基于半不变量和Gram-Charlier级数展开法的随机潮流算法  

A Probabilistic Power Flow Algorithm Based on Semi-variable and Gram-Charlier Series Expansion

作　　者：卫鹏[1] 刘建坤[1] 周前[1] 徐青山[2] 黄煜[2] 

机构地区：[1]江苏省电力试验研究院有限公司,江苏南京211103 [2]东南大学电气工程学院,江苏南京210096

出　　处：《电力工程技术》2017年第1期34-38,共5页Electric Power Engineering Technology

基　　金：国家自然科学基金51577028;国家电网公司科技项目(新能源发电预测误差对电网安全运行影响评价方法研究)

摘　　要：随着新能源规模的日益扩大,新能源电站的出力往往呈现较强的相关性,在传统的随机潮流算法中对强相关性随机变量考虑较少。综合考虑风电出力的随机波动、负荷的变化、发电机的强迫停运及线路的故障等各种不确定情况,根据节点电压和支路潮流的期望值及灵敏度矩阵,计算了负荷及常规发电机、风电机组出力、各节点注入功率的各阶半不变量,由Gram-Charlier级数展开求得概率密度函数和概率分布函数。IEEE-30节点测试表明:该算法能反映大规模新能源接入下系统的不确定性,将求取随机变量和的概率密度函数的卷积运算简化为半不变量的代数运算,极大地缩短了计算时间,并具有良好的收敛性。With the growing scale of new energy, new energy power contribute often exhibit a strong correlation, traditional random flow algorithm for strong correlation random variables was considered less. A method of probabilistic power flow algorithm based on semi-variable and Gram-Charlier series expansion was proposed in this paper. According node voltage and branch current expectations and sensitivity matrix, with wind power output, load changes, forced outages and generator fault lines and other uncertainties considered, load and conventional generators, wind turbine output and each node injection power of each order half invariant were calculated. Probability density function and probability distribution function were obtained by Gram-Charlier series expansion. IEEE-30 node test shows that the algorithm can reflect the uncertainty of large-scale new energy accessing to the system, and probability density function can be simplified to the semi-invariant algebra. Greatly reduce the computation time and it has a good convergence.

关 键 词：随机模型 半不变量 随机潮流 风电 Gram-Charlier级数 

分 类 号：TM732[电气工程—电力系统及自动化]