SERI PHOTOVOLTAIC VENTURE ANALYSIS: LONG TERM DEMAND
ESTIMATION
Richard D. Tabors, Susan Finger with Allen Burns,
Paul Carpenter, Thomas Dinwoodie, Jesse Tatum and
Gerald Taylor
Energy Laboratory Report - MIT-EL-78-032
July 1978
ABSTRACT
This report presents the results of a sectoral demand analysis for photovoltaic power systems used in the residential sector single family homes],
the service, commercial, and institutional sector [schools] and in the central
power sector.
The results described are the output of a set of three normative
modeling activities carried out by the MIT Energy Laboratory,
They are based
on the assumption that the actors, i.e,, the utilities, schools, and homeowners,
will switch to photovoltaic power systems when they are cost-effective relative
to the competition, that is, centralized power generation using conventional
fuels.
In each case the assumption is made that the market for photovoltaic
power systems will be a new market, not a retrofit market.
As a result the
annual (total for utilities) sales potential at a given price is estimated
for each. sector assuming a specific level of new installations in that sector,
i.e., new single-family homes, new schools, and additions to utility stocks,
As such.,the results presented are maxima for a given application.
While
the methodology presented does not allow for any early acceptors, it does
assume that once economic all new homeowners, school-builders, and utilities
will
buy to a fixed level.
SERI PHOTOVOLTAIC VENTURE ANALYSIS
Long Term Demand Estimation
I.
Introduction
This report presents the results of a sectoral demand analysis for photovoltaic power systems used in the residential sector [single family homes],
the service, commercial, and institutional sector [schools] and in the central
power sector. The results described are the output of a set of three normative
modeling activities carried out by the MIT Energy Laboratory. They are based
on the assumption that the actors, i.e. the utilities, schools, and homeowners,
will switch to photovoltaic power systems when they are cost-effective relative
to the competition, that is, centralized power generation using conventional
fuels. In each case the assumption is made that the market for photovoltaic
As a result the
power systems will be a new market, not a retrofit market.
is estimated
at
a
given
price
annual (total for utilities) sales potential
for each sector assuming a specific level of new installations in that sector,
i.e. new single-family homes, new schools, and additions to utility stocks.
As such, the results presented are maxima for a given application. While
the methodology presented does not allow for any early acceptors, it does
assume that once economic all new homeowners, school-builders, and utilities
will
buy to a fixed level.
Each of the three sectors
analyzed
will be discussed
in greater
detail
below. The methodology used for both the school and the residential analysis
is a modification of that developed by Carpenter and Taylor in An Economic
Analysis of Grid-Connected Residential Solar Photovoltaic Power Systems.
This methodology measures the "worth" of a photovoltaic power system to the
owner of the system. The worth is measured against the alternative available
energy system, the purchase of electric power from a utility grid. Analysis
of the value of photovoltaic power systems to a utility was carried out using
a more detailed utility systems operating model, SYSGEN. The SYSGEN methodology
allows the MIT Energy Laboratory to parallel the developmental work carried
out by General Electric Electric Utility Systems Engineering Department for
EPRI, in 1977. In the utility analysis we have looked at the value of a range
of penetrations of photovoltaic power systems - 2 to 12% - into specific regional
synthetic utilities as developed by EPRI.
This analysis utilized the solar planning regions developed by Tabors
and Carpenter.l
Figure
1 shows
the region
so defined.
It should
be noted
that throughout the analysis which follows there is no consideration of the
Northwest region. This region appears to have minimal potential for photovoltaic power systems as alternative power sources are relatively inexpensive,
solar insolation is poor and the region as a whole is predicted to have only
marginal growth in population.
The city of Omaha, Nebraska has been used as a surrogate for the North-
1
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central region. This location was chosen because of utility cooperation, data
availability, and similar weather conditions to much of the Northcentral region.
As such it offers a fair surrogate for the analysis undertaken in this region.
On the whole it is somewhat warmer and sunnier than such population areas such
as Chicago and Detroit. The utility system load is similar to an industrial
center with summer air conditioning and therefore is a good match with much
of the load shape for the Northcentral area.
II.
Single-Family Residences
It has been argued that single-family residences would offer an early
potential market for photovoltaic power systems. The major advantages of this
market to photovoltaics are 1) favorable financial borrowing power on the
part of the homeowner; 2) if constructed as a portion of a new building, photovoltaic systems do not require specific support structures and are able to
take advantage of a credit for roof material not utilized. 3) The load is
co-terminus with the generation source and as a result there are no transmission
and distribution losses.
The analyses which follow have utilized the methodology developed by
Carpenter and Taylor to evaluate the worth of photovoltaic power systems to
a residential user. The Carpenter and Taylor work, and that by Tatum which
preceeded it, utilizes a simulation model of a residence with fixed appliance
load and operating characteristics, as shown on Table 1. The photovoltaic
power system provides electricity to the load when there is sufficient insolation. When insufficient insolation exists, the load is supplied from the
grid in whole or in part. Because electric power is more expensive to generate
on peak than it is on base, the analysis utilizes a modified marginal cost
system or time-of-day pricing with which to value the electricity not purchased
from the utility by virtue of the existance of a photovoltaic power system
on an individual's roof. In addition, the homeowner is able to provide power
back to the utility
when
he has excess
capacity
relative
to his own demands.
Within the analysis, the value of power bought back by the utility is set
at three levels for parametric analysis. The first level is zero percent
buyback, i.e. no credit for excess generation. The second level is at fifty
percent buyback, i.e. that the utility is willing to buy from the homeowner
at half the time-specific price that the utility charges that homeowner for
his electric power; and third, one hundred percent buyback, i.e. that the
utility is willing to pay the homeowner exactly what they charge the homeowner
(the California model). We believe that the middle of these three options
is the most reasonable upper bound for the economic behavior of a utility.
Furthermore, an analysis is required on a utility-by-utility basis to justify
the precise value of excess electric power to the utility itself. In the
absence of that complete analysis, a 50% buyback rate represents a fair approximation of the split between fuel and operating costs. The resulting
values for the worth of photovoltaic power systems to an owner range between
$.80 and $.20 per watt(peak) module at a fifty percent buyback rate. These
values represent a conservative estimate of the value of photovoltaic systems
to residential owners. Table 2 summarizes the system configuration and efficiency
assumptions
used in this analysis.
Table 3 summarizes by region the worth of photovoltaic systems in the residential
market. The system net present value given in Table 3 is found by comparing
the homeowner's electric bill with and without the photovoltaic system.
The net present value of the difference in these electric bills is the amount
that the homeowner would be willing to pay for the entire system. The second
3
TABLE
1
APPLIANCE USE AND BEHAVIORAL ASSUMPTIONS
Load
Appliance
Rating
Yearly
Consumption
Comments
1
Refrigerator
615W
1829kWh
Unit is on continuously
but draws load (i.e. is
running) only one-third
of the time to maintain
proper temperature
(runs only 20 min. each
hour).
2 & 3
Dryer
4850W
1008kWh
Eight half-hour loads.
May be run at any time
during day/night but
four loads must be run
in each half of each
week.
4 & 5
Washer
500W
103kWh
Same as dryer.
6
Water
2500W
4270kWh
Unit is on from 6 a.m.
to midnight but runs
only one-fourth of each
hour to maintain
temperature.
1205kWh
Meal loads represent
different combinations
of oven, broiler, and
range-top burners that
might be used for each
meal. Each load is 30
min. in duration.
Breakfast, lunch, and
dinner start times are
6-7:30 a.m., 11:3012:30 p.m., and 6-8
p.m., respectively.
heater
Range loads
7.
Dinner
8.
Lunch
9.
Breakfast
9100W
2400W
2700W
4
TABLE 1
continued
APPLIANCE USE AND BEHAVIORAL ASSUMPTIONS (continued)
Load #
Appliance
Rating
10
TV
200W
Yearly
Consumption
44OkWh
11
Dishwasher
1250W
363kWh
Runs consist of two
30-min. or one 1-hour
cycle each day. May
be run after either
breakfast or dinner.
12
Lighting
2400W
1314kWh
Lighting for a 6-7
room home. Roughly
one-fourth of the
lights are on at any
time during evening
lighting hours.
13
Central Air
Conditioning
5000W
Variable
A/C mode is triggered
by two days with
temperatures greater
than 25.5 0 C. Once
in A/C mode unit is
turned on when temperature reaches 2 1.90 C
and runs continuously
until house is cooled
to 20.90 C.
5
Comments
Unit runs for 6 hours
per day beginning in
the late afternoon or
early evening.
TABLE
2
Residential Simulation (SOLOPS) Assumptions
Array Size
33M 2
Array Tilt Angle
Latitude
Encapsulated Cell Efficiency
.12
Wiring and Mismatch Efficiency
.95
Inverter Efficiency
.88
Packing Factor
.80
Storage
NONE
Utility Rate Structure
Time of Day
Cell Degradation Rate
5% years
.7% years
Less
100
1 and
System Lifetime
20 years
Discount Rate
3% (Real)
Fuel Escalation Rate
3% (Real)
Balance of System Cost
$500 Fixed
$11/m2 Variable
6
2
3 to 20
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column in Table 3 gives the net present value of the difference in the electric
bills per peak module watt. This gives the value of the entire system per
peak module watt. (To evaluate the worth for an individual homeowner, this
number should be converted into delivered watts since the homeowner would
be concerned with the delivered power rather than the power available from
the module.) The third column in Table 3 has the rest of system costs (fixed
and variable) subtracted out to give the net present value of the array per
module watt.
The analysis of potential market size for photovoltaic power systems
in residences is based upon a normative model of economic behavior. In
this model it is assumed that when a photovoltaic power system is cost effective in lifecycle cost economic terms, it will be purchased. As such
it represents
an upper bound on demand.
To calculate
the total
demand
re-
quires both an estimation of the value of the photovoltaic power system
to its owner at a specific location or within a specific region, and the
number of annual housing starts in a given region. Once again the analysis
takes into account only new housing starts as potential markets for photovoltaics. It does not utilize retrofits because it is felt that these will
be economic later in each region.
The analysis of new housing starts is based on estimates made by CONAES
as reported in "Macroeconomic Scenarios" prepared by Donavan, Hamester,
and Rattien, Inc. (DHR) for SERI's Photovoltaic Venture Analysis. DHR reports
a total housing stock for the United States in the year 1990 of 93 million
units. This is compared with a housing stock of 70.4 million housing units
in 1975. Using OBERS projection of population to 1990 by region in the United
States, we were able to allocate the total housing stock across the U.S.
Single family homes were then allocated within regions as a proportion of
the existing housing stock in single families. Table 4 shows the resulting
number of single family homes by region in the United States. The annual
additions to stock were taken as the geometric rate of increase in housing
stock, 1970 [the last date for which we had region housing stock] to 1990.
Table 4 contains the resultant annual increments to stock in the year 1990.
Assuming a normative behavioral pattern on the part of residential
consumers, the early penetration of photovoltaics into a southwest market
would account for an annual market for photovoltaics of 360 megawatts at
module prices of $.81/Wp or less given the system configuration in this
analysis. The second regions to be cost-effective for photovoltaics will
be the northeast and the south. Again assuming conservative parameter values
and a fifty percent buyback rate, these become economic at 44 cents per peak
watt. The total annual demand at 44 cents a peak watt is then calculated
to be the summmation of the Southwest plus the South plus the Northeast regions,
or roughly 1650 megawatts. The final point on the curve shows a value of
photvoltaics of 28 cents per peak watt and the addition of the Northcentral
region expanding the potential market to 2250 megawatts. These data are
summarized
in Figure
2.
In summary, then, it can be seen that using the assumptions contained
in this analysis, the value of photovoltaic power systems in the hot dry
climate of the southwest is roughly 81 cents per peak watt assuming a 50
percent buyback rate. The market potential for the Southwest at this price
in annual sales to new dwelling units would amount to approximately 114,000
housing units out of a total of 706,000 single family housing units estimated
to be added to the national housing stock per annum in the year 1990. The
8
TABLE 4
Regional Distribution of New Housing Starts 1990
(Values x 103 )
19701
1990
Single
Family
Housing
Total
Housing
19902
Single
Family
Housing
Annual 3
Additions
to Single
Family
Housing
(Housing Units)
11,470
21,101
15,485
234
2,633
4,122
2,948
17
11,900
23,063
15,190
187
IV. Northeast
9,615
26,715
12,567
169
V.
5,992
12,838
7,972
114
3,010
5,162
3,837
47
44,620
93,000
58,000
766
I.
South
II. Northwest
III. Northcentral
Southwest
VI. Texas
Total
1
Source:
2Source:
3
Bureau of Census, 1970
CONEAS as reported in Donavan, Hamester, and Rattier for SERI Venture
Analysis. Values proportional to OBERS Series E Population Projections
(State) for the U.S.
Assumed uniform geometric growth 1970 to 1990.
9
Values are 1990 increment,
Figure 2
.80
.70
.60
.50
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0
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69
.30
.20
.10
I
500
I
I
1000
1500
I
2000
Potential Megawatts of Annual Sale
71
I
2300
demand curve produced is downward sloping to the right, indicating an increase
in demand as a function of price decline. The steepness of the slope indicates
the likely sensitivity of the analysis contained herein to the regional setting
and the number of potential homes in each region. Because the Southwest
represents a major growth area in the United States, as does the South, these
areas require considerable further analysis if more accurate demand projections
are to be made.
III.
Service/Commercial/Institutional Sector, School Buildings in the United
States
The analysis of a representative building type (a school) from the
service/commercial/institutional sector in the United States was undertaken
using an abbreviated version of the SOLOPS model for residences described
above. Schools offer a potential early market for photovoltaics given their
relatively light daylight loads and their generally flat roofed structure.
Schools have been chosen as surrogates for the highly heterogeneous building
stock in the service/commercial/institutional sector. The simplifying assumptions
made were the following: 1) Four weekly runs were used to simulate longer
periods of time during the year. 2) These four weekly runs were then expanded
into a yearly estimate of energy savings. 3) Loads modeled were only lighting,
light appliances, and circulating fans. No account was made for potential
energy requirements for compressors for air conditioning or any resistance
heating. 4) The load curve for schools was adapted from that prepared by
Educational Services, Inc. 5) The area of the school was 100,000 square
feet. 6) The area of the array was 5,400 square meters. 7) The angle
of the array was latitude minus 10 degrees. 8) The assumed utility buyback
rates were set at zro and fifty percent. 9) Rest of system costs were set
at 82,000 and $18/m variable. All other assumptions are the same as those that
apply to residences as described above.
While the simplifying assumptions made above limit the precision of the
simulation model as it is run for school buildings, the baseline results are
expected to be ordinally correct. Table 5 summarizes the results of the school
analysis for the five case areas, Phoenix, Boston, Omaha, Fort Worth, and Miami,
for both a zero percent buyback rate and a fifty percent buyback rate. As can
be seen from Table 5, Phoenix has the highest value; and Omaha and Miami the
lowest value for photovoltaic power systems in solar applications.
The estimation of the number of school buildings constructed annually
in the United States was done in a manner similar to that discussed above for
residences.
Estimates of total school enrollment in 1990 were available from
the Census Bureau.
These numbers were then allocated to the six regions of
the United States as a function of OBERS projections of total U.S. population
by state to 1990. Given the number of anticipated students in 1990 relative
to 1970, it is possible to calculate an average annual increase in school-age
population. The final step in the estimation procedure was to assume that all
students would be housed in schools of 100,000 square feet at a density no greater
than 87 square feet per student, the average for school building in the U.S.
in 1975. As can be seen from Table 6, there are a relatively small number of
schools expected to be built annually in the six regions. If we assume, however,
that these schools will switch to photovoltaics when it is cost-effective within
that region,
it is possible
to develop
11
an annual
demand
curve
as shown
in Figure
3
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TABLE
6
Regional Distribution of New Schools, 1990
I.
South
II.
Northwest
19741
School
19902
Estimated
Building
Stock
School
Building Stock
Annual3
Additions
to Stock
1990
20,936
24,102
125
8,146
9,430
49
III. Northcentral
22,941
27,855
138
IV.
Northeast
20,214
25,338
121
V.
Southwest
10,522
12,164
63
VI.
Texas
5,287
5,994
32
Total
88,046
108,645
528
Source:
United States Department of Health, Education and Welfare; National
Center for Education Statistics. Digest of Education Statistics, 1976 Edition
2
Source:
Department of Commerce, Bureau of Census; Demographic Projections of the
United States, CPR, P-25, No. 476, Feb. 1972. Distributed according to
OBERS Series E Population Projections.
3
Assumes a uniform geometric growth 1974 to 1990.
13
Values are 1990 increment.
Figure 3
Annual Potential for Photovoltaics on
New School Buildings
A ssum ptions: *
I) Lighting (and Circulating
Load
.ondition ing
ige
Meters
50
Rate
0)
40
· Northeast
~0
:E
0
-
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0
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3c
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30
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20
_
10
_
Nort h
Central
·
25
·
75
50
I·
100
I·
125
Megawatts
*Calculations
therefore
cover specific weekly periods through seasons and
are not full simulation runs.
14
for schools in the United States. The Southwest region shows the largest potential benefits to photovoltaic-powered schools at a value of $.56 per peak watt.
The Northcentral region, represented by Omaha, has the lowest potential for photovoltaic systems. In summary, while schools do not offer a large potential market
for photovoltaics, they do offer, as do residences, a bridge between the dispersed
non-grid interconnected applications and the centralized central power applications.
IV.
Central
Power
The U.S. central utility market represents the major potential long-term
market for photovoltaics as it does for all other non-conventional electric
power generation systems. The level of infrastructure currently invested in
transmission and distribution systems make it unlikely that the utilities will
lessen their role in-provision of energy to U.S. consumers; the discussion
which follows is focused on long-term utility use of photovoltaic power systems.
Throughout this analysis it is assumed that the photovoltaic systems are located
on utility-owned
property
and as a result are charged
a land-cost
as well as
other area and land related costs not required by schools or residences. There
is no specific assumption concerning the size of individual generating units
or their location, only that they are utility-owned and that they gain no credit
by being
located
on a building.
The analysis which follows calculates the value of photovoltaic power systems
to a given utility as a function of the operating system of the utility and the
level of photovoltaic penetration into that utility. The hypothesis in this
work is that with decreasing cost to the utility, photovoltaic power systems
will become more attractive in larger quantities within a given system. As a
result, a "utility demand curve" will be downward sloping and to the right.
The analysis requires the use of a detailed utility-operations simulation model,
SYSGEN, a set of assumptions concerning the operating characteristics of the
photovoltaic power systems, and a matched data set of hourly loads and hourly
solar insolation. There are a set of critical uncertainties associated with
this analysis. The most significant of these is the total dollar requirements
for the systems costs.
The second critical uncertainty in this analysis is the rate of real escalation of capital costs of conventional generation equipment. Capacity costs
are required in calculation of the worth of photovoltaic systems to the regional
utilities. As little information is available on future capital costs, we have
assumed that the capital costs available for new installations in 1976 fairly
represent those anticipated over the nest decade and a half, i.e, that there
will be no real escalation costs of capital. Again, these results and underlying assumptions are reported for ease in parametric analysis.
The methodology employed in estimation of the value of photovoltaic
power systems to central utilities is centered on the use of the utility
operating simulation model, SYSGEN, developed at the MIT Energy Laboratory.
SYSGEN is a production costing/reliability model that assumes a fixed generating capacity for the utility. The version of SYSGEN used in this analysis
has been adapted to incorporate photovoltaic, weather-dependant, generation
sources into an operating system of a traditional utility. The SYSGEN model
15
was applied to four of the six synthetic utilities developed by EPRI.4 The
synthetic utilities represent scaled, simplified utilities developed by EPRI
for the purpose of general systems analysis. These synthetic utilities can be
modified for use in any given region within the United States without the need
to gather specific operating characteristics for individual utilities.
Because of the nature of photovoltaic power systems, it is desirable
to analyze their effectiveness within a specific utility power system by detailed
hourly simulations which match the electrical demand for such services as
air conditioning with the provision or availability of electric power from
the photovoltaic arrays. As a result, our work has matched data for loads
on specific utilities with solar insolation data taken from the SOLMET data
series. The load data was scaled to match the generating capacity and operating
characterisitics of the synthetic utilities. Load data was provided to the
MIT Energy Laboratory by four utilities: Omaha PUblic Power District, Florida
Power and Light, Arizona Public Service, and Boston Edison, whose assistance
in this effort is gratefully acknowledged.
The capital stock of each of the synthetic utilities represents roughly
seven thousand megawatts of installed capacity. For each region, a specific
synthetic utility was chosen for analysis and for load matching; the synthetic
utility matched as nearly as possible the regional generating mix within which
each case study utility was located. It should be pointed out that because
the purpose of this analysis was regional estimation, not estimation of the
operating characteristics of a specific utility, system generating characteristics
were matched to regional characteristics rather than those of a specific utility,
This was done more nearly to reflect an average or regional total system worth,
It is felt that the results of this analysis are more valid across the region
than would be the results of detailed analyses of only specific utilities.
The modeling process begins with the running of SYSGEN for a specific
load as a base case. The loss-of-load probability generated in this run is
held constant for all future runs with photovoltaic systems in place, The
second step is to install a fixed capacity of photovoltaic power systems in
the utility structure, re-run SYSGEN, and balance the loss-of-load probability
resulting from the introduction of the photovoltaic power systems. The result
is a decrease in the requirement for conventional generating capacity and a
decrease in use of conventional fuels. Fuel savings are calculated directly
by SYSGEN. Capacity savings are calculated from the final reliability curve
produced by SYSGEN, using a variation of the standard methodology for computing
the effective load carrying capability of a plant. The effective load carrying
capability of the photovoltaic plants represents the amount of generation that
the photovoltaics can replace without changing the reliability of the system.
The value of a megawatt saved is then evaluated parametrically as a function of
four types of conventional capacity: oil, coal, nuclear, or a weighted average
of all systems. While it is possible to calculate the precise capacity savings
given the simulation run, the sensitivity of the results to specific values
for capacity types was seen to be great enough to require that this be handled
parametrically. The results of this analysis will be seen in the section which
follows.
The results of the analysis undertaken for central power are summarized
in Tables
7 to 11
and Figures
4 and 5.
16
Table
7 shows
the analysis
carried
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out for the South region of the United States. The system load taken was that
for Florida Power and Light for 1975; it was matched against Miami weather
data for 1975. The nameplate capacity for the syste as a whole was 6,550
megawatts. As can be seen in Table 7, the dollars per watt peak of system
operating costs decreased as additional capacity was added from 200 through
1200 megawatts. Photovoltaic generation additions represented an increase from
roughly two percent of total capacity to twenty percent of name plate capacity.
The effect of the addition of photovoltaic systems was primarily to decrease
the required capacity of oil-fired intermediate generation through 800 megawatts
of photovoltaics. At roughly 800 megawatts of installed capacity, the photovoltaic
power systems became complementary with pumped hydro in the system, thereby
decreasing other oi-fired plants and showing an increase in the overall value
of the system. (See Figure 4.) The same pattern was seen at 1,000 and 1,200
megawatts of photovoltaic power systems, though here the value of the total
system per peak watt was less than had been the case at 800 megawatts of installed capacity.
Table 8 summarizes the worth calculations for an oil scenario for the
Northeast region. In this scenario, photovoltaics replaces intermediate and
peaking oil and other fossil systems. As was the case in the South region,
at roughly 800 megawatts of installed photovoltaic systems, photovoltaics becomes complementary with pumped hydro and as a result there is an upward swing
in the value of the photovoltaic system brought about primarily by a major
increase in use of hydro and photovoltaics relative to oil-fired systems.
This increase is quite striking both in operating cost savings and in capacity
credit.
The Southwest region synthetic utility chosen was Scenario B. This was
run utilizing Arizona Public Service load data for 1975 and SOLMET Phoenix
1975 weather data. The nameplate capacity for the system was 10,700 megawatts.
This is a typical structure for a southwest utility though less so for Arizona
Public Service. The impact of photovoltaics on such a system is to replace
initially the intermediate oil systems, some peaking combustion capacity, and
some hydro. As shown in Table 9, the value of the photovoltaic power systems
remains relatively even and high through the range of penetration levels.
Photovoltiacs replace intermediate oil and conventional hydro to the point
at which the competition with hydro ceases. Given that there is no operating
advantage to replacing hydro, it is only when photovoltaics replace in the
more expensive fuel systems such as coal and oil that one sees an increase in
the value of photovoltaics.
Table 10 summarizes the utility worth calculations for Omaha which has
been used as a surrogate for the Northcentral region. As can be seen the
worth to the utility is almost entirely in terms of fuel savings. A careful
analysis of Omaha peak demand periods and potential power provided by the
phtovoltaic systems showed little overlap between photovoltaic availability
and the peak accounting for the extremely small capacitycredit.
Table 11 summarizes the potential for total generating capacity in the
UinitedStates to the year 1990. The table; contains the regional distribution
uf installed capacity in 1975; it shows the total capacity projected to exist
21
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4
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200
400
600
800
1000
1200
Installed Megawatts of Photovoltaics
22
Rest of
System
Cost
50¢/wp
5
_
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m
Figure 5
30 /wp
PhotovoIta ic Demand
Uti I it ies
20 _
18
_
16
_
14
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40
80
60
Total Potential
Megawatts
23
Demand
Peak
100
in 1990 by Electric World in their 28th Annual Electrical Industry Forecast and
the predicted level of new capacity additions in 1990. Using 1990 as a baseline
for analysis of photovoltaic penetration, Figure 5 presents a composite of
regional demand calculations at specific prices for photovoltaic power systems.
The values on the y-axis of Figure 5 are ranged from $.30 to $.50 rest-of-systems
cost, showing the sensitivity of the results to the assumptions made concerning
other than photovoltaic power systems costs. Total demand ranges from 25,000
to 70,000 MW at module cost of 0 to $.70 per peak watt. In summary, the
potential for photovoltaics penetration into the central power sector appears
to be highly sensitive to the assumption one makes concerning the types of
capacity that will be displaced in the future, the costs of that capacity
and the non-array systems costs.
V.
Summary
The simulation work carried on by the MIT Energy Laboratory has focused
on measurement of the estimation of the worth of photovoltaic power systems
to owners in the residential, commercial, and central power sectors. This
analysis has focused on the benefits of load and weather-matching on an hourly
basis throughout the year. The analysis has shown the value of residential
systems to be greater than those of either school or central power systems.
All analyses are highly sensitive to assumptions concerning the non-modular
system costs. In addition, results are sensitive to fuel escalation, discount rate, and assumptions concerning future costs of alternative capital
stocks. One significant result which emerges from this analysis is that while
the potential for storage at the dispersal site appears minimal, centralized
storage in the form of pumped hydro as a complement to photovoltaics appears
to have a highpotential. Complementarity of these two potential sources requires additional analysis.
24
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FOOTNOTES
1
Richard Tabors and Paul Carpenter. Methodology and Definition of Solar
Photovoltaic Planning Regions, Massachusetts Institute of Technology
Energy Laboratory. Technical Report Forthcoming.
2
jesse Tatum. "Photovoltaic/Hybrid Simulation Model for Grid Interconnected
Residential Applications", April 1978, forthcoming.
3 U.S. Department of Commerce, Bureau of Census. Demographic Projections
Of The United States, CRR P-24, No, 476, February 1972.
4
EPRI, Synthetic Electric Utility Systems for Evaluating Advanced Technologies
EPRI EM-285
February
1977.
26
APPENDIX I
Methodology for Calculation of Capacity Credit Values
For Photovoltaic Central Power Applications
The value of capacity replaced (effective capacity) for photovoltaic
power systems used in Central Power Applications has been calculated using
1976 average cost of capital figures for small-scale plants (500 MW) taken
from Electrical World.
To equate the photovoltaic generation capacity to the actual quantity
of megawatts replaced requires consideration of the forced outage rate of
the conventional
system
as well.
Capital costs were:
$/KW*
Forced
Outage
Rate**
Cost
Comparison
($/KW)
Base/Intermediate Oil
212
.835
254
"All Stations"
275
.833
230
Coal
387
.835
463
Nuclear
450
.736
611
*Source:
**Source:
Electrical World, "20th Station Cost Survey,"
November 15, 1977.
EPRI Synthetic Utilities Assumptions Concerning Forced Outage Rates.
27
APPENDIX
28
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