Electric Vehicle Charging Sessions Generator Based on Clustered Driver Behaviors

Abstract

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.

An Analysis of Vehicle-to-Grid in Sweden Using MATLAB/Simulink

“…From the user perspective, V2G functionality may contribute to extra revenue if the electricity can be sold back to the grid, especially during periods of electricity price peaks [13]. Looking at the techno-economic assessment of V2G in a microgrid, the authors in [14] highlight that several parameters impact the feasibility of V2G, e.g., the price of the chargers and the available capacity per car. Moreover, there are sociotechnical aspects of V2G that need to be further investigated, including, e.g., aspects regarding the motivation of the drivers in utilizing V2G and the driver's view on data sharing in the charging/discharging events [15].…”