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DOE OSTI · 1976947

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

Kelley, Morgan T.·Tsay, Calvin·Cao, Yanan·Wang, Yajun·Flores-Cerrillo, Jesus·Baldea, Michael
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Abstract

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. Here 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.

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42 ENGINEERINGARXASUair separation unitsautoregressive with extra inputsdemand responsemodelsproduction schedulingscale bridging models

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Kelley, Morgan T., Tsay, Calvin, Cao, Yanan, Wang, Yajun, Flores-Cerrillo, Jesus, Baldea, Michael. 2022-02-08. A data-driven linear formulation of the optimal demand response scheduling problem for an industrial air separation unit. https://doi.org/10.1016/j.ces.2022.117468

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