Optimal Power and Energy Management Control for Hybrid Fuel Cell-Fed Shipboard DC Microgrid
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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2023, IEEE.
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Authors:
- Chen, Wenjie
- Tai, Kang
- Lau, Michael Wai Sheng
- Abdelhakim, Ahmed
- Chan, Ricky R
- Ådnanes, Alf Kåre
- Tjahjowidodo, Tegoeh
- Publication Date: 2023-12
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 14133-14150
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 24
- Issue Number: 12
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Direct current; Energy consumption; Grids (Transmission lines); Machine learning; Vessel operations
- Identifier Terms: Model Predictive Control
- Subject Areas: Data and Information Technology; Energy; Marine Transportation; Vehicles and Equipment;
Filing Info
- Accession Number: 01906449
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 30 2024 9:25AM