Learning to predictive scheduling for orderly charging of electric vehicles in renewable energy charging stations

To address the challenges of low control accuracy and profitability in renewable energy charging stations participating in auxiliary services, caused by the uncertainties of electric vehicles (EVs) and renewable energy, this paper proposes a learning to predictive scheduling for orderly charging of EVs. To reduce the scheduling cost for charging stations engaging in auxiliary services and enhance participation enthusiasm among different types of EVs, a price incentive mechanism of multi-charge mode is introduced into the day-ahead optimization model. This mechanism is formulated based on charge and discharge energy boundaries and incentive response analysis. Furthermore, a dynamic tracking control method based on PD-iterative learning model predictive control (PD-ILMPC) is proposed to achieve precise tracking of the day-ahead optimal targets. To improve stability and mitigate power fluctuations during the EV power control process, a control priority coefficient is incorporated into the optimization function of the PD-ILMPC. Finally, a wind-photovoltaic-energy storage charging station is used as a case study to demonstrate the effectiveness and optimality of the proposed method. Simulation results show that the proposed method achieves accurate control and ensures the economic operation of the charging station.