Genetic algorithm-based synthesis-, design-, and operation optimization of large sector coupled ship energy systems

The decarbonization of the maritime industry is crucial for mitigating climate change. Achieving this goal involves increasing power generation efficiency, adopting non-fossil fuels, and enhancing overall ship efficiency. This study introduces the Maritime Energy System Optimizer (MESO), a modular Python platform designed to enhance ship concepts through holistic synthesis, design, and operation optimization of sector coupled ship energy systems (ES). MESO uses a genetic algorithm for synthesis and design optimization, and, nested within the fitness calculation, a mixed-integer linear programming approach for operation optimization based on power flows and energy balances using the open energy modelling framework (oemof). Validation studies, based on a 2018 cruise liner with 30 MW propulsion power and high demand granularity, demonstrated the effectiveness of MESO for various levels of detail. MESO reduces unnecessary iterations compared to the conventional heuristic design process and enables more informed decision-making. Optimized ESs are at least 4.4 % cheaper than conventional systems, mainly due to the integration of storages for peak shaving and load balancing. MESO is designed for easy expansion and will be extended to include partial load efficiency, demand-side management, and load profile generation.

Subjects

coupled energy systems | energy system optimization | genetic algorithm | green ship design | synthesis design operation optimization

DDC Class

600: Technology

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Genetic algorithm-based synthesis-, design-, and operation optimization of large sector coupled ship energy systems