Long term individual load forecast under different electrical vehicles uptake scenarios

Poghosyan, Anush; Greetham, Danica Vukadinovi?; Haben, Stephen; Lee, Tamsin

Abstract

© 2015 Elsevier Ltd. 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.

Citation

Poghosyan, A., Greetham, D. V., Haben, S., & Lee, T. (2015). Long term individual load forecast under different electrical vehicles uptake scenarios. Applied Energy, 157, 699-709. https://doi.org/10.1016/j.apenergy.2015.02.069

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2015
• Publication Date: Nov 1, 2015
• Journal: Applied Energy
• Print ISSN: 0306-2619
• Electronic ISSN: 1872-9118
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 157
• Pages: 699-709
• DOI: https://doi.org/10.1016/j.apenergy.2015.02.069
• Keywords: low carbon technologies, long term forecasts, agent based modelling, low voltage networks
• Public URL: https://uwe-repository.worktribe.com/output/844015
• Publisher URL: http://dx.doi.org/10.1016/j.apenergy.2015.02.069