Probabilistic Load Flow Based on Holomorphic Embedding, Kernel Density Estimator and Saddle Point Approximation Including Correlated Uncertainty Variables

2020 ◽  
Vol 183 ◽  
pp. 106178
Author(s):  
Ali Reza Abbasi
Keyword(s):  
Saddle Point ◽  
Kernel Density ◽  
Load Flow ◽  
Kernel Density Estimator ◽  
Density Estimator ◽  
Saddle Point Approximation ◽  
Probabilistic Load Flow ◽  
Point Approximation ◽  
Holomorphic Embedding ◽  
Probabilistic Load

Nonparametric Probabilistic Load Flow With Saddle Point Approximation

2018 ◽  
Vol 9 (5) ◽  
pp. 4796-4804 ◽  
Author(s):  
Mohammad Mohammadi ◽  
Hooman Basirat ◽  
Amin Kargarian
Keyword(s):  
Saddle Point ◽  
Load Flow ◽  
Saddle Point Approximation ◽  
Probabilistic Load Flow ◽  
Point Approximation ◽  
Probabilistic Load

Probabilistic load flow computation using saddle-point approximation

2017 ◽  
Vol 36 (1) ◽  
pp. 48-61 ◽  
Author(s):  
Meghdad Tourandaz Kenari ◽  
Mohammad Sadegh Sepasian ◽  
Mehrdad Setayesh Nazar
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Distribution Networks ◽  
Load Flow ◽  
Cumulative Distribution ◽  
Wind Generation ◽  
Saddle Point Approximation ◽  
Content Type ◽  
Probabilistic Load Flow ◽  
Probabilistic Load

Purpose The purpose of this paper is to present a new cumulant-based method, based on the properties of saddle-point approximation (SPA), to solve the probabilistic load flow (PLF) problem for distribution networks with wind generation. Design/methodology/approach This technique combines cumulant properties with the SPA to improve the analytical approach of PLF calculation. The proposed approach takes into account the load demand and wind generation uncertainties in distribution networks, where a suitable probabilistic model of wind turbine (WT) is used. Findings The proposed procedure is applied to IEEE 33-bus distribution test system, and the results are discussed. The output variables, with and without WT connection, are presented for normal and gamma random variables (RVs). The case studies demonstrate that the proposed method gives accurate results with relatively low computational burden even for non-Gaussian probability density functions. Originality/value The main contribution of this paper is the use of SPA for the reconstruction of probability density function or cumulative distribution function in the PLF problem. To confirm the validity of the method, results are compared with Monte Carlo simulation and Gram–Charlier expansion results. From the viewpoint of accuracy and computational cost, SPA almost surpasses other approximations for obtaining the cumulative distribution function of the output RVs.

Probabilistic harmonic load flow using an improved kernel density estimator

2016 ◽  
Vol 78 ◽  
pp. 292-298 ◽  
Author(s):  
F. Nasrfard-Jahromi ◽  
M. Mohammadi
Keyword(s):  
Kernel Density ◽  
Load Flow ◽  
Kernel Density Estimator ◽  
Density Estimator ◽  
Harmonic Load

Probabilistic load flow using multilinearisations

1981 ◽  
Vol 128 (5) ◽  
pp. 280 ◽  
Author(s):  
R.N. Allan ◽  
A.M. Léite da Silva
Keyword(s):  
Load Flow ◽  
Probabilistic Load Flow ◽  
Probabilistic Load

scholarly journals Non-parametric probabilistic load flow using Gaussian process learning

2021 ◽  
pp. 132941
Author(s):  
Parikshit Pareek ◽  
Chuan Wang ◽  
Hung D. Nguyen
Keyword(s):  
Gaussian Process ◽  
Load Flow ◽  
Probabilistic Load Flow ◽  
Process Learning ◽  
Probabilistic Load ◽  
Non Parametric

scholarly journals Probabilistic load flow calculation based on sparse polynomial chaos expansion

2018 ◽  
Vol 12 (11) ◽  
pp. 2735-2744 ◽  
Author(s):  
Xin Sun ◽  
Qingrui Tu ◽  
Jinfu Chen ◽  
Chengwen Zhang ◽  
Xianzhong Duan
Keyword(s):  
Polynomial Chaos ◽  
Polynomial Chaos Expansion ◽  
Load Flow ◽  
Chaos Expansion ◽  
Flow Calculation ◽  
Probabilistic Load Flow ◽  
Sparse Polynomial ◽  
Sparse Polynomial Chaos Expansion ◽  
Probabilistic Load ◽  
Load Flow Calculation
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