Optimization of sail-hybrid electric power system for ships considering correlated environmental uncertainties

Jianyun Zhu, Li Chen and Rui Miao

Applied Energy, 2025, vol. 391, issue C, No S0306261925005926

Abstract: The sail-hybrid electric power system (sail-HEPS) has gained significant attention in the maritime industry as an eco-friendly solution to reduce greenhouse gas (GHG) emissions. The key to successful implementation of sail-HEPS lies in the integrated optimal sizing to effectively leverage the advantages of multiple energy sources. Considering sail-HEPS is constantly influenced by multiple uncertain and correlated factors in the environment, existing deterministic optimization methods based on single scenario are inadequate to ensure optimal performance of the system throughout its lifecycle. To address this issue, this study proposes a probabilistic optimization method that integrates multiple energy sources and considers correlated uncertainties. A vine copula method is employed to model the interdependencies among wave direction, significant wave height, wave period, wind direction, and wind speed. The design space exploration and multiple criteria decision making are performed with multi-objective particle swarm optimization (MOPSO) algorithm and the technique for order preference by similarity to an ideal solution (TOPSIS). A case study of a 20-m yacht in the South China Sea validates the proposed method, demonstrating its superiority over deterministic optimization and quasi-probabilistic optimization, which disregards the correlation among environmental variables. Furthermore, it is observed that there is no significant difference in the performance of the Pareto designs obtained from deterministic optimization and quasi-probabilistic optimization when correlated uncertainties are introduced, highlighting the importance of considering the correlation of the uncertainties.

Keywords: Sail-hybrid electric power system; Multi-objective probabilistic optimization; Correlated environmental uncertainties (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125862

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