2025/07/07 by Sarvarizadeh, Miad, Sigrist, Lukas, Rouco, Almudena +2
#FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2507.05062
This paper presents a novel corrective \glsfcuc formulation for island power systems by implementing data-driven constraint learning to estimate the optimal \glsufls. The Tobit model is presented to estimate the optimal amount of \glsufls using the initial rate of change of frequency. The proposed formulation enables co-optimizing operation costs and \glsufls. The aim is to account for optimal \glsufls occurrences during operation planning, without increasing them. This would potentially reduce system operation costs by relaxing the reserve requirement constraint. The performance of the proposed formulation has been analyzed for a Spanish island power system through various simulations. Different daily demand profiles are analyzed to demonstrate the effectiveness of the proposed formulation. Additionally, a sensitivity analysis is conducted to demonstrate the effects of changing the cost associated with \glsufls. The corrective \glsfcuc is shown to be capable of reducing system operation costs without jeopardizing the quality of the frequency response in terms of \glsufls occurrence.