The low accuracy of wind power scheduling influences the grid dispatch adversely, increasing the demand for spinning to reserve capacity and obstructing the grid frequency regulation. Considering the throughput characteristics of energy storage system, which can be used to compensate for wind farm power scheduling deviations, and smooth the grid power fluctuations, the hybrid energy storage (HES) is employed to enhance the dispatch ability of wind power generation. As one of the key techniques, desirable energy storage capacity configuration (ESCC) and control methods would accelerate the application of energy storage in the field of new resource. Combined with statistics and frequency decomposition of scheduling power deviation, HES capacity configuration and online dynamic power allocation method are proposed. First, by analysis of grid assessment indexes of wind power, scheduled wind power data are produced by improved adaptive error factor correction particle swarm optimization back-propagation neural network (AEFC-PSO-BPNN) prediction followed by wavelet packet smooth (WPS). After comparing with actual power, scheduling deviation statistics and frequency decomposition are applied in capacity and power configuration of energy storage, as well as dynamic power distribution control. With wind/storage simulation platform, then, feasibility of energy storage embedded in grid wind power scheduling deviation, regulation is verified under several combined methods, and the proposed ESCC methods are tested in application case by grid wind power indexes of root-mean-square error rate (RMSE), average volatility (AV), maximum throughout power and current (MTP, MTC), actual supercapacitor (SC), battery consumption capacity, and the number of crossings of state of charge (SOC) of HES. Finally, analyses and comparison of energy storage capacity requirements are carried out on different scheduling deviation control methods so as to explore the significant factors influencing capacity allocation. Applying these methods can improve the scheduling accuracy of grid wind power, reduce power fluctuations at the power common connected (PCC) point, and minimize the impact of accessed wind power to the grid as much as possible.