University Of Tasmania

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

journal contribution
posted on 2023-05-20, 07:14 authored by Al-Falahi, MDA, Shantha Jayasinghe ArachchillageShantha Jayasinghe Arachchillage, Hossein EnshaeiHossein Enshaei
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.


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Australian Maritime College



Place of publication

Oxford, England

Rights statement

Copyright 2019 Elsevier Ltd.

Repository Status

  • Restricted

Socio-economic Objectives

Transport energy efficiency; Energy storage, distribution and supply not elsewhere classified; Renewable energy not elsewhere classified