Fueling Industrial Flexibility: Discrete-Time Dispatch Optimization of Electric Arc Furnaces

Electric arc furnace technology is a key factor in the sustainable transformation of the iron and steel industry. This study compares two discrete-time multi-objective optimization models—integer and mixed-integer linear programming—that integrate unit commitment with economic and environmental dispatch. After evaluating both approaches, the integer linear programming model is used, due to its reasonable calculation time, to assess demand-side management potentials under real-world processes and day-ahead market conditions. The model is applied to various scenarios with differing energy price dynamics, CO2 pricing, EAF utilization levels, and weighting of the objective functions. Results indicate cost savings of up to 6.95% and CO2 emission reductions of up to 10.86%, though these are subject to a non-linear trade-off between economic and environmental goals. Due to process constraints and market structures, EAFs’ flexibility in energy carrier use (switch between electricity and natural gas) is limited to 3.07%. Additionally, lower furnace utilization does not necessarily increase flexibility, as downstream process requirements restrict scheduling options. The study underscores the importance of green electrification, with up to 36% CO2 savings when using 100% renewable electricity. Overall, unlocking industrial flexibility requires technical solutions, supportive market incentives, and regulatory frameworks for effective industrial decarbonization.