Low-carbon economic dispatch of iron and steel industry empowered by wind‑hydrogen energy: Modeling and stochastic programming

Haotian Wu, Deping Ke, Jian Xu, Lin Song, Siyang Liao and Pengcheng Zhang

Applied Energy, 2025, vol. 387, issue C, No S0306261925003290

Abstract: The advancement of iron and steel production techniques is facilitating the transition of the iron and steel industry (ISI) from coal as the primary energy source to renewable alternatives such as wind and hydrogen. This also implies that the traditional scheduling method of the ISI, which considers only a single form of energy, requires immediate upgrading. To address this issue, this paper proposes a low-carbon stochastic economic dispatch model that considers the multi-energy coupled ISI. The implementation of a resource task network, which defines discrete steel production, permits the incorporation of gas-based ironmaking and stochastic wind‑hydrogen scenarios into an extended resource task network (ERTN). This ERTN ultimately provides a mathematical representation of the overall operation of the ISI. Additionally, a carbon trading model for the ISI based on the actual carbon policies in southern China is constructed to provide additional guidance on the energy use of the ISI. To overcome the computational challenges posed by the considerable number of binary variables and scenarios inherent to the ERTN, a Lagrangian Benders decomposition algorithm (LBDA) has been developed. This approach entails decomposing the original model into a master problem and multiple subproblems, thereby facilitating more efficient optimization. The simulation results demonstrate that the proposed model is capable of rationally arranging iron and steel production and optimizing the energy utility to maximize the overall economy of ISI, and the LBDA is able to guarantee optimality while significantly enhancing the solution efficiency.

Keywords: Low-carbon iron and steel production; Hydrogen energy; Extended resource task network; Lagrangian benders decomposition; Scenario-based stochastic programming (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125599

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