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
Published in:Energies (Basel) 2016-03, Vol.9 (3), p.153-153
Main Authors: Sun, Yingyun, Mao, Rui, Li, Zuyi, Tian, Wei
Format: Article
Language:English
Subjects:
Accuracy
Alternative energy sources
Computation
Constants
Construction
Electricity distribution
Galerkin methods
generalized polynomial chaos
Jacobians
Methods
Nataf transformation
probabilistic load flow
Probabilistic methods
Probability theory
Random variables
Renewable resources
stochastic Galerkin method
Stochasticity
uncertainty quantification
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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
1996-1073
DOI:10.3390/en9030153