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Toward optimization of a wind/ compressed air energy storage (CAES) power system . Jeffery B. Greenblatt Samir Succar David C. Denkenberger Robert H. Williams Princeton University, Princeton, NJ 08544 Guyot Hall, (609) 258-7442 / 7715 FAX, [email protected]

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toward optimization of a wind compressed air energy storage caes power system

Toward optimization of a wind/ compressed air energy storage (CAES) power system

Jeffery B. Greenblatt

Samir Succar

David C. Denkenberger

Robert H. Williams

Princeton University, Princeton, NJ 08544

Guyot Hall, (609) 258-7442 / 7715 FAX, [email protected]

Electric Power Conference, Baltimore, MD, 30 March – 1 April 2004

Session 11D (Wind Power II), 1 April 2004

Foote Creek Rim, Wyoming

does wind power need storage
Does wind power need storage?

Three contexts:

Power

Make wind dispatchable (price arbitrage; potential at small market share)

Time

Time

Boost wind capacity factor at large market penetration (offsets fuel cost only)

Few markets currently exist

Value

Market share

Exploit high-quality but remote wind resources (by reducing transmission costs)

electric storage options
Electric storage options

Cost of 20

hrs. storage

($/kW)

Source: Schainker, 1997 (reproduced in PCAST, 1999)

Technology

Compressed Air Energy

Storage (CAES) (350 MW)

Pumped hydroelectric

Advanced battery (10 MW)

Flywheel (100 MW)

Superconductor (100 MW)

Capacity

($/kW)

Storage

($/kWh)

1

10

100

300

300

350

900

120

150

120

370

1100

2100

6200

6100

  • CAES is clear choice for:
  • Several hours (or more) of storage
  • Large capacity (> ~100 MW)
slide4
CAES system

Compressor train

Expander/generator train

Air

Exhaust

PC

PG

Intercoolers

Heat recuperator

PC = Compressor

power in

PG = Generator

power out

Fuel (e.g. natural gas, distillate)

Aquifer,

salt cavern,

or hard mine

Air

Storage

hS = Hours of

Storage (at PC)

a wind caes model
A wind/CAES model

PWF

PT

CAES plant

Wind farm

Transmission

PWF = Wind Farm max.

power out

(rated power)

PT = Transmission line

max. power

Underground

air storage

For this application CAES is needed to provide baseload power

research objectives
Research objectives
  • What is optimal wind/CAES system for baseload power transmission?
  • What is optimal capacity factor (CF) of that transmission line?
  • How much will such a system cost, and can it compete against other baseload systems (nuclear, coal, natural gas)?

Note: Costs of system components were not available in time for the Feb. 2 deadline. If component costs can be obtained, a cost optimization will be presented at the conference.

key parameters
vavg.

vrate

Key parameters

Gen

  • Size of CAES generation relative to transmission line (PG/PT)
  • CAES compression/generation ratio (PC/PG)
  • Relative size of wind farm (PWF/PT)
  • CAES storage time relative to wind autocorrelation time (hS/hA)
  • Ratio of turbine speed rating to resource wind speed (vrate/vavg)

Comp

Gen

hS

hA

secondary parameters
Secondary parameters

Eo

  • CAES electricity output/input ratio (Eo/Ei)
  • Wind turbine array spacing (xD2)
  • Weibull shape parameter (k) and wind power density (Pwind)

Ei

wind farm simulation
Wind farm simulation

Weibull dist.

Power curve

Rated power

(k2 > k1)

Power

Probability

PWF

Wind speed

Wind speed

Losses

Wind speed time series

Wind power time series

Autocorrelation

time (hA)

} Power “lost”

Rated power

Wind speed

Wind speed

Time

Time

caes model
Spilled power

CAES

capacity

Transmission

line

capacity

CAES model

Spilled power

(if storage full)

CO2

Compressor

Generator

Air

PC

PG

Losses

Losses

X

Fuel

Air

storage

hS

Direct output

(≤ PT)

Transmission losses

PWF

Total system output (≤ PT)

base case configuration
Base case configuration

Wind resource:

k = 3, vavg = 9.6 m/s,

Pwind = 550 W/m2 (Class 5)

hA = 5 hrs.

System

CF = 0.80

PC = 0.85 PT

(1700 MW)

PG = 0.50 PT

(1000 MW)

Comp

Gen

hS = 10 hrs.

(at PC)

Wind farm:

PWF = 2 PT (4000 MW)

Spacing = 50 D2

vrated = 1.4 vavg

Transmission:

PT = 2000 MW

Eo/Ei = 1.30

CAES system

compressor and generator sizes
Compressor and generator sizes

1.5

Cut along constant PG/PT:

Base case

Base case

1

CF

PC/PT

CF = 81%

0.5

CF = 76%

PC/PT

CF = 72%

CF improves (with diminishing returns) as either PC/PT or PG/PT increases

CF = 68%

0

0.5

1

1.5

PG/PT

compressor generator ratio
Compressor/generator ratio

Max. CF = 85%

1.5

Slope ~ 1.7

For given CF, least cost configuration appears to lie along slope line

Minimal increase in CF for PG/PT = 0.5  1

Slope expected to be controlled by PWF/PT and turbine rating

Base case

1

PC/PT

CF = 81%

0.5

CF = 76%

CF = 72%

CF = 68%

0

0.5

1

1.5

PG/PT

wind farm parameters
Wind farm parameters

Base case

Base case

CF

Small change in CF with array spacing

PWF

= PT

case

PWF/PT (oversizing)

Array spacing (D2)

Some improvement at large PWF/PT, but most improvement at PWF/PT ≤ 2

storage vs autocorrelation time
Storage vs. autocorrelation time

100

Cut along constant hS:

Base case

CF = 79%

10

CF = 74%

Base case

CF

Storage time (hS)

(hrs. log scale)

hS = hA

case

CF = 70%

1

CF = 65%

hA (hrs. log scale)

No improvement in CF if hS >> hA or vice-versa

0.1

0.1

1

10

100

Autocorrelation time (hA)

(hrs. log scale)

power derating
Probability-

weighted power

Wind speed

36%

Probability-

weighted power

Wind speed

Probability-

weighted power

72%

Wind speed

Power derating

Wind turbine power curve

7% above rated speed

vrate = 1.8vavg

vrate = 1.4vavg

Power

vrate = 1.0vavg

Wind speed

CF increases, but rated power decreases, so more turbines needed for same PWF

As vrate decreases, turbines run at rated (maximum) power more of the time

caes generation vs turbine rating
CAES generation vs. turbine rating

0.6

Base case

(“large CAES”)

Large vrate/vavg

0.5

0.4

CF = 80%

Alternative case

(“small CAES”):

Small vrate/vavg

CF = 60%

CF = 40%

PG/PT

0.3

0.2

Small CAES case may be more economical if

(COSTWT•NWT) + COSTCAES < 0

0.1

0

1

1.5

2

vrate/vavg

Alternatively, PWF/PT could be increased (may be more expensive)

dependence on e o e i
Dependence on Eo/Ei

Base case

CF

Little change in CF with CAES efficiency

Eo/Ei

wind resource parameters
Wind resource parameters

vrate/vavg

1.0

1.4

Base case

Base case

CF

1.8

Pwind (W/m2)

Weibull k

CF trend with k depends strongly on vrate/vavg

Virtually no change in CF over Pwind = 200-1000 W/m2 (classes 2-7+)

conclusions
Conclusions
  • Capacity factor (CF) of 80% is achievable for our base case:

PWF/PT = 2 PG/PT = 0.5 PC/PG = 1.7

hS = 10 h spacing = 50 D2 vrate/vavg = 1.4

  • Base case is somewhat improved by increasing PWF/PT, PG/PT or array spacing, but all likely to be expensive
  • Optimal storage time (hS) should be somewhat larger than the wind autocorrelation time (hA)

Base case

CF = 80%

Gen

hS

>

hA

conclusions cont d
Eo

Ei

Conclusions (cont’d)
  • Comparable CF is achieved by reducing CAES system size and rating turbines lower (alternatively, PWF/PT could be increased but this is probably more expensive).
  • Dependence of CF on k is coupled to turbine rating, with CF increasing with k for lower vrate/vavg, and decreasing for higher vrate/vavg.
  • Changing Eo/Ei, Pwind has little effect on CF.

+

CAES

size

acknowledgments
Acknowledgments
  • Dennis Elliott, Michael Milligan, Marc Schwarz, and Yih-Wei Wan, NREL
  • Al Dutcher, HPRCC
  • Marc Kapner, Austin Energy
  • Nisha Desai, Ridge Energy Storage
  • Bob Haug, Iowa Municipal Utilities District
  • Paul Denholm, University of Wisconsin, Madison
  • Joseph DeCarolis, Carnegie Mellon University
  • Al Cavallo, NIST
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