The United States electric power grid is the most complex and expansive control system in the world. Local generation control occurs at individual units based on response time and unit economics, larger regional control coordinates unit response to error conditions, and high level large-area regional control is ultimately administered by a network of humans guided by economic and resiliency related factors. Under normal operating conditions, the grid is a relatively slow moving entity that exhibits high inertia to outside stimuli, and behaves along repeatable diurnal and seasonal patterns. However, that paradigm is quickly changing because of the increasing implementation of renewable generation sources. Renewable generators by nature cannot be tightly controlled or scheduled. They appear like a negative load to the system with all of the variability associated with load on a larger scale. Also, grid-reactive loads (i.e. smart devices) can alter their consumption based on price or demand rules adding more variability to system behavior. This paper demonstrates how a systems dynamic modeling approach capable of operating over multiple time scales, can provide valuable insight into developing new ?smart-grid? control strategies and devices needed to accommodate renewable generation and regulate the frequency of the grid.