Sustainable retrofit of petrochemical energy systems under multiple uncertainties using the stochastic optimization method

•Sustainable retrofit of energy systems under uncertainties is investigated.•A multi-objective stochastic optimization framework is formulated for the retrofit.•Uncertainties including energy demands and renewable energy loads are considered.•SROM sampling method is introduced to describe the uncert...

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Vydané v:Computers & chemical engineering Ročník 151; s. 107374
Hlavní autori: Qian, Qiming, Liu, Hua, He, Chang, Shu, Yidan, Chen, Qing L., Zhang, Bing J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.08.2021
Predmet:
Mixed-integer non-linear programming
Multiple uncertainties
Retrofit
Stochastic reduced order model
Sustainable energy system
Retrofit
Stochastic reduced order model
Sustainable energy system
Multiple uncertainties
Mixed-integer non-linear programming
ISSN:0098-1354, 1873-4375
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Shrnutí:•Sustainable retrofit of energy systems under uncertainties is investigated.•A multi-objective stochastic optimization framework is formulated for the retrofit.•Uncertainties including energy demands and renewable energy loads are considered.•SROM sampling method is introduced to describe the uncertainty with less samples.•Uncertain optimization leads to a reliable retrofit with a 10% increase of TAC. We report a multi-objective stochastic mixed-integer non-linear programming (MINLP) framework for sustainable retrofit and capability expansion of traditional energy systems in petrochemical complexes. Multiple uncertainties including energy demands, solar radiation and wind speeds are considered in the optimization framework, these are characterized by historical data or normal distributions which are pre-defined with assumed mean values and standard variations. A stochastic reduced order model approach is introduced to describe the uncertainties by a small number of scenarios and their individual probabilities. The optimization framework further accounts for system configuration selection and sizing of the candidate energy conversion equipment, such as thermal storage units, gas turbines, boilers, steam turbines, as well as their operating capacities in each time period. A case study is investigated to demonstrate the performance of the proposed strategy and then the optimization results under three modes (deterministic, stochastic and semi-stochastic programs) are compared. [Display omitted]
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2021.107374