Constant Jacobian Matrix-Based Stochastic Galerkin Method for Probabilistic Load Flow

An intrusive spectral method of probabilistic load flow (PLF) is proposed in the paper, which can handle the uncertainties arising from renewable energy integration. Generalized polynomial chaos (gPC) expansions of dependent random variables are utilized to build a spectral stochastic representation...

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Bibliographic Details
Main Authors: Yingyun Sun, Rui Mao, Zuyi Li, Wei Tian
Format: Article
Language:English
Published: MDPI AG 2016-03-01
Series:Energies
Subjects:
Online Access:http://www.mdpi.com/1996-1073/9/3/153
Description
Summary:An intrusive spectral method of probabilistic load flow (PLF) is proposed in the paper, which can handle the uncertainties arising from renewable energy integration. Generalized polynomial chaos (gPC) expansions of dependent random variables are utilized to build a spectral stochastic representation of PLF model. Instead of solving the coupled PLF model with a traditional, cumbersome method, a modified stochastic Galerkin (SG) method is proposed based on the P-Q decoupling properties of load flow in power system. By introducing two pre-calculated constant sparse Jacobian matrices, the computational burden of the SG method is significantly reduced. Two cases, IEEE 14-bus and IEEE 118-bus systems, are used to verify the computation speed and efficiency of the proposed method.
ISSN:1996-1073