Dynamic ramping for demand response of processes and energy systems based on exact linearization. (October 2022)

• Record Type: Journal Article
• Authors: Baader, Florian Joseph; Althaus, Philipp; Bardow, André; Dahmen, Manuel
• Is Part Of: Journal of process control. Volume 118(2022)
• Page Start: 218
• Page End: 230
• Publication Date: 2022-10
• Subjects: Demand response -- Mixed-integer dynamic optimization -- Exact linearization -- Scheduling optimization; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.08.017
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: The increasing share of volatile renewable electricity production motivates demand response. Substantial potential for demand response is offered by flexible processes and their local multi-energy supply systems. Simultaneous optimization of their schedules can exploit the demand response potential, but leads to numerically challenging problems for nonlinear dynamic processes. In this paper, we propose to capture process dynamics using dynamic ramping constraints. In contrast to traditional static ramping constraints, dynamic ramping constraints are a function of the process state and can capture high-order dynamics. We derive dynamic ramping constraints rigorously for the case of single-input single-output processes that are exactly input-state linearizable. The resulting scheduling problem can be efficiently solved as a mixed-integer linear program. In a case study, we study two flexible reactors and a multi-energy system. The proper representation of process dynamics by dynamic ramping allows for faster transitions compared to static ramping constraints and thus higher economic benefits of demand response. The proposed dynamic ramping approach is sufficiently fast for application in online optimization. Highlights: Dynamic ramping constraints for scheduling of processes and energy systems. Allow for high-order dynamics, and non-constant ramp limits. Derived rigorously from exact input-state linearization. Mixed-integer linear optimization fast enough for online optimization. Cost savings close to nonlinear optimization.