Toward optimization of a wind/ Toward optimization of a wind/ compressed air energy storage compressed air energy storage

(CAES) power system (CAES) power system Jeffery B. GreenblattJeffery B. Greenblatt

Samir SuccarSamir Succar

David C. DenkenbergerDavid C. Denkenberger

Robert H. WilliamsRobert H. WilliamsPrinceton University, Princeton, NJ 08544Princeton University, Princeton, NJ 08544

Guyot Hall, (609) 258-7442 / 7715 FAX, jgreenbl@princeton.eduGuyot Hall, (609) 258-7442 / 7715 FAX, jgreenbl@princeton.edu

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

Session 11D (Wind Power II), 1 April 2004Foote Creek Rim, Wyoming

Does wind power need storage?Three contexts:

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

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

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

Time

Pow

er

Time

Market shareV

alue Few markets

currently exist

Electric storage options

TechnologyCompressed Air EnergyStorage (CAES) (350 MW)Pumped hydroelectricAdvanced battery (10 MW)Flywheel (100 MW)Superconductor (100 MW)

370

1100210062006100

Cost of 20hrs. storage

($/kW)Capacity($/kW)

Storage($/kWh)

350

900120150120

1

10100300300

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

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

Compressor train Expander/generator train

Fuel (e.g. natural gas, distillate)

CAES system

Intercoolers

Heat recuperator

PC PG

AirExhaust

AirStorage

Aquifer,salt cavern,

or hard mine

hS = Hours ofStorage (at PC)

PC = Compressorpower in

PG = Generatorpower out

A wind/CAES model

Wind farm Transmission

CAES plant

Undergroundair storage

For this application CAES is needed to provide baseload power

PWF = Wind Farm max.power out(rated power)

PT = Transmission linemax. power

PWF PT

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• 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

Gen

vrate

vavg.

hS hA

Secondary parameters

• CAES electricity output/input ratio (Eo/Ei)

• Wind turbine array spacing (xD2)

• Weibull shape parameter (k) and wind power density (Pwind)

Ei Eo

Wind farm simulationWeibull dist.

Wind speed

Prob

abil

ity

Wind speed time seriesAutocorrelation

time (hA)

Time

Win

d sp

eed

Power curve

Wind speed

Pow

er

Losses

PWF

Time

Win

d sp

eed

Wind power time series

Rated power

Rated power

(k2 > k1)

} Power “lost”

CAES model

Compressor Generator

Spilled power(if storage full)

Fuel

PWF

Transmissionline

capacity

CAEScapacity

Spilled power

Air

X

PG

Total system output (≤ PT)

Direct output(≤ PT)

Losses Losses

CO2

Transmission losses

PC

Airstorage

hS

Base case configuration

Wind farm:PWF = 2 PT (4000 MW)

Spacing = 50 D2

vrated = 1.4 vavg

Transmission:PT = 2000 MW

Comp Gen

PC = 0.85 PT (1700 MW)

CAES system

Wind resource:k = 3, vavg = 9.6 m/s,

Pwind = 550 W/m2 (Class 5)hA = 5 hrs.

SystemCF = 0.80

Eo/Ei = 1.30

PG = 0.50 PT

(1000 MW)

hS = 10 hrs.(at PC)

Compressor and generator sizesP

C/P

T

1

0 1PG/PT

CF = 81%CF = 81%

CF = 76%CF = 76%

CF = 68%CF = 68%

CF = 72%CF = 72%

Cut along constant PG/PT:

0.5

0.5

1.5

1.5C

F

PC/PT

Base case

CF improves (with diminishing returns)

as either PC/PT or PG/PT increases

Base case

Compressor/generator ratioP

C/P

T

1

0 1PG/PT

CF = 81%CF = 81%

CF = 76%CF = 76%

CF = 68%CF = 68%

CF = 72%CF = 72%

Base case

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

0.5

0.5

1.5

1.5

Max. CF = 85%

Wind farm parameters

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

Small change in CF with array spacing

Array spacing (D2)

Base case

CF

PWF/PT (oversizing)

Base case

PWF

= PT

case

Storage vs. autocorrelation time100

10

0.1

1

Sto

rage

tim

e (h

S)

(hrs

. log

sca

le)

Autocorrelation time (hA)(hrs. log scale)

0.1 1 10 100

Base case

CF = 70%

CF = 70%

CF = 79%

CF = 79%

CF = 74%

CF = 74%

CF = 65%

CF = 65%

No improvement in CF if hS >> hA or

vice-versa

hA (hrs. log scale)

CF

Cut along constant hS:

Base case hS = hA

case

Power derating

Wind speed

Pow

er

vrate = 1.8vavg

Wind speed

Prob

abil

ity-

wei

ghte

d po

wer

Wind turbine power curve

vrate = 1.4vavg

7% above rated speed

vrate = 1.0vavg

Wind speed

36%

Prob

abil

ity-

wei

ghte

d po

wer

Wind speed

72%

Prob

abil

ity-

wei

ghte

d po

wer

As vrate decreases, turbines run at rated

(maximum) power more of the time

CF increases, but rated power

decreases, so more turbines

needed for same PWF

0.6

0

PG/P

T

1 1.5 2vrate/vavg

0.4

0.3

0.2

0.1

0.5

CF

= 8

0%C

F =

80%

CF

= 6

0%C

F =

60%

CF

= 4

0%C

F =

40%

CAES generation vs. turbine ratingBase case

(“large CAES”)Large vrate/vavg

Alternative case(“small CAES”):

Small vrate/vavg

Small CAES case may be more economical if

(COSTWT•NWT) + COSTCAES < 0

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

Dependence on Eo/Ei

CF

Base case

Eo/Ei

Little change in CF with CAES efficiency

Wind resource parametersC

F

Pwind (W/m2) Weibull k

Base case Base case

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

CF trend with k depends strongly on

vrate/vavg

vrate/vavg

1.0

1.4

1.8

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)

Gen

hS hA>

Base caseCF = 80%

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.Ei Eo

+CAESsize

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