The integration of small-scale, distributed flexibility, such as electric vehicles (EVs), is becoming increasingly relevant for market-based system operation and redispatch-oriented applications. In this context, operators need both robust ex-ante estimates of feasible flexibility and operational checks of whether planned and realized behavior remains technically plausible. At the same time, available charging field data are often sparse and only partially observable, which limits direct transfer of historical patterns to changing market conditions, weather, calendar effects, user-group composition, and incentive structures.

This paper presents a hybrid workflow that combines measured charging data with stochastic driving-and-charging simulation. The approach maps field observations to a compact set of effective flexibility parameters and embeds them into a scenario-ready model chain. Key behavioral and technical drivers, including plug-in behavior, connection duration, charging power limits, state-of-charge constraints, calendar-related usage patterns, user-group characteristics, and incentive-related charging responses, are represented to derive context-specific flexibility envelopes.

These envelopes are used as an extended measurement layer for market analysis and can support forecasting and redispatch-related monitoring. In particular, they can support near-real-time plausibility checks of offered or scheduled flexibility and post-event assessment of dispatch outcomes against technically feasible ranges.

The presented results indicate that this physically grounded representation improves transparency and consistency when assessing decentralized EV flexibility under data scarcity. The framework therefore provides a practical basis for linking ex-ante flexibility estimation with operational monitoring in evolving market-based grid applications.