Data-Driven Mean-Field Game Approximation for a Population of Electric Vehicles

D. Bauso, T. Namerikawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For a population of electric vehicles (EVs) we design a data-driven mean-field game and provide analysis of approximated mean-field equilibrium points based on a receding horizon approach. The model involves stochastic disturbances on the data that drive the game. Some numerical studies illustrate the efficacy of the proposed strategies.

Original languageEnglish
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages285-289
Number of pages5
ISBN (Electronic)9781728107080
DOIs
Publication statusPublished - 2019 Jun
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: 2019 Jun 22019 Jun 5

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference2019 IEEE Data Science Workshop, DSW 2019
CountryUnited States
CityMinneapolis
Period19/6/219/6/5

Keywords

  • Mean-field games
  • Smart-grid

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Bauso, D., & Namerikawa, T. (2019). Data-Driven Mean-Field Game Approximation for a Population of Electric Vehicles. In 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings (pp. 285-289). [8755573] (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2019.8755573