Distributed robust optimization scheduling of a steel plant integrated energy system considering the uncertainty of byproduct coal gas

Abstract

The steel plant integrated energy system (SPIES) is an important form in the steel industry. Improving the utilization efficiency of steam, electricity, coal gas and other energy flows is of great significance for both economic and environmental benefits. In this paper, a SPIES scheduling model is established according to the operation characteristics of coal gas holders, boilers and other equipment in steel plants. Meanwhile, to cope with the uncertainty of byproduct coal gas, this paper adopts an imprecise Dirichlet model (IDM) to construct a fuzzy set containing multisource coal gas production information. Then, according to duality theory and the big-M method, the original distributed robust optimization (DRO) model is transformed into a traditional mixed integer linear programming (MILP) model, which is solved by the column-and-constraint generation (CC&G) algorithm. Finally, a real steel production system is given in a case study. Case study illustrate that compared with the traditional robust method, the method proposed in this paper for a SPIES can effectively reduce the conservatism of the scheduling decision. Numerical simulation show that the proposed method can reduce total cost by 55,307.1¥, accounting for 1.91% of the total cost compared with robust optimization method and save 1,326.94 s of computational time compared with the stochastic optimization method, thus reaching balance between conservatism and computational efficiency.