GIS-based probabilistic modeling of BEV charging load for Australia

Abstract:

Due to the unknown spatio-temporal distribution of Battery Electric Vehicles (BEVs) charging load, introducing large quantities of BEVs in the transportation sector has drawn growing concerns about its negative impacts on the power grid system. Based on real-world vehicle driving survey data, this paper presents a deterministic and a probabilistic model to quantitatively investigate the spatio-temporal distribution of BEV charging load for Australia. Whilst the trip-chain-related travel parameters for the deterministic model are directly taken from travel survey data, those for the probabilistic model are generated by the k-Nearest-Neighbour (kNN) algorithm. The probabilistic model is validated and applied to simulate the spatio-temporal distribution of BEV load based on GISgridded data for Australia. We are able to distinguish different temporal BEV charging load distributions for weekdays and weekends, and a heavy spatial concentration in capital cities.

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