  This paper studies the problem of stochastic dynamic pricing and energy
management policy for electric vehicle (EV) charging service providers. In the
presence of renewable energy integration and energy storage system, EV charging
service providers must deal with multiple uncertainties --- charging demand
volatility, inherent intermittency of renewable energy generation, and
wholesale electricity price fluctuation. The motivation behind our work is to
offer guidelines for charging service providers to determine proper charging
prices and manage electricity to balance the competing objectives of improving
profitability, enhancing customer satisfaction, and reducing impact on power
grid in spite of these uncertainties. We propose a new metric to assess the
impact on power grid without solving complete power flow equations. To protect
service providers from severe financial losses, a safeguard of profit is
incorporated in the model. Two algorithms --- stochastic dynamic programming
(SDP) algorithm and greedy algorithm (benchmark algorithm) --- are applied to
derive the pricing and electricity procurement policy. A Pareto front of the
multiobjective optimization is derived. Simulation results show that using SDP
algorithm can achieve up to 7% profit gain over using greedy algorithm.
Additionally, we observe that the charging service provider is able to reshape
spatial-temporal charging demands to reduce the impact on power grid via
pricing signals.
