A multi-time scale grid scheduling optimization method considering demand-side resources

Abstract

This research introduces a pioneering strategy for optimizing power grid scheduling across various time scales, addressing a previously unmet need in the field of energy regulation. Initially, we amassed and categorized tunable resources on the demand side into three distinct temporal categories—long-term, short-term, and immediate—reflecting their respective response attributes. This taxonomy informed the development of a cohesive scheduling framework, encompassing pre-emptive, daily, and instantaneous sub-models, designed to facilitate precise control over grid operations. The deployment of this framework has led to marked enhancements in both the efficiency and dependability of power grid management. The merits of this methodology were exemplified through its application to the IEEE33 network model, showcasing its proficiency in the acquisition of optimized scheduling parameters. This multi-tiered modeling and optimization technique offers an innovative pathway for the dynamic and effective management of smart grids.