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Load Forecast of Electric Vehicle Charging Station Considering Multi-Source Information and User Decision Modification

Author

Listed:
  • Zhiyuan Zhuang

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, China)

  • Xidong Zheng

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, China)

  • Zixing Chen

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, China)

  • Tao Jin

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, China)

  • Zengqin Li

    (China Railway Electric Industry Co., Ltd., Baoding 071051, China)

Abstract

In view of the current multi-source information scenario, this paper proposes a decision-making method for electric vehicle charging stations (EVCSs) based on prospect theory, which considers payment cost, time cost, and route factors, and is used for electric vehicle (EV) owners to make decisions when the vehicle’s electricity is low. Combined with the multi-source information architecture composed of an information layer, algorithm layer, and model layer, the load of EVCSs in the region is forecast. In this paper, the Monte Carlo method is used to test the IEEE-30 model and the traffic network based on it, and the spatial and temporal distribution of charging load in the region is obtained, which verifies the effectiveness of the proposed method. The results show that EVCS load forecasting based on the prospect theory under the influence of multi-source information will have an impact on the space–time distribution of the EVCS load, which is more consistent with the decisions of EV owners in reality.

Suggested Citation

  • Zhiyuan Zhuang & Xidong Zheng & Zixing Chen & Tao Jin & Zengqin Li, 2022. "Load Forecast of Electric Vehicle Charging Station Considering Multi-Source Information and User Decision Modification," Energies, MDPI, vol. 15(19), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7021-:d:924168
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    References listed on IDEAS

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    1. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    2. Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.
    3. Gilanifar, Mostafa & Parvania, Masood, 2021. "Clustered multi-node learning of electric vehicle charging flexibility," Applied Energy, Elsevier, vol. 282(PB).
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    Cited by:

    1. Francesco Lo Franco & Mattia Ricco & Vincenzo Cirimele & Valerio Apicella & Benedetto Carambia & Gabriele Grandi, 2023. "Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach," Energies, MDPI, vol. 16(4), pages 1-27, February.
    2. Kakkar, Riya & Agrawal, Smita & Tanwar, Sudeep, 2024. "A systematic survey on demand response management schemes for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    3. Manuel Jaramillo & Diego Carrión, 2022. "An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19," Energies, MDPI, vol. 15(22), pages 1-19, November.

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