IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v317y2022ics0306261922004639.html
   My bibliography  Save this article

A distributed real-time power management scheme for shipboard zonal multi-microgrid system

Author

Listed:
  • Xie, Peilin
  • Tan, Sen
  • Bazmohammadi, Najmeh
  • Guerrero, Josep. M.
  • Vasquez, Juan. C.
  • Alcala, Jose Matas
  • Carreño, Jorge El Mariachet

Abstract

The increasing demands of reducing fuel consumption for marine transportation have motivated the use of high fuel efficiency power plants and the development of power management systems (PMS). Current studies on shipboard PMS are mostly categorized as centralized, which are easy to be implemented and able to converge to the global optimum solutions. However, centralized techniques may suffer from the high computational burden and single-point failures. Considering the future trends of marine vessels toward zonal electrical distribution (ZED), distributed PMS are becoming an alternative choice. To achieve the ship high fuel-efficiency operation under high fluctuated propulsion loads, a real-time distributed PMS is developed in this paper that can acquire as good fuel economy as centralized PMS, but with faster computing speed. With a combination of filter-based, rule-based, and optimization-based approaches in a highly computationally efficient manner, the distributed PMS is constructed based on three layers that guarantees not only high fuel efficiency, but also sufficient energy reservation in different sailing modes and even in faulty conditions. Convergence tests and multiple case studies are conducted to prove the effectiveness of the proposed PMS in terms of fast convergence speed, improved fuel efficiency, and enhanced resilience.

Suggested Citation

  • Xie, Peilin & Tan, Sen & Bazmohammadi, Najmeh & Guerrero, Josep. M. & Vasquez, Juan. C. & Alcala, Jose Matas & Carreño, Jorge El Mariachet, 2022. "A distributed real-time power management scheme for shipboard zonal multi-microgrid system," Applied Energy, Elsevier, vol. 317(C).
  • Handle: RePEc:eee:appene:v:317:y:2022:i:c:s0306261922004639
    DOI: 10.1016/j.apenergy.2022.119072
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922004639
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.119072?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    2. Wen, Shuli & Lan, Hai & Yu, David. C. & Fu, Qiang & Hong, Ying-Yi & Yu, Lijun & Yang, Ruirui, 2017. "Optimal sizing of hybrid energy storage sub-systems in PV/diesel ship power system using frequency analysis," Energy, Elsevier, vol. 140(P1), pages 198-208.
    3. Xin Wang & Jason Atkin & Najmeh Bazmohammadi & Serhiy Bozhko & Josep M. Guerrero, 2021. "Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control," Sustainability, MDPI, vol. 13(24), pages 1-24, December.
    4. Haseltalab, Ali & Negenborn, Rudy R., 2019. "Model predictive maneuvering control and energy management for all-electric autonomous ships," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    5. Wu, Peng & Partridge, Julius & Bucknall, Richard, 2020. "Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships," Applied Energy, Elsevier, vol. 275(C).
    6. 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).
    7. Hou, Jun & Song, Ziyou & Park, Hyeongjun & Hofmann, Heath & Sun, Jing, 2018. "Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems," Applied Energy, Elsevier, vol. 230(C), pages 62-77.
    8. Chen, Hui & Zhang, Zehui & Guan, Cong & Gao, Haibo, 2020. "Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship," Energy, Elsevier, vol. 197(C).
    9. Armellini, A. & Daniotti, S. & Pinamonti, P. & Reini, M., 2018. "Evaluation of gas turbines as alternative energy production systems for a large cruise ship to meet new maritime regulations," Applied Energy, Elsevier, vol. 211(C), pages 306-317.
    10. Huang, Yuqing & Lan, Hai & Hong, Ying-Yi & Wen, Shuli & Fang, Sidun, 2020. "Joint voyage scheduling and economic dispatch for all-electric ships with virtual energy storage systems," Energy, Elsevier, vol. 190(C).
    11. Hou, Jun & Sun, Jing & Hofmann, Heath, 2018. "Control development and performance evaluation for battery/flywheel hybrid energy storage solutions to mitigate load fluctuations in all-electric ship propulsion systems," Applied Energy, Elsevier, vol. 212(C), pages 919-930.
    12. Mohammadpour Shotorbani, Amin & Zeinal-Kheiri, Sevda & Chhipi-Shrestha, Gyan & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Enhanced real-time scheduling algorithm for energy management in a renewable-integrated microgrid," Applied Energy, Elsevier, vol. 304(C).
    13. Yupeng Yuan & Tianding Zhang & Boyang Shen & Xinping Yan & Teng Long, 2018. "A Fuzzy Logic Energy Management Strategy for a Photovoltaic/Diesel/Battery Hybrid Ship Based on Experimental Database," Energies, MDPI, vol. 11(9), pages 1-15, August.
    14. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    15. Lai, Kexing & Illindala, Mahesh S., 2018. "A distributed energy management strategy for resilient shipboard power system," Applied Energy, Elsevier, vol. 228(C), pages 821-832.
    16. Hou, Jun & Sun, Jing & Hofmann, Heath, 2018. "Adaptive model predictive control with propulsion load estimation and prediction for all-electric ship energy management," Energy, Elsevier, vol. 150(C), pages 877-889.
    17. Tan, Sen & Xie, Peilin & Guerrero, Josep M. & Vasquez, Juan C., 2022. "False Data Injection Cyber-Attacks Detection for Multiple DC Microgrid Clusters," Applied Energy, Elsevier, vol. 310(C).
    18. Zhu, Jianyun & Chen, Li & Wang, Xuefeng & Yu, Long, 2020. "Bi-level optimal sizing and energy management of hybrid electric propulsion systems," Applied Energy, Elsevier, vol. 260(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiankai Gao & Yang Li & Bin Wang & Haibo Wu, 2023. "Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm," Energies, MDPI, vol. 16(7), pages 1-21, April.
    2. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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).
    3. Park, Chybyung & Jeong, Byongug & Zhou, Peilin, 2022. "Lifecycle energy solution of the electric propulsion ship with Live-Life cycle assessment for clean maritime economy," Applied Energy, Elsevier, vol. 328(C).
    4. Hou, Jun & Song, Ziyou, 2020. "A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity," Applied Energy, Elsevier, vol. 257(C).
    5. Nuchturee, Chalermkiat & Li, Tie & Xia, Hongpu, 2020. "Energy efficiency of integrated electric propulsion for ships – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    6. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Yang, Qidong & Yang, Xuchang, 2022. "Optimal control of transient processes in marine hybrid propulsion systems: Modeling, optimization and performance enhancement," Applied Energy, Elsevier, vol. 321(C).
    7. Hou, Jun & Song, Ziyou & Park, Hyeongjun & Hofmann, Heath & Sun, Jing, 2018. "Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems," Applied Energy, Elsevier, vol. 230(C), pages 62-77.
    8. 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).
    9. Wang, Zhuang & Chen, Li & Wang, Bin & Huang, Lianzhong & Wang, Kai & Ma, Ranqi, 2023. "Integrated optimization of speed schedule and energy management for a hybrid electric cruise ship considering environmental factors," Energy, Elsevier, vol. 282(C).
    10. Song, Tiewei & Fu, Lijun & Zhong, Linlin & Fan, Yaxiang & Shang, Qianyi, 2024. "HP3O algorithm-based all electric ship energy management strategy integrating demand-side adjustment," Energy, Elsevier, vol. 295(C).
    11. Haseltalab, Ali & Negenborn, Rudy R., 2019. "Model predictive maneuvering control and energy management for all-electric autonomous ships," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    12. Chiara Dall’Armi & Davide Pivetta & Rodolfo Taccani, 2023. "Hybrid PEM Fuel Cell Power Plants Fuelled by Hydrogen for Improving Sustainability in Shipping: State of the Art and Review on Active Projects," Energies, MDPI, vol. 16(4), pages 1-34, February.
    13. Chiara Dall’Armi & Davide Pivetta & Rodolfo Taccani, 2021. "Health-Conscious Optimization of Long-Term Operation for Hybrid PEMFC Ship Propulsion Systems," Energies, MDPI, vol. 14(13), pages 1-20, June.
    14. Inal, Omer Berkehan & Charpentier, Jean-Frédéric & Deniz, Cengiz, 2022. "Hybrid power and propulsion systems for ships: Current status and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    15. Liu, Hanyou & Fan, Ailong & Li, Yongping & Bucknall, Richard & Chen, Li, 2024. "Hierarchical distributed MPC method for hybrid energy management: A case study of ship with variable operating conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    16. 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.
    17. Luta, Doudou N. & Raji, Atanda K., 2019. "Optimal sizing of hybrid fuel cell-supercapacitor storage system for off-grid renewable applications," Energy, Elsevier, vol. 166(C), pages 530-540.
    18. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Liu, Zhijiang & Ke, Yun & Ding, Shunliang, 2023. "Novel enhancement of energy distribution for marine hybrid propulsion systems by an advanced variable weight decision model predictive control," Energy, Elsevier, vol. 274(C).
    19. Xu, Lei & Wen, Yintang & Luo, Xiaoyuan & Lu, Zhigang & Guan, Xinping, 2022. "A modified power management algorithm with energy efficiency and GHG emissions limitation for hybrid power ship system," Applied Energy, Elsevier, vol. 317(C).
    20. Bouguenna, Ibrahim Farouk & Azaiz, Ahmed & Tahour, Ahmed & Larbaoui, Ahmed, 2019. "Robust neuro-fuzzy sliding mode control with extended state observer for an electric drive system," Energy, Elsevier, vol. 169(C), pages 1054-1063.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:317:y:2022:i:c:s0306261922004639. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.