Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: balancing costs, emissions and make-span - data

As an energy-intensive industry, the steel industry grapples with increasing energy costs and decarbonisation pressures. Therefore, multi-objective optimisation is widely applied in the production scheduling of the steelmaking plant. However, the optimal solution prioritising energy savings and emission reductions may lead to impractical or less economically efficient solutions since the processing time requirement (PTR) of steel production orders in real-world production is neglected. A research titled "Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span" has been published on Journal of Cleaner Production regarding this topic. This study fills the research gap by discussing the impact of PTR on the make-span of the steelmaking process and incorporating it into the optimisation model. Considering the variability of PTR, solving the multi-objective scheduling problem is transformed into the selection from Pareto solutions with different make-spans. To better leverage the temporal flexibility of the steelmaking process, a what-if-analysis-based strategy coupled with the Normal Boundary Intersection method is proposed to generate a series of evenly distributed Pareto solutions. The energy storage system is integrated to improve the time granularity of the steelmaking plant's flexibility.

In case studies of the paper, cases were conducted to demonstrate the proposed method for reducing electricity and emission costs. The input dataset, such as the day-ahead electricity price profile, RES generation, and carbon intensity profile, has been provided for researchers to reproduce the results in the paper or to conduct further related studies. Also, the original numerical data of the results in the case studies of the paper have been provided for researchers to better understand the results or to use the results for other purposes.

The whole dataset includes 9 CSV files in total. The detailed description of them is presented as follows:

1. "Price_day_ahead.csv" provides a day-ahead hourly electricity price.

2. "RES_generation.csv" provides a day-ahead forecast of hourly RES generation, such as PV and wind generation; the unit is MW. 

3. "Carbon_Intensity_Data.csv" provides forcast carbon intensity data in the South Wales area. The unit is tCO2/MWh.

4. "Numerical results_ NBI_11P_BESS.csv" provides the numerical results of Section 5.2.1. It provides the data related to the MO-FlexSP + BESS optimal solutions in Fig. 10. The 'makespan' column corresponds to the value on the abscissa, and the 'EL_EM_Cost' column corresponds to the value on the ordinate. There are 11 optimal points in this case.

5. "Numerical results_ NBI_11P_woBESS.csv" provides the numerical results of Section 5.2.1. It provides the data related to the MO-FlexSP optimal solutions in Fig. 10. The 'makespan' column corresponds to the value on the x-axis, and the 'EL_EM_Cost' column corresponds to the value on the y-axis.There are 11 optimal points in this case.

6. "Numerical results_ WS_11p_woBESS.csv" provides the numerical results of Section 5.2.2. It provides the data related to the MO-FlexSP optimal solutions using weighted sum method in Fig. 11. The 'makespan' column corresponds to the value on the x-axis, and the 'EL_EM_Cost' column corresponds to the value on the y-axis.There are 11 optimal points in this case.

7. "Numerical results_ NBI_21p_woBESS.csv" provides the numerical results of Section 5.2.2. It provides the data related to the MO-FlexSP optimal solutions in Fig. 12. The 'makespan' column corresponds to the value on the x-axis, and the 'EL_EM_Cost' column corresponds to the value on the y-axis.There are 21 optimal points in this case.

8. "Numerical results_ WS_21p_woBESS.csv" provides the numerical results of Section 5.2.2. It provides the data related to the MO-FlexSP optimal solutions using the weighted sum method in Fig. 12. The 'makespan' column corresponds to the value on the x-axis, and the 'EL_EM_Cost' column corresponds to the value on the y-axis.There are 21 optimal points in this case.

9. "Numerical results_ emission sensitivity.csv" provides the numerical results of Section 5.2.3. It provides the data related to the Min EL-EM case  in Fig. 14, which shows the sensitivity of indirect emissions to carbon tax.

Some schematic diagrams in this paper are also provided as follows:

1. "Industrial information management system.pdf" provides the role of the proposed model in current industrial information management systems.

2. "Steelmaking Process.pdf" describes the typical steelmaking process, which consists of four stages: electric arc furnace (EAF), argon oxygen decarburisation (AOD), ladle furnace (LF), and continuous casting (CC).

Research results based upon these data are published at https://doi.org/10.1016/j.jclepro.2023.139350