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SYSTEIATIC METHODOLOGY FOR THE CO1PARISON
OF EIRONENTA,
CONTROL TECHNOLOGIES
FOR COAL-FIUD
ELECTRIC GENEIATION
J. Gruhl
F.C. Schweppe
NI.F. Ruane
S. Finger
MIT Energy
Lab R.eort
MIT-EL 76-012
NTovember 1976
Sponsored by Argonne National Laboratory with a subcontract fron ERDA
contract W31-109-ENG-38. The Argonne project "Environmental Control
Technology for Generation of Power from Coal" is being directed by
Dr. Norm Sather, Argonne National Lab, Argonne Illinois.
Abstract
This report describes work performed as a subcontract to Argonne
National Laboratory's project "Environmental Control Technology for
Generation of Power from Coal." The intention of thatArgonne Program
is to provide an independently developed assessment of alternative
environmental control technologies for coal-fired electric power
generation and to develop an independently evaluated set of recommendations for future environmental control technology research,
development, and demonstration programs for these processes. This
report describes a probabilistic, systems analytic methodology appropriate for use in comparing the alternative control technologies. In
addition to the discussions about this probabilistic framework, there
are examples.of the use of the framework for comparative purposes.
Information is presented on the methods and sources for making these
comparisons on health effects bases, as well as the relevant economic,
technological, availability, resource use, emissions, and ambient-level
information. In addition, there are bibliographies of key references
in the related areas.
-1-
Table of Contents
page
Abstract ..................................................
..............
1
Table of Contents.......................................
..............
2
Glossary
..............
4
.......................................
1. Introduction and Executive Summary ......................
2. Existing Sources of Information.........................
2.1 State-of-the-Art Energy/Environmental Modeling.....
2.2 Existing Data Bases ...............................
6
.
.............
.............
.............
11
11
13
3. Overall Mechanism Structure..........................................
3.1 Different Levels of Sophistication..............................
3.2 Modularity and Updating
.......................................
16
18
4. Technical Factors and Models
.........................................
4.1 Consistency Assumptions and Common Factors............. ..........
4.1.1 Assumptions on Categories of Technical Factors.. ..........
4.1.2 Levels of Probabilistic Sophistication ......... ..........
4.2 Fuels.................................................. ..........
4.2.1 Representative Coal Types....................... ..........
4.2.2 Low Sulfur Coal................................. ..... .....
4.3 Fuel Treatment and Conversion
.........................
..........
4.3.1 Coal Preparation and Cleaning................... ..........
4.3.2 Solvent Coal Refining........................... ..........
4.4 Combustion Technologies
.............................
4.4.1 Current Coal-Fired Combustion/Generation ........ ..........
4.4.2 Low Btu Gasification/Combined Cycle Systems..... ..........
4.5 Emission Controls....................................
..........
4.5.1 Particulate Removal Systems .....................
..........
4.5.2 Stack Gas Cleaning for Sulfur Removal........... ..........
4.5.3 NOx Control Technologies
.......................*..........
4.5.4 Intermittent Control and Tall Stacks............ ..........
4.6 Potential for Including Future Technologies............ ..........
21
21
19
25
26
28
28
38
43
43
46
...... .... 50
50
53
56
56
57
59
60
62
5. Simulation Mechanism ..................................
65
5.1 Modeling Options ................................................ 65
5.1.1 Assessment Options ...................... ................ 65
5.1.2 Non-Technical Factors...................
................ 68
5.2 Single Fuel/Plant/Control Probabilistic Emission Simulation...... 73
5.2.1 Example of Simulation Mechanism.......... ................ 78
5.2.2 Methodology for Improvement of Mechanism..
83
5.3 Potential for Emission-to-Health Modeling........ ................
85
5.3.1 Example of Health Effects Simulation...... ................ 99
5.3.2 Future Data and Modeling Requirements..... ............... 107
5.4 Regional and Power System Considerations......... ............... 108
5.4.1 Power System Integration.................. ............... 109
5.4.2 Regional Characteristics .................. ............... 114
5.4.3 Natural Aggregation Levels................ ............... 123
5.4.4 Overview of Regional Considerations....... ............... 129
5.5 National Aggregation
........................................... 142
5.5.1 Definitions - Single Plant................ ............... 142
5.5.2 Different Utility Regions ................ ............... 143
5.5.3 Utility Region Simulation ................ ............... 144
5.5.4 Representative Power Systems.............. ............... 145
-2-
.
.
page
5.5.5 National Resultant Factors ................................ 146
5.5.6 Recommended Approach ................. ................... 148
6. Resultant Factors
...................................................
6.1 Economic Resultant Factors......................................
6.2 Performance Resultant Factors...................................
6.3 Applicability Resultant Factors ..............................
6.4 Resource Requirements...........................
6.5 Environmental Consequences
....................................
150
152
152
152
152
154
....................
.................... 155
..................... 156
.................... 156
.................... 156
.................... 157
....................
.................... 159
.................... 159
.................... 162
.................... 169
7. Ordering Mechanisms and Critical Factors .........
7.1 Ordering Models
.............................
7.1.1 Elimination Strategies................
7.1.1.1 Threshold Criteria...........
7.1.1.2 Indifference Elimination.....
7.1.1.3 Relative Weighting...........
7.1.1.4 Strategies Using Uncertainty.
7.1.2 General Ordering Mechanism ............
7.1.3 Elimination Operations ...............
7.2 Example of Interactive Otdering Mechanism....
7.3 Examples of Critical Factors.................
8. Conclusions and Future Research Needs
.............................
9.0 References and Bibliography
...........................
9.I Fuels
....................
9.II Fuel Treatment and Conversion...............................
9.III Combustion Technologies
.....................................
9.IV Emission Controls .............................................
9.V Energy/Environmental Information and Modeling ...................
9.VI Ordering Mechanisms........................................
-3-
155
.157
178
179
179
181
183
185
190
210
Glossary
assessment options
- the choices of the fuel/plant/control combinations
to be simulated or the choices of regional or
national aggregations to be performed
critical factors
- the most crucial differences between the most
attractive Assessment Option alternatives that
have been examined
downstream technology
- an emission control technology is downstream from
a combustion/generation technology which is
downstream from a precombustion control technology
which is downstream from a fuel type
fuel/plant/control
combination
- any logistically valid combination of one fuel type,
none, one or more (concatenated) precombustion
control technologies, one combustion/generation
technology, and none, one or more (concatenated)
postcombustion control technologies
modeling options
- all of those choices made by the user that are
necessary to get the overall assessment mechanism
started, includes Assessment Options and NonTechnical Factors
modularity
- the capability for freely substituting models of
similar technologies and thus making possible the
simulation of any hypothetical fuel/plant/control
combination
'new' pollutants
- any air or water pollutants that are not currently
regulated by the ambient threshhold standards
non-technical factors
- those parameters that can be varied by the user;
factors that may play such a crucial role in the
decisions to be made that sensitivity studies with
respect to them may be in order
ordering mechanism
- an interactive procedure for imposing the decision
makers interests and priorities upon the large set
of information contained in the Resultant Factors
to produce Critical Factors
overall assessment
- the entire
priorities
Simulation
Mechanism,
mechanism
framework that accepts user options and
and contains the Technical Factors,
Mechanism, Resultant Factors, Ordering
and Critical Factors
priorities
- user opinions, interests, and biases that can be
used by the Ordering Mechanism to pare down the
number of Resultant Factors so that a small set of
Critical Factors can be developed
representative coal types
- various specified coal samples that taken as a
collection are both regionally representative and
constituently representative
representative power
systems
- a small set of power system types that taken as
a collection can be used to approximately simulate
the planning and operating characteristics of any
of the nation'smany utility regions
resultant factors
- performance measures for a fuel/plant/control
combination that describe the economics, performance,
applicability, resource use, and environmental
consequences of that combination
simulation mechanism
- the structure containing all models and logistics
for splicing together different valid fuel/plant/
control combinations to develop the Resultant Factors
technical factors
- the data and models that provide the adequate
characterization of each of the fuels, precombustion
technologies, combustion/generation technologies,
and postcombustion technologies
upstream technology
- a fuel type is upstream from a precombustion
technology which is upstream from a combustion/
generation technology which is upstream from a
postcombustion technology.
user
- the decision maker utilizing the overall assessment mechanism
utility perspective
- primarily an economic and applicability perspective
but more recently including concerns about fuel,
manpower, and resource availability and concerns
about the potential sensitivity of environmental
and human health problems
utility regions
- any geographic division of the country that roughly
corresponds,but is not limited to, power pools
-5-
1.
Introduction and Executive Summary
This document describes work that has been performed as a subcontract
under ANL project "Environmental Control Technology for Generation of
Power from Coal." The intention of the. ongoing ANL program is to
provide an independently developed assessment of environmental control
technology for the generation of electric power from coal and to develop
an independently evaluated set of recommendations for future environmental
control technology programs for these processes. Further details of the
ANL project can be found in ANL supplement no. 18, Contract W31-109-ENG-38,
"Environmental Control Technology Program Budget" and in (V; Argonne
National Lab; 1976).
The ANL project will augment the large number of recent and current
assessments of control technologies, in order:
(1) to provide current, comprehensive assessments of the
effectiveness and applicability of these control technologies;
(2) to develop a basis for comparative evaluations of the
alternatives;
(3) to identify unresolved issues, information gaps and needed
R&D programs related to the control technologies; and
(4) to establish and maintain an in-house group of experts on
the current developing technologies.
The primary function of this subcontract was to design a systems analysis
framework for making comparisons among alternative control technologies.
This work is therefore most closely related to objective (2) above. However,
since the systems analysis framework necessarily defines assessment criteria,
this subcontract also affects the successful completion of objectives (1)
and (3).
This program comes at a particularly important time in our nation's
energy history. The United States is heading toward an energy-tight situation
that has the potential to become of enormous importance to our economy and
lifestyles. The present national energy policy recommends pressing forward
with utmost speed on the research and commercialization of uses of coal,
our only domestic source that can introduce significant energy by the year
1990. If one Project Independence (V; FEA; 1974) scenario is correct the
commercialization of coal technologies in the next 10 to 20 years could rival
the most frantic wartime production efforts.
Historically several government agencies have been charged with the
concern for ensuring adequate supplies of energy and ensuring that these
technologies are consistent with the public's general expectation for
environmental and health protection.
-6-
In 1970 the Environmental Protection Agency was established and began
its involvement in the environmental aspects of agency technologies. Late
in 1974 an Office of Energy Research was formed within EPA. When Congress
enacted the Energy Reorganization Act of 1974 it established ERDA and gave
it the mission to aggressively pursue new energy sources and to expand
existing sources using the best technological, economic and environmental
means available. Thus in 1974 it became obvious that a more coherent and
coordinated government energy/environmental program must be developed.
To sort out the "turf" issues and the areas of necessary duplication
two interagency task forces were established by the Office of Management
and Budget. Their two reports covered the areas "Health and Environmental
Effects of Energy Use" and "Environmental Control Technology for Energy
Systems." These November 1974 reports essentially recommended that nearterm energy/environmental research should be centered at EPA, with mid- and
long-range research centered at ERDA.
Immediately upon being given those responsibilities ERDA stated in
(V; ERDA; 1975), that there loom possibilities for large-scale discrepancies
between the magnitude of coal use and the protection of man and the environment from damaging impacts. From that report (V; ERDA; 1975), "It is the
responsibility of the ERDA Administrator to submit to Congress plans for
solutions to near-, mid-, and long-term energy supply systems and associated environmental problems. The ERDA biomedical research capability must address
the environmental and health implications of coal combustion and conversion
technology and inform ERDA technology programs on these matters at all stages
of development. A broadly-based ERDA biological and environmental research
program completely interactive with and responsive to energy-technology
programs will provide assurance that promising technology developments are
compatible with the protection of human health and environment."
With this clearly defined charge, the Argonne project was begun in
March 1976 to evaluate technologies for controlling the environmental impacts
of coal-using electric power generation processes. This ongoing program is
sponsored by ERDA's Division of Environmental Control Technology and involves
comparison of technologies that include: conventional coal combustion processes
with add-on stack gas cleaning and intermittent emission control strategies;
processes that involve the production and use of a cleaned or solvent-refined
coal; and processes such as fluidized-bed coal combustion, low Btu gasification/
combustion, and other advanced systems in which the combustion and pollutant
removal operations are combined.
Tihesystem analysis framework described in this report requires that
the collection of a large number of factors, called Technical Factors
throughout this report, for the adequate characterization of the various
environmental control technologies. These factors are, in general, dependent
on the characteristics of the power plants using the control technologies.
In order that the ANL group and its subcontractors, who will evaluate most
of the individual technologies, can assure a consistent final comparative
assessment of alternate technologies, it is necessary to carefully define
the factors of interest and their associated assumptions. The broad
-7-
categories of these Technical Factors include representation of:
1.
2.
3.
4.
5.
6.
7.
direct environmental impacts,
indirect environmental impacts,
investment/operating costs.
operating characteristics including efficiency,
reliability, and characterization of operating
schedules,
resource, materials, and equipment availability,
potential and time scale for improved technology
regulatory, institutional, and other limitations.
These lists of Technical Factors for each particular type of each
control technology are then used as the input data base for the operation
of the Simulation Mechanism, see Figure 1.0-1. The Simulation Mechanism
ASSESSM:ENT
Il
OPTIONS
I _
USER
./
DECISION
__~~~~~~~~~~~~~~~~~
.MAER
I
A'D
NONTECHNICALNUTS
User choices of regional
ag-regations
or individual
fuel/plant/control combinations to be studied
antd
user choices of regional
economric, and other info
mis-
ation to be studied or
PRIORITIES
DISPLY
OF
RESULTANT
FACTORS
TO USER
1
I
I
para-eterized
SIMULATION
TECENICAL
.nforatton
MECHANISM
FACTORS
on
individual fuel,
plant, and control
apmlicability,
perforca-.ce,and
recuirements
DISPLAY OF
INTERIM
INFOPYATION
TO USER
Subjective user
choices of
methods for
Iordering
and
eliminating
information
and
combinations
"S
Models of accounting,
dispersion, and
regionalaggregation
concerns for fuels,
fuel treatment,
conversion
plants,
and abatement
_-V
LTANT FACTORS
Comparable
information
on each fuel/plant/
control combination
or
regional aggregations
chosen for study
ORDERING
MECHANISM
Mechanism
and report
generator
for sorting,
weighting,
and displaying the information of interest
CRITICAL
FACTORS
Final information
on
the crucial differences
co-ol
regional
chosen
l~~
betwee
then fuelplan
a
nations
afor
study
for
coto
or
study
obntos
Figure 1.0-1 Block diagram representation of the flow of information through
the overall assessment mechanism.
contains all of the models and logistics for splicing together different
valid combinations of control technologies and thus develops the performance
information, called Resultant Factors, that describe the economics,
performance, applicability, resource use, and environmental consequences of
that particular combination of fuel, pretreatment, combustion, and pretreatment technologies that comprise the particular plant configuration that
has been chosen for study. The diagram on the next page, Figure 1.0-2,
illustrates the manner in which fuel/plant/control configuration can be
chosen. Note especially how this flow chart leaves open the possibility for
concatenating pretreatment or post-treatment devices.
Again looking at Figure 1.0-1 it can be seen that
only the particular fuel/plant/control combinations he
also sets ground rules for accounting procedures to be
that might exist, and any of a number of other options
parameterization in sensitivity studies.
-8-
the user chooses not
wishes to consider but
used, regional differences
that are then open to
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Once this collection of resultant factors has been developed for each
of the various fuel/plant/control configurations the user has the choice of
combining them to simulate nationally or regionally aggregated results.
These aggregations, as well as a number of possible expressions of preferences
and appropriate criteria for comparison, are performed in the Ordering Mechanism of Figure 1.0-1. This Ordering Mechanism is a method of imposing the
decision maker's interests and priorities upon the large amount of information
held in the collection of Resultant Factors. In an interactive procedure the
decision-maker can pare down the information until it represents only the most
crucial differences between only the most attractive alternatives; these are
called Critical Factors. Another decision-maker with a different set of
preferences and interests need only retrieve the collection of Resultant
Factors and iterate his ideas with the Ordering Mechanism to arrive at his
own, probably different, set of Critical Factors.
The ground rule throughout this report has been to consider things from
the "electric utility perspective." This results in a more accurate simulation
of the way in which the control technologies will actually be chosen from the
open marketplace for use by utilities. However,i the phrase "electric utility
perspective" has, especially in recent years, come to mean more than an economic
and applicability perspective. For example, in order to avoid sensitive
issues in the future, utilities are now concerned with availability of
resources such as fuels, metals, manpower, and so on, and they are now
more concerned with the sensitivity of future plants to potential future
environmental standards, including stricter or looser levels of current
standards and the'new'pollutants of interest and the new formats for
standards(that can be predicted with health effects simulations). In fact,
with the present methods of plant licensing and the present atmosphere of
plant operation, electric utility concerns must now include all of the
concerns that any other special interest group might have. The assessment
procedure described here is thus aimed not only at being easily updated
with new processes or better information, but it would be also useable by
decision-makers from any of the possible special interest groups.
2.
Existing Sources of Infonnation
Energy/environmental modeling as it pertains to health effects is discussed
in section 5.3. The bibliographies of energy/environmental modeling information
are contained in section 9.V, with additional references available in (O; Gruhl;
1976a) and (O; Gruhl, 1976b).
2.1
State-of-the-Art Energy/Environmental Modeling
There is now a great deal of information available on the modeling of the
environmental effects of energy facilities. Much of this large body of information
does however have very limited scope. For example, much of it treats radiation
problems alone, or thermal water pollution, or common inorganic air pollutants, or
some other specific aspect of the environmental system. Another common limitation
of scope is in the treatment of only those environmental effects that come directly
from the energy facility, few sources deal with the entire fuel cycle effects. Because of this fragmentation in the literature, in the following discussion of the
state of the art, therefore, the components that make up the entire energy/environment system will be discussed separately.
The first component in the modeling of these effects is the careful characterization of the constituents in the fuel. There are important regional variations
that must be taken into account, and compilations of these coal constituencies by
regions are available in (V; NASA; 1976), (I; National Coal Association; 1971), and
many other sources some described in section 4.2.
Energy facilities themselves send out environmental effluents through a number
of different airborne and water pathways. Collections of the tracings of these pathways can be found in several sources, most notably (V; Hamilton; 1974) see Figure
2.1-1.
The major impact from energy plant emissions will probably be from the airborne
dispersion of pollutants. As such the important emissions characterizations for use
in this type of study would be the modeling of the atmospheric effluents from current
and advanced combustion processes. The MIT Energy Laboratory and the MIT Schools of
Engineering have emissions modeling efforts for all of the major coal-using energy
technologies, with just a few of the others involved being: MHD-NASA, Exxon, Argonne;
Fuel cells - NASA, Exxon, Argonne; Advanced gas turbines - NASA; Fluidized bed combustors - Batelle, Argonne, EPA, Exxon, TVA; Stack gas scrubbers - Argonne, EPRI, EPA,TVA: NO control - Argonne, EPA; Gasification - Exxon, ERDA, EPRI, Battelle;. Liquefaction - ERDA, Battelle. Some of these advanced processes involve a great many known
or suspect carcinogenic agents; trace and heavy metals, radionuclides, polycyclic
hydrocarbons, precursors to nitrosamines, and organic sulfur compounds. Liquefaction
plants are particularly rich in aromatics with 13 of the 14 most potent known carcinogens occurring in their process streams (private communication; J. Liverman; ERDA;
August 1975).
The completed and ongoing emission research programs include emission categories
of widely varying specificity. Some are collecting only SOx, NOx , and-particulates
while others contain great detail on radionuclides (V; Martin, Howard, and Oakley;
1971) (V; Eisenbud and Petrow; 1964), and trace and heavy metals (V; Argonne National
Lab; 1973), (V; Ragaini and Ondov, 1975), (IV; Berry and Wallace; 1974), (IV; Kaakinen,
Jordan, Lawasoni, and West; 1975) and (IV; Klein and Russell; 1973). Quantitative
surveys are generally not available for specific organic compounds that are emitted
from the conversion and combustion processes.
The timing of the release of the pollutants is an important aspect of an overall
simulation mechanism. This particular area is a field in which a great deal of expertise exists,most of it was originally developed for use on power plants and power
systems, for characterization of energy facility operating conditions to determine
the frequency and duration of pollutant emissions, (III; Gruhl; 1974), (III; Gruhl;
1973a), and (III; Gruhl; 1973b), including literature surveys cosponsored by ERDA
and EPRI (III; Schweppe, Ruane, and Gruhl; 1975) and (III; Gruhl, Schweppe, and
Effects module:
VJi
r/
?sus
I --
oended
solids
I -~~~~~~~~~~~~
I-.
Noter/dissolved
solids.. .
rclarity -..- ;.-
}
-
:
temperature....
r,,wate
. l
. .
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Figure 2.1-1
tI
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Tracings of pathways of a pollutant through the environment
(V; Hamilton;
1974).
-12-
Ruane; 19.75). The most useful available techniques for dealing with the simulation
of the emission timing come from the analogous area of power production simulation.
Work in this field has progressed quite far and can generally be divided into chronological simulators and time-collapsed simulators. Chronological studies preserve
time as a variable and simulate operation as if in real time. Time--collapsed simulators substitute other parameters for time, such as percentage of some time period,
and thus can substantially speed computations.
For the purpose of simulating atmospheric pollutant dispersion there are
a large number of excellent computerized models. The state-of-the-art knowledge
on the 1-100km multiple point source modeling that can be superimposed on area
background sources covers a great range of sophis-tication. The gross, regional
regression models are probably too coarse for use in the context proposed in this
project; the puff and microscale models are too detailed. It is likely that a
technique that is consistent with the accuracies and time consumptions of the
other portions of the described methodology would be some type of Gaussian plume
model, probably sector-averaged as is used in (III; Ruane, et al.; 1976).
Although most atmospheric dispersion modeling has been aimed at the more
commonly studied pollutants, extensive literature is available on special considerations for trace metals and other pollutants in the so-called "hazardous" category
(IV; Junge; 1969), (IV; Klein, et al.; 1975) and (IV; Mills and Reeves; 1973).
Long-range dispersion modeling (IV; Junge; 1969), (IV; Nord; 1973), (V;
Reiquam; 1970), (V; Rodhe, Person and Akeson; 1972), (V; Szepesi; 1964), and (V;
Zeedrick and Velds; 1973) is another area of modeling necessary in this type of
approach.
Information required for the characterization of the likely background
concentrations of the future is very difficult to find. Current background
levels of the common pollutants are, however, readily available, as are fairly
adequate data on trace metals [(V; Argonne National Lab; 1973), otherwise generally
collected in separate reports by elements, refer to the lists in (O; Gruhl; 1976)],
heavy metals (V; Schroeder; 1970), (V; Fowler; 1975), and organic compounds (V;
U.S. Environmental Protection Agency; 1973), (V; Ketserdies, Hahn, Joenicke, Junge;
1976), (V; Sawicki; 1967) and (V; Watson; 1970).
Sulfation is a far from well-known phenomenon, but at least in (V; NAS; 1975)
a starting model is available that can be improved as further information is developed.
Other atmospheric reaction rates and reactions are less well known:. inorganic and
organic nitrogen compounds (V; Butcher and Charlson; 1972), (V; Davis, Smith and
Kluaber; 1974), (V; Preussman; 1974), and (V; Systems Applications Inc.; 1974); organic sulfur and other organic compounds (V; National Research Council; 1972), and
(V; Yeung and Phillips; 1975).
From the aerochemistry, one path for going further in the assessment procedure
would be to go to demographic and human exposure pattern modeling, this is discussed
in section 5.3. Another path in the assessment procedure involves the examination
of the nonh man impacts of these pollutants. This field of systematic assessments of
environmental impacts has, particularly in the past five years, been an area of intense
and valuable research. From its beginnings decades ago in resource planning, mostly
of water or land use, the assessment methodologies have been developed and applied to
virtually all of the areas where choices are to be made that have different environmental impacts. A significant review of this general field was sponsored by EPA
(V; Warner and Preston; 1974).
2.2
Existing Data Bases
There are about 40 key sources listed in (0; Gruhl; 1976a) of emissions or
ambient concentration (but not public health) comparisons of alternative energy
technologies. Perhaps the most important data base of this type is the one annually
updated by the government: "Energy Alternatives: A Comparative Analysis" (V; University of Oklahoma; 1975) see Table 2.2-1. This data base, among other places, can be
accessed at Brookhaven National Laboratories and can be manipulated using the MERES
-13-
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system (V; U.S. Council on Environmental Quality; 1975). As comprehensive as
this information is on the current and advanced energy technologies, except for
the occupational health data there is not much of interest to this current discussion. Emissions are collected in the categories: particulates, NO , SOx,
hydrocarbons, CO and aldehydes. Even these values are displayed without the
measures of uncertainty which can be retrieved from some of the original data
bases at Hittman, Battelle, and Teknekron (V; Battelle Memorial Institute; 1973),
(V; Hittman Associates Inc.; 1974) and 1975).
The greatest single source of information on data bases that are available i$
in the FEILS documents, Federal Energy Information Locator System (V; FEA; 1975).
That system is being improved but already includes listings of several regional
data bases, such as for Applachian coals, and lists of annual reports and data
bases, with a particularly strong emphasis on coal information.
Some of the other data bases exist in industry (particularly Exxon, Gulf, and
EPRI's control technology data base at Battelle) and several of the government agencies: in particular EPA (SEAS system, described in section 5.3; control technology program especially the PEDCO data base; synthetic fuels program; and the
Teknekron ITA project described in section 5.3); and a number of ERDA programs including the National Coal Assessment program and programs and data bases at the
national labs (particularly Argonne, Brookhaven, and Oak Ridge).
3.
Overall Mechanism Structure
There are several basic premises upon which the formulation of the assessment framework was based. First was the condition that although a "least cost"
criteria should be uppermost in the list of measures of desirability, there must
be capabilities for using any of a great number of alternative performance measures.
The second premise was that as much modularity as possible should be introduced into
the mechanism's structure, to facilitate investigation of many different fuel/plant/
control conbimations and to simplify the addition to the mechanism of entirely new
technologies at future dates. Finally, the quality of the results was to be quantified in terms of probabilistic measures of the "hardness" of all of the data, models,
and assumptions.
Figure 3.0-1 shows the flow of information through the structure of the
assessment framework. The technical factors, as described in Chapter 1 and in great
detail in Chapter 4, are the necessary and sufficient pieces of information and models
for making specific technologies known to the Simulation Mechanism. As shown in
Figure 3.0-2 the technical factors must be collected for a great many different
technologies.
no further
Figure 3.0-2
Schematic of all possible fuel/plant/control combinations
According to the Assessment Options chosen by the user, different control
technologies can be concatenated to form specific fuel/plant/control combinations
within the Simulation Mechanism. The Non-Technical Factors are all of the information
requirements that have not been automatically built into the imulation Mechanism
because the user may wish to parameterize these factors in sensitivity studies.
Included in these input requirements could be the choice of site types (population
-16-
U
-l
U),
I
X
>
u
I
a
04.)
U)
z
0:
0
H
NI
'
0
44
r.
.
I
H
o
lE
I
I
0
r4
4:
o
(A
Cu
en
S-
40
O
4
n
U
a,
co
Co
0
L.
OE _
'
_
_
_
'
-17-
_
_
'
_
__
patterns, climatological data, resource availability, and so on), regional factors,
accounting procedure options, regulatory variations, disinterest in types of results
(such as disinterest in health effects which then causes bypassing of all such calculations), and so on.
The Simulation Mechanism takes the Technical Factors and the Assessment Options
and simulates the process technologies (processing steps, operating conditions, flow
of pollutant and pollutant precursors into and through the plant, and the control of
discharges), power system interactions, regional assessment, atmospheric dispersions
(transport, persistence of species, aerochemistry, and product distribution), dosage
assessments, and so on.
The Resultant Factors that emerge from the Simulation Mechanism represent all
of the raw material necessary for making a decision about the desirability of this
particular single fuel/plant/control combination. These single facility Resultant
Factors can then be compared with other types of facilities, can be compared under
various sensitivity parameters, or can be aggregated with other types of facilities
to simulate regional or national situations.
3.1
Different Levels of Sophistication
Presented here is an initial plan for approaching the problem along with
a brief description of the rationale behind this approach.
It appears that there are three important levels at which the systematic
analysis could be used to evaluate and compare the control technologies:
System Level I: Isolated Power Plant
-ignore electrical interconnection
·ignore considerations and constraints of specific
regions
System Level II: Interconnected Power System
* consider electrical interconnecting and power
system operation/planning issues
.ignore considerations and constraints of specific
regions
System Level III: Specific Region of the Country
consider interconnected power system
consider primary fuel availability, transportation
issues, and other regional issues
Presumably, the initial descriptions and comparisons of alternative plantcontrol pairings that ANL will perform will be done at Level I (Isolated Power
Plant). Particularly where far-future technologies are being evaluated, that is,
where detailed operating characteristics are not available, Level I is probably
the most sophisticated level of analysis that can be performed.
Level II would deal collectively with all of the issues that are important
to power systems in general. For example, for a particular plant-control configuration it is necessary to determine:
1. the compatibility of the alternative's operating
characteristics with the various possible choice/use
patterns of power systems,
2. the fixed/variable costs implicit in the frequency and
duration of the alternative's forced and planned outages,
3. the position the alternative holds on the investment versus
operating cost scale, and so on.
Analysis at Level III, that is, for specific regions of the country, has
different uses and somewhat different data/model requirements than the other levels.
-18-
For example, sensitivity studies performed for specific regions would still include
the same evaluations made in Levels I and II, but they would require data (and quite
possibly distinct models) reflecting regional variability in issues such as existing
utility practices and structure, environmental conditions, resources, and so 'on.
It is important, of course, to define a set of factors and assumptions that
cover all three system levels, so that any subsequent higher level description/comparison will not require a restudy of the control techniques themselves.
Environmental issues could also be evaluated at different levels, considering
both direct and indirect effects:
Environmental Level I: Physical Mass Flow
·resource consumption
.emission rates
Environmental Level II: Physical Impact on Ambient Conditions
* air and water quality issues
*land use
Environmental Level III: Ecological and Health Impacts
.pollutant dosages and health effects
'impacts on materials and biosphere
The initial descriptions and comparisons of alternative plant-control
pairs thatANL will perform will probably be done at Environmental Levels I and II
with only a few selected studies at Level III. However, it is important to define
a set of factors that will be sufficient to make possible those few subsequent
higher-level environmental studies. This means that the input requirements for the
systematic analysis must be made with regard to all of the factors and assumptions
needed for all three system levels and three environmental levels.
3.2
Modularity and Updating
One of the most important initial assumptions about the structure and procedure of attacking this problem involves the determination of the split of responsibilities between Argonne and technical subcontractors for the data and modeling
requirements.
To clarify the implications of different assignments of responsibility it is
useful to refer to the diagram that shows the problem in an input/output typ.e
structure, see Figure 3.0-1.
Presumably the Technical Factors would be developed by subcontractors following the guidelines of the system analysis framework. Then, one alternative,
which has the maximum responsibility lying with ANL, and is referred to as maximum
modularization, would have the separate subcontractors submit independent characterizations of the modules involved. Separate data would be prepared for the coals,
generating facilities, and control alternatives. The Simulation Mechanism, of Figurei<
3.0-1, would then splice these parts together to form a characterization of an entire
energy facility. On the other extreme, the assessment mechanism would require the
different subcontractors to submit all necessary economic/environmental/reliability
characterizations for specific coal/plant/control combinations. While this extreme
would result in the most accurate representation it would be a futile task (for
example, 12 coals times 3 precombustion controls times 10 generators times 8 postcombustion controls yields nearly 3000 combinations) and it would not be amenable
to any easy updating procedures.
Maximum modularization has the greatest versatility and is the approach followed
throughout this report. This, as previously mentioned, greatly facilitates the updating procedure. New data simply displaces old data. New technologies can be added
-19-
through a simple procedure:
1. input and output formats are specified for the new technology
based upon the output and input formats of the upstream and
downstream technologies respectively;
2. Resultant Factors are checked to make sure all relevant models
and data are available that will produce numbers in these
categories; and
3. Non-Technical Factors are pulled out of all of the models, to
become exogenous input variables available for parameterization
studies.
-20-
4.
Technical Factors and Models
As described in Chapter 3 the technical factors and models are the necessary and sufficient information required to fully characterize a fuel or a
technology that is to be assessed. Section 4.1 deals with some of the overall
concerns involved in collection and use of the technical factors. Lists that
represent a first attempt at organizing technical factors for each of the fuel
categories and for each of the technologies are displayed in Sections 4.2
through 4.6.
The following figure, Figure 4.0-1, shows the internal, driving force that
the Technical Factors provide. The reason these factors are shown separate
from the Simulation Mechanism is that these numbers and models should be kept
separate from that mechanism to allow for ease in updating this information.
The schematic on the following page, Figure 4.0-2, shows the relationship
between the various modules for which Technical Factors must be developed.
USER ./
DECISION
MAKER
I
Ix
I
ASSESSMEN.TOPTIONS Ah'
ONSTFCPNS.CAL
INPUTS
User choices of regional
agcrecations or individual
I1
DISPLAY O
fuel/plant/control
combin-
RESULTANT
ations to be studied and
FACTORS
userchoicesof regional,
economic,and otherinformation to be studiedor
Sn vtsnm
NIO~t~v:E5l
Subjective
user
choices of
ordering and
l
DISPLAY Or
INTERIM
INFOPATION
TO USER
eliminating
information
and
parameterized
combinations
------- ---J L
TECHICAL FACTORS
Information
on
ndvtu
uel,~
plantontro
and
applicability,
perfornance-, and
reSquire.-ents
Figure 4.0-1
4.1
Modelsof accounting,
dispersion,
and
regional aggregation
concerns for fuels,
fuel treatment,
conversion plants,
and abatement
RESULTANTFACTORS
1 Comparabl'
information
on each
fuel/plant/
or
ontrol combinaton
control
coml~bination
or
regional aggregations
chosen for study
-.,."
ORDERINGMECXANTSM
CRITICAL FACTORS
Mchanism and report
generator for sortingcrucial
d-between
and diseightin, and
weighting,
playing the information of interest
Final information on
fuel/plant/
thedtfferenes
control combinations or
control oninations or
region a
qreations
chosen
for
study
Representation of the relationship of the Technical Factors to the
other portions of the overall assessment mechanism.
Consistency Assumptions and Common Factors
It is important to organize the collection of the required technical information in a pattern that is consistent across all of the technologies.
Ideal lists, that is, containing all reasonable types of data, should be sought
from the various information gatherers by furnishing for them: (1) clear examples, (2) insight into the rationale of the overall procedure, (3) a strongly
recommended type of probabilistic format, and (4) priorities on the relative
importance of different categories of data. Forms should be developed and
provided to the data gathers to ensure consistency, see Table 4.1-1.
-21-
$4
$4
0
rlr
03
4.
(i
0O
r-I
14-
C
C)
HO
C,
tj
,-4
so
,-4o
Co
rl
0
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-22-
Table 4.1-1 One of the many forms developed for use in the ECAS study
data collection effort (V; NASA; 1976).
(;) System base cases
Case
1 (basc)a
2
3
Parameters
Power output, MWe
Furnace type
Conversion process
Coal type
Additional parameters as used on parametric lists
Summary of plant results
Thermodynamic efficiency,b percent
Powerplant efficiency, percent
Overall energy efficiency, percent
Capital costs, dollars
Capital costs, $/kWe
Cost of electricity, e mills/kW-hr:
Capital
Fuel
Cperation and maintenance
Total
Estimated construction time, dyr
Estimated availability datee
Breakdownof plant results
Capital costs, $/kWe:
Each major componentf
Total for all major components
Balance of plantg
Site labor
Escalation
Interest during construction
Cost of electricity, mills/kW-hr, at capacity factor of 0.50
0.65
0.80
Change in cost of electricity with 20 percent increase
in capital costs, rlills/kW-hr
Change in cost of electricity with 20 percent increase
in fuel costs, mills/kW-hr
ausc base delivered fuel cost.
bprovidc where applicable. Definedas altemnating.cturrent output from prime cycle (and
bottoming cycle) ivided by heat input into prilie cycle (i.e., not including furnace or
gasifier efficiency or power output from furnace pressurizing subsystem.
CFor O.65 capacity factor.
dFrom start of site construction to plant on-line operation.
eFirst plant commercial ope ration.
fUse otll alternating-current plant outlpit anedcomnponentFOB manufactl'zrinlg
plant price.
gDocs not include site labor.
-23-
Table 4.1-1
(continued)
(b) Summary for each base case and each parametric point
recommend:d for ECAS Phase 2
Value
Paramete r
Performance and cost
Powerplant efficiency, percent
Overall energy efficiency, percent
Plant capital cost, dollars
Plant capital cost, $/kWe
Cost of electricity, mills/kW-hr
Natural resources
Coal, lh/kW-hr
Water, gal/kW-hr
Total
Cooling
Processing
Makeup
NOX suppression
Stack-gas cleanup
Land, acres/10
8
MWe
Environmental intrusion
Amount of pollutant, lb/MBtu heat input; lb/kW-hr.
SO2
NOX
HC
CO
Particulates
lHeat,Btu/kW-hr.
To water, where applicable
Total rejected
Wastes (type and quantity),h lb/kW-hr; lb/day
Major
component
Number Total
Cost FOB
Module Module
cost
of
from
weight,
size
manufacturing modules
lb
(width,
required
plant
length,
or diDollars $/kWe
ameter)
hAssuming rated output throughout 24 hr.
As a minimum, the components listed in the statement of work
and including cooling towers and emission control equipment.
-24-
Table 4.1-1
(continued)
{) Mallrlal. revtew I*.r ail a)stm base.ses ad
each cse recmtr~l
for ECASPhse 2
¥.Jor
coal
4.1.1
,
W.,lmbenmt
M.WerL I
Cuam.rce
at
Assumptions on Categories of Technical Factors
The technical factors listed in Sections 4.2 through 4.6 show considerable
similarities from technology to technology. Some of this similarity is due to
the common categories of technical factors that have been used; these categories
are defined as:
TECHNOLOGY
-specific technology such as specific type of fuel
treatment, or plant type, or abatement alternative
Input characterization
-all the information from the previous upstream technology needed to simulate the TECHNOLOGY
Resources
-requirements of the process that do not come from the
upstream technology, and outputs that are not Environmental Consequences, e.g.. saleable products
Economics
-all dollar measures that characterize the performance
of the TECHNOLOGY
Performance
-all non-economic performance measures
Applicability
-all constraints/limitations to the installation of
facilities other than resource limitations
Environmental Consequences
-all environmental outputs except those that are passed on to downstream technologies
Output Characteristics
-all information needed by downstream technology.
Another reason for similarities among the lists of technical factors is
due to the modularity approach that has been followed, see Figure 4.1-1, that
is, technologies that perform the same type of function, such as specific
fuel treatment processes, must all have identical output and input characterizations if they are to be interchangeable. Also, the output from one technology must be identical to the input of any of the possible downstream technologies.
-25-
-25-
Resources
ra------1
Technology
:j
t
I
TECHNOLOGY
I
I
Upstream
I
I
I
input
I
I
Economics
Performance
Reliability
II
I
Downstream
output
Technology
I
l
II.
I
I
I
- --
-
_ -4
Environmental Consequences
Figure 4.1-1
Relationship of the various types of Technical Factors
Finally, there is an obvious and desirable similarity between resource,
economics, performance, applicability, and environmental consequence technical
factors because all of these must necessarily be aimed at fulfilling the information requirements represented in the set of resultant factors.
4.1.2
Levels of Probabilistic Sophistication
The technical factors should be collected with the most possible probabilistic information. This would seem to be obvious but apparently there are strong
tendencies to use letters rather than numbers,such as: blank, -, or U for
unknown; S for small; L for large or possibly very large; and so on. This
type of practice buries information, making it not retrievable and not usable
in quantitative assessments (although it is amazing how often these letters
get added
in as zeroes
in a string
of numbers-).
There is, fortunately, a great deal that can be done in the apparent lack
of information. Take for example, and an example would be an excellent method
of informing data gatherers of this procedure, the fact that lanthanum levels
in a specific coal seam may be unknown. First, the level is certainly greater
than or equal to zero. Distribution in the crust of the earth is known to be
about 20 ppm. Lanthanum ores may generally be in the range of 600 to 1000 ppm.
Levels i oil shale are known to be about 30 ppm, so
there appears to be
some concentration in the energy storage process. Thus, as a crude estimate
one might set the range at 10 ppm to 600 ppm with a mean possibly at 40 ppm.
If this spread of numbers causes problems in the overall assessment then there
is clearly a need for research to reduce this uncertainty.
A priority ordering of sophistication should be put on the alternative probabilistic displays so that if there is a choice in the data collection procedure the preference is clearly known. Such an ordering might be as follows:
(1)
graphic display of cumulative probability distribution versus the
values, see Figure 4.1-2.
(2)
mean, maximum, minimum, standard deviation
(3)
mean and two confidence limits such as 100% (maximum) and 0%
(minimum)
(4)
or 95% and 5%.
two confidence limits.
-26-
1
n
probability
value
.9
is
less than
given level
.8
.7
.6
.5
I.
.4
.3
.2
.1
value of
--.
.0
V
--
0
Figure 4.1-2
In addition
24
25
26
27
28
29
30
F .-t r
Shiv
Example of a cumulative probability distribution curve
to the shape,
or characterization,
of the probabilistic
nature
of the piece of data, it is desirable, in the ideal case, to know the nature
or type of uncertainty being considered. One could, for instance, talk about
a probabilistic distribution for each of four types of uncertainty associated
with a data point: the unknowable, averageable uncertainty; the unknowable,
unaveragable uncertainty; the descriptive, averageable uncertainty; and the
descriptive, unaverageable uncertainty. Definitions and examples of these
terms are contained in table 4.1-2,
Table 4.1-2
1)
Distinctions between different types of uncertainty
unknowable
-that component of uncertainty that is either totally random
or beyond any current method of analysis.
example: randomness about the expected value of cost of money 20
years in the future.
. Is
2)
descriptive
-that uncertainty that describes a range or distribution of
numbers which, for a specific dollar and time investment, can
be eliminated.
example: range on a ppm level of beryllium in a particular coal seam.
3)
averageable
-uncertainties, that are generally descriptive but could be
unknowable, which over a number of units or over a given period
of time will average out.
example: error in hour-by-hour knowledge of meteorological conditions
that over a year can be expected to average out.
4)
unaverageable
-uncertainties, that can be either descriptive or unknowable, that
will-not average out over a number of events.
example: any uncertainties for which there is only a single events
such as probability of technological feasibility, and that must
be treated by decision tree methodologies.
__
__
1
___
4.2
Fuels
There is a single set of technical factors.that should be used to describe
(1) all of the fuels that feed the fuel treatment equipment, (2) all the fuels
that result from the treatment processes such as physically cleaned coals, and
(3) all of the fuels that are combusted directly. An example of such a set of
fuel technical factors is given in Section 4.2.2, Low Sulfur Coal.
For the unprocessed coals many of those fuel-type technical factors are
available from Bureau of Mines publications and other documents listed in Section
9.1. Table 4.2-1 from (I; Ruch et al.; 1974) is an example of national average
constituency breakdowns. Other publications, such as (I; Abernathy and Gibson;
1953) and (; ESSO R&E Co.;1973), contain similar displays of data on seam-byseam bases.
For processed coals, except for some careful treatment of sulfur contents,
many of the technical factors, particularly in the constituency breakdowns, are
not published and are likely not to be known. In the absence of data, it would
be advantageous, initially at least, to have estimates available. Unfortunately, approximations of the treatment of some of the coal constituents in the
physical coal cleaning processes may be very difficult to develop. For example, one might expect mercury levels to decrease roughly in proportion to the
ash level reduction during the physical separation of pyritic sulfur. This is
apparently not the case since the mercury is sometimes closely associated with
the pyrite material and thus significantly 50% to 60%, removed in some coal
samples. These types of peculiarities, especially those involving the suspected "hazardous" components of coal, deserve careful further research.
4.2.1
Representative Coal Types
The various representative coal types should use the same technical factors
as those listed in Section 4.2.2, Low Sulfur Coal. The difficult question
discussed here is how many coal types, and which ones, should be used to make
the overall assessment mechanism as flexible as possible. For example, if 15
to 20 types are selected they should be regionally representative (see Figure
4.2-1) to allow for a good national overview, and they should be constituently
representative, so interpolations or extrapolations can be made from the information available on the few tested types to determine the approximate performance of some new specific coal type.
Three examples of previously used representative coal types are listed in
Table 4.2-2; they include 3 samples, 5 samples and 57 samples. These and
other choices of representative types are generally made by considering: (1)
the greatest number that can sensibly be handled; (2) the type of application
involved; and unfortunately (3) the availability of information. An example of
this selection process follows.
Table 4.2-1 Probabilistic display of the constituents of 101
representative U.S. coals (I; Ruch et al; 1974).
Mean
Consttuent
I
As
B
P.e
14.02ppm
102.21 ppm
1.61 ppm
StarndanDevIation
X4nim=.
Max lum
17.70
54-65
0.82
0.50
5.00
0.20
93.00
224.00
4.00
5.92
7.60
7.26
7.26
4.00
0.10
1.00
4.00
52.00
65.00
43.00
5.00
Br
Cd
Co
Cr
15.42 ppm
2.52 ppm
9.57 ppm
13.75ppm
Cu
15.16ppm
F
60.94 ppm
20.99
Ga
Ge
lg
Ma
Mo
Ni
P
Pb
Sb
Se
3.12ppm
6.59ppmn
0.20 ppm
49.40ppm
7.54ppm
21.07ppm
71.10ppia
34.78 ppm
1.26 ppm
2.08ppm
1.06
6.71
0.20
40.15
5.96
12.35
72.81
43.69
1.32
1.10
Sn
4.79 ppm
.6.15
V
Zn
Zr
Al
32.71 ppm
272.29ppm
72.46ppm
1.29%
12.03
6954.23
57.78
o.45
Ca
Cl
0.77 %
0.14 %
055
0.14
0.05
0.01
2.67
0.54
Fe
K
Mg
1.92%
0.16%
0.05%
0.79
.6
0.04
0.34
0.02
0.01
4.32
0.43
0.25
Ka
Si
Ti
0.05
2.49 %
0.0 %
0.04
0.80
0.02
0.00
0.58
0.02
0.20
6.09
0.15
Org. S
1.41%
0.65
0.31
3.09
Pyr. S
Sul. S
Tot. S
SXR?
ADl.
Mots.
Vol.
1.76%
0.10%
3.2 %
2.91 %
7.70 %
9.05 %
39.70 %
0.86
0.19
1.35
1.24
3.47
5.05
4.27
o.6
0.01
0.42
0.54
1.0
0.01
18.90
3.78
1.06
6.47
5.40
16.70
2o-0.70
52.70
FLx. C
48.82%
4.95
34.60
65.40
Ash
Btu
0
.
8.12
7.50
43.00
1.60
181.00
30.00
83.00
400.00
218.00
8.90o
7.70
1.00
51.00
11.00
6.00
8.00
0.43
78.00
5.350.00
133.00
3.
0.22
2.41
0.78
t.15
1.84
1.03
3.28
25.85
1.30%
8.68 %
N
0
1.10
1.00
0.02
6.00
1.00
3.00
5.00
4.00
0.20
.45
25.80
14,362.00
80.1;
2.89
4t.50
.
61.00
143.00
2.20
11,562.00
55.23
11.44 %
12,748.91
70.28 %
%
Hi1~ 4.95
5.00
25.00
3.87
0.31
W'A
11.41%
2.95
LTA
15.28
4. o3
4.o3
.82
5.79
31.70
other than standard chemicail symbols: organic sulfur (Org. S), pyritic
sulfur (Pyr. S), sulfate sulfur (u!. S), total sulfur (Tot. S), sulfur by X-ray
fluoresconce (SCRF), air-dry loss (ADL), moisture (Mois.), vglati!u rna[ter (Vol.),
fixed carbon (Fix. C), high-tmrnperatura ash (HTA), low-temperature ash (LTA).
Abbrevations
-29-
c~
rl
H
01
r4
a)
H
4J
a
0
C
3,
13
5
r-
c
W
44
O
,
o
a)
c(
r-
Li
in
C'~
-c~
~
r-
]
LI.f
J
.I
O)
a)
0
a)
jr-
to
Y11
r
0
10
0
0z
l
I
)
0
(O.
a,
To
-H
Pr4
-3Q-
Table 4,2-2
Representative Coal Types from Some Previous Studies
ECAS study (V; NASA; 1976);
Illinois #6 Macoupin County
Montana sub-bituminous Rosebud County
North Dakota lignite Mercer County
EPA study(IV; Forney et al.; 1974):
Pittsburgh seam coal
Western Kentucky
Illinois
#6
Wyoming sub-bituminous
North Dakota lignite
DOI study
(I; O'Gorman
et al.;
1972):
Elkhorn #3 15"-23" from bottom of seam Deane, Kentucky
Elkhorn #3 23"-31" from bottom of seam Deane, Kentucky
Elkhorn #3 31"-40" from bottom of seam Deane, Kentucky
Elkhorn #3 15"-22" different area Deane, Kentucky
C Seam 50½"-60" from bottom Benham, Kentucky
C Seam 40½"-50½" from bottom.Benham, Kentucky
Illinois #6 Victoria, Illinois
#2 Colchester Vermont, Illinois
Illinois #6 Carrier Mills, Illinois
Lower Sunnyside seam Horse Canyon, Utah
Buck Mountain seam 7"-19" from top Zerbe, Pennsylvania
Buck Mountain seam 31"-39" from top Zerbe, Pennsylvania
8½ seam Shamokin, Pennsylvania
8 seam Shamokin, Pennsylvania
8 leader Shamokin, Pennsylvania
Zap seam 106"-130" from top Zap, North Dakota
Zap seam top 18" Zap, North Dakota
Unnamed seam grab sample Gascoyne, North Dakota
Unnamed seam 45"-66" from top Savage, Montana
Unnamed seam top 45" Savage, Montana
Unnamed seam 66"-70" from top Savage, Montana
Queen or #4 seam Carbonado, Washington
#80 seam Hanna, Wyoming
School seam Glenrock, Wyoming
Roland seam Gilette, Wyoming (2 samples)
Pittsburgh seam 25"-35" from base Washington County, PA
Pittsburgh seam handpicked Washington County, PA
#1 Block seam handpicked Jefferson Twp., Indiana (2 samples)
Pittsburgh seam top 10" Marianna, Pennsylvania
Pittsburgh seam 10"-35" Marianna, Pennsylvania
Pittsburgh seam 35"-54" Marianna, Pennsylvania
Pittsburgh seam 54"-72" base Marianna, Pennsylvania
Lr. Kittanning Tire Hill, Pennsylvania (2 samples)
Lr. Freeport Ehrenfeld, Pennsylvania
Tioga seam 35" from base Tioga, West Virginia
Tioga seam 22" section near base Tioga, West Virginia
#5 Block seam top 18" Bickmore, West Virginia
#5 Block seam 18"-33" Bickmore, West Virginia
Lr. Freeport Hastings, Pennsylvania
Table 4.2-2 (continued)
_
__
_
Lr, Kittanning Ebensburg, Pennsylvania channel
Lr. Kittanning basal 9" Ebensburg, Pennsylvania
Lr. Kittanning top 11½" Ebensburg, Pennsylvania
Lr. Kittanning 15½" from top Ebensburg, Pennsylvania
Pocahontas #3 1"-14½" from top Gary, West Virginia
Pocahontas #3 bottom 13" Gary, West Virginia
Pratt seam 18" thick middle split Hueytown, Alabama
Pratt seam top 15" Hueytown, Alabama
Pratt seam lower 14" Hueytown, Alabama
Darco selected streaks Darco, Texas
Darco 34"-82" from top Darco, Texas
Darco top 33" Darco, Texas
Hartshorne seam channel Heavener, Oklahoma
Hartshorne seam lower 5" Heavener, Oklahoma
Colorado B seam Redstone, Colorado
First, to find coals representative of regional, non-coking quality reserves, the regional economically recoverable reserves must be examined, see
Table 4.2-3. Keys to that chart include:
AN - anthracite; BT - bituminous; SB - subbituminous; LN - lignite;
H - more than 7167 Kcal/kg; M -.4478 to 7167 kcal/kg; A - less than
1% sulfur by weight; B- 1-- 3% sulfur; and C - more than 3% sulfur.
From these figures a representative sample of the largest regions might
include: AppalachianBasin - AN, BT; Eastern Interior - BT; Western Interior
BT, LN; Northern Rocky Mountains (or Great Plains) - SB, LN; Southern Rocky
Mountains - SB, LN; Alaska - SB; and from a large unlisted area, Gulf - LN.
This yields eleven types.
Table 4.2-3
Regional coal resources of the United States (I; Peck(ed); 1974).
Annual
'ecent
-
-
Name of Continen it,
ainda
Reqion.Countr
Na*ional SubdIviision
PBae
of Total
Ref. Fuel Amount
in
Year
of
Place
(neqatonne)
i
1 d4i-
0esr V .
nosits
nT
,n'axieaUS itua
Pln
o
Pconoicaly
Recoverable
eserves
otal
I
A
_ ercent
ional ResoutSil- oZat ValuI Resources
ohur ieat asis
e gaCon- '1 - (As
onne)
ine4
tegaue
tont I
Feas
l ptelh
Cf
Amount By
or
as
tonne!
( ) Thick(mega- Sr- CokBurnness
tonnel face ing
-1i)
(P)
i n- ICualr
;_·_
_-,
ear Anount
(kilotonne)
Ba. ls
(GrosS
or
et)
I
(1) 12
(17)
-
10) I (1 1) I
(7)
)
l{v
I
111
11l
(1 2)
(1
(1 3!
)
({1)
(1A)
-i
-
'
NORPHAERICA
Northern America
ninite4States
Appalachian
(a.b,c)
asin
tastern Interior
western Interior
1972
1972
1972
1972
1972
1972
Worthern ocky ountains
Southern "ock? Fountains
Pacific Coart
Alaska
A
BT
BT
61.49
BT
1'19
LN
1972
197
1977
197;
197;2
United States
TOTAL
BT
SB
AN
IB
. a
LN
II
TOTAL ALL RANIKS
I1-
-
I
2177
835
17414
2 LI
?
3n1?8 3n.0n
616
PT
197; B
197i SB
0
7710 12.0 8.00
AY
SsB
AN
0
31.751
53698
39909
1.la1
707
LI
5714 50.0
113919 56960 1.09 65.0
8308t ut5 1 10.0 1.00
AN
1972 BT
1972 SB
1972
1972
11u21
26'7 59.0
19954 26.0
9.070
a15s 52.
7.00
R707
-
-
281.2
1U0.6
36.2R
1
10111 'aM.2
407.0
7075
.1
0
305.0
0
s.n
BC
o."00
A
-
--
ln.0
1.50n
-
- 16.0 3 5.0 0.7n
21.0
T i
10t°
0
1 s.n
23055 27.6
105.
0
105.0
363562 1A1781 -
0
-
_32-
I.so i
-
I.%50IA
7u q
176112
AN.
AMl
21
10
Al
1971
1971
72'19
1q71
1071
1071
1971
1971
120qa
1A 769
12602
A,
19906r
2902C1
130.0
12^
188 q.5
H
AR
S'91
1971
5q05
74. 1
56.21
q
1'
71R91
115733
1971
I0a227
57q9n40
i'
A
jA
0.7n0, A
0
11
AL'C
0.700
105.0
1t.0
0
5755 9.6
11510
235176 1176PR 09 31.
70565 15202 6; 7
46117
1.S0n
n 3cr.0 1.500 ABC
'ABC
0 315.0, 1.59
O 10.0 O.700 In
'
1.500 A
0 l.;.
-
.13.51001
3537
05.0 0.'C A
105. .7n0 aBC
305.0 0.7n AEC
.700 sC
.O
1 105
105.n O.,l0 APC
150Rn71
1071
u;.551
U.9 1 5
Iq71
1"505
AM
AM
137'q
997.
3197%
Al
15'067
A"I
h"m
204418
7u 37
1224 '1Af
A
I
1, 81:
I107111111
5 126 1E
I
_.,AX..
C
I Z7'·s
,
)
61P 711f
292? 4n:
--
l nnual
- .
rs
1v
ing
I
1971
22. 6
1971
C134
618.
7R9
4950uU
10SF
n89
51211
I
To totally cover the range of constituencies of coal is an impossible
task; there is a virtual periodic table of elements in coal to say nothing
of the compounds. A good starting point on classifications is available from
the American Society for Testing and Materials, see Table 4.2-4. Care in the
choice of the regionally typical lignite and subbituminous types might possibly
cover those groups in Table 4.2-4. The one regional anthracite type should
probably be from the second group of Table 4.2-4, and considering the scarcity of U.S. anthracite coal this should probably be all of that class that
is represented. To represent all the bituminous classes (plus possibly one
additional subbituminous) might add three new samples to the set. Additional
samples may be required to adequately cover the ranges of ash fusion temperatures.
It is interesting to make a comment on occupational health effects at
this point.
It has long been known that there is a significant difference in the occupational effects, specifically pneumoconiosis and massive
complications, of mining different coals. Recently correlations between these
effects and the ASTM coal rank have been noted (personal communication, Dr.
Bruce Stuart, Battelle Northwest Labs, October 1976) and thus occupational
health consequences per ton of coal can be modeled more adequately if the
ASTM breakdown is used in sample selection.
Because of the potential importance of trace metals in public health effects modeling it would be desirable to also include some coal samples that
contain high levels of some of the more hazardous, volatile elements. This
would require additions of 4 to 8 new samples of lower-rank coals from Texas,
Colorado, North Dakota and South Dakota. In particular,. some of those trace
elements, that it eastern sources are in about the lO0ppmrange and in western sources can be in the 100 to 1000ppm range, include: arsenic, beryllium,
chromium, cobalt, copper, lead,manganese, molybdenum, nickel, vanadium, and
zirconium (V; Hub, et al.; 1973).
Sulfur ranges, of course, are also very important in characterizations
of coal (I; Hoffman, et al.; 1972), especially since they can markedly affect
the performance of some system components, such as precipitators. The sulfur
content classes considered in that reference include 0.8 to 1.1 percent, 1.1 to
1.5 percent, 1.6 to 2.0 percent, 2.1 to 2.5 percent, and 2.6 to 3.0 percent.
These five sulfur classes can probably be covered in the regional and class
coal choices. They can then generally also be used to define the prices of
coals, see Figures 4.2-2 and 4.2-3.
Weight of coal can be calculated from regional and BTU information, see
Figure 4.2-4, for example, and from this data the transportation costs can be
estimated, see Figure 4.2-5.
-33-
IU
_YIUII
'C C'j N N
V)'
-
Cl
-S cS
I
e
rEC1
~c'C N
>
N
C
v
N
r-
^,
c
I
I
1'
ci
V
ci
N (N '0) O
1
rc ,
_
N
Z (l
:C
90'~
,
I3
Cr
l-
"0
X,
,
Acc cu
b
c,
>
A CNo
cc
-
*>
·-t
I
CA
AN z cc4 6et (N
N c c
N C
o
C x
o
(
rC--c
ct
\d
(N CN (N -
(S
4
-:
C1l
j Py
' ts e
q~~~~y
CI
'c
c
el
LS
co,
n
'c
< I
C
Cr
ti
c
In
'D
N
C
<
-
G1s
'o
0
'0
'I
Cl
*(
'
o
a
*-
V
1'
·-.
*0
to
ci 11~
a)
Cl
_Cl
Cl
0=
-t l - '0 rl) (N!
(N In
I.0
'T
1-q
1,
H
ct
-o ooc
r- ,4 'oo
0C
V)
_ Vc
C-1 < 4 C-4 C 1"
I
co
V
V
ci
C0
' 00000
`4
ui
000 o
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C-r- c00
C- -t
CO
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00 0 Ic .0 r-
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Cl
co o..
0ooo ,
I
I I I I I o
00ro
0000
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4
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rO
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o 'n
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pt
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ci
co
"t
Cd
cr
ci
--·
-4)
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)
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zc
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CSn
CS - C
'0
cc t 0 GO0
(N C C)
t
.O
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,j
zo
CO .-
I I
-
I
I
00 'v) o 'l cN
CS) CS C) (In CS
,-(N A m'
V
i
C- C
(-
Cl
-,'(N'Cl
V
>1
r. 4
clt
c-
o
,
c
C.
A cc c
CO
V) '-0 C (
1
mV) V)
4cn
(N
Ici.
c a
Cl
-- 0
-
'Cl (
,
tcCS
'n
C C-- ( 1.0
'
C CS
*4
U
,
cI c -r-
c0
C\ a:
i-
c:
m*o
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'.
0
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N (N
Inl n v"
.0 0·.
_
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1
Co
C
Cl
Zi
C)
,
co
I
I
Cl
'C-
-j-(
>
--
Cl
Cl
Cl
l
C,:
_
.
i
._
." Z 0
>>
z
_
_0 -0. .> c. 0S. . ( c
Cl<
-.0 .N
_nl
L·4,
C
'
-
0
0
C
Cl
:3
C)
.'t
C
C
O
.E
E
.0
Cl
Cl
CA
-:
·
_,
.
0
-
"
(0
-4
:
C
Ei
ri2:
(14
'
rU
0
-
r-
o
C0
o
0a)
E-4
'-.
'-C
cia
zl Cl ': c:
(A
u0
Zci)
0. Ql
Q
3
CDO
._
oC
4i
Cl
Cl
.0
-o
1f
zQ
'C
-Q
(1
r-
'
a)
E(H
f4
Zs
Cl
C._
Cl
._
c
:2
-Cl
It!
--
'C'
-
(.4
C
cI
r-4
I
I
130
120
1.0-1. 5%S
110
100
3
-
90
_
80
_
<1.0%S
70
_
,2.5%
60
_
50
40
30
_
20
_-
10
.
.
I
-
l
J
S
N
1972
Figure 4.2-2
J
: pa---------
M
M
I
I Ip~~~
us
I
I I CI
S
J
1973
I
I
N
J
MH
----
l
J
S
1974'
Prices (FOB) of coals by sulfur content (I; Lethi, et al.; 1975)
-35-
___I__
__
225 F
I
r:
200:
i.
175
.
Coal "
150
125
... ..
1
100 --
Uj
Coal
"B"'-
.1
I
|tw
-
_Of
.
,
501
..
Coal
.,
I
'.
.
-.
.,. :
. 0
"C" '
I
i
I
I
I
I
I
I
i
II
-i
I
L
_
0.2 0.4
0.6
I
_,_
0.8
1.0
I
I
. .
1.2
1.4 1.6
1.8
2.0
2.2
2.4 2.6
2.8
3.0
3.2
% SULFUR CONTENT
Figure
4.2-3 Prices of coals at minemouthas a function of sulfur content
(V; Center for Energy Policy;
1976).
3.0
2.0
m
EA
D
g:
BTU
Pc
0
U,
pi
w
.,"4
OIL)
en
0
1.0
L)
0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
PERCENT SULFUR BY WEIGIIT
Figure 4.2-4
Sulfur content, Btu, and tonnage relationships
(I; Rosenberg,
et al.; 1972).
_
7
__
6
5
ESTIMATED COST
DOLLARS/TON
4
3
2
1
0
I
I
I
I
100
20f3
300
400
DISTANCE
Figure 4.2-5
IN MIvLES
Transportation costs by coal types and mode (I; Ouellette; 1972).
-37-
4.2.2
Low Sulfur Coal
The previous section described the way in which a regional breakdown of coals
could be used for choosing representative samples. There is ample opportunity
to characterize the different low sulfur coals in these regionally typical
selections. By region and sulfur content a very representative list would
include (I, Given; 1974):
Table 4.2-5
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
A set of coal types that is representative of U.S. resources.
bituminous--Appalachian--low sulfur (less than 1.1%)
bituminous--Appalachian--medium sulfur (1.1 to 3.0%)
bituminous--Eastern Interior--high sulfur (greater than 3.0%)
bituminous--Western Interior--medium sulfur
bituminous--Rocky Mountain--low sulfur
subbituminous--Northern Great Plains--low sulfur
subbituminous--Rocky Mountain--low sulfur
subbituminous--Alaska--low sulfur
lignite--Northern Great Plains--low sulfur
lignite--Gulf--medium sulfur.
All of the coals that are chosen to represent the types available in the
United States must be systematically characterized. A number of excellent
breakdowns of the properties and constituents of coal can be found in sources
in the Fuels bibliography 9.I, or in some of the many Bureau of Mines documents,
see Table 4.2-6. The ranges of values as shown in this table can be very useful in developing worst and best case hypothetical samples from these particular regions. The samples that are actually used in any combustion testing, or
that are found in the literature associated with specific combustion tests,
should whenever possible be characterized to within measurement error.
Under certain assumptions about the location of use of the coals there
can be an immediate determination of the delivered prices, as in Table 4.2-7.
From the standpoint of retaining maximum flexibility in this project it would
seem advisable to tabulate prices at the mine and then make determinations of
the transportation costs under whatever regional assumptions may be desired by
the decision maker. The determining factor in how these transport costs should
be handled will have to come out of an examination of the magnitude of those
transportation costs relative to the magnitude of the uncertainties in the
other costs in the assessment.
Fuel type
Base fuel
Minimum
cost
range,
(delivered),$/j TU
$/MBTU
Coal:
TnhI
A
-
-7
Illinois 6
Montanasubbituminous
0.85
0.85
0.50 -1.50
0.30- 1.50
North Dakota lilgnite
0.85
0.25 - 1. 50
T) c %1n
n
^ -#-' ,
A
~4 C4:. - ,- -
-
r--
17
VCA
1 0-7 4
II_
lb
o
0
1I
§
I
I
I
l
II
I
II
I
I 10
0
l
l
tC-
-
.- l
CO
I.
In>
-4tO. CI
I
I
I~n
t--ol·
0
0
4
0~~~C-
10
0
Ci
.4
lb, C)
C'
-0C
co
c
-
Q
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C)
.
C
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C)
C-I
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c1
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C^ C6
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C
0
0
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%h
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I lb
JIC-O
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b
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C-I
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Lc-C
m
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o
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3
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-
,
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CD
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8
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C)
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0
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0
10 0
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.·:00
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=!f.2lg-2
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Coccn
0
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::
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The following, Table 4.2-8, is the first in a number of ideal lists of
technical factors for the control technologies. In many categories the data
does not exist and thus expert opinions should be sought. Not applicable
should be marked "n/a". Blanks should be left only if the number is known only
to be between plus and minus infinity. For example, cadmium levels may not
be recorded in the coal science literature but they are certainly between 0
ppm and the level of low-grade cadmium ore (perhaps 1000 ppm). The numbers
presented in the following lists of technical factors are only given as indications of the types of numbers to be sought.
Table 4.2-8
_
DESCRIPTORS
SCOPE:
Technical Factors for Representing Fuels
__
Restricted to U.S. coals
ASSUMPTIONS: Cost escallation per year for rail transportation + 3%, etc.
all bounds are 5% confidence limits, STDV are arithmetic standard dev,
Sample Name
#15
ASTM Rank
Bituminous; Medium volatile
Coal Geographic Origin
Central Appalachian Basin
_
L
Probability Levels
QUANTIFIERS
5%
Economics
mean
95%
Price
per ton versus sulfur content and other variables at specific years
Price per Kcal
"
"
I"
;I
Transport cost to regional markets:
Unit cost of tranport (mills/ton-mile)
5,5
7.2
10.5
Discount rate for est. ann. capital charges (%)
12
14
16
(parameterize w.r.t. this variable)
Applicability
Recent annual production(or time to develop)
year
net amount (kton)
Transportation limits on production
Manpower limits on production
Equipment availability limits on production
Resource Needs
Known Resources
Total amount in place
6
(10 tons)
Economically recoverable
(10 tons)
By strip mining %
By deep mining %
Of coking quality %
Known marginal/submarginal resources
Undiscovered Resources,
Bed Thickness
Recoverable
Overburden
Marginal/submarginal
-40-
1973
3500
0
0
0
1973
3900
0
1973
4200
0
0
0
10000
5000
11300
5600
8%
8%
92%
92%
0
0
13000
6200
8%
92%
62%
65%
65%
6000
6200
8000
2000
4000
3000
6000
5000
9000
Table 4.2-8
_
_
_
_
___
_
_
(continued)
__
____
__
Environmental Consequences
Environmental production restrictions(all in 10 tons)
0
0
By current regulations
400
450
By projected futute regulations
0
O
Water limited
Emissions fto be added if downstream processes are less efficient)
rom mining - (surface and deep-mined separately)
Output Characterization
Particle size distribution
Grindability index
Caking quality
Fixed carbon dry (%)
moist ()
Volatile matter dry (%)
Volatile matter moist (%)
Natural moisture (%)
Heating values
(kcal/kg)
Dry basis
Moist basis (kcal/kg)
ASh
42
45
47
78%
73%
14%
13%
80%
75%
18%
17%
86%
81%
22%
21%
5%
5%
5%
8600
8200
8640
8275
7.3%
5.0%
5.0%
1.5%
(%)
Oxygen
Hydrogen
Nitrogen
Sulfur Inorganic
Sulfur Organic
Phosphorous
Potassium
Chlorine
Silicon
Calcium
Magnesium
Sodium
min 0.2% max 0.8%
min 0.6% max 1.0%
'3%
8750
8350
STDV 1.2%
1.0%
0.8%
0.2%
.1%
.7%
.1%
0.2%
0.1%
3.2%
0.3%
1.0%
0.67%
0.20%
0.25%
Iron
0.05%
Aluminum
Other
0
500
O
0.03%
1.50%
25ppm
1000 ppm
500ppm
700ppm
300ppm
1000ppm
.025ppm
6ppm
500ppm
10ppm
.015ppm
3ppm
1000ppm
10ppm
100ppm
125ppm
(X)
Arsenic (PPM)
Barium
Beryllium
Boron
Copper
Germanium
Gold
Iodine
Lanthanum
Molybdenum
Platinum
Selenium
Strontium
Tin
Uranium
Zirconium (PPM)
Flourine
Cadmium
Mercury
Lead
-41-
lppm
200ppm
100ppm
200ppm
lOppm
500ppm
.002ppm
.2ppm
100ppm
lppm
.001ppm
lppm
10ppm
lppm
50ppm
50ppm
Table 4.2-8
_
_
_
_
(continued)
__
_I
_
_
_
_
Vanadium
Chromium
Cobalt
Nickel
Zinc
Gallium
Yttrium
Lithium
Scandium
Manganese
Ytterbium
Bismuth
NaCl,
Compounds of interest: SiO , Al 0
SO ,
F20 , CaO, Mg8, Na O K20,
TiO , FCO
3
FeS2 , CaO 3 , nC3, FeCO3, and nCO 3.
Free Swell Index
Fusion Temperature
Plasticity
Porosity
Number of Samples Analyzed
12
__
-42-
I
4.3
Fuel Treatment and Conversion
Fuel treatment modules represent information and models of all the
different types of fuel-to-fuel conversion technologies. The input as well
as the output from each of these modules should be the generalized characterization of fuels as previously shown in Table 4.2-8. Having identical input
and output information categories facilitates the use either of no fuel
treatment or, alternatively, the concatenation of two or more fuel treatment
technologies, such as one type of physical coal cleaning feeding into another
type, or a coal cleaning device sending fuel into a solvent refining facility.
The output from the fuel treatment module becomes the input information for
the generation technologies, as shown in Figure 4.3-1.
Economic and Technological
Resource and Commodity
Requirements
Economic and Technical
Information
Figure 4.3-1 Different modeling tasks for the single fuel/plant/control
simulation.
4.3.1
Coal Preparation and Cleaning
There may be 20 or 30 different technologies that will require assessment
as potential coal preparation and cleaning types. These processes are defined
as those that sort out certain constituents of coal by taking advantage of
some physical differences between those constituents and the rest of the coal.
Technical factor categories for describing these processes are shown in Table
4.3-1. Models of each of those processes must be capable of taking the input
characterization and turning out the economic, performance, applicability,
resource requirements, and environmental consequences as well as the output
characterization. For example, one particular type of physical coal cleaning
may have a functional model for relating mercury levels at the input to
mercury levels at the output, perhaps something like
Hgout = Hgin · 0.9 (Sulfurpyr/Sulfur
tota
pyr
uutotal)
-43-
Table 4.3-1
___
_
Technical Factors for Coal Preparation and Cleaning Processes
_
DESCRIPTORS
Scope: All coal preparation and cleaning processes current and future-known
that do not change molecular structures but sort on physical properties
Assumptions: Units are all tons of product, where there are variations for
different coals these should be tabulated, correlations between numbers in
ranges should be noted as well as whether ranges are due to choice or uncertainty
All bounds are 5% limits, STDV are arithmetic standard deviations
Process Name:
Process Description: Including "optimum capacity','i.e. concensus, typical size
Relative Complexity
QUANTIFIERS
Input Characterization
-(sameas low sulfur coal chart) including binding of materials (e.g. trace
metals to organic sulfur, etc.)
Economics
Capital Investment ($/10,000ton/day) for optimum size
Operating Costs
($/ton)
Labor
Maint
Energy
Transportation costs to Regional Markets(mills/ton-mile)
Typical Amortization Rate for This Type of Facility
15% 16% 17%
Effective total energy cost (price)
Additional investments for potential abatement req,
Performance
Capacity -optimum and range
Production -optimum and range
Availability
Reliability
Forced Outages (freq. and duration)
Maint Outages (freq, and duration)
Yield (tons'in/tons out)
}
Output per 1Manhour
Energy losses
Caloric losses
Ancillary energy needs
Cleaning efficiency
Organic sulfur (% extracted)
Inorganic sulfur
Total sulfur
Ash
Nitrogen
Trace elements
Moisture
Expected Facility Lifetime
Applicability
Current largest sixe
Operating experience
Commercialization date (2nd 10,000ton/day plant)
Expenditure to get to commercialization
Potential for advancement of technology(in reduced cost/output/year)
Retrofittability with advances (compatibility)
Institutional.constraints (including licensing time)
Probability of Technical Success (% at all feasible)
AA-
Table 4.3-1
(continued)
.
-I
-
-
Geographic limitations
Particle size distribution needed at input
Site-specific requirements/constraints
Time to get a plant on-line
Unresolved issue
Maximum rate of availability
(plants/yr)
Other constraints to development
Resource Needs
Manpower skills
Manpower numbers
Manpower availability
Land use
Principle equipment needs
Equipment availability
Materials needs
Water requirements
Chemical needs
consumption rates
recycling potential
waste disposal costs
land used in mining, disposal
Other resource needs
Saleable products
physical and chemical characterization
production rates
marketing price
market limits
-8
Environmental Consequences (consequences that fall out at this stage)
Occupational health
Emission standards
Emissions (normal and upset)
including emissions frOm mined materials (other than fuels)
Air pollutants
Water pollutants
Waste solids
Land requirements
Pondage req.
Noise
Other effects
Frequency and duration of upset conditions
Output Characterization (incl. any env. conseq. that are passed to downstream
(same as input characterization)
technologies)
particle size distribution
alkali (potential for additional sulfur capture in combustion)
-45-
4.3.2
Solvent Coal Refining
Solvent coal refining and coal liquefaction offer a variety of methodologies which have been brought to various stages of commercial development
in the past 100 years. Because the major impurities in coal sulfur, nitrogen,
and ash, especially trace metals are tightly bound in the original coal matrix,
their physical removal is difficult. One approach to the control of these
impurities is to change the coal matrix in such a way as to facilitate the
removal of those impurities.
The refined coal is essentially composed of carbon, hydrogen, and oxygen
and can be produced as a solid or liquid fuel. Processing chemicals are,however,
needed and the impurities must be disposed of after removal. Because of the
long history of these processes there is a considerable body of literature on
them and their basic data,
A comparison of solvent refining and other cleaning processes should consider the disposal of impurities after their removal. If the solvent refining
occurs off-site and supplies'clean fuel to the power plant, other methodologies
which must operate on-site, such-as scrubbers, will be penalized and this must
be considered.
Table 4.3-2 Technical Factors for Solvent Coal Refining
DESCRIPTORS
Scope
All coal processing technologies which use a solvent to extract undesirable substances from coal either by dissolving the undesirable substances directly, or by dissolving the nonmineral portion of the coal
and removing the residues through filtration or centrifuge. Solid,
liquid or gaseous fuels can result. Other forms of chemical refining,
such as catalysis, Fischer-Tropsch synthesis, or pyrolysis are included.
Assumptions
Process Name
Process Description
Relative Process Complexity
QUAiNTIFIERS
Input Characterization
ASTM Rank of Coal
Coal Geographic Region
Ash (% by weight)
Sulfur Pyritic (% by weight)
Organic (
"
)
Heating Value Dry (kcal/kg)
Wet (
"
Moisture (% by weight)
Hydrogen
Carbon
(These numbers will be
supplied by upsteam
technology -and should
be used in the model of
the solvent coal refinery
process)
"
"
Volatile Matter "
Nitrogen
"
Aluminum
"
Calcium
Chlorine
Iron
"
"
"
-46-
Table 4.3-2
(continued)
(% by weight)
Magnesium
Phosphorous
Potassium "
Silicon
"
Sodium
"
"
Other
Trace Metals
"
"
PPM
Arsenic
"
Barium
"t
Beryllium
"
Boron
It
Copper
"
Cobalt
Germanium
"
Gold
Iodine
Lanthanum
Molybdenum
Mercury
Nickel
Platinum
Selenium
Strontium
Tin
Uranium
Zirconium
"
"
"
"
"
"
"
"
"
"
"
Caking Quality (Free Swelling Index)
Grindability Index
Fusion Temperature °C
Probability Levels
Economic Characterization
Capital Investment 106 $/plant of 1000MW/day
Coal Preparation
Preheaters/Dissolvers
Mineral Separation/Storage
Solvent Recovery
Product Treatment/Storage
Power Production Plant
Hydrogen Plant
Sulfur Plant
Other
Plant lifetime (yr) 6
Operating Costs
10 $/yr/1000W/day plant
Labor and Supervision
Maintenance
Taxes/Insurance
Water
Steam Production
Coal Preparation
Energy Costs/Credits
Byproduct Credits
Storage
Chemicals, Catalysts, Solvents
-47-
5%
mean
95%
8
9
11
22
24
29
13
10
15
12
19
14.
5
.5
5
18
21
26
8
4
9
4
11
4
20
20
20
2
2
2
2
2
2
2
3
.2
2.5
.2
.5
.5
1
1
.2
1
.2
.5
.2
1.5
.2
.5
.2
.5
1
.2
3
.2
.5
Table 4.3-2
I
_
(continued)
_
_ __I
_
_I___
_
_
Performance Characterization
Plant Capacity (Ktpd of input coal)
Clean Fuel Produced (Ktpd)
Reliability Forced Outage Rate (%)
Duration of Outage (days)
Maintenance (wks/yr)
Energy Losses Caloric (mmbtu/day)
Ancillary
_
_
_
_
9500 900
6000 6000
12
15
1.4
2.0
6
6
_ _
10000
6000
19
4.0
6
"
Efficiency of Removal (%)
Ash
Organic Sulfur
Inorganic Sulfur
Nitrogen
Trace Elements (Specify as above)
Moisture
Other Minerals (Specify as above)
Product Heat Value (Btu/lb)
95
90
95
50
100
Applicability Characterization
Current Largest Size
Operating Experience
Commercialization Date (2nd 10014W/day capacity)
Expenditure
I__
to get to Commercialization
106$
Compatibility with conversion technologies
Potential for advancement of technology
Retrofit problems
Geographic limitations
Particle sizing needed at input
Construction period (months)
Site constraints
Institutional constraints
Maximum rate of Introduction (1000MW/day plants/yr)
Unresolved issues
Probability of technological feasibility
Resource Needs Characterization
Manpower skills
Manpower availability
Land requirements of plant
Water requirements
Principal equipment availability
Materials requirements
Chemical needs
Chemical recycling potential
Land requirements/mining/disposal
Other resource needs
Byproduct characterization (for each byproduct)
Description
Production rates kton/yr
$/ton
Market price
Market saturation limits kton/yr
--
-48-
165000
16000
16G00
1984
40
120mw/day
18 months
1990
1986
150
80
8-20mm
20
24
30
4
10
12
Table 4.3-2
(continued)
Environmental Consequences Characterization
Occupational health
Emission standards
Emissions (normal and upset/transient)
Air
Water
Waste solids
Pondage requirements
Noise
Others
Frequency and duration of upset conditions
Output Characterization
(Same as input characterization, less first two items)
-49-
4.4
Combustion Technologies
Two general types of generation devices are described in this section.
Any other generation processes can be incorporated into this assessment
framework by using factors described in section 4.6. The function performed
by each of these combustion/conversion type modules is to take the fuel
representation as input, quantify the process requirements and performance,
and output the characterization of emissions for use by any of the downstream
abatement modules, see Figure 4.4-1.
Resource and Commodity
Requ irements
Economic and Technical
Information
Figure 4.4-1
simulator.
4.4.1
Different modeling tasks for the single fuel/plant/control
Current Coal-Fired Combustion/Generation
In order to make comparisons among environmental control alternatives
such as low sulfur coal, fluidized bed combustion, and scrubbers, for instance,
a state-of-the-art, regular, coal-fired power plant must be used as the baseline combustion/generation component in the low sulfur coal and the scrubber
assessments. Enabling comparisons involving individual non-combustion control
technologies is, however, only one of the reasons why it would be useful to have
a characterization of current combustion technologies. Another very important
reason comes from the desirability of having a base case of best presently
available technology, against which to compare the future alternatives.
If it would not be outside the scope of this project it would, in addition,
be desirable to have comparably formated performance models for the present
state-of-the-technology for gas, oil, and nuclear facilities. This type of
information is generally easily obtainable from data bases such as those at the
FPC or in computerized information systems such as the CONCEPT program at Oak
Ridge National Laboratory.
For the coal-fired facilities the technical factors in Table 4.4-1 should
be collected for two or three common plant capacities, such as 150MWe, 300MWe,
and 600Me, and two or three design capacity factors, such as .50, .65, and .80.
-50-
--'-'
Table
4.4-1 Technical Factors
for1------11----4-----_.
Coal Combustion __and Generation
-_-4(-··--··11114--·-_-P-·--l
__
DESCRIPTORS
Scope Current state-of-the-art coal-fired boiler and turbine combinations
Assumptions Designed for 600MWe, .80 capacity factor
Process Name Coal-Fired Combustion/Generation
Process Description Furnace Type, etc.
Relative Complexity
QUANTIFIERS
Input Characterization
Same as low sulfur coal factors including physical characteristics,
elements, compounds and so on.
Economics
Capital investment ($/1000MWe)
Operating costs (R/MWhr)
Labor
Maintenance
Energy
Total cost of electricity ($/MWhr)
Transmission costs to demand centers
Additional investments for potential
abatement requirements
Typical amortization rates
Performance
Capacity (MWe)
Availability
Reliability(%)
Forced Outages (frequency and durations)
Maintenance Outages (frequency and durations)
Energy losses
Fixed ancillary energy needs
Total efficiency (%)
Applicability
Potential for advancement of technology
(in reduced cost per output per year)
Licensing time and other institutional constraints
Geographic limitations
Site-specific requirements/constraints
Maximum rate of availability (1000MWe/yr)
Probability of technological feasibility
Resource Needs
Manpower skills
Manpower numbers
Manpower availability
Land use
Principal equipment needs
Equipment availability
Materials needs
Water requirements
Process
Consumption
Pondage requirements
--
I
--
-51-
Table 4.4-1
(continued)
Chemical needs
Consumption rates
Land use in mining, disposal
Other resource needs
Saleable products (for each product)
Physical and chemical characterization
Production rates
Marketing price
Market limits
Environmental Consequences (that occur at this state only)
Occupational health
Emissions standards
Emissions (normal and upset) including
Emissions from mined chemicals
Waste solids
Noise
Other effects
Output Characterization
Frequency versus Duration versus MWe
Emissions
Air pollutants
various oxides, hydrocarbons, trace elements and
other compounds, including possible day-to-day
variability
Water pollutants
Gas flow versus MWe
Gas temperature versus MWe
-
-
The convention here is to classify as output technical factors any environmental consequences that might be treated in downstream abatement technologies.
If no further treatment is given to the emissions then a unity input/output
abatement alternative can be chosen which would simply relabel the output charac- terization as environmental consequences.
-52-
4.4.2
Low Btu Gasification/Combined Cycle Systems
Integrated low Btu/combined cycle plants rest on proven technology, with
the exception of high temperature gas cleaning equipment and mining technology
questions, which are common to all schemes for increased utilizationof coal.
It is important to note that the gasifiers can both utilize high pressure
air extracted from the combined cycle gas turbine compressors and process
steam generated in the heat recovery steam generators and extracted from the
main steam turbines. The integration of these complementary functions provided
for significant gains in plant efficiency and cost.
Methods for low-Btu gasification have been demonstrated commercially
but suffer in efficiency because the lack of a high temperature (9000 F)
gas cleaning process necessitates lower turbine operating temperatures.
The corrosiveness of gases produced from high sulfur coals is another serious
limitation. Assumptions regarding the operating conditions for the gasifierturbine combination wlbe
critical in determining overall attractiveness
of this methodology. The byproducts of gasification must also be considered
when comparing gasification with other technologies.
Table 4.4-2 Technical Factors for Low Btu Gasification Systems
DESCRIPTORS
Scope All coal processing technologies which produce a low Btu (100-300 btu/scf)
gaseous product from coal for use ina combined cycle power system. The
process will typically include pollutant removal for at least S0x and
particulates to protect the gas turbine from wear. The pollutant removal,
gas turbine and steam generator and turbine are all included in the scope.
Assumptions The same gas turbine/steam generator system should be used on all
evaluations of the gasification technologies, when possible. Changes in
the combined cycle assumptions should be noted.
Process Name
Process Description
Relative Process Complexity
QUANTIFIERS
Input Characterization
ASTM rank of coal
Coal geographic region
Ash (% by weight)
Sulfur Pyritic (% by weight)
Organic (
"
)
Heating value Dry (kcal/kg)
Wet (
"
)
Moisture
(% by weight)
Hydrogen
"
Carbon
Volatile Matter
"
Nitrogen
Aluminum
Calcium
Chlorine
"
"
"
"
(These numbers will
be the input variables
for this model)
Iron
Magnesium
Phosphorus
Potassium
Silicon
Sodium
Other
"
"
"
"
"
"
--
-53-
Table 4.4-2
(continued)
Trace Metals
PPM
Arsenic
Barium
Beryllium
Boron
Copper
"
"
"
"
"
Cobalt
Germanium
"
Gold
"
Iodine
"
Lanthanum
Iolybdenum
Mercury
"
"
Nickel
Platinum
Selenium
Strontium
"
"
"
Tin
Uranium
Zirconium
Caking quality (free swelling index)
Grindability index
Fusion temperature °C
Probability Levels
Economic Characterization
Capital investment 106 $/plant of 1000MW
Coal preparation
Gas production
Gas clean-up
Combustion turbines
Heat recovery steam generators
Steam turbines
Other
Plant lifetime (yr)
Operating costs 10 6 $/yr/1000yr
Labor and supervision
Maintenance
.Taxes/insurance
Water
Steam production
Coal preparation
Energy costs/credits
Byproduct. credits
Storage
Chemicals, catalysts, etc.
Temp
16.5
72.8
20
17
18.7
75
90
45
25
47
30
50
40
40
40
25
25
25
30
32
33
2
2
2
2
2
2
2
2.5
.2
.2
.5
.5
1
.2
"
-
-54-
-
.3
.5
.2
.2
3
.2
.5
.2
90 00
10 00
10 00
21 00
9200
1000
1000
2600
9300
1000
1000
2800
11
12
14
1.0
.5
.7
12 50
1450
1000
9 00
I-
3
.2
.5
5
°F
95%
1
1.5
1
Caloric (mBtu/day)
Ancillary
Mean
1
Performance Characterization
Plant capacity (Ktpd of input coal)
Clean fuel produced
Output MW
Heat rate for total system (kcal/kwh)
Reliability Forced outage rate (%)
Duration of outage (days)
Maintenance (wks/yr)
Waste heat steam conditions Pressure psig
Energy losses
5%
5
.5
5
2400
1000/1000
Table 4.4-2
(continued)
Probability
Levels
Levels~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Probability
4.4-2(continued)
Table
5%
Efficiency of removal
Ash
Organic sulfur
Inorganic sulfur
Nitrogen
Trace elements (specify as above)
Other minerals (
"
)
Applicability Characterization
Current largest size
Operating experience
Commercialization date (2nd 1000MW plant)
Expenditure to get to commercialization 106$
Compatibility with conversion technologies
Potential for advancement of technology
Retrofit problems
Geographic limitations
Particle sizing needed at input
Construction period (months)
Site constraints
Institutional constraints
Maximum rate of introduction (1000MW plants/yr)
Unresolved issues
Probability of technological feasibility
Resource Needs Characterization
Manpower skills
Manpower availability
Land requirements of plant
Water requirements
Principal equipment availability
Materials requirements
Chemical needs
Chemical recycling potential
Land requirements/mining/disposal
Other resource needs
Byproduct characterization (for each byproduct)
Description
Production rates kton/yr
Market price $/ton
Market saturation limits kton/yr
Environmental Consequences Characterization
Occupational health.
Emission standards
Emissions (normal and upset/transient)
Air
Water
Waste solids
Pondate requirements
Noise
Others
Frequency and duration of upset conditions
Output Characterization
(Same as output characterization in Table 4.4-1)
-55-
95
90
95
Mean
97
90
96
50
95%
99.5
90
98
250MW
3 months
1979
1983
1987
20
35
70
included
14
16
20
6
10
12
4.5
Emission Controls
Emission treatment modules should contain the data and input/output models
of all the types of emission-to-emission conversion technologies. The inputs
to these models, as well as the outputs, should be the general emissions
characterization as described in Table 4.4-1. The abatement equipments should
be capable of making any reasonable combination with other abatement equipments
and should be capable of being attached to any of the generation type modules,
as shown in Figure 4.5-1.
no further
Figure 4.5-1
4.5.1
Representation of possible fuel/plant/control options.
Particulate Removal Systems
Electrostatic precipitation represents a proven and highly effective emissions
control technology. Modeling of these devices is a relatively straightforward
task. Some complications do arise, however, in the modeling of decrease in
efficiency with the reduction in the resistivity of the effluents, such as for
the case of low sulfur levels in the emissions. Technical factors for particulate
removal should follow the same format as that presented in the next section in
Table 4.5-1.
-56-
4.5.2
Stack Gas Cleaning for Sulfur Removal
Information in the literature about flue gas desulfurization is either
data from experience on existing pilot plants or projections of data for
conditions in the future when large-scale implementation is under way.
The distinction is important, as are the distinctions between retrofit
and new installations, and throw away versus recoverable systems.
Scrubbers are generally restricted to modules according to the
gas flows involved. Since economies of scale apply up to the technical
limit of the module size, about 150 MW, vendors will probably provide
modular systems for most plants. The compatibility of scrubber module size
assumptions with combustion technology sizes must be checked during comparisons.
Table 4.5-1 Technical Factors for Sulfur Removal Equipment
DESCRIPTORS
Scope All technologies which treat the combustion flue gases to extract
sulfur compounds before emission to the atmosphere. Methods for control
of other air pollutants may or may not be part of the technology.
Assumptions The input to a stack gas cleaning technology is the flue gas
from the boiler or combustor of the generation technology being used.
Ancillary equipment, such as plume reheaters, are necessary because of
the stack gas cleaning and should be included.
Process Name
Process Description
Relative Process Complexity
QUANTIFIERS
Input Characterization
6
Full-load gas flow all train (ACFMxl06)
Gas temperature (F)
Ash loading (lb/ACFMxlO6 )
SOx loading (
"
NOx loading (
C02 loading (
CO loading (
H20 loading (
Hydrocarbons(
"
"
"
"
"
Aluminum
PPM
Calcium
Chlorine
"
"
Iron
"
Magnesium
Phosphorus
Potassium
Silicon
"
"
"
"
)
)
)
)
)
Sodium
Other
"
Trace Metals
Arsenic
Barium
Beryllium
Boron
Copper
PPM
"
"
"
"
"
-57-
Table 4.5-1
-
-
(continued)
_
_
-
PPM
I!
Cobalt
ti
Germanium
I!
It
Gold
Iodine
Lanthanum
II
II
I!
Molybdenum
II
Mercury
I1
Nickel
Platinum
II
Selenium
Strontium
Tin
I!
Uranium
II
Zirconium
Initial Plant Heat Rate (kcal/kwh)
Economic Characterization
Capital Investment 10 6 $/1000MW plant)
Scrubber and ductwork
Gas reheater
Catalyst system
Slurry system
Sulfur plant
Other
Plant lifetime (yr)
Operating costs 106 $/yr for 1000MW)
Labor and supervision
Maintenance
Taxes/insurance
Water
Reheat fuel
Slurry preparation
Chemicals, catalysts, etc.
Energy costs
Byproduct credits
Storage/disposal
Performance Characterization
Train size (ACFix106 )
Reliability Forced outage rate (%)
Duration of outages (days)
Maintenance (wks/yr)
Energy loss Reheater (mBtu/day)
Ancillary (mBtu/day)
Efficiency of removal
SOx
NOx
Particulates
Trace elements (specify as above)
Other minerals (specify as above)
Addition of water vapor (tons/day)
Final plant heat rate (kcal/kwh)
Slurry/sludge production
-58-
Probability Levels
5%
Mean
95%
15
17
19
5
7
9
4
7
10
12
20
15
25
17
30
2
3
2
2
3.2
2
3.5
3.0
.4
2.5
.55
.6
.8
1 .6
1.1
.8
.8
1.8
1.6
.4
.4
..6
1
.8
1.9
2.0
.4
10
14
20
4
5
7
6
6
6
85
90
95
60
90
65
95
70
96
2700
2900
3100
Table 4.5-1
(continued)
Applicability Characterization
Current largest size
Operating experience
Commercialization date (2nd 1000l
W
Expenditure
90M1W
24 months
plant)
to get to commercialization
Compatibility with conversion technologies
Potential for advancement of technology
Retrofit problems
Geographic limitations
Construction period (months)
Site constraints
Institutional constraints
Maximum rate of introduction (100OMW plants/yr)
Unresolved issues
Probability of technological feasibility
1978
1980
1983
14
20
35
limestone supply
12
15
18
25
30
40
Resource Needs Characterization
Manpower skills
Manpower availability
Land requirements of stack gas equipment
Water requirements
Principal equipment availability
Land requirements--mining/disposal
Materials requirements
Chemical needs
Chemical recycling potential
Other resource needs
Byproduct characterization (for each byproduct)
Description
Production rates kton/yr
Market price /ton
Market saturation limits kton/yr
Environmental consequences characterization
Occupational health
Emissions standards
Emissions (normal and upset/transient)
Air
Water/drainage
Waste solids
Pondage requirements
Noise
Other
Frequency and duration of upset conditions
Output Characterization
Gas temperature after reheat °F
220
220
(Same as input characterization from 3rd to next-to-last items)
- -- -4.5.3
220
--
NOx Control Technologies
In the event that combustion process modification results in NOx Control,
this reduction in NOx emissions will be reflected in that combustion modeling.
With the appearance of an NOx scrubber a possibility for the future, this
technology can be treated in a format exactly analogous to that presented for
sulfur scrubbers, in Section 4.5.2.
-59-
4.5.4
Intermittent Control and Tall Stacks
In a comparison based solely on tons of emissions, any strategy for use of
intermittent controls and tall stacks would not reduce, and might well add to,
those total tons of emissions. Some comparative assessments, such as (V; NAS;
1975), have attempted to determine effective emissions reductions computed as
the fractional decrease in the expected peak ambient concentration. Such a
characterization is suspect on a number of scores, and in any event is a simple
scaling procedure. Thus, an ambient comparison approach is described here.
Table 4.5-2 Technical Factors for Intermittent Control Systems
DESCRIPTORS
Scope Includes tall stacks, fuel switching, and load shifting, and is based
on the assumption that some ambient pollutant forecasting capability
exists.
Assumptions
Process Name
Process Description Alternative fuels, stack height, percent control, ratio
"fuel switch hours" to "load shift hours".
Relative Complexity
QUANTIFIERS
Input Characterization (for each fuel type)
Frequency versus Duration versus MWe
Emissions
Air pollutants
Water pollutants
Gas flow versus MWe
Gas temperature versus MWe
Economics
Capital investment ($/plant)
Operating costs ($/yr/% control)
Labor
Maintenance
Fuel/energy cost
Replacement power costs (increase)
Total increase in cost of electricity
(% increase/% control)
Performance
Availability (%)
Accuracy (ratio correct actions to false alarms)
Applicability
Potential for advancement of technology
(an increased accuracy ratio)
Licensing time and other institutional constraints
Geographic limitations
Maximum rate of availability (plants/yr)
Probability of technological feasibility
-60-
Table 4.5-2
(continued)
Resource Needs
Manpower skills
Manpoxwer numbers
Manpower availability
Land use (for monitors)
Principal equipment needs
Equipment availability
Other resource needs
Environmental Consequences
Frequency versus Duration versus MNWe
Emissions
Air pollutants
Water pollutants
Peak ambient conditions (1 hr, 3 hr, 24 hr,
annual)
Other ambient characterizations, such as concentration versus duration versus acres
(before and after control on top of assumed
background profile)
I-
'
-
-
-~I--
--
-61-
-
-
I-
-
4.6
Potential for Including Future Technologies
As this project progresses there will certainly be a need to add in new
options for generation of power. Some of these options will probably include
.fluidized bed combustion, MHD, fuel cells, and other advanced generation
technologies that are likely to become available in the future. Less urgency
on the decisions and less information on the processes have relegated these
advanced technologies to a delayed role in this assessment. The modularized
approach, however, should facilitate the easy incorporation of these and other
new options into the overall assessment procedure. The typical format for
the technical factors of these advanced generation technologies is presented
in Table 4.6-1.
Table 4.6-1 Technical Factors for Future Generation Technologies
DESCRIPTORS
Scope All future power generation technologies which convert coal to electric
power.
Assumptions
Process Name
Process Description
Relative Process Complexity
QUANTIFIERS
Input Characterization
ASTM rank of coal
Coal geographic region
Ash (%'by weight)
Sulfur Pyritic (% by weeight)
Organic (
"
)
Heating value Dry (kca]/kg)
Wet (
Moisture
I"
)
(% by weig]ht)
Hydrogen
"
Carbon
I"
Volatile matter
"
Nitrogen
Aluminum
"
"
Calcium
it
"
Chlorine
"
Iron
"
Magnesium
Phosphorus "
Potassium
Silicon
Sodium
Other
"
Trace Metals
Arsenic
Barium
Beryllium
Boron
Copper
P]PM
I'1
II
II
I
-62-
Table 4.6-1
(continued)
PPM
Cobalt
"
Germanium
Gold
Iodine
"
Lanthanum
Molybdenum
Mercury
Nickel
Platinum
"
Selenium
Strontium
Tin
Uranium
Zirconium "
"
"
"
"
"
"
"
"
Caking quality (free swelling index)
Grindability index
Fusion temperature °C
Economic Characterization
Capital Investment (06$/1000MW plant)
Materials and equipment
Coal preparation unit
Power production unit
Construction
Land investment
Operating costs (106 $/1000MW plant)
Labor and supervision
Maintenance
Taxes
Insurance
Water
Coal preparation
Power production
Energy costs/credits
Transportation
Byproduct credits
Storage/disposal
Chemicals/catalysts
Performance Characterization
Plant Capacity (MW) (rated)
Plant operating lifetime (yrs)
Plant heat rate (kcal/kwh)
Maximum theoretical efficiency (%)
Reliability: Forced outage rate (%)
Duration of outage (days)
Maintenance (wks/yr)
Energy losses Caloric (kcal/day)
Ancillary (kcal/day)
Waste heat steam conditions: Pressure (psig)
Temperature
Gas temperature entering stack (C)
Startup time (hrs)
Response time (hrs)
Coal feed rate (lb/hr)
Catalyst/seed feed rate (lb/hr)
-63-
(C)
I
Table 4.6-1
(continued)
-
-
-
|
Combusion temperature (C)
Design capacity factor (%)
Efficiency of removal
Ash
Organic sulfur
Inorganic sulfur
Nitrogen
Trace elements (specify as above)
Other minerals (specify as above)
Applicability Characterization
Manpower skills
Manpower availability
Land requirements of plant
Water requirements
Principal equipment availability
Materials requirements
Chemical needs
Chemical recycling potential
Land requirements/Mining/Disposal
Other resource needs
Byproduct characterization (for each byproduct)
Description
Production rates kton/yr
Market price $/ton
Market saturation limits kton/yr
Environmental Consequences Characterization
Occupational health
Emission standards
Emissions (normal and upset/transient)
Air
Water
Waste solids
Pondage requirements
Noise
Others
Frequency and duration of upset conditions
Output Characterization
(Same as output characterization in Table 4.4-1)
_
_
-..
.
5.
Simulation Mechanism
The central position of the Simulation Mechanism in the overall assessment
structure can be seen in the schematic in Figure 5.0-1. lThe Simulation Mechanism
draws data and models from the Technical Factor information base, receives information about Assessment Options from the user, and delivers the Resultant Factors
from which the crucial decision-aiding information can be extracted.
The first part of this chapter deals with the simulation of a single energy
facility, a concatenation of: a fuel type; none, one, or more precombustion
controls; a combustion/generation type; and none, one, or more post-combustion
controls. The flow chart for this type of concatenation procedure has been shown
back in Figure 3.0-2.
The last part of this chapter describes some of the methods by which several
single facility simulations could possibly be combined to yield regional or even
national simulations. These types of aggregations could in fact be performed as
part of the Ordering Mechanism, and some of the ways this could be done have been
mentioned at several points in this report. In the manner in which this report's
outline has been set down these aggregation procedures are included, however, as
part of the Simulation Mechanism. Here again there are optional ways in which this
procedure could be handled: the aggregation could be a fully automated procedure
stored in "cookbook" fashion within the Simulation Mechanism for each of several
optional scenarios; or the aggregation could be a manual task performed by the user.
While a reading of this chapter would seem to suggest a favoring of the latter type
of treatment, we wish not to recommend any particular regional or national aggregation procedure. Such a recommendation could only come from a detailed balance of
the difficulty of assembling an automated aggregator and the projected utility of
such a device in the future use of the assessment mechanism.
5.1
Modeling Options
The Modeling Options are those choices made by the user that are necessary
to get the Simulation Mechanism underway. These choices divide into two categories,
and are described in the following separate subsections. The category labelled
Assessment Options includes the choices of the type of energy facilities that are
to be simulated. The Non-Technical Factors are the parameters that must be set
to fix the assumptions under which the simulation is to be made.
5.1.1
Assessment Options
As just described the Assessment Options are the subset to be examined of
all possible fuel/plant/control combinations. A subset must be chose because obviously not all combinations can be examined. For example, if there are: 25 coal
types; 35 types of coal cleaning, preparation, and refining some of which could be
concatenated with one or two other types; 40 types and sizes of combustion/generation
equipment; and 35 types of emission treatment equipment some of which can again be
concatenated with one or two other types (e.g., precipitator-scrubber-intermittemt
control) this yields many millions of feasible combinations.
An example of the Assessment Options that might be chosen could be:
Fuel/Plant/Control Combinations
1. coal sample #11/unprocessed/500MWe MHD type #3/no abatement.
2. coal sample #11/unprocessed/500 e conventional coal boiler
type #1/wet limestone scrubber type #3
3. coal sample #22 (low sulfur)/unprocessed/500MWe conventional
coal boiler type #1/no abatement
4. coal sample #11/coal cleaning type #6/500MWe conventional
coal boiler type #1/no abatement
-65-
Ci
Cd
cJ
C.),
ct
0
H0 EJ
)
00
oo
ari
c0
0q
W4O
l 4
0
*H ·01
co
0Cd
-Hr
o40
ci
Each of these four combinations would then be simulated in turn by the Simulation
Mechanism. Technical Factors would be available to exactly describe that, for
example, coal sample #11 was Central Applachian, high sulfur, with a particular
set of typical constituencies, or for example coal cleaning type #6 might be Battelle
Hydrothermnl.
The capacity sizes of the various energy componentshave been a subject of
considerable attention in the formulation of this assessment framework. It would
be ideal to make choices of say 100, 250, 500, or 1000 MWe (busbar) and get all
of the equipments of the specified types that would be designed exactly to meet
that output of power in the "best" possible manner. There are several problems
with this approach, one is the "best" will not be defined in any unique manner.
Another problem results from the limited amount of information that is initially
available on advanced technologies. The solution to this second problem is, at
the outset, to offer as options only those capacity sizes of the various facilities
that are considered by concensus to be the modular size(s) that are most likely
to be built. For example, fluidized-bed combustors (FBC) would originally only
be available at about a standardized 125 MWe size, and this would be the only size
at which information would be available in the initial uses of the overall assessIf 400 MWe of FBC are called for then the appropriate performance
ment mechanism.
measures of the 125 MWe size could be linearly scaled.
For currently available technologies about which much information is known
the different types of equipment could include designs specifically aimed at a
variety of modular sizes, for example type 1 of conventional coal-fired boiler
might be 500 MWe, type 2 250 Mwe, and so on. In some cases if intermediate sizes
are still desired, interpolations (or extrapolations) can be made and these need
not be linear, see Figure 5.1-1.
-C
4J
a,
4;
C:
CJ
E
0
C:
4.
C_
0
200
400
600
Power Unit Size,
800
1000
1200
W
Figure 5.1-1 Method of displaying costs versus capacity sizes, here it is
for limestone scrubbers (V; Argonne National Lab; 1976; Fig. 4.1.6).
-67-
5.1.2
Non-Technical Factors
The Non-Technical Factors are those factors that may play such a crucial
role in the decisions to be made and that are of such an uncertain nature that
sensitivity studies with respect to those factors may be in order. One suchobvious example is the cost of money which may be instance be parameterized at
levels of 7.5, 9.0, and 10.5% per year. Suppose now that the user has chosen
these three parameters for the Non-Technical Factors and the four fuel/plant/
control Assessment Options described in the previous section, this would cause
twelve single plant simulation runs to take place. These twelve options would
produce twelve sets (or vectors) of Resultant Factors. The Ordering Mechanism
would then be used to sort out the preferences and important information from
among those twelve options. There are ways, discussed in Chapter 7, for combining
the three parameterizations for each of those four energy facilities to result
in just one set of Resultant Factors for each facility where now these factors have
probabilistic profiles to reflect the variability shown in the parameterization of
the Non-Technical Factors.
Examples of some types of Non-Technical Factors that might be considered
are given in the following outline. Defaults should be developed for any of these
parameters to simplify the process of making assessments where maximum use of
the parameterizations is not desired.
1. Basic Assumptions
A. Year (if a particular year is to be the time for the comparison)
- example: 1988
B. Regional Considerations
- Fuel
- example: fuel from Region #2
-
Plant
- example: located in Region #4, rural site
-
Load/Demand
- example:
C.
2.
customers in Region #4, load shape typical of Reg. 4
Power System
- example: power system Type #3, small predominately
oil-fired, this would then reflect upon the usefullness
of this particular facility with respect to the generation mix available on this type of power system and the
replacement cost of power that would be likely.
Economics
Whether or not, and which economic factors would be
available as Non-Technical Factors would depend entirely
upon the sophistication of the modeling of accounting
procedures that is available in the Simulation Mechanism.
Possible levels of accounting sophistication include:
Oth Order- exact accounting procedure to be used would be
specified to the subcontractors as would the
exact values for economic factors, such as
cost of capital
1st Order- subcontractors would provide informationnecessary
and sufficient for mechanism to be able to use
parameterization of economic factors and
for the major
different accounting procedures
expenditure items, such as construction, licensing,
equipment, and other primary investments.
Secondary expenses, such as transportation
investments that would affect transportation costs
as passed on to the utilities would be handled
-68-
as described in 0th Order procedure.
2nd Order- information would be made available so that primary
and secondary (or indirectly affecting) expenditures could be modeled with parameterized
economic factors and various accounting procedures
and tertiary influences would use prespedifaed
procedure as in 0th Order
The comparison of energy technologies on the basis of economic
evaluations is a conventional practice. It is rarely, however,
that these comparisons can be made on common economic assumptions,
and more rarely that competing technologies can be compared using
a series of assumptions or several values of key economic parameters. Some of the most detailed models that are capable of
handling such sensitivity studies for conventional coal, oil,
gas, and nuclear fueled facilities are available from ERDA and
Oak Ridge National Labs, including CONCEPT, PLANT, AND ORCOST
codes. In the present project it is likely that some options
and some parameters will be fixed in an inevitable accuracy
versus complexity tradeoff.
In a 0th effort no economic factors could be parameterized
(they would all be pre-fixed in specifications to subcontractors).
In a 1st Order procedure, the one that we recommend, examples
of Non-Technical Factors would include:
A. Accounting Procedures
1. Depreciation Options:
a. straight line
b. sum-of-the-years-digits
c. combination of a. and b. switching at a
given year
2. Fraction of Year to Discount Annual Expense
- example:
.50
3. Time Factors
a. Base year for escalations
- example: 1971.0
b. year construction started
- example: 1971.0
c. year of commercial operation
- example: 1977.5
d. length of workweek (hrs)
- example: 40.0
e. year for present-worthing of dollars
- example: 1975.0
B. Treatment of Debt and Equity
1. Bond Repayment Options:
a. proportional case
b. uniform principal reduction
c. uniform annual payment
d. delayed uniform principal reduction, include
starting year for delayed option
2. Annual Interest Rate on Debt (%)
- example: 7.5%
3. Fraction of Initial Investment Raised by Debt
4. Earning Rate on Equity (after tax)
5. Debt/Equity Ratio
C. Escalation Rates
1. Initial Equipment Escalation Rate (%)
- example: 5.0%
.-
_
.
2.
D.
E.
Equipment Escalation Rate (%)
- example: 5.0%
3. Initial Material Escalation Rate (%)
- example: 5.0%
4. Material Escalation Rate (%)
- example: 5.0%
5. Initial Labor Escalation Rate (%)
- example: 10.0%
6. Labor Escalation Rate (%)
- example: 10.0%
7. Uniform Overall Escalation Rate (%)
- example: 0.0%
8. Escalation Rate on O&M Cost (%/yr)
- example: 0.0%
9.- Escalation Rate on Fuel Cost (%/yr)
- example: 0.0%
Indexes for Uniform Parameterization
1. Site Labor Productivity Index
- example: 1.0
2. Equipment Cost Index
- example: 1.0
3. Materials Cost Index
- example: 1.0
4. Labor Cost Index
- example: 1.0
Insurances
1.
2.
F.
0
Property Insurance (fraction of plant investment/yr)
- example: 0.001
Additional Liability Insurance (for nuclear accidents,
oil conflagrations, and so on; $/yr or $/yr/MWth)
- example: 0.000
Taxes
1. Federal Income Tax Rate (fractional)
- example: 0.041
2. State Income Tax Rate (fractional)
3. State Gross Revenue Tax Rate (fractional)
4. Property Tax Rate on Plant (fraction/yr)
5. Other Taxes (fraction/yr)
It is clear that the amount of accounting and economic
parameter flexibility that is desired will greatly effect the
quantities and types of technical factors that will be necessary.
For example, if all of the above categories are dealt with as
user options in the overall mechanism then it would be necessary
from each of the technologies to have as a function of time the
equipment, materials, and labor outlays.
3.
Performance
A.
4.
Capacity Factor (design)
- example: 65% (i.e. baseloaded)
Environmental
A. Emission Standards
- example: current standards
B. Ambient Standards
examle:
disreard xml
Rather~then
preen
a yohtcl
alito o-ehnclFcos
Rather then present a hypothetical example of a list of Non-Technical Factors,
this section closes out with perhaps the most extensive such example that has ap-70-.
peared to date in the literature (V;
Cost of oxygen
Range of oxygen cost
Composite labor rate
Year for cost estimates
Initial Material escalation
rate
NASA; 1976):
$9.00/ton delivered
$5-15
$10.60/hour
1974.5
6.5%
Interest on Committed
10%
funding
7.5
Cost of Money (%/yr)
Federal Income Tax
4.1
3.3
Depreciation
Other taxes
2.8
Insurance
.
1
.2
Working capital
18.0
Total (%/yr)
Emission standards
current
(water site and emission levels assumed)
Environmental conditions .
0.65
Capacity factor
0.50-0.80
Capacity factor range
90%
Availability
500 kiovolt, 60 hertz
Power
After several pages of additional specifications on economic ground rules and sensitivity studies (V; NASA; 1976; pp. 44-49) the results in Figures 5.1-2 and 5.1-3
were developed along with numerous other comparisons. Some of the other sensitivity
studies that were developed in that project include + 50% changes in capital costs,
+ 30% changes in construction time, + 50% changes in fuel costs, .50-.65-.80 capacity
factors, 0%-10%-20% interest rates, and .000-.065-.130 uniform escalation rates.
w
so80
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steam
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·
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Electric
results
- current-yerdollars
endcommon
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dae.
(IbAvelaelfele COt' - GenerallcIri ctns; verqe inflation
ratefor30years.
) 25IernL
Figure 5.1-2 Sensitivity of results to variations in the Non-Technical Factors
involved in the calculation of the cost of electricity (V; NASA; 1976)
-71-
n
Supercritical
CO2
Closed-cycle
lID
SC
Liquid-metal
MHD
76
/-Low-temperature
fuel cells
60
-Open-cycleMIID
.- Htigh-temperature
fuelcells
-- Ciosed-cycle
gasturbinelorgank
'-Liquid-metalRankine
- Recuperated
closed-cycle
gas
turbine
,'-AAdvancedsteam
50
40
'-Open-cyclegasturbinelorgank
30
Combined
cycle{'iter cooled)
'-'Open-cycle gas turbine
-Combinedcyclefair cooled)
20
In
.D
v
__
I_
I
__i_
I .
I
_
__
7
_
(a)General
Electric
selected
points.
5
41
Q
Fixed-charge
rate.
Ib) Westinghouse
selected
points
Figure 5.1-3 Sensitivity of cost of electricity to variations in the Non-Technical
Factor: the fixed-charge rate (V; NASA; 1976)
5.2
Single Fuel/Plant/Control Probabilistic Emission Simulation
The single facility emissions simulation may be nothing more than a
bookkeepingsystem, collecting and using the best models of the alternative
technologies and properly matching the input and output Technical Factors as
it carries the simulations downstream. The different modules of this single
plant simulator are show in Figure 5.2-1
Economic and Technological
/
AAssumptions
Design
Modification
for Abatement
j/
I
I
i
i
Specific
Fuel
Generating
Treatment
-.,_
Facility
I
Emission
___N
i
I
I
Treatment
i
i-,- -
I
Resource and Commodity
Requirements
Economic and Technical
Information
Figure 5.2-1 Different modeling components of the single facility fuel-to-emission
simulation procedure
The first module of Figure 5.2-1 characterizes the coal preparation and
refining options. Inputs include the choice and Technical Factors associated with
the one or more coal samples to be used. The information that should drop out
of the fuel treatment modeling, and in fact from all the modules, includes the
various economic, resource, environmental, performance, and applicability factors.
These will then largely be accounted and totaled across the various fuel /plant/
control combinations, with the summation of these economic, resource, and other
factors contributing directly to the Resultant Factors. The second block of
Figure 5.2-1 is the modeling and data on the combustion/generation equipment.
The final block represents the abatement models.
The overall complexity of the single plant Simulation Mechanism will
depend largely on three factors:
1. the amount of flexibility required in the modeling
options and parameterizable Non-Technical Factors;
2. the complexity of the imput/output models required
for the individual technologies; and
3. the size of the array of Resultant Factors expected
as an output.
For example, section 5.1.2 showed how there is a real possibility for the number
of economic Non-Technical Factors to significantly increase the data and modeling
requirements of the Simulation Mechanism. As another example, it is not difficult
-73-
Table 5.2-1
List of pollutants collected for use in the SEAS system (V; USEPA; 1975)
The systerlconsists of a nine-cigit code, as follows:
1st and 2nd diaits:
Residual Category.
3rd and 4th digits:
(Continued)
Particulates
01
Antimony
03
Sulfur Oxides
Nitrogen Oxides
Hydrocarbons
Carbon Monoxide
Photochemical Oxidants
Other Gases and Mists
Odors
Biological Oxygen Demand
Chemical Oxygen Demand
Total Organic Carbon
Suspehded Solids
Dissolved Solids
Nutrients
Acids
Bases
Oils and Greases
Surfactants
Pathogens
Waste Water
Thermal Loading
Combustible Solid Waste
Non-Combustible Solid Waste
Bulky Waste
Hazardous Waste
Mining Waste
Industrial Sludges
Sewage Sludge
Herbicides
Insecticides
Fungicides
Miscellaneous Pesticides
Radionuclides to Air
Radionuclides to Water
Radionuclides to Land
02
Appliances
0.4
03
04'
Arsenic
05
Ash
06
07
Autordobiles
Bacteria
05
06
07
08
08
Bariumn-140
09
10
11
12
13
Asbestos
Beryllium
Boron
Botanical Insecticides
Cadmium
.Carbamate Insecticides
09
10
11
12
13
14
15
14.. Cesium-134
16
15 'Cesium-137
16. Cesiumn-144
17
17
Chloramine
19
18
Chlorine
20
19
Chromium'
21
20
21
22
23
24'
25
26
27
28
29
30
31
32
33
34
35
22
18
Not Applicable
00
Cobalt-60
Concrete, Masonry
Copper
Copper Fungicides
Crop Waste
Cyanide
Dithiocarbamate Fungicides
Ferric Chloride
Ferric Sulkate
Ferrous Metals
Fluorine
Food.Waste
Garden Waste
Glass
Household Furniture
.
Hydrogen-3
Inorganic Herbicides
Inorganic Insecticides
Iodine-129
Iodine-131
Krypton-85
Aluminum
01
Lanthanum-140
42
43
Ammonium Hydroxide
02
Lead
44
3rd and 4th digits:
Residual Component
--
-74-
23
24
25
26
27-
28
29
30
31
32
33
34
35
36
37
38
39
40
41
Table 5.2-1
continued
_
3rd and 4th dicits:
5th dicit: Carrier Medium/
Reporting Category
(Cont'inued)
Leather
Livestock aste
Mercury
Mine Overburden
Mine Tailings
Miscellaneous Fungicides
Nitrates
Non-Ferrous Metals,
Miscellaneous
Organic Herbicide3
Organi.c Mercury Fungicides
Organocldorine nsecticides
Organophosphorus
Insecticides
Other Synthetic 'Organic
Insecticides
Paper
Phenols
Phosphates
Pthalinide Fungicides
Plastics
Radiun-266
Radon-222
Rubber
Ruthenium-106
Sand, Stone,. Soil.
'Selenium
Slag
Strontium-90
Tellurium
Textiles
Thalium
Tires
Vanadium
Viruses
Water, Cooling
Water, Process.
Wood
Zinc
_
45
Air
46
47
48
49
Water
Land
Leachate
Pesticide
Radiation
50
51
6th dqi.t:
1
2
3
4
S
6
Source
52
1
53'
54
55
Point
Area
Mobile
56
7th digit:
2
3
Product of
Combustion
57.
58
59
60
61
62
63
64
65-
66
67
68
69
70
" 71
72
73
74
75
76
77
78
79
80
-75-
Yes
1
2
No
.N
8th
t:
.'
.
Type of
Economic Activity
Extraction
1
Production
2
Distribution
Consumption
Disposal
3
4
5
9th digit:Toxicity
None
1
Low
Medium
2
3
High
4
to imagine how in the emission characterization the number of different chemical
elements and compounds could greatly tax the overall mechanism, there are many
thousand different combustion-formed hydrocarbons, for instance. Also in the SEAS
mechanism being developed in the EPA (V; USEPA; 1975) there are many more emissions
collected than could be worked with in any type of sophisticated Simulation Mechanism,
see Table 5.2-1.
Priorities must be developed for including any of the different capabilities
in the Simulation Mechanism. As an example of a high priority item, in the accounting
procedures the future cost of capital is a very important, and very uncertain, economic parameter. On the other hand, the escalation rate for O&M costs is likely
to be unimportant due to the relatively small magnitude of these costs.
As another example of the systematic development of these modeling priorities
consider the air pollutant emissions categories that might be used. Different types
of concerns will have to be examined in order to determine which of the emissions
deserve to be taking up the efforts of the Simulation Mechanism. Some of these
concerns in the various categories include:
Great Variability among Coals
1. Arsenic (sometimes 100 to 1000 times national average)
2.
Barium
3.
4.
5.
6.
7.
Beryllium (0.1-1000ppm)
Boron (100-1000ppm)
Germanium (25-3000ppm)
Uramium (1-200ppm)
Sulfur (3000-120000ppm)
8.
Nitrogen
9.
10.
11.
12.
13.
14.
15.
16.
Chromium
Cobalt
Copper
Lead
Manganese
Molybdenum
Nickel
Vanadium
(1 to 3000ppm)
.17. Zirconium
Escape Abatement Equipment due to Volatility
1. Mercury (about 100%)
2. Arsenic (about 80%)
3. Beryllium (about 10%)
4. Nitrogen (about 100%)
5. Sulfur (about 99%)
Relative Importance of Power Plants as an Emissions Source (IV; Goldberg;
1973) and (V; Starr and Greenfield; 1972)
1. soX (73.5%)
2. Beryllium (68.0%)
3. Chromium (53.5%)
4. Selenium (50.5%)
5. NOx (43.8%)
6. Vanadium (34.2%)
7. Boron (32.2%)
8. Particulates (31.4%)
9. Nickel (26.8% but mostly oil)
10. Barium (24.8%)
11. Mercury (22.0%)
12. Flourides (17.7%)
13. Magnesium (8.5%)
14. Lead (7.7%)
15. Arsenic (5.5%)
16. Tin (4.5%)
-76-
Approaching Ambient Standards or Rccommended Levels
1.
SOx
2. Total Suspended Particulates
3. NO
4.
5.
Ozone/Oxidants
Hydrocarbons
6. Beryllium (20% of recommended levels)
7. Radiation (10% of recommended levels)
Important from Standpoint of Health Effects Research
1.
SO x
2.
3.
Particulate Sulfates
Sulfuric Acid Aerosols
4.
5.
6.
NOx
NO
Ammonia
7.
8.
9.
10.
11.
12.
Particulate Hydrocarbons
Particulate Hydrocarbons
Particulate Hydrocarbons
Particulate Hydrocarbons
Heat
Radionuclides
(carcinogenic
(carcinogenic
(carcinogenic
(carcinogenic
-)
potency
potency +++)
potency ++)
potency +)
13. SiO
14.
15.
16.
17.
18.
19.
20.
21.
22.
Arsenic
Asbestos
Beryllium
Chromium
Lead
Mercury
Nickel
Tin
Vanadium
23. Zinc
Synergistic Pollutants
Potentiators
1. SOx
2. NO x
3. Total Suspended Particulates
4. Ozone
5.
6.
Reactive Gaseous Hydrocarbons
Metal Oxides
7.
Iron
Antagonizers (beneficial to health)
1.
2.
3.
Arsenic
Cadmium
Copper
4.
5.
Manganese
Particulate Hydrocarbons (carcinogenic potency -)
6.
7.
Selenium
Titanium
8. Water Hardness
From these types of lists some priorities can be developed. For example,
highest priorities for incorporation should go to those compound that consistently
occur in these different lists, such as sulfur compounds, nitrogen compounds,
In this way the size
beryllium, arsenic compounds, and uranium/radioactivity.
Mechanism can
Simulation
the
in
procedures
manipulation
and
of thebookkeeping
be kept reasonable.
-77-
5.2.1
Example of Simulation Mechanism
A simplified hypothetical example of a simulation is presented here.
This example is intended to show some of the bookkeeping and modeling functions
of the proposed mechanism as well as to show the flexibility of the mechanism
that is due to the modularity of the different portions of the mechanism. The
motivation for this type of modularized approach is obvious:
(1)
it probably reduces the necessary amount of contracted work on
plant and control characterization;
(2)
it facilitates the-updating process; and
(3)
it reduces the turnaround time on studies of newly emerged
technologies.
It is not immediately clear to what extent this modularized approach
would affect accuracy of the results. For example, it is well known that
scrubber performance is very sensitive to the variation in the characteristics
of the fuel used. One would hope that the fuel-caused variations of the inflows to the scrubber could be characterized sufficiently to show this performance difference,
The example that has been chosen involves the use of six different
coal examples in a present state-of-the-art 1000 MWe coal-fired power
plant sited near Cairo, Ill. with and without a specific type of physical
coal cleaning process. There are thus twelve vectors of Resultant Factors
that are developed for these 6X2X1= 12 Assessment Options. All Non-Technical
Factors are set.at default values. Not all of the Technical Factors nor all
of the Resultant Factors are derived for this example, just to simplify this
computation and display procedure. So initially flags have been set to show
that the only Resultant Factors of interest consist of:
Economic Resultant Factors
Capital investment
Operating cost
Performance Resultant Factors
Availability
Energy efficiency
Environmental Resultant Factors
Air emission rates for NOx, SOx, CO, particulates,
arsenic, beryllium, mercury, nickel, and radium
In addition to this subset of Resultant Factors, it has also been preselected
that only the mean values are of interest in this particular assessment.
The first step in the Simulation Mechanism is then the recollection
of the Technical Factors for the coal samples chosen. Some of these Technical
Factors are shown in Table 5.2-2.
-78-
Table 5.2-2
samples.
Some Technical Factors associated with the six different coal
Sample
Number
Description
/l06 Btu
at mine
Transport cost
mills/ton-mile
1
2
3
4
5
6
Pittsburgh Seam
West Kentucky
Ill. No. 6
Wyom. Subbit.
S. Dak. Lig.
No. Dak. Lig.
90
72
74
123
118
96
17.0
17,0
32.0
7.2
7.2
7.2
(barge)
(barge)
(truck)
(unit train)
(unit train)
(unit train)
The distance that each of the six coals would have to be transported are 681,
178, 154, 1010, 744, and. 810 miles respectively. Other Technical Factors
for the six coal samples are shown in Table 5.2-3. The pyritic sulfur content
of the six samples is assumed to be .7, 1.2, 1.1, .1, .1, and .1%.
The coal cleaning process that is an option in this example is
presumed to be approximately a Level 4 Beneficiation as described in
(V; Argonne National Lab; 1976) and is located at the powerplant site.
Technical Factors for this process are listed in Table 5,2-4.
Table 5.2-4
Coal Cleaning Technical Factors example
Economics
Capital Investment ($1000/10,000 ton output/day)
Operating Cost ($/ton output)
Performance
Total Energy loss (%)
(%)
Availability
Cleaning Efficiency (% of input material removed)
Organic sulfur
Inorganic sulfur
Nitrogen
Organic (Total)
Minerals
Mercury
Arsenic
Beryllium
Nickel
Uranium
(tons in/tons out)
Yield
8,350
7.10
10.5
95.0
6.5%'
65.2%
18.3%
15.4%
52.0%
50.0%
x sulf. pyr.
sulf. total
15.0%
15.0%
15.0%
15.0%
75.4%
For the sake of simplifying this example the emissions from the
transportation and coal cleaning processes are not included.
The Technical Factors for the 1000 MWe coal-fired power plant are
shown in Table 5.2-5.
-79-
Table 5.2--3 Constituent Breakdowns of Coal Samples Used in Example
Pittsburgh
Seam Coal
Western
Kentucky
Illinois
Number 6
Wyoming
Subbitum.
So. Dak.
No. Dak.
Lignite*
Lignite
ASTM rank
BTU/lb
moisture %
vol. matter
fix. carb.%
ash %
hydrogen %
oxygen %
carbon %
nitrogen %
sulfur %
arsenic ppm
beryl. ppm
merc. ppm
nickel ppm
uran. ppm
* Values in this table are generally realistic averages from particular
coal seams, from (I; Abernathy and Gibson; 1963), (I; Bowen; 1966),
(I; ESSO; 1973), (I; Hall, Varga and Magee; 1974), (I; MclIeal and
Nielsen; 1976), (I; O'Gorman and Walker; 1971), (I; Ruch, Gluskoter
and Shimp; 1974), (I; Berry and Wallace; 1974), (I;Bertine and
Goldberg; 1971), (IV;Goldberg; 1973), and (I; Klein et al.; 1975).
The exception to this more or less realistic example is the South
Dakota lignite from Hardin County. Although the uranium level is
accurate, 3300ppm average with 7800ppm maximum, some of the other
trace metals were chosen at unrealistically high levels for this coal
field. These values are, however, typical of values from other sources
and thus South Dakota lignite represents a worst case example from the
standpoint of trace metal contaminents. At the very latest prices for
uranium,ores are being mined that have as little as 2000ppm uranium
content, thus values above this level in coals would probably only
be used as fuels if that uranium content of the coal was not known.
-80-
Table 5,2-5
-
Technical Factors for coal-fired plant of 1000MWe
-I-
Economics
(1000MRIe)
Capital Investment
$465 x 106
Operating cost -(Mills/kV hr,)
fuel
.11 x ¢/106 Btu
other
17,3
Performance
(%)
Total Energy Loss
Availability
(%)
(Btu/kW hr.)
Heat Rate
62.0
68.0
8530
Environmental Consequences
Nitrogen oxides (gm/min)
Sulfur oxides
Carbon Monoxide
ParticulatesArsenic
Beryllium
Mercury
Nickel
Radium 226
Radium 228
46900/lb N/10 Btu
15000/lb S/10 6 Btu
1200
385/%
ash
1.73/ppm
.35/ppm
4.3/ppm
.043/ppm
.032/ppm uranium
.009/ppm uranium
-
------
-
The assumed value for this table is 10,000 Btu/lb. coal, thus if the heat
content of the coal used is different then that then the number in the table
must be scaled to reflect the greater or lesser amounts of coal used.
With rather simple arithmetic, keeping track of the different heat
contents per ton of raw and processed coals the Resultant Factors for this
example are given in Table 5.2-6.
To get a rough idea of what the increases to ambient concentrations
will be, assume 17,250 m 3 /min outlet velocity and a dilution factor of
10,000 to reach the 24 hr, -max. concentration, The continuation of this
example into the area of ambient levels and health impacts is presented in
Section 5.3.1.
-81-
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5.2.2
Methodology for Improvement of Mechanism
.--
_
.
c
There is obviously a constant need for improvement of the data and
models in the Simulation Mechanism. Aside from these data needs, the
methodological extentions that would be most desirable are those described
in the following sections: 5.3 taking the results to the health effects
stage; 5.4 regional aggregations; and 5.5 national aggregations.
Some additional areas of future work might include efforts to
supplement the list of different emissions with some that may be important
but are diffecult to model: hydrocarbons by groups, heat, carbon dioxide,
moisture, noise, aesthetics, very low frequency electromagnetic waves, and
so on. Another area of possible future research is the area of more accurately
characterizing the timing of the release of emissions so that they can be
more accurately spliced together with timing of weather conditions and
eventually with timing of exposures of humans. Figure 5.2-2 shows one
attempt at a time-collapsed characterization of frequency, duration, and
magnitude of emissions releases.
The most important future area of research not covered in Sections
5.3 through 5.5 is the whole field of model validation. Modeling devices,
particularly those that are quite complex, are rarely well understood by
potential users. Either due to lack of good documentation or lack of careful
study of the mechanism, the results of the modeling tend to take on mysterious,
larger-than-life reputations. Such misuses have the potential for overshadowing any of the valuable contributions that could be made by such
models, Model validation research should include careful analysis of assumptions and scoping of the areas of model accuracy.
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5.3
Potential for Emission-to-Health Modeling
There is an increasingly urgent need for methods and mechanisms that
could be used to compare various energy options from an impact viewpoint.
Historically, the development of energy technologies has not been guided
by particular concern for the health effects that might result. There is
now more concern for health impacts of future technologies generally because
there is greater perception that significant health impacts exist and these
have not been internalized in decisions about energy technologies.
Still, however, there is relatively little known about the health
effects of any of the energy systems, past or future. A 1000 MW coalfired plant is believed to cause between 1 and 100 premature deaths in the
general public
(V; Hamilton;-1974) and even this guess does not include
any mutagenic or carcinogenic impacts. For a number of reasons assessments
of health effects of energy technologies will become more important in the
future:
(1)
health assessments are needed to guide in the development of
national energy policies, particularly the choice among alternative sources;
(2)
research needs and priorities for R & D funds could be greatly
influenced by projected health impact barriers to commercialization potential;
(3)
health assessments could be useful in the process of finding
for new energy facilities the sites with the least impact on
human health;
(4)
health assessments are necessary to formulate rational, balanced
programs for cost - effective control and safety equipment and
procedures, e.g. $100 million spent to save $3000 of fish cannot
be spent for a hospitalI and finally
(5)
the trial and error approaches of the past for slowly introducing
new energy technologies can not be used in the face of the projected, massive introduction rates for new coal-using technologies
with the possible enormous masses of new pollutants that will
suddenly be emitted.
Regardless of the scenerio that is projected there is certain to be
significant change in the energy choice/use patterns of the future. The
amount of health effects information that will be known and useable to those
who make our future energy choices is cause for concern. The outline of
.the major barriers to the implementation of an appropriate program to cover
these concerns is contained in the "Ray Report"
(V; Ray; 1973; 19:50, p. 32):
"Supporting Evidence:
It is clear that a sound base of scientific capability exists
for this work. No major difficulties with scientific feasibility
are foreseen in achieving the goals. Few engineering problems are
anticipated, but close cooperation between biologists, environmental
scientists and technology development engineers will be required to
minimize environmental impacts of present and new technologies.
The
major potential barriers are: (l) inadequate commmunication between
the environmental scientists and the energy technology developers and
-85-
(2) lack of established policy for the timely incorporation of environmental impact data into the development and implementation of energy
systems and associated technology."
co.-lsistent
ith barrier (.) is by providincg
Thei. :ideall manner n whicl L :o de:l 1 .assu,n1tions
and formats, and n.: .ethrdo.ogy
and rT.,ccha:n:i;smlfoir quick Jii
ter-retation
of the imlicaticns
(;ps,
:riori.t.ies nTc..'d-r iony) of
-tany of te dvelopc.
B<rrier (2) o he
of the rcla.:r frlds.
a,%1'
data ns i becoci:s -avilabl e .in
possi.ble p :.-tLicl.o solutlinnons, coulrl best be aittackedc
out,ide of t
R'RavReort,
by distrJibl-tin%! wideI. v the data t:1b:cc'.-s
available.,
r-!-.Jlg widely uS:eabl
some type( of- s.-r ltirIc
L
mcl.chnli_2snfor cia'y i:Lrc,;:i-Lpretat
i.on of results,
and, most
import-n!tl.y, Ar.!..ing
the rcsul
t , of environmenta~lla] t:h ceffect ases,
fol;rm
at rea. i.
ly u;cicLC
for te
technologies.
.~isn th-i.s informatio
int:elli.gelt dec:isi:cns wold .be.
and it wll
if rccw':ari.]se
our
of -ra.li:-
fitutre
tleal
lents
in a
diverse, complting energy
woi:.1d he]p to ensure
q avai.able
Ibr;;de about:
fac:ili::atet:lle task
of te
that
c-ncr,;ychoice/uls;e
patterns,
lderstanding
.c.l of iubll,,
of the al;:.ernltives.
There is, fortuantely, decades of literature in this area;' two
bibliographies contain more than 500 sources each (0; Gruhl; 1976a),
(0; Gruhl; 1976b). Some of the literature most relevent to this project
area is described here.
The general schematic for simulation of the health effects from the
emissions of a single energy facility is shown in Figure 5.3-1. Most of
Meteorological
Demographic
and Background
Emissio
40
Emission and Ambient
Standards Comparisons
Health and Cost/Benefit
Impl ications
Figure 5.3-1
Modeling tasks for extending emissions levels to health
effects.
the previous studies have concentrated on one or more of the functions in
that block diagram representation.
Actually, health impacts result form all of the different stages of
the fuel cycle of an energy facility, beginning with the health effects
coal itself. Perhaps the most important single factor
implicit in the mined
in characterizing coal is the occupational health consequences per unit heat
value. These health hazards, due to the carcinogenic dusts, suspended and
soluble organic and inorganic chemicals, can be found in many data bases,
on Environmental
(.V;QS5Council
(V; University of Oklahoma; 1975) or
Quality, 1973), for example. The physical processing of coal involves health
problems in the suspended or soluble inorganic and organic chemicals in
treated or untreated waste water. Modeling of the pathways and data on the
(V; Hamilton, 1974) and that
effects of these pollutants is available
refine their results.
to
and other projects like it are continuing
Ambient pollutant levels are created around the energy facilities
by using the same dispersion models described in section 2.1 and adding to
them the background concentrations known to exist. Measurements on these
(many sources are discussed in
background levels are generally available
section 2.1) and are even known for some of the rarer trace metals, see
Table 5.3-1.
Low
High
14
54
0
5
7
5
35
204
21
20
13
55
Calcium
Lead
1682
494
4274
1000
Bromine
Potassium
Zinc
Iron
Manganese
Gallium
Chromium
39
316
319
1838
75
6
39
143
1189
937
21,368
507
14
123
75,000
140,000
Nickel
Copper
Arsenic
Rubidium
Selenium
Strontium
Suspended
P'articulates
(ng/m /24 hrs.)
Table 5,3-1 Ranges of median values over several sampling stations of background
anbient levels of atmospheric trace elements (V; Winklestein et al; 1974)
Demographic assumptions are necessary in order to develop population
(V;
exposure patterns, Currently available is the model used by NAS
National Academy of Sciences; 1975) for a typical urban or a typical remote
siting location, See Figure 5.3-'2.
Most of the early research on population exposure patterns was
concentrated on the assessment of one-shot accidents. For example, one
study was performed on the effects of a nuclear reactor accident compared
with the impact of a conflagration of the fuel supply for an oil-fired
power plant, see Figure 5.3-3
Nuclear reactor accident assessments remain as
the most elaborate pollutant/demographic studies due to the licensing requirement for a very tedious complete characterization of the distribution of
population around a proposed site.
A number of excellent studies of
population exposure patterns have been developed for this purpose:
(V;
Frigerio, et al.; 1973), .(V; Hart; 1974),
(V; Honstead; 1970),
(V;
International Committee on Radiological Protection; 1969), (V; Kolde and
Kahn; 1970) and
(V; Sagan; 1971).
There are some additional considerations that must be incorporated
when extending the methodologies of radioactive exposure patterns to the
,87-
,Metr bpolitan Area
Remote
Poiint at Which Ambient
Leve,I Observed: 520 km
frorr I Power Plant and
40 1km Inside Metropolitan
Areat
.
i
|' '
.-
.- -...
--
-480
km
.--
.
60 km
Wind
Direction
Point at Which Ambient Level Observed:
From Power Plant,
Urban L
-
,
15 °
P
F
H
80 km
~~~~~~.
(All Metropolitan Area)
Figure 5.3-2
Geometry of a typical rural and urban demographic situation
(V; NAS; 1975)
i
I
.-
g
g
A
DISTANCEFPRO.PLANT MET£nS)
Figure 5.3-3
Cumulative mortality as a function of distance from oil
cor.flagration and nuclear releases from power plants(V;
Starr, Greenfield and Hausknecht; 1972)
-88-
chemical pollutants: the one-shot studies become series of erratic
bursts; and the indoor-outdoor patterns are more important
(V;
.S.
Environmental Protection Agency, 1972). Work in progress that has interim
results in.this field of chemical pollutant exposure patterns includes: a
TVA computer-graphic display of specific environmental distrubution patterns;
the EPA methodology guidelines
(V; U.S. Environmental Protection Agency; 1976)
for developing cancer risk exposure patterns; and the previous portions of
MITs program on chemical pollutant population exposure-patterns
(V; Gruhl;
1976). This previous work was directed toward the preliminary development of
probabilistic dispersion and demographic models to result in a method for
creating concentration-versus-population surfaces. These surfaces would
then characterize the impact of energy facility operation on exposed populations, see Figure 5.3-4.
log of potency of
::
log of nun
of exposur
(populatio
times
episoaesj
; of
ltion of
sure (e.g.
'ne year's time)
Figure 5,3-4 Concentration versus duration versus population surface for
characterizing the exposure history of a population around an energy facility
There are several indications that the surface in Figure 5.3-4 could
be characterized by a single curve
(due to the straight-line characteristic
of 'log of concentration' versus 'log of duration' for a stationary sensor).
With such a characterization the uncertainty associated with the exposure
pattern could easily be characterized
(using a similar curve for geometric
deviation, for example). This type of exposure pattern information would
splice together perfectly with dose-response information in the appropriate
format (assuming the conce-ntration in Figure 5.3-4 is actually a potency
index).
There have been few attempts to pull together overall energy/health
simulators and even these attempts have resulted in estimates that have
lacked precision, generally due to inadequate health/pollution data. The
-89-
(V; Comar, Sagan; 1976? p. 588)
following table is a collection of results
that shows the level of accuracy in current estimates.
Table 5.3-2 Premature deaths per year in the general public associated
with operation of a 100 MWe power plant
(values are lowest and highest
(V; Comar, Sagan; 1976; p. 588).
estimates from cited references)
Coal
Transport
.55-1.3
Processing
1-10
.067,100
Conversion
1.6111
Total
Note:
Oil
Natural Gas
Nuclear
-
.01-.16
1-100
1-100+%
0++
.01-.16++
Dashes indicate no data.
There are a number of reasons for these limitations and uncertainties,
some of these are listed in (V; Gruhl; 1976a) and most of these reasons are
due to the lack of good correlations between pollutant levels and health
impacts. This information is very difficult to develop because:
(1)
the air is filled with a huge number of potentially harmful
substances;
(2) various pollutants are often simultaneously present and thus
the effects are difficult to associate with the particular
causitive agents;
(3)
it is difficult to determine the impacts of chemical interactions
(that is, potentiating or
and synergistic combinations
antagonizing combinations of pollutants);
(4)
there may be many causative agents in addition to the air
pollutants;
(5)
there is a general lack of well-defined dose data for past
exposures; and
(6)
age, sex, latency periods, and pre-existing ailments all contribute to the susceptibility of populations.
The numbers that are developed so far have not been sophisticated enough to
account for genetic effects, carcinogenic effects, and other low-level effects
that may in the long run be the major consideration. Thus, this area of
research is sorely in need of new methods and information.
A starting point in the development of a simulation mechanism could
(V; National Academy of
be the National Academy of Sciences simulator
Sciences; 1975), see Figure 5.3-5. The options available for study using
the NAS assessment mechansim include:
Fuels:
Coal
(3 types:
eastern)
high sulfur, low sulfur western, low sulfur
,90-
Figure 5.3-5 Example of a methodology for the systematic assessment of health
effects of an energy facility (V; NAS; 1975)
Uranium
Generatorst
Existing coal-fired
620MW
New coal-fired
612MW
Old doal-fired plant reconverted from oil to coal
Nuclear
1000MW
Fuel Treatment;
Coal Cleaning
(Preparation only)
Emission Treatment;
Scrubber
Intermittent Control and Tall Stacks
Site Location:
Urban
Remote
The resultant health effects categories are;
1.
2.
Occupational Health
A. Mortalities
B. Morbidities
C. Man- Days Lost
D. Occupational Health Costs
Public Health
A. Mortalities
-91-
620MW
B.
3.
Morbidities
i. Chronic Respiratory
ii. Aggravated Heart--Lung Symptoms
iii.Asthma Attaches
iv. Children's Respiratory
C. Public Health Costs
Pollution-Related Damage Costs
A. Biota Costs
B. Material Damage Costs
C. Aesthetic Costs
This NAS study was admittedly quite crude, but it does offer an
excellent example of the extent to which an energy/environment assessment
could be carried. The analyses performed with this mechanism included a
number of excellent sensitivity studies with respect to uncertain parameters
such as sulfation rates. Some of the obvious places where advances on the
NAS model were in order included, in particular, the crude one meterological
condition dispersion model and the extension beyond the SO2-particulate
method for determining health effects.
Critical reviews of some of the other health effects assessments of
alternative electric power technologies can be found in (V; Comar and Sagan;
1976) and (V; Institutt for Atomenergie; 1975). The most important
comparative studies of the health effects of different energy technologies
include: (V; Argonne National Lab; 1973); (V; Energy Research and
Development Ad; 1975); (V; Carnow; 1974); (V; Gruhl; 1976c); (V; Hamilton;
1974);(V;Lave and Freeburg; 1973);
(V; Rose; 1975); (V; Sagan; 1974);
(V; Starr, Greenfield and Hausknecht; 1972); and (V; U.S. Atomic Energy
Commission; 1974). Many other articles are listed in (0; Gruhl; 1976a).
All of the references just listed skip one or several of the functional
modules shown in Figure 5,3,1,, For example, two blocks in almost all health/
energy assessments that are not dealt with by physically-significant models
are the "Air Pollutant Dispersion and Aerochemistry" and the "Exposure
Patterns," Generally this short--cutis taken by using correlations of
regional tons of emissions to regional health/damage estimates. Another
assumption used by all of these studies was the utilization of SOx (ometimes
with particulates) as a gross general indicator of approximate total air
pollution.
A number of other health/energy studies are underway. One effort
that is not completed but which does have substantial interim progress is
the SEAS-Strategic Environmental Assessment System
(V; Environmental
Protection Agency; 1975). SEAS currently exists in module form only,
the package to be put together by 1979-1980, Briefly it is a model of the
interaction of energy and environmental problems with the entire national
economy
(modeled using a large input-output scheme). Thus, SEAS takes
into consideration materials and resource availability, capabilities of supporting industries, and so on. The list of atmospheric pollutants collected
is impressive, still its direct application to health/energy assessments is
not possible. Hydrocarbons, for example, are all in one category with no
regard to carcinogenic potency.
Ambient concentrations in SEAS are estimated from emission-to-ambient
scaling procedures much like those used in Project Independence
(V; Federal
Energy Administration; 1974) see Figure 5.3-6. Health effects are included
only insofar as they are reflected in the dollar consequences of the emissionto-damage scaling estimates
(the emissions from the 1973 National Emission
Data System collection by industry and pollutant, the damages from 1971
estimates by region.)
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-93-
Some of the other outstanding examples of environmental methodologies
that are relevant to this report's topic are in the area of cost/benefit/risk
assessments of nuclear power.
some of the best results from this field are
in
(V; Gillette;
1974),
(V; Hammond;
1974),
(V; Jordan;
1970),
(V; Rudman;
1974), (V; Sagan; 1972), (V; U.S. Nuclear Regulatory Commission; 1975),
(V, Wilson; 1972) and an excellent critical review of this field in (V;
Starr, Rudman, and Whipple; 1976).
One of the areas of the energy/health field that is fast growing to be
of greatest concern is the cancer potential of coal-using power plants. The
principal carcinogenic air pollutants that result directly from the conversion
and combustion of coal include a list of polycyclic and other aromatic
hydrocarbons, trace elements and radionuclides. Given an opportunity to
react in the atmosphere, a whole series of organic nitrogen and sulfur
compounds join the list as indirect carcinogenic emissions. Filially, if the
suspected promoters are also included then the list of agents to be considered
expands to include almost all of the common pollutants. For example, S0 2 is
a suspected potentiator of the carcinogenic effect of polycyclic organics
such as benz(a)pyrene; some metal oxides, such as FE20 3 act similarly as
accelerators; NO 2 and ozone are suspected of interfering with complex
clearing mechanisms and thus contributing to carcinogenesis; NO and NO 2, in
the presence of ammonia and acids in coal combustion plumes could contribute
to the formations of nitrosamines of pronounced carcinogenicity
(V; Preussman;
1976). It is thus obvious that the systematic assessment of carcinogenic
hazards cannot concentrate on a small set of pollutants but must take an
across the board approach.
Systematic carcinogenic assessments have been developed largely in two
areas of the government, The National Cancer Institute has made a number of
contributions to this field, a summary of these efforts from the energy
technology viewpoint can be found in (V; Schneiderman; 1975). The EPA
in its Cancer Assessment Group has developed an assessment methodology for
determining chemical carcinogen risks
(-V;U.S, Environmental Protection
Agency; 1976).,
For the chemical carcinogens the
EPA has altered the stance of "lowest practicable level" regulations used
for carcinogenic ionizing radiation releases to a stance of "balancing
risks and benefits as a basis for final regulatory action." Thus, there was
a need for an assessment methodology.
While this EPA carcinogenic assessment methodology could make a
number of important contributions to this particular project it is not
wholly applicable by any means. Assessment mechanisms for regulatory use,
in general, have levels of risk aversion built in at any number of places,
One example is the prudent assumption of the direct linear non-threshold
relationship between biological effects and amount of dose. Another built-in
risk aversion is the use of the dose to the highest exposed individual as
the design criterion. In the context of this current discussion, in which
a curve of health risk versus probability might be a result, different
levels of risk aversion would be different points on a curve all of whose
points are important in a comparative assessment context.
The only source that really attacks the carcinogenic aspects of
coal-fired electric generation in (V; Watson; 1970). This is, however,
more a listing of carcinogenic suspects that are believed or known to be
in electric power plant emissions. A National Cancer Institute study
proposed by the MIT Energy Lab is in fact intended to be a way of extending
the current state-of~the-art beyond that type of listing effort by putting
real impact quantifications and measures of uncertainty on the results.
-94-
It is important initially
(and in future extrapolations) to have
the information on the carcinogenic potency of individual pollutants and
synergistic effects of whatever combinations of pollutants are available
from existing data bases
(V; National Cancer Institute; 1974). It is obvious,
however, that for this field to progress new and even uncharacterized
mixtures of pollutants likely to impact future populations would have to be
postulated and synthesized and these new mixtures would have to be tested
for carcinogenicity to more accurately account for synergisms.
In addition to the NCI data base
(V; National Cancer Institute;
1974) some of the many sources of information on atmospheric carcinogens
include (V; Buck and Brown; 1964),
(V; Carnow and Meier; 1972), (V; Fenter
and Margetter; 1973), (V; Hettche; 1971), (V; Hueper; 1966) (V; Stocks; 1966),
(V; Winklestein and Kantor; 1969), (V; U.S. Dept. of HEW- 1962), and
(V; Wynder and Hamond; 1967). These data vary considerably in applicability
to the systematic framework concept. Much of this data and all the otherpollutant/
health data is in terms of thresholds at which effects are noticed,- and even
when this format is used very systematically, it is not in the most useable
form, see Figure 5.3-7.
w
4d
0A.
0
Figure 5.3-7 Threshhold type display of dose-response information (V; Starr and
Greenfield; 1975)
A more useful format, from the assessment viewpoint, is a doseresponse relationship for predicting magnitudes of effects: linear nonthreshold
(V; Bruces; 1958), linear with threshold
(V; National Academy
of Sciences; 1975), log-profit, and half-power
(V; Starr, Greenfield,
Hausknecht; 1972) models have all been used. Some studies have bounded above
and below with different types of models
(V; Starr, Greenfield, Hausknecht;
1972).
The ideal format for operating on population exposure patterns has a
different dose-response pattern for each health effect to be modeled
(such
as mortality, mutation, stomach cancer, total cancer, or whatever). Associated
with each effect there is a functional combination of the potentiating,
antagonizing, or additive pollutants and this funtional combination acts as
-95-
an index of the potency of the particular combination. Ideally, thent
curves of probabilities of an effect at the different potency-duration
pairings would be plotted, see Figure 5.3-8. A display similar to Figure
pote
Figure 5.3-8
Dose-response information in a format ideal for assessment usie
5.3.8 showing instead the isopleths of geometric standard deviation associated
with each pairing would add the very useable probabilistic information.
There are, of course, problems with this postulated
'ideal' format,
for example in the treatment of abnormally susceptible populations. Hopefully,
socio-economic indexes or density implications might be worked directly into
the potency functions to the extent that they affected health responses. In
any event, Figure 5.3-8 can offer a useful initial format for assessment
purposes; the available health effects data can generally be easily fitted
into its appropriated position on this format. The regression analyses are
the primary exception.
The problem with regression analyses is that they never claim to
establish causal dose-response relations, just statistical association. The
variables of a regression analysis are certain only to be indexes of the true
(perhaps unknown) causative agents. The results of regressions can be
useful in the absence of knowledge of causative agents or, possibly, in
independent evaluation of results.
The most widely prblicized regression analyses of health effects of
air pollution have been reported in (V; Lave and Seskin; 1974) and
(V; Hickey; 1971). Common pollutants, trace metals, and organics have been
employed as indexes to carcinogenic hazards
(V; Hickey, et al.; 1970),
(V; National Research Council; 1972), (V; Schneiderman; 1975).
Many of the programs that have produced the previous references are
continuing. There are, in addition, several new projects that can be
expected in the future to provide significantly valuable inputs to this
physically significant simulation field.
EPA is increasingly being charged with short-term problems, however,
there is a considerable abount of information being developed that can be
of initial use in an overall assessment. The EPA has begun the funding
(2/76)
of an Integrated Technology Assessment program at Teknekron, Inc. (V;
-96-
Teknekron, Inc.; 1976). This involves the development, by about 1980, of an
simulation mechanism that will model the social, environmental, and economic
problems of electric utility systems. Included in their current plans are
modules to simulate dispersion and exposure patterns, see Figure 5.3-9. A
similar ERDA program, National Coal Assessment, is being carried out by
Argonne National Laboratory
as part of its Regional Studies Program.
Current ERDA programs in population exposure pattern studies include
research on the methodology for describing the populations surrounding
energy facilities in terms of demographic, socioeconomic, and health indexes.
The HNL work is concerned with areas of Tennessee; Argonne has chosen fifteen
different locations. Los Alamos Scientific Laboratories are presently
conducting tissue analyses of the general population in specific locations to
determine trace metal accumulations attributable to nearby fossil-fueled
facilities. These studies will attempt to create exposure patterns without
dispersion modeling.
Mutagenic and carcinogenic activity of fossil-fuel combustion components
(HNL, Argonne, UCLA,
are currently being studied at six ERDA laboratories
Pacific Northwest, Lawrence Livermore, and Franklin McLean). Accidental
exposures around pilot facilities are to receive top priority in the collection
of data on health effects. As far as the planned research is concerned,
the array of chemical agents is so complex and the expertise so rare that
only the potentially hazardous materials of highest "priority" are being
investigated. As far as breakdowns in methods of approach for determining the
effects of fossil-fuel pollutants they are roughly: pollutant-nucleic acid
(Brookhaven, Lawrence, Los Alamos); differentiation between
interaction
(Argonne, Holifield, Lawrence): rapid
inducing and promoting activities
(Holifield);
screening of coal conversion process streams and products
Argonne, Pacific Northwest, Brookhaven,
determination of likely doses to man
Inhalation Toxicology Research Institute, University of Tennessee, and
University of California-Davis); and pollutant interactions and dose-response
determinations
(Pacific Northwest, ITRI, Holifield, and University of
Rochester).
-97-
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-98-
5.3.1
v
Example of Health Effects Simulation
Extending the simulation mechanism from the emission characterization
stage to the health impact level involves including several additional
assumption categories and some new, and possibly complex, functional
modules.There are a few alternative methods that can be employed to avoid some
of these more difficult modeling tasks. The dashed lines of Figure 5.3-10
shows one example where scaling procedures, such as the emission-to-impact
regional modeling based on regression analyses, can be used as one alternative
to the dispersion and aerochemical modeling that would be required in a
physically significant simulation procedure.
The example presented here is just an extension of the hypothetical
example from section 5.2.1. To really be carried out in complete detail,
different dispersive models should be used for the different pollutants,
Climatological data should be used and pollution profiles should be developed
for the various distances at each of the 16 points of the compass. Table
5,3-3 from (V; Hamilton and Morris; 1974) shows this type of development,
although not at different wind directions and only for the "common"
pollutants.
Background levels should then be superimposed and comparisons
could then be made against the ambient standards, see Table 5.3-4.
At this stage
ose-response curves should be used for the various
pollutants. For example, Table 5,3-5 (V; NAS; 1975) shows some sulfur
curves, and Table 5,3-6 has dose-response relationships for some other
pollutants. From this material and demographic information on population
densities at the downwind distances in each of the wind directions, see
Figure 5.311, some information on health impacts can be developed, such
as in Table 5,37.
The example that is presented here does not go into this level of
detail. The pollutants collected in the example from section 5.2.1 are the
only ones used in this example, and the only health effects computed are
the mortality harvests indicated by the levels of the 24-hr. maximum
concentrations. These indications of the maximum 24-hr. concentrations are in
this example found by dividing the emission rate in gm/min by the 17,250m 3 /min
emission volume and using a standard diluting factor of 10,000. The health
effects are here approximated by the formula that proposes that for the population
exposed the annual mortalities are equal to the ratio of the 24-hr. maximum to the
24-hr. recommended standard. Although this is a hypothetical formula it does give
some indications of what the actual impacts might be. For example, ten times the
standard is presumed to annually cause ten premature mortalities in the exposed
population, one-tenth of the standards causes one-tenth of a premature mortality.
_99-
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Table 5.3-4
National ambient air quality standards for the common pollutants
__
Primary
_ ____ Standarda
__
Contaminant
Averaging
Interval
Sulfur
Dioxide
1 year
24 hours
1 year
(by vol.)
24 hours
75d
ppmb,c
Pg/m3
(by vol.)
60
260
80
--
ppmb,c
3
.
Suspended
Particulates
Secondary Standarda
d
150
d
365
0.03
0.14
3 hours
1, 300
0.5.
Carbon
Monoxide
'8 hours
1 hour
10,000
40,000
Photochemical
Oxidant
1 hour
160
0.08
160
0.08
1 year
100
0.05
100
0.05
itrogen
9.0
35.0
10,000
40,000
Dioxide
9.0
35.0
a
All values other than annual values are maximum
concentrations not to be exceeded more than once
per year.
b
PPM values are approximate only.
C
All concentrations relate to air at standard conditions of 250 C temperature and 760 millimeters
of mercury pressure.
d
Annual average refers to arithmetic mean for
gases and geometric mean for particulates.
-102-
Dose-response relationships* developed for use on suspended sulfate
(V; NAS; 1975)
Table 5.3-5
levels
BEST JUDG.ZNT THRESHOLD
FUNCTION
EXPOSURE
TRESII9LD
SLOPE"
ADVERSE
HEALTHEFFkCT
DURATION
-
INCREASED
DAILY MORTAJITY
ig/m)
-
24 HOURS'
OR LONGER .
25"
AGGRAVATION OF HEART 24 HIOURS
AND LUNG DISEASE
OR LONGER
AGGRAVATION
OF ASTHMA
-
24 HOURS
OR LONGER
"
.252
9
1.41
6
3.35
EXCESS LOWER
RESPIRATORYDISEASE Up to 10 YEARS
IN CHILDREN
13:
13
7.69
EXCESSRISK
FOR CRONIC RESP. Up to 10 YEARS
DISEASE IN ADULTS***
12
11.1
.
.
*These dose response relationshipswere developed in an unpublished:
study for the U.S. Envirornmental
ProtectionAgency. The "best
judgment threshold functions"representsubjectiveapproximations
to data, not precise mathematicalfits. -Thestudies upon which the
estimates were based are as follows: Mortality;Lindeberg (1968),
Martin and Bradley (1960),Lawther (196),
Glasser and Greenburg (1965),
Brasser et al. (1967), Watanabe and Kaneko (1971),Nose and Nose (1970),
Buechley et al. (1973). Aggravationof heart and lung disease; Carnow
et al. (1970), Goldberg et al. (1974). A/rava[on of asthma; French,
Sugita et al. (1970),Finklea et al. (1974a,-Tnlea
et a1.(1974c).
Excess lower respiratorydisease in children;Nelson et al. (1974)
Finklea et al.(1974b),Douglas and Waller (1966),Lunn et al. (1967),
Love et al. (1974),Hammer(1974). Excess chronic respiratorydisease;
Burn and Pemberton (1974),Goldberg et al. (1974), House et al. (1973),
Hayes et al. (1974),Yashizo (1968),House (1974),Galke and House
(1974a),Galke and House (1974b).
**Change in percent excess over base rate for population,per g/m 3;
change in suspended sulfate level.
t**For chronic respiratorydisease, difficultieswith available data
necessitatedthe unit of measurementto be excess risk rather than
direct incidenceof illness. Actually, in its originally calculated
form, separate dose response functionswere assessed for cigarette.
smokers and nonsmokers. The function described in the table is a
weighted linear average based upon the average prevalenceof cigarette
smoking in the adult populationat risk.
_
_
Table 5.3-6 Dose-response relationships including effects of trace metal
pollutants, developed by regression studies (V; Hickey et al; 1970)
Rea %oncficiont
Annua mnortalit rt
per 105 ppu;lazion
Tot: caier
Bseast cancer,L:asedon
toa! populatiOns
Stornachcancer
LLn!JcarnerrL
Deses of the hartl
Arteri,;clerotic
heart dcs:als
ISC
Kfcsm,
fworin c(Cab;r~-i
g:w'
0S4 2
Ti
Watcr
Consnt
A2
.0
Cd
2
Cu
140-205 -15;.33 +40.130 +181J43 -10.120
170
51
% of
.t
-
-25.6
28. D-
162-11t3
400-402,
410-43
-25.4
420
"-1.
+2.5
+4.
2
+D.9X01 +51.616
+71.A31 +41.3?
-...
~
-
-
hrdo;s
"cbpained"
If
2
x
-
5.4
-
.5
73.2
-Z9.r4
e5.1
23.14
47A
C0})
-~~~~
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47.
O.152
-1.43
-
*2.010
+d.117
-
A;
-24.225
~.Gr
1,E410
-
-'.0
-
-
-2.107 +5.462 -2.103
-
-
-
-
-
-
-
0.6255
-
-A
10
10
106
.,4
4
-J
5
.
4
O
104
10
10 2
60
30
In
,V
10
20
30
40
50
Distance From Site (miles)
5.3-11 Information about cumulative population versus distance downwind
Figure
from energy facilities (V; Argonne National Lab; 1973)
-104-
Table 5.3-7 Excess mortalities within an 80km radius due to pollution exposure
from a 1000lWe fossil-fueled plant with a 1000-foot stack (V; Hamilton and
Morris;
1.974)
I'
. Total Population Within 80 KIM= 3.8 x 10
· SO
2
Reaction Rate - 10% Per IHour
NUMBER
OF
EXCESS
TECIŽr1OLOGJtCALALTER.TATIVES
PERCENTAGE
INCREASE IN
MORTALITY
DEATHS
PRATE
.100
10
0.27
0.03
10
0.03
30
0.09
EASTERN HIGH SULTJ'R COAL
(12,000 Btu/#,
3% sulfur,
12% ash)
99% particulate removal
99% particulate + 90% sulfur removal
FASTERN LOW SULFUR COAL
(12,0C0 3u/#, 0.4% sulfur, 3
99.0 particulate removal
ash)
MONTAN.MA COAL
(8,750 Btu/#, 0.8% sulfur, 8.4% ash)
99% particulate removal
LOW SLFUR OIL
(153,000 Btu/gallon, 0.2% sulfur).
99% particulate removal
3
0.01
40
0.11
HIGH SULFUR OIL
(153,0'30 3tu/gallon, 2.5% sulfur)
99% particu.lite
re.moval
99, particulate + 90% sulfur rex:noval
4
0.01.
I
Number of expected annual deaths in the
population
-
---
36,000
I-
The 24-hr. recommended or standard levels for the pollutants used in this
example are:
NOx
455pg/m 3 (extrapolated from the annual standard)
SOx
365gg/m 3 (standard)
710,000pg/m3 (standard)
CO
-
0pg/m3 (standard)
particulates
26
arsenic
.15pg/m3 (IV; Goldberg; 1973)
beryllium
.01lg/m3 (V; USAEC; 1974a)
mercury
.10ug/m3 (IV; Goldberg; 1973)
nickel
.03ig/m3
radium
.02curies/yr (V; USAEC; 1974a)
(IV; Goldberg;
1973)
Assuming that background levels for each of these pollutants are ignored, these
levels lead to annual premature mortality rates for these different 1O000Me
coal-fired facilities at:
_·
Pitt Seam/ cleaned/ coal-fired
Pitt Seam/uncleaned/coal-fired
West Kent/ cleaned/ coal-fired
West Kent/uncleaned/coal-fired
Ill No. 6/ cleandd/ coal-fired
Ill No. 6/uncleaned/coal-fired
Wyo Subbt/ cleaned/ coal-fired
Wyo Subbt/uncleaned/coal-fired
S.Dak lig/ cleaned/ coal-fired
S.Dak lig/uncleaned/coal-fired
N.Dak lig/ cleaned/ coal-fired
N.Dak lig/uncleaned/coal-fired
-o06-
plant
plant
plant
plant
plant
plant
plant
plant
plant
plant
plant
plant
#1
#1
1.8
1.9
#1
4.3
#1
#1
#1
#1
#1
4.9
4.3
4.6
4.5
4.8
#1
154.
#1
#1
#1
167.
13.5
14.1
5.3.2
Future Data and Modeling Requirements
In a recent ERDA report (V; ERDA; 1975; p5) it was stated: "Information
about long-term health effects of continuous low level exposure is meager
and thus is not taken into account in the present standards even though they
may in the long run be the major health cost." This major gap in information
about health effects must be considered as a potential major change in
standards, and as such represents a large element of uncertainty in the
decision making process of electric utilities. These environmental uncertainties
will be particularly important factors for risk-averse persons deciding about
commitments of large amounts of capital for furture energy facilities.
This concern has been recognized by a number of utilities and, in fact, some
have rated as "high priority" the study of the "relationship of community
health to utility emisions" and the "identification of 'new' air pollutant
components of utility emissions which mightbecome major concerns of regulatory
or other agencies." Everyone seems to agree that there is a great need for
new research in this field.
There are several important goals that should be the focus of that type
of future energy/health research. First, it should make available new
information on the health impacts of various combinations and durations of
community-level pollutants both common and "new." Second, it should extend
to the dosage stage all of the sophisticated probabilistic models that have
been developed for energy facility operations and pollutant dispersion modeling.
Third, it should aim toward the development of a consistent, appropriate
population exposure patterns for
simply characterizing
methodology for
use in single plant siting surveys or in environmental evaluations of national
energy scenarios. In addition, it should develop new information on the health
effects not just frompollutantsbutfrom
specific types of energy facilities
and from long- and short-range background concentrations. Finally, through
the comparison of many available health/pollution models a determination should
be made of the predictive value of these models once they are removed from
the place and time of their development.
These goals represent a great amount of research, but it would certainly
be worthwhile, and the dividends are likely to be very large. In addition
to the realization of the goals previously listed, there are a number of
possible indirect benefits. For example, development of some type of health
impact assessor for energy facilities could produce a mechanism that could
serve as a vehicle for much current health effects data and would thus provide
direction, format, and incentive for future health research in the energy field.
Another application for such a health assessment procedure is apparent due to
the inadequacies of current air quality standards. Not only do present
standards not accurately protect human health but it is not possible to comply
with them. Even simple probabilistic dosage modeling can show that there is
always a finite probability of violation the "once per year" air quality
thresholds at least twice per year. Also with the new emissions sources that
will always be added and the realizations of new pollutant/health effects, it
is clear that the "once per year" threshold standards will have to soon give way
to individual health assessments and a weighing of costs and benefits.
There are strong indications that if the health assessment information were
available we could be making better choices in almost all of our energy decisions.
-107-
5.4
Regional and Power System Considerations
There exists a number of questions of regional and national scope that could
best be answered with aggregated
data on the comparative technologies for coalfired electric generation. Two examples of such questions might be:
a) How should ERDA development funds be allocated among the competing
developing technologies to maximize substitution of coal for oil by
1990?
b)
TWhatis the maximum commercial level of, say, fluidized bed combustion in the nation's fuel use pattern in 1995?
This section discusses the major issues to be considered in extending the
present systems analysis mechanism to consider such assessments.
The design of the present systems analysis mechanism has been limited to
considerations of isolated power plants. As a result, it does not currently
represent the interactions between power plants at different locations, although
several measures important to those interactions, such as reliability, have
been characterized. In the sense that a variety of coal types, transportation
costs and plant sizes are characterized, it is possible to use the current
mechanism to construct an assessment of isolated plants in different locations,
but this capability falls short of representing the complexity of practical
power systems operation and planning problems. This limitation was a deliberate strategic choice, made in order to address the most difficult and immedi-.
ate assessment problems first and does not reflect any serious technical
obstacle to obtaining aggregated results. The only direct concessions to
regional concerns were the technical and resultant factors of introduction rate
for new technologies, factors which are concerned with the cumulative limitation on the isolated plants.
The design of the present systems analysis mechanism has taken the "utility
industry perspective" on the choice of new equipment and fuel treatment methods.
This perspective could be loosely stated as follows:
The choice of new generation and its fuel is a constrained
minimization problem. The major constraints are reliability of
service, environmental and financial regulations and contractual
obligations. The objective function is the present-worthed value
of investments and operating costs for the total system.
Contrast this perspective with that of a hypothetical perspective of an
environmentalist:
The choice of new generation and its fuel is a constrained minimization problem. The major constraints are reliability of service, financial regulations and contractual obligations. The
objective function is the total emissions of all pollutants
from all the plants. Costs are secondary.
Or the hypothesis of an energy independence advocate:
The choice of new generation and its fuel is a constrained
minimization problem. The major constraints are reliability
of service, environmental and financial regulations, contractual obligations, and the elimination of oil and natural
gas as generating fuels by 1990. The objective function is
present-worthed value of investments and operating costs for
the entire system.
Both our hypothetical environmentalist and energy independence advocate raise
possible methods by which the comparative assessment of coal-fired technologies could proceed. But, barring new legislation, neither reflects the basis
on which the decisions between technologies and fuels are actually made by
persons in the utility industry.
For an isolated plant, the utility industry perspective is largely a good
engineering design approach. Costs for the total system and implications for
the system reliability cannot be calculated, so alternative measures, such as
-108-
plant costs for design-level operation and plant reliability are considered.
Unfortunately, for the reasons discussed in the next section, the isolated
plant with the most attractive assessment is not necessarily the most attractive choice for integration into a power system. As a result, simple aggregation from isolated plants to national data is potentially misrepresentative.
As more regional considerations are included the aggregate results should
improve. The questions of importance are the identification of the salient
regional and power system factors and the specification of the method for
assessing them prior to the aggregation procedure.
5.4.1
Power System Integration
Electric utility systems evolve primarily through the addition of new
facilities, rather than by the replacement of existing facilities. This situation is the result of the capital intensiveness of the industry, its high
growth rate and the long life of the typical plant. Consequently, when a
utility is deciding on the construction of a new generation facility-determining its capacity, steam system, fuel, abatement, installation
and so on--the choice is heavily influenced by the utility's existing and
committed system. (ii;Anderson;i972)
The choice of new facilities is influenced by the existing and committed
system primarily through plant reliability and plant energy production. The
impact of a given plant's reliability on total system reliability varies
according to the size of the new plant relative to the existing and committed
system and according to the availabilities of the existing and committed system units. Since total system reliability determines the margin (excess
capacity required over peak load) requirements, this factor impacts directly
upon the investment requirements of the total system.
The design capacity factor of a new technology will not necessarily be
its actual capacity factor. Since the overall economic attractiveness of a
plant is strongly dependent on its fuel consumption, a change in capacity factor could be a critical factor in the choice between two competing technologies. The amount of energy which a given facility can be expected to produce
is determined by its availability (a function of characteristics such as maintenance requirements, environmental restrictions, and forced outages) and its
incremental cost of producing energy. Depending upon the incremental costs
and availabilities of the plants in the existing and committed system, a new
facility may operate close to its availability (a baseloaded plant), may have
frequent periods of service but not operate close to its availability (an
intermediate or cycling plant), or may operate infrequently (a peaking plant).
Typically, baseloaded plants offer lower operating costs (10 mills/kWhr)
at the expense of high plant investment ($500-$1000/kW). Peaking units offer
low investment and short lead time for construction, but have high fuel costs
(40 mills/kcWhr). A plant designed for baseloaded use and operated as a cycling unit, or vice versa, will be uneconomical. When planning a new facility,
a utility usually simulates the operationof its new facility with its existing
and committed system to determine whether the design operation will actually
occur. The simulation techniques, called production costing methods, are
widely used and readily available (Ill;
Booth; 1972 ), III; Ringlee and Wood; 1969).
Table 5.4-1 0aI;Edison Electric Institute; 1975; p. 21) and Table 5.4-2
all;FPC; 1970; p. 1-18-23) indicate some of the differences in the existing
and committed systems found in different regions of the country. Table 5.4-1
indicates the MW installed capacity in each major generation class for each
state and census region. Figure 5.4-1 shows this data as the census region's
mix of energy generation in 1974, indicating the different emphasis that the
different regions have placed on hydro, conventional steam, nuclear steam, and
internal combustion.
-109-
Table 5.4-1 Generation - Total Electric Utility Industry, by states and type
of prime mover driving the generator ii; Edison Electric Institute; 1975; p2 l).
1973 AND 1974-KILOWATT-HOURS IN MILLIONS
Total Electric
Utility Industry
197 4 p
Total United States
.....
1973r
1 864 961
1 856 216
Maine..................
New Hampshire.........
Vermont................
Massachusetts...........
Rhode Island...........
Connecticut
...........
New England.........
7
4
3
28
1
23
69
557
637
605
816
231
391
237
7
5
2
33
1
21
72
New York..............
New Jersey .............
Pennsylvania ............
Middle Atlantic.......
103
32
98
3
750
9C0
884
Ohio ..
........
Indiana ................
Illinois .................
Michigan ...
Wisconsin .......
....
East North Central...
Minnesota ..............
Iowa ...................
Missoul i................
North I)akota...........
South Dakota...........
Nebraska ...............
Kansas.................
West North Central....
Delaware .............
Maryland ...............
District of Columbia.....
Vir9ginia .............
West Virinia...........
North Carolina ..........
South Carolina ..........
Georgia..............
Florida .................
South Atlantic.........
IKentucky ..............
Tennessez ..............
Alabama ...............
Mississippi ..............
. East South Central....
Arkansas...............
Louisiana
....
Oklahoma ..............
Texas ..................
West South Central....
Mlontana...............
Idaho ..................
Wyoming ...............
Colorado ...............
New Mexico............
Arizona ...............
Utah.: .................
Nevada ................
Mountain.............
Washington.............
Oregon ..............
California .....
Pacific...............
Alaska .................
Hawaii
.............
Alaska & Hlawaii......
Conventional
Steam
Hydro
19
71p
1973r
19711)
1973r
300 447
271 634
1 445 785
1.494 901
1 756
920
359
4
422
7,53
1 853
1 435
994
489
5
4.40
5 216
2 201
3 315
193
25 359
1 202
11 983
7 283
2 610
3 810
167
28 039
1 177
16 373
52 17
28 639
(285)
1 391
29 .45
29 154
(337)
1 371
30 188
65
29
90
185
5St
572
-151
60
10
445
106
1 050
1 706
8
4S0
113
890
2 107
98
56
71
59
22
033
366
342
350
536
9) 699
3 317
3 598
307 627
309 91
-135
587
931
776
539
6-18
521
736
890
1 713
2729
5 624
1 293
7
872
905
007
382
795
370
3
11 042
116 437
12 992
6 741
5 801
-'9594
98
8.. 191
56 862
91 453
6.......
61 452
32 559
340 617
22
15
38
8
6
12
18
28
2
33
61
58
31
37
75
171
545
025
475
324
302
200
542
405
781
505
520
159
611
881
336 I46
53
52
58
11
543
705
0146
462
175 756
12 172
105 778
36 261
103 841
2.5 880
99
59
92
62
28
920
524
514
561
734
34/3253
21
15
38
7
5
9
17
105 6'22
90
36
1...
120
2
246
407
019
3(63
819
2
2
4
1
-
-
19 313
949
524
060
060
3 398
11 767
10 344
-
3 823
11 452
11 778
-
179 593
25 509
27 053
4 263
4 236
52
59
56
11
964
474
497
000
1 690
2 16-1
1
1 270
1
69
33
102
20.
58
71
58
20
16
13
36
5
12 334
17 371
11 055
803
650
122
528
999
771
'19-
331 360'O
227 070
10
15
20
20
3
13
4
1 045
477
6 887
3 413
3606
252
33 129
142 263
9 694
1 292
27 646
3 264
35 716
56 530
62 178
28 029
35 916
76 280
12
41
30
135
3.....
39
9 506
853
245
771
874
204
129
076
7 107
3 862
4178
234
6 347
26
39
-
-
2 482
2 885
7 970
16 911
10
213
25
16
290
12
226
22
13
312
9 27:3
3 73
6 998
9iD9.4
7 226
3 585
362
11 173
25-
272
44
298
43
-Sj-320
-
19 592
416
8 256
20 051
2 834
5 952
148
51
413
636
61
213
48
444
551
71
2
288
-
96 666
97 781
9' 290
6 741
5794
293 307
50
40
41
11
145
938
413
462
4 363
931
--
375
263
561
033
067
001
738
094
11 057
43
7 877
293 926
930
49
48
43
11
126
072
96S
060
5 953
-
6 289
-
143 9,58'
152 226
6 289
8715
41 133
26 502
132 963
361
3 590
1 61
3 761
1 700
7
39
29
140
219 935
9 484
9 697
216 3383
209 313
361
9 136
9 725
7 520
1 330
-
1 411
1 414
74
7 393
941
1 601
1 110
1 281
65
7 187
1 111
1 669
391
099
971
124
0-17
159
1 616
9 252
13 093
19 238
10 338
2'500
12 162
32 24.5
28 195
73 129
68 199
1 213
85
71 071
75 3169
5 290
302
95 736
101 3.28
8 223
10
14
19
17
3
13
395
526
420
5-10
626
8-5
!6 711
78
28
137
244
-
-
9686
8222
542
138
347
311
8
9
14
19
13
3
12
1 871
5 288
1 7.1.t
5 087
326
17
286
20
1 383
5 118
1 306
4956
7
6 831
3j43
306
6 601
6 22
p
Preliminary.
68
28
38
135
r
8-14
139
751
73
Revised.
,
( )
-
-2,9§
3 996
-
82 301
35 964
t46
120
164 68
Source: Federal I'ower Commission.
5 989
.3 351
-1 598
5 120
4 303
14 372
-
565
-1-t
134
1,40
D159
83 334
937
19.1
529
392
722
7 321
16 686
25
3
27
56
55
18
31
71
1973r
3 57
16
14
36
5
752
404
758
028
625
689
962
348
197 -p
112 740
126
013
0I0
199
996
906
286(
604
Internal
Combustion
197.p 1973r
834
283
022
746
684-1
6 659
17 438
26
2
26
61
51
16
33
67
-1497
Nuclear
Steam
600
-
238
441
290
16
354
755
356
488
395
2
22
357
832
3 870
2 094
2 45il
-
-
-
100
6 858
-
6 166
-
-
25
-8
27
-4
-
4 681
404
271
17 705
.537
416
-
-
-
-
-
314
-
-
-
6
368
192
321
13
341
234
337
887
925
-
-
1
137
--
314
-
-
-
-
-
-
-
11
2,8
317
-
-
-
-
11
11
15
15
11
-11
-
-
3 890
-
2 861
' 751
4 431
-
2 632
7 063
1
3
152
117
15
15
14
-77
-
7
107
-
-
-
1327
1
3 270
-
-
-
-
162
153
152
111
-
-
315
263
-
Denotes negative figure.
Table 5.4-2 Generating capacity by type of prime mover; peak demand, and
reserve capacity by regions 11l; FPC; 1970; pI-18-23)
1970
1980
MWV
Percent
1990
I1NW
Percent
MW
Percent
Northeast
Conventional hydro .................
Pumped storage hydro ..............
IC and gas turbines ................
Fossil steam ......................
8.9
2.8
9.7
73.2
5.4
5,800
1,800
6,300
47,500
3,500
Nuclear
...........................
6.2
8.0
8.0
41.5
36.3
7,000
9,000
9,000
47,000
41,000
3.5
9.4
6.5
23.4
57.2
7,000
19,000
13,000
47,000
115,000
-
Total capacity ..................
64,900
100.0
113,000
100.0
Peak demand ...................
52,900
............
93,000
...........
Reserve capacity ....................
12,000
............
20,000
..
Reserves in percent of peak..........
23
21
............
o,.........
165,000
............
36,000
............
22
oo..........
100.0
201,000
............
East Central
Conventional hydro .................
Pulnped storage hydro ...............
IC and gas turbines .................
Fossil
steam..........
Nuclear
............
......
......
1,000
1.8
100
2,400
0.2
4.4
51,200
93.1
300
0.5
---
Total capacity ..................
Peakdemand.....
.................
Reserve capacity ....................
Reserves in percent of peaklc........
1.9
3.9
6.8
2,000
4,000
7,000
77,000
13,000
74.8
12.6
3,000
14,000
12,000
115,000
42,000
103,000
100.0
186,000
1.6
7.6
6.5
61.7
22.6
--
100.0
55,000
44,000
............
82,000
11,000
............
21,000
25
25
............
100.0
148,000
............
38,(00
............
26
............
Southeast
Conventional hydro ...............
Pumped storage hydro....:..........
IC and gas turbines .................
Fossil steam ........................
Nuclear...........................
0.0
11,000
4,000
6,000
77,000
34,000
8.3
3.0
4.5
58.3
25.8
100.0
132,000
100.0
9,300
14.6
100
2,700
51,600
0.2
4.2
81.0
0
63,700
13,000
13,000
14,000
121,000
94,000
5.1
5.1
5.5
47.4
36.9
255,000
100.0
---
Total capacity ..................
Peakdemand.....
............
Reserve capacity ..................
Reserves in percent of peak..........
52,900
10,800
20
.............
o.......o...
.......
o...
-111-
110,000
............
211,000
22,000
............
44,000
20
............
21
.........
............
o.o
Table 5.4-2
(continued)
1970
IMWN\
1980
Perceit
lW
1990
Percent
Percent
MW
WeYstCentral
Conventional hydro .................
Pumped storage hydro ...............
IC and gas turbines .................
Fossil steam ........................
8.2
0.9
9.9
3.5
50,000
19,000
3.7
2.4
9.8
60.9
.23.2
100.0
82,000
100.0
3,500
400
4,200
Nuclear
...........................
Total capacity ................
Peak demand ......................
Reserve capacity ....................
Reserves in percent of peak .........
33,000
1,500
77.5
42,600
3,000
2,000
8,000
3,000
4,000
2.0
2.6
9.2
14,000
54,000
35.6
77,000
50.6
152,000
100.0
35,700
............
69,000
............
128,000
............
6,900
............
13,000
............
24,000
............
............
19
............
19
19
.............
South Central
Conventional hydro ................
Pumped storage hydro ...............
IC and gas turbines .................
Fossil steam ........................
4.7
3,000
0.2
4.3
3,000
44,400
90.8
85,000
0
0.0
8,000
2,300
100
2,100
Nuclear...........................
2.8
2.8
6.6
80.2
7.6
7,000
4,000
1.9
8,000
14,000
46,000
3.8
6.6
65.9
21.8
211,000
100.0
139,000
----------------
Total capacity ..................
100.0
48,900
Peak demand ......................
40,600
Reserve capacity ....................
Reserves in percent of peak..........
100.0
106,000
............
91,000
............
8,300
............
15,000
............
20
............
16
............
182,000 ............
29,000
16
....
,........
............
West
Conventional hydro ................
Pumped storage hydro ..............
IC and gas turbines ..............
Fossil steam .....................
Nuclear..........................
29,700
45.8
1,100
1.7
1,500
31,400
1,200
48.4
54,000
32.5
3.9
2.3
41.9
1.8
25,000
19.4
101,000
32.1
39.6
100.0
129,000
100.0
255,000
100.0
2.3
42,000
5,000
3,000
20.4
4.7
3.2
52,000
12,000
8,000
82,000
__
Total capacity ..................
64,900
Peak demand ......................
49,600
............
Reservecapacity....................
15,300
.
Reserves in percent of peak..........
110,000
.O
31 ............
-112-
...........
19,000
17 ............
..
216,000
............
39,000
............
18 ............
__~
..
i~~~~~~
~
~
-I
1
i.
..
*
.
0
I
G"~~~-
·
...... !
...
?.........
.
,....
....
.
.
.
.-..
.
....-,-.
.~,
.......--
.
L-.
.......
___
.
...
~
,
...
'
.......
.
,,
................
.
rl
ci.
.~-
i I;
.
-6
.. .-...
.~.~......
i
·
, .
0'~
~ ~' ~~~~~f
~~~~~~~~~...............
~~...-
-......
·
.
4-
..-.....-.......
'
......
i
·
:
I ..........
.... .;..._.
':....... ;---... ................
....... :-!·-1·--·-~
....
h~~~~~~~~~~~~~,4.L~
-
...........
0
U1
4
*.
..........
....
i.
-.. .!....
i-·
...
!'-:... .a... ..........
-
1
··
~~~~~
~
· ·
i
:· i :
?i
I
I
L40
m
CJ
0~
N
0o
~'
I~~~~~~~~~~I.
.rt
·
{
,
..... I ---- ~-.
---·
-- i1 :~........i,
.
.
- -.--·-. . i....:...
....
~. . .......
'~i
. ....-t . .!-- ·
i·--i·
...-L .:.._ ,
~~~~·t-
'...........
r~
~ ·--;~ ~. ~~~r-
~-·
- -r- -:-- - ·- ·-------i
I
'·· ,- · ·- ·- ·--
·
·--
· ·
. .
.i4
0)
0
· ·-
"'~o
I~~~~~~~~~~~~~~~~~~
...
;..· i
"--.. ,.~
...
.,...r.
....
_:...
~. _:._ ~__~.._'
i
..... _i
,
·
-···- :-· · ·-:-
'L·--
I
... :
~....
-
(O
o
: 'a'
.
*14.
.~~~~~~~~~~~~~~~~~~~~~~~~
~~~~..
...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~·
...i...:..
..."'N...
'-i..:...
.~
!
.
I
a0
.
dk
'T
,
'
,
!
!
,-
4-4
-
I
.
-
I
: i e3
:,
-
·
*e
-
.. ·
I
-
I
.
'
,
·
"
.
'C
:;
I
1
!
-113-
.
"U
I
;'
I
Table 5.4-2 shows capacity information for 1970, 1.980and 1990 for the
FPC reporting regions and indicates not only the different existing mixes, but
the different rates at which the regions are planning to change their mix.
(Since the figures in Table 5.4-2 are pre-oil embargo, they should not be
considered reliable in terms of actual BMUT levels for 1980 and 1990.) Tle
utilities included in each of the regions and their most recent FPC load and
energy data (Nov. 1975) are given in Table 5.4-3(III;FPC; June 1976; p. 10).
5.4.2
Regional Characteristics
The choice
of a new facility is also nfluenced by conditions which are
beyond the utility's control, but are still characteristic of the particular
company's service area. Examples of such conditions are the climate (which
helps dictate the shape of the load and the type of plant cooling technologies), the status of the local economy (which helps to affect fuel costs and
alternatives, abatement options, and labor costs), and regulatory policy
(which helps to affect company finances, environmental performance and site
selection).
These regional considerations affect a utility's choice of new technology
in two ways: first, they directly affect the cost and competitiveness of the
new candidate technologies; secondly, these regional considerations will have
had a cumulative effect on the evolution costs and operation of the existing
and committed system. Thus, the Northwest is heavily hydro while New England
tends toward nuclear: the competitiveness of candidate technologies would vary
in those regions not only because of regional differences in their costs of
construction, materials and operation, but also because a new fossil plant
would. be utilized differently in Oregon than in Connecticut.
The tables and figures on the following pages are intended to illustrate
some of the significant differences among various regions and power pools.
Figure 5.4-2(III;FPC; 1972; p. I-18-22) shows the distribution of winter and
summer load peaking regions in 1970 actual data and 1990 projections. This
system factor influences reliability, maintenance scheduling, peaking requirements, and the necessity for backing off base load plants.
Table 5.4-4(III;FPC; 1972; p. 1-17-3) indicates the 1971 capacity, peak
load for 1970, and thelargest unit in each pool. These are primarily reliability measures for the pools, for instance a 100OMW low Btu plant installed
in the Iowa Power Pool would have a drastic effect on its reliability (being
40% of peak and 28% of installed capacity). Its effect on the New York Power
Pool would be much less.
Figure 5.4-3(III;FPC; 1972; p. I-10-19) displays the results of three FPC
studies of cooling water facility costs. Capital costs are found to vary in
1990, study C, as much as 67% (West Central-South East) and annual costs as
much as 80% (West Central-South East).. The significant factors here are climatological, requiring different sizings to achieve the same condenser cooling
capabilities.
Figure 5.4-4(III;FPC; 1972; p. I-19-11) indicates the actual 1968 and
projected 1990 power costs. Differences in this factor could be significant
for technologies with high ancillary power needs. The oil embargo impact has
been to accentuate the gap between the Northeast and other regions.
Table 5.4-5(III;FPC; 1972; p. I-19-9) indicates the annual costs of working capital in the various regions. This factor, coupled with stringent capital availability restrictions such as the utility industry has experienced
around the time of Consolidated Edison's dividend suspension, could be a significant constraint on the ability of utilities to invest in the more capitalintensive projects.
Table 5.4-6(III;FPC; 1972; p. -18-25) provides estimates of the expansion
projections of the industry, based on conventional coal-fired technologies.
-114-
Table 5.4-3 Electric energy for load and system peak loads, major electric
utility systems - November 1975 (III;FPC; June 1976; plO).
i
ENERGY- PEAK
LOAD
1000
KWH
KW
SYSTEM AND POWER SUPPLY AREA
RI:CION 11- EASr CENTRAL(Continued!
Bg Itives EicstricCocperative
East eritcxy Ruril ElectricCooperative
renilersois,MuncipalPower& Light
Coiblinid R.rlort: KentuckyUtilitiesCo.:
Old Dominion1owerCo.
Ownsboo, Kent.cky, MunicipalUtilities
REGIONI - NORTIIFAST
CombinedReport NewEnglandPowerExchange:
BangorHydroElectric Co.;Boston EdisonCo.;
Braintree Mass.)ElectricLight Dept.;CentralMaine
PowerCo.;CentralVermontPublicServiceCorp.;
ConnecticutYankeeAtomic PowerCo.; EasternUtilities
Associates;
Fitchirrg GasandElectric Light Co.;Green
MountainPowerCorp.;Holyoke(Mass.)GasandElectric
Dept.:;MainePublicServiceCo.;NewEnglandElectric
System;NewEnglandGas & ElectricAssociation;
NewportEiectric Corp.;NortheastUtilities;PublicService
Co. of NewHampshire;TaurntonMass.)MunicipalLighting
Plant;United IlluminatingCo.;Vermont ElectricPowerCo.
Inc.; YankeeAtomic ElectricCo.,andSmallerPublic
PrivateandCooperativeSystems
in Area.
I&2
CombinedReport- NewYork PowerPool- NewYork State
Interconnected
Systems:CentralHudsonGas& Electric
Corp.;Consolidated
EdisonCo. of N.Y,; Villageof
Freeport;Jamestown
MunicipalElectricSystenm;
LongSault
Inc.; LongIslandLightingCo.;NewYork State; Electric&
GasCorp.;Niagra Moh.awkPowerCorp.;Orange& Rockland UtilitiesInc.; PowerAuthority, Stateof New York;
Rcchester Gas& ElectricCorp.
3& 4
Cormbined
Report- PennsylvaniaNewJerseyMarylandInterconnection:Atlantic City Electric Co.;
BaltimoreGas& ElectricCo.;BethlehemSteelCo.
{SparrowsPoint Plant);Delrnarva
Power& Light Co.;
GeneralElectricCo.; JerseyCentralPower& Light Co.;
LuzerneElectric Division,UnitedGasImprovementCo..;
MetropolitanEdisonCo.;NewJ.erseyPower& Light Co.;
Pennsylvania
ElectricCo.;Pennsylvania
Power& Light
Co.;PhiladelphiaElectricCo.;PotomacElectricPower
Co.;PublicServiceElectricandGasCo.;Vineland,New
Jersey,Muiicipil System
5& 6
Sile HarborWaterPowerCorp.'
5& 6
troTAl. Rt:(lGON 11
19
19
19
Y1
477,'7
121.000
536,00
z2tDQ
1.34e000
o
I
.r
s445
19
62.000
.3
48,25.e00
.
Adininistriticn
Philpott
18
Project)
- - - m
- - -
U.S. Departmentof the Interior.SoutheasternPower
Administration
S,121.83
-
s,37.eog
(John H. Kerr Project)
VirginiaElectric and PowerCo.
Tennessee
Valley Authority includingAlcoaSystemand
U.S. Department of theInterior, Southeastern
Power
Administration(Cumberland BasinProjects)
Carolina Power& LightCo.
Duke PowerCo.
LockhartPowerCo.
South CarolinaElectric& Gat Co.
South Carolina PublicServiceAuthority
{Sontee-CooperProject)
Yadkin, Inc.
South MississippiElectricPowerAun.
U.S.Departmentof the Interior,SoutheasternPower
t2.4120
16.89.00
Administration
(Clark Hill Project)
11,801,023
CombinedReport TheSouthernCompany:Alabama
PowerCo.;GeorgiaPowerCo.;Gulf PowerCo.;
MississippiPowerCo.;SouthernElectric
GeneratingCo.
ElectricandPowerCo.
Savannah
U.S. Donaitment of theInterior, Southeastern
Power
Administration(Allutnona andBuford Projects)
U.S. Department of theIterior, Southes-ternPower
Administration IWater F. GeorgeProject,Carter,
2,5.13co
_,s
26,207.361
51,917,COQ
WestPointardJones
Bluff)
18
..
18
,5.521
9,3S365
2.054,8I1
3,754,16
24.431
741.12i
17,075.000
4,05,00
,,o
30,000
1,393,000
21
21
22
355,414
111.046
81.1t4
713,000
159,00
110.000
3.993000
951.,105
t1,833.00
1,366,795
43,985
253,235
2,633,000
2,191
13,0J0
627,000
48.000
632.572
1328.00
1,749,425
3,34,000
5$g.325
]35.394
109,31
1,100,000
110.01.Oe
5,304.
38
040^
34.7Ll.J
1.372.1G$
2,2.1 14
9,620,0DO
2,000
3,01,000
1,34.00
55.610
138.341
12
12
983.402
119,224
12'
12
12
12
12
28.177
138,11
625.193
so3r.teo
211.G00
1,00,O0O
190.000
32,000
1,208,000
950.000
928.031
12
12
12
1.217.011
39.001
1,431,.00
1.31.00
01110
t11lt'6I
3%0l00
I
-115-
a
-
10.552
168,00
22 & 23
22 & 23
6.447,306
16,3
12,brt,00
308.000
22 & 23
22& 23
- - -
-
-1,181.a
2,48.352
-
2.712,00
5120,000
GainesvilleElectricWiter & SewerUtil. Bd.
Jacksonvile, Florida,ElectricAuthority
Lakeland.Florida,Daparteentof ElectricandWaterUtilities
24
24
24
4.02
405,211
2,1.13
I
OrlandoUtilitiesCommission
Tallahassee,
Florida,City of
TampaElectricCo. Ilnterconnected
System)
U.S.Dep trnent of the Interior,Southeastern
Power
Administration IJimWoodruff Project)
24
24
24
13735
11s01
7tS,4;s
313,000
It.0
1,3t7,0
24
- --
'FOrAL RCt;ION III
,.10000
-.
22 & 23
U.S.Departmentof the Interior,Southeastern
Power
Administration(Hartwelland Millers FerryProjects) 22 & 23
Florida PowerCorporation
24
FloridaPower& Light Co.(InterconnectedSystem)
24
2,120.10
.
,0
20
21
21
21
21
21, 22 & 23
Alabama Electric Cooperative, Inc.
TOTAL REGI;ONI
Hamlton,Ohio.Mnicipal PowerPlant
HoosierEnergyDiv.; IndianaStatewideR E.C.; Inc.
ln'Jlarapo!isPwer LighltCo.
LouisvilleGasandElectricCo. and Subsidiary
NorthernIndianaPuble Service
Co. (Total CmnpanySstem)
Publc Service
Co.of Inliara, Inc. l{Interconrected
System)
Richmond,Indiana,Powerand Light
SouthernIndianaGasad ElectricCo.
^-
REGION Ill - SOUrlTIEASTr
U. S. Oepartment ol theInterior, Southeastern,
Power
REGION11- EASTCENTRAL
CombinedReport: AlleghenyPowerServiceCorp., and
Subsidiary,MononghelaPowerCo.,andSubsidiary,
The PotomacEdisonCo.,andSubsidiaries,
& WestPenn.
PowerCo.,andSubsidiaries
7
Duquesne
LightCo.
76
TheClevelandElectricIlluminatingCo.
8
Clave!and,Ohio, Divisionof Light& Power
8
BuckeyePower,Inc.
9
Columbus,Ohio,Municipal ElectricPlant
9
Columbus
andSouthernOhio ElectricCo.
(CompanySystem)
9
CombinedReport: Ohio EdisonCo.;Pennsylvania
PowerCo.
9
TheToledoEdisonCo.
9
Ohio Valle ElectricCorp. (includingsubsidiary
12
9
&
Indiar-Kentucky ElectricCorp.)
CombinedReport- AmericanElectricPowr
System: Appalachian
PowerCo.;Indiana& Michigan
ElectricCo.;KentuckyPowerCo.;KingsportPower Co.;
Ohio PowerCo.;WheelingElectricCo.
MichiganPowerCo.;Sewell ValleyUtilities Co.
10,9 & 12
Danvil:e,Virgiia, Water,Gas andElectric Department
10
Consumers
PowerCo. MainSystem)
11
The Detroit EdisonCo.
11
Detroit.Michigan,PublicLiglhtingCommission
11
Lansing,Michigan,Boardof Water& Light
II
The CincinnatiGas& ElectricCo. and Subsidiary
Companies
TheDaytonPowerandLight Co.
r- r",,
ENEfRGY PEAK
SYSTEM AND POWER SUPPLY AREA 100
LOAD
KWH
KW
31.037,0
110,000
s3,00
18,000
60.610,000
Table 5.4-3
(continued)
7NERGY
SYSTEM AND POWER SUPPLY AREA
RC(;ItONIV NORTII CENTI
RAL
Consolidated
WaterPowerCo.
13
EdisonSault ElectricCo.
13
Kaukauna,Wisconsin,
Eitctrical & WaterDepts
13
MadisonGasandClhctricCo.
13
Manitruoc,Wlsconsin,
PublhcUtilities
13
UpperPeninsula
PowerCo.(IntergratedSystem)
13
CombinedReport: WisconsinElectricPowerCo.;
Wisconsin
MichiganPowerCo.
13
Wisconsin
Power& LightCo. andSubsidiaryCo.)
13
Wisconsin
PublicServiceCorp.
13
Commonwealth
EdisonCo.
14
MissouriPower& Light Co.
15
Union ElectricCompany
15
DairylandPowerCooperative
(Alma.Cassville,
ChippewaFalls.Flambeau,Genoa,Wisconsin.
and
Twin Lakes,Minnesota
16
LakeSuperiorDistrict PowerCo.
16
CombinedReport: MinnesotaPower& LightCo.;
SuperiorWater,Lil ht andPowerCo.
16
NorthernStatesPowerCo.(Minnesota)and
Subsidiary
Company(intercoinectedSystem)
16
Rochester,
Minnesota,City Electric Department
16
UnitedPowerAssociation
16,26, & 27
InterstatePowerCo. (Main Interconnected
System)
17
CornBelt PowerCooperative
17
iowa ElectricLilqht& PowerCompany
17
Iowa IllinoisGas& ElectricCompany
7
IowaPower& Light Company
17
Iowa PublicService
Company
17
Iowa SouthernUtilitiesCompany
17
CentralIllinois LightCo.
40
CentralIllinois PublicServiceCo.
(Interconnected
System)
40
ElectricEnergy,Inc.
40
Illinois PowerCo.(Total System)
40
',utheril Illinois Po.werCooperative
40
ringfield,Illinois. City Water,LightandPowerOept.
40
TOTAL REGIONIV
(Narrows Dam Project)
CombinedReport. U.S.Departnment
of the Interior,
SoutlwasternPowerAdministrationSPAPool: SPA
InterconnectedHydroSystem,includingBeaver, Bull
Shoals.Dardanelle.
Denison,Eufaula,Fort Gbson,
GreersFerry, Keystone,Norfork,Ozark,TableRock,
TenkillerFerry, andWhitneyProjects
25, 33, 34.&
TheCentralKansas
PowerCo.
The Kansas
Powerad LightCo.
WesternPowerDivisionof CentralTelephone&
Utilities Corp.(Interconnected System)
OklahomaGasandElectricCo. (General
Transmission
System)
PublicServiceCo.of Oklahoma(Interconnected System
GrandRiverDamAuthority
Southwestern
ElectricPowerCo.(Interconnected System)
WesternFarmersElectricCooperative
The EmpireDistrict ElectricCo.
Kansas
GasandElectricCo.
Kansas
City.,Kansas,Board of PublicUtilities
KansasCity PowerandLight Co.
dissouri PublicServiceCo. IWarrensburg
System)
Springfield.MissourlCity Utilities
,
St. Jose;h Light& PowerCo.
25
RCt;ION V SOUSTI CI.NTRAI.
2 .51S
124,16
11.29
207,155
1,2716.11
46,131
400,00
4,72,934
20.16
1.530,440
2.60.000
927.000
8071,000
.991.003
241,.000
2.75,000
1.7,402
2,0os
407.000
ls5.000
497S132
0o.e000
641S5.734 3.076.000
32.50
174.000
174.659
301.000
25.6699
492.000
64,548
ll3.00O
075,931
74l,000
25,521
523,W00
309.130
633.000
2t,.412
4i7.00
11060so.
239,000
31.524
102.00
656.450
53,2471
953,201
45,051
1 25
S.92.00s
93,000
49,oo00
034,087
065.00e
---
---
-
-
l04,o47
33
33
33
33
33
34
34
34
34
34
34
34
1.035,161
069.062
21tt30
e59.314
14.l320
ss)s
410,24
43.611
54 11t2
lo.757
35
35
35
30.5.000
1.01,.5,5
42,700
201.970
29
35
1,261.000
744,00
1.024,000
0.000
16.9,000
64.6
11.521
41.,51
30132.31
1.51,712
---
61.000
76,000
t0,000
TOTAL REGIONV
REGIONVI - WiST CENTRAL
BlackHills Power& LightCo. (Interconnected
System)
MinnkotaPowerCooperative,
Inc.
Montana-Dakota
Utilities Co. (Interconnected
System)
NorthernStatesPowerCo. (Minnesota)(FargoGrand
ForksSystem)
NorthernStatesPowerCo. (M;nnesota)Minot System)
NorthwesternPublicServiceCo.
Otter Tail PowerCo.
U.S.Bureauof Reclamation(MisscuriRiver BasinEasternDivisionIntegratedSystem)
TOTAL RE.GIONVI
200,00
2.002.000
,.3060.00
311,000
1255.000
301.oo0
306.004
641.00
261.000
t.093,000
315.00
10s.008
S
1ts.000
,000
-
6l.00
3.692,000
---
-116-
35
Lubbock,Texas,MunicipalPowir & Light
36
Southwestern
PublicServiceCo. (Illtercnnected Srstem)
36
CombinedReport: razo0sElectricPower
Coctperatlve.
Inc.; BrazosR.verAuthority
37
ElectricRelihaility Councilof Tesas(ERCOT)
AustinWater, Lig!t & Po. er Departmrnent;
Central
Power& Lilht Conpany; D)allas
Power& Light
Company;HoustonLighting& PowerCompany;Lower
ColoradoRiverAuthority: SanArtcnio City Public
ServiceBoard;TexasElectric ServiceComprany;
Texas
Power& Light Companyand WestTexasUtilities
Company
37 & 38
WestTexasUtilities Co. (Interstate System)
37
U.S.Departmentof the Interior, SouthwesternPower
AdministrationWhitney DamnProject)
37
U.S.Bureauof ReclamationandIrnternational
Boundary
andWaterCommissionFalconDarnProject)
38
CombinedReport: U.S. Bureauof Reclamation
(RioGrandeProject);El PasoElectricCo.;
CommunityPublicServiceCo. (SilverCity, Alamogordo
and Hollywood.N.M.); PlainsElectricGeneration&
Transmission
Cooperative;
PublicSerice Co. of New
Mexico(Albuquerque-S3ntaFeLas Vegasand Deming
Division ·IntegratedSystem);Town of Gallup,N.M.; City
of Truth of Consequences,
N.M.
39
CombinedReport: NebraskaPublicPowerDistrict;
Nebraska
PublicPowerDistrict(EasternSystem);
CentralNebraskaPublicPowerand
IrrigationDistrict; Loup RiverPublicPower
District, City of LincolnElectricSystem
OmahaPublicDistrict
CombinedReport- Interconnected
PowerSystemPool,
AreasNo. 31 and32: U.S.Bureauof Reclamation's
WesternDivisionAreaof Regions
VI andVII;
PacificPower& Light Co.(Wyoming.Division);
CheyenneLight, FuelandPowerCo.;Nebraska
PublicPowerDistrict (Western
Div.); andother
utilities andmunicipalitieswithin the geographic
area
.
ColoradoSpring,Colorado,Dept.of PublicUtilities
Colorado-UteElectricAssociation,Inc.
PublicServiceCo.of Colorado(CentralSystem)
The WesternColoradoPowerCo.
SoutherilColoradoPowerDivision.CentralTelephone
&
UtilitiesCorp.
---
ENERGY
1000
KWH
PEAK
LOAD
KW
(Cuiitiiued)
Sabine River Authority
BasilEIet, ic Cooperative
30,022
41,417
'4.51,11
CentralLouisianaElectricCo. Inc.
Gulf StatesUtilties Co.IGull StatesSystem)
U.S. Departmenrt
of tile Interior,Southwestern
Power
AdministrationISJm RaylbirnDamProlect)
SYSTEM AND POWER SUFPILY AREA
LOAD
KW
51.000
52.000
T6.411
51,0cW
25.000
3.000
32,.000
35
29
29
Cajon Electric Power Co-op.
PEAK
24.15s
05S59,50
REGION V - SOUTIIICENTRAI.
CombinedReport: MiddleSouth Systenms
PowerPool:
Arkansas-Missouri
PowerCo.;ArkansasPower&
Light Co.;LouisianaPower& LightCo.,Mississippi
Power& Light Co.: NewOrleansPublicService,Inc.
25
MissouriUtilitiesCo. (Southeast Group-Northern
Division)
25
ArkansasElectricCooperative
Corp.
25,29 & 33
CombinedReport· Associated
ElectricCooperative,
Inc.:
CentralElectricPower,KAMOElectric,M. & A.
ElectricPower.N.W.ElectricPower,andNortheast
MissouriElectricPowerCooperatives,
Shoe-Me
Power
Corporation
25.33.,34 & 35
U.S. Departmentof the Interior,Soulhn.stern Power
Administration(BlakelyMountainProject)
25
U.S. Departlent of the Inter;or, SouthwesternPower
Administration
10()0
KWFH
-- - a
I
4.54W
0L6.56
- -
0,.006
1 1.409.000
106.51
27.000
,65s,401
20.541
1s.58.000
28.00'
---
---
---
---
606.41
1,113,C00
20.327.71
39946,00
26 & 27
26 & 27
26 & 27
70,121
10,43
Is56.10
13,00
220.000
331.000
26 & 27
26 & 27
26 & 27
26& 27
6.0,S
15.256
62 tl
207,200
142,000
34,000
135,00
325,000
26 & 27
339,751
630,009
26& 27
_
- - -
4425. i
3050762
970.000
708,000
28
28
31 & 32
32
32
32
32
32
o06.7180
,090.000
125.410
270.000
130.040 162,000
1.01.1777 1.92,000
1,0111777I
.s2000
10,566
3.174.11
141,000
000
181.000
Table 5.4-3
(continued)
SYSTEM AND POWER SUPPLY AREA
ENERGY
1000
KWH
PEAK
LOAD
KW
SYSTEM AND POWER SUPPLY AREA
REGION V1l - NORTIIWEST
ENERGY
PEAK
1000
KWH
LOAD
KW
7.111
43,16
102.392
12.353
"3,IO0
18,313
- -
I,0.0
811.00
181.000
24,010
13.000
38,000
--..
226,567
419.008
401.045
32.10
5i.1S91
8137,00
10.,0o0
5.000
453.,644
363.000
At ASKA
The MontanaPowerCo.
Pacil.cPower& Light Co. KalispellSystem)
U.S.
of Reclamation(Missouri RiverBasin·
oureau
Upper MissouriProjectsCanyon
Ferry Unit)
U.S. Departmentof the Interior, Bonneville Power
Admninistraton
lBureauof Reclamation-Boise,
Minidoka
andPalisades
Projects)
Ca3ifornia-PacificUtilities Co. (Baker-LaGrandeSysten)
Idaho Falls.Idaho, ElectricDivision
IdahoPowerCo.(Main System)
Utah Power& LightCo.
PublicUtility DistrictNo. 1 of DouglasCounty
PublicUtility District No. 2 of GrantCounty
P.blic Utility District No. 1 of Pend OreilleCounty
the Washington
WaterPowerCo.
Washinton PublicPowerSupplySystem
42. 43. 4 &
PacificPower& Light Co. (Oregon-WashingtonCaliforniaSystem)
44 &
PortlandGeneralElectricCo.
PublicUtility DistrictNo. 1 of CowlitzCounty
PublicUtility District No. of GraysHarbor County
U.S. Departmentof the Interior ·BonncvillePower
Administration
42, 43, 44. 30, 41 &
Public Utility DistrictNo. I of ChelanCounty
Pu-get
SoundPower& LightCo.
Seattle.Washington.
Dept. of Lighlting
Tacoma,
Washington,
Dept.of PublicUtilities,
Light Division
Eugene. Oregon,Water& ElectricBoard
30
30
4Ea.951
30
-
41
41
I1
41
41
42
42
42
42
45
4.47l3
21,005
32.121
632.76S
199.999
--- a
S.1
12.261
N01.50
-- -
340.0C0
4O,216
Alska Electric Light& PowerCo.
Anchsrag Mun.cipalLij:t & PowerPlant
Criujch ElectricAssocatl.,n,Inc.
F3irbanksMunicipalUtlitics System
GclirenVal'evElectricAs,ociation
KetchikanPublie Utilities
U.S. Bureauof Reclamation(Eklutna Project)
83,000
-- -
£40oo0
S4.000
1000
49
49
49
49
49
49
49
TOTAL ALASKA
t1.14.004
ItAWA!I
I.553,000
HawaiianElectricCompany,Inc.
Hlo ElectricLight Company,Ltd.
MaIi El.ctr;icCompany,Ltd.
178.000
21.000
1.072.000o
---
50
50
O
TOT'ALHfAWAII
t'NIrFI) STATES TryAL
45
44
44
44
1,341,749
1,129.482
222.800
109.725
2.00.004
2.203.000
414.000
201.000
151.3t17,36
201.t31.0c0
FOOTNOTES:
* Generates
for resale.
a Powerdevelopedby theseprojectslargely disposed
ol by other systemsin Ihe samearea.
45
43
43
43
4,347,.38
0.115.0039
663,991
7,118.000
31.,000
1.18,000
1.22.000
43
45
399,0t3
186,250
754.000
40200
TOrTALREGIONVIll
t188.84
1467
t?.464.800 f 22.67,006
REGION
VIII -SOU'rIWEST
CaliforniaDept. of WaterResources
CombincdReport: Oakdale IrrigationDistr;ct &
SouthSanJoaquinIrrigation District
46
NevadaIrrigationDistrict
Oroville-WyandlotteIrrigationDistrict
PlacerConty WaterAgency
46
46
46
46
46
Yuba Water Agency
46
Merced rrigation District
.
Combined Feport: Turlock IrrigationDistrict;
M
odestoIrrigationDistrict
.
46
CombinedReport: PacificGasandElectricCo.; andloads
of City of San Francisco,
CentralValleyProject.andStateof
Cahfomni3
Water ProjectsuppliedthroughCompany's
System
46
Sacramento,
California.MunicipalUtility District
46
SierraPacificPowerCo.
46
U.S.Bureauof Reclamation(Central ValleyProject)
46
Burbankc.California,PublicServiceDept.
47
I
Glen;ale,California.PublicServiceDept.
47
Inmoerial
IrrigationDistrict
47
LosAngeles.California,Dept. of WaterandPower
47
Pasaena,California,MunicipalLight andPowerDept.
47
SanDiegoGas& ElectricCo.
47
SouthernCaliforniaEdisonCo.
47
ArizonaElectricPowerCooperative,
Inc.
48
ArizonaPublicServiceCo.(All systems
interconnected
by U.S.Bureauof Reclamation
facilities)
.
48
TheMetropolitanWaterDistrict of SouthernCalifornia
48
48
NevadaPowerCompany
Salt RiverProject Agricultral Improvement
and
PowerDistrict
48
48
.
TucsonGas& ElectricCo.
U.S.Bureauof ReclamationIColoradoRiverStorage
Projectl
48. 31. 32 & 41
CombinedReport- U.S.Bureauof Reclamation
(Colorado lver System -Lowr Basin): Parker-Davis
Prolect;BoulderCanyonProject ITranesissinonlyl;
Sen.tor Wah Yumprng
Gerating Plant,Yuma Projects
48
TOI.)'A. R'fLIttN
(IN1,UOLgS
tINI:I)
VII
SI
---
a
--
a
-
-
-
- -
-
--
_
- a-
-
- -
125.8
251.000
.199.130
365.512
237,018
17,52
.126
04.565
76.844
1,21.3S07
a
3.46,000
7s1.000
78.552
4.023.734
39.087
046.000
1.000
128,000
1:5.000
145,000
2.71.,000
126.000
1.478.000
,.152.000
91.000
S6.1t5
82.060
342.7t1
1.059.000
116.000
6,2.00e
507.543
284,102
082.000
035.00
61.74
19,000
165.903
268.000
EO.a3
Is.1o.Zo
IIOTAL
I
-
- -
t402,900
8.
tse.t015.32.175 28.249.000
-117-
--
·
-L1
Figure 5.4-2 Estimated differences between August and December peak loads
within each power supply area (II;.FPC; 1970; pI-18-22).
Li.
OUliI|G
l(I
Ii
r.
-118~
Table 5.4-4 Generating capacity and peak loads of formal coordinating organizations or power pools (III;FPC;
1972; pI-17-3).
Generating
Capacity
as of
-:sJanuary
1, 1971
(MW)
NORTHEAST REGION
New England Power Pool (NEPOOL) '...................
New York Power Pool (NYPP) .........
: .................
Pennsylvania-New Jersey-Maryland Interconnection (PJM)...
Total for Region ......................................
SOUTHEAST REGION
Carolinas-Virginia Powcr Pool (CARVA) I.................
Southern Company System (Holding Company) ............
Total for Region ......................
19)70
Peak-Hlour
Load
................
EAST CENTRAL REGION
American Electric Power System (Holding Company) ........
Allegheny Power System (Holding Company) ...............
Central Area Power Coordination Group (CAPCO).........
Kentucky-Indiana Power Pool (KIP) ......................
Michigan Pool .........................................
Cincinnati, Columbus, Dayton Pool (CCD) ................
Total for Region ......................................
in 1971
(NMW)
12,918
22,616
29,899
11,622
17,037
23,838
65,433
52,497
18,515
13,154
17,357
12,589
31,669
29,946
10, 143
4,287
10,021
4,899
10,605
4,806
Nameplate Rating of
Largcst G;enerating
Unit in Operation
8,535
3,649
8,527
4,157
8,905
4,206
(MW)
661
1,028
936
730
806
761
576
680
518
750
580
44,761
37,979
8,467
5,991
7,207
5,305
605
580
Iowa Power Pool .......................................
2,614
2,582
212
Wisconsin Power Pool ..................................
Missouri Basin Systems Group (MBSG)... ................
1,845
4,373
1,755
3,940
406
216
23,290
20,789
4,739
6,753
17,659
8, 150
4,287
5,999
16, 126
7,444
29,009
26,487
20,340
18,284
18,077
16,080
38,624
34,157
232,786
201,855
NWESTCENTRAL REGION
Illinois-Missouri Pool ....................................
Upper Mississippi Valley Power Pool ......................
Total for Region ......................................
SOUTH CENTRAL REGION
Mhissouri-KansasPool (MOKAN) (excluding satellites) .......
Middle South Utilities System (Holding Co.)...............
South Central Electric Companies (SCEC) ................
Texas Utilities Company System (Holding Company)........
Total for Region 4 ...................
................
495
700
750
588
WEST REGION
California
Power Pool...................................
Pacific Northwest Coordination Agreement .................
Total for Region........................................
Total for All Regions ..................................
755
700
Source of generating capacity and peak-hour load data: Regional Council Reports and FPC Forms Nos. 12and 12-E.
1Data for the nine systems which initiated NEPOOL studies in 1967.
2 Pooling agreement terminated October 20, 1970, but the parties will adhere to principle of equalized reserves for an
additional three-year period.
3 Includes Middle South Utilities System power pool and two members of the MOKAN Pool with 8,292 MW of generating capacity and 7,369 MW of load.
Duplication referred to in footnote 3 eliminated in totals.
Figure 5.4-3 Estimated capital and annual costs for cooling water facilities
for projected steam-electric plants under alternative study assumptions (III;
FPC; 1972, pI-10-19).
2.5
2.0
A,
CD
1.5
1.0
o
C,
-J
C-)
0.5
0
WEST
WEST
SOUTH
CENTRAL
CENTRAL
WEST
SOUTH
EAST
SOUTH
NORTH
CENTRAL
EAST
EAST
EAST
SOUTH
NORTH
CENTRAL
EAST
EAST
co
<z2O00
CDI
_:
=0 200
WEST
CENTRAL CENTRAL
-120-
Figure 5.4-4 Electric power costs, 1968 and 1990, cents per kilowatt-hour in
1968 dollars(III;FPC; 1972; pI-19-11).
1990
PROJECTED
2.31
_
/
1968
1.99
1.82...
1.77
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SOUTH
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NORTH
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CENTRAL
CENTRAL
EAST
EAST
-121-
CONTIGUOUS
U.S.
Table 5.4-5
Estimated 1990 annual cost of working capital(III;FPC; 1972; p-19-9).
[1968 Dollars]
Region
Production
(million)
Northeast..................................
$
EIast Central ...............................
Southeast ..................................
West Central ................................
South Central ..............................
West ......................................
Total U. S.............................
'
Transmission
(million)
2
Distribution
(million)
Totals
(nillion)
169
$ 48
$ 99
$ 316
194
208
99
158
172
36
39
33
30
64
63
60
55
63
85
293
307
187
251
321
$1,000
$250
$425
$1,675
Production working capital is allocated among regions generally on the basis of the estimated 1990 generation, with
fossilplants allocatcd at twice the unit rate applied to otherplants.
2 Transmission working capital is allocated on the basis of the estimated 1990 transmission plant investment in each
region.
3 Distribution
working capital is allocated on the basis of the 1990 estimated numbcr of customers in each region.
Table 5.4-6 New capacity needs by regions and type of prime mover(III;FPC;
1972; p- 1 8 -2 5 ).
Type of Prime
Mover
Northeast
MW
East
Central
MW
Southeast
MW
WVest
Central
MW
South
Central
MW
West
MW
U.S.
. Total
MW
1971-1980
Conv. hydro ...........
P. S. hydro ............
IC and GT ............
Fossil steam ............
Nuclear ...............
1,000
7,000
3,000
8,000
37,000
1,000
4,000
5,000
33,000
13,000
2.000
4,000
3,000
27,000
34,000
0
1,000
4,000
20,000
18,000
0
3,000
5,000
43,000
8,000
12,000
4,000
1,000
25,000
24,000
16,000
23,000
21,000
156,000
134,000
56,000
56,000
70,000
43,000
59,000
66,000
350,000
Conv. hydro ..............
P.S. hydro ............
IC and GT ............
Fossil steam ............
Nuclear ...............
0
10,000
4,000
11,000
74,000
1,000
10,000
5,000
51,000
29,000
2,000
9,000
8,000
57,000
60,000
0
2,000
6,000
13,000
58,000
1,000
5,000
7,000
62,000
38,000
10,000
7,000
5,000
35,000
76,000
14,000
43,000
35,000
229,000
335,000
Total .............
99,000
96,000
133,000
656,000
Total .............
1981-1990
136,000
79,000
113,000
This table raises questions of availability of materials and labor, transportation and capital. Since the 1 numbers are based upon conventional technologies and outdated costs, they cannot be considered representative of
the actual installations now planned. They do, however, graphically show the
regional differences in new capacity requirements.
Figure 5.4-5(III;Electrical World; 1976; p. 98) indicates two final considerations in regional differences: interties and load density. Interties
allow a region to maintain lower reserves because the ties can carry power in
emergencies, or during periods of load diversity. This could affect the
reliability requirements placed on a new plant. Load density relates to the
siting problem in two ways. Load density usually indicates population density,
so high density areas will tend to have fewer sites and greater opposition to
construction of polluting facilities. On the other hand, load density implies
a simplified transmission problem by virtue of a strong existing network and
concentrated load centers.
Figure 5.4-5 Load densities and interties 1976(III;Electrical World, September
1976; p98).
There are also significant political and contractual differences in various regions that affect the choice of technologies. These issues straddle
the gap between purely power system and purely regional issues, and arise from
the historical manner in which utilities cooperated in pool agreements to
utilize economies of scale, load diversity, and other advantages. Under pooling agreements utilities generally agree to plan and operate so as to meet the
pool's objectives of capacity and energy supply. In return, individual companies can reduce reserves and purchase energy at rates which would be impossible otherwise. In a practical sense many pools are "super-utilities" under
normal operating conditions and the above power system considerations can be
applied to pools by considering them to be companies with very large service
areas. Because pools are basically contractual arrangements, quite often dealing in interstate transfers of electricity, they present a more complex problem
than a single utility. More discussions about pools are given in the next
section.
5.4.3
Natural Aggregation Levels
The most reasonable aggregations that could be used to account for
-123-
regional and power system differences in the assessment of new coal technologies would be those that have evolved naturally as part of the utility
industry. These aggregations are shown in Table 5.4-6 with the individual
plant, representing the lowest level of aggregation, shown for reference.
TABLE 5.4-6
Levels of Utility Aggregation
(Plant)
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)
Individual Utility
Holding Company
State
Power Pool
Interconnection Areas
National Reliability Councils
Power Supply Areas
National Power Survey Regions
Census Regions
Edison Electric Institute Regions
Of these ten types of aggregations, the last four were developed principally to serve record keeping functions, including data collection and analysis
tasks. While they are useful levels for analysis, they do not correspond well
to the decision making aggregation levels in the industry, and will not be
considered further. Figures 5.4.6, 5.4.7 and 5.4.8 show these four aggregations.
Since the assessment of new coal technologies is performed in the context
of system planning and operation, the first six levels of aggregation in Table
5.4.6 are most appropriate to be considered for that assessment purpose.
Individual utilities usually perform their own planning and operating functions,
especially those utilities that are relatively large. Holding companies,
which exist as a result of utility mergers or when a utility system serves more
than one state and has separate operating companies in each, can be considered
equivalent to individual utilities. These levels of aggregation are based on
the corporate boundaries as determined by service area, transmission system
and generation equipment, and they try to optimize their economic performance
within those boundaries.
One difficulty with aggregation at the utility and holding company levels
is the varying effects of state' regulation on the planning and operation processes. Environmental controls, siting councils, and actual state participation in the industry can make individual states the most consistent and
appropriate level of aggregation. Policy interpretation and data collection
can often be simpler at this level. On the other hand, for many states planning
and operation does not occur for the state as a unit and the differences among
individual utilities should be considered.
Power pools have evolved largely from contractual agreements among individual utilities or holding companies, being set up to perform common planning
and operating functions. By sharing resources the individual members can
reduce their own investment and operating costs, at the price of losing some
of their autonomy. Power pools perform planning and operating functions simultaneously for all their members, although the members, in their own systems,
can usually have the final say in the matter of specific design or operating
decisions. Pool-level aggregation maintains an accurate representation of the
actual planning and operating decisions of the member companies, but can be
complicated by varying state regulations and the ultimate flexibility of the
members to modify the pool decisions.
Formal power pools (or coordinating organizations) accounted for 68% of
the installed generation in 1970. Figure 5.4-9(III;FPC; 1972; p. -17-8)
-124-
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-126-
U4
34n
Power Supply Area
National
Power Survey Region
Figure 5.4-8 National Power Survey regions and power supply areas(III;Edison
Electric Institute; 1975).
-127-
1. New England
8. Central Area Power Coordination
15. Wisconsin
2. New York
9. Kentucky- Indiana
16. Missouri Basin Systems Group
3. P-J-MInterconnection
10. Michigan
17. Missouri - Kansas
4. California
11. Cincinnati, Columbus,Dayton
18. Middle South Uilities System.
5. The Southern CompanySystem
12. Illinois - Missouri
19. Texas Utilities CompanySystem
6. American Electric Power System
13. Iowa
20. South Central Electric Companies .
7. Allegheiy Power System
14. Upper Mississippi Valley
21. Pacific Northwest Coordination
NOTE:
Notall systems
operating
ineach
of the21areasareformal
power
poolmembers.
Figure 5.4-9
pi-17-8).
Formal coordinating organizations or power pools(III;FPC; 1972;
-128-
and Table 5.4-8(III;FPC; 1972; p.
tions
of the continental
-17-5) show the member companies and loca-
U. S. power pools and coordinating
organizations.
Figure 5.4-lO(II;UC;
1972; p. -17-10) and Table 5.4-9(III;FPC; 1972;
1-17-11) show the same information about the informal coordinating groups.
Interconnection areas, shown in Figure 5.4.11(III;FPC; 1972; p. -17-21)
are concerned with transmission interconnections, and have no significant influence on the choice of generation within the areas. Their function is primarily
one of coordination and making recommendations to committee members about
transmission requirements.
Reliability councils represent the largest existing aggregation in the
utility industry, set up to augment the reliability and adequacy of bulk power
supplies. These councils' regions are shown in Figure 5.4.12 and their member
companies listed in Table 5.4-10(1i[;FPC; 1972; p. I-17-16);(III;FPC; 1972;
1-17-18). The 1970 capacities of the U. S. members of the councils are shown
in Table 5.4-1(lii; FPC; 1972; 1-17-17).
The functions of these councils are restricted to planning and operation
problems and even here only those problems that arise during the review of the
plans and operating practices of member utilities and pools, and during the
identification of weaknesses that could result in shortages or outages. Reliability councils do not actually perform planning studies or direct operations.
They do, however, attempt to establish criteria to guide the members in those
functions and as a result the reliability councils usually encompass a variety
of planning and operation units. Since they are also interstate and
international, there is a wide variety of regulatory issues to consider at this
level of aggregation.
There is no existing national aggregation for performing or overseeing
planning and operation of the U. S. utility industry outside of the record
keeping functions performed within the Federal Power Commission.
5.4.4
Overview of Regional Considerations
The deliberate limitation of the scope of the assessment mechanism to
isolated plants does not lessen the importance of obtaining regional or
national assessments or of accurately reflecting power system level effects on
the choices of technology at individual plants. Indeed, national assessment
will be of the most significance in the longer term, and regional and power
system considerations are critical to successful national assessments. Current
planning and operating practices of the utility industry can only be represented accurately on a national basis by first considering their effect on the
regional and power system levels. National studies which aggregate directly
from local plants to national results must ignore or assume away too many factors to be successful. Although it means more data and effort, the regional
route is the only accurate path to national conclusions. A number of levels
of aggregation exist for attempting national aggregation; the most accurate
is probably the power pool level. A more convenient, but artificial, approximation could be made by aggregating at the reliability council level.
-129-
Table 5.4-8 Members of formal coordinating organizations or pools(III;FPC;
1972; p-17-5).
[January
1, 19701
NORTHEAST REGION
ANewEngland Power Pool (NEPOOL)
Northeast Utilities
Public Service Company of New Hampshire
2
Boston Edison Company
Eastern Utilities Associates 2
New England Electric System
Central Maine Power Co.
The United Illuminating Co.
New England Gas & Electric Assoc.2
Central Vermont Public Service Co.
NAew ork Power Pool (NYPP)
3
Consolidated Edison Company of N. Y.
Niagara Mohawk Powcr Corp.
Long Island Lighting Company
New York State Elctric and Gas Corp.
Central Hudson Gas & Electric Corp.
Rochester Gas and Electric Corp.
Orange and Rockland Utilities, Inc.
Power Authority of the State of N. Y.
Pennsylvania-New Jersey-Maryland Interconnection (PJM)
Public Service Electric and Gas Company
Philadelphia Electric Company
General Public Utilities Corporation
Metropolitan Edison Company
Pennsylvania Electric Company
Jersey Central Power and Light Company
New Jersey Power & Light Company
Pennsylvania Power & Light Company
Baltimore Gas and Electric Company
Potomac Electric Power Company
SOUTHEAST REGION
Caroli.nas-Virginia Power Pool (CARVA)
Virginia Electric & Power Company
Carolina Power & Light Company.
Duke Power Company
South Carolina Electric & Gas Company
Southern Company System (Holding Co.)
Alabama Power Company
Georgia Power Company
Gulf Power Company
Mississippi Power Company
EAST CENTRAL REGION
American Electric Power System (AEP) (Holding Company)
Appalachian Power Company
Indiana & Michigan Electric Co.
Kentucky Power Company
Kingsport Power Co.
Michigan Power Co.
Sewell Valley Utilities Co.
Wheeling Electric Co.
Ohio Power Company
Alleghety Power System (APS) (Holding Company)
Monongahela Power Company
Potomac Edison Company
West Penn Power Company
Central Area Power Coordination Group (CAPCO)
Cleveland Electric Illuminating Company
Duquesne Light Company
Ohio Edison System (IHolding Company)
Ohio Edison Company
Pennsylvania Power Company
Toledo Edison Company
-130-
Table 5.4-8
(continued)
EAST CENTRAL REGION-Continued
Cincinnati, Columbus, Dayton Pool (CCD)
Columbus & Southern Ohio Electric Co.
Dayton Power & Light Co.
Cincinnati Gas & Electric Co.
Kentucky-Indiana Power Pool (KIP)
Indianapolis Power & Light Co.
Public Scrvice Co. of Indiana
Kentucky Utilities Company
Michigan Pool
Consumers Power Company
Detroit Edison Company
WEST CENTRAL REGION
Illinois-MissouriPool
Central Illinois Public Service Company
Illinois Power Company
Union Electric Company
Upper Mississippi Valley Power Pool
Cooperatives
Cooperative Power Association
Dairyland Power Cooperative
Minnkota Power Cooperative
Northern Minnesota Power Assoc.
Rural Cooperative Power Assoc.
United Power Association
Investor-owned Companies
Interstate Power Company
Lake Superior District Power Co.
Minnesota Power & Light Company
Montana-Dakota Utilities Co.
Northern States Power Company
Northwestern Public Service Company
Otter Tail Power Company
Iowa Pool
Iowa Electric Light and Power Co.
Iowa-Illinois Gas and Elec. Co.
Iowa Power and Light Company
Iowa Public Service Company
Iowa Southern Utilities Company
Corn Belt Power Cooperative
Wisconsin Powir Pool
Wisconsin Public Service Corporation
Wisconsin Power and Light Company
Madison Gas and Electric Company
Mfissouri Basin Systems Group (MBSG)
U. S. Bureau of Reclamation
Basin Electric Power Cooperative
Central Power Electric Cooperative
Nebraska Public Power System
Other Members
SOUTH CENTRAL REGION
Missouri-KansasPool (MOKAN)
Kansas Power and Light Company
Missouri Public Service Company
Empire District Electric Company
Kansas City Power & Light Company
Kansas Gas & Electric Company
-131-
Table 5.4-8
(continued)
SOUTH CENTRAL REGION-Continued
lfiddleSouth Utilities System (Holding Co.)
Arkansas Pwer and Light Company
Louisiana Powcr and Light Company
Mississippi Power & Light Company
New Orleans Public Service, Inc.
South Central Electric Companies (SCEC)
Gulf States Utilities Company
Oklahoma Gas and Electric Company
New Orleans Public Service Company
Central Louisiana Electric Company
Public Service Co. of Oklahoma
Southwestern Electric Power Company
Texas Utilities System (olding
Arkansas Power and Light Company
Louisiana Power and Light Company
Mississippi Power and Light Company
Kansas Gas and Electric Company
Empire District Electric Company
Company)
Dallas Power & Light Company
Texas Electric Service Company
Texas Power and Light Company
WEST REGION
California Power Pool
Southern California Edison Company
Pacific Gas and Electric Company
San Diego Gas & Electric Company
PacificNorthwest Coordination Agreement
Bonneville Power Administration
City of Eugene, Oregon
City of Seattle, Washington
City of Tacoma, Washington
Coloekum Transmission Company
Montana Power Company
Pacific Power & Light Company
Portland General Electric Company
P. U. Dist. No. of Chelan County, Washington
P. U. Dist. No. I of Cowlitz County, Washington
P. U. Dist. No. of Douglas County, Washmngton
P. U. Dist. No. of Pend Oreiile County, Washington
P. U. Dist. No. 2 of Grant County, Washington
Puget Sound Power & Light Company
United States Corps of Engineers
Washington Water Power Company
1Data for the nine systems which initiated NEPOOL studies. As of January 1970, nearly all New England utilities were
represented in the expanded negotiations which were in process since June 1969.
2 Holding company.
3 Power Authority of the State of New York takes part in pool planning and operations, but not in commercial transactions of the pool.
4 Pooling agreement terminated as of October 20, 1970.
There are also five satellite members: St. oseph Light & Power Co.; Board of Public Utilities of Kansas City, Kansas;
City of Independence, Missouri; Central Telephone and Utilities Corp.-Western Power Division; and Associated Electric
Cooperatiye, Inc.
-132-
3. WUidUUOyste1m1s
bUUIU1111%W1
boU11nl
4. Florida Operating Committee
U. iULAylUUlltall rwe! FM
10. SouthernCalifornia Municipal Group
5. Joint Power Planning Council
II.
6. Mid-ContinentArea Power Planners
12. WesternEnergySupply & TransmissionAssociates
The IntercompanyPool
13. Wisconsin Upper MichiganSystems
NOTE:Areaboundaries
areonlygeneral;
notall systems
withina boundary
aremembers
o thedesignated
organizations
Figure 5.4-10
pI-17-10).
Informal coordinating groups, January 1, 1970(III;FPC; 1972;
Table 5.4-9 Informal coordinating organizations or power pools, January 1, 1970
(III;FPC; 1972; pI-17-11).
PLANNING ORGANIZATIONS
AND THEIR MEMBERS
Associated Mountain Power Systems (AMPS)
Utah Power & Light Co.
Washington Water Power Co.
Idaho Power Co.
Montana Power Co.
Pacific Power & Light Co.
Total 5 Systems
joint PowerPlanningCcncil(JPPC)
Washington Water Power Co.
Bonneville Power Administration
Publicly Owned Utilities in Oregon, Washington,
Idaho and Montana (104 Systems)
Pacific Power & Light Company
Portland General Electric Co.
Puget Sound Power & Light Co.
Total 109 Systems
Mid-Continent Area Power Planners (MAPP)
Black Hills Power & Light Co.
Northwestern Wisconsin Electric Co.
Omaha Public Power District
Nebraska Public Power District
Central Iowa Power Cooperative
Eastern Iowa Light & Power Coop.
Iowa Power Pool Members
Iowa Electric Light and Power Co.
Iowa-Illinois Gas and Electric Co.
Iowa Power and Light Co.
Iowa Public Service Co.
Iowa Southern Utilities Co.
Corn Belt Power Cooperative
Union Electric Company
Municipal Systems in Nebraska, South Dakota, Iowa
and Minnesota
Upper Mississippi Valley Power Pool
Cooperatives
Cooperative Power Association
Dairyland Power Cooperative
Minnkota Power Cooperative
Northern Minnesota Power Assoc.
Rural Cooperative Power Assoc.
Investor-owned Companies
Interstate Power Company
Lake Superior District Power Co.
Minnesota Power & Light Company
Montana-Dakota Utilities Company
Northern States Power Company
Northwestern Public Service Co.
Otter Tail Power Company
(28 Systems)
Manitoba Hydro-Electric Board
Total 54 Systems
Western Energy Supply & Transmission Associates (WEST)
Arizona Power Authority
Burbank Public Service Dept.
City of Colorado Springs
Colorado-Ute Electric Association, Inc.
Glendale Public Service Department
Imperial Irrigation District
Pacific Power & Light Co.
Pasadena Municipal Light & Power Dept.
Plains Electric G.&T. Coop., Inc.
Salt River Project
Central Telephone & Utilities Corp. (Southern Colo.
Power Div.)
Arizona Public Service Co.
Los Angeles Dept. of Water & Power
El Paso Electric Co.
Nevada Power Co.
Public Service Company of Colorado
San Diego Gas & Electric Co.
Sierra Pacific Power Co.
Southern California Edison Co.
Tucson Gas & Electric Co.
Utah Power & Light Co.
Arizona Electric Power Coop.
Public Service Co. of New Mexico
Total 23 Systems
OTHER INFORMAL COORDINATING
GROUPS AND THEIR MEMBERS--
Colorado Power Pool (COLOPP)
Public Service Company of Colorado
City of Colorado Springs
Southern Colorado Power Div. of C.T.U.
Total 3 Systems
-134-
Table 5.4-9
(continued)
OTHER INFORMAL COORDINATING
GROUPS AND TIHIEIR MEMBERS-Continuecd
The intercompany Pool (INTERPOOL)
Pacific Powcr & Light Company
Portland General Electric Co.
Puget Sound Power & Light Co.
Washington Water Power Co.
Total 4 Systems
Southern California Municipal Group (SCMG)
Los Angeles Department of Water and Power
Glendale Public Service Dept.
Burbank Public Service Dept.
Pasadena Municipal Light & Power Dept.
Total 4 Systems
Colorado stems Coordinating Council (CSCC)
Central Municipal Light & Power System
Colorado Springs Dept. of Public Utilities
Town of Estes Park
Fort Collins Light & Power Department
City of Fort Morgan
Glenwood Springs Municipal Elec. System
Julesburg Power & Light Department
La Junta Municipal Utilities
Utilities Board of the City of Lamar
Las Animas Municipal Light & Power System
City of Longmont
Loveland Electrical Department
City of Trinidad
Colorado-Ute Electric Assoc., Inc.
Arkansas Valley G. & T., Inc.
Tri State G. & T. Assoc., Inc.
Home Light & Power Co.
Public Service Company of Colorado
Central Telephone & Utilities Corp. (Southern Colo.
Power Div.)
Western Coloradd Power Co.
USBR
Total 21 Systems
Florida Operating Comnmittee
Florida Power & Light Co.
Florida Power Corp.
Tampa Electric Co.
City of Jacksonville
Orlando Utilities Commission
Total 5 Systems
Wisconsin-Upper Michigan Systems (WUMS)
Wisconsin-Michigan Power Co.
Upper Peninsula Power Co.
Wisconsin Power Pool (3 Systems)
Wisconsin Electric Power Co.
Total 6 Systems
Rocky Mountain Power Pool (RMPP)
Public Service Company of Colorado
Pacific Power & Light Co.
USBR Regions 4 and 7
Montana Power Co.
Consumers Public Power District
Southern Colorado Power Division of C.T.U.
City of Colorado Springs
Utah Power & Light Company
Black I-HillsPower & Light Co.
Tri-State G. & T. Assoc., Inc.
Colorado-Ute Elec. Association, Inc.
Cheyenne Light, Fuel & Power Co.
Western Colorado Power Co.
Total 13 Systems
JYNew
Mexico Power Pool (NMPP)
Community Public Service Company
El Paso Electric Company
Plains Electric G. & T. Coop.
Public Service Company of New Mexico
USBR Rio Grande Project
Total 5 Systems
-135-
Table 5.4-9
OTHER
(continued)
INFORMAL COORDINATING
GROUPS AND TH-IEIR IMEMNIBERS-Continued
,Vorthwest Power Pool (NIVPP)
Bonneville Power Administration
Eugcne Water & Electric Board
Idaho Power Co.
Montana Power Co.
P.U.D. No. 2 of Grant County
Seattle Department of Lighting
Tacoma Public Utilities (Lt. Div.)
Utah Power & Light Co.
Washington Water Power Co.
British Columbia 1-ydro & Power Authority
West Kootenay Power & Light Co.
Corps of Engineers-North Pacific Div.
USBR-BPA (Southern Idaho)
Pacific Powver & Light Co.
Portland Gcneral Electric Co.
Puget Sound Power & Light Co.
P.U.D. No. of Chelan County
P.U.D. No. 1 of Douglas County
Total
18 Systems
INTERCONNECTION
MARITIME
QUEBECSYSTEMS
A-SouthwesternPublic ServiceCompanySystem
B-Electric ReliabilityCouncilof Texas
AreasA and B are representedin NAPSICby the South
Central Systems,but are not in synchronousoperationwith
the interconnectedpowersystems in the South WestRegion
Figure 5.4-11 North American power systems interconnection committee areas,
January 1, 1970(III;FPC; 1972; pI-17-21).
-136-
ieluir
WSCC
MARCA
f-
Western Systems
CoordinatingCouncil
InrTllJnlJc]
IriiutIua4
1~Ilafo
iWM
CoordinatingCouncil
Mid-ContinentArea Reliability
CoordinationAgreement
Power Pool
~SPPSouthwest
ERCOT
Electric Reliability CouncilOf Texas
MAIN
Mid-AmericaInterpool Network
MAAC
Mid-AtlanticArea
CoordinationGroup
ECAR
EastCentralArea Reliability
CoordinationAgreement
SERC
SoutheasternElectric
Reliability Council
Figure 5.4-12 National Electric Reliability Council regions, Canadian portions
not included(III;FPC; 1972; p-17-16).
-137-
Table 5.4-10
pI-17-18).
Individual members of regional reliability councils (III;FPC; 1972;
Northeast Power Coordinating Council (PCC)
Boston Edison Co.
Burlington Electric Light Dept.
Central Hudson Gas & Electric Corp.
Central Maine Power Co.
Central Vermont Public Service Corp.
Consolidated Edison of N. Y., Inc.
Eastern Utilities Associates
Green Mountain Power Corp.
Hydro-Electric Powcr Comm. of Ontario
Long Island Lighting Co.
New England Electric System
New England Gas & Electric Assoc.
New York State Electric & Gas Corp.
Niagara Mohawk Power Corp.
Northeast Utilities
Orange and Rockland Utilities, Inc.
Power Authority of the State of New York
Public Service Company of New Hampshire
Rochester Gas and Electric Corp.
The United Illuminating Company
Mid-Continent Area Reliability CoordinationAgreement (MARCA)
Basin Electric Power Cooperative
Black Hills Power and Light Co.
Central Iowa Power Coop.
Cooperative Power Assoc.
Corn Belt Power Coop.
Dairyland Power Coop.
Eastern Iowa Light and Power Coop.
Interstate Power Co.
Iowa Electric Light & Power Co.
Iowa-Illinois Gas & Electric Co.
Iowa Power and Light Co.
Iowa Public Service Co.
Iowa Southern Utilities Co.
Lake Superior District Power Co.
Minnesota Power & Light Co.
Minnkota Power Coop., Inc.
Montana-Dakota Utilities Co.
Nebraska Public Power District
Northern Minnesota Power Association
Northern States Power Co.
Northwestern Public Service Co.
Omaha Public Power District
Otter Tail Power Co.
Rural Coop. Power Association
U. S. Bureau of Reclamation
Associates: Union Electric Co.
Manitoba Hydro-Electric Board of Canada
Southwest Power Pool Agreement (SPP)
Arkansas-Electric Coop. Corp.
Arkansas-Missouri Power Co.
Arkansas Power & Light Co.
Associated Electric Coop., Inc.
Board of Public Utilities, Kansas City, Kan.
Central Louisiana Electric Co., Inc. (The)
City Power & Light Dept., Independence, Mo.
City Utilities of Springtield, Missouri
Empire District Electric Co. (The)
Grand River Dam Authority
Gulf States Utilities Company
Kansas City Power & Light Co.
Kansas Gas and Electric Co.
Kansas Power & Light Co. (The)
Louisiana Power & Lt. Co.
Mississippi Power & Light Co.
Missouri Edison Co.'
Missouri Power & Light Co.'
Missouri Public Service Co.
Missouri Utilities Company
New Orleans Public Service, Inc.
Oklahoma Gas & Electric Co.
Public SrcnviceCo. of Oklahoma
St. Joseph Light & Power Co.
Southwestern Electric Power Co.
Southwestern Power Administration
Western Farmers Electric Coop.
Western Power Division-CT & U
Mid-Atlantic Area Coordination Agreemrent(MAAC)
Atlantic City Electric Co.
Baltimore Gas and Electric Co.
Delmarva Power & Light Co.
Jersey Central Power & Light Co.
Metropolitan Edison Co.
New Jersey Power & Light Co.
Pennsylvania Electric Co.
Pennsylvania Power & Light Co.
Philadelphia Electric Co.
Potomac Electric Power Co.
Public Service Elcctric and Gas Co.
UGI Corp.
-138-
Table 5.4-10
(continued)
Southeastern Electric Reliabilitv Council (SERC)
Alabama Electric Cooperative
Alabama Power Company
Carolina Power & Light Co.
City of Tallahassee
Crisp County Power Commission
Duke Power Company
Florida Power Corporation
Florida Power & Light Co.
Georgia Power Co.
Gulf Power Co.
Jacksonville Electric Authority
Lakeland Dept. of Elec. & Water
Mississippi Power Co.
Nantahala Power & Light Co.
Orlando Utilities Commission
Savannah Electric & Power Co.
South Carolina Electric & Gas Co.
South Carolina Public Service Authority
Southeastern Power Administration
Tampa Electric Co.
Tapoco, Inc.
Tennessee Valley Authority
Virginia Electric & Power Co.
Yadkin, Inc.
East Central Area Reliability Coordination Agreement (ECAR)
Appalachian Power Co.
Cincinnati Gas & Electric Co.
Cleveland Electric Illuminating Co.
Columbus & Southern Ohio Electric Co.
Consumers Power Co.
Dayton Power & Light Company
Detroit Edison Company
Duqucsne Light Company
East Kentucky Rural Electric Coop.
Indiana-Kentucky Electric Corp.
Indiana & Michigan Elect. Co.
Indianapolis Power & Light Co.
Kentucky Power Company
Kentucky Utilities Company
Louisville Gas & Electric Company
Monongahela Power Company
Northern Indiana Public Service Co.
Ohio Edison Company
Ohio Power Company
Ohio Valley Electric Corp.
Pennsylvania Power Company
Potomac Edison Company
Public Service Co. of Indiana
Southern Indiana Gas & Electric Co.
Toledo Edison Co.
West Penn Power Company
Mid-America InterconnectedAetlwork (MAIN)
Associated Electric Coop., Inc.3
Central Illinois Light Company
Central Illinois Public Service Co.
City Watcr Light & Power, Springfield, Ill.
Commonwealth Edison
Illinois Power Company
Interstate Power Company 6
Iowa Electric Light & Power Company
Iowa-Illinois Gas & Electric Co.4
Iowa Power & Light Company 4
Iowa Public Service Co. s
Iowa Southern Utilities Co.4
Madison Gas and Electric Co.
Northern States Power Co.4
Union Electric Company
Upper Peninsula Power Co.
Wisconsin Electric Power Company
Wisconsin-Michigan Power Company
Wisconsin Power and Light Compaiy
Wisconsin Public Service Corp.
ElectricReliabiliy Councilof Texas (ERCO T)
B-K Electric Coop., Inc.
Baird, City of
Bartlett Electric Coop., Inc.
Bluebonnet Elec. Coop., Inc.
Boerne Utilities
Bowic, City of
Brady Water & Light Works
Brazos Elec. Power Coop., Inc.
Brenham Municipal Utilities
Brownsville, City of
Bryan, City of
Cap Rock Elec. Coop., Inc.
Central Power & Light Company
City of Austin
City Public Service Board (San Antonio)
Coleman, City of
Comanche County Elec. Coop. Assoc.
Community Public Service Company
Crosbyton, City of
Cuero Electric Dept.
Dallas Power & Light Company
Deep East Texas Elec. Coop., Inc.
Denton Municipal Utilities
Denton County Elec. Coop., Inc.
DeWitt County Elec. Coop., Inc.
Fannin County Elec. Coop., Inc.
Farmers Electric Coop., Inc.
Fayette Electric Coop., Inc.
Garland, City of
Giddings, City of
Goldthwaite, City of
Gonzales Electric District System
Grayson-Collin Elec. Coop., Inc.
Greenville Municipal Utilities
Guadalupe Valley Elec. Coop., Inc.
Jackson Electric Coop., Inc.
Jasper-Newton Electric Coop., Inc.
Johnson County Electric Coop. Assn.
-139-
Table 5.4-10
(continued)
Flectric Reliability Council of Texas (ERCO T)-Continued
Kaufman County Electric Coop., Inc.
Kimble Electric Coop., Inc.
LaGrange, City of
Lamar County Electric Coop. Assn.
Limestone County Elec. Coop., Inc.
Livingston, City of
Lockhart Utilities
Lower Colorado River Authority
Luling Utilities
Magic Valley Electric Coop., Inc.
McCulloch Electric Coop., Inc.
McLennan County Electric Coop., Inc.
Medina Electric Coop., Inc.
Mid-South Electric Coop. Assn.
Midwest Electric Coop., Inc.
Navarro County Electric Coop., Inc.
New Braunfels Utilities
New Era Electric Coop., Inc.
Nueces Electric Coop., Inc.
Robertson Electric Coop., Inc.
Robstown, City of
Sam H-ouston Electric Coop., Inc.
San Bernard Electric Coop., Inc.
San Patricio Electric Coop., Inc.
Schulenburg, City of
Seguin, City of
Shiner, Light & Water Department
Southwestern Electric Service Co.
South Texas Elec. Coop., Inc.
Southwest Texas Elec. Coop., Inc.
Stamford Electric Coop., Inc.
Teaguc, City of
Hamilton County Elc. Coop. Assn.
Iearne Municipal Plant
Hemphill Electric Department
Hill County Electric Coop., Inc.
Houston Lighting & Power Company
Hunt-Collin Elec. Coop., Inc.
Texas Electric Service Co.
Texas Power & Light Co.
Tri-County Electric Coop., Inc.
Tulia Light & Power Plant
Weimar, City of
West Texas Utilities
Wise Electric Cooperative, Inc.
Western Systems Coordinating Council (WSCC)
Arizona Power Authority
Arizona Public Service Co.
Bonneville Power Administration
British Columbia Hydro & Power Authority
California Dept. of Water Resources
Central Telephone & Utilities (South Colorado Power
Division)
Chelan County P.U.D. No. I
City of Glendale, Public Service Dept.
City of Tacoma, Dept. Public Utilities
City of Seattle Dept. of Lighting
Cowlitz County P.U.D. No. I
Colorado-Ute Electric Association, Inc.
Douglas County P.U.D. No. I
El Paso Electric Company
Eugene Water & Electric Board
Grant County P.U.D. No. 2
Idaho Power Company
Los Angeles Department of Water & Power
Metropolitan Water Dist. of South Calif.
Montana Power Company
Nebraska Public Power District
Nevada Power Company
Pacific Gas & Electric Co.
Pacific Power & Light Company
Portland General Electric Co.
Public Service Company of Colorado
Public Service Company of New Mexico
Puget Sound Power & Light Co.
Sacramento Municipal Utility District
Salt River Project
San Diego Gas & Electric Co.
Sierra Pacific Power Company
Southern Calif. Edison Company
Tri-State G&T Association
Tucson Gas & Electric Company
U. S. Bureau of Reclamation
U. S. Corps of Engineers
Utah Power & Light Company
Washington Water Power Company
West Kootenay Power & Light Company
I Membership reported by all electric reliability councils as of September 1, 1970, except for the Electric Reliability
Council of Texas which is reported as of November 20, 1970.
2 Also members of MAIN trough their parent company, Union Electric Company.
s Also member of SPP.
4 Also member of
IMARCA.
I Member of MARCA. Resigned membership in MAIN as of June 30, 1971
-140-
Table 5.4-11 Organizations comprising the National Electric Reliability Council
(III;FPC; 1972; p-17-17).
Regional Organizations
Resources
Winter of 1970
(Megawatts)
East Central Arca Reliability Coordination
Agreement (ECAR) ..................
Electric Reliability Council of Texas
(ERCOT) ..........................
51,763
20,942
Mid-Atlantic Area Coordination Group
(MAAC)...
.......................
Mid America Interpool Network (MAIN).
Mid-Continent Area Reliability Coordination Agreement (MARCA) ...........
Northeast Power Coordinating Council
(NPCC) ............................
Southeastern Electric Reliability Council
(SERC)............................
Southwest Power Pool Coordination Agreement (SPP) .........................
Western Systems Coordinating Council
(WSCC)...........................
29,151
28,157
112,709
2 35,084
62,411
25,413
' 62,685
Source: Reliability Council Reports to FPC.
1 Resources of Manitoba Hydro-Electric Board, Canada,
not reported.
Excludes resources of Ontario IIydro-Electric Commission, Canada, which are reported to be 1,903 MW.
3 Excludes resources of the two Canadian
members,
British Columbia Hydro & Power Authority and West
Kootenay Power & Lght Co., which are reported to be
3,426 MW and 693 MW, respectively.
-141-
5.5
National Aggregation
The assessment mechanism of this report is designed for single fuel/plant/
control considerations. Section 5.4 discussed the many issues that have to
be considered for assessments at a regional power system level and emphasized
that national level assessments should be done by aggregating the results of
separate regional level studies. This section discusses options for performing
regional level assessments and their aggregation to a national level. There
are obviously tradeoffs between the value of the results and the developmental
and operational costs of the assessment mechanism. After defining and discussing the options, we provide our recommendations for the best way to proceed
and our perception of tasks that should be part of the implementation of the
assessment mechanism.
5.5.1
Definitions - Single Plant'
The following terms and concepts, related to the single plant assessment,
are redefined here for ease of reference for discussion on the problems of
regional assessment and national aggregation.
A "plant type" consists of a particular combination of
one pre-combustion process (which may be a "null" process)
one combustion process
· one post-combustion process (which may be a "null" process)
with prespecified electric generation capabilities such as 100, 250, or 1000MW.
Thus a "plant type" is one particular configuration of hardware that converts
unprocessed,mined coal into electrical energy at one of four such MW capacity
levels. If there are 5 types of precombustion, 3 types of combustion, and 2
types of post-combustion of concern, then there are 5 x 3 x 2 = 30 possible
plant types at a given capacity level. In practice, of course, not all combinations are of interest. A "coal type" consists of specification of one
particular coal's
dollar cost at mine mouth
chemical, mechanical, and other properties.
A "site type" consists of specification of a site's
land availability
water availability
ambient air quality due to background for each pollutant of concern.
A "plant configuration" consists of a particular combination of
one plant type
one coal type
· one site type.
If there are 120 plant types, 6 coal types, and 3 site types, then there are
120 x 6 x 3 = 2160 possible different plant configurations.
A "plant simulation" has as inputs
one particular plant configuration and all the associated technical
factors
cost of capital
cost of coal transportation (from mine mouth to plant)
plant capacity factor
· environmental constraints to be met (existing or hypothetical standards).
The outputs are "plant resultant factors". For a given set of environmental
constraints, the plant configuration may be such that the constraints cannot
be met. Only plant resultant factors corresponding to plant configurations that
can meet the standards have to be considered in regional studies.
-142-
5.5.2
Different Utility Regions
The continental United States is assumed to be divided into K different
"utility regions" where:
· a utility region is a geographical area that is considered to make
coordinated decisions on future generation expansion.
The kth Utility Region, k=l...K, is specified by:
regional expansion needs
available plant configurations
* coal transportation costs (for each coal type, site type combination).
Section 5.4.3 discusses some of the possible natural levels of utility
aggregation (see Table 54.6).
Since a utility region "makes decisions" it
actually corresponds to a power pool or an individual utility. An example of
a division of the continental U.S. into "utility regions" is shown in the
following map, Figure 5.5-1.
Figure 5.5-1 An example of one possible set of "utility regions" that includes
K=49 different geographic areas.
The regional expansion needs of a utility region can be specified as
either
static: needs for one specified year, or
dynamic: needs on year-by-year basis over a specified time span such
as 20 years.
The "desired mix" is defined to be a specification of
. number of plants of specified capacity ()
that are to be built.
The "existing mix" is a specification of
number of plants of specified characteristics that are already built
(or committed).
There are three different basic levels of sophistication at which regional
expansion needs can be specified:
Level I:
Specify desired mix, and capacity factors for each plant in
desired mix.
-143-
Level II:
Specify desired mix, existing mix, and load shape (load
duration curve).
Level III: Specify existing mix and load shape (load duration curve).
Since any of these three levels can be specified either "statically" or
"dynamically", there are 6 basic possibilities. As one example, a dynamic Level
I would specify number of plants to be built, size of plants, and capacity
factors for each year of, say, a 20-year time span. The choice of level of
sophistication is determined by the type of "Utility Region Simulation" to
be used (see Section 5.5.3).
5.5.3
Utility Region Simulation
A utility region simulation has as inputs
specification of kth utility region characteristics
cost of capital
environmental constraints to be met (existing or hypothetical standards)
characteristics of other electric generation technologies (such as
gas, oil, nuclear, or hydro) to be considered.
The outputs are the kth utility region's resultant factors.
A utility region simulation contains a decision making/optimization logic
which
chooses the minimum (capital plus operating) dollar cost plants
from the set of available plant configurations
meets
the specified expansion needs
satisfies
all environmental constraints.
This criterion is used to correspond, as best as reasonable, with the actual
decision making process (see section 5.4.2). The commercialization potential
of a given technology is determined by how competitive it is in the above decision making process. As discussed in section 5.4.2 there are many regionally
dependent political, social and regulatory issues that also affect the decision
making process.
Conceptually, a utility region simulation can be viewed as a computer
program that
· carries out the plant simulation for each plant in the set of available
plant configurations,
rejects those plant configurations that cannot meet the environmental
constraints (a screening" process),
searches over the economic resultant factors from the remaining plant
simulations to find a minimum cost solution (subject to other constraints such as rate of introduction of new technology, and so on),
outputs the chosen plant configurations, and all resultant factors for
the chosen plants.
There are then three levels of sophistication for a utility region simulation where each level has a correspondingly different specificity of regional
expansion needs, as defined in section 5.5.2. The role of the utility region
simulation at these different levels of sophistication is
Level I (Specify desired mix, capacity factors): Choose best set of
plant configurations to fit prespecified desired mix and prespecified
capacity factor.
Level II (Specify desired mix, existing mix, load shape): Choose
best set of plant configurations to fit prespecified desired mix while
choosing best set of capacity factors.
Level III (Specify existing mix, load shape): Choose best set of plant
configurations and corresponding capacity factors.
Level III gives the most meaningful outputs and requires the least a priori
decision making for its inputs, but also requires the development/use of fairly
sophisticated computer programs. Level I can almost be a simple "list-search"
code.
-144-
It is clear that a utility region simulation can have a wide range of
complexity and sophistication. Consider two extremes. A sophisticated dynamic
Level III effectively exists already in the MIT "GEM" computer code- see
(V; Schweppe, et al.; 1972).
The simplest static Level I
version results when the "desired mix" (for the year of interest) consists of
one plant (of specified MW size and capacity factor). For this case, the utility
simulation simply results in the choice of the one plant configuration that has
minimum dollar cost (and which meets environmental constraints). Amore general
discussion of these issues is contained i(III;Ruane, et al.; 1976).
A utility region simulation involves the choice of the least dollar cost
set of plant configurations that meets the expansion needs subject to environmental and other constraints. This approach corresponds to the actual utility
decision making process. It must, however, be emphasized that the use of the
resulting mathematical optimization logics can yield misleading results. For
example, one particular plant configuration might be chosen over all others even
though the actual cost difference between them is small. The choice patterns
might change completely with only a small variation in, say, a hypothesized
cost of capital. For this reason it is necessary to once again follow standard
utility practice and perform sensitivity studies to see how the chosen set of
plant configurations varies with changes in the exogenous input parameters.
Thus, multiple runs of the utility region simulation may be required fr each
region.
5.5.4
Representative Power Systems
There are more than 20 utility regions at a formal utility-pool level in
the United States. This number exceeds 40 if informal pools are included. Thus,
substantial computer time would be required to run a utility region simulation on all utility regions (especially if Level II or III sophistication is
used). Compounding the difficulty is the necessity for developing a data base
that defines each of the regions. Such data are available but their collection
and use require a major undertaking. Two alternative approaches that alleviate
some of these potential data collection and computer time problems are now
discussed.
Figure 5.5-2
An example of an aggregated set of "utility regions."
-145-
The first possibility simply involves working with gross aggregations of
the individual "utility regions." An example of this type of geographic breakdown is presented in Figure 5.5-2. This example shows aggregated utility
regions that are made up of between 2 and 9 of the individual regions in Fgure 5.5-1.
The other potential simplification is based not upon actual geographic
regions but on different types of power system situations. The basic idea
involves the definition of J types of "representative power systems." None of
these representative power systems is necessarily any particular utility or
pool (that is, not any of the actual utility regions). However, it is assumed
that for any particular utility region, there exists a representative power
system that corresponds to it in a sense that the operation of a utility
regional simulation on the kth utility region or on the corresponding jth
representative power system yield effectively the same conclusions. At the
present time we feel 5 to 10 representative power systems (J = 5 to 10) will
probably be sufficient for a relatively accurate national representation. An
example of a hypothetical representation of the "utility regions" from Figure
5.5-1 by a few "representative power systems" is given in Figure 5.5-3.
Figure 5.5-3 A hypothetical coverage of a number of "utility regions" by a small
( J=7 ) number of "representative power systems."
The representative power system concept can reduce data base problems and
the number of individual utility region simulations that have to be run. This
method has been applied to numerous studies at a national level in the past,
(V; FEA; 1974), see Figure 5.5-4.
recently in
5.5.5
National Resultant Factors
Use of a utility region simulation on K utility regions (or the approximately equivalent J representative power systems) yields K sets of regional
resultant factors. The aggregation of these regional resultant factors into
national resultant factors is now considered. Different methods of aggregation
are used for different regional resultant factors.
-146-
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Some of the regional resultant factors, such as dollar costs or barrels
of oil per year saved, can be simply summed over all regions to get a national
corresponding factor. Such simple summations, however, are often not satisfactory for all resultant factors, and, thus, other procedures have to be
utilized.
The nature of the desired aggregation can depend on the qu4estion that is
to be answered. For example, consider the two example questions given at the
beginning of section 5.4:
development funds be allocated among the competing
1. How should EA
developing technologies to maximize substitution of coal for oil by
1990?
2. What is the maximum commercialization level of, say, fluidized bed
combustion in the nation's fuel use pattern by 1995?
Both of these questions are economically oriented so a lot of detail on the
environmental side is not required. Environmental factors could therefore be
greatly aggregated. On the other hand, an environmentally oriented question
such as:
Do any of the competing developing technologies have a high probability
of a major health impact if things go wrong?
would involve minimal aggregation of the environmental factors as one goes from
the regional to the national level.
In some cases the method of aggregating regional factors to the national
level should depend on the nature of the regional resultant factors themselves.
For example, when comparing alternative coal burning technologies, a reasonable
national resultant factor is the percentage of the national market that each
technology captures. This national resultant factor can be obtained by simply
summing over the regional results. However, it can be very misleading when a
particular technology captures only a small share of the national market but
also happens to be critical to one particular region of the United States.
A simple summation of emissions over all K utility regions is possible but a
weighting by region would often be considered more reasonable. For example,
if sulfates are a prime concern, tons of SO2 emissions per year on the East
Coast might be weighted less than SO2 emissions in the Chicago area. Unfortunately the choice of such weighting factors is still more subjective
than objective.
The above discussions illustrate one very important point. Although
possible, it is extremely difficult and often very unsatisfactory to comrpletely prespecify a set of specific formulas for use in aggregrating
region resultant factors to national resultant factors. What is really needed
is a versatile, interactive computer code which:
1. computes and outputs certain standard national resultant factors
(such as dollars, percentage capture of national market, and so on)
using prespecified formulas, and
2. can be adapted to output other national resultant factors which are
chosen on the basis of the type of question to be answered, the
nature of the regional resultant factors, or other concerns as they arise.
3.
5.5.6.
Recommended Approach
The discussions in sections 5.5.1 through 5.5.5 covered many different
ways in which to proceed from a single plant assessment mechanism to
national assessment. The authors' recommendations on how to choose
between there options will now be presented. These recommendations are based
on the criteria that it is desirable to obtain a reasonable and accurate
national level capability with the least cost as measured in terms of
development time and computer requirements.
The choice of a recommended utility region simulation (see section 5.5.3.)
is not easy. The most accurate choice is a sophisticated dynamic Level III
-148-
simulation and the basic computer programs to do this are presently
operational (MIT-GEM is an example). However after much thought the use of
a package such as the MIT-GEM system is not recommended at this time
because the computer time required to run such code on even the J representative power systems appears to be excessive because of the need to do
multiple sensitivity studies. It is, of course, possible to develop a
less sophisticated Level II utility region simulation. The supply side of
of the Baughman-Joskow model (IV; Baughman; 1975) is an example of an operating computer code that might be modified to do the job. After considering
the costs associated with developing and verifying such a computer code
however, and considering what we perceive to be the needs of the overall
Argonne assessment program, we do not recommend the development of a Level
III capability.
We recommend that a dynamic Level I utility region simulation be
developed first but in a fashion such that a Level II capability can be
added easily, at some later time. This Level II capability would be obtained
by adding a Booth-Balerieux production cost program (IV: Finger; 1975)
which could iteratively adjust the prespecified capacity factors of the
Level I simulation.
The choice
between using K explicit utility regions and J
representative power systems (that are then mapped into the K region)
is relatively easy. We feel that the effort required to work with K explicit
utility regions is not worthwhile relative to the advantages that it
provides. We therefore recommend using the J representative power system
approach.
It is our opinion that a fixed set of formulas for national aggregation of
all regional factors should not be used. We strongly recommend that a versatile
computer code be develoedso that the specific method of aggregration for
some of the regional resultant factors can be adapted to the specific
questions of concern and the nature of the numerical results. This recommendation is consistent with our design and basic philosophy behind the
ordering mechanism. If the ordering mechanism itself is implemented
as discussed in chapter 7 of this report, the addition of a versatile
national aggregration capability is a relatively small additional computer
programming effort.
6.
Resultant Factors
The resultant factors play an important central role in the overall assessment procedure. In terms of the terminology used throughout this report
the technical factors are used in te simulation mechanism to develop resultant factors. A set of resultant factors would be developed for each combination of fuel/conversion/control and for each option to be explored among the
regional assumptions, accounting procedures, fixed capitalization charges, and
the various other economic and performance criteria. Thus, a single set of
resultant factors may be meaningful by itself, or a number of sets of resultant
factors could be developed for a single technology under various sensitivity parametrizations of the modeling options. The place of the resultant factors in
the overall assessment procedure is shown in Figure 6.0-1.
USER . /
DECISION
2AMER
I
I'
I
ASSESSMENT
CTIONS
NONTCHNICAL
A
D
INPUTS
User choices of regional
ac-recations
or individual
fel/plant/control
combinations to be studied
and
user choices of regional,
econcnic, and other information to be studied or
para-eterizoed
I1 1'
L1!
PRIORITIES
Subjectte
choices
f
DISPLAY
RESULTANTOF
FACTORS
"-
user-
DISPLAY
f
IiTERIM
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TO USER
f-
ordering and
eliminating
information and
combinations
TEC.'ICAL
lnor.ation
on
iu-.
plan
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FACTORS
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2
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ab
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RSLTANT
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__
V ie
Models of accounting,
dispersion
d
aregio1
,
n
totioo
concerns
for
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lconversion
plants,
and
abteent
ERING
MECHANISM
~Final
Mechanism
andreport
Co,p~r~ble
information
Comparable
information
M'2ch
on each
fuel/plant/
control
combination
aggregations
regional
chosen
for study
generator
ing,
wengoting,
and
is
playing
the information
of interest
or
|CRITICAL FACTORS
-
n
information
the
crucIal
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the
crucia'l
diffuren
es
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cntr
control
cen
chosen
th
f1/plant/
'cfatnr
rbrnations
frs
for study
r
n
Figure 6.0-1 Representation of the central role played by the resultant factors.
Note that a user with different subjective priorities need only go back and
retrieve the original set of resultant factors.
In any of these cases the resultant
set of numbers
actors should be a reasonably sized
that:
(1) contain as much as is possible of the information that might be useful to a decision maker from any of the many special interest groups,
and
(2) does
not presume hidden subjective tradeoffs between unlike quanti-
ties.
An example of a possible list of resultant factors is contained in Table 6.0-1
Table 6.0-1 Example of a list of resultant factors for a chosen energy plant/
control option.
1.
Economic
1.
2.
Resiltant Factors
Total Investment ($)
Capital Investment Normalized
-150-
($/1000MWe)
Table 6.0-1
(continued)
3.
4.
5.
2.
Operating Cdst
A. Fixed Operating Cost ($/MWe/yr)
B. Variable Operating Cost ($/MWhr)
Annualized Cost ($/Yr)
Total Cost per Unit Output (mills/kTlfr)
Performance Resultant Factors
(e)
1.
Capacity
2.
Production
3.
4.
Design Capacity Factor (%)
Operating Capacity Factor (%)
5.
Availability (%)
6.
7.
Energy Efficiency (overall losses and ancillary, %)
Expected Lifetime of Unit (yrs)
(lhr/yr)
3.
Applicability Resultant Factors
1. Commercialization Date (2000MBe production capacity, yr)
2. Operating Experience (e-yr)
3.
Licensing and Construction Time (yrs)
4. Maximum Rate of Installation (e/yr)
5. Potential for Advancement of Technology (e.g., mills/kWlhr
reduction in output price per year after commercialization)
6. Probability of Technological Feasibility
(fraction of 1)
4.
Resource Requirements
1. Renewable Energy (as % of primary energy)
2. Land Use (acres/biWe).
A. On-Site Requirements
B. Waste Disposal and Other
C. Pondage Requirements
5.
(non-operating, man-yrs)
3.
Manpower Requirement
4.
Water Consumption (gallons/tBhr)
5.
6.
Materials Requirements (tons/M9yr/material)
By-Products (disposal costs or sales, $/MWyr)
Environmental Consequences
1. Emission Standards (% of each standard)
2. Emissions (normal and upset)
for specific pollutants)
A. Air Pollutants (tons, BTU/Wyr
B. Water Pollutants (tons, BTU/bB1yr for specific pollutants)
C. Waste Solids (tons/MB1yr for specific wastes)
D. Radioactive Pollutants
E. Noise (decibels/full load)
3. Upset Conditions (hrs/Mlyr)
4. Ambient Standards (% of each standard)
5. Occupational Health
A.
6.
Mortalities (deaths/yr/W)
B. Morbidities (illnesses/yr/MW)
C. Man-Days Lost (man-days/yr/NMW)
D. Occupational HIealth Costs ($/yr/MW)
Public Health
A.
Mortalities (deaths/yr/MW)
-151-
Table 6.0-1
(continued)
B. Morbidities (illnesses/vr/N)
7.
6.1
i. Chronic Respiratory (cases)
ii. Aggravated
eart-Lung Symptoms (person-days/yr/MW)
iii. Asthma Attaches (cases)
iv. Children's Respiratory (cases)
C. Public Health Costs ($/yr/MW)
Pollution-Related Damage Costs (total health and other,
$/yr/MW)
A. Biota Costs ($/yr/MW)
B. Material Damage Costs ($/yr/MW)
C. Aesthetic Costs ($/yr/bB)
Economic Resultant Factors
These factors should include as much as is feasible of all of the necessary and sufficient information about the economics of a particular fuel/plant/
control option. Using the suggested categories in Table 6.0-1 most of the
numbers are normalized: fixed and variable costs and cost per unit output.
It may also be important to have non-normalized costs such as total investment
and annualized cost. The reason for this is that some technology such as
fusion may show up as an attractive alternative on a normalized cost basis,
but the $10 billion outlay for the typically (0,OOOMWe) sized unit may be
an important constraint to some decision makers. An example of an exhaustive
display of some economic Resultant Factors from (V; NASA; 1976; p355) is shown
in Figure 6.1-1.
6.2
Performance Resultant Factors
For interested parties with more focused perspectives, such as environmentalists, performance measures might be of little concern. In terms, however, of
fitting into a long-range utility plan, size, reliability, and lifetime can be
among the most important considerations. In the example in Table 6.0-1 energy
efficiency could as appropriately be considered in resource requirements as in
performance indexes.
6.3
Applicability Resultant Factors
This category of displayed results deals primarily with the logistical
problems associated with the fuel/plant/control options. Again using the
categories in Table 6.0-1 some of these results show whether and when this combination can be built, some show likelihood of difficulties (operating experience) or improvements, and some categories show numbers that can help in regional
and national scale-ups.
With some experience using the overall assessment mechanism should come
better ideas for categories and more precise assumptions for the quantification
of results. For example, in Table 6.0-1 the commercialization date is defined
as the year at which 2000MWe (equivalent) of production is on line. However,
it might be better to use the definition as the year the third nominally sized
facility begins operation; this would help display the difference between
technologies that would have typical full-sized capacities of 25MWe and those
of 10,OOOMWe capacities.
6.4
Resource Requirements
These resultant factors are the standard, often easily available, land,
manpower, water, energy, and materials requirements normalized to a common out-152-
System
steam(atlospheric
Advanced
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Case
69
Advanced
steam
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31
Advanced
steam
ipressurizedboiler)
49
Advanced
steamPi81
49
gasturbine
Open-cycle
(simplecycle)
26
LJ
L_
a'l Mjor components
i
balanceof plant
'11] Contingency
andinterest
L1 Escalation
1~~~~~~~~~~~~-
Open-cycle
gasturbine
Irecuperated
Open-cycle
gas
turbiine/organic
96
Combined
cycle
63
Closed-cycle
gaslturbine
Irecuperated)
27
Closed-cycle
gas
turbinelsteam
42
Closed-cycle
gas
turbine/organic
52
Llquid-metal
Rankine
13
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71
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700
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1000
110
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120
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2000
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Figure 6.1-1 Display of several economic Resultant Factors for a specific set of
economic Non-Technical Factors (V; NASA; 1976).
-153-
put number. Some additional categories that might be worthwhile for consideration include: domestic and mported ratios on material and resource uses,
quantitative measures of the cartel-vulnerability of imported materials, and
ppm or ppb measures of the ratio of resource use to total resource availability-a type of measure that spotlights the use of scarcer resources.
6.5
Environmental Consequences
This is the largest and most uncertain portion of the list of resultant
factors. Most comparative assessments stop at tons of emissions for 5 to 10
different pollutants, the common pollutants for which there are standards.
These common pollutants are sometimes extrapolated to ambient and damage
figures. Seldom are uncommon pollutants traced to ambient conditions and seldom are public health effects displayed for anything but common pollutant
impacts. Occupational effects are largely available. The rest of the environmental resultant factors listed in Table 6.0-1 are untried, and precisely
because this is new territory there are likely to be important and revealing
results in these categories that could evolve from the future Argonne efforts
on this project.
-154-
7.
Ordering Mechanisms and Critical Factors
The place of the ordering mechanism in the overall assessment procedure is
shown in Figure 7.0-1. The "priorities" shown feeding information back and
forth between the modeling options and the ordering mechanism represents the
interactive nature that could exist between the user and the computer terminal.
0
factors
operation
USER
~/Assessment
controls
Optionsand
/
/_ Non-Technical Inputs
r--
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.
.
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COMPUTERIZED ASSESSMENT MECHANISM
1
Figure 7.0-1
i
1
Block diagram representation of relationship between user and
the computerized assessment mechanism
The input to the ordering mechanism can best be visualized as a matrix
of entries. Each of the columns in such a matrix would represent a different
fuel/plant/control or a different assessment option. Each of the rows of this
matrix would represent a different type of resultant factor. In the most
general case each entry in the matrix would be not just a single number but a
set of numbers or a function that characterized the uncertainty of that piece
of information.
7.1
Ordering Models
Once the matrix of plant alternatives and resultant factors has been constructed, the decision maker is left with the task of sorting through those
alternatives to find which ones are most suitable based on selection criteria.
Even in the deterministic case, where each entry of the matrix is a single
number, it can be a very formidable task to seek out the most attractive alternatives. The inclusion of uncertainty in that analysis complicates the task to
the point where some kind of ordering or screening mechanism is essential.
There are several requirements for a useful ordering mechanism. It must
be flexible enough so that decision makers with different selection criteria
and different attitudes toward risk can use it. It must be capable of handling the uncertainty in the resultant factors in a meaningful way. And, because
the decision making is ideally an iterative process, the screening model should
be interactive to allow the decision maker to see the results of each step and
to backtrack or go forward using these results.
At some point, the screening model arrives at a subset of the original
resultant factors, a subset which here is defined to be the set of critical
factors. It may be that soma resultant factors, such as installment cost, go
directly into the final set of critical factors since the decision maker may
know he will always be concerned with cost; whereas other resultant factors,
such as plant efficiency, may become apparent as critical only during the course
-155-
of the analysis. For eample, there may be a large tradeoff between installment cost and plant efficiency of which the decision maker is not initially
aware. As the analysis proceeds, the decision aker may become aware of this
tradeoff and realize that plant efficiency will be critical in arriving at a
decision.
The screening model described below is designed solely to identify the
critical factors. Once the critical factors have been obtained, methods such
as tradeoff analysis or sophisticated graphics may be employed to arrive at a
fuller understanding of the relationships among the final alternatives.
The description of the screening model itself can be broken down into two
parts. First, there are the strategies available to the decision maker for
eliminating variables. Second, there are the operations performed within the
model which will reduce the matrix once a variable has been eliminated. The
elimination strategies will be discussed first, with the definitions of the
different types of elimination given in section 7.1.3.
7.1.1
Elimination Strategies
Descriptions of four major different types of elimination strategies are
given in the following subsections. There are other kinds of strategies, a
number of which have been developed and used in sources listed in section 9.VI,
but most of these are minor variations or combinations of the following types.
7.1.1.1
Threshold Criteria
This procedure requires the decision maker to indicate threshold values
above or below which an alternative would not be considered. These thresholds
may be government or industry standards, maximum resource availabilities, or
absolute system constraints. If there are uncertainty measures associated with
the data, the threshold criteria can be given in probabilistic terms. For
example, one criterion might be that all plants have a 90% chance of meeting the
1980 pollution standards.
Once all fuel/plant/control options that are unacceptable to the decision
maker have been removed,then the matrix can be further reduced using row and
column elimination techniques (described in section 7.1.3). Using threshold
criteria alone, however, is not likely to reduce the matrix to a small enough
set of critical factors to enable the decision maker to formulate any overall
conclusions. Therefore, threshold criteria most probably must be combined with one
of the other strategies described below.
7.1.1.2
Indifference Elimination
Elimination by indifference is an interactive strategy in which the decision maker successively eliminates particular resultant factors or specific
plant alternatives because they do not appear at that stage to be important
concerns. After each such procedure the matrix is thus reduced by the elimination of a row or a column.
At any time, in an ideal interactive mechanism, the decision maker should
be allowed to replace a variable previously eliminated and to start down a different elimination path. For example, a decision maker may look at the acreage
requirements for the various alternatives, decide that they are relatively
unimportant and eliminate them from the matrix. Later in the procedure he
may realize that he is
not indifferent to the acreage requirement and thus
elect either to return to the matrix as it was or to retain all the elimination
up to that point and just to reinstate the row of acreage requirements.
Some advantages of the indifference elimination strategy are that it does
not require the decision maker to quantify preferences among variables and that
-156-
uncertainties in the data do not change the effectiveness of this strategy.
It does not, however, allow for anything but binary, totally in or totally out,
decisions. The strategy described in the next section is designed to remedy this.
7.1.1.3
Relative Weighting
In the weighting model, the decision maker initially enters a relative
weight for each resultant factor. For example, a decision maker might put a
weight of 1.0 on all cost factors, a weight of 0.5 on all pollution factors,
and a weight of 0.25 on all resource requirement factors. This would mean that
there were no factors more important than costs, that pollution factors were
half as important as costs, and that resource requirements were one quarter as
important as costs.
For factors with zero weights, the strategy is identical to the indifference
elimination strategy, so that initially any factor with a zero weight is
removed from the matrix. The inclusion of uncertainty complicates the model
considerably, therefore, the weighting model will be discussed first assuming
that each resultant factor is represented by a single number.
Because the resultant factors are in different units (dollars, dollars/MWhr,
tons/4NWhr, and so on), they are difficult to compare, and must be reduced to
some common denominator. One method for accomplishing this is to compare them
to the best available technology for that type of plant. The resultant factors
could thus be converted to nondimensional
ratios comparing performance to
that of the best existing technology. For example, a plant which produced 50%
less S02 than the best existing technology would have a resultant factor of
1.5 for S02 pollution.
Once the resultant factors have been reduced to a common unit of measurement,
then the relative weight can be applied. It may be desirable to eliminate rows
of resultant factors for which the remaining plants are essentially the same.
This may be done either by the decision maker or by setting a tolerance range
within the model.
There are a number of other obvious ways that weighting factors can be
used. For example, for resultant factors with common units of measure, such
as air pollutant emissions, it is quite easy to combine these few rows into a
single index of, in this instance, air pollution. For environmental consequences, across-the-board weightings of radically different types of impacts
have been looked at previously (V; Reiquam, et al.; SY7).
Combinations of rows
are not, of course, restricted to the environmental factor. A weighted combination of investment and operating costs, for example, could be used to
determine which alternative technology best fills a certain type of slot in a
utility's future plans.
Weighted combinations of columns are also potentially useful. For example,
if several different coal samples or several different future economic assumptions are each followed through for a particular technology it might be helpful
to combine these columns to form a single vector that represents a typical composite or averaged view of the likely performance of that technology.
7.1.1.4
Strategies Using Uncertainty
All of the discussions above have assumed that each resultant factor is
represented by a single number, ignoring the upper and lower bounds. However,
the additional information on the uncertainty of a value may be important in
reaching a decision. To include the uncertainties explicitly in any of the
sorting mechanisms could be very cumbersome, since one would expect bounds to
overlap for many alternatives, so that no one alternative would ever be clearly
better than another.
There are several strategies for reducing probabilistic data to single
-157-
numbers that include some presumption about the treatment of the uncertainty.
One simple strategy is to take the expected value of each resultant factor and
to proceed without any reflection of the variance in that parameter. This can
have serious disadvantages, especially, for example, to a decision maker who
was greatly averse to taking risks. He might, for example, not be interested
in a plant if there was some chance of it being very costly, even though the
expected cost might be relatively low. To circumvent this, one could look at
points other than the expected value on the probability distribution curve.
For example, for a mildly risk-averse decision maker, one could elect to represent all resultant factors by that value that had a chance of 70 percent or
better of occurring. This technique could have the same disadvantage as did the
taking of the expected value in that it does not reflect the variance of a
parameter, nor the decision maker's attitude towards that variance.
Fortunately, decision analysis, or more specifically utility theory, has
been developed because of just this sort of problem. Decision analysis is a
relatively new field, so there are many unanswered questions, particularly for
multi-attribute types of decisions. The following paragraphs briefly outline
the theory of decision analysis. An excellent reference is (VI; Raiffa; 1968).
The first step in decision analysis is to quantify a decision maker's
attitude toward taking risks. It is easiest to think in terms of money, but
the theory is by no means limited to this; one could draw up curves that
showed a decision maker's risk attitude toward pollution, reliability, or
whatever.
In monetary terms, then, someone's attitude toward risk is usually assessed
using lotteries. One is given a choice between two lotteries, both with the
same expected value but different variances, and asked which is preferable.
Or one is asked for how much one would sell a given lottery. Through a series
of questions, a curve such as that shown in Figure 7.1-1 can be drawn up. A
"utility" of 1.0 is usually assigned to the largest value and a utility of zero
to the lowest. The intermediate values are scaled accordingly. Used in this
sense, utility" means the ultimate usefulness of an outcome to the decision
maker, rather than its face value.
The shape of the utility curve reflects one's attitude towards risk. A
concave curve means that one is risk averse. A straight line means one is
risk indifferent, or equivalently that one uses the expected values to make
decisions. A convex curve means one is risk prone or that one enjoys taking
risks.
As an example, suppose the decision maker were presented with a choice
between two plants. One plant has a 30% chance of costing $500 per installed
kW and a 70% chance of costing $1000 per W. The other plant has a 60% chance
of costing $750 per kW and a 40% chance of costing $1250 per kW. Suppose, in
addition, that we have exactly characterized someone's preferences and they
are displayed in the curve in Figure 7.1-1.The first plant then has an expected
utility of (.30 X 1.0 + .70 X .5) = .65. The second plant has an expected utility
of (.60 X .7 + .40 X .33) = .55. So the first plant is preferred by this
particular person.
Ideally, one could assess the decision maker's attitude toward risk for
each resultant factor, and his attitude toward the interactions among the
resultant factors in order to arrive at the overall utility for each plant.
One would then simply choose the plant with the highest utility. Unfortunately,
such multi-attribute decision problems are complex and only a few special
cases have been solved. Even if the plant assessment problem could be formulated as one of these special cases, there would remain the task of assessing
the utility curves for each of the resultant factors. Therefore, in the design
outlined below, it is anticipated that decision analysis would be used only in
the final stages.
-158-
1.0
.9
.8
.7
4O
"~ '6
4J
H5
4
0J .4
a;
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Pi
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0
Figure 7.1-1
7.1.2
$500
$1000
$1500
dollars per kW
$2000
$2500
Utility curve showing the preferences of a decision maker
General Ordering Mechanism
Combining all of the methodologies outlined above, the following ordering
mechanism emerges. First the decision maker enters any threshold criteria
that must be enforced. The resulting size of the matrix is displayed. The
decision maker may elect to stop, to have specific rows displayed, to have additional resultant factors eliminated, to enter a vector of weights for the
remaining factors, or to begin again with new threshold criteria or other strategies. If the decision maker arrives at a point where he can no longer eliminate variables, but the uncertainty in the data prohibits him from making a
decision, he may elect to have his risk profile assessed for various parameters.
New resultant factors would be displayed which would show, for example, the
cost utility or pollution utility, of each alternative. This may aid the
decision maker to reduce the size of the matrix to a small set of critical factors so that a reasonably simple decision can be made.
7.1.3
Elimination Operations
There are just two different types of elimination operations, column
elimination and row elimination. The column for a plant alternative may be
eliminated from the matrix when there exists another column that is superior
in all important aspects. This may occur after the decision maker has eliminated
a resultant factor from the matrix or after a row elimination. For example,
suppose there were two fuel/plant/control options, identical except that one
had been made more efficient in the conservation of resources and consequently
has a higher capital and maintenance cost. If the decision maker is indifferent
-159-
to resource usage, then the cheaper plant would be preferred and the more efficient one eliminated from the analysis.
A row of resultant factors mraybe eliminated when all elements are less desirable
, or are the same to within some specified tolerance. This
than some other row
may occur after a plant alternative has been eliminated, either by the decision
maker or by column elimination. For example, if solar technologies were eliminated because they would not be available soon enough, then the resultant
factor displaying fraction of energy need that is from renewable sources might
at that point be zero across the remaining technologies and thus the row of data
on renewable energy would be eliminated from the matrix.
-160-
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7.2
Example of Interactive Ordering Mechanism
All of the elimination strategies discussed above can be combined into a
general ordering mechanism. A flow chart of the procedures is given in Figure
7.2-1. The example below follows the flow chart and shows the result each decision has on the matrix. The original matrix is shown in Table 7.2-1.
1) In the initial scan of the matrix, the availability resultant factor
is eliminated. No other rows or columns can be obviously eliminated.
2) The decision maker enters threshold criteria:
a) S02 emissions must have a 95% chance of being less than 250 tons/
year.
b) Particulate emissions must have a 95% chance of being less than
50 tons/year.
c) Plant must be available in 1985.
Result on matrix:
a) High sulfur coal is eliminated.
b)
Liquefaction
is eliminated.
c) All plants will be available in 1985; no effect.
New matrix is 6 x 16 (see Table 7.2-2).
LOWSULFUR
COAL
-CCN.¶IfC
PHYSICAL COAL
CLEANING
FLUIDIZED BED
COBUSTION
LOW TU
GASIFICATION
445.-47O·-540.
470.-490.-650.
520.-555.-695.
8.4-8.9-10.0
9.8-10. 2-13.5
12.4-132.- 15.8
RESULTANT FACTORS
CAP~ITALI;EV-':.S7T
:OM.ALIZEO
C$1000/INVE0.-{6T·-
5LIE
CPERATING COST
(SI/MWH
R)
7.8-8.2-9.5
(PERCENT)
. 68.-68.-68
8..........68.-68.-68
....
PER1RMANC'2 RESULTANT FACTORS
AVAILABILITY
IN'RAY EFFICIENCY
(CVERALL
P-'FCENT)
34.-34.-34.
32.-32.-32.
32.-34.-35.
22.-24.-27.
1976
1976
1981
19d2
10
9
4
2
100.
80.
60.
AFPPECARILITYRES{LTANT FACTORS
CcMiERCIALiZATI)N
DATE
(2C'".C .~,~E/YEAR}
I.AXMU.1 RATE OF
NSTALLATION
..
(10.C .1 E/EAR)
FRC3ABILITY OF SUCCESS
(P. _-NC
NT)
ES7t-.':ANTAL
AIR- NOX
10c.
RESULTA.T FACTORS
(ICNS/MWYzAR)
...
70.-88. -105.
AlR - S02
(TONS/MW-YEA
R)
52.8-66.-79.
116. - 145. - 174-
AIR - CO
(TC.S/MW-YEAR
AIR - PARTICULATES
{ONS/nW-YEAR)
5.-6.1-7.3
10.-12 -15.
AIR - TOTAL ORGANICMATTER
(T3SS/MW-YEAR)
WATER- SUSPENDEDSOLIDS
1.1-1.42-1.7
(TOSS/;MW-YEAR)
SCLID - ASH
(TONS/M.-YEAR)
SOLID - SLUDGE
ATTER
.
0.0
15.4-19.3-23.
.87-1.09-1.31
632-790.-948.
408.-510.-612.
0.0
43.2-54.-64.8
HEAT RATE ASSUMED
1C,lCO ETU/KEW
CONVENTIONALLOLLER LFICIENCY
34%
CONVENTIONAL COAL-FIRED BOILER COST = $465/K"
CCNVENTIONAL PCILLR C'SIiATIG
.....
3 .4-62.-
.
31. [0-51.-7 . 2
b. 6
70.' 2
.9-1.32-1.7
9.9-14.1-1.3
10.B-13.5-16:2
..
47.6-41.-62.4
·
42.-71.-9J.4.
12.7-15.9-19.
13.2-22.-31.U
0.0
.16-.26-.36
1070.-1530.-1989.
738.-i23b.-1722.
14.7-21.-27.3
-.
..
18.-30.-42.
..
CCST = $8. 2C/MWI1R
ALL COSTS IN 1975 DCLLARS. PLANTS TO BE BUILT STARTING IN 1985
DEFINITO1~ OF BUNDS
...
----
'--
--
VARLAI'LF A.,; 576 CIANCE OF' BEING LESS ITIAN UPA'Eh bOUND
VARIAL'LE HAS 50% CHANCE CY BEING LESS THAN MIDDLE VALUEi(:'XPUCTED VALUE)
VARl1AiPLE AS 95% CHANCE CF BEI:IG GREATES TAh LGdL-( BOUND
$UMBERS DERIVED FRC
EA
STUDY (Ii;HALL,CF!IN
AND KROPP;1974)
WITH ADITIONAL
ASSUMPTIONS
AND HYPOTHETICAL QUANI3?ICATICIS
OF DATA PESrN1ED SUBJECTIVELY.
TABI.- 7.2-1
MATRIX O? INITIAL PESULTANT FACTORS
(continued on following page)
-162-
117, 164.
1.3-1.88-2.6
12.-19.8-27.7
0.0
.8-1.02-1.2
(TONS/3W-YEA
)
.
.87-1.09-1.31
48.-61.-73.2
(TONS/MW-YEAR)
(TOS/'1W-Y`A.)
b.6
.3
3.9-4.9-5.8
16.-20.-24.
21.-27.-32.
WATER- DISCLVED SOLIDS
WATER - TOTAL GRGANIC
0S.-
9.8-14.-18.2
LIQUVFICATICN
LIMESTGN£
SCRUEBI4NG
ECCVOC"IC RSULTAHT
FACTORS
CAPITALIN'VESrE'NT NOR.ALIZED
(S10C )/ 1)
CPERArI:;GcOsr
(:/
fi
HI;li SULFiIE
Cn A L
SC li BI G
11G'1
5 N
510,-535.-61C.
525.-545.-690
R)
1J.-14.2-17.0
PEF.FORMA.CEPSULTANT FACTOPS
AV.'AILA3ILITY
(?p RCZ-NT)
ZNERSY FFICIENCY
(OVERALLPE3CSNT)
AFPICABrLIETY !:'STILTANT FACTORS
CCv_`_.ClA LZ.ATI0N DAT'.
(2CO .,E/Y.3R)
eAxr.ll
RATEOF INSTALLArION
(1000 ,-4/YEAY)
PPFCBABILITYO? SUCCESS
10.3-lC,-
51 .- 35.-6 10.
1".3-10.7-12.1
12.1
68.-b8.-68.
68.-68.-68.
o8. -68.-6t.
s3.-25.-27.
32.-32.-32.
32.-J2.-3.
1984
1976
1976
2
8
li
60.
95.
95.
44n. -4,.
7.5-; .2- 'I.
1 76
..
(P ERC E)
-53C.
. 1
I01.
ENVIRO!;.INTAL RFS[ILT.NT FACTORS
AI OX
(TCNSS/tW-YEA R)
[IR - S02
(T0NS/tW- YEAR)
48.-96.-1444.
'I{ .
-...
52.-86.-12C.
AIR - CO
5.-8.3-11.6
(TONS/NiW-YEARi
AR - PARTICULATES
(TO N$5/.- YEAR)
A R - TO-AL ORGANIC
.8-1.6-2.4
( CNS/.W-YEAR)
WATER - SSPNDSD SOLIDS
(TC,S/.W-YEAR)
TER - DSCLVEZDSOLIDS
(IONS/.'lW-YEAR)
45.5-91.-
WATFR - TTAL ORGANIC MATTER
(TO NS/W- YEAR)
SOLID - ASH
137.
14. 5-29.- 43.5
.68-1.
36-2.04
960.-1920.-288.
19.-38.-57.
19.-38.-57.
(T NS/!W-YEA R)
SOLID - SLUDGE
(TCNS/.M -YE R)
1IEAT RATE ASSUMED = 10,100
-5
37.6-4,7.-5i
(.J'.b
1. 4- 23.-Z7.
1.0-1.21-1.5
1.0-1.21-1.5
AS 95
CANCE
.36-1.07-1.28
14 .-230.-,'76.
.8-1. CO
-1.2
134.-230.-276.
2030.-2600.-3120.
OF BEING LESS THAN IPEiR
bCUJND
F 3EING LESS rHAN MI1DL VLUJ(£XP'CTEU
GREATEP THAN L:;R
TABLE 7.2-1
MATRIX OF INITIAL
-163-
RESU'L.AN:T
V,LUZ)
IO1JI)]
NUMBERSDERIVEDFROM PA STUDY {III;IALL,CIlIIN ANDKRlOPP;1974) WIril AITINIIlAL
AND HYPOTHETICALQANTIuIL.TIONS OF DATA Il'ESNTi:D SJlECTIVEIY.
.7d- .
,- 1. 11
813
4.- 1.Q).-1272
16.3 -21 .- 25.
18. 4-23.-27.
E/KWH
VARIABLE HAS 95% CHANCE CF BING
. JJ-1 . 1 -1 .3 J
12.7-1,.9-19.
13. 4-16.7-20.
DEFTNITICN OF ECUNDS :
VARIABLE HAS 50% CANCE
,,
8
CONVENTIONALCILENi FFICIFNCY = 3L
CONVENTICNiAL
CCAL-FIEIC ECILER COST = $465/KHi
CON'VENTIONALCIL1ih 0P-RATING COST = 8.20/mHHe
ALL CSTS IN 1975 DOLLARS. LANTSTO E BUILT TA:ITIN,; IN 19'15
VARIAPLE
-S1. I,I
.4
18.4-23.-27.6
43.2-54.-64.
511.
U.13-5.4-6.5
4.3-5.4-6.5
33.-55.-77.
ATTER
37.6-47.-56.14
AS;I.I'"lONI;
,ACTORS
(continued)
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The decision maker indicates he is indifferent to: commercialization
date, maximum rate of installation and water environmental factors.
Result on matrix:
a) Indifference factors are eliminated reducing the matrix to 6 x 11
(Table 7.2-3).
b) With the water factors eliminated, MgO scrubbing dominates limestone scrubbing; that is, they are the same in all aspects except
sludge for which MgO scrubbing is clearly superior. Limestone
scrubbing is eliminated. No further reductions can be made.
The new matrix is 5 x 11 (Table 7.2-4).
3)
LOW SULFUR
FLUIDIZED
PHYSICAL COAL
LO0 BTU
GASIF ICATIr-N
BED
· CC¶B $ST
IO
CLEANING
CO.AL
SULTANT FACTOFS
gCOOIC
CAEITAL I:VS;TtEN£ NORMALIZED
440..-465.-530.445.-470.-540. 470.-490.-650.
($1000/m.4)
CFER.TING COST
9.8-10.2-13.5
'$/MiHR}
7.8-8.2-9.5· 8.4-8.9-10.0
PIFCRMANC:E:PESULT.ANTFACTCRS
T
E(OERLYEFVC
.
(OVERALLPCENT}
APTLTC%1ILTY
-
r)
o-n..-
34.
3
23CY
32.-32·.-32.
3a.
-.
100.
PESULTANTFACTORS
E.NVIRONiEXTAL
AIR - SOX
70.-88.-105.
'
( TONS/.'tW-YEAR)
52.8-66.-79.
AIR - S2
... .
-
9.8-14.-
43. 2-54.-tb4.
(7.ONS/:W)0.0
-
-l6.2
9. ')- 14l. 1--18.3
32.-32.-32.
95.
60
" 0.2.'117.-164 -37.6-47.-56.4
1.3-1./8-2.6
12.-19.8-27.7
14.3-5.4-6.5
18.4-23.-27.6
8
14.7-21.
-27.3
Id.-3);-42.
18.4-23.-27.!6
D&FINITIO5.OF BCUNDS
VARIABLL HAS 95. CHANCE CF BING LESS THAN UPPER BOUND
VALUE)
VARIABLE HAS 50% CHACE CF 3EING L3S TH.N MIDDLE VALT1E(eX:)ZCEI:D
VARIABLE HAS 95% CHANC C BEING GREATEN THAN LOWER BOUND
AND KOPP; 1974) WITH ADDITIONAL ASIJIPTIONS
40USBERSDERIVED ROMEPA STUDY (III;1[ALL,CHIIN
CF DATA PSENTED SUBJECTIVELY.
AND YP3THETIC&LQUANiZ71CATIONS
4)
MATRIX AFTER ELIMINATIoN OF LIMESTONESCRUBBING
The decision maker enters weighting factors for pollution. He
decides to use expected values as a basis for comparison with
weighting factors of:
.7
Air - NOx
1.0
Air- S 2
.5
Air - CO
1.0
Air - Particulate
Air - TOM
.3
.6
Solid - Ash
.7
Solid - Sludge
To find the relative weighting of each technology Physical Coal
Cleaning is arbitrarily chosen as the basis. The new entry in the
matrix is given by:
where
-15
1070.-1530.' -1989.'738.-1230.-1722. 184.-230.-276.
-.-.
HLAT R:ATEASSUMED 1 C. 1 o0 eTU/KV
CONVENTIONAL POILES EFFICIENCY =X
CONVrNTIONAL COAL-FIRED eOILL3 COSr = $465/KW
COIVE};TIONAL OILER OPERATING COST = 8.20/MHR
ALL COSTS IN 1975 OLLARS. PLANTS TO BE BILT STARTIN3 IN 1985
TABLE 7.2-4
...
31.8-53.-74.2 " 46.4-58.-69.6 ''
.10.8-13.5-1b.2.1.0-11
0.0
4)8.-510.-G12.
..
32.-90.-98.
-
- . .
-1.7
9-1..12
~~~~~~~~~~_
(TCGS/AW-YEAR)
22.-24.-27.
43.4-62.-80.6
108.-13b.-163.
1i6.-145.-174.
.............
' {TC.IS/MW-YLAR)
AIR - CO
3.9-4.95.8
5.-6.1-7.3
(0N5/'W-YEA 1)
AIR - PARrIC'/LATES
lb->O.-2
-712'-5
1
...
YEAR)
(TO8S/MWAIR - OrAL ORGANIC MATTER
2-1.7.. 87-1.09-1.31
-1 42
FA.)..........i (TNS.0W-- 1.1--I.
(TONS/?MW-YEAR)
SOLID - ASH
SOLID - SLUDGE
80.
51C.-535. -61.
12.4-132.-15.8 1C.3-1C.7-12. I
32.-34.-35.
ES3lTANT ACTCRS
OF_.O!E.A
F........ . .
100.
(PEiCENr)
520.-555.-69 5.
hGC
SCJ isINj
Wi
aij = [1.0 + P!, - Xil
Pi
aij = new element for factor i, alternative j
-166-
= expected value for factor i, alternative j
Xi
= expected value for factor i, basis technology
= weight for factor i.
The new pollution index factor for each alternative is:
fj
= Tdij
The weighted pollution matrix is given in Table 7.2-5.
resultant matrix is given in Table 7.2-6.
LOWSULFUR
COAL
The new
PHYSICAL COALFLUIDIED BED LOW U
CLEANING
coUSrION
GASIFICATION SCRU.b:,G
WAIGHTED ENVIRONMENT L
PESULTANTFAC , TORS
.-
AIR - NOX
(TONS/,K`W-YEAR)
AIR - S02
0.47
(TONS/3W-YEAR)
AIR - CO
(IONS/nW-YEAR)
AIR - PARrICTJLATES
0
0.70
.
(ICNS/3;;-Y?AR)
.
.....
1.:1 .
.1.5.
G.4I1
O.5
0.
1.3..
1.00
1.30
1.01
0.85
.....................0.21
0.30
.. .O.4.
-3.12
.
0.27
0.60
1.40
0.70
5.04
4.80
.
(TON$/~-¥EAR)
C.78
C.94
.
0.87
.
AIR - TOTAL ORGANIC MATTER
SCLI1 - ASH
1.5
.
0.50
.0.38.
(TOS/NW-YEAR)
1.25
1.0
.93
..
.
.
0.16
-0.25
0.93
1.13
1.u1
1.10
5.91
1.44
6.1.2
-0.60
SOLID - SLUDGE
(TCNS/32-YEAR)
NEg
_,V!...IFST.AL
INDEX
_
TABLE 7.2-5 MAIRIX OF WE1GHTEC ENVIrONMENTALFACTORS
PHYSICAL COAL FLUIDIZED BD
CLEANING
CO1BUSTION
LOW SULFUR
COAL
LOW TU
GASIFICATION
-
GC
SC;SUBBING
ECONOMIC RESULTANT FACTORS
CAPITAL NVESTMENTORMALIZED
440.-465.-530. 445.-470.-540. 470-490.-650.
( 1000/mW)
CPEiA ING COST
(s/%J H.)
7.8-8.2-9.5
P.FOR.ANC.: SESULTANT FACTORS
.NEI'.Y
FFFICI-NCY
(OVERALL
ERC!NT)
3:.-34.-34.
8.4-8.9-10.0 9.8-10.2-13.5
32.-32.-32.
520.-555.-695.
51C.-535.-61J.
12-112.-15.8
10.3-10.7-12.1
32.-34.-35.
22.-24.-27.
32.-32.-32.
AFPLrCA:'ILTTY ESUlrA47 FACTORS
£EC2AhILtTY F SCCESS
(EiF
V EN rAL I}:DEX
100.
--
.
5.04
HEAT RATE ASSUED
= 1C.100
100.
.. 4.80
.
5.91
80.
1. ...
60.
6.02
95.
TU/KVH
CIN'JENTICNAL 130ILLU FFICIEhCY = 3%
BOILER COST = $465/KW
CONVZNTICNAL[OIL,< OPERATINGCOST = S8.2/MWHR
ALL COSTS IN 1975 OLLARS. PLANTS TO BE BUILT STARTIN3 IN 1985
CONVENTb0NAL CCAL-FIbED
DEFINITION
OF BCUNDS
VARIAPLE HAS 97
CHA!CE CF BEING LESS TAN UPPE BOUND
VARIABLE HAS 5% CHANCE CF BEING LESS TdAN MIDDLE VALUE(XPECTEDVALUE)
VARIAbLE HAS 95,%CIiAUCE OF BEING G-CTER
TiAN LOWEi BOUND
NUMPERSDERIVEDFROM EPA STUDY (III;Ai.L,C!IN
AND KOPP;1974)
WITH ADITIO1:AL ASSUMPTIONS
]Nr HYPOTRETICALQ.%-TLCATIONS CF DTA PSLuIEO
SUBJECTIVELY.
TABLE 7.2-6
5)
MAIRIX WITH
NVIRONI.'1NTAL RSULTANT
FACTORS RLACED
BY THE ENVIHRt:JF-NTAL i!UDZX
The decision maker eliminates Low Btu Gasification since it has the
lowest efficiency, lowest probability of success, and the lowest
(Low Btu Gasification is left out only because of'
environmental index.
personal choice because it is not strictly dominated by any other
technology.)
At this point, the decision maker indicates that all factors are
critical and the process stops. The final table of critical factors
-167-
is given in Table 7.2-7. This matrix of critical factors has the most
attractive alternative fuel/plant/control combinations as its columns
and has the vital differences between those combinations displayed in
its rows.
LOW SULFDR
COAL
C S 'C
1
PHYSICAL COAL
CLEANING
(5130/ni)
4tO.-4u5.-530.
C?3ATIN~G cosr
PE':IANCE
7.8-8.2-9.5
?SLTANT
445.-470.-540.
470.-490.-650.
8. 4 -8.9-10.0
9.8-10.2-13.5
34.-34.-34.
RF5SILA''
F! 2 .3LLI_.Y
(F EC ENT)
OF SCCESS
32.-32.-32.
32.-34.-35.
5.100.
5.014
100.
4. 80
5.91
32.-32.-32.
HE1l
RATE ASSUMED = 1C,lCO
o80.
95.
6.02
BTU/KWH
C''%:VNTiCNAL ECILER FFFLCIENCY= 34%
CONIESICNAL
CCOA-FITF.D
OIIER
COST
31465/KW
C3NVENTIONALECILER FLEAT.£NGCST = .8.20/tiWtHR
ALL COSTS IN 1975 LOLLAiS. :L!NTS TO B BUILT SARTIN;
DEFINITION CF BCUNDS
V';R1ADLE HAS 95 CANCE
VARI A,'L HiAS
i 50S CANCE
IN 1985
F iBEING LESS THAN UPPER OUND
CF EING LESS THAN MIDDLE VA LUE(EXPECTED
~~VAPIAL'LE
AS 95 CANCE CF
NUI.ERS DERIVEDFRC E?A STUDY (I'I;HALL,CHIN
AND HYPOH-TICAL
10.3-10.7-12.1
FACTORS
E!;VION.'1NTAL INDEX
.
510. -535.-610.
?ACTCRS
PiCENT)
F7-.:C.T/trY
·*~
S
iINGO
SC.; UJDI1NG
S LTAN? FCTORS.
CAPITAL INVFSTSNT NOtYIALIZ:D
(CVRALL
FLUIDIZED
E3D
COz,3 1ST10N
VALUE)
BEING GREATEI IHAN .LOWLRBCUND
AD KRCP;1974)
WITiHADDITIONALASSUMPTIONS
QUANf-FICATIONS OF DATA PREISENtIED SUBJECTIVELY.
TABLE7.2-7 TABLE OF CRITICAL FACTORS
-168-
7.3 Examples of Critical Factors
The previous section showed an example of the process of developing
a set of critical factors. In the general usage of such an ordering
mechanism the types of factors that will finally end up as critical factors
will depend largely upon the special interests of the person operating the
ordering mechanism. The ultimate size of the unreducible set of critical
factors will depend upon the number of original fuel/plant/control
options being studied and upon! the extent to which the decision maker
is inclined to make judgements on the relative importance of the items displayed. Although the names "ordering mechanism" and "critical factors"
are not explicitly used, the following examples from the literature
demonstrate the great variety of potential sets of critical factors.
Tables 7.3-1 and 7.3-2 originally developedby J. Gruhl in (III;
White; 1974) show a quantitative, comparative display of the performance
of several different technologies. Despite the fact that this is a rather
large set of information it is by the definitions in this project a set
of critical factors because, for the purposes of that report, there was
a decision not to make any further reductions of the dimensionality of
that matrix. It should be interesting to note that some descriptive informations
occupy some of the entries of that matrix, certainly a possibility in the
context of the proposed framework for this project, with its descriptive
technical factors.
Another rather large set of critical factors comes from (V; Argonne
National Lab; 1973) and is shown in Table 7.3-3. Whereas the previous
example showed a decided emphasis on the economic and applicability informations
this set of critical factors is strongly aimed at environmental implications.
A third example is given in Table 7.3-4 from (V; NASA; 1976). These
two sets of critical factors, although only two among many displayed in
that study, show that in both cases a great deal of reduction has
been performed to get from the set of all available information, that is to
say, the resultant factors, to this particular set of critical factors. This is
one of the few displays of probabilistic information, here in the form of
ranges, and shows that there is no reason why this uncertainty can not be
left and displayed in the final set of critical factors.
The two extreme cases in the utilization of an ordering-mechanism are:
1) the case where the decsion maker will not make any subjective decisions
and thus the matrix of resultant factors becomes the matrix of critical
factors, and
2) the case where the decision maker feeds enough subjective criteria
into the ordering mechanism to result in a set of critical factors
that is just a single number rating of each fuel/plant/control option.
An example of the latter type can be found in the EPA project (V; Hall, Choi
and Kropp; 1974). In this study each of the resultant factors has been
converted from a physical quantity to a subjective,
through 10, number by a
type of weighting process, see Figure 7.3-1' Then in one collective process all
of these ratings have been weighted together to from a single critical factor for
each of the technologies, see Table 7.3-5.
Regardless of the extent of ordering that is accomplished in any given
decision making session it is clearly advantageous to have a record of the
information as it was before there was any subjective handling, that is the
matrix of resultant factors, and the documentation of the step-by-step
ordering and elimination procedure that was performed.
-169-
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-172-
0
cJ) -H
System
-
-
Cost of
Overall
electricity,
ene rg.
i
mills/k W-hr efficiency,
percent
Advanced steam
Powerplant
effic ien cy,
pelrcent
30 - 38
34 - 40
34 - 40
31 - 37
33 - 39
19
31 - 37
20 - 22
42 - 43
23-33
21 - 37
34 - 48
34 - 49
3:8 - 43
15 - 34
3. - 38
26 - 34
36 - 4t5
.20 -. 34
35 - 38
20 - :33
50 - 79
35 - 41
35 - 41
40 - 61
34 - -11
:3. - 41
41 - 48
40 - 53
-41 - 57
Open-cycle gas turbine:
No bottoming
Organic bottoming
Combined cycle
15-
Closed-cycle gas turbine:
No bottoming
Orlganic bottoming
Steam bottoming
Supel'critical
CO9
Liq uid-metal Rankine
MIII)
Open-cycle
Closed-cycle
MAIII)
,16 - 73
26 - 46
,5 - . (;
Liq uid-metal
M11D
58 - 110
17 - 39
28 - 39
12 - 45
31 - 60
24-1- 3:-14
2.t- :;-
12 - 31
25 - 51
FIuel cells:
i
llat LIue
iteml)e
l
Iow tempera tl re
System
Descriptionl
O,- rall Construcefficiency tion time,
Advanced
Capital costs,
$/kWe
a
malnten.ncee
yr
A
Operation
and
Mid-1974 Actual
co.ts
costs,
mills/kW-hr estimate (mid-1981)
0.38
5
2.5
500
843
steam
B
Combined
cycle with
.40
4
2.5
350
580
C
Open-cycle
.50
7
3.8
650
1137
MHJD
tH11
D._
alnclude$ 6.5 pelrcelltescalation and 10 percent nterest duringcstruction.
Table 7.3-4 Twosets of critical
columns the other reduced
factors, one reduced to just three
to three
-173-
rows (V; NASA; 1976).
10
8
f.
O6
3
4
2
0
Efficiency,-%
Figure 7.3-1 An example of a very subjective type of weighting used
to reduce factors to sets of common ratings (V; Hall, Choi, and Kropp; 1974)
Normalized
Weighted
Energy Technology
Rating
Stack Gas Cleaning,
throwaway
52.2
Physical Cal
51.9
Cleaning
Stack Gas Cleaning,
by-product
51.3
Resid Desulfurization
49.4
High Pressure Fluidized--Bad,
coal
43.38
Che=ically Active Fluidized
red, oil
48.7
Cheamlcal Coal Cleaning
43.2
Coal Gasification, low Btu
35.2
Ccl Refining (liquefaction)
Coal Gasification, high Btu
33.1
33.0
Table 7.3-5 A set of critical factors that represents the ultimate
reduction, to a single piece of information, for each technology (V;
Hall, Choi, and Kropp; 1974)
-174-
The step beyond the development of the set of critical factors is the
display f the information contained in those factors. Obviously one display
technique is the tabular exhibition of the critical factors. Another often
used display technique involved the utilization of a tradeoff curve, see
Figure 7.3-2 and Figure 7.3-3 from (V; Beller; 1976). Probabilistic information
aev
,4^
r
315
0
0
4*
0I)
310
-J
O
0
I-
305
300
-
2
I
295
!
500
550
ENVIRONMENTAL
I
"600
650
700
INDEX
Figure 7.3-2 An example of the use of a tradeoff curve to display
the possible choices between two critical factors (V; Beller; 1976).
can be introduced into these tradeoff curves by showing the concentric set
of curves associated with different levels of certainty. In the case where
instead of a tradeoffcurve there is just a collection of several points that
represent the opportunity set, the probabilistic information can be displayed
in the form of deviations or ranges, see Figure 7.3-4, about those points.
Regional aggregation results can also be displayed in a number of different
ways. One method, obviously, involves the tabulation of the weighted results of
the aggregations of the various different single plants. Another display technique
that has often been used, see Figure 7.3-5, consists of shading various regions of
a map according to threshholds of one or more of the critical factors.
-175-
zen _
315
0a
m
II
(o
00
(.11
6
310
-J
0I305
5
300
4
I
295
117.5
115
120
RESOURCE
122.5
5
USE-IO
125
127.5
Btu
Figure 7.3-3 An example of a tradeoff curve display of the best four
systems from the standpoint of two critical factors (V; Beller; 1976).
/
*N
/
r-CCMIO
I
I
I
!\S.,/ ,t
II
r Alkaline
fuelcells
I
Phosphork
acidfuelcells,
.-
E 4
/
i
-~~~~~~
/-
-ollen carbonate
fuelcells
----
r Solid
electrolyte
.- ___J'_fuel
cells
OGTIorganic
I
8
Iv,
/'-,
%
°%
CGT,
O%C "
/0
r
6';,6
OCMID
/AVAHD
OGT-' " -
~.--...... ~%_c,,l,,/P °(,
;I
I
1
Combined
cycle
LMR
'4A
Advanced
steam
in
.10
tA
-
IB
.20
t
IB
I
Ovrall
ener4y
eficiency
Overall
energy
efficiency
IJ
.50
II
!
.60
Figure 7.3-4 The display of points on a tradeoff curve with the
incorporation of probabilistic information associated with those
points (V; NASA; 1976).
-176-
.c
4-4
,-l)
.
ctro
c)
,{
0
0
0Ov
C.)
CA
~4.U)o
0
c)
en
CO0
U)
~0
CH l
W
rA
H
*H X
O)
-U
·. 4r-.
uD
!
a)r-)
0I
4
-177-
8.
Conclusions and Future Research Needs
It is becoming increasingly clear that the choice among future energy
technologies will be a choice among different degrees and balances of economic
and health insults. In that type of comparative analysis, magnitudes are of'
paramount importance. Early estimates on these magnitudes can also be used
to prioritize the future research efforts among the infinite number of alternative tasks.
A physically significant simulation approach is suggested rather than
use of regression studies. Regression of previous effects to previous emissions
carries a number of assumptions that are difficult to reconcile with the decreasing
quality of sites, increasing densities of populations, and widely varying siting
alternatives (river versus offshore, for example). Also, extrapolations to
situations far different from existing conditions is difficult, especially for
very different energy and emissions mixes, new background pollutant levels, and
'new' pollutants. A physically significant approach can be initially attacked
and continually improved on all fronts; fuel-characterization, combustion modeling,
dispersion, aerochemistry, exposure patterns, and dose-response relations.
Finally, a probabilistic framework is suggested rather than a deterministic formulation. The key assumptions, models, and data all contain different degrees of uncertainty. It is important to follow these uncertainties forward
through the simulation to determine the quality of the analytic results, so that
intelligent comparativesdecisions on risks can be made. It is also important to
be able to follow the critical, but poor quality, results backward through the
simulation to identify and prioritize research to reduce the responsible uncertainties.
There is a considerable amount of work that is implied by the modularity
and the capabilities for parameterizations that are implied in the previously discussed methodology. This approach is, however, believed to pay very great dividends
in versatility and sensitivity studies.
A significant amount of work has already been begun as described in (V;
Argonne National Lab; 1976). Combustion and control technologies are being examined to search out the information that is necessary for the comparative assessment task. Additional research areas will, however, certainly arise, particularly
once new applications for this assessment mechanism are found. For example, in
studying the penetration of some technology into the utility industry it might
be necessary to arrive at new factors that more accurately describe some of the
intangibles in the decision process. As the horizon is pushed ahead on the period
to be assessed new technologies will have to be added. Complex national studies
of the commercialization potential of some technologies may require more automated
procedures for arriving at valid scenarios of Non-Technical Factors. Or as a final
example, the "electric utility perspective" may switch to cover sensitive health
issues, conservation of fuel resources, lowest risk of energy shortfalls, or any of
a number of other possible perspectives.
There are strong indications that we could be making better environmental
control choices and that we could be directing searches toward more desirable energy/
health balances. The discussions in this report have revolved around the possibility
for developing a methodology that might aid in deciding those choices and directions
with more assurance than is otherwise possible.
-178-
I.
9.0 References and Bibliography
The references are listed in this chapter in the sections designated by
the Roman numerals that appeared with their citations. More comprehensive
bibliographic information can be found in:
Gruhl, J., 19 7 6 a. "Components for modeling the public health impacts of
energy facilities - A bibliography,
MIT Energy Lab Report #MIT-EL
76-024wp, Cambridge MA, 34pp, October.
Gruhl, J., 1976b6. "Health/environmental consequences of energy conversion
alternatives - A bibliography," MIT Energy Lab Report #MIT-EL 76-020wp,
Cambridge MA, 30pp, August.
9. I
Fuels
Abernathy, R.F. and F.H. Gibson, 1963. "Rare Elements in Coal," US B of Mines
I.C. 8163, US Dept. of Interior.
Abernathy, R.F., et al., 1969. "Spectrochemical Analyses of Coal Ash for
Elements," Report of Investigations #7281, Bureau of Mines, July.
Trace
American Chemical Society, 1966. Coal Science, Advances in Chemistry Series,
Number 55, American Chemical Society.
Asbury, J.G. and K.W. Costello, 1976. "Price and Availability of Western Coal in
the Midwestern Market," Argonne Lab., ASME Ind. Power Conference,
Memphis, Tenn., 48PP, May 17-18.
Austin, L.G., 1974. "Mechanical and Communitive Properties of Coal," NSF and
OCR Conference, Materials Problems and Research Opportunity in Coal
Conversion, Ohio State University, Columbus, Ohio, PP49-86, April 16-18.
Berry, W.L., and A. Wallace, 1974. "Trace Elements in the Environment-Their
Role and Proximity as Related to Fossil Fuels - A Preliminary Study," NTIS,
Waslhington, D.C. 66 pp.
Bowen, H.J.M., 1966. Trace Metals in Biochemistry, Academic Press, New York,N.Y.
Brown, R.L., 1960. "Crushing and Grinding, A Bibiliography, " Chemical
Publishing Company, PP18-26.
ESSO Research and Engineering Co., 1973. "Potential Pollutants in Fossil
Fuels," for EPA, NTIS# PP 225 039, Springfield, VA.
Evans, I, and C.D. Pomeroy, 1966. Strength, Fracture and Workability of Coal,
Pergamon Press.
Given, P.H., 1974. "The Chemistry of Coals as it may Relate to the Materials
Technology of Conversion Processes," NSF and OCR Conference, Materials
and Research Opportunities in Coal Conversion, Ohio State University,
Columbus, Ohio, PP87-107, April 16-18.
Hall, H.J., G.M. Varga and E.M. Magee, 1974. "Trace Elements and Potential
Pollutant Effects in Fossil Fuels, " EPA-650/2-74-118, PP35-48, May.
-X79-
I.
Desulfurization of CoalHoffman, L. and K.E. Yeager, 1971. "The Physical
Major Considerations for S02 Emissions Control," Mitre Corp. #MTR-4151.
Hoffman, L., F.J. Lysy, J.P. Morris and K.E. Yeager, 1972. "Survey of Coal
Availabilities by Sulfur Content," Mitre Corp., MTR-6086, McLean Va.,
123PP, May.
Leonard J.W. and D.R. Mitchell, 1968. "Coal Preparation," American Institute of
Mining, Metal., and Petroleum Engineering Inc., N.Y.
Lethi, M.T., J. Elliott, D. Ellis, and E.P. Krajeski, 1975. "Analysis of Steam
Coal Sales and Purchases," Mitre Corp., MTR-6878, McLean Va., 119PP , April.
Locklin, D.W., etal., 1974. "Power Plant Utilization of Coal," Battelle
Energy Program Report, Battelle Columbus Laboratories, Columbus, Ohio.
McNeal, W.H.,(Manager) and G.F. Nielsen (Ed), 1976. 1976 Keystone Coal Industry
Manual, McGraw-Hill Inc., New York, N.Y. 1092PP.
National Coal Association, 1971. "Keystone Coal Manual, 1971
Association, Washington, D.C.
Nephew, E.A., 1973. "The Callenge
Review, PP21-29, December.
National Coal
and Promise of Coal," Technology
O'Gorman, J.V. and P.L. Walker, 1971. "Studies on Mineral Matter and Trace
Elements in North American Coals," R&D Report 61, Int. Rep, 2,
Office of Coal Research, Us Dept of the Interior, Washington D.C.
Ouellette, R.P., 1972. "Coal-The Black Magic," Mitre Corp., Report #M72-170,
Mclean Va., September.
National Coal Association, 1973. Keystone Coal Manual, 1973, National Coal.
Peck, W.G. (ED), 1974. Survey of Energy Resources 1974, World Energy Conference
Survey of Energy Resources, U.S. National Committee of the World Energy
Conf., New, York, N.Y.
Peng, S.S., 1974. "Fracture Characteristics of Coal by Various Coal Mining
Methods-- A Review," NSF and OCR Conference, Materials Problems and
Research Opportunities in Coal Conversion, Ohio State University,
Columbus, Ohio, PP295-299, April 16-18.
Rosenberg. L.,L. Hoffman, F. Lysy, R. Ouellette, S. Stryker, and V. Wenk,
1972."Availability and Requirements of Stationary-Source Fossil Fuels1975 and 1977, " Mitre Corp., MTR-6221, McLean Va., 106PP, August.
Ruch, R.R., H.J. Gluskoter, and N.F. Shimp, 1974 . "Distribution of
Trace Elements in Coal," EPA-650/2-74-118,PP49-54, May.
Ruch, R.R,, H.J. Gluskoter and N.F. Shimp. 1974. "Occurrence and Distribution
of Potentially Volatile Trace Elements in Coal," Environmental
Geology Notes, Ill. State Geological Survey, No. 72, 96PP, August.
US
Department of the Interior, 1971. "United States Coal Resources
and Production, Bureau of Mines, NTIS#PP-202 166, Springfield Va., June.
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9. II
Fuel Treatment and Conversion
Argonne National Lab, 1976. "Balanced Program Plan: Analysis for Biomedical
and Environmental Research, Volume 3, Coal Extraction, Processing, and
Combustion." ERDA-116,Argonne, Ill., 74PP, April.
Attare, A., 1973 . "Fate of Trace Constituents of Coal During GAsification."
EPA-650/2-73-0043 August.
Bach, G., S. Nowak, R.A. Kalinenko, K.P. Lavrovski, L.V. Shevelikove,
M.G. Belostotskii and E.A. Feigin, 1975. "Kinetics and Mechanism of
the Pyrolysis of Hydrocarbons- Mathematical Model of the Pyrolysis
Process." Z. Chem.,15:5, PP165-171.
Beychok, M.R., 1975. "Environmental Factors in Producing Supplementary Fuels,"
Hydrocarbon Process, 54:10, PP78-81, October.
Deubrock, A.W. and P,S. Jacobson, 1974. "Coal Cleaning- State-of-the-Art,"
Proceedings Coal Utilization Symposium - Focus on S02 Emission Control,
National Coal Association, Louisville Ky., P3, October.
Hoffman, L. and K.E. Yeager, 1971. "The Physical Desulfurization of Coal Major Considerations for SOX Emissions Control,"Mitre Corp, #MTR-4151.
Hottel, H.C. and J.B. Howard, 1971. New Energy Technology- Some Facts
Assessments, MIT Press, Cambridge, Mass.
and
Katz, D.L., et al., 1974. "Evaluation of Coal Conversion Processes to
Provide Clean Fuels," EPRI Report 206-0-0, Parts I-III, Palo Alto, Ca.,
February.
Kilgroe, J.D., 1976. "Physical and Chemical Coal Cleaning for Pollution
Control," EPA, Research Triangle Park, N.C.
Leonard,J.W. and D.R. Mitchell, 1968. Coal Preparation, 3rd Edition,
American Institute of Mining, Metallurgical and Petroleum Engineers,
New York, N.Y.
Locklin, D.W., etal,. 1974. "Liquefaction and Chemical Refining of Coal,"
Battelle Energy Program Report, Battelle Columbus Laboratories,
Columbus, Ohio, July.
Lowell, P.S. and K. Schwitzgebel, 1974. "Potential Byproducts Formed from
Minor and Trace Components in Coal Liquefaction Processes," EPA-650/2-74-118
PP331-339,
May.
Matoney, J.P., 1975. "Coal Preparation," Annual Coal Review, Mining Engineering,
27:2, P65.
Matthews, C.W., 1972. "Coal Gasification for Electric Power." Proceedings
American Power Conference, April.
Mudge, L.K., et al., 1972. "The Gasification of Coal," Battelle Energy Program
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Robson, F.C., 1972. "Clean Power form Gas Turbine-Based
Combustion, July.
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Rubin, E.S. and F.C. McMichael, 1975. "Impact of Regulations on Coal Conversion
Plants, " Environmental Science and Technology, 9:2, PP112-117.
Stambaugh, E.P., et al, 1975. "Hydrothermal Process Produces Clean Fuel,"
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US Environmental Protection Agency, 1975. "Background Information for
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Standard," Government Report Announcements, 75:3, PP114, February 7.
US Environmental Protection Agency, 1974. "Proc. of the EPA Symposium on
Environmental Aspects of Fuel Conversion Technology," Held St. Louis,
Missouri, May 13-16, USGPO, Washington, D.C.
Walker, P.L., 1974. "Physical Chemistry of Coal Conversion," NSF and OCR
Conference, Materials Problems and Research Opportunities in Coal
Conversion, Ohio State University, Columbus, Ohio. PP109-125, April 16-18.
White,J.W. and M. Larsen (EDS), 1973. "Symposium Papers - Clean Fuels from
Coal," Institute of Gas Technology, Chicago, Ill., September.
Yavorsky, P.M. and S. Akhtar, 1974. "Environmental Aspects of Coal
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Combustion Technologies
III.
Anderson, D., 1972. "Models for determining least-cost investment in electricity
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Anderson, R.T., 1970.
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Argonne National Laboratory, 1976. "Balanced Program Plan: Analysis for Biomedical and Environmental Research, Volume 3, Coal Extraction, Processing,
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Booth, R.R., 1972. "Power system simulation model based on probability analysis,"
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Edison Electric Institute, 1975.
EEl publication, New York.
"Statistics of the Electric Power Industry,"
Ehrlich, S., 1970. "Air Pollution Control Through New Combustion Processes,"
Environmental Science and Technology, 4, pp. 396-400, May.
Electrical World, 1970. "Cut Pollution at What Price," Electrical World,172,
PP32-33, January 19.
Federal Power Commission, 1972 and 1976.
D.C., USGPO.
"National Power Survey,"
FPC, Washington
Finger, S., 1975. "Modeling Conventional and Pumped Hydro-electric Energy
Using Booth-Baleriaux Probabilistic Simulation," MIT Energy Lab Report
MIT-EL 75-009TP, August.
Fuller, R.L.C., et al., 1972. "ORCOST--A Computer Code for Summary Capital
Cost Estimates of Steam-Electric Power Plants," Oak Ridge National Laboratory, ORNL-TM-3743, September.
Gruhl, J., 1974. "Generator Maintenance and Production Scheduling to Optimize
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74-002P, January.
Gruhl, J., 1974a. "Large Systems Modeling for Coordinating Atmospheric Processes and Electric Power Operation," Proceedings 12th Annual Allerton
Conference
on Circuit
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Gruhl, J., 1973&. "Electric Power Unit Commitment Scheduling Using a Dynamically
Evolving Mixed Integer Program," MIT Energy Lab, NTrISNo. PB-224 006,
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Gruhl, J., 1972a. "Electric Generation Production Scheduling Using a Quasi
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Hottel, H.C. and J.B. Howard, 1971. New Enerfy Technology--Some Facts andAssessments, MIT Press, Cambridge, MIa.
Matthews, C.W., 1972. "Coal Gasification for Electric Power," Proceedings
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Montague, P.J., et al., 1972.
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Emission Controls
IV.
Agarwal, J.C., R.A. Gilberti, P.F. rminger, L.J. Petrovic and S.S. Sareen,
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Axtmann,
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Battelle Memorial Lab, 1976. "Environmental Assessment/Systems Analysis and
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Baughman, M. et al., 1975. "Regional electric supply model - the Baughman-Joskow
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Benedict, B.A. and L.R. Jones, 1973. "Review of Heated Surface Discharge
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Bolton, N.E., R.I. Van Hook, W. Fulkerson, W.S. Lyon, A.W. Andren, J.A. Carter,
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