基于NGO-VMD的混合储能功率分配策略

doi:10.11930/j.issn.1004-9649.202308010

摘要/Abstract

摘要：

为解决风电场并网时的功率波动影响电网稳定性的问题，提出一种基于北方苍鹰（northern goshawk optimization，NGO）算法优化变分模态分解（variational mode decomposition，VMD）参数的混合储能功率分配策略。首先，按照风电场并网技术规范，采用自适应平均滤波法对风力发电功率进行滤波，并由滤波后的并网功率计算出波动功率。然后，采用NGO优化VMD算法中分解模态数K值和二次惩罚因子α值的最优值组合，将波动功率信号经VMD分解后实现在锂电池和超级电容器的功率分配，最后，采用双重模糊控制对混合储能系统（hybrid energy storage system，HESS）的荷电状态（state of charge，SOC）进行优化，完成HESS功率的二次分配。仿真结果表明，该控制策略不仅能够满足风电并网最大功率波动要求，还可以保持SOC维持在合理范围，实现HESS长期安全运行。

关键词: 风电并网, 北方苍鹰算法, 变分模态分解, 混合储能, 模糊控制

Abstract:

Power fluctuations during wind power grid connection affect the stability of the power grid. To address this issue, a hybrid energy storage power allocation strategy based on the northern goshawk optimization (NGO) algorithm was proposed to optimize the parameters of variational mode decomposition (VMD). Firstly, the generation power of wind power was filtered in accordance with the regulations of wind power grid connection technology using the adaptive averaging filtering method, and the fluctuation power was calculated from the filtered power. Then, the optimal combination of K value (number of decomposition modes) and α value (quadratic penalty factor) in the NGO-VMD algorithm was used to realize power allocation between lithium batteries and supercapacitors after the fluctuation power signal was decomposed by VMD. Finally, the state of charge (SOC) of the hybrid energy storage system (HESS) was optimized using dual fuzzy control to achieve the secondary power allocation of the HESS. Simulation outcomes demonstrate that employing this control strategy achieves not only compliance with the maximum power fluctuation requirements of wind power grid connection but also the maintenance of SOC within a reasonable range, ensuring the long-term secure operation of HESS.

Key words: wind power grid connection, northern goshawk optimization, variational mode decomposition, hybrid energy storage, fuzzy control

王海燕, 钱林宇. 基于NGO-VMD的混合储能功率分配策略[J]. 中国电力, 2024, 57(11): 119-128.

Haiyan WANG, Linyu QIAN. Hybrid Energy Storage Power Allocation Strategy Based on NGO-VMD[J]. Electric Power, 2024, 57(11): 119-128.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202308010

https://www.electricpower.com.cn/CN/Y2024/V57/I11/119

图/表 13

图 1 NGO优化VMD流程

Fig.1 Optimization flow of VMD by NGO

图 2 风储发电系统

Fig.2 Wind storage power generation system

表 1 风电场有功功率变化最大限值

Table 1 Maximum value of active power variation of wind farm

风电场装机容量/ MW	10 min功率变化最大限值/MW	1 min功率变化最大限值/MW
＜30	10	3
30~150	装机容量/3	装机容量/10
＞150	50	15

图 3 自适应平均滤波流程

Fig.3 Flow chart of adaptive average filtering

图 4 混合储能控制策略

Fig.4 Hybrid energy storage control strategy

表 2 超级电容器模糊控制规则

Table 2 Fuzzy control rules for supercapacitors

$ S_{ \rm OCsc}(t-1) $	$ \Delta S_{ \rm OCsc}(t) $
$ S_{ \rm OCsc}(t-1) $	NB	NM	NS	PS	PM	PB
NB	NB	NM	NS	PB	PB	PM
NS	NM	NS	NS	PB	PM	PS
ZO	NS	PS	PM	PM	PS	NS
PS	PS	PM	PB	NS	NS	NM
PB	PM	PB	PS	NS	NM	NB

图 5 模糊控制器1隶属度函数

Fig.5 Membership function of fuzzy controller 1

图 6 平滑前后风电功率对比

Fig.6 Comparison of wind power before and after smoothing

图 7 NGO和遗传算法迭代计算对比

Fig.7 Comparison of NGO and GA iterative calculations

图 8 EMD和CEEMD分解的边际谱图

Fig.8 Spectral analysis of EMD and CEEMD

图 9 储能元件控制对比

Fig.9 Comparison of energy storage element control

表 3 模糊控制后SOC

Table 3 SOC after fuzzy control

储能类型	无二次功率分配	低通滤波法	二次模糊控制
锂电池	0.31~0.96	0.37~0.91	0.38~0.87
超级电容器	0.11~0.89	0.21~0.80	0.24~0.75

图 10 模糊控制后并网功率1 min和10 min有功功率变化值

Fig.10 Changes in active power after 1 minute and 10 minutes of grid connection with fuzzy control

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