针对新能源发电存在随机性、间歇性及初期投资成本高等问题，对面向船舶微电网的双层优化模型进行构建，本文的上层模型以系统初始投资成本最低、污染物排放最少为目标优化配置，下层模型以系统运行成本最低、系统出力波动最小为目标优化调度。提出黄金Halton-Levy麻雀算法（Golden Halton-Levy Sparrow Search Algorithm，GH-LSSA）求解上层配置模型，调用商业求解器CPLEX和YALMIP工具箱求解下层模型。针对船舶特殊工况，提出增益自整定PID偏航控制策略对调度进行优化。研究结果表明，与粒子群算法（Particle Swarm Optimization, PSO）相比，黄金Halton-Levy麻雀算法的单次航程运行成本比PSO算法低2.2万元，出力波动降低7.47 kW，在经济性、可靠性与环保性之间取得了最佳平衡，综合性能表现最优。使用增益自整定PID偏航控制策略的船舶微电网更适应船舶航行的特殊复杂工况，可靠性更高。
New energy generation faces challenges such as randomness, intermittency, and high initial investment costs. To address these issues, a bi-level optimization model for ship microgrids was constructed. The upper-level model optimizes configuration with the objectives of minimizing initial investment costs and pollutant emissions, while the lower-level model optimizes scheduling with the goals of minimizing operational costs and power output fluctuations. The Golden Halton-Levy Sparrow Search Algorithm (GH-LSSA) was proposed to solve the upper-level configuration model, and the commercial solvers CPLEX and the YALMIP toolbox were employed to solve the lower-level model. For the special operational conditions of ships, a gain self-tuning PID yaw control strategy was introduced to optimize scheduling. The results demonstrate that, compared to the Particle Swarm Optimization (PSO) algorithm, the GH-LSSA reduces single-voyage operational costs by 22,000 yuan and decreases power output fluctuations by 7.47 kW. It achieves the optimal balance among economy, reliability, and environmental friendliness, demonstrating the best overall performance. The ship microgrid utilizing the gain self-tuning PID yaw control strategy proves more adaptable to the complex and unique operational conditions of maritime navigation, exhibiting higher reliability.
2026,48(7): 53-60 收稿日期：2025-8-14
DOI：10.3404/j.issn.1672-7649.2026.07.010
分类号：U66；TM61
基金项目：国家自然科学基金资助项目（51807198）
作者简介：张倩(2001-),女,硕士研究生,研究方向为船舶电力系统、新能源电力系统
参考文献：
[1] YIN H, LAN H, HONG Y Y, et al. A comprehensive review of shipboard power systems with new energy sources[J]. Energies, 2023, 16(5): 2307
[2] 裴自来. 船舶新能源动力系统的现状与发展趋势[J]. 大众标准化, 2021(11): 18-20 PEI Z L. Current status and development trends of new energy power systems for ships[J]. Popular Standardization, 2021(11): 18-20
[3] 姜淇, 常玉红, 衣传宝, 等. 基于NSGA-Ⅱ串行模式搜索的新能源发电与抽水蓄能电站联合系统多时间尺度优化调度方法[J]. 太阳能学报, 2024, 45(4): 434-441 JIANG Q, CHANG Y H, YI C B, et al. Multi-time scale optimization scheduling of combined system of new energy power generation and pumped storage power station based on NSGA-II serial pattern search[J]. Acta Energiae Solaris Sinica, 2024, 45(4): 434-441
[4] 周燕伟. 风光发电储能技术发展趋势分析及投资建议[J]. 石化技术, 2024, 31(2): 134-136 ZHOU Y W. Analysis of development trends and investment suggestions in wind-solar power generation and energy storage technology[J]. Petrochemical Industry Technology, 2024, 31(2): 134-136
[5] YANG L X, WANG P Z, ZHAO W, et al. Research on capacity configuration and operation scheduling optimization of multi-energy complementary microgrid[J]. IOP Conference Series: Earth and Environmental Science, 2018, 188(1): 012080-012080.
[6] 陈钢, 黄国政, 邓瑞麒, 等. 结合网络重构的主动配电网日前无功电压双层优化[J]. 供用电, 2022, 39(5): 13-24 CHEN G, HUANG G Z, DENG R Q, et al. Bi-level optimization of day-ahead reactive-voltage in active distribution network with network reconfiguration[J]. Distribution & Utilization, 2022, 39(5): 13-24
[7] 张科峰, 王贺佳, 谢丽蓉, 等. 计及混合储能的水光互补制氢双层优化调度[J]. 新疆大学学报(自然科学版中英文), 2025, 42(2): 214-224 ZHANG K F, WANG H J, XIE L R, et al. Considering the dual-layer optimal scheduling of hydro-solar hydrogen production considering hybrid energy storage[J]. Journal of Xinjiang University (Natural Science Edition in Chinese and English), 2025, 42(2): 214-224
[8] 王勇, 刘梦晨, 王辉, 等. 计及源网荷储协同运行的城市配网侧储能系统规划调度[J]. 电工电能新技术, 2024, 43(3): 73-82 WANG Y, LIU M C, WANG H, et al. Planning and scheduling of energy storage system for urban distribution network considering cooperative operation of generation, grid, load, and storage[J]. Advanced Technology of Electrical Engineering and Energy, 2024, 43(3): 73-82
[9] 寇凌峰, 季宇, 吴鸣, 等. 多能互补系统全寿命周期优化配置方法[J]. 中国电力, 2020, 53(12): 75-82 KOU L F, JI Y, WU M, et al. Optimal configuration of multi-energy complementary system considering full life cycle[J]. Electric Power, 2020, 53(12): 75-82
[10] 王坤, 李岩, 于建成, 等. 电-热一体化综合能源系统容量配置与运行策略双层优化模型[J]. 供用电, 2024, 41(6): 55-63 WANG K, LI Y, YU J C, et al. Double layer optimization model for capacity configuration and operation strategy of electric-thermalintegrated energy system[J]. Distribution & Utilization, 2024, 41(6): 55-63
[11] 安源, 郑申印, 苏瑞, 等. 风光水储多能互补发电系统双层优化研究[J]. 太阳能学报, 2023, 44(12): 510-517 AN Y, ZHENG S Y, SU R, et al. Research on two-layer optimization of wind-solar-water- storage multi energy complementary power generation system[J]. Acta Energiae Solaris Sinica, 2023, 44(12): 510-517
[12] 张子烨. 基于新能源的船舶混合电力系统容量优化策略研究[D]. 镇江: 江苏科技大学, 2021.
[13] 蒋正宇, 戴晓强. 基于遗传优化粒子的船舶电网容量优化配置[J]. 计算机与数字工程, 2024, 52(2): 626-629+640 JIANG Z Y, DAI X Q. Optimal capacity allocation of marine microgrid based on particle swarm optimization algorithm[J]. Computer & Digital Engineering, 2024, 52(2): 626-629+640
[14] 汪永鑫, 许媛媛, 徐茂栋, 等. 基于混合储能的船用光伏微电网控制策略研究[J]. 能源工程, 2022, 42(5): 81-88 WANG Y X, XU Y Y, XU M D, et al. Research on control strategy of marine photovoltaic micro-grid with hybrid energy storage system[J]. Energy Engineering, 2022, 42(5): 81-88
[15] 司玉鹏, 王荣杰, 张世奇, 等. 新形态船舶能源系统多能互补优化管理[J]. 哈尔滨工程大学学报, 2022, 43(7): 1051-1058 SI Y P, WANG R J, ZHANG S Q, et al. Multienergy complementary optimal management fora new type of energy system for ships[J]. Journal of Harbin Engineering University, 2022, 43(7): 1051-1058
[16] WEN S L, LAN H, DAI J F, et al. Economic analysis of hybrid wind/pv/diesel/ess system on a large oil tanker[J]. Electric Power Components and Systems, 2017, 45(7): 705-714
[17] 张子烨, 姜文刚. 新能源船舶混合电力系统容量优化策略[J]. 船舶工程, 2020, 42(10): 84-89 ZHANG Z Y, JIANG W G. Capacity optimization strategy for new energyship hybrid power system[J]. Ship Engineering, 2020, 42(10): 84-89
[18] 付秀杰. 船舶电力系统可靠性优化策略研究[J]. 船舶物资与市场, 2024, 32(10): 87-89
[19] BANGYAL W H, BATOOL H, AHMED J, et al. An improved particle swarm optimization algorithm with chi-square mutation strategy[J]. International Journal of Advanced Computer Science and Applications, 2019, 10(3): 481-491
[20] 刘维莎, 石荣亮, 周其锋, 等. 基于改进麻雀搜索算法分数阶PI的PMSM调速策略[J]. 电子测量技术, 2024, 47(11): 78-85 LIU W S, SHI R L, ZHOU Q F, et al. Speed regulation strategy of PMSM based on fractional order proportional integral with improved sparrow search algorithm[J]. Electronic Measurement Technology, 2024, 47(11): 78-85
[21] 刘诗意, 张超宇, 王桐. 基于风向预测的风电机组偏航优化控制方法[J]. 风机技术, 2023, 65(4): 55-59 LIU S Y, ZHANG C Y, WANG T. Optimal yaw control method for wind turbine based on wind direction prediction[J]. Fan Technology, 2023, 65(4): 55-59
[22] LAN H, DAI J F, WEN S L, et al. Optimal tilt angle of photovoltaic arrays and economic allocation of energy storage system on large oil tanker ship[J]. Energies. 2015;8(10): 11515-11530.
[23] TAFRESHI S M , ZAMANI H A, EZZATI S M, et al. Optimal unit sizing of distributed energy resources in micro grid using genetic algorithm [C]// IEEE International Conference on Renewable Energy Research and Applications, 2017.
[24] 高媚. 基于改进遗传算法的直流微网 HESS 容量配置研究[D]. 武汉: 湖北工业大学, 2020.
[25] 吕鑫, 慕晓冬, 张钧, 等. 混沌麻雀搜索优化算法[J]. 北京航空航天大学学报, 2021(8): 1712–1720. LYU X, MU X D, ZHANG J, et al. Chaos sparrow search optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics , 2021 , 47 (8): 1712–1720.
[26] 吴吉军, 常占丁, 苏庆生, 等. 风电机组偏航调节策略分析与优化[J]. 中国设备工程, 2023(S1): 120-122 WU J J, CHANG Z D, SU Q S, et al. Analysis and optimization of yaw adjustment strategy for wind turbines[J]. China Plant Engineering, 2023(S1): 120-122
