Applied Energy 89 (2012) 474–481
Contents lists available at SciVerse ScienceDirect
Applied Energy
journal homepage: www.elsevier.com/locate/apenergy
Modeling and simulation of compressed air storage in caverns: A case study
of the Huntorf plant
Mandhapati Raju a,1, Siddhartha Kumar Khaitan b,⇑
a
b
Optimal CAE Inc., Plymouth, MI 48170, United States
Iowa State University, Electrical and Computer Engineering Dept., Ames, IA 50014, United States
a r t i c l e
i n f o
a b s t r a c t
Article history:
Received 17 November 2010
Received in revised form 12 August 2011
Accepted 12 August 2011
Available online 8 September 2011
An accurate dynamic simulation model for compressed air energy storage (CAES) inside caverns has been
developed. Huntorf gas turbine plant is taken as the case study to validate the model. Accurate dynamic
modeling of CAES involves formulating both the mass and energy balance inside the storage. In the
ground reservoir based storage bed, the heat transfer from the ground reservoir plays an important role
in predicting the cavern storage behavior and is therefore taken into account. The heat transfer coefficient
between the cavern walls and the air inside the cavern is accurately modeled based on the real tests data
obtained from the Huntorf plant trial tests. Finally the model is validated based on a typical daily schedule operation of the Huntorf plant. A comparison is also made with the results obtained from adiabatic
and isothermal assumptions inside the cavern to gain further insights. Such accurate modeling of cavern
dynamics will affect the design of the cavern storage beds for future explorations.
Ó 2011 Elsevier Ltd. All rights reserved.
Keywords:
CAES
Huntorf
Cavern
Simulation
Heat transfer coefficient
1. Introduction
There is an increasing need to harness the renewable energy
sources to reduce the nation’s dependence on fossil fuels, oils and
natural gas. On the other hand renewable energy from sources like
solar, wind, and tidal is inherently intermittent and thus nondispatchable leading to non-optimal operation. Therefore proven
storage technology is very crucial for technical viability and economic feasibility of renewable energy sources. Current available
storage technologies include potential energy (e.g. compressed air
energy storage [1–6], pumped hydro storage [7,8]), kinetic, thermal,
chemical energy storage (e.g. hydrogen [9]), batteries [10], super
capacitors [11], flywheels [12] and superconducting magnetic
energy storage (SMES) [13]. For large scale storage applications,
pumped hydro storage and compressed air storage are the most viable technologies. However pumped hydro storage is found to have
certain adverse environmental effects [7]. As compared to other
storage options (pumped hydro power and batteries), CAES has a
lower capital and maintenance cost [14,15]. Hence CAES has
received lot of attention in the recent times [4–6,16].
Historically CAES has been deployed for grid management
applications such as load shaving, load following, load shifting
and regulation. Earlier investigations focussed on peak shaving
and load-levelling applications in conjunction with base-load
⇑ Corresponding author. Tel.: +1 515 294 5499.
E-mail addresses: raju192@gmail.com
(S. Kumar Khaitan).
1
Tel.: +1 216 526 8384.
(M.
Raju),
skhaitan@iastate.com
0306-2619/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.apenergy.2011.08.019
thermal and nuclear power plants [17,18]. The technology for CAES
has a proven past although there are only two CAES plants in the
world namely the 290 MW plant belonging to E.N Kraftwerke,
Huntorf, Germany, 1978 [1], and the 110 MW plant of Alabama
Electric Corporation (AEC) in McIntosh, Alabama, USA, 1991 [2].
Both the plants have served us well over last two decades without
any problem. Recent global climatic concerns, widespread renewable portfolios and initiatives for a greener tomorrow open a host
of newer opportunities of application domains for large scale storage technologies such as CAES. Combining renewables with large
scale storage technology such as CAES has the potential to increase
the penetration [19] of the intermittent sources.
Currently, power generators are used, on average, approximately 55% of the time due to the variability of peak and off peak
demand. During peak demand times, power suppliers use costly
generators which increase the cost of energy for the customers.
Although a CAES turbine still requires some natural gas to heat
the compressed air that drives the turbine, it is less than one third
of the amount used by a conventional combustion turbine. Power
generators are also able to produce fossil fuel generated power
during the night when green house gases are least harmful and
the stored energy can be used during the day.
Compressed air energy storage can be achieved using manually
dug underground salt caverns, naturally occurring aquifers, depleted wells, manually built storage tanks and so on. The dynamics
of compressed air storage depends on the type of storage bed. In
naturally occurring aquifers, porous media modeling is required
to accurately model the storage bed. There is intimate contact
between the bed and the compressed air leading to heat transfer
M. Raju, S.K. Khaitan / Applied Energy 89 (2012) 474–481
between the bed and the air. Previous modeling effort has been
done by [20] to model the porous media based aquifer storage.
However, the heat transfer inside the aquifer has not been modeled. In the case of underground salt caverns, there is heat transfer
between the air in the cavern and the salt ground reservoir at the
cavern walls. Depending on the quantity of the heat transfer and
the operation of the cavern, the temperature of the cavern will
vary.
While designing the volume of the cavern for a particular project,
the operating temperature and pressure inside the cavern, plays an
important role in addition to the designed number of full load operating hours of the turbine [1]. However the temperature inside the
cavern is dependent on the heat transfer taking place between the
compressed air in the cavern and ground reservoir. Fluctuations in
this temperature can affect the operating capacity of the cavern storage, which has to be taken into account while designing the cavern
storage volume. In future, adiabatic compressed air storage systems
are to be driven only by cleanly-sourced power. The heat generated
by the compression is also to be stored and be mixed with the compressed air streaming out to the expansion turbine. The accurate
prediction of temperature inside the cavern is even more important
in the case of adiabatic compressed air storage. In reality, adiabatic
storage is difficult to achieve due to heat transfer to the surrounding
reservoir. The temperature of the stored air being high in the case of
adiabatic compressed air storage systems, the heat transfer will play
an important role in designing such systems.
In this paper, an accurate dynamics simulation model is developed for compressed air energy storage. Heat transfer to and from
the cavern walls is taken into account. Huntorf CAES system is
taken as a sample case study. The heat transfer coefficients are estimated based on comparison with test runs reported on Huntorf
CAES storage plant. Once having obtained the heat transfer coefficients, the dynamic performance of the Huntorf cavern storage is
obtained for a typical daily schedule operation of the plant and
the results are compared with the experimental results. The importance of including accurate heat transfer model is further illustrated by comparing the results obtained from isothermal and
adiabatic assumptions inside the cavern.
turbine operation; air is removed from the cavern. In principle,
both the compressor and the turbine can be operated simultaneously. The mass and the energy balance equations inside the
cavern are given below. Air is assumed to be ideal gas. In the operating range of pressures (46–66 bar) and temperatures (around
50 °C) within the cavern, ideal gas assumption for air is nearly valid
[21]. The mass and energy balance are written over the control
volume enclosing the air in the cavern. Fig. 2 shows the control
volume enclosing the cavern storage bed.
3.1. Mass balance
_ out
_ in m
dq m
¼
dt
V
3.2. Energy balance
Energy balance is written over the control volume enclosing the
air in the cavern as
dðMUÞ
_ out Hout hamb Acav ern ðT T amb Þ
_ in Hin m
¼m
dt
P
q
dH ¼ C p dT
ð3Þ
ð4Þ
Substituting Eqs. (3) and (4) in Eq. (2) leads to
qcp
_ in
dT m
dP hamb Acav ern
þ
þ
cp ðT T in Þ ðT T amb Þ ¼ 0
dt
dt
V
V
ð5Þ
The first term in the above equation is the rate of heat accumulation in the cavern air. The second term is the rate of convective
heat flux into the cavern due to the incoming air. The third term
is the heat of compression and the last term is the heat transfer
from the ambient. Tin is the temperature of the incoming air from
the compressor, P is the pressure inside the cavern and V is the volume of the cavern.
The heat transfer coefficient and the area of heat transfer are
not easy to determine. It has to be estimated based on trial experiments. Both of them are lumped together to obtain the following
equations
qcp
Compressed air is physically stored in a cavern. During the compressor operation, air is pressurized into the cavern and during the
ð2Þ
The left-hand side of this equation is the rate of increase in
internal energy of the cavern air, the first two terms on the righthand side of the equation represents the net rate of enthalpy
change between the incoming air and the outgoing air, the third
term represents the heat loss from the cavern air to the cavern surroundings. M is the mass of the air in the cavern, Hin is the specific
enthalpy of the incoming air, Hout is the specific enthalpy of the
outgoing air, hamb is the heat transfer coefficient between the cavern wall and the air, Acavern is the area of heat transfer between the
air and the cavern wall, T is the temperature of the air and Tamb is
the cavern wall temperature.
For an ideal gas
U ¼H
3. Modeling CAES
ð1Þ
q is the density of air inside the cavern and min is the mass flow rate
of the incoming air from the compressor and mout is the mass flow
rate of the out-going air to the turbine and V is the volume of the
cavern .
2. Schematic
A CAES plant mainly consists of (1) compressor train, (2) motorgenerator unit, (3) gas turbine and (4) underground compressed air
storage. Fig. 1 shows the schematic of a CAES plant. During lowcost off-peak low load periods, motor consumes the grid power
or power from the renewable source to compress and store air in
the underground cavern. Later, during peak load periods, the process is reversed. The compressed air is discharged from the storage
tank and is sent to combustion chambers to burn the natural gas.
The resulting combustion gas is then expanded in the 2-stage, high
pressure and low pressure gas turbines to generate electricity. The
electricity generated is fed back to the grid to meet the peak load.
In a pure gas turbine power station, around two-thirds of the output is needed for compressing the combustion air. In a CAES power
station, however, no compression is needed during turbine operation because the required enthalpy is already included in the compressed air. This has two advantages: (1) during off-peak periods
cheaper power can be used for compression; (2) the gas turbine
can generate thrice the electricity as compared to the conventional
gas turbine.
475
_ in
dT m
dP
þ
þ heff ðT T amb Þ ¼ 0
cp ðT T in Þ dt
dt
V
ð6Þ
heff is the effective heat transfer coefficient with the units (W/m3 K).
Eqs. (1) and (6) constitute the mass and balance energy equations for the compressed air storage in caverns.
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M. Raju, S.K. Khaitan / Applied Energy 89 (2012) 474–481
Power Grid
Exhaust
After-cooler
Motor
HP Turbine
LP Turbine
Compressor
Generator
Fuel(Natural Gas)
Salt Dome
Cavern
Fig. 1. Main components of a CAES plant.
min
H in
Table 1
Operating conditions for the Huntorf plant [1].
mout
H out
hamb Aamb (T – Tamb )
Operating condition
Value
Unit
Gas turbine
Rated turbine power
Air consumption
Inlet pressure HP turbine
Inlet temperature HP turbine
Inlet pressure LP turbine
Inlet temperature LP turbine
Fuel
290
417
42
550
11
825
Natural gas
MW
kg/s
bar
°C
bar
°C
Compressor
Air flow rate
Rated compressor power
Temperature at exit of after cooler
Pressure at exit of after cooler
108
60
50
46–72
kg/s
MW
°C
Bar
Cavern
Volume of the cavern storage
Cavern operating pressure
Maximum cavern pressure
Maximum discharge rate
Cavern wall temperature
300,000
46–66
72
10
50
m3
Bar
Bar
bar/h
°C
Fig. 2. Schematic of control volume enclosing the cavern storage bed.
To validate these model equations, Huntorf air storage gas turbine plant is taken as a case study. The following section outlines
the methodology adopted to validate the model for this case study.
4. Huntorf – a case study
Data collected from the Huntorf project as given in Table 1 is
taken as case study to validate our model. Fortunately some of
the real test data from the Huntorf plant is reported in the literature [22], which helped the authors to thoroughly validate the
model. In particular 2 data sets are extracted from [22]. First data
set is to use the trail runs conducted to examine the thermodynamic behavior of the cavern air storage (refer Fig. 4.2 of [22]). It
is reported that the heat transfer takes place at the peripheral zone
of the contact between cavern wall and the bulk air. Further the
irregular shape of the cavern wall surface led to an increased heat
transfer with the cavern surroundings, thereby leading to an
increase in storage capacity. This data set is used to evaluate the
unknown heat transfer coefficient in Eq. (6). This is presented in
Section 4.1. The next data set is the typical power production
schedule during a single day operation (see Fig. 4.1 of [22]). The
pressure variation inside the cavern during this operation is also
provided. The power consumption/production schedule is used to
determine the compressor and turbine mass flow rates which is
provided as input to the model. Knowing the accurate heat transfer
coefficients, the cavern pressure and temperature can be determined accurately. The pressure variation in the cavern predicted
by the model is compared with the observed pressure data. This
is presented in Section 4.2. Finally the results are compared with
the results obtained from the adiabatic and isothermal assumptions to demonstrate the errors that might be expected from such
assumptions.
4.1. Evaluating heat transfer coefficients
In this section Fig. 4.2 of [22] is taken as guideline to estimate
the heat transfer coefficients accurately. Preliminary comparisons
with the Huntorf test data set revealed that a constant heat transfer coefficient assumption is unable to reproduce the observed cavern behavior. This is to be expected since the mass flow rate is
changing. In general, the heat transfer coefficient is dependent on
the flow velocity. The flow velocity is affected by the charging
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M. Raju, S.K. Khaitan / Applied Energy 89 (2012) 474–481
NuL;FC ¼ 0:023ðReÞ0:8 ðPrÞn
ð7Þ
Since the flow velocities are dependent on the mass flow rate,
Eq. (7) could be written as
_ in m
_ out j0:8
NuL;FC / jm
ð8Þ
In Eqs. (7)–(9), NuL,FC is the Nusselt number based on forced
convection, Re is the Reynolds number, Pr is the Prandtl number.
Hence the heat transfer coefficient can be roughly approximated as
_ in m
_ out j0:8
heff ¼ a þ bjm
ð9Þ
The constant term a represents the heat transfer coefficient
caused by natural convection in the absence of any net flow in/
out of the cavern. The unknown coefficients a and b can be obtained by comparison with the cavern tests. These coefficients
are weak functions of temperature but given the fact that the temperature variation in the cavern is not very high, they can be assumed to be fairly constant. If the temperature variation is large,
even more accurate correlations are required. In such a case, a
more detailed CFD modeling may be required to accurately study
the temperature and pressure variation inside the cavern. However
for the current study, a lumped parameter correlation based on Eq.
(9) is a good approximation.
Fig. 4.2 of [22] provides dynamics of the cavern during a trial
discharge experiment where the cavern is vented out to the atmosphere. Up to 5 h, the cavern is discharged at a constant rate of
417 kg/s and then the discharge mass flow rate continually drops.
Fig. 3 shows the mass flow variation as a function of time. This data
is extracted from [22]. Based on this discharge mass flow as an
input to the model, the cavern pressure and temperature are simulated. The coefficients a and b are tuned in such a way that a good
match between the experimental and model results are obtained.
Thus Eq. (9) becomes
500
_ in m
_ out j0:8
heff ¼ 0:2356 þ 0:0149jm
ð10Þ
Fig. 4 shows the comparison of the experimental results and the
simulation model results. Quite accurate match between the
experimental and simulation model results are obtained for the given choice of a and b as shown in Eq. (10). Fig. 4a shows the variation of pressure with time. Since air is continually discharged
from the cavern, the pressure decreases with time. After 5 h, the
rate of decrease of pressure decreases since the mass flow discharge continually drops after 5 h. Fig. 4b shows the temperature
inside the cavern. As air is discharged from the cavern, the temperature of the air in the cavern decreases as a result of gas expansion.
Simultaneously heat is also transferred from the cavern walls to
the air. The cavern wall is assumed to be at 50 °C. The initial temperature of the air inside the cavern is assumed to be at 33 °C. Heat
is transferred from the cavern wall to the cavern air which is at
lower temperature. The temperature drops rapidly till 5 h when
the discharge is maximum. After 5 h, the discharge rate drops continuously and so the heat of expansion reduces and the heat transfer from the wall increases causing a lower decrease rate in
temperature. Interestingly at around 10 h, the temperature reaches
a minimum and it starts increasing. This implies the rate of heat
transfer from the cavern wall dominates the rate of heat loss from
gas expansion. This interesting trend is well captured by the
model.
In order to demonstrate the necessity of an accurate heat transfer model inside the cavern, comparison is made with the current
model results with the other two possible model assumptions –
isothermal and adiabatic. Both the above two model assumptions
are at the two extremes. The thermodynamics inside the cavern
will be in between the two extremes. Fig. 5 shows the comparison
of the current model results with the isothermal and adiabatic
models.
Note that in the case of isothermal assumption, the cavern
temperature is assumed to be constant at the initial temperature
of 33 °C. Fig. 5a shows the pressure variation inside the cavern for
the three models. The maximum pressure difference between the
experimental and the isothermal model is around 6 bar. Given the
80
pressure (bar)
and discharging characteristics of the cavern. The heat transfer inside the cavern is a combination of forced convection (induced by
the incoming or outgoing air) and natural convection (due to buoyancy effect). Due to lack of information on heat transfer correlation
for flows inside storage bed, the well known heat transfer correlations for flows inside tubes are taken as guidelines. The forced convection heat transfer coefficient is proportional to the convective
mass flow rate inside the cavern given by Dittus–Boelter equation
[23].
Model
Experimental
60
40
0
400
5
10
15
time (hr)
50
300
temperature (C)
mass flow rate (kg/s)
20
200
100
0
5
10
15
time (hr)
Fig. 3. Experimental data showing the discharge rate as a function of time.
Model
Experimental
40
30
20
10
0
0
5
10
15
time (hr)
Fig. 4. Comparison of model and experimental results for the discharge tests.
478
M. Raju, S.K. Khaitan / Applied Energy 89 (2012) 474–481
mass flow rate (kg/s)
250
turbine operation
compressor operation
0
-250
-500
0
5
10
15
20
time (hr)
Fig. 7. Mass flow rates during a single day of Huntdorf plant.
Fig. 5. Comparison of experimental results for the discharge tests with adiabatic
and isothermal assumptions.
3500
grid power w/o CAES
grid power with CAES
Power (MW)
3000
dynamics during actual plant operation. Fortunately, experimental
data is provided during a typical daily schedule operation of the
Huntorf plant. Ref. [22] provides the grid power demand at the
electric grid both with and without the operation of the Huntorf
plant as shown in Fig. 6. Based on the data provided in [1], it is assumed that for an intake of 108 kg/s during compressor operation,
60 MW of power is consumed by the compressor. The compressed
air is cooled in an after-cooler down to 50 °C (approximately corresponds to the salt wall temperature of the cavern) before entry into
the cavern [1]. Similarly, a discharge rate of 417 kg/s runs a turbine
which produces an output power of 290 MW. Based on these
assumptions, the flow rates during compressor operation and turbine operation are estimated. It is also assumed that the power is
directly proportional to the mass flow rates.
2500
5. Results and discussion
2000
1500
0
5
10
15
20
25
time (hr)
Fig. 6. Typical grid power operation of Huntdorf during a single day.
operating pressure of the cavern is the range of 20–70 bar, this pressure difference can lead to a significant error in the design calculations for the estimation of cavern volume and in estimating the
operational characteristics of the cavern. Similarly for the adiabatic
assumption, the maximum pressure difference is around 10 bar.
Fig. 5b shows the temperature variation inside the cavern. The isothermal model gives a temperature difference of around 25 °C. The
adiabatic model gives a very low temperature prediction of around
-100 °C at the end of 16 h discharge. The discharge flow rates are
huge (average 300 kg/s in 16 h). This caused a huge under-prediction
in the temperature for the adiabatic assumption. Hence it is to be
noted that if the flow rates are high, the adiabatic assumption will
give very misleading results.
4.2. Validation of the cavern storage model
Once having obtained the values of the heat transfer correlation
coefficients, the model is ready to accurately simulate the cavern
Fig. 7 shows the mass flow rates thus calculated based on the
above assumptions. The solid lines indicate the compressor operation and the dotted lines indicate the turbine operation. Cavern intake flow rates are treated as positive and cavern discharge flow
rates are treated as negative.
Fig. 8 shows the comparison of the model results with the actual Huntorf results for pressure variation inside the cavern during
a daily schedule operation. Fig. 8a shows the pressure variation inside the cavern. Fig. 8b shows the variation of temperature inside
the cavern. As shown in Fig. 8a, the simulated pressure variation
matches quite closely with the actual pressure variation inside
the cavern as reported in [22]. The pressure increases during the
compressor operation due to the fact that mass inside the cavern
increases. During compressor operation, the temperature inside
the cavern increases due to heat of compression. Similarly the
pressure decreases during the turbine operation due to fact that
mass is discharged from the cavern and the temperature decreases
resulting from cooling due to expansion. Ref. [22] does not report
the temperature variation inside the cavern. Using the model, the
temperature inside the cavern can be well predicted. It is to be
noted that the good match observed in the pressure variation between the simulation and the observed values is due to the incorporation of accurate heat transfer model in the energy balance
equation.
Fig. 9 shows the comparison of the observed pressure with the
simulated model and the isothermal and adiabatic models. Fig. 9a
shows the pressure variation. It is clear from the figure that the
479
pressure (bar)
M. Raju, S.K. Khaitan / Applied Energy 89 (2012) 474–481
75
model
70
Experiment
65
60
55
50
45
40
35
0
5
10
15
20
25
15
20
25
time (hr)
temperature (K)
350
340
330
320
310
0
5
10
time (hr)
Fig. 8. Cavern pressure and temperature variation during a single day operation.
actual model
experimental
isothermal model
adiabatic model
pressure (bar)
60
55
50
0
5
10
15
20
25
time (hr)
360
actual model
isothermal model
adiabatic model
temperature (K)
350
340
330
320
310
0
5
10
15
20
25
time (hr)
Fig. 9. Comparison of the cavern characteristics for the current model with the adiabatic and isothermal models.
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M. Raju, S.K. Khaitan / Applied Energy 89 (2012) 474–481
heat gained from the reservoir (kW)
2000
0
-2000
-4000
-6000
-8000
-10000
0
5
10
15
20
time (hr)
Fig. 10. Heat loss to the reservoir during daily operation of the cavern.
isothermal and adiabatic models compare poorly with the actual
one. In addition, the prediction of temperature inside the cavern
is quite different for all the three models showing the importance
of using an accurate heat transfer model.
Fig. 10 shows the heat transfer rate from the cavern walls to the
air inside the cavern. The cavern wall is 323 K. As shown in Fig. 8b,
the temperature of the cavern air is higher than 323 K during the
compressor operation. During this phase, heat is lost to surroundings through the cavern walls. The cavern surface act as a heat sink
during this phase. During the turbine operation, the temperature
inside the cavern decreases and reaches a stage where the temperature drops below 323 K. Then the heat is transferred from cavern
surroundings to the air inside the cavern. This is depicted in Fig. 10
as a shift from negative values to positive values. The heat transfer
rate is in the order of a few megawatts. This heat transfer has to be
taken into account for accurate calculations while designing cavern
storage beds.
This current simulation model can be used to conduct feasibility
studies of the CAES technology for various practical scenarios. The
efficiency of a CAES system can be improved by identifying the
waste heat losses and reusing it in the system. For example, the
waste heat of the exhaust gases from the LP turbine can be used
for preheating the air at the inlet of the HP combustor. Similarly,
the heat losses from the after-cooler during compressor operation
can be stored in suitable storage materials like PCM’s, concrete and
reused later. Feasibility studies can also be done for hybrid CAES
systems with renewable energy sources like wind and solar. The
optimal design parameters and operating conditions of the cavern
storage can be estimated based on the types of the renewable energy sources available, energy available from such sources and the
nature of their intermittency. The current storage model can also
be modified appropriately for using it in hydrogen storage applications [24] and other applications.
5.1. Technical and economical issues
There are various technical challenges to be met to bring the
CAES technology to the real market. First, is the choice of the air
storage system (mines, reservoir, salt domes, etc.). The geological
factors play an important role in the selection of proper air storage
systems. The size of the storage system needed for optimal performance of the CAES system might become technically unfeasible
depending on the local conditions [25]. Efficient recuperator systems are needed to extract the waste heat from the exhaust gases
to increase the efficiency of the CAES system. The usage of fuel
could be further reduced with semi-adiabatic systems using efficient storage and heat recuperation. In principle, the usage of fuel
could be totally eliminated with advanced adiabatic systems. However, this would require advanced research on high heat production by compressors, heat capture and transmission systems [26].
The current air driven turbine designs are based on the steam driven turbine technology and hence the operating conditions of the
CAES system are limited.
Although the technical challenges are there, the real concern is
how to address these issues in a cost effective way because bulk
storage systems like CAES have high capital cost. Thus, economic
feasibility of the CAES is an important bottleneck in the implementation of the technology. The economics of the CAES plant design is
affected by the location of the CAES plant and the plant operational
costs. The CAES plant should be located such that there is geographical suitability for CAES storage, easy delivery of natural gas
or a suitable fuel, suitable infrastructure for electric transmission
network. The plant operational costs include on-peak and off-peak
electricity costs, ancillary service revenues and fuel prices. The economics is further affected by the ability to capture multiple
revenue streams and environmental regulations. Detailed cost
analysis models need to be developed to justify the development
of the bulk storage CAES systems [26].
6. Conclusions
Accurate modeling of compressed air storage cavern is done
by including the mass and energy balance inside the cavern. It
is shown that heat transfer plays an important role in determining the behavior of the cavern. Data from Huntorf cavern operation is used to validate this model. By incorporating accurate
heat transfer model, the cavern behavior can be accurately simulated. Comparisons are made with the isothermal and adiabatic
models and are found to inadequately describe the behavior of
the cavern. Such modeling efforts will be useful in the future
design of cavern based compressed air storage beds, in terms
of making a good estimate of the cavern volume and operating
conditions.
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