Allocation of Hybrid Energy Storage Capacity Based on Pearson Correlation Analysis and T-tFD Algorithm under VMD Decomposition

doi:10.11930/j.issn.1004-9649.202403025

Abstract

Abstract:

In the context of rapid development of clean energy, the stochasticity and volatility of wind power output have significant impacts on the stability of power system, so wind power fluctuation smoothing is a basic problem for the current clean energy development. A hybrid energy storage capacity allocation strategy based on SCNGO-VMD is proposed to smooth wind power fluctuations. After variational mode decomposition (VMD) of the wind power parameter optimization, the Pearson correlation analysis is used to judge the boundary points of strong and weak correlation, and the grid-connected power and hybrid energy storage power are obtained after two allocations; The hybrid energy storage power is allocated based on T-test frequency division (T-tFD) algorithm, and the capacity configuration of battery/ultra-capacitor is obtained. Based on this strategy, the annual comprehensive cost of energy storage components is used as the model to evaluate the economics through case study. And the fluctuation of grid-connected power and the superiority of the SCNGO are analyzed. The results show that the energy storage capacity allocation strategy based on SCNGO-VMD can effectively smooth wind power fluctuations. The maximum fluctuation of the smoothed grid-connected power for 1 minute and 10 minutes is only 18.2% and 45.52% of the national requirements, and the corresponding energy storage configuration cost is the lowest among traditional configuration strategies. The configured hybrid energy storage capacity is more economical. Meanwhile, it is verified that the SCNGO is superior to the traditional intelligent optimization algorithm in iteration speed and accuracy.

Key words: hybrid energy storage, capacity allocation, variational mode decomposition, northern goshawk optimization, Pearson correlation analysis, t-test

Shurui LIU, Peiqiang LI, Jiayu CHEN, Yashi GUO. Allocation of Hybrid Energy Storage Capacity Based on Pearson Correlation Analysis and T-tFD Algorithm under VMD Decomposition[J]. Electric Power, 2024, 57(7): 82-97.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202403025

https://www.electricpower.com.cn/EN/Y2024/V57/I7/82

Figures/Tables 25

Fig.1 Topology of wind power-storage joint system

Table 1 Wind power fluctuation requirements by national standard

装机容量/MW	1 min最大波动量/MW	10 min最大波动量/MW
＜30	3	10
30~50	装机容量/10	装机容量/3
＞50	15	50

Fig.2 Power allocation method used in this article

Fig.3 Step search factor and weight value curve

Fig.4 SCNGO-VMD algorithm flowchart

Fig.5 Power allocation algorithm flowchart

Fig.6 Wind power output

Table 2 Analysis of 1 min and 10 min fluctuations

数据	10 min最大波动/MW	1 min最大波动/MW
国家标准并网要求	17.1	5.00
本文P_wind	18.1	9.46

Fig.7 The distribution of optimal values in spatial fitting and scatter plots

Fig.8 Results of the first correlation analysis

Fig.9 Comparison between wind power output and some IMF components

Fig.10 Results of the second correlation analysis

Fig.11 Characteristics of strongly correlated component curves

Table 3 T-test results

IMF分量		1		2		3		4		5		6		7		8		9
t检验结果		1		1		0		0		0		0		0		0		0

Fig.12 IMF(1) and IMF(9) envelope lines

Fig.13 Battery/supercapacitor power distribution

Fig.14 Comparison of 1-minute volatility

Fig.15 Comparison of 10-minute volatility

Table 4 Energy storage related parameters

储能类型	参数	数值
蓄电池（BAT）	单位功率成本/(元·kW^–1)	3400
	单位容量成本/(元·(kW·h)^–1)	2200
	年运行成本/(元·(kW·h)^–1)	370
	年维护成本/(元·(kW·h)^–1)	90
	年报废置换成本/(元·(kW·h)^–1)	120
	充放电效率/%	85
	SOC上下限	(0.8, 0.2)
	蓄电池寿命/年	10
超级电容器（SC）	单位功率成本/(元·kW^–1)	1100
	单位容量成本/(元·(kW·h)^–1)	12000
	年运行成本/(元·(kW·h)^–1)	200
	年维护成本/(元·(kW·h)^–1)	0
	年报废置换成本/(元·(kW·h)^–1)	30
	充放电效率/%	95
	SOC上下限	(0.9, 0.1)
	超级电容寿命/年	20
其他	风电工程寿命/年	20
其他	贴现率/%	0.06

Fig.16 Power/capacity curves at different boundary points

Fig.17 Cost curves at different boundary points of EMD and VMD

Fig.18 Spectra of each IMF after VMD decomposition

Fig.19 Spectra of each IMF after EMD decomposition

Table 5 Results of different allocation strategies

策略	P_bN/MW	E_bN/(MW·h)	P_sN/MW	E_sN/(MW·h)	C_HESS/万元
蓄电池	7.595	0.207	—	—	4398.212
超级电容	—	—	7.195	0.153	4458.735
EMD+ T-tFD	5.878	0.205	6.198	0.097	4178.735
VMD+ T-tFD	5.807	0.201	5.797	0.059	2198.760

Fig.20 Comparison of iteration times

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