New Type Power System and the Integrated Energy

Power Allocation Strategy for Wind Power Hybrid Storage Systems Based on Variational Modal Decomposition-Multifuzzy Control

  • LI Jianlin ,
  • SUN Haoyuan ,
  • ZHAO Wending ,
  • LIANG Ce ,
  • LIANG Zhonghao ,
  • YUAN Xiaodong
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  • 1 National User-Side Energy Storage Innovation Research and Development Center, North ChinaUniversity of Technology, Beijing 100144, China
    2 Electric Power Research Institute ofState Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China

Received date: 2023-11-10

  Revised date: 2023-12-10

  Accepted date: 2023-12-26

  Online published: 2024-02-27

Abstract

A power allocation strategy based on variational modal decomposition-multifuzzy control for wind power hybrid storage system is proposed to address the poor grid-connected power quality caused by the uncertainty of wind power output and the poor power allocation of hybrid storage systems. First, Latin hypercubic sampling and Euclidean distance method are used to generate a typical scenario of wind power considering the uncertainty of wind power output. Then, the variational modal decomposition optimized by the positive cosine algorithm is used for the initial allocation of wind power to obtain the grid-connected power of wind power which is lower than the upper limit of the grid-connected fluctuation and the levelling power of the hybrid energy storage system. Finally, the load state of the hybrid energy storage system is partitioned, and a power redistribution strategy under multi-fuzzy control is proposed to achieve the correction of the power of the hybrid energy storage system by considering the system load state and the characteristics of the hybrid energy storage. Simulation results show that the proposed strategy can suppress wind power fluctuations and obtain stable grid-connected power. Additionly, it can effectively solve the overcharging and over-discharging problems of the hybrid energy storage system.

Cite this article

LI Jianlin , SUN Haoyuan , ZHAO Wending , LIANG Ce , LIANG Zhonghao , YUAN Xiaodong . Power Allocation Strategy for Wind Power Hybrid Storage Systems Based on Variational Modal Decomposition-Multifuzzy Control[J]. Journal of Shanghai Jiaotong University, 2025 , 59(10) : 1498 -1509 . DOI: 10.16183/j.cnki.jsjtu.2023.572

References

[1] 李建林, 张则栋, 梁策, 等. 计及源-荷不确定性的综合能源系统多目标鲁棒优化调度[J/OL]. 上海交通大学学报. https://doi.org/10.16183/j.cnki.jsjtu.2023.238.
  LI Jianlin, ZHANG Zedong, LIANG Ce, et al. Multi-objective robust optimal scheduling of integrated energy systems taking into account source-load uncertainty[J/OL]. Journal of Shanghai Jiao Tong University. https://doi.org/10.16183/j.cnki.jsjtu.2023.238.
[2] 袁小明, 程时杰, 文劲宇. 储能技术在解决大规模风电并网问题中的应用前景分析[J]. 电力系统自动化, 2013, 37(1): 14-18.
  YUAN Xiaoming, CHENG Shijie, WEN Jinyu. Prospects analysis of energy storage application in grid integration of large-scale wind power[J]. Automation of Electric Power Systems, 2013, 37(1): 14-18.
[3] 林森, 文书礼, 朱淼, 等. 考虑碳交易机制的海港综合能源系统电-热混合储能优化配置[J]. 上海交通大学学报, 2024, 58(9): 1344-1356.
  LIN Sen, WEN Wenli, ZHU Miao, et al. Optimal allocation of hybrid electric-thermal energy storage for harbour integrated energy system considering carbon trading mechanism[J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1344-1356.
[4] 赵永熹, 高鹏超, 范宏. 基于虚拟阻抗-模糊算法的交直流微电网混合储能功率协调策略[J/OL]. 上海交通大学学报. https://doi.org/10.16183/j.cnki.jsjtu.2023.308.
  ZHAO Yongxi, GAO Pengchao, FAN Hong. Hybrid energy storage power coordination strategy for AC/DC microgrid based on virtual impedance-fuzzy algorithm[J/OL]. Journal of Shanghai Jiao Tong University. https://doi.org/10.16183/j.cnki.jsjtu.2023.308.
[5] 李建林, 李雅欣, 刘海涛, 等. 计及储能电站安全性的功率分配策略研究[J]. 电工技术学报, 2022, 37(23): 5976-5986.
  LI Jianlin, LI Yaxin, LIU Haitao, et al. Research on power distribution strategy considering the safety of energy storage power station[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5976-5986.
[6] 乔亮波, 张晓虎, 孙现众, 等. 电池-超级电容器混合储能系统研究进展[J]. 储能科学与技术, 2022, 11(1): 98-106.
  QIAO Liangbo, ZHANG Xiaohu, SUN Xianzhong, et al. Advances in battery-supercapacitor hybrid energy storage system[J]. Energy Storage Science & Technology, 2022, 11(1): 98-106.
[7] 蒋新科, 刘春, 张雪松, 等. 基于双储能的风电功率波动平抑策略研究[J/OL]. 电测与仪表. https://kns.cnki.net/kcms/detail/23.1202.TH.20230322.1512.006.html.
  JIANG Xinke, LIU Chun, ZHANG Xuesong, et al. Research on wind power fluctuation smoothing strategy based on dual energy storage[J/OL]. Electrical Measurement & Instrumentation. https://kns.cnki.net/kcms/detail/23.1202.TH.20230322.1512.006.html.
[8] 韩晓娟, 田春光, 程成, 等. 基于经验模态分解的混合储能系统功率分配方法[J]. 太阳能学报, 2014, 35(10): 1889-1896.
  HAN Xiaojuan, TIAN Chunguang, CHENG Cheng, et al. Power allocation method of hybrid energy storage system based on empirical mode decomposition[J]. Acta Energiae Solaris Sinica, 2014, 35(10): 1889-1896.
[9] 付菊霞, 陈洁, 邓浩, 等. 平抑风电波动的混合储能系统控制策略[J]. 电测与仪表, 2020, 57(5): 94-100.
  FU Juxia, CHEN Jie, DENG Hao, et al. Control strategy of hybrid energy storage system for mitigating wind power fluctuations[J]. Electrical Measurement & Instrumentation, 2020, 57(5): 94-100.
[10] 杨锡运, 曹超, 李相俊, 等. 基于模糊经验模态分解的电池储能系统平滑风电出力控制策略[J]. 电力建设, 2016, 37(8): 134-140.
  YANG Xiyun, CAO Chao, LI Xiangjun, et al. Control strategy of smoothing wind power output using battery energy storage system based on fuzzy empirical mode decomposition[J]. Electric Power Construction, 2016, 37(8): 134-140.
[11] 郭志彬. 基于模糊控制的光储联合系统控制策略研究[D]. 沈阳: 沈阳农业大学, 2023.
  GUO Zhibin. Research on control strategy of optical storage joint system based on fuzzy control[D]. Shenyang: Shenyang Agricultural University, 2023.
[12] 陈洪磊, 孙泽贤. 模糊控制下混合储能平抑风电波动控制策略[J]. 低温与超导, 2023, 51(7): 82-89.
  CHEN Honglei, SUN Zexian. Hybrid energy-storage control strategy based on fuzzy control to stabilize wind power fluctuation[J]. Cryogenics & Superconductivity, 2023, 51(7): 82-89.
[13] 程龙, 张方华. 用于混合储能系统平抑功率波动的小波变换方法[J]. 电力自动化设备, 2021, 41(3): 100-104.
  CHENG Long, ZHANG Fanghua. Wavelet transform method for hybrid energy storage system smoothing power fluctuation[J]. Electric Power Automation Equipment, 2021, 41(3): 100-104.
[14] 陈景文, 周婧, 张文倩. 基于小波包-模糊算法的混合储能功率分配策略[J]. 智慧电力, 2023, 51(1): 61-68.
  CHEN Jingwen, ZHOU Jing, ZHANG Wenqian. Hybrid energy storage power allocation strategy based on wavelet packet-fuzzy algorithm[J]. Smart Power, 2023, 51(1): 61-68.
[15] 毛志宇, 李培强, 郭思源. 基于自适应时间尺度小波包和模糊控制的复合储能控制策略[J]. 电力系统自动化, 2023, 47(9): 158-165.
  MAO Zhiyu, LI Peiqiang, GUO Siyuan. Control strategy of composite energy storage based on wavelet packet with adaptive time scale and fuzzy control[J]. Automation of Electric Power Systems, 2023, 47(9): 158-165.
[16] LI Y M, DING Z M, YU Y H, et al. Hybrid energy storage power allocation strategy based on parameter-optimized VMD algorithm for marine micro gas turbine power system[J]. Journal of Energy Storage, 2023, 73: 109189.
[17] 李亚楠, 王倩, 宋文峰, 等. 混合储能系统平滑风电出力的变分模态分解-模糊控制策略[J]. 电力系统保护与控制, 2019, 47(7): 58-65.
  LI Yanan, WANG Qian, SONG Wenfeng, et al. Variational mode decomposition and fuzzy control strategy of hybrid energy storage for smoothing wind power outputs[J]. Power System Protection & Control, 2019, 47(7): 58-65.
[18] 车兵, 李轩, 郑建勇, 等. 基于LHS与BR的风电出力场景分析研究[J]. 电力工程技术, 2020, 39(6): 213-219.
  CHE Bing, LI Xuan, ZHENG Jianyong, et al. Scenario analysis of wind power output based on LHS and BR[J]. Electric Power Engineering Technology, 2020, 39(6): 213-219.
[19] 鲍俊文, 胡欣宇, 邢明源, 等. 可靠度分析中拉丁超立方和传统舍选抽样法对比研究[J]. 华北科技学院学报, 2021, 18(3): 81-84.
  BAO Junwen, HU Xinyu, XING Mingyuan, et al. Comparing LHS with traditional rejection sampling methods in the analysis of reliability[J]. Journal of North China Institute of Science & Technology, 2021, 18(3): 81-84.
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