Stochastic Optimization of Onboard Photovoltaic Hybrid Power System Considering Environmental Uncertainties

Environmental uncertainties present a significant challenge in the design of onboard photovoltaic hybrid power systems (PV-HPS), a pivotal decarbonization technology garnering widespread attention in the shipping industry. Neglecting environmental uncertainties associated with photovoltaic (PV) output and hull resistance can lead to suboptimal solutions. To address this issue, this paper proposes a stochastic optimization method for PV-HPS, aiming to minimize greenhouse gas (GHG) emissions and lifecycle costs. Copula functions are employed to establish joint distributions of uncertainties in solar irradiance, ambient temperature, significant wave height, and wave period. Monte Carlo simulation, the bi-bin method, and the multi-objective particle swarm optimization (MOPSO) algorithm are utilized for scenario generation, scenario reduction, and design space exploration. The efficacy of the proposed method is demonstrated through a case study involving an unmanned ship. Additionally, deterministic optimization and two partial stochastic optimizations are conducted to underscore the importance of simultaneously considering environmental uncertainties related to power sources and hull resistance. The results affirm the proposed approach's capability to reduce GHG emissions and lifecycle costs. A sensitivity analysis of bin number is performed to investigate the tradeoff between optimality and computation time.