Affiliations: Department of Electrical and Computer Engineering, Dariun Branch, Islamic Azad University, Dariun, Iran
Correspondence:
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Corresponding author. Amir Ghaedi, Department of Electrical and Computer Engineering, Dariun Branch, Islamic Azad University, Dariun, Iran. E-mail: f.power.co@gmail.com.
Abstract: The appearance of Plug-in Electric Vehicles (PEVs) in the electric grids is providing new opportunities when some new challenges are also created. Technically, PEVs are movable loads that can benefit to both owners and utilities in case of using Vehicle-to-Grid (V2G) technology. Therefore, this article aims to investigate the Distribution Feeder Reconfiguration (DFR) effect to optimally manage PEV performance in a probabilistic framework. The proposed stochastic framework will capture the uncertainties of location of PEVs as well as driving pattern and battery State-of-Charge (SOC). In addition, a new self-adaptive evolutionary swarm algorithm based on Social Spider Optimization (SSO) algorithm is proposed that will search the problem space globally. The simulation results on the IEEE standard test system shows the high performance of the proposed method.
Keywords: Plug-in Electric Vehicle (PEV), Vehicle-to-Grid (V2G), feeder reconfiguration, Social Spider Optimization (SSO)
