Induction in Optimal Control of Multiple-Kite Airborne Wind Energy Systems

Leuthold, Rachel; Gros, Sébastien; Diehl, Moritz

doi:10.1016/j.ifacol.2017.08.026

Cited by 20 publications

(11 citation statements)

References 4 publications

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“…The axial induction factor a quantifies the strength of the induction phenomenon. The values of axial induction factors reported in AWE literature (Leuthold et al, 2017;Haas and Meyers, 2017;Kheiri et al, 2018) are lower than the known Betz limit a = 1/3 of conventional wind turbines (Jenkins et al, 2001), suggesting that axial induction is less significant for airborne wind energy systems although it cannot be fully neglected. In the current approach, the LES-based wind velocity sampled by the wind speed estimator already contains the effects of axial induction.…”

Section: Wind Speed Estimatormentioning

confidence: 90%

“…For individual systems, recent investigations of flow induction and wake effects were performed, mainly considering axissymmetric AWES configurations in uniform inflows using either analyses based on momentum theory (Leuthold et al, 2017;De Lellis et al, 2018), vortex theory (Leuthold et al, 2019;Gaunaa et al, 2020) or the entrainment hypothesis (Kaufman-Martin et al, 2021), or high-fidelity CFD simulations (Haas and Meyers, 2017;Kheiri et al, 2018). Further numerical investigations of large-scale airborne wind energy systems in the atmospheric boundary layer (ABL) (Haas et al, 2019b) have shown that the wake development downstream of the system is considerable, hence suggesting that wake effects of utility-scale airborne wind energy systems can not be neglected when operating in parks.…”

Section: Introductionmentioning

confidence: 99%

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Large-eddy simulation of airborne wind energy farms

Haas

Schutter

Diehl

et al. 2021

Preprint

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Abstract. The future utility-scale deployment of airborne wind energy technologies requires the development of large-scale multi-megawatt systems. This study aims at quantifying the interaction between the atmospheric boundary layer (ABL) and large-scale airborne wind energy systems operating in a farm. To that end, we present a virtual flight simulator combining large-eddy simulations to simulate turbulent flow conditions and optimal control techniques for flight-path generation and tracking. The two-way coupling between flow and system dynamics is achieved by implementing an actuator sector method that we pair to a model predictive controller. In this study, we consider ground-based power generation pumping-mode AWE systems (lift-mode AWES) and on-board power generation AWE systems (drag-mode AWES). For the lift-mode AWES, we additionally investigate different reel-out strategies to reduce the interaction between the tethered wing and its own wake. Further, we investigate AWE parks consisting of 25 systems organized in 5 rows of 5 systems. For both lift- and drag-mode archetypes, we consider a moderate park layout with a power density of 10 MW km−2 achieved at a rated wind speed of 12 m s−1. For the drag-mode AWES, an additional park with denser layout and power density of 28 MW km−2 is also considered. The model predictive controller achieves very satisfactory flight-path tracking despite the AWE systems operating in fully waked, turbulent flow conditions. Furthermore, we observe significant wake effects for the utility-scale AWE systems considered in the study. Wake-induced performance losses increase gradually through the downstream rows of systems and reach in the last row of the parks up to 17 % for the lift-mode AWE park and up to 25 % and 45 % for the moderate and dense drag-mode AWE parks, respectively. For an operation period of 60 minutes at a below-rated reference wind speed of 10 m s−1, the lift-mode AWE park generates about 84.4 MW of power, corresponding to 82.5 % of the power yield expected when AWE systems operate ideally and interaction with the ABL is negligible. For the drag-mode AWE parks, the moderate and dense layouts generate about 86.0 MW and 72.9 MW of power, respectively, corresponding to 89.2 % and 75.6 % of the ideal power yield.

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Section: Wind Speed Estimatormentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Large-eddy simulation of airborne wind energy farms

Haas

Schutter

Diehl

et al. 2021

Preprint

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“…Upscaling of installed capacity per occupied area of land or sea may be implemented by staggering operational heights, thereby using AWE systems with a different tether length and inclination angles, and developing plantwide control strategies (Leuthold et al 2017(Leuthold et al , 2018.…”

Section: Upscalingmentioning

confidence: 99%

Airborne Wind Energy

Weber

Marquis

Cooperman

et al. 2021

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This section briefly describes the way in which this report addresses the congressional request regarding focus and scope, the consideration of AWE in relation to traditional wind energy, and the approach taken to develop the report. Addressing the Congressional Request: Focus and ScopeThis report describes technical analyses of various aspects of AWE provided to the U.S. Department of Energy (DOE) Wind Energy Technologies Office to underpin DOE's response to the congressional request for a report on AWE in the Energy Act of 2020, which states:-Not later than 180 days after the date of the enactment of this Act, the Secretary shall submit to the Committee on Science, Space, and Technology of the House of Representatives and the Committee on Energy and Natural Resources of the Senate a report on the potential for, and technical viability of, airborne wind energy systems to provide a significant source of energy in the United States.

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“…and C L;n and C D;eq;n are the nominal total lift and equivalent drag coefficients that occur at c L c L;n and C D;k;a C D;k;a;min 0 in Eqs. (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). That optimal value v a;opt is used as the set value of the airflow speed v a;set , but bounded by the minimum and maximum values, that is, v a;set limitv a;min ; v a;opt ; v a;max (70)…”

Section: Tangential Speed Set Valuementioning

confidence: 99%

“…One important aspect is the control of the kite. Many researchers proposed nonlinear model predictive control for this task (cf., e.g., [2,5,[14][15][16][17][18][19][20]). A drawback is the high computational load.…”

Section: Introductionmentioning

confidence: 99%

Control of a Drag Power Kite over the Entire Wind Speed Range

Bauer

Petzold

Kennel

et al. 2019

Journal of Guidance, Control, and Dynamics

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A control scheme for drag power kites, also known as airborne wind turbines, for the entire wind speed range is proposed, including 1) a temperature controller allowing for temporary overloading of the powertrain; 2) a limitation controller ensuring that power, force, speed, and actuator constraints are satisfied; 3) a tangential flight speed controller; and 4) a tangential force control allocation, which inverts the nonlinearities of the plant and allocates the flight speed controller's tangential force demand to the available actuators. The drag power kite plant model is based on a point-mass model and a simple aerodynamics model with various drag contributions. Simulations are conducted with the parameters of the 20 kW Wing 7 developed by Makani Power, Inc. The proper working of the control scheme is indicated by the good match of the simulation results with independent simulation results and measurements published by Makani. A temporary overloading of the powertrain with about twice the nominal power can be concluded as a requirement; otherwise the mean power would be significantly lower. Because of the reduction of the lift and thus reduction of the centripetal force at high wind speeds, the inside-down figure eight can be concluded as the best pattern.

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Induction in Optimal Control of Multiple-Kite Airborne Wind Energy Systems

Cited by 20 publications

References 4 publications

Large-eddy simulation of airborne wind energy farms

Large-eddy simulation of airborne wind energy farms

Airborne Wind Energy

Control of a Drag Power Kite over the Entire Wind Speed Range

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