This paper proposes and experimentally validates a grid-aware scheduling and control framework for Electric Vehicle Charging Stations (EVCSs) for dispatching the operation of active distribution networks (ADNs). The framework consists of two stages. In the first stage (day-ahead), we determine an optimal 24-hour power schedule at the grid connection point (GCP), referred to as the dispatch plan. Then, in the second stage, a real-time model predictive control (RT-MPC) is proposed to track the day-ahead dispatch plan using flexibility from EVCSs and other controllable resources (e.g., batteries). The dispatch plan accounts for the uncertainties of vehicles connected to the EVCS along with other uncontrollable power injections, by day-ahead predicted scenarios. The RT-MPC accounts for the uncertainty of the power injections of stochastic resources (such as demand and generation from photovoltaic – PV plants) by short-term forecasts.The framework ensures that the grid is operated within its nodal voltage and branches power-flow operational bounds, modeled by a linearized optimal power-flow model, maintaining the tractability of the problem formulation. The scheme is numerically and experimentally validated on a real-life ADN at the EPFL hosting two controllable EVCSs (172 kWp and 32 kWp), multiple PV plants (aggregated generation of 42 kWp), uncontrollable demand from office buildings (20 kWp), and two controllable BESSs (150kW/300kWh and 25kW/25kWh).