  The performance of frequency regulating units for automatic generation
control (AGC) of power systems depends on their ability to track the AGC signal
accurately. In addition, representative models and advanced analysis and
analytics can yield forecasts of the AGC signal that aids in controller design.
In this paper, time-series analyses are conducted on an AGC signal,
specifically the PJM Reg-D, and using the results, a statistical model is
derived that fairly accurately captures its second moments and saturated
nature, as well as a time-series-based predictive model to provide forecasts.
As an application, the predictive model is used in a model predictive control
framework to ensure optimal tracking performance of a down ramp-limited
distributed energy resource coordination scheme. The results provide valuable
insight into the properties of the AGC signal and indicate the effectiveness of
these models in replicating its behavior.
