Rational capacity investment for renewable hydrogen-based steelmaking systems: A multi-stage expansion planning strategy

Abstract

To improve energy efficiency and mitigate environmental impact, the iron and steel industry (ISI) is responsible for implementing a low-carbon transition. The renewable hydrogen-based steelmaking system (RHSS) has emerged as an innovative energy system in the pathway of reducing the carbon footprint of ISI. Nonetheless, the sustainable development requirements of RHSS emphasize the critical need for well-planned capacity investments to boost its economic viability. This study introduces a novel planning strategy for RHSS referred to as a multi-stage expansion planning (MSEP) model, which is aimed to minimize the levelized cost of crude steel over the planning horizon from 2025 to 2050 (EU climate neutrality year). The MSEP model incorporates the construction sequence and dynamic techno-economic parameters to formulate investment strategies that adapt to changing system requirements and external conditions over the planning horizon. To address the complexity of the MSEP model, a rolling-horizon approach (RHA) is proposed to solve the model sequentially while integrating updated information. The proposed approach is benchmarked against both the single-stage approach and the perfect-sight approach through case studies. The numerical results show that the proposed RHA (i) guarantees a rational capacity investment strategy for RHSS considering the construction sequence and dynamic techno-economic parameters over multiple planning stages; (ii) achieves a reduction in the levelized cost of crude steel by approximately 50% relative to the single-stage approach; (iii) resolves the convergence issues encountered with the perfect-sight approach while achieving an optimal solution in a reasonable time frame. Additionally, sensitivity analysis evaluates the uncertainties of multiple factors on the planning results, while the robustness analysis confirms the applicability and adaptability of RHA across various scenarios.