Increasing utilization of the low-voltage (LV) grid, driven by heat pumps, distributed energy resources, and electric vehicles (EVs) with bidirectional charging capabilities, introduces new operational challenges. In particular, charging/discharging of EV fleets can lead to local congestion and voltage deviations. Traditional grid operation approaches are not designed for these bidirectional and dynamic power flows, while many existing EV fleet control strategies focus primarily on economic optimization without fully considering their impact on grid behaviour.

This work presents a modular and scalable co-simulation framework for monitoring and analysis of EV fleet integration in LV grids, focusing on use cases where grid operators have direct or indirect control over aggregated EV fleets, such as in airport or commercial charging infrastructures. The framework combines a grid model implemented in DIgSILENT PowerFactory with Python-based modules for charging management, congestion monitoring, voltage control, and EV fleet modelling. All modules are executed in parallel and exchange data continuously using interfaces aligned with industrial communication protocols. The setup reflects a realistic system architecture for aggregated control of charging stations.

The framework is applied to a sample German LV grid with 25 charging stations to evaluate bidirectional charging strategies under different operating conditions. The results provide insight into the interaction between fleet-level charging behaviour and grid constraints, supporting the assessment of congestion and voltage control measures. The modular design supports flexible adaptation to different grid sizes and varying shares of bidirectional charging.

The proposed framework provides a practical basis for studying grid integration aspects of EV fleets and supports data-driven decision-making for grid operation and planning. Its modular structure enables the replacement of individual components with field data or industrial systems, such as SCADA-based grid models, commercial charging management systems, or real charging station data, supporting transferability to real-world applications in both research and operational environments.

This work is conducted within the ReSkaLa@FRA research project, a real-world laboratory for scaling bidirectional charging at Frankfurt Airport (Fraport). The simulation framework will be used to investigate the integration of bidirectional charging infrastructure on airport grid, using real grid model and EV profile from Fraport.