System design and scaling trends for airborne wind energy

Abstract. So far, the size of horizontal axis wind turbines (HAWTs) has steadily increased, but recent studies and market decisions suggest that this trend may come to an end. Airborne wind energy (AWE) is an innovative technology that differs from the operating principles of 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 and the design drivers of AWE systems is essential for developing this technology further. To this end, we developed a multi-disciplinary 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 kW 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 kW 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.