Airborne Wind Energy Systems Research Articles

Drag-mode airborne wind energy vs. wind turbines: An analysis of power production, variability and geography

Airborne wind energy (AWE) is a wind power technology that harvests energy at high altitudes. The performance of AWE systems relative to traditional wind power turbines (WT) is of key relevance to any future commercialization. In particular, the power generation as well as its consistency over time will be key performance indicators. This study aims at identifying crucial factors that will influence the competitiveness of drag-mode AWE systems against WTs. To that end, the hourly power production of several drag-mode AWE designs is investigated using realistic wind data, and compared to the hourly power production of classical WTs. These results are then analyzed through three performance indicators: total annual power production, Gini coefficient, and correlation coefficient. The results show that AWE systems with multiple smaller wings have the highest annual production. The AWE power production of all AWE systems correlates in time at all sites with the production of WTs, and the Gini coefficients are similar. This observation challenges the belief that AWE systems will outcompete WTs thanks to a more consistent power output than WTs. However, the wing design as well as the local wind shear have a significant impact on the AWE performance.

Improving reliability and safety of airborne wind energy systems

AbstractAirborne wind energy systems use tethered flying devices to harvest wind energy beyond the height range accessible to tower‐based wind turbines. Current commercial prototypes have reached power ratings of up to several hundred kilowatts, and companies are aiming at long‐term operation in relevant environments. As consequence, system reliability, operational robustness, and safety have become crucially important aspects of system development. In this study, we analyze the reliability and safety of a 100‐kW technology development platform with the objective of achieving continuous automatic operation. We first outline the different components of the kite power system and its operational modes. In the next step, we identify failure modes, their causes, and effects by means of failure mode and effects analysis (FMEA) and fault tree analysis (FTA). Potentially hazardous situations and mechanisms which can render the system nonoperational are identified, and mitigation measures are proposed. We find that the majority of these measures can be performed by a failure detection, isolation, and recovery (FDIR) system for which we present a hierarchical architecture adapted from space industry.

A Metric Space with LCOE Isolines for Research Guidance in wind and hydrokinetic energy systems

AbstractThis paper introduces a new Metric Space to guide the design of advanced wind energy systems and hydrokinetic energy converters such as tidal, ocean current and riverine turbines. The Metric Space can analyse farms that combine different or identical turbines and stand‐alone turbines. The first metric (M1) of the space considers the efficiency of the turbines in the farm, which is also proportional to the specific power per swept area at a given wind/water velocity (W/m2). The second metric (M2) describes the specific rotor area per unit of mass of the turbines (m2/kg). Both metrics depend on the primary design characteristics of the turbines, such as swept area, system size and mass, materials and efficiency, and are independent at first from external characteristics, such as atmospheric and ocean site conditions, cost of materials and economic factors. Combining both metrics, and for a given set of external characteristics, the resulting Metric Space M2/M1 displays the Levelized Cost of Energy (LCOE) standards as isolines. This graphical representation provides a quick understanding of the cost and state of the technology. It also offers a practical guidance to choose the research tasks and strategy to design advanced wind and hydrokinetic energy systems. The paper applies the new Metric Space to several case studies, including large and small onshore wind turbines, floating and bottom‐fixed offshore wind turbines, downwind rotors, multi‐rotor and hybrid systems, airborne wind energy systems, wind farms and tidal energy converters.

Scalability and compatibility analyses of airborne wind technology for maritime transport

Wind energy is a source freely available in the oceans. New developments in the wind industry are towards high altitude wind turbines, known as Airborne Wind Energy Systems. The airborne wind technology is rapidly developing with several different design concepts, e.g. Skysail, Makani, KiteMill. There are mainly two types of airborne wind technology: propulsion-supporting and electricity-generating. Electricity generating airborne technology is mainly used for land-based buildings and fixed offshore structures and with small-scale airborne turbines. Thus, the applications of this technology in the maritime industry are limited to ship propulsion-support only and no scalable airborne wind turbine is used on board of a vessel. Therefore, the objective of this study is to explore the application of electricity-generating airborne turbines on the board of a ship. The exploration is mainly to determine how scalable these turbines shall be to satisfy a ship’s electricity needs and how compatible the design of these turbines can be with the ships design. . Therefore, a scalability analysis based on crosswind kite power model is applied to determine the technical requirements to scale up the existing airborne turbine to meet the electricity demand of a ship. Moreover, a compatibility analysis is applied to determine the technical interfaces and constrains between the airborne turbine configuration and the ship architecture. In this case, a commercial electricity-generating airborne turbine and a commercial floating storage unit are considered as part of the case study. The developed scalability model indicates that the traction force is the most critical design parameter to scale up a wind turbine. On the other hand, the developed compatibility model shows that there is a notorious complexity in merging airborne and ship technology due to their contexts. Consequently, this research appears to be relevant for both, the airborne technology industry and the maritime industry toward more innovative and cost-effective applications.

Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

Abstract. Airborne wind energy systems (AWESs) aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind light detection and ranging (lidar) measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This study investigates whether assimilating measurements into the mesoscale Weather Research and Forecasting (WRF) model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the lidar measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long-term statistics. Finally, wind speed data are used to estimate the optimal traction power and operating altitudes of AWES. Observation nudging improves the WRF accuracy at the measurement location. Close to the surface the impact of nudging is limited as effects of the air–surface interaction dominate but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed frequency distribution shows a multi-modality caused by changing atmospheric stability conditions. Therefore, wind speed profiles are categorized into various stability conditions. Based on a simplified AWES model, the most probable optimal altitude is between 200 and 600 m. This wide range of heights emphasizes the benefit of such systems to dynamically adjust their operating altitude.

Viability of Airborne Wind Energy in the United Kingdom

Abstract To meet the worldwide requirements of carbon emission reduction, the European Council has set the UK a 15% energy target to come from renewable energy by 2020. The biggest renewable energy sources in the UK are bioenergy, wind, solar, and hydro. The UK is located in prime geography, considered to be the best in Europe, for harvesting, and over the last three decades, the number of wind farms has increased greatly. However, the interaction of wind speed and structural strength has limited the height of platform-based wind turbines to a maximum height of around 100 m. Airborne wind energy (AWE) systems enable the extraction of more energy from the wind at elevated altitudes beyond 150 m using a device termed a kite. A method is required to determine suitable locations for AWE system implementation. In this work, a regional feasibility study has been conducted to establish an ideal suitable location to implement AWE systems. Extensive research has been carried out to assess the electricity costs, energy savings, area availability, as well as regional airborne wind energy power densities at different regions within the UK. A standardized method has been developed to assess the viability of AWE in various geographical locations. It was found that Scotland was the most suitable location for the implementation of AWE systems due to the high wind power density in this region and existing high costs of electricity, thus greater potentials for energy cost savings.

Wake characteristics of pumping mode airborne wind energy systems

Airborne wind energy is an emerging technology that aims at harvesting wind power at high altitudes. In the present work, we propose a framework combining optimal control and large-eddy simulation to investigate the wake characteristics of large-scale airborne wind energy systems. We consider systems operating in pumping mode which alternate between power-generating and -consuming phases. We investigate the downstream wake development in non-turbulent and turbulent sheared inflow conditions. The optimal system operation leads to a non-uniform radial wake development and results show that the maximal wake velocity deficit for the current system is half the deficit predicted by Betz limit for a conventional wind turbine.

Engineering Wake Induction Model For Axisymmetric Multi-Kite Systems

Multi-kite airborne wind energy systems (MAWES) fly two or more kites in a high-altitude orbit that allows a main tether to unroll a generator. An engineering wake induction model could simplify the (axisymmetric) aerodynamics of such an unsteady actuator-annulus with variable thrust-coefficient, flight-radius, and downwind-direction apparent velocity, in comparison to high-fidelity wake models. To the authors’ knowledge, existing engineering models do not predict the influence of variable flight radius. The goal of this paper is to synthesize a MAWES engineering model, a closed-form function approximating the axial induction factor ã, specifically at the kite mid-span, using linearized wake parameters, as available from a dynamics solver. A vortex tube model is used to construct a plausible approximation of the induction, which is then heavily simplified for computation tractability while retaining important flow physics.

Reduced-Order Dynamic Model of a Morphing Airborne Wind Energy Aircraft

Airborne wind energy (AWE) is a power production technique aiming to harvest energy from high-altitude winds through tethered aircraft. In the ground-based power generation concept, the aircraft flies dynamic trajectories at high flight speeds and lift coefficients, thus creating large lift forces. These forces drive a ground-based generator via a tether, thereby producing energy. The tethered aircraft operates over a wide range of wind speeds, with distinct operating modes. Current rigid-wing AWE aircraft adapt to these conditions by using discrete control surfaces. Morphing wings, in contrast, can adjust to the different flight conditions and operating modes by continuously altering their shape, and thus can increase the power production of AWE systems. This paper presents the modeling of a camber-morphing AWE system, focusing in particular on the coupled aeroelastics and flight dynamics of the aircraft and on the reduced-order structural and aerodynamic model of the morphing wing. The resulting framework allows analysis of the entire AWE system consisting of the aircraft, tether, and ground station. Owing to its high computational efficiency, the model enables comparison of different trajectories and flight control strategies, and it permits identification of optimal aerodynamic- and structural-design parameters of morphing AWE wings.

Iterative Learning-Based Path Optimization for Repetitive Path Planning, With Application to 3-D Crosswind Flight of Airborne Wind Energy Systems

This paper presents an iterative learning approach for optimizing the course geometry in repetitive path following applications. In particular, we focus on airborne wind energy (AWE) systems. Our proposed algorithm consists of two key features. First, a recursive least squares (RLS) fit is used to construct an estimate of the behavior of the performance index. Second, an iteration-to-iteration path adaptation law is used to adjust the path shape in the direction of optimal performance. We propose two candidate update laws, both of which parallel the mathematical structure of common iterative learning control (ILC) update laws but replace the tracking-dependent terms with terms based on the performance index. We apply our formulation to the iterative crosswind path optimization of an AWE system, where the goal is to maximize the average power output over a figure-8 path. Using a physics-based AWE system model, we demonstrate that the proposed adaptation strategy successfully achieves convergence to near-optimal figure-8 paths for a variety of initial conditions under both constant and real wind profiles.

Cascaded Pumping Cycle Control for Rigid Wing Airborne Wind Energy Systems

Airborne wind energy is an emerging technology that uses tethered unmanned aerial vehicles for harvesting wind energy at altitudes higher than conventional towered wind turbines. To make the technology competitive to other renewable energy technologies a reliable control system is required that allows autonomously operating the system throughout all phases of flight. In the present work a cascaded nonlinear control scheme for reliable pumping cycle control of a rigid wing airborne wind energy system is proposed. The high-level control strategy in the form of a state machine as well as the flight controller consisting of path-following guidance and control, attitude, and rate loop is presented along with a winch controller for tether force tracking. A mathematical model for an existing prototype will be derived, and results from a simulation study will be used to demonstrate the robustness of the proposed concept in the presence of turbulence and wind gusts.

In-Flight Estimation of the Aerodynamics of Tethered Wings for Airborne Wind Energy

A new filtering approach is presented, able to estimate online the aerodynamic characteristics of tethered wings used in airborne wind energy systems. The approach is based on the extended Kalman filter (EKF) and on a representative model of the flight dynamics. The filter is fed with measurements available at the ground station, namely, the line angles and their rates, the traction force on the tether, and the wind speed and direction a few meters above the ground. Convergence and accuracy of the estimates are first evaluated through simulations. Then, the approach is demonstrated using extensive experimental data, collected during field tests with two small-scale prototypes built independently by different research groups and four different flexible airfoils (power kites), of both ram-air type and of leading-edge-inflated type. The experimental results show that the algorithm can effectively estimate both the state and the aerodynamic parameters of the wings, as well as the wind speed and direction at the wing's altitude, and makes it possible to analyze the kite aerodynamics during highly dynamical tethered flight.

Combined Plant and Controller Design Using Batch Bayesian Optimization: A Case Study in Airborne Wind Energy Systems

This paper presents a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored toward instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necessary. Specifically, Bayesian optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve a nested optimization problem. The underlying objective function is modeled by a Gaussian process (GP); then, Bayesian optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent control or plant parameters. The proposed framework differs from the majority of codesign literature where there exists a closed-form model of the system dynamics. Furthermore, we utilize the idea of batch Bayesian optimization at the plant optimization level to generate a set of plant designs at each iteration of the overall optimization process, recognizing that there will exist economies of scale in running multiple experiments in each iteration of the plant design process. We validate the proposed framework for Altaeros' buoyant airborne turbine (BAT). We choose the horizontal stabilizer area, longitudinal center of mass relative to center of buoyancy (plant parameters), and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that these plant and control parameters converge to their respective optimal values within only a few iterations.

LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

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.

Airborne wind energy resource analysis

We compare the available wind resources for conventional wind turbines and for airborne wind energy systems. Accessing higher altitudes and continuously adjusting the harvesting operation to the wind resource substantially increases the potential energy yield. The study is based on the ERA5 reanalysis data which covers a period of 7 years with hourly estimates at a surface resolution of 31 × 31 km and a vertical resolution of 137 barometric altitude levels. We present detailed wind statistics for a location in the English Channel and then expand the analysis to a surface grid of Western and Central Europe with a resolution of 110 × 110 km. Over the land mass and coastal areas of Europe we find that compared to a fixed harvesting height at the approximate hub height of wind turbines, the wind power density which is available for 95% of the time increases by a factor of two.

A reference model for airborne wind energy systems for optimization and control

Airborne Wind Energy (AWE) is a promising new technology, and attracts a growing academic and industrial attention. Important research efforts have been deployed to develop prototypes in order to test the technology, generate control algorithms and optimize the efficiency of AWE systems. By today, a large set of control and optimization methods is available for AWE systems. However, because no validated reference model is available, there is a lack of benchmark for these methods. In this paper, we provide a reference model for pumping mode AWE systems based on rigid wings. The model describes the flight dynamics of a tethered 6 degrees of freedom (DOF) rigid body aircraft in form of differential-algebraic equations, based on Lagrange dynamics. With the help of least squares fitting the model is assessed using real flight data from the Ampyx Power prototype AP2. The model equations are smooth and have a low symbolic complexity, so as to make the model ideal for optimization and control. The information given in this paper aims at providing AWE researchers with a model that has been validated against flight data and that is well suited for trajectory and power output simulation and optimization.

Spatiotemporal Optimization Through Gaussian Process-Based Model Predictive Control: A Case Study in Airborne Wind Energy

This brief presents a model predictive control (MPC)-based spatiotemporal optimization strategy that is applied to the problem of optimizing the altitude of a type of airborne wind energy (AWE) system, specifically a buoyant airborne turbine. Altitude optimization for AWE systems represents a challenging problem under which the wind speed varies with both time and altitude, is only instantaneously observable at the altitude where the AWE system is operating, and dictates the net power produced by the system. The proposed MPC strategy avoids the need for a computationally expensive Markov process model for characterizing the wind speed and is structured in a way that the need for instantaneous power maximization (termed exploitation ) is balanced with the need to maintain an accurate map of wind speed versus altitude (termed exploration ). The MPC strategy is calibrated through a Gaussian process regression framework. Real wind speed versus altitude data have been used to validate the strategy.

A lagrangian flight simulator for airborne wind energy systems

• A constraint-free simulator for airborne wind energy systems is presented. • Fly- and ground-generation systems are considered. • Trade-off analysis of parallelization and lagrangian versus Hamiltonian formulations. • Self-consistent figure-of-eight trajectories are presented. • Open- and close-loop control strategies are considered. A parallelized flight simulator for the dynamic analysis of airborne wind energy (AWE) systems for ground- and fly-generation configurations is presented. The mechanical system comprises a kite or fixed-wing drone equipped with rotors and linked to the ground by a flexible tether. The time-dependent control vector of the simulator mimics real AWE systems and it includes the length of the main tether, the geometry of the bridle, the torque of the motor controllers of the rotors, and the deflections of ailerons , rudder and elevator. The use of a lagrangian formulation with a minimal coordinate approach and discretizing the main tether as a chain of inelastic straight rods linked by ideal (dissipative-less) rotational joints, yielded a non-stiff set of ordinary differential equations free of algebraic constraints. Several verification tests, including a reel-in maneuver that admits an analytical solution, are presented. The efficiency of the parallelization with the number of tether segments, and trade-off analysis of the lagrangian and hamiltonian formulations are also considered. The versatility of the simulator is highlighted by analyzing two maneuvers that are relevant for AWE scenarios. First, the simulator is used to compute periodic figure-of-eight trajectories with an open-loop control law that varies the geometry of the kite’s bridle, as frequently done in ground-generation AWE systems. Second, an unstable equilibrium state of a tethered drone equipped with two rotors for energy harvesting is stabilized by implementing a closed-loop control strategy for the deflection of the control aerodynamic surfaces .

Feasibility study of pumping cycle kite power system implication in Scotland UK

To meet the worldwide requirements of carbon emission reduction, the European Council set a target for 15% of final energy consumption in the UK to come from renewable energy by 2020. In the UK, the Climate Change Act (2008) set a target for the reduction of 80% of carbon emissions by 2050. The biggest renewable energy sources in the UK are bioenergy, wind, solar and hydro. The UK is located in a windy area and is one of the top locations in the world for wind power, often considered to be the best in Europe. Since the 1980’s, the number of wind farms has increased greatly. Wind turbines extract wind energy from 100 metres above the ground. Airborne Wind Energy (AWE) systems enable the extraction of more energy from the wind at elevated altitudes beyond 150 meters using a technique termed pumping cycle kite power system. Scotland is the most suitable location for the implementation of AWE systems in the UK. In this work, the annual energy production of such a pumping cycle kite power system in Scotland was analysed using Luchsinger’s analytical models. The annual energy production was found to be over 3.4MWh/m2 in all five locations in Scotland.

Power Control of Direct Interconnection Technique for Airborne Wind Energy Systems

In this paper, an offshore airborne wind energy (AWE) farm consisting of three non-reversing pumping mode AWE systems is modelled and simulated. The AWE systems employ permanent magnet synchronous generators (PMSG). A direct interconnection technique is developed and implemented for AWE systems. This method is a new approach invented for interconnecting offshore wind turbines with the least number of required offshore-based power electronic converters. The direct interconnection technique can be beneficial in improving the economy and reliability of marine airborne wind energy systems. The performance and interactions of the directly interconnected generators inside the energy farm internal power grid are investigated. The results of the study conducted in this paper, show the directly interconnected AWE systems can exhibit a poor load balance and significant reactive power exchange which must be addressed. Power control strategies for controlling the active and reactive power of the AWE farm are designed, implemented, and promising results are discussed in this paper.