Optimal siting and sizing of rapid charging station for electric vehicles considering Bangi city road network in Malaysia

Md Mainul Islam, Hussain Shareef, Azah Mohamed

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Recently, electric vehicles (EVs) have been seen as a felicitous option towards a less carbon-intensive road transport. The key issue in this system is recharging the EV batteries before they are exhausted. Thus, charging stations (CSs) should be carefully located to make sure EV users can access a CS within their driving range. Considering geographic information and traffic density, this paper proposes an optimization overture for optimal siting and sizing of a rapid CS (RCS). It aims to minimize the daily total cost (which includes the cost of substation energy loss, traveling cost of EVs to the CS, and investment, variable, and operational costs of the stations simultaneously) while maintaining system constraints. The binary gravitational search algorithm, genetic algorithm, and binary particle swarm optimization algorithm were employed to optimize the daily total cost by finding the best location and sizing of the RCS in a given metropolitan area in Malaysia. The results show that the proposed methods can find optimal locations and sizing of a RCS that can benefit EV users, CS developers, and the power grid.

Original languageEnglish
Pages (from-to)3933-3948
Number of pages16
JournalTurkish Journal of Electrical Engineering and Computer Sciences
Volume24
Issue number5
DOIs
Publication statusPublished - 2016

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Electric vehicles
Costs
Particle swarm optimization (PSO)
Energy dissipation
Genetic algorithms
Carbon

Keywords

  • Electric vehicles
  • Genetic algorithm
  • Gravitational search algorithm
  • Optimal planning
  • Particle swarm optimization
  • Rapid charging station

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science(all)

Cite this

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abstract = "Recently, electric vehicles (EVs) have been seen as a felicitous option towards a less carbon-intensive road transport. The key issue in this system is recharging the EV batteries before they are exhausted. Thus, charging stations (CSs) should be carefully located to make sure EV users can access a CS within their driving range. Considering geographic information and traffic density, this paper proposes an optimization overture for optimal siting and sizing of a rapid CS (RCS). It aims to minimize the daily total cost (which includes the cost of substation energy loss, traveling cost of EVs to the CS, and investment, variable, and operational costs of the stations simultaneously) while maintaining system constraints. The binary gravitational search algorithm, genetic algorithm, and binary particle swarm optimization algorithm were employed to optimize the daily total cost by finding the best location and sizing of the RCS in a given metropolitan area in Malaysia. The results show that the proposed methods can find optimal locations and sizing of a RCS that can benefit EV users, CS developers, and the power grid.",
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AU - Mohamed, Azah

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N2 - Recently, electric vehicles (EVs) have been seen as a felicitous option towards a less carbon-intensive road transport. The key issue in this system is recharging the EV batteries before they are exhausted. Thus, charging stations (CSs) should be carefully located to make sure EV users can access a CS within their driving range. Considering geographic information and traffic density, this paper proposes an optimization overture for optimal siting and sizing of a rapid CS (RCS). It aims to minimize the daily total cost (which includes the cost of substation energy loss, traveling cost of EVs to the CS, and investment, variable, and operational costs of the stations simultaneously) while maintaining system constraints. The binary gravitational search algorithm, genetic algorithm, and binary particle swarm optimization algorithm were employed to optimize the daily total cost by finding the best location and sizing of the RCS in a given metropolitan area in Malaysia. The results show that the proposed methods can find optimal locations and sizing of a RCS that can benefit EV users, CS developers, and the power grid.

AB - Recently, electric vehicles (EVs) have been seen as a felicitous option towards a less carbon-intensive road transport. The key issue in this system is recharging the EV batteries before they are exhausted. Thus, charging stations (CSs) should be carefully located to make sure EV users can access a CS within their driving range. Considering geographic information and traffic density, this paper proposes an optimization overture for optimal siting and sizing of a rapid CS (RCS). It aims to minimize the daily total cost (which includes the cost of substation energy loss, traveling cost of EVs to the CS, and investment, variable, and operational costs of the stations simultaneously) while maintaining system constraints. The binary gravitational search algorithm, genetic algorithm, and binary particle swarm optimization algorithm were employed to optimize the daily total cost by finding the best location and sizing of the RCS in a given metropolitan area in Malaysia. The results show that the proposed methods can find optimal locations and sizing of a RCS that can benefit EV users, CS developers, and the power grid.

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KW - Genetic algorithm

KW - Gravitational search algorithm

KW - Optimal planning

KW - Particle swarm optimization

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