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Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles

Author

Listed:
  • Jun Yang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Wanmeng Hao

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Lei Chen

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Jiejun Chen

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Jing Jin

    (State Grid Hubei Electric Power Company, Wuhan 430077, Hubei, China)

  • Feng Wang

    (Computer School of Wuhan University, Wuhan 430072, Hubei, China)

Abstract

Electric vehicles (EVs) have received wide attention due to their higher energy efficiency and lower emissions. However, the random charging and discharging behaviors of substantial numbers of EVs may lead to safety risk problems in a distribution network. Reasonable price incentives can guide EVs through orderly charging and discharging, and further provide a feasible solution to reduce the operational risk of the distribution network. Considering three typical electricity prices, EV charging/discharging load models are built. Then, a Probabilistic Load Flow (PLF) method using cumulants and Gram-Charlier series is proposed to obtain the power flow of the distribution network including massive numbers of EVs. In terms of the risk indexes of node voltage and line flow, the operational risk of the distribution network can be estimated in detail. From the simulations of an IEEE-33 bus system and an IEEE 69-bus system, the demonstrated results show that reasonable charging and discharging prices are conducive to reducing the peak-valley difference, and consequently the risks of the distribution network can be decreased to a certain extent.

Suggested Citation

  • Jun Yang & Wanmeng Hao & Lei Chen & Jiejun Chen & Jing Jin & Feng Wang, 2016. "Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles," Energies, MDPI, vol. 9(7), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:560-:d:74226
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    References listed on IDEAS

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    Cited by:

    1. Munan Li & Alan L. Porter, 2018. "Facilitating the discovery of relevant studies on risk analysis for three-dimensional printing based on an integrated framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 277-300, January.
    2. Zain Anwer Memon & Riccardo Trinchero & Yanzhao Xie & Flavio G. Canavero & Igor S. Stievano, 2020. "An Iterative Scheme for the Power-Flow Analysis of Distribution Networks based on Decoupled Circuit Equivalents in the Phasor Domain," Energies, MDPI, vol. 13(2), pages 1-16, January.
    3. Md. Mosaraf Hossain Khan & Amran Hossain & Aasim Ullah & Molla Shahadat Hossain Lipu & S. M. Shahnewaz Siddiquee & M. Shafiul Alam & Taskin Jamal & Hafiz Ahmed, 2021. "Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment," Sustainability, MDPI, vol. 13(19), pages 1-18, October.
    4. Yuttana Kongjeen & Krischonme Bhumkittipich, 2018. "Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis," Energies, MDPI, vol. 11(6), pages 1-16, June.
    5. Chunlin Guo & Jingjing Yang & Lin Yang, 2018. "Planning of Electric Vehicle Charging Infrastructure for Urban Areas with Tight Land Supply," Energies, MDPI, vol. 11(9), pages 1-17, September.
    6. Bishnu P. Bhattarai & Kurt S. Myers & Birgitte Bak-Jensen & Sumit Paudyal, 2017. "Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks," Energies, MDPI, vol. 10(1), pages 1-18, January.

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