Model predictive control for on–off charging of electrical vehicles in smart grids

Ye Shi, Hoang D. Tuan, Andrey V. Savkin, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Over the next decade, a massive number of plug‐in electric vehicles (PEVs) will need to be integrated into current power grids. This is likely to give rise to unmanageable fluc-tuations in power demand and unacceptable deviations in voltage. These negative impacts are difficult to mitigate because PEVs connect and disconnect from the grid randomly and each type of PEVs has different charging profiles. This paper presents a solution to these problems that involves coordination of power grid control and PEV charging. The proposed strategy minimises the overall costs of charging and power generation in meeting future increases in PEV charging demand and the operational constraints of the power grid. The solution is based on an on–off PEV charging strategy that is easy and convenient to implement online. The joint coordination problem is formulated by a mixed integer non‐linear programming (MINP) with binary charging and continuous voltage variables and is solved by a highly novel computational algorithm. Its online implementation is based on a new model predictive control method that is free from prior assumptions about PEVs' arrival and charging information. Comprehensive simu-lations are provided to demonstrate the efficiency and practicality of the proposed methods.

Original languageEnglish (US)
Pages (from-to)121-133
Number of pages13
JournalIET Electrical Systems in Transportation
Volume11
Issue number2
DOIs
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Model predictive control for on–off charging of electrical vehicles in smart grids'. Together they form a unique fingerprint.

Cite this