Journal of Intelligent & Fuzzy Systems 31 (2016) 1329–1340 DOI:10.3233/IFS-162199 IOS Press 1329 Probabilistic scheduling of smart electric grids considering plug-in hybrid electric vehicles Amir Ghaedi a , Saeed Daneshvar Dehnavi b and Hadi Fotoohabadi a,∗ a Department of Electrical and Computer Engineering, Dariun Branch, Islamic Azad University, Dariun, Iran b Department of Electrical and Computer Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran Abstract. Optimal feeder reconfiguration is a precious and valuable strategy that can improve the distribution system from different aspects such as power loss reduction, reliability enhancement, load balance improvement and power quality. Nev- ertheless, the charging demand of electric vehicles (EVs) can affect the optimal switching greatly. Therefore, this paper introduces a new stochastic framework to solve the optimal feeder reconfiguration in the presence of plug-in hybrid electric vehicles (PHEVs). The high volatile stochastic behavior of PHEVs is modeled in the proposed formulation and is considered in determining the optimal status of remotely controlled switches (RCSs). Also, a stochastic framework is constructed based on point estimate method (PEM) with 2m-scheme wherein m is the number of uncertain parameters to capture the uncertainty effects. In addition, a new optimization algorithm based on teacher learning optimization (TLO) algorithm with a two-stage modification method are proposed to explore the entire search space globally. The objective function to be optimized is the total cost of the network incorporating the cost of supplying loads and PHEVs charging demand, cost of power losses and the cost of switching. The performance of the proposed method is examined on the IEEE standard distribution test system. Keywords: Reconfiguration, plug-in hybrid electric vehicle (PHEV), teacher learning optimization (TLO) algorithm Nomenclature Cost Total network cost ($) C switching Cost of switch operation ($) C bat Battery capacity of PHEV (kWh) DOD Depth of discharge in PHEV battery f zl Probability function of z l X teacher best student in the class Iter Iteration number I t i Current magnitude of ith branch at hour t (A) ∗ Corresponding author. Hadi Fotoohabadi, Department of Electrical and Computer Engineering, Dariun Branch, Islamic Azad University, Dariun, Iran. Tel./Fax: +9871267825; E-mail: Hadi.Fotoohabadi@gmail.com. I max i Maximum current of ith branch (A) X b i Position of best individual in ith movement X mut Improved new individual based on mutation process X Testi ith test individual generated in modification strategy N branch /N bus /N loop Number of branch/bus/main loops of network N RCS Number of RCSs N switching RCS Daily switching actions for each RCS N max switching Maximum number of daily switching actions 1064-1246/16/$35.00 © 2016 – IOS Press and the authors. All rights reserved