TY - JOUR
T1 - A novel bi-level distributed dynamic optimization method of ship fleets energy consumption
AU - Wang, Kai
AU - Li, Jiayuan
AU - Yan, Xinping
AU - Huang, Lianzhong
AU - Jiang, Xiaoli
AU - Yuan, Yupeng
AU - Ma, Ranqi
AU - Negenborn, Rudy R.
N1 - Accepted Author Manuscript
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Distributed model predictive control
KW - EEOI
KW - Fleet energy consumption
KW - Speed dynamic optimization
UR - http://www.scopus.com/inward/record.url?scp=85078535552&partnerID=8YFLogxK
U2 - 10.1016/j.oceaneng.2019.106802
DO - 10.1016/j.oceaneng.2019.106802
M3 - Article
AN - SCOPUS:85078535552
VL - 197
JO - Ocean Engineering
JF - Ocean Engineering
SN - 0029-8018
M1 - 106802
ER -