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An Energy Optimization Strategy for Hybrid Power Ships under Load Uncertainty Based on Load Power Prediction and Improved NSGA-II Algorithm

Author

Listed:
  • Diju Gao

    (Key Laboratory Marine Technology and Control Engineering, Ministry of Communications, Shanghai Maritime University, Shanghai 201306, China)

  • Xuyang Wang

    (Key Laboratory Marine Technology and Control Engineering, Ministry of Communications, Shanghai Maritime University, Shanghai 201306, China)

  • Tianzhen Wang

    (Key Laboratory Marine Technology and Control Engineering, Ministry of Communications, Shanghai Maritime University, Shanghai 201306, China)

  • Yide Wang

    (Key Laboratory Marine Technology and Control Engineering, Ministry of Communications, Shanghai Maritime University, Shanghai 201306, China
    Institut d’Electronique et Telecommunications de Rennes, UMR CNRS 6164, Universite de Nantes, 44300 Nantes, France)

  • Xiaobin Xu

    (School of Automation, Hangzhou Dianzi University, Hangzhou 100084, China)

Abstract

In this paper, a hybrid ship powered by diesel generator sets and power batteries in series is considered. By analyzing the characteristics of hybrid ship cycle operating conditions, the load power of the hybrid ship under load uncertainty is firstly predicted. Then, considering the economy, emissions and continuous navigation time (endurance) of the hybrid ship, an energy optimization strategy based on the predicted load power is proposed to achieve the goal of minimum fuel consumption, minimum emissions and maximum endurance of ship operation. The experimental results show that, compared with the fuzzy logic rules based strategy, the fuel economy of the ship is increased by 9.6% and the ship’s endurance is increased by 24% for the proposed strategy.

Suggested Citation

  • Diju Gao & Xuyang Wang & Tianzhen Wang & Yide Wang & Xiaobin Xu, 2018. "An Energy Optimization Strategy for Hybrid Power Ships under Load Uncertainty Based on Load Power Prediction and Improved NSGA-II Algorithm," Energies, MDPI, vol. 11(7), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1699-:d:155496
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    References listed on IDEAS

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    1. Bigdeli, Nooshin, 2015. "Optimal management of hybrid PV/fuel cell/battery power system: A comparison of optimal hybrid approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 377-393.
    2. Lincun Fang & Shiyin Qin & Gang Xu & Tianli Li & Kemin Zhu, 2011. "Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms," Energies, MDPI, vol. 4(3), pages 1-13, March.
    3. Nan Zhou & Nian Liu & Jianhua Zhang & Jinyong Lei, 2016. "Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response," Energies, MDPI, vol. 9(8), pages 1-24, July.
    4. Diju Gao & Wei Zhang & Aidi Shen & Yide Wang, 2017. "Parameter Design and Energy Control of the Power Train in a Hybrid Electric Boat," Energies, MDPI, vol. 10(7), pages 1-12, July.
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    Cited by:

    1. Xin Peng & Hui Chen & Cong Guan, 2023. "Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-15, January.
    2. Nien-Che Yang & Yan-Lin Zeng & Tsai-Hsiang Chen, 2021. "Assessment of Voltage Imbalance Improvement and Power Loss Reduction in Residential Distribution Systems in Taiwan," Mathematics, MDPI, vol. 9(24), pages 1-17, December.
    3. Nivolianiti, Evaggelia & Karnavas, Yannis L. & Charpentier, Jean-Frederic, 2024. "Energy management of shipboard microgrids integrating energy storage systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    4. Planakis, Nikolaos & Papalambrou, George & Kyrtatos, Nikolaos, 2022. "Ship energy management system development and experimental evaluation utilizing marine loading cycles based on machine learning techniques," Applied Energy, Elsevier, vol. 307(C).
    5. Yuan, Yupeng & Wang, Jixiang & Yan, Xinping & Shen, Boyang & Long, Teng, 2020. "A review of multi-energy hybrid power system for ships," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).

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