Impact of Reservation on the Performance of Optimal Control in Electric Vehicles Fast Charging Stations

Service stations equipped with multiple delivery points for plug-in electric vehicles fast charging are expected to gradually replace petrol-based service stations. Operators interested in managing the new stations are going to face key challenges in terms of sizing the station's point of connection to the grid so as to render the provision of the service economically viable. As connection fees depend on the plant nominal power, sustainability is expected to come from the integration in the area of local generation from renewable energy sources and an electric energy storage device. This paper presents an approach based on optimal control methodology for regulating the energy flows at the point of connection of a service station with the grid. The control goal is to optimally balance the Plug-in Electric Vehicle (PEV) recharging power through the Energy Storage System (ESS), while also taking into account an uncertain number of charging requests and local generation. The overall objective is to lower as much as possible the power flowing at the point of connection with the grid, while providing as much as possible maximum power to the single PEVs. A model predictive control (MPC) algorithm is proposed to control the ESS. The single MPC iteration is based on continuous time optimal control. The core contribution of this paper is in the study of the impact of the level of uncertainty of the charging power (i.e., the number of charging requests by the PEV owners) on the performance of the controller. Simulation results show that reservations of the charging service represent a key aspect to take into account for taking decisions on the size of service station point of connection, even with the aforementioned control architecture.