Multipurpose Charging Schedule Optimization Method for Electric Buses: Evaluation Using Real City Data

The use of electric vehicles (EVs) and photovoltaics (PV) is increasing worldwide. Transportation networks require the effective renewable energy (RE) for EVs, while power local consumption PV energy, mainly at initiative governments. Although many previous studies have addressed these requirements using private EVs as mobile storage resources, their uncertainty uncontrollability are major issues. Therefore, our focused on buses having high controllability certainty, developed evaluated two independent minimization problems kilowatts (KW) kilowatt-hours (KWH) surplus RE, a charging schedule optimization method mixed integer linear programming. However, there were still technical issues regarding simultaneous feasibility KW KWH minimization. This study aims to extend generalize PV-derived reverse flow (RPF). With this multiobjective optimization, peak-cut RPF improves hosting capacity expands amount connectable RE promotes realization decarbonized public transportation. Through simulations detailed actual bus operation data real city, simultaneously optimizing relationship between indicators number EV chargers confirmed. optimized 17 achieved maximum 211.4 kW 1318.4 kWh RE-RPF absorption, in which reduced CO2 emissions by 495.7 kg-CO2/day.