  High integration of intermittent renewable energy sources (RES), in
particular wind power, has created complexities in power system operations. On
the other hand, large fleets of Electric Vehicles (EVs) are expected to have
great impact on electricity consumption, and uncoordinated charging process
will add load uncertainty and further complicate the grid scheduling. In this
paper, we propose a smart charging approach that uses the flexibility of EV
owners to absorb the fluctuations in the output of RES in a vehicle-to-grid
(V2G) setup. We propose an optimal scheduling algorithm for charge/discharge of
aggregated EV fleets to maximize the integration of wind generation as well as
minimize the charging cost for EV owners. Challenges for people participation
in V2G, such as battery degradation and feeling insecure for unexpected events,
are also addressed. We first formulate a static model using mixed-integer
quadratic programming (MIQP) with multi-objective optimization assuming that
every parameter of the model is known a day ahead of scheduling. Subsequently,
we formulate a dynamic (dis)charging schedule after EVs arrive into the system
with updated information about EV availabilities, wind generation forecast, and
energy price in real-time, using rolling-horizon optimization. Simulations
using a group of 100 EVs in a micro-grid with wind as primary resource
demonstrate significant increase in wind utilization and reduction in charging
cost compared to uncontrolled charging scenario.
