A novel bi-level distributed dynamic optimization method of ship fleets energy consumption. (1st February 2020)

• Record Type: Journal Article
• Authors: Wang, Kai; Li, Jiayuan; Yan, Xinping; Huang, Lianzhong; Jiang, Xiaoli; Yuan, Yupeng; Ma, Ranqi; Negenborn, Rudy R.
• Is Part Of: Ocean engineering. Volume 197(2020)
• Publication Date: 2020-02-01
• Subjects: Fleet energy consumption -- EEOI -- Speed dynamic optimization -- Distributed model predictive control; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2019.106802
• Languages: English
• ISSNs: 0029-8018

Abstract

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. Graphical abstract: A bi-level distributed dynamic optimization method adopting distributed model predictive control (DMPC) strategy is proposed to optimize fleet energy efficiency. It mainly includes an upper-level optimization model for the fleet operational decision-making, and a lower-level dynamic optimization model for the fleet energy consumption considering multiple time-varying influencing factors. Based on these, a control algorithm for the dynamic optimization of fleet energy efficiency (DOFEE) is developed. A case study is carried out to demonstrate the validity of this optimization method. Our results show that this method can reduce energy consumption of ship fleets effectively.The bi-level distributed dynamic optimization for the fleet energy consumption. Image 1 Highlights: A fleet energy efficiency model considering multiple influencing factors is established. A bi-level distributed dynamic optimization method adopting DMPC strategy is proposed. A control algorithm for the dynamic optimization of fleet energy efficiency is developed. The proposed method can reduce fleet energy consumption and CO2 emissions by about 6.8%.