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Probabilistic Power Flow Method Considering Continuous and Discrete Variables

Author

Listed:
  • Xuexia Zhang

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Zhiqi Guo

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Weirong Chen

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

Abstract

This paper proposes a probabilistic power flow (PPF) method considering continuous and discrete variables (continuous and discrete power flow, CDPF) for power systems. The proposed method—based on the cumulant method (CM) and multiple deterministic power flow (MDPF) calculations—can deal with continuous variables such as wind power generation (WPG) and loads, and discrete variables such as fuel cell generation (FCG). In this paper, continuous variables follow a normal distribution (loads) or a non-normal distribution (WPG), and discrete variables follow a binomial distribution (FCG). Through testing on IEEE 14-bus and IEEE 118-bus power systems, the proposed method (CDPF) has better accuracy compared with the CM, and higher efficiency compared with the Monte Carlo simulation method (MCSM).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:590-:d:96835
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    References listed on IDEAS

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    1. Chao Long & Mohamed Emad A. Farrag & Donald M. Hepburn & Chengke Zhou, 2014. "Point Estimate Method for Voltage Unbalance Evaluation in Residential Distribution Networks with High Penetration of Small Wind Turbines," Energies, MDPI, vol. 7(11), pages 1-15, November.
    2. 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.
    3. 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. Pinto, Edwin S. & Gronier, Timothé & Franquet, Erwin & Serra, Luis M., 2023. "Opportunities and economic assessment for a third-party delivering electricity, heat and cold to residential buildings," Energy, Elsevier, vol. 272(C).
    2. Bowen Zhou & Xiao Yang & Dongsheng Yang & Zhile Yang & Tim Littler & Hua Li, 2019. "Probabilistic Load Flow Algorithm of Distribution Networks with Distributed Generators and Electric Vehicles Integration," Energies, MDPI, vol. 12(22), pages 1-24, November.
    3. 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.
    4. 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.
    5. Ziwei Zhu & Shifan Lu & Sui Peng, 2018. "An Improved Stochastic Response Surface Method Based Probabilistic Load Flow for Studies on Correlated Wind Speeds in the AC/DC Grid," Energies, MDPI, vol. 11(12), pages 1-14, December.
    6. Morshed, Mohammad Javad & Hmida, Jalel Ben & Fekih, Afef, 2018. "A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems," Applied Energy, Elsevier, vol. 211(C), pages 1136-1149.

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