The online scheduling of electric vehicle (EV) charging stations faces significant challenges due to the volatility of real-time electricity prices and the uncertainty of charging demand caused by stochastic EV arrivals. Furthermore, the participation degree of station operators is pivotal to the effective execution of the grid’s demand response. To effectively capture the complex variability of electricity prices, a novel ensemble prediction-probability mapping framework is proposed to probabilistically characterize real-time peak-valley price features. Unlike traditional methods that rely on predetermined price prediction curves, the framework quantifies price uncertainty through probability interval values, offering a more robust and adaptive decision-making process. To respond to these real-time electricity signals, a risk-aware dynamic price subsidy mechanism is designed to provide differentiated incentives, aligning station operators’ economic interests with grid load-balancing objectives. A hybrid ambiguity set that integrates both moment-based and distance-based information is developed to accurately characterize the uncertain charging demand. Additionally, a distribution robust optimization (DRO) model with this ambiguity set is proposed to mitigate conservatism in worst-case scenarios. The model is reformulated as a finite-dimensional convex program to ensure that the set remains sufficiently expressive without becoming intractable. Experimental results demonstrate real-time response in dynamic EV-grid interaction scenarios, validating the robustness and cost-risk balance of the proposed method against price and demand uncertainties.