平抑风电出力波动的混合储能功率分配策略

doi:10.19799/j.cnki.2095-4239.2023.0912

摘要/Abstract

摘要：

储能技术逐渐成为平抑功率波动、提高电网接纳能力的有效手段，但风电功率波动情况复杂且多变，单一储能并不具备高能量、高功率密度特性。因此为解决风力发电出力平滑问题，针对其随机性间歇性等典型特征，本工作拟采用混合储能技术手段，结合锂离子电池和超级电容两种储能的优势，设计一种基于自适应滑动平均滤波-小波包分解双层功率分解分配策略，利用滑动平均滤波算法完成风电功率的自适应分解，获得并网功率和混合储能所需最低功率。考虑到混合储能输出功率依然含有丰富的信息以及小波包分解层数和功率分界点的确定规则，借助小波包算法对平抑的风电功率进行二次分解，并结合变频熵值策略优化混合储能的功率分配，有效改善风电功率的波动情况，实现混合储能功率的最优分解和合理分配。采用国家标准时间尺度波动情况、引入储能前后波动情况、功率幅值、充放电次数等评价指标对不同功率分配方式进行对比分析，结果表明，所提策略能够满足国家标准对风电场出力波动要求，减小对电网的冲击，与传统小波包分解算法对比具有较好自适应性，整体波动情况提升23.69%，锂电池充放电次数降低58.2%。模型评估结果表明，所提方法能够提升混合储能系统整体经济性并延长电池使用寿命。

关键词: 混合储能, 自适应滑动平均算法, 小波分解, 变频熵值, 功率分配

Abstract:

In recent years, energy storage technology has become an effective means of smoothing wind power fluctuations and improving the acceptance capacity of wind power in the grid; however, wind power fluctuations are complex and variable, and a single energy storage mechanism does not have the required high energy and high power density characteristics. Therefore, to solve the problem of wind power generation power smoothing in terms of its stochastic gap and other typical characteristics, this study intends to use a hybrid energy storage technology, that combines the advantages of lithium-ion batteries and supercapacitors, to design a two-layer power decomposition and allocation strategy based on the adaptive sliding average filtering-wavelet packet decomposition; further, it aims to utilize the sliding average filtering algorithm to complete the adaptive decomposition of wind power, and obtain the minimum power required for grid-connected power and hybrid energy storage systems. The minimum power required for grid-connected power and hybrid energy storage systems is obtained. Considering that the output power of a hybrid energy storage system continues to contain rich information and the rules for determining the number of wavelet packet decomposition layers and the power cut-off point, the wavelet packet algorithm is used to decompose the suppressed wind power twice and optimize the power allocation of the hybrid energy storage system by combining with the inverter entropy value strategy. This improves the fluctuation of the wind power effectively and realizes the optimal decomposition and reasonable allocation of hybrid energy storage power. Comparative analysis of different power allocation methods uses evaluation indexes such as the fluctuation of the national standard time scale, fluctuation before and after the introduction of energy storage, power amplitude, charging and discharging times, etc. The results show that the proposed method can meet the requirements of national standards for wind farm power fluctuation, reduce the impact of fluctuations on the grid, and show better adaptability compared with the traditional wavelet packet decomposition algorithms, with the overall fluctuation situation being improved by 23.69%. The proposed method reduces the lithium battery charging and discharging times by 58.2%. The model evaluation results show that the proposed method can improve the overall economy of the hybrid energy storage system and extend the life of lithium batteries.

Key words: hybrid energy storage, adaptive sliding average algorithm, wavelet decomposition, variable frequency entropy, power allocation

中图分类号:

TM 614

郭东泽, 张继红, 王庆宇, 张帅. 平抑风电出力波动的混合储能功率分配策略[J]. 储能科学与技术, 2024, 13(5): 1564-1573.

Dongze GUO, Jihong ZHANG, Qingyu WANG, Shuai ZHANG. Hybrid energy storage power allocation strategy for smoothing wind power output fluctuations[J]. Energy Storage Science and Technology, 2024, 13(5): 1564-1573.

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风电场装机容量/MW	10 min波动限值/MW	1 min波动限值/MW
<30	10	3
30~150	装机容量1/3	装机容量/10
>150	50	15

经济指标	锂电池	超级电容
功率成本/(万元/MW)	930	186
容量成本/(万元/MWh)	930	1240
功率运维成本/(万元/MW)	6	8
容量运维成本/(万元/MWh)	0.001	0.001
充放电效率/%	95	90
SOC上下限	0.2~0.8	0.2~0.8
运行年限	—	20

场景	L	F_1，MAX/MW	F_10，MAX/MW	F_diff/MW
场景1	L₁-1	1.37	10.11	3.43×10³
	L₁	1.29	9.69	3.50×10³
	L₁+1	1.23	9.27	3.55×10³
场景2	L₂-1	2.34	10.79	1.44×10³
	L₂	2.29	9.66	1.49×10³
	L₂+1	2.05	9.66	1.55×10³

指标	F_1，MAX/MW	F_10，MAX/MW	F_all/MW
P_w1(t)	14.61	21.05	—
P_WPD(4,0)	1.15	9.43	2522.36
P_AMAF	1.28	9.69	3119.82

方法	N_bat/(次/天)	P_bat,max/MW	N_sc/(次/天)	P_sc,max/MW
方法1	256	15.29	888	8.50
方法2	107	8.92	650	11.10