Dynamic optimization of ship energy efficiency considering time-varying environmental factors

Abstract Nowadays, optimization of ship energy efficiency attracts increasing attention in order to meet the requirement for energy conservation and emission reduction. Ship operation energy efficiency is significantly influenced by environmental factors such as wind speed and direction, water speed and depth. Owing to inherent time-variety and uncertainty associated with these various factors, it is very difficult to determine optimal sailing speeds accurately for different legs of the whole route using traditional static optimization methods, especially when the weather conditions change frequently over the length of a ship route. Therefore, in this paper, a novel dynamic optimization method adopting the model predictive control (MPC) strategy is proposed to optimize ship energy efficiency accounting for these time-varying environmental factors. Firstly, the dynamic optimization model of ship energy efficiency considering time-varying environmental factors and the nonlinear system model of ship energy efficiency are established. On this basis, the control algorithm and controller for the dynamic optimization of ship energy efficiency (DOSEE) are designed. Finally, a case study is carried out to demonstrate the validity of this optimization method. The results indicate that the optimal sailing speeds at different time steps could be determined through the dynamic optimization method. This method can improve ship energy efficiency and reduce CO2 emissions effectively.

[1]  Aris Kornelakis,et al.  Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems , 2010 .

[2]  Xinping Yan,et al.  Multiparameter Sensitivity Analysis of Operational Energy Efficiency for Inland River Ships Based on Backpropagation Neural Network Method , 2015 .

[3]  Bing Wu,et al.  Analysis of the operational energy efficiency for inland river ships , 2013 .

[4]  Xinping Yan,et al.  Real-time optimization of ship energy efficiency based on the prediction technology of working condition , 2016 .

[5]  Inge Norstad,et al.  Tramp ship routing and scheduling with speed optimization , 2011 .

[6]  Wenming Yang,et al.  Power management of vessel propulsion system for thrust efficiency and emissions mitigation , 2016 .

[7]  Gabriel Lodewijks,et al.  Closed-loop scheduling and control of waterborne AGVs for energy-efficient Inter Terminal Transport , 2017 .

[8]  Evangelos F. Magirou,et al.  The economic speed of an oceangoing vessel in a dynamic setting , 2015 .

[9]  Christos A. Kontovas,et al.  Ship speed optimization: Concepts, models and combined speed-routing scenarios , 2014 .

[10]  Chen Chen.  Key Technology of Collecting Traffic Flow on the Yangtze River in Real-Time , 2010 .

[11]  Ching Chin Chang,et al.  Energy conservation for international dry bulk carriers via vessel speed reduction , 2013 .

[12]  Qiang Meng,et al.  Shipping log data based container ship fuel efficiency modeling , 2016 .

[13]  Inge Norstad,et al.  Reducing fuel emissions by optimizing speed on shipping routes , 2010, J. Oper. Res. Soc..

[14]  D. Ronen,et al.  The effect of oil price on containership speed and fleet size , 2011, J. Oper. Res. Soc..

[15]  Christos A. Kontovas,et al.  Speed models for energy-efficient maritime transportation: A taxonomy and survey , 2013 .

[16]  Huarong Zheng,et al.  Predictive path following with arrival time awareness for waterborne AGVs , 2016 .

[17]  Qiang Meng,et al.  Sailing speed optimization for container ships in a liner shipping network , 2012 .

[18]  Kenji Sasa,et al.  Numerical ship navigation based on weather and ocean simulation , 2013 .

[19]  Atilla Incecik,et al.  A semi-empirical ship operational performance prediction model for voyage optimization towards energy efficient shipping , 2015 .

[20]  Osman Turan,et al.  An application of fuzzy-AHP to ship operational energy efficiency measures , 2016 .

[21]  Xinping Yan,et al.  Optimizing ship energy efficiency: Application of particle swarm optimization algorithm , 2018 .

[22]  Rudy R. Negenborn,et al.  Energy-efficient container handling using hybrid model predictive control , 2015, Int. J. Control.

[23]  R L Townsin,et al.  MONITORING THE SPEED PERFORMANCE OF SHIPS , 1975 .

[24]  A. Stromman,et al.  Reductions in greenhouse gas emissions and cost by shipping at lower speeds , 2011 .

[25]  J. Holtrop,et al.  AN APPROXIMATE POWER PREDICTION METHOD , 1982 .

[26]  J. M. Maestre,et al.  Distributed Model Predictive Control: An Overview and Roadmap of Future Research Opportunities , 2014, IEEE Control Systems.

[27]  Chu Xiumin.  Green Yangtze River Intelligent Shipping Information System and Its Key Technologies , 2010 .

[28]  Ching-Chih Chang,et al.  Evaluating the effects of speed reduce for shipping costs and CO2 emission , 2014 .