Two-Stage Event-Triggered Model Predictive Power Control of EV Charging Stations With V2G Capability

To achieve optimal power control of an electric vehicle (EV) charging station, this paper presents a two-stage model predictive control (MPC) approach considering environmental and economic impacts. The charging station that includes a battery energy storage system (BESS) and a photovoltaic (PV) system is also connected to the main power grid. The proposed approach includes two stages: the first stage is introduced to reduce carbon dioxide (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CO</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>) emissions and costs associated with interaction with the main power grid. This stage itself consists of two sub-levels: in the first sub-level, an offline single-step optimization problem is solved based on expected data for the entire day, while the second sub-level is designed to reduce the impact of data uncertainties used in the previous step. Then, a real-time event-triggered MPC (ETMPC) is configured based on the new constraints of the power to be purchased from the main power grid resulting from the first stage. The proposed scheme considers EV energy demands and departure times, and by leveraging vehicle-to-grid (V2G) capabilities and the constraints obtained from the first stage, it achieves more efficient power control. To reduce the computations needed to solve the online optimization problem at each time step, new conditions based on an event-driven approach are introduced. Also, to improve the feasibility of the ETMPC, an ellipsoidal terminal constraint is introduced. In addition, due to the event-triggered mechanism, new conditions are presented for proving the descent of the cost function, which guarantees the stability of the closed-loop system. Finally, various numerical simulations and experimental tests are carried out in order to highlight the environmental, economic, and computational impacts of the proposed method.