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The increasing share of volatile renewable electricity production motivatesdemand response. Substantial potential for demand response is offered byflexible processes and their local multi-energy supply systems. Simultaneousoptimization of their schedules can exploit the demand response potential, butleads to numerically challenging problems for nonlinear dynamic processes. Inthis paper, we propose to capture process dynamics using dynamic rampingconstraints. In contrast to traditional static ramping constraints, dynamicramping constraints are a function of the process state and can capturehigh-order dynamics. We derive dynamic ramping constraints rigorously for thecase of single-input single-output processes that are exactly input-statelinearizable. The resulting scheduling problem can be efficiently solved as amixed-integer linear program. In a case study, we study two flexible reactorsand a multi-energy system. The proper representation of process dynamics bydynamic ramping allows for faster transitions compared to static rampingconstraints and thus higher economic benefits of demand response. The proposeddynamic ramping approach is sufficiently fast for application in onlineoptimization.

Dynamic Ramping for Demand Response of Processes and Energy Systems based on Exact Linearization