IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i10p2681-d174305.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/10/2681/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/10/2681/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rouzbeh Haghighi & Van-Hai Bui & Mengqi Wang & Wencong Su, 2024. "Survey of Reliability Challenges and Assessment in Power Grids with High Penetration of Inverter-Based Resources," Energies, MDPI, vol. 17(21), pages 1-26, October.
    2. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    3. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    4. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    5. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    6. Liu, Shuai & Wei, Li & Wang, Huai, 2020. "Review on reliability of supercapacitors in energy storage applications," Applied Energy, Elsevier, vol. 278(C).
    7. Jiashen Teh, 2018. "Adequacy Assessment of Wind Integrated Generating Systems Incorporating Demand Response and Battery Energy Storage System," Energies, MDPI, vol. 11(10), pages 1-12, October.
    8. Yuansheng Huang & Shijian Liu & Lei Yang, 2018. "Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    9. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    10. Wang, Huaizhi & Xue, Wenli & Liu, Yitao & Peng, Jianchun & Jiang, Hui, 2020. "Probabilistic wind power forecasting based on spiking neural network," Energy, Elsevier, vol. 196(C).
    11. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    12. Tolga Kara & Ahmet Duran Şahin, 2023. "Implications of Climate Change on Wind Energy Potential," Sustainability, MDPI, vol. 15(20), pages 1-26, October.
    13. Qin, Li & Liu, Shi & Long, Teng & Shahzad, Muhammad Ali & Schlaberg, H. Inaki & Yan, Song An, 2018. "Wind field reconstruction using dimension-reduction of CFD data with experimental validation," Energy, Elsevier, vol. 151(C), pages 272-288.
    14. Yu, Chuanjin & Fu, Suxiang & Wei, ZiWei & Zhang, Xiaochi & Li, Yongle, 2024. "Multi-feature-fused generative neural network with Gaussian mixture for multi-step probabilistic wind speed prediction," Applied Energy, Elsevier, vol. 359(C).
    15. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    16. Gensler, André & Sick, Bernhard & Vogt, Stephan, 2018. "A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 352-379.
    17. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    18. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
    19. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    20. Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2681-:d:174305. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.