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Recursive Method for Distribution System Reliability Evaluation

Author

Listed:
  • Huaizhi Wang

    (College of Mechatronics and Control Engineering, Shenzhen University, Nanshan District, Shenzhen 518060, China)

  • Xian Zhang

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

  • Qing Li

    (Maintenance & Test Center, CSG EHV Power Transmission Company, Guangzhou 518026, China)

  • Guibin Wang

    (College of Mechatronics and Control Engineering, Shenzhen University, Nanshan District, Shenzhen 518060, China)

  • Hui Jiang

    (College of Optoelectronic Engineering, Shenzhen University, Nanshan District, Shenzhen 518060, China)

  • Jianchun Peng

    (College of Mechatronics and Control Engineering, Shenzhen University, Nanshan District, Shenzhen 518060, China)

Abstract

This paper proposes a novel hybrid recursive method for distribution system reliability evaluation to deal with the computational limit and low-efficiency problem which exist in previously developed techniques as the system becomes larger. This method includes a bottom-up process and a top-down process, which are developed on the basis of a recursive principle, and the synthesis of both processes yield the reliability performance of each bus of the system. The bottom-up process considers the effects of downstream failures on upstream customers, and the top-down process considers the effects of upstream failures on downstream customers. In addition, a novel switch zone concept is defined and introduced into the bottom-up recursive process to save the computation cost. Besides, section technique (ST) and shortest path method (SPM) are employed to effectively simplify the recursive path and thus, the computation efficiency can be improved. The most significant feature of the proposed method over ST, SPM, failure mode and effect analysis (FMEA) is that it provides a more generalized equivalent approach to maximally simplify the network for reliable evaluation irrespective of the network topology. The effectiveness of the proposed method has been validated through comprehensive tests on Roy Billinton test system (RBTS) bus 6 and a practical-sized distribution system in China.

Suggested Citation

  • Huaizhi Wang & Xian Zhang & Qing Li & Guibin Wang & Hui Jiang & Jianchun Peng, 2018. "Recursive Method for Distribution System Reliability Evaluation," Energies, MDPI, vol. 11(10), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2681-:d:174305
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    References listed on IDEAS

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    1. Wenxia Liu & Dapeng Guo & Yahui Xu & Rui Cheng & Zhiqiang Wang & Yueqiao Li, 2018. "Reliability Assessment of Power Systems with Photovoltaic Power Stations Based on Intelligent State Space Reduction and Pseudo-Sequential Monte Carlo Simulation," Energies, MDPI, vol. 11(6), pages 1-14, June.
    2. Jiazheng Lu & Jun Guo & Zhou Jian & Yihao Yang & Wenhu Tang, 2018. "Resilience Assessment and Its Enhancement in Tackling Adverse Impact of Ice Disasters for Power Transmission Systems," Energies, MDPI, vol. 11(9), pages 1-15, August.
    3. Farihan Mohamad & Jiashen Teh & Ching-Ming Lai & Liang-Rui Chen, 2018. "Development of Energy Storage Systems for Power Network Reliability: A Review," Energies, MDPI, vol. 11(9), pages 1-19, August.
    4. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    5. Xi, Lei & Chen, Jianfeng & Huang, Yuehua & Xu, Yanchun & Liu, Lang & Zhou, Yimin & Li, Yudan, 2018. "Smart generation control based on multi-agent reinforcement learning with the idea of the time tunnel," Energy, Elsevier, vol. 153(C), pages 977-987.
    6. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    7. Farihan Mohamad & Jiashen Teh, 2018. "Impacts of Energy Storage System on Power System Reliability: A Systematic Review," Energies, MDPI, vol. 11(7), pages 1-23, July.
    8. Xi, Lei & Yu, Tao & Yang, Bo & Zhang, Xiaoshun & Qiu, Xuanyu, 2016. "A wolf pack hunting strategy based virtual tribes control for automatic generation control of smart grid," Applied Energy, Elsevier, vol. 178(C), pages 198-211.
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    Cited by:

    1. Gustavo L. Aschidamini & Gederson A. da Cruz & Mariana Resener & Maicon J. S. Ramos & Luís A. Pereira & Bibiana P. Ferraz & Sérgio Haffner & Panos M. Pardalos, 2022. "Expansion Planning of Power Distribution Systems Considering Reliability: A Comprehensive Review," Energies, MDPI, vol. 15(6), pages 1-29, March.
    2. Hak-Ju Lee & Byeong-Chan Oh & Seok-Woong Kim & Sung-Yul Kim, 2020. "V2G Strategy for Improvement of Distribution Network Reliability Considering Time Space Network of EVs," Energies, MDPI, vol. 13(17), pages 1-19, August.

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