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Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic

Author

Listed:
  • Rob Shipman

    (Department of Architecture & Built Environment, University of Nottingham, Nottingham NG7 2RD, UK)

  • Rebecca Roberts

    (A.T. Kearney Limited, 1-11 John Adam Street, London WC2N 6HT, UK)

  • Julie Waldron

    (Department of Architecture & Built Environment, University of Nottingham, Nottingham NG7 2RD, UK)

  • Chris Rimmer

    (Cenex, Holywell Building, Holywell Park, Ashby Road, Loughborough LE11 3UZ, UK)

  • Lucelia Rodrigues

    (Department of Architecture & Built Environment, University of Nottingham, Nottingham NG7 2RD, UK)

  • Mark Gillott

    (Department of Architecture & Built Environment, University of Nottingham, Nottingham NG7 2RD, UK)

Abstract

Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a service are described in the context of a commercial service that is currently under development. Key among these components is the prediction of available capacity at a future time. In this paper, we extend a previous work that used a deep learning recurrent neural network for this task to include online machine learning, which enables the network to continually refine its predictions based on observed behaviour. The coronavirus pandemic that was declared in 2020 resulted in closures of the university and substantial changes to the behaviour of the university fleet. In this work, the impact of this change in vehicles usage was used to test the predictions of a network initially trained using vehicle trip data from 2019 with and without online machine learning. It is shown that prediction error is significantly reduced using online machine learning, and it is concluded that a similar capability will be of critical importance for a commercial service such as the one described in this paper.

Suggested Citation

  • Rob Shipman & Rebecca Roberts & Julie Waldron & Chris Rimmer & Lucelia Rodrigues & Mark Gillott, 2021. "Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic," Energies, MDPI, vol. 14(21), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7176-:d:670270
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    References listed on IDEAS

    as
    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. DeForest, Nicholas & MacDonald, Jason S. & Black, Douglas R., 2018. "Day ahead optimization of an electric vehicle fleet providing ancillary services in the Los Angeles Air Force Base vehicle-to-grid demonstration," Applied Energy, Elsevier, vol. 210(C), pages 987-1001.
    3. Shipman, Rob & Roberts, Rebecca & Waldron, Julie & Naylor, Sophie & Pinchin, James & Rodrigues, Lucelia & Gillott, Mark, 2021. "We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network," Energy, Elsevier, vol. 221(C).
    4. Charilaos Latinopoulos & Aruna Sivakumar & John W. Polak, 2021. "Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models," Energies, MDPI, vol. 14(4), pages 1-27, February.
    5. Chu Donatus Iweh & Samuel Gyamfi & Emmanuel Tanyi & Eric Effah-Donyina, 2021. "Distributed Generation and Renewable Energy Integration into the Grid: Prerequisites, Push Factors, Practical Options, Issues and Merits," Energies, MDPI, vol. 14(17), pages 1-34, August.
    6. Rob Shipman & Julie Waldron & Sophie Naylor & James Pinchin & Lucelia Rodrigues & Mark Gillott, 2020. "Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid," Energies, MDPI, vol. 13(8), pages 1-15, April.
    7. Mahmud, Khizir & Town, Graham E. & Morsalin, Sayidul & Hossain, M.J., 2018. "Integration of electric vehicles and management in the internet of energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4179-4203.
    8. Gough, Rebecca & Dickerson, Charles & Rowley, Paul & Walsh, Chris, 2017. "Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage," Applied Energy, Elsevier, vol. 192(C), pages 12-23.
    9. Mostafa Shibl & Loay Ismail & Ahmed Massoud, 2021. "Electric Vehicles Charging Management Using Machine Learning Considering Fast Charging and Vehicle-to-Grid Operation," Energies, MDPI, vol. 14(19), pages 1-22, September.
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    Cited by:

    1. Marius Minea & Cătălin Marian Dumitrescu, 2022. "On the Feasibility and Efficiency of Self-Powered Green Intelligent Highways," Energies, MDPI, vol. 15(13), pages 1-32, June.
    2. Qin Chen & Komla Agbenyo Folly, 2022. "Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review," Energies, MDPI, vol. 16(1), pages 1-26, December.

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