Long term individual load forecast under different electrical vehicles uptake scenarios

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

Research output: Contribution to journalArticle

16 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)699-709
Number of pages11
JournalApplied Energy
Volume157
Early online date12 Mar 2015
DOIs
Publication statusPublished - 1 Nov 2015

Keywords

  • Agent based modelling
  • Long term forecasts
  • Low carbon technologies
  • Low voltage networks

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

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