Currently, distribution system operators (DSOs) are asked to operate distribution grids, managing the rise of the distributed generators (DGs), the rise of the load correlated to heat pump and e-mobility, etc. Nevertheless, they are asked to minimize investments in new sensors and telecommunication links and, consequently, several nodes of the grid are still not monitored and tele-controlled. At the same time, DSOs are asked to improve the network’s resilience, looking for a reduction in the frequency and impact of power outages caused by extreme weather events. The paper presents a machine learning GIS-based approach to estimate a secondary substation’s load profiles, even in those cases where monitoring sensors are not deployed. For this purpose, a large amount of data from different sources has been collected and integrated to describe secondary substation load profiles adequately. Based on real measurements of some secondary substations (medium-voltage to low-voltage interface) given by Unareti, the DSO of Milan, and georeferenced data gathered from open-source databases, unknown secondary substations load profiles are estimated. Three types of machine learning algorithms, regression tree, boosting, and random forest, as well as geographic information system (GIS) information, such as secondary substation locations, building area, types of occupants, etc., are considered to find the most effective approach.

The paper aims to provide primary substations’ optimal siting and timing to expand existing distribution networks. The proposed methodology relies on three main features: a geographic information system for capturing, elaborating, and displaying spatial input data; a particle swarm optimization algorithm to locate and timing the new primary substations; a Voronoi diagram-based approach to find the primary substation service areas and loading. The optimization criteria follow the approach of serving every customer from the nearest primary substation to ensure that the distribution delivery distance is as short as possible, reducing feeders’ cost, electric losses, and service interruption exposure. The algorithm also considers the primary substation transformers’ capacity limit. Thanks to Unareti, the distribution system operator of Milan and Brescia, the methodology was tested by carrying out several simulations, progressively increasing the number of new primary substations. The results obtained confirm the proposed approach’s effectiveness and show that the methodology is a valuable tool to guide Unareti, and distribution system operators in general, in expanding distribution networks to face the challenges of the energy transition.