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Optimal operation of hybrid energy system for intelligent ship: An ultrahigh-dimensional model and control method

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  • Tang, Ruoli
  • An, Qing
  • Xu, Fan
  • Zhang, Xiaodi
  • Li, Xin
  • Lai, Jingang
  • Dong, Zhengcheng

Abstract

Due to the upcoming regulations for decreasing emissions from shipping, the hybrid energy system is becoming a more popular and feasible option for large ships. This work focuses on the cooperation and optimal operation of onboard hybrid energy system and on-land shore power, in order to obtain cost savings from the perspective of shipping company. Firstly, this work proposes a novel concept, namely “data acquisition and sharing mechanism at shipping company level”, then the corresponding operation model and control methodology are developed. To be specific, the ship’s energy-management is modelled as an ultrahigh-dimensional optimization problem, in which the binary/continuous hybrid encoding scheme is developed, and a high time-resolution (15 min per point) is achieved. Then, by analysing the variable-coupling characteristic, the proposed model is proved to be non-separable in the early evolution stage, and nearly separable in the later stage. Finally, the novel “power/time/random” variable-grouping and “guided mutation of context-vector” mechanisms are proposed for enhancing the co-evolutionary algorithm to optimize the model. Experimental results show that even when the model dimensionality increases to more than 3000, the evaluated system can strictly satisfy the regulations and constraints from both the port and ship, and also obtain great cost savings.

Suggested Citation

  • Tang, Ruoli & An, Qing & Xu, Fan & Zhang, Xiaodi & Li, Xin & Lai, Jingang & Dong, Zhengcheng, 2020. "Optimal operation of hybrid energy system for intelligent ship: An ultrahigh-dimensional model and control method," Energy, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:energy:v:211:y:2020:i:c:s0360544220321848
    DOI: 10.1016/j.energy.2020.119077
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    Cited by:

    1. An, Qing & Peng, Jian, 2023. "Parameter identification of lithium battery pack based on novel cooperatively coevolving differential evolution algorithm," Renewable Energy, Elsevier, vol. 216(C).
    2. Tang, Ruoli & Zhang, Shangyu & Zhang, Shihan & Zhang, Yan & Lai, Jingang, 2023. "Parameter identification for lithium batteries: Model variable-coupling analysis and a novel cooperatively coevolving identification algorithm," Energy, Elsevier, vol. 263(PB).
    3. Maja Perčić & Nikola Vladimir & Marija Koričan, 2021. "Electrification of Inland Waterway Ships Considering Power System Lifetime Emissions and Costs," Energies, MDPI, vol. 14(21), pages 1-25, October.
    4. Tang, Ruoli & Zhang, Shihan & Zhang, Shangyu & Lai, Jingang & Zhang, Yan, 2023. "Semi-online parameter identification methodology for maritime power lithium batteries," Applied Energy, Elsevier, vol. 339(C).

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