Modeling Charging Demand and Quantifying Flexibility Bounds for Large-Scale BEV Fleets
Maria Parajeles Herrera, Gabriela Hug
TL;DR
This paper tackles the challenge of future BEV charging by developing a bottom-up framework that links geo-referenced mobility and driving energy needs to user-comfort-based charging decisions, enabling high-resolution modeling of baseline demand and charging flexibility. It introduces a SOC-based charging decision driven by a truncated normal SOC distribution ($\mu=0.6$, $\sigma=0.2$), region-specific battery-size distributions, and location-dependent charging rates, then extends to a quantitative, region-wide flexibility analysis with hourly power bounds and daily flexible energy. A case study for Switzerland demonstrates non-symmetric upward/downward flexibility, substantial weekday flexibility (up to ~68% of daily energy), and clear urban–rural differences in charging patterns and opportunities. The findings provide actionable inputs for grid planning, optimization of charging strategies, and infrastructure investments in the context of a large-scale, electrified transport system.
Abstract
This paper presents a bottom-up method to model baseline charging power demand and quantify available flexibility for large-scale BEV fleets. The method utilizes geographic and sociodemographic information to represent the fleet's mobility and driving energy needs. It models the charging decisions of drivers based on their driving energy needs and range comfort level using real-world data. The flexibility quantification provides an hourly maximum and minimum bound for the charging power and limits the amount of daily flexible charging energy. We apply the methodology to the future fully electrified fleet of Switzerland as a case study and compare the spatio-temporal characteristics of the charging demand and flexibility of different geographic areas and urbanization levels.