LI Xiongfei, LI Kangping, HUANG Chunyi, et al. Demand Response Potential Estimation of EAF Steelmaking Users Considering Production Coupling Constraints[J]. 2026, 46(7): 2852-2864.
DOI:
LI Xiongfei, LI Kangping, HUANG Chunyi, et al. Demand Response Potential Estimation of EAF Steelmaking Users Considering Production Coupling Constraints[J]. 2026, 46(7): 2852-2864. DOI: 10.13334/j.0258-8013.pcsee.242879.
Demand Response Potential Estimation of EAF Steelmaking Users Considering Production Coupling Constraints
such as the electric arc furnace (EAF) steelmaking
boast significant demand response (DR) potential and have been formally recognized as an ancillary service provider. Accurate DR potential assessment forms the foundation for market participation and operational optimization
which is of great significance for enhancing the flexibility of the new power system. Current methods inadequately address production process coupling constraints while relying on single power adjustment modes
resulting in evaluation inaccuracies. This paper proposes a DR potential assessment method based on equipment-level production scheduling simulation. By considering two different regulation methods
load shifting and power curtailment
it fully taps the DR potential of EAF steelmaking. First
a resource-task network is employed to establish an EAF steelmaking production scheduling model that considers the coupling constraints of the production process. Second
with the shortest completion time as the optimization objective
the baseline scheduling scheme is determined. Finally
for price-based DR
the adjustable load boundaries of the steel plant's load are determined with electricity costs as the objectives. For incentive-based DR
the DR potential of the steel mill is evaluated with the objective of maximizing the response during the DR period. A case study carried out in a steel plant in Hebei Province shows that the upward and downward adjustable capacities of EAF steelmaking account for 17.8% and 18.3% of the total load respectively. The total electricity cost can be reduced by 3.01% to 15.59% under time-of-use pricing and by 7.51% to 20.93% under real-time pricing
demonstrating the effectiveness of the proposed method.