Modelling the charging probability of electric vehicles as a Gaussian mixture model for a convolution based power flow analysis

Markus Gödde, Tobias Findeisen, Torsten Sowa, Phuong H. Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

This paper presents an approach for modelling the charging probability of electric vehicles as a Gaussian mixture model. The model is built up by assembling adapted multivari-ate normal probability density functions. This is done because the expectation maximization algorithm fails finding maximum likelihood estimates in respect of the charging power of the generated charging profiles. This Gaussian mixture model enables for capturing the charging profiles comprehensively with a few parameters and therefore it enables for calculating the charging probability dynamically for individual parameter intervals. The underlying assumptions about battery capacity, consumption, charging infrastructure, type of weekday and settlement structure determine the generation of the charging profiles. The proposed approach makes these parameters available for the density. Thereby, the provision of the charging profiles gets obsolete. This density can be used for a convolution based power flow analysis which offers benefits regarding the computational effort and random access memory usage com-pared to Monte Carlo-like simulations.

Original languageEnglish
Title of host publication2015 IEEE Eindhoven PowerTech, PowerTech 2015
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-4799-7693-5
DOIs
Publication statusPublished - 31 Aug 2015
EventIEEE Eindhoven PowerTech, PowerTech 2015 - Eindhoven, Netherlands
Duration: 29 Jun 20152 Jul 2015

Conference

ConferenceIEEE Eindhoven PowerTech, PowerTech 2015
CountryNetherlands
CityEindhoven
Period29/06/152/07/15

Keywords

  • Convolution
  • Electric vehicles
  • Gaussian mixture model
  • Load flow

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