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

D. Bauso; T. Namerikawa

doi:10.1109/DSW.2019.8755573

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

D. Bauso, T. Namerikawa

Department of System Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	285-289
Number of pages	5
ISBN (Electronic)	9781728107080
DOIs	https://doi.org/10.1109/DSW.2019.8755573
Publication status	Published - 2019 Jun
Event	2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duration: 2019 Jun 2 → 2019 Jun 5

Publication series

Name	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference	2019 IEEE Data Science Workshop, DSW 2019
Country/Territory	United States
City	Minneapolis
Period	19/6/2 → 19/6/5

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/DSW.2019.8755573

Cite this

Data-Driven Mean-Field Game Approximation for a Population of Electric Vehicles. / Bauso, D.; Namerikawa, T.

2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 285-289 8755573 (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 8755573, 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 285-289, 2019 IEEE Data Science Workshop, DSW 2019, Minneapolis, United States, 19/6/2. https://doi.org/10.1109/DSW.2019.8755573

@inproceedings{2f92d4c3c5c74e81bc193d8685946748,

title = "Data-Driven Mean-Field Game Approximation for a Population of Electric Vehicles",

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.",

keywords = "Mean-field games, Smart-grid",

author = "D. Bauso and T. Namerikawa",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Data Science Workshop, DSW 2019 ; Conference date: 02-06-2019 Through 05-06-2019",

year = "2019",

month = jun,

doi = "10.1109/DSW.2019.8755573",

language = "English",

series = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "285--289",

booktitle = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",

}

TY - GEN

T1 - Data-Driven Mean-Field Game Approximation for a Population of Electric Vehicles

AU - Bauso, D.

AU - Namerikawa, T.

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - Mean-field games

KW - Smart-grid

UR - http://www.scopus.com/inward/record.url?scp=85069513188&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069513188&partnerID=8YFLogxK

U2 - 10.1109/DSW.2019.8755573

DO - 10.1109/DSW.2019.8755573

M3 - Conference contribution

AN - SCOPUS:85069513188

T3 - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

SP - 285

EP - 289

BT - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Data Science Workshop, DSW 2019

Y2 - 2 June 2019 through 5 June 2019

ER -

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this