A Capacity Allocation Method for Long-Endurance Hydrogen-Powered Hybrid UAVs Based on Two-Stage Optimization

Due to the challenges associated with the application of existing two-stage optimization methods in energy system capacity configuration, such as uncertainty scenario generation, multi-timescale coupling, and balancing economic and environmental benefits, this paper proposes a two-stage optimization configuration method based on Particle Swarm Optimization (PSO) for the capacity configuration of long-endurance hydrogen-powered hybrid unmanned aerial vehicles (UAVs). By constructing a hydrogen-powered hybrid UAV energy system model, an uncertainty model for the energy system, and multi-timescale comprehensive evaluation indicators and corresponding objective functions, the capacity configuration is determined using a two-stage stochastic programming model solved by CPLEX in MATLAB. The two-stage stochastic programming model consists of the first stage, which involves capacity optimization through PSO, and the second stage, which employs Monte Carlo method for random wind field sampling. The research provides a theoretical foundation for the application of the two-stage optimization capacity configuration method in the field of long-endurance hydrogen-powered hybrid UAVs.