Towards the optimum system design of ground-generation airborne wind energy systems

Airborne wind energy (AWE) is an emerging technology with various concepts of flying devices being developed within the industry. This work focuses on the fixed-wing ground-generation concept in which power generation happens in reel-out and reel-in phases cycles. We have developed an analytical power generation model based on steady-state aircraft dynamics, which captures the interaction between all the critical high-level design and operational parameters. This model is based on [1] and tries to fill the gaps in [2,3]. The critical system design parameters are the kite wing area, aspect ratio, wing aerodynamic properties, tether material, dimensions and the required electrical rated power. The critical operational design parameters are the pattern elevation angle, opening cone angle, pattern radius, reel-out length, reel-in speed and the wing aerodynamic properties. This work aims to understand the holistic behaviour of the system and the interaction between these design parameters.

Table 1 shows some of the critical specifications defining a particular system. All the above-mentioned operational parameters are optimised for maximising the cycle power output at every wind speed. Figure 1 shows the resulting power curve of the system and the cycle efficiencies. The system reaches its rated electrical power at the wind speed of 16m/s. Figure 2 represents the instantaneous cycle power at this wind speed. The maximum reel-out power is around 1.5 times the cycle average. The power has to be capped after the rated wind speed to maintain it at the rated value. This can be achieved by reducing the lift, decreasing the reel-out time, and increasing the pattern elevation, as seen in Figures 3, 4 and 5. Figure 6 shows the average operating altitude, pattern radius, reel-out length, and tether length at every wind speed. Amongst others, the three primary constraints driving the system’s behaviour are the limit on the maximum tether tension, length, and the required rated electrical power.

Setting different requirements and constraints will lead to the different behaviour of the system. To find the optimal system design, we need to observe the sensitivity of these design parameters to the system’s performance. The next step is to couple this power generation model with a cost model to evaluate the performance of systems based on standard design metrics such as the Levelized cost of electricity (LCoE). These insights will be a starting point for understanding the design space's feasibility, limits and critical trade-offs.