Probabilistic Load Flow Method Based on Spline Reconstruction and Quasi Monte Carlo Simulation

FANG Sidun; CHENG Haozhong; SONG Yue; ZENG Pingliang; YAO Liangzhong

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Probabilistic Load Flow Method Based on Spline Reconstruction and Quasi Monte Carlo Simulation

更新时间：2025-12-08

- Probabilistic Load Flow Method Based on Spline Reconstruction and Quasi Monte Carlo Simulation
- Vol. 41, Issue 10, Pages: 3447-3453(2015)
- 作者机构：
  
  1. 上海交通大学电力传输与功率变换控制教育部重点实验室
  2. 香港大学电机电子工程系
  3. 中国电力科学研究院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：30 October 2015，
  
  Published：2015
- 稿件说明：
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FANG Sidun, CHENG Haozhong, SONG Yue, et al. Probabilistic Load Flow Method Based on Spline Reconstruction and Quasi Monte Carlo Simulation[J]. 2015, 41(10): 3447-3453.
DOI：

FANG Sidun, CHENG Haozhong, SONG Yue, et al. Probabilistic Load Flow Method Based on Spline Reconstruction and Quasi Monte Carlo Simulation[J]. 2015, 41(10): 3447-3453. DOI：

摘要

为准确分析大规模风电并网电力系统运行风险

在随机潮流（PLF）分析中需对输入随机变量的概率分布函数（PDFs）进行准确建模。因此

提出了一种基于样条重构和准蒙特卡洛（Monte Carlo）方法的PLF计算方法。该方法可直接根据变量矩信息重构概率分布函数

使用基于样条重构的Nataf变换获得相关的输入变量样本

并采用准蒙特卡洛方法获得系统输出变量的概率特征。对IEEE 30节点系统和某大区域电网进行仿真试验验证了该方法的有效性。结果表明:所提方法可准确重构变量分布

且具有计算速度快、可灵活处理输入变量间相关性的优点。

Abstract

To accurately analyze operational risk of large-scale wind power integration system

we need to build a accurate model of probability distribution functions（PDFs） of input variables in probabilistic load flow（PLF） analysis. Therefore

we proposed a PLF method based on spline reconstruction and quasi Monte Carlo simulation. This method can establish PDF of input variables according to variable torque information

and obtain the relevant input variables sample using Nataf conversion which is based on spline reconstitution

then quasi Monte Carlo simulation based on Sobol sequence is adopted to obtain the probability distribution of the output variables. Simulation on IEEE 30 bus system and a real power system demonstrate the validity of the proposed method. The results suggest that the proposed method not only has the advantages of modelling input variables accurately and fast computation speed

but also can deal with correlation with convenience.

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