Optimal vehicle to grid planning and scheduling using double layer multi-objective algorithm

Kang Miao Tan, Vigna Kumaran Ramachandaramurthy, Jia Ying Yong

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Vehicle to grid is a revolutionary technology that allows energy exchange between electric vehicles and power grid for mutual advantages. The implementation of appropriate vehicle to grid energy management system can maximize the potential of electric vehicles to provide grid ancillary services. This paper proposes an optimal vehicle to grid planning and scheduling by utilizing a novel double layer multi-objective algorithm. This optimization algorithm utilizes the grid-connected electric vehicles to perform peak load shaving and load levelling services to minimize the power grid load variance in the first layer optimization. Meanwhile, the second layer optimization minimizes the reactive power compensation for grid voltage regulation and therefore, optimizes the vehicle to grid charger's capacitor sizing. The second layer optimization algorithm utilizes an approximated formula from the simulation of a vehicle to grid charger. The proposed vehicle to grid optimization algorithm considers various power grid and electric vehicle constraints for practicality purpose. With the real time implementation of the proposed algorithm, the optimization results show that the power load curve is effectively followed the preset constant target loading, while the grid voltage is successfully regulated to the predetermined voltage level with minimal amount of reactive power supply from the optimal charger's capacitor.

Original languageEnglish
Pages (from-to)1060-1073
Number of pages14
JournalEnergy
Volume112
DOIs
Publication statusPublished - 01 Oct 2016

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All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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