The variable output of a large wind farm presents many integration challenges, especially at high levels of penetration. The uncertainty in the output of a large wind plant can be covered by using fast-acting dispatchable sources, such as natural gas turbines or hydro generators. However, using dispatchable sources on short notice to smooth the variability of wind power can increase the cost of large-scale wind power integration. To remedy this, the inclusion of large-scale energy storage at the wind farm output can be used to improve the predictability of wind power and reduce the need for load following and regulation hydro or fossil-fuel reserve generation. This paper presents sizing and control methodologies for a zinc-bromine flow battery-based energy storage system. The results show that the power flow control strategy does have a significant impact on proper sizing of the rated power and energy of the system. In particular, artificial neural network control strategies resulted in significantly lower cost energy storage systems than simplified controllers. The results show that through more effective control and coordination of energy storage systems, the predictability of wind plant outputs can be increased and the cost of integration associated with reserve requirements can be decreased.