Data-Driven Adjustable Potential Assessment of Large-Scale Aggregated Charging Station

Large-scale electric vehicles (EVs) are important flexibility resources in power grid due to their characteristic of energy storage, and evaluating their adjustable potential is of great significance for participating in the interaction with power grid. Hence, this paper proposes a data-driven adjustable potential assessment method for large-scale charging stations. Firstly, the number of charging piles in different states is calculated by using station level non-private data. Based on the state changes of charging piles, a method for calculating the adjustable potential upper/lower limit value of a single charging station is proposed. Then, based on k-means clustering algorithm, the evaluation results and geographical location display of adjustable potential of large-scale charging stations are given. Finally, the effectiveness of the proposed method is verified by using the data of 247 charging stations in Shenzhen.