Abstract Optimizing ship power systems with diesel generators such as the platform supply vessel (PSV) has become a pressing issue due to the emission of carbon dioxide. This paper investigates the optimal operation of diesel generators in marine power systems, particularly the PSV. The investigation is mainly focused on carbon dioxide emission and fuel consumption in a ship's mission. In this regard, this paper presents a clear optimization strategy, called a trip-ahead to determine the best operation schedule of generators to supply the electricity demand for the next days of a PSV. The PSV has six generators (i.e., four primary and two auxiliary diesel generators) with two different fuel consumption curves and carbon dioxide emission. The trip-ahead algorithm's objective functions consist of minimization of cost and emission during the complete PSV mission. A power level is assigned to each generator for each hour of the next day of a 5-day trip, taking into account the fuel consumption per kilowatt-hour (kWh). The prepared sample of the load profile is the historical demand records of a real PSV. Additionally, this paper compares the results of the proposed approach with other optimization algorithms such as the Genetic algorithm (GA), Particle Swarm Optimization algorithm (PSO), and the software HOMER Pro optimization tool. Moreover, this paper presents the sensitivity analysis to compensate for possible errors in prediction demand for the next trip. The results prove the proposed algorithm in comparing GA and PSO is more accurate and the calculation velocity of the proposed algorithm when objective functions are cost and emission is about 27% and 46% better than the PSO and about 36% and 62% better than the GA, respectively.

中文翻译：

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船舶电力系统优化能源调度的提前航行策略

摘要 由于二氧化碳的排放，优化配备柴油发电机的船舶动力系统，如平台供应船（PSV）已成为一个紧迫的问题。本文研究了船舶动力系统中柴油发电机的优化运行，特别是 PSV。调查主要集中在船舶任务中的二氧化碳排放和燃料消耗。在这方面，本文提出了一个明确的优化策略，称为提前行程，以确定发电机的最佳运行时间表，以满足 PSV 未来几天的电力需求。PSV 有六台发电机（即四台主柴油发电机和两台辅助柴油发电机），具有两种不同的燃料消耗曲线和二氧化碳排放量。提前旅行算法' 目标函数包括在完整的 PSV 任务期间最小化成本和排放。考虑到每千瓦时 (kWh) 的燃料消耗，为每台发电机在 5 天行程的第二天每小时分配一个功率级别。准备好的负载曲线样本是真实 PSV 的历史需求记录。此外，本文将所提出方法的结果与其他优化算法，如遗传算法 (GA)、粒子群优化算法 (PSO) 和软件 HOMER Pro 优化工具的结果进行了比较。此外，本文提出了灵敏度分析，以补偿下一次旅行的预测需求中可能出现的错误。