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, [email protected] 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 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