This paper proposes a novel approach to energy exchange between electric vehicle (EV) load and wind generation utilities participating in the day-ahead energy, balancing, and regulation markets. An optimal bidding/offering strategy model is developed to mitigate wind energy and EV imbalance threats, and optimize EV charging profiles. A new strategy model is based on optimizing decision making of a wind generating company (WGenCO) in selecting the best option among the use of the balancing or regulation services, the use of the energy storage system (ESS) and the use of all of them to compensate wind power deviation. Energy imbalance is discussed using conventional systems, ESS, and EV-Wind coordination; results are compared and analyzed. Stochastic intra-hour optimization is solved by mixed-integer linear programming (MILP). Uncertainties associated with wind forecasting, energy price, and behavior of EV owners based on their driving patterns, are considered in the proposed stochastic method and validated through several case studies.