HomeMy WebLinkAboutRemote Power Systems with Advanced Storage Tech for Remote AK - LLNL 12-1997Lawrence
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NationalLaboratoryUCRL-ID-129289
Remote Power Systems with
Advanced Storage Technologies for
Remote Alaskan Villages
W. Isherwood, R. Smith, S. Aceves, G. Berry, W. Clark,
R. Johnson, D. Das, D. Goering and R. Seifert
December 1997
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UCRL-ID-129289
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Remote Power Systems with Advanced Storage Technologies
for Alaskan Villages
by
William Isherwood, Ray Smith, Salvador Aceves, Gene Berry and Woodrow Clark
Lawrence Livermore National Laboratory (LLNL)
and
Ronald Johnson, Deben Das, Douglas Goering, and Richard Seifert
University of Alaska Fairbanks (UAF)
Abstract
Remote Alaskan communities pay economic and environmental penalties for electricity,
because they must import diesel as their primary fuel for electric power production, paying
heavy transportation costs and potentially causing environmental damage with empty
drums, leakage, and spills. For these reasons, remote villages offer a viable niche market
where sustainable energy systems based on renewable resources and advanced energy
storage technologies can compete favorably on purely economic grounds, while providing
environmental benefits. These villages can also serve as a robust proving ground for
systematic analysis, study, improvement, and optimization of sustainable energy systems
with advanced technologies.
This paper presents an analytical optimization of a remote power system for a hypothetical
Alaskan village. The analysis considers the potential of generating renewable energy (e.g.,
wind and solar), along with the possibility of using energy storage to take full advantage of
the intermittent renewable sources available to these villages. Storage in the form of either
compressed hydrogen or zinc pellets can then provide electricity from hydrogen or zinc-air
fuel cells when renewable sources are unavailable.
The analytical results show a great potential to reduce fossil fuel consumption and costs by
using renewable energy combined with advanced energy storage devices. The best
solution for our hypothetical village appears to be a hybrid energy system, which can
reduce consumption of diesel fuel by over 50% with annualized cost savings by over 30%
by adding wind turbines to the existing diesel generators. When energy storage devices are
added, diesel fuel consumption and costs can be reduced substantially more. With
optimized energy storage, use of the diesel gensets can be reduced to almost zero, with the
existing equipment only maintained for added reliability. However about one quarter of the
original diesel consumption is still used for heating purposes. (We use the term ‘diesel’ to
encompass the fuel, often called ‘heating or fuel oil’, of similar or identical properties.)
Introduction
Most remote Alaskan communities pay economic penalties for electricity (ARECA, 1996),
because they must import diesel as their primary fuel for electric power production, paying
heavy transportation costs and potentially causing environmental damage. Furthermore,
the consumption of fossil fuels and the local negative environmental impact caused by
communities befouling the region with leaking tanks and discarded drums must be
considered when examining remote energy options. High fuel costs and environmental
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impacts occur not just in Alaska but also many locations worldwide where remote
communities need power, regardless of climate.
In theses remote locations, renewable resources and advanced technologies, coupled with
state-of-the-art energy storage methods, can compete favorably with conventional fossil
fuel generation, when analytical comparisons are optimized to include life-cycle costs for
the entire integrated energy system. This is true particularly where electric costs are high
because of fuel transportation expense, there is a reasonable renewable resource available
(e.g., wind, low-head hydro, solar, geothermal, etc.), and there is no inter-connection to a
large-scale power grid. A modular approach to energy systems further allows the
transition from a hybrid (for example, the combination of fossil fuel and renewable
energy generation) to a totally renewable system as new technologies and applications
become commercially available.
Resources such as wind and sunlight, however, are not continuously available in any
region. The greatest reduction in fossil fuel consumption can be achieved, therefore, by
using energy storage strategies and newly available technologies, capable of storing
energy for periods of several days to more than a week. Effective long-term storage can,
for example, be provided by using surplus power from renewable resources to electrolyze
water, producing hydrogen, which can be later used to re-generate electricity in either fuel
cells or with internal combustion engines. Alternatively, energy can also be stored in the
form of recovered zinc which is later used to generate electricity in a zinc-air fuel cell. In
both cases, the technologies exist today and are now being commercialized (see Moore,
1997, and The Economist, 1997).
Renewable energy combined with energy storage also has the potential to provide the very
important benefit of increased system reliability, which has been recognized as one of the
highest priorities in the design of remote power systems (Brown et al., 1996). Fuel cells,
for example, have no moving parts, require almost no maintenance, and have
demonstrated long lives. Reliability can be enhanced by a distributed generation facility,
combined with storage, and optimized through systems codes; potentially using the
existing diesel generating system as a backup.
Public and private sector research has developed and demonstrated numerous renewable
energy technologies. Widespread use of renewable energy technologies has been limited,
however, by high costs (US Department of Commerce, 1997, and EPRI, 1997).
Among other problems cited that prevent the commercialization of “environmentally
sound technologies” have been (1) the market has been insufficient to stimulate mass
production, (2) competition from inexpensive fossil fuels (the price of which commonly
fails to include full environmental costs), and (3) the lack of integrated systems that take
advantage of synergies possible between new technologies (The Economist, 1997).
Nevertheless, a growing literature indicates that environmental and energy market
demand is being created and supported through changes in governmental regulations.
This leads to a stronger competitive advantage for private sector firms marketing
‘alternative’ technologies (Porter and van der Linde, 1995; UN Reports, 1994 and 1995;
Clark, 1997; and Clark and Paolucci, 1997).
Scope
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This paper presents an analysis of remote power systems for an Alaskan community,
demonstrating how a hybrid of technologies is superior in optimizing energy efficiency,
reducing environmental degradation, and reducing costs. Two computer codes provide the
basis for our analysis. The first is a renewable grid analysis tool, and the second is an
optimizer. These two codes combine to obtain optimum designs for any number of
decision variables, as well as equality and inequality constraints.
This hypothetical remote village analysis treats optimization primarily as an energy cost
problem, not as an environmentally driven problem. Thus no externalities (such as
environmental regulations, legislative initiatives, and system reliability), nor potential
linkages to water and waste disposal infrastructure are included in the cost analysis.
Chapman (1996) estimated the substantial cost of environmental degradation due to
emissions and spills that result from diesel engine operation at $0.80 per liter of fuel
($3/gal). If such ‘hidden costs’ and further integration with other community needs were
taken into account, we expect the advanced technologies discussed herein would appear even
more favorable.
Costs are very sensitive to a long list of parameters, both local and external to the village.
This sensitivity makes cost comparisons difficult (Guichard, 1994). The results obtained in
the analysis are expected to indicate trends that would exist in an actual village in which the
conditions are not too different from those assumed here.
Remote Village Scenarios
The coastal village used in the analysis is fictional, in that it has the demands of Deering
(48 homes, population 150), and the solar insolation, wind and temperature resources of
Kotzebue. The parameters, data, and verification are based on profiles provided by the
University of Alaska, Mechanical Engineering Department. Further data were provided
directly from actual remote Alaskan communities. Wind speed data have been scaled to 8
m/s average wind speed, which is a realistic value for sites along coastal Alaska. Although
we considered the possibility of including photovoltaic (PV) cells in the system, this
evaluation indicated that wind was the preferred renewable resource in this sample case.
Consequently, the optimized solutions presented below all show zero PV component.
Other analyses could include PV for remote communities, especially those in sunnier
regions lacking reasonable wind resources.
Space and water heating are major contributors to the total energy demand in Alaskan
villages (Koniag, Inc., 1995). For this reason, our integrated approach considers the
possibility of covering part of the heating load with waste heat from power generation
equipment, or with surplus renewable energy obtained during periods of high wind speed,
to reduce the fuel consumption for heating homes and public buildings.
Four modular energy systems are analyzed and compared in this paper. The systems are:
Diesel-Only , Base-Case : This is the system that currently exists in most Alaskan villages.
Diesel gensets produce electricity, with heating provided by available waste heat first, then
by diesel-burning furnaces (except that most real villages do not fully utilize available waste
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heat from gensets, partly because noise and safety factors place gensets far away from the
greatest heating loads.). Our hypothetical village uses 250,000 liters per year (250 kl/yr) of
diesel for electrical generation and 135 kl/yr for heating.
Hybrid Wind-Diesel System : This system includes wind turbines and diesel generators.
Wind turbines generate electricity to satisfy the power demand (70 kW average, 118 kW
one hour peak). If there is surplus electricity after the power demand is satisfied, the
surplus electricity provides heating for homes. As in the base-case system, diesel
generators cover the electrical load and diesel-furnaces provide the heat when there is not
enough wind to satisfy the electrical demand.
Wind - - Hydrogen Storage -- Fuel Cell -- Diesel : This system includes wind turbines, an
electrolyzer for producing hydrogen, vessels for low-pressure compressed hydrogen
storage (4.1 MPa, 600 psi), a commercially available phosphoric acid fuel cell (PAFC),
and backup diesel generators. (Proton Exchange Membrane -- PEM, and Solid Oxide
Fuel Cells -- SOFCs -- may soon be available with similar or even more suitable
characteristics, but for simplicity, the current analysis included only the PAFCs for use
with hydrogen.) Wind turbines first satisfy the power demand. If there is surplus
electricity after the power demand is satisfied, it can be used for either heating homes or for
generating hydrogen for storage. When the wind turbines cannot satisfy the electrical
demand, the fuel cell provides power to the system. If stored hydrogen becomes
exhausted, the genset comes on line. Diesel continues to used for heating, when waste heat
is not available.
Hydrogen storage has an economic advantage over lead-acid batteries for long-term
storage, in that increased energy storage (measured in kilowatt-hours) is added by
increasing only the hydrogen storage, at relatively low cost per kilowatt-hour. Low-
pressure hydrogen storage is a safe, proven, and readily available technology. Fuel cells
utilize the hydrogen to generate electricity. Although the overall turnaround efficiency of
energy storage and retrieval (electricity to electricity) from the system is only about 30%,
heat from the fuel cells and electrolyzers can be used for space or process heating,
substantially increasing the overall energy efficiency. Because fuel cells are practically
noiseless, they can be placed close to facilities that can utilize their ‘waste’ heat.
Wind - - Zinc storage - - Zinc-Air Fuel Cell -- Diesel : This system is similar in strategy and
components as the previous one, the only exception being that zinc pellets produced in an
electrolytic process are used for energy storage, and a zinc-air fuel cell is used to generate
electricity. Prototype zinc-air cells have demonstrated a turn-around electric energy storage
efficiency of about 60%, compared to 70% for lead-acid batteries (Cooper et al., 1995). As
with hydrogen fuel cells, use of the waste heat from a zinc-air cell can bring the overall
energy efficiency significantly higher. Zinc-air cells present none of the disposal problems
of lead-acid batteries and have a considerable per-unit-energy weight advantage, which is
important for shipping.
Zinc-air fuel cells should soon (within 2 years) become commercially available and the
total production costs should easily compete with lead-acid batteries on a per kilowatt (kW,
power) basis. But as with hydrogen, the cost of incremental energy storage capacity
(kilowatt-hour, kWh) is quite low, making these cells particularly advantageous for long-
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term storage. Zinc-air fuel cell costs used in this study are based upon an industrial
partner’s estimate for commercialization of new technologies.
Optimization Code Analyses
A remote village system analysis code has been developed specifically for this project and
then integrated with an optimizer. This work does not to duplicate the extensive capabilities
of an existing hybrid systems code (HYBRID2, Baring-Gould, 1996; Manwell et al.,
1996), which can also be linked to an optimizer (Flowers, 1997). Instead, the purpose is to
evaluate new advanced technologies (such as energy storage devices), waste heat recovery
systems, and operating strategies, for optimization into modular systems suitable for
remote villages.
The U.S. Magnetic Fusion Program at Lawrence Livermore National Laboratory originally
developed SUPERCODE in the early 1990’s for optimizing tokamak reactors and
experimental designs (Galambos et al., 1995). SUPERCODE is a shell that incorporates
process models, uncertainty, and non-linear equations, which has subsequently been used
to optimize inertial fusion devices, rail-guns, and hybrid-electric vehicles (Haney et al,
1992; Aceves et al, 1995), in addition to the present application.
A powerful programmable shell that takes input using a variant of the C++ language
controls SUPERCODE, and has recently been converted to Mathematica (Perkins et al.,
1997). Input can be from a terminal or from files, allowing interactive or batch operation.
The user can define real, integer, complex, array, and string variables. In addition, the
language supports control statements, loops and functions. The SUPERCODE shell can
exploit the multi-processing capabilities of UNIX to run external programs, such as this
village simulation code, to compute constraint and figure-of-merit values. It is also
possible to use the parallel virtual machine system (Beguelin et al., 1991) to simultaneously
run multiple copies of the external program in parallel on a number of workstations thereby
dramatically reducing execution time.
This programmable shell offers tremendous flexibility for the user to specify an
optimization problem. Once the optimization is completed, the user can interrogate the
shell for variable and figure-of-merit values. Also, variables can be fixed, or new
constraints applied to investigate "what-if" scenarios. Loops can also be written to perform
parameter scans.
Our village optimization code includes:
1. Electricity generation components: These are defined by vectors that specify electricity
output for every value of energy input (wind speed, solar irradiation, or fuel
consumption for a diesel genset or fuel cell).
2. Loads: Electrical loads are taken from Deering, Alaska. Average demand is 70 kW,
and the 1-hour peak is 118 kW. Average heating load is assumed equal to 150 kW for
the whole village. We assumed that 85% of the heating load goes for space heating and
15% for water heating. The space heating load is distributed along the year based on
the temperature data for the village. The water heating load is distributed uniformly
throughout the year.
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3. Energy storage components: These vectors specify the efficiency as a function of
power input.
4. Waste heat recovery: This component specifies the fraction of the total waste heat that
can be used for heating, and the maximum percentage of the village that can be heated
with waste heat.
5. Energy storage strategy: Surplus electricity can be either stored by electrolyzing water
to make hydrogen or recovering zinc from the zinc-air fuel cell residue of zinc oxides,
or used for heating the homes. The systems analysis code analyzes both of these
options for any particular scenario.
6. Economic analysis: The code calculates annualized operating costs and years for return
of investment as a function of all the system cost parameters, fuel consumption and
maintenance of the system, which are in turn functions of equipment performance and
use. Options include separate rates for the cost of capital (interest rate), fuel cost
escalation, and maintenance cost escalation.
Optimization Methodology
Table 1 list the parameters and assumptions used in the test village analysis. The analysis
assumes that 40% of the waste heat generated from the diesel engine can be used for
heating. This value corresponds closely to the amount of waste heat transferred to the
cooling water (Malosh et al., 1985). The rest of the waste heat is lost through the exhaust,
and it is not recovered in current power plants. For fuel cells, we assume that most of the
waste heat (60%) is transferred to the cooling water, making it available for heating. We
also assumed that in the diesel-only base-case a maximum of 30% of the village can be
heated with waste heat. This is because diesel engines are likely to be located at a central
power plant located away from the village center, so that waste heat can only be
economically used in a few buildings. Fuel cells can be located within or distributed
throughout the village. If desired, each home could potentially have its own fuel cell. This
affords a significantly higher potential for heating with waste heat recovery (50%).
Diesel engines present operating difficulties if operated at very low load. For that reason, a
minimum operating power (40% of full load, Malosh et al., 1985) is defined.
We present here two separate sets of economic assumptions to illustrate their effect. First
we assumed no escalation of fuel or maintenance costs and used a 0% interest rate on
capital, due to State or Federal subsidizes or other investment incentives. Published
scenarios project fuel cost escalation to approximate or exceed the borrowing rate, making
this approach a plausible first assumption. Furthermore, the state of Alaska currently
subsidizes the electricity for the remote villages to a level of $0.27/kWh (Jensen, 1997), so
this case assumes that the State might be willing to provide low- or no-interest loans in
order to reduce the future amount of the subsidies. The results are also presented in terms
of simple payback, which is independent on the interest rate.
For comparison, we also show results based on the fairly conservative economic
assumptions that (1) diesel fuel costs do not escalate (based on recent history rather than
escalating predictions such as those of the Energy Information Administration [1997]), (2)
maintenance costs escalate at a 3% general rate of inflation, and (3) money for capital
improvements can be borrowed at an 8% interest rate, As will be seen, these economic
UCRL-ID-129289
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assumptions do not significantly alter the magnitude of benefit derived from the optimized
power system scenarios described above.
Table 2 gives cost parameters for the main components in the power grid. Capital costs
include transportation of equipment to the village and power system installation.
Maintenance costs are very important since they can make or brake the economics of an
installation (Energy Mines and Resources Canada, 1988; UN 1994 and 1997). Renewable
modular energy systems are expected to have significantly lower maintenance costs than
diesel systems (Malosh et al, 1985; Ontario Hydro, 1997; Bergey Windpower, 1997). The
individual components of wind-turbines, electrolyzers, and fuel cells have a good history
from which to estimate maintenance costs (see, for instance, Guichard, 1994). Zinc-air
technology is new, but the simple principles and similarity to hydrogen fuel cell technology
provide a basis for assuming similar low maintenance costs.
Five parameters are used as decision variables:
1. Total wind turbine power capacity, in kW
2. Total PV energy capacity, in kW
3. Energy storage capacity, in kWh
4. Maximum possible power into storage (maximum electrolyzer or zinc recovery unit
power).
5. Maximum possible power out of storage (maximum fuel cell power).
The specific figure-of-merit used for this optimization example is the yearly cost of the
system. This includes capital, maintenance and fuel costs. The cost of fuel is assumed to
be in the range of $0.40/liter ($1.50 per gallon) to $0.92/liter ($3.50 per gallon).
The energy control strategy for the storage system is critical to the operation of the grid
itself. Two of the possible options are:
1. Heating first: Surplus renewable electricity is used for resistive heating within the
village. If there is surplus electricity after providing all the required heat, the electricity
is used for generating either hydrogen or zinc for the storage.
2. Storage first: Surplus renewable electricity is stored as either hydrogen or zinc. If the
storage system is full, surplus electricity is then used for heating the homes.
A preliminary analysis has shown that the heating first strategy has an advantage for the
conditions analyzed in this paper. Heating first is the strategy selected because heating
with renewable sources is more efficient than storage and recovery of energy.
Results
Table 3a shows the results of the example system optimization for minimum yearly cost,
for a $0.66/l ($2.50/gal) fuel cost, and the zero interest rate, no cost escalation scenario.
Table 3b shows results of the same optimization with the alternative economic
assumptions; 8% interest, 3% maintenance cost escalation, but still no fuel cost escalation.
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The tables list the values of the five decision variables, as well as the fuel consumption and
cost values.
The tables indicate that none of the lowest cost systems have a photovoltaic component.
Intrinsic solar irradiation is low at our model village, but the lack of PV here is most
directly a consequence of today's still relatively high PV costs. However, PV costs have
declined sharply in the past, and further declines are expected, perhaps sufficient for PV
electricity to compete economically with other sources used in rural Alaskan communities
in the future.
The results in Tables 3a and 3b indicate that maintenance costs dominate the economics for
the base-case system. The importance of maintenance costs has been stressed in previous
reports (e.g., AVEC, 1996; Harris et al, 1997). Most of the maintenance cost is associated
with diesel genset operation. For this reason, optimum renewable systems tend to reduce
diesel genset operation as much as possible. For example with the zinc-air fuel cell
system, there is almost no need to operate the diesel genset, although the analysis considers
that the diesel genset is kept as a part of the system for increased reliability (i.e., capital cost
for the genset is included).
The wind-diesel system reduces diesel genset use to about a third, and total fuel
consumption to less than half of the base-case values. Considering the moderate
investment and the short time for payback required for these systems, installation of wind
turbines constitutes a good first step that can later be enhanced to include energy storage as
additional capital is available for investment.
Figures 1a and 1b show optimization results for the same two economic assumptions. The
figures show total system cost and total diesel consumption for the four systems being
considered. In addition to the fuel cost considered for Table 3 ($0.66/l; $2.50/gal), two
more values are used: $0.40/l ($1.50/gal), and $0.92/l ($2.50/gal). The figures show that
the yearly cost for the base-case system is very sensitive to fuel cost. For the systems with
storage, fuel consumption is significantly reduced so that the yearly cost is less sensitive to
fuel cost. The figures illustrate clearly the potential for cost and fuel consumption reduction
obtainable by using renewable electricity generation in the village. Note the general
similarity in results between the different economic cases. For brevity, the following
figures present data only from the simple -- no interest, no escalation -- case, with
confidence that general conclusions will not differ significantly over a broad spectrum of
realistic economic assumptions.
Figure 2 illustrates the results of a system optimization when only the electricity demand is
considered (no heating load is satisfied). The cost and fuel savings for systems with
storage appear even greater when heating is neglected. We show this for comparison with
other studies that do not integrate heating with power. Consideration of the total energy
picture makes more sense for village planners. Optimum (lowest yearly cost) designs for
the zinc-air fuel cell can reduce diesel fuel consumption to almost zero, so that the operating
cost is independent of fuel cost (note, however, that the time for payback is still a function
of fuel cost). Wind-diesel systems can reduce fuel consumption to about 40% of the
original value, and the cost to almost 50%.
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Figures 3 and 4 show the results of a parametric analysis for a system with hydrogen
storage and a PAFC. Figure 3 shows lines of constant total fuel consumption as a function
of energy storage capacity and installed wind power. The numbers on the curves indicate
the fraction of the fuel consumed in the base-case (384,000 liters/year). Decision variables
are set to the optimum values for the PAFC system from Table 3a. For low wind capacity
(‘low-penetration’), storage provides little benefit, since all of the electricity produced is
immediately used to satisfy either electrical load or electrical-heating load, and the storage
system remains empty. As the wind capacity increases (‘high penetration’), the benefit of
energy storage increases. A point in the figure indicates the optimum design from Table
3a.
Figure 4 shows lines of constant fuel consumption for electricity generation only, as a
function of energy storage capacity and installed wind power for the same system with
hydrogen storage and a PAFC, with the optimum values for the decision variables from
Table 3a. The numbers on the curves indicate the fraction of the fuel consumed in the
base-case (250,000 liters/year). As previously discussed, operation and maintenance of the
diesel engines is expensive, and therefore the optimum design reduces considerably the
fuel consumption for electricity generation.
Figures 5 and 6 show results that are similar to those presented in Figures 3 and 4, except
that now the system being considered is the zinc-air fuel cell as energy storage. The
decision variables take their optimum values from Table 3a. Zinc-air fuel cells and storage
are expected to be cheaper that PAFC and hydrogen storage, so that the optimum amount
of storage, indicated by a point in the figure, is higher than for hydrogen. The higher
efficiency of the zinc-air fuel cell results in a very low fuel consumption for electricity
generation. Total fuel consumption remains at about the same level as obtained for the
PAFC system.
Conclusions
Application of an energy production and use simulation code and an optimizer to the
problem of sizing a renewable electricity generation grid in a remote Alaskan village
demonstrates significant potential for life-cycle cost savings. We compared a base-case
system, which consists of (1) diesel gensets and diesel heaters, to three highly reliable
systems that include renewable electricity generation: (2) a wind-diesel system, (3) a wind-
diesel system with hydrogen storage and a phosphoric acid fuel cell (available ‘off-the-
shelf’), and (4) a wind-diesel system with zinc storage and a zinc-air fuel cell (expected to
be available within 2 years). The results show that, for the conditions used for this
analysis, fuel consumption and annualized life-cycle costs can be substantially reduced by
using renewable electricity generation technologies as well as energy storage devices.
Specific results from the analyses demonstrate that:
1. When wind turbines are added to diesel gensets (“wind-diesel” hybrid), the saving of
diesel fuel can be more than 50% at a cost savings of over 30%. This is the most cost
effective, quickest payback configuration for a remote village that has sufficient wind
resource. Furthermore, wind turbines can be added incrementally, with additional
maintenance and operational savings at every increment.
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2. When energy storage devices are added (e.g. hydrogen or zinc), diesel fuel
consumption and costs can be reduced substantially more. Optimized energy storage
allows diesel gensets to be eliminated. However, about one quarter of the original
diesel consumption is still required to satisfy heating demands.
3. Costs using optimized hydrogen storage can be 10-20% lower than for wind-diesel
alone, while displacing about an additional 20% of the original diesel fuel consumption.
4. Using estimated costs for zinc-air technology for energy storage, as much as 75% of
the current diesel fuel can be displaced with 30-40% cost savings over wind-diesel
without storage. This result provides a strong incentive to further speed the
commercialization of this technology.
There are a number of externalities that have not been factored in at this point, but could be
in future analyses. For Alaska, we suggest the externalities include: environmental impact
and regulations, political legislation and funding (direct investment, incentives, and taxes),
and specific community-based variables such as employment opportunities and need for
reliable service.
It is expected that refinements possible during the analysis of a real village could
potentially make the economics more or less favorable. In general, we believe the benefits
shown possible in these analyses should be realizable at numerous sites throughout Alaska;
certainly at those sites that have significant wind resources.
The systems described in this paper should be robust enough for application in real
communities and could be modular enough for additions and substitutions of new
technologies as they become available. In short, a key concept is the creation of new
energy systems that are economically viable and sufficiently flexible for implementation of
new technological advances.
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UCRL-ID-129289
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Table 1. Parameters for remote village electric grid.
distance from wind turbines to village, km 10
average electric demand for village, kW 70
1-hour peak demand for village, kW 118
average wind speed, m/s 8
1-hour peak wind speed, m/s 35
average heating demand, kW 150
1-hour peak heating demand, kW 365
efficiency of diesel-fueled heater, % 80
fraction of waste heat that can be used for heating, fuel cells, % 60
fraction of heating load that can be met with waste heat, fuel cells, %50
fraction of waste heat that can be used for heating, diesel genset, % 40
fraction of heating load that can be met with waste heat, base-case, % 30
number of diesel gensets 3
maximum diesel genset power, kW 60
minimum diesel genset power, kW 24
wind turbine maximum power output, kW 20
maximum solar irradiation, kW/m2 0.82
fuel energy density, kWh/liter (kWh/gal) 9.51 (36)
fuel cost, $/liter ($/gal)0.40-0.92
(1.50-3.50)
interest rate, % 0.0 and 8.0
maintenance cost escalation, % 0.0 and 3.0
fuel cost escalation, % 0.0 and 0.0
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15
Table 2. Parameters for cost analysis of grid.
Component life, yrs cost maintenance,
$/kWh
transmission lines 20 $10000/km 0.001
compressed hydrogen storage 20 $10/kWh 0.001
electrolyzer 20
a $1000/kW 0.001
diesel heater 10 $100/kW 0.001
electrical resistive heaters 20 $20/kW 0.001
engine-generator 4
a $200/kWa 0.12c
wind turbine 20 $2400/kW (installed) 0.03
PAFC fuel cell 20
a $1500/kWb 0.01
photovoltaic cells 20
a $770/m2 or $5000/kW (peak) 0.0a
zinc storage 20 $4/kWh 0.001
zinc-air fuel cell 20 $150/kW 0.01
zinc recovery unit 20 $150/kW 0.001
________________________
a. From Guichard (1994).
b. From Guichard (1994). Projection to 1998.
c. From Malosh et al., 1985.
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16
Table 3a. System parameters for optimum designs presented in Figure 1a, for a fuel cost of
$0.66/l ($2.50/gal), zero interest and no cost escalation.
Base-case wind-diesel hydrogen PAFC zinc-air
optimum system parameters:
wind power, kW 0.0 403 580 451
energy storage, MWh 0.0 0.0 35.7 82.2
electrolyzer power, kW 0.0 0.0 358 287
fuel cell power, kW 0.0 0.0 96.8 122
photovoltaic power, kW 0.0 0.0 0.0 0.0
annual diesel fuel consumption, kl (kgal):
for electricity generation 250(66.0) 85.6(22.6) 11.1(2.94) 0.0(0.0)
for heating 135(35.6) 78.1(20.6) 78.9(20.8) 95.9(25.3)
total 384(101) 164(43.3) 90.0(23.8) 95.9(25.3)
system costs:
capital, k$/yr 13.4 66.5 130 91.4
maintenance, k$/yr 286 164 77.5 49.2
fuel, k$/yr 253 108 59.5 63.3
total, k$/yr 553 338 267 204
years for payback - 4.0 5.84 3.68
Table 3b. System parameters for optimum designs presented in Figure 1b, for a fuel cost
of $0.66/l ($2.50/gal), 8% interest, 3% maintenance cost escalation, and no fuel cost
escalation.
Base-case wind-diesel hydrogen PAFC zinc-air
optimum system parameters:
wind power, kW 0.0 351 538 446
energy storage, MWh 0.0 0.0 22.3 47.6
electrolyzer power, kW 0.0 0.0 294 298
fuel cell power, kW 0.0 0.0 75.6 153
photovoltaic power, kW 0.0 0.0 0.0 0.0
annual diesel fuel consumption, kl (kgal):
for electricity generation 250(66.0) 90.6(24.0) 24.0(6.38) 6.55(1.73)
for heating 135(35.6) 95.9(25.4) 82.5(25.4) 94.5(25.0)
total 384(101) 187(49.4) 106(28.2) 101(26.8)
system costs:
capital, k$/yr 19.0 115 225 165
maintenance, k$/yr 386 224 123 78.0
fuel, k$/yr 274 133 76.1 72.3
total, k$/yr 679 472 424 315
years for payback - 3.75 5.34 3.44
n 1 I
;:niEsEil ~j~700
Zj
diesel-only .
zv 500 base-case
E
g 400 A
#*
>
m 300
n wind-diesel
PAFC
f
A76
E-200 ~zinc-air
100
0%interest rate
o 1 1 t
o 100 200 300 400
total diesel consumption,kllyr
Figure la.Total system cost and total diesel consumptionfor the four systems being
illustrated,for fueI costs of $0.66/l ($2.50/g@,$0.40/l ($1.50/gal),and $0.92/l
($2.50/gal),zero interest and no cost esc~ation.
a
800
1 I
G $0.92/1 ($3.50/gal)m
n $0.66/1 ($2.501gal)~700 diesel-only
$A $0.40/1 ($1 .50/gal).base-case =.
.600
:A
u 500 #
E N wind-diesel
~400 PAFC*~A
:m
a 300 ~zinc-air
~
~200 8%interest
O%fuel cost escalation100
3%maintenance cost escalation
‘o
1 1
100
I
200 30(3 40(3
total diesel consumption,kllyr
Figure lb.Total system cost and total diesel consumptionfor the four sys~ms betig
illustrat@ for fuel costs of $0.66/i ($2.50/gd),$0.40/I ($1.50/gal),and $0.92/I
~~~~~)$‘ith 8%‘teEst ‘n caPIM~3%maintenancecost escalation,and no cost.
1 I 1
800:W ~g 700
~
.600
(s)
:500 l diesel-only
E
-.
:base-caseg4“”
m
>
m 300 tAwind-dieselG
z-200
E PAFC
1001;zinc-air
O?fO interest rate
‘o
1 !1
100 200 300 400
diesel consumption for electricity generation,kllyr
Figure 2.Total system cost and diesel consumptionfor a system optimization when only
the electricity demand is considered (no heating load is satisfie&for the four systems being
illustrated,for fuel costs of $0.66/l ($2.50/gal),$0.40/l ($1.50/gal),and $0.92/l
($2.50/gal).
tatal tifi ccmsumptian
fracthn Of base-case (384 kllyear)
t I *i I 1
.
c1
in
.
J
8 mh vvhd speed
Optimum
H 1 #I
1(Xl 200 300 400 5(NI 600 7(IO 800 -900 1000
I t i I 1 1 1’1 I I I I I
installed maximum wind power,kW
FQure 3.Lines of constant total fbel consumption as a function of energy storage capacity
and installed wind power for a system with hydrogen storage and a PAFC.The number on
the curves indicates the fraction of the i%el consumed in the base-case (384,000 liters/year).
Decision variables are set to the optimum values for the PAFC system from Table 3a.Note
that this indicates storage becomes more important with higher wind power penetration.
.
1(10
90
(1
—
#
c1
fhel consumed for electricity generaticm
Raction of bas=case (250 kl&ear)
-1 1
-.
(3
“L3
,i-—
-0 1(lo 230 300 400 500 600 700 800 900 1000
installed maximum wind power,kW
Figure 4.Lines of constant fuel consumptionfor electricity generationas a function of
energy storage capacity andinstalledwindpowerfora systemwithhydrogenstorageanda
PAFC.Thenumberon thecurvesindicatesthefractionof thefiel consumedin thebase-
case (250,000 liters/year).Decision variables are set to the optimum values for the PAFC
system nom Table 3a.
,.
tdal fuel cmsumptim
fmcticm of base-case (384 kllyear)
I I I I !’I 1 I 1 i I
8 mh m“nd speed
\
E optimum zinc-air
100 200 300 400 500 600 700
installed maximum wind power,
800 900 1000
kW
Figure 5.Lines of constant total fuel consumptionas a function of energy storage capacity
and installed wind power for a system with zinc storage and a zinc-air fuel cell.The
number on the curves indicates the fraction of the fuel consumed in the base-case (384,000
liters/year).Decision variables am set to the optimum values for the zinc-air system from
Table 3a.
l ,.
fuel ccmsumed for electricity generaticm
,
c1
—
fractim of base-case (250 kllyr)
7
-=
c1
“A
C2
“m
T-1 I 1 I i I I I 1 1 I
8 mls wind speed
I zinc-air
n optimum
)
m+%
1,1
(-’j C3
J 1
-0 1CM 200 300 400 500 600 700 800 900 1 CIOCI
installed maximum wind power,kW
Figure 6.Lines of constant fiel consumptionfor electricitygeneration as a function of
energy storage capacity and installed wind power for a system with zinc storage and a zinc-
air fuel cell.The number on the curves indicates the fi-actionof the fuel consumed in the
base-case (250,000 liters/year).Decision variablesare set to the optimum values for the
zinc-air system from Table 3&
Technical Information Department • Lawrence Livermore National LaboratoryUniversity of California • Livermore, California 94551