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This paper proposes a stochastic optimization model for optimal bidding strategies of electric vehicle (EV) aggregators in day-ahead energy and ancillary services markets with variable wind energy. The forecast errors of EV fleet characteristics, hourly loads, and wind energy as well as random outages of generating units and transmission lines are considered as potential uncertainties, which are represented by scenarios in the Monte Carlo Simulation (MCS). The conditional value at risk (CVaR) index is utilized for measuring EV aggregators risks caused by the uncertainties. The EV aggregators optimal bidding strategy is formulated as a mathematical programming with equilibrium constraints (MPEC), in which the upper level problem is the aggregators CVaR maximization while the lower level problem corresponds to the system operation cost minimization. The bi-level problem is transformed into a single-level …