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In the early stages of hybrid power system design, it is beneficial to have means of quickly estimating the performance of candidate systems in order to make trades between the components of the system, to incorporate changing requirements, or to deal with uncertainty in assumptions. If the candidate systems incorporate energy storage, the behavior of the system is complicated by the nature of load/renewable/storage interactions, which motivates the use of time-series simulations to accurately evaluate the performance of a candidate system. However, the execution time of a simulation does not lend itself well to real-time engineering design studies. An approach is demonstrated which uses Neural Network surrogate models created to represent a more complex simulation code to enable rapid trades and sensitivities, instant performance results and visualizations, and rapid reoptimization for the design of an on-grid hybrid wind/fossil power system with storage. These surrogate models enabled the creation of a tool that presents a decision maker with the ability to generate endless hybrid mix scenarios and determine which various renewable and non-renewable energy systems meet annual energy load requirements, acquisition and operation costs, and individual solution attributes.