  In Part-I, we presented an optimal day-ahead scheduling scheme for
dispatching active distribution networks accounting for the flexibility
provided by electric vehicle charging stations (EVCSs) and other controllable
resources such as battery energy storage systems (BESSs). Part-II presents the
intra-day control layer for tracking the dispatch plan computed from the
day-ahead scheduling stage. The control problem is formulated as model
predictive control (MPC) with an objective to track the dispatch plan setpoint
every 5 minutes, while actuated every 30 seconds. MPC accounts for the
uncertainty of the power injections from stochastic resources (such as demand
and generation from photovoltaic - PV plants) by short-term forecasts. MPC also
accounts for the grid's operational constraints (i.e., the limits on the nodal
voltages and the line power-flows) by a linearized optimal power flow (LOPF)
model based on the power-flow sensitivity coefficients, and for the operational
constraints of the controllable resources (i.e., BESSs and EVCSs). The proposed
framework is experimentally validated on a real-life ADN at the EPFL's
Distributed Electrical Systems Laboratory and is composed of a medium voltage
(MV) bus connected to three low voltage distribution networks. It hosts two
controllable EVCSs (172 kWp and 32 F~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).
