基于VMD分解下的皮尔逊相关性分析及T-tFD的混合储能容量配置

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

在清洁能源发展迅速的大环境下,风电出力的随机性和波动性会对电力系统的稳定造成影响,因此对风电波动平抑是当前清洁能源发展的一个基础性问题。提出一种基于改进后的北方苍鹰算法(sinecosine northern goshawk optimization,SCNGO)优化变分模态分解(VMD)参数平抑风电波动的混合储能容量配置策略,对风电功率进行参数优化的VMD过后利用皮尔逊相关性分析判断强弱相关分界点,经过2次分配后得到并网功率与混合储能功率;对混合储能功率进行基于t检验分频算法的功率分配,得到蓄电池/超级电容的容量配置。基于此策略,以储能元件年综合成本作为模型,结合算例进行经济性评估并对并网功率进行波动量分析及改进北方苍鹰算法的优越性分析。结果表明:基于SCNGO-VMD的储能容量配置策略能有效平抑风电波动,平抑后的并网功率1 min、10 min的最大波动量仅为国家要求的18.2%、45.52%,相应的储能配置成本为传统配置策略中的最低值。其配置的混合储能容量更具经济性,验证了改进的北方苍鹰算法在迭代速度与精度上均优于传统的智能优化算法。

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.