## 1 Introduction  
Primary Substations (PSs) siting, sizing, and timing are strategic decisions in planning distribution networks (DNs). Although PSs represent a minority of the entire DN cost, PSs outline DNs' global shape. PSs are the meeting place between transmission and distribution, and their locations define the endpoints of the transmission system and the starting points of the distribution level. Therefore, selecting a poor site from the feeder standpoint can significantly increase the cost of the whole DN, removing, for instance, the benefits of using advanced feeder routing planning procedures [1]. Expansion planning of PSs is even more crucial nowadays to prepare DNs to face the challenges of the energy transition, both in terms of the end user's consumption electrification [2] and the deployment and integration of distributed generators [3, 4]. The DN of Milan and Rozzano is not immune to these challenges. The DN is currently managed by the Distribution System Operator (DSO) Unareti, which needs to install new PSs to improve reliability and resilience and face the energy transition [5]. Several papers in the literature deal with DN expansion planning [6]. Some of them make use of Geographic Information Systems (GIS) [7] and evolutionary algorithms, such as Genetic Algorithm (GA) [8], Particle Swarm Optimization (PSO) [9], and Tabu Search (TS) [10]. For instance, paper [11] identifies the potential location for substation development by conducting spatial data analysis using GIS software. The method minimizes labor and logistic costs while at the same time providing better database storage compared to conventional methods. In [12], the authors propose GIS application to distribution substation planning for the metropolitan electricity authority distribution systems. The main objective is to serve future load growth and increase the reliability of the distribution systems. GIS is used in the planning process because it contains comprehensive topological information on electrical equipment from transmission substations, distribution substations, power transformers, and distribution transformers to revenue meters at customer sites. It also gives the number of customers in each feeder and power demand in small areas. Paper [13] successfully defines the location of substations' optimal use of ArcGIS aid applications and tools inside it. With this application, the authors may determine the location that allows building the substation to meet the East Java region's electricity needs. Based on data from the state electricity company, the method can optimize the electricity distribution network using the Voronoi diagram and the Delaunay triangulation. Paper [14] presents an improved method based on the weighted Voronoi diagram and transportation model for substation planning, which can optimize the location, capacity, and power supply range for each substation with minimum investment, evaluating the cost of the lines, substations' cost, and annual operating expenses. Many experiments show that the improved method can get more reasonable and optimized planning results within a shorter time than other algorithms in the literature. Authors in [15] present a novel and applied approach based on clustering to determine optimal locations and sizing of sub-transmission substations with their associated service areas. This optimization aims to minimize fixed and operation costs while respecting all technical constraints. The proposed model includes a method for adding planning constraints in a K-means-based algorithm that converts the K-means-based algorithm into a tool with the most accuracy. The algorithm is then tested on a real urban network to verify the effectiveness and feasibility of the proposed model. Paper [16] presents an enhanced evolutionary algorithm to solve large distribution networks' static distribution substation planning problem.