Airborne wind energy systems (AWESs) leverage the generally less variable and higher wind speeds at increased altitudes by utilizing kites, with significantly reduced material costs compared to conventional wind turbines. Energy is commonly harnessed by flying crosswind trajectories, which allow the kite to achieve speeds significantly higher than the ambient wind speed. However, the airborne nature of these systems demands active control and makes them highly sensitive to changes in wind conditions, making accurate wind measurements essential for steering the kite along its optimal trajectory. This paper presents an advanced sensor fusion technique based on an iterated extended Kalman filter (EKF) for state and wind estimation for AWESs. By integrating position, velocity, tether force, and reeling speed, this method provides accurate estimations of system dynamics, including kite orientation and tether shape. The estimates of the wind speed and direction are compared to lidar measurements, showing good agreement across various atmospheric conditions, with 10 min averaged root mean square error (RMSE) values below 1 m s−1 and 5°, respectively. The results demonstrate that this approach can effectively capture the transient dynamics of atmospheric wind using sensors typically already present in AWESs, making it suitable for supervisory control strategies and ultimately enhancing energy efficiency and system reliability across diverse atmospheric conditions.

Airborne wind energy (AWE) is an innovative technology that differs from the operating principles of horizontal axis wind turbines (HAWTs). It uses tethered flying devices, denoted as kites, to harvest higher-altitude wind resources. Kites eliminate the need for a tower but introduce a penalty in power generation since the kite has to spend part of its aerodynamic force to counter its weight. The differences between the two technologies lead to different scaling behaviours, and understanding these as well as the design drivers of AWE systems is essential for developing this technology further. To this end, we developed a multidisciplinary design, analysis, and optimisation (MDAO) framework which employs models evaluating the wind resource, power curve, energy production, overall component and operation costs, and various economic metrics. This framework was used to design fixed-wing ground-generation (GG) AWE systems based on the objective of minimising the levelised cost of energy (LCoE). The variables used to define the system were the wing area, aspect ratio, tether diameter, and rated power of the generator. The framework was employed to find optimal system designs for rated power ranging from 100 to 2000 kW. The results show that kite mass, energy storage, and tether replacements are the key LCoE driving factors. Moreover, in contradistinction to HAWTs, the total lifetime operational costs are equal to or higher than the initial investment costs. This distribution of costs over the project's lifetime, rather than as a large upfront investment, could make it easier to secure project financing. The scaling results show that the LCoE-driven optimum lies within the 100 to 1000 kW system size. The reason for this is that the kite mass penalty increases the cut-in and rated wind speeds, reducing the capacity factor of the larger systems. Sensitivity analyses with respect to extreme scenarios considering technological advancements, financial uncertainties, and environmental conditions show that this optimum is robust within our modelling assumptions.

This study investigates the relationship between sound quality metrics (SQMs) and noise annoyance caused by airborne wind energy systems (AWESs). In a controlled listening experiment, 75 participants rated their annoyance on the International Commission on Biological Effects of Noise (ICBEN) scale in response to recordings from in-field measurements of two fixed-wing and one soft-wing ground-generation AWES. All recordings were normalized to an equivalent A-weighted sound pressure level of 45 dBA. The results revealed that sharpness was the only SQM predicting participants' annoyance. Fixed-wing kites, characterized by sharper and more tonal and narrowband sound profiles, were rated as more annoying than the soft-wing kite, characterized by higher loudness values. In addition, the effect of some SQMs on annoyance depended on participant characteristics, with loudness having a weaker impact on annoyance for participants familiar with AWESs and tonality having a weaker effect on annoyance for older participants. These findings emphasize the importance of considering psychoacoustic factors in the design and operation of AWESs to reduce noise annoyance.

MegAWES is a reference design and simulation framework for ground-generation, fixed-wing airborne wind energy systems with a nominal power output of 3 MW. The winch size of MegAWES is based on a smaller system and needs to be scaled up because the current size leads to unrealistically fast dynamics, which require saturation. However, there is no available method to select an appropriate size for the winch. Additionally, while it has been hypothesized that the size of the winch has a significant effect on the dynamics of the overall system for ground-generation concepts, this effect has not been quantified. In this work, we first analyze the effects of the winch size on the system dynamics using a linearized model. Second, we present a method to find the upper bound for the size of the winch based on a selected maximum tether force overshoot during nominal operation. Third, we apply this method to find an upper bound for the winch size for the MegAWES reference design. Using the nonlinear MegAWES simulation framework, we validated this upper bound. At the upper bound, the system accurately tracked the reference tether force without overshoot and when exceeding our upper bound, the tether force response was oscillatory and overshot its ideal value.

Airborne wind energy (AWE) is an emerging renewable energy technology that uses kites to harvest winds at higher altitudes than wind turbines. Understanding how residents experience a local AWE system (AWES) is important as the technology approaches commercialization. Such knowledge can help adjust the design and deployment of an AWES to fit locals' needs better, thereby decreasing the technology's burden on people. Although the AWE literature claims that the technology affects nature and residents less than wind turbines, empirical evidence has been lacking. This first community acceptance study recruited residents within a 3.5 km radius of an AWE test site in Northern Germany. Using structured questionnaires, 54 residents rated the AWES and the closest wind farm on visual, sound, safety, siting, environmental, and ecological aspects. Contrary to the literature's claims, residents assessed the noise, ecological, and safety impacts similarly for the AWES and the wind farm. Only visual impacts were rated better for the AWES (e.g., no shadows were perceived). Consistent with research on wind turbines, residents who rated the site operation as fairer and the developer as more transparent tended to have more positive attitudes towards the AWES and to experience less noise annoyance. Consequently, recommendations for the AWE industry and policymakers include mitigating technology impacts and implementing evidence-based strategies to ensure just and effective project development. The findings are limited to one specific AWES using soft-wing kites. Future research should assess community responses across regions and different types of AWESs to test the findings' generalizability.

The economic viability of future large-scale airborne wind energy systems critically hinges on the achievable power output in a given wind environment and the system costs. This work presents a fast model for estimating the net power output of fixed-wing ground-generation airborne wind energy systems in the conceptual design phase. In this quasi-steady approach, the kite is represented as a point mass and operated in circular flight manoeuvres while reeling out the tether. This phase is subdivided into several segments. Each segment is assigned a single flight state resulting from an equilibrium of the forces acting on the kite. The model accounts for the effects of flight pattern elevation, gravity, vertical wind shear, hardware limitations, and drivetrain losses. The simulated system is defined by the kite, tether, and drivetrain properties, such as the kite wing area, aspect ratio, aerodynamic properties, tether dimensions and material properties, generator rating, maximum allowable drum speed, etc. For defined system and environmental conditions, the cycle power is maximised by optimising the operational parameters for each phase segment. The operational parameters include cycle properties such as the stroke length (reeling distance over the cycle), the flight pattern average elevation angle, and the pattern cone angle, as well as segment properties such as the turning radius of the circular manoeuvre, the wing lift coefficient, and the reeling speed. To analyse the scaling behaviour, we present a kite mass estimation model based on the wing area, aspect ratio, and maximum tether force. The computed results are compared with 6-degree-of-freedom simulation results of a system with a rated power of 150 kW. The results show the interdependencies between key environmental, system design, and operational parameters. Gravity penalises performance more at low wind speeds than at high wind speeds, and excluding gravity does not yield optimistic performance since it assists in the reel-in phase by reducing the required power. Thin tethers perform better at lower wind speeds but limit power extraction at higher wind speeds and vice versa for thick tethers. Upscaling results in a diminishing gain in performance with an increase in kite wing area. The proposed model is suitable for integration with cost models and is aimed at sensitivity and scaling studies to support design and innovation trade-offs in the conceptual design of systems.

Three unsteady aerodynamic tools at different levels of fidelity and computational cost were used to investigate the unsteady aerodynamic behavior of a delta kite applied to airborne wind energy. The first tool is an in-house unsteady panel method that is fast but delivers low to mid fidelity predictions. The second tool uses the open-source CFD code SU2 to solve the unsteady Reynolds-averaged Navier–Stokes equations with the (Formula presented.) SST turbulence model. At an intermediate level of fidelity, a semiempirical dynamic stall model that combines the panel method with a phenomenological dynamic stall module is proposed. The latter has free parameters that are fine-tuned with CFD results from the second tool. The research on the dynamic stall model has been inspired by two flight test campaigns suggesting dynamic stall phenomena possibly driven by the periodic variation of the angle of attack (aerodynamic pitching motion) during crosswind maneuvers. The recorded inflow along the flight path was prescribed in the three aerodynamic tools. As expected, the price to pay for the low computational cost of the panel method is its inability to capture the dynamic stall phenomenon. The results from unsteady CFD qualitatively matched the experimental data identifying a leading-edge vortex that forms and detaches cyclically during the pitching motion. Using RANS data, the semiempirical tool was fined-tuned to reproduce the dynamic stall behavior, becoming an accurate and fast aerodynamic tool for coupling with any kite flight simulator. Further discussions on the effects of kite aerostructural deflections are included.

In the original version of the book, on page xi, one of the authors listed for Chapter 2 is “R. Schnmehl”, which should be “R. Schmehl”. On page 21, the same correction needs to be made twice, in the listed authors at the top of the page and also in the footnotes. “Schnmehl” should become “Schmehl” in both the cases. This has now been rectified and the author’s name has been corrected. The correction to this book have been updated with the changes.

Airborne wind energy is an emerging technology that uses tethered flying devices to capture stronger and more steady winds at higher altitudes. Compared to smaller systems, megawatt-scale systems are substantially affected by gravity during flight operation, resulting in power fluctuations. MegAWES, a 3 MW reference model, experiences power fluctuations between -5.8 MW and +20.5 MW every 12.5 seconds during the traction phase when using its baseline controller at a wind speed of 22 m/s. The baseline controller does not have a power limit, leading to high peak power, and aims to keep the tether force constant, causing it to consume power when the kite is flying upwards. In this paper, we implement an optimal torque controller in the MegAWES framework and show that this eliminates the power consumption during the traction phase. Furthermore, we propose a kite tether force controller that allows setting a power limit when combined with the 2-phase reeling strategy, which decreases the peak power. Our new architecture reduces the power output range by 75% to between +3.7 MW and +9.4 MW in strong wind conditions.

Novel wind technologies, in particular airborne wind energy (AWE) and floating offshore wind turbines, have the potential to unlock untapped wind resources and contribute to power system stability in unique ways. So far, the techno-economic potential of both technologies has only been investigated at a small scale, whereas the most significant benefits will likely play out on a system scale. Given the urgency of the energy transition, the possible contribution of these novel technologies should be addressed. Therefore, we investigate the main system-level trade-offs in integrating AWE systems and floating wind turbines into a highly renewable future energy system. To do so, we develop a modelling workflow that integrates wind resource assessment and future cost and performance estimations into a large-scale energy system model, which finds cost-optimal system designs that are operationally feasible with hourly temporal resolution across ten countries in the North Sea region. Acknowledging the uncertainty on AWE systems' future costs and performance and floating wind turbines, we examine a broad range of cost and technology development scenarios and identify which insights are consistent across different possible futures. We find that onshore AWE outperforms conventional onshore wind regarding system-wide benefits due to higher wind resource availability and distinctive hourly generation profiles, which are sometimes complementary to conventional onshore turbines. The achievable power density per ground surface area is the main limiting factor in large-scale onshore AWE deployment. Offshore AWE, in contrast, provides system benefits similar to those of offshore wind alternatives. Therefore, deployment is primarily driven by cost competitiveness. Floating wind turbines achieve higher performance than conventional wind turbines, so they can cost more and remain competitive. AWE, in particular, might be able to play a significant role in a climate-neutral European energy supply and thus warrants further study.