Statistical Modeling and Forecasting of Automatic Generation Control Signals

TL;DR

This paper develops statistical and predictive models for AGC signals in power systems, enhancing controller design and system performance by accurately capturing signal properties and providing reliable forecasts.

Contribution

It introduces a novel statistical and time-series-based predictive modeling approach for AGC signals, improving tracking and forecasting capabilities for power system control.

Findings

Models accurately capture AGC signal second moments and saturation effects.

Predictive models enable effective model predictive control for energy resource coordination.

Results demonstrate improved AGC signal tracking and forecasting accuracy.

Abstract

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.