LAPSE:2019.0350
Published Article
LAPSE:2019.0350
A Parallel Probabilistic Load Flow Method Considering Nodal Correlations
Jun Liu, Xudong Hao, Peifen Cheng, Wanliang Fang, Shuanbao Niu
February 27, 2019
With the introduction of more and more random factors in power systems, probabilistic load flow (PLF) has become one of the most important tasks for power system planning and operation. Cumulants-based PLF is an effective algorithm to calculate PLF in an analytical way, however, the correlations among the nodal injections to the system level have rarely been studied. A novel parallel cumulants-based PLF method considering nodal correlations is proposed in this paper, which is able to deal with the correlations among all system nodes, and avoid the Jacobian matrix inversion in the traditional cumulants-based PLF as well. In addition, parallel computing is introduced to improve the efficiency of the numerical calculations. The accuracy of the proposed method is validated by numerical tests on the standard IEEE-14 system, comparing with the results from Correlation Latin hypercube sampling Monte Carlo Simulation (CLMCS) method. And the efficiency and parallel performance is proven by the tests on the modified IEEE-300, C703, N1047 systems with distributed generation (DG). Numerical simulations show that the proposed parallel cumulants-based PLF method considering nodal correlations is able to get more accurate results using less computational time and physical memory, and have higher efficiency and better parallel performance than the traditional one.
Keywords
Correlation Latin hypercube sampling Monte Carlo Simulation (CLMCS), correlation matrix, cumulants, distributed generation (DG), parallel computing, probabilistic load flow (PLF)
Suggested Citation
Liu J, Hao X, Cheng P, Fang W, Niu S. A Parallel Probabilistic Load Flow Method Considering Nodal Correlations. (2019). LAPSE:2019.0350
Author Affiliations
Liu J: Shaanxi Key Laboratory of Smart Grid, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Hao X: Shaanxi Key Laboratory of Smart Grid, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Cheng P: Shaanxi Key Laboratory of Smart Grid, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Fang W: Shaanxi Key Laboratory of Smart Grid, State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Niu S: Northwest Subsection of State Grid Corporation of China, Xi’an 710048, China
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Journal Name
Energies
Volume
9
Issue
12
Article Number
E1041
Year
2016
Publication Date
2016-12-10
Published Version
ISSN
1996-1073
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PII: en9121041, Publication Type: Journal Article
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LAPSE:2019.0350
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doi:10.3390/en9121041
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Feb 27, 2019
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Calvin Tsay
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