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智能船舶综合能源系统及其分布式优化调度方法

滕菲 单麒赫 李铁山

滕菲, 单麒赫, 李铁山. 智能船舶综合能源系统及其分布式优化调度方法. 自动化学报, 2020, 46(9): 1809−1817 doi: 10.16383/j.aas.c200176
引用本文: 滕菲, 单麒赫, 李铁山. 智能船舶综合能源系统及其分布式优化调度方法. 自动化学报, 2020, 46(9): 1809−1817 doi: 10.16383/j.aas.c200176
Teng Fei, Shan Qi-He, Li Tie-Shan. Intelligent ship integrated energy system and its distributed optimal scheduling algorithm. Acta Automatica Sinica, 2020, 46(9): 1809−1817 doi: 10.16383/j.aas.c200176
Citation: Teng Fei, Shan Qi-He, Li Tie-Shan. Intelligent ship integrated energy system and its distributed optimal scheduling algorithm. Acta Automatica Sinica, 2020, 46(9): 1809−1817 doi: 10.16383/j.aas.c200176

智能船舶综合能源系统及其分布式优化调度方法

doi: 10.16383/j.aas.c200176
基金项目: 国家自然科学基金(61803064), 中央高校基本科研业务费专项资金(3132020103, 3132020125)资助
详细信息
    作者简介:

    滕菲:大连海事大学船舶电气工程学院讲师. 主要研究方向为分布式优化技术及其在综合能源系统领域相关应用.E-mail: brenda_teng@163.com

    单麒赫:大连海事大学航海学院副教授. 主要研究方向为多智能体控制, 分布式优化, 船舶能耗优化. 本文通信作者.E-mail: shanqihe@dlmu.edu.cn

    李铁山:电子科技大学自动化工程学院教授. 主要研究方向为智能船舶控制理论与技术, 非线性系统智能控制理论与应用研究.E-mail: litieshan073@uestc.edu.cn

Intelligent Ship Integrated Energy System and Its Distributed Optimal Scheduling Algorithm

Funds: Supported by National Natural Science Foundation of China (61803064), the Fundamental Research Funds for the Central Universities (3132020103, 3132020125)
  • 摘要: 船舶航运污染是阻碍海洋经济发展、海洋强国建设的瓶颈问题. 智能船舶为航运业绿色环保发展提供了重要手段. 为进一步开发船载新能源, 提升能源综合利用效率, 降低船舶航运污染排放, 本文构建以能量优化调度系统为核心、以能源转换中心为枢纽的智能船舶综合能源系统; 考虑其特有的动力系统负荷需求、航行低污染排放量标准以及电−热多能流耦合供能特性, 建立智能船舶综合能源系统能量优化调度目标函数及相关约束条件; 并基于宽度学习、带有广义噪声的多智能体分布式优化相关理论, 提出可快速准确地预测全航程各时段负荷需求、可容纳复杂干扰的分布式优化调度方法, 实现高效的智能船舶综合能源系统能量优化调度, 保障智能船舶经济、可靠、稳定航行. 仿真分析验证了所提出智能船舶综合能源系统分布式优化调度方法的有效性.
  • 图  1  智能船舶综合能源系统基本结构框图

    Fig.  1  The typical architecture of intelligent ship integrated energy system

    图  2  智能船舶综合能源系统仿真模型

    Fig.  2  The simulation model of intelligent ship integrated energy system

    图  3  智能船舶综合能源系统全航程分布式优化调度考虑的广义噪声干扰

    Fig.  3  The general noise considered in the distributed optimal scheduling during the whole voyage of intelligent ship integrated energy system

    图  4  船舶航行$6\sim 10 $小时时段各供能设备电输出功率

    Fig.  4  Electricity output of each energy supply equipment during $6\sim 10 $ hours sailing

    图  5  船舶航行$6\sim 10 $小时时段各供能设备热输出功率

    Fig.  5  Heat output of each energy supply equipment during $6\sim 10 $ hours sailing

    图  6  智能船舶航行航线全航程各时段各供能设备最优电输出功率

    Fig.  6  The optimal electricity output of each energy supply equipment of intelligent ship in different periods of the whole voyage

    图  7  智能船舶航行航线全航程各时段各供能设备最优热输出功率

    Fig.  7  The optimal heat output of each energy supply equipment of intelligent ship in different periods of the whole voyage

    表  1  智能船舶全航程各时段电−热负荷预测结果

    Table  1  The forecast results of electric and thermal load of intelligent ship in different periods of the whole voyage

    全航程各时段热
    负荷预测量 (MW)
    1小时2小时3小时4小时5小时6小时7小时8小时9小时10小时11小时12小时
    19.000028.988933.000034.000032.000027.000020.000016.000018.000027.978033.000034.0000
    13小时14小时15小时16小时17小时18小时19小时20小时21小时22小时23小时24小时
    36.000029.000020.000016.000019.000029.967130.000035.000031.000028.000019.495718.0000
    全航程各时段电
    负荷预测量 (MW)
    1小时2小时3小时4小时5小时6小时7小时8小时9小时10小时11小时12小时
    29.360055.325561.610062.430060.830048.850033.730025.250032.160057.388561.080059.7900
    13小时14小时15小时16小时17小时18小时19小时20小时21小时22小时23小时24小时
    65.180055.480035.250026.600032.700054.362954.590064.240056.610054.930032.903928.2700
    下载: 导出CSV
  • [1] 十八大之海洋强国战略[Online], available: https://www.sohu.com/a/195834582_811190, 2017年10月1日

    Sea Strategic thought at the 18th CPC National Congress [Online], available: https://www.sohu.com/a/195834582_811190, October 1, 2017
    [2] 十九大报告全文[Online], available: http://sh.people.com.cn/n2/2018/0313/c134768-31338145.html, 2018年3月13日

    The 19th National Congress of the Communist Party of China [Online], available: http://sh.people.com.cn/n2/2018/0313/c134768-31338145.html, March 13, 2018
    [3] 国务院印发《中国制造2050》[Online], available: http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm, 2015年5月19日

    Notice of the State Council on Issuing the “Made in China (2025)” [Online], available: http://www.gov.cn/zhengce/con-tent/2015-05/19/content_9784.htm, May 19, 2015
    [4] IMO签署协议: 2050年海运业碳排放量减少一半[Online], available: http://www.tanpaifang.com/jienenjianpai/2018/0416/61777.html, 2018年4月16日

    IMO Signed an Agreement to Halve Carbon Emissions of Shipping Industry in 2050 [Online], available: http://www.tanpaifang.com/jienenjianpai/2018/0416/61777.html, April 16, 2018
    [5] 范爱龙, 贺亚鹏, 严新平, 王骏腾. 智能新能源船舶的概念及关键技术. 船舶工程, 2020, 42(3): 9−14

    Fan Ai-Long, He Ya-Peng, Yan Xin-Ping, Wang Jun-Teng. Concept and key technologies of intelligent new energy ship. Ship Engineering, 2020, 42(3): 9−14
    [6] Tsekouras G J, Kanellos F D, Prousalidis J. Simplified method for the assessment of ship electric power systems operation cost reduction from energy storage and renewable energy sources integration. IET Electrical Systems in Transportation, 2015, 5(2): 61−69 doi: 10.1049/iet-est.2013.0011
    [7] Michalopoulos P, Kanellos F D, Tsekouras G J, Prousalidis J M. A method for optimal operation of complex ship power systems employing shaft electric machines. IEEE Transactions on Transportation Electrification, 2016, 2(4): 547−557 doi: 10.1109/TTE.2016.2572093
    [8] Khooban M H, Dragicevic T, Blaabjerg F, Delimar M. Shipboard microgrids: A novel approach to load frequency control. IEEE Transactions on Sustainable Energy, 2018, 9(2): 843−852 doi: 10.1109/TSTE.2017.2763605
    [9] 贾宏杰, 王丹, 徐宪东, 余晓丹. 区域综合能源系统若干问题研究. 电力系统自动化, 2015, 39(7): 198−207

    Jia Hong-Jie, Wang Dan, Xu Xian-Dong, Yu Xiao-Dan. Research on some key problems related to integrated energy systems. Automation of Electric Power Systems, 2015, 39(7): 198−207
    [10] 孙秋野, 滕菲, 张化光. 能源互联网及其关键控制问题. 自动化学报, 2017, 43(2): 176−194

    Sun Qiu-Ye, Teng Fei, Zhang Hua-Guang. Energy internet and its key control issues. Acta Automatica Sinica, 2017, 43(2): 176−194
    [11] 孙宏斌, 潘昭光, 郭庆来. 多能流能量管理研究: 挑战与展望. 电力系统自动化, 2016, 40(15): 1−8 doi: 10.7500/AEPS20160522006

    Sun Hong-Bin, Pan Zhao-Guang, Guo Qing-Lai. Energy management for multi-energy flow: challenges and prospects. Automation of Electric Power Systems, 2016, 40(15): 1−8 doi: 10.7500/AEPS20160522006
    [12] 郭创新, 王惠如, 张伊宁, 何宇斌. 面向区域能源互联网的“源—网—荷”协同规划综述. 电网技术, 2019, 43(9): 3071−3080

    Guo Chuang-Xin, Wang Hui-Ru, Zhang Yi-Ning, He Yu-Bin. Review of “source-grid-load” co-planning orienting to regional energy internet power system technology. Power System Technology, 2019, 43(9): 3071−3080
    [13] 王佳颖, 史俊祎, 文福拴, 李继红, 张利军, 徐晨博. 计及需求响应的光热电站热电联供型微网的优化运行. 电力系统自动化, 2019, 43(1): 176−189

    Wang Jia-Ying, Shi Jun-Yi, Wen Fu-Shuan, Li Ji-Hong, Zhang Li-Jun, Xu Chen-Bo. Optimal operation of CHP microgrid with concentrating solar power plants considering demand response. Automation of Electric Power Systems, 2019, 43(1): 176−189
    [14] 孙宏斌, 郭庆来, 吴文传, 王彬, 夏天. 面向能源互联网的多能流综合能量管理系统: 设计与应用. 电力系统自动化, 2019, 43(12): 122−128, 171 doi: 10.7500/AEPS20190228003

    Sun Hong-Bin, Guo Qing-Lai, Wu Wen-Chuan, Wang Bin, Xia Tian. Integrated energy management system with multi-agent flow for energy internet: design and application. Automation of Electric Power Systems, 2019, 43(12): 122−128, 171 doi: 10.7500/AEPS20190228003
    [15] 唐昊, 刘畅, 杨明, 汤必强, 许丹, 吕凯. 考虑电网调峰需求的工业园区主动配电系统调度学习优化. 自动化学报, DOI: 10.16383/j.aas.c190079, 2019.

    Tang Hao, Liu Chang, Yang Ming, Tang Bi-Qiang, Xu Dan, Lv Kai. Learning-based optimization of active distribution system dispatch in industrial park considering the peak operation demand of power grid. Acta Automatica Sinica, DOI: 10.16383/j.aas.c190079, 2019.
    [16] 陈刚, 李志勇. 集合约束下多智能体系统分布式固定时间优化控制. 自动化学报, DOI: 10.16383/j.aas.c190416, 2019.

    Chen Gang, Li Zhi-Yong. Distributed fixed-time optimization control for multi-agent systems with set constraints. Acta Automatica Sinica, DOI: 10.16383/j.aas.c190416, 2019.
    [17] 殷爽睿, 艾芊, 曾顺奇, 吴琼, 郝然, 江迪. 能源互联网多能分布式优化研究挑战与展望. 电网技术, 2018, 42(5): 1359−1369

    Yin Shuang-Rui, Ai Qian, Zeng Shun-Qi, Wu Qiong, Hao Ran, Jiang Di. Challenges and prospects of multi-energy distributed optimization for Energy Internet. Power System Technology, 2018, 42(5): 1359−1369
    [18] 席磊, 周礼鹏.分布式多区域多能微网群协同AGC 算法. 自动化学报, DOI: 10.16383/j.aas.c200105, 2020.

    Xi Lei, Zhou Li-Peng. Coordinated AGC algorithm for distributed multi-region multi-energy micro-network group. Acta Automatica Sinica, DOI: 10.16383/j.aas.c200105, 2020.
    [19] Yang S P, Tan S C, Xu J X. Consensus based approach for economic dispatch problem in a smart grid. IEEE Transactions on Power Systems, 2013, 28(4): 4416−4426 doi: 10.1109/TPWRS.2013.2271640
    [20] Guo F H, Wen C Y, Mao J F, Song Y D. Distributed economic dispatch for smart grids with random wind power. IEEE Transactions on Smart Grid, 2016, 7(3): 1572−1583 doi: 10.1109/TSG.2015.2434831
    [21] Binetti G, Davoudi A, Lewis F L, Naso D, Turchiano B. Distributed consensus-based economic dispatch with transmission losses. IEEE Transactions on Power System, 2014, 29(4): 1711−1720 doi: 10.1109/TPWRS.2014.2299436
    [22] Wang Z G, Wu W C, Zhang B M. A fully distributed power dispatch method for fast frequency recovery and minimal generation cost in autonomous microgrids. IEEE Transactions on Smart Grid, 2016, 7(1): 19−31 doi: 10.1109/TSG.2015.2493638
    [23] Chen G, Ren J H, Feng E N. Distributed finite-time economic dispatch of a network of energy resources. IEEE Transactions on Smart Grid, 2017, 8(2): 822−832
    [24] Zhang H G, Li Y S, Gao D W Z, Zhou J G. Distributed optimal energy management for energy internet. IEEE Transactions on Industrial Informatics, 2017, 13(6): 3081−3097 doi: 10.1109/TII.2017.2714199
    [25] Kanellos F D, Tsekouras G J, Hatziargyriou N D. Optimal demand-side management and power generation scheduling in an all-electric ship. IEEE Transactions on Sustainable Energy, 2014, 5(4): 1166−1175 doi: 10.1109/TSTE.2014.2336973
    [26] Chen C L P, Liu Z L. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 10−24 doi: 10.1109/TNNLS.2017.2716952
    [27] Yang S F, Liu Q S, Wang J. A multi-agent system with a proportional-integral protocol for distributed constrained optimization. IEEE Transactions on Automatic Control, 2017, 62(7): 3461−3467 doi: 10.1109/TAC.2016.2610945
    [28] Zhang H G, Teng F, Sun Q Y, Shan Q H. Distributed optimization based on a multiagent system disturbed by genera noise. IEEE Transactions on Cybernetics, 2019, 49(8): 3209−3213 doi: 10.1109/TCYB.2018.2839912
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出版历程
  • 收稿日期:  2020-03-31
  • 录用日期:  2020-06-28
  • 网络出版日期:  2020-09-28
  • 刊出日期:  2020-09-28

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