Spatial-Temporal Simulation to Estimate the Load Demand of Battery Electric Vehicles Charging in Small Residential Areas

Abstract

This paper presents a spatial-temporal approach for estimating the load demand of battery electric vehicles (BEV) charging in small residential areas. This approach is especially suited for simulating the driving pattern of BEVs in cities without this kind of information. The service zone is divided into several sub-zones; each of these has a probability that represents how likely it is for a BEVs to cross the sub-zone. The driving pattern of BEVs is simulated using a multi-agent framework, which estimates the spatial distribution of these in a city. To determine the hourly charge in each place identified in the spatial area, the model considers the battery charging profile via two charging scenarios. The main contribution of this method is the estimation of BEV charging in feeders or transformers using small-scale simulation. The proposed approach was tested on a real distribution system of a mid-sized city in Brazil. For this specific system, the simulation was able to identify two different levels of agglomerations; when the worst-case scenario with a 20 % penetration is analyzed, an increase in peak demand up to 34.04 % was determined in the most affected part of the distribution system while the rest of the distribution system is almost unaffected.