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

TL;DR

This paper introduces dynamic ramping constraints based on exact linearization to improve the scheduling and demand response of flexible processes and energy systems, enabling faster transitions and higher economic benefits.

Contribution

It develops a novel dynamic ramping constraint formulation for input-state linearizable processes, allowing efficient mixed-integer linear programming solutions.

Findings

Faster process transitions with dynamic ramping constraints.

Higher economic benefits in demand response scenarios.

Applicable for online optimization in energy systems.

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.