@article{8d82ba7099a646b1beef0a87b573ee91,
title = "Model predictive control for smart grids with multiple electric-vehicle charging stations",
abstract = "Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEV arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. This paper develops a model predictive control-based approach to address joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, knowledge of PEVs' future demand, or unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla model S PEVs show the merit of this approach.",
keywords = "Model predictive control, Optimal power flow, Plug-in electric vehicles, Smart power grid",
author = "Poor, {H. Vincent} and Ye Shi and Tuan, {Hoang Duong} and Savkin, {Andrey V.} and Duong, {Trung Q.}",
note = "Funding Information: Manuscript received July 27, 2017; revised October 27, 2017 and December 10, 2017; accepted December 24, 2017. Date of publication January 3, 2018; date of current version February 18, 2019. This work was supported in part by the Australian Research Council{\textquoteright}s Discovery Projects under Project DP130104617 and Project DP170103750, in part by a U.K. Royal Academy of Engineering Research Fellowship under Grant RF1415/14/22, in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/P019374/1, and in part by the U.S. National Science Foundation under Grant ECCS-1549881. Paper no. TSG-01065-2017. (Corresponding author: Trung Q. Duong.) Y. Shi and H. D. Tuan are with the School of Electrical and Data Engineering, University of Technology Sydney, Broadway, NSW 2007, Australia (e-mail: ye.shi@student.uts.edu.au; tuan.hoang@uts.edu.au). Funding Information: This work was supported in part by the Australian Research Council's Discovery Projects under Project DP130104617 and Project DP170103750, in part by a U.K. Royal Academy of Engineering Research Fellowship under Grant RF1415/14/22, in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/P019374/1, and in part by the U.S. National Science Foundation under Grant ECCS-1549881. Paper no. TSG-01065-2017. Publisher Copyright: {\textcopyright} 2017 IEEE",
year = "2019",
month = mar,
doi = "10.1109/TSG.2017.2789333",
language = "English (US)",
volume = "10",
pages = "2127--2136",
journal = "IEEE Transactions on Smart Grid",
issn = "1949-3053",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",
}