Application of generalised cross-entropy method in probabilistic power flow

Abolfazl Kazemdehdashti; Mohammad Mohammadi; Ali Reza Seifi

Application of generalised cross-entropy method in probabilistic power flow

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Author(s): Abolfazl Kazemdehdashti¹ ; Mohammad Mohammadi¹ ; Ali Reza Seifi¹
- Affiliations: 1: Department of Power and Control, Engineering Faculty , School of Electrical and Computer Engineering, Shiraz University , No. 1, Zand Street, Shiraz , Iran
Source: Volume 12, Issue 11, 19 June 2018, p. 2745 – 2754
DOI: 10.1049/iet-gtd.2017.1892 , Print ISSN 1751-8687, Online ISSN 1751-8695

Received 12/12/2017, Accepted 06/03/2018, Revised 05/02/2018, Published 16/03/2018

This study presents a new powerful method for probability density function (PDF) estimation of the probabilistic power flow problem results. The proposed method works on a prior density function to improve its potential PDF estimation. The generalised cross-entropy method is used to refine the initial dataset. Most of the existing methods for PDF estimation do not provide a sparse result and rely on approximation and simplification. Compared to the other conventional methods, the proposed method has some remarkable features in density estimation. Low computational burden or high-speed response and high-precision results are the most prominent features of the proposed method. The method can deal with a correlated problem which is considered the correlation between uncertain parameters. It is tested on IEEE 14-bus and IEEE 118-bus systems, considering the non-stationary loads, some renewable energy resources and their correlations. The results of the proposed method are compared against more accurate results obtained from Monte Carlo simulation and some other conventional methods in density estimation. Comparisons confirm the high accuracy level and speed of the proposed density estimator against the other methods.

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Application of generalised cross-entropy method in probabilistic power flow

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