含速度-功率耦合约束的两阶段港口微电网多岸桥柔性能量管理方法

doi:10.19595/j.cnki.1000-6753.tces.251472

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Abstract The increasing electrification of container ports has strengthened the operational coupling between automated quay cranes (AQCs) and port microgrids, particularly under high penetration of renewable energy. The large and rapidly fluctuating power demands of multiple AQCs, combined with highly variable wind and photovoltaic generation, impose stringent requirements on system flexibility. To address the challenges of instantaneous multi-AQC power aggregation, renewable-generation uncertainty, and lifetime degradation of large-scale energy storage, this paper develops a two-stage flexible energy management strategy for port microgrids. The proposed framework integrates a refined velocity-power coupling model for AQCs and a linearized healthy-operation-region model for energy storage, enabling coordinated logistics execution and energy scheduling, reduced peak load, and enhanced renewable-energy utilization.
The methodology begins with the derivation of a physics-based dynamic model for six standard AQC movements: hoisting, trolley travel, and lowering under both loaded and unloaded conditions, each following a trapezoidal velocity profile. Analytical relationships among velocity, acceleration, displacement, and instantaneous power are established, capturing the second-level power variations. Based on this model, controllable operating velocities and adjustable start sequences are formulated to exploit the operational flexibility of individual AQCs. A coordinated de-peaking mechanism is then introduced to prevent simultaneous high-power actions across multiple AQCs. By identifying overlapping movement intervals using binary variables and imposing upper bounds on aggregated crane power, the mechanism effectively mitigates peak-power surges in multi-AQC scenarios. Meanwhile, to ensure the long-term reliability of the storage system under frequent cycling, a linear healthy operational region model is developed. Through hyperspace projection of the nonlinear lifetime surface defined over average state of charge (SoC_mean) and maximum depth of discharge (DoD_max), followed by geometric convex approximation, the original nonlinear constraints are transformed into a tractable linear form. This enables the battery system to participate in second-level regulation while remaining within a safe degradation boundary.
A two-stage scheduling architecture is built on these components. In the day-ahead stage, hourly decisions on AQC allocation, container-handling quantities, grid purchases, and renewable-energy utilization are optimized under the linearized storage-health constraints. In the intra-day stage, a rolling optimization with a 1-hour window is performed, during which second-level AQC power trajectories are reconstructed from optimized velocity profiles to accommodate deviations in actual renewable generation. The energy storage system and AQC operation are jointly coordinated to absorb forecast errors while ensuring timely completion of logistical tasks.
Case studies based on operational data from a U.S. port verify the effectiveness of the proposed framework. Compared with conventional strategies, the method reduces second-level peak load by 33.61%, increases renewable-energy utilization by 15.12%, and extends battery lifetime by 19.88%. The coordinated de-peaking mechanism effectively prevents overlapping high-power stages among cranes, producing a smoother load profile and reducing contracted transformer capacity requirements. The linearized storage-health model constrains deep cycling and maintains SoC trajectories within the safe operating region throughout both scheduling stages. Under conditions with large forecast deviations, dynamically adjusted AQC velocities provide additional demand-side flexibility, enabling full renewable absorption without jeopardizing operational deadlines.
Overall, the study establishes an integrated logistics-energy co-optimization framework that combines detailed electromechanical AQC modeling at the second scale, coordinated multi-crane scheduling, and health-aware energy storage operation. The results demonstrate its capability to enhance microgrid flexibility, suppress peak load, and support renewable-rich green port operations.

Key words： Automated quay crane microgrid energy management mixed-integer nonlinear programming renewable energy consumption two-stage optimization

Received: 21 August 2025

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	Xing Ailing
	Fang Sidun
	Niu Tao
	Chen Guanhong
	Liao Ruijin

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Xing Ailing,Fang Sidun,Niu Tao等. Two-Stage Flexible Energy Management Method for Port Microgrid with Multiple Quay Cranes Containing Velocity-Power Coupling Constraints[J]. Transactions of China Electrotechnical Society, 2026, 41(17): 5834-5856.

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[1] Iris Ç, Lam J S L. Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty[J]. Omega, 2021, 103: 102445.
[2] 董晊兴, 文书礼, 朱淼, 等. 基于双层博弈理论的绿色港口集群能源经济优化调度[J]. 电工技术学报, 2025, 40(15): 4804-4817.
Dong Zhixing, Wen Shuli, Zhu Miao, et al.Two-level game theory-based economic scheduling of multi-energy in green port clusters[J]. Transactions of China Electrotechnical Society, 2025, 40(15): 4804-4817.
[3] Zhao Nan, Schofield N, Niu Wangqiang.Energy storage system for a port crane hybrid power-train[J]. IEEE Transactions on Transportation Electrification, 2016, 2(4): 480-492.
[4] Iris Ç, Lam J S L. A review of energy efficiency in ports: operational strategies, technologies and energy management systems[J]. Renewable and Sustainable Energy Reviews, 2019, 112: 170-182.
[5] Kermani M, Shirdare E, Parise G, et al.A comprehensive technoeconomic solution for demand control in ports: energy storage systems integration[J]. IEEE Transactions on Industry Applications, 2022, 58(2): 1592-1601.
[6] Cheimanoff N, Fontane F, Kitri M N, et al.Exact and heuristic methods for the integrated berth allocation and specific time-invariant quay crane assignment problems[J]. Computers & Operations Research, 2022, 141: 105695.
[7] 黄逸文, 黄文焘, 卫卫, 等. 大型海港综合能源系统物流-能量协同优化调度方法[J]. 中国电机工程学报, 2022, 42(17): 6184-6196.
Huang Yiwen, Huang Wentao, Wei Wei, et al.Logistics-energy collaborative optimization scheduling method for large seaport integrated energy system[J]. Proceedings of the CSEE, 2022, 42(17): 6184-6196.
[8] 魏皓, 黄文焘, 余墨多, 等. 集装箱海港级联物流-能源耦合系统协同优化方法[J]. 中国电机工程学报, 2025, 45(7): 2461-2472.
Wei Hao, Huang Wentao, Yu Moduo, et al.Collaborative optimization method for container seaport cascading logistics-energy coupling systems[J]. Proceedings of the CSEE, 2025, 45(7): 2461-2472.
[9] Tan Caimao, Yan Wei, Yue Jiantao.Quay crane scheduling in automated container terminal for the trade-off between operation efficiency and energy consumption[J]. Advanced Engineering Informatics, 2021, 48: 101285.
[10] Lou J, Huang W, Yang Y, et al.Energy management of port electric power system under the trajectory optimization of electric quay crane[C]//12th International Conference on Renewable Power Generation (RPG 2023), Shanghai, China, 2023: 712-717.
[11] Parise G, Parise L, Malerba A, et al.Comprehensive peak-shaving solutions for port cranes[J]. IEEE Transactions on Industry Applications, 2017, 53(3): 1799-1806.
[12] Geerlings H, Heij R, van Duin R. Opportunities for peak shaving the energy demand of ship-to-shore quay cranes at container terminals[J]. Journal of Shipping and Trade, 2018, 3(1): 3.
[13] 郭飞, 雍培, 刘欣宇, 等. 考虑状态相依老化特性的电池储能参与调峰投资效益评估[J]. 电工技术学报, 2025, 40(13): 4330-4342.
Guo Fei, Yong Pei, Liu Xinyu, et al.Evaluating the investment benefits of battery energy storage participating in peak shaving considering the state-dependent aging characteristics[J]. Transactions of China Electrotechnical Society, 2025, 40(13): 4330-4342.
[14] Kong Laiqiang, Luo Yingbing, Fang Sidun, et al.State estimation of lithium-ion battery for shipboard applications: key challenges and future trends[J]. Green Energy and Intelligent Transportation, 2025, 4(3): 100192.
[15] 方斯顿, 黄莘杰, 孔赖强, 等. 含储能健康运行域与电压安全约束的配电网重构方法[J]. 中国电机工程学报, 2025, 45(2): 557-587.
Fang Sidun, Huang Xinjie, Kong Laiqiang, et al.Distribution network reconfiguration method with health operating area of energy storage life and voltage security constraints[J]. Proceedings of the CSEE, 2025, 45(2): 557-587.
[16] 李弈, 张金龙, 漆汉宏, 等. 基于变分深度嵌入-带有梯度惩罚的生成对抗网络的锂离子电池老化特性建模[J]. 电工技术学报, 2024, 39(13): 4226-4239.
Li Yi, Zhang Jinlong, Qi Hanhong, et al.Ageing performance modeling of Li-ion batteries based on variational deep embedding-Wasserstein GAN with gradient penalty[J]. Transactions of China Electro-technical Society, 2024, 39(13): 4226-4239.
[17] 贺鸿杰, 张宁, 杜尔顺, 等. 电网侧大规模电化学储能运行效率及寿命衰减建模方法综述[J]. 电力系统自动化, 2020, 44(12): 193-207.
He Hongjie, Zhang Ning, Du Ershun, et al.Review on modeling method for operation efficiency and lifespan decay of large-scale electrochemical energy storage on power grid side[J]. Automation of Electric Power Systems, 2020, 44(12): 193-207.
[18] 邸鹏宇, 蔡新雷, 孟子杰, 等. 基于考虑通信延迟改进分布一致性算法的多储能电站快速低寿命损耗功率均衡分配策略[J]. 电力自动化设备, 2024, 44(8): 18-26.
Di Pengyu, Cai Xinlei, Meng Zijie, et al.Fast and low life loss power equalization allocation strategy of multiple battery energy storage stations based on improved distributed consistency algorithm considering communication delay[J]. Electric Power Automation Equipment, 2024, 44(8): 18-26.
[19] 孙毅, 单禹钦, 陈明昊, 等. 考虑生命周期与储能损耗的光-储系统低碳运行优化策略[J]. 电力系统自动化, 2025, 49(12): 60-68.
Sun Yi, Shan Yuqin, Chen Minghao, et al.Low-carbon operation optimization strategy for photovoltaic-electrical energy storage system considering life cycle and energy storage degradation[J]. Automation of Electric Power Systems, 2025, 49(12): 60-68.
[20] Seddiek I S.Application of renewable energy technologies for eco-friendly sea ports[J]. Ships and Offshore Structures, 2020, 15(9): 953-962.
[21] Song Shuang, Poh K L.Solar PV leasing in Singapore: enhancing return on investments with options[J]. IOP Conference Series: Earth and Environmental Science, 2017, 67: 012020.
[22] 交通运输部. 港口零碳转型试点探路各显其“能”[EB/OL]. (2024-10-29)[2025-07-23]. https://www.mot.gov.cn/jiaotongyaowen/202410/t20241029_4158629.html.
[23] 胡婷. 计及风电功率波动的微电网储能规划研究[D]. 北京: 华北电力大学, 2019.
Hu Ting.Research on energy storage planning of microgrid considering the fluctuation of wind power[D]. Beijing: North China Electric Power University, 2019.
[24] 李虹, 韩雨萌. 计及新能源不确定性的配电网-多微电网协同优化调度[J]. 电工技术学报, 2025, 40(17): 5571-5588.
Li Hong, Han Yumeng.Coordinated optimization scheduling of distribution networks and multiple microgrids considering the uncertainty of new energy[J]. Transactions of China Electrotechnical Society, 2025, 40(17): 5571-5588.
[25] Ahamad N B B, Su C L, Xiao Zhaoxia, et al. Energy harvesting from harbor cranes with flywheel energy storage systems[J]. IEEE Transactions on Industry Applications, 2019, 55(4): 3354-3364.
[26] 程曹阳, 杨知方, 余娟, 等. 面向全场景安全的储能投资高效规划方法[J]. 电工技术学报, 2025, 40(1): 64-79.
Cheng Caoyang, Yang Zhifang, Yu Juan, et al.An efficient method for energy storage planning considering full-scenario security[J]. Transactions of China Electrotechnical Society, 2025, 40(1): 64-79.
[27] Sayfutdinov T, Ali M, Khamisov O.Alternating direction method of multipliers for the optimal siting, sizing, and technology selection of Li-ion battery storage[J]. Electric Power Systems Research, 2020, 185: 106388.
[28] 重庆市发展和改革委员会. 重庆市发展和改革委员会关于贯彻落实煤电容量电价机制有关事项的通知[EB/OL]. (2023-12-26)[2025-07-25]. https://fzggw.cq.gov.cn/zwgk/zfxxgkml/jgxx/jgzc/202312/t20231226_12745289.html.
[29] National Renewable Energy Laboratory. Cost Projections for Utility-Scale Battery Storage (2025) [EB/OL]. (2025-01-01)[2025-11-16]. https://docs.nrel.gov/docs/fy25osti/93281.pdf.
[30] 魏震波, 李杰, 杨超, 等. 基于动态掺氢策略的综合能源系统低碳经济调度[J]. 电网技术, 2024, 48(8): 3155-3164.
Wei Zhenbo, Li Jie, Yang Chao, et al.Low-carbon economic scheduling for integrated energy system based on dynamic hydrogen doping strategy[J]. Power System Technology, 2024, 48(8): 3155-3164.