Journal of Intelligent & Fuzzy Systems 30 (2016) 1067–1075 DOI:10.3233/IFS-151829 IOS Press 1067 Optimal distribution reconfiguration considering high penetration of electric vehicles Amir Ghaedi ∗ , Elaheh Taherian Fard, Hadi Fotoohabadi and Farzaneh Kavousi-Fard Department of Electrical and Computer Engineering, Dariun Branch, Islamic Azad University, Dariun, Iran 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) Nomenclature C sub t /C t loss /C t v Hourly energy price/ loss cost/ V2G price. d ic/d ib Cartesian distance to closest/best individual. E D,v t Energy for PEVs in fleet v to drive at time t . E v t Available energy in batteries of fleet v at time t . E v ini /E v fin Initial/final energy in PEV fleet v. E v min /E v max Min/max energy in batteries of PEV fleet v. f i /f b /f w Objective value of ith/best/worst individual. Iter Iteration counter. ∗ Corresponding author. Amir Ghaedi, Department of Electrical and Computer Engineering, Dariun Branch, Islamic Azad University, Dariun, Iran. E-mail: f.power.co@gmail.com. Iter max Maximum number of iterations. L´ evy(.) L´ evy flight function. M w Weighted mean of male spiders in the colony. N mod Number of individuals which select a strategy. N p Population size. N F/N M Number of female/male spiders in the colony. N v Total number of PEVs. n v Number of control variables. P sub t /P sub max Hourly/max imported power from upstream grid. P loss t Hourly active power loss of network. P c,v t /P d,v t Charging/discharging capacity of PEV fleet v. P c,v min /P c,v max Min/max charging capacity of PEV fleet v. P d,v min /P d,v max Min/max discharging capacity of PEV fleet v. 1064-1246/16/$35.00 © 2016 – IOS Press and the authors. All rights reserved