EV Charging Scheduling with Genetic Algorithm as Intermittent PV Mitigation in Centralized Residential Charging Stations

Abstract

Residential charging stations are a necessity by increasing electric vehicles (EVs) that promise to be environmentally friendly. Residential charging stations systems that use renewable energy such as PV have become an important concern in an effort to build green energy. Unfortunately, intermittent PV is an unavoidable problem. The EV charging demand characteristics of residential charging stations can be utilized to work together with the generator side to achieve a power balance which suitable for the smart grid concept. Our contribution is to propose a novel charging scheduling based on intermittent PV. The charging schedule is used to reduce peak loads during intermittent PV events. The charging scheduling scenario is run using the genetic algorithm with Google Colab to find out that the charging schedule has succeeded in maintaining power balance in the residential charging station systems. The innovative formula of fitness calculation used in the genetic algorithm is a way to get optimal charging scheduling according to PV capabilities. The simulation results confirm that the peak load can be decreased by 30% according to the intermittent PV value in all case modes.