Cost-Optimal Operation of Dynamic Wireless Charging Stations for Electric Vehicles Through Predictive Energy Trading and Storage Scheduling

The transition to electric vehicles is a cornerstone of global efforts to reduce carbon emissions, and dynamic wireless charging is instrumental in facilitating this shift by providing continuous charging capabilities. Renewed interest in this technology has emerged due to its reported advantages, including smaller battery requirements and reduced range anxiety. To effectively leverage the benefits of dynamic charging, forecasting and operational strategies must be designed to work synergically to achieve cost-optimality. This paper proposes a predictive model to optimize the interaction between electric vehicles charging infrastructure, localized energy storage, and photovoltaic systems under variable electricity market conditions. By framing the decision-making process as a financial bidding problem, machine learning-based day-ahead price predictions are employed to minimize energy costs. The proposed approach aims at providing insights for charging stations operators to formulate and adjust their operational and planning strategies from a financial perspective.