Flexibility analysis for demand-side management in large-scale chemical processes: An ethylene oxide production case study. (2nd November 2021)

• Record Type: Journal Article
• Authors: Bruns, Bastian; Di Pretoro, Alessandro; Grünewald, Marcus; Riese, Julia
• Is Part Of: Chemical engineering science. Volume 243(2021)
• Publication Date: 2021-11-02
• Subjects: Plantwide flexibility analysis -- Demand-side management -- Optimization -- Demand response -- Continuous production -- Process design; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2021.116779
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: A framework to assess DSM potentials in large-scale processes was proposed. A new categorization between direct and indirect demand response was elaborated. The plantwide flexibility analysis of the EO production process was carried out. Flexibility indices were reconsidered in compliance with the framework requirements. Simultaneous evaluation and optimization of the operating window became possible. Abstract: The increasing generation of electricity from renewables requires the stabilization of the grid but also offers the opportunity to reduce operating costs for electricity consumers. Chemical processes are favorable participants of this energy evolution due to their localized, high electricity demands. Large-scale continuous processes that do not have electricity as a primary feedstock are not commonly utilized as consumers. However, coupling these processes with fast-responding upstream processes that employ electricity as major feedstock can offer great potential for demand-side management (DSM). This research work provides a framework to assess DSM potentials tested on the ethylene oxide production process. Complex process modeling and simulation were employed. The conventional flexibility analysis approach was rethought to simultaneously perform system flexibility assessment and operating window optimization. Moreover, the detection of critical parameters was possible, which can be exploited to increase process flexibility. This work sets the basis for a flexible DSM optimization procedure.