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Optimal Port Microgrid Scheduling Incorporating Onshore Power Supply and Berth Allocation Under Uncertainty

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  • Zhang, Yue
  • Liang, Chengji
  • Shi, Jian
  • Lim, Gino
  • Wu, Yiwei

Abstract

The high environmental impacts of maritime transportation have led to an increasing interest in adopting electricity as the ideal energy source within the sector. In this paper, we propose a novel integrated day-ahead scheduling algorithm to jointly optimize the seaside/yard operation and the port energy system management within one unified framework by harnessing the synergy between two of the most prominent maritime electrification techniques: onshore power supply and microgrid. We formulate the joint scheduling problem as a two-stage model. In the first stage, the port authority determines the optimal berth allocation for the incoming vessels considering their cargo volumes, energy demands, and the availability of OPS facility and cargo handling equipment (i.e., quay/yard cranes). In the second stage, acting as the port microgrid operator, the port authority determines the optimal day-ahead scheduling of the container handling activities and operation of port microgrid assets for each time slot. Uncertainty from renewable energy generation and port load forecast is also incorporated in the problem formulation. The simulation-based case study shows that the proposed joint scheduling algorithm is capable of enhancing energy independence, system-wide efficiency, operational reliability, and economy of the port microgrid in comparison with the conventional berth allocation strategy. We hope our work provides insights into how electrification can help the maritime sector reinforce its commitment to sustainability while remaining competitive.

Suggested Citation

  • Zhang, Yue & Liang, Chengji & Shi, Jian & Lim, Gino & Wu, Yiwei, 2022. "Optimal Port Microgrid Scheduling Incorporating Onshore Power Supply and Berth Allocation Under Uncertainty," Applied Energy, Elsevier, vol. 313(C).
  • Handle: RePEc:eee:appene:v:313:y:2022:i:c:s0306261922002926
    DOI: 10.1016/j.apenergy.2022.118856
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    References listed on IDEAS

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    4. Wang, Lifen & Liang, Chengji & Shi, Jian & Molavi, Anahita & Lim, Gino & Zhang, Yue, 2021. "A bilevel hybrid economic approach for optimal deployment of onshore power supply in maritime ports," Applied Energy, Elsevier, vol. 292(C).
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    Cited by:

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    5. Chengji Liang & Yue Zhang & Liang Dong, 2022. "A Three Stage Optimal Scheduling Algorithm for AGV Route Planning Considering Collision Avoidance under Speed Control Strategy," Mathematics, MDPI, vol. 11(1), pages 1-18, December.
    6. Bakar, Nur Najihah Abu & Bazmohammadi, Najmeh & Çimen, Halil & Uyanik, Tayfun & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Data-driven ship berthing forecasting for cold ironing in maritime transportation," Applied Energy, Elsevier, vol. 326(C).
    7. Zejun Tong & Chun Zhang & Xiaotai Wu & Pengcheng Gao & Shuang Wu & Haoyu Li, 2023. "Economic Optimization Control Method of Grid-Connected Microgrid Based on Improved Pinning Consensus," Energies, MDPI, vol. 16(3), pages 1-31, January.
    8. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    9. Alexander Micallef & Josep M. Guerrero & Juan C. Vasquez, 2023. "New Horizons for Microgrids: From Rural Electrification to Space Applications," Energies, MDPI, vol. 16(4), pages 1-25, February.

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