PDF available. See the Figure 3:
Figure 3. Masses of AWE systems as a function of the wing surface area. MegAWES [25], Ampyx
Power AP2 and AP3 [20], AP5 low and AP5 high [26], Makani Power M600, MX2 (Oktoberkite), and
M5 [27,28], Haas et al. 2019 [29], and conventional aircraft wing scaling [30].
The curve of conventional aircraft wing scaling looks to be almost linear. An explain page 4/20:
The aircraft wing scaling law (dashed line) is optimistic because it only includes the mass of the wing, omitting the fuselage, tail, and other electronic and electrical subsystems. The correlation provides a reference to compare the mass of conventional, untethered aircraft to that of fixed-wing kites for airborne wind energy harvesting designed for a substantially higher wing loading.
The main purpose seems to be the levelized cost of energy (LCOE). Power to space use ratio does not look to be considered.
Abstract
In the current auction-based electricity market, the design of utility-scale renewable energy systems has traditionally been driven by the levelised cost of energy (LCoE). However, the market is gradually moving towards a subsidy-free era, which will expose the power plant owners to the fluctuating prices of electricity. This paper presents a computational approach to account for the influence of time-varying electricity prices on the design of airborne wind energy (AWE) systems. The framework combines an analytical performance model, providing the power curve of the system, with a wind resource characterisation based on ERA5 reanalysis data. The resulting annual energy production (AEP) model is coupled with a parametric cost model based on reference prototype data from Ampyx Power B.V. extended by scaling laws. Ultimately, an energy price model using real-life data from the ENTSO-E platform maintained by the association of EU transmission system operators was used to estimate the revenue profile. This framework was then used to compare the performance of systems based on multiple economic metrics within a chosen design space. The simulation results confirmed the expected behaviour that the electricity produced at lower wind speeds has a higher value than that produced at higher wind speeds. To account for this electricity price dependency on wind speeds in the design process, we propose an economic metric defined as the levelised profit of energy (LPoE). This approach determines the trade-offs between designing a system that minimises cost and designing a system that maximises value.