Wake characteristics of pumping mode airborne wind energy systems

Haas, Thomas; Schutter, Jochem De; Diehl, Moritz; Meyers, Johan

doi:10.1088/1742-6596/1256/1/012016

Cited by 21 publications

(25 citation statements)

References 15 publications

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“…Due to the large size of the swept area of the kite trajectory -typically an annulus or a lemniscate on the spherical surface around the ground station -AWE systems typically lead to lower induction effects than conventional wind turbines. However, induction effects are particularly relevant for the optimization of multiple kite systems (De Schutter, Leuthold, Bronnenmeyer, Paelinck, & Diehl, 2019;Zanon, Gros, Andersson, & Diehl, 2013), and their inclusion into modeling and optimal control of AWE systems is the subject of ongoing research (Haas, De Schutter, Diehl, & Meyers, 2019;Leuthold et al, 2017;Trevisi, Gaunaa, & McWilliam, 2020).…”

Section: Fundamental Principles and Aerodynamic Considerationsmentioning

confidence: 99%

“…When this physical phenomenon, abbreviated as the induction effect, is not included in AWE OCPs, the conversion efficiency does tend towards unity, as in . However, when (Haas et al, 2019) flies OCP-generated lift-mode trajectories within an atmospheric large-eddy-simulation (a high-fidelity induction model), the resulting induction effects are far too large to be safely neglected. Second, (Leuthold et al, 2017;Zanon, Gros, Meyers, & Diehl, 2014) find that the inclusion of a quasi-steady actuator model (a very rough induction model) into an OCP leads to a large drop in predicted power.…”

Section: Some Open Modeling Questions In Awe Optimal Controlmentioning

confidence: 99%

See 1 more Smart Citation

Electricity in the air: Insights from two decades of advanced control research and experimental flight testing of airborne wind energy systems

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Annual Reviews in Control

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Section: Fundamental Principles and Aerodynamic Considerationsmentioning

confidence: 99%

Section: Some Open Modeling Questions In Awe Optimal Controlmentioning

confidence: 99%

Electricity in the air: Insights from two decades of advanced control research and experimental flight testing of airborne wind energy systems

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et al. 2021

Annual Reviews in Control

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“…For a more detailed description of the model and the optimization algorithm see [135,84,29,19,71,67].…”

Section: Model Overviewmentioning

confidence: 99%

Ground-generation airborne wind energy design space exploration

Sommerfeld

Dörenkämper

Schutter³

et al. 2020

Preprint

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Abstract. While some Airborne Wind Energy System (AWES) companies aim at small-scale, temporary or remote off-grid markets, others aim to integrate utility-scale, multi-megawatt AWES into the electricity grid. This study investigates the scaling effects of single-wing, ground-generation AWESs from small to large-scale systems, subject to realistic 10-minute, onshore and offshore wind conditions derived from the numerical mesoscale weather research and forecasting (WRF) model. To reduce computational cost, wind velocity profiles are grouped into k = 10 clusters using k-means clustering. Three representative profiles from each cluster are implemented into a nonlinear AWES optimal control model, to determine power-optimal trajectories, system dynamics, as well as instantaneous and cycle-average power. We compare the performance of three different aircraft masses and two sets of nonlinear aerodynamic coefficients for each aircraft size, with wing areas ranging from 10 m2 to 150 m2. We predict size and weight-dependent, optimal AWES power curves, annual energy production (AEP) and capacity factor (cf). Tether impacts, such as power losses associated with tether drag and the tether contribution to total system mass are quantified. Furthermore, we estimate a minimum average cycle-average lift to weight ratio, above which ground-generation AWES can operate, to explore the viable AWES mass budget.

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“…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%

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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Wake characteristics of pumping mode airborne wind energy systems

Cited by 21 publications

References 15 publications

Electricity in the air: Insights from two decades of advanced control research and experimental flight testing of airborne wind energy systems

Electricity in the air: Insights from two decades of advanced control research and experimental flight testing of airborne wind energy systems

Ground-generation airborne wind energy design space exploration

Large-eddy simulation of airborne wind energy farms

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