EPA-R2-73-161
MARCH 1973 Environmental Protection Technology Series
Analysis of Engineering Alternatives
for Environmental Protection
from Thermal Discharges
^°ST^
Office of Research and Monitoring
U.S. Environmental Protection Agency
Washington, DC. 20460
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and
Monitoring, Environmental Protection Agency, have
been grouped into five series. These five broad
categories were established to facilitate further
development and application of environmental
technology. Elimination of traditional grouping
was consciously planned to foster technology
transfer and a maximum interface in related
fields. The five series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
U. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the ENVIRONMENTAL
PROTECTION TECHNOLOGY series. This series
describes research performed to develop and
demonstrate instrumentation, equipment and
methodology to repair or prevent environmental
degradation from point and non-point sources of
pollution. This work provides the new or improved
technology required for the control and treatment
of pollution sources to meet environmental quality
standards.
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EPA-R2-73-161
March 1973
ANALYSIS OF ENGINEERING ALTERNATIVES
FOR ENVIRONMENTAL PROTECTION FROM
THERMAL DISCHARGES
By
State of Washington Water Research Center
University of Washington/Washington State University
Pullman, Washington 99163
Project 16130 FLM
Project Officer
Dr. Bruce Tichenor
Environmental Protection Agency
National Environmental Research Center
Corvallis, Oregon 97330
Prepared for
OFFICE OF RESEARCH AND MONITORING
U.S. ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
For sale by the Superintendent of Documents, C.S. Government Printing Office, Washington, D.C. 20402
Price $2.60 domestic postpaid or $2.25 OPO Bookstore
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EPA Review Notice
This report has been reviewed by the EPA, and
approved for publication. Approval does not
signify that the contents necessarily reflect
the views and policies of the Environmental
Protection Agency, nor does mention of trade
names or commercial products constitute
endorsement or recommendation for use.
ii
BXVIHOMMEHTAL PROTECTION AGEWCY
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ABSTRACT
This report summarizes a two year effort to develop an analytical framework
to evaluate current and proposed engineering practices used in the protec-
tion of the environment from the impact of thermal power systems. The
engineering practices modeled included the heat source/generating system,
the electrical power transmission system, the water intake system, the
cooling system and the chemical control of the water systems. An
ecological accounting system was developed to provide a non-monetary
measure of biological response to the engineering alternatives. Efforts
to formulate a socio-economic accounting system to internalize externali-
ties created by these engineering practices were unsuccessful. A computer
model was developed employing a decision tree format to evaluate the
environmental and economic impacts of these engineering alternatives for
any given site. Methodologies available to assess the impact of a series
of power plant developments in a region were evaluated and the Forrester
type (1971) feedback simulation of a few variables was found to be best
suited for dynamic assessment problems. Both static and dynamic analytical
frameworks have been verified to be computationally correct, but an
extensive demonstration program is required to prove their applicability.
iii
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TABLE OF CONTENTS
Page
I. Conclusion 1
II. Recommendations 3
III. Introduction 5
IV. Single Plant Assessment 9
Literature Review 9
History and Problems of Thermal Power Plant Siting 9
Existing Modeling Efforts for Assessment of the Impact
of Thermal Power Plants 12
Previous Regional Siting Studies 21
Decision Trees as an Analytical Framework 25
Task 1: Baseline Definition 27
Task 2: Accounting Systems 32
Socio-Economic Accounting 32
Benefit-Cost Analysis 32
Peak Power Pricing to Reduce Demand 35
Methods to Include Externalities 40
Social Accounting 47
Ecological Accounting 51
Freshwater Fish 52
Freshwater Invertebrates 60
Freshwater Algae 63
Marine Fish and Invertebrates 64
Chemical Impact 67
Task 3: Engineering Alternatives 74
Screening of Intakes 74
Chemical Discharges from Thermal Power Plants 78
Cooling Systems 90
Control of Non-Water Impacts 94
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Computer Model 97
Calculational Routines 97
Input Data 104
V. Dynamic Assessment 113
Models for Dynamic Assessment 114
VI. Evaluation 123
VII. Acknowledgement 125
VIII. References 126
IX. Glossary 135
X. Appendices 141
vi
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FIGURES
Page
1. Management of Project Tasks and Outputs 8
2. Equation Set and Key for Marks-Borenstein Model 15
3. Equation Set and Key for Capacitated Plant Model 16
4. Basic Components in Fields' Decision Model 18
5. Graphical Definition of Admissible Solutions for
Fields' Model 20
6. Number of Species and Mean Lethal Limits for Freshwater
Fishes and Invertebrates 62
7. Water and Chemical Balances for Thermal Power Plants 79
8. Power Plant Water System 82
9. Water Discharge as a Function of Cooling Water Chemical
Concentration 85
10. Sample Data Set for Static Siting Model 105
11. Static Siting Model Decision Tree 107
12. Linear Programming Formulation 116
13. Interaction Matrix within a Region 118
14. Flow Diagram Based on Interaction Matrix 118
15. Typical Cost Function for Dynamic Model 120
vii
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TABLES
Page
1. Summary of ark 6
2. Nuclear Power Plant Cooling Requirements and River Flows 11
3. Power Plant Sites Discussed by Levin jajt al_, (1972) 13
4. Baseline Plant Characteristics 28
5. Predicted Effect of Increasing Water Temperature on the
Fish Community of the Columbia River 54
6. Predicted Effect of Increasing Water Temperature on the
Fish Community of the Sacramento River 55
7. Predicted Effect of Increasing Water Temperature on the
Fish Community of the Upper Mississippi River 56
8. Predicted Effect of Increasing Water Temperature on the
Fish Community of the Lower Mississippi River 57
9. Predicted Effect of increasing Water Temperature on the
Fish Community of the Tennessee River 58
10. Predicted Effect of Increasing Water Temperature on the
Fish Community of the Delaware River 59
11. Thermal Tolerance of Various Groups of Marine Fishes and
Invertebrates 66
12. Summary of Chemicals Used in Cooling System Treatment 68
13. Disposal Characteristics and Treatment Requirements
of Cooling Tower Chemicals 88
14. Major Cooling System Types and Relevant Environmental
Design Parameters 91
15. Evaporative Cooling System Discharges to Air and Water 93
16. Transmission Line Parameters . 95
17. Screening Cost Factors as a Function of Flow Rate 101
viii
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SECTION I
CONCLUSIONS
1. This research has identified a need for a systematic examination of
the environmental impact of thermal power plants. Impact evaluations
tend to be fragmented, lack systematic or holistic perspective, and are
difficult to interrelate.
2. The results of this study include a model format for preliminary
planning and evaluation of the environmental impact of thermal power
plants that is systematic, holistic and consistent.
3. To augment economic evaluations in this model an ecological accounting
system can be formulated based on the response of fish communities to
environmental impact.
4. The engineering alternatives to protect fishes from fatalities in
power plant intake structures are not as sensitive to cost as to proper
design. A high cost system incorrectly designed may create more damage
than a lower cost unit, correctly designed. Salt water intake protection
systems may require new technological developments as these waters are
abundant with eggs and larvae of valuable fishes that can penetrate
existing screens. Alternatively, the use of recirculating cooling
facilities will reduce the demand for make-up water and the impact of
intake structures.
5. The decision tree format provides a systematic framework to evaluate
engineering alternatives and their environmental impact. This procedure
has a disadvantage that feedback and iterative decision making is deleted
but it offers consistency and simplicity of analysis of a complex situa-
tion.
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6. Dynamic assessment of the second and third order impacts of a thermal
site decision can best be made by using feedback-loop structure models
with a minimum number of parameters. These models can be constructed,
modified, and applied inexpensively, yet they provide much insight con-
cerning dynamic environmental impacts.
7. The major problem encountered in developing a format for environmental
impact assessment of thermal power plants is the fragmented studies that
focus in great detail on fairly restricted problems. There is insufficient
information to create a detailed description of the overall problem, thus
the only alternative is to develop less detailed general models that
emphasize interactions among processes rather than a simple process.
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SECTION II
RECOMMENDATIONS
1. As originally proposed, this study was one of two parts. The
complementary study to verify and test these models in practical
applications must be conducted before the models can be evaluated.
2. Planning and evaluation is not a single action but a continual
process that responds to changing technical advances, demographic and
economic conditions, and social values. There is a lack of methodology
available to conduct continuous assessment at a reasonable cost. The
results of this study are only a start in the development of evaluation
techniques that are responsive to changing conditions. Further develop-
ment of these techniques is required if environmental impacts of thermal
power plants are to be properly evaluated.
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SECTION III
INTRODUCTION
Man's attempts to engineer the environment have been myopic with each new
solution creating a problem of equal or larger magnitude. In an attempt
to increase the vision used in planning new thermal power generating
facilities, the State of Washington Water Research Center in 1970 proposed
a nine task program to analyze the institutional and information con-
straints that create the myopic vision encountered in power plant siting,
to formulate a methodology to clearly assess the engineering alternatives,
and to verify the methodology that was developed. • The nine tasks
originally proposed are listed in Table 1.
Only tasks 1, 2, 3 and 7 were funded and the level and duration of funding
required that graduate students rather than faculty conduct the study.
A graduate student and faculty advisor were assigned to each task and
theses or reports were usually produced as a by-product of the research:
Bush _et al. (1972), Geitner (1972), Meyer (1972), Porter (1972), Saad
(1971). This project report summarizes and integrates the results of
these studies.
The specific goals of the funded tasks were:
1. A computerized model that defines the thermal discharges, the chemical
discharges, the resulting physical environmental impacts, and the costs
for a baseline 1000 MWe power system.
2. The development of economic, social and ecological accounting systems
for use in the evaluation of the effectiveness of engineering alternatives
for the protection of the environment from thermal power system discharges.
3. The collection of data and formulation of a model that describes the
cost, performance and environmental impact of engineering alternatives
used in the protection of the environment from thermal power systems.
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Table 1. Summary of Proposed Work
Task
Purpose
Procedure
Baseline Definition
1. Document existing once
through thermal power plant
environmental impact
Provide economic, eco-
logical and social
baseline for study
Use FWPCA, BPA,
industrial data
plus literature
and
Environmental Accounting
2. a. Develop ecological
environmental account-
ing system and define
ecological response
Many changes cannot be
valued in dollars; must
use some quantitative
measure
Survey literature,
current research, and
expert opinion, and
then develop
b. Develop social
accounting system and
social response
Relate economic and
environment change to
social value system
Economic-social
research study
Alternative Engineering Solutions
3. Define current alter-
natives for control of
environmental changes from
thermal power plants
Provide models for
evaluation
Survey of FWPCA, BPA,
EEI, etc. studies
Environmental Analysis
4. Examine environmental
effectiveness of alterna-
tives for 4 selected cases
To define subsequent
changes necessary to
protect environment
Employ output of
Task 2a and computer
system analysis, etc.
Social Analysis
5. Examine social effec-
tiveness of alternatives
for 4 selected cases
To define subsequent
changes necessary to
protect environment
A team of socio-
economic researchers
will conduct the
analysis using
computer analysis
Engineering Analysis
6. Evaluate economics of
primary plus secondary ac-
tions necessary to achieve
environmental quality
Determine effectiveness
of total required
alternatives
A team of ecology and
water quality experts
will evaluate the en-
vironmental response
Dynamic Analysis
7. Examine dynamic impli-
cations of multiple-plant
system on environment
Determine if single
plant criteria is
optimum
Use models in DYNAMO
analyzing gaming
studies
Critique
8. Analyze results of
analysis and recommend
management alternatives
Discuss results
The interdisciplinary
team will focus on
this task
Report
9. Prepare final report
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4. The development of a dynamic or sequential analytical framework to
assess the environmental impact of a series of thermal power plants in
a region.
A decision tree was employed as a focal point for integration of the
task outputs, and as the format for the final static siting computer
model. This format is schematically presented in Figure 1.
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Figure 1. Management of Project Tasks and Outputs
Branch
Decisions
Define
Power
Needs
Define
Chemical
System
Define Environ-
mental , Economic
& Social Impact
oo
Schematic
of Decision
Tree
Task
-(4) Dynamic -
Model
.(1) Baseline^. (2) Engineering
Model -^ Alternatives for-
Environmental
Protection
(3) Develop
Economic,
•*— Social and—»-
Environmental
. Accounts
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SECTION IV
SINGLE PLANT ASSESSMENT
LITERATURE REVIEW
History and Problems of Thermal Power Plant Siting
Geitner (1972), as part of this research effort, reviewed an extensive
list of literature relevant to thermal plant siting problems, engineering
alternatives and environmental impact of thermal power plants. The
results are presented and analyzed in this section. Thermal power plant
siting has progressed largely on an ad hoc basis throughout most of the
history of the electric power industry. Traditional trade-offs among
prospectively sites for fossil-fueled power plants have been between fuel
costs and transmission costs. Little attention was paid to the external-
ities of thermal power generation with the exception of the soot problems
experienced with early plants which, combined with limited land availa-
bility and high land cost, forced the thermal plants outside the cities.
Externalities were not perceived in the early years of power development
to be a problem principally due to their small magnitude and very localized
impact. The number and density of plants was small. The size of generat-
ing stations was also small.
As the magnitude of electric power production grew, the number and density
of thermal generating plants increased. Although the accompanying
environmental impact, which had been small and localized, increased
noticeably, it still was not a factor considered in siting decisions.
Gilleland (1969) presents a list of siting parameters in his article
concerning Tennessee Valley Authority (TVA) siting experience. The
parameters discussed were:
1. Load Center
2. Fuel Transportation
3. Transmission Lines
4. Aesthetics
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5. Exclusion Area
6. Meteorology, Hydrology, Geology and Seismology
7. Cooling Water Supply
8. Access
Noticeably absent from this list of traditional engineering and economic
parameters is direct mention of environmental impact. Only the categories
of aesthetics and exclusion imply consideration of some measure of
environmental impact. Gilleland included in his discussion a statement
that a properly chosen site should not necessitate the use of cooling
towers. Seemingly, utilities continued to view the cooling tower as an
engineering solution to the scarce water problem rather than an alterna-
tive solution to prevent adverse environmental impact from thermal dis-
charges to native receiving water bodies.
Thermal discharges to receiving waters increased despite improvements in
plant efficiencies. Older, less efficient plants became a smaller per-
centage of the generating capacity, and added, efficient, new plants
soon over burdened receiving waters. The introduction of water-cooled
nuclear central generating stations actually placed a larger burden of
heat rejection per unit electrical output than an electrically equivalent
modern fossil-fueled unit. These facts and other basic thermally
related problems were discussed in the "Industrial Waste Guide on Thermal
Pollution" by the Federal Water Pollution Control Administration (FWPCA,
1968). Larger generating plants began to remove sizeable portions of the
rivers for condenser cooling water and the subsequent increases in river
temperature caused concern over the health of the resident aquatic com-
munity. The Division of Reactor Development and Technology, U. S. Atomic
Energy Commission (AEC), 1971, published a summary document concerning
thermal effects and nuclear power stations. Table II of the AEC report
is summarized in Table 2 to show the impact of increasing station size
on the water requirements and the associated relationship to the adjacent
river. While the table is not inclusive, it does demonstrate that certain
sited plants have the potential to remove sizeable quantities of the
10
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Table 2.
Nuclear Power Plant Cooling Requirements and River Flows
Plant
Dresden Station
Monticello
Quad Cities
Cooper
Fort Calhoun
Location
Illinois River, IL
Mississippi River, MN
Mississippi River, IL
Missouri River, NB
Missouri River, NB
Cooling Water
Requirements
(cfs)
2,660a
623b
2,100
1,450C
702
River Flows (cfs)
Low Flow
2,694
220
12,096
4,320
3,000 to
5,000
Average Flow
8,000
3,200
50,000
20,000
25,000
Ratio of
Cooling Water
Required to
Low Flow (%)
99
283
17
34
23-14
Key:
a
Ref: Table II, AEG (1971)
430 cfs once-through for unit 1. 2230 cfs variable-cycle flow-through cooling pond for
units 2 and 3
To use variable cycle, mechanical-draft cooling tower system
To use cooling pond
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adjacent natural flowing river. The table shows however, that some of
the plants use once through or partially recirculating cycle condenser
cooling devices to reduce the amount of water withdrawn from the river.
Table 3 presents results of studies by Levin et^ al. (1972) at nine thermal
power plants operating in various environments. Of the nine sites only
one, Turkey Point, Florida, has experienced large biological impact on
the receiving water. It should be noted that some of the plants discussed
are small and are located on relatively large water bodies. While the
results of their survey of the aquatic impact of thermal power plants is
not complete, it does elucidate the point that properly sited thermal
power plants may cause minimal environmental impact. The difficult
questions remaining are those concerning determination of sublethal
effects and determination of criteria for defining a properly placed
plant. Or, as an opposite accomplishment, what information is necessary
to fairly precisely define the places where it is environmentally unwise
to construct a large thermal power plant?
Existing Modeling Efforts for Assessment of the Impact of Thermal Power
Plants
Three comprehensive modeling efforts were found to exist in the literature.
Two of these efforts involved application of linear programming techniques,
Mark and Borenstein (1970) and Millham (1971). The third method by Fields
(1971) involves use of graph theory methods. A good description of linear
programming and its applications can be found in Daellenbach and Bell
(1970) or Hillier and Liebermann (1967). Graph theory and its applications
are discussed in Berge (1962) and Busacker and Saaty (1969).
Marks and Borenstein (1969) used a specialized form of linear programming
to analyze thermal power plant siting. This model set out to find:
1. Optimum number of generating sites
2. Optimum size of generating plants
12
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Table 3. Biological Survey Sites Discussed by Levin, et al. (1972)
Site Name
Hanford, WA
Chalk Point, MD
Contra Costa, CA
Morro Bay, CA
Humboldt Bay, CA
Connecticut
Yankee , CT
Turkey Point, FL
Petersburg, IN
Martins Creek, PA
Size
(MW )
various
2 x 335
1298
1030
172
562
2 x 432
220
not
given
Water Body
Columbia River
Patuxeut River'
Estuary
San Joaquin River
Pacific Ocean
Humboldt Bay
Connecticut River
Biscayne Bay
White River
Delaware River
Noticeable
Detrimental
Effects
none
no major changes
no major changes
none
none
no major changes, not
enough information
about sublethal effects
yet
1. 125 acre area + 4°C
kill area
2. 170 acre area + 3°C
algal growth dis-
turbed; diversity
and abundance shifts
only fish avoidance of
93°F plume water
none
13
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3. Optimum location of generating plants
The objective function for this work was minimization of total cost
(defined as capital costs plus lifetime operating costs). The con-
straints placed on the solution were:
1. Predicted power demands of the region must be satisfied.
2. Temperature standards for receiving waters must not be exceeded.
The model equations and explanations appear in Figure 2.
The formulated problem was solved for the integer case only. The performed
solution was not a temporal one. It is similar to the basic transportation
problem formulation of linear programming with an added temperature
constraint and imposition of integer solution variables. It addresses
the question of optimum plant size with respect to a temperature con-
straint only. It is a good beginning effort that is bounded by: (1) its
static nature, (2) its treatment of only a limited size system (no allow-
ance for outside trading of power), and (3) solution of only a hypothetical
test case.
Another linear programming model was developed by C. B. Millham (1971) as
part of an environmental Research Center, Washington State University
(1971) research proposal to the National Science Foundation. It is a
formulation very similar, but not identical to, a time base resolution
form of the transportation problem form discussed in Hillier and
Liebermann (Ch. 6, 1967). Its mathematical formulation is shown in
Figure 3.
This formulation is called the Capacitated Plant Model. It seeks to
minimize the cost of supplying power. The formulation divides the
objective function as shown into three parts:
1. Capital Costs
2. Transmission/Peak Costs
3. Operating/Base Energy Costs.
14
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Objective Function:
Minimization of total costs
Z[X.F. + ZY..L.(P. + T. . + D.)]
..i1 j 13 33 13 3
Constraint Equations:
Satisfaction of region load
ZY..L. < X.M.
j 13 3 11
Receiving water excess temperature limit
ZA., . R.[ZY..L.] < C.
± ik i ij j k
Where:
F. = Summation of intercept capital costs at site i ($/wk)
X^ = Integer plant selection coefficient (0,1) and Z 51
S = m set of plant alternatives
m
Y.. = Load assignment coefficient (0,1) for all i, j
L. = jth load (MW)
P. = Load dependent production cost rate ($/MW-wk)
J
T.. = Total transmission cost ($/MW-wk)
D. = Distribution cost rate ($/MW-wk)
M. = Maximum generating capacity for site
A., = Temperature transfer coefficient
R. = Temperature rise at site i
C, = Permitted excess temperature at location k
K.
i = site index
j = load index
k = temperature monitor location index
Figure 2. Equation Set and Key for Marks-Borenstein Model
15
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Objective Function:
Minimization of total cost
m m n m n
I f Y. + Z Z C..X. . + Z Z C, .Y..
1-1 ± ± i-1 J-l 1J lj i-1 J-1 lj lj
Constraint Equations:
Total generation is bounded by (peak capacity) x (load factor)
n
Z X < K • F • 8760
j-1 J
Summation of peak demands is less than or equal to source capacity
Summation of energy sources equals or exceeds load center energy
demand
m
Z X > e
Summation of individual peak demands equals or exceeds peak demand
value
m
Z Y. . > d .
1-1 lj J
Where:
f = Plant i amortized construction cost ($/kW-yr)
C. = Plant i operating cost ($/k.W-yr)
Y. = Plant i allocation coefficient, constrained to 0 or 1
X = Energy transfer (non-peak) plant i to load j (kW-yr)
Y = Peak demand transfer plant i to load j (kW-yr)
K. = Plant i peak load capacity (kW-yr)
F± = Plant i load factor (%)
6 = Load j energy demand
D. = Load j peak demand (kW-yr)
J
Figure 3. Equation Set and Key for Capacitated Plant Model
16
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The constraint functions are structured so that all base and peak load
demands are met.
This model is a temporal one but its complex design with respect to peak
and base loads negates treatment of environmental parameters. These are
left to be specified exogenously. This formulation does not account for
any time changing values for siting and transmission cost. A sample
case has been run utilizing data from the Harty, et^ al^. (1967) study to
seek an answer only with respect to economic considerations. It should
be viewed as useful only for this purpose. Additional constraint rela-
tions would have to be developed for it to be useful in environmental
judgments of alternative sites for thermal power generation.
Fields (1971) developed two algorithms for scoring alternative methods of
heat disposal for steam power plants. Fields' methods generate:
1. Cost/Benefit ratios for heat disposal systems
2. Ancillary effects scores for heat disposal systems
3. A decision process for selecting a best heat disposal system based on
1 and 2 above plus a cost ceiling
Both algorithms are based on a graph with three dimensions. The dimensions
of the model are methods of heat disposal, M , characteristics of the
receiving water, C., and hydro-environmental effects, E . Figure 4 shows
the graph and its components. A pseudosystem, WQS, is defined for the M
dimension to furnish a standard for comparison of the various M .
Fields' algorithms do not attempt to calculate microvalues or discharge
quantities. His algorithms are decision tools only. Expert decision
makers in the three perceived fields are needed to supply information
to the decision system. Opinions of participating experts are used to
establish:
17
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DIMENSIONS
NODES
oo
Hydro-environmental
Effects, E.
Characteristic of Body
of Water Receiving
Heat, C.
Methods of Heat
Disposal, M.
Figure 4. Basic Components in Fields Decision Model
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1. Dimensions of the graph (the number of M.'s, C.'s, E.'s)
2. Sets of constant-sum preference decisions rating the interaction
factors between respectively M. and C., and the C.'s and the E.'s.
3. Ancillary effects dimensions and importance for use as a secondary
decision variable.
The M.'s reflect the different possible condenser cooling means considered
for a given site. The C.'s represent the different types of water bodies
under consideration (e.g. river, estuary, ocean, lake). A set of hydro-
environmental effects might include:
E - Effects on fish
£„ - Effects on plankton
E - Effects on benthos
Interaction parameters between the dimensions are entered as constant-sum
decision pairs. Information on intralevel interactions and interlevel
interactions are input to the model as constant-sum paired decision com-
parisons. The two algorithms differ in the level of information detail
and calculation procedures for interaction parameters.
Both methods share a common decision rule set for selection of the optimal
M.. The principal decision criteria is the cost/benefit ratio. This
ratio is determined by dividing the system cost ($) by the percent system
performance compared with the performance of the Water Quality Standard
(WQS) pseudosystem. Equation IV-1 gives the cost/benefit relation.
., ,„ Cost of System Mn-
C/B. = i i (IV-1)
1 % Performance better than WQS v '
The WQS standard is evaluated in the method and is considered as the
minimum performance limit. Two secondary decision variables are the
ancillary effects scores for the various M.'s and the upper cost bound.
Figure 5 shows how the region of acceptable solutions is defined and
19
-------
o
o
0)
I
o
M
TJ
0) M
O ttf
ct) C
M jx,
01 4-1
En)
co
C/J Q)
4J
4-1 rt
c &
QJ
O l-i
M 0)
Q) >
P^ O
o
o
/
• /
./
Region of
Admissible
/ Solutions
/
\
WQS point
\ 1 1 1 1 1"
Performance
Limit of
WQS
cost
of W(^S
System Cost ($)
Line of
Maximum
Cost Limit
Note: Scales and bounds are arbitrary and are for illustrative
purposes only.
Figure 5. Graphical Definition of Admissible Solutions for Fields'
Model
20
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bounded. System performance worse than the WQS is not acceptable.
Acillary effects criteria (land use, visual impact, and air discharges
for example) are significant only for deciding among M.'s with close C/B
ratios. The ancillary effects scores are not viewed as controlling
parameters.
Total system cost is the final bound on the optimal solution. After
determining the C/B ratio for a given M. the system cost is compared with
the specified maximum cost. The solution is not allowed if the cost ex-
ceeds the maximum value.
fields' methods are only preliminary planning tools. The decision
process emphasizes aquatic impacts which may not be the controlling
factor for a given site. Fields' classification of receiving water
characteristics is too coarse to be of benefit at any given site or for
deciding among a set of competing sites. To be of use for a particular
site the characteristics category would have to include several of the
usual water quality parameters used to describe water quality at a site
(e.g. temperature, dissolved oxygen, dissolved solids). Fields' method
relies heavily on exogenous inputs from experts in the related problem
areas. There is no control over the methods used to determine the input
to the model.
Previous Regional Siting Studies
Electric utilities and related government agencies began to realize that
expansion of already large power systems implied increasing siting
problems. On the West Coast two comprehensive but different studies were
undertaken to assess the status of thermal power plant siting (Harty et
al., 1967, and The Resources Agency, State of California, 1970). The
Committee on Power Plant Siting, National Academy of Engineering (NAE),
"Engineering for Resolution of the Energy-Environmental Dilemma," (1972),
21
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released their findings as this study was concluding. Not all of their
results could be incorporated in this work. Both the Pacific Northwest
and California had always been areas where power had been plentiful.
Bonneville Power Administration (BPA), a regional wholesale distributor
of electric power, contracted with the Pacific Northwest Laboratories of
Battelle Memorial Institute to conduct a study of prospective nuclear
power generating sites. The resultant study by Harty ej: all (1967) con-
sidered the viability of siting 16 nuclear power plants throughout the
Pacific Northwest on a site-by-site basis.
The 1970 Resources Agency study was not a site-by-site analysis of thermal
siting in California, nor did it review with great detail the characteris-
tics required for a site. Instead it reviewed the need for further develop-
ment of nuclear power plant sites and encouraged continued, cooperative
planning among the various public and private electrical utilities in the
State. This overview study did establish some policy measures which
demonstrate the multiplicity of problems predicted for future power plant
siting efforts.
1. An environmental protection agreement with the State Resources Agency
must be consummated as a condition of construction of all new thermal
power plants.
2. Future siting must be compatible with objectives of planning and zoning
authorities.
Harty e± ail. (1967) considered the following factors in studying the 16
example sites:
1. Technical feasibility from geological, hydrological, seismological and
meteorological standpoints
2. Economic feasibility
3. Ease of approval by regulatory agencies
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4. Minimization of adverse public and government agency reactions
5. Illustrative problems typical of the Pacific Northwest environment
Two factors specifically not included by Harty et al. were transmission
and land acquisition costs.
Reflecting then current power plant design practices, 11 of the sites
proposed by Harty et al. were planned for once-through condenser cooling
with five sites using recirculating cooling systems in locations where a
water shortage was seen to exist. Once-through cooling was recommended
even though the following statement was issued at the beginning of the
report:
"Biological effects associated with thermal discharge are
sufficiently defined (though not completely understood) to
allow identification of sites and designs of heated effluent
systems that minimize effects upon many aquatic species and
communities. Insufficient information is available to
predict with confidence all of the significant effects of
heated discharges on aquatic life in the PNW (Pacific
Northwest)."
The criteria used by Harty et al. to determine the applicability of once-
through cooling must be questioned. Of the 11 once-through cooling sites,
two sites are currently under preparation. The Trojan site near Rainier,
under construction, will utilize a single, large natural draft evaporative
cooling tower. Hanford II, on the Hanford Atomic Energy Commission
Reservation, will use mechanical-draft evaporative cooling towers. Much
of the pressure for cooling towers comes from lack of understanding of
thermal effects. The cost figures set forth by Harty et^ al^, (1967), which
were claimed to be reliable for one decade are also out of date.
In view of such inadequacies in previous comprehensive power plant siting
analyses, a need was perceived for a complete, comprehensive definition
of the effects of a baseline thermal power system. The literature of the
various disciplines indicated little interaction and collective synthesis.
It was felt that an overview, comprehensive type project might provide
23
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info rmation necessary to analyze the current status of the collective
fields involved in thermal power plant siting. This definition would
be keyed to identifying the interaction pathways between the power plant
system and the environment and have the characteristic of continual up-
dating. Such a definition could then be used as a starting point for
analysis of plant/site interactions.
Considerable benefits were seen to accrue from an assembly of a multi-
discipline team to examine the thermal power plant as a complete system
from the generating station to the distribution point. The goal of the
study was to quantify the impact of the thermal power generating system
wherever possible so that trade-offs could be evaluated with some degree
of certainty. Where effects could not be quantified or defined, it was
hoped that a comprehensive listing of data insufficiencies could be
assembled for the identifying and guiding of new research projects.
24
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DECISION TREES AS AN ANALYTICAL FRAMEWORK
Decision trees have been employed as a systematic method to define and
analyze sequential decision making that involves a hierarchical struc-
ture of choices. The literature is rich with the theory and application
of decision trees. Poage (1970) presents a concise review and Raiffa
(1968) discusses the analysis of decision making in the presence of un-
certainty. Figure 1 presents a decision tree for the analysis of engi-
neering and planning decisions for thermal plant siting. A node in the
tree represents a decision point and the branches indicate alternative
decisions that can be made and the resulting outcomes. The outcomes can
be interpreted as deterministic or probabilistic events. By tracing any
given path through the tree, the final result of any given set of deci-
sions becomes apparent.
The tree serves as an information organization and transfer device (each
stage adds to its input information and passes the result on to the next
stage). The tree also transforms the externally supplied and internally
generated information into final outputs (costs, quantitative environ-
mental discharges, land use data, and thermal impact) for each alter-
native path evaluated.
This decision tree relates the alternative engineering decisions sequen-
tially to the ultimate environmental and sociological impacts. This
method permits the analysis of a large number of alternative decisions
in a logical sequence. The ecological and sociological effects are keyed
to the engineering decision chain. The decision chain proceeds from left
to right. Knowledge from the left side of the tree is used to specify
the operation and performance of the next level to the right. Thusly,
sociological and environmental effects are developed as the last element
or payoff. All engineering alteratives must be specified before the
sociological payoff can be accurately specified. The level of complexity
of the tree increases with the detail of engineering analysis specified.
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The static siting model (SSM) developed in this study is a computerized
tool employing a decision tree format to specify inputs concerning a
particular power plant siting analysis and to evaluate the various
environmental impacts through the siting tree. The SSM does not attempt
to determine an optimum system for a particular site or plant. Its
purpose is to display at a preliminary stage the spectrum of possible
impacts accruing from alternative engineering courses of action.
The ordering of the thermal discharges tree is based on information flow
requirements. The first and most basic level for single site analysis is
the site/transmission path level. Parameters concerning access and on-
site environmental conditions are required before accurate appraisal of
engineering systems can be undertaken. The next level specifies the plant
type under consideration. This level follows logically after the site
information. Plant operating information must be available before cooling
system performance (level 3) can be calculated. Following an evaluation
of cooling system performance, screening calculations can be made. Only
after specification of plant type, cooling alternative and screening
system can complete evaluation of the power plant's chemical system be
analyzed. The complete spectrum of decision alternatives encompasses:
1. Sites/transmission path
2. Plant types
3. Cooling systems
4. Intake systems
5. Chemical systems
The environmental and social payoffs are a function of the particular
path evaluated. The SSM is capable of demonstrating variance in impact
of the alternative decision paths.
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TASK 1: BASELINE DEFINITION
The Baseline definition provides a reference point for comparison of
engineering alternatives to reduce the environmental impact of thermal
power plants. The Baseline configuration represents one path on the
decision tree that contains a 1000 MWe gross steam cycle, once-through
river cooled plant, with screens to protect the plant from damage, chlorine
treatment of cooling water, and a submerged diffuser outlet. In the sub-
sequent sections of this report each decision point of the tree is de-
scribed indicating the analytical and computational models developed in
this study, and the alternatives that can be examined.
Two rather than one baseline heat sources are available in the model.
Either a nuclear water-cooled system or a fossil-fuel plant are available
as baselines. All other systems can be analyzed by additional input
statements describing the alternative heat sources. Table 4 presents the
basic information describing the baseline configurations used in this
analysis. The analysis of decisions through the cooling node employs the
Dynatech (1971) cooling computer program. Much of the effort in baseline
development was devoted to correction of this program and modification of
the computer program to permit integration with the decision model. The
decision tree has been constructed to permit alternative descriptions for
the baseline characteristics shown in Table 4.
One of the early modeling decisions made in this study was the resolution
of the data and information provided at each step in the decision tree.
Information is provded to make inter-site comparisons at a gross level;
there is no attempt to compare intra-site decisions to optimize engineer-
ing alternatives. For example, all water discharges are considered to be
well mixed in evaluating thermal discharge effects, no attempt is made
in the analysis to describe thermal plume behavior or the impact of float-
ing or dispersing the heated effluent. These effects can only be incorpo-
rated at large costs, since the data requirements are increased many fold.
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Table 4. Baseline Plant Characteristics
Heat Source
Gross Electrical Output
Condenser Pressure
Condenser Temperature
Gross Plant Heat-Rate
In-Plant Losses
Heat Discharge-Rate
Cooling
Chemical Discharge
Intake Screens
Cooling Discharge
Land
Nuclear
1000
1.5 in Hg
91.7°F
10,340 Btu/kw-hr
5%
6.4x10 Btu/hr
River-Once Through
.5 ppm Cl Residual
minimal
Submerged diffuser
500 acres
Fossil
1000 MWe
1.5 in Hg
91.7 °F
8,630
15%
3.8xl09 Btu/hr
River Once-Through
.5 ppm C12 Residual
minimal
Submerged diffuser
500 acres
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The thermal discharge model provided in this study integrates the results
of FWPCA (1968), Hauser (1970, 1971) and Jaske (1971, 1972a, 1972b) with
the Dynatech (1971) efforts. Five percent changes in heat rate have
minor environmental impacts but create major thermal plant design changes.
Chemical discharges to the aquatic environment from the once-through
cooled plant are a direct function of the intake water quality. Liquid
chemical discharges emanate from either the circulating cooling water or
untreated discharges from the steam system. The latter system in new
plants has almost zero blowdown. Boiler water make-up is only required
for system leaks and is generally less than 1 cfs. Complete discussions
of cooling water chemical treatment processes and impacts on the aquatic
ccmmunity appear in Task 2 and Task 3 of this report.
Baseline plant condenser cleaning is assumed to be by the method of
intermittent, break-point chlorination. While some plants are using
mechanical means (abrasive balls), chlorination was seen to be a more
typical method.
The high flowrate open systems for once-through cooling prohibit the use
of a high concentration of chemical treatment and cleaning agents. Inter-
mittent break-point chlorination has proven and effective means for control
of biological fouling. The dose required to achieve break-point or free-
residual chlorination dose is a function of the inlet water quality. The
i i i i
presence of both reducing compounds (Fe , Mn and H-S) and ammonia (NH.)
affect the necessary dosage. Their uptake requirements for the chlorine
must be satisfied before an uncombined residual is available for biofouling
control. Metcalf and Eddy (1972) give a dose range of 1 to 10 mg/1 of Cl.
for these applications. Cooling waters containing large industrial,
agricultural or municipal waste loads will require doses at the upper end
of the range to achieve satisfactory results. The dose period typically
is short (20 to 30 minutes) with a frequency of 2 to 3 times daily.
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The residual chlorine concentration is a function of the dose necessary
to achieve the free residual. For the new plant at Hanford, WA, a
residual value of 0.5 mg/1 is predicted using the relatively clean
Columbia River in a recirculating cooling system (WPPSS, 1971). A similar
value would be valid for a once-through system at the same location.
The baseline model assumed a residual of 0.7 mg/1 unless an input value
is specified. This value will be reduced by uptake of slime growths in
the condenser system. The uptake of chlorine by the slime growths is a
difficult parameter to model being a function of contact time and slime
concentration. Ideally, chlorine would be dosed so that all free residual
was absorbed within the confines of the condenser system for a time period
that would either limit slime growth to a negligible level or kill it all
together. The chlorine residual in the cooling water discharge is subject
to EPA regulation. Current recommendations are for a residual of 0.1
mg/1 for not more than 30 minutes per day or 0.05 mg/1 for not more than
2 hours per day. If the dosage can be regulated to these levels, no
additional treatment or costs will be necessary. Dosing costs are
derived from the work of Smith (1967). Extra costs incurred to meet the
standard are not a part of the baseline system. These cost constitute
treatment above baseline and can be accommodated in the chemical treatment
branch of once-through cooling.
Physical environment changes are varied and are, in general, difficult to
quantify. While Thackstone and Parker (1971) give a number of from 75 to
100 acres as the minimum plant size for a nuclear generating station, most
plants are located on larger sites. The baseline plant model assumes 500
acres as a base fingure for land use. Nuclear plants impact land use
further by requiring certain population density zones around the plant to
correspond with plant design requirements.
Other land use parameters are not standardized to the extent of precise
quantification. Some of these parameters are site preparation cost and
30
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debris, specialized access construction (barge or rail transit), site
aesthetics and surface water contamination during plant construction.
Transmission system land use is discussed in a later section of this
report.
The baseline plant is considered to have intake screening only to the
extent necessary to protect the plant components from damage. The screen
face for an intake system is assumed to cost $240/cfs. (1 ft/sec, approach
velocity). Fish damage is assessed on the yearly capitalized value of the
fish as shown in the following equation.
„. , ,$ . condenser flow ... , ,
Fish damage (—) = : —^ x fish value
yr river flow
This is a very crude approximation to a fish damage function.
Air discharges from the baseline plant consist of small quantities of
long-lived radioactive gases. This project was instructed not to in-
vestigate in detail radioactive aspects of the site. A base figure of
0.238 microcuries/kWh was readily available and was used in the baseline
model (Wegmann, 1970) . The baseline fossil fueled emissions are modeled
using the parameters discussed the non-water impact section of Task-3
Engineering alternatives.
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TASK 2 - ACCOUNTING SYSTEMS
The introduction of a thermal power plant, can cause major changes in the
social, economic and ecological environment of the region. Any attempt
to assess these impacts will encounter a maze of information of widely
varying accuracy and completeness. In order to define engineering
alternatives that can protect the environment, it is necessary to define
the appropriate yardsticks to measure the increased protection and to
define what needs to be protected.
Approximately one half of this study effort was devoted to the development
of economic, social, and ecological accounting systems for use in evalu-
ating the effectiveness of engineering alternatives for the protection
of the environment from large, stationary thermal power systems. His-
torically, the evaluation of power plant development has employed
engineering economics which ignored costs or benefits that were not
priced in the market place. The introduction of accounting systems
that reflect environmental benefits and costs is a difficult task.
This project employed the strategy of examining three complementary
accounts: engineering economics, ecological, and social. The ultimate
goal would be to understand each account completely so transforms could
be made from one system to another. Without these transforms three
accounts must be used, and costs and benefits identified and quantified.
The results of the research to establish the three accounting systems
have been summarized for this report. The socio-economic systems are
combines followed by a description of the ecological accounting system.
Task 2: Socio-Economic Accounting
Protection of the environmental resources from the impact of thermal
power plant involves the allocation of scarce resources in the form of
money, labor, technology, natural resources, and .the natural environment.
In order to formulate a rationale for resource allocation, some framework
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for analysis must be established with a common set of units. As part of
this research program, Meyer (1972) reveiwed the existing analytical
frameworks that could be applied to the socio-economic accounting of
thermal power plant siting impacts and sought specific data and methods
for this model. The major thrusts of Meyer's efforts were (1) extension
of benefit-cost analysis to include social accounting, (2) analysis of
the effectiveness of pricing for controlling power plant siting impact,
and (3) examination of transmission impacts and fisheries damages as
special cases of socio-economic impact of thermal power plants. Each
of these efforts are summarized in this report and the analytical frame-
work is presented for model development.
Benefit-Cost Analysis - Economic theory provides a rational framework for
allocating scarce resources using the market mechanisms. The problem
encountered when this is applied to the benefit-cost analysis of thermal
plant siting are well documented but unfortunately the solutions to
these problems are lacking. Prices are supposed to reflect the willing-
ness of users to pay and the willingness of suppliers to sell. Un-
fortunately many of the resources employed in power production are not
correctly priced. Water, air, fish and certain other elements of the
ecosystem are not owned, and no prices have been established for their
use. A dollar value should be established for the use of these re-
sources. If this is infeasible, the cost or benefit to these resources
must be quantified in some manner. The suppliers of power have a
monopoly in many instances and prices are controlled by government
agencies. Thus the price of power may not reflect the willingness to
sell in a competitive market. Given these imperfect prices, the user
cannot efficiently substitute other resources as economic theory would
predict.
The problems of proper pricing of the benefits and costs are only one of
several economic problems in the assessment process. The costs of power
production are incurred before the benefits, and a time preference must
33
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be developed in evaluating power plant development. The literature is
rich in the discussion of appropriate interest rates and what should be
the accepted opportunity costs used in the construction of power generating
facilities. The model must accept interest rate as a variable in order to
test the significance of changes.
Similarly, the benefits or damages created as a-result of power plant
siting are not fully reflected in the price power users are willing to
pay. In many cases a project can generate benefits .greater than costs
on a national basis, yet be unacceptable because those bearing the costs
do not receive any benefits. Conversely a vocal group of beneficiaries
may argue for a project where costs exceed benefits since they face no
costs themselves. Social accounting is a catchall phrase suggesting
that all factors be considered in a benefit-cost analysis. An alterna-
tive is to conduct the benefit-cost analysis based on available economic
data and concurrently conduct a similar analysis quantifying social
impact and environmental impacts. In either case it is necessary to
identify who pays and who benefits.
/
Lind (1968) provides an excellent discussion of benefit-cost analysis
and cites the major analytical problems of prices, discount rates, op-
portunity costs, and externalities. Federal water resources programs
have strongly influenced benefit-cost analysis and the recent "Proposed
Principles and Standards for Planning Water and Related Land Resources"
by the Water Resources Council (1971) provides a model that can be used
for thermal plant siting evaluation. National, regional, and environmental
accounts are proposed to permit groups with various preferences to deter-
mine whether an effect is adverse or beneficial. The model developed for
this study does not address the regional and national accounts, and is
limited to the environmental account.
The analytical framework for this study was•formulated to evaluate the
engineering alternatives to protect the environment from impacts of thermal
34
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plant siting. This approach assumes that more power production would have
a net benefit, and the problem is to maximize these benefits by minimizing
environmental costs. Clearly, the social costs of providing more power
when other opportunities for investment would yield larger returns are
ignored, even in the face of lessons learned from investments in water
resources development. Discussion of the evaluation of engineering
alternatives includes sections dealing with the abortive attempts to
address the broader problem of optimum investment in power production.
The economic assessment of engineering alternatives developed in this
study focuses on the cost aspects of power generation. Each engineering
alternative is identified and the direct costs computed and adjusted to
current dollars. Since costs can be sensitive to amortization period,
plant life, and discount rates, these parameters are considered to be
variables in the analysis. The sensitivity of project cost to these
variables can be identified in this model. The factors that enter the
economic analysis were established in a review of specific cost proposals
for new power generating facilities and the work of Harty et^ al. (1967)
that evaluated 16 sites in the Pacific Northwest. The basic factors
used in the model are the cost of the acquisition and preparation of the
site, the construction and operation of the power generating units, the
transmission facilities, and the environmental protection devices.
Peak Power Pricing to Reduce Demand - A non-engineering alternative to
environmental protection is to not provide all the power that is demanded
and reduce the potential impacts of new power generating facilities. From
an economic view the adjustment of price to reduce the demand for power is
a proper and effective alternative if the price of power does not reflect
marginal costs and/or during the relevant demand periods, the quantity
demanded varies at a particular price. Since the storage of electricity
is very expensive (most machines used in the home and factory are not
built to store electricity, and people expect instantaneous electric
service), demand is met exclusively on-line by production. Utilities are
35
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faced with a fluctuating demand schedule, daily and seasonally.
With its supply possibilities the utility must decide how much electricity
and at what price to produce at each point in time. Average cost pricing
may violate efficiency as well as equity conditions: time-uniform rates
ignore the possibility that generation costs vary through time; and
uniform rates fail to provide an incentive to substitute low cost (off-'
peak) for high cost (peak) commodities. If a high cost commodity is
under priced, demand for this commodity will exceed supply. When peak'
power is under priced excessive generating capacity, the use of inefficient,
non-price rationing practices or the subsidization of one group of "con-
sumers by another group of consumers is the result. Capacity will be
under utilized during off-peak periods. And to the extent that off-peak
consumers finance fixed costs incurred by peak period consumers, income'
will be transferred among consumers. A peak load problem exists if, at
a specific price, the quantities demanded during two time periods are un-
equal. The objective is to specify a set of prices which encourage con-
sumers to purchase the optimum quantity of electricity in each period, the
optimum quantity being that specified by the equality of incremental cost
and price.
Pricing below cost during peak periods really means that some customers
pay less than the opportunity cost of those resources used during peak
periods, since peak demand is greater than it would be if prices reflected
the cost of expanding capacity. If a utitlity does not use peak period
pricing procedures, and capacity is sufficient to meet demands upon the
system, either extra-price rationing devices are used, or off-peak :users
are paying a portion of the peak period consumers' capacity costs.
Since utilities do not usually charge prices which 'Vary with time,
capacity costs are borne fairly evenly by both peak and off-peak consumers
and, to the degree this is true, off-peak consumers subsidize peak :con-
sumers. Subsidization of this sort leads to excess'generating capacity,
-------
as well as under use of that capacity during off-peak periods. Higher peak
period prices would restrict peak demand, while lower off-peak prices
(equated with marginal cost) would stimulate off-peak demand.
It is this purported misallocation of resources, over investment in capa-
city, and under use of existing capacity, that is of concern. Subsidiza-
is tantamount to saying that there is over investment in system capacity,
although there is no present estimate of the magnitude of this over
investment.
In the ideal world, with optimal adjustment of plant capacity, the proper
prices are those which, during peak periods, are equated with incremental
operating and capacity costs or are high enough to ensure that potential
demand does not exceed supply, and are equated with marginal operating
cost during off-peak periods.
Estimation of new peak period prices was approached from two standpoints.
Comparing the average price advertised by BPA of 2.4 mills per kWh
($18.60 per Kilowatt year) with a peak period price of 6.5 mills per kWh
allows conclusions to be drawn.
There is the interdependence problem; peak/off-peak electricity are sub-
stitutes for each other. The position of the demand for a particular time
period will be influenced by the price charged during another demand
period. Instituting a price differential will bring into play cross-
elasticities which are not now operative. It seems logical that if the
cross-elasticity coefficients are positive, raising peak period prices
will shift the off-peak demand curve to the right. This being the case,
analysis of direct price elasticities will underestimate the effect on
a peak period price increase on the quantity purchased.
For households, using an elasticity of -.4, the new peak period price
might be expected to reduce peak period consumption by 50 percent. This
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means that 10 years after-the price change, household consumption will be
50 percent less than presently anticipated. For the primary metals in-
dustry, into which category the aluminum industry falls, the quantity
response is 230 percent. The magnitude of the adjustments is dependent
upon industry's ability to shift peak consumption to off-peak periods.
These numbers must be used with extreme caution. They suggest that if
peak period prices were adjusted, the additions to system capacity pres-
ently envisioned might exceed what in fact is necessary. If peak period
pricing sufficiently reduced peak period demand, allowing the postponement
of additional generation and transmission capacity, the economy or savings
would be the annual fixed interest charges times the years delayed. Annual
fixed interest charges are estimated as $19,100,000 for a hypothetical
1,000 MW plant generation plant and $5,016,000 for the related transmission
facilities. Thus, $24,116,000 of capital resources can be used elsewhere
if construction is postponed for a year.
By applying the household elasticity to all electric consumption in the
Pacific Northwest, it is possible to gain a notion of the possible savings.
If all anticipated consumption were reduced by 30 percent during the
period 1970 to the end of 1980 the savings can be estimated, assuming that
the reduction is linear over the 10 year period. Furthermore, it is
necessary to assume that the fixed costs, $24,350,000 per year, are
realistic, and that the mix of generation capacity and transmission
capacity will be constant.
In its 1969 Annual Report BPA indicates that over 11 million kilowatts of
generation capacity will be required during the period 1970 to 1980 in the
Pacific Northwest. A 30 percent reduction in anticipated growth will
reduce the additions to capacity by 3.3 million kilowatts. At the end of
1973, the end of 1976 and the end of 1979 the proposed addition of a
1,000 megawatt nuclear power plant would not be necessary if prices were
raised. By applying an annuity factor for the seven, four and one year
38
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time periods to the $24,350,000, at the end of 1970 the present value of
the potential gross savings amounts to over 150 million dollars.
There are situations in which peak period pricing reduced peak period
demand. The French reduced their national peak by 5 percent after
instituting a pricing schedule which more precisely reflected the costs
of production; the reduction amounted to the equivalent of approximately
50 percent of the load growth anticipated during that first year. Turvey
(1968) cites an example in New Zealand in which those customers who have
their water heaters switched off during peak daily periods are sold
electricity at approximately 1/3 the rate of those who use peak energy.
This strategy reduced peak load demand by 20 percent.
For multiple-dial meters the French use ripple control equipment; high
frequency electricity is used to switch meters. It is also possible to
use clocks to regulate multiple-dial meters, although clocks which are
electrically powered are not without their problems. Outages desyn-
chronize the clocks with the daily or weekly load cycle. However,
clocks can be powered by other means. In the United States demand
controllers have been used to limit loads.
It is possible that residential customers will find metering and billing
costs too high for even a two-dial meter, in which case there are other
possibilities. Higher winter costs could be used as a rationale for dis-
couraging electric home and water heaters. Or, electric home and water
heaters can, as they are in England, be designed to store heat energy,
and then be switched off during peak demand periods. Turvey (1968) cites
examples of hot water heaters with a six-hour supply of hot water.
Obviously, any effort to promote peak period demand is not rational; and
it seems likely that there are more marginal cost pricing possibilities
than have been or are in use.
39
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With potential gross savings of 150 million dollars, and given the examples
of capital cost savings in other countries, peak period pricing obviously
deserves further investigation. If it is decided that the anticipated
additional costs of metering, billing, administration and the consumers'
additional equipment costs do not exceed the potential gross savings,
there are net benefits to be captured. If the costs of household appli-
ance modification or industrial modification do exceed the potential
savings, the case for marginal cost, peak period pricing is not as strong.
There may be grounds for peak period pricing if it is no longer acceptable
to transfer income from off-peak period to peak period consumers.
Lastly, it may be argued that a marginal cost pricing schedule will reduce
peak period demand growth sufficiently to allow for a greater margin of
reserve capacity or more time to plan for the proper siting of nuclear
power plants.
In order to accommodate peak pricing evaluation, costs for all power
generation components of a regional system must be available. Thus the
peak pricing issue is not of concern in the siting of a specific plant as
much as it is in the dynamic analysis of a series of site selections. The
dynamic assessment model is constructed to integrate the results of in-
dividual site assessment and test the impact of alternative time sequences
of power plant sitings.
Methods to Include Externalities - Faced with the choice to quantify ex-
ternalities in dollar terms or maintain separate accounts to permit com-
parison of engineering alternatives, Meyer elected to examine specific
externalities related to fishery damage from power plant cooling and
transmission of power and develop a modeling methodology. While these
studies identified methods to estimate the rent from the use of water
or land and introduce some social accounting into the analysis, it is
not a complete social accounting.
40
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The market place fails to efficiently allocate common property fishery
resources for reasons very similar to the market's failure to allocate
external costs; fishery property rights are not defined, transferable
or enforceable. The fluidity of the product, the fishery, prohibits the
establishment of well-defined property rights.
Without well-defined property rights, labor and capital can freely enter
the fishery. When the price (not the cost) of entrance is zero, the
fishery will be exploited as long as it yields positive rents. As long
as entry is not restricted, the contribution of the fishery, rent, will
be viewed as excess profits and additional capital and labor will be
attracted to the industry. Entry continues until rents are dissipated,
and the net economic contribution of the fishery is zero. The relevant
consideration, rent, is the value of the fishery optimally regulated.
Royce _ejt _al. (1963) feel that 50 percent of the gross value of the Puget
Sound salmon fishery is potential rent. By eliminating redundant labor
and capital and allowing the remaining gear to fish a full week, instead
of two days per week, the State of Washington may capture the fishery
rent. If rules enforcing the inefficient use of capital are eliminated,
net rent can be expected to exceed 50 percent of the gross fishery value.
The commercial Columbia River catch averages approximately $6.7 million a
year, 75 percent of which is potential rent (Crutchfield and Pontecorvo
(1969)).
For the Sacramento River, Fry (1962) has estimated potential net economic
yields to be 90 percent of the gross value of the fishery. Mathews and
Wendler (1968) have made similar estimates for the potential yield from
the 1965 Columbia River fishery for the spring and fall Chinook runs.
For the sport fishery, the economic value is not the fish, but the fishing
experience; although catching the fish is intrinsically involved, the
41
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service is the fishing activity.
Estimating net yields from a sport fishery is very difficult. Since only
a nominal or zero price is charged, there is little price information about
how intensely fishermen like to fish. For the commercial catch, some have
used the price of commercially caught fish. Although gross annual sport
fishing expenditures may be important from a local standpoint, they are
not pertinent in a national assessment.
Economists have resorted to other methods of net yield estimation, since
the traditional indicators are absent. An Oregon study, published by
Brown, Singh and Castle (1964), and other studies have used distance-cost
models for estimating recreational values. The estimated minimum net
value for all Oregon salmon and steelhead sport fisheries in 1962 was
$2.5 million for about 282,000 fish caught on 1,100,000 angler days.
Brown and Mathews (1970) have used the questionnaire approach. Although,
like the other procedures, there are disadvantages, this method requires
that sport fishermen establish a minimum annual price for which they would
be willing to sell their right to salmon fish. The authors recommend using
a minimum net value of $28 per salt-water sport fishing day in Puget Sound
and along the Washington coast. This is an estimate of how much sport
fishermen are willing to pay for the opportunity to fish. The authors
also found that net benefits increase as catch per day rises.
If, as Mathews and Brown show, there is a positive relationship between
net value and catch per trip, and their spo.rt catch were increased, net
rents would increase by $28 per angler-day and decrease by the amount of
the commercial fishery rent or 50 percent of the gross value. It is
assumed that the possibility of doing so would not be so great as to
alter the incremental values involved.
42
-------
Crutchfield and MacFarlane (1968) estimate that if similar restrictions
were placed on the halibut fishery, the net rents accruing to society
would be in the range of 40 to 50 percent of the gross value of the
annual catch.
These data indicate that if the impact of a power plant on fishes can be
determined in terms of the increase or decrease in the annual catch, some
estimate of the economic value of that change in catch can be made. In
the case of fishery that are damaged by the release of heat or chemicals,
the full cost of these damages should be charged to the cost of producing
power. Conversely, if fish stocks are increased the benefit should be
credited to the power plant. When the fishery affected is privately
owned, such as for oyster or fish farming, the estimate of benefits or
costs created by a power plant could be directly obtained from the an-
ticipated changes in net earnings of the firm in question.
Based on these findings, the model has been developed to estimate the
changes in fisheries in terms of diversity of fishes, and value of the
fishery, as a result of heat and chemical discharges, and physical
losses from intake structures.
Transmission lines were evaluated for three externalities: visual impact;
erosion, sedimentation and water quality changes; and non-payment for
land use changes.
Since BPA has modified some of its transmission practices, avoiding scenic
areas and adopting its own environmental standards, it is logical to argue
that a portion of average transmission costs are the result of efforts to
minimize transmission externalities. The magnitude involved cannot be
defined. Whether BPA activities are optimal has not been shown.
Personal communication with BPA representatives is the source of the wheel-
ing formulas (cost to involved in delivering power).
43
-------
$1.32/kW-year - Plant west of the Cascades to the major load center
$2.33/kW-year - Hanford
$4.37/kW-year - Libby
$ .81/kW-year + $.01/mile kW-year - general formula, subject to annual
negotiation
If economies of scale exist for the transmission of electricity and this
service is financed so that total revenue equals or exceeds total cost,
then the wheeling costs could be viewed as an upper limit to incremental
transmission costs. BPA indicates that transmission of power is self-
supporting; i.e. revenues including interest charges, exceed costs.
The wheeling formula costs were calculated on the basis of financial costs,
not economic costs. Since interest rates influence costs, improper
interest rates results in erroneous costs. Through fiscal year 1963 the
rate was 2-1/2 percent; subsequently, the rate has gradually risen to
6-3/8 percent.
As mentioned earlier, the choice of interest rate is crucial and under-
estimation of interest rate results in an underestimation of the oppor-
tunity cost of capital. This is especially important in the case of
transmission cost since capital costs are a major fraction of the total
costs. For a double circuit, 500-KV, 200-mile, 1000 MWe capacity line,
using an interest rate of 7 percent, an economic life of 45 years, BPA
average system line losses of 4-1/2 percent valued at $18.60 per kW-year,
15 percent overhead charges, and the construction, terminal facility and
operation and maintenance costs presented in the section on peak period
pricing yields an annual, incremental transmission cost of approximately
$5,700,000. For 200 miles and 1,000- MWe, the general wheeling formula
generates a figure of about $2,810,000. The difference between $5,700,000
and $2,810,000 is caused by an inappropriate interest rate, although the
interest rate used by BPA is specified by law.
44
-------
Based on this comparison, transmission costs should not be based on
wheeling formulas developed by utilities unless appropriate discount
rates have been used.
The visual costs that property owners adjacent to transmission line
right-of-way are willing to pay to diminish the visual impact are less
than the costs of transmission practice modification. To appreciably
increase transmission distance or to underground wires may increase costs
in excess of the amount people are willing to pay to reduce external
visual effects. The apparent lack of property value depreciation due to
transmission line proximity lends support to this position. In urban
areas, particularly adjacent to high value residential areas or heavily
used scenic areas, the cost of alternative routing or undergrounding may,
however, be less than the external cost.
It also seems likely that the visual costs borne by the traveling public
are directly related to the frequency of encountering towers. And if, as
is possible, the public is willing to forego something in order to reduce
visual costs, there are options which allow the planner to arrange the
roadside view in a cost-minimizing manner. These options include under-
grounding, alternative routing and the adoption of visual-impact-
minimizing techniques.
Since the external costs involved are difficult or impossible to quantify,
the values involved can only be imputed by alternate routing, and conse-
quently lengthening the transmission distance, undergrounding or including
the costs of constructing aesthetically superior transmission lines.
Displaying the costs of alternative strategies will assist the decision
maker to select the desirable alternative.
Presently, in central business districts, underground transmission lines
are often the least-cost alternative; this is evidenced by the existence
of underground lines, although utility decisions may be influenced by
45
-------
visual considerations. The Federal Power Commission (1966) indicates that
underground transmission costs exceed overhead transmission costs by from
10 to 40 times in rural areas and from 1 to 20 times in urban-suburban
areas. Using an escalation factor of 10, applied to the above baseline
costs, yields an average annual cost of $57,000,000. Assuming there are
no visual costs associated with underground lines, this figure represents
one of the variants of the transmission cost alternatives. Another pos-
sibility for eliminating visual costs is to locate the power plant near
the load center.
If, as was previously argued, the proper goal is environmental cost
minimization, undergrounding may be optimal in particular localities.
For instance, if, to avoid residential areas or scenic areas, 20 miles of
undergrounding is being considered, the additional cost of $523,000 is the
relevant value. The decision making body would then decide if the visual
values exceeded the additional $523,000.
The estimation of social costs related to erosion, sedimentation and water
quality changes caused by land use for power transmission can be obtained
from rents and costs of right-of-way management practices. Engineering
alternatives such as paving of roads, use of aerial transport to install
facilities, and improved methods of maintaining vegetation can preserve
fish, wildlife and water quality. When the land is privately owned, the
rent will reflect costs to satisfy environmental criteria. However, if
rent is not collected or if federal agencies do not require land manage-
ment to protect the environment in transmission rights-of-way, these
costs will be difficult to estimate and internalize.
Transmission and right-of-way management strategies which minimize external
costs are obviously desirable, but in normal situations it does not seem
likely that internalization of external costs will appreciably alter the
location or length of transmission lines. For example, if a change in
procedures caused right-of-way acquisition and management process costs
46
-------
to increase from $19,000 to $36,000 per mile, a highly unlikely increase,
annual transmission costs for the example used above would increase by
less than $300,000 a year. Considering the large incremental cost of
altering plant location it is very unlikely that internalization of these
costs will appreciably change any plant's location. However, the small
magnitude involved should not be used as a rationale for ignoring the
diseconomy.
Social Accounting - This research has failed to produce any significant
improvement in the formulation of a social account for assessing the
environmental impact of thermal power plant siting. The internalization
of externalities, the estimation of rents, and the selection of proper
discount rates were existing concepts prior to this study. Peak period
pricing is suggested to reduce the demand for power and internalize
specific benefits and costs. This allows the market mechanism to
allocate costs to power consumers, but does not contribute to the inter-
nalization of costs associated with environmental degradation. The model
developed in this research does not reflect any other social factor.
An exploratory attempt to develop a single surrogate for social impact did
not yield an acceptable solution to this problem. The most promising
surrogate appears to be lead time between an identified need for power and
the ability to satisfy this added demand.
Lead time costs are described by several utilities as one of the more
important considerations in siting a nuclear power plant. It has been
argued elsewhere that because proper consideration has not been given
to the negative external effects of power generation and distribution,
the public responds by impeding or, in some cases, completely blocking
prospective power plants. Examples are numerous, and this occurs in
spite of the fact that the utilities conduct extensive educational and
advertising programs to promote public acceptance. To properly analyze
public acceptance or opposition and its effect upon lead time requires
47
-------
extensive research, since with the comparison of two locational alterna-
tives all other things will inevitably not be equal.
Seattle City Light's representatives have indicated that it takes
approximately nine years to plan and have a nuclear power plant in
operation. Felton (1971) indirectly states that the total time involved
is a little over eight years. These figures seem appropriate only after
preliminary studies have narrowed down the range of locational alterna-
tives. Prior to the nine-year period resources are allocated to load
forecasting, planning and preliminary site investigations. Facilities
established during earlier power development may be the controlling
factor on current efforts.
Felton (1971) analyzed the time period necessary to receive a construction
permit. Since 1967 construction permit applications have required pro-
gressively more time. The average time was 10.5 months in 1967, 13.25
months in 1968, 19 months in 1969 and 18.25 months in 1970. Most of the
increase took place prior to the final Advisory Council on Reactor Safe-
quard's review. For 1971 Felton calculated, excluding anomalies, that
the 10 plants which received construction permits required an average time
of 20.5 months. These data bias any predictions since 21 applications
were filed; the numerical time applies only to the 10 which received
permits.
Construction and delivery times have also increased. Delivery time from
contract to operation for 14 plants in operation in 1968, ranged from four
to 10 years with a mean of six years (Nuclear News, January, 1970).
Larger numbers of orders in 1966 and 1967 led to delays in plant delivery.
Only two of the 13 nuclear power plants scheduled for commercial operation
in 1969 were in operation on schedule.
One investigation into the causes of delays, Hogerton (1970), of 70 nuclear
power plants for which construction permit application had been submitted
48
-------
concluded that delays averaged 4.5 months, ranging from one to 13 months.
Causes of delay are listed in order of importance: labor, licensing,
delivery, public opposition, construction problems and scheduling prob-
lems. Hogerton also states that utilities are now allowing more
construction time, approximately 5.3 years.
Until very recently, public opposition has been most active prior to
initiation of construction. It has forced the utilities to invest more
resources in planning and ecological research. It also has produced
significant changes in the licensing requirements at both the state and
federal level. The recent Calvert Cliffs decision, U. S. Court of
Appeals, Washington, D. C., required that 110 proposed reactors, 46 of
which have construction permits and could be modified, must be subject to
ij
environmental reviews. The court held that the A.E.G. was not meeting
its responsibilities for implementation of the National Environmental
Policy Act, and the A.E.G. must, at all stages of licensing, evaluate
environmental values.
After analyzing the proposed Bell Station on Lake Cayuga, Nelkin (1971)
has argued for coordinated regulatory activities, and greater flexibility
on the part of the utilities. The Bell Station was postponed approximately
1-3/4 years after formal announcement of plans. The controversy was pre-
dominantly centered about possible thermal effects. New York State
Electric and Gas Corporation learned that the burden of proof rested
with them; and that regardless of the misinformation about their plans,
the public's tastes and perception of property-use rights had changed
sufficiently to stop the plant.
Jopling and Gage (1971) considered five case studies, identifying the
salient issues for each siting controversy. The authors recommend public
consultation by the utilities, incorporation of public opinion into the
decision process and more public hearings.
49
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Additionally, proposals have been made for changes in the A.E.C.'s reactor
licensing rules. These proposals have been made by an ad hoc group of
lawyers from the Atomic Energy Industrial Forum and the A.B.C. If
enacted, the changes are expected to accelerate methods of handling
license applications.
At the state level new regulations have emerged also. To assure more
certainty in the siting of thermal power plants some states have created
comprehensive site evaluation agencies. Of note is the Washington State
Thermal Power Plant Siting Council and the New York State Atomic and Space
Development Authority.
Designed to protect the public and provide the utilities with a one-stop
state evaluation, these agencies can be expected to reduce the red tape
associated with plant siting and provide the public with the feeling that
all issues are being evaluated.
The New York Agency is authorized to issue bonds to acquire and develop
thermal power plant sites for resale or lease to utilities. The Washington
Council is composed of the pertinent state agencies, representatives of
local governments and an independent consultant to represent the public.
The available information and the experience in power plant siting of the
proposing and opposing groups will be major factors in the time required
to win approval for plant construction. Currently the time required
reflects the delays caused by lack of proper forums for debate and
decision-making, as well as time required to resolve critical issues. In
the future the former delays will be minimized since one-stop approval
agencies are being created and formats for impact statements are becoming
standardized. The delay time will reflect more closely the concern over
environmental impacts and the willingness of society to accept or reject
these. It may be possible then to identify delay time as the surrogate
for social .cost. Unfortunately insufficient empirical data were available
50
-------
to develop a function that could be incorporated in the model.
Task 2: Ecological Accounting
In response to the predicted increase in use of natural waters for cooling
purposes, several comprehensive literature reviews have been compiled to
provide quick reference to effects of changes in water temperature upon
aquatic organisms (Brett, 1956; Wurtz and Renn, 1965; Altman and Dittmer,
1966; Welch and Wojtolik, 1968; Jensen et^ al. , 1969; Parker and
Krenkel, 1969; deSylva, 1969; Hawkes, 1969; Coutant, 1968, 1969, 1970,
1971; Garble and Mowell, 1971). Despite this concentration of literature
there has been a great reluctance by biologists to predict the response
of an aquatic system to increased water temperatures. One reason, and
perhaps the basic one, is that many of the multitude of interactions
within an aquatic community are unknown and the understanding of the
known is hazy at best. Thus, it may seem unrealistic to predict the
consequences of altering one basic environmental parameter.
Other reasons for reluctance stem from the fact that the great majority
of the data available are from research programs investigating the effects
of temperature elevation on single test organisms. There is a paucity of
studies at the population or community level. Also, much of the research
has been performed in laboratories, and there is hesistance to extra-
polate laboratory results to field situations.
However, in planning the use of a waterway for cooling water discharge,
it is essential to be able to determine the amount of heated water that
can be safely introduced or to be able to make some estimation of the
consequences of altering the natural temperature regime. The authors
recognize the immediate need for studies designed especially for the
purpose of estimating the response of aquatic communities to elevated
temperature, but feel in the absence of these data there is information
enough to at least be able to set forth guidelines and highlight possible
problems that may be expected to result from increased water temperature.
51
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Even though only a first approximation, such guidelines would be very use-
ful in siting power plants if properly applied and qualified.
This study has been made as a guide to the possible effects of increased
water temperature on both freshwater and marine aquatic sy3terns with
special consideration to fishes as the principal indicator organisms.
Freshwater Fish - Data on the thermal requirements for 85 species of
freshwater fish are presented in tabular form by Bush &± ad. (1972).
Although the data are the result of numerous studies using a variety of
experimental techniques, they provide a good approximation of the thermal
tolerance limit and preferred temperature for a significant sample of the
important fish species.
By tabulating the typical fish species inhabiting a river system and
compiling the available data on thermal requirements for the species,
predictions were made on which fish species would be adversely affected
by increased water temperature. Such predictions are, presented in this
manuscript for six representative river systems in the- U. S. The pre-
dictions are based upon three assumptions: (1) that a fish species
would be lost from an area, either by death or emigration, if the water
temperature was raised to the highest lethal limit for that species
reported in the literature, (2) that a fish would be subjected to sub-
optimal conditions that would have a detrimental effect on its activity,
growth and survival or would avoid the reach of the river if the water
temperature in that reach was raised above the preferred temperature of
the species (the highest reported in the literature 'at the highest
acclimation temperature) and (3) that a change in the temperature regime
that did not raise the water temperature above a fish's preferred temper-
ature would not have an adverse effect on that species.
The estimated community response of freshwater and anadromous fish
species to elevated temperature for six representative river systems is
52
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presented in Tables 5 through 10. The river systems chosen were the
Columbia River, Sacramento River, Upper Mississippi River, Lower Missi-
ssippi River, Tennessee River and the Delaware River. No one reach of
each river will contain all the fish species indicated for the river
system. Thus, in using these tables, the reader must be aware of the
fish species in the reach of the river concerned and must adjust for fish
species that are predicted to be harmed but are not present. Similarly,
the user must acknowledge that species not listed in the tables may
indeed occur in the reach of the river concerned, and these should be
considered in the predictions of community response. Estimation of the
effect on any fish species not listed for the river systems (Tables 5 -
10) can be derived from the original data compiled for that species,
(Bush, et^ al. (1971)). If compiled data for that species are not avail-
able, compiled data from fish of the same family or genus could be used
as a best estimate of the response of the fish concerned.
The predictions in Tables 5-10 are based on preferred and lethal tem-
perature data for adult and juvenile fish. They exclude the effect of
increased water temperature on spawning and egg hatching success. Where
specific data for a species were not available, data from closely related
•
species (species from the same genus or family) were used.
A fish species is assumed to be within its preferred temperature range
if the given water temperature (or mixed river temperature) is lower
than the highest reported preferred temperature determined at the highest
acclimation temperature for the fish. The suboptimal temperature range
is the range above which a fish prefers but below its lethal limit. In
this temperature range the fish is considered stressed and an adverse
effect on its activity, growth and survival is predicted. Exposures to
sublethal temperatures over long periods of time can be just as harmful
as lethal temperatures and may cause the elimination or drastic reduction
in the population size of stressed species even to the eventual elimina-
tion of species (Tarzwell, 1970). Except for the anadromous fish, the
53
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Table 5. Predicted Effect of Increasing Water Temperature on the Fish Community of the Columbia River ^ '
°c
8
10
12
14
16
18
20
22
24
°F
46.4
50.0
53.6
57.2
60.8
64.4
68.0
71,6
75.2
% of species.
within pre-
ferred temp.
range
100
95
93
88
60
54
51
28
26
X of species
in suboptimal
temperature
conditions W)
0
5
7
12
40
44
44
65
48
% of species
expected to
be lost from
the system
0
0
0
0
0
2
5
7
26
species
expected
to be lost
from the system
Columbia River smelt
freshwater smelt
Puget Sound smelt
sockeye salmon, chinook salmon, chum salmon, pink salmon,
coho salmon, Dclly Varden, Rocky Mt. whitefish, chisalmcuth
26 78.8 16 44 40 brook trout, Aleutian sculpin, prickly sculpin, Columbia
sculpln, sculpin, starry flounder
28 82.4 9 49 42 lake trout
30 86.0 9 26 65 white sturgeon, green sturgeon, American shad, cutthroat
trout, brox^n trout, longnose sucker, tulchub, squawfish
32 89.6 7 12 81 rainbow trout, coarsescale sucker, yellow perch, fresh-
water burbot, Columbia finescale sucker, chiselmouth,
longnose dace, western speckled dace, Columbia R. chub
34 93.2 0 14 86 redside shiner, threespine stickleback
36 96.8 0 7 93 tadpole madtom, smallmouth bass, black crappie
38 100.4 0 0 100 carp, largemouth bass, bluegill
(1) based on preferred and lethal temperature data for adult and juvenile fish. Where specific data for a
species were unavailable, data from closely related species were used.
(2) the temperature range above the preferred temperature and below the lethal temperature.
-------
Table 6 . Predicted Effect of Increasing Water Temperature on the Fish Community of the Sacramento
species
expected
to be lost
from the system
°c
8
10
12
14
16
18
20
22
24
26
28
30
°F % of species.
within pre-
ferred temp.
range
46.4
50.0
53.6
57.2
60.8
64.4
68.0
71.6
75.2
78.8
82.4
86.0
100
97
95
92
72
72
69
59
38
33
23
15
% of species
in suboptimal
temperature
conditions "'
0
3
5
8
28
28
28
36
44
41
51
49
% of species
expected to
be lost from
the system
0
0
0
0
0
0
3
5
13
26
26
36
freshwater smelt
Sacramento smelt
pink salmon, chum salmon, coho salmon, sockeye salmon,
01 chinook salmon
Aleutian sculpin, prickly sculpin, rough sculpin
white sturgeon, green sturgeon, brown trout, Sacramento
squawfish
32 39.6 8 46 46 rainbow trout, California sucker, Sacramento smallscale
sucker, stripped bass
34 93.2 0 33 67 hardhead, Sacramento blackfish, splittail, hitch, western
roach, tuichub, thicktail chub, threespine stickleback
36 96.8 0 13 87 white catfish, channel catfish, brown bullhead, black
bullhead, smallmouth bass, black crappie, Sacramento
perch, green sunfish
38 100.4 0 0 100 threadfin shad, carp, largemouth bass, spotted bass,
bluegill
(1) based on preferred and lethal temperature data for adult and juvenile fish. Where specific data for a
species were unavailable, data from closely related species were used.
(2) the temperature range above the preferred temperature and below the lethal temperature
-------
Table"7. Predicted Effect of Increasing Water Temperature on the Fieh Community of the Upper Mississippi River
(1)
°c
18
20
22
24
26
28
30
64.4
68.0
71.6
75.2
78.8
82.4
86.0
Z of species
within pre-
ferred temp.
range
100
98
75
61
49
18
14
% of species
in suboptimal
temperature
conditions **'
0
2
25
39
51
82
79
Z of species
expected to
be lost from
the system
0
0
0
0
0
0
7
32 89.6
34 93.2 2
36 96.8 0
38 100.4 0
40 104.0 0
49
35
12
2
0
44
63
88
98
100
species
expected
to be lost
from the system
shovelnose sturgeon, pallid sturgeon, lake sturgeon,
emerald shiner
blue sucker, largemouth buffalofish, snallraouth buffalofish,
quillback carpsucker, rabbitmouth sucker, silver redhorse,
golden redhorse, greater redhorse, blacknose uace, long-
nose dace, creek chub, white bass, yellow bass, yellow
perch, sauger, walleye, log perch, slenderhead darter,
greenside darter, Johnny darter, banded darter
stoneroller, golden shiner, redside dace, fathead minnow,
bluntnose minnow, bullhead minnow, redfin shiner, mimic
shiner, blackchin shiner, ironcolor shiner, spottail
shiner
northern pike, blue catfish, channel catfish, flathead
catfish, yellow bullhead, brown bullhead, black bullhead,
stonecat, madtom, smallmouth bass, black crappie, white
crappie, warmouth, green sunfish
gizzard shad, carp, largemouth bass, bluegill, orange-
spotted sunfish, pumpkinseed
banded killifish
(1) based on preferred and lethal temperature data for adult and juvenile fish. Where specific data for
a species were unavailable, data from closely related species were used.
(2) the temperature range above the preferred temperature and below the lethal temperature.
-------
Table 8.
°C
18
20
22
24
26
28
30
32
Predicted Effect of Increasing Water Temper;
°F % of species
within pre-
ferred temp.
range
64.4
68.0
71.6
75.2
78.8
82.4
86.0
89.6
100
98
78
64
50
26
22
10
7. of species % of species
in suboptimal expected to
temperature be lost from
conditions (2) the system
0
2
22
36
50
74
74
54
0
0
0
0
0
0
4
36
34 93.2
36 96.8
38 100.4
40 104.0
0
0
38
18
4
0
58
82
96
100
species
expected
to be lost
from the system
shovelnose sturgeon, emerald shiner
river herring, blue sucker, largemouth buffalofish, small-
mouth buffalofish, quillback carpsucker, carpsucker,
Alabama chubsucker, rabbitmouth sucker, black redhorse,
yellow bass, white bass, Arkansas sand darter, yellow
perch, log perch, dusky darter, goldstripe darter
stoneroller, golden shiner, fathead minnow, pugnose minnow,
silvery minnow, cypress minnow, blacktail shiner, flagfin
shiner, common shiner, blackfin shiner, river shiner
redfin pickerel, blue catfish, channel catfish, brown
bullhead, black bullhead, flathead catfish, flier, black
crappie, rockbass, warmouth, green sunfish
gizzard shad, carp, largemouth bass, spotted bass, spotted
sunfish, bluegill, redear sunfish
blackstripe topminnow, blackspotted -topminnow
(1) based on preferred and lethal temperature data for adult and Juvenile fish. Where specific data for
a species were unavailable, data from closely related species were used.
(2) the temperature range above the preferred temperature and below the lethal temperature.
-------
Table
ec
12
14
16
18
20
22
24
26
28
30
32
9.
•F
53.6
57.2
60.8
64.4
68.0
71.6
75.2
78.8
82.4
86.0
89.6
Predicted Effect of Increasing
% of species
within pre-
ferred temp.
range
100
99
99
97
96
72
61
51
21
15
6
% of species
in sub optimal
temperature
conditions (2)
0
1
1
3
4
28
39
48
78
81
43
Water Temperat
% of species
expected to
be lost from
the system
0
0
0
0
0
0
0
1
1
4
51
species
expected
to be lost
from the system
brook trout
shovelnose sturgeon, brown trout
skipjack herring, rainbow trout, blue sucker, smallmouth
buffalofish, largemouth buffalofish, highfin carpsucker,
carpsucker, spotted sucker, hogsucker, silver redhorse,
shorthead redhorse, river redhorse, black redhorse,
golden redhorse, white sucker, longnose dace, white bass,
walleye, sauger, log perch, gilt darter, dusky darter
speck darter, greenslde darter, Tennessee snubnose
darter, Johnny darter, goldstripe darter, banded darter,
redline darter, spottail darter, Cumberland fantail darter
34 93.2 1 30 69 stoneroller, golden shiner, bluntnose minnow, river chub,
blotched chub, spotfin chub, bigeye chub, common shiner,
popeye shiner, mimic shiner, Tennessee shiner, silver shiner
36 96.8 0 12 88 muskellunge, blue catfish, channel catfish, flathead catfish,
brown bullhead, stonecat, smallmouth bass, black crappie,
white crappie, warmouth, longear sunfish, orangespotted
sunfish, redear sunfish
38 100.4 01 99 gizzard shad, threadfin shad, carp, largemouth bass,
spotted bass, rockbass, bluegill
40 104.0 0 0 100 white streaked killifish
(1) based on preferred and lethal temperature data for adult and juvenile fish. Where specific data for a
specieswere unavailable, data from closely related species were used.
(2) the temperature range above the preferred temperature and below the lethal temperature.
-------
Ul
Table
•c
12
14
16
18
20
22
24
26
28
30
10.
°F
53.6
57.2
60.8
64.4
68.0
71.6
75.2
78.8
82.4
86.0
Predicted Effect of Increasing
% of species
within pre-
ferred temp.
range
100
100
100
96
82
72
52
36
26
20
% of species
in suboptimal
temperature
conditions (2)
0
0
0
4
13
28
48
62
70
66
Water Tempers
% of species
expected to
be lost from
the system
0
0
0
0
0
0
0
0
4
14
.(1)
32 89.6
34 93.2
36 96.8
38 100.4
40 104.0
0
0
24
32
12
22
66
88
98
100
species
expected
to be lost
from the system
smelt
alewlfe
tomcod, Atlantic sturgeon, shortnose sturgeon, American
shad, Atlantic salmon
alewife, hickory shad, common sucker, spotted sucker,
chubsucker, eastern redhorse, hogsucker, blacknose dace,
pearl dace, silverline shiner, striped bass, white perch,
yellow perch, blackside darter, Johnny darter, creek chub
fallfish, golden shiner, fathead minnow, bluntnose minnow,
northern chub, satinfin shiner, bridled shiner, swallow-
tail shiner, silvery minnow, silverfin shiner, threespine
stickleback
chain pickerel, redfin pickerel, white catfish, yellow
bullhead, brown bullhead, madtom, banded sunfish, blue-
spotted sunfish, rockbass, yellowbelly sunfish, small-
mouth bass
gizzard shad, carp, largemouth bass, bluegill, pumpkinseed
variegated cyprinodon
(1) based on preferred and lethal temperature data for adult and juvenile fish.
a species were unavailable, data from closely related species were used.
Where specific data for
(2) the temperature range above the preferred temperature and below the lethal temperature.
-------
temperature at which a species is expected to be lost from the system was
derived from the highest recorded lethal limit reported in the literature.
During their migration through a reach of a river with elevated temper-
atures, anadromous fish would not have time for full acclimation to the
heated waters. Therefore the temperature at which an anadromous fish was
estimated to be lost from the system was derived from lethal limits
determined at intermediate acclimation temperatures.
The tables of predictions of alterations in fish communities in a river
are meant, to set forth guidelines and highlight possible problems that
are expected to result from increased water temperature. The accuracy
of these predictions must be qualified not only because of the varied
techniques used by researchers who produced the data upon which the pre-
dictions are based, but also because they do not consider the response of
a fish species to the numerous other environmental variables that interact
with a temperature increase to affect the species.
It is impossible at this time to integrate all the biotic and abiotic
environmental variables that change with increased water temperature
into a comprehensive table to predict effects on fish. If possible, such
integration would undoubtedly indicate a loss of more native fish species
from a river system than would result from consideration of temperature
alone. Recognizing the need for such qualification and more detailed
studies at most sites, these predictions will be useful as guidelines in
predicting the consequences from altering the natural temperature regime
in a section of a river. Their purpose is to aid the initial evaluation
of thermal power plant siting and cooling water discharges. In no way do
these preliminary predictions replace the need for extensive studies to
define thermal environmental impacts at the selected site.
Freshwater Invertebrates - The data compiled from the literature search
for lethal temperature limits for freshwater invertebrates are reported
by Bush et j^L. (1972). Temperature tolerances have been determined for
60
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only a small fraction of the total number of freshwater invertebrate
species. There is estimated to be about 8,500 described species of fresh-
water invertebrates excluding protozoa and parasitic classes (Pennak,
1953). The relatively small number (94) of species for which data on
thermal requirements are available precludes community response estima-
tions similar to those given for freshwater fish. However, there seems
to be evidence that in protecting fish, the invertebrate fauna will also
be protected. According to Mount (1969):
"There is a substantial amount of evidence that fishes
frequently are more sensitive to elevated temperatures
than are most of the food chain organisms. This is not
to say that some varieties of invertebrates or phytoplankton
are not more sensitive than fishes, but it does imply that,
under increased temperature, sufficient food organisms will
be present, though they may be of different kinds, to support
the harvested crop."
Further there does not seem to be an example in the literature of a heated
discharge destroying the bottom fauna to the extent that the fish popula-
tion was adversely affected by lack of food organisms. Parker and Krenkel
(1969) report that the heat tolerance of most macroscopic invertebrates
for which data on lethal limits reported by Bush et^ al. (1972) are repre-
sentative of the tolerance range of freshvrater invertebrates in general,
then Figure 6 supports the thesis that protection of the fish will in
general result in the protection of most invertebrate fauna or at least
an adequate fish food supply. Figure 6 gives the mean lethal temperature
for each freshwater fish and invertebrate respectively for which data
were found. The figure suggests that when nearly all fish species are
protected from heat water discharges, the invertebrate organisms should
also be protected. At least, as Mount (1969) suggested, there would be
food organisms present. In an extreme case, Figure 6 indicates that
invertebrate species, many of which are fish food items, will still exist
after all fish are killed by heat.
Because of the regional variation in the composition of invertebrate fauna,
results from the few studies reported in the literature may not be indica-
61
-------
X X X X $
X X
o
m
/M
oo
in
CM
m
-------
tive of the temperature tolerance for an invertebrate community in general
and, therefore, are not a sound basis for guidelines. However, if indeed
the adequate protection of fish species results in like protection of the
principal invertebrate fauna, at least to the extent that there will be
preservation of fish food organisms to support the fish population, then
the use of the tables on the estimated effect of elevated temperature on
freshwater fish communities will suffice as a guideline for both the
protection of fishes and invertebrates.
Freshwater Algae - The composition of an algal community in a river may
change with variations in environmental conditions as the competitive
advantage of some forms is changed. Because of the acute competition
among algal species for space, nutrients, light, etc., conditions need
not reach the lethal limit for an algal species to be replaced (out-com-
peted) by a more tolerant form. The conditions need only be less favor-
able for that species and for more favorable another. Thus, in evaluating
the effect of temperature on an algal community, the optimum temperature
for the species is of prime concern. Differences in temperature optimum
are among the major factors causing succession and dominance in algal
communities. Whether or not increased water temperature would result
in succession to more tolerant forms would also depend on other environ-
mental conditions such as light and nutrient supply. Given sufficient
light and nutrients, changes in water temperature can cause shifts in. the
species composition of an algal community.
In general the optimum temperature for growth of diatoms is from 15 to
25°C (59-77°F), that for green algae 25 to 35°C (77-95°F) and for blue-
green algae 30 to 40°C (86-104°F) (Hawkes, 1969). According to Patrick
(1969) the blue-green algae may assume dominance when temperatures are
maintained above 35°C (95°F) for fairly long periods of time. If '-he
temperature is held between about 32.2 and 35°C (90-95°F) , an increase In
green algae can be expected. Below these temperatures the. native diatom
population will be maintained unless the stream is polluted from other
63
-------
sources and the diatoms have already been replaced by green or blue-green
algae. The effect of passage of algae through the condenser system of a
power plant is uncertain. Temperatures above 34°C (93.2°F) will have a
negative effect on algal survival and temperatures below this may decrease
the photosynthetic activity of the algae. Since complex and dynamic algal
communities are an integral part of any ecosystem, statements of conse-
quences from altering the algal community structure by increased water
temperature can only be speculative at best.
The selection preference towards blue-green algal forms is an advancement
towards dominance by nuisance algae. The community increase in blue-green
algal mass is characteristically greater than the concurrent decrease of
other algal forms resulting in greater total biomass (Coutant, 1966).
Further, the blue-green algae are not utilized in the food web to the
extent that diatoms and green algae are. Thus, the increased production
resulting from blue-green growth is lost to the higher trophic levels.
The consequences of this are relatively unknown.
Marine Fish and Invertebrates - Data on thermal lethal limits for marine
fish and invertebrates have been compiled by Bush et al. (1972). Because
of the relatively small number of marine organisms for which there are
available data, predictions of community response to elevated temperatures
similar to those made for freshwater fish are not possible. Instead,
general statements must suffice for guidelines and more emphasis must be
placed on special site studies.
The thermal tolerances of the major groups of marine fish and invertebrates
are summarized in Table 11 in order of increasing sensitivity or decreas-
ing tolerance. As was the case in freshwater, the most sensitive groups
of organisms are the fish with mean values for thermal tolerance (lethal)
limits of 26.5°C (79.7°F) and 28.3°C (82.9°F) for Osteichthys and
Chondrichthys, respectively. Data are also most prevalent for the bony
fishes. Marine phytoplankton, macroalgae and rooted plants are not in-
64
-------
eluded in the table. However Marble and Mowell (1971) do indicate that
thermal tolerance values are higher for these groups than for fish.
Based on 22 values for marine phytoplankton, they gave a man tolerance
value of 32.2°C (90°F) with a range of 16-41°C (60.8-105.8°F). The mean
reported for red algae (Rhodophyceae) is 30.5°C (86.9°F) with a range of
27.0-35.08C (80.6-95°F) (based on four species). The mean reported for
brown algae (Phaeophyceae) is 37.0°C (98.6°F) with a range of 27.0-42.5°C
(80.6-108.5°F). Three species of sea grasses were reported to have a
mean thermal tolerance of 37.6°C (99.7°F) with a range of 33.0-45.0°C
(91.4-113.0°F).
Although studies in the vicinity of power plants are scarce, several are
significant. The flora of Morrow Bay, California, was found to be much
more sensitive than the fauna to heated water discharge. Abundance and
species diversity of both the aquatic plants and benthic organisms were
reduced for about 500 feet away from the power plant; the flora however
was affected much more severely. The fish population did not seem to be
affected. The mean intake temperature of the water to the power station
at Morrow Bay was 13.3°C (~56°F) with an annual range of 10-15.6°C (50-
60°F). Temperature increments of 5.6°C (10°F) above normal were found
within 500 feet of the discharge (Adams, 1969; Zeller and Rulifson,
1970). North and Adams (1969) suggested that if the water temperature
increased 5.6°C (10°F) above summer ambient in areas on the Pacific coast
all cold water biota would be eliminated and many of the species consid-
ered to be able to tolerate warm water would be adversely affected. An
increase of 1.1°C (2°F) over normal summer temperatures was predicted
to reduce or eliminate cold water biota during warm summers. They report
that the Laminarian kelps of California will be severely affected by a
5.6°C (10°F) increase in water temperature in the summer months and that
canopy deterioration of Macrocystis spp (kelp) frequently occurs when
summer temperatures 1.1°C (2°F) greater than normal are maintained for
several weeks. Such predictions are supported by the Morrow Bay experience.
65
-------
Table 11. Thermal Tolerance of Various Groups of Marime Fish and
Invertebrates
Mean Temperature Range Number
°C °F °C of species
Order Thoracia 47.3 117.1 42.3 - 53.7 5
(barnacles)
Class Xiphosura 42.7 108.9 42.7 1
(horseshoe crabs)
Phylum Protozoa 38.5 101.3 34-43 2
(unicellular animals)
Phylum Mollusca 37.2 99.0 29 - 45.5 44
(molluscs)
Phylum Coelenterata 36.6 97.9 27.4 - 40.9 17
(jellyfishes, hydroids,
sea anemones, corals)
Phylum Annelida 36.2 97.2 31.6 - 42.7 3
(segmented worms)
Phylum Echinodermata 35.9 96.6 29 - 40.7 13
(sea urchins, starfishes)
Phylum Arthropoda* 34.5 94.1 17 - 53.7 52
(arthropods)
Order Amphipoda 34.4 93.9 27.5 - 41 13
(amphipods)
Order Decapoda 32.9 91.2 17 - 43 26
(shrimps, lobsters, crabs)
Phylum Ctenophora 32.1 89.8 26.5-38.2 4
(comb jellies)
Order Eucopepoda 30.3 86.5 28 - 32.8 3
(copepods)
Order Anostraca 30.0 86.0 28-42 2
(fairy shrimp)
Class Chondrichthys 28.3 82.9 26.7 - 29.8 4
(fishes with cartilaginous
skeletons)
Class Osteichthys 26.5 79.7 10 - 38 58
(fishes with at least partly
ossified skeletons)
Order Euphausiacea 25.1 77.2 23.2 - 27 2
(euphasids)
**Includes several orders and a class listed separately
66
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Fewer species and numbers of fish were reportedly found in the discharge
area of the Turkey Point power station on Biscayne Bay, Florida. The
effluent also altered the aquatic plant and benthic communities. Summer
ambient temperatures were 30-31°C (86-87.8°F). Many plants and animals
in the zone +4°C (7.2°F) above ambient were killed or greatly reduced.
The turtle grass (Thalassia testudium) population was killed within the
+4°C (7.2°F) isotherm. The turtle grass provided an important habitat and
food source for many of the invertebrate species of the area. Within the
+3°C (5.4°F) isotherm, species diversity and abundance of algae was re-
duced. The mollusks and crustaceans increased while the number of fish
decreased in this area (Levin £t al., 1972; Marble and Mowell, 1971).
Chemical Impact - The economic efficiency of thermal power cooling systems
is heavily dependent upon chemical treatment in controlling corrosion,
scaling, wood deterioration and biological growth. The chemical treatment
used in cooling systems is discussed in detail by Saad (1971) and in
section Task-3 Engineering Alternatives, of this report. A summary of
chemical treatment employed in cooling systems is presented in Table 12.
Because of the poor and scattered information available in the literature,
the table is far from complete but offers a useful guideline and serves
as a base for future study. Several of the chemicals used in water treat-
ment are toxic to aquatic organisms. Of particular concern are the
chromium and zinc compounds used in prevention of corrosion and the
biocides used to control biological growth.
The scope of this section is to briefly discuss the impact of these
chemicals if released to receiving waters. A discussion of the methods
and cost of treatment of cooling water containing the chemicals used in
control of corrosion, scaling, etc. is presented in section Task-3
Engineering Alternatives and by Saad (1971). The most widely used
corrosion inhibitors in cooling systems are polyphosphates, silicates,
the dianodic combination of phosphates and chromates and the zinc dianodic
combination of zinc, phosphate and chromate.
67
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Table 12.
Summary of Chemicals Used in Cooling System Treatment
PROBLEM CHEMICAL TREATMENT
Scaling Lime softening
Sulfuric acid addition
Chlorination
Orthophosphates (10-30ppm)
pH control
Magnesium oxide
Tannins, lignins,
starches (30-100ppm)
Polyacrylates, chelates
Corrosion Polyphosphates (2-10ppm)
sodium tripolyphosphate
sodium hexametaphosphate
sodium decaphosphate
pH adjustment
Dianodic
phosphate (30-100ppm)
zinc (8-35ppm)
fluoride
chromium
Lignins
Other inhibitors
sodium nitrite
silicates
Chromates (up to 10,000ppm)
Nitrates
Soluble oils
Micro-
biological
Fouling
Tannins, lignosulfanates
(20-50ppm)
Synthetic polyphosphates
Stabilizing chelates
Chlorine
Organic phosphates
Thiocyanates, Copper salts
Other Polyelectrolytes
Fouling Polyacrolytes
Lignosulfanates
Polyphosphates
Wood Biocides (60-120ppm)
Deteriora- Chlorine, Bromines
tion Fungicides
CONTROL OF:
Calcium carbonate
Calcium carbonate
Organic slime control
Calcium phosphate, iron
Calcium sulfate
Silica
Iron
Calcium carbonate, CO,
mineral acids '
Reduce 50-100 times phosphate
needed
Pitting control
Aluminum contamination
Hydrogen sulfide, mercaptors
Corrosion cells
Corrosion
Control for high pH waters
Minimize couples
Anaerobic sulfur reducing
forms
Spore formers
Aerobic sulfur bacteria
Fungi
Algae
Muds, silt, etc.
Oils
68
-------
Examples of the polyphosphates are sodium tripolyphosphate, sodium
hexametaphosphate and sodium decaphosphate. Although very little data
are available, sodium phosphates do not seem to be strongly toxic to fish.
Henderson et al. (1959) report the lethal level of sodium tripolyphosphate
to the fathead minnow (Pimephales promelas) to be 140 mg/£ in soft water
and from 1,300-1,350 mg/£ in hard water.
Phosphorus is one of the nutrients most often cited as the cuase of
accelerated eutrophication resulting in nuisance concentrations of algae.
Significantly increasing the phosphorus loading of a stream or lake may
result in nuisance levels of algae. Surely algal growth can be limited
by many nutrients such as nitrogen, carbon, iron and many trace elements.
However phosphorus is most scarce naturally relative to algal needs. To
limit nuisance growth of algae the National Technical Advisory Committee
(1968) recommended as a guideline that the concentration of total
phosphorus should not be increased to levels exceeding 100 yg/& where
streams enter lakes or reservoirs.
Sodium silicate used in corrosion prevention does not seem to be toxic
to aquatic organisms in concentrations expected to be released from
cooling systems. The toxicity threshold of sodium silicate to Daphnia
magna, a zooplankton species, has been reported at 247 mg/&. Sodium
silicate was found not to be lethal to fingerling rainbow trout (Salmo
gairdnerii) at a concentration of 256 mg/& (McKee and Wolf, 1971). Some
species are much more tolerant.
The use of the dianodic combination of phosphates and chromates and the
zinc dianodic combination of zinc, phosphate and chromate to control cor-
rosion can result in chromate concentrations as high as 500 mg/£ as CrO,
(220 mg/Jl as Cr) and zinc concentrations as high as 35 mg/& in recirculat-
ing waters (Saad, 1971). Heavy metals such as zinc and trivalent chromium
are considered highly toxic to fish (Doudoroff and Katz, 1953). Hexavalent
chromium (in solutions of chromates or dichromates) was reported to be less
69
-------
toxic to fish than trivalent chromium and was classified as intermediate
in toxicity to fish.
A wide range of chromium concentrations are reported as toxic to fish.
For example, McKee and Wolf (1971) show a range in toxicity of hexavalent
chromium to many species of fish from 5 to 520 tng/£ as Cr. In part the
wide range reported in the literature is due to the widely varied experi-
mental conditions and methods used in determining and reporting a toxic
value. This makes it difficult to choose a limit that will prevent fish
mortality from chromium salts. Moreover, the concentration that can be
tolerated indefinitely is more important and also elusive. The National
Technical Advisory Committee (NTAC) (1968) has recommended a fraction of
the TLm (an application factor) that would provide protection indefinitely
(1/20 of the 96 hour TLm value in freshwater at any given time or place).
Further, the 24 hour average of the toxic substance should not exceed
1/100 of the TLm. They also recommend an application factor for metals
in marine and estuarine environments of 1/100 of the 96 hour TLm. Where
application factors have been determined for a specific toxicant NTAC
recommends the use of that factor.
The range in 96 hour TLm values for hexavalent chromium are from 17.6 to
177 with a mean of about 104 mg/Jl as Cr for six species,of fish (Cairns,
1957; Kempetal., 1971; McKee and Wolf, 1971; Patrick et al., 1968;
Trama and Benoit, 1960; Wallen ^t al., 1957). Using an application
factor of 1/100 of the 96 hour TLm, the concentration of hexavalent
chromium assumed to be safe for fish would be 1.0 or 0.176 mg/£ Cr for
the mean or lowest 96 hour TLm value, respectively.
Species in the aquatic community besides fish, such as zooplankton and
algae, seem to be more sensitive to chromium (Dowden and Bennet, 1965;
Patrick _et al., 1968; McCann, 1972; and the National Technical Advisory
Committee, 1968). Data are inadequate to permit a well defined guideline,
but the value of 0.05 mg/£ of chromium recommended by McKee and Wolf (1971)
70
-------
to protect all aquatic life seems to be the best estimate available.
Zinc is extremely toxic to fish. The range of 96 hour TLm to eight
species of fish is from 0.88 to 35.5 with a mean of 10.14 mg/£ Zn (Ball,
1967; Brungs, 1969; Cairns, 1957; Kemp _et_ al. , 1971; McKee and Wolf,
1971; Mount, 1966; Patrick et^ al., 1968). Using the recommended National
Technical Advisory Committee application factor of 1/100 of the 96 hour TLm
for the mean and lowest TLm value gives an assumed safe concentration of
about 0.1 and 0.01, respectively. Since concentrations as low as 0.01
mg/£ of zinc have been reported to be toxic to fish (McKee and Wolf, 1971),
0.01 mg/£ seems more desirable a£ a limit. When exposure to the toxicant
is for short periods, 0.1 mg/£ may provide adequate protection. Other
forms of aquatic life do not seem to be more sensitive to zinc than are
the fishes.
The chemicals used to control biological fouling and wood deterioration
are presented in section Task-3 Engineering Alternatives and by Saad (1971).
Those chemicals for which toxicity results are available are chlorine,
acrolein and copper salts used in prevention of biological fouling and
chlorophenol used for control of wood deterioration. Chlorine is the
most widely used agent in controlling biological fouling and wood
deterioration.
Hydrolysis and ionization of Cl- gas occur when added to water resulting
in HOC1 and OC1 . The toxicity of HOC1 to microorganisms is from 40 to
80 times that of OC1 . There exists a wide discrepancy in the concentra-
tions of free chlorine (HOC1 and OC1~) found toxic to fish (0.03 to 3.0
mg/£) and the concentrations in which fish have been reported to survive
(0.1 to 5.0 mg/&) (McKee and Wolf, 1971). The discrepancy results from
differences in the toxicity of total chlorine due to variations in water
quality and make it impossible to estimate a toxic limit for free chlorine.
Free chlorine will combine with reducing agents such as NO- , H..S, Fe ,
71
-------
Mn and organic matter. Of importance in fish toxicity are the
chloramines. Data cited by McKee and Wolf (1971) give conflicting
evidence as to whether chloramines or free chlorine are more toxic to
fish. The dosage range of chlorine to control slime growth ranges from
1 to 10 mg/& which is similar to that used in an activated sludge treat-
ment plant for disinfection (Metcalf and Eddy, Inc., 1972). Since chlori-
nation procedures used in activated sludge treatment plants have resulted
in fish mortality in receiving waters, it is strongly recommended that the
chlorine residual in the effluent be kept below 0.05 mg/& unless contact
time is less than one hour.
Few data exist on the toxicity of acrolein to aquatic organisms. Concen-
trations recorded toxic to salmon, killifish and shrimp were 0.08, 0.24
and 0.1 mg/£ respectively (Kemp £t al.., 1971). Data are too incomplete
to set a guideline; caution is urged in the discharge of acrolein.
The copper salt most utilized in the control of biological growth is
copper sulfate. Concentrations of copper sulfate from 0.002 to 200 mg/fc
have been reported lethal to many kinds of fish in a variety of water
conditions. Also concentrations from 0.14 to 900 mg/& have been reported
as non-toxic to a variety of fish. Copper sulfate requirements to control
plankton range from 0.05 to 12 mg/&. For control of other types of aquatic
forms concentrations of 0.1 to 20 mg/& have been used (McKee and Wolf,
1971). Because of its high toxicity to aquat'ic life McKee and Wolf (1971)
recommended a threshold concentration of 0.02 mg/& Cu to protect aquatic
organisms in fresh water and 0.05 mg/£ Cu in sea water. The freshwater
limit is very close to that Mount (1968) found as the maximum acceptable
concentration for indefinite exposure (threshold concentration) of copper
sulfate - Cu for the fathead minnow, Pimephales promelas. He reported
the threshold concentration to be between 0.015 and 0.033 mg/£ Cu.
Chlorophenol has been reported to be toxic to fish in concentrations from
8.1 - 58 mg/&. 96 hour TLm values for the fathead minnow (Pimephales
72
-------
promelas), bluegill (Lepomls macrochirus), goldfish (Carassius auratus)
and the guppy (Lebistes reticulatus) have been reported at 12, 10, 14 and
23 mg/£ respectively (Kemp e_t_ al., 1971). Using an application factor
of 1/100 of the mean 96 hour TLm would give a value of about 0.15 mg/Ji as
the maximum concentration assumed to be safe for fish.
The National Technical Advisory Committee (1968) has recommended the
following formula to estimate the permissible concentration of mixtures
of toxic substances:
Ca Cb Cn
~i _ , . • • "T ~^ X
La Lb Ln —
where Ca, Cb and Cn are the measured concentrations of toxic substances
in the water and La, Lb and Ln are the concentrations permissible for
each toxic substance. If the sum of the fractions exceed 1, then restric-
tion of one or more of the toxic substances is necessary. This method of
summing the effects of toxic materials was also reported by Brown (1968).
The method assumes that all toxic materials can contribute in a similar
manner to the total toxicity of a mixture. Brown (1968) cites several
examples where this condition was satisfied often enough to make such a
method a reasonable estimation of the toxicity of a mixture of toxicants
in the environment.
73
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TASK 3 - ENGINEERING ALTERNATIVES
Evaluation of engineering alternatives that can reduce the environmental
impact of thermal power plants is the major goal of this research. Re-
search emphasis has been placed on developing an analytical framework
that will specify the type and resolution of data to be used in such
evaluations. Previously, the evaluation of engineering alternatives has
been accomplished in a fragmented manner. It was necessary in this study
to equalize data; data on heat sources and cooling systems were aggregated
and abstracted to simplify the evaluation, while data on chemicals, intakes
and non-water environmental impacts were generated in this study. The
project officer directed that nuclear radiation impacts be excluded from
this study.
The following sections describe the specific models developed for the
decision tree analysis. The first two sections discuss the results of
major research efforts in this study to (1) define and evaluate engineer-
ing alternatives for screening intakes to prevent damage to the physical
plant and protect fishes and (2) to identify chemicals used in the control
of corrosion, scaling, and fouling of process and cooling waters. The
chemical study also produced a model to evaluate the impact of windage
losses and blowdown chemical concentrations on plant cost and ecological
response. The final parts of this section describe the models that were
modified rather than developed by this study. The engineering alternatives
for cooling were modified from Dynatech (1971), and the engineering alter-
natives for transmission of power were abstracted from BPA data reported
by Meyer (1972). The air pollution, solid waste, and land management
impacts were defined by previous decisions in the tree and are modeled
as dependent variables.
Task 3: Engineering Alternatives, Screening of Intakes
The impact of screening on the environment and the cost of power generation
is directly related to the amount as well as the fraction of available
74
-------
water diverted by the system. For a 1,000 MWe plant a once-through cool-
ing system will require on the order of one to two thousand cubic feet
per second (cfs) of cooling water, while a recirculating evaporative
cooling system will require less than 100 cfs. Screening is required to
protect the plant from debris in the intake water as well as to prevent
aquatic life from entering the plant. Obviously, screens and trash racks
will prevent debris from entering the system and damaging or plugging the
cooling system. Screens are also the most effective device to prevent
fish from entering the cooling system. An alternative to screening are
the use of artificial guidance devices to direct fish away from intakes.
An extensive literature review conducted as part of this research revealed
that little success has been achieved with any form of artificial guidance
including light, velocity, channel configuration, temperature, electrical
shock, bubble curtains or chemicals. These methods can be used to com-
plement mechanical screens; proper design will utilize these factors of
fish guidance. Electric screens have proven successful as barriers but
not as guidance mechanisms.
The engineering alternatives for intakes are basically screens or no
screens. Once an engineering decision has been made, the effectiveness
of a screen is not so much a function of cost, but of design. The design
for minimum fish mortality is almost independent of screen cost. Fish
mortality in screens can be as high as 50 percent when they are trapped
on the screens (either fixed or rotary). Once trapped the fish usually
suffocate unless by-pass configurations are properly designed. Head
losses across the screens should not exceed 0.02 feet of water. The
screen and by-pass configuration must provide a smooth path along the
screens to permit lateral movement of the trapped fishes. Improper
screen designs have corners that trap fish as they move laterally across
the screen or force fish to swim upstream to escape. A comparison of
proper and improper screen configurations is shown in Appendix A.
The cost differences between configurations are minimal and costs for
75
-------
mechanically operated screens are estimated as $240 per square foot of
contact surface ($100 for screen face and $140 for support structure).
Intake structures and pumps are not included in these costs. The annual
operation and maintenance costs were found to be approximately 10 percent
of the capital investment. If a design approach velocity of 0.5 fps is
used, the screening costs would be $480 per cfs.
The purpose of a screen will vary with the type of cooling water source.
Screens for river intakes are usually be designed to prevent the intake
of young anadromous species and they must accommodate the debris, sand
and silts, that can clog the screen and abrade the cleaning mechanism.
Screening design for lake intakes is simplified because of the lack of
currents and the smaller level fluctuations that are encountered in rivers.
In lakes, the location of the intake sometimes can be selected to avoid
areas where fishes are present, while a river intake will usually en-
counter all the fishes. In both lakes and rivers the exit from the
screen by-passes must be designed so the fishes are not guided to areas
where predators can concentrate.
The design of screens for salt water cooling systems is complicated by
(1) the tidal fluctuations that create positive, negative, and slack
flow conditions, (2) the presence of active fouling organisms present in
the water, (3) the presence of organisms and larvae of most important local
food fishes, and (4) the corrosive nature of sea water. A pump is re-
quired to provide flow for proper operation of a screen by-pass. Pumps
are available that can pump fish without major mortalities. Unlike
freshwater fouling organisms, those in salt water can freeze guides and
bearings. Barnacles are attracted to intake, screen, and outfalls and
can plug these systems. These can be controlled by chemicals (see
chemical treatment section) or freshwater backwashes. The most signifi-
cant difference between screens in fresh and salt water is the presence
of eggs and larvae of demersal or pelagic fishes and bivalves (clams,
76
-------
scallops, and oysters), crustaceans (crabs and shrimp), and gastropods
(abalones) in salt water. The screening decision must be based on the
size of organism to be screened. In the case of river screens only the
migrants and adults are of prime concern. In the case of salt water
screens, the abundant eggs and larvae of high valued food fish present a
serious problem. The alternatives may be to select locations in non-
nursery areas or employ infiltration beds. Screens are not effective
against organisms such as larvae and eggs.
Very little cost or performance data are available for infiltration beds
but rough cost estimates suggests $1,000 per cfs. Major cleaning or back-
washing problems will be encountered with infiltration beds. When
screens are used in salt water, they must be constructed of stainless
steel in order to avoid corrosion. While impeding corrosion, the use
of stainless steel increases screening costs. The problem of fouling
remains despite the change in screening material.
The engineering alternatives to minimize fish damage at intakes are a
zero/one decision. Once the type of protection is selected, the design
of the system must consider the behavior of the fishes. Increasing the
cost of screen systems will not always increase the protection. The
proper design appears to be an ill-defined "art" rather than a science.
The research effort of this study was directed toward documenting this
art so that it may be approached as a science.
The screening and intake alternatives modeled in the decision tree are
of the zero/one form. The performance, cost and impact of the intake
system are functions of the intake water flow and the fraction of the
receiving water removed by the intake.
77
-------
Task 3: Engineering Alternatives, Chemical Discharges from Thermal
Power Plants
Treatment of cooling waters used in thermal power plants to reduce corro-
sion or fouling is a common practice. Chemicals can be removed by pre-
treatment of waters supplied to the cooling system or chemicals may be
added directly to the water in the cooling system (Table 12). In either
event, such treatment will create wastewater streams that contain sig-
nificant levels of chemicals that may damage the environment. The
environmental impact of chemicals released by thermal power plants has
not been well documented in the literature, and definition of the
problem is difficult. To provide sufficient data to construct a model
of chemical discharges from thermal power plants, Saad (1971) conducted
a literature review and assessment. No information was uncovered that
would permit the estimation of chemical concentrations in water from air
cleaning devices and only chemical discharges from the cooling system
could be modeled. Over 70 journal articles and documents were analyzed
as a basis for this model. Based on these data, a simple model is de-
veloped for the estimation of chemical discharges from thermal power
plants.
The model is formulated to estimate the chemical discharges from (1) once-
through cooling systems, (2) cooling ponds, (3) evaporative cooling
towers, (4) dry cooling towers, and (5) any combination of these basic
systems such as tapping or variable cooling systems cycles. These cooling
systems are widely discussed in the literature (FWPCA, 1968) and their
characteristics are not repeated in this report.
The general water'balance for thermal power plant cooling system is
shown in Figure 7 and is expressed as
M = E + B + W' (1)
where M = make-up water flow rate
B = blow down flow rate
78
-------
%£
M
A
B
Definitions:
M = Make-up Water (gpm)
B = Blow-down (gpm)
C = Circulation (gpm)
E = Evaporation (gpm)
R = Condenser
W = Windage or drift (gpm)
X = Salt Concentration (ppm)
X = Concentrations (ppm) of the make-up and circulating
m,c
waters respectively
Figure 7. Water and Chemical Balances for Thermal Power Plants
79
-------
E = evaporation rate
W = windage or drift loss rate
C = circulation flow rate in system
Water is discharged from ponds and wet cooling systems by two basic means
besides evaporation. Slowdown is term used to described water withdrawn
from the cooling system to maintain a desired concentration of chemicals
in the recirculating system. Windage and drift are losses due to natural
processes such as ambient wind or tower draft carrying away, but not
evaporating, water droplets. Both methods achieve the same effect in
terms of system water loss. If the drift and windage losses were of the
magnitude necessary to maintain the desired chemical concentration in the
circulating water, no blowdown would be necessary. Dry cooling towers
utilize on only blowdown to maintain chemical concentrations in the re-
circulating system.
For the various cooling system alternatives, this equation will have the
following specific forms;
1. Once-through system
M^C = B; W = 0; E ^ 0
since the water is not recirculated and evaporation occurs after
discharge
2. Wet cooling towers and ponds
M = E + B + W
3. Dry cooling towers
M = B^C; E^W = 0
since the recirculating cooling water is not exposed to the atmosphere
A total solids material balance can be defined for these cooling systems
with the following general expression;
A + MX = (B + W)X (2)
m c
80
-------
where X = concentration of material in make-up water
m r
X = concentration of material in circulating water
A = mass of material added/unit time
Equation 2 can also be used to describe the material balance for specific
chemicals that are added to the cooling system, but do not react chemical-
ly or biologically. In this case X , X and A refer to a specific
me
chemical. When the chemicals added to the cooling system react, two
situations are of great interest. The first is when the chemical added
is toxic or has significant environmental impact and is consumed or
reacts in the system.
In this case can be expressed by modifying equation 2;
YA + MX = (B + W)X
m c
where y is tne fraction of A that is in stoichiometric excess at the amount
of A required for chemical reaction. The second case would be when the
addition of a chemical would cause a reaction with other chemicals in the
cooling system to form a product that is undesirable. The production of
chloro-phenals is an example. The mass balance in these cases must be
based on stoichiometric balances for the specific reactants.
The other major source of chemical discharge from cooling systems is the
chemicals removed by pretreatment of make-up water. The expression for
these wastes is given as;
M(X.n - Xm) + P (3)
where X. = concentration of material in make-up water prior to treatment
in
P = mass of chemicals added in treatment
The expression can be written on a total solids basis or for specific
conservative chemicals. Figure 8 summarizes the alternative chemical
adding processes that can be introduced into the cooling water system.
The volume flow rates for the chemical discharge model are defined by
81
-------
Combined Treatment
B1owd own
Separate Treatment
Pretreatment NO Slowdown
Pre-
Separate Cooling
Water Treatment
No Cooling Treat-
ment Required
oo
to
treatment
Cooling .System Makeup
Plant
Intake
Non-cooling
Uses
No Slowdown
Separate Trt.
Combined Treatment
Combined
Separate Domestic
Waste
Combined Plant Waste
Treatment Facility -
Primary, Secondary,
CoolfngTertiary
treatment
Separate
Separate Domestic
Waste Treatment
Va <-1' 1 i' t~ v
Separate Domestic Waste
No Pretreatment
Non-cooling Discharg
7
Combined w Cooling
~ Discharge
Separate
Non-cooling
Waste Treatment
Figure 8. Power Plant Water System
-------
the turbine heat rate, the type of cooling system, the type of cooling
water, environmental conditions, and the water quality standards for the
effluent or receiving waters. The amount of water passing through the
condensers C, is determined by the amount of heat to be transferred and
the allowable increase in cooling water temperature:
C = -£-|^ (Ib/sec) (4)
P
where H = heat removed per unit time, Btu/sec
AT = allowable temperature increase of coolant, °F
C = heat capacity of coolant, Btu/lb F°
P
When the heat from the coolant is dissipated by evaporation, the water
lost in evaporation is given by:
E = | a (Ib/sec) (5)
where L is the latent heat of evaporation of the coolant water in Btu/lb.
For water L is approximately 970 Btu/lb and C is approximately 1 Btu/lb/°F.
The parameter a is the fraction of the heat rejected from the system via
the evaporative cooling process. Some heat is rejected by advection or
in the form of temperature increase in the blowdown. The fraction of the
circulating water evaporated can be determined by dividing equation (6)
by equation (5). The resulting equation gives the ratio of E to C as a
function of a and AT.
The percent of cooling water evaporated can be expressed as:
E. ctH/L 100 = alOO C AT _ IQQ ATa
C X H/C AT I ~ 1006"
f - • m («
However, as water is lost, the chemical concentration of the remaining
cooling water increases. In order to maintain the concentration at a
desired level, some of the coolant can be withdrawn from the system as
the "blow-down", B.
83
-------
For freshwater coolauu, uu ulow-down may be required in an open recycle
system if windage losses remove a significant fraction of the coolant
stream and maintain the desired chemical concentration.
Using the mass balance of Equation (2) and neglecting the addition of
make-up chemicals and treatment, the ratio of chemicals in the coolant
to that in the make up is:
X
c M
X B + W (7)
m
The sum B + W can be denoted as D, the water discharged from the system.
D can be expressed in terms of the flow rate of circulation water and
allowable temperature use of this water:
X D
m
D - — = (8)
- 1) C (970) (^ - 1)
m m
This expression can be used to define the water discharge required to
maintain any given level of chemical in the cooling water stream.
The parameter a is equal to 1.0 for the case of pure evaporative heat
transfer. For mechanical and natural draft evaporative cooling tower
recirculating system a is usually less than 1.0 but greater than 0.8.
Cooling ponds have natural evaporation in addition to the evaporative
load imposed by the steam plant and thus have a values greater than 1.0.
Figure 9 shows that a small fraction of the circulating water need be
discharged to maintain stable chemical concentrations for cycles of
84
-------
CM _
o
o —
m
o
CJ
14-1
O
O
in
o
CM —
o
o
o
-aAT - 30°F
aAT =
1
5
1
10
15
20
Figure 9.
f*
— ( Cycles of Concentration)
A.
m
Water Discharge as a Function of Cooling Water Chemical
Concentration
85
-------
concentration in excess of 5. Slowdown is necessary for all systems as
current generation evaporative cooling towers are designed for drift
losses of 0.01% or less. Salt water natural draft towers are designed
for 0.002% (circulation) drift in order to minimize detrimental effects
of salt spray. Drift losses can be seen to only be significant in dis-
charge calculations for very high cycles of concentration (greater than
20). The maximum value of cycles of concentration is not limited by
drift in modern towers but by inlet water quality. Both blowdown and
drift remove chemicals from the recirculating system. One discharges to
the air. The other to the water environment. It is essentially impossi-
ble to treat drift as a source. Drift can only be minimized by design.
For sea water systems the cycles of concentration is usually kept lower
than in fresh water systems. The cycles of concentration are kept low to
prevent possible problems with salt precipitation and corrosion. Sea water
systems have correspondingly higher blowdown rates. Drift, as mentioned
early, is designed to be minimal due to possible damaging effects. Some
salt systems with cycles of concentration less than 2 are essentially
flow through devices used to lower discharge temperatures.
It should be restated that all of the above work is concerned with con-
servative materials in open recirculating systems. For example the above
work is not applicable to chlorine which is removed from the system by
the scrubbing action of evaporative towers. The above equations are good
for other chemicals used in the cooling systems for which reaction rate
constants are very slow or which are added in excess concentrations. For
a particular system it will be necessary to determine which water quality
parameter is limiting and set the cycles of concentration by that param-,
eters. Chemical additions may be necessary to adjust other water quality
parameters to values desired. Determination of an optimal chemical
system is a matter for detailed plant design.
86
-------
Calculation of Treatment Costs - The water streams in a power plant that
may require treatment are the make-up water M, the blow-down B, and the
noncooling water stream. The use of water recirculation significantly
reduces the volume of water to be treated to a few percent of that re-
quired for once through cooling. The previous discussion develops the
models to predict the volume of water to be treated. This section
describes the procedure to estimate the degree and cost of treatment.
An alternative to chemical control is mechanical cleaning of the system.
This process was not modelled.
Assuming that water quality standards are established for each type of
pollutant in the wastewater from a power plant, the degree of treatment
can be defined as the difference between the blow-down or wastewater
concentration and the allowable effluent concentration defined by these
standards. Similarly, given the quality of intake water and the quality
criteria for make-up water the treatment requirements for intake water
can be established. Table 13 summarizes the treatment requirement for
removal of anti-corrosion or fouling agents added to cooling water.
Since most of these agents are dissolved and cannot be effectively re-
moved by biological treatment, chemical or physical treatment is required.
Processes such as ion exhange, electrodylasis, or other forms of desalina-
tion, chemical precipitation, or carbon absorption are required. The
cost of these types of treatment reported for waste waters can be expres-
sed as:
T.C. = AFb c/1000 gallons
b, A = empirical constants
F = water volume/day to be treated, mgd
Typical values for the constants are:
• • A b
micros training -:—=- ,.,.-1
coagulation and sedimentation 4 -.02
sand filtration 8 -.4
87
-------
Table 13. Disposal Characteristics and Treatment Requirements of Cooling
Tower Chemicals (after Saad, 1971)
Inhibitor
System
Chromate
only
Zinc
Chromate
Chromate
Phosphate
Zinc
Phosphate
Zinc
Phosphate
Phosphate
Organic
Organic
only
Organic
Biocide
Concentration
in Recirculating
Water
200-500 as in
Cr04
8-35 as Zn
17-65 as Cr04
10-15 as Cr04
30-45 as P04
8-35 as Zn
15-60 as P04
8-35 as Zn
15-60 as P04
15-60 as P04
3-10 as organic
100-200 as organic
10 est. as BOD
100 est. as COD
50 est. as CC14
extract
5 est. as MBAS
30 as Chlorophenol
5 as sulfone
1 as thiocynate
Stream
Standards
0.05 as Cr04
3.0 as Cr
5 as Zn
0.05 as Cr04
0.05 as Cr04
3.0 as Cr
0.3 as P04
5 as Zn
0.05 as Zn
0.3 as P04
5 as Zn
0.3 as P04
0.3 as PO.
4
20 as BOD
40 as COD
0.2 as exotic
organic
0.2 as phenol
0.1 as is (est.)
5.0 as SCN (est.)
Ratio Blow-
down Cone.
Stream Std.
10,000
80
7
1,300
300
2
150
7
35
200
7
200
200
2.5
250
100
50
Disposal
Cost $/
1000 gal
Blow-down
0.70
1
0.16
0.13
0.12
0.14
0.13
1.25
88
-------
carbon adsorption 16 -.3
electrodialysis 21 -.18
These are based on the values of Smith (1967) and can be adjusted for
inflation, cost increases, and amortization schedule. There are no data
for cooling water treatment specifically and these data of Smith are re-
ported for an estimate. The coagulation and sedimentation, and sand
filtration are primarily pretreatment processes, while all can be used
for post treatment.
These costs do not include costs of ultimate disposal of brines or sludges
produced by water treatment. Many water treatment systems discharge these
wastes to the receiving waters or employ land disposal. Cost data for
such systems are inadequate to formulate cost equations. The model will
account for these ultimate disposal costs by a constant, a, to reflect
these added costs. The treatment cost will be represented by the expression
T.C. - aAFb
The levels of treatment have been classified into three major types:
physical-chemical for removal of solids, hardness, and microorganisms;
biological, to remove oxygen demanding waste; and advanced, to remove
toxic chemicals, biocides, fungicides, salts, and corrosion and scaling
inhibitors. Make-up water treatment is limited to only physical-chemical,
while discharges can provide any or all types of treatment depending on
the flow separation designed for the discharge streams. With the excep-
tion of once-through cooling, the cost of treatment should not be large
even for complete purification of the water since the volumes are small
(a few percent of once-through cooling volumes). All costs for treatment
include ultimate disposal of sludges and brines produced in the treatment
process. Treatment is assumed to be sufficient to avoid environmental
impact from the chemicals since total rather than partial treatment is
proposed. Cost data are insufficient to permit the estimation of partial
treatment costs.
89
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Task 3: Engineering Alternatives, Cooling Systems
All thermal power systems require an engineered system to dissipate heat
in a manner not damaging to the surrounding environment. There are six
major methods commonly considered for heat rejection. These systems are
shown in Table 14 together with relevant design parameters as summarized
by Hauser, et^ al. (1971). Several comprehensive discussions (Edinger and
Geyer, 1965, Parker and Krenkel, 1969a and 1969, NAE, 1972, FWPCA, 1970)
have been published giving extensive reviews of alternative cooling sys-
tems. More recent publications (e.g. Thackston and Parker, 1971, Brady,
1970 and Kadel, 1970) have tended to deal with the performance of individ-
ual components or to elucidate solutions and refinements of cooling tower
performance and designs previously set forth by McKelvey and Brooke (1959).
Matching of all the components in a large steam-electric power plant is a
complicated, multiparametric process. Steam plant operating characteris-
tics, environmental conditions (air and water data) and economic factos
must be combined in a calculational package to yield an optimal configura-
tion. Two methods of cooling system calculation appear in the recent
literature (Hauser jit al., 1971 and Dynatech, 1971).
Hauser et al. (1971) discuss the multiplicity of problems inherent in
cooling system optimization studies. They point out that the system under
consideration extends from the last stage design of the steam turbine
through physical system components of condensers, pumps, pipes and heat
dissipation devices. Hauser jet^ £]L. conclude that final detailed design
methods are necessary to predict system performance with enough accuracy
to discern savings and cost potentials of alternative systems.
The Dynatech (1971) sutdy does not contain all parameters specified in
the work of Ha,user et al. (1971) . It was available in a computerized
package and did contain many of the major design parameters of the Hauser
et al. model. The Dynatech package was evaluated and found to give
90
-------
DESIGN PARAMETERS
Once-Through
Cooling Pond
Spray Pond
Natural Draft
Wet Tower
Mechanical Draft
Wet Tower
Dry Cooling Tower
1
1
1
1
1
2
1
1
1
1
2
1
2
2
2
2
2
2
1
1
1
0
0
0
1
1
2
0
0
0
2
2
2
0
0
0
1
1
2
0
0
0
1
2
2
2
2
0
Key:
0 = Parameter is of minor importance in determining system
performance
1 = Parameter is of prime importance in determining system
performance
2 = Parameter is of secondary importance in determining
system performance
Table 14. Major Cooling System Types and Relevant Environmental
Design Parameters (after Hauser et al., 1971)
91
-------
adequate results of necessary accuracy for use in a preliminary site
planning and evaluation model. This package contained routines for cal-
culating the performance of a given steam turbine system with alternative
cooling devices (once-through river, cooling pond, mechanical-draft
evaporative tower and natural-draft evaporative tower). This package of
cooling routines slightly modified, formed the cooling calculational level
in the thermal siting analysis decision tree.
Traditional analysis of engineering cooling alternatives ends with the
determination of the economic parameters for a given system and possibly
a determination of the resulting river temperature from a thermal dis-
charge. All cooling analyses are extended in this study to include pre-
diction of biological impact based on the mixed river temperature at the
point of thermal discharge. This provides an additional measure of the
impact of thermal power plants. Table 5 through 10 presented in Task 2
are used as the basis of this analysis to predict the response of fishes
to the river in question with and without the thermal plant in operation.
Similarly, the chemicals used in cooling systems are defined and their
impact estimated.
Once-through, pond, mechanical draft wet tower and natural draft wet tower
performances are calculated using the basic Dynatech (1971) cooling pack-
age. Hauser and Oleson (1970) summarize the effects of alternative cool-
ing devices on water consumption as functions of several parameters. For
most closed cycle evaporative devices, the water consumption is roughly
twice that of a once-through cooled system (- 1 gal/net kWh v. -
0.5 gal/net kWh).
Use of an evaporative cooling system does not necessarily eliminate dis-
charges to the natural waters. These discharges to the atmosphere and
receiving waters are summarized for the various cooling systems in
Table 15.
92
-------
COOLING DEVICE
Cooling Pond
Spray Pond
Mechanical Draft
Evaporative Tower
Natural Draft
Evaporative Tower
WATER DISCHARGES
WATER
QUANTITY
Variable
Variable
Blowdownl
Blowdown
QUALITY2
System
System
System
System
AIR DISCHARGES
QUANTITY
Evaporation
Evap. + Windage
Drift + Windage
3r Evaporation
Drift + Windage
•f Evaporation
QUALITY
Pure
System-*
System-^
3
System
Notes:
1. Slowdown quantity applies to recirculating systems only. Some ponds may have no
discharge. Topping(flow-through) devices will have large discharges of essentially
the same magnitude as the condenser flow.
2. Assumes no treatment. Quality of effluent is the same as is found in the operating system.
3. Quality factor applies to windage and drift losses only. The evaporite is assumed pure.
Table 15. Evaporative Cooling System Discharges to Air and Water
-------
Task 3: Engineering Alternatives, Control of Non-Water Impacts
The decision tree has been constructed to compute land requirements and
non-water waste loads that can be used in evaluating the impact of power
plant siting. The land requirements for any given path of the decision
tree are the sum of the power plant, cooling and transmission require-
ments. The power plant land requirements are 500 acres for the baseline
plant, unless modified by input. The cooling system land requirements
are computed from the Dynatech (1971) model for cooling ponds, or .0046
acres/MW for mechanical draft system and .0007 acres/MW for natural draft
systems (Kolflat, 1971). The transmission land requirements are correlated
with transmission voltage and distance. Table 16 summarizes the values
used for this study. The total land requirements can be used to estimate
land cost as well as the increasing demand for land as power demands
increase in a region.
When fossil fuels are used to generate power, the air emissions are
estimated by the model. The pollutant emission factors of Duprey (1968)
and Marks (1958) have been incorporated in this model. The emission
factors are:
pounds/ton of coal
Aldehydes 0.0005
Carbon Monoxides 0.5
Hydrocarbons 0.2
Oxides of Nitrogen 20
Oxides of Sulfur 38
The ash is estimated as 0.24 pounds per kWh and is either a solid waste
or emission problem. The water emissions are computed as the sum of
vapor and droplets. Vapor is equated to evaporation loss and droplets
are equated to windage losses. The amount of chemicals released in the
windage is based on the system concentration.
94
-------
Transmission
Voltage (KV)
345
500
700
01
•H
O
tu-
ft
td
u
4J
•H
O
^1
•rl
U
d)
l_l
o
3
O
Q
500
1,500
3,000
/— s
J-l
5
CO1
1
W-l
O
1
4->
(~f
60
*H
PH
13
Q)
H
•H
a*
125
150
175
CO
OJ
t-i
u
<^
N*^
QJ
H
«!-)
y
^|
cu
cx
CQ
a)
15
18
21
QJ
•i-\
&
0)
t=5
J_j
cu
ft
n)
cu
M
0.03
0.012
0.007
Q)
a
c ^^
n) .
4-1 -H
en g
•r^ ^"^
P
*^^
co
CO
0
j_3
^-^
S £*^
•3 ^i— /
c
a
2.2/200
1.2/300
1.0/300
13
0)
4-1
4-1
•H
B
CO
CO C
C^ Co
O M
•H 4J
4J
O •
C -d
3 B
fx^
o
CO O
en t-H
O ^j
2.2 + .05(x-2)2
1.2 + .04x
1.0 + .03x
x = transmission distance in 100's of miles
Table 16. Transmission Line Parameters (Mclntyre, 1970)
95
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The computations of these emissions provide bases for treatment decisions
to reduce emission levels. The introduction of additional nodes for these
decisions is simple in the proposed analytical framework.
96
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COMPUTER MODEL - Calculational Routines
The following sections discuss the elements of the program. The discussion
begins with the main call sequence and proceeds through the subroutines
used at each tree level.
The thermal analysis tree structure was conceived to contain six decision
levels. The levels are:
1. site - transmission path
2. plant type
3. condenser cooling means
4. water intake screening alternatives
5. water chemical treatment alternatives
The tree diagram was selected as the method for portraying the range of
alternatives for a given site. Evaluation of the tree proceeds from left
to right with the information from the previous level(s) providing input
to the calculational routines in the subsequent levels. For each iden-
tified set of alternatives the payoff routine outputs:
1. water use information
2. air and water discharge information
3. thermal status of representative water body
4. land use information
5. power cost information
A complete program listing and list of variables are provided in the
Appendix B.
The main program (BUILD) part of this work is composed of two sets of
call sequences. The main routine is an executive routine only. All
system calculations are performed in the subroutines for each level of
the tree diagram. The first action of the main program is to list or
not list the thermal plant tree diagram depending upon the value of tree
print flag (ITO). The tree is printed by calling the subprogram TREE.
97
-------
The program call sequence can operate in either of two modes depending
upon the call sequence control flag (CF). For ICF = 1 the program is
placed in the single payoff mode. For ICF = 0 the program will evaluate
the payoffs for all options subject to the values of the other control
variables. For the complete tree mode the number of payoffs calculated
depends upon the number of sites being evacuated (NSEI), the maximum
number of transmission options for each site (MNTR) , the maximum number
of plant types to be considered (MNT) and the maximum number of cooling
alternatives (MNC).
For the individual payoff claculations the control flags are specified
previously. Only two maximum bound flags (MNTR, NSEI) must be input for
this calculation option.
The program will process calculations for any number of sites. The
program treats each site as a separate case. Either a complete set of
alternative payoffs or a single payoff can be calculated for one site.
Subroutines SITE and PLANT are input routines. The subprogram names
describe the functions of each subprogram. All variables with respect
to the site to be valuated are input to the calculation via the SITE
routine. Input blocks, $ C0NFLG, $INPUT, $ WATRV and $VARAMB in addition
to the two title cards, are handled by this subprogram. Control flag and
site related data are input to common blocks for access by the calcula-
tional routines. The listing of input data is controlled by the 100
flag. For short form output (100 = 1 or 2) the input data is not listed.
Subroutine PLANT handles all data relevant to the type of power plant
being evaluated. Power plant data may either be input or internal. Two
complete internal sets of power plant input data are provided. For
NT = 1 and IPF = 0 a representative set of light water reactor type
parameters are available. For NT = 2 and IPF = 0 a representative set
of coal-fueled plant parameters are available.
98
-------
Following the input section of PLANT are data conditioning and checking
routines from Dynatech (1971). Printing of generated values is con-
trolled through the use of the 100 flag. At the conclusion of the plant
subroutine sufficient information is available for transmission calcula-
tions. The transmission calculational routine TRANS is called as the
final action of PLANT.
Length of transmission lines is a significant parameter in the determina-
tion of system costs and efficiencies. Transmission calculations are
treated in two discrete pieces. A new construction distance DISTR (I))
is used to treat the length of new transmission lines (in miles) that
must be added to the existing network in order to accommodate the new
plant and its added generation capacity. With respect to the new
construction distances the following parameters must be available for
use in determining capital and operating costs of the new lines for
every transmission option I.
Variable Definition and Units
CCPM(I) Capital cost of new lines ($/MI-1000MW)
Y0PCM (I) Yearly Operation and Maintenance Cost
($/MI-1000MW-YR)
The fixed charge (.81 $/KW-YR) and the TMCST (I) value can be adjusted to
fit the nature of the grid under study. The total transmission cost is
the sum of the new construction and load center related costs.
TRCST = TPEM + TC
Land use by transmission lines represents another significant parameter
relevant to the siting of thermal power plants. Land use is calculated
using data obtained from Mclntyre (1970). Using 500 KV as a transmission
voltage, a right-of-way 150 feet wide is required for transmitting 1000
to 1500 MW of power. Land used is then classed into new construction
(SPNC) and grid (SPTR).
99
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SPNC = (DISTR(I)*150.*5280.)/43560. (ACRES)
SPTR = (DISLC(I)*150.*5280.)/43560. (ACRES)
Cooling calculations are based on the slightly modified cooling calcula-
tion package written by Dynatech (1971). A dummy routine C00LING is used
to call the appropriate cooling routine depending upon the value of the
cooling index (NC). The options available are:
NC = 1, once-through cooling
NC = 2, artificial cooling poind
NC = 3, mechanical draft evaporative cooling tower
NC = 4, natural draft evaporative cooling tower
The logic of all Dynatech cooling routines is similar. Calculation is
started at the minimum condenser operating temperature. For systems with
a specified set size condenser the approach temperature is incremented
until the maximum condenser temperature is reached. For systems designing
both condenser and cooling device for a recirculating system, the program
increments both condenser and approach temperatures to determine the
minimum cost system. Design of topping systems for a system with a fixed
condenser is a one stage process with condenser temperature determined by
trial and error to obtain the design outlet temperature. Topping systems
that contain a condenser design are iterated until a maximum condenser
temperature is obtained. For all systems the least cost system is the
design output. For the backfitting case (condenser specified) with
topping operation only one system is evaluated.
Three basic screening configurations are available for power plant use:
1. basic intake only
2. rotating/self-cleaning screens
3. infiltration beds
For this work the basic intake system was taken to be that which was
necessary to withdraw water from the environment and protect the plant
100
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machinery and piping from damage that might be caused by ingested objects,
living or inert. This intake system design does not protect fish only
plant components.
Two design options are available in the current model:
NSC = 1, basic intake structure only
NSC = 2, rotating screens installed.
Screening cost functions have been developed. Costs are a function of
the intake flow rate. The costs are taken to be the sum of a fixed fac-
tor plus a flow dependent term.
CC0STS = Fl * FLOI + F2 ($)
The screened flow rate (FL0I) is in cubic feet per second (cfs). The
values of Fl and F2 are a function of flow rate and are illustrated in
Table 17. The cost per unit power generated is:
CC0STS*ANFCR
CCPUP =
(mills/kWh)
PSIZE*1000.*CAPFAC*8.76
Operation and Maintenance yearly costs are taken to be ten percent of the
capital costs
0.01 * CC0ST
OCPUP =
(mills/kWh)
PSIZE * 1000.*CAPFAC*8.76
The total cost to the plant is the sum of the two unit costs. Fish damage
from intake into the plant is considered to be zero.
Intake-only costs are valued at sixty percent of system costs with screens.
X2 = 0.6 * CCPUP
X3 = 0.6 * 0CPUP
Flow Rate (cfs)
FL0I > 500
10 < FL0I < 500
FL0I < 10
Fl ($/cfs)
240
1000
0.0
F2 ($)
0.0
0.0
8000
Table 17. Screening Cost Factors as a Function of Flow Rate
101
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An intake damage function is constructed for the intake without screens.
The damage function assumes that the percentage of fish damaged is propor-
tional to the percentage of river flow withdrawn by the plant. The value
of the resident fishes in dollars per year is assumed to be known and in-
put. This valuation should reflect both commercial and sport fishery
elements .
Using this relation a first approximation to the impact of a non-screened
plant can be determined. A benefit/cost ratio may be constructed if
desired. The benefits would be damage prevented. The costs are the
difference between the intake only costs and the screening system costs.
This formulation assumes that all fish damage is prevented with the
installation of the rotating screens.
Chemical discharge costs are calculated in this version of the code.
Current options are no treatment (NCH = 1) and treatment (NCR = 2) . Costs
involved are calculated on estimated treatment procedures and are based on
the cost estimates of Smith (1967).
Discharged effluents contain original chemicals from the intake water
plus chemicals added to protect plant components. The concentration of
the original chemicals is increased with the use' of a closed-cycle cool-
ing system through water loss to evaporation (cycles of concentration) .
On a total solids basis the amount of original chemicals remains roughly
the same with system losses occurring due to deposition of material in
the cooling system, blowdown, drift and windage. Added chemicals are
assumed discharged at their operating concentrations. Nonconservative
chemicals such as chlorine may be removed from the system or reduced in
concentration through the scrubbing action in the system tower or biologic
uptake by resident biota in a cooling pond. Disregarding reactions in
the relative short residence time within the system, once-through cooling
systems do not modify the natural chemical content of the water used for
102
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cooling. Chlorination in once-through systems was discussed in the base-
line section. Mechanical condenser cleaning would not involve addition
of chemicals. Removed material that was not collected and treated would
be discharged to the receiving water.
The subrountine PAY0FF calculates the air, water, solid waste, and land
use impacts and an itemized total system cost for the set of alternatives
specified. For each plant type at a site there are sixteen identified
alternative decision paths per transmission alternative.
Air impacts of the plant are specified by fuel type. Coal emissions from
Duprey (1968) were used for emissions. The calculation gives emissions
for the base heat rate condition plus the perturbation effects (if any)
on the air emissions from non-once-through cooling means. Nuclear emis-
sions are not closely tied to the heat rate value and are estimated at
0.238 microcuries/kWh.
Water impacts of the plant are specified as a function of cooling means,
screening and chemical discharges. Tables of fish community response for
the six representative rivers evaluate the thermal status of the community
with respect to before and after the presence of the plant.
Land use impacts represent the total acreage used for plant, cooling and
transmission. Each component is itemized.
Cost factors are output with respect to the capital costs involved at
each decision level plus the cost per unit power production (mills/kWh).
Solid waste impact is keyed to fuel residues from the fossil fired plant.
Information from Duprey (1968) and Baumeister (1958) was used to obtain
a value of 0.24 Ib/kWh for ash and other solid matter resulting from
fossil fired combustion. This value is increased as the heat rate
increases. Nuclear fuel presents no on-site solid waste problem as its
103
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processing is handled in a separate area.
The payoff calculation is presented in a two page summary form that gives
the above information without the detailed results of the sublevel calcu-
lations. This output form makes the program more desirable for remote
terminal acess.
COMPUTER MODEL - Input Data
This section describes the input data necessary to run the static siting
model. It is not a complete listing of all variables contained within
the program. The term data card and information line will be used inter-
changeably in the text. A complete listing of the input values, names
and units is described in the following section of text. With the excep-
tion of title cards and the tree print-out control card. All data is
input via NAMELIST. This input method is standard on most F0RTRAN IV
compilers and eases the field counting problems encountered when using
standard, format-dependent input statements. The problems of counting
spaces and setting tabs are alleviated with the NAMELIST input procedure.
The input quantities are now described in the order in which they are
used by the program. Figure 10 is a sample data set.
Variable Definition Permissible Range
NS Number of site being analyzed 1 to 4
NSEI Total number of sites to be analyzed 1 to 4
NTR Number of transmission options for site (NS) 1 to 5
NT Plant type designation number: 1 or 2
1 = Water Reactor 2 = Fossil fueled
MNT Number of plant types to be evaluated for 1 to 2
sites (NS)
NC0 Cooling System Code Number 1 to 4
1 = Once-Through
2 = Pond
3 = Mechanical Draft Wet Tower
4 = Natural Draft Wet Tower
104
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IPF=0,ICF=1, 100=2 $
SAMPLE DATA INPUT CASE FOR SITING PROG.
PLANT ON RIVER CLASS NO 1
$ INPUT TOB=90.»TWB=73. »WIND=b. »KAD = J^OO. »PKPAGK=100U
5.,YOPCI"H1)=325.,TMLS<1)=O.UUU5»TMCST<1)=0.01»ANFCK =
CCPM(l) =300000. .DISTRt 1)=15. tDISLCl 1)=150. $
*WATRV
FISH = 6000000. » NR=1 $
SVARAMB TAMDBd ) =9i>. »29. » 80. » ^0. • TAMvi/B ( 1 ) =75 . .20. »4b. » 33. » TAM^V ( 1 ) =oa. ,
60.»67.»60.iAMWlND( 1 ) =6. » 20. » 8. , 7. »A'"'KAD ( 1 ) =5600. » 2i4Q. » 5500. » 1000. *
Figure 10. Sample Data Set for Static Stiting Model
105
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MNC Maximum number of cooling alternatives 1 to 4
to be evaluated
NSC Screening alternative code 1 to 2
1 = No environmental screening
2 = Environmental screening
NCH Chemical treatment alternative code 1 to 2
1 = No chemical treatment
2 = Complete chemical treatment
IFF Power plant input factor 0,1
0 = Program uses internal power plant data
1 = Power plant data in input
ICF Program control mode 0,n
1 = Individual payoff calculation
Any other value full tree mode
100 Output option control 1 to 3
=1 Only payoff values output
=2 Level calculations and payoff values output
=3 Input data, level calculations and
payoff values output
MNTR Total number of transmission paths to be evaluated
for Site (NS) 1 to 5
The first value input is the control fig (ITO) for printing out the tree
display. The format for ITO is II. A value of 3 causes the tree to be
displayed. Any other value entered (including 0 or a blank card) will
defeat the printing of the tree. It utilizes almost the full width of
the line printer and is not cropped to the 72-column width requirement
of a teletype terminal. Therefore, IT0 must not be set equal to 3 for
remote terminal use. A copy of the tree may be kept with the input
instructions for ready reference.
The first NAMELIST input block is named C0NFLG. This input block contains
the information that controls the calculational sequences. All input
values in this block are integers. When using the NAMELIST input routine,
it is not necessary to input variables in any particular order. The tree
printout (Figure 11) can be referred to as an aid in selecting individual
payoff calculation input controls. The tree shows the range of options
106
-------
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-------
that are available.
For an individual payoff calculation concerning a particular site, plant
type, cooling system, screening alternative, and chemical treatment
process, all of the above variables must be input with ICF equal to one.
With ICF equal to one the internal call sequence for alternative combina-
tion generation is defeated. When this is accomplished, values for NS,
NTR, NT, NC0, NSC, and NCR, which are usually internally controlled, must
be supplied as specified above. For a complete tree evaluation these
values must not be input.
Output option control is provided to shorten the printed output for remote
terminal use. The entire tree contains 32 discrete payoff calculations
in its present form. Thus, if the tree were evaluated in its entirety
with input data and level calculations, a teletype unit would become out-
put bound and non-optimally utilized. Lengthy runs may be submitted
remotely and channeled directly to the central computer line printer.
For remote terminal output individual payoff calculation mode (ICF = 1)
and shortened output 100 equal 1 or 2 are recommended.
The next two cards are title cards. The first forty spaces on each card
are utilized. Any characters may be entered including blanks. For
example, the first card may contain a plant name and the second card
the site location.
The next NAMELIST input block (input) is for variables specifically re-
lated to the plant site. All of these variables are required input for
complete program execution. Variables input are:
Variable Description Range and/or Units
TDB Design dry bulb temperature °F
for cooling systems
TWB Design wet bulb temperature °F
for cooling systems
108
-------
WIND Design wind speed
RAD Design net radiation
PRPAGR Land cost
NTAMB Number of alternative ambient
operating conditions
ACC Accessibility parameter for
adjustment in base plant cost
due to moving of major
material and components
C0NSCT Escalation parameter for
adjustment in base plant cost
due to local labor costs
Y0PCM (I) Yearly operating cost of
transmission lines on Itn
path
TMLS (I) Fraction of power lost per
mile transmitted on Itn path
TMCST (I) Transmission cost
ANFCR Fixed charge rate for plants
at this site
CCPM (I) Capital cost of new transmission
lines on Itn path
DISTR(I) New construction transmission
distance for ltn path
DISLC (I) Load center distance (total
including DISTR (I)) for Ith
path
ra.p.h.
Btu/ft2/day
$/Acre
0-4
0 - 100
(% of base cost)
0 - 100
(% of base cost)
$
MI-YR-1000 MWe
1=1, MNTR
fraction lost
mi
1=1, MNTR
$
1000MW-YR-MI
% x .01
$/1000MW-Ml
(1=1, MNTR)
mi
(1=1, MNTR)
mi
Input values specifically, pertaining to the native water body are input
in the NAMELIST block WATRV. All variables in this block are required
input.
Variable Description
QFLRIV Design river flow rate
WIDTH Design river width
DEPTH Design river depth
Range and/or Units
c.f.s.
ft.
ft.
109
-------
TAVH20 Design available river temp. °F
NH20 Available water type -1 to 1
(Integer) = -1 salt water
= 0 untreated freshwater
- +1 treated freshwater
FISH Yearly capital value of fish $/yr.
at site (including transient
and resident populations)
NR River classification index 1 to 6
= 1 Columbia River
= 2 Sacramento River
= 3 Upper Mississippi River
= 4 Lower Mississippi River
= 5 Tennessee River
= 6 Delaware River
Next follows the NAMELIST input block VARAMB. VAKAMB is the input data
block of the evaluation of the design plant at the various operating con-
ditions at the site under consideration. The entering of this data block
into the input information is optional. The program will only seek this
data block if NTAMB is greater than 0. If NTAMB is equal to 0, this data
block must be omitted from the input string.
The variables are input in sets with incident radiation, dry bulb tem-
perature, wet bulb temperature, wind speed, and available water temper-
ature needed to comprise an input data set. The variables are divided
into sets by the storage index. Variables stored in these arrays should
be keyed into sets based on the storage index value (e.g. all variables
in the first set entered as the first element in their respective arrays).
Variable Description Range and/or Units
TAMDB (I) Dry bulb temperatures °F
(1=1, NTAMB)
TAMWB (I) Wet bulb temperatures °F
(1=1, NTAMB)
TAMRV (I) River water temperature °F
(1=1, NTAMB)
110
-------
AMWIND (I)
AMRAD (I)
Wind speeds (1=1, NTAMB)
Net over-water radiation
values (1-1, NTAMB)
m.p.h.
Btu
ft2/day
All of the following NAMELIST input blocks pertain to internal steam plant
detailed operating characteristics. If IFF = 1, all must be included. If
IFF = 0, all data blocks must be omitted. The namelist data sets will be
discussed in order of their appearance should they be included. All
variables listed must be entered for proper program execution (except as
noted under $ SYSDTA). The variables listed under their namelist block
names are:
Variable Description
$ PLPARM
PSIZE
CCPKW
FUCST
SPGR
NCAPS
(integer)
$ CATURL
CAP (I)
T0TLD (I)
C0LPCT (I)
NHRPTS (I)
(integer)
PCMIN (I)
PCMAX (I)
Plant electrical output
Plant capital cost
Fuel cost
Land needed by steam
(excluding cooling and
transmission facilities)
Number of plant operating
capacities
I design operating capacity
(1 to NCAPS)
Number of hours spent @ CAP (I).
Must total to 8760 hours (1 year).
Fraction of time cooling system
is in use for CAP (I)
Number of turbine heat rate points
input for CAP (I)
Minimum is 3; maximum is 6
Minimum condenser operating
pressure for CAP (I)
Maximum condenser operating
pressure for CAP (I)
Range and/or Units
Megawatts (MWe)
$_
kW
C/106 Btu
Acres
1 to 6
1 to 6
hours
O.-KL.
3 to 6
in Hg.
in Hg.
Ill
-------
$ TUR0PD
HRP (I,J)
TURBHR (I,J)
PCBASE
PCTAMB (I,J)
$ SYSDTA
NSYS0P
(integer)
NSPC0N
U0VALL
(optional)
AREAC
SPFL0W
Condenser pressure (I)
corresponding to TURBHR (I)
for capacity J
Ithturbine heat rate for Ith
capacity, minimum of 3 for each J
Base design condenser pressure
t*H
Fraction of time spent at I
capacity for J1-" ambient
condition. Sum over J must
equal 1.0 for every I.
System operating flag
= 0 Closed cycle operation
= 1 Cooling system is of helper
design (once-through base)
Condenser design flag
= 0 Condenser is designed
internally
= 1 Condenser is specified
by the following data
Heat transfer coefficient
Input only if NSPC0N = 1
Condenser area.
if NSPC0N = 1
Input only
Condenser flow rate. Input
only if condenser is
specified (NSPC0N = 1)
in Hg.
Btu
kWh
in Hg.
0.0*1.0
0, 1
0, 1
Btu
hr/ftV°F
ft2
Ibm
hr
NAMELIST input blocks have a few basic rules. Column one must always
be left blank. The NAMELIST block name is the first entry starting in
column two. The proper entry is $NAME with no imbedded blanks. One
blank space is left between the name and the first data entry (e.g.
$NAME A = 1.,). Each input value is separated from the next with a
comma. Each line of information (data card) must end with a complete
record (e.g. A = 1, or Z = 100._$). The last value input is followed
by one space and a dollar sign indicating the end of the NAMELIST block
(e.g. Z = 100._$). Variables may be input in any order desired.
112
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SECTION V
DYNAMIC ASSESSMENT
Incremental decision making is a symptom of our society. The fragmenta-
tion of decision making, the market mechanism that ignores externalities,
and the lack of adequate feedback mechanisms permit rational appearing
incremental decisions to become a chain of events with sufficient intertia
that environmental degradation cannot be avoided without major societal
changes. Many water pollution problems were not the result of initial
decisions to locate waste discharges, but the result of the magnetic
attraction of development for other development with accompanying increases
in waste discharges. Another aspect of incremental environmental decision
making is the satiating factor where "too much of a good thing" can create
environmental damage. In the Pacific Northwest the hydroelectric projects
on the Columbia River are at a satiation point from both a physical and an
environmental standpoint. Future power generation must come from thermal
systems.
The static model developed for this study is an incremental decision making
tool. It does not address the questions of established precedence of a
siting decision, nor the subsequent options that such a decision may
eliminate, nor any of the problems cited above. While it is naive to
think that a perfect model of the future can be developed or that a
twenty year plan for thermal plant locations can be constructed without
periodic updating, it may be possible to examine alternative futures and
select a siting pattern that is least sensitive to social change.
The goal of the dynamic assessment effort in this study was to evaluate
methods that permit the examination of a sequence of siting decisions
rather than just the impact of a single plant location. Questions that
were addressed included:
1. Given that n plants must be located in a region in t years, identify
the sequence of sitings that minimize environmental impact.
113
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2. Will an incremental optimal siting decision now create irreversible
conditions that preclude optional siting in the future?
3. How sensitive is a given site location to alternative patterns of
urban and industrial growth in a region?
4. What are early warning signals that indicate current siting patterns
will create serious environmental impacts in the future?
Models for Dynamic Assessment
A major concern of siting models is that they are severe abstractions of
the real world which contain so many assumptions that all reality is
lost. In this study, each model was studied to determine what real world
conditions were simulated and what assumptions were necessary. Since the
focus of the dynamic assessment was time variance, this was given greater
value than specific static details concerning power plant performance.
Three different dynamic models were studied for applicability to the ther-
mal siting problem; the linear-programming (LP) transportation model,
the Forrester-"dynamics" models; and dynamic programming. Each model was
constructed for a 20 site system and a 20 year time period.
The linear programming-transportation model addresses the problem: given
i generation sources and j load centers, and a demand for service as a
function of loads, determine the optimum assignment of sources and load
centers over time. The model requires as input the cost to generate and
transmit power from i to j, C.., the demand at each j, D., and the
maximum source capacity for each i, S.. The linear programming algorithm
will allocate the power from sources to the sinks, P... The mathematical
model is written:
Min. I E C.. P. .
, U U
114
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Subject to the constraints that
Z P. . > D.
U- J
Obviously this model lacks a time variable and cannot be used directly as
a dynamic model. Two modifications are possible; (1) let j represent
time rather than space, so that D. represents load demands at different
time periods, or (2) add a subscript koto represent time periods. The
first modification sacrifices spatial resolution of load centers, but
does introduce the time variable. The second modification increases the
number of variables geometrically and presents a serious computational
problem. Both require the introduction of a constraint establishing the
maximum power generation at each source for each time period. All linear
program formulations impose an abstraction that the cost functions are
independent of power produced or transmitted (no economy of scale).
This constraint can be avoided by repetitively processing the model
employing different coefficients until the cost coefficient and magnitude
of power P.. are correct, but this can be very time consuming. The ad-
vantages of the linear program are that standard computer programs exist
to process the model, there is minimal input preparation, and sensitivity
analysis can be easily obtained. The disadvantages are that demand and
supply are independent and no feedback can be represented. The model
cannot be made to relate increased supply at a point to increased power
demand or to increased cost of producing power. One would expect power
costs to increase in aregion as more development occurs or population
increases. The linear program cannot provide an abstraction of these
interactions.
An example of the dynamic assessment of power plant siting using linear
programming is shown in Figure 12. Very complex models are costly; for
115
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Objective Function:
Minimization of total costs
EEC. .P. .
i j 1J 1J
Constraint Equations :
Demand must be satisfied
EP . . * D .
i ij J
Power produced at site i and time j must not exceed the maximum
capacity of. site i
IP. .> S.
31J X
Water interaction limitation factor
I E w..P. < W.
i k ik U J
Air interaction limitation factor
I 2 a..P < A.
i k lk «" J
i
Transmission limitation path
I 1. .P. . < L.
1J iJ 3
Where: i = site index
j = time index
k = dummy site index
Cij = cost to produce and transmit power from site i at time j
P.ji = Power produced at site i and time- j
D^ = Power demand at time j
Si = Power production capacity of site i
wik = Water quality interaction coefficient between plants i and
W- = Water quality limit at time j
= Air quality interaction coefficient between plants i and k
I-M = Transmission line area per unit power factor from site i
at time j
L-: = Total amount of land available for transmission at time j
Figure 12. Linear Programming Formulation
116
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example, Watt at UC Davis is funded for over a million dollars to create
such an energy model.
An example model using Forrester's concept with a linear programming
optimization imbedded in the structure was constructed for evaluation.
The model considered the power demands and generating capacities for the
PNW power supply areas in Washington, Oregon and Idaho using the following
variables in each region:
Power loads
Power generation
Population
Power cost
Environmental quality
Figure 13 defines the cause-effect relationships included in the model in
matrix form and Figure 13 translates the matrix into flow graph form. A
transform must be defined for each arrow in the flow graph and these trans-
forms can be programmed for the computer. Initial conditions are defined
for each parameter at the initial time period; these values are used in
the program to compute new values at the next time period. For example,
the population in region J can be expressed as:
APOP(J,T) = POP(J,T-1) * K(J)EQ(J,T-1) * [KPOP(J) - POP(J.T-l)]
where APOP(J,T) = change in population of region J at time T
POP(J,T-1) = population of region J at time T-l
K(J) = annual rate of population growth
EQ(J,T-1) = quality of environment of region J at time T-l
KPOP(J) = maximum population desired in region J
This transform indicates that the population increase is a function of
the existing population times a growth constant, times a correction for
limiting growth. Such a function is commonly used in population dynamics.
Dynamic models of this type permit flexibility in modifying transforms and
117
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Power Load (PL)
Power Generation (PG)
Population (POP)
Power Cost ($P)
Environmental (EQ)
Quality
•3
o
hJ
O
Pn
C
O
•rl
4-1
CO
a)
o
o
PM
a
o
•H
rH
3
P.
O
co
o
o
OJ
S
O
4J
0)
O H
H CO
1
1
1
1
1
1
1
1
1
1
1
1
1
Figure 13. Interaction Matrix Within.a Region
Figure 14. Flow Diagram Based on Interaction Matrix
118
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cause-effect relationships. The purpose of these models is to test the
sensitivity of the system to various assumptions concerning transforms.
If the matrix in Figure 13 is not acceptable, it can be easily changed
and the model reevaluated. For example, if one feels that environmental
quality should directly impact power cost, this can be introduced by
modifying the statement defining power cost.
In the example model the linear program has been imbedded by computing
the power load at time t and employing the power generation values of
time t-1 for each sector. These values define D. and S. for the linear
J i
program (see p. 116) and the optimization of new power units and trans-
mission can be performed using an LP. The cost functions for each region
were defined as constant for each type power source. Figure 15 shows a
typical cost function. The step increments A, B, C, D indicate the
maximum incremental power that can be supplied in this region by a par-
ticular type of power generation system. Again the purpose of this type
of model is to explore the impact of assumptions concerning the values in
the transforms. Variation of costs and limits can be simple model changes.
A program listing, sample input, sample output and list of variables for
this model are presented in Appendix C.
The major criticism of these types of models is the lack of resolution.
Notice that in the population transform there is no definition of age,
sex or income structure. Many would argue that each of these additional
variables have significant impact in a model and cannot be aggregated.
Proponents argue that the purpose of gross models is to demonstrate the
complex interactions of such gross concepts. (Looking at forests rather
than trees.) For the purpose of dynamic assessment of power plant siting,
this method does provide a rapid method to examine the gross system
structures and to test assumptions concerning such structure. Any
criticism can be incorporated as a model modification to test its validity.
The model can continually be adjusted with minimal effort to reflect the
119
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mills/
kw hr
12-
10-
8-
6-
4-
2
0
C
B .
D
• . J
dry cooling
wet cooling
A
. once through
hydro
. , _ .... . 4- ..-. 1 . . J.
10
20
GWe
30
Figure 15. Typical Cost Function for Dynamic Model
120
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current state of knowledge.
The final method examined for dynamic assessment of power plant siting
was dynamic programming (Bellman, 1957). This concept has been exten-
sively used to optimize sequential decision making. Using an example
of a region requiring 20 new power generating sources in the next t years,
the dynamic program assessment can be formulated as follows:
1. The first decision is to select which of the 20 plants to construct.
2. Depending upon this choice the regional demands, economy, population
and social values may change. This change may depend on which plant is
selected.
3. These changes may in turn change the costs for the remaining 19 plants,
so new estimates must be made based on the first decision.
4. The choice is made1 for the next plant to be constructed, and steps 2,
3 and 4 repeated.
5. The problem is to find the proper sequence of plant installation to
minimize power costs, environmental impact or other parameters of interest.
Using the preceding methods of linear programming, dynamic modeling and
the static siting model, the analysis outlined could exhaustively be made
by the following algorithm:
1. Use the static siting model (SSM) to evaluate the costs for each of
the 20 sites
2. For each site use the dynamic model to estimate the change in conditions
that would affect the construction of the remaining plants
3. Use these new conditions and the SSM to evaluate costs for the next
site selection
A. Repeat
These data can be placed in a decision tree with the construction costs
determined by the algorithm representing the pay off at each node
(Figure 11). By assuming that each site can accommodate only one plant
121
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the tree will converge so that the 20th decision will be deterministic
(only one plant is left). If this assumption is not made there will be
20
20 nodes to evaluate in order to define the optimum path. In order to
reduce the number of evaluations one can use the theorem of dynamic
programming: "Given a current state, an optimal policy for the remaining
stages is independent of the policy adopted in previous stages." In this
definition, a stage is one of the twenty decisions to build a specific
plant, and a state is the number of existing plants. For example, in the
20 plant problem, at stage 1 the decision to be made is what plant to
build, with the system in its original condition. Once the decision is
made, the state of the system becomes the original plus the selected
plant (20 possible). At stage 2, the decision required is which plant
to add given the system in the new state. This simplifies the problem
20
from evaluating 20 paths, each 20 nodes long to evaluate 20 stages with
20 states, but each equivalent path is 2 nodes rather than 20 nodes long.
The problem is reduced to a simple case of cost minimization of each point
in time using the SSM to generate the costs. If costs are time dependent,
the computation process would be too complex to practically apply given
the varying quality of input data.
122
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SECTION VI
EVALUATION
The models developed in this study have emphasized the overview rather
than the specific. This strategy immediately will draw concern from most
readers who concentrate on only one aspect of the siting impact assessment,
since the models will not contain the resolution and richness that is de-
sired. The purpose of the models developed in this study were to provide,
for the specialist on any aspect of plant siting, an insight into the re-
mainder of the system, and the impact his evaluation of alternatives will
have on the total system. The goal was to distill from specific models
of system components, those essential features that would strongly in-
fluence other components.
One of the most significant concepts used in this study was the hierar-
chical order of decisions used in power plant development. This order
of decision indicates which decision can still be changed after previous
decisions have been made, and which cannot. The analysis of siting
problems with such a hierarchy is independent of the resolution of data
used in the decision making. If the level of information developed in
this study is inappropriate, other data can be substituted without
modifying the analytical framework.
In each section of this report, the need for model verification has been
stressed. The models developed in this study are gross, and in many cases
data were found to be proprietary so model refinement was not possible.
In other cases, the model resolution was designed to be intentionally
gross to be compatible with other segments of the model. Each user of
these models should examine the components of the siting problem that he
is familiar with and ask "Given the resolution specified is the model an
adequate representation?" The resolution of the developed models was
established by financial and informational constraints. Limits of
resolution can be modified by relaxing these constraints.
123
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The principal application of this mddel is to explore alternatives and
to obtain relative rankings of potential alternatives that should be
explored. If higher resolution components were placed in the analytical
framework, the models could be used for preparation of detailed impact
statements.
124
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SECTION VII
ACKNOWLEDGEMENTS
The research was administered by Allen F. Agnew, Director of the State
of Washington Water Research Center. Brian Mar was the principal
investigator and the following faculty and students conducted the
research:
Social-Economic Accounting
James Crutchfield, Professor of Economics
Thomas Meyer, Ph.D.
Ecological Accounting
Eugene Welch, Associate Professor of Applied Biology
Ronald Bush, graduate student in Applied Biology
Intake Systems
Milo Bell, Professor of Fisheries
Russell Porter, graduate student in Fisheries
Cooling Systems
Aziz Saad, graduate student in Civil Engineering
Analytical Methods and Project Integration
Neil Geitner, graduate student in Civil Engineering
125
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SECTION VIII
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133
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SECTION IX
GLOSSARY
Acclimation temperature (°C or °F) - The temperature to which an organism
has become adapted thru prolonged exposure.
Approach temperature (°C or °F) - The difference between the outlet
temperature of an evaporative cooling device and the wet bulb temperature
of the surrounding air (theoretical cooling limit).
Benefit-Cost analysis - A technique used to evaluate the economic feasibility
of a project. Includes in analysis all benefits and costs in an effort to
determine best allocation of monies to project or set of projects designed
to achieve a given goal.
Biological impact - Change in the aquatic community caused by the state
parameter change.
Biomass - Weight (per unit area) of biological organisms under study.
Slowdown - The quantity of fluid discharged per unit time from a recircu-
lating system in order to maintain the desired chemical concentrations.
Break-point chlorination - That minimum dosage of chlorine necessary to
form a free residual of chlorine after uptake by ammonia and reducing
compounds.
Capital recovery factor - The uniform, end-of-period payment required for
the amortization of capital investment (of magnitude one) over n periods.
The sum of the sinking fund payment plus interest on the capital.
Community diversity - The ratio of the number of types of organisms to the
total number of organisms.
135
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Condenser cooling water - That water flow rate which is circulated through
the steam system condenser in order to provide a heat sink for the heat
released by the condensing the steam.
Constraint equation - An equation used in a mathematical model which
describes a system boundary as a function of a certain combination of
system variables.
Constant-sum decision pairs - Sets of variables considered two per time
in which both are compared to a given third parameter. The sum of the
comparison scores given the comparison variables is required to be a
constant value.
Cooling tower - A device used in a recirculating of topping system which
transfers heat from the condenser cooling water to the adjacent air mass.
It may accomplish this by conductive heat transfer alone (dry tower) or
evaporative, conductive and convective heat transfer (wet tower) through
the use of a naturally generated draft or a mechanically generated draft.
Cross-elasticity coefficients - Elasticity coefficients related the
predicted change in demand as a function of price per unit. Cross-
elasticity coefficients are used to describe a change in two related
goods that may substitute for each other.
Cycles £f concentration - A number expressing the ratio of chemical con-
centration in a recirculating system to the concentration in the makeup
water (X /X ).
v c m
Damage function - A mathematical relation giving ecological damage or
change as a function of environmental change.
Decision tree - A branching graph ordering and portraying all possible
decision alternatives and associated probabilities and payoffs for a
given situation.
136
-------
Discount rate (%/yr) - That value used to reflect the value of sacrificed
consumption in capital investment in order to achieve a benefit at some
future time.
Drift loss - The time rate of loss of water and associated dissolved and
suspended materials from a evaporative cooling tower system due to the
velocity of incident air carrying droplets from the packing area.
Economic rent - The maximum profit attainable from a natural resource
optimally regulated on the basis of economics.
Exclusion area - The buffer area for a given thermal power plant required
to abate the negative effects of that plant.
Externality - In economic terms a non priced or inaccurately priced
consequence of the provision of a certain good or service not considered
in the analysis of benefits and costs associated with provision of the
good.
Feedback model - A mathematical model that utilizes outputs as future
inputs in simulating properly system performance.
Annual fixed charge rate (/yr) - That fraction of the original cost which
must be paid each year for amortization of investment and for all charges
associated with the investment and use of the capital (e.g. depreciation,
taxes, insurance).
Fouling - Impediment to heat transfer in a system due to biologic growths,
material deposition or surface corrosion.
Hardness (usually mg/& CaCO,,) - The measure of the presence of certain
I i i j I j [ 1
divalent cations in natural waters (usually CA , Mg , Sr , Fe ,
I |
Mn ). Such ions are capable of procuding undesirable precipitates.
137
-------
Heat source - In a thermal electric generating station that part of the
plant which generates steam for use in the turbine.
Helper tower - A recirculating cooling system only with capacity to dis-
sipate part of the total plant heat load during extreme conditions.
Hydro-environment effects - Those effects on members of the aquatic
community caused by a particular event (e.g. temperature change).
Incremental decisions - Decisions that are made in stages or finite part,
utilizing information generated from previous decisions in the sequence.
In-plant losses - Those losses of heat that occur within a thermal power
plant between the boiler or steam generator and the turbine exhaust.
These losses are the difference between the heat added to the system and
the useful work plus the heat rejected in the cooling water.
Intake structure - The facility designed especially for removing the power
plant's water from the natural water body serving as the source.
Lead time - That time required between perception of a need for more
electric power and the actual commencement of planning for delivery of
the power at the desired time.
Load center - The hypothetical point where all electric power .use is
centered. The load center is the point considered as the terminus of the
high voltage transmission line facilities and the beginning of the distribu-
tion facilities.
MWe - The units of electrical power for the system (megawatts - electrical).
As differentiated from thermal power output (MWt).
138
-------
Objective function - The function relating problem variables that is to
be optimized in any optimization problem.
Opportunity cost - The cost associated with use of capital in a project
yielding revenue at a future data due to forgone benefits from alternative
investments and to forgone consumption.
Payoff - The resultant element that arises from a particular decision or
sequence of decisions.
Peak power - Power demanded in excess of the base power load during certain
time spans.
Recirculating cooling system - Any cooling system for a thermal power
plant that does not involve the direct intake and immediate discharge of
the condenser cooling water supply.
Scale - Deposition of dissolved solids from circulating cooling water on
exposed surfaces of the condenser cooling water equipment.
Sludge - Those solids separated from water during its treatment by various
physical, biological or chemical processes.
TLm - A median tolerance limit for 50 per cent mortality from exposure to
the stated substance for the stated time.
Topping system - An open loop cooling system designed to reject only part
of the system heat to the atmosphere through either a tower or pond before
discharging the remainder to an adjacent water body. The devices used in
these systems are termed helper tower or flow-through ponds. These systems
remove that part of the heat load that is above the discharge standard.
139
-------
Windage loss - Water loss from an evaporative tower due to lateral wind
pressure on the packing. The loss is in droplet form containing system
chemicals that are not subject to removal by air scrubbing action.
Wheeling charge - That charge assessed for the transport of electric
power over a high voltage transmission system.
140
-------
SECTION X
APPENDICES
Appendix A. Inlet Screening Configurations
141
-------
5
0
j
ll
!...
a
\i
IL
1
ffSZSSfL
OR
RECESSED SCREEN
NO BY-PASS
POOR peSi«N
0
J
IL
jfi
A«SN J
\ ^
^
at
u
£
A*
1
—\
;H RACK-'
FLOW
OR
STIL1.JVATER
SMOOTH-FACED SCREEN
NO BY-PASS
SOMEW/HA~T BETTER DESIGN!
142
-------
PIPI= RISER
TO UNDERGROUNO
RETURN PIPE
RWSK
SJWFACI= SLOPE
SMOOTH-FACED SCREEN
WITH BY- PASS
BETTER DESIGN
O
SCRB6W
O
J
ICE
r
J
J_L
n
TRASH RACK-
-^- FLOW
5MOOTH-FACED SCRETEN
SECOMES BY-PASS
DESIGN
143
-------
PJPE RISER
TO UNOSB6ROIWO
ANGLE- FACE
WITH
s \
\ \
I I
< I
\ \
\ \
\ \
\ \
\ \
I 1
FUOW
OR S-TJLLWATBR
ANGLE FACE
US»NG ONE I3Y-PASS
BEST
144
-------
LIST OF VARIABLES
This is an abbreviated list of variables which does not include those
previously discussed in the text nor those given in the Dynatech (1971)
program.
VARIABLE
CC0ST
CC0STS
CCSC
GDIS(I)
CFISH
CHSC
C0NCR
SCS
DISCH(I)
L0CI(I)
NAMES(I)
PNAME(I)
SPC0
SPGR
SPNC
SPTR
TDIS(I)
TRCST
TZER0
WATER(I)
DEFINITION
Capital cost of new transmission facilities ($)
Capital cost of screening system ($)
Capital cost of cooling alternative ($)
Discharged chemical concentration (x inlet)
Cost of fish damaged by plant intake ($)
Cost of chemical system (mills/kW-hr)
Cycles of concentration
Cost of cooling system (mills/kW-hr)
Water quantity discharged by cooling system (cfs)
Storage variable for second title card
Storage variable for first title card
Storage variable for plant type name
Land required for cooling devices (acres)
Land required for base plant (acres)
Land required for new transmission lines (acres)
Land required for existing transmission lines (acres)
Temperature of water discharged from cooling system (°F)
Total transmission system cost (mills/kW-hr)
Temperature of river without plant operating (°F)
Water quantity intake by cooling system (cfs)
145
-------
PROGRAM BUlLnUNPUi't UU1 PuT , f APE5=INPUJ, 1APE6=OUrPU])
C dASlC JtCIdlON TREE GALL iEUUtNCE FUR THERMAL PLANT ill ING
C THI> ?ARi JF THt PROGRAM GcNERAits T He WALuE.^ 10 B£ UiED
COMMON PiIZc,GGPK«,ANFCR,FuGSj ,NCAPi,CAP(b) ,TOiL-(5) . CGLPCi (5) ,TC
XMIN(fa),PCMIN(b),1CMAX(6),PCMAX«6), HRCOF2C6) ,HRGOFl(b) ,HRCOFO(6),
XTJd, iHd f RH,i Al/H2u, TCtJA.it, Nl AMB , AMbjFCC 51 ,AMdOPC<5) ,1 AML8(5) ,TAMW
Xd(5» ,AMbRH(5) , TAMRVC5) ,PJT AMb < 5 , b) ,NilTbUP , 1J1SMX, NiPCON,UOVALL , A
XR£AC,»PFLOt^, NH^O, WIjjH,PRPAGR,CAPFAGf OotFACf JKWHRbtlRII EtIREAU»
6 AMWINu(5) ,AMRAu(b) ,HINu,RAJ,UFLRIV ,uEP7H
COMNON/Nh.Wtf/ GONCR,LOCI C^O > , AGG ,CONiGT,UlSLL (51 ,UlilR(5l , FLCW1
2,1TOPG,1{ 5) ,CCPM(i>) ,[MLj(5) ,TMCSI (5) ,1 RCS I , WAI ER (4) ,UliCH (^»l ,TDI
-J,CJli(<+* ,FIiH,T2tRO, NR,C6C,oiC»CFI^H,CHiC,PNAMtC*l ,NAMEi>(20J ,
'tiPGR, aPiR» iPGO, uELHR, CC^G, GCOii , CCOili, iPNC,IOO
REAJ(5,5) ITO
5 FORMArC ID
IFIIIO.cQ.3) GALL fREE
PRIN1 11
11 FORMAT! 1H1,» POWER PLANT aliING ANALVi,Ii> PROGRAM*//)
C INIl'IALlZc I HE LEVEL COuNiERj
Ni = 0 & Nit = 0
G CARu 1 COMMcNGci THE ANAL/ilj FOR EACH ilTE
1 Ni=Ni * 1 $' NT=0 4 N»R=0 $ NP=0
GALL il TttNj>,NiEI,NJ R,MNi R,NT ,MNT ,NCO ,MNC,NbC,NCH,NP ,IPF , IGF)
IF( ICF.EQ.ll GO TO 2u
NP = 0
C iTATEMcNT 2 COMMtNuto i Hi. ANALYili FOR EAGH • RANiMIbilON OPTION
C GALGuLAIiON lo NOT PLRFORMcD UN1IL AFj'tR I HE PLAN* bUBROUl INE
G A» I'RANbMIiilON CO^Ho ARE A FuNGTION OF BOTH PLANT ANu -ilTE
2 NT< = N1R *• 1 * NT = 0 » NCO = 0
C aTATEMcNI 3 COMMcNCcS THc ANALYSIo FOR cACH iVPE PLANT FOR ANY
G SITE
3 NT = NT +1 $ NCO = 0
CALL PLAN1 INJ>iNl R»NT ,IPF*
G jfATEMENl <» GOMMcNCto THE COOLING ANALYili FOR EACH iYPE/i>ITE
k NG3 = NCO * 1
GALL COOLlNG(Ni>,M , NCO)
NP = NP + 1
NiC = 1
CALL dCktENSINo, NT, NCO, Ni>C>
NCH = 1
CALL CH£M(Ni,NT,NCO,NiiC,NCHJ
CALL PAYOFF(Ni>,NiR,NJ ,NCO,NbC|NGH,NP,lCF>
NCH = 2
CALL CHEM(Ni,NI,NCO,NSC,NCH)
NP = NP* 1
CALL PAYOFF
-------
c
C
C
20
CALL PAYOFF(Nb»NiR»NT»NCO,NiCtNGH,NP,lCFJl
IF(NCO.LT.MNC) GO TO k
IF(Ni.LT.NNT) GO TO 3
IFCNJR.Lf.MNyRJ GO TO 2
Nit = NbE + 1
IFCNiE.LUNSEI) GO TO i
GO TO 1000
CALL ROUTINE FOR bINGLc PAYOFF CALCULATION
CALL PLANT(Ni»,NTRtNTfIPFl
CALL COOLlNGCNi, NT, NCO)
GALL iC«E£Nc,CNb,Nr,NCO,N^C)
CALL CHEMCNb,NT>NCO,NbC>NCH)
CALL PAYOFFTOP
ENJ
GO TO 1
147
-------
c
c
c
c
c
c
c
c
1
2
3
4
5
6
1
8
9
10
11
12
13
14
16
17
Id
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
3d
39
40
41
iUBROJ]
oEI UP
FOR THE
JECK MO
DECK M
FORMAT
FORMAT!
2,1HO,10
3E ROUTI
40WER PL
5RT. OPT
FORMAT!
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
27)
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FOR1AT
FORMAT
20FF 10
FORMAT
FORMAT
FORMAT
FORMAT
1
!
I
G
!
!
!
:<[
!
!
!
I
!
•«
!
1
!
I
1
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!
!
1!
I
!
I
fi
!
1
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a
1
i
!
,
{
«
!
(.
INE TREE
OF FORMAT aTAiEMENls FOR PRINTING OUT OF DECISION TREE
RMAL DISCHARGED BAsELlNE ANALYSIS PROGRAM
DIFICAIIONi AS OF 22 MARCH 1972, 1115 HRS
Ou NOi. ORIGINATED ON 19 MARCH AT 1600 RUN
iTATEMENTS FOR PRINTING THE DECISION TREE
1H1,20X,*IHERMAL DISCHARGE DECISION TREE ANALYSIS PROGRAM*/
X,* THE TREE BELOW SHOW:* THE VARIOUS DECISION LEVELi AND TH
NES PROGRAMMED WITHIN EACH LEVEL*/*0 SITE/TRANSMISSION P
ANT TYPE COOLING METHOD ^GREENING OPTIONS CHEM T
IONS PAYOFF N0.*/t
1HO,80X,30H *CHEM. UISCH NOT TR**PAYCFF 1)
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
1H
t
»
»
t
i
»
t
»
,
»
,
,
»
»
*
»
,
t
80X
60X
59X
58X
57X
40X
57X
39X
59X
38X
80X
37X
36X
»
,
t
,
,
,
»
l
»
i
»
1:H»)
2 OH*
1H*,
1H*,
1H*)
17H*
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1H*,
1H»,
1H*,
1H*)
,2H*
»
1H*)
NO
I
20X,
21X
,
NTAKE oCREENS**)
1H*)
30H *CHEM DISC TREAT ED***PAYOFF 2)
ONCE-THROUGH***)
18X
20X
21X
,
»
»
,41X
1H*,21X,30H *CHEH OISCH NOT TRT**PAYOFF
1H*I
20H*SCREENED INTAKES***)
,30H *CHEM uliC TREATEQ***PAyOFF 4,/)
3)
80X,30H *CHEM uliCH NOT TRT**PAYOFF 5)
35X
60X
34X
58X
,33X
i
»
»
,
t
,
40X
»
»
,
t
,
»
32X,
39X
31X
38X
30X
,37X
,
i
?
,
,
,
(
»
29X
20X
29X
19X
30X
18X
31X
17X
•
»
1H»,
1H*,
1H»,
1H»,
17H*
1H*,
1H*,
1H*,
> 1 H* ,
»
»
,
>
»
»
,
,
,
»
1H*,
1H*,
1H»)
17H*
1H»)
1H»,
1H*,
1H»,
1H* »
1H»,
44X
24X
21X
23X
>
,
*
,
1H»)
20H*NO INTAKE SCREENS**)
1H*,20 X, 1H*>
30H *CHEM uISC TR£AT,ED***PArOFF 6)
1H*)
COOLING PONO***)
24X
»
1H*»
18X,1H*,22X,29H*CHEM uliCH NOT TRT**PAYOFF
27X
21X
49X
,
»
»
H20
17X
49X
19X
48X
21X
,
»
i
,
,
1H*,20X,1H*I
20H*.»CREENEO INTAKE^** I
1H»)
42X,30H *CHEM DISC T REAl ED***PAYOFF 81
NuC PWR PLT)
1H*,43X,29H*CHEM . DICSH NO i TRT**PAYOFF
1H*)
1H*,21X,20H*NO INIAKE SCREENS**!
1H*)
9)
1H*,19X,1H*,21X,30H*CHEM UISC T REAl ED***FAY
/,1H ,32X,1H*J
1H
1H
1H
1H
,
»
,
,
1 6X
15X
34X
14X
»
»
,
,
1H»,
1H*,
1H»,
1H»,
23X
43X
45X
45X
»
t
,
,
19H*M£C uFT WET TOWER*, /,1H ,33X,1H»)
1H*,21X,30H*CHEM DISCH NOT TRi**PAYCFF
1H*)
20H*SCREENEu INTAKES***)
11)
148
-------
<*3
FORMATS
FORMAT!
<*
, 60X,20H*iCR£ENEQ INTAKES***)
,6X,1H*,73X,1H»)
, 81X,30H*CHEM bliCH TREATEG^'PAYOFF 16)
,5X,1H»)
SITE(Ni)/TRANS(NTR))
151
NOT TRT**PAYOFF 171
31X,30H*CHEM
1H ,5X,1H»,7<»X,1H*)
1H , 60X,20H*NO INTAKE iCREENS**)
1H ,6X,1H*,52X,1H*,20X,1H*)
1H , 58X,lH*,22Xf30H*CHtK ulsCH TREATED**?AVOFF 18)
1H ,7X,1H*,<»9X,1H*)
1H , <»QX,17H* ONCE-THROUGH***)
1H , 39X,lH*,iax,lH*,22X,30H*CHEM DISCH NOT 1RT**FA¥
1H ,9X,lH*,*»9X,lH*,20XtlH»)
1H , 38X,lH*,21X,20H*SCRfc£N£u INTAKE;****)
1H ,10X,1H*,69X,1H*)
,
1H , 37X,1H*,<»3X,30H*CHEM U
1H ,i!X,lH*t/,lH ,36X,1H»)
1H ,12X,lh*,68X,30H*CHEM ClbCH NOT
S1H ,35X, *<*
22)
TR£ATEu***PAYOFF 20)
S1H ,35X, 1H*,4<*X,1H*I
1H ,13X,1H*,«»6X,20H*NO INTAKE SCREENS**!
1H ,3'»X,1H*,2'*X,1H*,20X,1H*)
1H , 1*»X,1H*,«»3X,1H*,22X,30H*CHEM uISC TREATtD***PAYOFF
1H ,33X,1H*,23X,1H*>
1H ,15X,1H*,2<»X,17H* COOLING PONU***)
1H ,32X,1H*,2<»X,1H*)
1H ,16X,1H*,22X,1H*,18X,1H*,22X,30H*CH£M uISCH NCI iRT**PAY
1H ,31X,1H*,27X,1H*,20X,1H*I
1H ,17X,lH*,20X,lH*,21X,20H*iCREENEu INiAKES***)
1H ,30Xt 1H*,<»9X,1H*)
1H ,18X,lH*,18X,lH*,'»3X,30H*CHcM uliC TREATED***?AYOFF 2i»)
1H ,29X,1H»)
1H ,19X,lflH* FOSSIL FIRtu PLT)
1H ,29X,lh»)
1H ,37X,1H*,'»3X,30H*CHEM UI^CH NOT TRT**PAYOFF 25)
!1H ,30X,1H*,^9X,1H*»
1H ,38X,1H*,21X,2UH*NO INTAKt iCREENS**)
1H ,31X,lh*,<»8X,lH*)
1H ,39X,lH*,19XilH*,21X,30H*CHEM DISC TREATED***?AYCFF 26)
1H ,32X,1H*»
149
-------
95
96
97
98
99
100
101
102
103
HJ^
105
106
107
108
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
2,1H )
FORMAT
FORMAT
FORMAT
FORMAT
PRINT
PRINT
PRINI
PRINT
PRINI
PRINT
PRINI
PRINI
PRINT
PRINT
PRINT
PRINT
PRIN'i
PRINT
PRINI
PRINT
PRINT
PRINT
PRINI
RETURM
ENi)
f
1
I
(
(
«
8
f
(
I
f
(
<
C
1
7
1
1
2
3
3
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6
6
7
7
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t
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<»5X,1H*I
60X,2CH*i>CREENEJ
81X,31H*CHEM uli
36X,1H*,
37X,1H»,
80X,1H*,
8UX,1H*)
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1HO
1H
1H
$
£
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INTAKES***, /,1H ,35X,lH*,i»
27)
|
-------
SU3ROU1INE COOLING(NS, NT,NCO)
C COOLING !;> A uUMMY SUBPROGRAM UbEJ TO CALL THE APPLICABLE
C JfNATtCH SUBPROGRAM FOR COOLING ANALYblb
IF(NCO. tCl.l) CALL ONCE (NS |NT , NCO )
IF(NCO.tQ.2) CALL PONO < Ni> ,NT , NCOI
IF(NCO.£Q.3I CALL MCHjFT(NS,NT,NCO)
IF(NCO.tQ.«») CALL NATjFTI NS» NT , NCO)
KEfURN
151
-------
SUBROUTINE PONUIZE,CCPKW,ANFCR,FUC;jT,NCAPS,CAP(&) ,TOTLJ(5) , CCLPCT(5) ,TC
XMIN(6) ,PCMIN(6) ,]CMAX(fa> ,PCMAX(6) , HRCOF2(6) ,HRCOF1<6) ,HRCOFO<6) ,
XT03,TWB,RH,TAVH20,T CEASE, NT A«B, AMBQFCt 5) , AM80PC(5) ,T AMGB<5 ) , T A KM
XB(5) ,AMBRH(5) , TAMRIM5) ,PCi'AM6 <5,5) , NSYSOP tTOISMX, NSPCCN,UOVALL, A
XR£AC,iPFLOW, NHk:0, WIJTH,PRP AG«»CAPF AC»UiEFAC,TKWHRi, , IRITfc ,IR£AO,
6 AMWIND(5) ,AMRAb(5> ,WINU ,RAu , QFLRI V ,UEPTH
COMMON/NtWU/ CONCRi LOCK20) , ACC ,CONSCT,U ISLC 15) ,L)IiTRfl5l , FLOW1
2,YOPCMC5J,CCPM(5),TMLS<5) ,TMCS1 (5J ,T RCST ,WATEKC») , JlbCH ««) ,IOIi C<«)
) *FIbH,l ZERO, NR,C!iCfSSC,CFISH,CHSCtPNAHE(
-------
50 IFd =CHPPMP*.7i»57/«Pi>IZE*1000.> ) *PWCi>T*USEFAC
COaMAI=.001*CAPCOi)/fiPSIZE*1000.)-t-.l*OPCOS+.01*bYSCSl
TOrC03=(CAPCOS*ANFCRi/CPbIZE*1000.*CAPFAC*8.76) *OPCOS
XCiT*UELFC
IF(TOTCOi-TOiCil»15<»,156,156
156 IF«NiPCON.EQ.l)GO TO 157
IF(NSY^OP.EQ.2)GO TO 151
T2=T2*1.
GO TO 50
157 IFJNiYbOP.EU.2)GO TO 190
151 TC = TC+1.
IF(TC-TCMAX(NCAP5s -HI 1100,100, 190
15i» RA1=RA
AR£AP1=AR£AP $ I11=T1 $ T21=T2 * iYSl=^rSCiT I CAPCS1=CAPCOS
COSPKl=COiPKW $ OPCbl=OPCOa $ COSMA1=COSMAI $ AFLR1=0. $ HPF1=0.
HPP1=HPPMP $ b£LFl=OELFC S TCALC1=TCALC $ QRJ1=QREJ SQRJ1 1=QREJT
FLOW1=FLOW $ GPM1=GPM i UA1=UA $ WEtf APl=WEtf AP
W8JWN1 = 0.0 $ WATER(NCO) =WEVAP/22WOO. S OISCH SNCO)=0 . 0
TUli(NCOI=0.0 S CQIS(NCO) =0.0 S CiC = TOTCS1 S SPCO = AREAP
TC1 = TC $ TOICil = lCTCOi> $ CCSC = CAPCsl $ GO TO 156
190 IFUOTCS1-1. E3I200, 195, 200
195 WRITEC IRITE,196)TC,11,RA
196 FORHA1 «/3X9*FOR THE GIVEN CONuITIONS A SOLUTION CANNOT BE FOUN
Xu*B/,SX,^fG =»,F5.0,» VI =*,F5.0,» RA =*,F5.0I
GO TO
-------
197 WRITE( IRITE,198*
198 FORMAT «/»3X,*MAX OIS I LtSi THAN EQUILIBRIUM T»)
GO TO 400
200 CONTINUE
IFCIOO. C.Q.1) GO TO 500
WRITE(IIRITE,212)
212 FORMAT «*1*,15X,» ----- COOLING PONG --- --*,//, 10X, *THE DESIGN tf
XALJEi AND COiTS ARE -*,//)
CALL PRTji>l(QRJl,TCl,HPFl,HPPl,W£tfAPl,WBDWNl,AFLRl)
WRITE«IRITE,227)FLOW1,T11,RA1,TCALC1,AREAP1
227 FORMAT«3X,*CONU FLOW =*,E12.4,» 1 IN =*,F4.0,» RANGE -*
X,F<».Of/,3X,*tQULI8RIUH TEMP =*,F4.0,* UEG F. POND AREA =*,F8.0f
X* ACR£b*J
IF(Ni>lTbOP-2) 23 0,223, 230
228 WRI1£« IRITE,229)QRJT1
229 FORMAT (/3X,*Q RtJ PONu =*,E12.4,» BTU/HR*!
230 CALL P«TUS2(CAPCSl,OPCiil,COSMAl,i»Ybl,UELFl,T01CSl,COSPKl)
WRITEJIRIT£,330)
3JO FORMAT «//15X,*V ARI ABLE AM8IEN1 CONu ITION^*/)
500 IF(NTANB.EQ.O)GO TO <»00
TOPMIL=0.0 $ TDFML=0.0
uO 350 I=l,NCAPb
IF(CAP8 I).LU.O.)GO TO 350
AMBOPCC I)=0.0 $ AMBUFC(I)=0.0
DO 340 J=1,NTAMB
IFCPCFANBlIt JJ.cC.O.IGO TO 340
TC=TCMIN(I) i NTCOu=0. $ 1A=TAMD6»ECOF
ALP ACT = (AREAP1*XK*'»3560.*/{24.*FLOW1I
301 CALL PAFCiT(I,TC,PWCbT,DFCOJ,QREJ)
Dr2=(GlR£J/FLOWll/CEXP«UAl/FLOWl)-l.)
UT1=OI2*QREJ/FLOW1
Tl=TC-t/T2
I2=TC-uTl
IF(NiYi>OP-2)316,303,316
316 IF«T2-TCALCI304,304,302
303 IF(T2-TAMRV(J))30'*,305,306
305 NTCOJ=1
GO TO 306
306 T2=fCALC+U 1-1 CALC) /EXP{ ALPACT 1
IF(r2-TjIi)MXJ310, 31 0,307
307 -WRIIEd IRITE,308)CAP(I) ,TAMWB(J) ,TC,T2
154
-------
.508 FORHAJ «/dX*FOR CAP = *,F<*.2,*, f WB = *,F<*.0,*, ANU TC =
X,Fi».0,/ ,3X»] uli> EXCEtuS IJlo MAX - CCN1INUING*)
GO TO 315
302 ALPHA = -ALOG«12-TCALC> /Cil-TCALCM
IF(ALPHA.Lf.ALPACT)GO TO 310
NTCOu=l
IFCTC tLl. TCMAXdMGO TO 301
IF(IUO.GT.l) PRINT 309, CAPCI), J
309 FORMAK*- 1 Hi. PONJ Ib tsSENUAn./ TOO iKALL FOR»,F<*.2,
i * CAPACIfy ANu ArtBIcNT NO.*,12/» PROGRAM fcliCONT INUING*)
GO 10 <»00
310 IFINTCOU.G1 .0)GO TO 315
IF(IOO.GI.l) PRINT 312, CAPCI), J
312 FO*MAf«*01'HE PONu Ii LARGtR THAN NcCtbSARV FO^*,F<».2,* CAPACITV*,
* * AMu AHBIENI NO.*,I2/
** COMPUTING COiaTi AioClMlNG MObl £FFICIEN1 CCNJITION (PC = PCMIN)*>
IF( jFCOJ.Gf. 0.)uFCOU=0.0
315 CONTINUE
OPCOU=« HPPl*.7<»57/(PiIZE*1000.))*PWC;iT
AM30PCC I)=AMBOPC(I)+OPCOu*PCTAMB( J)
AMBUFCC I»=AM3UFC(I)+uFCOU*PCTAMB(JJ
3^0 CONTINUE
TOPHIL=TOPMIL + AMBOPC(II*TUTLO(I)*COLPCT (I)*CAP(I)
TjFMIL=iuFMIL*AMBJFC,AVl Ci>T I
i*00 CONJINUL
RETURN
ENJ
155
-------
iUdROJT INE MCHuFI (NS ,NT,NCO»
COMMON PiIZc.,CCPKW,ANFCR, FJCS1 , NCAPb ,CAP (6) ,TOTLu«5) , COLPCi C5J ,TC
X MINK 6) ,PCM1N(6.) , i CMAX « b) , PCMAX « 6 ) , HRCOF2 ( 6) , HRCOFl (6) ,HRCOFO«6) ,
XUd, 1MB , RH,TAVH2(J,T CEASE, NT AMb , AMtiUFC( 5) , AMQOPCI51 ,1 APL8 <5) ,T AMW
Xd(5),AMdRH<5), TAMRV<5) ,PCi AMB (5, 5) ,NbYSOP ,Tci liMX, NbPCCN ,UO VALL ,A
XREAC,iPFLO^, NH£0,WIDIH,PRPAGR,CAPFACtU5.tFAC,TKWHRi,IRne,IRE*b,
6 AMWINU(5) ,AMRAU(5) ,^IND ,RAu , QFLRI V ,UEPTH
COM M ON/ NEW V/ CONOR, LOCI (20) , ACC , CONSCT,U liiC (5J ,u!ilR«5l , FLCW1
2, \T OP CMC 5) ,CCPM(p), rMLS(5* ,TMCjT(5) , I RCil , WAT ER (4J ,UIbCH (V) ,TD Ib Ci)
,FIaH,12ERO, NR, CoC ,3bC , CFIi,H,CHSC, FNAME C+J , NAHEb(20) ,
bPJR, oPCC, uELHR, CCiC , CCOil , CCCiTi:, bPNC,IOO
UlMENilON XK(20)
FANEF=0.8 ii PMPtF=0.8 i TOTCil=l.c3 $ TC=TCMIN( NCAPi,*!*
100 CONTINUE
IF(Ni>PCON-l) 30,4b,JO
VO CALL PAFCol (NCAPi>*i ,TC ,PWCiI ,QLLFC, (JREJI
OT2= (QREJ/^PFLOW)/lcXP(UOVALL*AREAC/^PFLGW) -1.)
Il=rC-U12
IFtNiriOP-2) kkt^
VI IF(L)T1-(TC-IAVH20) *15, 15,^2
<»2 IF(lC-TCMAX(NCAFi*l))13,190,190
13 TC = 7C+1
GO TO (*0
IS T2 = TJIi>MX
APPR=f2-TW3
IF(APPR.LI ,7.)GO TO 190
IFtAPPR.GT.20.IGO TO 190
RA=r 1-J 2
IFCRA.LI.10.IGO TO 190
GO TO <»&
kit T2 = 1C-UT1
APPR=f2-lW3
IF(APPR.Lr.7.IGO TO 151
IF(APPR.Gi.20.)GO TO 190
GO TO 45
30 IFCNalTSOP-2131,32,31
31 APPR=7.
50 r2=THB+APPR
IFUC-T2)151,151,51
32 T2=lJIiMX
APPR=T2-TW3
IFCAPPR.LT.7.IGO TO 190
IF(APPR.GT.2fl.)GO TO 190
51 012=5.
CALL PAFCiTCi,T,
* COSPKW)
GO TO 47
46 CALL CONO«TC,IAVH20,QREJ,PWCi,l ,ul2,Tl,UA,FLOW,GPM,5>YiCS>'l ,
* COi>PKW)
QRtJT = QREJ*ni-T2)/(Tl-TAVH20J
47 RA=T1-T2
156
-------
IFCRA.LT.10.JGO 10 151
TAXT=«Tl+I2)/2.
H1=HUWB)
H2=H(TAXT)
AFLR=i}REJT/(H2-Hll
WACT=RH + «.b22*PnDB) I / ( 1^.696-P
APjAT=
RJH3=l./(H(T5)-Hi:«-.<4*C01J
RjH<»=l./
-------
IFCAB.tQ.AAIGO TO ( 10,12, Ik , 16, 18 ,20 ) IXY 2
GO 70 ( 10,12,14,16,18) IXYZ
21 Z = 2.
GO 10 C 12, 1<+, 16,18, 201IXYZ
10 X< CIO )=.a7b57520E-OH-. 2597637 9*RANGE-.69589515E-02*RANGE**2
X *. H.542869£-03*RANGE**3-.69832755£-06*RANG£**^
IFJAB.cQ.AAJGO TO 25
IFCZ-2. 121,23,21
12 XK (12) = .30 7559 83&-02*. 22692621* RANGE-. 575 1566<»E-02*RANGE**2
X «-. 89248959t-Q<«*SANGE**3-.5038iO'»6E-06*RANGE**<»
IF(AB.EQ.AA)GO 10 25
IFCZ-2. )21, 23, 21
i<» XKC1<») =-.3 3616133*. 22612638* RANGE-. 579310 <«3E-02*RANGE»*2
X*.8989fa029E-OVRANGE**3-.50893025E-06*RANGE**<»
IF(AB.tQ.AA) GO TO 25
IFCZ-2. 121, 23, 21
16 XKC16)=-.'»3379805*.20785695*RANGE-.51672b32t-02*RAN&E**2
X«-.77053656E-04»RANG£**3-.-TQB)
WtVAP=ULAT/970.3
CONCR=5.
C HATER FOR BLOHOOWN
WB3WN=C ,06*UREJT*62.'»)/C500.*7.'t8*CCONCR-l.))
WNJG = 0.002*FLOh
WNcEu = WEVAP + HBOWN 4-WNbG
GPMr=GPM+WN£ED/C8.34*60.l
FOJGE=GPMT*XKCIK)*CWB
C LOCKHARJ iAYo COol = 2*FUQGE BU I CONVERito GOSlb ARE
C ABOUT 2*LOCKHARIi - uit AVG. = 3*FUuGE
CAPCOi=3.*FUuGE
ACFM=AFLR/CbO.*UINJ
HPFAN=C ACFM*DELP*5.2)/C33000.*FANEF)
HPPMP=GPMl*tPHi«-10.)/C39bO.»PMPEFI
TOTHP=HPFAN+HPPHF
UELFC=UELFC*UiEFAC
OPCOi=CTOi HP*.7<*57/CPSIZe*lOOO.) ) *PWCbT *Ui>EF AC
TOi C0i= CCAPCOi>*ANFCR)/CPiIZc*lCOO.*CAPFAC*8.76) +OPCOS
X *COil1AI*iYi,CiT*utLFC
158
-------
IFCST $ CAPCSl=CA.PCOi $ COSFK1 = COSFKW
OPCSl=OPCOa $ COSMA1-COSMAI $ 1HP1=TOTHP * HPFl=HPFAN $ HPP1=HPPMP
DtLFl=uELFC * GRJ1=QKEJ S QRJT1=QREJT S FLOW1=FLOW $ GPM1=GPM
WARI1=WART S UA1=UA i WEVAP1=WEVAP $ WBJWNl=WBuWN $ WNEEul=WNEEU
TC1=TC i XK1=XK(1K) t TOTCS1=TOICOS
WArERINCO)=WNE£ul/22l-l.£3)200,195,200
195 WRITci IRIT£,196)TC,APPR,RA
196 FORMAT(/3X,*FOR THE GIVEN CONDITION^ A aOLLTION CANNOl BE
X FOUNJ*,/,3X,*iC =*,F5.0,» APPR =*,F5.0,* RA=*,F5.0»
GO TO kQQ
200 CONTINUE
IFCIOO.EQ.l) GO 10 500
210 WRITE*IRITE,212)
212 FORMATC*1*,15X,» MECHANICAL DRAFT WEI TOWER *,//, 10
XX,*TH£ DESIGN VALUES AND COSI a ARE -*,//»
CALL PRTDil(QRJl,iCl,HPFl,HPPl,WEVAPl,W8(jWNl,AFLRl)
WRITt
IF(NS/iOP-2)230,228,230
22d WRITE!IRITt,229)QRJll
229 FORMATC/3X,*Q REJ TOWER =*,E9.2,» BTU/HR*J
230 CALL PRTJS2(CAPCol,OPCol,COoMAl,iYi>l,D£LFl,I01CSl,CUiPKl)
WRITEJIRIT£,330)
330 FORMAT(//15X,*VARIAdLE AMBIEN1 CONuITIONS*/)
500 IF(NTAMB.EQ.O)GO TO 400
TOPMIL=0.0
TDFMIL=0.0
uO 350 I=1,NCAPS
IF(CAPI D.EQ.O*) GO TO 350
AMBOPCC I) = 0.0
AM3uFC(I)=0.0
DO 340 J=1,N1AMB
IFCPCI AMBd, J).£Q.O.)GO TO 340
TC=TCMIN(I*
NTCOD=0
301 CALL PAFCS4CI,TC,PWCST,DFCOD,QREJ>
uT2=(QREJ/FLOWll/(£XP(UAl/FLOWll-l.»
jTl=DT2*QRcJ/FLOWl
Tl=fC-OT2
J2=TC-D II
IFINSlTSOP-21316,303,316
159
-------
316 IFU2-TAMW8(J))30<»,30<*,302
303 IF(T2-TAMf?V(J))30<»,305,306
305 NTCOJ=1
306 T2=TJISMX
IF(T2.GT.TAMWB(J)IGO TO 302
WRITt{IRITE,313)TAMWB{J)
313 FORMAT (3X*T uli MAX Ltai THANJOR =) 1 W8 = *,F<*.0,/,
X 3X»R£(iUlRe;> NEGATIVE APPROACH - DISCONTINUING*)
GO 10 <»00
302 TAX| = Ul
RA=T1-T2
T3=T2*. 1*RA
T'»=T2+.<»*RA
T6=Tl-.i*RA
C01=WART1*RA
H1=H(TANW3(J) I
H2=H(TAXT)
Rim=l./(H(T3)-Hl-.l*C01)
RJH2=1./ EXCEEDS TOIS MAX - CONTINUING*!
GO TO 315
30<» TC = TC«-lt
NTCOU=1
IF(fC .LT. 1CMAX(I))GO TO 301
WRITE! IRITE,309)PCMAX(I) , CAP(I) , TAMWBCJ)
309 FORMAT (/,3X,*CONO£NiER PKEbii MUST EXCEELi 1 HE GIVEN MAX OF*,/ 8
XX,F<».2,* FOR THE CAPACITY OF*,F<*.2,» AT T WET BULB =*,F5. 0,/ , 3X, »
XPROGRAM DIbCONIINUING*)
GO TO <»00
310 IF! NTCOu.GT.O)GO TO 315
IF(IOO.GT.l) PRINT 312, CAPtI), TAMUtHJ! , TC
312 FORMAT (/8X,*FOR CAP =*,F<».2,*, T HB -=*,F5.0,», AND
X 1C =*,F<*.0,/,3X*PC LESS THAN PC MIN - AiSUME PC MIN - CONTINUE*)
315 CONTINUE
OPCOu=t?HPl*.7'»57/
-------
370 WRIiE
-------
SUBROUTINE NflTCFl (NS f NT , NCO)
COMNON P$IZE,CCFKW,ANFCR,FUCS1 , NC APb ,CAP (6) ,TCTLC(5I , CCLPCT<5I ,TC
XMIN86) ,PCMIN(61,KMAX(6),PCMAX(6), HRCOF2(6) ,HRCOF1<6) , HRCCFOC6) ,
XIuB,TWB,£H,TAVH2C,TCEASE, NT AK8 , AMBCFCS 5) , AK80PC«5) ,1 AKCBf 5) tTANN
XBG5) ,AMBRH(51 , TAMRVG5),PCTAMB«5,51 , Ni>lf bOF ,1 LlihX , NSPCCN ,UO V AL L , A
XREAC,iPfLOW, NH20, WlDTH,PRPAGR , C APF AC,UiEF/lC ,TKWHRS , IR I7E , IREAO ,
6 AMWINDC&) ,AMRAC(5) , WINu ,RAQ,GF LRIV ,CEPI H
COMHON/ NEW V/ CONCR t LCCI «2C> , ACC, GONSCT,0 ISLC (5.1 ,CIS1R{5) , FLChl
2,YOPCM(5),CCPM(5) ,T«Lst5) .TMCST «51 ,TRCST , WAT Efi «*i) ,OI!sCH(<«),TCIS(<4)
3,CuIi>(<») ,FIi)HC101,TZERC,NR,CSC,SSC,CFISH,CHS)C,PNAME(i«) ,N/!MES(20),
4SPGR, iPTR, SPCCi UELHR, CCiCt CCOST f CCCbTS, SPNC,ICC
PI=3. 14159
PMPEF=0.8
TOTCS1=1.E3
HORMAX=1.5
TC=TCMIN
uT2= IQRE J/SP FLOW ) / It XP (UCVALL'ARE AC/SFFL CW) - 1.1
UT1=DT2+QREJ/SPFLOW
T1=TC-OT2
<»1 IF«UT1-(TC-TAVH20)»15,15,'»2
^2 IF«TC-TCMAX(NCAFS*1)I13,190,1SO
13 TC = TC*1
GO TO kQ
15 T2=TUIb«X
APP«=T2-TWB
IFSAPPR.LT.12.) GC 1C 190
IFCAPPR.GT.20.JGG TC 190
RA=T 1-T 2
IF(«A.LT.10.IGO TO 190
GO TO <»6
-------
* COSPKWI
QRfcJT=QR£J*Ul-U)/Ul-TAVH20)
V7 RA=T1-T2
IF«RA.LT.10.)GO TO 151
WLOAD=1250.
C INITIAL WATER LOADING 1250 L8M/FT2/HR
8SA=FLOW/WLCAD
C TAXT - AIR EXIT TEMP - FRAAS + OZISIK
TAXT=«Tl+T2)/2.
H1=HITW8)
H2=Hl/{53.35*{TAXT*'*60,l»
C VIN - INLET VELOCITY
VIN=5.
C VHDI - INLET VEL HEAD
VHOI=UIN**2)*CIN/6<».'»
OELP=TPOP*VHliI
OPHr=JAFLR/36flO.)/(PI*OIA»DIN»VIN)
C SPRAY NOZZLES ASSUMED k F'T ABOVE PACKING
THT = DELP/(uIN-DCLTI + PHT + <*.0 + OPHT
HTDIA = THT»CIA
CAPCOS = 3.4E5*
-------
WNJG = 0.006*FLCk
WNEEO = WEVAF * KBuWN + WNOG
OPCOb=FFAC*8.76» +CPCCS
X +COSMAI + SYbCi>T+LcLFC
IFYSOF.EG.2)GC fC 151
APPR=APPR*1.
IF(APPR-20.)50,5L,151
157 IFJNSYSOP.tC.2)GC TC 190
151 TC=TC*1.
IFCTC-TCMAX(NCAPb+l) J 100, 10 0,150
RA1=RA
CHAR1=CHAR
PHT1=PHT
AFLR1=AFLR
APPR1=APPR
HWB1=H1
i>YSl=iYi>CSI
CAPCS1=CAPCOS
GOiPKl=CCSPKW
OPC^1=OPCOS
COiMAl=CCSMAI
HPF1=0. 0
HPP1=HPPNP
0£LF1=DELFC
QRJl=QkEJ
QRJT1=QREJT
FLOW1=FLCW
GPM1=GPM
WART 1=W ART
UA1=UA
WEVAP1=WEVA-P
WNEE01=WNEED
TC1 = TC
OIA1=OIA
THT1=THT
TAXT1=TAXT
OPHT1=OPHT
TPDP1=TPDP
70TCil=TOiCCS
WATER(NCC) = WNEEC/224700. $ DliCH (NCC) =WBCWN 1/22^700 .
Ti)IS«NCO)=TOUTS $ CDIS«NCOI= CONCR $ CSC = TOTCol $ SPCC = 20.
CCSC = CAFCS1
GO TO 156
190 IFJTOTCS1-1.E3) 200fl95,200
195 WRITECIRITE,1<=6)TC,APPR,RA
196 FORMAT I/3X, *FOR THE GIVEN CCNDITICNi A SC1UTICN CANNOT 8E FCLN
XO*,/,3X,*TC =*,F5.0,» APPR =*,F5.0,» RA =*,F5.0I
GO TO <»00
164
-------
200 CONTINUE
IFtIOO.EC.il GC 1C SCO
WRITE«IRITE,<:12J
212 FORMAT <*1*,15X,» ..... NATURAL uRAFT Wtl TOKER - ---- *,//, 10X,»
XTH£ DESIGN VAlbEi ANC COiTi> /Ft -*,//)
CALL PRTbal(Cfijl,TCl,HFFl,HPFl,l«EVAPl,WBCWNl ,/>FLRl>
WRITUIRITE,227)lFl,FLCWl,RAl,AFFRl,ThTl,UlAl,CHARl
227 FORMAT C3X ,*FRESi>URE DROP =*,F5.1,» CCNO FLOH = *, 1 12 . <« ,/ ,3X»
XRANGE = *,Fl,COiMAl,SirSl,uELFl,JCTCjl,CCi>FKl)
300 CONTINUE
WRITE(IRITE,33C)
330 FORMAT C//15X ,*V ARI A8LE AMBIENT CCNu ITIONJi*/)
500 IF«NTAM6.EG.O)GO TO ^00
TOPNIL=0.0
TUFMIL=O.O
CO 350 I=l,NGAPi>
IFKCAPt I).EQ.O.)GC TG 350
AMBOPC«I)=0.0
ANBtiFCfl 11=0.0
00 3*»0 J-1,NTAKE
IFlPCTAMBtlt JJ .EC.O.JGO TC 3t»0
TC=TCMINdI)
NTCOD=0
301 CALL PAFCiTII,TC»PWCST,CFCOO,QREJ)
lJT2=CQREo/FLCWl)/(EXP«UAl/FLOhl)-l.}
LT1=OT2«-CREJ/FLOM
T1=TC-OT2
T2=TC-UI1
IFS NSYS OP- 2) 316,303,316
316 IFU2-T/»MH8(JJ)20<»,30/2.
RA=T1-T2
T3 = T2 + 0.1'RA
T't = T2 + 0.^*RA
T5 = Tl - 0.<4*RA
T6 = Tl - 0.1*I
RQH2 = l./CHtT^) - HI - 0
ROH3 = 1./JHCT5) - H2 + O
= 1./HHCT6) - H2 + 0.1*C01I
165
-------
CHAR = (RAM.IMROH1 + RUH2 * RUH3 + RQH«»)
THI = 0.0
IFfiCHAR.LT. CHARD GO TO 320
321 IFCNSYSCP-2I30<«, 307,30'*
307 WRITECIRITE,3(J8)CAPP4T A MOB ( J) ))
APSAT=WACT*l<«.69e/(.622 + WACT>
DIN= Ik* . * ( Ik . 696- APS * T ) / ( 53 . 3 5* U AMiJ 6 (J ) •» <«6 0 . 1 1
OOUT=Cl<»'«.*tl'«.6S6-P(TAXT)l}/(53.35*(TAXT+'teO.))
AFLR=QR£j/tH2-Hl)
tf IN= •CAF4.fi/36flfl. I /
-------
SUBROUTINE ONCE(Na,NT,NCO)
COMMON PiIZE,CCPKW,ANFCR,FUCST,NCAPb,CAP(&) ,TOTLuI5) , CGLPCH5) ,IC
XMIN(6) ,PCMIN(6) , 1CMAXC6) , PCM AX (6) , HRCOF2(6) ,HRCOF1(6) ,HRCOFO(6),
XI J8,1WB ,RH,1 AVH20,TCdASE, NT AMB, AHBUFCC 5» , AMBOPC«5) ,T AKOB(5) ,T AKW
XB(5J .AMBRHC5) , TAMRVI5) ,PCTAMB(5,5) ,NiYSOP,T JISMX, NSPCGN, UOVALL, A
XRtACiaPFLOW, NH20, W UTH, PRPAGR ,CAPF AC,UStFAC,l KWHRS , IR IT £, IRE AD t
6 ArtWINU(5) ,AMRAb(5) , WINU , RAO , QFLRItf , DEPTH
COMMON/ NEWV/ CONCR, LOCI (201 , ACCtCONi>CT .OliLC C5J ,UIS7 R( 5» , FLOW1
2,rOPCM<5) ,CCPM«5),TMLS(5) .FMCil (5),TRCS1 ,WATER<'») ,OIbCH(4) ,TuISC«)
3,CJI jl^) ,FIaH,TZERO, NR,CiC,iiC,CFISH,CHSC,PNAhE C») , NAMES « 20) ,
<»:>PGR, iPiR, SPCO, JELHR, CCiC, CCOST, CCOSTS, SPNC,IOO
WCOFA=0. * WCOFB=15. $ PMPEF=0.8 $ TOTCS1=1.£3 $ IA=TuB $ IG=TCB
SPCO = 0.0
C
C CALCULATION OF THE EQULIBRIUM TEMPERATURE. TCALC, TO
C iTAlEMENT 1 — tQUAIION TAKEN FROM EOINGER
c.COF=WCOFA+WCOF8*WlNU
TA=TuB
TG=TU3
7 jH=RAL»-180i.*(rG/^60. + l.)**4-ECOF*5l.7*(P{TG)-RH*P(TA) ) -,26*ECC
XF*(fG-T Al
UHP= RAU-1801. *( OG + 1.)/<*6U. *•!.)**'»-£ GOF* 51. 7* (Pi ,
* COiPKW)
RA=T1-T2
13,14,15
167
-------
C PLAC- COST PER KW FOR WATER DUCTING
13 PLAC=1.5
GO TO 16
14 PLAO1.25
GO TO 16
15 PLAC=1.0
16 CONTINUE
IF(NSPCON.tQ.l)PLAC=0.0
:>r,iCST = i>ySCST+PLAC*ANFCR/(CAPFAC*8.76)
C ASSUME 5 FT OF PUMPING HEAD (FRICTION)
PHc.AD=5.
HPPMPsGPM*PHEAU/U960.*PMPEF)
CAPCOS=0.0
C CHANGE OPCOS STATEMENT TO MATCH EPA— CORVALLI:> VERSION Auu UsEFAC
C OPCOa=« HPPMP*.7457/(P;»IZE*1UOO.) )*PWCiT
OPCOS=(HPPMP*.7457/(PSIZEMOOO.) >*PWCST*UiEFAC
COSH AI=,OOi*CAPCO.]>/(PSIZt MOO 0.>+.1*OPCOS«-.01*SYSCS1
IF( f Of C OS- TO I Gill 154,156, 156
156 IF(NSPCON.tQ.l)GO TO 190
151 TC=TC*1.
IFUC-TCMAXINCAPS+l) >100,100,190
154 RA1=RA
T11 = T1 $ T21=T2 $ silTil=Srj)C*T $ CAPCS1=CAPCOS $
OPCSl=OPCOi $ COiMAl-COSHAI $ PLAC1=PLAC £ HPF1=0. I HPP1=HPPMP
OELFl=uELFC * TCALC1=TCALC t Q«J1=QREJ $ FLOW1=FLOW I GPM1=GPM
FCFS=FLOW1/224700. X JA1=UA { iCl=TC $ i OTCS1=T 01 COi
WATERdNCOl * FCFi, i L)ISCH(NCO)= FCFi> $ TUIi>(NCO) « Tl
CJIs(NCO) = 1.0 i CSC = TOTCS1 $ CCoC=CAPCi»l $ GO TO 156
190 IF(TOTCi>l-l. £31200, 195, 200
195 HRITE(IRirE,196)TC,Tl,RA
196 FORMA] (/3X, *FOR THE GIVEN CONUITIONb A iOLUTION CANNOT BE FOUN
XU»,/,3X,*TC =*,F5.0,* Tl =*,F5.0,» RA=*,F5.0I
GO TO VOO
200 CONTINUE
IF(IOO.LT.2) GO TO 23
WRITc(IRITE,212J
212 FORMAT (*1*15X» ----- iTRAIGHI CONDENSER COOLING ----- *,/)
IF(NH20) 220,228,230
220 WRITEC IRITEtl97)
197 FORMAT t20X» (WITH 6EA WATERI*//)
GO TO 23
228 WRITE( IRITE,198J
198 FORMAT (20X*(WITH JNTREATcJ FRtiH WATER)*//)
GO TO 23
230 WRITE! IRI1E,157)
157 FORMAT C20XMWITH TREATEU FRESH WATER)*//!
23 IFCIOO.Gl.il PRINT 227, QRJ1, TC1» FCFS, FLOW1, HPP1, TCALC1, RA1
227 FORMAT 4iOX*lHE bEiIGN VALUES AND COSTS ARE -*,//, 3X*Q REJECT = »,
1E12.4, * 8TU/HR AT T CONDENSER =*, F4. 0» /,3X*CONDENSER FLOW =*,£12
2.4,* CFS <*,E12.<»* LB/HR)*/,1H ,
3 * PUMP POWER =*,E12.4, * HP*,/3X*EQUIL I8RIUM TEMP =*FU.O
X,» RANGE -*,F4.0,/)
IFdOO. GT.l) CALL PRTDS2 ( CAPCi 1 ,OPCS 1 ,COSMA1 ,a¥Sl ,bELFl ,TOT Gil tCCS
2PK1I
IFdOO. GT.l) PRINT 302
168
-------
302 FORMAT«//15X*--fiIVcR TEMPERATURES—*,//,
XiX,14HOI$TANCE-MIL£l>,3X,17HSTREAM TEMP UEG.F, 3X,17HPLUR£ TEMP.-DE
XG.F ,3X,1<»HPLUME WIOTH-MI ,/19X, 8HNO PLANT ,2X ,5HMIXEL ,//)
CALC OF PLUME lEMPa AND WIDTH ANQ RIVER TEMPS- TO SJAT 22
V£LRIV=CUFLRIV/CWIJTH*OEPTH)I*3600.*2<«.
DTRIV= PLUMEW
308 FORMAT'(I6X,F5.1,6X,F6.2,<»X,F6.2,5XfF6.2,llX,F6.<*l
IFd.LT .20)00 TO 307
IF(IOO.GT.l) PRINT 330
330 FORMAT C//15X ,*VARIABLE AMBIENT CONDITIONS*/)
IFfiNTAMB.EQ.UJGO TO ^00
TOPNIL=0.0
TUFHIL=0.0
00 350 I=l,NCAPi>
IF(CAP{I».£Q.O.)GO 10 350
AMBOPCCI)=0.0
AM3uFC(IJ=0.0
DO 3 THAN PC MIN - AbSUMc PC MIN - CONTINUE*!!
IF(JFCOi).GT*Ot)JFCOO=0.0
169
-------
315 CONilNUt
OPCOU=(HPPi*.7<»57/CPSIZE*lGOO.) )*PWCST
AMBOPC< I) = AM80PCU) «-OPCOO*PCr AMB < J)
AMBJFCH I) = AM3uFC(IH-uFCOD*PCTAMB(J>
3<*0 CONTINUE
TOPMIL=TGPMIL*AMeOPC(I)*TOTLu(I)*CULPCTm»CAP
-------
oUGROJTINE TRANi>«N.»,NTR,NT>
COMMON Ps>IZE,CCPKW,ANFCR,FUCSl » NC APS ,CAP<6) ,TOTLU«5J , CCLPCK5S ,TC
XMIN(6) ,PCMIN<6) ,7 CM AX (6) ,PCMAX<6) , HRCOF2(6) ,HRCOFl<6) ,HRCOFO(fal »
Xi JtJ,lrfd ,RH,TAVH20,ICBAiE, NTAM6 , AM80FC<51 ,AMBOPC45) ,TAMUB«5),TAMW
XB85K ,AMBRH«5) , TAMRVJ5) ,PCTAN855,5> , NiaYSOP ,TUISMX, NSPCCN,UOVALL, A
XRtAC,iPFLOW, NH20,WIJfH,PRPAGR,CAPFAC,U;>EFAC,TKWHRS,IRIT£,IREAO,
6 AHWINU(5),AMRAU(5),WINU,RAO,QFLRIV,DEPTH
COMMON/ NtWtf/ CONCR,LOCI (20) i ACC,CONSCr,uIi>LC«5» ,OIS1R«5) , FLCWi
2,YOPCM«5) ,CCPM«5),I'MLiJ5) ,TMCiT C 5) fTRCiT f WATERS) ,UlSCH«<») ,TOIS(<«)
3»CJldUI ,FIi)H,JZERO, NR ,CaC,SbC, CFl5H,CHi>C ,PNAME (4) , NAMEi. (201 ,
<»:>PGR, oPIR, i>PCO, uELHR, CCSC> CCOiT, CCOiTS, SPNC,IOO
1 FORMAfC*'* REbULTi OF JRANSMISSION COisT CALCU^Ar ICNo FOR ilTE N
2UMBER *I3,/» WI1H PLANT TYPE *I3f//,25X,* CAPITAL COSTS *10X* COS
Jl PER UNIT POWER */,23X,» (jOLLARS) »15X* fMILLS/KW-HR) »/• TRAN&M
<»ISiION OPTION IS *I2»/I
2 FORNATi* NEW TRANiMIiilON LINES */*0 CONSTRUCTION CCsT *8X,E9.2t
2i8X,£9.2, /* MAINTENANCE COJ»T *8X,E9.2,18X, E9.2/* NEW TRANS T
304"AL COi,I*33Xt£9.2//» LOAD CENTER RELATED COSTS */» WHEELING CO*
4TC8PA»*35X,E9.2//* TOTAL TRANSMISSION COSTS AT THIS SITE ARE *E9.
52* MILLS/KW-HR. *//J
U = PiIZE*i.E3*CAPFAC*8.76
R = PSIZE/1000.
CCOaT = OISTR(NTR)*R*CCPM(NTR)
OMCOiT = JIiTR{NlRJ*R*YOPCM(NTR»
UCC = CCOiT*ANFCR/J
OOC = OMCOSl/0
TC = JCC + UOC
C CALCULATE COiT IN MILLS/KW-HR FOR TRANSMISSION OF POWER TO LOAO
C CENTER UilNG THE BPA WHEELING FORMULA
IPEN = JUISLC«N|*)*TMCST(NTR1 + 0.811/8.76
TRCST = TP£N * TC
C
C LAND USE CALCULATION FOR 500 KV TRANSMISSION LINES8 FOR 1000 MW
C ASSUME CORRIuOR IS 175 FT WIDE
C
iPNC = CJISTRINTR)*5280.*175.)/<*3560.
iPTR = «uISLC{NIR)*5280.*175.)/'»3560.
IF(IOO.EQ.l) GO TO 10
PRINT 1, Ni,NT,NlR
PRINT 2, CCObT, UCC, OMCObT, UOCi TC, TPEN, TRCST
10 RETURN
ENJ
171
-------
SLuWSl(NS,Nf ,NCO,NSC,NCH,NP)
COMMON Pi>IZE,CCPKW,ANFCR,FUC:>T,NCAPS,CAP«&) ,T01LUJ5I » COLPCT (5) ,TC
XMINJ6) , PCMIN(fa) ,] CM AX to) ,PCMAXt6) , HRCOF2 « 6) , HRCOF1 (61 ,HRCOFO J6),
xrjB,T kB,RH,TAVH20,TCBAiE, NT AMB, AMBuFC I 5) , AMBOPCJ5) ,1 AfU8<5) ,TAHW
XBC5) ,AMBRH(5) , 1 AMRV (5) ,PCT AMB (5 ,5) ,NSYSOP , TJIiMX,NSPCON ,UO VALL, A
XREAC,iPFLOW, NH20, WIJTH,PRPAGR, CAPF ACt USEFAC,! KWHRS , IRIT E , IRE AD ,
6 AMWINu(5) ,AMRAu(5) ,WINU ,RAG , QFLRIV ,OEP1 H
COMMON/ NEWtf/ CONGR,LOCI «20) , ACC .CONbCT ,0 I6LCJ5) ,DIS1 R!«5) , FLOW1
2,rOPCM(5) ,CCPM(5),TMLS(5) ,TMCi>» <5) ,TRCS1 , WATER (4) ,OIbCH(4) )TOIi>l'«)
3,CdI;>«<*> ,FIbH,TZ£RO» NR,CbC,3SC ,CFIiH ,CHSC, PNAME (i») ,NAM£S«20I ,
<»iPGR, i>PTRf SPCO, JELHR, CCSC, CCObT, CCOSTS, SPNC,IOO
C
C iLOWbF Ib A SUBROUTINE WHICH CALCULATES SOLID WASTE GENERATEQ BY
C A FOSSIL FIREu POWER PLANHCOAL) . THE SUBROUTINE GIVES
C INFORMATION ON BASELINE SOLlO WASTES GENERATED ANJ THE EXTRA
C BURUEN CAUSEu BY EXTRA FUEL CONSUMPTION FROM THE USE OF NON-ONCE
C THRU COOLING. BA^E HEAT RATE IS ASSUNEO TO bE 7200 8TU/KWHR.
C
1 FORMAfl*0 BASELINE SOLlu WAiTE LOAu li *F7.2* LB/KW-HR»/» THIS
2 AMOUNTS TO GENERATING *E9.2* LB/HR OF SOLID WAS1E FOR THIS PLANT*
JJ
2 FORMAf(*0 SOLIU WASTE GENERATED BY THIS OPTION IS *F7.Z* LB/KW-H
2R*/* THIi> AMOUNTS TO GENERATING *E9.2* LB/HR OF SCLIC WASiE.*/)
3 FOi?MAft*0 THE NUCLEAR FUEL SYSTEM IS A CLOSED LOOP.*/* NO FUEL S
20LIO WASTE IS HANDLED AS A SEPARATE, EXTERNAL PROBLEM.*/* ALL FUEL
3CO.*rsCINCLUOlNG oOLIJ WASTE ARE INTERNALIZED IN FUEL PRICE)*/!
SWGBH = 0.2«**CAPFAC*PSIZE*1000.
SWG = 0.2
-------
FUNCIION HU )
H =21.5721<»2-.935;i9227*H-.233b52<»3t-01*T«1*2 -. 26faC 5772E- 03*T**3+
X.12bCa99b£-05*l**'«
RETURN
FJNC1ION P(T)
P = .i&8i31b6E-H-.l<«<»610a9E-2*l+.83<»6u2<(7E-5*T**2+.<»987537E-6
X *r**3-.2065ad<»3L-9*T**'»+. 22620 22<»t-10*T**5
RETJRN
ENO
iUBROU* INt PRIOuCOPCOU, jFCOu, TCGu)
COMMON PiIZE,CCPKW,ANFCRj,FJC il , NCAPS » CAP 16) ,T01 Lu«5) , C04.PCTJ5) ,TC
XMIN(6) , PCMINC6) , ICMAXtbl ,PCMAXE, NT AMB f AMBJFC I 5) , AM30PCC5) ,TAMuB(5) ,TAMk
XB«5J ,AMBRH«5) , JAMRVJ5) ,PCTAMB«5,5) ,NSYoOP ,T JliMX, NoPCCN ,UOVALL , A
XREAC,sPFLOW, NH20, WI JTH}PRPAGR ,CAPF AC,USEF AC,TKWHRS , IR IT E > IRE AD ,
6 AMWINUC5) ,AMRAb<5) , WINu ,RAu , QFLRI V ,UEP1 H
COMNON/NEWV/ CONCR»LOCI<20) ,ACG»CONSCi"»uISLC«5) ,u!i>lR(5) » FLOW1
5) ,CCPMJ5) ,i'MLS<5) ,FMCiJ (5) ,iRCi'l ,WATERU) ,UliCHU) ,TUISK)
) ,FIi»H,TZtRO, NR,Ci>C,iiC,CFIbH,CHi)C,PNAME(Iit) , NAMES (120 I ,
i>PT«, 6PCO, UELHR, CCi>C, CCO^T , CCGiTS, SPNC,IOO
IRITE,10) OPCOj,OFCOJ,TCOu
10 FORMAi C/5X,*WI»H THE VARIOUS AMBIENT TEMPERATURES*,/,
X 5X,*fH£ CO^TS ARE -*,//,
X 3X, "OPERATING CCiT =*,Ffa.3,* MILLS/ KW-HR*,/,
X 3X,*JIFFLRENTIAL FUtL COiT =*,F6.3,» MILLS/M-HR*,/ ,
X /,3X,*T01AL iY^lEM COiT =*,F6.3,» MILLS/KW-HR*)
RETURN
ENJ
INE
COMMON PiIZE,CCPKW,ANFCR,FUCiT,NCAPS,CAP(6) ,TOTLO(5) , CCLPCF(5) ,TC
XMIN(6) ,PCMIN(6) ,1 CM AX (6) , PCM AX (6) , HRCOF2(6) ,HRCOF1 (b) ,HRCOFO(6I,
XTja,TW8,RH,TAVH20,J CBASE, NT A MB, AMBuFC « 5) , AMBOPC45) ,TANuB C 5) ,T AMW
XBC5) ,AMBRH«5) , 1AMRV«5) , PCI A MS (5, 5) , NS>V SOP , TUliMX, NSPCCN ,UOVALL ,A
XREAC,aPFLOW, NH20, WI JTH ,PRPAGR ,CAPFAC,USEF AC, I KWHRS , IRI T£, IRE AD,
6 AM WINu (5) ,AMRAu(5) , HIND ,RAu , QFLRIV , CEPTH
COMMON/NEWV/ CONCR,LOCI «2 0) , ACC, CONiCT, 0 IbLC 15) ,DIi1 R?5) , FLOhl
) ,CCPM«5J,TMLo«5) ,TMCil (151 , TRCil , WATE*«<») ,DISCH (<») ,TQIS ( <«)
,FIi>H,lZERO, NR, CSC,Sc>C ,CFIiH ,CHSC, PNAME C») , NAMES* 20) ,
oPCO, JELHR, CCi>C , CCOST , CCOSTS, bPNC,IOO
WRITE«I«ITE,10) CAPCOj, CCiPKW, OPCOi, COiMAI , iV^CO^,UELFC ,1 OiCOS
10 FORMAT«*0 CAPI1AL COiT =*,E12.<*,* U01LARS*/* CONLENiER ANU PUMP
ICOoT =*,E12.'»,* bOLLARS/KW*/* OPERATING COST =*,
X F6.3,* MILLS/KW-HR*,/, 3X , "MAINTENANCE COST =*,F6.3,» MILLS
X/KW-HR*,/, 3X,*CCNuENiER ilTiltM COSI =*,Fb.3,» MILLS/KW-HR*,/ , 3X,
X*UIFFERENTIAL FUEL CCiT =*,F6.3,» MILLS/KW-HR*,//, 3X,» TOTAL SYi
XTEM COoF =*,F6.3,» MILLS/KW-HR*/)
RETURN
ENO 173
-------
~«,-,x^. ..„_ PRTUi>lT,NCAPi,,CAP(6) ,TOiLut5) , CCLPCK5) ,TC
XMINI 6) ,PCMIN(b) ,TCMAX(b) ,PCMAX«b>, HRCOF2J6J,MRCOF1(6J,HRCOFO<6>,
XUa,lWB,RH,l AVH2C,ICEAbE, NT A MB , AMBOFCC 51 ,AMBOPC(I5) ,1 Ahu8 (51 ,T AMW
XB(5) ,AMBRH(C^I , 1 AMRV(5) , PCT AMB ( 5 ,5) ,NilTi>OP ,TCIbMX,N£jPCCN ,UOVALL,A
XREAC.iPFLOW, NHi:0,WIJTH,PRP£GR,CAPFAC,UL.tFAC,TKWHRi,,IRITE,IREAL,
b ANWIND(S) ,AMRAb(5) ,WIND,RAJ,QFLRIV ,DEPTH
COMMON/ NEW \l/ CONCR,LOCI (20) , ACC »CONbCT ,u IsLC J5) ,Uli»l R«5> , FLCW1
2»yOPCM(5) »CCPM(5) »IHLd(5) ,TMCiT (5) ,TRCiT ,WATERC*I ,0 l!sCH ( ^1 ,TQ
3,CJIb«»> tFIi»H,]2tKOi NRtCaCfSiCiCFIbH.CHSC.PNAMEC*! » NAMES (20)
i»oPGR, iPTRj sPCC, UELHR, CCSC, CCOST , CCOoTb, SPNCflOO
WE(/CFi=WtV/22<*700.
WRirt(IRIIc,10)UREJiTC»HPF,HPP,W£VCFi,,WEV,WBuCFi,WBGtAFLR
10 FORMAT J/,3X,*Q REJECT =*,E12.'»,» 8TO/HR AI 1 CONDENSER =*, F5.0
1,/,3X,*FAN POWER =*,E12.^,*HP PUMP POWER =*,£12,^,*HP*,/,3X,»
2H20 EVAP =*,t.l2.<*,» CFS (*,E12 . k ,*LB/HR* */
3 » H20 BLOWuOWN =*,E12.'*,* CFb (»tE12.<»t* LB/HR)*/
k * AIR FLOW RATE =*,c!2.*»,* LQ/HR»»
REf JRN
ENJ
174
-------
iUBROJTINE PAFCil (I , 1 C,PWCSf , utLFC,QREJ)
COMMON PiIZt,CCPKW,ANFCR,FJGSY,NCAPi>,CAP(6) ,TOTLOI5) , COLPCT<5> ,TC
XMINIb) , PCM IN 1 6) , TCMAX( 6) , PCM AX ( 6> , HRCOF2C 6) ,HRCOFl (6) ,HRCOFO<6),
XfOB,lWB,RH,i AVH20|TCBA.»E, NT AMB , AMBOFCI 5) , AMBOPC85) ,TAMGBC5J ,TAMW
X8I5) ,AMBRH(5) , T AMR VI 5) ,PCi AM8«5,5) , N^f SOP, TO IS MX, NSPGCN ,UOVA,LL ,A
XREAC.aPFLOW, NH20, WIJTHfPRPAGR,CAPFAC,USEF AC,TKWHRS, IRIT£» IR£A(J,
6 AMWIND(5) ,AMkAL(5l , WIND , RAD ,QFLRItf, DEPTH
COMMON/NEW*// CONCR,LOCI <20) , ACC , CONbCT ,OISLC 15) ,JIS1R-I5J , FLCW1
2,YOPCH«5) ,CCP«C5) ,TMLS(5) ,rMCi>T{5) ,? RCST » WATER Ik ) ,uIi>CH<4} ,lul
f*) fFIiH,TZERO, NR,CbC,SbC,CFIiH,CHSC,PNAMECf) , NAMEb(I20> ,
iPTR, SPCO, UELHR, CCSC, CCOST , CCOSTS, SPNC,IOO
HRBAS>E=HRCOF2«II*TCBAdE*»2*HRCOFllI)»ICttASE*HRCOFO(I)
HEArR=HRCOF2II)*lC**2*HRCOFl(I)*TC*HRCOFO(I)
uELHR=HEATR-HRBASE
UELFC=FUCSr*l»tLHR*l.c-5
PMa*lsFUC5r*HEATR*l.E-5+CCCPKW*ANFCR)/(CAPFAC*8.76)
RETURN
cNJ
175
-------
SU3ROJTINE CONimCONU,TIN,Q.RE,J,PWCST,uT2,TOUT,UA,FLOW,GPK,
COMMON PbIZE,CCPKW,ANFCR,FUCST,NCAPS,CAP<6) ,TOTLU<5> , COiPCT (5) |TC
XMIN(6»,PCMIN<6) ,TCMAXJ&) ,PCMAX(6) , HRCOF2S 6) ,HRCOF1 C6) .HRCOFOI6J,
XlUtJ,TWB,RH,TAVH2C,TC8AaE, NT AMB, AMBOFC8 51 , AMBOPCC5) ,T AKu8 <5) ,T AMW
XB«5» ,AMSRH(5I , 1 AMRV85J ,PCTAMB<5,5) ,NSYSOP ,TOISMX, NSPCON,UOV/ALi ,A
XREAC,SPFLOW, NH20, WIJTH, PRP AGR, CAPFAC,Ui£F AC,TKWHRi , IRITEi IR£Au ,
6 AMWlNu(5) ,AMRAu(5) , WIND, RAO .QFLRIV ,UEPTH
COMMON/NEWV/ CONCR,LOCI(20I , ACC ,CONiCT,u Ii>LC 15) ,UIS1R(I5), FLOW1
5) ,CCPM(5),fMLi(5) ,TMC^,I (5) ,TRCSI ,WAT£RC*> ,DIbCH(^J
) ,FIi,H,TZ£RO, NR,CiCtiiC,CFIbH,CHSC,PNAM£(i4) » NAMES (20 )
iPTR, S>PCO, uELHR, CCiC, CCQiT, CCOaTS, bPNC
PMP£F=.8
IF(NiPCON.£Q.l)GCi TO 30
C VARIATION OF HEAT TRANSFER COEFFICIENT UALL, WITH TYPE
C OF WATER
10 UALL=340.
GO TO 25
15 UALL=<*20.
GO TO 25
20 UALL=250.
25 CONTINUE
UTl=TCONU-fIN
TOJT=TCONU-U12
UELT=[OUT-TIN
ALOG MtAN TEMPERATURE OIFFtRENCE, LMTU
uTLGM=tDTi-uT2)/{ALOGCUTl/JT2n
ACOND=QREJ/CL)TLGM*UALLI
UA=UALL*ACOND
CONCST=20.*(ACONL,*1.05)**.9
65 PtRCtNT INCREASE IN MATERIAL COiTi IF iALT WATER UaED
IF«NH20.U.O)CONUl=CONCi>T*l.fa5
FLOW=QREJ/JEL1
A6i>UM£ 35 FT OF HEAO
CHcAU=35.
PMi»Ci>r=T)/<396li.*PMFt.F*PSIZE:*1600.»
1 OOLLAfJ PER GPM FOR COSi OF PUMPS
COiPKW=lCONCbT*l.*CiPM)/(PaIZE*l.£3)
^YsCbT=l(COSPKW*ANFCR)/tCAPFAC*6.76»+PMPCST
GO TO 50
30 UA=UOVALL*AREAC
FLOW=iPFLOW
GPM=FLOW/(8.3't»bO.»
50 RETURN
ENJ
176
-------
C
C
C
SUBROUi INE aCREENS(NS,NT,NCU,NSC)
COMMON PSIZE,CCPKW,ANFCR,FJC SI » NCAPS , CAP (5) ,TMCbl C5)ylRCd) , WATER «») ,01SCHU) fTu!b«t)
NR,CoC,iSC,CFI^H,CHSC,PNAM£«i»l ,NAMEb((20l ,
OtLHR, CCSC, CCOST , CCOS1S, SPNC,IOO
<»SPGR, SPIR,
UIMENdlON NAMECt)
OAFAlNAHEd) , 1-1 ,
210HMECHUFT TR/
1 FORNAT(*0 THE
2»MlLLa/KWHR*/ *
3ARE*E9.2*
2 FORMAr«*0
/lOHONCE-THRu .10HPONU
, 10HNATLF1 TWR,
SCREENING COi>TS ARE */*0 CAPITAL COaTS * £9.2
OPERATING COST J*E9. 2*MlLLa/KWHR*/» TCTAL
MILLi/KWHR*/)
INTAKE COaTb WITHOUl SCREENING*//
2* CAPITAL COST*E9.2* MIJ.LS/KWHR* /* OPERATING CO^Tis *E9.2 *
3MILLi>/KWHR*//» 10TAL SYSTEM COST IS* £9.2* MILLS/KWHRVJ
3 FORHAr«*0 THE SCREENING DAMAGE TO FISH HAS A VALUE CF *t9«2* MILL
2*/KW-HR*/» COMFARtu TO SCREENING COSTS OF *E9.2» MILLS/KW-HR*/)
k FORNAn*! SCREENING CALCULATIONS FOR *A10* COOLlNG*/o THE
ZFLQ* 10 BE CONSIuERcU FOR -SCREENING Io »E9. 2* CFi.»)
FLOI = WATER(NCO)
IFdOO.NE.ll PRINT <», NAht(NCO), FLOI
F2= 0.
IF(FLOI.LT.500.) Fl = 1000.
IF (FLOI.G£.500«) Fi = 2kQ.
IF«Fi.OI.LT.10.) GO TO 8
GO TO 10
fl F2=8.0E3$F1=0.
CONPJIE CAPITAL COoTS FOR A SCREENED INSTALLATION
10 CCOS1S = F1*FLCI * F2
COMPUFE COoT PER UNIT ENERGY GENERATED
0 = PSIZE*l.E3*CAPFAC*8.7b
CCPJP = CCOS1S»ANFCR/U
THE MAINTENANCE FACTOR IS TAKEN TO BE 10 PER CENJ OF ?HE CAP CS1
OCPUP = fl.01»CCOSTi/0
TCiF = CCPUP + OCPUP
IFdOO.NE.li PRINT 1, CCPUP, OCPUP, TCS1
WI1HOJI SCREtNS ARE 60 PERCENT OF THOSE WITH SCREENS
$ XJ=0.b*OCPuP£ X<»= X2 + X3
PRINT 2, X2, X3, Xk
UAMAGE IS PROPORTIONAL TC THE FRACTION OF RIUEK WATER
INTAKE CObTS
X2=0.b*CCPUP
IFdOO.NE.ll
ASSUME FISH
FUNCTION OF THE RIVER ON
PUMPEU THROUGH I HE PLANT.
R = FLOI/QFLRItf
UAM = R*FISH
UAMP = uAM/u
IFtNSC.EC.il SSC^Oo
IF(NSC.EQ.l) CFISH
IFCNSC.EQ.2) SSC -
IF(NSC.EQ.2) CFISH
IFdOO.NE.l) PRINT
RE1URN
WHICH THE PLAN! IS LOCATED.
SCREENING PREVENTS ALL FISH DAMAGE IN
= UAMP
TCST
= 0.0
177
-------
SUBROUTINE. CHEM(Ni,NT,NCO,NSC,NCH)
COMMON Pl»IZt.,CCPKW,ANFCR,FUCST,NCAPS,CAP<6l ,TOTLO(5J , CCLPCK5) ,TC
XMIN(6) ,PCMIN(b) ,1CMAX(6),PCMAX(6), HRCOF2«6),HRCOF1(6) ,HRCOFOt6),
XTJB,TW8,RH,TAVH2(J,rCBASE, NT AMb , AMBUFC I 51 , AM80PCJ5) ,1 AMuB (5) ,1 AMW
X8(5) ,AMBRH(5), TAMRV(S) ,PCTAM8<5,5) ,NSYSOP ,TUISMX,NSPCCN ,UOVALL,A
XREAC,SPFLOW, NH20, WIDTH, PRPAGR ,GAPFAC,Ui>EF AC ,TKWHRi>, IRITt, IRE AD f
6 AM WIND(5) ,AMRAulS),WINO,RAj,QFLRIV ,OEPTH
COMMON/NEWV/ CONCRfLOCI(201,ACC,CONSCT,UliLC(5>,OISTR15) , FLCW1
) ,CCPM(5) ,THLi<5) ,TMCbT (5) ,TRCiiT ,WATt« (4) ,UISCH<<») ,TOISC«)
,FIi>H,TZcRO, NR,CiC,^SC , CFIiH ,CHSC, PNAME U) ,NAMEi>C20) ,
i>P]R, SPCO, JELHR, CCjC, CCOS1 , CCOiTS, SPNC,IOO
C SELECT IREAiHENT PROCEio BAiED ON COOLING IYPE AND TREATMENT FLAG
GO 10(100,100,200,200* NCO
C ESTABLISH TREATMENT COiTi FOR iYbTEMS UTILIZING CHLORINATICN
C ONLY(PONuj ANu ONC£-iHRU)
C CONVERi' FLOW TO MGU.
100 FL = HFLCW1*2.89I/1.£6
C CALCULATE CAPITAL COST
CAPC = «0.015*FL**.67)*l.t6
OUTPUT,CAPC,FL»FLOW1
C 0 AND M CURVE Ils VERY NEARLY FLAT PICK A VALUE OF .07 S/1000.
C GAL AND GENERATE 0 + M FOR FLOWRATE
C CONVERT BOTH COi>Ti> TO PER UNII POWER COSli AND ADO FOR TOTAL
UCAP = CAPC*1000.*ANFCR/TKWHRS
UOP = 0.07*FL*0.01/2<».
CHSC = UCAP * UOF
OUTPUT, UCAP,UOP,CHiC
GO TO 5UO
C ESTABLISH TRtAlMENT COiiTb FOR TOWER SYSTEMS FOR THIS CASh TREAT
C MENT IS, ASSUMtu TO CONSIST OF COAGULATION ANU SEDIMENTATION
C PLUS SANu FILTRATION
C NO TREATMENT IF NCH = 1
200 IF(NCH.EQ.l) GO TO <*UO
C CONVERT 8LOWGOWN TO MGu
FL = JISCHCNCO)/1.55
C COMPUTE COST(CEN1S/1QOO. GAL)
TC = <*./(FL**.02) + S.
C CONVERT COST TO MILLS/KW-HR
CHiC = TC*FL*G.01/2<».
OurPUT,OIaCH(NCO),FL,TC,CHSC
GO TO 500
400 CMSC = 0.0
500 RETURN
ENJ
178
-------
INE PLANT(Nj,NrR,Nl ,IPF)
COMMON PilZE, CCPKW, ANFCR, FUCoT , NCAPi ,CAP (6) ,T01LU(5) , CCLPCIC5),TC
XMINC6) , PCM IN 16) , TCMAX (6) , PCMAX ( b) , HRCOF216I ,HRCOF1(6) ,HRCOFO(6I,
XUB,Tdb,RH,l AVH20,iCBASE, NT AMB, AMBuFCt 5) ,AM30PC(5) ,T AHCBJ5) ,T AKH
XB(5) ,AMBRH(5) , i AMR V( 5) , PCTA M6 ( 5, 5) , NSYiOP ,TUISMX, NSPCCN, UOVALL ,A
XR£AC,iPFLOW, NH20, WIui H, PRPAGR , CAPF ACjUiEF AC, TKWHRS , IRI1 E, IRE AO ,
6 AMWINU(5) ,AMRAu(») , WINu ,RAU ,QFLRIV , DEPTH
COMMON/ NEW V/ CONCR,LOCI (20) , ACC ,CON$CT .GISLC (5) , UIS1R45) , FLOW1
2,YOPCM(!5),CCPMC5),rMLc»II5) ,TMCS)1 «5),TRCS1 , WATER U) ,DISCH «*) ,TDISC<»)
3,CJIS«<*) iFIbH,lZEKO, NR ,CsC ,SiC ,CFIiH,CHbC,PNAME Cf) , NAMES C20),
5PTR, i>PCO, uELHR, CCiC, CCOiT , CCOiTb, SPNC,IOO
N NHRPlb (b) ,HRP(6,6) ,IHR(6,6) ,TUR8HF 86, 6) , HR«6,6)
NAMELIaT/PLPARM/ PilZE, CCPKW, FUCST, iPGR, NCAPS
NAMELlbT/CAPTuRL/ CAP, TOiLO, COLPC* , NHRP1S, PCMIN, PCfAX
NAMELIbl/TUROPu/ HRP, TURBHR, PC3ASE, PCTAM8
NAMELIbl/ilTSjTA/ NbYiOP, N3PCON, UOVALL, AREAC, SPFLOW
IF(IPF.cU.O) GO TO 90
REAJdREAQ, PLPARMI
NCAPbl=NCAPb*l
R£AQ(IREAd,CAPlURL)
NNCAP=NCAPi
IFCNHRPTb(NCAPbl) .GT. 0) NNCAP=NNCAF-H
REAJ(IREAj,TUROPH
REAJ
GO TO 100
C
C SELtCr POWER PLANT INPUT INFORMATION 8ASEO ON EliHER DEFAULT OR
C INPJ1 FLAG INFORMATION
C UEFAULT INPU1 INFORMATION FOR NUCLEAR POWER PLANT
C
90 GO 10(91,92) NT
91 PSIZE=1000. $ CCPKW=250. $ ANFCR=0.10 $ FUCST=20.
TURSHRCl,l)=97fa5. $ i URBHRd, 2 ) =9815 . $ TUKBHR( 1 ,3) = 9865.
1UR8HR(2,1)=9815. $ TJRBHR(2 ,2) =9915. $ TUREHR (2 ,3) = 10300 .
IJRBHRff 3,1) =9815. i « URBHR( 3 , 2) =9915. $ TURBHR(3 ,3) = 10300 .
TdR8HR«<»,l) = 9615. $ TURBHR(<» ,2) =9915. $ 1 URBHR«^ ,3) =10300'.
TURBHRI5,1)= 0. $ TJRBHR(5,2)= 0. $ 1 URBHR (V, 3)= 0.0
TUR8HR(5,1)= 0. i TURBHR(5,2)= 0. $ 1URBHR(5,3)= 0.0
iPGR = 500. S HRBASE = 9815. S PNAME(NT)= 7HNUCLEAR
GO TO 99
C
C FOSSIL FIR£(j(COALI oEFAULT INPUT UAI A
C
92 PSIZt = 1000. » CCPKW = 190. £ ANFCR = 0.10 $ FUCST = 27.
iUR3HR((l,l) = 7200. $ 1 URBHRd ,2 ) =7220 . 2 TURBHRfll ,3) =7250.
TURBHR«2,1J=7220. S TUR8HR(2 , 21 =7280 . $ TURBHR (2 ,3) =7350.
IURBHRJ 3,11=7220. $ 1 URBHR (3, 2) =7280 . S IURBHR(3 , 3)=7350.
JJRBHR«<»,1)=7220. $ TURBHRU , 2) =7280 . $ i URBHRD ,3) = 735 0.
JURBHrMl 5,1) = 0. £ iUR8HR(5,2)= 0. { TURBHR(5,3)= 0.
C.PGR = 3UO. $ HRfaASE = 7220. $ PNAME(NT) = 7H FOSSIL
99 NSYSOP=0 $ NSPCON=0 $ NCAPS1=6 $ NCAPS = 5 $ PC8ASE = 1.5
CAPI1) = 1.0 $ CAP(2)=U.8 $ CAP(3)=0.6 $ CAP(<»)=0.25 i CAF(5) = 0.0
TOTLD(5)=360.
179
-------
TOf Lo(l)=fa450. £ TOTLJJ2) =750. $ TOTLJC3)=700. $ TOTLlH <<) = 500.
COLPCni)=1.0 S COLPCTI2) = 1.0 i COLPCT(3) = 1.0 $ COLPCT (41=1.0
COLPCT«5) = 1.0
PCMIN(1)=1.G * PCMINI2)=1.5 $ PCMINI3)=1.5 $ PCMIN(4I=1.5
PCMINC5)=1.5 $ PCMIN(6)=0.0
PCMAXC1)=2.0 i PCMAX(2)=3.5 i PCMAX(3)=3.5 $ PCMAX(4)=3.5
PCMAXJ5J=3.5 S PCMAX(6)=0.0
NHRPTSK1) = 3 $ NHRPTS<2)=3 $ NHRPfo«3)=3 $ NHRPTi(4)=3
NHRPTiS5)-3 $ NHRPTS(6)=0
PCTAMB«1,1)=0.5 $ PUfAMBIl,2Js0.1 S PCTAMBt1,3)=0.2
PCTAiMB(2,l) = 0.1 i PCTAMB(2,2)=0.4 $ PCTAMB« 2 ,3) = 0 .
PCfAM3(3,1) = 0.2 S PCTAMBt3,2)=0.^ » PCTAMB83 ,3) = 0.3
PCIAMB* 3,<*) = 0.1
PCrAMJC»,l)=0.3 $ PCIAMBUt 21=0.1 $ PCT AMB( ^ , 3J =0. >*
PCr A MB «<»,'+) = 0.2
PCTAMB«5,1) = 1.0 $ PCTAMB15,2).= 0.0 S PCT ANSI 5 ,3)-0 .0
HRPCifl»=1.0 $ HRP<1,2»*1.5 i HRP(1,3) = 2.0
HRP(2,1)=1.5 $ HRP(2,2»=2.5 $ HRP(2,3)=3.5
HRP«3,11=1.5 t HRP(3,2»=2.5 $ HRP(3,3)=3.5
HRPJ4tl)=1.5 £ HRP(4,2)=2.5 $ HRP«4,3)=3.5
HRP15,11=1.5 i HRP(5,2)=2.5 S HRPC5,3)=3.5
C
C LOAD DURATION HOURS CHECK - iO STATEMENT 246
100 TOTJUR = 0.0
uO 243 I = 1,NCAP;>
TOTAM=0 .0
C PERCENT AMBIENT TIME CHECK - TO STATEMENT 242
00 238 J=1,NTA«B
238 TOTAr1=TOTAM+PCTAMB
-------
uo y I=I,NNCAP
IFlNHRPTi>JI) .Gl . 2) GO 10 7
WRIT£«IRITE,6II
6 FORMAT (* Ltoia THAN 6 HEAi RATEb FOR CAP NO *,I2>
GO TO 500
7 Xl=G.iX2=0.iX3=C.iX<«-0. JXY=0 . kX2Y-0 . m=0.
M2=NHRPTiII)
UO 8 J=1,M2
C
HRU,J) = iJRBHR(I,J)
C
C CALC OF ISAT FROM PREiS. (IN. HG»
BLOG=ALOGi*(8*X3-X2*Xl)+X2*(X3*Xl-X2**2)
ANJM=X2 Y*ia*X2-Xl**2)-X3*«B*XY-Xl»Yl)+X2*JXY*Xl-X2*yi)
BNJM=X^*(B*XY-X1*Y1I-X2Y*(IB*X3-X2*X1}+X2*(X3*Y1-XY*X2>
CNUM=X<»*(X2*Y1-XY*X1)-X3*(X3*Y1-XY*X2)+X2Y*{X3*X1-X2**2)
HRCOF2( I) = ANUM/UEN
HRCOF1C I)=BNUM/bEN
HRCOFOJ I)=CNUM/DEN
9 COMTINUE
C
C MAKING OESIGN HR = 100PCT CAPACI I Y HR (IF NOT SPECIFIED)
IFtNHRPlijCNCAPill .GT. OJ GO TO kS
00 <*0 I = i,NCAPS
1FICAP( I) .EQ. 1.) GO TO ^2
kQ CONTINUE
WRITESIRITE,**!)
<»1 FORMAT (* NO 100 PCT CAPACITY OR DESIGN HR*J
GO TO 500
it 2 HRCOF2(NCAPS1) = HRCOF2(!I)
HRCOF1«NCAP5>U = HRCOFKH
HRCOFOi NCAPbl) = HRCOFO(I)
PCMIN(NCAPill=PCMIN(I) $ PCMAX(NCAPSU=PCMAX(I)
V5 CONTINUE
C
UO 11 I=i,NCAPbl
BLOG=ALOG(PC«IN(D)
ICMIN([I)=79.035793*30.'»62i»09*BLOG*1.97A>0'»16*
X JdLOG) **2 + 0.131 2<4i) 35* «Bi.OG>**3
BLOG=ALOG(PCMAX(D)
11 TCMA X (I) =7 9. 03579 3+ 30.462^0 9* BLOG+ 1.97^0^16*
X (BLOG) **2*0.1312<*035*«8LOG)**3
aLOG=ALOG(PCBASEJ
X (BLOG) **2+0.13.12<«035*
-------
c
C CALC OF AVG CAPACITY FACTOR AND COOLING USE FAC1OR
TKWHRb=0.0 $ CKWHRi-0.0
UO 47 I=1,NCAPS
TKrfHRS=TKWHRi-MCAPd)*TOTLJd))
47 CKWHRS = CKWHRS+T($/KW) ANFCR(Ffc) FUEL COST (CTS/MI1LION BT
3U) TYPE*/,5X,F5.0,3X,F9.0,6X,F3.3,F9.0,15X,A7//>
IFdPF.EQ.O) PRINT 1000
1UOO FO(?MATI*0 NOlt POWER PLAN! DATA SPECIFIEJ IS INTERNAL DATA*/*
2 NO POWER PLANT uATA WERE INPUT.*///J
IFdOO.NE.3) GO TO 500
C
WRI ltd RITE, 360) (CAP (I) , I = i,NCAPS)
WRITECIRIIE,361) (TOTLQd) ,I=i,NCAPbl
WRITt(IRIT£t3b2) (COLPCT11),1 = 1,NCAPS)
WRIiECIRIT£,363) (PCMIN8IJ,I=1,NCAPS1)
WRIT Ed RITE, 364) (TCMINdl ,I = 1,NCAPS1I
WRIT£(IRIT£,365) IPCMAXdl ,I=1,NCAPS1»
WRI1 £dRITE,366) (TCMAXd) ,I = 1,NCAPS1)
360 FORMAT (5X,*CAPACIlIEi ANo CORRESPONUING DATA*,/, 7X, MEXTRA V
XALJEi ARc uEbIGN UAJA)*,//, 3X,*CAPACITY - *,5F7.2I
361 FORMAT**
362 FORNAM*
363 FORM AH*
364 FORM AH*
365 FORMAT!*
366 FORNAM*
HRS/YtAR -
PCT COOLING -
MIN P CONQ -
MIN 7 CONU -
MAX P CONU -
MAX T CONU -
*,5F7.0)
*,5F7.2)
*,6F7.2)
*,6F7.2)
*,6F7.2J
*,6F7.2)
WRIT£dRITc,370ICAPFAC,UsEFAC
370 FORMAT CSX,*CAPACITY FACTOR =*,F5.2,/,5X,*COOLING USE FACTOR =*
X ,F5.2»//I
C
WRITEdRIlE,
-------
c: - *,6F8.2,/)
WRITt«IRII£,410) ICBAi£
429 FORMAT ((3X,*UAoE P CONu BAi>E I COND* ,/, 7X , F6.2 , 8X, F6. 1//I
C
WRITtCIRITE,'*!/*
<*17 FORMAT«//5X,»PERCENT OF COOLING iY^TEM TIMt AT A80VE*,/,
X aX,*AM6IENT CONLITIONS*,/>
uO <»18 I=l,NCAPb
<*18 WRIiEC I«ITt.,'*19)CAP(II , (PCTAM6 (I, J) ,J=1,NI A MSI
419 FORMAT«/3X,*CAP = *,F^.2,* -*,5F7.2)
C
C
<»24 IF(NiPCON-l) 500,^25,500
425 WRJ/icC IRITE,<»26)JOVALL»AREAC,SPFLOW
426 FORMAT «5X ,*GONuENoER SPECIFICA1 IONi>* ,/ , 3X,*OVERALL U = *,F6.
10,/, 3X,*lUbE AREA = *,£12.4,/,3X,*H20 FLOW = *,E12.4,//>
C
5QO CALL TRANbtNitNiR,NT)
RETURN
ENO
183
-------
SUBROUTINE i>nE(N;>,NSEI,NTR,MNIR,NT,MNT,NCO,MNC,NSC,NCH,NP,IPF,ICF
2)
C
C SUE IS I HE SUBROUTINE WHICH INPUTS ALL I HE sITE RELATEL DATA
C
COMMON PSIZE,CCPKW,ANFCR,FUCSr,NCAPs,CAP(6) ,TOTLO«5), CCLPCT<5) ,IC
XMINI 6),PCMINI6I,TCMAX(6),PCMAX(6), HRCOF2(6),HRCOF1(6),HRCOFO(6),
XTJB,TW8,RH,TAVH20,rCBASE, NTAM8,AMBOFCI5),AM80PC<5) ,TAK£B<5) ,TAKW
X8(5) ,AMBRH(5) , IAMRV(5) ,PCTAMB(5,5) ,NsYSOP ,IUISMX,NsPCCN,UOVALL , A
XREAC,SPFLOW, NH20,WI3IH,PRP /GR,CAPFAC,USEFAC.TKWHRS,IRITE,IREAD,
6 ANWINu(5),AMRAL(5),WIND,RAO,QFLRIV,OEPIH
COMMON/NEWV/ CONCR,LOCI<20),ACC,CONSCT,UISLC(51,OIS1R(5), FLCW1
2,Y3PCM(5),CCPM(5).,TMLS(5) ,TMCST 15) ,TRCi>T » WATERUI ,UlSCHI4) ,IOISC<«)
J,CJliC*») ,FIS>H,IZERO, NR,CaCi;»SC»CFI&H»CMSCiPNAHEC<»l , NAMES (120 I,
'tjPGRt iPTRi iPCO, JELHR, CCSC, CCOSI , CCOSTS, SPNC,IOO
NAMELIiT/CONFLG/ Nc. f NSEIf NFRf MNT R ,NT , MNT ,NCC ,MNC,NSC ,NCH , NP , IPF ,
2ICF.IOO
NAMELIiJ/VARAMfl/ TAMu3,TAMW8,TAMRV,AMWINu,AMRAO
NAMEUST/INPUT/ 10B, TWB, WIND, DIR, RAD, PRPAGR, NlAMti, ACC,
2CONiCrf MtF, yOPCM, CCPM, TMLS, TMCST, ANFCR, GEO, Ul^TR, DISLC
NAMELIil/WATRV/ GFLRIV, WIJTH, uEP'iH, TAVH20, NH20f TJISMX, FISH,
2 NR
1 FORNAM20A2I
2 FORMAT(*0 blTL NUMBER *I3* IS *20A2/» ITS LOCATION IS *20A2/J
3 FORMATC*<» THE VARIOUS AMBIENT CONuITIONS ARE »/*0 UP TO (5) OPERAT
2ING CONDITIONS CAN 8£ SUBMITTEb BESIOEi,OEsIGN VALUtS»A*0 ZEROS IN
3UICATE LEsS 1HAN (5) VARIABLE CONuITIONS WERE INPUI*//,1HO ,21X,1H1
<»,9X,1H2 ,9X,1H3,9X,1H4,9X,1H5, iX*UESIGN*/*0 DRY BULB(F) *6F10
5.1,/*0 WEI BULB(F)*6F10.1,/»0 WINU(MPH) *6F10.1,/*0 RADIATION *6
6F10.1,/*0 H20 J£MP(F)*6Flli.i,/*0 LANU COiT IS *F10.0* $/ACRE»///l
4 FORMAT«*0 FOR THIS SlfE» INPul IRANaMISSION LINE INFORMATION IS *
2/*0 NUMBER OF TRANSMISSION OPTIONS IS »13//» OPTION NO. NEW CO
3NSTR JIST. LOAJ CTR. DISI. */,20X,» (MILEs)*14X*(MILES)»/)
5 FORMAT«*0 SITE INPUI INFORMATION*/)
104 FOKMAHlHOf5X,I2,13X,F7.1,14X,F7.1l
420 FORHAT(*0 RltftR INPUT PARAMETERS •//* FLOW(CFS) WIOT
2H(FD JLPTH(FT)*/3F12.1//» FIoH VALUE AT PLANT IS *E9.2* UO
3LLARs.*/*0 WAIER iYPc = *I2,2X,*(0 = UNIRT. FRtSH WATER, 1 = TRT F
4RsH WATER, -1 = SALT H20)*//l
IREAu=& $ IRITE=6
REAU(IREAj.CONFLG)
C INPUT i HE SITE NAME ANu I'lS LOCATION
REAj(IREAu,l) (NAMES CD , 1 = 1,2 01
REAj(IREAj,!) (LOCI(I),1=1,20)
WRITE(IRIf£,2) NS,NAMES,LOCI
C INPUT silt INFORMAJION WR1 AIR ANu LANU PARAMETERS
REAUdREAU, INPUT)
C INPUT WAIER BOut PARAMETERS FOR THIi SITE.
REAU(IREAjtWATRV)
C INPUT OPERATING CONuIflONs (ATMOSPHERIC AND WATER) CNucR WHICH THE
C PLAN1 WILL PERFORM.
C INITIALIZE ALL AMBIENT OPERATING CONu ARRAYS TO 0.0
UO 100 1=1,5
TAMUBGI) = 0.0
TAMWB2I) =0.0
184
-------
TAMRVCI) = 0.0
AHRAUdl) = C.O
AflrilNul(I) = 0.0
100 CONTINUE
RtAUdREAu, VARAMEI
WRITE OUT JtiIGN PARAM£TERa FOR COOLING bYSTEMS
IF(IOD.tU.3l PRINT 5
IFtIOO.EQ.3) PRINT 3, IAMu3,TQB,TAMWBfTWBfAMKINJiWINC,ANRAu,RAU
2TAMRV,TAVH20,PRPAGR
WRITE OUT TRANSMISilON UlaTANCES
IFIIOO.EU.3I PRIM i«,MNTR
IFCIOO.EQ.3) PRINT 10'*, CI ,ulsl R 11) ,u loLC C I) , 1 = 1, MNTR)
IF(IOO.tC.3) PRINT 1*20, QFLRItf, WlulH, DEPTH, FISH, NH2C
RETURN
ENJ
185
-------
SUBROUTINE PArOFF ,AMBRH(5) , TAMRV(5) ,PCTAMB<5,5) ,NSITSOP ,TUISMX, NSPCON ,UOVALL ,A
XREAC,SPFLOH, NH20 ,WIt)TH,PRPAGR , C APF AC.UiEFAC ,TKWHRS t IR ITE , IRE AD ,
& AMWINiKb) ,AMRAD(5) ,WING, RAO, QFLRIV ,UEP1 H
COMNON/NEWV/ CONOR, LOCI (20) , ACC ,CONSCT ,U ISLC (5) ,uISTRC5» , FLOW1
2,fOPCN(5),CCPM(5),TMLS<5) ,TMCiT ( 5) ,T RCST , WATER (<*J , J ISCH U) ,TuIS (<«)
3,CJli(<*l ,FISH,IZLRO, NRtCi>C»SbC, CFIiH ,CHSC, PNAME C4) , NAMES 120) ,
iPTR, SPCO, JELHR, CCiC , CCOST i CCOilii, SPNC,ICO
N NAME(2) ,RNA(6) , CNAC*) ,SNA C 2) ,RT lu (61 , SOK5 b , 16) , SSL ( 6 , 161
2*SL:>(6> 16J , TCDISCi)
QATA STATEMENTS
OAJACNAMEd) , 1=1 ,2) /7HNUCLEAR , 7H FOSSIL/
OATAIRNAdl ,I=i,6)/8HCOLUM3IA,10HiACRAMtNTO,10HUPPER MISS, 9HLOWER
2MISS ,9HT£NNESiEt,8HOcLAWARE/
DATA (CNA(I) ,!-=!, «») /5HRI V£R,4HPONU ,7HMECHT WR , 7HNATOTHR/
UA1 A(SNA(I) ,I=1,2)/8HOOES NOT,4HOOES/
JATAtRT 1L(I> ,I=1,6)/6*10.0/
UArA{iOK(I),I=l,9bl/iOO.,100.,100.,100.,100.,100.,90.,8'5.,100.,
2100.,100.,10C.,82.,78.,10G.,iOO.,100.,lGO.,b5.,6i*.,100.,100.,
3100.,100.t55.,6'*.,100.,100.,99.,93.,53.,fal.,100.,100.,9l3.,9'*.,
4^1. ,47. ,67t,a8. ,8<*.|80. |26.|*»5. , U6. , 69. , 66. ,57., 12. ,8., 26., 31.,
521. ,39. 1 10. ,b., 12., 20. tlk,t 27., 8., 3. ,10. ,16. ,10. ,18. ,8. ,3. ,U. ,
612., 9. ,12.,G.,0.,0.,<».,3.,10.,18*0./
OAT A (SSL (I) , I=l,96)/6*0. ,10.,11.»'4*0.»18.,22.,'**0. ,35. ,36. ,
-------
7 FORMAT 11*0 NO aPECIES IN uANGER WITHOUT THERMAL PLANT.*)
6 FORMAT«*0 PROdLEMa W/ PLANT *F5. 1 ,<*X ,F5 . 0 , 10X, F5.0 , 10X ,F5. 0 ,/)
9 FORMAI
-------
PRINT 3, WATERCNCOJ, JlSCH(NCO), UNET
C PRINT OUT WATER QUALITY OF UISCHARGE WITH RESPECT TO TEMPERATURE
C ANJ CHEMICAL CONTENT
IF ALTERNATIVE
C
IFINCO.tQtl.ANu.NT.EQ.ll PRINT 19
IF(NCO.EQ.1.ANO.NT.EQ.2I PRINT 20
IF(NCO.NE.l.ANU.NT.EU.l) PRI hT 22
C CALCULATE EXTRA AIR EMISSIONS CAoS£U BY CHANGE IN HEATRATE DUE
C HO UiE OF A NON-CNCt-THRU COCLING SYSTEN
XFJEL = (u£LHR/2.<«)*!.E-7
N02 = XFu£L*6.0t-<«
N02H = 1000.*PbIZE*N02
$02 = XFU£L*5.7E-5
i>02H = 1000.*Pi>IZE*S02
CO = XFUEL*1.5E-5
COH = 1000.*Pi>IZE*CO
HYJ = XFUtL*6.0E-6
HYiJH = 1000.*PS>IZE*HYL)
ALJ = XFUEL*1.5L-7
ALJH = IOOU.*PSIZE*ALU
PAR = XFUtL*5.9E-3
PARH = 1000.*Pi>IZE*PAR
IF(NCO.N£.l.ANb.NT.EQ.2) PRINT 21, N02,N02H ,i02tS02H,CO,COH.HYOtHY
2UH.ALd» ALJH,PAR,PARH
CALL SLuWST(NS,N1,NCO,NbC,NCH,NP»
C
C PRINI OU1 JOLLAR COSfi FOR THIS ALiERNATIVE
C COMPARE iTANuARU PLANT CAPITA. COST WITH CAPITAL COST AT THIS
C LOCATION.
PRINT 10
COAT = CCPKWM1. + (ACC * CONSCT)/10 0 .J
Cl = CCPKW*PSIZE/1000.
C2 = COAT*PiiIZE/1000.
C3 = C2*1000.*ANFCR/TKWHRi>
TC2 = C2*1.E6
PRINT 11, Cl, C2
188
-------
C CCbJ OUT COOLING AL1 ERNAT
C
PRINT 111
PRINT 112, C3, TC2
IF(Nl.tQ.l) HRBAiE = 9815.
IF(NT.tQ.2) HRBAbE = 7220.
CFJ = H«BASE*FUCi>T*l.E-5
PRINi Hi, CFU
PRINT 12, Cso, CCiC
C
C COsT OUT ^GREENING ALTERNATIVE
C
u = PSIZt*1000.*CAPFAC*8.76
CCF = CFISH*b
PRINT 13, sSC, CCOsTo, CFIsH, CCF
C
C ESTIMATE CHEMICAL TRcAIMENT COilS
C
CCT = CHbC'D
PRINT lC, CCT
C
C TRANSMISSION COsli FOR THE ilTE ARE
C
PRINI 15, TRCiT, CCOST
C
C AJJ UP COMPONENT COaTS 10 GET 1 HE COiTS OF 1 HE POWER DELIVERED TC
C 1HE GRID SYSTEM ANJ 1 HE LOAu CENTER.
C
1C = TRCsT *• CHsC * 3CC + CFI^H * CSC * C3 + CFU
PRINI 18, TC
C UEFERMINE TOJAL LANd CONSUMEu BY THE PLANT, COOLING MEANS, AND
C TRANsrllsSION LINES.
PRINT 16
oPT = sPGR + sPTR + sPCO
PRINT 17, SPGR, SPCO, SPTR, SPNC, SPT
RETURN
ENJ
189
-------
POWER PLAN] ill ING ANAL
PROGRAM
t NoMdER 1 IS SAMPLE uATA INPJI CAoE FOR CITING FROG,
LOCATION la PLAN! ON RIVER CLAiS NO 1
-NOTt POWER PLANT DATA SPECIFIcJ la INTERNAL DATA
NO POWER PLAN! uAJA WtRc INPUT.
REiOLfi) OF TRANiMISblON CObT CALCULATIONS FOR SITE NUMBER 1
WITH PLANT '
TRANoNliilON OPTION IS
NtW TRANiMIoilON LlNEi
CONSTRUCTION COST
MAINTENANCc COiT
NEW TRAN6 JOTAL COST
CAPITAL COoTb.
(UOLLARS)
<».5Cit+Ofa
<*.88E+03
LOAu CENTER RdLAFEu COSTS
WHEELING COSTOPA)
TOTAL IKANSNIiilON 30STS A] JHIa SITE ARE
COST PER UNIT POWER
CMILLS/KW-HR)
5.92t-02
b.
-------
MECHANICAL DRAFT WEi TOWER
THE DEoIGN VALUES AND COiTS ARE -
Q RcJECT = b.<»51<»£+09 BTU/HR AT T CONDENSER = 101
FAN POWER = 5.798<*E+03HP PUMP POWER = It 3<»36E+0<«HP
H20 tVAP - 2.9<»'»&E*-G1 CFS ( &.6165E+06LB/HRI
H20 BLOWUOWN = 7.1d5i»t. + 00 CFS i 1.61<»6£+06 LB/HRJ
AIR FLOW RATE = 3.3621E+08 LE/HR
PRESSURE UROP = .37 CONO FLOW -= 5.3602E+08
RANGE = 12 APPROACH = 11
CAPITAL COS I = 6.8169E+06 DOLLAR*
CONutNSER ANO PUMPCOST = 1.0569£-«-Oi uOLLARS/KW
OPERATING COoT = .076 MILLS/KW-HR
MAINTENANCE COST = .016 MILLS/KW-HR
CONuENitR SYSTEM COS 1 = .186 MILLS/KW-HR
UIFFERENflAL FUEL COiK = .010 MILLS/KW-HR
TOTAL alTSTEM COST - .377 MILLS/KW-HR
VARIABLE. AMBIENT CONDITIONS
FOR CAP = .80, T WB = 20, ANO TC = 92
PC LEii THAN PC NIN - AiSUME PC MIN - CONTINUE
FOR CAP = .80, T WB = «»5, ANO TC = 92
PC LESS THAN PC MIN - ASSUME PC MIN - CONTINUE
FOR CAP = .80, T WB = 33, ANJ TC = 92
PC LEiS THAN PC MIN - AiSUME PC MIN - CONTINUE
FOR CAP = .60, T WB - 20, ANu TC = 92
PC LESo THAN PC MIN - AiSUMc PC MIN - CONTINUE
FOR CAP = .60, T WB = i»5, ANj TC = 92
PC LEii THAN PC MIN - ASSUME fC MIN - CONTINUE
FOR CAP = .60, T WB = 33, ANJ TC = 92
PC LESS THAN PC MIN - AoSUMt PC MIN - CONTINUE
FOR CAP = .25, I WB = 75, ANO 1C = 92
PC LEii THAN PC HIN - ASSUME PC MIN - CONTINUE
FOR CAP = .25, { WB = 20, ANO 1C = 92
PC LEiS THAN PC MIN - AaaUME FC MIN - CONTINUE
FOR CAP = .25, T WB = <»5, AND 1C = 92
PC LEiS THAN PC MIN - ASSUMt PC MIN - CONTINUE
FOR CAP = .25, T WU = 33, ANO TC = 92
PC LEii THAN PC MIN - ASSUME PC MIN - CONTINUE
WITH THc VARIOUS AMBIENi lEMPERATURtS
IHt COSTS ARE ~
OPERATING COS1 = 0Q83 MILLS/KW-HR
191
-------
DIFFERENTIAL FJEL COS1 = -.00<« MILLS/KW-HR
IOIAL iY^IEM COS! = .371 MILLS/KW-HR
SCREENING CALCULATIONS FOR NATiiFT TWR COOLING
THt FLOW TO BE CONilOERfcU FOR SCREENING IS 1.1<»E*01 CFS,
THE SCREENING COSTS ARE
CAPITAL COSli S
OPERATING COSTo 5 .<»5 E-05MILLS/KWHR
TOTAL COSTS ARE S^OE-O't MILLS/KWHR
INTAKc COSTi WITHOJ1 SCREENING
CAPITAL COiT J.27£-0^ MILLS/KWHR
OPERATING COSTi 3 .17 E-OSMILLi/KWHR
IOTAL SYiTtM COaT IS S.bOt-O'* MIcLS/KWHR
THc SCREENING JAMAGE TO FISH HAa A VALUc OF 2.18E-0** MILLS/KW-HR
COMPARED 10 SCREENING COSlS OF 6.00E-0<* MILLS/KW-HR
uISCHINCO) = 7.185<»36 FL = k. 635765 TC = 6.210690
CHSC = .015tt595
192
-------
THIS Ii> AN INDIVIDUAL PAYOFF CALCULATION FOR A NUCLEAR POWER PLANT.
RIVER CLASS IS COLUMBIA
PLAN! UTILIZES MECHTWR COOLING
IT DOES HAVE SCREENED INTAKES.
THE PLANT DOES TREAT ITS CHEMICAL DISCHARGE.
THE TRANSMISSION OPTION NO. IS 1
WATER UsE INFORMATION-
JHE PLANT WITHDRAWS <».1<»£ + 01 CFs OF WATER FOR'SUPPLY
THE PLANT ulSCHARGta 7.19E+00 CFs BACK FO THt RIVER.
NET WATER REMOVED 8/ THE PLANT !.> 3.U2E*01 CFS
CNOI COUNTING WATER EVAPORATEu A^ A RESULT OF A THERMAL DISCHARGE.I
DISCHARGE lEMPERATURt Ii 90.39 QEG-F
CHEMICAL CONCENTRATION Ii 0 TIMES THE INLE.T CONCENT RA1 IUN
INFORMATION ABOUT THERMAL STATUi OF FISH SPECIES AT UISCHARGE POINT
PER CENT SPdCIEi, IN
CONDITION TEMP TEMP ZONE 1 TtMP ZONE 2 TEMP ZONE 3
LlEG-C
PROBLEM^ W/0 PLANT 18.9 65 35 0
DEFINITION OF THERMAL ZONES
ZONE 1 = PREFFERREJ TEMPtRATURE RANGE
ZONE 2 = iUBOPTIMAL TEMPtRATURE CONuITION
ZONE 3 = TEMPtRATURE ZONE WHERE SPECIES
li EXPECTED 10 BE LCST FROM SYSTEM
DATA li INSUFFICIENT AT THIS TIME TO CALCULATE EXTRA NUCLEAR
PLANT EMISSIONS CAUSED BY NON-ONCE-THRU COOLING.
THE NUCLEAR FUEL SYSTEM IS A CLOSED LOOP.
NO FUEL SOLID WASTE Is HANDLED AS A SEPARATE* EXTERNAL PROBLEM.
ALL FJELCOsTSdNCLUDlNG SOLID WASTE ARE INTERNALIZED IN FUEL PRICE)
193
-------
COST CALCULATION^ FOR THIS SET OF ALTERNAiIYES.
BAsE PLANT COSI 13 250 MILLION JOLLARS
PLANT CSI AT THIs SITE Ii 267 MILLION DOLLARS.
VARIOUS COMPONENT COSTS FOR I HIS PLANT ARE
MILLS PER KH-HR CAP COSIS(S)
BASIC PLANT COsT 3.52E+00 2.67E+08
BAsIC PLANT FUEL COsT 1.96E+00
COOLING SYSTEM COiTS ARE 3.77E-01 6.82E+06
INIAKE SYSTEM COST IS 6
FIsH UAMAGtS FROM SCREENS ARt 0. 0.
CHcMICAL TREATMENT SYSTEM COSTS ARE 1.59E-02 1.20E+05
TQ"(AL TRANSMISSION COsTs ARE 3.2<»E-Ol <«.50E+06
TOiAL COST OF POWER AT 1 His SITE IS 6.20E+00 MILLS/KW-HR.
'TOTAL LANQ UTILIZED FOR GENERATION, COOLING ANu TRANSMISSION(ACRES)
PLANi LANJ COOLING LAND TRANSMISSION RT OF WAV
10 LJ CENTER NEW CONST.
500 20 3182 318
TOTAL LANO UstJ IS 37u2 ACRES.
194
-------
THIS IS AN INUIVIDUAL PAYOFF CALCULATION FO* A MlCLFAH POWFQ PLANT,
RIVER CLASS IS COLUMBIA
PLANT UTILI7F.S POND COOLING
IT DOES HAVE SCPEEN'ED INTAKES.
THE PL&NT DOE? MOT T=>EAT ITS CHEMICAL n
THE TRANSMISSION OPTION NO. IS 1
WATER USE INFORMATION
THE DLANT WITHDRAWS T.^SE+OI CFS OF WATER FOP SUPPLY
THE PLANT DISCHARGES o. CFS BAC< FO THE RIVER.
NET HATER REMOVED BY THE PLANT IS 7.4SF+C1 CFS
(NOT COUNTING WATER EVAPORATED AS A RESULT OF A THERMAL DISCHARGE*)
DISCHARGE TEMPERATURE IS 0.00 DEG-F
CHEMICAL CONCENTRATION IS 0 TIMES THE INLET CONCENTRATION
INFORMATION ABOUT THERMAL STATUS OF FISH SPECIES AT- DISCHARGE POINT
PE-- Olr.'T SPECIES IN
CONDITION TEMP TEMP. ZONE 1 TEMP ZONE 2 TEMP ZONES
DEG-C
PROBLEMS W/0 PLANT 18.9 65 35 0
DEFINITION OF THERMAL ?0\'ES
ZONE 1 = PPEFFF.&P.E9 TEM&EPATUPE FVjC-c
ZONE 2 = SU"GDTIMAL TEM-E.^ TiJ-E CC'MITIOM
ZONE 3 = Tc.>-'PpP'\TUK'Z ZONE WHE^'E SPFCIPS
IS EXPECTED TO 8E LOST FPOM SYSTFM
DATA IS iNS'.iFFICJF'-JT AT IHll ll>'r: TO C-'LC''LATE f.XTRA MUCLEAR PLANT
.EMISSIONS CAUSED RY NON-OUCL: -THHU COOL If. o.
THE NUCLEAR FKE!. SYSTEM IS A C'..0C^0 'Or>P.
NO ni£L SOLIO ••!(••''-. ', r T ?, ^./.rjOL ED AS •'- S~f^-?-~E - T.r Tr-*^'A-L
FUFLCOSTS( ir-.TLUPlNG SOLIH '.v^STT. /i»L' IN T T '^.i. ! 7LD IN FUifL ^PIC
-------
Appendix C. Dynamic Model Description
Program MAIN
MAIN is the executive routine. It calls the analysis subprograms in the
proper order. A flow chart for MAIN is shown below.
T = 1
V
CALL READ
±[
CALL C0ST
CALL LPG0G0
_y
Check for
Abnormal Output.
If Abnormal,
Print Diagnosis,
STOP
196
-------
Subroutine READ
This subroutine reads all input variables and performs partial initializing
for future processing. Format for the input data is given below. The in-
put data cards are discussed in the order of their appearance in the input
file. A sample data set for the sample output is supplied immediately
following the deck listing.
Card 1: Title A72 A72
Card 2: M, N, NSTA, NYEAR, NTYPE, ICHECK 615
Note: ICHECK = 1 gives complete output
= 2 gives abbreviated output
Card 3: C0STP0 (I,J) 4F10.4
I = 1 to No. of Regions (7 maximum)
J = 1 to No. of Types of Power Generation
within each region (4 maximum)
Each card is for a region.
As many cards are needed as there are regions.
Next I Cards:
1+4: TR (I, J) 7F10.4
Transmission cost from region I to J.
One card per region.
Next I Cards:
PEX(I) Power expansion Unit (MWe) 8F10.4
Next I Cards:
PRES(J) 7F10.4
Reserve power required in region I (MWe)
Next I Cards:
PSDM(I,J) 4F10.4
One card per each region I, gives
PSDM(I,J), J = 1, NTYPE. Zeros acceptable
Next Card: PSDIN(I) 7F10.4
197
-------
Next Card: P0P(I,1) 7F10.4
Initial population for the I regions (10 people)
Next Card: P0PMAX(I) 7F10.4
Maximum population for region I (10 people)
Next Card: GR0W(I) 7F10.4
Growth rate in region I (%/yr)
Next Card: PWD(I,1) 7F10.4
Initial power demand in region I = 1, No. of regions (MWe)
The above input is followed by a lengthy input section for the linear
programming optimization routine. For the above input reference can be
made to the following list of variables and comment cards in the program
for added helpful information. The linear optimization routine is
slightly modified from Daellenbach and Bell (1970). A complete explanation
of the routine and input is found therein. The following discussion applies
to the input in the sample case:
14 Cards: PWD1 A6
PWD7
PSD1
PSD7
Identifying constraint equation names.
Two cards for each region. Constraints are
supply and demand satisfaction. Names begin
in column 1 and must be no longer than 6 characters.
The next n cards are variable cards. Each decision and slack variable
must be identified. In this case power interties between each region are
identified. Names begin in column 9 and must end by column 14. The
following short example is shown for region 1 to all others. 56 cards
198
-------
Pll
P12
„,., others begin with
P14 P21, P31, P41,. . .
P15
P16
P17
must input identifying all such interchanges PIJ (49) plus 7 slack variables
(SLACK 1 SLACK 7) .
Input next are the cards giving the left hand side coefficient of the
variables in each constraint relation. For 7 regions there are 7 sets of
7 cards each. Again one set is shown with n sets being necessary for n
regions. Columns are indicated above the significant letters.
19 28
PWD1 Pll 1.00
PWD1 P21 1.00
PWD1 P31 1.00 ,
PWD1 P41 1.00
PWD1 P51 1.00
PWD1 P61 1.00
PWD1 P71 1.00
Input Format is A6, 2X, A6, 2X, F12.6. After all similar variables above
are entered. The supply constraints are input to construct the supply
relations. The same format is used as shown above. The sample data cases
are not precisely right adjusted because blanks count as zeros. Care
should be exercised in use of such methods, however.
199
-------
PSD1
PSD1
PSD1
PSD1
PSD1
PSD1
PSD1
PSD1
PSD1
PSD1
PSD1
PSD1
PSD1
P12
P13
P14
P15
P16
P17
P21
P31
P41
P51
P61
P71
SLACK1
-1.00
-1.00
-1.00
-1.00
-1.00
-1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Sample for
Region = 1
Such a data set is input for each region. The Last card input is S0LVE
beginning in col. 1.
200
-------
Subroutine C0ST
The routine determines the cost of power production in each region. Since
the objective is cost minimization, all costs are multiplied by a -1 for
processing by the linear program and stored in ARRAY (M + 1, L). M is
the number of constraints, L is the number of variables.
If power production in any region is done by methods other than hydro
power, new units are added in increments of 1 MW. The cost of production
is an average value over all costs within the region. The flow chart for
C0ST appears on the next page. Variable B generates the right hand side
coefficients for the constraint relations. Coefficients are generated
for power demands and power reserves for each region.
201
-------
L = 0
V
1 = 1
NO
YES
J = 1
COST = COST OF PRODUC-
TION + TRANSMISSION
COST
L = L + 1
A(M+1) = - COST
POWER COST
COST OF
PRODUCTION
V
FIND THE AVERAGE COST
L = L + 1
A(M+1) = - COST
POWER COST = COST OF
POWER PRODUCTION
SET COEFFICIENT OF
SLACK VARIABLES TO
ZERO
B(l) to B(7) = POWER
RESERVE + PEX
B(8) to B(14) = POWER
RESERVE
SET A(I,J)
I = 1, M
J = 1. N
= 0
V
( RETURN J
Flow Chart for Subroutine C0ST
202
-------
Subroutine LPG0G0
The routine is a modified linear optimization routine from Daellenbach
and Bell (1970). It is calculationally unmodified. A performance check
variable has been installed to check for improper functioning.
IN0FES = 1, Normal Execution
IN0FES = 2, No Feasible Solution
" =3, Inconsistent Names in Input
= 4, Unbounded Solution
Output from LPG0G0 is stored in arrays IR0W1 and XI.
Subroutine DYNAMI
This routine takes results from LPG0G0 and calculates future demand, future
supply and future power cost for each region. Program flow is explained
by the detailed comment cards included in the listing.
Subroutine PL0TR0
This routine outputs results by region on a line printer. The routine
utilizes UMPL0T for the CDC-6400. Call statements should be checked for
compatibility with local routines. Seven plots are generated for each
region.
1. Power Demand v. Year
2. Power Produced v. Year
3. Power Price v. Year
4. Population v. year
Sample outputs show results for sample data so that program functioning
can be checked.
203
-------
LIST OF VARIABLES
VARIABLE
C0STP0
ELAS
GR0W
ICHECK
IN0FES
L0SS
M
N
NSTA
NTYPE
NYEAR
PADD
PC0S
P0P
P0PMAX
P0WEXI
PRES
PDS
PSDIN
PS DM
PS0LD
PWD
T
TR
VAL
DEFINITION
Cost of power production (mills/kW-hr)
Price elasticity of demand
Population growth rate (%/yr)
Variable used to suppress output
Output check variable
Power loss in transmission (%)
Number of constraints
Number of variables
Number of regions with power generating capacity
Number of types of power generation within a region
Number of years in analysis
Power added each year (MWe)
Cost of power production
Population (millions of people)
Maximum -population (millions)
Existing power production capacity (MWe)
Power reserve required (MWe)
Power supplied (MWe)
Sum of maximum power production capacity (MWe)
Power supplied by each method (MWe)
Selling price of power (mills/kW-hr)
Power Demand
Time (years)
Transmission cost region to region (mills/kW-hr)
Value
204
-------
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
PROGRAM MAIN(INPUT,OUTPUT,TAPE5=INPUT,TAPE6=OUTPUT)
DIMENSION COSTPO(10,5),P(10,10),NEWP(10), POWEXIUO),
18(52),JCOL(100) ,IROW(50),1 BASIS(50) ,ITITLE(12),PEX(50),
1PWO(7,50),PSO(7,5C) ,PSOM(1G,5),PCOS(10t50),ELAS(10),PPC(10),
1IROWK100) , XI (100) tPRES(lO) ,PSOIN(10) ,POP(10,50).POPMAX(10) ,
IGROW(IO),A(52,150),PPC1(10),STACK(10),POPX(10,50),TR(10,10),
IDPOP(IO) ,PAOO(10),IDUP(150> ,JOUP<150) , \l ALOUP (150) ,PSOLO(10,50),
ICON(IO),3ROCON(1G)
COMMON T,A,8,JCOL,IRGW,IBASIS,ITITLE,M,N,NSTA,PEX,PWO,PSD,
1P,NEWP,POWEXI,PCOS,ELAS,PPC,PPC1,STACK,LOSS,VAL,POPX,NTYPE,
1IROW1,X1,MPLUS2,NM,N*EAR,PRES,PSOIN,POPMAX,POP,3ROW,TR,PADO,
1COSTPO,PSOM,IDJP,JOUP,VALDUP,INOFES,ICHECK,PSOLD,CON,GROCON
1 NAME OF PROBLEM IN COL. 1 TO 12
2 NO OF CONSTRAINTS,NO OF VARIABLES INCLUDING SLACK VARIABLE
NO OF 3ENE.RATIN3 STATIONS,NO OF YEAR,NO OF TYPES OF
POWER PRODUCTION AND ICHECK
ALL SIX INPUTS IN ONE CARO FORMAT 615 DATA RIGHT AOJUS
ICHECK = 2 HILL SUPRESS MOST OF THE INTIAL PRINTOUTS
3 COSTPO(I,J) COST OF PRODUCTION OF POWER BY VARIOUS METHODS
IN EACH STATION I = STA, J = METHOD
FORMAT <*F10.<*
PER SARD STATION 3Y STATUON
TRANSMISSION COST FROM STATION I
7F10.**
PER CARO STATION I TO ALL THE OTHER STAIONS
DATA <*
TR(I,J)
FORMAT
7 DATA
PEXU)
FORAMT
PRES( J)
TO STATION J
8F10 .k
POWER RESERVE
FORMAT(7F10.(*>
PSDM(I,J) MAXIMUM POWER THAT CAN BE PRODUCED BY EACH METHOD
FORMAT t»F10.it
NO OF CARDS NO OF STATIONS
IF NO POWER IS PRJUCEO BY ANY
ZERO
PSOIN(J) SUM OF POWER PRODUCED
FORMAT(7F10.<»)
POP(J,1) P3PULATION IN EACH REGION
FORMAT 7F10.<»
POPMAX(J) MAXIMUM POPULATION IN EACH RE3ION* (10**6)
FORMAT (7F10.<*)
GROW(J) GROWTH RATE OF POPULATION FOR ALL THE REGION IN PERC
FORMAT 7F10.<»
PWO(J,1) POWE* DEMAND FOR THE FIRST YEAR FOR ALL RESIGNS
FORMAT 7FlQ.it .
INPUT FOR LPGOGO EXPLAINED IN SUBROUTINE LPGOGO
METHOD LEAVE THAT COL. BLANK OR R
BY ALL THE METHODS IN A STATION
* (10**6) FOR THE BASE YEA
20
10
11
12
13
INTEGER T,STACK
REAL NEWP,LOSS
INOFES = 1
T = 1
SUBROUTINE *EAO READS IN ALL THE INPUT VARIABLES ACCORDING TO
CIFICATION 3IVEN IN COMMENTS 1 TO 12
CALL READ
SUBROUTINE COST DETERMINES THE AVERAGE COST OF POWER PRODUCTI
CALL COST
205
-------
C SUBROUTINE LPG-OGO FINOS THE OPTIMAL SOLUTON
CALL LP3030
GO TO (1,2,3,^) ,INOFES
C SUBROUTINE 3YNAMI CALCULATES POWER DEMAND,POWER SUPPLIED,POPULA
C ETC FROM THE OPTIMAL SOLUTIONS 03TAINEO FROM LPGOGO
1 CALL OYNAMI
IF (T .GT. NYEAR) GO TO 15
GO TO 20
C SUBROUTINE PLOTRO PLOTS THE OUTPUT ON LINE PRINTER
15 CALL PLGTRO
GO TO 1C
C THE FOLLOWING 3 W*ITE STATEMENTS PRINTS THE OIAGONOSIS FROM LPG
2 WRITE(6,31)T
GO TO 10
3 WRITE(6,32)
GO TO 10
*» WRITE(6,33)
10 STOP
31 FORMAT(10X,*ND FEASIBLE SOLUTION FOR TH YAER *,I5)
32 FORMAT(10X,*IM CONSISTANT NAMES IN THE INPUT UNABLE TO
1 FORM CONSTRAIMF EQUATIONS CHECK INPUT*,/)
33 FORMAT(10X,*SDLJTIO.>I UN30UNDEO CHECK THE CONSTAINT3 AND
1 AND THE OBJECTIVE FUNCTIONS*,/)
END
206
-------
SUBROUTINE READ
DIMENSION COSTPO(10,5),P(10,10) ,NEWP(10) , POWEXI(IO),
1B(5Z) , JCOL(IOO) , I , PEX (50 ) ,
1PWO (7,50) , PSO(7,5C) ,PSOM(10t5) ,PCOS < 10 , 50 ) , EL AS ( 10) ,PPC(10) ,
1IROW1(10C),X1(100),P*ES(10) ,PSOIN(10) , POP (10, 50) ,POPMAX(10) ,
13ROW(10) , A (52,150) ,PPC1(10) , STACK (10) ,POPX(10,50) ,TR(10,10) ,
lOPOP(lO) ,PAOO(10) ,IOJP(150) ,JOUP(150) ,V ALDUP (150 ) ,PSOLO (10,50) ,
ICON(IO) ,GROCON(10)
COMMON T,A,B,JCOL,1ROW,IBASI5,ITITLE,M,N,NSTA,PEX,PWD,PSO,
ip,NEwp,POHExi,pcos,ELAS,PPC,PPCi,STACK,LOSs,\/AL,POPxfNTYPE,
Hi
-------
REAO(5,2070MPRES< J),J=1,NSTA)
REAO(5,2001)UPSOM(I,J),J=1,NTYPE),I=1,NSTA)
30 TO <30131),I CHECK
30 WRITE(6,220)
00 200 J=1,NSTA
WRITE(6,20<»0)J,
200 CONTINUE
31 RE AD (5, 20 70) (PSDINU ) , J = l ,NST A)
REAO(5,2070) (POP(J,1) ,J=1,NSTA)
READ(5,20/0)(POPMAX(J),J=l,NSTA)
READ(5,2070) (GROW( J) ,J=1,NSTA)
REAO(5,2070)(PWD(J,T),Jsl,NSTA)
00 260 J = 1,NSTA
3ROWU) = 3*OW(J)/100.
GROCON(J) = 3ROWU)
260 CONTINUE
DO 250 I=1,NSTA
IF(PSOM(I,1) .EQ. 0.0) 30 TO 251
GO TO 250
251 STACK(I) = STACKd) «• 1
MI = STACK(I)
PADO(I)= 1.0
IF(PSOM(I,MI) .EQ. 0.0) GO TO 251
250 CONTINUE
00 230 I=1,N5TA
MI = STACK(I)
POWEXKI) = P30M(I,MI)
CUN(I) = (PHO(Itl)/POP(Itl)) * 0.03
230 CONTINUE
30 TO (510,511),ICHECK
510 WRITE(6,281) (PSOINU) »J=1,NSTA)
WRITE(6,282)(PDP(J,1) ,J*1,NSTA)
WRITE(6,283)(POPMAX(J),J=l,NSTA)
WRITE<6,28<0 (3^0W(J),J=1,NSTA)
WRITE (6,2 80) (POWEXKI) ,I=1,NSTA)
WRITE(6,285)(CON(I),I=1,NSTA)
220 FORMAT(5X,7HSTATION,8X,6HMAXHrO,7X,llHMAXONCE THR,6X,
112HMAX COOL TOH,7X,8HMAX DRY ,//)
280 FORMAT(5Xt*EXl5ITING POWER*,7(5X,F10.4))
1001 FORMAT(12A6)
500 FORMAT(10X,*N3 OF CONSTRAINTS = *,I5,/f10X,*NO OF VARIABLES
1 = *,I5,/,1GX,»NO FO YEAR = *,15,/,10X,»NO OF GENERAING STAT
1IONS = *,I5,/,10X,*NO OF TYPES OF POWER PRODUCTIONS = *,I5,/)
1002 FORMAU6I5)
2001 FORMAT(<*F10.'«)
2010 FORMAT(10X,»COST OF PRODUCTION POWER BY VARIOUS METHODS*,///)
20«»0 FORMAT(5X,I5,<»(5X,F12.U)///)
2050 FORMAT(7F10.<»)
2020 FORMAT(5X,7r«STATION,8X,5HHYORO,10X,8HONCE THR, 12X, 8H COOL TOW,
17X,5HORY ,///)
2051 FORMAT(1H1,10X» TRANSMISSION COST REGION I TO REGION J*///)
2052 FORMAT(1*»X,1H1,9X,1H2,9X,1H3,9X,1H^,9X,1H5,9X,1H6,12X,1H7//)
2055 FORMAT(2X,12,7(2X,F10.<»)//)
2060 FORMAT(8F10.<»)
2070 FOKMAT(7F10.'«)
2080 FORMAT(8E10.<»)
208
-------
1131 FORMAT(lHlfl2A6«///)
231 FUaMAT(lHli5Xt*PSDIN*«7(5X,Fl2.<»)
282 FORMAT(5X,*PO? J »1* , M5X ,F12. •*) )
283 FUKMAT(5Xi*POPMAX*»7(5X,F12.^))
231» FORMAT(5X,*GRO<<».?'(5X|F12.'»))
285 FORMAT(5>Xi»COrt » t7 ( 5XfF12.«») )
511 RETURN
END
209
-------
C
C
C
SUBROUTINE COST
THIS SUBROUTINE FINDS THE COST C(I,J> =
C(I) IS THE COST TO PROOUCE POWER AT ST
,A,B,JCOL,IROW,I9ASIS,ITITLE,
00 IOC I = ItNSTA
MA = STACK(I)
1F(MA .NE. 1) GO TO 81
00 170 J=1,NSTA
COST = COSTPO(I,MA> + TR(ItJ)
L = L * 1
A(M+1,L) = (-l.*COST)
170 CONTINUE
PCOS(I,T)=COSrPO(l,HA)
GO TO 100
81 COST2 =0.0
COST1 = g.O
Ml = MA-1
00 160 LB = 1,M1
COST1=COST1 + COSTPO(I,L8) * PSOM < I ,L3)
COST2 = COST2 * PSO~I(I,L3)
160 CONTINUE
COST1 = COST1 + PAOd(I) * COSTPO(I,HA)
COST2=COST2 <- PAOO(I)
CUST3 = COST1/COST2
00 180 J=1,NSTA
COST = COST3 * T*(I , J)
L = L * 1
A(M+1,L) = (-l.'COST)
180 CONTINUE '
PCOS(I,T> =COST3
100 CONTINUE
NSLAK = N-L
00 30 1 = 1, NSLAK
L = L-H
A(M+1,L) = 0.0
30 CONTINUE
UO llu J=1,^STA
110 3( JJ=PWD( J,T)+PEX(T)
NOTE PRES SIGN CHANGED
00 12G J=1,MSTA
120 3(NSTAtJ)= PRCS(J)
210
-------
00 50 1=1,M
00 50 J=1,N
A(I,J> = 0.0
5o CONTINUE
RETURN
ENO
211
-------
SUBROUTINE LPGOGO
DIMENSION CCSTPO(10,5),P(10,10) ,NEWP.(10) , PQWEXK10),
18(52),JCOL(100>,IROW(50),I8ASIS(50),ITITLE(12),PEX(50)»
1PWO(7,5C)tFSO{7,5C),PSOM(10,5),PCOS(10,50),ELAS(10),PPC(10),
lIROrtl(10C),Xl(iaO),PRF$<10),PSOlN(10)tPOP(10,50)«POPMAX(10>,
IGROW(IO) ,A(52,150),PPei(lQ),STACK(10) ,POPX(10,50),TR(10,10) f
10POFUO) ,PACC(10) ,IOUF(150) , JGIPC150) ,VALOUP(150),PSOLQ(10,50) ,
ICON(IO) ,SROCON(10)
COMMON T,A,e,JCOL,IROW,ieASIS,ITITLE,M,NtNSTA,PEX,FWQ,PSC,
1P,NEWP,POWEXI,PCO?,ELAS,PPC,PPC1,STACK,LOSS,\/AL,POPX,NTYPE,
lIROkl»Xl,MPLUS2,NM,NYEAR,PRES,FSCIN,POPHAXtPOP,3KOW,TR,FAODt.
1COSTPO,PSOM,IOJP,JDUP,7ALDUP,INOFES,ICHECK,PSOLO,CON,GROCON
INTEGER T,STACK
REAL NEWP.LOS3
C LPGOGO MAXIMIZES A LINEAR FUNCTION SUBJECT TO LINEAR EQUATIONS.
C ALL RHS PARAMETERS MUST BE NONNEGATIVE.
DATA KBASIC/5H8ASIC/
DATA L9ASIC/5H /
DATA M8ASIC/6H3INONG/
DATA N3ASIC/5HSLACK/
DATA ISOLVE/6HSOLVE /
C INPUT
ISTOP = 0
ITERS = C
IA1 =0
181 = C
00 5 I=1,KFLUS2
IBASISd) = 0
5 CONTINUE
IF (T .GE. 1) ICHECK * 2
C REAO EQUATION NAMES AND NON-NEGATIVE RHS PARAMETERS
IF(T .3T. 1) 30 TO 12
READ(5,2003) (IROW(I),I=1,M)
REAO(5,20Qi») (JCOL( J) , J=1,N)
200** FORMAT(8X,A6)
2'C03 FORMAT(A6)
12 GO TO (10,50),ICHECK
10 WRITE(6,1103)(IROW(I),8(I),I=1,M)
WRITE(6,110'»)(JCOL(J),A(Mtl,J),J = l,N)
C READ LHS CCEFFICIFNTS
50 12=0
J2=0
IF(T .NE. 1) GO TO 2«»0
131 = 181 + 1
READ(5,1005)IOUP(I81), JOUP(IBl) ,(/ALDUP(IRl)
I = IOUPCI81) J J =JOUP(I51) I VALUE = VALOUP(I81)
IF(I .EQ. ISOLVE) GO TO 99
30 TO 2<»5
2i»0 131 = IB1 + 1
I = lOUP(IPl) S J = JOUP(I91) J VALUE = tfALDUP(IBi)
IFCI .EC. ISOLVE) GO TO 99
2«f5 DO 60 II = 1,M
IF( I .EQ. IROW(ID) GO TO 62
60 CONTINUE
GO TO 700
212
-------
62 12 = II
00 65 Jl = 1,N
IF( J .EG. JCOU Jl) ) GO TO 66
65 CONTINUE
30 TO 7CC
66 J2 = Jl
90 A(I2, J2)=VALUE
GO TO 50
99 WRITE<6,1107>
IF(ISTOP.EO.l) GO TO 1
100 CONTINUE
GO TO (20,21),ICHECK
20 WRITE(6,1105)(IOUP(LC),JOUP(LC),VALDUP(LC),LC=1,I91>
21 K=2
N1=N+1
SETUP PHASE I ROW
00 120 J=1,N
A(M+2,J) =0.0
00 120 1=1,M
A(M*2,J)= A(M+2, J)+A(I,J)
120 CONTINUE
SET UP INITIAL BASIS AND ARTIFICIALS
00 110 1=1,M
NPLUSI = N + I
A(I,NPLUSI) = 1.0
I8ASIS(I)=0
9(M+2) = 9CM+2) + 3(1)
110 CONTINUE
FIND PIVOT COLUMN
399 OPS=0.0
MPLUSK = M + K
<*00 00 <»10 J = 1,N
5 IF( A(MPLUSK,J)-OPS)U10,'»10,'«2C
^20 OPS = AtMPLUSK,J)
JPIV=J
i*10 CONTINUE
IF(OPS-1.0E-C6) 501,501,««50
FIND PIVOT RCH
«t50 RATMIN = l.OE+06
IPIV=M * 3
00 i»70 1 = 1,M
IF( A(I, JPIV) .LE.l.OE-06) GO TO i*7Q
RATIO = g(I)/A(I,JPIV)
IF(RATIO.GE.RATHIN) GO TO k7Q
RATHIN = RATIO
IPIV=I
<*70 CONTINUE
IFCK.EQ.2) GC TO ^75
00 <*75 1 = 1,M
IF(I8ASIS(I).NE.O) GO TO U75
IF(AES(A(I,JPIV) ) .LE.l.OE-06) GO TO
IPIV = I
«»75 CONTINUE
PIVOT = ACIPIV, JPIV)
I8ASISCIPIV) = JPIV
ITERS=ITERS+1
213
-------
C IF PIVOT FCUNO, TRANSFORM TABLEAU
C IF NOT, EXIT, SOLUTION UNBOUNDED
IFdPIV.EQ.M + 3) GO TO <»96
DO 500 I = 1,MPLUSK
IF(I.EQ.IPJV) 30 TO 500
oo <«ec J=I,KM
IF( J.EQ. JPIV) GO TO «f80
A (I, J) = A(I,J)-A (I,JPIV)*A(XPIV«J)/PIVOT
<*80 CONTINUE
8(1 )&B(I)-A(I,JPIV)*31ZPIV) /PIVOT
A(I,JPIV) = 0.0
500 CCMTINUE
9(1PIV)=9(IPIV) /PIVOT
DO IV, J)/PIVOT
<»95 CONTINUE
30 TO 399
<»96 WRITE(6,10G6)
INOFES = ^
&0 TO 571
501 IF(K.EQ.l) 30 TO 510
C NO FEASIBLE SOLUTION EXISTS
505 WRITE(6,1007)
INCFES = 2
30 TO 571
50<» K=l
GO TO 399
C OPTIMAL SOLUTION OUTPUT
510 CONTINUE
WRITE(6,1008> ITERS
ZIMfiOX = -B(M-H)
WRITE(6,1010)ZIH30X
30 T0(650 ,651) ,ICHECK
650 WRITE(6,1011)
651 DO 580 J=itN
JCOLJ = JCOL(J)
OELTAJ = ACM + 1, J)
00 520 1=1, M
11=1
IF(I9ASIS(I) .EQ. J) GO TO 550
520 CONTINUE
X=0.0
J8ASIC = L8ASIC
GO TO 560
550 X = 9(11)
J3ASIC = KBASIC
560 IA1 = IA1 + 1
IROW1(IA1)=IROWI $ X1(IA1)=X
IF(T .GT. 1) GO TO 580
WRITE (6, 10 09) JCOLJ, J BASIC, X, OELTAJ
580 CONTINUE
561 GO 10(660,661) ,ICHECK
660 HRITE(6,1012)
661 00 570 1=1, M
J8ASIC = M8ASIC
214
-------
IROWI=IROW(I)
NPL'JSI = N+I
X = -A(M+1,NPLUSI)
IF(A9S(X) - 1,OE-09)562,562,606
562 IF(IBASISU) )5&i4,563,56<*
563 J8ASIC =L8ASIC
FLOHER = 0.0 '
FUPFER = 0.0
30 TO 569
56<» JBASIC = NEASIC
606 FLOWER = -l.OE+10
FUPPER = l.OE+10
00 900 K=1,M
IF(A(K,NPLUSI))601,900,605
601 OUOT = -8(K)/A(K,NPLUSI)
IF(GUOT.GE.FUPPER) GO TO 900
FUPPER = QUOT
GO TO 900
605 QUOT = -B(K)/A(K,NPLUSI)
IF(QUOT.LE.FLOWER) GO TO 900
FLOWER = QUOT
900 CONTINUE
FLOWER = -FLOWER
IF(FLOWER.EG.+1.OE+10iAND.FUPPER.LT . 1.OEf10)GO TO 57k
IFtFLOWER.LT.H.OE + lO.AND.FUPPER,EQ.1«OE + 10)30 TO 576
IF(FLOWER,EQ.-H.OE«-10i,ANO. FUPPER. EQ.1.OE+10)GO TO 572
C NOTE THE FOLLOWING FOUR STATEMENTS ARE CHANGED TO SUPRESS
C OUTPUT FRCM LPGOGO AFTER T=5
C TO 3ET THE ORI3INALCHAN3E STATE NOS TO THE FOLLOWIN3 IF CARD
569 IF(T .GT. 1) GO TO 56<91
C 569 WRITE(6,1013)IROWJ,JBASIC,X,FLCWER,FUPPER
WRITt(6,1013)IRQWI,JBASIC,X,FLOWER, FUPPER
5691 IA1 = IA1+1
GO TO 570
572 IF(T .GT. 1) GO TO 5721
C 572 WRITE(6,1017)IROWT.,J3*SIC,X
WRITE(6,1017)ROW!,J3fiSIC,X
5721 IA1 = IA1U
GO TO 570
57<4 IF(T .GT. 1) GO TO 57*1
C 57<* WRITE<6,lQ18)IROWI,J3fiSIC,X,FUPPER
WRITE(6,1018)IROWI,J3flSIC,X,FUPPER
57*»1 IA1 =IA1 * 1
GO TO 570
576 IF(T ,3T. 1) 30 TC 5761
C 576 WRITE(6,1019)ROWI, J3 ASIC ,X ,FLC KER
WRITE(6,1019)IROWI,JBASIC,X,FLOWER
5761 IA1 =IA1 * 1
570 CONTINUE
C FULL TABLEAU PRINTOUT AVAILABLE GY REMOVING THE FOLLOWING CARD.
GO TO 1
571 WRITE(6,1015)
MPLUS2 = M + 2
DO 800 I = 1,MPLUS2
WRITE(6,1016)(A(I,J),J=1,NM),B(I)
800 CONTINUE
215
-------
WRnE(6,1015)
GO TO 1
700 WRITE(6il014)
ISTOP = 1
INOFES = 3
GO TO 50
1003 FORMAT(12X,A6,1X,F10.0)
100
1005 FOKMAK A6,2X,A6,2X,F12.6)
1006 FORMATU9H SOLUTION UNBOUNDED)
1007 FORMAT(21H NO FEASIBLE SOLUTION)
1008 FORMAT(23H1SOLUTION OPTIMAL AFTER,2X,15 ,11H ITERATIONS)
1009 FORMAT(<»X,A6,2X,A5,5X,F12.6,<4X,F12.&)
1010 FORMAT(20H MAXIMAL OBJECTIVE = ,F16.6)
1011 FORMAT (2X,8Hy/ARIA3LEt 2X,6HSTATUS,8X,5HVALUE,9X,6HOELTAJ)
1012 FORM AT(11HOCONSTRAINT,IX,6HSTATUS,8X,5HVALUE,9X,8HOECREASE,9XtSHIN
1CREASE)
1013 FORMATC*Xf A6»2X,A6,'»XtF12.6t'»X,F12.6»«»X,F12.6)
101^ FORMATU8H INCONSISTENT NAME)
1015 FORMATdOX,////)
1016 FORMAT(1X,8F1«*.7)
1017 FORMAT(<»Xf A6,2X,A6»*fX,F12.6,'»X,12H OPEN ,<»X,12H OPEN )
1018 FORMATt^X, A6f2X,A6,UX,F12.fJ,«4X,12H OPEN ,«fX,F12.6)
1019 FORMATC»X,A6i2X,A6,«»X,F12.6t<»X,F12.6,itX,12H OPEN )
1103 FORMAT(1XA6,10X,F12.6)
110<» FORHAK9X, A6»2X,F12.6)
1105 FORMAT(lXA6,2XfA&,2X,F12.6)
1107 FORMAT(1X,5HSOL\/E)
1 RETURN
ENO
216
-------
c
c
20
C
C
SUBKOUTiixE OYNAMI
DIMENSION COST?0(1G,5) ,P(10,iO) ,NEHP(10) , POrtEXKlO),
18(52) tJCUL (103) , I-.OW(5Q) ,1 BASIS(50) tITITLE (12),PEX<50)t
IPiO (7,50) ,PS'J(7t5u) ,PSJM(1G,5) ,PCOS(10,50) , EL AS (10) ,PPC(10) ,
1IRUW1 (ICC) tXKlOG) tPRES(lC) ,PSOIN(1G) , POP (10, 50) tPOPf1AX(10) ,
13,iUW(10) .A(52,15G)»PPCl(li])»STACK(10) tPOPX(10,50) ,TR(10»1C) ,
lOPUP(lO) ,PAOO(10),IOUP(15C) ,JOUP(15U> ,VALOUP(150),PT*IN(10) ,
1PTR(1G,5C) ,EQUL(1C,50),PPROD(lo) ,P3OtO (10,50) ,CON(10),GROCON(10)
CUflMON T ,A,9,JCOL,IRJK,I3ASIS,ITITLE,^,N,NSTA,PEX,PW;3,PSO,
lP,NtWP,PUWEXI,PCO^,ELA3,PPC,PPCl,STACK,LOSS,\/AL,POPX,NTYPE,
llRUWl,Xl,MPLUS2,N-1,NYEAR,PRE5,PSOI^tPOPMAX,POP»GROW,TR,PAOO,
lCOSTPOfPSOM,IOJP,JOUPtVALOUP.INOFES»ICHECK,P30LD,CON,GROCQN
INTEGER T,STACK
REAL NEWPiLOSa
THESE OATA MAY 3E SUPPLIED IN RE A3
DATA N8ACK/5/
OATA CONST/.03/
L = 0
IFLAG = 2
VAL =0.1
LOSS = 0.05
ARRAY XKL) CONTAINS OFTI1I3EQ OOTPUT FRO^ LP&OGO. IT IS REARR
INTO ARRAY P(I,J) POWER TRANSMITTED FROM STATION I TO STATION
QU 20 I=1,NSTA
00 20 J=lfNSTA
L = L <• 1
P(I,J)= XKL)
CONTINUE
FINOS THE POWER PRODUEO PLUS THE POWER TRANSMITTED
NEHP(I) IS THL POWER PROOUED PLUS TRANSMITTED
Ou
-------
WRITE(6,211>T
212
218
400
C
C
C
191
425
43C
WRITE(6,214)
WRITE(6,215)
1=1
WRITEC&, 213)1, (?(I,J) ,J = 1,NSTA)
1=1 + 1
IF(I .LE.NSTA) GO TO 212
IF(T .NE. 2) 30 iO 400
WR1TE(6,21G)
00 218 I=1,NSTA
WKITE(6,219)PWO(i,T-l),PSO(I,T-l) , PSOLD ^
PPROO(l) = PSiMI,T-l)
CONTINUE
30 TO 470
WRITE(6,210)
IF THE PRESENT POWER PRODUCTION IS L E.
REGION THE PREVIOUS YEARS VALUE ARE U.^
ASTRITCH (**) IN THE COLOUM PSO DENOTE;
00 415 I=1,NSTA
IF(PPROO(I> .LE. PSJCItT-D) GO TO 191
PSO(I,T-1) = PPROO(I)
IFLAP = 1
GO TO 425
IFLAP = 2
PPROO(I) =
IF(ELAS(I)
IF(ELASd)
IF (ELAS(I)
IF (ELAS(I)
,POP(I,T-1)
vAN PREVIOUS YEARS AT
. IN THE OUTPUT DOUBLE
THIS
PSD(I,T-1)
.LE. VAL .AND,
.LE. */AL .AND,
.GT. VAL .AND,
.GT. VAL .AND,
,EQ,
>EQ,
,EQ,
,EQ,
1)30
2) GO
DGO
2) GO
TO
TO
TO
TO
43G
435
440
445
435
440
445
415
471J
221
220
C
C
C
IFLAP
IFLAF
IFLAP
IFLAP
WRITE(6,450)PW3(I,T-1),PSD(I,T-1),PSOLD (I,T-1) ,POP(I,T-1),
1ELAS(1),EQUL(I,T-1)
GO TO 415
WRlTE(6,451)PHQ(i,T-l),PSO(I,T-1),PSOLO(I,T-l),POP(I,T-1),
lELAS(I) ,EQUL(I,T-1)
GO TO 415
WRITE(6,452)PWO(I,T-1),PSO(I,T-l) ,PSOLD(I,T-l) ,POP(I,T-l) ,
lELAS(I) ,£QUL(I,T-1)
30 TO 415
WRITE(6,453>PWJ(I,T-1),PSO(I,T-l),PSOLO (I,T-1),POP (I,T-l) ,
lELAS(I) ,EQUL(I,T-1)
GO TO 415
CONTINUE
DO 22G I=1,NSTA
SUM = O.C
00 221 J=1,NSTA
IF(I .EQ. J) 30 To 221
SUM = SUM *• P(J,I)
CONTINUE
PTR(I,T-1) = SUM
CONTINUE
FIND WEATHER HiHE'? COST OF PRODUCTION IS REQUIRED
DO 130 I=1,NSTA
IN THE NEXT STATEMENT , NOT SURE PSO OR NEWP
IF THE POWER DEMAND IS LAR3ER THAN POWER SUPPLIED
THEN A POWE* UNIT OF 1 MW 13 A03EQ TO THAT STATION
218
-------
132 IF(PSO(I,T-1) .GE. POWEXKD) GO TO 131
GO TO 130
131 PADiHI) = PAOJ(I> + 1.
POWEXKD = PDWEAKi) +1.
15 Ml =STACK(I)
IF(Mi .EQ. 1) GDTO 13
IF (PSOM(I,MI) ,3E. PAOD(D) 30 TO 132
IF(PSOM(I,MD .EQ. 0.0) GO TO Ik
PAOO(I)=1.
STACK(I) = STACK(I) + 1
30 TO 132
13 IF(PSOM(I,MI) .LT. POWEXKD) STACK(I) =STACK(I) + 1
GO TO 132
14 STACK(I) = STACK(i) + 1
3D TO 15
13C CONTINUE
C CALCULATES EL AS ,EQUL , PPC ,3POP ,PWC,POP FRO* THE PREVIOUSLY CALCU
C OATA
00 140 I = luMSTA
SUM = O.ij
MC = STACK(I)
DO 1«»1 J = 1» "1C
SUM = SUM + PSiJM (I t J)
CONTINUE
PTRIN(I) = SUM
CONTINUE
00 70 J = l.hlSTA
C NOTE ELAS SET TO 1. AND PSOIN(J) SET TO PTRIN(J)
C ELASU) = 1.4 -(0.4»(PSOLD( J,T-l)/PSOLO(JtD) »
ELAS(J) = 1.0
PTRIN(J) = PSJIN(J)
EUUL(J,T) = <1.-IPTR(J»T-1)/PSDIN(J)))»
Kl.-lPSO(JtT-l) /PTRId (J) ) )
IF( T .LT. 7) GO TO 200
3ROHU) = 3ROCONCJ) » EQUL (J tT-M3AC<)
2CO IF(ELAS(J) .LT. VAL) IFLAG = 1
PPC(J)=CON(J)» (POP(J,T-1)/POP(j,l))
OPOP(J) = (POP( JiT-l)*Gt^OW(J)*(PJPMAX( J)-PO^(J,T-1) ) )-POPX(J,T)
PWO(J,T) = (P^O(JtT-l) *(OPOP(J)*PPC(J)) ) * ELAS(J)
POP(J,T) = PO?(J,T-1) + OPOP(J)
IFdFLAG .NE. DGO TO 70
WRITE(G,9G) J
ELAS(J) =&.!
70 CONTINUE
ICHECK = 1
GO TO (485,48b) ,ICHECK
485 WRITE(6tl54)(?TR(JtT-1)tJ=ltNSTA)
WRITE(btl55)(PTR1N(J)tJ=1»NSTA)
6,156)(3ROWCJ) ,J = 1,NSTA)
,15C) (3^0(J,T),J=1,NSTA)
WRIlE(6tl51)(?PC(J)tJ=ltNSTA)
WRITElb,152)(JPOP(J),J=1,NSTA)
WRITE(b,153)(PAOD(J) , j=l,NSTA)
WRITE(b,158)(STACK(I),!=!,NSTA)
W^ITE(6,159J (POWEXI(I) ,1=1 ,NSTA»
219
-------
WRIT E( 6, 160)
21<» FORMAT (lCX,*POrfER DISTRIBUTION BETWEEN STATIONS *,//)
215 FORMAT <15X,1H1,17X,1H 2, !7X,lH3»17Xt IH^t, 17X» 1H5, 17X ,1H6, 17X , 1H7 , //)
216 FORMAT(5X,I5,7<5X,F12.<+>)
210 FOR1AT(1GX,//,10X,»PJWER DEMAND *,5X,*POWER SUPPLIED *,10X,*PJWER
1COST *,9X,*POPULATION*,6X,* ELASTICITY *,13X,*EQUL *,//)
FOR,1AT(5X,2(5X,F15.<*) ,!X,2H**,2X,Fl5.'+,2(5X,Fl5.t»),lX,2H**j2X,
FORMAT (5X,4(5X,Fl5.'t) ,!X,2H**,2X,F15.if,5X,Fl5.'t)
FORMAT (5X, 2 (5X , F15. 4 ) ,1X ,2H** , 2X ,F15. 4,3 (5X,F15.4) )
453 FORMAT <5X , 6 (5X , F15. 4 ) )
9C FORMAT(10X,*ELAST1CITY OF REGION* , I 3»2X ,*LESS THAN 0.1 CHECK*)
1CHECK = 2
486 RETURN
END
220
-------
SUBROUTINE PLOTR'J
DIMENSION COSTPO(10,5),P(10,10),NEWP(10), POWEXI(IO),
18(52), JCUL(100) ,IxOW(50) ,1 BASIS(50) ,ITITLE(12),PEX(50) ,
1PWQ(7,50),PSO(7,5G),PSOM(10,5),PCOS(10,5G),EL AS (1G) ,PPC(10) ,
IIROWKIGG) ,X1(100),P-*ES(1C),PSOIN(10) , POP (10, 50) ,POPMAX<10) ,
13ROWUO) ,A(52,150) ,PPC1(1C) ,STACK(10),POPX(10,50) ,TR(10,10) ,
IDPOP(IO) ,PAOO(10) ,IOUP(150) ,JO'JP(15C) ,VALOUP(150> ,PSOLO (10,150 ) ,
1IMAGE(867),CON(1G),GROCON(1G)
COMMON T,A,B,JCOL,IROW,I3ASIS,ITITLE,M,N,NSTA,PEX,PWO,PSO,
lP,NEWP,POWEXI,PCOb,ELAS,PPC,PPCl,STACK,LOSS,\/AL,POPX,NTYPE,
UROWl,Xl,MPLUSa,U1,NYEAR,PRES,PSOIN,POPMAX,POP»GROW,TR,PAOO,
1COSTPO,PSOM,IOJP, JOUP,^/ALOUP,INOFES,ICHECK,P30LO,CON,GROCON
INTEGER T,STACK
REAu NE^P,LOSS
XMAX = NYEAR
XM1N = 1.
1= 1
7G WRITE(6,80)I
WRITE(6,100)
00 85 J=l,NYEAR
85 WRITE(6,90)PWD(I, J) ,PSD(I, J) ,PSOLO(I, J) ,POP(I, J)
CALL PLOT2(IMA3E,XMAX,XMIN,5.0,Q.a)
WRITE(b,6G)
WRITE(6,61)I
00 50 J=l,NYEAR
TIME = J
PE1 = POP(I,J)
PS1 = PbO(I,J)
COST = PSOLO(I,J)
PWI = pwod,j)
CALL PLOT3(1HP,T1ME,PE1,1)
CALL PLOT3(lri5.TI1E,PSl,l)
CALL PLOT3(1HC,TIME,COST,1)
CALL PLUT3(1H3,TIME,PHI,1)
50 CONTINUE
CALL PLUT<»U3,
-------
POWtR bl^lRIBUTION PROBLEM
NO OF CONS1RAIN1S = 1<*
NO OF VARIABLES = 56
NO FO YEAR = 15
NO OF GENERAING STATIONS = 7
NO OF lYPES OF POWER PROjUCTIONb = «*
COiT OF PRODUCTION POWER BY VARIOU^ MtTHCJS
STAiION
HYDRO
ONCE THR
COOL row
DRY
2.0000
5.OOUO
7.0UUU
13.GOOD
S3
to
ro
2.0000
<4.0000
5.500Q
11.5000
2.0000
(+.0000
5.5000
11.5000
2.0000
O.OUOO
6.0UOO
.12.0000
2.OOUO
5.0000
6.5000
li:.5000
2.0000
5.0000
6.5000
12.5000
2.0000
5.0UOO
6.5000
12.5000
-------
i RANiMI-jilON COi i R?G1JN I TO REGION J
.1500
.1500
.2700
.2700
.3000
.3300
.3300
ION
.1500
.1500
,15uu
.1500
.3301,
.3300
M 4 X h r
to
to
UJ
27JO
2700
1500
1500
2 /"JO
2 f. 5 j 0 d
. 0 U u u
. . u a a o
2 . 1.1.' 0 )
2 I-. 0 J 0 0
i.
>. u 0 0 0
2 L . U 0 0 0
-------
PS DIN
POP(J,11
POPMftX
SROH
SOLVE
3,?.,';3Q1
'••. jOCfl
. o i-:< o
? . 3 0 ', J
1 . G :I2 5
? 7 . 3 3 'J 0
1 .2000
5. 3533
.9150
2.0003
I . 5 ?S 3
i> 3 . -1 0 3 3
.Mao
5.!' 000
.0131
t O.COOO
.78QQ
16.0003
.6000
5.0000
.0230
1.Q300
1.1967
32.0300
.6000
5.0300
.0180
5.5030
.3833
29.0000
.6300
5.P100
.013C
2.0300
1.36.33
27. 3000
..2 3 0 1
5. 1 300
.0 1 SO
7.030C
1 .0 !01
SOLUTION OPTIMAL wrlF.R
OOJFCTIVE =
?7 IT-.?iHONS
-?"..225033
Ni
P12
PI 3
Pl«.
PI 5
Plb
P17
P21
P22
P23
P2<*
P25
P2&
P27
P31
P32
P33
P3<»
P35
P36
P37
PM
Pi.2
P<.3
P>»<»
P<»5
P<»b
P<»7
P51
P52
P53
P5<»
P55
P56
PS 7
P61
P62
Pb3
P6<*
P&5
P56
P67
P71
P72
P73
P7I,
P75
P76
P77
dASIC
•JASIC
OASIC
BASIC
BASIC
BASIC
8ASIC
BASIC
9ASIC
.7)100."
1. 3iU03D
C. 0100 03
0 . 0 • 1 0 '3 0 0
O.CO-33C-:
o.oojyoa
C . G i] 3 1 0 0
i.dJOOOC
0.01003';
G.au joe
O.OOOQQO
G. 3033 V-:
C.OUOG30
C.DQ300C
Q . 0 J :) rj Q 0
. J3J300
O.OCOOOC
0.030C3\!
G. OOQOOG
0.000000
o.oaoooo
0.000030
.7Q030G
.auooao
i.aonooc
O.O'JGDOG
0.000 300
G. iisaooo
0.000300
O.COU033
G.OOQQO!;
O.OCOOOC
.730000
0.000000
0.003300
o.oooooc
O.OCOOOC
0.00300G
0.000000
0.000300
1.3UOOOC
O.GOGOOC
O.OOOOOG
O.OCIQOOG
o.onoooc
O.OL'OOGG
C. 000000
0.03300G
.700300
3.000000
0.00 00 30
-^.270003
-.1230Q3
-.153000
-.160030
-.11000-3
3.033000
-.100330
-.120030
-.120000
-.150030
-.181003
-.110000
-.120000
J. 003003
3.030003
-.15C301
-.120000
-.120000
-.150000
-.120000
3 . 3 0 0 0 0 3
3.300000
0.030000
-.120000
-.1*0300
-.193000
-.150000
-.150000
-.120000
-.150000
0.000000
-.120003
-.150000
-. 140000
-.180033
-.123000
-.120030
3.003300
0.000000
-.120000
-.180000
-.180000
-.120000
-.120000
-.150003
-.1200GC
3.330380
SLACK1
5L6CK2
SLACKS
SLACK!.
SL&CK5
SLAC<6
SLACK7
PUCSL1
PXUSL2
PWCSL5
PKDSLij
PWOSL7
PHD1
PWD2
PWD3
PUDI.
PX05
PHD 6
PWC7
PSD1
PSD 2
PSD 3
PSQi.
PS35
PSD6
PSD 7
•JASIC
34SIC
-;ASIC
r>ASIC
HINUN3
'JINONT,
dINONG
BIMONG
EJI N D NG
3INONG
6INDNG
SLACK
BINONS
SLACK
SLACK
SLACK
SLACK
SLACK
o. ceo 3 on
C.t0030C
O.COQOO'J
2.003300
.5C3QO!;
.5C300G
.5naooc
O.CUjGOO
0.000000
o.OQaoao
a.0033oo
C.GOOOOO
0.00033?
O.OGOOOC
-2.15000C
-2.15330C
-2.15QOOO
-2.150000
•2.15000C
•2.153QOC
•O.C0030C
2.0Q300G
•0.00300C
•a.OOiJOOQ
•O.OOQCOO
•U.00300C
•c.ocoooc
3.000000
-Z.QQ3090
a. o 3 c o o u
3 .000033
3.QJCGOO
3 . 3 0 0 U J 5
0 . 0 0 C 3 0 0
-2.150003
-2.150000
-?.150000
-2.150003
-2.150000
-2.150000
.700000
1.300000
.330000
1.200000
.730000
1.300000
.700000
.730000
.700030
.700000
2.000000
.500000
.500COG
.500000
OPEN
.703000
.f00000
OPEN
OPEN
OPEN
OPEN
1.303 COO
1.300000
.300000
OPEN
OPEN
OPEN
OPEN
-------
CALCULATION FOR THE YEflR
POKER DISTRIBUTION BETWEEN REGIONS
1
2
3
<»
5
6
7
POWER
J9T-1)
RIM
OH
PHO
PPC
OP
DO
ACK
HEXI
OS( JST)
.70QC
1.8000
C.OOOO
0.0030
C.CCOC
O.CQ30
C.OCOO
DEMAND POWER
2.0000
1.8COC
.3COO
.7COO
.2COO
.8000
.2COQ
1.300G
32.0030
.C23C
2 . 1 <«S 7
1 . 0 82 f,
.1355
l.OOGC
2
3.0COO
2.3000
1.3030
o.gooo
.3030
.7000
0.0030
O.OOOQ
0.3000
SUPPLIED
2.1000
1.9901
.3150
2.9350
.7350
1.3b5fl
.73 = 1
2.30QQ
27.0033
.0130
1.9937
1.5333
.0593
1.D3QO
2
2.C033
«..33TO
O.G003
0.0000
0.000*]
.8003
C.OOOO
0.0003
0.0003
POWER COST
3.5903
3.1500
2.1500
3.1500
2.1500
2.1500
2.1500
.8000
«.3.QC60
.0123
.3172
.7300
.0221
0.0000
1
1C.0003
2.0930
0.0000
0.0000
0.0030
1.2000
0.0000
0.0000
0.0000
= OPIJL«TION
1.9000
1.2003
.1.000
0.0300
16.0033
.0233
.7727
1.1967
.0607
2.0000
3
3.0000
2.0030
.6000
.6330
.2000
0.0030
3.0000
0.0030
0.0000
.7000
0.0003
3.COOO
0.0000
C.OOOO
0.0000
0.3000
Q.QOQC
1.3000
0.0030
ELASTICITY
Q.OCOG
32.QGOO
.0193
.2173
.3633
O.OCOD
1
5.5000
O.OOOC
28.0300
.0150
.03U3
o.ooco
1
2. DODO
2 . -J 0 3 0
0.0100
Q.31CO
0.31CO
C.0100
0.3 ICO
0.3 120
.7500
EO'.-L
C . 0 C'T 1
"7.0 ICQ
.OTIC
.> J7g
1 .3
-------
CALCULATION FOR THE YEAR
POWER DISTRIBUTION 3ETWTEM REGIONS
. 75f> 0
i . 8 90 7
0 . 000 0
0 . 00!) C
0 . COO u
0. 000 C
0. 000 J
.890 7
0 .000 1
1.503 3
0.0000
o. o o 3 a
0.0030
0 .0000
0.0000
C..JOOO
.8172
C.OO 00
0.0003
0.0000
0.0000
.5000
0.0300
O.OCOO
.7727
0.0000
0.0000
0.0009
0.0000
a.oooo
0.0000
0.0200
. 7173
0.0000
0.0000
0.0000
o.oano
O.OOGO
0.0000
0.0000
1.3<»68
0.0000
0 100
0100
0 100
0 300
0 300
0.0330
.7 }79
POWER OEMAN'3
POWER SUPPLItJ
POWER COST
POPULATION
ELASTICITY
EQbL
PTR< J.T-1)
PTRIN
GROW
PWO
PPC
DPOP
PAOO
S T A C.<
P'J WEXI
PCOS( J,T)
SOLVE
2.1W57
1.6907
.3172
.7737
.2173
,3'»68
.2079
1
. 3 9 C 7
32. 003 G
. C23 0
2. 3076
1 . 1 ?<) 3
. 1388
i.ooo :
1
3.0000
3.CQ 00
1. -)352
2. ".331
.7531
. 7!»33
Z.3927
27. COO 0
. U 13 0
1.9310
1 .6036
.0612
1.0000
2
2.0000
<•. 0 0 0 0
3.86<*-V
2 .5226
2.1500
1..2090
2.150 0
2.1500
2.1500
0.COOG
<• 3 . 0 0 0 0
.C12G
.3363
.8231
.0232
c.cooo
1
10.0000
2.0000
2.D355
1.2593
.6<»75
.631.3
.2077
.5000
16.0000
.0230
.8595
1.3173
.0659
2.0003
3
3.0003
<».6667
0000
,oaoo
,0000
0000
,0000
,0000
,0000
1813
,1533
0.0300
32.0000
.0130
.2372
.3921
.0507
O.OOCO
1
5.50GO
2.0000
0.0900
23.0000
.0130
.3987
.0360
0.0000
1
2.0000
2.0000
, 5223
3770
3512
3 7 > 3
0.0000
27.0300
.0-380
.216<»
1.0696
.0030
0.00=0
1
7.0 300
2. 3 COO
-------
COMPRESSED OUTPUT DATA USED FOR PLOTTING FCR THE REGION
POWER DEMAND
POWER SUPPLIED
POWER COST
POPULATION
2.0000
to
ho
2.3076
2.<*827
2.6713
2.3727
3.0603
3.251*2
3.<»527
3.8575
-------
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-------
'SELECTED WATER
RESOURCES ABSTRACTS
INPUT TRANSACTION FORM
7. Report No.
4. Title Analysis of Engineering Alternatives for
Environmental Protection from Thermal
Discharges
7. Author(s) Mar> B.W., Crutchfield, J.A., Welch, E.B., Bell.M.C
Geitner, N.M., Bush, R.M., Meyer, T., Porter, R., Saad, A.H.
9. Organization
State of Washington Water Research Center, University of
Washington/Washington State University, Pullman, Washington
99163
it. Sponsoring Orgsmation U.S. Environmental Protection Agency
/';. Supplementary Notes
Environmental Protection Agency report
number, EPA-R2-73-161, March 1973.
.?. Accession No.
w
5. Report Date
6.
S. Performing Organization
Report No.
1 j
10. Project No.
16130 FLM
11. Contract/Grant No.
;./'. 7'/,)« ol Report and
f'f:ic-! Covered
16. Abstract
A decision tree framework was utilized to integrate engineering decisions con-
cerned with the control of environmental impacts from stationary thermal power plants.
The engineering costs and the ecological response of fish communities to any sequence
of decisions in the tree can be computed with the models developed in this study. A
series of formulations were also developed to describe the environmental impact of
siting a series of power plants in a region. Both the static and dynamic models re-
quire verification before they are applied. Impacts of thermal and chemical discharges
to the receiving waters and mechanical damage from screening devices are modeled.
17a. Descriptors
Thermal Powerplants Water Temperature
Mathematical Models Thermal Effects
Decision Trees Socio-economic aspects of power generation
Intake Systems for thermal plants Cooling system chemicals for thermal
power plants
776. Identifiers
Thermal Power Plants, Thermal effects, Socio-economic effects, intake systems,
chemical system, single plant assessment, dynamic assessment
77c. COWRR Field & Croup
18. Availability
19. Security Class.
(Report)
20. Security Class.
(Page)
21. No. of
Pages
22. Price
Send To :
WATER RESOURCES ^r.ll N"l .-iC IKTOHMAI ,UN •'. T:., ! KF,
U S. DEPARTMENT Of 1 t: L INTI F« t
4U.S. GOVERNMENT PRINTING OFFICE:1973 514-155/Z97 1-3
------- |