Electricity-intensive process industries are seeking for new ways to achieve savings on energy costs, taking advantage of the liberalization of electrical markets and integration of distributed generation. Also, the increasing share of renewables requires the grid to adapt to varying electricity availability and consumption patterns. Optimal production scheduling of power-intensive steel making processes with regard to energy cost may lead to significant savings in the electricity bill. For the purpose of this research the steel plant melt shop model described in Harjunkoski & Grossmann (2001) has been adopted as a use case. The steel plant is assumed to participate in a day-ahead electricity market with hourly varying electricity prices and to face financial penalties in case the plant's actual electricity consumption deviates from the committed level. The resulting scheduling problem is formulated as a continuous-time Mixed Integer Linear Programming (MILP) problem, enhanced by introducing energy price-related time slots and tested on realistic production data for various problem sizes. The results show that compared to the standard makespan-driven approaches, the process plant is able to save on operational expenditures while satisfying the critical production rules and due dates.
|Julkaisu||Computer Aided Chemical Engineering|
|DOI - pysyväislinkit|
|Tila||Julkaistu - 2013|
|OKM-julkaisutyyppi||A1 Julkaistu artikkeli, soviteltu|