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A Parallel Probabilistic Load Flow Method Considering Nodal Correlations

Author

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  • Jun Liu

    (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)

  • Xudong Hao

    (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)

  • Peifen Cheng

    (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)

  • Wanliang Fang

    (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)

  • Shuanbao Niu

    (Northwest Subsection of State Grid Corporation of China, Xi’an 710048, China)

Abstract

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.

Suggested Citation

  • Jun Liu & Xudong Hao & Peifen Cheng & Wanliang Fang & Shuanbao Niu, 2016. "A Parallel Probabilistic Load Flow Method Considering Nodal Correlations," Energies, MDPI, vol. 9(12), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1041-:d:84872
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    References listed on IDEAS

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    1. Sun, Can & Bie, Zhaohong & Xie, Min & Jiang, Jiangfeng, 2016. "Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation," Renewable Energy, Elsevier, vol. 93(C), pages 68-76.
    2. Yingyun Sun & Rui Mao & Zuyi Li & Wei Tian, 2016. "Constant Jacobian Matrix-Based Stochastic Galerkin Method for Probabilistic Load Flow," Energies, MDPI, vol. 9(3), pages 1-18, March.
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    Cited by:

    1. Qais Alsafasfeh & Omar A. Saraereh & Imran Khan & Sunghwan Kim, 2019. "Solar PV Grid Power Flow Analysis," Sustainability, MDPI, vol. 11(6), pages 1-25, March.
    2. Xuexia Zhang & Zhiqi Guo & Weirong Chen, 2017. "Probabilistic Power Flow Method Considering Continuous and Discrete Variables," Energies, MDPI, vol. 10(5), pages 1-17, April.
    3. Chen, Zhang & Liu, Jun & Liu, Xinglei, 2022. "GPU accelerated power flow calculation of integrated electricity and heat system with component-oriented modeling of district heating network," Applied Energy, Elsevier, vol. 305(C).
    4. Xiaoyang Deng & Jinghan He & Pei Zhang, 2017. "A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables," Energies, MDPI, vol. 10(10), pages 1-21, October.
    5. Pei Bie & Buhan Zhang & Hang Li & Yong Wang & Le Luan & Guoyan Chen & Guojun Lu, 2017. "Chance-Constrained Real-Time Dispatch with Renewable Uncertainty Based on Dynamic Load Flow," Energies, MDPI, vol. 10(12), pages 1-20, December.

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