Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience

Alessandro Bosisio, Matteo Moncecchi, Andrea Morotti and Marco Merlo
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Alessandro Bosisio: Department of Energy, Politecnico di Milano, 20156 Milano, Italy
Matteo Moncecchi: Department of Energy, Politecnico di Milano, 20156 Milano, Italy
Andrea Morotti: Planning Department, Unareti S.p.A., 20138 Milano, Italy
Marco Merlo: Department of Energy, Politecnico di Milano, 20156 Milano, Italy

Energies, 2021, vol. 14, issue 14, 1-23

Abstract: 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.

Keywords: geographic information systems; machine learning; power distribution networks; system resilience (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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