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Long term individual load forecast under different electrical vehicles uptake scenarios

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  • Poghosyan, Anush
  • Greetham, Danica Vukadinović
  • Haben, Stephen
  • Lee, Tamsin

Abstract

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences – that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Suggested Citation

  • Poghosyan, Anush & Greetham, Danica Vukadinović & Haben, Stephen & Lee, Tamsin, 2015. "Long term individual load forecast under different electrical vehicles uptake scenarios," Applied Energy, Elsevier, vol. 157(C), pages 699-709.
    • Handle: RePEc:eee:appene:v:157:y:2015:i:c:p:699-709
    DOI: 10.1016/j.apenergy.2015.02.069
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    2. Ghadimi, Noradin & Akbarimajd, Adel & Shayeghi, Hossein & Abedinia, Oveis, 2018. "Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting," Energy, Elsevier, vol. 161(C), pages 130-142.
    3. Luciano C. Siebert & Adriana Sbicca & Alexandre Rasi Aoki & Germano Lambert-Torres, 2017. "A Behavioral Economics Approach to Residential Electricity Consumption," Energies, MDPI, vol. 10(6), pages 1-18, June.
    4. Zarazua de Rubens, Gerardo, 2019. "Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market," Energy, Elsevier, vol. 172(C), pages 243-254.
    5. Yunusov, Timur & Frame, Damien & Holderbaum, William & Potter, Ben, 2016. "The impact of location and type on the performance of low-voltage network connected battery energy storage systems," Applied Energy, Elsevier, vol. 165(C), pages 202-213.
    6. Li, Wei & Jia, Zhijie & Zhang, Hongzhi, 2017. "The impact of electric vehicles and CCS in the context of emission trading scheme in China: A CGE-based analysis," Energy, Elsevier, vol. 119(C), pages 800-816.
    7. Sovacool, Benjamin K. & Kivimaa, Paula & Hielscher, Sabine & Jenkins, Kirsten, 2017. "Vulnerability and resistance in the United Kingdom's smart meter transition," Energy Policy, Elsevier, vol. 109(C), pages 767-781.

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