Probabilistic scheduling of smart electric grids considering plug-in hybrid electric vehicles

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.