Optimal Planning of a Photovoltaic-Based Grid-Connected Electric Vehicle Charging System Using Teaching–Learning-Based Optimization (TLBO) †

Abstract

In this paper, an energy management schedule is proposed for a renewable energy (RE)-based grid-connected electric vehicle (EV) charging system (commercial charging station). This energy management schedule was converted into an energy management algorithm (EMA) on which teaching–learning-based optimization (TLBO) was used to determine the optimum size of the photovoltaic (PV) array and energy storage unit (ESU) required to charge electric vehicles with the help of the proposed energy management scheme. It was designed in such a way that the EVs are charged without incurring economic losses to the station owner. The objective function of the TLBO was formulated based on a financial model that comprised the grid tariff, EV demand, and the purchasing as well as selling prices of RE and ESU energy. By integrating the financial model with the energy management algorithm (EMA), the TLBO computed the minimum number of PV modules (Npv) and ESU batteries (Nbat) for various vehicles.