Optimal Integration of Plug-In Hybrid Electric Vehicles in Microgrids

Abstract

Plug-in hybrid electric vehicles' (PHEVs) integration can result in additional electricity consumers and electricity suppliers in microgrids (MGs). This paper presents a new method for optimal integration of PHEVs in MGs, which considers the optimal number of parking numbers under optimal scheduling of PHEVs. Due to the uncertainty of solar energy, the radial basis function network (RBFN) techniques are used for forecasting photovoltaic (PV) power output. Monte Carlo simulation is used to deal with the uncertainties associated with the daily distance driven of the PHEVs, load values, and electricity market price. The objective is the minimization of total cost (TC) and optimal set of the optimization problem is found using genetic algorithm (GA) method. In order to verify the effectiveness of the proposed method, two market policies are used as case studies. The computation results can be used to evaluate the impact of PHEVs' integration on economical performance of MGs.