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

Abstract

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