LAPSE:2018.0892
Published Article
LAPSE:2018.0892
Constant Jacobian Matrix-Based Stochastic Galerkin Method for Probabilistic Load Flow
Yingyun Sun, Rui Mao, Zuyi Li, Wei Tian
November 27, 2018
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.
Keywords
generalized polynomial chaos, Nataf transformation, probabilistic load flow, stochastic Galerkin method, uncertainty quantification
Suggested Citation
Sun Y, Mao R, Li Z, Tian W. Constant Jacobian Matrix-Based Stochastic Galerkin Method for Probabilistic Load Flow. (2018). LAPSE:2018.0892
Author Affiliations
Sun Y: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Mao R: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Li Z: Robert W. Galvin Center for Electricity Innovation, Illinois Institute of Technology, Chicago, IL 60616, USA
Tian W: Robert W. Galvin Center for Electricity Innovation, Illinois Institute of Technology, Chicago, IL 60616, USA
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Journal Name
Energies
Volume
9
Issue
3
Article Number
E153
Year
2016
Publication Date
2016-03-03
Published Version
ISSN
1996-1073
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PII: en9030153, Publication Type: Journal Article
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LAPSE:2018.0892
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doi:10.3390/en9030153
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Nov 27, 2018
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Nov 27, 2018
 
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Calvin Tsay
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