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

1
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, [phone] / 7715 FAX,
  jgreenbl_at_princeton.edu

Electric Power Conference, Baltimore, MD, 30
March 1 April 2004 Session 11D (Wind Power II),
1 April 2004
Foote Creek Rim, Wyoming
2
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)

3
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)
370 1100 2100 6200 6100
350 900 120 150 120
1 10 100 300 300

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

4
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)
Air Storage
Aquifer, salt cavern, or hard mine
hS Hours of Storage (at PC)
5
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
6
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.

7
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)

Gen
Comp
Gen
hS
hA
8
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
9
Wind farm simulation
Weibull dist.
Power curve
Rated power
(k2 gt 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
10
CAES model
Spilled power (if storage full)
CO2
Compressor
Generator
Air
PG
PC
Losses
Losses
X
Fuel
Air storage
hS
Direct output ( PT)
Transmission losses
PWF
Total system output ( PT)
11
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
12
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
1
0.5
1.5
PG/PT
13
Compressor/generator ratio
1.5
Max. CF 85
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
1
0.5
1.5
PG/PT
14
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
15
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 gtgt hA or vice-versa
0.1
0.1
1
10
100
Autocorrelation time (hA) (hrs. log scale)
16
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
17
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 ?(COSTWTNWT) ?COSTCAES lt 0
0.1
0
1
1.5
2
vrate/vavg
Alternatively, PWF/PT could be increased (may be
more expensive)
18
Dependence on Eo/Ei
Base case
CF
Little change in CF with CAES efficiency
Eo/Ei
19
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)
20
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
gt
hA
21
Conclusions (contd)

• 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
22
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