Unbiased automated optimization of ship energy systems

Shipping is highly efficient yet a significant greenhouse gas emitter. Achieving the IMO's 2050 carbon-neutrality target demands exceptionally efficient new vessel designs. However, ship design is complex due to the vast number of interacting options–e.g. hybrid electric architectures, renewable integration, sector coupling, and energy storage. Energy system design remains a largely manual, iterative process based on prior experience, often overlooking unconventional concepts. The Maritime Energy System Optimizer (MESO) addresses this gap. MESO is a modular Python platform optimizing the architecture, dimensioning, and operation of sector-coupled ship energy systems via a genetic algorithm. Fitness is evaluated through operational, component, and volume costs, with operation optimization using the open energy modeling framework. Its novelty lies in unbiased architecture optimization over dynamic load profiles, reducing design iterations. MESO is demonstrated in two case studies–a cruise ship's multi-energy system and a container ship's auxiliary electrical system–identifying efficient, unconventional configurations.

Subjects

architecture optimization

coupled energy systems

Energy system optimization

genetic algorithm

green ship design

mixed-integer linear programming

partial-load operation optimization

DDC Class

623.8: Naval Architecture; Shipbuilding

Lizenz

https://creativecommons.org/licenses/by/4.0/

Publication version

publishedVersion

Name

Unbiased automated optimization of ship energy systems.pdf

Type

Main Article

Size

2.19 MB

Format

Adobe PDF

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Unbiased automated optimization of ship energy systems