Robust Scheduling under Time-sensitive Electricity Prices for Continuous Powerintensive Processes

Optimization models for the production scheduling of power-intensive processes such as air separation can help realizing significant economical savings. However, if the electricity is procured in the dayahead or real-time market, the forecast for the hourly electricity prices contains a significant amount of uncertainty. In this work, we apply robust optimization to the uncertain electricity prices using an uncertainty set that features multiple ranges and can account for correlated data. We describe how the robust counterpart is constructed and provide a case study that shows the differences in the solutions for the deterministic and the robust schedule.