A Critical Assessment of Airborne Wind Energy Systems

<strong class="journal-contentHeaderColor">Abstract.</strong> <span>Wind energy is foreseen to be a cornerstone of the future energy mix, with a total capacity projected to increase drastically in the coming decades. To this end, the size of horizontal‑axis wind turbines is continuously increasing, which poses significant structural challenges and requires increasing material resources. Those challenges have triggered interest in alternative technologies. Airborne wind energy (AWE) shows great potential and has recently gained a great deal of interest. However, the implementation of airborne wind energy systems (AWES) is in its infancy, and the only existing systems operate isolated. For AWES to take an active part in wind energy, their operation in turbulent environments must be further studied, and wind farms must also be considered. This work proposes a framework based on computational fluid dynamics for studying AWES in ambient turbulent wind and wakes, as will be encountered when arranged in farms. The present work focuses on ground-gen rigid-wing AWES. The framework relies on a large eddy simulation flow solver, in which the kites are represented using a model based on an actuator line for the main wing with its ailerons, and complemented with models for the tail control surfaces (rudder and elevator). The flow solver is coupled, via a two-way coupling, to a control module based on model-predictive control, to follow optimal trajectories. The framework is presented in some detail and is then used to investigate the MegAWES aircraft, a MW-scale AWES of 42.5 m wingspan, here flying four-loop trajectories. The first part of the investigation focuses on a single system. Its ability to fly in a turbulent wind is demonstrated and analyzed, and its wake is also characterized. It is demonstrated that the controlled kite can handle the turbulent wind. The deviation from its reference trajectory is less than 15 % of the wingspan. In the second part, a tandem configuration is considered, with the same foor-loop trajectory for each kite. It is found that there is a configuration where the second kite, even fully aligned with the first one, can fly in unperturbed flow (other than the turbulence of the wind). A second case is investigated where the second kite is forced to fly in the wake from the first one. It is found that the wake produced by the first kite does not compromise the trajectory tracking of the second kite. However, the second kite feels the velocity deficit and its power production is reduced by 6 %.</span>

The United Kingdom (UK) has pledged to reduce its greenhouse-gas emissions to net zero by 2050. However, in July 2022, the High Court ruled that the government’s net zero strategy failed to outline policies that would enable it to meet the target. As a result, the government published a new plan that sets out opportunities for innovative technologies like Floating Offshore Wind Manufacturing, and hydrogen, which will not only help us reach net zero. For wind, the government’s goal is to develop up to 50 GW of offshore wind by 2030. The challenge of such an expansion is huge. It means the United Kingdom will need to install an estimated 3,200 new, and much larger, wind turbines by 2030—roughly three new turbines every 2 days. Airborne Wind Energy (AWE) systems are part of a new class of wind energy converters that is receiving considerable attention in the renewable energy generation arena. AWE systems benefit from the steadier and stronger wind streams at high altitudes to generate more energy from wind while avoiding the expense of tower construction. This paper presents a feasibility study of using AWE systems in the UK and particularly in the City of Aberdeen to generate renewable energy. The characteristics of wind energy distributions were theoretically investigated by developing a wind speed distribution model, and then the annual power production of a kite system and a turbine system with 30 kW generator were analysed by applying the annual wind profile in Aberdeen to the performance models of these two systems. It was found that the annual power production of the kite system was two times higher than that of a normal turbine system.

<strong class="journal-contentHeaderColor">Abstract.</strong> Understanding and modelling the aerodynamic wake of airborne wind energy systems (AWESs) is crucial for estimating the performance and defining the design of such systems, as tight trajectories increase induced velocities, and thus decrease the available power, while unnecessarily large trajectories increase power losses due to the gravitational potential energy exchange. The aerodynamic wake is here studied with vortex methods, as momentum methods cannot be applied to AWESs in a physically consistent way. The velocities induced at the AWES from a generic helicoidal vortex filament, trailed by a position on the AWES wing, is modelled with an expression for the near vortex filament and one for the far vortex filament. The near vortex filament is modelled as the first half rotation of the helicoidal filament, where its axial component is neglected. The induced drag due to the near wake, built up from near vortex filaments, is found to be similar to the induced drag the AWES would have in forward flight. The far wake is modelled as two semi-infinite vortex rings cascades with opposite intensity. An approximate solution for the induced axial velocity at the AWES is given as function of the radial (known) and axial (unknown) position of the vortex rings. An explicit and an implicit closure model are introduced to link the axial position of the vortex rings with the other quantities of the model. The aerodynamic model, using the implicit closure model for the far wake, is validated with the lifting line free vortex wake method implemented in QBlade. The model is suitable to be used in time-marching aero-servo-dynamic-elastic simulations and in design and optimization studies.

As global demand for renewable energy continues to grow, high-altitude wind energy, characterized by high speed, wide distribution, and strong stability, has emerged as a promising alternative to low-altitude wind energy. Airborne Wind Energy systems (AWEs) are key to harnessing high-altitude wind, and Ground-Generator (Ground-Gen) AWEs are favored for their lower costs and simpler deployment. This study focuses on the umbrella–ladder-type Ground-Gen AWEs, aiming to address the research gap by exploring the influence of canopy permeability on the aerodynamic and structural response characteristics of flexible wind-capturing umbrellas. A single-umbrella model of the high-altitude wind-capturing umbrella was established, and bidirectional fluid–structure interaction (FSI) numerical simulations were conducted using the Arbitrary Lagrangian–Eulerian (ALE) method. Simulations were performed under a 30° angle of attack with two canopy thicknesses (5 × 10−5 m and 1 × 10−4 m) and varying permeability (adjusted via viscosity coefficient a and inertial coefficient b). Results showed that higher permeability (smaller a and b) hindered upper canopy inflation, while lower permeability promoted full inflation and more uniform stress distribution. The max/min in-plane shear stress for the model with the lowest permeability (Model F) was approximately 85% lower than that of the model with the highest permeability (Model A). The tension coefficient increased with decreasing permeability. Full inflation resulted in a slightly higher axial load in the upper suspension lines due to the lift force, with a difference of up to 92.3% during slight collapse. This difference becomes significantly more pronounced during severe collapse. Asymmetric flow fields at a 30° attack angle generated a lift force, resulting in higher tension coefficients than those at a 0° attack angle. These findings provide valuable references for the design and optimization of high-altitude wind-capturing umbrellas.

The robotic control of tethered parafoils enables a number of exciting applications, primarily a novel approach to wind energy generation. The airborne wind energy (AWE) paradigm aims to reduce greatly the mass and cost of materials required to generate electrical power from the wind. With innovative, distributed control systems and advanced materials, free flying tethered wings can replace the tower and blades of a conventional wind turbine, with the power take off system relocated to ground level. The critical enabling challenge faced by such systems, is the development of suitable control system hardware and software architectures with robust controllers maintaining the safe and optimal operation of the system. Suitable power take off methods form an integral part of AWE systems, as the mechanical power is produced on a periodic cycle. A holistic approach to the design, development and testing of a distributed control system for the flight of tethered kites has been undertaken. The airborne control element must operate harmoniously with the ground based power take off unit. A series of flight tests of a prototype control system developed has been carried out in the field. A detailed analysis of results and outcomes is provided. Paralleling the prototype development a mathematical model and simulation tool for the power take off synchronisation of multiple AWE systems has been developed with analysis of the performance and requirements for such a system. Continuous power output is achieved from multiple interconnected cyclical sources in a model that presents one feasible AWE farm topology. Having gained low cost access to altitude, additional payload applications for the automatic flight of parafoils emerge such as aerial sensor and observation platforms or ‘over the horizon’ communications links.

This study presents a data-driven approach for optimizing the operating altitude of Airborne Wind Energy (AWE) systems to maximize net energy production. Determining the optimal operating altitude of an AWE system is challenging, as the wind speed constantly varies with both time and altitude. Furthermore, without expensive auxiliary equipment, the wind speed is only measurable at the AWE system's operating altitude. The work presented in this paper shows how tools from machine learning can be blended with real-time control to optimize the AWE system's operating altitude efficiently, without the use of auxiliary wind profiling equipment. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is applied to the real-time control of an AWE system. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent operating altitude. In the AWE application, conventional Bayesian Optimization is extended to handle the time-varying nature of the wind shear profile (wind speed vs. time). Using real wind data, our method is validated against three baseline approaches. Our simulation results show that the Bayesian Optimization method is successful in dramatically increasing power production over these baselines.

An Airborne Wind Energy System (AWES) is a concept to convert wind energy into electricity, which comprises a tethered aircraft connected to a ground station. These systems are capable of harvesting high altitude winds, which are more frequent and more consistent. Among AWES, there are Pumping Kite Generators (PKG) that involve a rigid or flexible kite connected to a motor/generator placed on the ground through a light–weight tether. Such PKG produces electrical power in a cyclical two–phased motion with a traction phase and a retraction phase. During the traction phase, the aim is to maximize power production. This goal is achieved by controlling the kite such that it performs an almost crosswind motion, keeping a low elevation angle in order to maximize the tether tension. During the retraction phase, the tether tension force is minimized by steering the kite while the tether is reeled–in. Such strategy assures that the cyclical two–phased motion has a positive electrical balance at the end of the overall cycle. In a first stage, we solve an optimal control problem to compute the optimal plan for the kite trajectory during the traction phase, maximizing power production. Such trajectory is then used to define a time–independent geometrical path, which in turn is used as the reference path for the path–following control procedure that is developed in a second stage, and for which results are also presented.

The focus of this paper is centered on the Buoyant Airborne Turbine (BAT) developed by Altaeros Energies. This system is part of a new class of wind energy system known as Airborne wind energy systems (AWES). To inform the reader, a brief review of the current control architecture for different types of AWES is presented. The paper then evaluates the control architecture for the Altaeros system and illustrates through a frequency domain analysis the impact that operating conditions will have on movement in roll, pitch and altitude. The primary contribution of this paper rests in the design of a multiloop novel adaptive low level PD controller that is developed from linearized models covering the full operating envelope. PD gains are adapted as a function of the system operating conditions and tuned as a function of the multi-loop bandwidth. As such it is shown that the control design varies with wind conditions and so facilitates the need for this adaptive approach. This work illustrates that a model free multiloop controller can be achieved without the need for extensive calibration parameters as in the classic LQR approach and demonstrates the merits of simplified PD tuning strategies when faced with complex multivariable problems.

SummaryEngineers and researchers working on the development of airborne wind energy systems (AWES) still rely on oversimplified wind speed approximations and coarsely sampled reanalysis data because of a lack of high‐resolution wind data at altitudes above 200 m. Ten‐minute average wind speed LiDAR measurements up to an altitude of 1100 m and data from nearby weather stations were investigated with regard to wind energy generation and impact on LiDAR measurements. Data were gathered by a long‐range pulsed Doppler LiDAR device installed on flat terrain. Because of the low overall carrier‐to‐noise ratio, a custom‐filtering technique was applied. Our analyses show that diurnal variation and atmospheric stability significantly affect wind conditions aloft which cause a wide range of wind speeds and a multimodal probability distribution that cannot be represented by a simple Weibull distribution fit. A better representation of the actual wind conditions can be achieved by fitting Weibull distributions separately to stable and unstable conditions. Splitting and clustering the data by simulated surface heat flux reveals substate stratification responsible for the multimodality. We classify different wind conditions based on these substates, which result in different wind energy potential. We assess optimal traction power and optimal operating altitudes statistically as well as for specific days based on a simplified AWES model. Using measured wind speed standard deviation, we estimate average turbulence intensity and show its variation with altitude and time. Selected short‐term data sets illustrate temporal changes in wind conditions and atmospheric stratification with a high temporal and vertical resolution.

The tether structure plays the role of transferring the traction force of an airborne wind energy system (AWES) to the fixed or mobile ground system with less motion and maintains the flying airborne system as a critical component. The implementation of a geometrically tailored tether design in an AWES could avoid unwanted snap-through failure, which can be take place in a conventional tether structure under impulsive loading. This concept relies on the redundant load path of the composite structure composed of tailored length and strength. In this study, the dynamic response of this composite tether structure to airborne wind energy systems, such as a kite wind power system, was analytically investigated. Also, for very long tether applications, an approximate model of the tether response was developed, which resulted in a dramatic reduction of computational efforts while preserving the accuracy quite well compared to the exact solution.

This paper presents an iterative learning approach for optimizing waypoints in repetitive path following applications. Our proposed algorithm consists of two key features: First, a recursive least squares fit is used to construct an estimate of the behavior of the performance index. Secondly, an iteration-to-iteration waypoint adaptation law is used to update waypoints in the direction of optimal performance. This waypoint update law parallels the mathematical structure of a traditional iterative learning control (ILC) update but replaces the tracking error term with an error between the present and estimated optimal waypoint sequences. The proposed methodology is applied to the crosswind path optimization of an airborne wind energy (AWE) system, where the goal is to maximize the average power output over a figure-8 path. In validating the tools from this work, we introduce a simplified 2-dimensional analog to the more complex 3-dimensional AWE system, which distills the problem to its core elements. Using this model, we demonstrate that the proposed waypoint adaptation strategy successfully achieves convergence to near-optimal figure-8 paths for a variety of initial conditions.

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.

An existing attitude tracking control method developed for a FlyGen airborne wind energy (AWE) system is extended to control a GroundGen AWE system. The GroundGen system produces energy through kite-generated aerodynamic forces that tension a flexible tether attached between the kite center of gravity and a ground generator system. The kite is modelled using a six degree of freedom aircraft dynamics model. We first provide an extension of a previously developed attitude tracking theorem which shows the possibility of simultaneously tracking both the effective angle of attack and sideslip angle on the kite. A detailed aerodynamic model with empirical airfoil data and three dimensional wing tip effects is included. A feedback linearization controller is designed, and a baseline simulation is studied for confirming the tracking behavior.

Airborne Wind Energy (AWE) systems generate energy by flying a tethered airfoil across the wind flow at a high velocity. Tethered flight is a fast, strongly nonlinear, unstable and constrained process, motivating control approaches based on fast Nonlinear Model Predictive Control (NMPC) and state estimation approaches based on Moving Horizon Estimation (MHE). Dual-Airfoil AWE systems, i.e. systems with two airfoils attached to a Y-shaped tether have been shown to be more effective than systems based on a single airfoil. This paper proposes a control scheme for a dual-airfoil AWE system based on NMPC and MHE and studies its performance in a realistic scenario based on state-of-the-art turbulence models.

Abstract. The optimal control problem for flight trajectories of Fly-Gen airborne wind energy systems (AWESs) is a crucial research topic for the field, as suboptimal paths can lead to a drastic reduction in power production. One of the novelties of the present work is the expression of the optimal control problem in the frequency domain through a harmonic balance formulation. This allows the potential reduction of the problem size by solving only for the main harmonics and allows the implicit imposition of periodicity of the solution. The trajectory is described by the Fourier coefficients of the dynamics (elevation and azimuth angles) and of the control inputs (onboard wind turbine thrust and AWES roll angle). To isolate the effects of each physical phenomenon, optimal trajectories are presented with an increasing level of physical representation from the most idealized case: (i) if the mean thrust power (mechanical power linked to the dynamics) is considered as the objective function, optimal trajectories are characterized by a constant AWES velocity over the loop and a circular shape. This is done by converting all the gravitational potential energy into electrical energy. At low wind speed, onboard wind turbines are then used as propellers in the ascendant part of the loop; (ii) if the mean shaft power (mechanical power after momentum losses) is the objective function, a part of the potential energy is converted into kinetic and the rest into electrical energy. Therefore, the AWES velocity fluctuates over the loop; (iii) if the mean electrical power is considered as the objective function, the onboard wind turbines are never used as propellers because of the power conversion efficiency. Optimal trajectories for case (ii) and (iii) have a circular shape squashed along the vertical direction. The optimal control inputs can be generally modeled with one harmonic for the onboard wind turbine thrust and two for AWES roll angle without a significant loss of power, demonstrating that the absence of high-frequency control is not detrimental to the power generated by Fly-Gen AWESs.