ALTERNATIVE ELECTRIC GENERATION IMPACT SIMULATOR AEGIS, DESCRIPTION AND EXAMPLES
James Gruhl
Energy Laboratory Report No. MIT-EL 79-010
November 1978
I
ALTERNATIVE ELECTRIC GENERATION IMPACT SIMULATOR AEGIS, DESCRIPTION AND EXAMPLES
by
James Gruhl
MIT Energy Laboratory
Massachusetts Institute of Technology
Cambridge, Massachusetts 02139
sponsored by
Northeast Utilities Service Company
New England Electric System
under the
M.I.T. Energy Laboratory Electric Utility Program
Energy Laboratory Report No. MIT-EL 79-010
November 1978
NOTICE AND ACKNOWLEDGMENTS
This report was prepared at the MIT Energy Laboratory, Cambridge,
Mass., as an account of a small portion of the work performed on
prediction of air pollution standards and strategies for meeting those
standards. This research was sponsored by Northeast Utilities Service
Corporation, under the principal direction of Denning Powell and with the
assistance of William Renfro, and was sponsored by New England Electric
System, under the direction of Bradley Schrader. These people are to be
congratulated on their foresight, as the use and/or sponsorship of this
program has now begun at E.P.A., E.P.R.I., and, ina major new effort, at
D.O.E. This research was conducted as part of the M.I.T. Electric Power
Program, William Hinkle, program manager.
None of these organizations nor any person acting on behalf of
organizations: (a)makes any warranty or representation, express orthese
implied, with respect to the accuracy, completeness, or usefulness of the
information contained in this report, or that the use of any information,
apparatus, method, or process disclosed in this report may not infringe
privately owned rights; or (b)assumes any liabilities with respect to
the use of, or for damages resulting from the use of, any information,
apparatus, method, or process disclosed in this report. Mentions of
products or policies do not necessarily imply endorsements of those
products or policies by any of these organizations.
2
U
ABSTRACT
This simulator should be viewed as a framework for assembling and
manipulating information about the economics, emissions, ambient
concentrations, and potential health impacts of different types and
configurations of electric power generating facilities. The framework is
probabilistic, and thus results in several measures of the range of
various consequences, inother words, a graphic display of the quality of
the various predictions. The current version of the model is to be
considered a testing version, as there are certain approximations
implicit in some of the manipulations, scaling procedures, and the data
base is incomplete inportions. As a result of additional fundings, the
data base should be at the state of the art by September 1979 and
additional refinements to various manipulations, inparticular the
economic and dispersions, should be completed by June 1980. There are,
nevertheless, a tremendous number of useful exercises that can be
performed on the current model version.
In addition, the simulator is
structured so that it iseasy to improve the sophistication of certain
manipulations, or to replace generic data, or update or add new data.
Versions of the simulator are available so that itcan be operated in
batch or interactive modes.
3
TABLE OF CONTENTS
Page
NOTICE AND ACKNOLWEDGMENTS........
ABSTRACT.
........................
...
2
..............................
TABLE OF CONTENTS
................... ...
......................
............................
1. Summary of Capabilities ...........................................
2. General Modeling Philosophy ..
.
3. Input/Output Procedures ...
..
............
4. Current Status of Modules and Recommendations..
5. References
...
..
5
........ ..................
16
....................
53
.............. 61
.....................................................
Appendix A -- Additional Simulation Examples ..........................
4
4
0
81
1. Summary of Capabilities
This document contains discussions about a computerized tool for
predicting the economics, resource uses, emissions, ambient
concentration, and health impact levels from combinations of:
1)
fuel types and sources,
2)
pretreatment equipment,
3)
generation equipment,
4)
abatement equipment,
5)
site types for different dispersive potentials,
6)
site types for different population densities, and
7)
available health effects models.
The framework of this mechanism has been the principal focus of this
portion of the project, although a number of existing government and
industry sources have been searched for data relevant to this mechanism.
Some of the structural issues addressed have included:
1)
types of components that should explicitly be incorporated,
2)
mechanisms for modular addition or updating of data,
3)
generic pieces ofinformation that could easily be used for
testing and simple exercises,
4)
specific air pollutants that should be collected within the
simulator,
5)
the treatment and display of probabilistic information models,
and finally
6)
means for evaluating the validity of complex computerized
models.
It is important that there be a realization of the magnitude of this
effort.
This has been an exploratory project, altogether encompassing
5
about one-half of a man-year.
Half of this time was used to address the
task of predicting, with measures of uncertainty, the air emission and
This research
ambient standards expected over the next 30 years.
culminated in oral presentations on "speculations about Future Ambient
Air Quality Regulations."
The remainder of this project was used to
address the possibility of a simulation of the consequences of electric
power production.
The majority of this time was spent on the structural
issues previously listed.
The data base, thus, is the weakest portion of
this project, and any uses of the simulator should be carefully augmented
with a study of the adequacy of the underlying data.
Fortunately, it is
not difficult to update the data base in any of the sections of the
simulator.
Also, fortunately, there is a major undertaking under way to
complete and update this data base by September 1979.
There are,
nonetheless, a tremendous number of important exercises that can be
conducted with the current version of the model, and examples are
presented of some of these.
First, it is important to understand the basic structure of the
model.
All of the quantities collected or manipulated within the model
correspond to actual physical flows.
Figure 1 shows some of these flows
that take place in the standard use of the model.
There are two
principal advantages of such a "physically significant" framework, namely:
1)
all of the data requirements correspond to real measurements
that can be made, and
2)
the structure is simple enough to allow for a quantification of
the profile of the uncertainty associated with any of the flows
or eventual outputs.
6
MODELS
MEANS OF
IDENTIFICATION
OPTIONS
Combustion
I
.TjFuel
sources and
constituents
- lists
fuel
OJ'
- input/output
or transfer
functions
Pretreatment
_J,~
I
fuel
Combustion
Analytic Chemistry
- emission
constituents
--
U
emissions
- modified
Guassian
- static aero'chemistry
- density roses
Health
nemissions
Atmospheric
Characteristics
.1]
meteorological
profiles, background
pollutant levels
ambient
7t
Demographic
CharacteristicsI
IL
I
I
Abatement
11
Dispersion and
Exposures
process types,
operating and
design parameters
population density
profiles
exposures
specific effects
Health
Impacts
- dose-response
curves
Figure 1 Combined Probabilistic Simulator of a Technology-Specific and
Site-Specific Situation
7
It is not immediately obvious what types of internal variables must be
collected to ensure the appropriate performances of the various options,
and this has been an area of considerable effort in the setup of the
framework of the current model version.
Although there is considerable unevenness in the qualities of the
different data, Table 1 is a listing of the modules which are in place in
the current version of the simulator.
The question of accuracy, or validity, is paramount in the minds of
informed users of any large computerized models.
An extensive
undertaking into the area of model validity has been conducted as part of
this project.
The conclusion was that the ideal situation would be to
quantitatively display the validity of all outputs as a normal course of
the report generating phase.
This has been accomplished in this project,
and may be a unique and important aspect of this project. Tables 2 and 3
display some of the uncertainties that have been studied in an attempt to
quantify the validity of the model's outputs. There may be some
considerable ground left to cover concerning the capabilities of this
simulator with respect to accuracy, but hopefully the majority of this
ground will be covered in the process of updating the data base.
In closing this section on capabilities it is perhaps useful to
present a list of potential questions that could be addressed with this
simulator, since it is probably difficult to appreciate the potential of
this probabilistic simulator as a highly sophisticated and expert
planning and policy tool.
Some of the questions to which it could offer
quick turnaround answers include:
1.
What level of validity exists in the ever more frequent
assessments of health impacts of electric power?
8
Table 1
'J
1 TF ;JNaTIVE ELECTDIC rrI.eTIP
vnr1U
ES TTtr
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9
SIuJLATO)R -
aEGIS
Table 1 (continued)
LIGHT
WTE. 0=AC0'0 P-KS
LIGHT WtATED :,ACT,)D 31IL
HIGH T:P GAS PrACTOO
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Table 1 (continued)
HFALITH
fJr) PLIJTTON! 1'JDFX n9TIJ4S .5F:
CHE'MICL
E.ILTH ;A:DFLS:
rJnNF
AVF. OF ALL C
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11
Table 2. Common Structural Deficiencies That Cause Loss of
Predictive Qualities in Environmental Models
_
__
Aggregation
_
- averaging of factors and effects over
model regions and time periods (although
aggregation over time periods usually
enhances predictive quality)
Simplification
- components capable of coupling complex
effects are simplified or missing;
deterministic, instead of probabilistic,
format is used
Indexes
- use of measures loosely correlated with
effects that are otherwise difficult to
predict, e.g. S02 as sole index of air
quality, species diversity as index of
aquatic community health, and so on
Crudeness
- lacking capabilities for identifying
coal types, plant types, or other
necessary details
-. structural mistakes in model due to
Invalidity
logical or behavioral inaccuracies,
lack of variables, etc.
Unverifiable
- structural mistakes due to inaccuracies
of algorithmic or computational
implementation
Undocumented
- misuses of model due to undocumented
features in structure, or undocumented
limits to range of applicability'
_____
__
·_
12
Table 3 Example of Data Deficiencies That Will Cause Compromises
in the Quality of Public Health Impact Predictions
_
Fuels
___
_ _
- uncertainties in levels of various
contaminants in fuels, uncertainties
in characterization of volatility,
heat content, and other properties of
the fuels
Precleaning
- general lack of knowledge of.
capabilities of precleaning options
Combustion
- unknown levels of pollutant'removal,
formation, and form inemissions
Posttreatment
- uncertainties in performance of
control equipment with respect to
certain pollutants
Dispersion
- uncertainties in dispersion; effects
of correlation of operating and
natural cycles; uncertainties in
climatological data
- lack of information about the
Aerochemistry
transformation of pollutants in the
atmosphere
uncertainties with respect to
population densities and population
susceptibilities
unknown magnitudes and causes of
health impacts
Demography
Health Impacts
_
__
_ __
13
2.
Where are the faults and implications in EPA standards,
abatement strategies and retrofit programs? For example,
further siting limitations may leave no acceptable sites, see
Figure 2.
3.
What are the actual comparative effects of coal versus nuclear,
or coal versus oil obtainable from state-of-the-knowledge
information, and what is the certainty with which these effects
are known?
4.
How can generation and control equipment be chosen to minimize
retrofit and shutdown problems?
5.
How can sites be selected on the basis of minimizing future
sensitive situations concerning health effects?
6.
What is the best information on the quality of fuels from
various sources, such as different coal sources, and what kinds
of long-term fuel contracts will look best in light of
predicted future standards?
and the most important question of course is the one which will first
arise the day the answer is needed.
14
Figure 2
Example of current siting difficulties faced by 5000MWe FEC (fossil energy
centers) on the basis of exclusion from national public lands, urban areas,
air quality maintenance areas, and particulate and S02 levels only (NTIS report#
ANL-76-XX-11)
F]S Arcas
tnlikoely to lrevide
sitSable :.!' sites.
Areas orst likely to provide suithbc ' ": sites wit!out comtsidrin. distal. u to ad,u!nt eC:t! rerernes
Cc3os5it Screen withnut Crnn iderin r] l :oServes
15
2. General Modeling Philosophy
What data are necessary for a decision maker to properly choose
among several similar advanced energy technologies?
To answer this
question it is necessary to first gain perspective of the overall
regional energy situations.
The potential performance of any new energy
technology must be measured in this overall context.
On the national
level Tables 4 and 5 show the objectives, constraints, and controls that
define this overall problem area.
The performances of electric power
related energy technologies within this broad context must also be
weighed against their performance in the related but slightly different
problem area defined by their regional power pool or individual utility
perspectives. The constraints, objectives and controls of concern at
this region level are outlined in Table 6.
A brief scan of these two problem formulations shows that an overall
evaluation of the potential performance of any upcoming energy technology
requires information about:
1)
economics, in terms of cost of unit energy output, investment
and operating costs;
/
2)
timeliness, availability for commercial use and fit into
energy-economic context;
3)
resource consumption, including use of unpolluted air and
water, materials, fuels, manpower, and capital;
4)
environmental, safety, and health characteristics;
5)
Basic research, meaning those contributions that will also
apply to other processes;
6)
institutional factors such as public image and
government-industry interference and cooperation; and
16
Table 4 Constraints and Performance Measures or Objectives
of the National Electric Energy Situation
-- --___ __..
-·
-J
_ -I ·
1
EConstraints:o
Energy Resources
Fuel
Non-Fuel
Conservation Potential
Materials
Capital
Manpower
Time
ectivesj:(all are essentially public perception of
quality of life)
Survival of System
Vulnerability to Foreign Disruption
Infrastructure Problems
Satisfying Energy Demands with Minimum Cost
Environmental Protection
Materials
Vegetation
Animal Life(Non-Human)
Human Well-Being
Other Quality-of-Life Measures
lCorntros:(related to electric power)
Policy Actions(see next page)
----
- --
- ~I-s"U"e
-
-- -
----
17
--
Table 5 Actions That Can Be Used to Control the National
Energy Situation
PoliCv Act ions)
Information
Public Awareness of Technologies Risks/Benefits
Public Awareness of Conservation Measure Cost/Benefits
Fiscal Chances, Incgntives, Uses
R&D Funding
Advanced Generation
Centralized and Distributed
Conventional and Unconventional
Co-Generation
Fuel Conversion
Nuclear
Storage
Advanced Transmission
Abatement Alternatives
System Studies
Operating Strategies
Planning Strategies
Potential Uncertainties
Research Payoffs
Health and Environmental Effects Information
Cost of Financing
State Financing
Incentives for Market Penetration
Tariffs, Regulation of Fuel, Plant, Wage Costs.
Btu Taxes
i
I
Regulatory Changes
Change Rate of Return
Accelerate Licensing of Attractive Alternatives
Include Work-In-Progress in Rate Base
Forced Retirement of Plants and Retrofitting
Regulate Reserve Margin
Load Management
Peak Load Pricing
Seasonal Pricing
Rationing and Source Regulations
Influence Economic/Demographic Trends
Conservation Policies
Environnentallv Motivated Actions
Change Environmental Standards
Change Siting Restrictions
Capital Equipment Requirements
Operating and Planning Strategy Requirements
-
---
I--
-18
--
I
Table 6 The Formulation of the Information and Components
of the Electric Utility Energy Alternatives Problem
Constraints: 1
Energy Resources
Fuel
Non-Fuel
Conservation Potential
Energy Technologies
Capital and Cash Flow
Sites
Manpower
Materials
Time Frame
Environmental Standards
i
[Objectives:
Survival of Private Power Sector
Satisfactory Reliability (meeting demand)
Satisfactory Cost of Electricity
Profit to Investors
Cost Effectiveness
High Efficiency-.._
Awareness of National Research and Trends
Avoid Retrofits
Avoid Costly Interventions and Moratoriums
Contrs:
i (all depend on time frame constraint)
R&D Funding
.Influence Policy Actions
Generation Expansion
'
Technology and Fuel Choices
Site Selection
Transmission Expansion
Production Scheduling
Nuclear Refueling
Hydro Scheduling
Maintenance Scheduling
Unit Commitment
Economic Dispatch and Faster Controls
Demand Modification
Pricing Policies
Conservation and Growth Incentives
Public Awareness of Conservation
_
g
I
I
I,-
7)
national security enhancement, primarily in aiming at replacing
or avoiding cartel-vulnerable products, such as foreign oil or
imported rare metals, and avoiding disruptions that could
affect the survival of the establishment.
The effort required to develop these kinds of information can be broken
into two separate tasks:
1) The assessment work that can be done by scientists and
engineers, which should result inobjective, unambiguous
technical information; and
2)
The evaluation work that is performed by a decision maker,
which will incorporate subjective judgments and which will vary
according to the perspective of each individual.
What will be discussed here is the importance of separating tasks
(1)and (2), the set of information that should be transferred from (1)
to (2), and the potential for feedback and interaction from task (2)back
to task (1).
First, it should be emphatically stated that it ismost important to
make as clear a division between the engineering work and the policy work
as ispossible. Qualitative or subjective arguments imbedded in the
results of engineering assessments not only reduce the usefulness of those
assessments but render them suspect in the view of people with slightly
different judgmental perspectives.
The question then arises as to what infact is the necessary and
sufficient information about an energy technology that can be made
available by an engineer that will allow the policy makers to make the
most intelligent decisions.. Here there are basically two types of
information needed:
20
1) Assumptive information
-
exactly what is the basis of the assessment, has there been
scaling up from small facilities, what sort of economic
judgments are imbedded in the cost information, what exactly
are the fuel, the plant design, and modeling assumptions;
2) Assessment information
-
expected values of all of the economic and engineering data,
reduced to their most usable form but not incorporating any
hidden subjective trade-offs between unlike quantities.
Assumptive Information
The assumptive information should consist of descriptive and
numerical information, specifically that information which must be
assumed to develop assessment numbers.
Some of the many types of
assumptions that may be involved are included in Table 7. Of course, in
making comparisons between different energy technologies, it is not
sufficientto use the same assumptions for all technologies, since some
assumptions will tend to bias the comparison.
The comparison of energy technologies on the basis of economic
evaluations is a conventional practice.
It is rarely, however, that
these comparisons can be made on common economic assumptions, and more
rarely that competing technologies can be compared using a series of
assumptions about several values of key economic parameters.
Some of the
most detailed models that are capable of handling such sensitivity
studies for conventional coal, oil, gas, and nuclear fueled facilities
are available from the Department of Energy and Oak Ridge National
Laboratory.
programs-
Examples of these models are the CONCEPT, PLANT, and ORCOST
In most projects it is likely that some options and some
21
Table 7 Example List of Assumptive Information
from Gruhl, et al., 1976]
1) Basic Assumptions
A. Year (if
a particular year is to be the time for the
-comparison) - example:
1988
B. Regional Considerations
-
Fuel
- example:
- Plant
- example:
fuel from Western Kentucky
located in New England, rural site.
- Load/Demand
- example:
customers in New England, load shape typical of
New England.
C. Power System
- example:
power system is small, predominately oil-fired,
this would then reflect upon the usefulness of this
particular facility with respect to the power generation
mix available on this type of power system and the
replacement cost of power that would be likely.
2)
Economics
Whether or not, and which, economic factors should be
available as parametric factors would depend entirely upon
the sophistication of the modeling of accounting
procedures that is desired.
Possible levels of accounting
sophistication include:
Oth Order - Exact accounting procedure to be used would be
specified as would the exact values for
ecomonic factors, such as cost of capital.
1st Order - Ability to use parameterization of economic
factors and different accounting procedures
for the major expenditure items, such as
construction, licensing, equipment, and other
22
Table 7 (continued)
Secondary expenses, such
primary investments.
as transportation investments that would
affect transportation costs as passed on to
the utilities would be handled as described in
Zeroth Order procedure.
2nd Order - Primary and secondary (or indirectly
affecting) expenditures could be modeled with
parameterized economic factors and various
accounting procedures and tertiary influences
would use prespecified procedure as in Zeroth
Order.
3)
Performance
A. Capacity Factor (design)
-example:
65% (i.e. baseload)
B. Exact descriptions of Equipment Designs and Assumptions.
4) Environmental
A. Emission Standards
- example:
B.
Ambient Standards
- example:
1
current standards
disregard
.
.
__
73
parameters will inevitably be fixed in an accuracy versus complexity
trade-off.
In a Zeroth Order effort no economic factor could be parameterized,
they would all be pre-fixed.
In a 1st Order procedure, such as in the
ECAS study (NASA, 1976), examples of parametric economic factors would
include those shown in Table 8.
In many cases, such as cost of capital, assumptions must be made in
order to develop meaningful cost information.
Since the exact numbers
are unknown,sensitivity studies with respect to these parameters are
essential.
Assessment Information
Here some attempt is made at displaying the set of quantitative
performance information that should ideally be passed from the
engineering studies to the policies studies.
As shown in Table. 6 there
are two broad categories of environmental assessment information:
(1) binding constraints that is,regulatory requirements, such as
air quality standards or limited sites, and
(2) performance objectives, that is,obligations to society, such
as preservation of materials, vegetation, animal, and human
health.
The overwhelming majority of energy system models that include
environmental information explicitly are aimed directly at testing (1),
the binding constraints.
Accommodating those binding constraints, to a
large extent, dictates the level of geographic disaggregation that is
carried by those models.
Table 9 shows the types of environmental information that can be
incorporated if a site-specific energy model is used.
24
Table 8
Varied,
A.
Example List of Economic Information That Might be Parametrically
principally from the ECAS study (NASA, 1976)].
Accounting Procedures
1) Depreciation Options:
a) straight line
b)
sum-of-the-years-digits
o) combination of (a) and (b) switching at a given year
2) Fraction of Year to Discount Annual Expenses
- example:
3)
0.50
Time Factors
a) base year for escalations
- example:
1971.0
b) year construction started
- example:
1971.0
c) year of commercial operation
-
example:
1979.5
d) length of workweek (hrs)
- example:
40.0
e) year for present-worthing of dollars
- example:
1975.0
B. Treatment of Debt and Equity
1) Bond Repayment Options
a) proportional case
b) uniform principal reduction
c)
uniform annual payment
d)
delayed uniform principal reduction, include starting year
for delayed option
2)
Annual Interest Rate on Debt (%)
- example:
7.5%
3) Fraction of Initial Investment Raised by Debt
4) Earning Rate on Equity (after tax)
5) Debt/Equity Ratio
C. Escalation Rates
1) Initial Equipment Escalation Rate (%)
- example:
i
i
2) Equipment Escalation Rate ()
- example:
-
5.0%
-
5.0%
-·---`------`D·--·-"-I
25-
Table 8
(continued)
,~~~~~~~~~~~~~~-
3)
Initial Material Escalation Rate ()
- example:
4)
5.0%
Material Escalation Rate ()
- example:
5.0%
5) Initial Labor Escalation Rate (%)
- example:
10.0%
6) Labor Ecalation Rate (%)
- example:
7)
10.0%
Uniform Overall Escalation Rate ()
- example:
0.0%
8) Escalation Rate on O&M Cost (%/yr)
- example:
9)
0.0%
Escalation Rate on Fuel Cost (%/yr)
- example:
0.0%
D. Indexes for Uniform Parameterization
1) Site Labor Productivity Index
- example:
1.0
2) Equipment Cost Index
- example:
3)
Materials Cost Index
- example:
4)
1.0
Labor Cost Index
- example:
E.
1.0
1.0
Insurance
1) Property Insurance (fraction of plant investment/yr)
- example:
0.001
2) Additional Liability Insurance (for nuclear accidents, oil
conflagrations, etc., $/yr or $/yr/MWh)
- example:
0.000
F. Taxes
1) Federal Income Tax Rate (fractional)
- example:
0.041
2) State Income Tax Rate (fractional)
3) State Gross Revenue Tax Rate (fractional)
4) Property Tax Rate on Plant (fraction/yr)
5) Other Taxes (fraction/yr)
26
Table 9 Categories of Environmental Information That Have Been
Included in Site-Specific Energy Models
RESOURCE REQUIREMENTS
1) Renewable Energy (as % of primary energy)
2) Land Use (acres/yr)
A. On-Site Requirements
B. Waste Disposal and Other
C. Pondage Requirements
3) Manpower Requirement (non-operating, man-yrs)
4) Water Consumption (gallons/yr)
5) Materials Requirements (tons/yr/material)
6) By-Products (disposal costs or sales, $/yr)
ENVIRONMENTAL CONSEQUENCES
1) Emission Standards (% of each standard)
2) Emissions (normal and upset)
A. Air Pollutants (tons/yr, BTU/yr for specific pollutants)
B. Water Pollutants (tons/yr, BTU/yr specific pollutants)
C. Waste Solids (tons/yr for specific wastes)
D. Radioactive Pollutants (curies/yr)
E. Noise (decibels/full load at plant boundary)
3) Upset Conditions (hrs/yr)
4) Ambient Standards (% of each standard)
5) Occupational Health
A. Mortalities Onsite (per year)
B. Morbidities Onsite (per year)
C. Days Lost Onsite (per year)
D. Mortalities Offsite (per year)
E. Morbidities Offsite (per year)
F. Days Lost Offsite (per year)
6) Public Health (Routine)
A. Mortalities (per year)
Cancer (total)
Cardiovascular
Bronchopulmonary
CNS
B. Mutations (total)
C. Morbidities (per year)
Respiratory
Asthma
Pneumonia
Chronic Respiratory
Children's
Thyroid
Cardiovascular
D. Days Lost (per year)
7) Public Health (Offsite)
A. Mortalities (per year)
Cancer (total)
B. Mutations (total)
27
Table 9 (continued)
C. Morbidities (per year)
D. Days Lost (per year)
8) Public Health (Catastrophic)
A. Probability of Occurrence (per year)
B. Mortalities (per year)
Cancer (total)
Bronchopulmonary
Cardiovascular
C. Mutations (total)
D. Morbidities (per year)
Thyroid
E. Days Lost (per year)
9) Pollution Related Damage Costs and Indexes
A. Public Health Costs ($/year)
B. Biota Costs ($/year)
C. Materials Damage Costs ($/year)
D. Aesthetic Costs ($/year)
E. Subjective Indexes
28
ii
iI
I
I
I
i
i
The portion of the list in Table 9 that perhaps requires more
amplification is the specification of the set of pollutants that should
be included to define the set of pollutants to be investigated the
argument that is generally put forward involves including every pollutant
about which there exists adequate emission information.
This, of course,
strongly biases the investigation in favor of the technologies about
which the least information is available, and this bias need not
necessarily be accepted as inevitable.
It can be avoided by not allowing
question marks or "unknown" to show up in the list of emissions, and
instead forcing a speculation on the possible levels, even if it is just
"zero to worst case."
Of course, it is impossible to include levels of emissions for all
pollutants, there are more than 602 inorganic and 491 organic air
emissions from coal facilities, so some aggregation is unavoidable.
Table 10 displays systematic considerations that can be useful for
determining which pollutants should be incorporated.
From these types of
lists some priorities can be developed concerning which pollutants to
investigate.
For example, highest priorities for incorporation should go
to those items that consistently occur in these different lists, such as
compounds of sulfur, nitrogen, beryllium, arsenic, and
uranium/radioactivity.
In this way the size of the bookkeeping and
manipulation efforts necessary for including this emission information
can be kept reasonable.
From the perspective of commercialization potential, another set of
pollutants which should receive consideration is those that are now and
may in the future be regulated.
Some foresight as to what these
29
TablelO Dfferent Ways in Which Pollutants Are Shown to be Important for
Inclusion in Energy/Environmental Systems Models (Gruhl.et al..1976)
_~~~~--
-
Great Variability among Coal
1)
Arsenic (sometimes 100 to 1000 times national average)
2)
Barium (1 to 3000 ppm)
3)
Beryllium (0.1-1000 ppm)
4)
Boron (100-1000 ppm)
5)
Germanium (25-3000 ppm)
6)
Uranium (1-200 ppm)
7)
Sulfur (3000-120000 ppm)
8)
Nitrogen
9)
Chromium
i
I
I,
10)
Cobalt
11)
Copper
12)
Lead
13)
Manganese
14)
Molybdenum
15)
Nickel
16)
Vanadium
17)
Zinc
I
18) Zirconium
Eseane Pollution Control Euiment Due to Volatility (or other
1)
Mercury (about 100%)
2)
Arsenic (about 80%)
3)
Beryllium (about 100%)
4)
Nitrogen (about 100%)
5)
Sulfur (about 99%)
roperties)
Relative National moortance of Power Plants as an Emission Source
(Goldberg, 1973) and (Starr, Greenfield and Hausknecht, 1972)
1)
SOX (73.5%)
I"
2) Beryllium (68.0%)
3)
Chromium (53.5%)
--
-
30
Table 1 0 (continued)
I4) Selenium (50.5%)
5) NOx (43.8%)
6)
-Vanaditum (34.2%)
7)
Boron (32.2%)
8)
Particulates (31.4%)
9)
Nickel (26.8% but mostly oil)
10)
Barium (24.8%)
11)
Mercury (22.0%)
12)
Flourides (17.7%)
13)
Magnesium (8.5%)
14) .Lead (7.7%)
15)
Arsenic (5.5%)
16)
Tin (4.5%)
1
Annroaching Ambient Standards or Recommended
1)
SO,
2)
Total Suspended Particulates
3)
NOX
4)
Ozone/Oxidants
5)
Hydrocarbons
6)
Beryllium (20% of recommended levels)
evels (site sDecific)
II
i
II
I
7) Radiation (10% of recommended levels)
Imortant from Standooint o.f Health Effects Research
1)
sox
2)
Particulate Sulfates
3)
Sulfuric Acid Aerosols
4)
NOx
5)
6)
NO
Ammonia
7)
Particulate Hydrocarbons (carcinogenic potency ++++)
I
i
I
1I
3
i
i
I
8) Particulate Hydrocarbons (carcinogenic potency ++)
31
I
Table 1](continued)
9)
Particulate Hydrocarbons (carcinogenic potency I)
10)
11)
Particulate Hydrocarbons (carcinogenic potency +)
Heat
12)
Radionuclides
13)
1#)
SiO
Arsenic
15)
Asbestos
16)
Beryllium
17)
18)
19)
20)
21)
22)
Chromium
Lead
Mercury
Nickel
Tin
Vanadium
23)
Zinc
Svnergistic Pollutants - Potentiators.
-.
1) so,
2)
NO
z
3)
Total Suspended Particulates
4)
'5)
Ozone,
Reactive Gaseous Hydrocarbons
6)
Metal Oxides
7)
Iron
Svnergitic Pollutants - Anta&onizers (Rossiblv benefigial to health)l
1) Arsenic
2) Cadmium
3) Copper
4) Manganese
5) Particulates Hydrocarbon (carcinogenic potency-)
6) Selenium
7) Titanium
8) Water Hardness
32
pollutants may be can be gleaned from several sources:
the revised Clean
Air Act Amendments, the concerns expressed by members of the
epidemiological, toxicological, and regulatory communities, the lowest
emissions levels from available control technologies, and the pollutants
investigated in various mechanisms aimed at policy evaluations or stated
as being important such as are displayed in Tables 11, 12, and 13.
Whereas the Teknekron model contains perhaps the most site-specific
capabilities of any current national energy system model, the SEAS
mechanism of Table 11 collects more pollutants than any of the commonly
used energy system models.
A crude first attempt at a list of air
pollutants that would be important for electric power and environmental
models is presented in Table 14.
It must be realized, however, that such
a list will vary considerably depending upon the intended applicatons of
those models.
Evaluating and Improving Predictive Quality
There is no such thing as perfect predictive quality, only varying
degrees of assurance.
The extent to which perfect validity can be
approached depends overwhelmingly upon whether the data upon which the
model is based are:
(1) accurate or inaccurate
(2) sparse or dense in the region of interest, and
(3) historical data or designed data; designed in that the ranges
of the variables of interest have been filled parametrically by
well-placed replicable experiments.
Historical data, which play a major role in energy/environmental models,
are inherently weaker in that the resulting models are susceptible to
"capitalization on chance" effects.
33
With that intrinsic weakness it is
Table 11 List of Pollutants Collected for Use in the SEAS
Mecnanism (USEPA, 1975)
_1
__
I
II
I
iIi
II
II
ys..tc-a consists uf a ninc-cligit coc, as follow:
The
1st and 2nd diaits:
Residual Category
Particulates
Sulfur Oxides
Nitrogcn Oxides
Hydrocarbons
Carbon Monoxide
Photochemical Oxidants
Other Gases and tlists
Cdors
Biological Oxygen Demand
ygen Demand
Chemical
Total Organic Carbon
Suspehded Sl-ds
i
Dissolved Solids
i
Nutrients
Acids
Bases
Oils and Greases
Surfactants
Pathogens
Waste Water
Thermal Loading
Combustible Solid Waste
lion-Combustible Solid :iste
Bulky Waste
· 'Hazardcus Waste
Hining Waste
, Industrial Sludges
Sewage Sludge
Herbicides
Insecticides
Fungicides
iiscellaneous Pesticides
Radionuclides to Air
Radionuclides to Water
Radionuclides to Land
i
3rd and 4th dinits:
(Continuca)
13 .Carbar.ate Insecticides
14 . 'Cesium-134
15
16.
'17
'18
19
20'
213
22
23
24
25
26
27
28
29
30
31
32
33
34
35
3rd and 4th digits:
Residual Component
I
Not Applicable
-
Aluminum
Ammonium Hydroxide
-
__
____
03
0.4
OS
'06
07
' 08
09
10
Antirony
01
Appliances
02
03
Arsenic
04 '. Asbestos
05 A sh
Automobiles
.06
Bacteria
07
.
Darim-10 '
.08
'
Berylliu.
09
10
Doron
11
Botanical Insecticides
12
Cadmium
00
'Cesium-137
Cesiun-144
Chlora;xAdrif
Chlorine
ChromiurCobalt-60
GO
Concrete, masonry
.
Copper
Copper Fungicides
Crop Waste
Cyanide
Dithiocarbamate Fungicides
Ferric Chloride
Ferric Sulfate
Ferrous letals
Fluorine
Food .Waste
Garden Waste
Glass
Household Furniture
.
Hydrogen-3
Inorganic Herbicides
Inorganic Insecticides
Iodine-129
Iodine-131
. KCrypton-8S
01
02
______
34
Lanthanum-140
Lead
___
__
12
13
14
S15
.16
17
19- 20
.21
22.
23
24
25
26
27
* 28
-29
30
31
32
33
34.
35
36.
37
38
39
40
41
42
'43
44
Table 11
continued
_
__
___
3rd and 4th dicits:
5th diqit: Carrier Medium!
Reporting Category
(Contilued)
Leathler
Livestock Wastoe
Mercury
Mine Overburden
Mine Tailings
Mtiscellareous Fungicides
Nitrates
'Non-Ferrous Metals,
Miscellaneous
. Organic Herbicideta
Organic Mercury Fungicides
Organochlorine Insecticides
Organophosphorus
Insecticides
Other Synthetic Organic
Insecticides
Paper
Phenols
Phosphates
Ptlhalimide Fungicides
Plastics
Radiunt-266
Radon-222
Rubber
Rutheniu-l106
Sand, StonG, Soil .
'Selenium
Slag
Strontim-90
Tellurium
Textiles
Thalium
Tires
Vanadium
Viruses
Water, Cooling
Water, Process.
Wood
Zinc
_
_
45
46
47
48
49
,50
51
1
2
3
4
S
Air
Water
Land
Leachate
Pesticide
Radiation
6
6th d.igit.
52
53' * point
54 Area
55 Mobile
Source
56
Product-of
57.
58
59
60
'61
62
63
64
65
66
67
.68
69
70
71
72
73
74
75
76
77
78
79
80
_
35
7th digit:
Combustion
1
2
3
.
Yes
2
No
8th digit: Type of
Economic Activity
1
2
.3
4
Extraction
Production
Distribution
Consumption
Disposal
9th digit:
None
Low
Medium
High
..
.
5
Toxicity
1
2
3
4
Table 12
* List of pollutants. that EPA conveys to its researchers as its perception of
potential probelems in the e!lergy field.
IxM*Ii)ir,[i LIST 01 C:c!ICAi, A'D PrlrYSCAI.
FORMS (01 )':3I,!tJ i':'TS OF CONCitR''
c . ;:OP.;S
POI.I,U1r;:T C!: TCI,
Sulfur Co.llpolnds
o
Elcmental sulfur
o
Oxidized (sulfur dioxide, sulfur trioxidc,
sulfites, sulfates)
. Reduced* (hydrogen sulfide, carbonyl sulfide, sulfides, disulfides)
Related air measurencment:
TRS (total reduced sulfur)
Nitrogen ComnounLds
· Oxidized (nitric oxide , nitrogen dioxide , nitrates, nitrites)
o
Reduced (armonia, cyanides, canates, 'hydrazines).
Related air measure:ent:
.
NO
x (nitrogen oxides)
..-
.---
--
--
.
.
.
Organic Conpounds
.
l
.
° Hydrocarbons
- aliphatics
- olefins
-aromatics
(benzene, toluene, xylene, etc.)
polmnuclcar aromatic (benzo(a)pyrene,
o
etc.)
..
Oxygenated Hydrocarbons
-acids (acetic, benzoic, etc.)
.
a nhydrides
I
- ketones
- epoxides
- ethers
- lactoncs
(cresols, xyienols, etc.)
- alcohols. (methyl, etc.)
-phenols
- peroxides
-polynuclear
- ozonides
(oxa.arencs, ring carbonyls)
- aldchydes (formaldchyde, -etc.)
_
36
i
i
iI
I
I
i
Tablel2 (continued)
° lalolc:it ed ly)tldrocarbonis
- chloronated, pol chlcronitc!
- fluorinated
Sulfur-containirg Hldrocarbons
.-Mercaptans
- Thiophecnes
- Ileteroc)clic/monlocyclic
o
(thio)
- Polynuclear (tlio arenes)
Nitrogen-containing IlHydrocarbons
- amines
- aliphatic
I
- aromatic (analines, naphthylamines, etc.)
- Heterocyclic monocyclic (aziridines, pyridines, pyrolles)
- Nitro compounds (PkN, methylethylnitrites)
- Nitrosamines (Dimethylnitrosamine, etc.)
- Polynuclcear
- Aza arcnes (acradene, etc.)
Organomcetallics (metal-containing Hydrocarbons)
- metal carbonyls
- chelates
- sul-fonates
·I
- metal alkyls
I
Related air and water mcasurements:
*~ ~~ ,~
Total gaseous hydrocarbons (FID) - air
Naphtha - water
Tar, oil and grease - water
TOC (Total Organic Carbon) - water
BOD' (Biological Oxygen Demand) - water
COD (Chmical Oxygen Demand) - water
t
.
.
* Carbon;
o Carbon monoxide
Carbon dioxide
*O Carbonates
-
* Ozone
---.
.
I~~~~
.
J
i
Carbon Comnnounds
O
~.I*
-
I
~~~~~~~~~~~~~~~~~~~~~~~~
·
r.a·
Table 12 (continued)
(liC1, etc.)
o
Cho:i;'
o
Fluorides (oiF, ctc.)
Iydrocar
n Co:;aoJldl
ion
Hydroxides
(illorgnic acids)
)lydrogen
o
o
.
Relatcd water m:asure-ment:
pH
Arsenic Co.:ounds
Beryllium Copounds
.
·
Cadmium Compounds
Chromium Comnounds
Lead Comlounds
r
-----
-rr·-·rrr-·
-~r~r~·-·
r
·
..
r·r.
e·.
e..
-$.,.
..
_-*·
e
.
Manganese Cornounds
Mercury Co:o.ns
.
.
Nickel Cornhounds
Phosphorous Co..:ounds
Selenium Copotmunds
V'anaditum Co.rounds
Zinc
.
Zinc Co. unds
t
.
.ds
.
.
-
I
Table 12 (cbntinued)
Pol lutants from coal processes of interest due to carcinogenic
suspicions (NTIS report# FE 2213-1)
dI4sse O t(wm or 'JSvetted CdC1:w:enic Or Cocarc.nogenic
Prot.esstnq nd t;tilization Of Coal
d:wttla
J
Co.po;nds A soCat-.
(13. 14 15. 16. 17).
Copound C asS
Poinuclear Ac-jtc y.vdrocarbon
Representat
e Cor Pound
Structure
CH 3
Anthracernes-'
3;10-diethylOa:
tn racere
CM4
3
* Chrysenes
(hrysene
Bedza thracenes.
benzo(a)an thtracene
Fluranthenes
- benzo(j Iflucrantene
Cholanthrenes
O-ethylychol danthrenP
Benzcpyrenes
benzo(a )pyrene
Dibenzpyrenes
dibenzo(a,h)yrene
:itrcqen-. Sulfur-
C.,,
ane Ow.Fc--Cotaiiirl POlvCvjic C-oosrds
dibenz(o,h'acridine
mono-and diben:acridines
-benzocarbe:oles
-co
'
f-.bn:ojc)cara:ao le
I-beqz(c ,9carbazole
dibenzocerbazoles
k
. 0 .- I
t
'
711-b(..nz d,e)anthra:en-7-one
benzathrones
u)
.39
I
a
Table-12 (continued)
Classes of .l..n
or Suspected
Cdrcifoeflic r Cd,'CtrOnenic
'al:J til:ation of Coal
Coound$ AsciateJ vit: 'r:e;seq
(contl .ued)
R
Cao-.Zund Class
esentative Co:o:,d
Structure
rcr.t'aTi
A
tC ... i.r
t
Amninoaobenzenes
4-dtetll.nodzobenzene ',o¥2 0
4'nO
"H
7
taphthylmines
. %-nphthylairi~lne
,I,
noroanic Substences
As
tricalciun arsenate
Ca3AsO4 )2
Se.
selenid salt
[.12]se
Co
cobalt sulfide
CoS
.;i
-if
nickel cerbonyl
Be
Beryllium oxide
BeO
Cr
chro;ate salts
[H2]CrO4
Fb
lead chroiate
PbCrO4 ..
Zn
zinc chr* ate
ZnCrO'
ele;rnrtal cercury
Ilg
cadmiui sulfide
CdS
*lg
-
Cd
Cocarcinoens end Prooting Acents
Phenol s/nnapthols
Long chain aliphatic hydrocerbons' n-dodecane
sulfur dioxide
S02
O
.
014
2-naphthol
nitrogen oxides
·
.
.
-; 40
,
C.3-(c1
SO2
2 )8 -C3
Table 13 c;n::ca;
S
XA.LLS
.Oz:xC
F:C.cc.:
I--
*, C,
I1.
,Ind,
7 .12
CI,3r
V
*'::*
enth:c
3.
4.
+4
nec
C
2.
3
'+ 3
,abtcI ac n-
Dlbenr:[n,l
4
+3
eno cJi;'cian:hrene
3
- --
- I-Z---
C'-
.Str
it
.ur
+4
cthylc.oacth:cna
H.3
S.
6.
+3
eno:o[orpyrc:e
Dberrot*r.,jpvr.ce
1
. 1
-NII,
.+3
. 00(
*,
Dlbenzot[a,jpvr.e
8.
c,Ccar:ol
Dtbenzotc
.+3
t:nore Found
-ocation
Stack
PO*-1
W.3C-2
°3
JPI-
l
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Excerpts from NTIS report "Hazardous and Organic.M atcrial from Industrial Boilers"
41
Table 14 List of the Most Important Air Pollutants to be Included
in an Electric Power/Environmental Model
SOx
Sulfates
Sulfuric Acid Aerosol
NOx
NO
NO2
NO3
Ozone and Oxidants
Particulates, Total
Particulates, Respirable (.less than 3 microns)
CO
CO2
Hydrocarbons, Total
Inert Hydrocarbons
Reactive Hydrocarbons
Hydrocarbons of Carcinogenic Potency ++++
Hydrocarbons of Carcinogenic Potency +++
Hydrocarbons of Carcinogenic Potency ++
Hydrocarbons of Carcinogenic Potency +
Trace Elements:
Arsenic
Beryllium
Cadmium
Chromium
Cobalt
Lead
Manganese
Mercury
Nickel
Selenium
Tin
Vanadium
Zinc
Radioactivity to Air
42
most important to try to improve the predictive quality by corrective
measures that can be suggested by appropriate breadth and quality of the
evaluation processes.
The array of validation techniques available for
this evaluation and improvement process includes those listed in Table
This list was compiled from 761 references collected for (Gruhl,
15.
Gruhl, 1978).
These validation techniques involve two steps - first some
piece of the model is examined or changed - then this action is evaluated
with respect to some basis for comparison.
Of the 20 possible actions
times7 possible comparisons only about half of these 140 combinations
makes sense.
In Table 15:
Observed data are historical or other data
used to build the model; input and output are those data associated with
a particular predictive use of the completed model; and parameters are
those constants or specified functions that are intended to remain
unchanged for different model applications.
There is clearly an undisguised and urgentneed for probabilistic
measures of the quality of predictions from energy/environmental models
before they are used in important policy decisions.
Treatment of Probabilities
The probabilistic treatments in the current version of AEGIS are
somewhat of an approximation.
Before discussing how it is now
accomplished it is instructive to discuss how it should be done ideally.
Beginning with the deterministic notation:
y= f(x)
where
y = the vector of model outputs,
x = the vector of model inputs, and
f = the functional combinations of inputs that create the outputs.
43
Table i5 Outline of Various Schemes for Evaluating and Enhancing
Predictive Quality
EXAMINATIONS OR CHANGES
ACTIONS:
OBSERVED DATA:
1.1
Examinations of the observed, historical, or estimation data
OBSERVATIONS-TO-STRUCTURAL:
Observed data perturbation effects on structure and parameters
Propagation of estimation error on structure and parameters
Measures of fit of structure and parameters to observed data
Effect of correlated or irrelevant observed or estimation
data on structure and parameters
2.5 Sensitivity analysis: quality of fit of structure and
parameters to observed data for altered structure and
parameters (includes ridge regression)
2.1
2.2
2.3
2.4
OBSERVATION-TO-OUTPUT:
3.1
Effects of correlated or irrelevant observed data on outputs
INPUT:
4.1
Base case or recommended input data examinations
INPUT-TO-OUTPUT:
5.1 Examine outputs with respect to base case input data
5.2 Simplify, e.g. linearize, this relationship to provide
understanding
5.3 Simplify (e.g. linearize) structural form analytically, or
group parameters to provide better understanding,
elimination of small effects, grouping of equations,
grouping of parallel tasks
5.4 Develop confidence measures on outputs by propagating input
error distributions through structural error distributions
STRUCTURE:
6.1 Structural form and parameter examinations
6.2 Respecification, that is,make more-sophisticated some
of the structural components
6.3 Decompose structure physically or graphically
6.4 Provide new model components to check effects of
assumed data or relationships
44
Table 15
(continued)
_
__
·
STRUCTURAL-TO-OUTPUT:
7.1
Examination of outputs for various structural and parametric
perturbations and error distributions
OUTPUT:
8.1
Examination of outputs
OUTPUT-TO-INPUT:
Examination of optimal inputs (controls) to reach target
outputs
9.2 Contribution analysis, percentage changes in outputs due to
changes in inputs
9.1
BASES FOR COMPARISON
A. Comparison with other empirical models
B. Comparison with theoretical or analytical models
C. Comparison with hand calculations or reprogrammed versions
of model components
D. Data splitting on observed data, by time or region
E. Obtain new estimation/prediction data with time, new
experiments, or in.simulated environments
F. Examination of reasonableness and accuracy, that is,
comparison with understanding
G. Examination of appropriateness and detail
45
Now instead of constant deterministic values, suppose the inputs are
specified as functions representing the probabilistic distribution of the
values of the inputs, say xp.
Likewise the outputs would then be
functions yp generated by convolutions and other combinations, F, of
the inputs.
Thus, the ultimately precise probabilistic formulation would
be
p = F(xp)
The problem with this ideal method is that the functions cannot be
precisely stored in a computer, thus a discretized representation of the
input and output are the best that can be used
n, Zn
In the
current version of the simulator there are five discrete points that
represent the probablistic distribution, the points at which the
probability of being less than that value is 0%, 16%, 50%, 84%, and
100%. Now the problem with these discrete values is that neither
f nor
There are two possible
F is the appropriate transforming function.
approaches to the development of the appropriate discrete transform.
The
first requires the fitting of functional relationships, from a set of
generalized Probability functions, to the discrete points.
regenerates
p from Xn and is termed Xn.
This somewhat
Now, assuming Xn is
x
very close to Zp
4 %p F(Xn).
This yp can be discretized to develop yn.
For a given set of
generalized probability functions it should be possible to develop a
general formula, G, for obtaining the -n:
=n
G(Xn).
The details of this have not been worked out.
46
The second approach is the more approximate approach and involves
worst case analysis.
Suppose m is such that m is 2, that is the
minimum and maximum values of p. It is computationally quite easy,
for any f, to determine the m, that is, the minimum and maximum values
of 4p, by simple tests using f over the range between the minimum and
maximum, am. Call this transform :
4
= j(xm)-
What would happen if we were to operate
Yn
that is
on n, n = 5; in fact does
(xn),
]1,5
g(x1,5),
-2,4 n(2
-3 :"
),
9(x3).
It can be thoughtout that
middle point
3
l1and 5 will be perfectly accurate.
is not precisely the same as
The
(X3), but it does
happen that
(.3), while not the median, is the deterministic case,
which has some value. The deviation points, -2,4, are also not
precise, but they are very close.
The advantages of this second approach
are the ease of its implementation and the speed of its computation.
Further analytic work is in progress on this point, along with some
examples, to see if this probabilistic treatment should be modified in
the future.
Illustrative Example
For the sake of example it is easiest to choose an environmental
quantity that is estimated quite often. This example is thus concerned
with the annual public health mortalities from the routine operation of a
large, coal-fired facility. Table 16 shows the results of a collection
47
Table 16 Collection of a Number of Estimates of Public Health Impacts
of Coal and Nuclear Power Plants
Ooal Po-er Plant
IoooufeA)
Diseases
0
uown
-0
-O
.7-2.8
.1
0.
0
0
0
70,000
| 4710
.
-r-
0
.001-.008
.14-8.5
9,OOQ .
.
*
-
.13-.57
k'
tnown
-
0
.
........
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.0
0
uO-uOwn
0
471nt11
.----
'..
...----
1
.:0
.
O
-
.
conosic
.18.
-- 0
.0Onown '
.
_
_l
Ases
Deaths.
canceor
.
.-
-O
.9-3
chz'on res
Deaths
non-cancer
Total
.
0O
0
._ow -O
t.o
Expoted
Other
f£fcts
.
- -'
.
mutations
Nuclear 2w
-61
ransport Caste
.92-7.3
-0·
. 17-150
Genetic
heart/lun
T
iccidents Dispoal
.
Deaths
non-cancer
Deaths
cancer
Jatastroph
outine
ZShFissions Accidents
0
.
0
.003-.0154 -. 71-1.9
0
*001
-. 44
Genetic
cono
numbbr
ic
L'.°
.
displayed as neans, or men
-
' ,,
an.d maium; lon-tm
48
-'
485,000
-
effects ntgrate.
of several published estimates.
There are some unknowns in this table
but on the whole the estimates do not show a tremendous amount of
variability, especially considering the lack of specifications of fuel
sources, combustion types, site types, population density patterns, and
so on.
If one wanted to incorporate public health mortalities in a
national energy model are these the numbers that should be used?
As was just mentioned these numbers are to be tested against
site-specific and technology-specific cases in which the uncertainties in
all of the data are carried forward to the uncertainty in the output.
First a Base Case is generated:
=
size = 1900 MWe, year = 1998, coal
Southern West Virginia Bituminous, plant = MHD, capacity factor = 70%,
stack = 235 meters, meteorology = average U.S. site, demographics = 0.8
times Indian Point in 1980 (estimated), and health impact model = LAMM a deterministic linear additive mortality model based upon consensus and
recommended standards.
Even assuming that the deterministic health model
is perfect, there is still enough uncertainty in the description of
characteristics and components of the coal seam, performance of the MHD
facility, and so on, to cause a two order of magnitude variation in
predicted mortalities, see Table 17.
As can be seen, the principal
causes for these uncertainties are due to the variations in the nickel
and beryllium emissions from the facility.
These ranges can be traced
back, in part, to uncertainties in the levels of these constituents in
the coal seam, but the primary uncertainties exist in the state of the
knowledge about MHD removal of nickel (1 to 99%) and beryllium (1 to 75%).
This clearly defines the data limitations to the predictive quality
of this power plant/mortality information.
Give this Base Case and its
inherent uncertainties the next question becomes:
49
What are the
Table 17 Annual Public Health Mortalities From the
Facility Simulated
900MWe MHD
I
BASE CASE
minimum
0.10
1 dev low
0.97
median
2.94
1 dev high
6.26
maximum
9.70
Percent of Base Case Range Attributible to Various Pollutants
Nickel
Beryllium
Particulates
NOx
SOx
Arsenic
Uranium/Radium
64.0%
22.1%
5,1%
3.2%
2.9%
1.1%
0.6% -
I
I
50
magnitudes of the additional uncertainties introduced by structural
inaccuracies, specifically aggregations and lack of specificity?
Table
18 gives an idea of how the magnitude of the uncertainty changes with
lack of specificity in several individual components.
As can be seen
from the bottom of Table 18 there is almost seven orders of magnitude
variation in the various estimates of mortality data. Two orders of
magnitude are, again, due to data inaccuracies; another two orders are
due to variations between health models; and almost three orders of
magnitude are due to aggregation of different generic site types.
51
Table 18
Effects That Certain Lacks of Specificity Have on the Range
of Uncertainty About Annual Public Health Impacts
Changes From Base Case
Assumptions
Annual Mortalities
Maximum
Minimum
No Change from Base Case
0.10
9.70
COALS
Whole Range of U.S.
Bituminous Coals
0.08
48.08
COMBUSTORS
Range for Various Fluid Bed,
MHD, and Advanced Conventional
0.10
32.60
METEOROLOGIC SITES
Range Over All U.S. Site Types
0.08
11.87
DEMOGRAPHIC SITES
Range Over All U.S. Site Types
0.02
22.31
HEALTH IMPACT MODELS
Range Over Several Models
0.0005
9.70
6.4X10 -5
454.
ALL COMBINATIONS
Range Over All Combinations from
Best Case to Worst Case
52
3. INPUT/OUTPUT PROCEDURES
The input and output procedures for this simulator are quite
straightforward.
For the sake of example, the procedures for the use of
the interactive form of the simulator are presented.
A flowchart of the
procedure is presented in Figure 3, showing the potential paths through
the various subroutines in the simulator.
Table 19 shows the interactive nature of the program's input
routine.
Table 20 displays the listing of the input assumptions and acts
both as a means for the user to verify the assumptions and for the formal
record of the simulation run.
The output from the open cycle magnetohydrodynamic power plant
simulation is shown in Table 21, displaying the range of uncertainty
associated with each of the 109 performance measures.
Minus numbers,
such as -1., or letters, such as NA, are indications that these are
performance values that are not predicted by the particular modules
chosen by the user. Additional examples of the use of this simulator are
presented in Appendix A.
Assimilating the important information from these long lists of
performance measures could be a formidable task.
It could be even more
difficult to make a comparison of several alternative sets of performance
measures.
Some though has been given as to how such comparisons and
evaluations could be made.
Although it has not been computerized, Figure
4 represents a procedure that could be operated manually or possibly even
examined for ideas about comparative techniques.
53
NA = #
Examples
NB = Prob. Display
NC = # Displays
ND = Max, in or No Prompti
Figure 3 Flowchart of Procedures for the Use of the
Interactive Version of the Simulator
54
Table 19
Input session for use of AEGIS program - terminal responses
are in CAPITALS, user responses are in lower case; this is an
example of the no-prompt option.
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(1
4. CURRENT STATUS OF MODULES AND RECOMMENDATIONS
It was the original intent for the structure of this simulator to
carry with it the documentation for every number and every function in an
on-line retrievable file.
Table 22 shows the retrieval index; and Tables
23 and 24 showsamples of the way in which this documentation was
initially intended to be set up.
idea.
There were two major problems with this
First, the storage requirements for this material grew to thepoint
where it was resulting in anunjustifiable expense.
Second, all of the
users initially interested in this simulator were only interested in the
batch model version.
carried on-line.
For these reasons the documentation is now
Due to the immature stage of this model, this
documentation and the current listing of the code are made available
separate from this report -- in that way hopefully preserving the useful
life of this report past the upcoming revisions in the data base.
It is appropriate here to discuss some of the general
characteristics of the various modules.
As shown in Figure 5, the first
module encountered is the fuels module.
For each different fuel type
there is information about its cost, heat content, mineral and moisture
contents, elemental constituencies, and occupational hazards per million
BTU.
The costs of the fuels are collected on a per-quantity basis as
delivered to the center of the New England area and prepared for power
plant use.
To make these costs generally applicable would require
changing the costs to mine-mouth costs, querying the user about the
specific geographic locaton of the facility, and creating a lookup table
of transportation modes and costs between the supply and demand regions.
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in many national energy system models, for example the ICF Coal and
61
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72
Electric Utilities Model. All costs are in terms of 1978 dollars, and in
the current version there are no real escalation rates for facilities
planned for some time far in the future.
Precleaning is an option.
If a type of coal or oil precleaning is
selected, the fuel data are massaged to account for losses, costs,
removal efficiencies, additional occupational hazards, and so on.
Precleaning adds only to the cost of the fuel, not to the investment cost
of the generating facility.
In the case of dedicated precleaning
facilities it may be important to recode this portion to carry forward
the investment costs.
The generation options are modeled with their principal
sophistication in the emissions portions.
Economics, availabilities,
resource consumptions, and other factors are carried along, but without
some of the flexibility one might like to have. Table 25, for example,
shows some additional sophistication that might be important to add to
the economic capabilities of the simulator.
In addition, storage
capabilities are not included in the simulator, except some inflexible
proportions that are tied to some of the alternative sources, such as
solar and wind generators.
The intent of the simulator was to be a
collection of the front runners, as far as designs were concerned, in
each of the major generator types.
For fluid bed combustors, where there
are several "front runners" it was necessary to create several different
choices. This, of course, would be possible for some of the other
combustion types, or it would be possible to pull out some key design
parameters as options.
The latter course would require only a little
more effort to implement in the current version of the simulator, but
73
Table 25
Some Possible Future Directions for Improving the
Fiscal Modeling in the Simulator
--
--
-variable year dollars
1. 6 per year
2. Handy-'fiitman electric light & power extrap.
3. Engineerinz 1.ews Record index xtrap
-various de-recation schemes
1. strai1ht line
2. sum of year's dizits
3. combination switchin3 at given ear
-insurance costs
1. property
2. liability
-debt/equitty features
I. debtoL/equity ratio
2. annual interest rate on debt
3. earning rate on euity
4. bond repayment otions
-proportional
-uniform principl reduction
-uniform annual pzayment
-uniform rln red starting at given year
-variable interest durin
onstruc, on
-taxes
1. federal inone ta: rte
2. state income tax rate
3. state ross revenue tax rate
4. proper'ty tax on plant
5. other
-various
resent worth com outations
__
_I_
__ICI·___
_
74
__ ___
I
P.,
would require extensive information about the system's overall
performance as a function of the variable parameter.
Additional sophistications in the generation module could include
explicit handling of costs during construction, different materials use
problems and variations in costs as a function of greater or lesser
amounts of ash, etc., or a more explicit and accurate handling of water
and solid pollutants.
The abatement module, in the current version, is not very
sophisticated. The procedure is much the same as the precleaning module
except that emissions constituencies are treated rather than fuel
constituencies.
One of the major improvements that is needed in this
module is a modeling and treatment of particular size distributions.
There are very different expectations and costs of precipitators based on
the size distributions of the particulates.
There was an initial effort to make the atmospheric dispersion and
the population density information responsive to specific situations.
Figure 6 shows the general capabilities included in the dispersion model,
and Figure 7 is a flowchart of that POLCON model.
It quickly became
apparent that the amount of input information would create a tremendous
burden for the user. The principal reason for this burden is that there
is apparently no existing technique for taking time-collapsed statistics
about emissions, wind speed, wind direction, mixing depth, and stability,
and creating time-collapsed statistics about concentrations.
A
Department of Energy contract is currently examining precisely this
issue, partly as a result of the problem identified in this project.
Whether (1) this results in a viable technique, or whether (2) there is a
tie-in capability to draw out meteorologic and demographic information
75
ii:ure 6
Point Source Dispersion to Ambient Levels
Modified Sector-Averaged Gaussian Dispersion Model
source superposition
turbulence measures
downwash modeling
variable winds modeling
terrain corrections
plume slipstreaming
mixing layer slipstreaming
surface roughness
variable plume rise
rain
washouts
backgroun
modeling
76
TIME,
SOURCE,
RECEPTOR
LOOPS
GAUSS. OR SECTOR,
MAX. OR PROFILE
Figure 7 Flowchart for the Creation of Ambient Concentrations from
Meteorologic and Emissions Information
77
from EPA and Brookhaven data bases and then using a chronological
simulation, in either case the amount of information actually passed from
these dispersion and demographic exercises to the simulator is quite
small.
For this reason, the current version of the model just carries
generic dispersion and demographic information, set up and scalable to
cover the full range of situations that might be of interest. One
potential future direction for some of this work would involve the
investigation of the differences in simulator results that would occur
due to the use of different dispersion models, such as:
1) sector-averaged POLCON,
2) Gaussian POLCON,
3) Gaussian PTMAX/PTDIS/PTMTP (EPA),
4) Gaussian CDM profiles (EPA),
5) Gaussian CRSTER yr. max. (EPA),
6) probablistic Hoult-Weil, or
7) long range dispersion modeling.
The final module of the simulator contains a wide range of different
air pollution/health impact models.
These models are different than
anything else modeled in the simulator in that they are all
deterministic. The reason for this is that they have been reported in
the literature only as deterministic models. This is a serious problem
in that it carries the presumption of exactness.
An attempt has been
made to somewhat correct for this difficulty by displaying a large number
of these models, so their spread can somehow be indicative of their
validity.
The ideal solution to this problem would be to go back into
the data used to develop these models and create the probabilistic models
that should have been reported in the first place.
78
Some research along
this line has been conducted by John Viren (Viren, 1978), and additional
work along this line is planned as part of an MIT-Harvard Medical School
contract with D.O.E. (contact J. Gruhl for further details).
79
5. REFERENCES
Goldberg, A.J., 1973. "Survey of Emissions and Controls for Hazardous
and Other Pollutants," EPA, NTIS #PB-223 568, Springfield, VA.
Gruhl, J., 1978.
"Predictive Qualities of Environmental Information in
Energy Models," Proceedings Integration of Environmental
Considerations into Energy-Economic Models, EPRI Special Report,
42pp, October.
Gruhl, J. and N.H. Gruhl, 1978. "Methods and Examples of Model
Validation - An Annotated Bibliography," MIT Energy Lab Working
Paper MIT-EL 78-022WP, July.
Gruhl, J., F.C. Schweppe, M.F. Ruane and S. Finger, 1976. "Systematic
Methodology for the Comparison of Environmental Control Technologies
for Coal-Fired Electric Generation," MIT Energy Lab Report MIT-EL
76-012 for DOE Contract W31-109-Eng-38, pp. 216, November.
Gruhl, J. and A.E. Sotak, 1978. "Comparing Advanced Energy Cycles and
Developing Priorities for Future R&D," MIT Energy Lab Report MIT-EL
78-030, 54 pp., A ugust.
Gruhl, J. et al., 1978. "Evaluation of Health Effects of Air Pollution
in the Chestnut Ridge Area," U.S. Department of Energy, Human Health
Studies Program, Report C00-4968-1, December.
NASA, 1976. "Comparative Evaluation of Phase One of the Energy
Conversion Alternatives Study," NASA-Lewis Labs Report TM X 71855,
February.
Starr, C., M.A. Greenfield and D.H. Hausknecht, 1972. "A Comparison of
Public Health Risks: Nuclear vs Oil-Fired Power Plants," in
ERDA-69, NTIS, Springfield, VA.
Viren, J.R., 1978. "Cross-Sectional Estimates of Mortality due to Fossil
Fuel Pollutants: A Case for Spurious Association," TR-17900,
Division of Policy Analysis, U.S. Department of Energy, Washington,
D.C., August.
80
APPENDIX A
Available upon special request from the author are three additional examples
of the use of the AEGIS simulator.
They have been excluded from this final
version of the report partly because of volume and partly because it has been
decided that they should not stand alone without listings of the associated data
and structural assumptions.
Readers who still have interest in obtaining additional
information about this model are encouraged to request from the author the latest
listing of code and documentation. All of the material developed during the
course of this contract is in the public domain.
81