
Cogeneration
Capacity
-Year -(MW) --
Tariff
1981
H-Rate
1982
H-Rate
Plant
Operating
Mode
Choice
Annual
Production
(MWH)
1983
4.0
H/Arbitrage
Always
31825
1984
4.0
H/Arbitrage
Time-ofOil
28792
1985
4.0
H/Net
Always
31825
1986
4.0
H/Arbitrage
Time-ofOil
25321
1987
4.0
H/Arbitrage
Time-ofOil
24994
Change in Net
(revenue less cost)
(thousand 1981 $)
-b5
(b) The Impact by Scenario for 1985
Utility Coal Conversion Scenario
Operation; Tariff
Choice; Net Utility
Impact
High
Medium
Fast
Time-of-Oi 1;
Time-of-Oil;
H-Rate/Arbitrage H-Rate/Arbitrage
Slow
Run Always;
H-Rate/Arbitrage
Fuel
0
0
0
Price EsMiddle
calation
Run Always;
H-Rate/Net Sale
&
-189
Run Always;
H-Rate/Net Sale
-65
Run Always;
H-Rate/Arbitrage
0
Scenario
Low
Run Always;
H-Rate/Net Sale
-272
Run Always;
H-Rate/Net Sale
-173
Values in thousand 1981 $
Run Always;
H-Rate/Net Sale
-98
176
escalation/medium utility coal conversion scenario.
By presenting tne
impact for one year, contingent upon the fuel and electricity price
conditions, Table 3.4 better demonstrates the uncertain nature of the
customer's choice of tariff and the resulting change in operating
profits for the utility.
If environmental restrictions prohibit the customer from building
a coal-fired system, the Large Manufacturer's second choice would be
an oil-fired steam turbine system. This system adapts its operating
modes as well as tariff choices to the current fuel and electricity
price conditions, as demonstrated in the middle/medium scenario
presentation in Table 3.5a and the contingent operation demonstrated
in Table 3.5b.
The change in tariff cnoice in 1985 shown by Table
3.5a results from a peak in the difference between the tariff charge
and the utility's marginal fuel costs under this price scenario.
The total impact upon the utility from a single customer can be
calculated as a present value of the expected losses.
This entire
loss estimation effort required the detail employed in the modeling
described in Chapter 2 (see Figure 2.11).
First, studying the
influence of the non-fuel tariff provisions such as the demand ratchet
required the multi-year plant performance simulation within tne
cogeneration operating decision forecasting.
Second, the operating
decisions needed to be calculated for each of the fuel ana electricity
price scenarios in order to capture the negative impacts on the
utility, which occurs only when the customer can reduce the tariff
charges more than the utility's marginal production cost.
Finally,
' 177
this calculation must be made over a long time horizon to evaluate the
cogeneration plant investment decision and the operating policies over
the years.
TaDle 3.6 provides the present value of the losses
expected for the utility given the customer's plant investment
decision; this has been calculated for only the first 10 years of the
horizon, using the same subjective probabilities for the scenarios as
the customer's, at a utility rate of return (15.1 percent/year or 4.6
percent/year real return).
As opposed to tne 15-year horizon for the
customer's investment decision, the shorter 10-year horizon for the
utility impact calculation implicitly assumes tnat tne utility can
somehow change conditions by the end of the horizon to reduce the
losses from customer cogeneration.
3.1.2.2
Modeling Utility Policies to Reduce the Impact from Customer
Cogeneration by Limiting the Tariff Menu
Up to this point, the customer modeling has assumed that the
cogenerator had an unrestricted choice of the tariffs listed in Taole
2.6 and the operating modes in Table 2.5.
Within legal limits, 8 the
utility may be able to reduce the negative impacts from tne net sale
of the cogenerated electricity, although it is likely tnat this can oe
accomplished only through changes in the overall industrial tariff
structure.
8 PURPA
Section 210 requires rates for tne sale of electricity to
cogenerators to be non-discriminatory.
178
Table 3.6:
Losses for the Utility Under Different Fuel and
Electricity Price Scen arios
(a) The First Choice P1ant:
Coal-Fired Steam Turbine Cogeneration
Utility Coal Conversion Scenario
Fast
Slow
Medium
Fuel
High
239
Price
Middle
861
140
Escalation
Low
1,553
1,005
Expected Value:
(b) The First Choice Non-Coal Plant:
Cogeneration
517
$495
Oil-Fired Steam Turoine
Utility Coal Conversion Scenario
Fast
Medium
Slow
Fuel
High
Price
Middle
Escalation
Low
0
641
1,327
Expected Value:
830
443
$377
Values in thousand 1981 $ representing discounted 1981-1990 impact.
a
179
Table 3.7 summarizes the losses for the utility as a result of the
cogeneration system choices forecast for the Large Manufacturer. 9
The Base Case (No. 1) reflects the choices made under the Base Case
for the examples in Tables 2.14 and 2.9.
As noted above, the
customer's first choice is a coal-fired steam turDine system; the
second choice is an oil-fired steam turbine system; and the third
choice is a diesel system, except under the asterisk-marked
conditions, in which the customer would prefer the existing boiler
system to the diesel system.
If the time-of-oil operating mode is not possible because such a
pricing scheme is not available for the purchase of electricity from
the cogenerators (Case No. 2), tne losses from a coal-fired system
will not diminish because it operates constantly in any case.
There
are, however, significant advantages for both the customer
9 The
values here understate the impact from diesel cogeneration
because the operating mode alternatives do ot include an option that
would be very beneficial to diesel cogeneration. As implemented here,
the most important choice for oil-fired cogeneration systems is
between operating continuously with net sale of the electricity and
operating on a time-of-oil basis with arbitrage of the electric
output. Since diesel coqeneration usually produces electricity in
excess of the site's loads, and since time-of-oil operation is very
valuable for diesel systems, the least cost operating policy is
usually time-of-oil operation with arbitrage if coal is ever a
marginal cost fuel. In many cases, especially for the low or middle
fuel price escalation cases, a better option would be to serve the
site's entire electric loads always and then export electricity on a
net sale/time-of-oil basis; this means electric load-following, wnich
is a substantial change in the way tne model functions now. The
omission of this operating/tariff choice option is not as important
for oil-fired steam turbine systems because they only rarely produce
electricity in excess of the site's electric loads under net sales, so
the cnoice oetween arbitrage/time-of-oil operation and net
sale/continuous operation adequately covers tne important alternatives.
180
Table 3.7:
Sensitivity of the Impact from Customer Cogeneration on
the Utility for the Large Manufacturer
Plant Selected Doy the Customer
Tariff
Restrictions
Coal-Fired
Steam Turbine
Base Case
(no tariff
restrictions)
495
377
165
No Time of Oil
Pricing
495
412
960*
Oi l-Fired
Steam Turbine
Mandatory TOU
Type Tariff for
"Net Sale"
Cogeneration
No CG Rate;
95% Marginal
Fuel Cost as
Purchase Rate
Lower Base Year
Oil Prices;
High Coal Prices;
No Tariff
Restrictions
Diesel
154
319
179
(2. 180)*
1,110
941
1,232
Losses (gains) in thousand 1981 $ representing expected discounted
1981-1990 impact.
*Plant investment is not economic in comparison to existing Doiler.
181
and the utility if the cogeneration system is oil-fired.
This
supports the theoretical discussions in Sections 2.2.2.2 and 3.1.1.2.
The present value of the expected combined benefits for both parties
is $167,000 for an oil-fired steam turbine cogenerator, or about
$42/KW; for a diesel cogeneration system the combined benefits are
$2,665,000 or $121/KW.
If the utility requires all customers with on-site cogeneration to
either sell net under only time-of-use tariffs or arbitrage under any
tariff (case No. 3), the structure of the revenue reductions for tne
utility better matches the production cost reductions.
The losses for
the utility are substantially reduced, while, as TaDle 2.14
demonstrated, the economic advantages for the cogenerator are not
significantly diminisned.
If the utility can limit its price for the purchase of electricity
to 95 percent of its marginal fuel costs (Case No. 4), this provides a
double protection to the utility's other customers.
First, it
provides operating cost savings to the utility for the electricity
purchased directly.
Second, it creates a "price gap" in which the
customer selects a net sale tariff under some fuel/electricity price
scenarios without a negative impact upon the utility.
Tnis tariff
restriction, however, reduces the operating cost savings for the
cogenerator.
The losses for the utility are sensitive to the difference between
the average tariff cost and the utility's marginal production.
No.
Case
5 shows the influence of oil price decreases on the unrestricted
182
tariff options policy (No. 1).
Since marginal costs drop faster than
average tariff costs, this shifting cogeneration toward net sale
tariff choices, the impact of cogeneration increases substantially.
3.1.2.3
Overview of the Different Site Studies
As in Chapter 2, the description of the modeling analysis uses the
Large Manufacturer as an example.
This section compares the results
from all the major case study sites.
Table 3.8 lists the impact on the utility from different plant
types for the case study sites.
Included witn the net present value
is the impact per kilowatt of cogeneration plant electrical capacity.
Not only does the impact vary substantially because of differences in
the type and scale of the cogeneration systems at each site, but the
impact depends upon internal electricity usage patterns at the
individual sites.
For example, a coal-fired steam turbine system
reduces the net sale electric energy usage and moves the load factor
at the Hospital from 421 to 107 hours-use, a big savings in the
expensive energy blocks of the H Rate tariff; at the Paper Mill, the
net sale reduction is for energy sold in the cheaper energy blocks of
the H Rate, where tne load factor drops from 610 to 428 hours-use.
Table 3.9 presents tne combined value of time-of-oil pricing for
both the utility and the private cogenerating firm. On the basis of
the cogeneration plant's electrical capacity, this value is more
stable between the individual sites than the utility impact in Table
3.8.
As illustrated in the analytical discussions in Sections 2.2.2.2
183
Table 3.8:
Present Value of the Utility Impact from tne Cogeneration
Plants
Total Impact; Impact per Unit Capacity
Coal
ST
Cogen.
Oil #6
ST
Cogen.
Diesel
Cogen.
Gas
Turbine
Cogen.
First Cnoice;
Second "No
Coal" Choice
Plants
New #6 boiler;
# 6 oil ST
Site
Office
.034
34
.019
38
.011
41
0
0
Paper
Mill
.150
34
.128
38
.037
41
.5UO
0
Coal ST;
oil ST
Medium
Mfr.
.127
106
.103
116
.052
8
0
0
Coal ST;
oil ST
Large
Mfr.
.495
89
.377
95
.165
7
.5/5
52
Coal ST;
oil ST
Hospital
.066
145
.057
159
.021
12
.046
67
Coal ST;
# 6 diesel
College
.046
46
.032
64
.009
6
.001
1
Coal ST;
existing
Doi ler
Unrestricted choice of tariffs; impact in present value for 1981-1990
in million 1981 $; impact per kW of cogeneration capacity in 1981 $/KW.
I
184
Table 3.9:
Combined Value of Time-of-Oil Pricing for Utility
and lu stomer
I
In
lUl
I
ComDined Utility +
Firm
For the
Utility
(000$)
(000$)
For tne
Site/Plant Type
Firm Value per
Unit of Cogen.
Capacity
($/kW)
Large Manufacter
Diesel, #6 oil
Steam Turbine, #6 oil
1,870
132
795
35
121
42
Hospital
Diesel, #6 oil
Steam Turbine, #6 oil
168
5
5
2
101
19
College
Diesel, #6 oil
Steam Turbine, #6 oil
105
17
48
5
138
44
Paper Mill
Diesel, #6 oil
Steam Turbine, #6 oil
1,081
77
418
13
119
44
Medium Manufacturer
Diesel, #6 oil
Steam Turbine, #6 oil
515
27
204
9
114
40
Office Building
Diesel, #2 oil
Steam Turbine, #6 oil
81
60
Based on detailed simulation; values in 1981 expected discountea value.
s 185
and 3.1.1.2, the value of time-of-oil pricing depends upon the
difference between the utility's marginal costs and the cogenerator's
incremental operating costs, which is the same for a given plant type
at all sites, because time-of-oil pricing must ordinarily be
implemented under arbitrage sale of the electricity, differences in
between each site's internal electricity usage patterns do not affect
the results except by customers remaining on net sale tariffs and
operating their plants continuously when they would otherwise operate
their plants on a time-of-oil basis if selling the cogeneration plant
output under arbitrage.
3.1.2.4
Simplification of the Modeling
The modeling simplifications tested in Section 2.3.3, while
acceptable for the most important aspects of the cogenerator's
investment decisions, work only in part for the calculation of the
impact on the utility.
For utility capacity planning, the level of
cogeneration capacity is the primary concern of tne forecaster.
Since
the current model implementation addresses the decision to switch to
cogeneration only in one year, it is adequate only for determining
conversions by existing customers when fuel and electricity prices
vary over a limited range.
The decision to invest in a cogeneration
system is not extremely sensitive to the future tariffs available to
the cogenerator because of the incentives for a utility to keep its
tariffs near marginal long-run costs, so the simplified modeling does
work acceptably for cogeneration capacity forecasting.
111111
186
For utility tariff policy planning, however, the difference
between the average tariff and marginal costs is the crucial issue.
This is because of the one-sided nature of the negative results from
"net sale" cogeneration on the utility system--as Figure 3.1 shows,
the adverse impact from cogeneration results only when the potential
tariff reductions are greater than marginal costs.
In the fuel and
electricity price scenarios selected for the modeling, this occurs
primarily in the cases with extreme utility coal conversion and lower
price growth.
The first column of Table 3.10 lists tne utility impact from tne
full range of possible plant alternatives at the four main case study
sites using the detailed modeling under all scenarios. 10
Note that
the losses are largest for the most economically attractive
cogeneration plant design, the coal-fired steam turbine cogeneration
system.
In the second and third columns, the aggregate modeling
simplification works very well for both data sources, which were
descriDed in Section 2.3.3.
The second simplification, the estimate of tne losses Dased on
only a middle fuel/medium coal conversion scenario, fails completely
in the estimation of the utility losses because of the asymmetry cited
above.
10 As
Unfortunately, the creation of the correct estimate by the
in Table 2.17, an extra plznt alternative has been aaded in
Table 3.10 for the Large Manufacturer in the simplified modeling
cases: an existing oil-fired, back-pressure steam turbine
cogeneration plant. This shows the compelling economics for the firm
and the present burden to the utility's ratepayers from an existing
system.
187
Table 3.10:
Effect of Modeling Simplifications on the Estimated
___
Impact on Utility System
Expected Impact of Plant
Site/
Plant (Fuel)
Detai led
Simulation
Middle Scenario Impact of Plant
Aggregate
SimulaAggregate tion
Detailed
SimulaSimula- Survey
tion
tion
Data
Aggregate
SimulaAggregate tion
Simula- Survey
ation
Data
Large Manufacturer
Boiler coal
-.165
-. 202
-. 174
Gas Turbine #6 -.575
ST #6 (new)
-.377
ST #6 (old 6 MW)
-.614
-. 342
-.290
--
-.867
-. 476
Diesel,
#6
ST coal
Hospital
Boiler coal
-.495
Diesel, #6
Gas Turbine #6
ST #6
-.021
-.046
ST coal
0
0
-. 621
-. 052
--
-.450
-.345
-. 306
-. 140
-. 114
-. 056
0
0
-.042
0
0
-.057
-.024
-.063
-.057
-.020
-.046
-.051
0
-.015
-.032
-.038
-.021
0
-.023
-.014
-.066
-.062
-.057
-.040
-.032
-.025
0
0
0
0
0
0
Diesel, #6
Gas Turbine #6
-.009
-.013
-.025
0
0
0
-.001
-.003
-.004
0
ST #6
ST coal
Paper Mill
-.032
-.037
-.039
0
0
-.006
0
-.007
-.046
-.056
-.054
-.012
-.023
-.026
Boiler coal
Diesel, #6
0
-.037
0
-.049
0
-. 043
0
0
0
0
0
0
Gas Turbine #6
ST #6
ST coal
-.500
-.500
-.318
-.271
-.265
-.128
-.116
-.097
0
0
-.150
-.133
-.116
-.003
0
Net present values in million 1981 $
0
College
Boiler coal
-.105
0
0
188
analysis of all scenarios over the planning horizon is one of the most
expensive stages of the modeling process. To affect reductions in the
modeling costs when addressing tariff planning, the customer's
investment decision can be calculated using the middle/medium scenario
simplification over the plant life horizon and, then the
multi-scenario approach can be used to explore the customer's possiole
operating and tariff decisions for only the selected plant type over
the more limited tariff planning horizon.
Table 3.11 lists the estimates of the value of time-of-oil pricing
for the utility under different modeling simplifications.
As was the
case for tne overall impact of cogeneration on the utility, tne
simplification of the plant performance mooeling works reasonaoly well
given the complex timing required
Dy
time-of-oil pricing (detailea
versus aggregate simulation on an expected value basis).
Since the
simplification of che analysis to include only tne midale fuel price
escalation/medium coal conversions scenario did not work well for tne
overall utility impact, the differences between the unrestricted and
restricted tariff choice cases need to determine the value of
time-of-oil pricing do not yield a close estimate in any case.
3.1.2.5
Comparison of Analytical and Modeling Estimates of the
Utility Impact
Section 2.3.4 compared tne modeling results with an analytical
approach for the customer's cogeneration plant operating and
investment choices.
This section demonstrates that analytical and
modeling estimates of the utility impact for a single year are similar
189
Table 3.11:
Influence of Model Simplifications on the Value of
Time-of-Oil Pricing for tne Utility
Midale Scenario Value Only
Expected Value
Site/
Plant
Detailed
Simulation
Aggregate
Aggregate
SimulaSimulaAggregate tion
Detailed Aggregate tion
Simula- Survey
SimulaSimula- Survey
tion
Data
tion
ation
Data
Large Manufacturer
Diesel #6
.795
.308
.342
.748
0
0
ST#6 Oil
.035
.036
.081
.051
.033
.066
Diesel #6
.005
.050
.039
.003
.034
.023
ST#6 Oil
.002
.008
.005
.001
.012
.008
Diesel #6 Oil
.048
.050
.050
.015
.021
.030
ST#6 Oil
.005
.006
.008
.011
.011
.018
Diesel #6 Oil
.418
.130
.128
.256
0
0
ST#6 Oil
.013
.011
.010
.006
.001
.004
Hospital
College
Paper Mill
Net present value in million 1981 $
190
provided that a good estimate is available for the reduction in the
customer's non-fuel tariff charges because of net sale cogeneration.
The sensitivity of these results to changes in the non-fuel tariff
reduction has been discussed in Chapter 2.
Table 3.12 presents the utility impact by scenario as determined
analytically using the tariff changes and utility marginal costs in
Table 2.21.
For every scenario in which the customer selects net sale
cogeneration, the impact isz
[Total
[Impact]=
( Average
H-Rate i
Tariff
[Utility's
IMarginal
LFuel Costs
rcogeneration
C
x
On-Site
LE lectricity Usage
For scenarios in which the customer selects arbitrage, the net impact
due to cogeneration is zero.
These results compare very closely with
those in Table 3.4.
As noted in Chapter 2, examination of oil-fired cogeneration
requires a much more complex analysis.
The results would not oe as
similar between the modeling and analytical estimates for the
coal-fired case because, at least in the one scenario notea in Section
2.3.4, the operating choice differs between the two methoas.
3.2 THE AGGREGATE FORECAST OF THE IMPACT FROM COGENERATION
This section discusses approaches for aggregating the cogeneration
forecast from the level of a single customer to the level of the whole
utility service territory.
The physically-based approach to
forecasting typically creates the projection deriving from any aevice,
usaqe, or customer class.through an enumeration of opportunities for a
load change in that class.
Table 3.12:
Impact on the Utility from a Coal-Fired Cogeneration
Plant in 1985 as Estimated by the Analytical Approach
Utility Coal Conversion Scenario
Tariff type
choice;
Net Impact
Fast
Fuel
High
Price
Middle
Escalation
Low
Net Sale
-112
Net Sale
-224
Net Sale
-336
Medium
Arbitrage
0
Net Sale
-112
Net Sale
-224
Slow
Arbitrage
0
Arbitrage
0
Arbitrage
-150
Scenario
Non-fuel tariff reduction assumed to be 2.2 /kWn; plant output is
37413 MWh per year; values are in thousand 1981 $; other assumptions
in Table 2.21.
192
The information available on cogeneration opportunities, wnich
would comprise the basis for the forecasting enumeration, can be
separated into the four general categories descried in Table 3.12.
In the case of cogeneration, the most profitable locations--and
therefore the most likely to develop--are at the large industrial and
commercial sites that use extensive quantities of steam.
Tne effort
expended on determining the potential for cogeneration, especially
based on information from the more nebulous C and D classes, should
depend upon the context of the utility policy decisions employing the
forecast and the economic environment of the particular utility; any
effort should start with information from the A and B classes.
If cogeneration tariff design is the primary utility policy Deing
reviewed, the greatest concern is the rapid conversion of existing
customers to cogeneration or changes in operating and electricity
purchases by existing cogenerators.
Since tariff policies can be
reviewed and changed in a period that is short relative to the time
period in which major industrial growth might occur, the impact from
cogeneration by existing customers indicates changes necessary in the
tariff policies.
The direction of the impact from tariff changes is
the most important aspect of the forecasting effort;
a fundemental
insight into tne results from tariff policy changes can be obtained
from case studies on individual sites alone.
The secondary aspect,
the magnitude of the tariff policy changes, can be estimated Dy a
sample of the utility's existing large commercial and industrial
customers who do or might cogenerate.
This is an easy group to
193
Table 3.13:
Classes of Customer Information Employea in the
Construction of a Cogeneration Forecast
Information
Class
A
Description of
Group
Existing customers on which detailed information is
available, such as a firm that has conducted extended
negotiations with the utility concerning a long-term
electricity purchase for the cogenerated electricity
B
Existing customers on which limited site-specific data
are available, such as those covered in the survey
described in this chapter and Appendix C.
Existing customers which have not been specifically
surveyed but on wnich aggregate energy usage data are
available.
Future customers which are forecast by commercial or
industrial category and on which only general
characteristic information is availaole.
194
identify since the prospects with the greatest potential for
conversion are among the largest industrial sites with large boiler
systems, which are generally known to the utility or can be traced
through a review of air pollution emissions data on industrial sites
collected by the individual states.
Furthermore, the aggregation effort should be concentratea on the
potential for cogeneration at existing sites because the revenue
reduction problem influences the utility through the switching of
existing customers to cogeneration.
For a new customer, if average
tariff levels are greater than marginal costs, then tne new customer's
cogeneration will diminish the incremental short-term profits for the
utility's stockholders arising through the firm's arrival; in the long
run, the new customer's purchases will reduce the average fixed costs
borne by the other customers, but not as much as it might nave if the
new firm did not cogenerate.
If average tariff levels are less than
marginal costs, the new customer will arbitrage, so cogeneration does
not pose a "loss in operating profit" problem; instead, it is a more
fundamental problem in utility economics, involving the general
utility tariff-setting and regulation process, not to be resolved in
the context of cogeneration forecasting.
If the cogeneration forecast is used in capacity planning for the
utility, the aggregation effort deserves careful attention because the
total cogeneration capacity is important.
The conversion of existing
industrial sites can be calculated with relative ease.
The
significant questions are the growth of steam-using industries ane
195
whether or not they will select cogeneration as a heat source for the
new facilities or the expansion of existing ones.
This report has narrowed the research task Dy selecting one
utility for case study analysis.
Anticipated load growtn for this
utility is very low, especially in the industrial sector, and in some
of the specific industries that are noted for steam use, as
illustrated in Table 3.14.
In the context of this case study,
therefore, the primary utility policy decisions involve tariff policy
rather than capacity expansion issues, so the focus of the research
here has been on understanding the effects of cogeneration by existing
customers.
The estimates developed in the remainder of this chapter employ
entirely class B data from an extensive questionnaire completed by 123
of the utility's largest industrial and commercial customers in the
late 1970's.
Tne information collected is described in Appendix C in
Tables C.1 through C.3.
The customers included in the survey are all
served or are eligible to be served under a tariff very similar to the
H Rate, which is described in Appendix B. As demonstrated in Taole
3.15, the survey sample constitutes a large share of the total
electric energy sales for the class of customers eligible for the
"large light and power" H Rate; the commercial ana industrial
customers represented significant shares of their respective groups;
and the survey group as a whole was a substantial share of the utility
sales to ultimate consumers.
No effort was made to explore elaborate
methods for extending the process estimates from the survey to the
196
Table 3.14: Utility Sales in 1.978 and Growth Rates Expected to 1990
1978
Revenue
Share
1978
Energy
Sales
Snare
Forecast Energy
Growth Rate
1978-1990
By Sector/Share of Total Sales
Residential
43.3
39.7
1.0
Commercial
31.7
31.5
2.3
Manufacturing/Mining
23.1
27.7
2.6
By Industry/Share of Total Manufacturing and Mining
The 6 Key Cogeneration Inaustries (with SIC):
20 Food and Kindred Products
5.3
5.4
1.6
22 Textile Mill Products
4.3
4.2
.5
26 Paper and Allied Products
7.1
7.7
1.5
28 Chemicals and Allied Products
9.2
10.2
2.4
29 Petroleum Refining &
Related Industries
33 Primary Metal Industries
.7
.7
0
6.9
7.7
1.8
33.5
35.3
1.7
30 Rubber & Misc. Plastic Prod.
11.8
12.4
3.4
36 Electrical & Electronics
13.3
14.5
3.2
58.6
62.2
2.4
Subtotal of 6 Industries
Other Key Local Industries:
Total of 8 Industries
197
Coverage of the Survey Relative to the Total Utility
Sales
Table 3.15:
I.
Number of Customers (% in survey)
A. Total:
B. By Class:
Commercial
Industrial
C. By Tariff:
H-Rate Eligible
.016%
.072%
3.0%
27.4%
II. Energy Sales (% in survey)
A. Total:
11.8%
B. By Class:
Commercial
Industrial
9.1%
32.2%
C. By Tariff:
H-Rate Eligible
41.7%
D. By the Eight Major Industries (% of Total SIC energy sales in
survey by SIC group):
20
22
26
28
29
30
33
36
Food and Kindred Products
Textile Mill products
Paper and Allied Products
Chemicals and Allied Products
Petroleum Refining and Related
Industries
Rubber and Miscellaneous
Plastic Products
Primary Metals Industries
Electrical.and Electronics
27.1%
21.0%
19.6%
20.5%
5.6%
22.7%
47.3%
60.4%
198
full customer base because utility tariff policies ratner than
capacity expansion issues were the primary concern in the case study
examined in this report.
Therefore the values reported in the
aggregate projections in this section have not been scaled up to
reflect the wnole utility territory population; the totals are only
based on the sites in the sample.
In analyzing the impact of cogeneration at this sample of 123
sites, the next subsection describes two simple approaches for
examining the survey data and making a crude forecast of the maximum
total cogeneration capacity development.
The second subsection
applies the simplified cogeneration economics model to the site survey
data in order to estimate the total cogeneration capacity development,
the electric energy output over a multi-year under differing fuel an
utility coal conversion scenarios, and the expected net impact of this
cogeneration development on the utility.
3.2.1
Exploratory Forecasts of the Maximum Cogeneration Capacity
Development
Sensitivity studies on the economics of cogeneration at a few
sites, together with some insight from the conceptual background on
cogeneration investment and operating decisions outlined in Section
2.2.2, suggest simple decision rules for determining, within the case
study region, the sites that are economic for cogeneration.
Applying
these rules to each of the sites in the survey allows a simple
cogeneration capacity forecast with little computational effort.
section presents two examples using this approach.
Tnis
Tne first looks at
0
199
the total cogeneration capacity of each type that could be built if
only a single technology was available at one time, using simple rules
for the sizing of the new capacity and the ratio between electricity
and steam output from each cogeneration technology.
The second
example applies a simple ranking of the technologies that can be used
at a site, depending on the size, duration, and pressure of the site's
steam usage.
A Simple Single Technology Forecast
The total possible cogeneration capacity in an area depenas upon
the economics of the individual sites and the combined actions at all
these sites.
The analytical and modeling results in Cnapter 2
demonstrated that the installed cogeneration type and its size at a
site depends, in part upon the steam usage patterns at the site.
Under the case study conditions examined in this report, it appears
that:
*
Coal-fired steam turbine cogeneration systems can be
economically sized to serve the peak month's average steam
loads at large steam-using sites.
*
Oil-fired steam turbine and aiesel systems must be sizea for
the base steam loads if they are to be economic at all.
Economies of scale in cogeneration plant capital and operating costs
place lower limits 6n the economic sizes for the installation of these
plants.
A very simplistic approach to exploring the maximum possiole
installed cogeneration capacity involves looking at the peak, average,
2Uu
and all larger sites in the survey.
For example, assuming that
coal-fired cogeneration systems are economic only down to 50 MBtu/nr
peak month's load, the total hourly peak steam load at all sites with
a peak steam load larger than 50 MBtu/hr is about 700 MBtu/hr.11
For the typical electrical/steam output ratio of 60 kWh/MBtu, this
implies a total installed capacity of 42,000 kW.
The effect of changing the minimum economic scale for a and base
steam loads at all 123 sites in the aggregate survey.
Figures 3.3a,
b, and c give the frequency distribution of the peak month's steam
use, the annual average steam use, and the base month's average steam
use for all the sites in the survey.
Note that there are very few
sites with large base month steam loads.
Projecting the total coal-fired steam turbine cogeneration
capacity requires estimating the economic size at each site:
by the
rule-of-thumb above, this is the peak month's average heat load in
MBtu/hr times a typical electric/steam output ratio for coal-fired
cogeneration, about 60 kWn/MBtu.
For example, a site witn 50 MBtu/nr
peak load would select a 3.0 MW coal-fired cogeneration plant.
These
individual site estimates can then be summed from the largest peak
load site down to the smallest site that could be served by the
minimum economic scale plant assumed for each technology.
Alternatively, the peak month heat loads can be summed for eacn site
1 1These
peak steam loads do not occur at the same time for all
sites. This is only a crude way of estimating installed capacity, not
a way of estimating the annual electric energy production from the
whole group.
201
THE FREQUENCY OF PEAK MONTH STEAM
LOADS
FOR THE SURVEY DATA SITES
80-
60
L0
U
40-
rr
20
0.0
I
0
I
_I
I
k -1---l
_
21'
31
41
51
61
71
81
91
1
PEAK MONTH STEAM LOAD AT SITE
Figure 3.3a
I! 121
131
(MBTU/HR)
141
202
THE FREQUENCY OF AVERAGE STEAM LCADS
FOR THE SURVEY DATA SITES
80-
60-
40-
20!
0.0
O
I
I
'c,
21
31
-- -
41
51
61
71
81
__
1 101 Ill
AVERAGE STEAM LOAD AT SITE
Figure 3.3b
'
121
131 141
(MBTU/HR)
203
THE FREQUENCY OF BASE MONTH STEAM LOACS
FOR THE SURVEY DATA SITES
80-
60
40-
20-
0.0
i
i
I
I
I
5I
I
21'
I
31
I
I
41
i
51
,
,, ,,
61
I
71
"
81
J,
91
-
101
'
BASE MONTH STEAM LOAD AT SITE
Figure 3.3c
.
III
121
I
•
131
141
(MBTU/HR)
l
204
down to the minimum scale and, then, the electric/steam output ratio
can De applied.
Figure 3.4a presents the cumulative peak month's
average steam usage plotted against the minimum scale for a given size
cogeneration technology can be seen immediately in Figure 3.4a.
Eacn
additional survey site is reflected oy a jump in the cumulative peak
steam capacity.
Although the largest sites are few in number, the
first five constitute over half of the total capacity even if the
minimum economic scale is reduced to the size of the smallest
commercially available coal-fired high-pressure boilers, about 20
MBtu/hr.
This approach can be similarly applied for oil-firea steam
turbine and diesel cogeneration except that the plant sizing would be
based on the base or lowest month's average steam loads.
For example,
assuming that #6 oil-fired diesel cogeneration systems were only
economic down to 25 MBtu/nr base heat load, Figure 3.4c indicates that
base heat loaas of that size and larger total about 200 MBtu/nr.
a typical electrical/steam output ratio of about 380 kWn/MStu,
For
this
implies a total installed capacity of 76 MW if only aiesel systems
were installed.
Again notice the concentration of the total potential
at a few large sites--sites which would most likely consider
coal-fired steam turbine or at least an oil-fired steam turbine before
considering a diesel system.
A Simple Multi-Technology Forecast
The single technology approach above does not reflect the
hierarchy of different plant alternatives possible at an individual
CUMULATIVE PEAK MONTH STEAM LOADS AT SURVEY DATA SITES
2000
1600
CUMULATIVE
PEAK MONTH
STEAM LOAD
AT SITES
WITH
GREATER
THAN THE
MINIMUM
PEAK LOAD
FOR GROUP
(MBTU/HR)
1400C)
12001000-
800600-
0 .0
I
0.0
12.5
25
.
I
37.5
, .. ...
50
62.5
I
75
87.5
MINIMUM PEAK MONTH STEAM, LOAD AT SITES
Figure
1.4a
I
100
112.5
IN GROUP
I
I
125
137.5
(MBTU/HR)
150
-----1
CUMULATIVE AVERAGE STEAM LOADS AT SURVEY DATA SITES
000
1800'
1600.
1400,
CUMULATIVE
AVERAGE
1200
STEAM LOAD
AT SITES
1000,
WITH
GREATER
800.
THAN THE
MINIMUM
AVERAGE LOAD
FOR GROUP
(MBTU/HR)
400200.
0.0 +0.0
112.5
MINIMUM AVERAGE STEAM LOAD AT SITES IN GROUP
125
137.5
150
(MBTU/HR)
Figure 3.4b
.
0
p
CUMULATIVE BASE MONTH STEAM LOADS AT SURVEY DATA SITES
2000180016001
CUMULATIVE 1400BASE MONTH
STEAM LOAD
1200AT SITES
WITH
1000GREATER
THAN THE
MINIMUM
BASE LOAD
FOR GROUP
(MBTU/HR)
800800400200-~1
!
0.0 i
0.0
I-
-
12.5
I
25
--- I
37.5
I
50
I
62.5
T
75
I
87.5
T
100
112.5
I
125
137.5
MINIMUM BASE MONTH STEAM LOAD AT SITES IN GROUP (MBTU/HR)
Fioure 3.4c
I
150
208
site.
The metnod in this subsection applies a simple ranking of plant
alternatives for each site to account for differences in the economic
sizing of plant types, the influence of steam load pressure
requirements on the electrical/steam output ratio for each techology,
and the presence of existing cogeneration.
1.
If the site has peak month steam loads above a given minimum
economic size (50 MBtu/hr in the base case), a coal-fired
steam turbine cogeneration plant will be selected for tne
site and sized for the peak month's average steam loads.
For sites with existing cogeneration capacity, the new
coal-fired capacity is assumed to be the greater of the
existing oil-fired capacity or the new size estimated by the
peak month sizing approach.
2.
If the site has peak steam loads too small for coal Out a
base month steam load that would result in oil-fired steam
turbine cogeneration with electrical capacity above a given
minimum economic size (500 KW in the base case), oil-fired
steam turbine cogeneration will be selected for the site and
sized for tne base month's average steam loads.
In sites
with existing capacity, the new capacity is the assumed to
be greater of the existing or tne new capacity forecast Dy
this method.
3.
If neither oil- nor coal-fired steam turbine systems can oe
selected by sites with low-pressure base-montn steam loads
that
would result in a #6 oil-fired diesel cogeneration
209
system with electrical capacity above a given minimum
economic size (1000 kW in the base case), diesel
cogeneration wil be selected for the site and sized for the
base month's average low-pressure steam loaas.
Tne
low-pressure steam load duration curve is assumed to have
the same profile as the total steam load duration curve,
buut it is a fixed fraction of the total load at any time.
There was no significant existing cogeneration in this size
range.
The electrical/steam output ratio for the steam turbine systems at
each site varies depending upon the fraction of steam loads requiring
low-pressure steam.
Since the diesel systems are assumed to serve
only low-pressure loads, they all have an electricity/steam ratio of
aDout 240 kWn/MBtu from Table U.2.
Figure 3.5 illustrates the results of tnis method as a function
of the indivioual site's average steam loaos.
Tnis figure, like
Figure 3.4a-c, presents the cumulative forecast cogeneration capacity
for all sites of tne given average steam load and larger.
Since coal
systems are sized on peaK load, ana oil systems are sized on base
load, tnis presentation of the results wita respect to average load
provides only a convenient standara of comparison.
First note that
the existing installed cogeneration capacity is at tne largest sites
and is about a third of the final cogeneration capacity.
The new,
coal-fired capacity includes incremental additions to sites with
existing capacity and new coqeneration at sites that previously were
TOTAL COGENERATION CAPACITY PROJECTION USING SIMPLE MULTI-TECHNOLOGY
APPROACH AS A FUNCTION OF AVERAGE STEFPF LOAD FOR SURVEY DATA SITES
100
90
80
70
TOTAL
COGENERATION
CAPACITY (MW) 60
AT SITES WITH
AVERAGE STEAM
LOAD GREATER
THAN THE
MINIMUM4 FOR
40
THE GROUP
New CoalFired
Steam Turbine
30
Cogeneration
20
N,.
ew #6
Oil
Diesel Cogeneration
7'New Oil-Fired Steam Turbine Cogeneration
-
10
.\'Existing Steam Turbine Cogeneration \\\\\\\\\\\\\\\\\\\\\\
0.0
II~ -0.0
->-\..I
xxxxxxxxxxxx~hb~~bb~>>b>>>S
--- ~~
~I - - -~- - - ~-- i~- -- ~--~~--~~~--~- ~~-~50
10
20
30
60
>>>->
70
60
>S-\
90
100
110
MINIMUM AVERAGE STEAM LOAD AT SITES IN GROUP (MBTU/HR)
Fi'.iqure 3.5
120
211
without it. Because of the sizing method based on the peak month's
average load, additions of coal-fireo cogeneration continue down to
average loads of about 25 MBtu/hr, although oil-fired steam turbine
capacity additions start at high load factor sites at about 30 MBtu/nr
average load.
Diesel cogeneration capacity additions are at the
lowest end of the scale, assuming that diesel systems are economic at
all.
Table 3.16 provides a sensitivity analysis of the simple,
multi-technology approach to changes in the minimum economic plant
scale assumptions.
Note the case that is most optimistic about the
minimum economic plant scales is not the case with the highest
electrical capacity forecast.
The sensitivity analysis here
demonstrates how one cogeneration type replaces another in any
forecast as economic assumptions cnange.
3.2.3
Forecasting Using the Cogeneration Plant Investment and
Operation Model
This section discusses the application of the simplified
cogeneration plant investment and operation model to the survey
sites.
This allows a multi-year forecast of cogeneration capacity
development with contingent forecasts of cogenerated electric energy,
and the net economi.c impact on the utility; the forecast depends upon
subsequent cogeneration plant operation and tariff choice decisions as
a result of changes in electricity and fuel prices.
The model
development has been discussed in Sections 2.3 and 3.1 for the
1___
212
Table 3.16:
Sensitivity of tne Simple Multi-Technology Forecast
Assumed Minimum Economic
Plant
---- Size
"
U011
Diesel
on
ST on
Coal ST
#6(z)
Cogen. (x) #6(y)
(kW)
(kW)
(MBtu/hr)
--
Case
Base
50
Pessimistic
on Coal
100
Survey Forecast Capacity (MW)
Coal
ExisST
ting
Oil
inST Diesel Total
ST
crement
I-.
500
1000
23.7
24.5
9.7
8.3
66.2
500
1000
23.7
.6
17.9
9.6
51.8
Optimistic
on Coal
25
500
1000
23.7
39.4
4.9
8.3
76.3
Optimistic
on Diesel
50
500
500
23.7
24.5
9.7
12.7
70.6
Very Optimistic
on Diesel
50
500
100
23.7
24.5
9.7
39.7
97.6
Pessimistic
on Oil ST
50
1000
1000
23.7
24.5
1.2
21.3
70.7
Optimistic
on Oil ST
50
250
1000
23. 7
24.5
14.4
Optimistic
on Coqen.
in general
25
250
100
23.7
39.4
1000
2500
23. 7
Pess imi st i c
on Cogen.
in general
100
.6
0
62.6
8.8
8.2
80.1
8.7
5.9
39.9
x:
Based on coal cogeneration plant sizing for peak month's steam
loads.
y:
Based on #6 oil-fired steam turoine sizing for base montn's steam
loads.
z:
eased on #6 oil-fired diesel sizing for base montn's low-pressure
steam loads.
213
investment and operating decisions at a single site; this section
describes the results of the simplifed model's application to all 123
sites in the aggregate survey.
To reduce the computational costs of this analysis, the forecast
for each site is conducted in several stages:
1.
The steam and electric loads are constructed from tne data
on the given site (see Appendix C and Table 2.16) and the
pre-sizing of different plant types takes place subject to
lower limits for tne commercially available cogeneration
equipment (1000 kW for coal-fired cogeneration; 500 KW for
oil-fired steam turbine cogeneration; 1000 kW for #6
oil-fired, and 250 kW for #2 oil-fired diesels;about 1000 KW
for #6 oil-fired, and 400 KW for f2 oil-fired gas turbines;
and 15 MBtu/hr for stand-alone coal-fired boilers). 12
If
the site has existing oil-fired steam turbine cogeneration
larger than 500 kW, it is included as a zero capital cost
alternative in place of a new, approximately optimally sized
oil-fired steam turbine cogeneration system.
2.
If the site has more than .2 MBtu/nr average heat load, tne
simplified operating simulation and the middle fuel price
escalation/medium coal conversion scenario financial
analys-is is performed as described in Section 2.3.3.
12 The economics of .the system are tested at sites smaller than
the minimum commercial.scale, but the cap-ital costs are not reduced
below the cost estimate for a plant of tne minimum commercial scale.
__
214
3.
If the site has existing cogeneration capacity, or if a
cogeneration system is determined in step #2 to be an
economic alternative to the existing boiler system, the
site's minimum operating costs and its operation/tariff
strategies are calculated under eacn fuel ana electricity
price scenario.
This provides the multi-year forecast of
energy and the expected utility impact for each site.
The average computation cost per site with four different tariff
restriction options is less than $1 per site on MIT's IBM 370/168
computer.
Tne forecast for the survey sample is simply the sum of the
results for the individual sites.
Table 3.17 provides a time series forecast resulting from tne
analysis for the full survey of 123 customers.
Five of the group
already had oil-fired steam turbines of 500 KW or larger at tne start
of the forecasting period; all the existing oil-fired cogeneration
could economically convert to a completely new coal-fired steam
turbine system.
Forty-three of the other sites provided economic
opportunity for coal-fired cogeneration under the ease case cost and
financial assumptions; two sites preferred stand-alone coal boilers to
cogeneration systems.
No oil-fired cogeneration was the first cnoice
plant at any of tnese sites.
As shown in Table 3.17, once the new coal-fired capacity is in
place, it operates at all times.
Tne impact on the utility, nowever,
changes witn the fuel price/utility coal conversion scenario and year;
again, as Section 3.1 demonstrated, the middle scenario provides a
,215
Table 3.17:
Model Forecast of Cogeneration Capacity, Energy, ana
IITllrtrr
Im~~rt
JI
I
I
VLJ
III.IQIL
Utility Loss
(thousand 1981 $)
Middle/Medlan
Expected
Scenario
Value
Year
Cogeneration
Capacity (MW)
Cogeneration
Energy
(MW average)
1981
22.5*
14.4*
1982
22.5*
14.4*
1983
22.5*
14.4*
1984
77.6
53.
260
672
1985
77.6
53.
870
1357
1986
77.6
53.
621
1262
1987
77.6
53.
405
1177
1988
77.6
53.
241
1103
1989
77.6
53.
'135
1040
1990
77.6
53.
55
983
-1,440
-17
NC
NC
1973
Present value of utility impact for years 1984-1990.
*This is the existing oil-fired steam turoine coqeneraton capacity
sites of 1000 kW or larger; new coal systems replace-all existing
oil-fired steam turbine systems.
NC = not calculateo.
NC
5559
-
216
very poor estimate of the utility impact.
Table 3.18 shows the composition of the capacity forecast and
demonstrates now major factors such as environmental restrictions on
coal use or the starting fuel prices and financial assumptions can
affect the forecast capacity development.
Under the base case
economic asssumptions, if coal-fired cogeneration and Doilers are
excluded for environmental reasons, existing oil-fired cogeneration
systems stay in operation, and a mixture of oil-fired steam-turbine
and diesel systems are developed where most of the coal-fired systems
would have been economic.
Under higher cost of capital and lower oil
price assumptions, the five existing oil-fired cogeneration systems
stay in place, and coal-fired cogeneration systems are economic only
at 22 larger sites; a small amount of new oil-fired steam turbine
based capacity is economic at 6 sites, 13 but no diesel cogeneration
is a viable alternative.
Under the higher cost of capital/lower oil
price assumptions, if coal-fired cogeneration and boilers are
excluded, both new oil-fired steam turbine and diesel cogeneration
capacity develop insteao.
Figure 3.6 illustrates the composition of the cogeneration
capacity forecast by the site average steam load unoer the base case
and higher cost of capital/lower oil price case.
Figure 3.7 presents
time series plots for capacity development, cogenerated electric
energy output, and expected utility impact for these two cases.
13This may be because of tne advanta es of switcning from #2
oil to #6 oil at these sites rather than Ne value of the cogeneration.
217
Table 3.18:
Sensitivity of Forecast Cogeneration Capacity and
Energy Output in 1985
Steam
Capacity Expec-
Electric Capacity (MW)
Existing
Oil ST
1. Base Case (15.1%
return witn 10%
inflation; low S
#6 oil at $5.45/
MBtu and coal at
$2.05/MBtu startin 1981).
0
2. Coal cogeneration
not allowed..
22.5
3. Higher cost of
capital and lower
fuel prices (18.9,
return with 10%
inflation; low S
#6 oil at $4.56/
MBtu and coal at
$2.31/MBtu startin 1981).
4. Hiqher cost of
capital and lower
fuel prices; coal
cogeneration not
allowed.
Coal
ST
New
Oil
ST
77.6
0
Total
(MBtu/
nr
Total
77.6
1228
83.6
977
NC
ted
Energy
(Mwavg)
53
0
15.4
45.7
22.5
24.8
3.7
0
51.0
848
38.3
22.5
0
12.6
19.6
54.7
729
NC
No restrictions on tariff choice.
NC = not calIculatea.
Diesel
- -~----I
FORECAST OF CUMULATIVE COGENERATION CAPACITY BY TECHNOLOGY:
BASE CASE
100
90
TOTAL 1985
COGENERATION
CAPACITY (MW)
AT SITES WITH
AVERAGE STEAM
LOAD GREATER
THAN THE
MINIMUM4 FOR
THE GROUP
New Coal-Fired Steam
Turbine Cogeneration
0.0
10
20
30
40
50
60
70
80
90
MINIMUM AVERAGE STEAM LOAD AT SITES IN GROUP
Figure 3.6a
4
4
r
100
110
(MBTU/HR)
120
FORECAST OF CUMULATIVE COGENERATION CAPACITY BY TECHNOLOGY:
EIGHER COST OF CAPITAL & LOWER OIL PRICE CASE
100 9080 70
-
TOTAL 1985
COGENERATION
CAPACITY (MW) 60
AT SITES WITH
AVERAGE STEAM 50 LOAD GREATER
THAN THE
MINIMUM FOR
THE GROUP
30
New Oil-Fired Steam Turbine Cogeneration
1
New
Coal-Fired Steam Turbine Cogeneration
..................
20
Existing Steam Turbine CoTeneration,
10
1Cogeneration,\k
\\\\\\\\\\\\\\\x
,\\k\\\\_k\\\\\\\k\\\\kk\\\
\
0.0
0.0
---- --10
20
30
40
--50
\\
\\
\\\\k\\\\\\
------~-----60
70
80
\\kk\\
---90
MINIMUM AVERAGE STEAM' LOAD AT SITES IN GROUP
Figure
'.6b
- I ~---~- -120
100
110
(MBTU/HR)
TEN YEPR COGENERATION FORECAST:
CAPACITY
100-
se
Ca
Base Case
COGENERATION
CAPACITY
(MW)
0-
O
O
.
7
8
9
5
6
7
8
9
0
0
of
Capital
Higher Cost
& Lower Oil Price Case
50-
I
i
I
2
3
4
YEAR
Figure 3.7a
I(
TEN YEAR COGENERATION FORECAST:
EXPECTED ENERGY CUTPUT
.100-
EXPECTED
ANNUAL
AVERAGE
COGENERATED
ELECTRIC
50ENERGY
OUTPUT
(MW)
Base Case
0
-e-
-
*
-&&
Higher Cost of Capital
& Lower Oil Price Case
I-'.
YEAR
Figure 3.7b
TEN YEAR COGENERATION FORECAST:
EXPECTED UTILITY IMPACT
2Higher Cost of Capital
& Lower Oil Price Case
e
ADVERSE
IMPACT ON
UTILITY
(MILLION
1981 $)
Base Case
I-
0-
I
2
4
3
Figure
r 0
0
5
YEAR
3.7c
6
7
8
9
1(
223
The impact of the cogeneration on the utility varies
substantially depending upon fuel prices and restrictions by the
utility on customer's tariff choices.
TaDle 3.19 summarizes the
results of sensitivity studies on tariff restrictions and their
influence upon the forecast utility impact from cogeneration.
First,
note that time-of-oil pricing has no worth for the utility if the
forecast includes only coal-fired cogeneration.
Also note that it is
more valuable for tne base case/no coal example (2.A less 2.8) than
for the higher cost of capital/lower oil prices/no coal example (4.A
less 4.B) because of the higner coal/oil price differentials.
Second,
forcing cogenerators to use time-of-use tariffs when tney "sell net"
reduces the impact on the utility substantially in all cases for two
reasons:
in some cases, the cogenerator finds it advantageous to
arbitrage rather than sell net; second, the marginal revenue reduction
from cogeneration during tne peak and off-peak periods better matches
the pattern of changing marginal costs for the utility.
Finally, the
reduction of the utility purchase rate from 100% to 95% of marginal
fuel costs is effective only when the cogeneration capacity mix must
sell a substantial amount of electricity in excess of internal site
loads, e.g., in case 2.D, which has a high forecast for diesel
capacity; otnerwise the plants can protect themselves by "selling net"
and reducing their tariff charges for internal loads.
Table 3.19 only provides tne expected impact; Taole 3.18 and
Figure 3.8 show
the distribution of the utility impact for the base
case and high cost of capital/lower oil price case, demonstrating the
224
Table 3.19:
Sensitivity of the Utility Impact to Economic and
Environmental Assumptions
Base Case
Tariff
Restrictions
I
Coal Cogen OK
2
No Coal
Higher Cost ot Capital and
Lower
1
- Uil Prices
4
3
Coal
Cogen
OK
No
Coal
L
A. No.restrictions on
tariff choice
5.56
6.03
12.46
13.85
B. No time of
5.56
7.90
12.53
14.35
1.33
C. Mandatory TOU
tariff for
net sale cogen.
2.36
4.68
6.06
11.87
13.26
oil pricing
D. No CG rate,
95% marginal
fuel cost as
utility purcase rate.
2.94
.53
Present value of expected utility impact in million 1981 $. Tne
calculations assume that, if a site converts from existing oil
cogeneration to coal, the oil system ceases cogeneration at the start
of 1981.
225
Table 3.20:
Utility Impact By Scenario
(a) Base Case
Utility Coal Conversion Scenario
Fast
Medium
Fuel
High
2.89
.01
Price
Middle
9.30
1.97*
Escalation
Low
16.88
10.93
(b)
Higner cost of capital,
Slow
.35
6.26
lower oil price case:
Utility Coal Conversion Scenario
Fast
Medium
Slow
Fuel
High
10.58
5.27
3.39
Price
Middle
15.83
11.57
8.30
Escalation
Low
21.11
18.39
16.34
Discounted impact in million 1981 $ for 1981-1990:
restrictions.
no tariff
DISTRIBUTION OF UTILITY IMPACT FOR AGGREGATE FORECAST
1.00
.8
CUMULATIVE
PROBABILITY
OF THE
ADVERSE
.6,
DISCOUNTED
UTILITY
IMPACT
BEING LESS .4
THAN OR
EQUAL TO
THE GIVEN
LEVEL
.2,
0'
DISCOUNTED ADVERSE UTILITY IMPACT FOR 1981-1990
(MILLION 1981 $)
Fiqure
3.8
227
highly contingent nature of these impacts.
3.3
SUMMARY AND CONCLUSIONS
This chapter has applied the physically-based approach for
electric load forecasting to the estimation of cogeneration
development in a utility's service territory.
The forecast provides
information on three aspects of the customer cogeneration
development:
the electric energy output; the change in utility tariff
revenues; and the change in production costs for the utility.
In applying the physically-based method, tnis research supplement
the methods employed in previous electric load forecasting studies.
While previous studies considered changes in operation and the choice
of tariff as part of an electric load forecast, as in Manicnaikul
(1978), tnese choices were not combinea over time to snow the impact
on the initial capital investment decision for the elemental device or
the customer's generation source.
Furthermore, these operating
choices by tne customer are aetermined for several scenarios, so the
'initial investment decision reflects strategic uncertainties in fuel
and electricity prices along with the impact of operating
uhcertainties from cogeneration plant outages and the changing steam
and electric loads served by the cogeneration system.
This study demonstrated that this approach, using strategic
uncertainties with consideration of the subsequent customer's
operating decisions, is necessary for the estimation of cogeneration's
economic impact on the utility.
A "middle" or "best" case is not
228
sufficient because the negative results occur only when the utility's
average costs, as reflected in the tariffs, are greater tnan the
utility's marginal costs; tne one-siaea nature of the impacts is not
reflected in single "best estimate" approcn for tne cost and price
assumption.
Applying these improvements in physically-Dased load forecasting
to the projection of customer cogeneration yields valuable information
for several aspects of utility planning.
First, tne total
cogeneration capacity may be important for a utility's capacity
planning.
Second, tne combined revenue ana cost forecast can De
employed in tariff aesign ana planning for tne cogeneration policies
from tne utility's perspective.
The case stuay here illustrates the
applications for tne utility's cogeneration tariff policy.
What is needed to make such a forecast?
As general information
to be applied at all sites, the forecaster must have:
1.
Estimates of cogeneration and boiler equipment capital
costs, operating and maintenance costs, and performance
capabilities for a broad range of equipment sizes.
2.
Forecasts of possible future fuel prices and changes in
utility tariffs and marginal costs.
For each site or class of customers that might consider adopting
cogeneration, the forecaster must know:
1.
The load duration of the site's heat loads:
it is even
better if this information is available in a form indicating
a correlation with ambient temperature, such as an estimate
229
of the shares of total energy used for process, space
heating, and space cooling.
2.
The steam or hot water temperatures required by the site.
3.
The existing boiler and cogeneration equipment on tne site.
4.
The site's electric load, preferably on a time-of-day
basis.
This information is vital when trying to estimate
the economic impact on the utility from changes in tariff
policy.
Applying this method to survey data of 123 sites in soutnern New
England, coal-fired cogeneration was found to be economic at
substantially smaller scale sites than previously anticipated.
The
entire base case forecast was for coal-fired cogeneration; if no coal
cogeneration or stand-alone coal boilers are allowed, the forecast is
a mixture of existing and new oil-fired steam turbine and cogeneration
along with some diesel cogeneration at the smaller sites with
low-pressure steam loads.
The impact on the utility is substantial
under scenarios with lower oil prices and extensive conversion of the
utility capacity to coal; the impact can be controlled to some degree
by restrictions in the tariffs that better match revenue reductions
from cogeneration with the operating cost savings by the utility.
s
230
* Chapter 4
CONCLUSIONS
The primary purpose of this research has been to refine
cogeneration forecasting methods to supplement the information usea in
electric utility tariff design and capacity pl-anning.
This chapter,
first, summarizes the report's findings in each of three areas.
The
second section suggests directions for further work in the fiela.
RESEARCH RESULTS
4.1
In pursuing the aim of this research, this report nas contriDuted
to a Detter understanaing in three areas:
*
the major economic motives Dehino a customer's decision to
bulla ana operate a cogeneration pldnt; and tne impact tnis
aecision has on the local utility;
*
the tecnniques for tne forecasting of cogeneration
development within a utility service territory, witn special
reference to the comDined projection of electric loaa,
revenue, and cost impacts from changes in the utility's
tariffs;
*
the general methodology of the physically-based or process
approach to electric load forecasting, in particular, the
problems involved in including long-term strategic
uncertainties in a customer's decision to invest in a
electricity generation or consumption device along with the
subsequent uncertainties in the device's operating
oprtn
, 231
performance.
4.1.1
Cogeneration Economics from the Utility's Perspective
In exploring cogeneration's impact on the utility, the report
addresses itself to two questions:
*
What chiefly influences a customer's decision to build ana
operate a cogeneration plant?
*
How aoes customer cogeneration affect an electric utility's
loads, revenues, and costs?
Since many firms can obtain steam and electricity from a package
boiler and through electricity purchases from the local utility, the
decision to replace these sources by a cogeneration plant involves a
major capital investment in anticipation of future operating cost
savings.
The customer can view the adoption of cogeneration in two
stages involving four decisions:
at the investment stage, the firm
must select first the type and then the size of the cogeneration
plant; at the operating stage, the firm must determine simultaneously
both the plant's operating strategies and the disposition of the
plant's electrical output.
At least within the context of the
southern New England economic conditions, each of these four decisions
influences the ultimate effect on the local utility.
These aecisions
will be reviewed in'reverse order.
In the short term, as summarized in Table 4.1, if owners of
cogeneration systems choose to reduce the tariff charges through the
net sale of the plant output by using the output of the plant to first
232
TaDle 4.1:
Impact on tne Utility from a Customer's Cogenerated
Electricity Sales Choice
Electricity Cost and Price Conditions
Reduction in Standard
Reduction in Standard
Tariff Cost Exceeds
Tariff Cost is Less Than
Utility's
Marginal
Utility's Marginal Cost
Cost
I
I. The customer's
economic choice
II.
Impact on the
combined stockholder and rate
payer interests
with respect to
the cogeneration
Sell cogeneratea electricity
to utility net of the site's
internal electric loads
(net sale)
Sell all cogeneratea
electricity to tne
utility; buy all
electricity for the
site's internal
electric loads from
the utility
(arbitrage)
Utility revenue is reduced
more than the decrease in
production costs; losses
shared by stockholders and
ratepayers over time, as
aiscussed below.
No net effect from
cogeneration
233
serve the site's internal electric loads, the utility's revenues are
reduced more than its production costs.
This operating profit loss
for the utility affects both the utility's stockholders and its
ratepayers.
If economic conditions encourage the cogenerating
customer to sell the entire plant output to the utility while
simultaneously purchasing the site's electric loads, the utility
suffers no damage from the cogeneration system. This situation,
however, indicates that tne utility may be collecting less than its
marginal costs through its tariffs, a more general tariff policy
problem.1
The cogeneration plant's operating strategy determines
the amount of electric energy produced ano, hence, the amount of
utility electricity production that is replaced by the cogeneratea
output.
At the investment stage, the size that the firm sets for tne plant
fixes the maximum electric output.
The choice of plant type
determines the cogeneration system's economic characteristics, setting
the anticipated performance within the utility's cogeneration forecast.
From the perspective of utility tariff planning for customer
cogeneration, losses for the utility can be minimized when the tariff
reductions possible by the customer's selection of "net sale"
cogeneration are set equal to the utility's marginal costs of
ITnis aiscussion assumes that the utility purchases all
cogenerated electricity at tne utility's marginal fuel costs. If the
utility can purcnase the electricity at sligntly less tnan tne
marginal costs, it will have more flexibility in the design ot tarifs
to avoid losses from customer cogeneration.
* 234
production.
Since a utility's marginal costs typically fluctuate mucn
more than tne average costs embodied in the utility's tariffs, this
requires unusual flexibility in the design of tariffs.
The Denefits
of even instantaneously fluctuating "time-of-oil" pricing have been
illustrated for the utility's purchases from oil-firea cogeneration
systems.
For utility capacity planning, at least within the southern iew
England region studied, any cogeneration development will oe limited
to major industrial sites with large year-around steam loads.
From
the private firm's perspective, coal-fired cogeneration systems are
the most economic plants, even at sites that are subtantially smaller
than have been economic for coal in the recent past.
uil-fireo
cogeneration is a second best choice at some sites, but it carries
some substantial risks, especially for the high electricity output
plant types like diesel cogeneration.
Furthermore, economics dictate that oil-fired cogeneration systems
must be sized to serve the constant portion of the steam loads.
Industrial and, especially, commercial steam loads change
substantially from season to season in New England; only a few sites
have year-around, constant or base steam loads sufficiently large for
oil-fired cogeneration while being too small to be served economically
by a peak-load size, coal-fired cogeneration system.
4.1.2
Forecasting Cogeneration at the Utility Level
Cogeneration development in a utility's service territory was
235 forecast by calculating cogeneration project economics from the
customer's perspective using a large sample of industrial and
commercial sites.
This modeling effort differs from previous studies
in this area by its inclusion of tariff effects and the resulting
impact on the utility's revenue and cost changes.
Detailed studies of six sites indicated that the modeling could oe
simplified wnen making the forecast for a large sample of sites.
In
both the detailed studies of the six individual sites and the analysis
of a large 123 site survey, the evaluations followed the customer's
decision-making stages outlineo in tne section above.
calculations proceeded in two steps.
The survey
First, since it was possiDle to
simplify the projection of tne customer's decision to build a
cogeneration plant, an estimate was made of tne customer's plant type
and sizing decisions, wnicn determine the cogeneration capacity in tne
forecast.
Secona, if a plant was found to be economic at a surveyec
site, more detailed calculations were made on the site's operating and
-tariff decisions under uncertain fuel and electricity price
conditions, which determine the utility's electricity sales,
purchases, revenues, and costs for the site contingent on the price
conditions.
Simple comparisons between the modeling results and
simple analytical calculations of the optimal cogeneration plant
investment and operating decisions showed that the level of modeling
is necessary to capture the influence of the utility tariffs studied
here.
Tne effort requires several types of information.
First, the
236
forecaster needs a general understanding of cogeneration capital
equipment costs, operating costs, ana plant performance parameters
along with projections of fuel and electricity prices, factors which
are similar at all potential cogeneration sites.
Secona, the
forecaster must have information on steam load patterns ana
temperature requirements for a large sample of large commercial ana
industrial sites.
These must be representative of the sites in the
local territory since substantial regional differences in steam use
patterns exist.
At least in the context of economic conditions in the
case study region, site-specific information was availaDle for a wide
sample of the potentially economic cogeneration sites.
It was
possible to obtain this information because cogeneration plants are
financially attractive at large heat-using industrial sites, which are
i.
easy to find and enumerate as part of a forecasting effort.
The combined projections for electric energy, revenue, and
production cost changes for the utility because of cogeneration allow
the forecaster to assist in the evaluation of the utility's
cogeneration tariff policies and capacity plans.
4.1.3
Physically-Based Electric Load Forecasting
Physically-based electric loaa forecasting has been employed to
project and understand such factors a the influence of weather on
electric loads and time-of-use tariffs on electric loaas and the
customer's tariff choice.
This research added the analysis of a
dispersed customer generation source, whlich requires the projection of
. 237
utility revenue and cost changes as well as electricity purchases and
sales.
A customer's equipment operating choices at the device level and
the customer's tariff choices have been considered in previous
physically-based electric load forecasting research.
Tnis report,
because of the capital investments assopciated with cogeneration
development, included the customer's capital investment decisions.
Previous studies have considered short-term uncertainties in usage of
equipment and the impact of weather conditions, as this study has.
The long-term strategic uncertainties in fuel and electricity prices
also had to be included for the forecasting of changes in plant
operating and the site's tariff choices, which are contingent on those
price conditions.
Because of the delays involved in implementing
utility tariff policy and capacity expansion decisions, this operating
and tariff choice must be forecast over a multi-year period to develop
a useful electric load, revenue, and cost projection for the utility.
4.2
DIRECTIONS FOR FURTHER RESEARCH
To address inadequacies in the knowledge of cogeneration
economics, the art of cogeneration forecasting, and the methodology of
physically-based electric load forecasting, this section discusses
weaknesses in the research and suggests areas of research that might
improve the state of the art.
~I
238
4.2.1
Cogeneration Economics and Utility Planning Research
This study addressed conditions in a region tnat has substantial
excess capacity with the potential for coal conversions to reduce the
marginal fuel costs for electricity.
The general framework shoula be
applied to two different utility conditions to improve the general
understanding of cogeneration economics.
First, researching the
development of cogeneration in a region with high economic and
electric load growth would require consideration of the utility's
capacity expansion costs in relationship to the cogeneration and the
forecasting problems when projecting cogeneration capacity at
industrial sites that do not yet exist.
Second, researching the
impact of cogeneration upon a utility with very high electricity
tariffs would require consideration of electric-load-following
operation by the cogeneration systems and the aggregation of
cogeneration opportunities at a larger number of smaller sites than
those surveyed in this study.
In addition, customer cogeneration systems have the capability to
adapt their operating policies in response to short-term changes in
electricity prices.
The research here demonstrated the value of
"time-of-oil" pricing, but tne changes in operating modes were only
made on an annual basis.
.1
Volatility in the oil markets and the
suddenness with which utility capacity problems can strike suggest
opportunities for further research on the value of operating
flexibility for cogeneration, other dispersed generation sources, and
customer loaas.
To capture the importance of this flexibility, the
239
oil market modeling and utility capacity expansion analysis, which act
as input assumptions for this research, must be adapted to provide
information on oil and electricity price fluctuations in a
probabilistic format that can be utilized in the electric load studies.
4.2.1
Cogeneration Forecasting Research
This research took the customer's current steam and electricity
usage patterns as given, and assumed the customer made tne
cogeneration and new boiler investment decisions on the basis of the
project economics alone.
At the fundamental level, the current art of
cogeneration forecasting could be extended in two ways.
First,
especially when considered at the aggregate forecast level instead of
at a single site, tne conservation of steam and electricity Dy all
potential cogenerators could substantially alter the magnitude of the
cogeneration capacity, energy, and revenue, and production cost
forecasts.
Second, as a substantial professional literature in
industrial markets contends, 2 an organization's capital investment
decisions are not entirely motivated by economics--this, comDinea with
the inevitable environmental difficulties likely to occur in siting a
cogeneration plant or major new boiler, warrants additional research
on non-economic factors in the forecasting of cogeneration
development. 3
The explicit consideration of the steam demand
2 Choffray
and Lilien, 1980.
3Research
is in progress on this topic by Radcliffe and Tabors
o
240
elasticity should become a part of more advanced cogeneration
forecasting efforts.
In addition to these major factors, several improvements are
needed to match the research suggested in the area of cogeneration
economics.
With the analysis of cogeneration in a high electricity
price utility area or with the evaluation of increased operating
flexibility, more operating modes must be considered in the
year-by-year optimal operation.
The case study for this report did
not require significant attention to the issue of aggregating the
results from single-plant sites to the utility level; -in other areas,
this could become a major difficulty, especially if smaller-scale
cogeneration technologies are likely to become viable.
Finally, since
steam load patterns at each site are a significant determinant of the
cogeneration plant size and type, the simple estimatjon methods used
to develop steam load duration curves need to be verified in detailed
comparisons of actual steam loads and the simulated steam load shape.
4.1.3
Physically-Based Electric Load Forecasting Research
First, this study, when analyzing the simple "Homeostatic"
time-of-oil pricing scheme for the purchase of cogenerated electricity
by the utility, considered only two price levels varying only in
response to ambient temperature.
Recent theoretical advances on plant
operation in response to homeostatic pricing x need to be includea in
4 Bohn
(1981).
(1981), Bohn (1982), and Bohn, Car amanis, and Schweppe
241
more general physically-based studies on the worth of flexible pricing
methods to be fully evaluated.
Second, this study bases the cogeneration forecast on capital
investment decisions that can only be made in the initial year of tne
forecast.
This needs to be generalized to allow for new load or
customer generation capacity additions throughout the forecast horizon
as well as the availability of new technologies at sometime other tnan
the beginning of the planning period.
The difficult aspect is
allowing for changing probabilistic expectations over time by the
customer as fuel ana electricity price changes occur--the fuel ana
electricity price scenario probabilities become conditional in any
year on prior fuel and electricity price changes.
Furthermore, if a
customer selects a plant type and economic conditions change, the
customer must be allowed to abandon the plant and build a new one;
this abandonment option may be particularly important for the
adoptions of new technologies.
To follow up on the suggestions made for research in the other
research areas, advances must be made on the incorporation of
non-economic behavioral influences in operating and investment
decisions and on the aggregation of site conditions.
4.3
CLUSING
This study supplements earlier researcn in tnree areas:
the
economics of cogeneration and the utility; techniques for cogeneration
forecasting; and the methodology of physically-based electric load
_
_____
242
forecasting.
The economic effects from cogeneration derive from a firm's
decision to build a cogeneration plant, which involves the firm making
a major capital investment in anticipation of future operating
savings.
The firm's economic analysis must therefore consider future
cogeneration plant operating and tariff choices in the investment
decisions on the plant's type and size.
Within the southern New
England case study conditions, coal-fired steam turbine cogeneration
is economic at sites much smaller than previously thought to be
economic.
Cogeneration has an adverse economic impact on the
utility's customers and stockholders when the utility's marginal cost,
which is assumed to be the price for electricity purchases from
cogenerators, drops below the utility's average tariff costs.
The
impact can be diminished by designing tariffs so that the reduction in
tariff costs to a customer through cogeneration matches the utility's
marginal cost as closely as possible.
When the utility can pay
slightly less than marginal cost, a possibility for utility cost
reduction exists in addition to the possibility for an adverse cost
impact, so'the latitude for tariff design increases.
Further research
on the economic aspects of cogeneration is recommended in two areas:
case studies for utilities which are capacity short or have average
tariff levels that are substantially above marginal costs; and for
cogeneration plant operation under more flexible spot pricing of
electricity than the simple time-of-oil pricing discussed here.
~I_
*
243
The techniques for cogeneration forecasting can assist in utility
planning for generation capacity expansion and tariff policies.
Assuming that the individual customer choices are based on economic
considerations alone, the load, revenue, and cost impact on the
utility is forecast by calculating eacn firm's present value for
different cogeneration systems along with investment and operating
choices at 123 surveyed commercial and industrial sites; the model for
the survey study was developed from detailed studies at six sites.
Further cogeneration forecasting research is recommended on the
influence of industrial steam conservation on the forecast and on the
inclusion of non-economic factors in the cogeneration plant choice
analysis.
The physically-based approach to electric load forecasting is
employed to make the utility load, revenue, and cost. projections over
a multi-year period with consideration of strategic fuel and
electricity price uncertainties as well as cogeneration plant
performance uncertainties; the forecast aepends upon cogeneration
plant investment decisions made in the first year of the forecasting
period.
This analysis includes the modeling of realistic utility
tariffs, in particular a simple Homeostatic pricing method for
electricity purchases by the utility.
Further research in the
physically-based electric load forecasting area is recommended on the
modeling of more complex spot pricing ane on the possiDility for
cogeneration capacity additions throughout the forecasting horizon.
244
Appendix A
SYIBOLS, ABBREVIATIONS, AND CONVERSION FACTORS
Table A.l:
Conversion Factors and Abbreviations
Fuel oil heating value for #2 and # 6 oil
(Erickson and Mathewson, 1978;
Baumeister et al., 1978)
Heating value of steam net of feedwater,
assuming feedwater is 130 F, which
is equivalent to a 50/50 mixture
of 60 F make-up water and 200 F
return water; unless otherwise noted,
all conditions assume 150 psig
process steam
(These assumptions are similar to
those in Resource Planning Associates,
1977)
.145 MBtu/gal
6.1 MBtu/bbl
15 psig steam = 1.066 MBtu/klb
150 psig steam = 1.098 MBtu/klb
Standard for dollars
mid-1981 real dollars except
cost estimates in Appendix D,
which are in mid-1980 dollars
Thousand pounds
klb
Million British thermal units
MBtu
245
Table A.2:
Subscripts and Superscripts for Symbols
B
boiler
Bi
a specific boiler technology
backup
associated with the boiler "backing up" the steam supply from
a cogeneration plant when the cogeneration system is
unavailable or uneconomic to run
CG
cogeneration
CGi
a specific cogeneration technology
EL
electricity
fuel
a fuel component
H
high electricity credit period
i
a given device
j
a given usage class
k
a given customer or site
L
customer's load or low electricity credit period
n
a given time interval
OM
a non-fuel operation and maintenance component
q
indicates high (H) or low (L) electricity credit period
s
a specific tariff type from a menu of alternatives
.ST
steam
utility
utility related components
o
"null," the base value
246
Table A.2:
BETA,
BETA L, BETAST
or
&,EL'
ST
Symbols
Capital cost economies of scale factor for
the total plant, the electric components, and
the steam components, respectively.
Total annual cost for steam supply at the site
as a function of cogeneration plant capacity
C(XCG)
ST
($/yr)
The total cost of serving customer k over the
Ck (tn)
period t, ()
The instantaneous cost for the utility to serve
ck(t)
customer k ($/unit time)
CAP(X), CAPST(XST)
Direct plant capital cost for the total plant,
CAPEL(XEL)
the steam component, and the electricity
component, respectively ($)
CAPCG
Total direct capital costs for a cogeneration
plant ($)
capo , capEL,
capST
The base per unit direct capital
cost for a total plant, the electric
components, and the steam component,
respectively ($/unit capacity)
capCG
Annual per unit capital costs of new
cogeneration capacity in terms of steam output
assuming constant returns to scale ($/MBtu/hr
per year)
L..
247
Table A.2:
Symbols (continued)
CO l
Total annual operations and maintenance costs
for a system ($/yr)
cop CG, copB
The net marginal fuel and non-fuel operating
cost of steam from a cogeneration plant and a
backup boiler, respective ($/MBtu)
The unit incremental steam operating costs
(including electricity credits for cogeneration
cST,q
technologies) for technology i during a time
with electricity credit level q
CG
C°POM
utility
COPoM
B
OM
The gross non-fuel marginal operating
costs for cogeneration and boilers,
respectively ($/MBtu)
The non-fuel marginal operating costs for the
utility ($/kWh)
crq
The unit operating cost savings for a
cogeneration system versus a backup boiler at
electricity credit level q ($/MBtu)
ctotCG
Levelized capital and operating cost of steam
from a cogeneration plant ($/MBtu)
•D(t )
The "billing demand" for the tariff demand
change in month tn; this may reflect peak
demands, d(.), from other months (kW)
d(tn )
Peak power demand during month tn (kW)
248
Table A.2: Symbols (continued)
The rate of return appropriate for a given
class of investments
ESR
Cogeneration plant electricity to steam energy
output ratio (kWh/MBtu)
e(t)
Vector of economic conditions or anticipations
fCG
EL'
fCG
ST
The ratio of electric energy and steam energy
(heated from 130 0 F feedwater to the gien
pressure saturated steam), respectively, to the
cogeneration plant fuel input (higher heating
value)
fbackup
fB
ST' ST
The ratio of steam energy to boiler
fuel input (higher heating value)
futility
fuel
The fractional electric energy output from the
utility central generation as a ratio to fuel
input (high heating value)
H(x)
The load duration function; hours per year that
the steam load is less than or equal to the
load level x (hours)
HI (H)
The inverse load duration curve; the steam load
level that is exceeded for H hours per year
(MBtu/hr)
hH(t)
i-~
Number of hours in year t that electricity is
at the high price level (hours)
249
Table A.2: Symbols (continued)
The cogeneration/backup boiler system
incremental heat rate for the production of
electricity (Btu/kWh)
I HR
m(t)
Vector of meteorological data or device
availability at time t
mtbf
Mlean time between failures (hours)
mtf
Mean time to failure (hours)
mtr
Mean time to repair (hours)
NPV 1 , NPV 2 , NPV
3
The net present value of installing a
cogeneration plant, as computed by analytic
method 1,.2, or 3, versus an existing boiler
system ($)
NP VCGi
The net present va.Teof selecting cogeneration
system CGi versus an existing boiler ($)
n
Life of the plant (years)
PEL
Price of electricity paid by the utility (S/kWh)
011
L' OMST
Per unit operation and maintenance costs for a
given plant in terms of electricity and steam
output ($/unit energy)
PH' PL
Price of electricity during the high and low
price periods ($/kWh)
250
Table A.2:
pCG
fuel'
pB
fuel
PCG i
max
Symbols (continued)
Price of cogeneration plant or boiler
fuel ($/MBtu)
The maximum price of fuel for cogeneration
system CGi for which it is economic to operate
the cogeneration plant rather than the backup
boiler ($/MBtu)
Putility
fuel
Price of fuel for the utility ($/MBtu)
p
Plant availability
R(t )
Total utility tariff charges for month tn ()
The monthly tariff customer charge component ($)
Rd(.)
The monthly tariff demand charge component ($)
Re(.)
The monthly tariff energy charge component ($)
Rk(.)
Revenue from customer k over a billing period
The tariff schedule function type s, which was
selected by the customer from a menu of
alternatives
T(t )
Dry bulb average air temperature during period
tn
Instantaneous time; a given year or month
251
Table A.2:
Symbols (continued)
tf
The time at which the fixed interval
time-of-supply price increases from PL
to PH
tn
The time period n
to
ti
The earliest and the latest times,
respectively, that a transition to a high
electricity price might occur
UHR
CG
The utility heat rate (Btu/kWh)
XELk
EL,k
Cogeneration plant electrical capacity for
customer k (kW)
XE
CG
L , XST
Cogeneration plant maximum electric and steam
output capacity (kW, MBtu/hr)
XCG*
The minimum cost capacity of the cogeneration
plant (tMBtu/hr)
Xk
Device capacity (usage or output) for
technology i in category j for customer k
Xo, XEC, XST
The base plant size for a plant,
the electrical subsystem (kW) and the steam
subsystem (MBtu/hr)
CGk(.)
ELkactor
Instantaneous cogeneration plant utilization
for customer k
factor for customer k
252
Table A.2:
ui
jk
Symbols (continued)
Instantaneous device i (in class j) utilization
factor for customer k
Plant economic utilization subfactor, which
represents the economic choices made by the
CG (.)
VELk()
customer k with regard to the plant operation
CG
WEL, k
The cogeneration plant availability and steam
load following subfactor, which represents
usage response, for example, by the automatic
control of electrical output in response to
changing steam loads for a thermal load
following cogeneration system
Total annual energy output from a plant in
YEL' YST
electricity and steam (kWh, MBtu)
Total electric energy sold to a customer in
YEL (tn)
month tn (kWh)
CG CG
ST(X T)
B CG
YST(XT)
Annual steam energy output from the cogeneration
plant and backup boiler, respectively, as a
function of the cogeneration plant's capacity
(MBtu)
YCGh
ST,h
The annual steam energy cogenerated during a
year with hH peak electric price credit
hours, where high price levels and high steam
load levels are perfectly correlated (MIBtu)
YI(t)
A vector describing the loads or cogeneration
for customer k in period tn (kW and/or kWh)
253
Table A.2:
Symbols (continued)
YL
ST
Annual steam energy usage at the site (MBtu)
T
Yk
Annual utility sales to customer k (kWh)
Annual utility purchases from customer k (kWh)
Instantaneous net sale of electricity at time t
YEL(t)
(kW)
T
CG yB
yYST'
ST
Cogeneration plant and backup boiler steam
output at a given time (MBtu/hr)
CG
ELqk
Instantaneous site cogeneration (kW)
An instantaneous load or device output from
jk
device i (e.g., boiler or cogeneration) in
class j (e.g., steam or electricity) by
customer or site k
Instantaneous net load from a customer k (kW)
Yk(t)
Site steam load at the instant t (MBtu/hr)
YLST(t)
YLO
ST'
LH yLC
ST' ST
The estimated coefficients for steam loads as a
function of ambient temperature, these are the
fixed, the heating degree, and the cooling
degree coefficients, respectively
yk(t)
Sales of utility's electricity to customer k
(kW)
254
Table A.2:
Symbols (continued)
Yk(t)
Instantaneous purchases of electricity by the
utility from customer k (ki)
A(t
A time-average during period t n of the
n)
instantaneous power system marginal cost, X(t),
($/kWh)
X(t)
Instantaneous power system marginal operating
cost ($/kWh)
255
Appendix B
EXAMPLES OF TYPICAL INDUSTRIAL AND LARGE COMMERCIAL ELECTRICITY TARIFFS
This appendix contains several sample electricity tariffs.
The
tariffs included are:
1.
The H Rate, a traditional industrial tariff.
2.
The C Rate, an industrial tariff that charges only for energy
use, albeit at a comparatively high unit rate.
3.
The X Rate, a time-of-use industrial tariff.
4.
The Auxiliary Service Provision, a rider to the tariffs
required for customers that have their own regular generation
source.
5.
The CG rate, a tariff for customers that cogenerate, which
combines a time-of-use energy charge with multi-comnponent
demand charge.
There have been a variety of approaches to the design of special
tariffs for cogeneration.
The sample tariffs here are a series of
industrial tariffs and adaptations that have been proposed for
customers wishing to use cogenerated electricity for their internal
loads and, perhaps, sell the excess energy.
Since tariff design has
had to adapt to rapidly changing economic and regulatory conditions,
the samples here reflect only a series of examples, not necessarily
tariffs that have been implemented.
256
B.1
A Traditional Industrial Tariff
OPTIONAL LARGE-POWER RATE H
AVAILABILITY
This rate isavailable for all purposes except resale.
All service
delivered at a given location shall be billed hereunder, and all
charges shall ,be based on a Demand of 500 kilowatts or more.
If
delivery is through more than one meter, except at the Company's
option, the Monthly Charge for service through each meter shall be
computed separately under this rate.
For a customer having another
surce of generation, the auxiliar service rider also applies.
MONTHLY CHARGE
The Monthly Charge will be the sum of the Demand and Energy
Charges.
Demand Charge
$330.00
1.57
for the first 500 kilowatts or less of Demand,
per kilowatt of Demand in excess of 500 kilowatts.
Energy Charge
3.002 cents per kilowatt-hour for the first 50,000
kilowatt-hours.
2.704 cents per kilowatt-hour for the next 50,000
kilowatt-hours.
2.397 cents per kilowatt-hour for the excess over 100,000
kilowatt-hours.
Notwithstanding the foregoing, the following reduced prices shall
apply:
2.281 cents per kilowatt-hour for all kilowatt-hours in
excess of 200 kilowatt-hours per kilowatt of Demand,
257
1.818 cent per kilowatt-hour for all kilowatt-hours in excess
of 300 kilowatt-hours per kilowatt of Demand,
1.716 cent per kilowatt-hour for all kilowatt-hours in excess
of 400 kilowatt-hours per kilowatt of Demand,
1.664 cent per kilowatt-hour for all kilowatt-hours in excess
of 500 kilowatt-hours per kilowatt of Demand,
.
plus the average cost of fuel in cents per kilowatt-hour for
each kilowatt-hour.
ADJUSTMENT FOR COST OF FUEL
The amount determined under the preceding provisions shall be
adjusted in accordance with the Company's Standard Fuel Clause as from
time to time effective in accordance with law.
DEMAND
The Demand for each month under ordinary load conditions shall be
the greatest of the following:
a)
The greatest 15-minute peak occurring during the Peak Period
of such month as measured in kilowatts,
b)
80% of the greatest fifteen-minute peak occurring during the
Peak Period of such month as measured in kilovolt-amperes,
c)
One-half the greatest fifteen-minute peak, either KW or 80%
KVA, occurring during the Off Peak period during such month.
d)
80% of the greatest Demand as so determined above during the
preceding eleven months,
e)
500 kilowatts.
Any Demands established during the eleven months prior to the
application of this rate shall be considered as having been
established under this rate.
258
TERMS OF AGREErIENT
The agreement for service under this rate will continue for a terra
of one year if
electricity can be properly supplied to a Customer
without an uneconomic expenditure by the Company.
259
B.2
An Energy-Only Industrial Tariff
C-RATE
AVAILABILITY
Service under this rate is available for all purposes.
For a
customer having another source of generation, the auxiliary rider also
applies.
MONTHLY CHARGE
$2.00 for the first 20 kilowatt-hours or less of electricity
delivered each month,
7.151 cents per kilowatt-hour for the next 80,000
kilowatt-hours.
6.552 cents per kilowatt-hour for the next 200 kilowatt-hours I
5.437 cents per kilowatt-hour for the next 1700
kilowatt-hours,
4.241 cents per kilowatt-hour for the excess over 2000
kilowatt-hours,
plus the average cost of fuel in cents per kilowatt-hour for
each kilowatt-hour.
ADJUSTMENT FOR COST OF FUEL
The amount determined under the preceding provisions shall be
adjusted in accordance with the Company's Standard Fuel Clause as from
time to time effective in accordance with law.
I
i
....
260
B.3
A Time-of-Use Industrial Tariff
OPTIONAL X-RATE
AVAILABILITY
This rate is available for all purposes except resale.
All
service delivered at a given location shall be billed hereunder, and
all charges shall be based on a Demand of 500 kilowatts or more.
If
delivery is through more than one meter, except at the Company's
option, the Monthly Charge for service through each.meter shall be
computed separately under this rate.
For a customer having another
source of generation, the auxiliary rider also applies.
MONTHLY CHARGE
The Monthly Charge will be the sum of the Customer, Demand and
Energy Charges.
Customer Charge
$112.90 per month.
Demand Charge
During the Billings months of
Off Peak Months:
Peak Months:
March-May, September-November
June-August, December-February
ir
$3.79 per KW
$6.02 per KW
Energy Charge
Peak Hours
2.166 cents iper kilowatt-hour
Off-Peak Hours
1.150 cents per kilowatt-hour
plus the average cost of fuel in cents per k ilowatt-hour for each
kilowatt-hour.
261
PEAK AND OFF-PEAK PERIODS
Peak hours will be from 8:00 A.M. to midnight daily on Monday
through Friday except for legal holidays.
Off-peak hours will be from
midnight to 8:00 A.M. daily Monday through Friday and all day on
Saturdays, Sundays, and legal holidays.
ADJUSTMENT FOR COST OF FUEL
The amount determined under the preceding provisions shall be
adjusted in accordance with the Company's Standard Fuel Clause as from
time to time effective in accordance with law.
DEMAND
The Demand for each month under ordinary load conditions shall be
the greatest of the following:
a)
The greatest 15-minute peak occurring during the Peak Period
.within such month as measured in kilowatts,
b)
80% of the greatest fifteen-minute peak occurring during the
Peak Period of such month as measured in kilovolt-amperes,
c)
One-half the greatest fifteen-minute peak, either KW or 80%
KVA, occurring during the Off Peak period during such month.
d)
500 kilowatts.
TERM OF AGREEMENT
The agreement for service under this rate will continue for an
initial term of one year if electricity can be properly supplied to a
Customer without an uneconomic expenditure by the Company.
262
B.4
A Supplemental Provision for Customers That Cogenerate
AUXILIARY SERVICE PROVISIONS
AVAILABILITY
Service is available under any applicable filed rate of this
Company for Auxiliary Service, sometimes referred to as Standby or
Breakdown Service, and more fully defined as service available at all
times to a Customer having another source of power, electrical or
mechanical, from which to supply his requirements of light, heat or
power, or a portion thereof.
Where such other source is used only in
case of failure of the Company's service, the Company's service shall
not be considered as Auxiliary Service.
RATE
The charge for electricity shall be computed under the Rate
applied with this provision, but not less than $2.00 per month per KVA
of contractual transformer capacity for the non-fuel components of the
Rate applied.
The contractual transformer capacity shall not be less
than the highest fifteen-minute net kilowatt demand during the prior
twelve months.
TERM OF AGREEMENT
The agreement for service under this rate will continue for an
initial term of one year if electricity can be properly supplied to a
Customer without an uneconomic expenditure by the Company.
263
B.5
A Special Cogeneration Tariff
COGENERATION RATE (CG)
APPLICABILITY
This rate is applicable to Customers who generate electricity as a
by-product of the production and use of heat for other purposes.
service delivered at a given location will be billed hereunder.
All
The
Company will not transmit and/or distribute any power generated by the
Customer except for any power purchased by the Company.
Delivery is to be at the Company's available primary voltage with
any transformers to be provided by the Customer.
This rate is not available for resale.
MONTHLY CHARGE
The Monthly Charge will be the sum of the Customer, Distribution
Standby, Demand, and Energy Charges.
Customer Charge
$112.90
per Month.
Distribution Standby Charge
$2.00 per kilovolt-ampere for Contracted Capacity.
If in any
month demand on the Company exceeds the Contracted Capacity,
such demand will become the Contracted Capacity.
Demand Charge
$4.50 per KOW of Demand in the billing months of January,
February, June, July, August, and December.
$1.79 per KW of Demand in the billing months of March, April,
May, September, October, and November.
264
Energy Charge
Peak Hours
2.166 cents per kilowatt-hour
Off-Peak Hours
1.150 cents per kilowatt-hour
plus the average cost of fuel in cents per kilowatt-hour for each
kilowatt-hour.
PEAK AND OFF-PEAK PERIODS
Peak hours will be from 8:00 A.M. to midnight daily on Mronday
through Friday except for legal holidays.
Off-peak hours will be from
midnight to 8:00 A.M. daily Monday through Friday and all day on
Saturdays, Sundays, and legal holidays.
ADJUSTMENT FOR COST OF FUEL
The amount determined under the preceding provisions shall be
adjusted in accordance with the Company's Standard Fuel Clause as from
time to time effective in accordance with law.
DETERMINATION OF BILLING DEMAND
The Billing Demand for each month under ordinary load conditions
will be thre greatest of the following:
a) The greatest 15-minute peak of total load less customer
average generation occurring between the hours of 8:00 A.M.
and midnight daily except Saturday, Sunday and Holidays.
b) One-half of the quantity of the greatest 15-minute peak of
total load at any other time less customer average generation
occurring in the peak period.
In no case will the Demand be greater than the Demand on the
Company, or in no case will the Demand be less than zero. The
265
standard Auxiliary Service Provision will not apply to this rate.
DEFINITIONS
1. Contracted capacity - The peak 15-minute demand as measured in
KVA, which the Company would be required to supply to the
Customer at any time, for the purpose of backup and/or normal
power requirements.
2. Total load - Total electrical requirement of the Customer.
is the sum of the purchased power plus generated power as
measured IN KU.
3. Customer Average Generation - The total KWH generated for a
specified period divided by the number of hours occurring
between the hours of 8:00 A.M. and midnight daily except
Saturday, Sunday and holidays.
EQUI PHENT
The Customer will install and maintain protective devices and
apparatus satisfactory to the Company.
PARALLEL OPERATION
The Customer will hold harmless the Company from all claims for
damage to the Customer's equipment or injury to the Customer's
employees or others on the Customer's property arising out of or
resulting from the parallel operation of the Customer's and the
Company's systems.
Also, the Customer will be required to maintain a minimum power
factor of 80% on all purchases from the Company.
It
266
TERM OF AGREEMENT
The agreement for service under this rate will7 continue for an
initial term of one year.
267
Appendix C
SITE AND SURVEY DATA ASSUMPTIONS
This appendix discusses the major assumptions and data sources
used in developing steam and electricity load characteristics for the
individual site studies and for the aggregate survey sites.
The first
section describes the methods employed for the individual sites, and
the second section describes the approaches used for the surveyed
sites.
C.I
DETAILED MODELING DATA
Section 2.1 summarizes the characteristics of the individual sites
that were selected for detailed study.
On the basis of monthly and
hourly information provided by these sites, electricity and steam load
information was developed for each site, except for the Computer
Assembly Plant, which could not easily be adapted to cogeneration, and
was therefore excluded from further study.
C.1.1
Steam Loads
Since the detailed modeling required electricity generation and
load data on a 8-hour working shift basis, and since the research time
limitations did not permit transcription of boiler steam output
charts, a short-cut method to project fluctuation steam loads had to
be developed.
An initial inspection of hourly and monthly steam usage
at the individual sites indicated that outside air temperatures
heavily influenced steam loads at even the large industrial sites.
Assuming that ambient air temperatures are the principal influence
on steam usage, the following method was used to create "synthetic
268
steam loads" for each site:
1. Take monthly boiler fuel usage, adjusted for boiler
efficiency, to estimate monthly steam usage.
Using data on
Boston heating and cooling degrees for the given months,
perform a linear regression to project monthly average steam
loads.
In most cases, no steam was used for cooling, so the
regression was based solely on heating degree data.
2. Assuming that the steam load regression can be temporarily
disaggregated, the coefficients estimated in step 1 can be
used with temperature data on a shorter time scale to
stimulate steam loads.
This was done using 8-hour average air
temperatures at Logan Airport, Boston, for the years 1953 and
1963 to 1967 from the SOLMET weather data tapes, where
yST(tn)+
YT+
) max(O; T(t ) - 65)
(L
(Y(T
+ (yT) max(0O; 65 - T(tn))
and
(C.1)
YLST(tn):
steam load during the 8-hour period tn
yLO.
YST"
the estimated base load steam load coefficient
yLH
YST"
the estimated steam load coefficient for
heating degrees
yL.
the estimated steam load coefficient for
cooling degrees
T(tn):
Boston dry bulb average air temperature during
the 8-hour period tn (degrees F)
Figure C.1 compares an actual hourly steam load duration curve prepared
COMPARISON OF STEAM LOAD DURATION DATA AND ESTIMATES
FOR THE HOSPITAL SITE
2018-
Data directly complied from
hourly steam usage at site
1614-
/
12-
Synthetic steam usage simulated
from monthly fuel use regression
analysis and 8-hour average
ambient temperature history
STEAM
LOAD
10
(MBTU/HR)
'--1
0.0iI
0.0
Data based directly on
monthly fuel history
10
20
30
40
50
60
70
80
90
100
PERCENTAGE OF THE TIME THAT STEAM LOAD IS ABOVE GIVEN LEVEL
Figure C.1
270
by the hospital site personnel with load duration curves prepared from
monthly steam load data and from the 8-hour shift simulation.
C.I.2
Electric Loads
Electric load data for the individual sites were obtained from
15-minute monitoring of electric sales to the individual sites for a
one-year period.
averages.
periods.
The 15-minute data were aggregated to 8-hour
Missing periods were replaced with data from similar
If the site had cogeneration in operation, the data were
adjusted to compensate for the cogeneration plant operation using
monthly generation information supplied by the site.
For one site, the 15-minute data were not available.
For this
site, monthly billing information was extrapolated to average monthly
peak and off-peak loads using the same technique described in Section
C.2.2, except the hours-use to peak/off-peak energy share
extrapolation was based on the monthly hours-use rather than annual
hours-use data.
"Hours-use" is a term employed in electric tariffs;
it is the ratio of a period's energy usage to the peak load in the
period.
C.2
MODELING DATA FOR SURVEY SOURCES
Table C.1 summarizes the types of information collected from the
individual sites in the aggregate survey of 123 industrial and large
commercial sites.
These data were then used to simulate 12 monthly
steam and electricity load patterns for a one-week period of 8-hour
shifts.
271
Table C.l:
Information from Utility Survey of Major Industrial &
Commercial Customers
1. Site .identification number
2. Site name
3. Town
4. State
5. Alternate name for site
6. SIC as reported in survey (up to 4 digits)
7. SIC as corrected (3 digits)
8. Other SIC codes for size (up to 4 digits)
9. MFBI status under FUA (on 1975 FEA list)
10. On-site steam turbine cogeneration (KW)
11. On-site hydroelectric generation (KW)
12. On-site diesel cogeneration (KW)
13. On-site standby diesel or gas turbine generation (KW)
14. Percentage of electrical load generated on-site
15. Percentage of steam load generated on-site
16. Share of electric energy used for process
17. Share of electric energy used for heating
18. Share of electric energy used for cooling
19. Share of electric energy used for other needs
20. Share of oil energy used for process
21. Share of oil energy used for heating
22. Share of oil energy used for cooling
23. Share of oil energy used for other needs
24. Share of gas energy used for process
272
Table C.l:
Information from Utility Survey of Major Industrial &
Commercial Customers (continued)
25. Share of gas energy used for heating
26. Share of gas energy used. for cooling
27. Share of gas energy used for other needs
28. Share of other fuel energy used for process
29. Share of other fuel energy used for heating
30. Share of other fuel energy used for cooling
31. Share of other fuel energy used for other needs
32. Estimated number of operating shifts
33. Ability to use distillate oil in current system
34. Ability to use natural gas in current system
35. Ability to use residual oil in current system
36. Ability to use coal oil in current system
37. AlIl electriic installation
38. Units used for reporting oil usage data
39. Units used for reporting gas usage data
40. Monthly boi ler fuel usage available (yes/no)
41.
Annual oil usage for boiler fuel (MBtu)
42. Annual gas usage for boiler fuel (MBtu)
43. Oil usage during month with highest combined boiler fuel usage
(MBtu)
44. Gas usage during month with highest combined boiler fuel usage
(MBtu)
45. Oil usage during month with lowest combined boiler fuel usage
(MBtu)
273
Table C.l:
Information from Utility Survey of Major Industrial &
Commercial Customers (continued)
46. Gas usage during month with lowest combined boiler fuel usage
(iBtu)
47. Percentage of steam loads that could be served by low pressure
hot water.
48. Annual electric energy purchases (kWh)
49. Peak electric purchase'in year (KW)
50. Average "hours use" for electric purchases in year (kWh/KW or hrs)
51. Comments on data, especially on problems relating to units used
for reporting boiler fuel usage.
274
C.2.1
Steam Loads from Survey Sources
The steam load simulations were based primarily on the average
heat load
for the site and the distribution of the total steam energy
usage between process needs and heating or cooling needs.
Fluctuating
steam loads were generated by setting the heating and cooling load
components in proportion to heating and cooling degree frequency
distributions for each month so the steam loads are in direct
proportion to the heating and cooling degrees for the respective
component.
The heating and cooling degree frequency distributions
were developed from the temperature data described in Section C.l.l.
If site data were also available on the peak month and lowest month
steam loads, the shift-by-shift simulation was adjusted to conform
with these monthly aggregations on a linearly interpolated basis for
the peak, average, and base steam loads.
Table C.2 summarizes the steam load information generated using
these survey data.
Cogeneration plant performance was adjusted to
reflect the steam pressures required at each site.
C.2.2
Electric Loads from Survey Sources
The only electric load data on the surveyed sites were the total
annual electric energy purchases (kWh) and the peak load (kW).
Other
surveys, however, indicated that the typical industrial or large
commercial customer used about 48% of the total electric energy during
the peak 13 hours per day.
The peak and off-peak load levels were
generated by first assuming that a site's electric load factor is
related to the share of energy used on peak by a linear interpolation
275
Table C.2:
Steam Usage Pattern Information Derived from Survey Data
1. Share of combined oil and gas boiler fuel used for process and
"other" loads.
2. Share of combined oil and gas boiler fuel used for heating loads.
3. Share of combined oil and gas boiler fuel used for cooling loads.
4. Residual oil capabilities (yes/no)
5. Average hourly steam load (using interfuel consumption and assumed
83% boiler efficiency with corrections for those using steam
turbine cogeneration)
6. Peak month average steam load durectly from fuel data (MBtu/hr).
7. Lowest month average steam load durectly from fuel data (MBtu/hr).
8. Peak month average steam load estimated froii process/heating/
cooling shares and monthly distribution of heating and cooling
degree data (MBtu/hr).
9. Lowest month average steam load estimated from process/heating/
cooling shares and monthly distribution of heating and cooling
degree data (MBtu/hr).
10. Lowest month average steam load estimated by linear extrapolation
of peak and average load (MBtu/hr, if Tess than zero, see No. 11).
11. Fraction of year that steam load is greater than zero; estimated
using linear extrapolation of peak (#6 or #8 if direct data are
not available) and average loads.
12. Load factor for steam loads based on fuel data (Avg/Peak).
13. Load factor for steam loads derived from usage shares (Avg/Peak).
14. Estimated average steam usage for process and "other" loads
276
Table C.2:
Steam Usage Pattern Information Derived from Survey Data
(continued)
(MBtu/hr).
15. Estimated average steam usage for heating loads (MBtu/hr).
16. Estimated average steam usage for cooling loads (MIBtu/hr).
04
277
(i.e., if a site has a 100% load factor, its peak and off-peak loads
are at a constant level; if a site has an average load factor or
hours-use, its load distribution between peak and off-peak is average;
and if a site has a very low load factor, the peak usage approaches
100% of the total usage).
Second, the total energy was distributed to
the peak and off-peak periods, where every month has the same peak and
off-peak load level for the typical week.
Finally, peak demands were
estimated to be higher than the peak average loads to allow for load
roughness in the shift.
with existing generation.
Adjustments were made in the loads for sites
Table C.3 summarizes the electric load
information generated from the survey data.
278
Table C.3:
Electrical Usage Pattern Information Derived
from Survey Data
1. Average monthly electric energy purchases (.flJH)
2. Average monthly electric energy on-site generation (MWH) derived
from data on share of loads generated internally
3. Average monthly electric energy usage (MWH)
4. Average electric purchase (KW)
5. Average electric generation (KW)
6. Average electric usage (KW)
7. Peak purchases in year (KW)
8. Maximum possible load in year (KW), combined maximum purchases and
on-site hydro and cogeneration capacity
9. Average monthly hours-use for internal loads, based on purchases
10. Average monthly hours-use for internal loads, based on internal
load divided by peak purchase
11. Average monthly hours-use on internal load divided by peak
possible load
12. On-site hydro and cogeneration capacity factor
13. Subjective estimate of average monthly hours-use for load (even
weighting of #9, #10, and #11)
14. Estimated fraction of electric energy load consumed on-peak, based
on interpolation using hours-use for load in relation to average
hours-use and the average on-peak usage fraction from separafe
load research study
15. Estimated average electric usage on-peak (KW) from on-peak fraction
16. Estimated average electric usage off-peak (KW) from on-peak
279
Table C.3:
Electrical Usage Pattern Information Derived
from Survey Data (continued)
fraction
17. Ratio of on-peak/off-peak loads
18. Ratio of peak purchases (#7) to estimated average peak load (#15)
19. Ratio of maximum possible load (#8) to estimated average peak load
(#15)
20. Estimated average electric usage off-peak (KW) based on 16-hr peak
assuming same peak usage as in #15 over 16-hr peak period
__
280
Appendix D
COGENERATION TECHNOLOGY AND COST SUMIMARY
This appendix discusses the sources and assumptions made in
deriving performance and cost relationships for the cogeneration
technologies used in the preceding analysis.
The first section
describes the components of cost and performance.
The second section
comments on the specific technologies.
The main purpose of this report is not the detailed examination of
cogeneration at just one site but the survey of cogeneration economics
at a range of sites.
Therefore, instead of concentrating on the
detailed costing and engineering performance analysis of specific
cogeneration plant designs, this appendix tries to capture the general
performance and cost characteristics for a wide size range of general
plant designs.
Three key factors that differentiate the basic cost and
performance relationships are the prime mover or boiler technology, the
plant fuel, and the scale of the plant.
The main boiler types (coal,
field-erected oil, and package oil) and cogeneration technologies
(back-pressure steam turbine, diesel,.and gas turbine) are each
represented with a fixed output mix and unit capital and O&M costs,
which may vary with the scale of the plant and its fuel type.
Someone
unfamiliar with the technologies for cogeneration should see Resource
Planning Associates (1978 or 1979) or Diamont (1970) for an
introduction to these systems.
The major sources for the data in this appendix are given in Table
281
D.l.
Chief among these:
the reports by ThermoElectron (1976) and TRW
and ThermoElectron (1979); the Burns and Roe costing studies used by
Resource Planning Associates (1977, 1978) and Manuel et al (1980); and
engineering design, simulation, and costing studies performed for MIT
and summarized in Charmichael (1978) and Steding and Charmichael
(1980).
It takes considerable effort to consolidate information on
cogeneration and boiler equipment because of differing definitions,
years, and plant conditions between the studies.
Every attempt was
made to reduce all costs to mid-1980 dollars for a complete plant (note
that this is in contrast to the 1981 dollar standard used for the rest
of this study).
The final assumptions used here consolidate regression
analysis and extensive judgemental comparison of the cost and
performance data from the listed sources.
D.1
COMPONENTS OF COST AND PERFORMtANCE
The major components of boiler and cogeneration cost and
performance needed to describe a plant are:
fuel efficiency and shares
of electricity and steam output; plant reliability; environmental
effects; capital costs; and operation and maintenance costs.
In describing cogeneration systems, definitions may often create
problems.
Substantial confusion arises when cogeneration equipment is
described in terms of its incremental efficiency or cost with respect
to a typical boiler.
While this can be meaningful in specifying the
results of economic calculations, unless the basic assumptions are
clearly stated, the underlying total costs and efficiency of the whole
282
Table D.
EQUIPMENT PERFORMANCE AND COST DATA SOURCES
Equipment Type:
Diesel
Source
TRW/ThermoElectron (1979)
P
ThermoElectron (1976)
Burns & Roe Cost Studies:
RPA (1977, 1979)
P
Manuel et al. (1979)
MIT Studies:
Charmichael (1978)
P
Erickson & Mathewson (1978);
Mathewson (1978);
Steding (1980); and
Steding and Charmichael (1980)
MacKay at EPRI (1979)
Solt at EPRI (1979)
Casten (1980)
P
Burch (1980)
P
ASME (1973)
P1
Synergic Resources (1980)
Kindl and Daniels (1979)
Coffin (1979)
Dittrich & Allon (1977)
US GAO (1980)
Murgatroyd at EPRI (1979)
S
Schweizer and Sieck (1978)
C. T. Main (1980)
Williams (1978)
S
Gas
Turbine
Steam
Turbine
Boiler
P
p
p
P
P
P
P
P
P
P
S
P
p1
P1
P2
P
P
S
P
S
S
S
P
pl
S
S
P
S
P = Primary source of historical, direct historical survey, or
engineering costing data
S = Secondary source using data from other studies
1) Source on O&M costs only
2) Source on effects of #6 oil upon gas turbine costs and
performance
283
plant can be left unclearly defined.
This appendix refers to three
appendix refers to three types of cost or efficiency:
unit; and gross per unit.
total; net per
Total cost or efficiency is for the total
plant, including generation and boiler components.
Net cost or
efficiency refers to a single component of the system, such as a boiler
or a steam turbine.
Gross per unit cost is the total cost divided by
one of the plant's outputs.
D.I.1
Fuel Efficiency and Output Shares
This report defines fuel efficiency and output shares on the basis
of the enthaplies of the associated input and output streams since
these output shares are used to compute fuel consumption to meet
thermal and electric loads.
The plant outputs are assumed to be linear
in fixed proportions with the fuel input.
Equipment output fractions
can vary over a plant's operating range, but since most oil-fired
cogeneration plant designs were found to be more cost-effective when
designed to serve only the
for base-load, it was assumed that the
fractions were constant over the resulting limited operating range.
Let:
fEL =
the ratio of electric energy output of the plant to
the fuel input (higher heating value)
fST =
the ratio of steam output (heated from 130 F
feedwater to the given pressure saturated steam) to
the fuel input (higher heating value).
]For simplicity in this appendix, the device superscripts Si and
CGi have been omitted. For example, a cogeneration plant's steam and
and fCGi
electricity shares would be fCGi
EL
ST
284
Because of the widely differing steam conditions and fuels between
industrial sites, this report approximates all plants on the basis of
150 psig or 15 psig steam conditions, which represent high and low
pressure steam loads, respectively.
The output share information is
given for two levels of steam pressures because of the substantial
difference in performance betweem the two common levels of 15 psig and
150 psig steam.
Building heating and low temperature processes
typically use pressures below 15 psig; in most states, if the entire
system has no pressures above 15 psig, the operator qualifications are
substantially reduced with consequent reductions in the labor component
of operating costs.
Many typical industrial process loads and 2-stage
absorbtion air conditioning water chillers require steam in the 100-200
psig range (Pickel, 1978, p.4 5 ).
Table D.2 gives the plant output shares along with the ratio of
electric to steam energy outputs and the technology's incremental heat
rate.
The incremental heat rate is the additional fuel required (in
Btu/kWh) by a cogeneration plant to generate a unit of electricity
beyond the fuel that a regular boiler would use to produce the same
amount of steam as the cogeneration plant would while generating the
unit of electricity:
IHR = incremental heat rate (Btu HHV/kWh)
IHR =
3412
[(I/fST
EL
- (1/fbackup
(D.)
ST
where
f backup:
ST
the first law efficiency for the relevent backup boiler.
285
Table D.2
PLANT OUTPUT SHARES
Process Steam Pressure (saturated)
150 psig
15 psig
IncrePlant Type
(fuel)
fT
Increratio mental
f1
(kWh/
Heat Rate (%J
JIBtu) (Btu/kWh)
fST
EL/ST mental
ratio Heat
(kWh/
Rate
MBtu)
(Btu/kWh)
EL/ST
High speed
diesel coeneration
no.2)
30
30
292
7260
30
37
238
6300
Low or
medium
speed
diesel co-
35
27
380
6580
35
35
293
5640
38.8
140230
7090*
18-31
43.8
121200
6380*
38.8
135220
7370*
17-30
43.8
117192
6640*
70.7
53
4450
16.6
66.6
73
4450
?eneration
no.6)
Gas turbine
18-31*
cogeneration
(no.2)
Gas turbine
17-30*
cogeneration
(no.6)
Backpressure
steam
turbine
cogeneration
(any fuel,
900 0 F)
12.9
(continued)
* The electricity output fraction for gas turbines is very dependent upon
unit size ( see text in Section D.2.2); performance examples given for a unit
serving 50 MBtu/hr heat load (about 9.5 MW electric).
286
Table D.2 (continued)
PLANT OUTPUT SHARES
Process Steam Pressure (saturated)
150 psig
Plant Type
(fuel)
F
(
New boiler
or back-up
boiler for
steam turbine cogen
(oil or
coal fuel)
Existing
boiler or
new package
boiler
f
(
15 psig
EL/ST
ratio
(kWh/
MBtu)
Incremental
f
Heat Rate (
(Btu/kWh)
fjT
(
85
85
83
83
95
95
97
97
IncreEL/ST mental
ratio Heat
(kWh/ Rate
MBtu) (Btu/kWh)
(oil)
Supplementary
fired boiler
(oil; incremental to
cogenerated
steam):
- diesel
(2x cogen
steam)**
- gas turbine
(4.5x
cogen
steam)**
**This is the upper limit on the high efficiency output from
supplementary fired boiler steam as a ratio to the current directly
cogenerated steam output (e.g., if a diesel 5 MBtu/hr cogeneration unit
is operating at 75% output, the supplementary boiler can produce 7.5
MBtu/hr at 95% efficiency).
287
For the steam turbine based cogeneration technologies, the backup
boiler is assumed to be a new, efficient design with fbackup = 85
ST
percent; for diesel and gas turbine systems, an existing boiler with a
slightly lower efficiency of 83 percent is assumed.
The variation of
gas turbine electrical efficiencies with plant size will be discussed
later.
A statistic that is very convenient in demonstrating the
differences between the electrical output of cogeneration technologies
serving the same steam load is:
ESR =
(fST)
EL
.00412
where
ESR:
the electrical to steam output ratio in kWh/M4Btu.
One common difficulty in comparing cogeneration system efficiencies
is that steam systems are often described in terms of fuel consumption
on a higher heating value (HHV) basis while engine-based systems, such
as gas turbines and diesels, typically cite fuel consumption measured
on a lower heating value (LHV) basis.
Wherever possible, the
performance comparisons here have converted all figures to a higher
heating value basis.
The LHV basis represents about a 6 percent
smaller Btu measure for the same fuel mass than the HHV basis
(Baumeister et al, 1978).
D.I.2
Plant Reliability
No consistent data are available on boiler or cogeneration plant
288
Table D.3
ASSUMED PLANT RELIABILITIES
Plant Type
fean Time
to Failure
(hours)
Mean Time
to Repair
(hours)
Availability
(percent)
Diesel Cogeneration
995
96.1
Gas Turbine Cogeneration
714
92.6
Steam Turbine Cogeneration
(combined boiler
and turbine)
2334
97.9
Boilers (coal or oil)
3343
98.5
Source: derived from the Edison Electric Institute, Equipment
Availability Task Force (1976, pp. 8, 38, 40)
289
failure rates or availability.
Private communications indicate that
the availability of these plants is at least as good as for small
electricity generating plants, which are generally the most reliable
class of plants. Since the analysis of utility tariffs on cogeneration
plant economics requires information on the failure and repair rates
for cogeneration equipment, this information was therefore assumed to
be similar to that for the small steam electric, gas turbine, and
diesel generating plants compiled by the Edison Electric Institute
(1976).
Equivalent full forced outage rate information along with mean time
to repair from forced outages was used to derive the availabilities
given in Table D.3.
The failure of each type of plant was modeled as
an exponentially distributed
distributed time to repair.
random process with exponentially
The associated boiler and turbine of a
steam turbine cogeneration plant are combined as one unit failing at an
exponential rate, rather than by the separate failures of the turbine
and boiler sub-systems.
Note the relationship between these factors:
mtf
= mean time to failure for the plant
mtr
= mean time to repair for the plant
mtbf = mtf + mtr = mean time between failures for the plant, and
.p
= the availability of the plant,
where
P :m--mtf
-g-
(D.2)
290
D.1.3
Environmental Factors
This study did not address the environmental impacts of
cogeneration systems.
All equipment cost and performance information,
however, includes environmental control equipment that will typically
be required.
Specifically, coal-fired boilers include the cost of
particulate and sulfur controls.
D.1.4
Capital Costs
Two key factors differentiate the capital cost relationships for
cogeneration and boiler systems:
first, the specific technology and
fuel type; and, second, the scale of the plant.
This is further
complicated by the dual output nature of cogeneration systems.
In this
study, diesel and gas turbine cogeneration systems and boilers have
their capital costs characterized on the size of one output, while
steam turbine cogeneration is characterized by the scale of both
outputs.
The costs include engineering, land, buildings, and
environmental controls, but not interest during construction.
For boilers, the total capital is given by the function:
6-1
(D.3)
cap o
CAP(X) = X
where for a given
CAP(X):
X:
cap :
boiler type:
the total plant cost in dollars,
the plant size in MBtu/hr,
the base per unit capital cost in $/tlBtu/hr,
291
XV:
the size of the base plant, and
B :
the scale factor for this technology.
Table D.4 gives constants cap 0 , Xo, and B along with the effective
federal investment tax credit, which is applied to the total plant
cost.
The table also notes the deviation found in capital costs
between sites of the same scale.
Figure D.1 illustrates the change in
per unit cost (CAP /Xj) with scale.
For diesel and gas turbine cogeneration systems, the total capital
costs for all electrical and steam components are defined as in
Equation D.3, except the size units are in kilowatts of plant
capacity.
This is the total cost, which includes the prime mover and a
heat recovery boiler of a size appropriate for the performance
specifications in Table D.2.
Any direct oil or gas supplementary
firing capacity for the waste heat boiler requires additional capital
expenses as if it was a separate, very efficient boiler.
The total back-pressure steam-turbine cogeneration plant capital
costs are given by a boiler component, based on the steam capacity of
system, and by a steam turbine/generator component, based on the plant
electrical capacity:
CAP(XST, XEL) = CAPST(XST) + CAPEL(XEL)
(D.4)
with
CAPST(XST)
= XST
caPT0
XST
)
ST
and
B -1
ST
(D.5)
292
Table D.4
CAPITAL COSTS AND TAX CREDITS
Plant Type
(fuel)
Base Size
(Xo)
Cost per Unit
(capO)
Scale Factor
(B)
---High speed
diesel cogeneration
(no.2)
Effective
Site Cost Combined
Tax
UncerCredits
tainty
(percent) (percent)
1000 kW
750 $/kW
.80
+ 15 %
1000 kW
900 $/kW
.75
+ 20
Gas turbine
cogeneration 1000 kW 650 $/kW
(no.2)
.85
+ 20
Gas turbine
cogeneration 1000 kW 715 $/kW
(no.6)
.85
+ 20
Low or
medium
speed
dieset cogeneration
(no.6)
Back-pressure steam
turbine
(boiler
extra)
Boiler
(coal)
1000 kW
1000 $/kW
.67
+ 15
100
MBtu/hr
80 $103
tIBtu/hr
.75
+ 35
(continued)
i
10/20*
293
Table D.4 (contined)
CAPITAL COSTS AND TAX CREDITS
Plant Type
(fuel)
Base Size
(Xo )
High
pressure
boiler
100
MBtu/hr
(oil for
cogeneration)
Package
boiler
100
MBtu/hr
Cost per Unit
Scale Factor
Site Cost
Uncertainty
(percent)
Effective
Combined
Tax
Credits
(percent)
(cap o )
(B)
30 $103/
M3tu/hr
.93
+ 15
10**
15 $103/
MBtu/hr
1.00
+ 15
0
25 $10 3
'MBtu/hr
1.00
+ 15
20
(oil)***
Supplementary fired
boiler (oil; 100
incremental MBtu/hr
to diesel
or gas
turbine cogeneration
plant)***
*Combined tax credit depends fuel for accompanying boiler;
percent for oil and 20 percent for coal systems.
it is 10
**For tax credit to be used, the boiler must be for cogeneration.
***Not usable in conjunction with steam turbine systems.
294
CAPITAL COST OF BOILERS
200
XST, MBTU/HR CAPACITY
Figure D.1
295
CAP
EL
(X ) = X capo
EL
EL
El
(D.6)
where
CAP(XST, XEL):
the total capital cost of the plant in dollars,
CAPST(xST):
the boiler related capital costs of the plant in
dollars,
the turbine/generator related capital costs of the
CAPEL(XEL):
plant in dollars,
XST:
the plant steam output capacity in MBtu/hr,
XEL:
the plant electrical output capacity in kW,
' capST
capEL
the base per unit capital costs in $/kW
and $/IIBtu, respectively,
0
0
XEL
XST
.
the base plant size in kW and tlBtu/hr,
respectively,
EL'
the scale factor for the electrical and
ST
steam cost components, respectively.
Figures D.2 through D.4 demonstrate the differences between the
alternative definitions of capital cost used in many of the
cogeneration studies.
Figure D0.2 gives the net electrical costs, so
this reflects the total cost of the plant less the cost of any high
pressure boilers (it is assumed that old package boilers already at the
sites).
For gas turbine and diesel cogeneration systems, this is
CAP/XEL , while for steam-turbine systems it is only the
turbine/generator average cost CAPEL/XEL
.
Figure D.3 and D.4
296
NET CAPITAL COST OF COGENERATION EQUIPMENTX
.04-
500
1000
10,000
5000
XEL, KW CAPACITY
*Excludes all direct-fired boiler costs
Fioure D.2
50opoo
297
GROSS CAPITAL COST PER MBTU/HOUR
FOR BOILER AND COGENERATION EQUIPMENT
>w or Medium Speed Diesel
(#6 -oil)
-4
E
Coal-Fired Back-Pressure
SSteam Turbine
sel
0
H
4:
C>
0U
4:
0U,
zn
0
X
1
10
100
1000
XST, MBTU/HR CAPACITY
*Includes electrical and steam system costs
Figure D.3
298
GROSS CAPITAL COST PER KILOWATT
FOR COGENERATION EQUIPMENT *
0,000
XEL, KWJ CAPACITY
*Includes electrical and steam system costs
Figure D.4
299
illustrate the gross capital cost of the systems, which is the total
cost divided by the given output, CAP/XEL or CAP/XST.
Since the larger cogeneration systems are installed over a
multi-year period, the present value of the plant
and the resulting
tax effects depend on the distribution of the capital expenditures over
the construction period.
Table D.5 gives the assumed distribution of
capital expenditures employed in detailed calculations of cogeneration
plant financing in this report.
As noted above, the costs in Table D.4
and Figures D.1 to D.4 are direct and indirect construction expenses in
1980 dollars and do not include any interest during construction.
D.I.5
Operation and Maintenance Costs
Three major problems complicate operation and maintenance costing.
A considerable disparity exists between various historical,
accounting-based sources and engineering estimates of operation and
maintenance expenses for cogeneration and boiler plants.
This could be
caused by numerous factors, such as:
*
The accounting data may include fixed costs, such as overhead
expenses that are unaffected by the type of steam plant at the
manufacturing site;
*
The engineering estimates may not include all relevant 0&11
costs that be influenced by the change in design.
An second problem is the separation of steam O&M from electricity O&IM
costs; this is especially important for a realistic costing of
back-pressure cogeneration, where the boiler-related O&M costs have
300
Table D.5
TIME DISTRIBUTION OF CAPITAL EXPENDITURES
Plant Type
Percentage of Direct Capital Expenditures
Made in Year Prior to Operation
1
2
3
High speed diesel
cogeneration (#2)
100
O0
Low or medium speed
diesel cogeneration (#6)
Gas turbine cogeneration (#2)
90
Gas turbine cogeneration (#6)
80
Coal-steam cogeneration
50
Oil-steam cogeneration
60
Coal boiler (only)
80
Oil boiler (only)
100
301
nearly identical costs to a similar stand-alone boiler.
Finally, few
discussions of cogeneration O&M costs carefully define whether the
costs are the gross average costs for the plant or are net of the
steam-related 0&H costs.
This report assumes that the total annual operation and maintenance
costs for a boiler or cogeneration system are the sum of two
components, one for total plant steam output and one for total plant
electrical output.
Although some studies indicate there may be slight
scale economies for 0&M costs, the information is adequate to project
this for all the boiler and cogeneration technologies.
Instead, each
component of the total O&M cost is assumed to be linear with respect to
the plant output for that component.
For plants having high-pressure
steam boiler.s (greater than 15 psig in any regular or heat recovery
boiler), it is assumed that the minimum total annual O&M costs for the
cogeneration system and boilers must be at least $30,000/year; this
reflects the legal requirement for the presence of a licensed boiler
operator at such sites in the state where this study was conducted.
The total O&M costs for a plant are:
COM(YSTYEL) = omST* YST
+
omEL . YEL
(D.7)
where
COM(YSTYEL):
YST' YEL:
total annual 0&M cost (dollars),
the annual energy output of the plant in steam (MBtu)
and electricity (kWh) respectively,
OmST,
OmEL:
the unit 0&1 costs for energy from the given plant in
steam ($/MBtu) and electricity ($/kWh)
302
The total COM is subject to the minimum noted for high pressure plants.
Table D.6 lists the unit O&M cost components for different plants.
In addition, the table gives the gross average O&M costs in terms of
steam and electricity output to illustrate the differences with the
separate components costs, where the gross average cost is the annual
cost divided by the total steam or electrical output.
D.2.
COGENERATION AND STEAM TECHNOLOGIES
This section discusses the special problems in describing the costs
and performance of specific cogeneration and steam production
technologies.
In addition, since the type of air conditioning systems
that a site uses may influence the cogeneration system that a site uses
may influence the cogeneration system economics, this chapter includes
comments on the costs and performance of air conditioning water
chillers.
D.2.1
Diesel Cogeneration Systems
Diesel cogeneration systems generate electricity using the
mechanical power from a diesel engine while steam is generated through
the recovery of heat from the exhaust gases and engine cooling water.
Because of the relatively low temperature of the exhaust gas and
especially the cooling jacket water, the amount of steam that can be
produced is very sensitive to the pressure and temperature of the steam
required.
The type of fuel that can be used in a diesel system depends upon
303
Table D.6
OPERATION AND MAINTENANCE COSTS
O&M Costs Per Unit
By Component
Steam
Electricity
OmST
OmEL
($/Mbtu)
(mills/kWh)
High speed
diesel cogeneration (#2)
Steam
Electricity
($/MBtu)
(mills/kWh)
Fixed**
Minimum for
High Pressure
Steam Systems
($/year)
Gross Average O&M*
.25
9.0
2.89
9.9
30,000
Low or medium
speed diesel cogeneration (#6) .25
8.0
3.29
8.7
30,000
Gas turbine cogeneration (#2)
.25
8.0
1.80
9.3
30,000
Gas turbine cogeneration (#6)
.25
Coal-fired
back pressure
steam turbine
cogeneration
Oil-fired
back pressure
steam turbine
cogeneration
1.25
.25
Steam turbine
(boiler extra)
Boiler (coal)
Boiler (oil,
regular or
supplementary
fixed)
10.0
4.0
4.0
2.11
11.3
30,000
1.46
27.4
30,000
.46
4.0
1.25
.25
8.7
30,000
30,000
30,000
30,000
*Based on the electricity to steam ratio for 150 psig steam cogeneration
systems with 50 MBtu/hr steam output capacity.
**At sites with a boiler pressure above 15 psig.
304
the design.
High speed diesel engine systems, which are used in small
250 kW to 2000 kW systems, use distillate fuel oil (#2).
Low and
medium speeddiesel engine-based plants, which start at about 1000 MW
and have a higher initial capital cost, can use the less expensive
residual oils provided that meet certain quality specifications.
Diesel engine based systems, in particular, have been subject to
environmental objections concerning their NOx emissions.
This study
did not consider the effects of environmental problems for diesels;
this presumes that the NOx emissions can be controlled acceptably
without major increases in cost.
Considerable difference of opinion exists on the capital and O&M
costs for diesel systems.
For example, differing sources estimated the
total cost of a 6000 MW diesel cogeneration system to be from $400/kW
to $lO00/kW.
Estimates of operation and maintenance costs vary even
more widely:
on a gross average basis, the historical data indicated
higher costs than the engineering estimates.
The waste heat boilers associated with diesel cogeneration plants
can product additional steam beyond that generated in exhaust gas heat
recovery alone through the supplemental firing of the boiler directly
by oil or gas.
Because of the high temperature of the exhaust gases
from the diesel and remaining oxygen, the supplementary firing
efficiency is much better than that of an isolated regular boiler
(95-97% versus 83-85%).
The benefits of supplementary firing are
limited by the excess oxygen in the exhaust gases.
For diesel systems
in this study, the improved efficiencies were assumed to be available
305
for up to twice the current level of cogeneration-only steam output.
For example, take a 6000 kW medium speed diesel cogeneration system
operating at an output of 3800 kW electricity and 10 Btu/hr high
pressure steam; the heat recovery boiler can be supplementary fired for
up to a 20 MBtu/hr additional steam output (total 30 MBTU/hr) at this
operating point with the higher supplementary firing efficiency of
95%.
The capital costs for supplementary firing capacity are added to
the total unit cost as if it.was a separate boiler (Tables D.2, D.4,
D.6).
D.2.2
Gas Turbine Cogeneration Systems
Gas turbine cogeneration systems generate electricity using
mechanical power from a gas turbine while steam is generated through
the recovery of heat from the exhaust gases.
Because of the
temperature differential limitations in the exhaust gas/steam boiler,
the amount of steam that can be produced is sensitive to the pressure
and temperature of the steam required, although not as sensitive as
diesel systems.
The electrical output fraction from gas turbine designs depends on
the scale of the gas turbine to a greater degree than any of the other
cogeneration systems, necessitating special treatment of the gas
turbine electrical output fraction.
The survey of cost and performance
estimates showed the electrical efficiencies ranged from less than 20%
for units under 1 MW to over 30% for the very large units above 60 MW.
This was captured in the plant performance simulations by assuming the
306
full load electrical efficiency of the gas turbine systems varied with
the design size of the plant according to a logit-type formula:
f
=1
EL
1 + exp(1.3964 - 1.4479 In (XEL /1000))
(D.8)
where
XEL:
the gas turbine electrical capacity in kW
This relationship was estimated using a linear regression of the
transformed data on 12 different gas turbine systems; the coefficients
are significant at the 99.5% level.
The resulting heat rates may
appear lower than usual, in part because they values here are computed
on a higher heating value basis (see Section D.1.1).
Gas turbine cogeneration systems have been designed around the
burning of natural gas or distillate oil (#2).
Modifications are
possible, however, to allow operation on higher quality levels of
residual oil (low sulfur #6).
Capital cost additions are necessary for
the fuel treatment facilities at the plant; the operating efficiency
also decreases slightly to 96.6% of the electrical efficiency for a #2
fired gas turbine of the same electrical output.
Gas turbine-based cogeneration systems are environmentally clean.
In some regions, especially in areas of California, however,
NOx
emissions have been an issue in the approval of gas turbine systems.
This study did not address these problems, presuming instead that NO
x
emissions can be controlled without major increases in cost.
Like diesel cogeneration systems, the waste heat boilers associated
with the plant can produce additional steam beyond that generated by
VARIATION OF GAS TURBINE EFFICIENCY WITH UNIT CAPACITY
0.40-
0.35Estimated Efficiency and
95% Confidence Intervals
0.30-
ELECTRIC
OUTPUT
SHARE,
0.25-
fEL
0.20(HIGHER
HEATING
VALUE)
0.15-
0.10*
Data on Individual
Gas Turbine Designs
0.05-
n
v
n0
I
0vv
0.1
II0
100
GAS TURBINE CAPACITY (MW)
Figure D.5
1000
308
the exhaust gas heat recovery through the supplemental firing of the
heat recovery boiler directly with oil or gas.
The higher temperatures
in the exhaust stream given better supplementary firing efficiencies
than diesel systems (97% for gas turbines, 95-97% for diesels, and
83-85% for regular boilers).
For gas turbine systems, the improved
efficiencies were assumed to be available for up to 4.5 times the
current cogeneration-only steam output.
For example, take a 4000 kW
gas turbine operating at full output with an associated steam output of
22.9 MBtu/hr of high pressure steam; the heat recovery boiler can be
supplementary-fired for up to 103 1fBtu/hr additional steam (total 126
MBtu/hr) with an efficiency of 97% for the additional fuel used in
supplementary firing.
The capital costs for this capability are added
to the total unit cost as if it were a separate boiler (see Tables 0.2,
D.4, and.D.6).
D.2.3
Steam Turbine Cogeneration
Steam turbine cogeneration systems produce steam and electricity by
generating steam in a boiler at a higher pressure than required for
process use.
In the most basic back-pressure steam turbine designs,
the steam is then reduced in pressure by running it through a turbine,
which generates electricity; the steam exits the turbine at the
required process pressure or pressures.
A broad variety of steam-turbine cogeneration system designs
exist.
The boilers can vary in the maximum throttle pressures and
temperatures for the same resulting process steam output, with the
'309
8
higher pressure/temperature combinations allowing a higher electricity
output per unit of.process steam output.
The turbine design can
exhaust all the steam input at the process pressure, exhaust at several
process pressures, or extract some process steam and release the
remainder to a condenser after generating more electricity in a low
pressure turbine section.
The design process outlet steam conditions
can vary from steam at hot water temperatures to the maximum attainable
from today's boiler designs.
The final mix of electricity and steam energy from these systems
similarly varies widely depending on the exact boiler and process steam
outlet conditions for which the unit is designed and operated.
To
simplify this range of design and operating choices to be considered in
comparing cogeneration plants at the many industrial sites studied in
this report, this analysis limited the steam turbine cpgeneration
systems types to a single back-pressure design with a boiler outlet
temperature of about 900 degrees F with the resulting steam turbine
output at either 15 psig or 150 psig.
This general design best
represents the efficiency advantages associated with steam-turbine
cogeneration without burdening the system with the extra capital costs
and technical complexities associated with generating extra electricity
through a condensing system.
The embodied economic assumption is that
the extra costs associated with condensing generation must be similar
to the cost of large-scale condensing (utility) generation but without
the economies of scale; if the utility is more limited in fuel choices
than the cogenerator using the condensing op.tion, this scale argument
310
is negated by the fuel choice advantages available to the cogenerator.
Since steam turbine systems are designed around a boiler, they can
burn anything from distillate oil to wood chips.
This study limited
the fuel choices for boilers to three representative fuels:
oil, low sulfur residual oil, or coal.
small sites.
distillate
The distillate oil is for very
The low sulfur residual oil was selected because most
industrial or commercial steam turbine cogeneration installations were
at sites large enough to handle residual oil but they were often in
locations that require the lower sulfur oil.
Coal is the primary
non-petroleum alternative fuel; although wood chips or waste might be a
viable alternative at some sites, the boiler and fuel costs would be
close enough to coal for coal to represent it as a proxy.
It is
assumed that the coal boiler emissions could be handled in a way that
will make the system environmentally acceptable.
The capital cost estimates for steam turbine systems vary
considerably because of site variations and differences in the
components included within the estimate.
To maintain consistency
between the oil and coal-fired steam turbine cogeneration systems, the
boiler and steam turbine components have been costed separately, as
described in Section D.l.4.
Even on a net basis, oil-fired boiler cost
estimates vary by a factor of four, and coal boilers by a factor of
two.
Steam turbine estimates for machines of the same scale vary by
30%.
Operation and maintenance cost estimates for steam turbines and
boilers in these cogeneration designs exhibit the same problem
-311
associated with such estimates for other systems.
Engineering
estimates are often in the $.30/IlBtu range while historical accounting
costs for many systems are over $2.00/MBtu.
D.2.4
Air Conditioning Chilled Water Systems Combined with Cogeneration
Many suggested cogeneration plant designs associated with
commercial buildings include steam absorption water chillers to
increase the cogeneration system's summer capacity factor.
This study
did not explore this alternative in detail because the energy
efficiencies are so poor for the steam chilling systems; the
steam-electric energy price difference does not appear to be sufficient
to counterbalance this situation, especially in a study location where
the utility has substantial excess capacity and is converting to coal
as its primary fuel.
Steam absorption water chillers are thermally less efficient and
are higher in capital costs than comparable electric chillers, as shown
in Table D.7.
For example, take an oil-fired central electricity
generating plant with a 10,000 Btu/kWh heat rate supplying power to an
electric chiller and compare this to an oil-fired steam turbine
cogeneration plant supplying 150 psig to a two-stage steam absorption
chiller.
Assume neither is capacity limited.
Using the plant
performance information from Tables 0.2 and 0.7, absorption chilling
requires .0106 !Btu oil per ton of chilling (allowing for a credit in
electricity not produced by a central oil-fired station) while the
electric chiller requires .008 Mi3tu oil per ton of chilling.
In
I~L~ga~
__IJUdP______~
312
Table D.7
COMPARATIVE AIR CONDITIONING CHILLED WATER COSTS
Chiller
Plant
Type
Installed Capital Cost
(/ton cniller + Incremental
cooling tower capacity cost) 1
Operating Energy Use 2
(kWh/ton-hr)
(Btu/ton-hr)
Electric
centrifugalhermetic
150
Steam absorber,
1-stage
210 (150+60)
18,000
(app. 15 psig)
Steam absorber,
2-stage
360 (320+40)
12,000
(app. 130 psig)
.80
1From
communications with various manufacturers (1980). These are
the typical installed costs for the chiller and the incremental
installed costs for the cooling tower beyond the sizing required by an
ordinary electric chiller.
2 From
Baumeister et al. (1978, p. 12-111) and communications with
manufacturers (19~U). These are only the direct chiller energy
requirements; they do not reflect differences in auxiliary pumping
costs owing to larger cooling tower sizes under steam absorbers.
313
addition, the electric chiller would have less than half the capital
cost.
The economics may be different if the cogenerator can burn
sufficiently cheaper fuel than the utility or if the utility has to add
generating capacity to meet electric loads which occur at the same time
the electric chiller must operate.
In the case study for this report,
the capacity problem did not exist, and sites with large cooling loads
were typically more restricted in their use of cheaper fuels than the
utility.
The model used in Chapters 2 and 3 for evaluating plant
designs, however, could be easily modified to accommodate the
complications involved in incorporating chilled water within the
cogeneration plant selection decision.
D.3.
SUMMARY AND COMMENTS
This appendix documents the cogeneration plant operation and cost
assumptions used in this report.
Substantial variation occurs in
capital and O&M expense estimates for these systems.
This analysis has
attempted to represent these plants across a broad range of sizes for
median cost facilities.
The final results of this study, which are
presented in the main body of the report, indicate coal-fired
cogeneration may be economic at much smaller scale sites than
previously imagined--so the capital cost estimates are being used on
plant sizes below the previous levels experienced and must be treated
with some scepticism.
technologies.
This applies to a lesser extent to the other
The cogeneration capital costs, however, are only a part
314
of the total net present value computation in the comparison of plant
economics.
As a convenient reference, Table D.8 gives a summary of the
performance and cost assumptions used in the sample calculations in
Chapters 2 and 3 for plants designed to produce 50 MBtu/hr steam.
315
Table D.8
COST AND PERFORMANCE SUMMARY FOR COGENERATION PLANTS SERVING
A CONSTANT 50 MBTU/HR HIGH-PRESSURE STEAM LOAD
Plant Type
Size
EL/ST
Ratio
(kWh/MBtu)
fST
TFuelT)
Medium or
low speed
diesel cogeneration
#6 oil)
Total)
Component
O&M Costs
(mills/
W$I
MBtu) + kWh)
27
35
380
8.19
.25
9 .3
38.8
25
186
4.76
.25
10.0
(oil)
2.7
70.7
12.9
53
3.51
.25
4.0
Back pressure
steam turbine
with boiler
(coal)
2.7
70.7
12.9
53
6.69
1.25
4.0
Boiler (coal)
--
85
0
4.76
1.25
0
0
0
.25
0
Gas turbine
cogeneration
(#6 oil)
19
Direct
Capital
Co t
8.0
Back pressure
steam turbine
with boiler
Existing
boiler (oil)
--
--
316
Appendix E
MODELING SUMMARY
This appendix summarizes the structure and major assumptions
employed in the electricity and steam cogeneration analysis model
discussed in Chapters 2 and 3. Since this analysis has been developed
for a specific utility system, and the model must incorporate the
electricity tariffs for that system, the model has not been designed
specifically for general purpose usage.
The description in this
appendix, therefore, is not intended to be thorough documentation for
the model.
The model, in order to be easy to modify, was written in
the APL language for operation on an IBM/370 system under VM/CMS
running VSAPL release 3. (For further information on APL, see Polivka
and Pakin, 1975).
317
Table E.l:
Summary of Detailed Modeling Assumptions
I. Timing
1981
A. Year of plant selection decision:
15 years
B. Planning horizon
II.
Financial Conditions
A.
10%/yr
Inflation
B. Return on 100% equity cogeneration/boiler plant investments
4.6%/yr real
15. 1%/yr
8.1%/yr real
18.9%/yr
(1) Base case (Asset Beta = .5)
(2) High cost of capital case (Asset Beta = .9)
C.
0.2%/yr real
10.2%/yr
Short-term debt
1.9%/yr real
12.1%/yr
D. Long-term debt
E.
Standard target debt fraction for incremental
investment associated with new plants
50.0%
F. Implied equ.ity return at 50% debt
III.
(1) Base case
18. 1%/yr
(2) High cost of capital case
25.7%/yr
Fuel Prices at start of decision year ($/MBTU)
A. Initial year Prices
Base Case
Low Oil
(1) #2 oil
$6.36
$7.16
(2) #6 low-sulfur oil
$5.45
4.56
(3) #6 high-sulfur oil
$4.55
4.09
(4) #6 mix for utility
(50% high and low sulfur)
$5.00
4.33
(5) coal
$2.05
2.31
(6)
nuclear
.68
.64
318
Table E.l:
Summary of Detailed Modeling Assumptions (continued)
B. Escalation depends on inflation and scenarios; all oil prices
escalate at "oil" rate, coal and nuclear at "coal" rate for
"base case" and "lower oil price case" from the starting year
assumptions.
C. See Table E.2 for the fuel price escalation scenarios.
IY. Electricity Prices
A. Non-fuel portion of tariffs escalate with inflation
B. Average fuel cost/fuel adjustment depends on fuel prices
and utility fuel mix by scenario (see Table E.3 for
utility fuel mix assumptions by scenario).
C. Marginal cost depends upon fuel prices and the share of time in
each year that oil or coal is the marginal fuel, which varies
as a simple function of the fraction of oil in the average
utility fuel mix.
D. The fraction of the year with oil as the marginal cost fuel
depends on the amount of oil in the average utility fuel mix:
(Fraction of year with oil on margin) = min(l; 1 (45/13)(share of oil in mix - .7))
*The fraction of the peak period with oil as the marginal cost
fuel depends on the fraction of the year with oil as the
marginal cost fuel: (Fraction of peak period with oil on
margin) = min(l; (fraction of total year with oil on
margin)/.84))
These relationships are crude approximations developed from a
detailed series of utility production costing analyses.
E. The peak period is 8 am to midnight on weekdays; all other
times are off peak.
V. Steam and Electric Loads
A. Weather characteristics for steam loads and time-of-oil periods
simulation: 8-hour average temperatures at Boston, Logan, for
the years 1953 and 1963-1967 (from SOLMET tapes).
B. Steam loads: a linear function of heating and cooling degrees
with a fixed base load in all periods for a 6-year synthetic
time series.
319
Table E.l:
Summary of Detailed Modeling Assumptions (continued)
C. Electric loads: 8-hour averages for a one-year history at
site or an extrapolation based on electric energy use and
electric load factor.
VI.
Cogeneration and Boiler Costs and Technologies (Appendix D)
A. Fuel use and outputs are in fixed linear ratios.
B. Capital and O&M costs are escalated at .inflotion from 1980
cost presented in Appendix D.
C. Outages are by an exponential random process.
VII.
Selection of Plant Types and Sizes for Detailed Modeling
A. Types
(1) If site currently uses #6 oil: coal boiler; coal steam
turbine coyeneration, #6 oil-fired steam turbine
cogeneration; #6 diesel cogeneration; #6 gas turbine
cogeneration.
(2) If site currently uses #2 oil: #6 oil boiler; #6
oil-fired steam turbine cogeneration, #2 diesel cogeneration;
#2 gas turbine; coal steam turbine cogeneration.
B. Sizing
(1) Basic plant size based on a levelized cost calculation
using 1985 middle fuel/medium coal conversion scenario prices
with the "NPV 3 " method (Section 2.2.1.3) using a fixed
charge that embodies the standard financial and tax
assumptions.
(2) Supplementary boilers for diesel and gas turbine systems
are sized to efficiency limit or to the top 20 percent of the
load, whichever is less.
VIII.
Operating Simulation
A. Monte Carlo outage generation and temperature simulation for
steam loads over a 6-year period with 12 billing months/year,
30 days/month, 3 eight-hour periods/day; outages based on an
exponential event process, and loads are from a time series.
320
Table E.1:
Summary of Detailed Modeling Assumptions (continued)
B. Simulation produces an annual average plant (cogeneration,
supplementary boiler, and back-up boiler) output and fuel use
along with non-fuel utility tariff charges fcr each mode and
tariff combination.
C. Non-fuel tariff charges are computed as described in Appendix
B.
IX. Operating Cost Escalation
15 years
A. Horizon
B. Different plant types start up in different years
C. (1) Fuel - by type and fuel scenario according to
inflation and real escalation
(2) O&M - at inflation
(3) Tariffs:
a) non-fuel - at inflation
b) fuel adjustment - by utility coal conversion
and fuel scenario
c) purchase rate - by utility coal conversion
and fuel scenario
D. Site combined steam and electricity supply operating costs are
minimized by year for each scenario over all feasible tariff
and operating mode combinations.
X.
Financial Analysis and Investment Decision
A.
Capital Expenditures
1. Financial conditions as above
2. Capital costs are escalated at inflation until funds are
expended in construction
3.
Taxes
(a) Property tax on assessed value (income tax depreciated
value increased for inflation)
2.5%/yr
(b) Income tax (state and federal combined)
48%
321
Table E.l:
Summary of Detailed Modeling Assumptions (continued)
(i) tax depreciation on 15-year life:
digits
sum of years
(ii) investment and energy tax credits are absorbed
by company as construction expenditures are made;
tax credits as given in Appendix D.
B. Investment Decision
1.
Based on present value of expected cashflows using adjusted
present value method (see Brealey and Myers, 1981).
2.
Fuel and utility coal conversion assumed uncorrelated with
financial conditions.
3.
Expectation across scenarios assumes fuel and utility coal
conversion are independent.
(a) Fuel:
high 33%, middle 33%, low 33%
(b) Coal conversion:
XI.
fast 35%, medium 35%, slow 30%
Utility Impact
A. Analysis of utility impact made by comparing the utility's
operating profit for the customer without cogeneration against the
case with cogeneration
B. The utility operating profit is estimated by the tariff revenues
in the given year less the marginal energy costs to serve the
customer. The fixed costs of local distribution of other
investments to serve this customer are not used to adjust the
operating profit
C. Time-of-oil periods based on the most extreme temperature periods,
evenly split between temperatures above and below 65 degrees F.
322
Table E.2:
Percentage real
escalation per year
oil
Real Fuel Price Escalation Rates
High
Fuel Escalation Scenario
Middle
Low
(#2, #6 high and low sulfur)
2-5
5.1
1.6
6-10
2.7
2.6
11-15
4.8
2.1
16-20
1.5
1.2
2-5
3.9
3.9
3.8
6-10
1.7
1.5
1.4
11-15
0.6
0.9
0.8
16-20
0.8
0.6
0.6
-1.5
Coal and Nuclear Energy
--
323.
Table E.3:
Share of Fuel Types in Average Utility Fuel Mix
Fast
Coal Conversion Scenario
Medium
Slow
1
0
0
0
2
45
45
45
5
61
55.6
45.6
10
54.5
49
39
15+
50
44.4
34.4
Percentage in Total
Fuel Mix by year
Coal
-
Oil (50% Low S #6; 50% High S #6)
1
78
78
78
2
33
33
33
5
18.8
24.2
34.2
10
17.3
22.7
32.7
15+
24.1
29.7
39.7
1
22
22
22
2
22
22
22
5
20.2
20.2
20.2
10
28.3
28.3
28.3
15+
25.9
25.9
25.9
Nuclear
-
324
Table E.4:
Share of Coal as Marginal Cost Utility Fuel
Coal Conversion Scenario
Fast
Medium
Medium
5
38.8
20.1
10
44.0
25.3
15+
20.4
1.0
Percentage
Slow
Slo
Year"
1
2
0
325 .
Table E.5:
I.
Summary of Aggregate Modeling Assumptions
General
The analysis is the same as in the Detailed Modeling (Table E.1)
unless noted.
II. Steam and Electric Loads
A. Weather characteristics for steam loads and time-of-oil
simulation:
the monthly frequency distribution for the peak
and off-peak periods, as developed from 6 years of SOLMET
temperature data (years 1953, 1963-1967)
B. Steam loads:
by month for one week of 7 days with three
8-hour shifts per day
C. Electric loads:
peak/off-peak pattern as discussed in
Appendix C.
III. Operating Simulation
A. Twelve month simulation with one week per month; shifts of 8
hours
B. Plant outages based on fixed intervals using mean time to
failure and repair statistics rather than a random process;
an additional adjustment to match FBTF statistics is made by a
reduction in cogeneration plant output capacity
C. Loads based on frequency distribution by month rather than a
time series
IV.
Investment Decision
Economic plant selection isbased on a staged process to lower
computation costs:
326
Table E.5:
Summary of Aggregate Modeling Assumptions (continued)
A. Alternative plants are evaluated under the middle
escalation/medium coal conversion scenario;
B. If a cogeneration plant is economic, the analysis continues
for all other scenarios.
After these results are calculated,
the optimum net present value plant is selected; the utility
impact is computed as a part of this stage.
1
327
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334
BIOGRAPHICAL NOTE
Frederick H. Pickel was born in Seattle, Washington, on June 12,
1952 to Hugh E. Pickel, Jr., and Dorothy J. (Miller) Pickel. He was
raised in Seattle, attending secondary schools there, graduating from
Seattle Preparatory School in 1970.
In 1974, he received the B.S. degree with distinction from Harvey
Mudd College, Claremont, California, in engineering and, through
Pomona College, in economics. He completed the S.M. degree in
Operations Research and the S.M. degree in Civil Engineering at the
Massachusetts Institute of Technology in 1978. Finally, he received
his Ph.D. degree in Engineering-Economic Systems Analysis at M.I.T. in
1982.
From 1974 to 1975 he worked with the Office of Energy Systems at
the Federal Power Commission. In 1976 and 1977 he was with the
Decision Analysis Group at SRI International, Menlo Park, California.
During 1977 and 1978, he was appointed to the Governor's Commission of
Cogeneration, Commonwealth of Massachusetts. He has consulted on the
economic viability of cogeneration and alternate energy systems for
organizations planning cogeneration projects and for firms studying
marketing opportunites. While in graduate studies at M.I.T., he was a
senior teaching assistant in mathematical optimization and systems
analysis, and was a research assistant in a variety of water resource
and energy projects. He is now working for New England Electric on
the development of alternate energy sources: since 19817, he has been
Administrator, Special Energy Projects, in the corporate legal
department; he was a consultant to New England Electric starting in
1979.
He is a member of the International Association of Energy
Economists, the Institute of Electrical and Electronics Engineers/
Power Engineering Society, The Institute of Management Science, the
Operations Research Society of America, the American Economic
Association, and Sigma Xi. He is currently a vice president of the
New England chapter of the IAEE. He received an NSF energy-related
graduate trainee scholarship from 1975-1976 and the Lorne D. Cooke
Memorial Award in Economics from Pomona College in 1974.
Selected publications:
"Cogeneration in the U.S.: An Economic and Technical A,.alysis,"
Frederick H. Pickel, MIT Energy Laboratory Report #MIT-EL78-039.
Cambridge, NA: Mass. Inst. of Tech., November 1978.
"Homeostatic Utility Control," Fred C. Schweppe, Richard D.
Tabors, James L. Kirtley, Hugh Outhred, Frederick H. Pickel, and
Alan J. Cox, presented at the IEEE Power Engineering Scciety 1979
summer meeting, Paper #F79-685-9, IEEE Transactions on ?cwer
Apparatus and Systems, Vol. PAS-99, No. 3;lay/June
u; pp.
1151-1163.
--
335
"Why Did the Role of Cogenerat ion Diminish?" Frederick H. Pickel,
invited paper. Proceedings ofI the Sixth Energy Technology
Conference. Washington, D.C.: Government Institutes, Inc.,
February 26-28, 1979.
"Electric Utility Forecasting of Customer Cogeneration and the
Influence of Special Rates," Frederick H. Pickel. Proceedings of
C•.T
the Eighth Energy Technology Conference. Washington,Government institutes, inc., .March 9-1~, 1981 (reprinted in MIT
Energy Laboratory Report #MIT-81-006, NTIS #PB-82-180-191).
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