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Optimal Power and Energy Management Control for Hybrid Fuel Cell-Fed Shipboard DC Microgrid

Lookup NU author(s): Dr Michael Lau

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Abstract

The all-electric ship (AES) with DC-grid configuration has demonstrated advantages compared to the traditional AC system and has become the state-of-the-art for ships with electric propulsion in the low to medium power range during the past decade. However, the integration with different power sources, such as fuel cells, batteries and diesel gen-sets, increases the system complexity and requires an advanced power management system (PMS) to handle vessel operation and to achieve optimal power control. This paper presents an optimized power management strategy to reduce the total cost of ownership of such vessels, considering not only the fuel cost and emission penalty, but also the power device degradation and equipment replacement cost. In this study, Model Predictive Control (MPC) and Reinforcement Learning (RL)-based PMS control methods are approached respectively. In order to demonstrate the performance of MPC and RL techniques, a typical tugboat load profile is simulated. The testing results are also compared with a traditional rule-based power management control.

Publication metadata

Author(s): Chen W, Tai K, Lau MWS, Abdelhakim A, Chan RR, Adnanes AK, Tjahjowidodo T

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Intelligent Transportation Systems

Year: 2023

Pages: Epub ahead of print

Online publication date: 22/08/2023

Acceptance date: 26/07/2023

ISSN (print): 1524-9050

ISSN (electronic): 1558-0016

Publisher: IEEE

URL: https://doi.org/10.1109/TITS.2023.3303886

DOI: 10.1109/TITS.2023.3303886

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