<p>The scheduling of wind energy is a complex problem due to its inherently uncertain nature. The operation of an energy storage system (ESS) is exposed to random failures as well as high uncertainties from renewable energy sources (RESs) and loads. The smooth operation of the power grid is further complicated by the demand response program (DRP) constraints and electric vehicle (EV) charging stations. Therefore, energy storage, electric vehicles, demand response and electric vehicle charging stations can be used to provide flexibility to manage the uncertainty of wind power. This paper proposes a transactive control-based model for wind, electric vehicles, demand response in stochastic scheduling. The vehicle-to-grid (V2G) integration system allows EVs to exchange power, but it adds operational complexity. A mixed-integer nonlinear programming (MINLP) challenge was used to formulate the issue. These problems might pose difficulties with local optima and are frequently time-consuming. To obtain the global optimal solution, the proposed approach uses a decomposition mechanism with a short computing time. Ultimately, it is discovered that the proposed optimization tool can manage sizable and realistic power systems since it is simulated using IEEE 24-bus test systems as a scenario-based model. A thorough evaluation of environmental and economic factors is carried out. According to the results, scheduling costs for uncertain wind and EV fleets can be reduced by 8.14% overall, while EV fleet expenses can be reduced by 12.24%.</p>