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

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

doi:10.1016/j.oceaneng.2019.106802

Cited by 17 publications

(6 citation statements)

References 40 publications

(38 reference statements)

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“…Unlike the system plant, which includes all the ships in the fleet, the prediction model only considers a single ship; therefore, it involves fewer binary variables compared with the system plant and, thus, its computation capacity is much lower. This decentralized arrangement increases the computing speed of a local controller and enables real-time optimization of the ships' speed (Negenborn and Maestre, 2014;Zheng et al, 2017;Wang et al, 2020).…”

Section: Decentered Model Predictive Control Frameworkmentioning

confidence: 99%

Hybrid dynamic modeling and receding horizon speed optimization for liner shipping operations from schedule reliability and energy efficiency perspectives

2023

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Uncertainties in port handling efficiency can cause port delays in the liner shipping system. Furthermore, policies on carbon emission reduction, such as EEXI standards, restrict the potential for speed optimization in liner shipping operations. Traditional tactical planning speed optimization is unsuitable for operational-level decision making, leading to unreliable schedules. From a schedule-reliability and energy-efficiency perspective, we propose a real-time speed optimization method based on discrete hybrid automaton (DHA) and decentered model predictive control (DMPC). We use a dynamic adjustment of sailing speed to offset the disturbance caused by port handling efficiency uncertainties. First, we establish a DHA model that describes each ship’s hybrid dynamics of state switching between sailing and berthing; then, we develop a prediction model for the DMPC controller, which is analogous to the DHA model. The schedule is transferred into time–position coordinates as controller reference trajectories in the receding horizon speed optimization framework. We consider determining tracking errors, carbon emissions, and fuel consumption as our objectives, and we carry out engine power limitation (EPL) analysis for the sample ship, which turns the EEXI standards into constraints. We attain the recommended speed by solving a mixed-integer optimization. We carry out a case study, and our results indicate the effectiveness of our proposed DHA-DMPC scheme in lowering port delays and achieving the best trade-off between schedule reliability and energy efficiency. Additionally, we conduct further experiments to analyze the impacts of various carbon reduction policies on the performance levels of liner shipping operations.

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Section: Decentered Model Predictive Control Frameworkmentioning

confidence: 99%

Hybrid dynamic modeling and receding horizon speed optimization for liner shipping operations from schedule reliability and energy efficiency perspectives

2023

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“…Qi, Wang, and Psaraftis [34] conducted a comprehensive review of bi-level optimization models for air emission management in the shipping industry. Wang et al [35] proposed a novel bi-level model aimed at optimizing the energy consumption of a fleet. The upper-level optimization model determined the loading and speed of each ship, taking into account relevant factors such as port information, the navigational environment, time requirements, and ship parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Level Programming Approach to Optimize Ship Fouling Cleaning

Wang,

Guo,

et al. 2023

JMSE

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Ship fouling has significant adverse effects on vessel performance and environmental sustainability. Therefore, this research study develops a bi-level programming model to simultaneously optimize cleaning equipment deployment by cleaning service providers in the upper level and cleaning decisions by shipping companies in the lower level. To address the interaction within the bi-level problem, the model is transformed into a single-level formulation using the big-M method. This transformation greatly simplifies the complexity of the computation and reduces computation time. Numerical experiments are conducted using real-world data to evaluate the performance of the proposed models. In addition, sensitivity analyses are performed to investigate the influence of key parameters. The results indicate that cleaning service providers primarily purchase equipment in the first year based on the demand distribution. To maximize profit, they may choose to forgo a portion of the demand. The sensitivity analysis reveals that sacrificing part of the demand can lead to an additional USD 27 million in profits compared with satisfying all demand. Moreover, increasing the cleaning price reduces both demand and equipment purchases but increases total profits. Conversely, increasing purchase costs reduces profits and the total amount of equipment purchased. When service providers can no longer generate profits, they are likely to exit the market. These findings offer valuable insights for service providers and shipping companies in the practical deployment of cleaning equipment and foul cleaning decisions, respectively.

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“…The shipping industry has put forward a variety of countermeasures to reduce the carbon dioxide emissions from ships [9,10], including clean energy [11][12][13], alternative fuels [14,15], collaborative optimization of ship energy efficiency [16][17][18], hull optimization, and power system optimization [19]. As a kind of clean energy, wind energy can significantly improve the energy-saving effect and reduce emissions [9,20].…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method for Energy Saving of Sail-Assisted Ship Based on Wind Resource Analysis of Typical Route

Wang

et al. 2023

JMSE

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Sail-assisted technology can reduce greenhouse-gas emissions by saving the energy consumption of ships with wind energy utilization. The distribution characteristics of marine wind resources are critical to the energy-saving effect of sail-assisted ships. However, due to the lack of effective energy-saving evaluation methods for improving the utilization rate of wind energy, a high potential for wind energy utilization still exists. A novel energy-saving evaluation method based on the wind energy resource analysis of typical ship routes is proposed in this paper. First, a three-degree-of-freedom motion model for sail-assisted ships considering the wing sail forces is constructed. Then, a wind resource acquisition and analysis method based on spatial–temporal interpolation is proposed. On this basis, the wind field probability matrix and wing sail force matrix are established. Ultimately, an energy-saving evaluation method for sail-assisted ships on typical routes is proposed by combining the sailing condition of ships. The case study results show that the energy-saving effect of a wing sail-assisted oil tanker that sailed on the China-to-Middle East route was more than 5.37% in 2021 and could reach 9.54% in a single voyage. It is of great significance to realize the popularization and application of sail-assisted technology, thus improving the greenization of the shipping industry.

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

Cited by 17 publications

References 40 publications

Hybrid dynamic modeling and receding horizon speed optimization for liner shipping operations from schedule reliability and energy efficiency perspectives

Hybrid dynamic modeling and receding horizon speed optimization for liner shipping operations from schedule reliability and energy efficiency perspectives

A Bi-Level Programming Approach to Optimize Ship Fouling Cleaning

Evaluation Method for Energy Saving of Sail-Assisted Ship Based on Wind Resource Analysis of Typical Route

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