基于高斯求积的智能配电网三相概率潮流点估计法

Three-phase Probabilistic Load Flow for Smart Distribution Network Based on Gauss-quadrature-based Point Estimate Method

随着大规模风电、光伏和电动汽车的接入,智能配电网运行状态的不确定性大幅增加。文章通过分析不确定性变量的概率分布特征,提出一种基于高斯积分的概率潮流点估计法。该方法利用Gauss-Hermite求积与正态变换技术选取输入变量的估计节点和对应权重,通过多项式逼近方法估计多随机变量响应函数的输出变量统计矩。针对配电网参数不对称的特征,文章构建三相不平衡潮流模型,并论述利用基于高斯积分的点估计法计算三相不平衡概率潮流的实现流程。以改进的IEEE33节点配电系统为例进行了仿真验证,结果显示所提方法的估计精度高于传统点估计法,适用于含有非正态分布或相关性不确定变量的配电网。

With the integration of large-scale wind power generation,photovoltaic generation and electric vehicles,the operational state of the smart distribution network becomes gradually uncertain.By analyzing the probability distribution characteristics of the uncertain variables,this paper proposes a probabilistic load flow(PLF)method for the distribution network based on the Gauss-quadrature-based point estimate method(GPEM).The Gauss-Hermite quadrature and the normal transformation are used to select the estimated nodes and the corresponding weights of the input variables.In the multiple random variable response function,the statistical moments of the output variables are estimated through polynomial approximation.Taking the unbalanced parameters of the distribution network into consideration,a three-phase power flow model is constructed.Then the whole process of the three-phase probabilistic load flow based on GPEM is directly given.Simulation results of a modified IEEE-33 bus system show that this method has a higher precision compared with the traditional point estimate method,and that it can solve the non-normal distribution and correlation of new sources and loads.