Long voyage planning and battery charging/swapping strategy of pure electric green ships
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摘要: 针对纯电动船舶长航程航次规划和充换电站选址的动态规划问题，提出了一种可行的多变量、多目标优化模型，实现航速和充换电策略的同步优化；建立了能耗最低和航时最短的双目标函数，分析了影响纯电动船舶长航程航次规划的环境和操作因素，构建了航速、航时和能耗约束条件；研究了集装箱式电池运营管理模式，建立了能量补给按照实际耗电量计费和更换电池一次性计费2种优化模型；利用非支配排序遗传算法完成了模型求解并进行试验案例验证。研究结果表明：基于案例数据条件，优化前以保证续航到第2个充换电站进行补给的最大平均航速8.5 kn计算，需耗能96 637.7 kWh、总航时62.1 h；以实际耗电量计费时，采用优化模型得到的充换电策略均是在第2个充换电站进行补给，以最短航时为优化目标时总航时仅需56.9 h，以最低能耗为优化目标时总能耗仅需65 762.5 kWh；以更换电池一次性计费时，优化模型的结果存在2种策略，即全程不换电池和在第2个充换电站更换一次电池，并且满足在更换电池前累积能耗接近船载电池的总容量，可最大程度利用能量；优化模型能够给出不同电池运营管理模式和用户偏好条件下的最优航速和充换电策略，并且能够应用于充换电站选址决策，对提高纯电动绿色船舶的运营效益具有重要意义。Abstract: To solve the problems of long voyage planning and dynamic planning of site selection of battery charging/swapping stations for pure electric ships, a feasible multi-variable and multi-objective optimization method was proposed to realize the synchronous optimization of speed and battery charging/swapping strategy. A double objective function of the lowest energy consumption and the shortest voyage time was established, the environmental and operational factors affecting long voyage planning for pure electric ships were analyzed, and the constraints of speed, voyage time and energy consumption were established. The operations management mode of the containerized battery was studied, and two optimization models of energy supply charging according to actual power consumption charging and one-time battery swapping charging were built. The models were solved by a non-dominated sorting genetic algorithm and verified by experiment cases. Research results show that based on case data conditions, before optimization, the maximum average speed of 8.5 kn ensuring continuous sailing to the second battery charging/swapping station for recharging requires 96 637.7 kWh of energy consumption and 62.1 h of total sailing time. When charging for actual power consumption, the battery charging/swapping strategy obtained by the optimization models requires recharging at the second battery charging/swapping station. When the optimization goal is the shortest sailing time, the total sailing time is only 56.9 h. When the optimization goal is the minimum energy consumption, the total energy consumption is only 65 762.5 kWh. In the case of one-time battery swapping charging, there are two strategies in the optimization model, including no battery swapping in the whole voyage and one-time battery swapping at the second battery charging/swapping station. The accumulated energy consumption before battery swapping is close to the total capacity of the battery on the ship, which can maximize the energy utilization. The optimization model can provide optimal speed and battery charging/swapping strategy under different battery operations management modes and user preferences, and can be applied to determine site selection of battery charging/swapping stations, which is of great significance to improve the operating efficiency of pure electric green ships.
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图 1 纯电动船舶航速优化建模航线
Figure 1. Route of speed optimization modeling for pure electric ship
图 5 航路点流速及航段平均流速
Figure 5. Speed of waypoints and average velocities of route segments
图 6 考虑充换电策略和环境的航速优化算法
Figure 6. Speed optimization algorithm considering battery charging/swapping strategy and environment
图 10 航路点流速与航段平均流速
Figure 10. Flow velocities of waypoints and average flow velocities of route segments
图 12 50个帕累托解的阶段累积能耗
Figure 12. Cumulative energy consumptions at each segment of 50 Pareto solutions
图 16 箱式电源更换一次性计费的帕累托解集
Figure 16. Pareto solution set of one-time charging for box-type power replacement
图 17 更换箱式电源采用一次性计费的充换电策略集
Figure 17. Battery charging/swapping strategy set of one-time charging for box-type power replacement
图 18 改进后50个帕累托解集的累积能耗
Figure 18. Cumulative energy consumptions of 50 Pareto solutions after improvement
图 19 逆流时帕累托解集中充换电站利用频数
Figure 19. Utilization frequencies of battery charging/swapping station in Pareto solution set with countercurrent sailing
图 20 顺流时帕累托解集中充换电站利用频数
Figure 20. Utilization frequencies of battery charging/swapping station in Pareto solution set with current sailing
表 1 航速-功率数据
Table 1. Speed-power data
航速/kn 主机功率/kW 5.0 195.0 6.0 320.0 7.0 526.4 7.5 646.2 8.5 938.4 9.5 1 308.2 10.0 1 525.0 10.5 1 685.0 11.0 1 936.6 
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表 2 帕累托解集按目标排序结果(案例1)
Table 2. Results of optimization model Pareto solution set sorted by target (case 1)
参数 v0/kn v1/kn v2/kn p1 p2 e0/kWh (e0+e1)/kWh (e0+e1+e2)/kWh 总时长/h 航时最短 8.6 8.5 11.0 0 1 32 988.9 59 998.9 114 829.0 56.9 能耗最低 6.4 6.2 6.3 0 1 22 141.2 39 844.6 65 762.5 80.0 优化前 8.5 8.5 8.5 0 1 32 171.8 58 850.4 96 637.7 62.1
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表 3 优化算法单一指标排序对比结果
Table 3. Comparison results of single indicator ranking for optimization algorithms
优化算法 总能耗/kWh 总时长/h 航时最短 NSGA-Ⅱ 114 829.0 56.9 RVEA-RES 114 745.8 57.0 能耗最低 NSGA-Ⅱ 65 762.5 80.0 RVEA-RES 65 893.5 80.0
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表 4 帕累托解集按目标排序结果(案例2)
Table 4. Results of Pareto solution set sorted by target (case 2)
参数 v0/kn v1/kn v2/kn p1 p2 e0/kWh (e0+e1)/kWh (e0+e1+e2)/kWh 总时长/h 航时最短 9.2 9.0 11.0 0 1 33 373.1 60 000.0 111 584.5 51.3 能耗最低 5.2 5.3 5.4 0 0 16 672.1 30 626.2 51 038.1 80.0 优化前 9.0 9.0 9.0 0 1 32 473.8 59 336.4 97 825.6 54.7
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