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Data-Driven EV Load Profiles Generation Using a Variational Auto-Encoder

Author

Listed:
  • Zhixin Pan

    (State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210029, China)

  • Jianming Wang

    (Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

  • Wenlong Liao

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Haiwen Chen

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Dong Yuan

    (State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210029, China)

  • Weiping Zhu

    (State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210029, China)

  • Xin Fang

    (Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

  • Zhen Zhu

    (State Grid Wuxi Power Supply Company, Wuxi 214062, China)

Abstract

Although the penetration of electric vehicles (EVs) in distribution networks can improve the energy saving and emission reduction effects, its random and uncertain nature limits the ability of distribution networks to accept the load of EVs. To this end, establishing a load profile model of EV charging stations accurately and reasonably is of great significance to the planning, operation and scheduling of power system. Traditional generation methods for EV load profiles rely too much on experience, and need to set up a power load probability distribution in advance. In this paper, we propose a data-driven approach for load profiles of EV generation using a variational automatic encoder. Firstly, an encoder composed of deep convolution networks and a decoder composed of transposed convolution networks are trained using the original load profiles. Then, the new load profiles are obtained by decoding the random number which obeys a normal distribution. The simulation results show that EV load profiles generated by the deep convolution variational auto-encoder can not only retain the temporal correlation and probability distribution nature of the original load profiles, but also have a good restorative effect on the time distribution and fluctuation nature of the original power load.

Suggested Citation

  • Zhixin Pan & Jianming Wang & Wenlong Liao & Haiwen Chen & Dong Yuan & Weiping Zhu & Xin Fang & Zhen Zhu, 2019. "Data-Driven EV Load Profiles Generation Using a Variational Auto-Encoder," Energies, MDPI, vol. 12(5), pages 1-15, March.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:849-:d:210935
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    References listed on IDEAS

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    Cited by:

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    2. Xinghua Wang & Xixian Liu & Fucheng Zhong & Zilv Li & Kaiguo Xuan & Zhuoli Zhao, 2023. "A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    3. Giacomo Talluri & Gabriele Maria Lozito & Francesco Grasso & Carlos Iturrino Garcia & Antonio Luchetta, 2021. "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, MDPI, vol. 14(24), pages 1-23, December.
    4. Xuejiao Gong & Bo Tang & Ruijin Zhu & Wenlong Liao & Like Song, 2020. "Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder," Energies, MDPI, vol. 13(17), pages 1-14, August.
    5. Silva, Walquiria N. & Bandória, Luís H.T. & Dias, Bruno H. & de Almeida, Madson C. & de Oliveira, Leonardo W., 2023. "Generating realistic load profiles in smart grids: An approach based on nonlinear independent component estimation (NICE) and convolutional layers," Applied Energy, Elsevier, vol. 351(C).
    6. Michel Noussan & Francesco Neirotti, 2020. "Cross-Country Comparison of Hourly Electricity Mixes for EV Charging Profiles," Energies, MDPI, vol. 13(10), pages 1-14, May.

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