Agglomerative Hierarchical Clustering-based Analysis of Charging Station Load Patterns

In the management of power system, categorizing charging station load profiles is crucial for enhancing operational efficiency and reliability of the system. This paper presents a agglomerative hierarchical clustering-based (AHC) algorithm to effectively analyze and categorize charging station load profiles. First, a substantial dataset of multiple charging stations is preprocessed and normalized to address the problem of varying load scales across different charging stations. Then, the AHC algorithm is used to cluster charging stations based on their similarity in load profiles. To demonstrate the AHC algorithm's superiority in computation time and effectiveness metrics over the K-means algorithm, the comparison is conducted by clustering charging station load data from Perth, Palo and Boulder. This algorithm can accurately identify the variation in load pattern, providing insights for resource allocation and maintenance prediction.