This paper presents a dynamic pricing and energy management framework forelectric vehicle (EV) charging service providers. To set the charging prices,the service providers faces three uncertainties: the volatility of wholesaleelectricity price, intermittent renewable energy generation, andspatial-temporal EV charging demand. The main objective of our work here is tohelp charging service providers to improve their total profits while enhancingcustomer satisfaction and maintaining power grid stability, taking into accountthose uncertainties. We employ a linear regression model to estimate the EVcharging demand at each charging station, and introduce a quantitative measurefor customer satisfaction. Both the greedy algorithm and the dynamicprogramming (DP) algorithm are employed to derive the optimal charging pricesand determine how much electricity to be purchased from the wholesale market ineach planning horizon. Simulation results show that DP algorithm achieves anincreased profit (up to 9%) compared to the greedy algorithm (the benchmarkalgorithm) under certain scenarios. Additionally, we observe that theintegration of a low-cost energy storage into the system can not only improvethe profit, but also smooth out the charging price fluctuation, protecting theend customers from the volatile wholesale market.
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