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).

作为高耗能产业，钢铁工业正面临能源成本持续上涨与脱碳压力不断加大的双重困境。为此，多目标优化在钢铁炼钢分厂的生产调度中得到了广泛应用。然而，仅以节能降碳为优先目标的最优解，可能会导致方案脱离实际或经济效率低下——这是因为其忽略了现实生产中钢铁生产订单的加工时间要求（processing time requirement, PTR）。
一篇题为《集成可再生能源与储能系统的钢铁炼钢分厂多目标调度：平衡成本、排放与制造工期》的研究已发表于《Journal of Cleaner Production》（清洁生产期刊），聚焦该研究主题。本研究填补了相关研究空白：探讨了加工时间要求对钢铁炼钢流程制造工期的影响，并将其纳入优化模型之中。考虑到加工时间要求的波动性，多目标调度问题的求解被转化为从不同制造工期的帕累托（Pareto）最优解集中进行选择。
为更好地利用钢铁炼钢流程的时间灵活性，本文提出了一种基于假设分析（what-if-analysis）的策略，并结合正态边界交集（Normal Boundary Intersection, NBI）方法，以生成一系列均匀分布的帕累托最优解。本文还集成了储能系统，以提升钢铁炼钢分厂灵活性的时间粒度。
在本文的案例研究中，通过多组测试案例验证了所提方法在降低电费与排放成本方面的有效性。本文提供了如下输入数据集，以供研究人员复现论文结果或开展进一步相关研究：日前电价曲线、可再生能源（RES）发电量数据以及碳排放强度曲线等。同时，本文还提供了案例研究中所得结果的原始数值数据，便于研究人员更好地理解研究结果或用于其他用途。本整套数据集共计包含9个CSV文件，详细说明如下：
1. "Price_day_ahead.csv"：提供逐小时的日前电价。
2. "RES_generation.csv"：提供逐小时的可再生能源发电量日前预测数据，涵盖光伏（PV）与风电发电，单位为兆瓦（MW）。
3. "Carbon_Intensity_Data.csv"：提供南威尔士地区的预测碳排放强度数据，单位为吨二氧化碳/兆瓦时（tCO₂/MWh）。
4. "Numerical results_ NBI_11P_BESS.csv"：提供5.2.1节的数值结果，对应图10中集成储能系统（BESS）的MO-FlexSP最优解相关数据。其中，`makespan`（制造工期）列对应横轴数值，`EL_EM_Cost`（电力与排放成本）列对应纵轴数值，本案例共包含11个最优点。
5. "Numerical results_ NBI_11P_woBESS.csv"：提供5.2.1节的数值结果，对应图10中未集成储能系统的MO-FlexSP最优解相关数据。其中，`makespan`列对应横轴数值，`EL_EM_Cost`列对应纵轴数值，本案例共包含11个最优点。
6. "Numerical results_ WS_11p_woBESS.csv"：提供5.2.2节的数值结果，对应图11中采用加权求和法的MO-FlexSP最优解相关数据。其中，`makespan`列对应横轴数值，`EL_EM_Cost`列对应纵轴数值，本案例共包含11个最优点。
7. "Numerical results_ NBI_21p_woBESS.csv"：提供5.2.2节的数值结果，对应图12中未集成储能系统的MO-FlexSP最优解相关数据。其中，`makespan`列对应横轴数值，`EL_EM_Cost`列对应纵轴数值，本案例共包含21个最优点。
8. "Numerical results_ WS_21p_woBESS.csv"：提供5.2.2节的数值结果，对应图12中采用加权求和法的MO-FlexSP最优解相关数据。其中，`makespan`列对应横轴数值，`EL_EM_Cost`列对应纵轴数值，本案例共包含21个最优点。
9. "Numerical results_ emission sensitivity.csv"：提供5.2.3节的数值结果，对应图14中最小化电力与排放成本的案例相关数据，该案例展示了间接排放对碳税的敏感性。
本文还提供了如下示意图文件：
1. "Industrial information management system.pdf"：展示了所提模型在当前工业信息管理系统中的角色定位。
2. "Steelmaking Process.pdf"：描述了典型钢铁炼钢流程，该流程包含四个阶段：电弧炉（EAF, Electric Arc Furnace）、氩氧脱碳炉（AOD, Argon Oxygen Decarburisation）、钢包精炼炉（LF, Ladle Furnace）以及连铸（CC, Continuous Casting）。