Distributed MPC for Cost-Optimal Control of FC-Battery Shipboard Microgrids

Conference Paper (2025)
Author(s)

Timon Kopka (TU Delft - Transport Engineering and Logistics)

Andrea Coraddu (TU Delft - Ship Design, Production and Operations)

Henk Polinder (TU Delft - Transport Engineering and Logistics)

Research Group
Transport Engineering and Logistics
DOI related publication
https://doi.org/10.1109/icdcm63994.2025.11144727
More Info
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Publication Year
2025
Language
English
Research Group
Transport Engineering and Logistics
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
ISBN (print)
979-8-3315-1275-0
ISBN (electronic)
978-8-3315-1274-3
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Abstract

The electrification of ship power systems plays a center role in the mobility transition towards sustainable transport solutions. It allows the integration of various power sources, energy storage systems, and intermittent generation. The integration of an increasing number of components with distinct characteristics shapes the notion of a shipboard microgrid which benefits from a modular approach in its design to reduce costs and uncertainties. DC distribution facilitates the modular design by simplifying the control, and, combined with power electronics interfaces, increases the controllability of power flows in the system. To handle the increasing system complexity, this work proposes a distributed and predictive control approach, addressing the modular topology of future shipboard power systems and leveraging load power forecasting. Investigations show that a distributed, predictive energy management reaches a similar performance as a centralized implementation. For a modular shipboard power system, the proposed method decreases both fuel and degradation costs with increasing performance gains for longer prediction horizons.

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