Estimating the impact of electric mobility on distribution networks through GIS techniques

This paper presents a novel GIS-based (Geographic Information System) approach, integrated with graph theory algorithms to predict the additional power demand from EVs (Electric Vehicles) on electric distribution grids. The energy consumption of an EV is primarily influenced by distance travelled, which is affected by factors such as traffic congestion and road network design. To consider all these factors, a weighted graph is constructed using the layout of Lombardy's traffic network in northern Italy. The traffic flow patterns are simulated utilizing a regional travel survey that provides the trips between the 1450 different travel zones of the region. The trips are simulated within the roads graph using Dijkstra's algorithm to find the fastest paths. The spatial resolution of the trips’ origins and destinations is increased using a further sectionalization of the region and gravity model using GIS-empowered probability density functions. The output from the traffic simulation is overlaid with the service areas of primary substations to estimate the added load from EVs. This integration of the two layers enables the identification of when, where, and to what extent electric mobility will impact the electric distribution grids. The novelties of the research work encompass the following key contributions: modelling a large-scale and real-world transportation network represented in graph theory and integration with the corresponding primary substation service areas, consequently enabling the estimation of added load from EVs with a high spatial–temporal resolution.