Model predictive control for smart grids with multiple electric-vehicle charging stations

H. Vincent Poor, Ye Shi, Hoang Duong Tuan, Andrey V. Savkin, Trung Q. Duong

Research output: Contribution to journalArticle

18 Scopus citations

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.

Original languageEnglish (US)
Article number8245844
Pages (from-to)2127-2136
Number of pages10
JournalIEEE Transactions on Smart Grid
Volume10
Issue number2
DOIs
StatePublished - Mar 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Keywords

  • Model predictive control
  • Optimal power flow
  • Plug-in electric vehicles
  • Smart power grid

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