A Joint Optimization Method for Berth Allocation and Energy Scheduling Based on Non-dominated Sorting Dung Beetle Optimizer
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摘要: 综合泊位分配与能量调度联合优化的港口微电网是物流和能量紧密耦合的系统，为兼顾港口物流运输效率和能源系统经济性，保证港口能源系统稳定可靠运行，建立了综合考虑船舶泊位分配和微电网运行成本的多目标联合优化模型，针对单目标求解算法的局限性研究了多目标蜣螂优化算法（non-dominated sorting dung beetle optimizer，NSDBO）模型求解方法，将非支配排序策略引进算法以提高算法精度和收敛速度，同时为维持种群个体的多样性与分布均匀性，引入拥挤距离计算，衡量经非支配排序后每一层解的密集程度并对种群重新排序，获得了分布较好的Pareto解，解决了蜣螂优化算法易陷入局部最优解、全局搜索能力欠缺、收敛精度低等问题，在保证种群均匀性和多样性的同时降低了计算复杂度。通过测试系统验证了改进的多目标蜣螂优化算法与基于支配排序的NSGA-Ⅱ算法、基于分解的MOEA/D算法、改进多目标粒子群算法（improved multi-objective particle swarm optimization，IMOPSO）、拥挤距离多目标粒子群优化算法（crowding distance multi-objective particle swarm optimization DCMOPSO）相比具有更好的分布性、收敛性和均匀度。以天津某港口为算例，对多目标联合优化模型进行求解，并设置多种方案进行对比分析，结果表明，所提泊位分配模型使各个泊位上的船舶分配更加均匀，与泊位分配和能量调度独立优化相比船舶等待时长增加了4 h，但总运行成本减少了121 283元；与单目标联合优化相比总运行成本仅增加了40 225元，船舶等待时长却减少了28 h，证明了泊位分配与能量调度的合理联合优化可以在兼顾船舶等待时长和运行成本的同时，不大幅增加船舶等待时长，验证了该模型和算法策略在泊位分配与能量调度问题中的有效性和准确性，凸显了优化模型的经济优势及其优化物流系统运输效率的卓越能力。Abstract: The integrated berth allocation and energy scheduling optimization in port microgrids is a system where logistics and energy are closely coupled. In order to balance the efficiency of port logistics transportation and the economic viability of the energy system while ensuring the stable and reliable operation of the port energy system, a multi-objective joint optimization model is established. The model considers both berth allocation of ships and the operating cost of the microgrid. To address the limitations of single-objective solution algorithms, the use of non-dominated sorting dung beetle optimizer (NSDBO) is investigated to solve the multi-objective problem. The non-dominated sorting strategy is introduced into the algorithm to enhance its accuracy and convergence speed. To maintain the diversity and uniform distribution of the population, a congestion distance calculation is introduced to measure the density of solutions at each layer after non-dominated sorting and to reorder the population, obtaining a well-distributed Pareto optimal solutions. This addressed the issues inherent in dung beetle optimization algorithm, such as local optima, poor global search capability, and low convergence precision. While ensuring the uniformity and the population diversity, the computational complexity is reduced. The performance of the improved multi-objective dung beetle optimization algorithm (NSDBO) is tested and compared with the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), the decomposition-based multi-objective evolutionary algorithm (MOEA/D), the improved multi-objective particle swarm optimization (IMOPSO), and the crowding distance multi-objective particle swarm optimization (DCMOPSO). The results show that the NSDBO algorithm provides better distribution, convergence, and uniformity. Taking a port in Tianjin as an example, the multi-objective joint optimization model is solved, and several alternative solutions are compared. The results indicate that the proposed berth allocation model distributes ships more evenly across the berths. Compared with the independent optimization of berth allocation and energy scheduling, the waiting time for ships increases by 4 hours, but the total operational costs are decreased by 121 283 Yuan. Compared with the single-objective joint optimization, the total operational costs increased by only 40 225 Yuan, while the waiting time for ships is decreased by 28 hours. This demonstrates that the reasonable joint optimization of berth allocation and energy scheduling can effectively balance the waiting time of ships and the operational costs without significantly increasing the waiting time of ships. The results verify the effectiveness and accuracy of the proposed model and algorithm strategy for berth allocation and energy scheduling problem, highlighting the economic advantages of the optimization model and its outstanding ability to optimize the transportation efficiency of the logistics system.
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Key words:
- energy dispatch /
- port microgrid /
- joint optimization /
- dung beetle algorithm /
- berth allocation
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图 1 泊位分配与微电网能量调度联合优化模型
Figure 1. Joint optimization model of berth allocation and microgrid energy dispatch
图 4 NSDBO算法在ZDT系列测试函数上的近似帕累托前沿
Figure 4. Approximate Pareto front for the NSDBO algorithm on the ZDT series of test functions
图 6 多目标联合优化模型的帕累托前沿
Figure 6. Pareto frontiers for a multi-objective joint optimisation model
图 7 多目标联合优化前后泊位分配方案
Figure 7. Berth allocation scheme before and after multi-objective joint optimization
图 8 多目标联合优化后的能量调度方案
Figure 8. Energy scheduling scheme after multi-objective joint optimization
表 1 多目标优化算法对比测试结果
Table 1. Comparative test results of multi-objective optimisation algorithms
测试函数 指标 模型 NSGA-Ⅱ MOEA/D IMOPSO DCMOPSO NSDBO ZDT1 IGD mean 0.00458 0.00583 0.00632 0.00639 0.00234 std 0.00133 0.00072 0.00093 0.00118 0.00006 SP mean 0.05920 0.05830 0.00537 0.00680 0.00331 std 0.00958 0.00035 0.00012 0.04380 0.00018 ZDT2 IGD mean 0.00619 0.00436 0.00368 0.03570 0.00240 std 0.00037 0.00068 0.00254 0.00395 0.00006 SP mean 0.00917 0.01390 0.00769 0.00915 0.00332 std 0.00065 0.00027 0.00044 0.00150 0.00024 ZDT3 IGD mean 0.00597 0.00729 0.00670 0.03580 0.00254 std 0.00202 0.00671 0.00057 0.00395 0.00007 SP mean 0.10100 0.09820 0.00693 0.08030 0.00350 std 0.05370 0.06320 0.00064 0.00575 0.00021 ZDT4 IGD mean 0.03190 0.00124 0.01290 0.25300 0.00232 std 0.05790 0.00119 0.08210 7.24100 0.00208 SP mean 0.04020 0.04970 0.37200 0.23500 0.00335 std 0.00826 0.00419 0.04720 0.73000 0.00016 ZDT6 IGD mean 0.07350 0.08400 0.18500 0.16500 0.00382 std 0.00657 0.00918 0.09980 0.98000 0.00014 SP mean 0.09830 0.05790 0.03800 0.32100 0.0338 std 0.00547 0.00098 0.00828 0.04530 0.10100 
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表 2 船舶靠港计划
Table 2. Ship berthing plan
船舶编号 船舶规模 预计到达时刻 预计靠港时长/h 1 大型 1 30 2 小型 2 8 3 小型 3 10 4 中型 6 18 5 小型 10 9 6 小型 13 11 7 中型 16 19 8 中型 20 21 9 中型 24 20 10 大型 28 33 11 中型 33 20 12 小型 37 11 13 小型 40 9 14 中型 45 15
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表 3 港口能源系统相关参数
Table 3. Relevant parameters of port energy system
类型 参数 数值 电网 峰时电价/（元/kW·h） 1.35 平时电价/（元/kW·h） 0.82 谷时电价/（元/kW·h） 0.38 出力上限/（MW） 100 储能系统 容量/（MW·h） 30 荷电状态上/下限 $ 0.9 /0.1 $ 维护系数 0.02898 充/放电效率 0.95 光伏 维护系数 0.096 使用寿命/年 20 风机 维护系数 0.0293 使用命/年 10
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表 4 各方案结果对比
Table 4. Comparison of results between schemes
方案 船舶等待时长/h 船舶物流成本/元 运维成本/元 能量交互成本/元 总运行成本/元 方案1 270 61200 5120 1805129 1871449 方案2 265 59200 6828 1823845 1889873 方案3 297 73600 3582 1651183 1728365 方案4 269 60800 5371 1702418 1768590
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[1] TAO Y C, QIU J, LAI S Y, et al. Flexible voyage scheduling and coordinated energy management strategy of all-electric ships and seaport microgrid[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 24(3): 3211-3222. [2] 蒋一鹏, 袁成清, 袁裕鹏, 等". 双碳"战略下中国港口与清洁能源融合发展路径探析[J]. 交通信息与安全, 2023, 41(2): 139-146. doi: 10.3963/j.jssn.1674-4861.2023.02.015JANG Y P, YUAN C G, YUAN Y P, et al. Pathway for Integrated development of port and clean energy under strategy of carbon peaking and carbon Neutralization in China[J]. Journal of Transport Information and Safety, 2023, 41(2): 139-146. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.02.015 [3] AHAMAD N B B, GUERRERO J M, SU C L, et al. Microgrids technologies in future seaports[C]. 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, Palermo, Italy: IEEE, 2018. [4] 方斯顿, 赵常宏, 丁肇豪, 等. 面向碳中和的港口综合能源系统(二): 能源-交通融合中的柔性资源与关键技术[J]. 中国电机工程学报, 2023, 43(3): 950-969.FANG S D, ZHAO C H, DING Z H, et al. Port integrated energy systems toward carbon neutrality(Ⅱ): flexible resources and key technologies in energy-transportation integration[J]. Proceedings of the CSEE, 2023, 43(3): 950-968. (in Chinese) [5] BOUZEKRI H, ALPAN G, GIARD V. Integrated laycan and berth allocation and time-invariant quay crane assignment problem in tidal ports with multiple quays[J]. European Journal of Operational Research, 2021, 293(3): 892-909. doi: 10.1016/j.ejor.2020.12.056 [6] BACALHAU E T, CASACIO L, DEAZEVEDOAT. New hybrid genetic algorithms to solve dynamic berth allocation problem[J]. Expert Systems With Applications, 2021, 167: 114198. doi: 10.1016/j.eswa.2020.114198 [7] YU J J, TANG G L, VOSS S, et al. Berth allocation and quay crane assignment considering the adoption of different green technologies[J]. Transportation Research Part E: Logistics and Transportation Review, 2023, 176: 103185. doi: 10.1016/j.tre.2023.103185 [8] 桂小娅, 陆志强, 韩笑乐. 集装箱码头连续型泊位与岸桥集成调度[J]. 上海交通大学学报, 2013, 47(2): 226-229.GUI X Y, LU Z Q, HAN X L. Integrating optimization method for continuous berth and quay crane scheduling in container terminals[J]. Journal of Shanghai Jiaotong University, 2013, 47(2): 226-229. (in Chinese) [9] YU J J, TANG G L, SONG X Q. Collaboration of vessel speed optimization with berth allocation and quay crane assignment considering vessel service differentiation[J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 160: 102651. doi: 10.1016/j.tre.2022.102651 [10] YU J J, TANG G L, VOSS S, et al. Berth allocation and quay crane assignment considering the adoption of different green technologies[J]. Transportation Research Part E: Logistics and Transportation Review, 2023, 176: 103185. green technologies[J]. Transportation Research Part E: Logistics and Transportation Review, 2023, 176: 103185. doi: 10.1016/j.tre.2023.103185 [11] 代永该, 庞利宝, 黄麟富. 考虑能耗的集装箱码头泊位与岸边集装箱起重机联合调度研究[J]. 港口装卸, 2023, (3): 33-38.DAI Y G, PANG L B, HUANG L F. Study on joint dispatching of container terminal berths and quayside container cranes considering energy consumption[J]. Port Operation, 2023, (3): 33-38. (in Chinese) [12] SONG T L, LI Y, ZHANG X P, et al. Integrated port energy system considering integrated demand response and energy interconnection[J]. International Journal of Electrical Power & Energy Systems, 2020, 117: 105654. [13] 王萧博, 黄文焘, 邰能灵, 等. 面向源储优化配置的港口微电网运行场景高保真压缩与重构方法[J]. 中国电机工程学报, 2023, 43(15): 5839-5850.WANG X B, HUANG W T, TAI N L, et al. A high-fidelity compression and reconstruction method of port microgrid operation scenarios for optimal source-storage allocation[J]. Proceedings of the CSEE, 2023, 43(15): 5839-5850. (in Chinese) [14] THIRUNAVUKKARASU G S, SEYEDMAHMOUDIAN M, JAMEI E, et al. Role of optimization techniques in microgrid energy management systems: a review[J]. Energy Strategy Reviews, 2022, 43: 100899. doi: 10.1016/j.esr.2022.100899 [15] WANG Y, DONG W, YANG Q. Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets[J]. Applied Energy, 2022, 310: 118528. doi: 10.1016/j.apenergy.2022.118528 [16] MERABET A, AL-DURRA A, EL-SAADANY E F. Energy management system for optimal cost and storage utilization of renewable hybrid energy microgrid[J]. Energy Conversion and Management, 2022, 252: 115116. doi: 10.1016/j.enconman.2021.115116 [17] ROSLAN M F, HANNAN M A, KER P J, et al. Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction[J]. Applied Energy, 2021, 292: 116883. doi: 10.1016/j.apenergy.2021.116883 [18] FANG S D, WANG Y, GOU B, et al. Towards future green maritime transportation: an overview of seaport microgrids and all-electric ships[J]. IEEE Transactions on Vehicular Technology, 2020, 69(1): 207-219. doi: 10.1109/TVT.2019.2950538 [19] MAO A J, YU T T, DING Z H, et al. Optimal scheduling for seaport integrated energy system considering flexible berth allocation[J]. Applied Energy, 2022, 308: 118386. doi: 10.1016/j.apenergy.2021.118386 [20] IRIS C, LAM J S L. Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty[J]. Omega-international Journal of Management Science, 2021, 103: 102445. [21] 普月, 刘皓明, 王健, 等. 考虑多源激励的港口能流-物流全过程协同调度优化[J]. 中国电机工程学报, 2023, 43 (20): 7912-7929.PU Y, LIU H M, WANG J, et al. Co-scheduling optimisation of the whole process of energy flow-logistics in ports considering multi-source incentives[J]. Proceedings of the CSEE, 2023, 43(20): 7912-7929. (in Chinese) [22] ZHANG Y, LIANG C J, SHI J, et al. Optimal port microgrid scheduling incorporating onshore power supply and berth allocation under uncertainty[J]. Applied Energy, 2022, 313: 118856. doi: 10.1016/j.apenergy.2022.118856 [23] FAN S L, AI Q A, XU G D, et al. Cooperative coordination between port microgrid and berthed ships with emission limitation and peak awareness[J]. Energy Reports, 2023, (9): 1657-1670. [24] XUE J K, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2023, 79(7): 7305-7336. doi: 10.1007/s11227-022-04959-6 [25] RAMIREZ-OCHOA D D, PEREZ-DOMINGUEZ LA, MARTINEZ-GOMEZ E A, et al. PSO, a swarm intelligence-based evolutionary algorithm as a decision-making strategy: a review[J]. Symmetry, 2022, 14(3): 455-458. doi: 10.3390/sym14030455 [26] WU R C, SONG J C, HAO M R, et al. Short term load forecasting method for regional intelligent distribution network based on deep learning[C]. 7th International Conference on Smart Grid and Smart Cities, Lanzhou, China: IEEE, 2023. [27] SHOU S A, LUO H R, ZHANG R H, et al. Power quality monitoring point configuration of distribution network based on dung beetle optimization algorithm considering panoramic perception[C]. 2023 3rd International Conference on Electrical Engineering and Mechatronics Technology, NanJing, China: IEEE, 2023. [28] WANG Z D, HUANG L L, YANG S X, et al. A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization[J]. Alexandria Engineering Journal, 2023, 81: 469-488. doi: 10.1016/j.aej.2023.09.042 -
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