TY - GEN
T1 - Data-Driven Mean-Field Game Approximation for a Population of Electric Vehicles
AU - Bauso, D.
AU - Namerikawa, T.
N1 - Publisher Copyright:
© 2019 IEEE.
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 -