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Reliability Evaluation of a Distribution Network with Microgrid Based on a Combined Power Generation System

Author

Listed:
  • Hao Bai

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Shihong Miao

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Pipei Zhang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhan Bai

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Distributed generation (DG), battery storage (BS) and electric vehicles (EVs) in a microgrid constitute the combined power generation system (CPGS). A CPGS can be applied to achieve a reliable evaluation of a distribution network with microgrids. To model charging load and discharging capacity, respectively, the EVs in a CPGS can be divided into regular EVs and ruleless EVs, according to their driving behavior. Based on statistical data of gasoline-fueled vehicles and the probability distribution of charging start instant and charging time, a statistical model can be built to describe the charging load and discharging capacity of ruleless EVs. The charge and discharge curves of regular EVs can also be drawn on the basis of a daily dispatch table. The CPGS takes the charge and discharge curves of EVs, daily load and DG power generation into consideration to calculate its power supply time during islanding. Combined with fault duration, the power supply time during islanding will be used to analyze and determine the interruption times and interruption duration of loads in islands. Then the Sequential Monte Carlo method is applied to complete the reliability evaluation of the distribution system. The RBTS Bus 4 test system is utilized to illustrate the proposed technique. The effects on the system reliability of BS capacity and V2G technology, driving behavior, recharging mode and penetration of EVs are all investigated.

Suggested Citation

  • Hao Bai & Shihong Miao & Pipei Zhang & Zhan Bai, 2015. "Reliability Evaluation of a Distribution Network with Microgrid Based on a Combined Power Generation System," Energies, MDPI, vol. 8(2), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:2:p:1216-1241:d:45516
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    Citations

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    Cited by:

    1. Quddus, Md Abdul & Kabli, Mohannad & Marufuzzaman, Mohammad, 2019. "Modeling electric vehicle charging station expansion with an integration of renewable energy and Vehicle-to-Grid sources," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 251-279.
    2. Jichao Hong & Zhenpo Wang & Peng Liu, 2017. "Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-16, July.
    3. Jose L. López-Prado & Jorge I. Vélez & Guisselle A. Garcia-Llinás, 2020. "Reliability Evaluation in Distribution Networks with Microgrids: Review and Classification of the Literature," Energies, MDPI, vol. 13(23), pages 1-31, November.
    4. Bingke Yan & Bo Wang & Lin Zhu & Hesen Liu & Yilu Liu & Xingpei Ji & Dichen Liu, 2015. "A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet," Energies, MDPI, vol. 8(11), pages 1-24, November.
    5. Qunli Wu & Chenyang Peng, 2016. "Comprehensive Benefit Evaluation of the Power Distribution Network Planning Project Based on Improved IAHP and Multi-Level Extension Assessment Method," Sustainability, MDPI, vol. 8(8), pages 1-18, August.
    6. Andrés Felipe Pérez Posada & Juan G. Villegas & Jesús M. López-Lezama, 2017. "A Scatter Search Heuristic for the Optimal Location, Sizing and Contract Pricing of Distributed Generation in Electric Distribution Systems," Energies, MDPI, vol. 10(10), pages 1-16, September.
    7. Feng Wang & Lizheng Sun & Zhang Wen & Fang Zhuo, 2022. "Overview of Inertia Enhancement Methods in DC System," Energies, MDPI, vol. 15(18), pages 1-25, September.
    8. Escalera, Alberto & Hayes, Barry & Prodanović, Milan, 2018. "A survey of reliability assessment techniques for modern distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 344-357.
    9. Xin Li & Xiaodi Zhang & Yuling Fan, 2019. "A Two-Step Framework for Energy Local Area Network Scheduling Problem with Electric Vehicles Based on Global–Local Optimization Method," Energies, MDPI, vol. 12(1), pages 1-17, January.
    10. Yinze Ren & Hongbin Wu & Hejun Yang & Shihai Yang & Zhixin Li, 2018. "A Method for Load Classification and Energy Scheduling Optimization to Improve Load Reliability," Energies, MDPI, vol. 11(6), pages 1-19, June.

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