Load flexibility evaluation of fast-charging stations considering Drivers' choice uncertainty and Price-varying spatial correlation.

Power systems need flexible resources to support renewable energy integration, where electric vehicles (EVs) provide a prospective solution. This study utilizes a charging station operator as a spatial aggregator to evaluate load flexibility of fast-charging stations (FCSs) with price incentives. First, FCSs' load flexibility is related with drivers' charging station choice uncertainty. Therefore, this uncertainty is analyzed by the logistic regression model and real-world data is utilized to obtain the model parameters and clarify drivers' choice preferences. Second, the price-varying load-transfer probabilities and spatial correlation parameters among FCSs are analyzed. Based on them, the probability theory is employed to obtain the spatially correlated probability distribution of FCS loads with price incentives. Third, the evaluation model of FCSs' load flexibility with real characteristics is established with drivers' choice uncertainty and spatial correlation as the basis of building a future general virtual power plant that considers spatial correlations, considering that the flexibility range varies with the response probability in a single FCS and that the price-varying spatial correlation between multiple FCSs flexibility. Case studies validate the effectiveness of the proposed method. The results validate the driver preferences obtained from real-world data, the price-varying spatial correlation of FCSs for different types of drivers, and the feasibility of grid congestion alleviation with obtained FCSs' load flexibility. • Drivers' charging station choice uncertainty is analyzed using real-world data. • Price-varying load-transfer probabilities among FCSs are analyzed. • The spatially correlated probability distribution for FCS loads is obtained. • The evaluation model of FCSs' load flexibility is established. [ABSTRACT FROM AUTHOR]