  This paper proposes a grid-aware scheduling and control framework for
Electric Vehicle Charging Stations (EVCSs) for dispatching the operation of an
active power distribution network. The framework consists of two stages. In the
first stage, we determine an optimal day-ahead 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 is proposed to track the day-ahead
dispatch plan using flexibility from EVCSs. The dispatch plan accounts for the
uncertainties of vehicles connected to the EVCS along with other uncontrollable
power injections, by day-ahead predicted scenarios. We propose using a
Gaussian-Mixture-Model (GMM) for the forecasting of EVCS demand using the
historical dataset on arrival, departure times, EV battery capacity,
State-of-Charge (SoC) targets, etc. The framework ensures that the grid is
operated within its 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 distribution network at the EPFL connected to two EVCSs, two
batteries, three photovoltaic plants, and multiple heterogeneous loads. The
day-ahead and real-time stages are described in Part-I and Part-II papers
respectively.
