TECHNO ECONOMIC EVALUATION OF COMPRESSED … Evaluation... · TECHNO‐ECONOMIC EVALUATION OF ......

SCHOOL OF CHEMICAL ENGINEERING

TECHNO‐ECONOMIC EVALUATION OF

COMPRESSED AIR ENERGY STORAGE

IN UNCONVENTIONAL APPLICATIONS Thomas B. McConnaughy, Conor Young and Eric McFarland

ABSTRACT

Compressed air energy storage systems consist of an electrically driven air

compressor, pressure vessels for storage of high pressure air, and an expander

motor coupled to a generator to produce electricity from the compressed air

supply. Unconventional configurations of CAES were analysed for compression in

underutilised water pipelines or tanks, and the system costs were compared to

conventional batteries and hydroelectric alternatives. For arbitrage of peak and off‐

peak electricity pricing, use of water pipelines for gas storage will only justify the

capital expense if the average electricity price difference exceeds $800/MWh.

Water pipeline storage may prove feasible in locations where PHS is not possible

and if price spreads increase significantly. In data centre UPS applications, CAES is

shown to be competitive with battery systems. Household CAES integrated with PV

systems are economically favourable to batteries, however, neither have

favourable overall economics. Batteries are superior to CAES for peak shaving

applications and may be profitable for certain power consumption profiles.

INTRODUCTION

Electrical energy storage encompasses a broad range of

technologies and storage time scales spanning seconds

to months (capacitors to hydroelectric dams). The

different technologies often have very different

economic and reliability characteristics.

Intermediate storage times, hours to days, have received

recent attention due to increased interest in

intermittent, renewable energy sources such as wind

and solar, for which energy storage can increase supply

reliability. Additionally, storage of stranded or excess

electricity generation, back‐up power systems and peak

demand reduction are other applications that can

benefit from intermediate storage times. At present

electrical energy storage technologies deployed

commercially at significant scales are pumped

hydroelectric storage (PHS), batteries, and CAES. Several

technologies are trying to establish their

competitiveness including flow batteries,

supercapacitors, flywheels, and superconducting

magnets1.

Electricity generation and storage using hydroelectric

systems is an established technology with generally the

lowest energy storage costs2. However, hydroelectric is

limited in capacity due to geographical and

environmental constraints1. Batteries are ubiquitous in

consumer product applications, however, their use in

large scale energy storage applications is limited due to

their high unit cost, limited cycle lives, and often their

use of hazardous materials3.

CAES has been proposed for many different energy

storage applications, however, identifying an

economically competitive application remains for

widespread adoption. In this report four scenarios are

investigated to assess the market competitiveness of

CAES.

CONFIGURATIONS

In general, electrical energy storage systems are

configured similarly to independent electricity

generators, Figure 1.

Their “fuel” is delivered and stored at the cost of the

charging electricity (adjusted for the round‐trip charging

and discharging efficiency), and the cost of the storage

capital required to store and deliver the energy when

needed. Displacing a combustion generator system

requires that the cost of the energy storage system and

the cost of charging are less than the cost of the

combustion generator and the fuel use.

CAES systems consist of an electrically driven air

compressor, pressure vessels for storage of high

pressure air, and an expander coupled with a generator

to generate electricity using the compressed air supply.

In addition, a control system is needed to ensure safe

operation and produce electricity on‐demand. Two

storage configurations that consist of four unique

applications are investigated in the analysis.

Conventional grid‐scale CAES technology has been

implemented in a limited number of sites which make

use of disused mines and salt caverns for the storage of

compressed air1. As with PHS, the potential growth of

the technology is geographically limited. Alternative

pressure vessels for storage are required to enable

distributed, widespread application of the technology.

Grid

Storage“Fuel”Electricity Product

Figure 1: Generalised electricity storage system

UNCONVENTIONAL CAES

CONFIGURATIONS

WATER PIPELINES

Empty or partially filled water distribution pipelines

could theoretically be utilised as CAES systems for grid‐

scale applications, similar to natural gas pipeline energy

recovery. Abandoned pipes can be pressurised with air

during off‐peak hours and expanded during peak hours

as a form of arbitrage. Underwater CAES (UWCAES) is a

technology which makes use of the hydrostatic pressure

of water (i.e. 1 bar per 10 m depth4) to store pressurised

air. The high hydrostatic pressure allows for a flexible

bladder to be used rather than an expensive, fixed

volume pressure vessel. Partially filled pipes can utilise a

bladder, in order to control the distribution of air within

the pipe, and the nominal water pressure for expansion.

This has the added benefit of a constant discharge

pressure, similar to underwater UWCAES5, which

simplifies the selection process of the expander and can

increase the system efficiency. The main advantage of

using pipelines is that the storage is likely to be closer to

the end user than conventional underwater storage; the

challenges and limitations of repurposing existing

infrastructure must also be considered. Figure 2 shows

the water pipeline configuration.

Grid

Compressor

Turbine

Storage

Bladder

Figure 2: Water pipeline CAES configuration

Here, we perform a case study on a water pipeline in

Brisbane, Australia. The Western Corridor Recycled

Water Pipeline is 200 km of pipeline which was designed

to transport water from various water recycling plants to

Wivenhoe dam and two power stations in the area6.

Arbitrage can be leveraged to generate daily revenue by

storing energy at off‐peak prices and releasing it during

peak demand. The difference between peak and off‐

peak electricity cost represents the profit margin of the

system and therefore is the key determining factor in the

viability of the application.

HIGH PRESSURE TANK STORAGE

Compressed air is used in a variety of commercial

applications and high pressure gas cylinders used as

storage vessels are common. We examined whether

these configurations would be appropriate for dedicated

electrical energy storage. This configuration is depicted

in Figure 3. There is flexibility with the size and pressure

of the tank, which can be customized to the application.

Grid

Compressor

Turbine

Storage

Figure 3: High pressure tank CAES configuration

Applications: The following applications share a

common mechanical model, however have different

value propositions due to energy delivery requirements.

The frequency of operation of the devices varies from

daily to annually, whilst power output varies from tens

to thousands of kilowatts.

a) Uninterruptable Power Supply (UPS)

UPS systems are used to avoid abrupt losses of power to

critical equipment which can result in significant loss of

revenue. Lead acid battery UPS systems are the

dominant technology for data centre backup power

supply, however, of those that experienced unplanned

power outages, battery failures were the leading root

cause7. The increased reliability of CAES systems, due to

their mechanical rather than electrochemical operation8,

holds promise to reduce the frequency of power outages

in UPS applications.

From a techno‐economic perspective, there is

substantial savings potential in mitigating power outages

due to the high costs associated with blackouts. Here,

the business case for a CAES UPS system is explored as

well as a comparison to existing lead acid battery UPS

systems.

Water Pipeline

b) Household & Photovoltaic

Recently, flexible pricing schemes have been introduced

by electricity providers to reduce the difference between

peak and off‐peak energy demands. Energy costs shift

depending on the demand associated with the period of

the day, known as peak (morning and evening), shoulder

(afternoon), and off‐peak (night). Passing on the

difference in cost between producing energy at peak and

shoulder or off‐peak times to the consumer incentivises

a reduction in peak power consumption9 and thus the

need for inefficient, expensive, and high carbon intensity

peaking power plants10. Coupling CAES and

photovoltaics (PV) can provide stored solar energy for

on‐demand use during peak hours, while grid electricity

can be used exclusively during off‐peak and shoulder

hours when electricity is cheaper.

Households in Queensland either pay a flexible or

variable rate for their electricity usage11. In order to

utilise flexible pricing economically, the price difference

between the standard rate and shoulder or off‐peak,

depending on time of use, must be high enough such

that the cost to generate and recover the electricity is

less than the standard electricity rate. More simply, the

annual savings, or ‘profits’, can be seen as the money

saved each year on the household electricity bill.

c) Peak Shaving

In many electricity markets, customers are charged a fee

based on their highest power consumption during the

billing period, known as a ‘demand charge’. The

infrastructure provider’s cost are related to their

capacity to provide peak power which can be greater

than the costs for generating the electricity12. The total

electricity bill for customers can be a combination of

demand charges and time‐of‐use adjusted total energy

consumption. Customers whose power demand profile

features brief power consumption spikes have

significant potential for savings if those demand spikes

can be removed through energy storage. The proposed

energy storage system is charged at times of reduced

power consumption and low cost energy for use at later

times to reduce the peak power required from the

supplier.

ANALYSIS

THERMODYNAMICS

The CAES system is charged by compression of air and

discharged by expansion of the air in a motor to drive a

generator producing electricity. Each step has associated

energy losses which determine the overall round trip

efficiency.

An estimate of the system efficiency can be determined

based on thermodynamic considerations of the

compression, storage, and expansion operations. Using

Equations 1 and 2, the energy required for compression

is calculated assuming ideal gasses and adiabatic

operation, where η is the expansion efficiency or inverse

compression efficiency (ηe = ηc = 0.8)13, γ is the heat

capacity ratio, (γair = 1.4), 1 and 2 refer to the inlet and

outlet of compressor respectively, and C is constant

between compressed and expanded states. This is

opposed to modelling isothermally, which is currently

unrealistic given current heating/cooling technologies.

In reality, the cycle will be neither adiabatic nor

isothermal. Equation 1 can be modified to calculate the

power required of compression or expansion given the

volumetric flow rate, q [m3/s] (Equation 3). The

compressed air storage volume is dictated by the system

work‐output requirement and expansion inefficiency

(Equation 1).

Equation 1: Adiabatic compression/expansion work

11

Equation 2: Adiabatic ideal gas relationship

Equation 3: Power

11

TECHNO‐ECONOMIC MODEL

An analysis of the costs was performed on several of the

system configurations and applications described above.

Table 1 lists the assumptions that were consistently used

for multiple application examples; any inconsistencies

are addressed in scenario specific tables. The

compressor, expander, and storage capital including

efficiency values13 were determined based on available

industrial information. The installed cost of the

equipment was then determined by using an installation

factor14. A storage pressure of 300 bar was used,

assuming multi‐stage expansion to ambient pressure.

The CAES system is expected to have a lifespan of 30

years2, however, a period of 10 years is used to assess

investment potential. Operating expenses (OPEX) are

the sum of the fixed and variable costs. Fixed costs are

assumed to be 5% of the discounted annual capital

expenditure15 (CAPEX) while the variable costs include

expenses such as charging the system. The discounted

annual CAPEX can be found by dividing the total CAPEX

by the total discount factor (TDF [years], Equation 4),

where i is the discount rate, n is the current year, and N

is the plant life.

Table 1: Model assumptions

Parameter Value Unit

Expansion Efficiency 80 %

Compression Efficiency 80 %

Tank Storage Pressure 300 bar

Tank Storage Capital 950 $/kWh

Installation Factor 1.25 n/a

CAES Lifespan 30 years

Investment Period 10 years

Discount Rate 10 %

Fixed Costs 5% of annual CAPEX $/year

TDF 9.43 years

Equation 4: Total discount factor

1

A key metric to compare and value CAES and other

storage technologies is the cost, per kilowatt hour, of

energy recovered over the lifetime of the system

(CoERlife [$/kWhlife], Equation 5), which is a function of

CAPEX and OPEX over the device lifetime, and the total

energy delivered during that time. This value provides a

basis for comparison against existing storage

technologies, such as batteries, that have lower capital

costs, but shorter lifetimes. This comparison provides

insight on the economic feasibility of CAES for different

applications.

Equation 5: Cost of energy recovered over the lifetime of the

system

$ $

$

The annualised costs of any electrical energy storage and

generation system used for a cycle duration or period,

tcycle [hours/cycle], can be placed in the same framework.

Using the unit capital cost based on delivered electric

power [$/kWrated], the capital cost to store the energy

[$/kWhrated], and the operating costs to charge the

system [$/kWh], the annual cost of power (ACoP

[$/kW‐y]), neglecting fixed costs, for a unit lifetime

approximately equal to the TDF, is given by:

Equation 6: Annual power cost of competing technologies

$

)

Where η is the round trip efficiency of the system and

fcycle is the cycle frequency [cycles/year]. Equation 6 has

the advantage of having two terms that include usage

dependence and independence. Here we use this basis

to compare CAES to competing technologies.

Performance metrics of competing technologies in

literature are shown in Table 2. Energy storage systems

have time‐dependent as well as cycle‐dependent failure

modes, measured in years and cycles respectively. The

ultimate failure mode will be whichever limit is reached

first, depending on scenario. CAPEX takes into account

the discounted future value of replacement batteries.

Internal rate of return (IRR) is calculated for each

scenario, 10% being used as the cut‐off for a feasible

investment prospect. A discount rate of 10% is applied

to determine the future value of profits and expenses.

Table 2: Comparative technology performance metrics2

Parameter PHS Lead‐acid Battery

Cycle Lifetime 13000+ 1000

Lifespan [years] 30 5

Capital [$/kWh] 222 703

WATER PIPELINE ARBITRAGE

Here, we perform a case study on a water pipeline in

Brisbane, Australia. The Western Corridor Recycled

Water Pipeline is 200 km of pipeline which was designed

to transport water from various water recycling plants to

Wivenhoe dam and two power stations in the area6.

Arbitrage can be leveraged to generate daily revenue by

storing energy at off‐peak prices and releasing it during

peak demand. The difference between peak and off‐

peak electricity cost represents the profit margin of the

system and therefore is the key determining factor in the

viability of the application.

Since completion of the Western Corridor Recycled

Water Pipeline in 2008 much of the pipe remains

unused; 140 km was considered in this analysis for

partial or total repurposing for CAES6. The maximum

pressure and average diameter of the pipeline were

estimated because no publically available data was

found. Data from the nearby Gold Coast Water network

was used to estimate an average diameter of 650 mm for

the pipeline in question. The pipeline storage volume,

4,646 m3, was calculated using the length and 10% of the

cross‐sectional area. Analysis of the pipeline path

revealed a steep 85 m elevation change at the Mt Petrie

balance tank. Assuming a continuous water column

across the elevation change, nearby sections of pipe

must be rated to at least 8.3 bar. It was assumed that

average water pressure in the pipeline is 5 bar, for the

purpose of estimating storage capacity. Air in a flexible

bladder cannot be pressurised any higher than the

surrounding fluid. The assumptions for the techno‐

economic analysis can be seen in Table 3.

Expander and compressor power requirements were

calculated using the known storage volume and pressure,

peak and off‐peak period durations, and equipment

efficiency. These quantities were then used to specify

capital costs. The CAPEX, $874,000, is broken down by

component in Figure 4. 50% of the compressor and

expander cost was set aside to pay for air storage bags

for the system. This allocation equates to $437/kWh of

storage.

The potential storage capacity of the pipeline was

assessed for three operating conditions: 10% (base‐case),

50%, and 100% of the pipeline cross‐section filled with

compressed air, as seen in Table 4. Depending on the

operation mode, the pipeline may store between 0.7 and

7 MWh of energy.

Table 3: Model assumptions for water pipeline arbitrage


Pipeline Length 140 km

Diameter 0.65 m

Storage Volume 4646 m3

Storage Pressure 5 bar

Expansion Power 416 kW

Expander Capital 1245 $/kW

Compression Power 139 kW

Compressor Capital 463 $/kW

Delivered Storage Potential

666 kWh

Air Storage Bag 50% of major equipment

$

Peak Power Duration 2 hours/day

Off‐peak Power Duration

6 hours/day

Peak Energy Cost 0.10 to 0.90 $/kWh

Off‐peak Energy Cost 0.5 $/kWh

Cycles per Day 1 day‐1

Cycles per Lifetime 10950 cycles

PHS CoERlife 0.04 $/kWhlife

Table 4: Storage potential under other operating modes

Parameter 50% Air Filled 100% Air Filled

Storage Volume [m3] 23,228 46,456

Delivered Storage Potential [kWh]

3,330 6,660

Figure 4: Breakdown of CAPEX for water pipeline arbitrage

6%

47%27%

20%

Compressor Expander Storage Installation

UPS

UPS specifications of 1.36 MW power output and 2.7

MWh energy per cycle correlate to average data centre

size, blackout duration, and frequency in the United

States. On average, data centres experience one full

power outage per year lasting 119 minutes7. The average

cost of such blackouts are $7900/min due to business

disruption and lost revenue7. Compression power was

determined assuming a 7 day recharge duration is

acceptable because of the infrequencies of power

outages. Due to the increased reliability of CAES when

compared to batteries, it was also of interest to see how

the lifetime storage cost was affected by a battery UPS

failure, however unlikely. The cost of a UPS failure

caused power outage was discounted and assumed to

occur at the halfway point of the system lifetime. This

investigation was done to quantify the step change in

CAPEX given a system failure. A sensitivity analysis of IRR

was conducted by varying the blackout cost to simulate

the feasibility for differing levels of power outage

consequence.

The total CAPEX of $4.8 million includes storage,

expansion, and installation (see Figure 5).

Table 5: Model assumptions for UPS


Energy Cost 0.10 $/kWh

Required Energy 2689 kWh



Compression Power 20 kW


Blackout Avg. Frequency 1 yr‐1

Blackout Avg. Duration 119 min

Blackout Avg. Cost 7900 $/min


Battery CoERlife 54 $/kWhlife

Figure 5: Breakdown of CAPEX for UPS (compressor negligible)

HOUSEHOLD & PHOTOVOLTAIC

A typical Australian household uses 20 kWh of energy

per day16. Half of this amount (10 kWh) was assumed to

be during peak time; this amount will be generated and

stored so the household will purchase no electricity

during peak time. The compressor was sized according

to the maximum power output from the PV system

during peak insolation. The expander was sized in order

to provide one hour of power at the maximum rating. A

5% discount rate was used as a reasonable value for

household finance. Table 6 lists the assumptions

associated with this model and a breakdown of the

CAPEX can be seen below in Figure 6. The $40,000 CAPEX

accounts for PV, compression, storage, expansion, and

installation. The lifetime energy recovered equals thirty

years of peak electricity usage for an average household.

The PV capital was reduced to reflect the improved

efficiency of batteries as compared to CAES, then added

to the battery capital cost to calculate CoERlife.

A sensitivity analysis for IRR was conducted to conclude

which variables have the largest impact on the recovered

energy cost. These were determined to be daily energy

consumption, peak energy usage, PV capital, and storage

capital. The parameters can be seen below in Table 7.

The daily energy consumption was varied by ±10 kWh, as

this is reasonable to replicate a small and large

household17. The PV capital was varied to present the

possibility of utilising a pre‐existing set of solar panels

(zero purchase price) to a current, more conservative

estimate18. Lastly, the lower and upper bounds for

storage cost correlate to 100% efficient isothermal

expansion and 80% efficient adiabatic expansion from a

low‐pressure (14 bar) air receiver tank, respectively.

54%

26%

20%

Storage Expander Installation

Table 6: Model assumptions for household PV energy storage


Flat Rate Energy Cost 0.31 $/kWh

Peak Energy Cost 0.37 $/kWh

Shoulder Energy Cost 0.26 $/kWh

Off‐peak Energy Cost 0.22 $/kWh

Daily Energy Consumption 20 kWh/day

Peak Usage 50 %

Shoulder Usage 40 %

Off‐Peak Usage 10 %

Insolation (annualised) 200 W/m2

PV Capital 520 $/m2

PV Efficiency 15 %



Compression Power 3.3 kW


Discount Rate 5 %


Battery CoERlife 0.55 $/kWhlife

Figure 6: Breakdown of CAPEX for household PV energy

storage

Table 7: IRR sensitivity analysis values for household PV energy

storage

Parameter High Base Low

Daily Energy Consumption [kWh/day]

30 20 10

PV Capital [$/m2] 750 520 0

Storage Capital [$/kWh] 2600 950 380

PEAK SHAVING

To decide upon the system specifications, a prospective

company’s power consumption profile must be assessed.

Figure 7 shows the power consumption of an industrial

operation over the course of a week; each data series

signifying one day. The prospective company has a

power consumption peak once per week caused by

starting a large piece of machinery. An assumption was

made that this same power consumption profile will

repeat each week over the lifespan of the system.

The peak to be levelled is chosen such that it will cause

the highest reduction in maximum power consumption

whilst requiring the least possible energy storage to

maximise profits. For the system to work as designed,

there must be sufficient ‘area’ (energy) below the new

maximum power usage cut‐off. This is designated as the

charging window for the CAES system (blue shaded

areas). Power demand will be increased during the time

used for charging, which is acceptable so long as the

demand remains below the new maximum power. The

height of the peak dictates the power output required of

the expander whilst the area under the peak dictates the

energy storage required.

For the purposes of the TEA, a peak demand charge of

$22/kW was applied, as well as an energy cost of

10c/kWh. See Table 8 for values used in the analysis. The

compressor was sized assuming it will be able to charge

the CAES system overnight to accommodate scenarios

involving daily peaks. The CAPEX of $105,000 considered

the cost of compression, storage, expansion, and

installation (Figure 8).

A sensitivity analysis of IRR for different variables was

conducted, shown in Table 9. The upper and lower

bounds of the demand charge were chosen to simulate

rising prices due to increasing network stresses and

smaller metropolitan areas than New York, respectively.

The frequency of power peaks will depend on the

operational schedule of the customer so the effect of

variation between daily and monthly was considered.

The lower and upper bounds for storage cost correlate

to 100% efficient isothermal expansion and 80% efficient

adiabatic expansion from a low‐pressure (14 bar) air

receiver tank, respectively. The expander upper limit is

derived from scaling a purpose built Honeywell MTG 160

kW gas pressure recovery turbine19 whilst the lower limit

is 50% of the base case expander price.

28%

30%

20%

15%

7%

Photovoltaics Storage Installation

Expander Compressor

Table 8: Model assumptions for peak shaving


Energy Storage Required 35 kWh

Peak Power Reduction 40 kW


Compression Power 5.5 kW


Energy Cost 0.10 $/kWh

Demand Charge 22 $/kW

Billing Period 30 days

Peaks per Billing Period 4 billing period‐1


Battery CoERlife 1.08 $/kWhlife

Figure 8: Breakdown of CAPEX for peak shaving

Table 9: IRR sensitivity analysis values for peak shaving

Parameter High Base Low

Demand Charge [$/kW] 30 22 13

Energy Charge [$/kWh] 0.30 0.10 0.05

Peaks per Period 30 4 1

Storage Capital [$/kWh] 2600 950 380

Expander Capital [$/kW] 8839 439 219

23%

44%

20%

13%

Expander Storage Installation Compressor

Power [kW

]

Time [½ hr]

Figure 7: Daily load data, Factory 'A'

RESULTS & DISCUSSION

WATER PIPELINES

The base case of a 10% air filled pipeline resulted in a

CoERlife of $0.18/kWhlife. This can be compared to PHS

that has a CoERlife of $0.02/kWhlife, seen in Table 3.

Considering the 5000 MWh storage of the nearby

Wivenhoe pumped hydro power station20, the 7 MWh

pipeline storage potential spread out over 140 km seems

negligible and difficult to justify.

Whilst it is proposed that a pipe with 10% cross‐sectional

area taken by air storage could still be used to transport

water, inevitably it would come at the cost of increased

pumping work. Reduction in the effective pipe diameter

caused by the air storage causes increased fluid velocity

and therefore frictional losses. Whether or not this

aspect would render the proposal unfeasible is still

unknown. The risk of a detached air storage bag blocking

pipe flow may make adaptation of in‐use pipelines

unfeasible.

Figure 9 plots IRR against wholesale energy price

difference. The techno‐economic analysis on the 10% air

filled pipeline predicted that a consistent peak/off‐peak

wholesale electricity price difference of $800/MWh

would be required for the project to break even. In

January 2014, when price spreads are large, the

maximum intraday spread was just $284/MWh21. More

importantly, the median for 2013‐2014 wholesale price

spread was only $3.38/MWh, which is the key metric to

predicting economic sustainability. Even with the low

CoERlife, the minimal profits result in an uneconomical

proposal given the current energy market.

Figure 9: IRR sensitivity as a function of price spread for water

pipeline arbitrage

UPS

As seen in Table 10, the capital ($/kWh) of CAES as well

as the CoERlife is more than the equivalent six sets of

batteries required for the same lifespan. If the cost of

one unmitigated blackout over the course of 30 years is

added to the NPV analysis, simulating a battery failure,

the capital and CoERlife are both increased by ~5%.

Table 10: Cost comparison of CAES and battery UPS systems

Parameter CAES Battery Battery with 1 Failure

Capital [$/kWh] 1777 1624 1707

CoERlife [$/kWhlife] 62 54 57

The cost per minute of power outage is highly variable

between different industries. This variable determines

the sensible upper limit on spending to avoid power

outages. Figure 10 depicts the effect cost per minute of

blackout has on IRR for the model, demonstrating the

business case for having an effective CAES UPS system so

long as the facility’s predicted blackout cost is greater

than $6800/min for the given scenario. The IRR is 14%

for the base case, which explains the widespread

adoption of UPS systems.

Figure 10: IRR sensitivity as a function of blackout cost per

minute for UPS

‐25%

‐20%

‐15%

‐10%

‐5%

0%

5%

10%

15%

0 200 400 600 800 1000

IRR

Price Spread [$/MWh]

‐30%

‐20%

‐10%

0%

10%

20%

30%

40%

50%

0 5000 10000 15000 20000

IRR

Blackout Cost [$/min]

HOUSEHOLD & PHOTOVOLTAIC

The model delivers peak energy at a rate of $0.38/kWhlife,

somewhat cheaper than the battery and PV setup which

delivers power at $0.55/kWhlife. As can be seen, the PV

system takes up a large portion of the total capital. This

is largely due to the inefficiencies of the system, as well

as the high installed cost of residential PV. The

competitiveness of CAES arises due to the frequency of

use in this scenario. Batteries cycled daily need to be

replaced every 2.7 years. IRR over 10 years is ‐16%.

The results of the sensitivity analysis in Figure 11 exhibit

the current futility of the application. As the energy

consumption increases, the profitability decreases. Even

with PV capital reduced to zero the investment is

unviable. Altering the storage capital has a similarly

minimal effect. A significant increase in flat rate energy

cost coupled with a decrease in off‐peak and shoulder

variable pricing rates is required to warrant investment.

Admittedly, the required photovoltaic system size is

highly dependent on the level of insolation, which is

highly variable due to season and weather. Therefore

the size of the system has an element of intrinsic error

due to the fact that during times when consumption is at

its greatest, the system will not be adequately sized. This

model assumes a constant insolation year‐round, which

is an optimistic approach. During times of low insolation,

some grid peak energy will need to be purchased which

is not factored into the analysis. It was assumed that

purchasing peak energy during these times would be

cheaper than oversizing the system to accommodate for

a worst‐case scenario.

PEAK SHAVING

The base case IRR was found to be 5.9% with a CoERlife of

$1.56/kWhlife. Lead acid batteries provide cheaper

energy for the same application, at $1.08/kWhlife.

The shape of the peak to be shaved has great influence

on the profitability of the installation; the reduction of a

sharp peak is more feasible than one which is wide. This

is due to the cost of energy storage, which does not have

a revenue stream associated with it. See Figure 12 for the

IRR sensitivity as a function of the required peak power

to energy ratio. A high ratio indicates a very sharp peak,

minimising storage capacity and maximising demand

charge reduction.

Figure 12: IRR as a function peak power/energy ratio for peak

shaving

At low power to energy ratios, the tanks make up the

vast majority of capital expenses. So much is spent

storing the compressed gas that the profitability of the

savings is diminished.

‐20%

‐15%

‐10%

‐5%

0%

5%

10%

15%

20%

25%

0.0 0.4 0.8 1.2 1.6 2.0 2.4

IRR

Peak Power to Energy Ratio [kW/kWh]

Figure 11: IRR sensitivity analysis for household PV energy storage

‐6.0%

‐10.6%

‐13.1%

‐24.5%

‐18.1%

‐22.1%

‐30% ‐25% ‐20% ‐15% ‐10% ‐5% 0%

Daily Energy Consumption [kWh/day]

PV Capital [$/m^2]

Storage Capital [$/kWh]

IRR

Low High

Figure 13 shows how changing the power to energy ratio

of the peak to be levelled affects the magnitude and

breakdown of capital expenditure. This was done by

simultaneously increasing peak power and reducing the

energy storage capacity required of the system. As the

peak becomes sharper, the proportion spent on storage

capacity and compression drops whilst expander

equipment makes a larger fraction of expenditure.

Overall, CAPEX is significantly reduced whilst at the same

time the savings derived from demand charge reduction

increase.

Figure 14: CAPEX versus power to energy ratio

Results from the sensitivity analysis can be seen in Figure

14. Changing the demand charge has a large effect on

IRR as this is the only source of profit. Targeting areas

with the highest demand charges, such as New York, will

reap the greatest benefits22. The energy cost and peaks

per period do not have as much of an effect because of

the relatively small amount of energy used by the system.

Increasing the amount of peaks that require shaving per

billing period only affects the operating expenses, which

as previously mentioned are small. Varying the capital

costs associated with storage and expansion result in

large changes in IRR as they make up a large proportion

of CAPEX.

COMPARISONS

Unconventional applications of CAES were compared to

conventional energy storage solutions for: water

pipeline, UPS, household photovoltaic, and peak shaving

applications. The CoERlife was determined for each, as

seen in Table 11.

For all scenarios except household + PV, CAES is more

expensive over the lifetime than existing solutions. This

is due to the daily operation of the household PV system

and the relatively low cycle‐lifetime of lead‐acid

batteries. The frequent replacement of battery packs

caused by daily cycling drives up the lifetime energy cost,

making CAES the less economically unfavourable option.

The annual cost of power of CAES applications can be

compared to battery storage, PHS, and combustion

based power generators given a frequency of 52 cycles

per year (Figure 15). It is shown how the ACoP changes

with respect to usage per cycle. As usage increases,

0

20

40

60

80

100

120

140

160

0.2 0.3 0.5 0.7 1.0 1.4 2.0

CAPEX

[$k]

Peak Power to Energy Ratio [kW/kWh]

Storage Expander Installation Compressor

Table 11: CoERlife summary [$/kWhlife]

Water Pipeline UPS Household + PV Peak Shaving

CAES 0.18 62 0.38 1.56

Current Solution (PHS/Battery) 0.04 54 0.55 1.08

Figure 13: IRR sensitivity for peak shaving

‐4.4%

6.0%

6.0%

15.7%

9.3%

13.3%

5.5%

4.6%

‐6.8%

‐24.2%

‐30% ‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20%

Demand Charge [$/kW]

Energy Charge [$/kWh]

Peaks per Period

Storage Cost [$/kWh]

Expander Cost [$/kW]

IRR

Low High

batteries and CAES costs also increase, while PHS and gas

generators stay relatively the same. This is evident of the

minor capital costs associated with storage in PHS and

gas generators. Therefore, in scenarios with high usage

cycles, PHS and gas generators prove to be much more

economical than alternatives. In scenarios with high

cycle duration and where PHS and gas generation are

unsuitable, CAES may have a market opportunity over

battery systems. The storage capital and resulting ACoP

for battery systems will increase, for a given cycle

duration, if the cycle frequency is increased above 200

cycles per year, due to batteries requiring earlier

replacement.

CONCLUSIONS

The application specific economics of unconventional

uses of CAES in configurations in underutilised water

pipelines or compressed gas tanks were compared to

conventional alternatives for applications in: arbitrage,

UPS, household photovoltaics, and peak shaving.

a) Water pipeline energy storage will only justify the

capital expense when average differences in

peak/off‐peak electricity prices exceed $800/MWh.

Based on existing data, a compelling business case

does not exist in the present energy market. PHS is

currently a cheaper alternative however, water

pipeline storage may prove feasible in locations

where PHS is not possible and if electricity price

spreads increase significantly.

b) There may be cause for further investigation into

UPS applications of CAES as it was shown to be close

to competitive with battery systems. The value of

energy storage for UPS is proven and its usage in

battery form is widespread.

c) Household CAES integrated with PV systems are less

economically unfavourable than batteries. PV’s

without storage are the least unfavourable however

implementation of demand charges would

incentivise storage.

d) Batteries are superior to CAES for peak shaving

applications and are feasible and advantageous for

certain power consumption profiles.

In all scenarios, it is important to note how each

equipment specification affects the overall conclusion of

the analysis. Storage pressure dictates the energy

density and overall size of equipment required. The cost

of energy recovered depends largely on the cylinder

specification. Tanks are available in a range of working

pressure depending on application and this must be

considered when specifying a system. Expansion and

compression efficiencies affect the CAPEX and OPEX of

the CAES system. The efficiency of expansion dictates

how much excess storage capacity is required to achieve

the desired energy output on discharge. The round trip

efficiency determines the increase in total electricity

consumption caused by use of the system which has a

mild effect on profitability.

Further research of each application is needed to

determine if there are more specific situations in each

scenario that do warrant investment. This may be the

case for extended sections of large and disused water

pipeline, facilities that have very high cost per minute

blackout, or businesses with occasional sharp peaks in

their electricity usage. These situations are likely

uncommon and therefore may not have widespread

impact. The most favourable scenarios will be those

which can utilise existing infrastructure to reduce CAPEX,

such as, empty water pipelines or facilities already using

compressed air. Future research into associated

technologies, which may be coupled with enticing

energy price spreads, will also assist in providing a stable

economic landscape for feasibility. In summary, CAES

research should be directed toward applications where

the delivered energy provides high value savings to the

customer and where high reliability is required.

0

200

400

600

800

1000

1200

1400

0 1 2 4 6 8 10Annual Cost of Power [$/kW‐y]

Cycle Duration, tcycle [hours/cycle]

Battery CAES PHS Gas Generator

Figure 15: ACoP comparison of competing technologies23

CONTACT UQ DOW CENTRE

e: [email protected]

w: dowcsei.uq.edu.au

t: +61 7 334 63883

UQ Dow Centre

Level 5 Hawken Engineering Building (50)

The University of Queensland

Brisbane QLD 4072

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