Agglomerative Hierarchical Clustering Applied to Medium Voltage Feeder Hosting Capacity Estimation

In this paper, we will assess the relevance of applying hierarchical agglomerative clustering algorithms on medium voltage feeder descriptive parameters (average line impedance, capacity, total length, etc.) in order to select representative feeders for long term planning studies. To achieve this, we start by creating a dataset of medium voltage feeders (bus and line geometries and characteristics) by combining domain knowledge with open datasets of distribution network layouts, district level electricity consumptions and building footprints. We then use this dataset to calculate descriptive attributes for each feeder as well as their maximal load hosting capacity and the associated type of constraint. Afterwards we perform a statistical analysis of the descriptive attributes in order to select the most relevant to use as inputs of the clustering algorithms. Finally, we apply a hierarchical agglomerative clustering algorithm with varying number of clusters, assess the quality of the results using internal and external validation and evaluate the ability of the medoids of each cluster to represent the behavior of the corresponding feeders.