Smart charging for electric vehicles to minimize charging cost

Yue Wang, David Infield, Simon Gill

Research output: Contribution to journalArticle

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Abstract

This paper assumes a smart grid framework where the driving patterns for electric vehicles are known, time variations in electricity prices are communicated to householders, and data on voltage variation throughout the distribution system is available. Based on this information an aggregator with access to this data can be employed to minimize EV owner charging costs whilst maintaining acceptable distribution system voltages. In this study EV charging is assumed to take place only in the home. A single-phase LV distribution network is investigated where the local EV penetration level is assumed to be 100%. EV use patterns have been extracted from the UK Time of Use Survey data with 10-minute resolution and the domestic base load is generated from an existing public domain model. Apart from the so-called real time price signal, which is derived from the electricity system wholesale price, the cost of battery degradation is also considered in the optimal scheduling of EV charging. A simple and effective heuristic method is proposed to minimize the EV charging cost whilst satisfying the requirement of state of charge for the EV battery. A simulation in OpenDSS over a period of 24 hours has been implemented, taking care of the network constraints for voltage level at the customer connection points. The optimization results are compared with those obtained using dynamic optimal power flow.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalProceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy
Early online date23 Jan 2017
Publication statusPublished - 30 Jun 2017

Keywords

  • electric vehicles
  • demand side management
  • real time price signal
  • cost minimization
  • dynamic optimal power flow

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