基于变分模态分解-多模糊控制的风电混储系统功率分配策略

doi:10.16183/j.cnki.jsjtu.2023.572

上海交通大学学报 ›› 2025, Vol. 59 ›› Issue (10): 1498-1509.doi: 10.16183/j.cnki.jsjtu.2023.572

• 新型电力系统与综合能源 • 上一篇下一篇

基于变分模态分解-多模糊控制的风电混储系统功率分配策略

李建林¹, 孙浩元¹(), 赵文鼎¹, 梁策¹, 梁忠豪¹, 袁晓冬²

¹ 北方工业大学国家能源用户侧储能创新研发中心, 北京 100144
² 国网江苏省电力有限公司电力科学研究院, 南京 211103

收稿日期:2023-11-10 修回日期:2023-12-10 接受日期:2023-12-26 出版日期:2025-10-28 发布日期:2025-10-24
通讯作者: 孙浩元,硕士生;E-mai: 1366604911@qq.com.
作者简介:李建林( 1976—),教授,博士生导师,从事大规模储能技术研究.
基金资助:
国家电网公司指南项目(5400-202318247A-1-1-ZN)

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

LI Jianlin¹, SUN Haoyuan¹(), ZHAO Wending¹, LIANG Ce¹, LIANG Zhonghao¹, YUAN Xiaodong²

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:2023-11-10 Revised:2023-12-10 Accepted:2023-12-26 Online:2025-10-28 Published:2025-10-24

摘要/Abstract

摘要：

针对风电出力的不确定性导致并网电能质量下降以及混合储能系统功率分配不佳的问题,提出一种基于变分模态分解与多模糊控制的风电混储系统功率分配策略.首先,考虑风电出力的不确定性,采用拉丁超立方抽样和欧氏距离法生成风电典型场景;其次,利用正余弦算法优化变分模态分解,对风电功率进行初次分配,得到低于并网波动上限的风电并网功率和混合储能系统平抑功率;最后,对混合储能系统的荷电状态进行分区划分,结合系统荷电状态与混合储能特性,提出一种多模糊控制下的功率再分配策略,以实现对混合储能系统功率的修正.仿真结果表明:所提策略不仅能够有效平抑风电波动、获得稳定的并网功率,还可缓解混合储能系统的过充和过放问题.

关键词: 功率分配, 混合储能系统, 拉丁超立方抽样, 变分模态分解, 多模糊控制策略

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.

Key words: power allocation, hybrid energy storage systems, Latin hypercubic sampling, variational modal decomposition, multi-fuzzy control strategies

中图分类号:

TM614

李建林, 孙浩元, 赵文鼎, 梁策, 梁忠豪, 袁晓冬. 基于变分模态分解-多模糊控制的风电混储系统功率分配策略[J]. 上海交通大学学报, 2025, 59(10): 1498-1509.

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 Jiao Tong University, 2025, 59(10): 1498-1509.

图/表 14

图1

图2

图3

表1

图4

图5

图6

图7

表2

图8

表3

图9

图10

图11

参考文献 19

[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. doi: 10.16183/j.cnki.jsjtu.2022.428
	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. doi: 10.19799/j.cnki.2095-4239.2021.0229
	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. doi: 10.3969/j.issn.1000－7229.2016.08.021
	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. doi: 10.3969/j.issn.1000－7229.2016.08.021
[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.

k₁,k₂		ΔS_SOC
k₁,k₂		NB	NM	NS	PS	PM	PB
S_SOC	VS	VS	VS	VS	VB	VB	VB
	S	S	MS	MB	VB	VB	VB
	MS	MS	MB	B	VB	VB	MS
	M	M	B	VB	VB	MB	M
	MB	MB	B	VB	VB	MB	M
	B	MB	VB	VB	B	MB	S
	VB	VB	VB	VB	VS	VS	VS

典型场景	总天数	概率
1	45	0.120
2	51	0.140
3	36	0.100
4	36	0.100
5	5	0.012
6	55	0.152
7	26	0.070
8	31	0.086
9	33	0.090
10	47	0.130

参数名称	最大充/放电功率限值/kW	额定容量/ (kW·h)	充放电效率/%	初始荷电状态/%	荷电状态下限/%	荷电状态上限/%
超级电容	158	35	90	50	10	90
蓄电池	206	105	80	50	20	80

基于变分模态分解-多模糊控制的风电混储系统功率分配策略

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

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 19

相关文章 9

编辑推荐

Metrics

本文评价

[1]	杜云海, 李恩玉, 马隆. 非理想串行干扰消除下的非正交多址接入系统性能可控的中继选择技术[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(6): 1181-1190.
[2]	邱革非, 冯泽华, 沈赋, 何超, 何虹辉, 刘铠铭. 考虑车网互动的园区电网动态双层能量管理策略[J]. 上海交通大学学报, 2024, 58(6): 916-925.
[3]	张诚, 琚长江, 熊灿, 杨根科. 基于双级主动式拓扑的锂电池寿命优化[J]. 上海交通大学学报, 2024, 58(5): 719-729.
[4]	翟玮昊, 龚敏浩, 林名润, 匡婷玉, 文珊珊. 基于主动学习的Kriging模型的可靠性分析[J]. 空天防御, 2023, 6(1): 1-5.
[5]	江春冬1,2，王景玉1，杜太行1,2，郝静1,3，龙超1. 基于变分模态分解算法的单通道无线电混合信号分离[J]. 上海交通大学学报, 2018, 52(12): 1618-1626.
[6]	徐峰1，范春菊1，徐勋建2，李丽2，倪佳筠3. 基于变分模态分解和AMPSO-SVM耦合模型的滑坡位移预测[J]. 上海交通大学学报, 2018, 52(10): 1388-1395.
[7]	吴志伟, 张建龙, 吴红杰, 殷承良. 低速电动汽车混合能源存储系统效率分析 [J]. 上海交通大学学报（自然版）, 2012, 46(08): 1304-1309.
[8]	刘忠军1, 2, 张冬梅1, 张祯松1, 徐友云1. 协同认知网络中功率分配与协同节点选择[J]. 上海交通大学学报（自然版）, 2012, 46(06): 882-886.
[9]	刘市1, 丁铭1, 2, 刘伟1, 罗汉文1. 多入多出中继系统的一种贪心天线选择算法[J]. 上海交通大学学报（自然版）, 2011, 45(07): 1054-1057.