A Multivariate Dimension-Reduction Method for Probabilistic Power Flow Calculation

Abstract

The rising penetration of renewable generation as a result of environmental concerns generates increased uncertainties in power systems. This necessitates probabilistic analyses of the system performance, which include probabilistic power flow (PPF). The PPF suffers from the curse of dimensionality due to a large number of random loads. To address this issue, a multivariate dimension-reduction (MDR) method is proposed for PPF studies in this paper. The MDR decomposes the PPF problem into lower dimensional PPF subproblems which are further solved with promising accuracy. The computation time of the proposed method is proportional to the number of wind farms, which noticeably facilitates computation. The proposed method is applied to the IEEE 118-bus system and 2383-bus system. Simulation results demonstrate the accuracy and effectiveness of the proposed method.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

National Natural Science Foundation (China)
National Key Technology R&D Program of China

Comments

This work was supported in part by National Natural Science Foundation of China (51307107, 51477098), National Key Technology R&D Program of China (2015BAA01B02).

Keywords and Phrases

Linearization; Wind power; Curse of dimensionality; Dimension reduction; Dimension reduction method; Environmental concerns; Hermite; Probabilistic analysis; Probabilistic power flow; Renewable generation; Electric load flow; Gauss-Hermite formula; Multivariate dimension-reduction

International Standard Serial Number (ISSN)

0378-7796

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Elsevier, All rights reserved.

Publication Date

01 Dec 2016

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