Scheduling Strategy for Electric Vehicle Aggregators Participating in Energy–Frequency Regulation Markets Considering User Uncertainty

The continuous growth of electric vehicle (EV) penetration offers electric vehicle aggregators (EVAs) opportunities to increase revenue by participating in both the energy market and the frequency regulation (FR) market. However, the uncertainty of user behavior poses challenges for formulating effective scheduling strategies. To address these issues, this paper first establishes a charging probability prediction model that considers battery state, travel distance, and user driving habits. Subsequently, a distributionally robust optimization (DRO) model is adopted to characterize the uncertainties associated with EV clusters, and the Column-and-Constraint Generation (C&CG) algorithm is employed to decompose the original model into a master–subproblem framework for solution. Finally, the proposed scheduling strategy for EVAs is validated within the PJM market framework. The results demonstrate that simultaneous participation in the energy and FR markets can significantly enhance the operational revenue of EVAs, achieving a total daily revenue of USD 547.47 compared to USD 427.35 from coordinated charging only. Moreover, the scheduling strategy based on the DRO model achieves a trade-off between economic efficiency and risk resilience, yielding a higher average daily revenue with lower volatility (standard deviation of USD 40.46) compared to Stochastic Optimization (UD 500.98 and USD 49.57, respectively).