Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153457
Title: Power system design optimization for a ferry using hybrid-shaft generators
Authors: Oo, Thant Zin
Ren, Yan
Kong, Adams Wai Kin 
Wang, Yi
Liu, Xiong
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Oo, T. Z., Ren, Y., Kong, A. W. K., Wang, Y. & Liu, X. (2021). Power system design optimization for a ferry using hybrid-shaft generators. IEEE Transactions On Power Systems. https://dx.doi.org/10.1109/TPWRS.2021.3128239
Project: IAF-ICP Grant - I1801E0033 
Journal: IEEE Transactions on Power Systems 
Abstract: Ferry contributing a significant amount of greenhouse gas is one of the critical vessels to be electrified. Designing a power system for a ferry with hybrid-shaft generators is different from designing a power system for other vessels because of its fixed route. More clearly, ferries repeatedly travel between their port of origin and port of destination, and before the next voyage, the battery must be recharged to the initial state so that the optimal energy management scheme can be repeatedly applied. Furthermore, the flexibility of hybrid-shaft generators, which allow more fuel saving, increases design complexity. In this paper, a mixed-integer non-linear programming problem is first formulated, and a power management algorithm with an initialization step for fulfilling the battery recharging requirement and a refinement step for minimizing the fuel consumption is proposed. The simulation results obtained from data of an actual ferry show that the proposed power management algorithm can fully recharge the battery and consumes less fuel than a rule-based power management scheme. Simulations also reveal that fuel consumption depends on available shore power, highlighting the necessity to develop charging infrastructure for practical electrification. Because of its speed, the algorithm can support hardware sizing, e.g., battery sizing.
URI: https://hdl.handle.net/10356/153457
ISSN: 0885-8950
DOI: 10.1109/TPWRS.2021.3128239
Schools: School of Computer Science and Engineering 
Research Centres: Rolls-Royce@NTU Corporate Lab 
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TPWRS.2021.3128239.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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