According to the EPA, greenhouse gas (GHG) emissions in the United States increased by 7% from 1990 to 2014 and carbon dioxide accounted for 82% of GHG emissions in 2015. Electricity transmission and production accounted for 29% of the greenhouse gas emissions, and industry accounted for 21%. This work analyzes the possibility of simultaneously lowering the industrial and electricity sector greenhouse gas emissions by changing the electricity demand profile of industrial plants, focusing in particular on lowering carbon dioxide emissions, referred to as emissions minimizing production (EMP). EMP is a promising candidate for lowered CO2 emissions—production rate (and thereby electricity consumption) can be increased during peak renewable generation hours and excess product stored (in either liquid or gas form) for use during off-peak renewable generation hours when production rate is lowered. EMP can therefore effectively lower the GHG emissions related to production and transmission of non-renewable electricity sources by altering the demand profile of industrial users with no additional capital cost. We pose a general EMP strategy and apply it to optimal production scheduling for an air separation unit (ASU) as a prototypical electricity-intensive chemical process. EMP operation of ASUs requires that process dynamic characteristics and performance be included in production scheduling calculations to ensure feasible production rate changes that do not violate product quality and safety constraints. However, the first-principles mathematical models that are needed to describe and optimize the behavior of industrial facilities cover multiple time scales and are inevitably large-scale, nonlinear, and stiff, posing computational challenges for simulation and optimization. To address this, we implement nonlinear low-order data-driven models of the scheduling-relevant dynamics of the plant and present a set of reformulation and exact linearization techniques that allow us to cast the overall demand response (DR) scheduling problem as Mixed Integer Linear Program (MILP). We illustrate these concepts using an Air Separation Unit (ASU) capable of producing 50 tons of N2 per day. We show that EMP can reduce emissions by 2.33-4.91%, compared to a scenario where the production rate is kept constant. Furthermore, we demonstrate that EMP is cost effective in most cases, reducing operating cost (from electricity prices) by up to 9.31% from traditional operating modes.

Demand response operation of electricity-intensive chemical processes for reduced greenhouse gas emissions: Application to an air separation unit

2019 Fall National Meeting

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