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Clustered multi-node learning of electric vehicle charging flexibility

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  • Gilanifar, Mostafa
  • Parvania, Masood

Abstract

Forecasting the available flexible load provided by electric vehicles would enable electric utilities to make informed decision in utilizing these loads for enhancing the operational efficiency of distribution systems. To overcome the lack of historical loads data at newly-installed EV charging stations, this paper proposes a clustered multi-node learning with Gaussian Process (CMNL-GP) method to fuse data from multiple charging stations and to learn them simultaneously. The proposed method improves the forecasting accuracy in each node by transferring meaningful information among multiple nodes. The proposed method also performs a clustering algorithm within its objective function to obtain within-cluster similarity, since all the nodes may not be related equally, and the nodes within a cluster may have a stronger correlation. To characterize the clustered structures and to transfer the shared information among multiple nodes, different regularization terms are imposed in the objective function of the proposed method. The proposed clustered multi-node learning also utilizes the Gaussian Process for statistical attributes of the residual stochastic process, which refers to the information that may not be shared among multiple nodes and can be node-specific. The proposed method is validated by real-world EV charging stations data in State of Utah, USA, to demonstrate the effectiveness of the proposed algorithm.

Suggested Citation

  • Gilanifar, Mostafa & Parvania, Masood, 2021. "Clustered multi-node learning of electric vehicle charging flexibility," Applied Energy, Elsevier, vol. 282(PB).
    • Handle: RePEc:eee:appene:v:282:y:2021:i:pb:s0306261920315403
    DOI: 10.1016/j.apenergy.2020.116125
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Genov, Evgenii & Cauwer, Cedric De & Kriekinge, Gilles Van & Coosemans, Thierry & Messagie, Maarten, 2024. "Forecasting flexibility of charging of electric vehicles: Tree and cluster-based methods," Applied Energy, Elsevier, vol. 353(PA).
    2. 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.
    3. Norouzi, Mohammadali & Aghaei, Jamshid & Niknam, Taher & Alipour, Mohammadali & Pirouzi, Sasan & Lehtonen, Matti, 2023. "Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting," Applied Energy, Elsevier, vol. 348(C).
    4. Joel Alpízar-Castillo & Laura Ramirez-Elizondo & Pavol Bauer, 2022. "Assessing the Role of Energy Storage in Multiple Energy Carriers toward Providing Ancillary Services: A Review," Energies, MDPI, vol. 16(1), pages 1-31, December.
    5. Qing Li & Xue Li & Zuyu Liu & Yaping Qi, 2022. "Application of Clustering Algorithms in the Location of Electric Taxi Charging Stations," Sustainability, MDPI, vol. 14(13), pages 1-15, June.

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