| 18 Jun 2026
Wind resource assessment in flow-distorting terrain: Techno-economic comparison of fixed-wing UAVs & profiling LiDAR
Abstract. This study conducts a head-to-head techno-economic comparison of vertical profiling Light Detection and Ranging (LiDAR) and fixed-wing Unmanned Aerial Vehicles (UAVs) deployments for wind resources assessment at a representative 120 MW onshore project in flow‑distorting terrain in Germany. A simplified energy yield model and a Monte Carlo framework propagate literature-based measurement and flow model uncertainties through different measurement scenarios to the exceedance levels (P50, P90) of Annual Energy Production (AEP), Net Present Value (NPV) and Levelized Cost of Electricity (LCOE). The analysis shows that both technologies achieve similar point measurement uncertainties (±0.4–0.5 m/s, ±7–10°). The dominant lever for reducing AEP uncertainty and improving P90 is increased direct spatial coverage, not marginal gains in instrument accuracy. Under consistent cost and financing assumptions, UAV-based deployments deliver the largest P90 uplift, higher NPV and lower LCOE than LiDAR configurations.
Competing interests: Danial Hassani is the founder of Alveo AB, which is commercially developing the UAV platform mentioned in this study, and provided the technical data used in designing four measurement scenarios. The remaining authors declare no competing interests.
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The manuscript compares four wind-resource-assessment deployment scenarios by propagating assumed measurement and flow-model uncertainties through an AEP and financial model. Several parts of the methodology require clarification before the numerical differences among the scenarios can be interpreted.
The largest issue is the treatment of uncertainty. Measurement errors are introduced as zero-mean Gaussian perturbations to 10-minute wind-speed and wind-direction data. When these perturbations are applied over a full year and across many turbine locations, much of the random error cancels. This appears to contribute to the very small AEP standard deviation reported for UAV_4, only 0.02% of P50. At the same time, long-term correction, interannual variability, wake uncertainty, power-curve uncertainty, calibration bias, and correlated spatial or temporal errors are excluded. The manuscript should distinguish between random sampling error and persistent or correlated uncertainty and should explain exactly how each uncertainty term is sampled in the Monte Carlo analysis.
The assumed flow-model uncertainties also control much of the result. The scenarios use 8%, 5%, 5%, and 0% flow-model uncertainty, respectively. The reduction from 8% to 5% is based on literature values, while the UAV_4 case removes flow-model uncertainty entirely because each turbine location is directly sampled. Direct sampling at a turbine location does not remove all uncertainty associated with representativeness, vertical extrapolation, temporal coverage, long-term correction, or spatial variability between flight passes. The basis for assigning zero residual flow-model uncertainty should therefore be reconsidered or more narrowly defined.
The increase in P50 from 468,893 MWh in LiDAR_1 to 472,423 MWh in UAV_4 also requires explanation. If the same underlying wind field is used and only zero-mean uncertainty is reduced, the main expected effect should be a narrowing of the AEP distribution rather than a systematic increase in its median. A deterministic no-uncertainty result should be included so that the source of the P50 shift can be identified.
The financial model requires additional detail. Equation (2) is described as an equity NPV calculation, but it discounts after-tax EBIT using WACC and subtracts total CAPEX. This is closer to a project-level discounted cash-flow formulation than an equity cash-flow formulation. The debt-sizing procedure, debt-service schedule, treatment of interest and principal, and derivation of the reported debt shares should be shown explicitly. The use of P90 energy in the LCOE denominator should also be separated from a conventional P50-based LCOE, since using P90 makes reduced uncertainty appear as a reduction in generation cost even when expected energy changes only slightly.
Several smaller inconsistencies should also be corrected. Twenty V162-6.2 MW turbines give 124 MW rather than 120 MW. The manuscript alternates between describing the site as low-complexity terrain and using it to support conclusions for moderate-to-high-complexity terrain. The notation “±0.4 m/s” is also used for what appears to be a Gaussian standard deviation, which should be stated directly as σ = 0.4 m/s rather than as an uncertainty bound. Clarifying these points would make the comparison more reproducible and would separate results produced by the data from those produced by the scenario assumptions.