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Probabilistic Power Flow Methodology for Large-Scale Power Systems Incorporating Renewable Energy Sources

Author

Listed:
  • Van Ky Huynh

    (The University of Danang, 41 Le Duan st., Danang 59000, Vietnam)

  • Van Duong Ngo

    (The University of Danang, 41 Le Duan st., Danang 59000, Vietnam)

  • Dinh Duong Le

    (Faculty of Electrical Engineering, The University of Danang—University of Science and Technology, 54 Nguyen Luong Bang st., Danang 59000, Vietnam)

  • Nhi Thi Ai Nguyen

    (Faculty of Electrical Engineering, The University of Danang—University of Science and Technology, 54 Nguyen Luong Bang st., Danang 59000, Vietnam)

Abstract

In this paper, we propose a new scheme for probabilistic power flow in networks with renewable power generation by making use of a data clustering technique. The proposed clustering technique is based on the combination of Principal Component Analysis and Differential Evolution clustering algorithm to deal with input random variables in probabilistic power flow. Extensive testing on the modified IEEE-118 bus test system shows good performance of the proposed approach in terms of significant reduction of computation time compared to the traditional Monte Carlo simulation, while maintaining an appropriate level of accuracy.

Suggested Citation

  • Van Ky Huynh & Van Duong Ngo & Dinh Duong Le & Nhi Thi Ai Nguyen, 2018. "Probabilistic Power Flow Methodology for Large-Scale Power Systems Incorporating Renewable Energy Sources," Energies, MDPI, vol. 11(10), pages 1-12, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2624-:d:173258
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    References listed on IDEAS

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    1. Arman Oshnoei & Rahmat Khezri & Mehrdad Tarafdar Hagh & Kuaanan Techato & SM Muyeen & Omid Sadeghian, 2018. "Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering," Energies, MDPI, vol. 11(2), pages 1-19, February.
    2. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    3. Carpinelli, Guido & Caramia, Pierluigi & Varilone, Pietro, 2015. "Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems," Renewable Energy, Elsevier, vol. 76(C), pages 283-295.
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    Cited by:

    1. Michał Jasiński & Tomasz Sikorski & Paweł Kostyła & Zbigniew Leonowicz & Klaudiusz Borkowski, 2020. "Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(8), pages 1-21, April.
    2. Yue Chen & Zhizhong Guo & Hongbo Li & Yi Yang & Abebe Tilahun Tadie & Guizhong Wang & Yingwei Hou, 2020. "Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
    3. 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.

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