A Two-Stage Distributionally Robust Optimization Model for Managing Electricity Consumption of Energy-Intensive Enterprises Considering Multiple Uncertainties

Energy-intensive enterprises (EIEs), as vital demand-side flexibility resources, can significantly enhance the power system's ability to regulate demand by participating in demand response (DR). This helps alleviate supply pressures during tight demand-supply conditions, ensuring the system's safe and stable operation. However, due to the current level of electricity management in EIEs, their participation in demand response has disrupted the continuity of production to some extent, which may hinder the sustainability of demand-side management mechanisms. To address this issue, this paper proposes a two-stage distributionally robust optimization (DRO) model for managing production electricity in EIEs, considering multiple uncertainties. First, a production electricity load model based on the state task network (STN) is developed, reflecting the characteristics of industrial production lines. Next, a two-stage DRO model for day-ahead and intra-day electricity management is formulated, integrating an uncertainty set for distributed generation output based on the Wasserstein distance and probabilistic constraints for the day-ahead DR capacity. Finally, a cement plant in western China is used as a case study to validate the effectiveness of the proposed model. The results show that the proposed model effectively guides EIE in participating in DR while optimizing electricity costs, enabling cost savings of up to 27.7%.