Nodal charging demand forecast of EVs considering drivers’ psychological bearing ability based on NMC‐MCS

Abstract

Abstract Widespread adoption of electric vehicles (EVs) would significantly increase the electrical load demand in power distribution networks. Most previous studies investigated EV charging demand based on drivers’ trip habits, but the impact of psychological bearing ability (PBA) about the range anxiety on EV drivers’ charging decision are ignored. Here a novel forecast method considering drivers’ PBA for predicting nodal charging demand of EVs is proposed. The charging decision model considering PBA is established based on an improved Richards model, and the spatial‐temporal dynamics model is established based on the non‐homogeneous Markov chain (NMC) and random trip chain. Meanwhile, the Monte Carlo simulation (MCS) is adopted to avoid the disaster of dimensionality in large scale EVs charging problem. The proposed method is illustrated by an actual system integrated traffic network and power grid. The simulation results demonstrate that drivers’ PBA will significantly affect the charging decision, then changes the spatial‐temporal distribution of charging power demand. The conclusion is that the drivers with lower PBA have a higher charging demand, and the impact of drivers’ PBA on charging power has a close relation with the initial battery level (IBL) of EVs.

Keywords

• Monte Carlo methods
• Markov processes
• Power system management, operation and economics
• Power system planning and layout
• Distribution networks
• Automobile electronics and electrics