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Recent increases in renewable power generation challenge the operation of the power grid: generation rates fluctuate in time and are not synchronized with power demand fluctuations.Demand response (DR) consists of adjusting user electricity demand in order to balance available power supply. Chemical plants are appealing candidates for DR programs; they offer large, concentrated loads that can be modulated via production scheduling. Price-based DR is a common means of engaging industrial entities; its benefits increase significantly when a longer (typically, a few days) scheduling time horizon is considered. DR production scheduling comes with its own challenges, related to uncertainty in future (i.e., forecast) electricity prices and product demand. In this work, we provide a framework for DR production scheduling under uncertainty based on a chance-constrained formulation, that also accounts for the dynamics of the production facility. The ideas are illustrated with an air separation unit case study.

A recent push for reduced greenhouse gas (GHG) emissions has led (in part) to the addition of renewable electricity generation sources to the power generation mix. Renewables such as wind and solar are desynchronized from grid demand, requiring the use of fossil fuels to bridge the gap. We propose a novel production scheduling formulation, emissions-minimizing production (EMP), which utilizes time-based information on the nature of the power generation mix to lower GHG emissions related to the transmission and generation of electricity for industrial users. We demonstrate the application of EMP on a single-column air separation unit. The scheduling problem is cast as a mixed integer linear program that can be solved in a practical amount of time. Extensive numerical studies are used to place EMP in the context of other production scheduling methods (such as demand response), and demonstrate its potential for significant reductions in GHG emissions.

Demand-side management/demand response (DSM/DR) are key strategies for mitigating the inherent variability in electricity generation rates by renewable sources.This article represents-to our knowledge-the first foray into assessing the DR potential of ammonia plants. Ammonia plants are interesting candidates for DR initiatives because of their significant electricity use (for operating compressors driving the synthesis loop) and the ability to store the ammonia product relatively easily and safely. Our approach is based on formulating and solving an optimal DR scheduling problem for an ammonia plant while accounting for the process dynamics. To this end, we introduce a new Hammerstein-Wiener-inspired modeling framework based on injecting linear dynamics in a first-principles static nonlinear model of the process.The results are encouraging; for the cases considered, peak-time power consumption decreases between 3.57% and 7.40%, coupled with 1.39% to 3.70% reductions in operating cost.