Optimization Configuration of Hybrid Energy Storage Capacity for Wind Power Fluctuation Smoothing Based on Variational Mode Decomposition

doi:10.12096/j.2096-4528.pgt.24077

Abstract

Abstract:

Objectives To address the issues of variability and intermittency of wind power generation, a configuration scheme of hybrid energy storage system for wind power smoothing is proposed. The system integrates lithium-ion batteries and supercapacitors into the proposed wind power system to reduce its instability. Methods Firstly, filtering techniques of exponential smoothing and recursive weighted average are used to dynamically adjust their weights, determining both the grid-connected wind power and the reference power for the energy storage system. Then, particle swarm optimization algorithm is applied to adjust the parameters of variational mode decomposition, decomposing the reference power of the energy storage system into high-frequency and low-frequency components, which are handled by supercapacitors and lithium-ion batteries, respectively. Finally, a lifecycle optimization configuration model for the hybrid energy storage system is developed to minimize the configuration costs of storage facilities. Results The proposed solution can make the output power better meet the grid-connected standard, reduce the grid-connected power output time, and realize the functional complementarity between lithium-ion batteries and supercapacitors. Conclusions The proposed solution effectively addresses the variability and intermittency of wind power generation, achieving optimal energy storage configuration and striking a balance between economic efficiency and stabilize fluctuation performance.

Key words: wind power fluctuation smoothing, wind power system, weighted algorithm, hybrid energy storage, variational mode decomposition, particle swarm, capacity configuration, cost effectiveness

CLC Number:

TK 83

Ping ZHANG, Yongqiang LI, Hualiang XING. Optimization Configuration of Hybrid Energy Storage Capacity for Wind Power Fluctuation Smoothing Based on Variational Mode Decomposition[J]. Power Generation Technology, 2025, 46(6): 1144-1153.

Figures/Tables 11

References 27

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风电场装机容量	1 min最大波动量	10 min最大波动量
<30	3	10
30~150	3~15	10~50
>150	15	50

风电场装机容量	1 min最大波动量	10 min最大波动量
<30	3	10
30~150	3~15	10~50
>150	15	50

参数	原始数据	递推平均滤波	加权滤波
最大波动量(1 min)/MW	10.710 9	4.620 7	4.478 2
最大波动率(1 min)/%	21.42	9.24	8.96
最大波动量(10 min)/MW	14.876 4	12.800 4	12.843 2
最大波动率(10 min)/%	29.75	25.60	25.70

参数	原始数据	递推平均滤波	加权滤波
最大波动量(1 min)/MW	10.710 9	4.620 7	4.478 2
最大波动率(1 min)/%	21.42	9.24	8.96
最大波动量(10 min)/MW	14.876 4	12.800 4	12.843 2
最大波动率(10 min)/%	29.75	25.60	25.70

参数	锂离子电池	超级电容
单位功率投资成本/(元/kW)	9 200	1 750
单位容量投资成本/[元/(kW⋅h)]	9 300	12 300
单位功率置换成本/(元/kW)	2 460	1 850
单位容量置换成本/[元/(kW⋅h)]	9 300	12 300
单位功率运维成本/(元/kW)	160	81
单位容量运维成本/[元/(kW⋅h)]	0.015	0.013 5
单位功率辅助成本/(元/kW)	610	610
单位容量辅助成本/[元/(kW⋅h)]	0	0
处理成本/(元/kW)	460	94
充放电效率/%	85	95
SOC范围	0.15~0.85	0.10~0.95