Multi-stage Pricing Strategy of Charging Station Based on EV Charging Load Forecasting

Electric vehicle charging station pricing plays a critical role in guiding user charging behavior. This paper proposes an optimal pricing strategy for charging stations based on load forecasting. Firstly, a method for load forecasting is presented to predict the charging station's future demand. Secondly, a machine learning-based evaluation method is proposed to assess the price sensitivity of electric vehicle charging demand. The proposed two-stage pricing strategy involves setting an invitation price for users based on the price sensitivity of electric vehicle charging demand model in the day-ahead stage and a guiding price for the intra-day stage. In the day-ahead stage, users are invited to charge their vehicles based on the predicted demand, taking into account the price sensitivity of electric vehicle charging demand. In the intra-day stage, the price is adjusted to guide user behavior and avoid charging station deviation costs. Simulation experiments demonstrate that the proposed two-stage pricing strategy effectively guides user charging behavior, reduces charging station deviation costs, and increases charging station revenue. The results show that this strategy is suitable for various load conditions and can be used to achieve optimal pricing under different scenarios.