Electric Vehicle Charging Sessions Generator Based on Clustered Driver Behaviors

Increasing penetration of electric vehicles brings a set of challenges for the electricity system related to its energy, power and balance adequacy. Research related to this topic often requires estimates of charging demand in various forms to feed various models and simulations. This paper proposes a methodology to simulate charging demand for different driver types in a local energy system in the form of time series of charging sessions. The driver types are extracted from historical charging session data via data mining techniques and then characterized using a kernel density estimation process. The results show that the methodology is able to capture the stochastic nature of the drivers' charging behavior in time, frequency and energy demand for different types of drivers, while respecting aggregated charging demand. This is essential when studying the energy balance of a local energy system and allows for calculating future demand scenarios by compiling driver population based on number of drivers per driver type. The methodology is then tested on a simulator to assess the benefits of smart charging.

Automated summary

The increasing use of electric vehicles presents challenges for the electricity system's energy, power, and balance adequacy. This paper proposes a methodology to simulate charging demand for different types of drivers in a local energy system, using time series of charging sessions. The methodology extracts driver types from historical charging session data and characterizes them through kernel density estimation. The results demonstrate that the methodology accurately captures the stochastic nature of drivers' charging behavior, allowing for future demand scenarios and assessment of smart charging benefits.