Electric Vehicle Charging Coordination on Distribution Network by Using Particle Swarm Optimization and Genetic Algorithm
M. R. M. Dahalan1, N. M. Sapari2, M. F. Darus3

1M.R.M. Dahalan, Marine Electrical & Electronics Technology, Universiti Kuala Lumpur, Lumut Perak, Malaysia
2N.M. Sapari, Faculty Information and Science Engineering, Management & Science University, Shah Alam Selangor, Malaysia.
3M.F. Darus, Marine Electrical & Electronics Technology, Universiti Kuala Lumpur, Lumut Perak, Malaysia
Manuscript received on September 11, 2019. | Revised Manuscript received on 05 October, 2019. | Manuscript published on October 10, 2019. | PP: 5673-5676 | Volume-8 Issue-12, October 2019. | Retrieval Number: L39941081219/2019©BEIESP | DOI: 10.35940/ijitee.L3994.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The Electric Vehicles becoming very popular in the recent years. Typically, Electric Vehicles propulsion systems come from one or more electrical motors built inside the vehicles. This motor used electricity as energy combustion method. Due to the limited energy storage capacity, Electric Vehicles need to replenish by plugging into an electrical source. The problems appear during multiple Electric Vehicles perform charging process in an Electric Distribution Network. This process will be causing line overload and efficiency degradation of Distribution Network. In performance to evaluate the potential of different of charging coordination, a classification has been made. The new coordinated process may consider minimum power losses and acceptable voltage limit. The process also needs to define the optimal uncoordinated and coordinated charging point. Therefore, a simulation-based framework will be performed, that use two algorithms which are Particle Swarm Optimization and Genetic Algorithm.
Keywords: Index Terms: Electric Vehicle, Electric Distribution Network, Genetic Algorithm, Particle Swarm Optimization
Scope of the Article: Swarm Intelligence