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Optimization‐based process synthesis under seasonal and daily variability: Application to concentrating solar power
Author(s) -
Peng Xinyue,
Root Thatcher W.,
Maravelias Christos T.
Publication year - 2019
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.16458
Subject(s) - process (computing) , selection (genetic algorithm) , mathematical optimization , solar power , concentrated solar power , solar energy , stochastic programming , power (physics) , computer science , engineering , mathematics , physics , quantum mechanics , artificial intelligence , electrical engineering , operating system
We propose an optimization‐based framework for process synthesis under variability in two frequencies. Low‐frequency variability is represented through scenarios and high‐frequency variability is modeled using modes. The proposed framework allows for the selection of different process configurations during different modes, a feature necessary to model systems under wide high frequency variability (e.g., solar‐based technologies). The optimization problem is formulated as a two‐stage stochastic programming model with mode subproblems nested inside each scenario. The proposed framework is applied to the design of concentrating solar power plants with thermochemical energy storage, leading to the formulation of a computationally efficient model, as well as the identification of a superior design. © 2018 American Institute of Chemical Engineers AIChE J , 65: e16458 2019