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A flexible design framework for process systems under demand‐side management
Author(s) -
Liu Yu,
Fan Yueyue,
Palazoglu Ahmet,
ElFarra Nael H.
Publication year - 2020
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16249
Subject(s) - stochastic programming , scheduling (production processes) , energy management , computer science , renewable energy , demand response , control reconfiguration , grid , electricity , integer programming , robust optimization , network planning and design , operations research , mathematical optimization , operations management , engineering , energy (signal processing) , computer network , statistics , geometry , mathematics , electrical engineering , embedded system , algorithm
Abstract Demand response (DR) has been an appealing strategy for both residential and commercial/industrial customers of electricity due to the increasing penetration of renewable energy resources into the power grid and the deregulation of the energy markets. For industrial DR participants, the management of energy consumption along with the satisfaction of production expectations is generally referred to as demand‐side management. Recent research in this area has focused mostly on process scheduling and capacity planning, especially for energy‐intensive processes. This article presents a new direction through an optimization‐based process design paradigm that allows for the potential reconfiguration of the process flow sheet in real time. Based on a case study of pump network design, a network superstructure is first defined, followed by a two‐stage stochastic optimization model. The method of progressive hedging is used to solve the resulting mixed‐integer stochastic programming problem. The results demonstrate that, under various scenarios, the reconfigurable design offers significant benefits compared to a fixed network structure in terms of total design cost and expected operating cost.