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Hybrid algorithm for optimal operation of hybrid energy systems in electric ferries

机译:电动渡轮混合能源系统最优运行的混合算法

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摘要

The move towards electrification of marine vessels enables the development of more efficient vessels by reducing fuel consumption and emissions. This includes incorporating electrical energy sources, storage systems and interfacing power electronic converters which increase system complexity. Therefore, an accurate and efficient power management system (PMS) is essential to achieve the optimum operation. This study aims to develop a novel hybrid meta-heuristic algorithm-based PMS for the fuel savings of hybrid electric ferries. The ferry power system used in this study comprises two diesel generator sets and a battery storage system. The proposed hybrid PMS method applies an interactive approach on the basis of a grey wolf optimizer (GWO) and fuzzy expert system to improve the computational efficiency of the algorithm. Measured load data from an existing short-haul ferry are used in the simulation under two load scenarios: normal and high load demands. The proposed fuzzy logic-grey wolf optimizer (FL-GWO) aims to minimize the operating cost of the proposed system while satisfying all operational and technical constraints of the ferry. Results show that the proposed FL-GWO provided a more accurate optimal solution set with less standard deviation than the GWO. The proposed method realized up to 3.14% and 1.81% fuel savings in normal- and high-load scenarios, respectively, compared with GWO. Moreover, the sensitivity analysis indicates that charging the battery from the onboard generators in a more uniform rate over the entire cruising period reduces the fuel consumption. (C) 2019 Elsevier Ltd. All rights reserved.
机译:船舶电气化的发展通过减少燃料消耗和排放,使更高效的船舶得以发展。这包括并入电能源,存储系统和接口电力电子转换器,这会增加系统的复杂性。因此,准确有效的电源管理系统(PMS)对于实现最佳运行至关重要。本研究旨在开发一种基于混合元启发式算法的新型PMS,以节省混合动力轮渡的燃料。本研究中使用的渡轮动力系统包括两个柴油发电机组和一个蓄电池存储系统。提出的混合PMS方法在灰狼优化器(GWO)和模糊专家系统的基础上应用了交互式方法,以提高算法的计算效率。在两种负载情况下,模拟中使用来自现有短途渡轮的测量负载数据:正常和高负载需求。拟议的模糊逻辑灰狼优化器(FL-GWO)旨在在满足轮渡的所有运营和技术约束的同时,将拟议系统的运营成本降至最低。结果表明,与GWO相比,所提出的FL-GWO提供了更准确的最优解决方案集,且标准偏差更小。与GWO相比,该方法在正常和高负荷情况下分别节省了3.14%和1.81%的燃油。此外,敏感性分析表明,在整个巡航期间,从车载发电机为电池充电的速率更加均匀。 (C)2019 Elsevier Ltd.保留所有权利。

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