  The large adoption of EVs brings practical interest to the operation
optimization of the charging station. The joint scheduling of pricing and
charging control will achieve a win-win situation both for the charging station
and EV drivers, thus enhancing the operational capability of the station. We
consider this important problem in this paper and make the following
contributions. First, a joint scheduling model of pricing and charging control
is developed to maximize the expected social welfare of the charging station
considering the Quality of Service and the price fluctuation sensitivity of EV
drivers. It is formulated as a Markov decision process with variance criterion
to capture uncertainties during operation. Second, a two-phase on-line policy
learning algorithm is proposed to solve this joint scheduling problem. In the
first phase, it implements event-based policy iteration to find the optimal
pricing scheme, while in the second phase, it implements scenario-based model
predictive control for smart charging under the updated pricing scheme. Third,
by leveraging the performance difference theory, the optimality of the proposed
algorithm is theoretically analyzed. Numerical experiments for a charging
station with distributed generation and energy storage demonstrate the
effectiveness of the proposed method and the improved social welfare of the
charging station.
