Domestic peak-load management including vehicle-to-grid and battery storage unit using an artificial neural network

K. Mahmud, S. Morsalin, M. J. Hossain, G. E. Town

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

14 Citations (Scopus)

Abstract

Controlled charging and discharge of electric vehicles (EVs) in distributed power systems can reduce the peak demand on the grid. This paper presents an energy management strategy (EMS) using an artificial neural network to shave the domestic peak grid load by the coordinated response of distributed energy resource (DER) units including photovoltaic (PV) systems, V2G (vehicle to grid)-capable EVs, and battery energy storage systems (BESS). The developed EMS is implemented on a test system including 15 houses and the realistic load pattern of California, U.S. From the simulation results for this network it is found that an artificial-neural-network controller can effectively coordinate the system to reshape the load curve and shave the domestic peak loading by up to 77%.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Industrial Technology (ICIT)
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages586-591
Number of pages6
ISBN (Electronic)9781509053209, 9781509053193
ISBN (Print)9781509053216
DOIs
Publication statusPublished - 2017
Event2017 IEEE International Conference on Industrial Technology, ICIT 2017 - Toronto, Canada
Duration: 23 Mar 201725 Mar 2017

Other

Other2017 IEEE International Conference on Industrial Technology, ICIT 2017
CountryCanada
CityToronto
Period23/03/1725/03/17

Keywords

  • artificial neural network
  • demand management
  • peak-load management
  • peak shave

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