A method for evaluating carbon reduction potential of steel industry under source-load interaction

Abstract

This invention discloses a method for assessing the carbon reduction potential of the steel industry under source-load interaction, belonging to the field of carbon emission control technology in the steel industry. The technical problem this invention aims to solve is that existing carbon emission accounting methods are static and cannot reflect real-time changes in power system carbon intensity and the impact of source-load interaction on emission reduction. The key technical points of this invention are: constructing a full-process carbon emission accounting model for the steel industry; determining the dynamic electric carbon factor of each node based on the power grid topology and power flow tracing method; assessing carbon emissions and emission reductions based on the dynamic electric carbon factor and short-process alternatives for electric arc furnaces; constructing an energy optimization model to optimize electricity load and energy storage configuration with the goal of minimizing overall cost; and regulating the charging and discharging of the energy storage system based on the optimization results. This invention achieves dynamic and accurate carbon emission accounting, quantifies the emission reduction potential of electricity substitution, reduces electricity costs and carbon emissions, and provides decision support for the low-carbon transformation of the steel industry.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission control technology in the steel industry, and particularly relates to a method for assessing the carbon reduction potential of the steel industry under the interaction of source and load. Background Technology

[0002] As a typical high-energy-consuming and high-emission sector, the steel industry's green transformation is crucial for achieving dual-carbon goals. Under the global carbon neutrality context, short-process electric arc furnace steelmaking, due to its lower carbon emission intensity, is gradually becoming the industry's transformation direction, and the country is actively promoting the shift from long-process to short-process technologies. However, existing research assessing the steel industry's emission reduction potential mostly focuses on changes in user-side energy consumption, failing to fully characterize the interconnected impacts of electricity substitution on the power generation structure, time-series electrical carbon factor, and system-level carbon emissions. Simultaneously, the high proportion of renewable energy integrated into the power system presents challenges such as insufficient regulation capacity and uncertainty in carbon emission reduction efficiency in the traditional load-driven operation mode. While existing methods assess the steel industry's emission reduction potential from the perspectives of energy efficiency improvement, energy structure adjustment, and scrap steel recycling exist, their generalizability and decision-making value are limited under high-proportion renewable energy scenarios. Specifically, traditional carbon emission accounting uses a static average electrical carbon factor, which is insufficient to accurately reflect the true emission reduction effect of electricity substitution, cannot track the specific transfer of carbon flow in the power system, and cannot characterize the production sources of carbon emissions generated by electricity consumption by load-side users. Furthermore, carbon emissions from the steel industry exhibit significant fluctuations on monthly, daily, and hourly scales. These fluctuations are influenced by policy factors such as production control and emergency responses to heavy pollution weather, as well as by market mechanisms such as peak-valley electricity pricing. Existing methods lack quantitative assessment tools for system-level carbon emissions under the source-load interaction mechanism. Accurately calculating the dynamic carbon factor and quantifying the systemic emission reduction potential of the steel industry through electricity substitution, given a high proportion of renewable energy integration, has become a fundamental issue in formulating a low-carbon path for the steel industry. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a method for assessing the carbon reduction potential of the steel industry under source-load interaction, thereby resolving the issues present in the existing technologies.

[0004] Firstly, to achieve the above objectives, this invention provides a method for assessing the carbon reduction potential of the steel industry under source-load interaction, comprising the following steps: S1. Construct a full-process carbon emission accounting model for steel enterprises, and calculate the total carbon emissions of enterprises based on data on fuel consumption, production process, purchased electricity and carbon sequestration products. S2. Based on the power grid topology and power flow section data, the dynamic carbon factor of each node in the power grid is determined by the power flow tracing method. S3. Based on the dynamic electric carbon factor and the electric energy substitution scheme, assess the carbon emissions and emission reductions of steel enterprises after implementing the electric arc furnace short process to replace the long process. S4. Construct an energy consumption optimization model with the goal of minimizing overall cost, and optimize the enterprise's electricity load and energy storage system configuration by combining electricity price and carbon factor data; S5. Based on the optimization results, regulate the charging and discharging of the energy storage system to achieve coordinated optimization of load and green electricity.

[0005] Optionally, in S1, the process of calculating the total carbon emissions of an enterprise includes: calculating fuel combustion emissions based on fuel consumption and fuel carbon emission factors for each process; calculating production process emissions based on flux consumption and flux carbon emission factors; calculating purchased electricity emissions based on purchased electricity and purchased electricity dynamic carbon factors; and calculating the implicit emissions of carbon sequestration products based on crude steel production and crude steel carbon emission factors.

[0006] Optionally, in S2, the process of determining the dynamic carbon factor of each node in the power grid using the power flow tracing method includes: based on the power grid topology, using the forward-backward substitution method to perform power flow calculations to obtain the branch active power flow matrix and node voltage; based on the generator carbon emission rate and active power flow distribution, constructing the node active power flux matrix and calculating the carbon factor of each node.

[0007] Optionally, in S3, the electricity substitution scheme is to replace the traditional blast furnace-converter long process with an electric arc furnace short process, and use all scrap steel as the raw material for electric arc furnace steelmaking; the process of assessing carbon emissions and emission reductions includes: calculating the carbon emissions after the transformation based on the carbon emission intensity of the electric arc furnace process and the enterprise's production capacity after the transformation, and calculating the emission reductions based on the total carbon emissions before the transformation.

[0008] Optionally, in S4, the process of building an energy optimization model includes: integrating enterprise hourly load data, time-series electricity prices, and time-series carbon factors to set the configuration range of energy storage system capacity and power; and using mixed integer linear programming with the objective function of minimizing electricity costs, minimizing carbon emissions, or minimizing overall costs to solve for the optimal electricity load curve and energy storage charging and discharging strategy.

[0009] Optionally, in S5, the process of regulating the charging and discharging of the energy storage system includes: based on the energy storage charging and discharging power timing determined by the optimization results, the energy storage system is controlled in real time to charge during periods of low electricity prices or low carbon factor, and to discharge during periods of high electricity prices or high carbon factor, so as to smooth load fluctuations and reduce electricity costs and carbon emissions.

[0010] Optionally, the method further includes: adjusting the dynamic carbon factor of each node of the power grid in S2 according to the new energy power generation forecast results, adjusting the scrap steel ratio of the power substitution scheme in S3 according to the scrap steel resource forecast results, and re-executing S4 and S5 to dynamically update the energy consumption optimization results.

[0011] In a second aspect, the present invention also provides a computer terminal device, comprising: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the method for assessing the carbon reduction potential of the steel industry under the source-load interaction described in the first aspect above.

[0012] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the method for assessing the carbon reduction potential of the steel industry under source-load interaction in the first aspect described above.

[0013] Fourthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method for assessing the carbon reduction potential of the steel industry under source-load interaction as described in the first aspect.

[0014] Compared with the prior art, the present invention has the following advantages and technical effects: This invention provides a method for assessing the carbon reduction potential of the steel industry under source-load interaction. By constructing a dynamic electrical carbon factor accounting method based on power flow tracking, it achieves accurate tracking of the carbon flow distribution of the power system, overcoming the limitation of traditional static emission factors that cannot reflect the real-time carbon intensity of the power grid. This invention introduces a source-load interaction mechanism, systematically characterizing the linkage impact of electricity substitution on the generation-side structure and system-level carbon emissions, and quantitatively assessing the real emission reduction benefits of short-process substitution. This invention establishes an energy consumption optimization model to achieve coordinated control of electricity load and energy storage systems while ensuring production continuity, achieving a balance between economic efficiency and carbon reduction benefits. This invention reveals the emission reduction mechanism and timing response characteristics of electrification transformation from the perspective of power system and industrial load coordination, providing a scientific basis for optimizing the low-carbon transformation path of heavy industry and refining regional pollution reduction and carbon reduction policies. Attached Figure Description

[0015] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating a method for assessing the carbon reduction potential of the steel industry under source-load interaction, according to an embodiment of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0018] Example 1 like Figure 1 As shown, this embodiment provides a method for assessing the carbon reduction potential of the steel industry under source-load interaction, including: S1. Construct a full-process carbon emission accounting model for steel enterprises, and calculate the total carbon emissions of enterprises based on data on fuel consumption, production process, purchased electricity and carbon sequestration products. S2. Based on the power grid topology and power flow section data, the dynamic carbon factor of each node in the power grid is determined by the power flow tracing method. S3. Based on the dynamic electric carbon factor and the electric energy substitution scheme, assess the carbon emissions and emission reductions of steel enterprises after implementing the electric arc furnace short process to replace the long process. S4. Construct an energy consumption optimization model with the goal of minimizing overall cost, and optimize the enterprise's electricity load and energy storage system configuration by combining electricity price and carbon factor data; S5. Based on the optimization results, regulate the charging and discharging of the energy storage system to achieve coordinated optimization of load and green electricity.

[0019] Furthermore, in S1, the process of calculating the total carbon emissions of an enterprise includes: calculating fuel combustion emissions based on fuel consumption and fuel carbon emission factors for each process; calculating production process emissions based on flux consumption and flux carbon emission factors; calculating purchased electricity emissions based on purchased electricity and purchased electricity dynamic carbon factors; and calculating the implicit emissions of carbon sequestration products based on crude steel production and crude steel carbon emission factors.

[0020] Specifically, the implementation process of this embodiment includes: Based on the accounting scope covered in Part 5 of the "Greenhouse Gas Emissions Accounting and Reporting Requirements: Steel Enterprises," and combined with regional electricity consumption and related energy activity data, a systematic accounting of the main carbon emission sources of steel enterprises is conducted, covering carbon emissions from fossil fuel combustion, flux decomposition, purchased electricity, and carbon sequestration products. Due to the lack of data on heating and electricity output from steel enterprises, this embodiment will not consider CO2 emissions from electricity output and heating by steel enterprises. Furthermore, to avoid double counting, this embodiment will not consider CO2 emissions generated during the self-generation of electricity by steel enterprises. The calculation formula is as follows: (1); In the formula: For steel companies CO2 emissions, in tons; For steel companies CO2 emissions from fuel combustion, in tons; For steel companies CO2 emissions from the production process, in tons (t); For steel companies CO2 emissions from purchased electricity, in tons; For steel companies CO2 emissions implied by crude steel production, in tons.

[0021] The main steel production process includes: coking, pelletizing, sintering, ironmaking, steelmaking, and hot rolling. The formula for calculating CO2 emissions from fuel combustion in the steel industry is as follows: (2); (3); (4); In the formula: For enterprises fuel Consumption amount, t; fuel CO2 emission factor, t·t -1 ; fuel Average lower heating value, TJ·t -1 ; fuel The carbon content per unit calorific value, t·TJ -1 , For enterprises product Energy intensity, kgce·t -1 The average energy consumption of the sintering process in several steel companies in a certain city is 51 kgce·t. -1 The average energy consumption of the blast furnace process is 412 kgce·t. -1 ; For enterprises product The output, in tons, is derived from model calculations. fuel The fuel composition for the sintering process is 70% coke and 30% anthracite, while the fuel composition for the blast furnace process is 77% coke, 14% anthracite, and 9% bituminous coal. The required parameters and values ​​for the model are shown in Table 1.

[0022] Table 1

[0023] The formula for calculating CO2 emissions from the steel industry production process is as follows: (5); In the formula: for Corporate Cosolvents The consumption, in tons, is mainly composed of dolomite and limestone. The fluxing agent consumption intensity data comes from the World Steel Association; the limestone consumption required per ton of pig iron is 1.7 t·t. -1 The consumption of dolomite was 0.014 t·t. -1 The pig iron data comes from model calculation results; as a cosolvent CO2 emission factor, t·t -1 .

[0024] The formula for calculating CO2 emissions from purchased electricity during steel production is as follows: (6); In the formula: For enterprises Net purchased electricity, kW∙h; The CO2 emission factor for purchased electricity is expressed in t·(kW·h). -1 .

[0025] The formula for calculating the CO2 emissions implied by carbon sequestration products is as follows: (7); In the formula: The model calculates the crude steel output (tons) of the enterprise based on the data. The CO2 emission factor for solid product crude steel, t·t -1 , is 0.008 t·t -1 .

[0026] Furthermore, in S2, the process of determining the dynamic carbon factor of each node in the power grid using the power flow tracing method includes: based on the power grid topology, using the forward-backward substitution method to calculate the power flow, obtaining the branch active power flow matrix and node voltage; based on the generator carbon emission rate and active power flow distribution, constructing the node active power flux matrix, and calculating the carbon factor of each node.

[0027] Specifically, the implementation process of this embodiment includes: Method for calculating carbon emission factors of purchased electricity: To track the specific transfer of carbon flow in the power system and the sources of carbon emissions generated by electricity consumption by load-side users, this study conducts power flow calculations on the system based on a large amount of power grid topology and power flow section data, and establishes a calculation model for the carbon flow distribution correlation analysis of branches, nodes, loads, and active power losses.

[0028] The power flow calculation adopts the forward-backward substitution method. Based on the known power of the tail node, the current of the head branch is obtained by forward substitution. Based on the calculation results, the tail end is calculated by backward substitution. The study studies how to satisfy the voltage constraint conditions by verifying the voltage value at each node and performing multiple forward-backward substitutions.

[0029] The calculation formula is as follows: (8); (9); (10); (11); In the formula: For nodes To the node The active power flow, kW; For nodes To the node The reactive power, kV; For nodes The voltage increase, kV; For nodes The set of child nodes; For the line Reactive power loss on the line, kV; For the line The resistance, in Ω; For the line The reactance, Ω.

[0030] Based on the power flow operation results, the inflow and outflow of active power at nodes are determined, and a branch active power flow matrix is ​​constructed. Simultaneously, combining known conditions, the unit injection matrix and generator carbon emission vector are listed. The calculation is completed through the definition of the node active power flux matrix, analyzing the connections between nodes in the distribution network system, and thus determining the node electrical carbon factor. (Nodes in the power grid) Electrocarbon factor The calculation formula is: (12); In the formula: branch road The carbon flux density. After replacing it with the branch-starting node electric carbon factor, the electric carbon factor formula is... Rewritten as: (13); In the formula: The One element is 1, and the rest are 0.

[0031] Based on the active flux matrix of the nodes We can obtain: (14); After simplification, the carbon factor of each node in the power system is obtained, as shown in equation (15): (15); The carbon flow analysis method based on power flow tracing combines future annual electricity consumption with the power flow calculation results for the future year as input. Through the principle of forward and backward substitution, it constructs a power flow distribution matrix, accurately decomposing the active power of generators into the load of each node, the power of each branch, and network losses. At the same time, it combines the real-time carbon emission rate model of different generators and the power distribution in the network, and utilizes the characteristic that carbon emission flow is attached to active power flow to realize the carbon emission flow analysis and calculation of the distribution network.

[0032] Furthermore, in S3, the electricity substitution scheme is to replace the traditional blast furnace-converter long process with an electric arc furnace short process, and to use all scrap steel as the raw material for electric arc furnace steelmaking; the process of assessing carbon emissions and emission reductions includes: calculating the carbon emissions after the transformation based on the carbon emission intensity of the electric arc furnace process and the enterprise's capacity after the transformation, and calculating the emission reductions based on the total carbon emissions before the transformation.

[0033] Specifically, the implementation process of this embodiment includes: Calculation of carbon emissions from steel companies after "electricity substitution": (16); (17); (18); In the formula: , , These represent short-process electric arc furnace steelmaking, blast furnace-converter long-process steelmaking, and steelmaking processes, respectively. The CO2 emissions of the steel plant after the renovation, in tons; CO2 emission intensity, t·t -1 References for CO2 emission data from electric arc furnaces (Yao Conglin, 2020): Analysis and Research on CO2 Emissions in the Steel Industry Based on Production Processes; CO2 emission intensity of the all-scrap electric arc furnace process is taken as 0.67 t·t. -1 ; For CO2 emission factors, t·t -1 ; The capacity of steel enterprises after renovation is t.

[0034] Calculation of carbon emission reductions for steel companies after "electricity substitution": (19); In the formula, The CO2 emission reduction of the enterprise after the renovation is expressed in tons (t). The figure represents the CO2 emissions of the steel plant before the renovation, in tons.

[0035] Furthermore, in S4, the process of constructing the energy optimization model includes: integrating enterprise hourly load data, time-series electricity prices, and time-series carbon factors to set the configuration range of energy storage system capacity and power; and using mixed integer linear programming with the objective function of minimizing electricity costs, minimizing carbon emissions, or minimizing overall costs to solve for the optimal electricity load curve and energy storage charging and discharging strategy.

[0036] Furthermore, in S5, the process of regulating the charging and discharging of the energy storage system includes: based on the energy storage charging and discharging power timing determined by the optimization results, the energy storage system is controlled in real time to charge during periods of low electricity prices or low carbon factor, and to discharge during periods of high electricity prices or high carbon factor, so as to smooth load fluctuations and reduce electricity costs and carbon emissions.

[0037] Furthermore, the method also includes: adjusting the dynamic carbon factor of each node of the power grid in S2 according to the new energy power generation forecast results, adjusting the scrap steel ratio of the power substitution scheme in S3 according to the scrap steel resource forecast results, and re-executing S4 and S5 to dynamically update the energy consumption optimization results.

[0038] Specifically, the implementation process of this embodiment includes: Optimization methods for "electricity substitution" in the steel industry: Based on a modular architecture, the system integrates data standardization, intelligent parameter generation, multi-objective optimization modeling, and batch solution functions. First, the system cleans and aligns the original load data of enterprises to generate hourly time series data. It then integrates regional electricity price and grid carbon factor data to construct a unified time series input set. Based on this, a dynamic parameter range generation strategy is proposed: the energy storage capacity range is capped at 12 times the average annual load, with an initial value set at [5, 10, 20] MW∙h, incremented and rounded, and ultra-long ranges are sparsely sampled to 10 typical scenarios; the power range is referenced to 10%~100% of the maximum load, increasing in 5 MW increments, with a minimum retention of [2.5, 5, 10] MW, ensuring a reasonable configuration range for enterprises of different sizes.

[0039] The optimization model adopts MILP and supports three types of objective functions: minimizing electricity costs, minimizing carbon emissions, and minimizing overall costs.

[0040] With the optimization goal of reducing enterprise electricity costs, its mathematical form is: (20); With the optimization objective of reducing carbon emissions, its mathematical form is: (twenty one); In actual production, enterprises often need to balance economic and environmental benefits, therefore a comprehensive objective function is set: (twenty two); In the formula, To optimize the total number of time periods; Electricity price for the specified time period, in yuan per kilowatt-hour (kW·h). -1 ; No. Carbon factor over time, kg·(kW·h) -1 ; , The energy storage system in the first Charge / discharge power over time period, kW·h; Electricity purchased for the power grid, kWh; For energy storage efficiency, % For carbon price, yuan·t -1 .

[0041] Example 2 In this embodiment, a computer terminal device is provided, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the above-described method for assessing the carbon reduction potential of the steel industry under source-load interaction.

[0042] In this embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the above-described method for assessing the carbon reduction potential of the steel industry under source-load interaction.

[0043] In this embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the above-described method for assessing the carbon reduction potential of the steel industry under source-load interaction.

[0044] This invention provides a method for assessing the carbon reduction potential of the steel industry under source-load interaction. By constructing a dynamic electrical carbon factor accounting method based on power flow tracking, it achieves accurate tracking of the carbon flow distribution of the power system, overcoming the limitation of traditional static emission factors that cannot reflect the real-time carbon intensity of the power grid. This invention introduces a source-load interaction mechanism, systematically characterizing the linkage impact of electricity substitution on the generation-side structure and system-level carbon emissions, and quantitatively assessing the real emission reduction benefits of short-process substitution. This invention establishes an energy consumption optimization model to achieve coordinated regulation of electricity load and energy storage systems while ensuring production continuity, achieving a balance between economic efficiency and carbon reduction benefits. This invention reveals the emission reduction mechanism and timing response characteristics of electrification transformation from the perspective of power system and industrial load coordination, providing a scientific basis for optimizing the low-carbon transformation path of heavy industry and refining regional pollution reduction and carbon reduction policies.

[0045] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing the carbon reduction potential of the steel industry under source-load interaction, characterized in that, Includes the following steps: S1. Construct a full-process carbon emission accounting model for steel enterprises, and calculate the total carbon emissions of enterprises based on data on fuel consumption, production process, purchased electricity and carbon sequestration products. S2. Based on the power grid topology and power flow section data, the dynamic carbon factor of each node in the power grid is determined by the power flow tracing method. S3. Based on the dynamic electric carbon factor and the electric energy substitution scheme, assess the carbon emissions and emission reductions of steel enterprises after implementing the electric arc furnace short process to replace the long process. S4. Construct an energy consumption optimization model with the goal of minimizing overall cost, and optimize the enterprise's electricity load and energy storage system configuration by combining electricity price and carbon factor data; S5. Based on the optimization results, regulate the charging and discharging of the energy storage system to achieve coordinated optimization of load and green electricity.

2. The method according to claim 1, characterized in that, In S1, the process of calculating the total carbon emissions of an enterprise includes: calculating fuel combustion emissions based on fuel consumption and fuel carbon emission factors for each process; calculating production process emissions based on flux consumption and flux carbon emission factors; calculating purchased electricity emissions based on purchased electricity and purchased electricity dynamic carbon factors; and calculating the implicit emissions of carbon sequestration products based on crude steel production and crude steel carbon emission factors.

3. The method according to claim 1, characterized in that, In S2, the process of determining the dynamic carbon factor of each node in the power grid using the power flow tracing method includes: based on the power grid topology, using the forward-backward substitution method to calculate the power flow matrix of the branch active power flow and the node voltage; based on the generator carbon emission rate and active power flow distribution, constructing the active power flux matrix of the node and calculating the carbon factor of each node.

4. The method according to claim 1, characterized in that, In S3, the electricity substitution scheme is to replace the traditional blast furnace-converter long process with an electric arc furnace short process, and to use all scrap steel as the raw material for electric arc furnace steelmaking. The process of assessing carbon emissions and emission reductions includes: calculating the carbon emissions after the transformation based on the carbon emission intensity of the electric arc furnace process and the enterprise's production capacity after the transformation; and calculating the emission reductions based on the total carbon emissions before the transformation.

5. The method according to claim 1, characterized in that, In S4, the process of building an energy optimization model includes: integrating enterprise hourly load data, time-series electricity prices and time-series carbon factors, and setting the configuration range of energy storage system capacity and power; using mixed integer linear programming, with the objective function of minimizing electricity costs, minimizing carbon emissions or minimizing overall costs, to solve for the optimal electricity load curve and energy storage charging and discharging strategy.

6. The method according to claim 1, characterized in that, In S5, the process of regulating the charging and discharging of the energy storage system includes: based on the energy storage charging and discharging power sequence determined by the optimization results, the energy storage system is controlled in real time to charge during periods of low electricity prices or low carbon factor, and to discharge during periods of high electricity prices or high carbon factor, so as to smooth load fluctuations and reduce electricity costs and carbon emissions.

7. The method according to claim 1, characterized in that, The method further includes: adjusting the dynamic carbon factor of each node of the power grid in S2 according to the new energy power generation forecast results, adjusting the scrap steel ratio of the power substitution scheme in S3 according to the scrap steel resource forecast results, and re-executing S4 and S5 to dynamically update the energy consumption optimization results.

8. A computer terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the steps of the method as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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