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

Kai Wang, Jiayuan Li, Xinping Yan, Lianzhong Huang, Xiaoli Jiang, Yupeng Yuan, Ranqi Ma, Rudy R. Negenborn

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Article number106802
Number of pages13
JournalOcean Engineering
Volume197
DOIs
Publication statusPublished - 2020

Keywords

  • Distributed model predictive control
  • EEOI
  • Fleet energy consumption
  • Speed dynamic optimization

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