印波, 王锡淮, 肖健梅. 基于改进粒子群优化算法的船舶能量管理方案[J]. 中国舰船研究, 2020, 15(6): 37–45. doi: 10.19693/j.issn.1673-3185.01890
引用本文: 印波, 王锡淮, 肖健梅. 基于改进粒子群优化算法的船舶能量管理方案[J]. 中国舰船研究, 2020, 15(6): 37–45. doi: 10.19693/j.issn.1673-3185.01890
YIN B, WANG X H, XIAO J M. Ship energy management scheme based on improved particle swarm optimization algorithm[J]. Chinese Journal of Ship Research, 2020, 15(6): 37–45. doi: 10.19693/j.issn.1673-3185.01890
Citation: YIN B, WANG X H, XIAO J M. Ship energy management scheme based on improved particle swarm optimization algorithm[J]. Chinese Journal of Ship Research, 2020, 15(6): 37–45. doi: 10.19693/j.issn.1673-3185.01890

基于改进粒子群优化算法的船舶能量管理方案

Ship energy management scheme based on improved particle swarm optimization algorithm

  • 摘要:
      目的  针对目前日益紧缩的船舶行业利润空间及因船舶航行造成的环境污染问题,提出一种满足各项约束条件的船舶能量管理方案。
      方法  首先,建立船舶能量管理系统的数学模型,明确到港时间限制、二氧化碳排放标准限制和船舶运行限制等一系列约束条件;然后,采用改进粒子群优化算法,对发电机组及柴油机组的启停状态和运行状态、船舶航速等参数进行实数编码并迭代寻优,以降低总体运行成本和温室气体排放量;最后,以某客渡船的航行标准数据为例,设计3种优化方案,用以验证改进粒子群优化算法的可行性和有效性。
      结果  仿真对比结果表明:方案1(初始方案)的运行成本为37 022.75 m.u.,方案2(仅对发电机组的电功率进行优化分配)的运行成本为36 993.27 m.u.,降低了0.079 6%;方案3(整体优化分配)的运行成本为35 851.25 m.u.,降低了3.164 2%,故其兼顾了经济性和环保性的要求。
      结论  基于改进粒子群优化算法的船舶能量管理方案可以显著降低运行成本,且保证相对稳定的船舶航速和负载分配曲线,有利于提升船舶能量系统的工作效率和经济效益。

     

    Abstract:
      Objectives  Aiming at the increasingly compressed profit margins of the ship industry and environmental pollution caused by ship navigation, a shipboard energy management system (EMS) scheme that satisfies various constraints is proposed.
      Methods  First, a mathematical model of the EMS is established to clarify a series of constraints such as arrival time restrictions, carbon dioxide emission standards and operational constraints. Then, an improved particle swarm optimization algorithm (PSO) is used to reduce overall operating costs and greenhouse gas emissions, taking the place of real-number coding and iterative optimization of the start and stop status, operating status, sailing speed and other parameters of generator sets and diesel generator sets. Finally, taking the sailing standard data of a passenger ferry as an example, three optimization schemes are set to verify the feasibility and effectiveness of the PSO algorithm.
      Results  The results of the simulation comparison show that the operating cost of scheme 1 (the initial scheme) is 37 022.75 m.u., and the operating cost of scheme 2 (only the optimal distribution of the electric power of the generator set) is 36 993.27 m.u., a reduction of 0.079 6%. Option 3 (overall optimization) is 35 851.25 m.u., a reduction of 3.164 2%, which takes the requirements of the economy and environmental protection into account.
      Conclusions  The EMS scheme based on the improved PSO algorithm can significantly reduce operating costs, and ensure a relatively stable sailing speed and load distribution curve, which is conducive to improving the working efficiency and economic benefits of the EMS.

     

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