Demand response strategy for coordinated optimization of furnaces in discrete production processes of steelmaking-continuous casting

Unlocking the inherent potential for flexible adjustment in steel enterprises is crucial for achieving green transformation in high energy-consuming industries and meeting national “carbon reduction” goals. However, traditional research has focused on reducing process wait times, often neglecting electricity costs, thereby limiting demand response capabilities. To address this, this paper proposes a mixed-integer linear programming (MILP) model for the steelmaking-continuous casting (SCC) section. The model uses the big M method to introduce $0-1$ decision variables to determine the processing order of furnace batch and analyze their operation times and corresponding power consumption across different time-of-use (TOU) tariff. By integrating the TOU tariff mechanism, the model aims to minimize electricity costs and reduce wait times between processes. To demonstrate the superiority of this method, it is compared with a genetic algorithm (GA) optimization model. The results show that, compared to the GA, this method reduces job completion time by $6.6 \%$, lowers electricity costs by $10.02 \%$, and decreases electricity consumption during high-price periods by $57 \%$, thereby effectively enhancing the grid’s regulatory capacity.