Due to the complexity of electricity pricing policies, multiple implementation steps, and difficulty in regulation, errors in electricity pricing implementation occur from time to time. This not only damages the fairness and efficiency of the electricity market but also affects the economic benefits of power enterprises and the electricity costs of users. A method was proposed for detecting electricity price anomalies at charging stations based on secondary clustering. Firstly, the electricity price anomalies were classified and the electricity consumption characteristics were analyzed. Secondly,the K-means clustering algorithm was used to extract electric vehicle users. Then, in the second clustering, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to accurately identify more complex default situations,such as high price and low connection. The proposed method improves the accuracy and efficiency of electricity price implementation through two rounds of cluster analysis, and has certain theoretical significance and application value
