Quantifying energy transport by electric vehicles: A Monte Carlo and optimization framework for flexible energy communities

Abstract

Rising grid congestion raises concerns that conventional grid expansion may not keep pace with projected increases in demand and renewable generation. This research proposes a supplementary solution: leveraging the inherent mobility of Electric Vehicles to enable energy transfer within different locations. Through an analytical modelling approach, a stochastic energy transport model using Monte Carlo sampling is developed. This model generates synthetic weekly charging and driving profiles to quantify the energy transport potential of private EV fleets, accounting for battery capacity, mobility patterns, and energy consumption. Building on this concept, the study introduces Flexible Energy Communities (FlexECs), where members share the same living location but commute to different workplaces or vice versa. By enabling electric vehicles to charge at one location and discharge at another during routine daily travels, Flexible Energy Communities exploit private EV mobility as a mechanism for spatial energy transfer, extending Energy Community operation beyond purely grid energy exchange.

Results reveal that approximately 35 kWh, equivalent to 60% of the total energy stored in the EV battery at the time of departure, can be discharged upon arrival. Additionally, when charging patterns are optimized, up to 30 kWh, representing 70% of the energy charged prior to the journey, can be effectively transported and discharged at the destination. By quantifying the impact of EV-imported energy on cost, peak demand, and overall energy consumption reduction, this study underscores the potential of EVs as dynamic energy transporters, providing a technical foundation for energy sharing through private EVs daily travelling patterns.