Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed U.S. electricity markets. The presence of renewables has increased variability and uncertainty on the supply side of the grid. Coupled with significant variability on the demand side, this has led to an increasingly challenging environment for balancing supply and demand. Managing demand rather than generation, referred to as demand response (DR), has emerged as an attractive approach for mitigating grid imbalance. Electricity-intensive processes are promising industrial DR candidates. Production can be increased during off-peak hours and excess product stored for use during peak demand, when the production rate is lowered. Grid-balancing benefits notwithstanding, DR participation has the potential to significantly lower operating costs of the industrial entities due to the inherent fluctuations in electricity prices generated by a mismatch between power supply and demand.
Industrial air separation units (ASUs) are great candidates for DR due to their high electricity consumption, minimal process inputs (air and electricity) and relative flexibility. In order to implement DR in ASUs, dynamics must be included in scheduling problems to ensure that proposed optimal schedules are feasible for plant operation. ASUs can be modeled using first-principles models, but these result in optimization problems that are highly nonlinear, inhibiting solution in a reasonable amount of time. In my work I have identified both linear and nonlinear low-order models to represent ASUs. Further implementations of parallel computing in the optimization problem have helped to significantly reduce the solution time from 97 hours to 1.5 minutes.
