The Point Estimate Method Using Third-Order Polynomial Normal Transformation Technique to Solve Probabilistic Power Flow with Correlated Wind Source and Load

2012 ◽  
Author(s):  
Huan Yang ◽  
Bin Zou
Keyword(s):  
Power Flow ◽  
Point Estimate ◽  
Third Order ◽  
Transformation Technique ◽  
Point Estimate Method ◽  
Order Polynomial ◽  
Estimate Method ◽  
Probabilistic Power Flow ◽  
Normal Transformation ◽  
Wind Source

Comparing unscented transformation and point estimate method for probabilistic power flow computation

2018 ◽  
Vol 37 (3) ◽  
pp. 1290-1303 ◽  
Author(s):  
Qing Xiao ◽  
Shaowu Zhou
Keyword(s):  
Power Flow ◽  
Quadrature Rule ◽  
Point Estimate ◽  
Unscented Transformation ◽  
Content Type ◽  
Point Estimate Method ◽  
Estimate Method ◽  
Gauss Type ◽  
Probability Spaces ◽  
Probabilistic Power Flow

Purpose Unscented transformation (UT) and point estimate method (PEM) are two efficient algorithms for probabilistic power flow (PPF) computation. This paper aims to show the relevance between UT and PEM and to derive a rule to determine the accuracy controlling parameters for UT method. Design/methodology/approach The authors derive the underlying sampling strategies of UT and PEM and check them in different probability spaces, where quadrature nodes are selected. Findings Gauss-type quadrature rule can be used to determine the accuracy controlling parameters of UT. If UT method and PEM select quadrature nodes in two probability spaces related by a linear transform, these two algorithms are equivalent. Originality/value It shows that UT method can be conveniently extended to (km + 1) scheme (k = 4; 6; : : : ) by Gauss-type quadrature rule.

Probabilistic Power Flow Analysis Based on Point-Estimate Method for High Penetration of Photovoltaic Generation in Electrical Distribution Systems

2020 ◽  
Author(s):  
Cordero B. Luis ◽  
Franco B. John
Keyword(s):  
Power Flow ◽  
Edgeworth Expansion ◽  
Distribution Networks ◽  
Point Estimate ◽  
Photovoltaic Systems ◽  
Distributed Energy ◽  
Photovoltaic Generation ◽  
Point Estimate Method ◽  
High Penetration ◽  
Estimate Method

Environmental awareness and energy policies led to decarbonization targets, fostering the adoption of distributed energy resource in the distribution network. Particularly, photovoltaic systems have been gaining momentum due to cost-competitive option and financial benefits. However, traditional distribution networks were not designed for intermittency in power generation. This poses technical issues such as reverse power flow, overvoltage, and thermal overloading. Furthermore, the growth in intermittency and variability of distributed energy resources increases the uncertainty, hence, it brings challenges for the operation, planning, and investment decisions. In this context, probabilistic methods to cater for these uncertainties are essential to address this issue. This paper presents a probabilistic power flow method based on point estimate method combined Edgeworth expansion for high penetration of photovoltaic generation in distribution networks. Normal distribution and Beta distribution are considered for load and solar irradiation modelling, respectively. The method is assessed for different cases using the IEEE 33-bus distribution test system with photovoltaic systems installation. The point estimate method combined Edgeworth expansion provided satisfactory results with lower computational effort and high fitting accuracy of statistical information compared to Monte Carlo simulation.

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