Stochastic Model for Estimation of Aggregated EV Charging Load Demand

The transformation of smart cities can be greatly aided by the electrification of transportation. Adoption of Electric Vehicles (EVs) is enhanced by deploying fast charging stations and by providing drivers with the convenience of fast charging their vehicles to full capacity. Prior to installing the charging infrastructure, it is important to assess the EV charging demand. The demand for EV charging at a given station depends on specific characteristics of EVs, the driving patterns, charging rate, location of charging. In this work, a scenario-based temporal stochastic fast EV charging demand is developed considering the uncertainties associated with the aforementioned factors. Model is able to estimate both weekdays and weekends demand where the peak and valleys occur at different time of the day. Monte-Carlo simulation is utilized to model the scenario-based demand. The uncertainties are incorporated into the charging demand estimation by establishing suitable probability density functions (PDF) as per the surveys available from the realistic data. By employing this simulation approach, the model aims to simulate and predict EV owners’ charging patterns, enabling a comprehensive evaluation of the expected charging demand at fast charging stations. The model gives interval estimation of the EV charging demand with confidence interval varying from 85% to 99%.