Efficient Probabilistic Load Flow Calculation Considering Vine Copula⁃Based Dependence Structure of Renewable Energy Generation  

作　　者：MA Hongyan WANG Han XU Xiaoyuan YAN Zheng MAO Guijiang 马洪艳;王晗;徐潇源;严正;毛贵江(College of Information Science and Technology,Donghua University,Shanghai 200051,China;Key Laboratory of Control of Power Transmission and Conversion(Shanghai Jiao Tong University),Ministry of Education,Shanghai 200240,China;State Grid Quzhou Power Supply Company,Quzhou 324000,China)

机构地区：[1]College of Information Science and Technology,Donghua University,Shanghai 200051,China [2]Key Laboratory of Control of Power Transmission and Conversion(Shanghai Jiao Tong University),Ministry of Education,Shanghai 200240,China [3]State Grid Quzhou Power Supply Company,Quzhou 324000,China

出　　处：《Journal of Donghua University(English Edition)》2021年第5期465-470,共6页东华大学学报（英文版）

基　　金：Fundamental Research Funds for the Central Universities,China(No.2232020D⁃53)。

摘　　要：Correlations among random variables make significant impacts on probabilistic load flow(PLF)calculation results.In the existing studies,correlation coefficients or Gaussian copula are usually used to model the correlations,while vine copula,which describes the complex dependence structure(DS)of random variables,is seldom discussed since it brings in much heavier computational burdens.To overcome this problem,this paper proposes an efficient PLF method considering input random variables with complex DS.Specifically,the Rosenblatt transformation(RT)is used to transform vine copula⁃based correlated variables into independent ones;and then the sparse polynomial chaos expansion(SPCE)evaluates output random variables of PLF calculation.The effectiveness of the proposed method is verified using the IEEE 123⁃bus system.

关 键 词：probabilistic load flow(PLF) vine copula sparse polynomial chaos expansion(SPCE) Rosenblatt transformation(RT) 

分 类 号：TM711[电气工程—电力系统及自动化]