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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FINAL VERSION.pdf | manuscript | 2.14 MB | Adobe PDF | View/Open |
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