A Reactive-Iterative Optimization Algorithm for Scheduling of Air Separation Units under Uncertainty in Electricity Prices

Abstract

The high energy demand in power-intensive processes and the possibility of reducing the energy bills by an optimal scheduling are the motivation for incorporating energy consideration in the production scheduling of air separation plants. Optimization opportunities exist at different time scales for day-ahead scheduling decisions and real-time decisions regarding all fluctuations in electricity prices. Consequently, this paper presents a reactive-iterative optimization approach, integrating the rolling horizon (RH) con- cept into an iterative solution algorithm, for optimizing production decisions when an industry partic- ipates in both the day-ahead electricity market and the spot electricity market. A novel discrete-time MILP formulation is used as a basis of the proposal, which allows adjusting production rates to electricity prices varying hourly or faster. Several scenarios from a real-life air separation industrial plant are solved to show interesting trade-offs between the predictive approach and the reactive-iterative strategy.