A data-driven linear formulation of the optimal demand response scheduling problem for an industrial air separation unit

•Focus on optimal demand response scheduling using dynamic process models.•Models in ARX form are identified from operating data.•The scheduling problem is formulated as a linear program.•An application to an industrial air separation unit is presented. Demand response (DR) has become a key element...

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Published in:Chemical engineering science 2022-04, Vol.252 (C), p.117468, Article 117468
Main Authors: Kelley, Morgan T., Tsay, Calvin, Cao, Yanan, Wang, Yajun, Flores-Cerrillo, Jesus, Baldea, Michael
Format: Article
Language:English
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Summary:•Focus on optimal demand response scheduling using dynamic process models.•Models in ARX form are identified from operating data.•The scheduling problem is formulated as a linear program.•An application to an industrial air separation unit is presented. Demand response (DR) has become a key element in balancing the power grid as the contribution of time-varying renewable power generation increases. Chemical plants are appealing candidates for DR programs as they offer large, concentrated and flexible loads. DR participation calls for frequent production rate changes over time scales that overlap with the dominant dynamics of the plant. Production scheduling should therefore consider the process dynamics explicitly. We present a data-driven approach for modelling the scheduling-relevant dynamics based on historical closed-loop operating data using autoregressive with extra inputs (ARX) models. We introduce a new, linear scheduling problem formulation based on the ARX representation, and demonstrate its implementation on an industrial air separation unit.
ISSN:0009-2509
1873-4405
DOI:10.1016/j.ces.2022.117468