Optimal Framework to Maximize the Workplace Charging Station Owner Profit while Compensating Electric Vehicles Users

Electric vehicles (EVs) are one promising technology for an improved sustainable transportation sector, particularly when they are charged with electricity from renewable energy sources. However, the EV user behaviour uncertainties as well as the fluctuating generation of renewable energy sources make the interaction between these technologies challenging. In this work, a new approach to coordinate the charging process of multiple EVs parked at workplace charging station (WCS) equipped with Photovoltaic panels (PV) is proposed. Considering the PV incremental cost and the day-ahead electricity price (DAEP), an optimal framework is introduced to maximize the WCS owner profit while compensating the EV users for discharging their EVs' battery. The EV user behaviour uncertainties are modeled by probability distribution functions, and the PV generation is forecasted by the backpropagation neural network model (BPNN). The optimization problem is solved by mixed-integer linear programming (MILP) while the Monte Carlo sampling methods have been applied to handle the EV user behaviour uncertainties. The results show that the proposed method increases the WCS owner profit and the EV user compensation by 54% and 50.7%, respectively, compared to uncoordinated charging. Moreover, the estimated WCS owner profit and the EV user compensation generated by coordinated charging are 1.72% and 1.35%, respectively, higher than the profits based on real user behaviour data.