# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 2 Networked Microgrids' Configuration
### Formation
#### 2.1.1 Clustering Approaches  
The discourse on clustering algorithms concerning the segmentation of distribution networks into multi-microgrids underscores the significance of employing data-driven methodologies to identify nodes or regions with similar attributes such as load demand, geographical proximity, or DER capacity. These methodologies can generally be categorized into partitional clustering, hierarchical clustering, and density-based methods, each serving distinct purposes.  
Partitional clustering algorithms play a key role in forming NMGs by pinpointing cluster centers and grouping data points into distinct clusters. The objective is to minimize the squared difference between data points and centroids. These data points represent DERs characterized by features such as power output, energy storage capacity, and geographical location. Typically, K-means and K-medoids algorithms are utilized to cluster DERs based on these features. In K-means, the centroid is the mean of all data points in a cluster, while K-medoids uses actual data points as centroids or medoids. These algorithms initialize random centroids, assign each DER to the closest center, and calculate new centroids iteratively. The iterative refinement process aims to minimize the sum of dissimilarities between data points and cluster representatives. In the context of forming NMGs, the goal of these algorithms is to minimize power loss and maximize load pickup while balancing demand and supply constraints [8; 9; 10; 11]. Their advantages include simplicity and scalability, making them well suited for large-scale microgrid systems. However, notable limitations encompass reliance on predefined cluster numbers, sensitivity to initial configurations, challenges with non-convex cluster shapes, and limited consideration of the spatial or geographical factors crucial for microgrid planning [12].  
Hierarchical clustering offers an alternative algorithm for organizing data points into clusters, and it involves two primary methods: divisive and agglomerative. In divisive clustering, all DERs are initially grouped into a single cluster, and recursive splitting occurs until each DER forms its own cluster. On the other hand, agglomerative clustering takes a bottom-up approach, starting with each DER in a separate cluster and iteratively merging the most similar pairs until all DERs belong to a unified cluster. This hierarchical structure is employed to form multi-microgrids by allocating DERs and identifying boundaries, taking into account distribution system features such as topology, line impedance, and load distribution. The objective is to maximize the restoration-path availability of critical loads after power outages [13], and minimize maintenance costs [14]. The hierarchical structure enhances microgrid formation by being adaptable to different datasets, allowing the creation of isolated yet dense and large clusters. This adaptability is crucial, especially in sparsely populated and remote locations. However, a significant drawback of hierarchical clustering algorithms is that once objects are linked, they cannot be reconsidered for linkage in another branch of the hierarchical tree. This limitation leads to a lack of robustness and sensitivity to noise and outliers [15].  
Density-based clustering algorithms are designed to identify clusters with arbitrary shapes, allowing for the detection of high-load-density clusters and the establishment of microgrid boundaries [16]. These algorithms excel in handling noise in data, offering advantages over hierarchical or partitional methods [17]. However, they may face efficiency challenges when dealing with higher-dimensional data due to their dependence on non-trivial user-defined parameters in the dataset.