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April 7, 2026Mathematics1 citationsOpen Access

A Mixed-Integer Linear Programming Framework for Optimal Scheduling of Maritime Mobile Energy Storage

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YSYunxiang ShuHong Kong Polytechnic UniversityYGY. Q. GuoHong Kong Polytechnic UniversityYDYuquan DuLa Trobe University

Key Points

  • The research aims to optimize the scheduling of maritime mobile energy storage systems to maximize energy delivery to the onshore grid.
  • Utilized a mixed-integer linear programming framework
  • Integrated a replicated port node mechanism for multi-trip operations
  • Considered energy transfer loss coefficients
  • Implemented a speed discretisation strategy to optimize propulsion consumption
  • Increasing vessel storage capacity from 500 to 600 megawatt-hours eliminated multi-stop trips, reducing propulsion consumption significantly
  • Expanding fleet size from five to six vessels allowed full retrieval of offshore electricity while decreasing energy consumption
  • The solver achieved optimal solutions in an average of 0.88 seconds

Abstract

The offshore wind energy sector requires efficient logistics to retrieve generated electricity using maritime mobile energy storage systems. This study addresses the maritime mobile energy storage scheduling problem to maximise the total net energy delivered to the onshore grid. The proposed approach utilises a mixed-integer linear programming framework. The mathematical formulation integrates a replicated port node mechanism to plan multi-trip operations over a continuous planning horizon. Additionally, the model accounts for energy transfer loss coefficients and incorporates a speed discretisation strategy to balance propulsion consumption against retrieved electricity. Numerical experiments based on simulated operational scenarios demonstrate the effectiveness of this method. The results indicate that expanding vessel storage capacity from 500 to 600 megawatt-hours eliminates the necessity for multi-stop trips, thereby reducing propulsion energy consumption from 270.79 to 73.65 megawatt-hours. Furthermore, increasing the fleet size from five to six vessels enables the full retrieval of available offshore electricity while decreasing fleet propulsion consumption to 91.08 megawatt-hours. The solver consistently achieves optimal solutions within an average of 0.88 s. Consequently, this framework provides operators with precise decision support for determining fleet capacity and configuring offshore energy retrieval networks.

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Cite This Study

Shu et al. (2026) studied this question.

synapsesocial.com/papers/69d49fa9b33cc4c35a2281adhttps://doi.org/10.3390/math14071216
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