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Today, maritime transport on traditional fuels is recognized as environmentally unsustainable being one of the causes that are accelerating the climate crisis. The usage of alternative renewable energy sources stored, for example, in large Li-ion batteries, is a solution for decreasing its environmental impact as well as cost. While large-scale fuel-free navigation is being developed, in this work we discuss a hybrid energy management system that obtains a close to the optimal way to utilize the available energy from batteries given the power demand. In this way, we decrease fuel consumption and the emissions associated with it. A large number of constraints make the problem challenging and an algorithm was developed such that it would satisfy each of them. The algorithm is implemented using the Simulated Annealing method. Obtained results show a significant decrease in fuel consumption and emissions.

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Hybrid Energy System Based on Constrained Optimization Using Simulated Annealing

  • 会议论文

  • 发表日期：

    2025年

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  • Anna Nikishova,
  • Martina Teruzzi,
  • Lorenzo Brigati,
  • Nicola Demo,
  • Giovanni Stabile,
  • Gianluigi Rozza

摘要

Today, maritime transport on traditional fuels is recognized as environmentally unsustainable being one of the causes that are accelerating the climate crisis. The usage of alternative renewable energy sources stored, for example, in large Li-ion batteries, is a solution for decreasing its environmental impact as well as cost. While large-scale fuel-free navigation is being developed, in this work we discuss a hybrid energy management system that obtains a close to the optimal way to utilize the available energy from batteries given the power demand. In this way, we decrease fuel consumption and the emissions associated with it. A large number of constraints make the problem challenging and an algorithm was developed such that it would satisfy each of them. The algorithm is implemented using the Simulated Annealing method. Obtained results show a significant decrease in fuel consumption and emissions.