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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, jgreenbl@princeton.edu.

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

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  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, (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 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) 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)

  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) Aquifer, salt cavern, or hard mine Air Storage 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. 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

  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 > 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. 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)

  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 0.5 1 1.5 PG/PT

  13. 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

  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 >> hA or vice-versa 0.1 0.1 1 10 100 Autocorrelation time (hA) (hrs. log scale)

  16. 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

  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 (COSTWT•NWT) + COSTCAES < 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 > hA

  21. 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

  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

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