Modelling of Charging Demand for Electric Vehicle based on Person-trip Survey Data

Abstract

In electricity distribution networks, voltage might be dropped significantly by simultaneous charging of high penetration Electric Vehicle (EV). The impact of EV on voltage drop can be different among areas depending on various factors such as original electricity demand profile without EV, EV penetration level and usage pattern, etc. For the statistical assessment of impact of high penetration EV in various distribution networks, this study proposes a model to estimate time-series data of electricity demand including EV charging. The proposed model is based on Grid Square Statistics Data on geographical distribution of population, employee, etc. and Person-trip Survey Data. As an example using the proposed model, this study compares three EV charging strategies in suburban area of Nagoya, Japan. As a result, if the EV charging starts simultaneously at 23:00 at which the night-time discount electricity rate is applied, the night-time electricity demand can be much larger than the day-time peak demand without EV. The increase can be mitigated by autonomous charging control according to the time and SOC of EV returning home in most areas. Finally, by using the calculated electricity demand profile, this study assesses the impact of EV charging on voltage profile. The result shows that the impact of EV charging on distribution network voltage can be avoided by applying the proposed autonomous charging control scheme.