2022/05/15 by Sarnaduti Brahma, Brahma, Sarnaduti, Hamid R. Ossareh +4 · 1 citation
Computer Science · Engineering · Mathematics · #Artificial intelligence #Automatic Generation Control #Computer science #Control (management) #Control theory (sociology) #Controller (irrigation) #Electric power system #Energy (signal processing) #Energy Load and Power Forecasting #Frequency Control in Power Systems #Machine learning #Mathematics #Model predictive control #Power (physics) #Predictive power #Real-time computing #SIGNAL (programming language) #Statistical model #Statistics #cs.SY #eess.SY
paper · pdf · doi:10.48550/arxiv.2205.07401
published in arXiv (Cornell University) (Cornell University) · In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canada
openalex publication_date 2022/05/15 · openalex created_date 2022/05/22 · arxiv created 2022/07/10 · arxiv updated 2022/07/12 · openalex updated_date 2026/07/28
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.