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The architecture of a new multidisciplinary design and optimization framework for rigid wing Airborne Wind Energy Systems, named T-GliDe , is introduced and a Ground-Gen AWES is analyzed to illustrate the features of the design approach. T-GliDe features an optimization module and an uncertainty quantification module, allowing for a number of algorithm-based design techniques. T-GliDe performs an AWES design optimizing market-based objective functions, while satisfying constraints related to fight stability. The fight dynamics is modelled with analytical aerodynamic theories, allowing for fast and consistent design evaluations, without the use of time simulation codes and thus of active control. In this work, a reduced optimization problem is run to find optimal working-set points of a Ground-Gen AWES with respect to power production. The reel-out factor and the pitch angle as function of wind speed are considered as design variables and output trends are shown. A variance based sensitivity analysis is then run to investigate how the rigid body eigenvalues are influenced by a set of independent variables. Among the chosen independent variables, a sub-set of variables which bear an impact on the dynamics is identified and they will be considered in future design and optimization activities as design variables.
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In pumping airborne wind energy (AWE) systems, the kite is operated in repetitive crosswind patterns, pulling the tether from a winch that drives a generator on the ground. During the reel-out phase of its operation, it produces power, whereas, during the reel-in phase, it consumes a small fraction of the produced power. This leads to an oscillating power profile that requires smoothing before it can be supplied to the electricity grid. This paper proposes three drivetrain concepts as a solution to this power smoothing challenge. The three concepts are based on three different types of storage technologies: electrical, hydraulic and mechanical. Techno–economic models of the drivetrains were developed and a case-study on sizing and costing of the three drivetrain concepts for a MW–scale AWE system was performed. Conclusions were drawn that provide guidance to AWE developers for choosing a suitable drivetrain concept for their systems.
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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). The aircraft have wingspans of approximately 60 m and fly large loops of approximately 200 m diameter centred at 200 m altitude. 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 organised in five rows of five 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 up to 17 % in the last row of the lift-mode AWE park and up to 25 % and 45 % in the last rows of the moderate and dense-drag-mode AWE parks respectively. For an operation period of 60 min 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 and 72.9 MW of power respectively corresponding to 89.2 % and 75.6 % of the ideal power yield.
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This paper presents some results from a computational fluid dynamics (CFD) model of a multi-megawatt crosswind kite spinning on a circular path in a straight downwind configuration. The unsteady Reynolds averaged Navier-Stokes equations closed by the k−ω SST turbulence model are solved in the three-dimensional space using ANSYS Fluent. The flow behaviour is examined at the rotation plane, and the overall (or global) induction factor is obtained by getting the weighted average of induction factors on multiple annuli over the swept area. The wake flow behaviour is also discussed in some details using velocity and pressure contour plots. In addition to the CFD model, an analytical model for calculating the average flow velocity and radii of the annular wake downstream of the kite is developed. The model is formulated based on the widely-used Jensen’s model which was developed for conventional wind turbines, and thus has a simple form. Expressions for the dimensionless wake flow velocity and wake radii are obtained by assuming self-similarity of flow velocity and linear wake expansion. Comparisons are made between numerical results from the analytical model and those from the CFD simulation. The level of agreement was found to be reasonably good. Such computational and analytical models are indispensable for kite farm layout design and optimization, where aerodynamic interactions between kites should be considered.
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Airborne wind energy (AWE) systems use tethered flying devices to harvest higher-altitude winds to produce electricity. For the success of the technology, it is crucial to understand how people perceive and respond to it. If concerns about the technology are not taken seriously, it could delay or prevent implementation, resulting in increased costs for project developers and a lower contribution to renewable energy targets. This literature review assessed the current state of knowledge on the social acceptance of AWE. A systematic literature search led to the identification of 40 relevant publications that were reviewed. The literature expected that the safety, visibility, acoustic emissions, ecological impacts, and the siting of AWE systems impact to which extent the technology will be accepted. The reviewed literature viewed the social acceptance of AWE optimistically but lacked scientific evidence to back up its claims. It seemed to overlook the fact that the impact of AWE’s characteristics (e.g., visibility) on people’s responses will also depend on a range of situational and psychological factors (e.g., the planning process, the community’s trust in project developers). Therefore, empirical social science research is needed to increase the field’s understanding of the acceptance of AWE and thereby facilitate development and deployment.
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Several structural models that could serve as building blocks for a next-generation FSI-based kite design tool have been developed. The models represent the leading-edge inflatable V3 soft wing membrane kite of the TU Delft and Kitepower B.V., by using a wireframe multi-plate representation. Each plate has three edges of constant length, each being a rigid representation of a tube segment. Only when including canopy billowing does the plate have a varying trailing edge length. The corners of the plates are represented by bridle line attachment points, as the tubular frame of the V3 is bridled on both the leading and trailing edges. The deformations of the shape are modelled through these attachment points, whose coordinate changes are predicted using bridle line system representations and calculation algorithms. A photogrammetry analysis of in-flight footage provided kite width change upon symmetrical actuation (up) and empirical relations, used to model phenomena like billowing. By including empirical relations sufficient bridle line system detail could be modelled allowing a geometric constraint-based 7-plate model to predict the width change upon symmetrical actuation within 1% of the photogrammetry results. The existence of slack made predicting asymmetrical actuation inaccurate, therefore a force-based particle system model (PSM) was developed. For higher computational costs the PSM was able to predict asymmetrical actuation and the width change accurately. Therefore, the PSM is considered an excellent building block for the next-generation kite design models.
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An airborne wind energy system (AWES) can harvest stronger wind streams at higher altitudes which are not accessible to conventional wind turbines. The operation of AWES requires a controller for the tethered aircraft/kite module (KM), as well as a controller for the ground station module (GSM). The literature regarding the control of AWES mostly focuses on the trajectory tracking of the KM. However, an advanced control of the GSM is also key to the successful operation of an AWES. In this paper we propose a cascaded control strategy for the GSM of an AWES during the traction or power generation phase. The GSM comprises a winch and a three-phase induction machine (IM), which acts as a generator. In the outer control-loop, an integral sliding mode control (SMC) algorithm is designed to keep the winch velocity at the prescribed level. A detailed stability analysis is also presented for the existence of the SMC for the perturbed winch system. The rotor flux-based field oriented control (RFOC) of the IM constitutes the inner control-loop. Due to the sophisticated RFOC, the decoupled and instantaneous control of torque and rotor flux is made possible using decentralized proportional integral (PI) controllers. The unknown states required to design RFOC are estimated using a discrete time Kalman filter (DKF), which is based on the quasi-linear model of the IM. The designed GSM controller is integrated with an already developed KM, and the AWES is simulated using MATLAB and Simulink. The simulation study shows that the GSM control system exhibits appropriate performance even in the presence of the wind gusts, which account for the external disturbance.
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The validity of using a low-computational-cost model for the aerodynamic characterization of Airborne Wind Energy Systems was studied by benchmarking a three-dimensional Unsteady Panel Method (UnPaM) with experimental data from a flight test campaign of a two-line Rigid-Framed Delta kite. The latter, and a subsequent analysis of the experimental data, provided the evolution of the tether tensions, the full kinematic state of the kite (aerodynamic velocity and angular velocity vectors, among others), and its aerodynamic coefficients. The history of the kinematic state was used as input for UnPaM that provided a set of theoretical aerodynamic coefficients. Disparate conclusions were found when comparing the experimental and theoretical aerodynamic coefficients. For a wide range of angles of attack and sideslip angles, the agreement in the lift and lateral force coefficients was good and moderate, respectively, considering UnPaM is a potential flow tool. As expected, UnPaM predicts a much lower drag because it ignores viscous effects. The comparison of the aerodynamic torque coefficients is more delicate due to uncertainties on the experimental data. Besides fully non-stationary simulations, the lift coefficient was also studied with UnPaM by assuming quasi-steady and steady conditions. It was found that for a typical figure-of-eight trajectory there are no significant differences between unsteady and quasi-steady approaches allowing for fast simulations.
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Several novel wind energy systems produce wakes with annular cross‐sections, which are qualitatively different from the wakes with circular cross‐sections commonly generated by conventional horizontal‐axis wind turbines and by compact obstacles. Since wind farms use arrays of hundreds of turbines, good analytical wake models are essential for efficient wind farm planning. Several models already exist for circular wakes; however, none have yet been proposed for annular wakes, making it impossible to estimate their array performance. We use the entrainment hypothesis to develop a reduced‐order model for the shape and flow velocity of an annular wake from a generic annular obstacle. Our model consists of a set of three ordinary differential equations, which we solve numerically. In addition, by assuming that the annular wake does not drift radially, we further reduce the problem to a model comprising only two differential equations, which we solve analytically. Both of our models are in good agreement with previously published large eddy simulation results.
Article
Airborne wind energy (AWE) is a fascinating technology to convert wind power into electricity with an autonomous tethered aircraft. Deemed a potentially game-changing solution, AWE is attracting the attention of policy makers and stakeholders with the promise of producing large amounts of cost-competitive electricity with wide applicability worldwide. Since the pioneering experimental endeavors in the years 2000–2010, there has been a clear technology convergence trend and steady progress in the field. Today, AWE systems can operate automatically with minimal supervision in all operational phases. A first product is also being commercialized. However, all-around fully autonomous operation still presents important fundamental challenges that are conceptually similar to those of other systems that promise to change our lives, such as fully autonomous passenger cars or service drones. At the same time, autonomous operation is necessary to enable large-scale AWE, thus combining challenging fundamental problems with high potential impact on society and the economy. This article describes the state of the art of this technology from a system perspective and with a critical view on some fundamental aspects, presents the latest automatic control results by prominent industrial players, and finally points out the most important challenges on the road to fully autonomous AWE systems. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.