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Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling

Author

Listed:
  • Quan Li

    (Huanggang Power Supply Company, Hubei Electric Power Co., Ltd., State Grid Corporation of China, Huanggang 438000, China)

  • Xin Wang

    (Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Shuaiang Rong

    (College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200082, China)

Abstract

The growing amount of distributed generation has brought great uncertainty to power grids. Traditional probabilistic load flow (PLF) algorithms, such as the Monte-Carlo method (MCM), can no longer meet the needs of efficiency and accuracy in large-scale power grids. Latin Hypercube Sampling (LHS) develops a sampling efficiency and solves the correlation problem of distributed generation (DG) access nodes for accuracy analyses. In this paper, a modified Latin Hypercube-Important Sampling method is proposed for higher efficiency and precision by using the importance sampling method before LHS and the Cholesky decomposition method in correlation calculations. The simulation results are presented using a modified IEEE 30-bus system and are compared with traditional MCM and LHS.

Suggested Citation

  • Quan Li & Xin Wang & Shuaiang Rong, 2018. "Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling," Energies, MDPI, vol. 11(11), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3171-:d:183160
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    References listed on IDEAS

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    Cited by:

    1. Tiago P. Abud & Andre A. Augusto & Marcio Z. Fortes & Renan S. Maciel & Bruno S. M. C. Borba, 2022. "State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation," Energies, MDPI, vol. 16(1), pages 1-24, December.
    2. Yue Ma & Xiaodong Chu, 2022. "Optimizing Low-Carbon Pathway of China’s Power Supply Structure Using Model Predictive Control," Energies, MDPI, vol. 15(12), pages 1-20, June.

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