Cost-effective scheduling of a hydrogen-based iron and steel plant powered by a grid-assisted renewable energy system

The iron and steel industry contributes approximately 25% of global industrial CO2 emissions, necessitating substantial decarbonisation efforts. Hydrogen-based iron and steel plants (HISPs), which utilise hydrogen-based direct reduction of iron ore followed by electric arc furnace steelmaking, have attracted substantial research interest. However, commercialisation of HISPs faces economic feasibility issues due to the high electricity costs of hydrogen production. To improve economic feasibility, HISPs are jointly powered by local renewable generators and bulk power grid, i.e., by a grid-assisted renewable energy system. Given the variability of renewable energy generation and time-dependent electricity prices, flexible scheduling of HISP production tasks is essential to reduce electricity costs. However, cost-effectively scheduling of HISP production tasks is non-trivial, as it is subject to critical operational constraints, arising from the tight coupling and distinct operational characteristics of HISPs sub-processes. To address the above issues, this paper proposes an integrated resource-task network (RTN) to elaborately model the critical operational constraints, such as resource balance, task execution, and transfer time. More specifically, each sub-process is first modelled as an individual RTN, which is then seamlessly integrated through boundary dependency constraints. By embedding the formulated operational constraints into optimisation, a cost-effective scheduling model is developed for HISPs powered by the grid-assisted renewable energy system. Numerical results demonstrate that, compared to conventional scheduling approaches, the proposed method significantly reduces total operational costs across various production scales.