A new approach to wind energy. John O. Dabiri California Institute of Technology
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1 A new approach to wind energy John O. Dabiri California Institute of Technology
2 Our goal: Develop wind energy technologies that match the global reach of the wind itself Only 4 countries get more than 10% of electricity from wind Global wind map Many developing countries have wind but little electricity Wind speed over water Wind speed over land 3Tier, Inc.
3 Evaluating Wind Energy Technologies Turbine efficiency Wind farm power density Cost of electricity U A $ C p = P AU 3 PD = P WF A WF LCOE = kwh
4 Evaluating Wind Energy Technologies Turbine efficiency Wind farm power density Cost of electricity U A $ C p = P AU 3 PD = P WF A WF LCOE = kwh
5 From closer spacing to lower cost? Closer turbine spacing can be used to achieve More efficient wind farms, which can generate sufficient power using Smaller wind turbines, which can achieve on a per-watt basis Lower costs for materials, manufacturing, installation, O&M LOWER COST OF ENERGY
6 The usual starting point: turbine efficiency Evaluating Wind Energy Technologies 100 meters VS. 10 meters Horizontal-axis wind turbine (HAWT) Vertical-axis wind turbine (VAWT)
7 Evaluating Wind Energy Technologies The usual starting point: turbine efficiency What fraction of wind energy flux through the swept area is converted to electricity? 100 meters > 10 meters Horizontal-axis wind turbine (HAWT) Vertical-axis wind turbine (VAWT)
8 Evaluating Wind Energy Technologies The usual starting point: turbine efficiency What is the maximum fraction of wind energy flux through the swept area that can be converted to electricity? Efficiency 60% Betz Efficiency Limit 50% 40% Modern HAWTs Modern HAWTs approach theoretical maximum efficiency 30% 20% Is there room for fundamental improvement in wind energy? 10% Tip Speed Ratio netfirms.com
9 Evaluating Wind Energy Technologies Our starting point: wind resource utilization
10 Evaluating Wind Energy Technologies Our starting point: wind resource utilization What fraction of wind energy flux into the wind farm volume is converted to electricity?
11 Evaluating Wind Energy Technologies Our starting point: wind resource utilization What fraction of wind energy flux into the wind farm volume is converted to electricity? Frontal kinetic energy flux P horz = ½ A frontal U 3
12 Evaluating Wind Energy Technologies Our starting point: wind resource utilization What fraction of wind energy flux into the wind farm volume is converted to electricity? Frontal kinetic energy flux P horz = ½ A frontal U 3 Planform kinetic energy flux P vert - A planform U <u w > Turbulence velocity fluctuations u (streamwise) and w (wall-normal) Ensemble average < >
13 The planform kinetic energy flux ( A planform U <u w >) 1. is the primary power source for most turbines in large-scale wind farms (Cal et. al. 2010) 2. supersedes the Betz limit as the relevant constraint on wind farm performance WHY? The Betz calculation does not account for wind power in the turbine wake that is extracted by neighboring turbines The Betz calculation does not account for wind power that is not extracted in the region between turbines leftover wind power C p = 0.5 C p = 0.5 C p = 0.5 C p = 0 C p = 0
14 P vert A planform Assume log wind profile: Modelling the planform flux limit U u w u w u 2 * ln U z d z 0 U friction velocity, u * von Karman constant, 0.4 zero plane displacement, d 2H / roughness length, z0 H /10 3 H H Foken 2008
15 P vert A planform Assume log wind profile: Modelling the planform flux limit U u w u w u 2 * ln U z d z 0 friction velocity, u * von Karman constant, 0.4 zero plane displacement, d 2H / roughness length, z0 H /10 3 The energy flux through the top of a wind farm is 68 watts per square meter in 8 m/s mean wind speed
16 How do existing wind farms compare to this upper limit? 2.5 versus 68 W m -2 DJC Mackay 2010
17 2.5 versus 68 W m -2 Whereas modern wind turbines achieve power coefficients that approach the theoretical maximum, existing wind farm performance remains far below the flux limit Why? Wake interactions reduce wind farm performance Horns Rev Offshore Wind Farm
18 2.5 versus 68 W m -2 Whereas modern wind turbines achieve power coefficients that approach the theoretical maximum, existing wind farm performance remains far below the flux limit Why? Turbine spacing requirements directly affect wind farm power density Power Density Calculation PD = 4 x rated power x capacity factor (turbine diameter x turbine spacing) 2 For six-diameter average turbine spacing and 30% capacity factor D x S Turbine #1 (2.5 MW, 100 m dia.): PD = 2.7 W/m 2 Turbine #2 (3.0 MW, 112 m dia.): PD =2.5 W/m 2 If turbine spacing is reduced to four diameters Turbine #1 (2.5 MW, 100 m dia.): PD = 6.0 W/m 2 Turbine #2 (3.0 MW, 112 m dia.): PD =5.7 W/m 2 i.e. power density is more than doubled.
19 Additional challenges for conventional wind energy Structure size, associated design requirements and materials costs johnrsweet.com
20 Additional challenges for conventional wind energy Structure size, associated design requirements and materials costs Logistics of installation and maintenance Paul Anderson
21 Additional challenges for conventional wind energy Structure size, associated design requirements and materials costs Logistics of installation and maintenance Societal acceptance - impact on birds/bats - visual signature - acoustic signature - radar signature Google Images
22 Additional challenges for conventional wind energy Structure size, associated design requirements and materials costs Logistics of installation and maintenance Societal acceptance Access in the developing world limited infrastructure Google Images
23 A new approach: Optimized arrays of smaller vertical-axis wind turbines Smaller structure size, materials costs, and wind farm signatures Simpler logistics of installation, operations, and maintenance Scalable from distributed to utility power Safer for birds and bats
24 A new approach: Optimized arrays of smaller vertical-axis wind turbines Power Density Calculation Revisited PD = 4 x rated power x capacity factor (turbine diameter x turbine spacing) 2 D x S For six-diameter average turbine spacing and 30% capacity factor Windspire Energy VAWT MW, 1.2 m dia. PD = 8.8 W/m 2 3X power density at 1/10 HAWT height!
25 A new approach: Optimized arrays of smaller vertical-axis wind turbines 150 Megawatt Wind Farm Comparison HAWT VAWT Turbine capacity 1.5 MW 3.0 kw Number of turbines ,000 Wind farm footprint (6D spacing) 77 square km 18 square km Wind farm footprint (4D spacing) N/A 8 square km
26 What is the optimal arrangement of VAWTs in a wind farm?
27 UW APL GLIDER Bioinspired Engineering Ocean Monitoring Heart Failure Diagnosis Jellyfish Wind Energy
28 What is the optimal arrangement of VAWTs in a wind farm? Biological inspiration: Fish Schooling
29 primary wind Triantafyllou et al. (1995)
30 Optimal fish schooling provides our starting point Weihs (1975)
31 2D potential flow VAWT farm model 2a U 2c b Model predicts order-of-magnitude increase in power density Rated Power Density (W m -2 ) a = 1.2D, b = 0.4D, c = 2D Whittlesey, Liska, Dabiri (Bioinspiration and Biomimetics, 2010)
32 Clever idea, but does it work for real turbines? Challenges: computer modeling requires empirical data inputs wind tunnel testing requires assumptions to scale-up measurements neither can replicate natural wind conditions end of story?
33 Clever idea, but does it work for real turbines? Challenges: computer modeling requires empirical data inputs wind tunnel testing requires assumptions to scale-up measurements neither can replicate natural wind conditions end of story?
34 Caltech Field Laboratory for Optimized Wind Energy (reduced visual signature)
35 Field Study Results 6-VAWT array measured over 250 continuous hours 4D 4D
36 Field Study Results 6-VAWT array measured over 250 continuous hours mean power above cut-in wind speed mean power, continuous Order-of-magnitude improvement of wind farm performance existing wind farms Planform Kinetic Energy Flux = U<u w > (W m -2 )
37 Potential order-of-magnitude enhancement of wind farm power density via counter-rotating vertical-axis wind turbine arrays Volume 3, July 2011
38 Common questions ( Yeah, but ) Can these results be achieved in a larger array? Will wind pass around a larger array instead of through it? Wind profile measurements using 7 three-component sonic anemometers Anne Cusack, Los Angeles Times
39 Common questions ( Yeah, but ) Can these results be achieved in a larger array? Will wind pass around a larger array instead of through it? Wind profile measurements using 7 three-component sonic anemometers 8 diameters measurement transect 8 diameters prevailing wind reference met tower
40 Common questions ( Yeah, but ) Can these results be achieved in a larger array? Will wind pass around a larger array instead of through it? Wind speed at rotor mid-span VAWT pair locations 1 Wind speed decreases upstream of the VAWT array i.e. blockage effect However, wind speed achieves asympotic flow (blue line) after third row (consistent with HAWT arrays) Power density based on third row VAWTs is 11 W/m 2 4X HAWT farms with no VAWT array optimization
41 Unanswered questions How do aerodynamic interactions within the VAWT farm determine the overall performance? Suppression of vortex shedding by turbine rotation? U CFD Experiment TSR = 1.2 Snow particle image velocimetry TSR = 1.5 TSR = 3 Chan et al. 2011
42 Unanswered questions How do aerodynamic interactions within the VAWT farm determine the overall performance? How can canopy flow analogies be applied to understand turbulent transport in VAWT farms?
43 Unanswered questions How do aerodynamic interactions within the VAWT farm determine the overall performance? How can canopy flow analogies be applied to understand turbulent transport in VAWT farms? How can we leverage the smaller VAWT size to improve materials costs, O&M, environmental impact, storage, LCOE? autoanything.com
44 From closer spacing to lower cost Closer turbine spacing More efficient wind farms Smaller wind turbines WE Lower costs for materials, manufacturing, installation, maintenance LOWER COST OF ENERGY
45 Acknowledgments Robert Whittlesey Aeronautics Grad Student (array modeling) Matthias Kinzel Aeronautics Postdoc (field measurements) Anna Craig Applied Physics Undergrad (array modeling) Quinn Mulligan Visiting Undergrad (field measurements) Brad Saund Mechanical Engineering Undergrad (wind turbine testing)
46
47 Caltech Program in Bio-inspired Wind Energy Field Laboratory (Dabiri) Wind Tunnel Research (Gharib, McKeon) Computational Design (Colonius) Materials and Manufacturing (Greer, Grubbs)
48 Questions & Comments? John O. Dabiri California Institute of Technology
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