OPTIMUM MEDIUM SCALE WIND-CAES … · simulate system operation, a sizing algorithm has been developed in C# (Wind-CAES-DM) The proposed solution is accordingly applied to three different

OPTIMUM MEDIUM SCALE WIND-CAES CONFIGURATIONS FOR

THE ELECTRIFICATION OF REMOTE COMMUNITIES

A. Marcogiannakis1, P. Pasas1, D. Zafirakis2, J.K. Kaldellis1

1Soft Energy Applications & Environmental Protection Lab

TEI of Piraeus; www.sealab.gr 2Norwich Business School

University of East Anglia

email: [email protected]

http://www.sealab.gr/

INTRODUCTION

Increased interest is recently noted in the

promotion of the so-called distributed generation

There are several areas across the globe that

cannot appreciate connection to a solid electricity

grid and thus rely on stand-alone energy

solutions, normally employing autonomous

power stations operating on imported oil

quantities

In many of these regions medium to high quality

RES potential that encourages installation of

RES solutions is met

Although such technologies are granted as

established, back up power is still required in

order to support the variable energy generation

ENERGY STORAGE SYSTEMS

There are various energy storage

technologies, that may interact with the

primary RES energy source and achieve

100% energy autonomy for the load

consumption each time investigated

Grown interest is recently noted in the

investigation of compressed air energy

storage (CAES) systems, normally used in

energy management applications

Operation of such systems is based on the

exploitation of waste/surplus or off-peak low

price energy amounts, feeding a motor-

compressor system that compresses air at

either an underground air cavern or a high

pressure tank.

POSITION OF THE PROBLEM

The current study investigates the solution of an

integrated Wind-CAES system used to serve

remote communities

An optimization methodology is developed on

the basis of a techno-economic analysis under

the restriction of 100% energy autonomy

offered to the remote community

The effect of the local wind potential on the

results obtained is examined

Two different system versions are studied; i.e.

the conventional CAES cycle and the dual-

mode CAES cycle, where the system may allow

shift to the Brayton cycle when energy stores

are not sufficient to cover energy demand

PROPOSED SYSTEM CONFIGURATION

A storage cavern or tank of given volume storage and maximum depth of discharge

A combustion chamber where the required amount of compressed air and natural gas

are mixed together for the production of gases that will operate the gas-turbine

A natural gas tank, used for fuel storage

A gas turbine (GT) and an electrical generator configuration of a certain power output,

directly related with the maximum appearing power deficit

A wind park comprising of a number of

wind turbines

A motor used to exploit any wind energy

surplus and feed the compressor

A multi-stage compressor, used to

compress ambient air into the air

cavern/tank

SYSTEM OPERATION

A. In the case that wind energy production is sufficient to cover energy

demand, wind energy is directly fed to the local consumption and

any appearing energy surplus is used to compress air inside the

cavern, provided that the latter is not full

B. In the case that wind energy production is not sufficient to cover the

load demand, the required amount of compressed air and fuel are

drawn in order to operate the GT

C. In case that both wind energy and energy stores are not able to

cover load demand, the appearing energy deficit is covered by the

dual-mode system operation, i.e. the GT is used to operate the

compressor and produce the appropriate energy, under a different

heat rate or efficiency, in comparison to the CAES cycle

PROBLEM INPUTS & VARIABLES (1/3)

Main problem inputs require wind speed, ambient temperature & pressure plus

hourly load demand

Main problem variables include wind farm capacity, compressor power and

storage volume

Technical characteristics of main system components are also required while to

simulate system operation, a sizing algorithm has been developed in C# (Wind-

CAES-DM)

The proposed solution is accordingly applied

to three different wind potential areas, being

representative of low, medium and high wind

potential cases of islands found in the Aegean

Sea, Greece

Note that the respective regions correspond to

isolated electricity systems, depending heavily

on oil imports

Plans concerning the introduction of LNG

terminals in certain island areas stimulate

investigation of the Wind-CAES solution

Three representative areas currently selected,

with the annual mean wind speed at 10m

height corresponding to 8.2m/sec, 6.2m/sec

and 4.7m/sec respectively

Annual Wind Speed Measurements on an Hourly Basis for

the Three Areas of Investigation

0

5

10

15

20

25

0

40

0

80

0

12

00

16

00

20

00

24

00

28

00

32

00

36

00

40

00

44

00

48

00

52

00

56

00

60

00

64

00

68

00

72

00

76

00

80

00

84

00

88

00

Hour of the Year

Win

d S

pee

d (

m/s

ec)

High WindMedium WindLow Wind


At the same time, the hourly load

demand profile of a medium scale

island for an entire year is used

The peak load demand reaches 6MW

and the respective minimum load

demand drops to 1MW, while the

annual energy demand exceeds

30GWh

Furthermore, a typical wind turbine

power curve is currently used in order

to estimate wind energy production on

the basis of wind potential

measurements available

Annual Variation of Load Demand on an Hourly Basis

(Medium Scale Island)

0

1

2

3

4

5

6

7

0

40

0

80

0

12

00

16

00

20

00

24

00

28

00

32

00

36

00

40

00

44

00

48

00

52

00

56

00

60

00

64

00

68

00

72

00

76

00

80

00

84

00

88

00

Hour of the Year

Load D

em

and (

MW

)


Non Dimensional Power Curve of a Typical Wind Turbine

0,0

0,2

0,4

0,6

0,8

1,0

1,2

0 2,5 5 7,5 10 12,5 15 17,5 20 22,5 25

Wind Speed (m/sec)

Non D

imensio

nal P

ow

er

Ou

tput

ENERGY AUTONOMY LEVELS (1/2)

By applying the proposed methodology, first application results obtained concern hours of load rejection per year for the operation of the Wind-CAES scheme Variation of both main parameters, i.e. wind power capacity and storage volume, from 4 to 60MW and from 10,000 to 100,000m3 respectively Increase of wind power capacity increases energy autonomy, with the parallel increase of storage volume allowing greater exploitation of wind energy surplus Energy autonomous configurations (i.e. configurations that guarantee zero load rejections for the entire year period) are designated in all cases examined

The Impact of Wind Power Capacity & Storage Volume on

the Levels of Energy Autonomy (Low Wind Potential Case)

0

1000

2000

3000

4000

5000

6000

7000

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60

Wind Farm Capacity (MW)

Ho

urs

of L

oa

d R

eje

ctio

n p

er

Ye

ar V=100,000m3 V=90,000m3

V=80,000m3 V=70,000m3

V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3

V=20,000m3 V=10,000m3

ENERGY AUTONOMY LEVELS (2/2)

In the case of low wind energy potential, one

needs a wind farm capacity that exceeds

50MW and a storage volume in the order of

100,000m3

In the case of the medium wind potential,

energy autonomous configurations result for

wind power capacity higher than 40MW, with

the respective min storage capacity required

even approaching 50,000m3 for the highest

wind power capacity, i.e. 60MW (half the one

corresponding to the low wind potential case)

In case that a high wind potential area is taken

into account, wind farm capacity of even

14MW is able to provide 100% energy

autonomy, if the highest storage capacity is

employed

The Impact of Wind Power Capacity & Storage Volume on the

Levels of Energy Autonomy (Medium Wind Potential Case)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

6000

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


Ho

urs

of L

oa

d R

eje

ctio

n p

er

Ye

ar

V=100,000m3 V=90,000m3

V=80,000m3 V=70,000m3

V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3

V=20,000m3 V=10,000m3


the Levels of Energy Autonomy (High Wind Potential Case)

0

500

1000

1500

2000

2500

3000

3500

4000

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


Ho

urs

of L

oa

d R

eje

ctio

n p

er

Ye

ar

V=100,000m3 V=90,000m3

V=80,000m3 V=70,000m3

V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3

V=20,000m3 V=10,000m3


the Levels of Energy Autonomy (Low Wind Potential Case)

0

1000

2000

3000

4000

5000

6000

7000

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


Hours

of Load R

eje

ction p

er

Year V=100,000m3 V=90,000m3

V=80,000m3 V=70,000m3

V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3

V=20,000m3 V=10,000m3

CAES FUEL CONSUMPTION

The algorithm also calculates the annual fuel consumption attributed to the operation of the CAES cycle only Vast increase of CAES fuel consumption for the early stages of wind power capacity increase, while max fuel consumption is recorded once energy autonomy levels approximate 100% From that point onward, fuel consumption is reduced due to increased participation of wind power (more intense for the high wind potential) The impact of the local wind potential is of primary importance, with the required fuel amount even exceeding 1600 tones of NG for the low wind potential and the highest storage volume achieving full energy autonomy


CAES Fuel Consumption (Low Wind Potential Case)

0

200

400

600

800

1000

1200

1400

1600

1800

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


CA

ES

Fuel C

onsum

ption (

t NG)

V=100,000m3 V=90,000m3

V=80,000m3 V=70,000m3

V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3

V=20,000m3 V=10,000m3


CAES Fuel Consumption (Medium Wind Potential Case)

0

150

300

450

600

750

900

1050

1200

1350

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


CA

ES

Fuel C

onsum

ption (

t NG)

V=100,000m3 V=90,000m3

V=80,000m3 V=70,000m3

V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3

V=20,000m3 V=10,000m3


CAES Fuel Consumption (High Wind Potential Case)

0

150

300

450

600

750

900

1050

1200

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


CA

ES

Fuel C

onsum

ption (

t NG)

V=100,000m3 V=90,000m3

V=80,000m3 V=70,000m3

V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3

V=20,000m3 V=10,000m3

DUAL-MODE CAES FUEL CONSUMPTION

Results obtained also include fuel consumption attributed to the operation of the dual-mode CAES cycle, i.e. the typical GT cycle The option of using zero storage capacity is also examined, corresponding to the parallel operation of the wind farm and a typical GT plant The impact of using even 10,000m3 of storage volume is critical in the reduction of the dual-mode CAES fuel consumption by more than 50%, 73% and 92% for the low, medium and high wind potential cases respectively As expected, dual-mode fuel consumption becomes zero once hourly load rejections also become zero, since from that point, the system relies on the operation of the Wind-CAES scheme only

The Impact of Wind Power Capacity & Storage Volume on Dual

Mode Fuel Consumption (Low Wind Potential Case)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


Dual M

ode F

uel C

on

sum

ption (

t NG)

V=100,000m3 V=90,000m3 V=80,000m3

V=70,000m3 V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3 V=20,000m3

V=10,000m3 Zero Storage

The Impact of Wind Power Capacity & Storage Volume on Dual

Mode Fuel Consumption (Medium Wind Potential Case)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


Dual M

ode F

uel C

on

sum

ption (

t NG)

V=100,000m3 V=90,000m3 V=80,000m3

V=70,000m3 V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3 V=20,000m3



Dual Mode Fuel Consumption (High Wind Potential Case)

0

400

800

1200

1600

2000

2400

2800

3200

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


Dua

l M

ode F

uel C

onsum

ptio

n (

t NG) V=100,000m3 V=90,000m3 V=80,000m3

V=70,000m3 V=60,000m3 V=50,000m3

V=40,000m3 V=30,000m3 V=20,000m3


ECONOMIC EVALUATION (1/2)

Evaluation of the above energy results is undertaken using the economic

criterion of long-term electricity production cost on the basis of typical

market data values

All three alternative energy solutions are evaluated, i.e. the dual-mode

Wind-CAES scheme, the GT only scheme and finally the wind-farm and GT

parallel operation

In order to better interpret the economic performance of different dual-

mode Wind-CAES configurations, participation of the dual-mode cycle in

terms of fuel consumption (in comparison with the total including also fuel

consumption of the pure CAES cycle) is also given

ECONOMIC EVALUATION (2/2)

The long-term production cost of the dual-mode Wind-CAES solution presents a gradual increase for the low wind potential as wind power increases, although for the medium and high wind potential, a minimum optimum point is obtained for NWP=8MW The GT-only solution cost is almost 120€/MWh, which comprises the most cost-efficient solution in case that wind power exceeds a certain limit The most cost-efficient solution corresponds to the dual-mode Wind-CAES solution with a storage volume of 10,000m3, that however implies extreme levels of the GT cycle participation As the quality of wind potential improves, additional dual-mode Wind-CAES systems of greater storage capacity become cost-competitive to both the GT-only and the wind park & GT solutions, achieving at the same time minimum fuel consumption

Long-Term Electricity Production Cost of Different Energy

Autonomous Configurations (Low Wind Potential Case)

0

40

80

120

160

200

240

280

320

360

400

4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


El. P

rod

. C

ost

(€/M

Wh

)

0

10

20

30

40

50

60

70

80

90

100

DM

-CA

ES

Co

ntr

ibu

tio

n (

%)

GT only Wind & GTV=100,000m3 V=80,000m3V=60,000m3 V=40,000m3V=10,000m3 V=100,000m3-DMV=80,000m3-DM V=60,000m3-DMV=40,000m3-DM V=10,000m3-DM


Autonomous Configurations (Medium Wind Potential Case)

0

40

80

120

160

200

240

280

320

360

400

4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


El. P

rod

. C

ost

(€/M

Wh

)

0

10

20

30

40

50

60

70

80

90

100

DM

-CA

ES

Co

ntr

ibu

tio

n (

%)



Autonomous Configurations (High Wind Potential Case)

0

40

80

120

160

200

240

280

320

360

400

4 8 12 16 20 24 28 32 36 40 44 48 52 56 60


El. P

rod

. C

ost

(€/M

Wh

)

0

10

20

30

40

50

60

70

80

90

100

DM

-CA

ES

Co

ntr

ibu

tio

n (

%)


CONCLUSIONS

Based on the development of an energy analysis algorithm for dual-mode Wind-CAES configurations, applications results were currently obtained on the basis of different quality wind potential areas The impact of the local wind potential was reflected on both the energy autonomy levels achieved by the use of a Wind-CAES only scheme and the total fuel consumption required to operate the CAES and GT cycles Economic evaluation of the alternative solutions designated optimum dual-mode Wind-CAES configurations that however implied maximum contribution of the dual-mode cycle As the local wind potential quality improves, dual-mode Wind-CAES configurations that allow min participation of the dual-mode cycle become more cost-competitive, especially in comparison with the GT-only solution The rationale of adopting the proposed solution is illustrated in terms of both long-term electricity production cost and limited fuel consumption

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OPTIMUM MEDIUM SCALE WIND-CAES … · simulate system operation, a sizing algorithm has been developed in C# (Wind-CAES-DM) The proposed solution is accordingly applied to three different