A Corrective Frequency-Constrained Unit Commitment with Data-driven Estimation of Optimal UFLS in Island Power Systems

TL;DR

This paper introduces a data-driven corrective frequency-constrained unit commitment model for island power systems that optimizes operational costs and UFLS deployment using a Tobit model, validated through simulations on a Spanish island system.

Contribution

It proposes a novel formulation that co-optimizes costs and UFLS, using data-driven constraint learning to improve frequency response without increasing UFLS.

Findings

Reduces system operation costs

Maintains frequency response quality

Effective across various demand profiles

Abstract

This paper presents a novel corrective \gls{fcuc} formulation for island power systems by implementing data-driven constraint learning to estimate the optimal \gls{ufls}. The Tobit model is presented to estimate the optimal amount of \gls{ufls} using the initial rate of change of frequency. The proposed formulation enables co-optimizing operation costs and \gls{ufls}. The aim is to account for optimal \gls{ufls} 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 \gls{ufls}. The corrective \gls{fcuc} is shown to be capable of reducing system operation costs without jeopardizing the quality of the frequency response in terms of \gls{ufls} occurrence.