A novel bi-level distributed dynamic optimization method of ship fleets energy consumption

Journal article (2020)

Authors

K. Wang Dalian Maritime University, Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Jiayuan Li Dalian Maritime University
Xinping Yan Wuhan University of Technology, MOST
Lianzhong Huang Dalian Maritime University
X. Jiang Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Yupeng Yuan Wuhan University of Technology, University of Cambridge
Ranqi Ma Dalian Maritime University
R.R. Negenborn Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering , MOST
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2020 K. Wang, Jiayuan Li, Xinping Yan, Lianzhong Huang, X. Jiang, Yupeng Yuan, Ranqi Ma, R.R. Negenborn
DOI
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https://doi.org/10.1016/j.oceaneng.2019.106802
EEOI Distributed model predictive control Fleet energy consumption Speed dynamic optimization
More Info
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Published Date

2020

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

Abstract

The optimization of ship energy consumption is attracting a great deal of attention, as societies seek to save energy and reduce emissions. Shipping companies are more concerned with the energy consumption of a ship fleet, as opposed to that of a single ship. Because the energy consumption of a fleet is influenced by multiple factors including environmental factors, port operations and transport demands, an improvement in a single ship's energy consumption does not necessarily mean that the overall energy consumption of a fleet is good. In addition, those factors are usually varying over time, making it hard to optimize the fleet's energy consumption by methods that do not consider these time-varying factors. Therefore, a bi-level distributed dynamic optimization method based on distributed model predictive control is proposed. Moreover, an upper-level optimization model for fleet operational decision-making and a lower-level dynamic optimization model of fleet energy consumption are established. Based on these, a control algorithm for the dynamic optimization of fleet energy consumption is developed. Finally, a case study is carried out to demonstrate the effectiveness of the method. It can further reduce the energy consumption of each ship by at least 1.1% and about 6.8% for the whole fleet.