A scheduling method and system for a long-process steel enterprise to participate in power grid peak shaving

Abstract

A scheduling method and system for long-process steel enterprises participating in power grid peak shaving. First, the long-process steelmaking process is divided into intermittent and continuous processes, and production constraints and material inventory constraints are constructed for the two types of processes; then, the output of the self-provided power plant is coupled with the long-process steelmaking process through by-product coal gas, and supply and demand balance constraints and self-provided power plant operation constraints of the by-product coal gas are established; then, according to the long-process steelmaking process, the adjustable process is distinguished, the peak shaving period is determined, the power load of the adjustable process and the power generation output of the self-provided power plant are jointly adjusted, and the adjustment boundary under the condition of not affecting the production is solved for the peak shaving period; finally, a power grid side scheduling optimization model is established to minimize the comprehensive cost as the solving target to solve the optimal scheduling scheme. The present application improves the energy regulation capability of the steel enterprise in the long-process production.

Description

Technical Field

[0001] This invention belongs to the field of industrial load dispatching and power system collaborative control technology, specifically relating to a dispatching method and system for long-process steel enterprises to participate in power grid peak shaving. Background Technology

[0002] With a high proportion of renewable energy connected to the grid, the peak-to-valley difference in the power system is constantly widening. Traditional methods relying on peak-shaving units are insufficient to meet the system's flexibility requirements, and the power grid urgently needs to provide substantial and controllable regulation capabilities on the load side. Existing research has proposed several methods to assess the power dispatch potential of steel enterprises in multi-energy coupled systems and to explore their application in peak-shaving dispatch.

[0003] For example, one study proposed a power dispatching potential assessment method combining multi-energy coupling and production process adjustment, focusing on optimizing the dispatching strategy for adjustable loads in steel production. Another study, by optimizing the dispatching framework, combined solar power generation with steel production load dispatching, which not only improved the utilization rate of solar energy but also contributed to achieving low-carbon goals, demonstrating the potential of steel enterprises in terms of adjustable loads. Furthermore, some scholars, considering the adjustability and coupling characteristics of the internal energy supply and demand system of steel enterprises, as well as the operational uncertainties of self-owned power plants and energy equipment, have adopted an objective function method to reduce maximum load, thereby assessing their peak-shaving potential.

[0004] As typical high-energy-consuming users, long-process steel enterprises have large load scales and stable operations, making them potentially important peak-shaving resources. However, their production processes are highly continuous and tightly coupled, and the generation and utilization of by-product coal gas makes their regulation capacity subject to multiple constraints. Therefore, it is crucial to consider their production characteristics and the characteristics of their self-owned power plants to provide reliable dispatching strategies for steel enterprises to participate in grid peak shaving. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a scheduling method and system for integrated steel enterprises to participate in grid peak shaving. By dividing the steelmaking process into intermittent and continuous processes, output and material inventory constraints are constructed. By coupling by-product gas with the output of self-owned power plants, the peak shaving periods of adjustable processes are identified, and electrical load and power generation output are adjusted. Finally, the overall cost is minimized through a grid-side scheduling optimization model, thereby optimizing the steel plant's peak shaving capacity and improving energy regulation efficiency.

[0006] The first aspect of this application discloses a dispatching method for integrated steel enterprises to participate in power grid peak shaving, which adopts the following technical solution: Based on the characteristics of the production cycle, the long-process steelmaking process is divided into intermittent output process and continuous output process, and output constraints and material inventory constraints are constructed for the two types of output processes. Among them, in the output constraint of the intermittent output process, the actual total output at the end of each production cycle is equivalent to the output of each production period within that cycle. Construct a power output model for self-owned power plants, couple the power output of self-owned power plants with long-process steelmaking through by-product gas, and establish supply and demand balance constraints for coke oven gas, blast furnace gas and converter gas, as well as operating constraints for self-owned power plants. Calculate the adjustable capacity of the steel plant; distinguish between adjustable and non-adjustable processes based on the production continuity and product storage characteristics in the long-process steelmaking process; calculate the net load curve of the power grid to identify the peak shaving period; and determine the adjustment boundary without affecting the output by jointly adjusting the electrical load of the adjustable process and the power generation output of the self-owned power plant for the peak shaving period of the steel plant. A power grid-side dispatch optimization model is established. The peak-shaving cost of steel enterprises is included in the comprehensive cost calculation, and the adjustment boundary of the peak-shaving period of steel plants is used as the constraint for calculation. The optimal dispatch scheme is solved with the goal of minimizing the comprehensive cost.

[0007] Furthermore, the supply and demand balance constraints for various types of by-product gas include: definition For the time slice index of the scheduling period; the first By-product gas in The amount of gas produced during a given period is the product of the product output per unit of gas yield coefficient for the corresponding process during that period; the unit gas yield coefficient is the product of the output per unit of gas yield coefficient for the process during that period. The yield coefficient of by-product gas represents the amount of gas (g) produced per ton of product. The types include coke oven gas, blast furnace gas, and converter gas.

[0008] Furthermore, the supply and demand balance constraints for various types of by-product gas also include: The amount of gas produced, the amount released from the gas holder, the consumption in the process, the consumption for power plant generation, the storage capacity of the gas holder, and the amount released are balanced. Specifically: exist Time period, the The sum of the amount of gas produced by the by-product coal gas and the amount released from the gas holder is equal to the sum of the consumption in the process, the consumption in the power plant's power generation, the amount released, and the amount stored in the gas holder. Among them, the consumption of each process is the consumption of the first process. The amount of by-product gas consumed by the power plant is the first-order amount of gas consumed by the self-owned power plant's gas generator units. The amount of coal gas produced as a by-product.

[0009] Furthermore, in the aforementioned self-owned power plant output model: exist During the specified period, the relationship between the power generation of the self-owned power plant and the amount of coal gas consumed is represented by a coal consumption characteristic curve containing quadratic, linear, and constant terms; it can be described as an equation: The left side of the equation is a quadratic function related to the power generation and coal consumption characteristic coefficient of the self-owned power plant, and the right side of the equation is the total heat energy consumed; the total heat energy is the sum of the heat energy of by-product coal gas and the heat energy of purchased natural gas; the heat energy of by-product coal gas is the product of the power generation consumption of all types of by-product coal gas and the corresponding calorific value, and the heat energy of purchased natural gas is the product of the consumption of purchased natural gas used for power generation and the corresponding calorific value.

[0010] Furthermore, the process of determining the adjustment boundary includes: An objective function is established, the objective of which is to minimize the total electricity purchased by steel enterprises from the power grid during the peak shaving period; the total electricity purchased is the difference between the electricity consumption of production processes and the power generation of self-owned power plants during the peak shaving period; the production processes during the peak shaving period include intermittent production processes and continuous production processes; In the process of obtaining the objective function, a weighting coefficient reflecting the difference in demand during peak-shaving periods is introduced; Treating the steel rolling process as the final production stage, the constraint for solving the objective function is that the output of the steel rolling process should not be lower than the target output.

[0011] Furthermore, the calculation process of the weighting coefficients includes: For scheduling period The corresponding weighting coefficient is calculated as a fraction; the numerator is the scheduling period. The difference between the net load of enterprises and the average net load; the denominator is the sum of the differences between the net load of enterprises and the average net load for all scheduling periods within the peak-shaving period.

[0012] Furthermore, the calculation of the comprehensive cost includes the operating cost of conventional thermal power units, the cost of energy storage units, the cost of new energy units, and the compensation cost for steel companies participating in peak shaving. The calculation method for the compensation cost of steel enterprises participating in peak shaving is as follows: calculate the difference between the steel enterprise's baseline load and the electricity purchased from the grid, and then multiply the difference with the compensation price.

[0013] The second aspect of this application discloses a dispatching system for integrated steel enterprises participating in power grid peak shaving, implementing the dispatching method as described in the first aspect of this application. The system includes: The output process modeling module is used to divide the long-process steelmaking process into intermittent output processes and continuous output processes according to the characteristics of the production cycle, and to build output constraints and material inventory constraints for the two types of output processes. Among them, in the output constraints of intermittent output processes, the actual total output at the end of each production cycle is equivalent to the output of each production period within that cycle. The energy coupling module is used to construct the output model of the self-owned power plant. It couples the output of the self-owned power plant with the long-process steelmaking process through by-product gas, and establishes the supply and demand balance constraints of coke oven gas, blast furnace gas and converter gas, as well as the operation constraints of the self-owned power plant. The steel plant-side adjustable capacity calculation module is used to calculate the adjustable capacity of the steel plant; based on the production continuity and product storage characteristics in the long-process steelmaking process, it distinguishes between adjustable and non-adjustable processes; it identifies the peak-shaving period, and by jointly adjusting the electrical load of adjustable processes and the power generation output of the self-owned power plant, it calculates the adjustment boundary without affecting the output during the peak-shaving period of the steel plant. The grid-side collaborative scheduling module is used to establish a grid-side scheduling optimization model. It incorporates the peak-shaving cost of steel enterprises into the comprehensive cost calculation, uses the adjustment boundary of the peak-shaving period of steel plants as the constraint for calculation, and solves the optimal scheduling scheme with the goal of minimizing the comprehensive cost.

[0014] The beneficial effects of this invention are that, compared with the prior art, This invention enhances the energy regulation capabilities of steel enterprises in long-process production. Specifically, most existing peak-shaving strategies for steel enterprises employ short-process steelmaking, primarily relying on electric arc furnace regulation. However, China's steel industry mainly originates from long-process steel enterprises, whose by-product gas generated during production is typically used as fuel for self-owned power plants, possessing unique regulation advantages. Existing methods rarely consider the coordinated regulation between self-owned power plants and various production processes, failing to effectively integrate the connection between the gas system and production scheduling.

[0015] This application proposes a peak-shaving strategy based on the coordinated regulation of internal processes and self-owned power plants within steel enterprises. It fully considers material coupling, production timing, and the scheduling of by-product gas in the long-process steel production process. By analyzing the net load curve, the time periods requiring regulation are identified, and the load regulation boundary of the steel plant during peak-shaving periods is optimized. This ensures that production load and power plant output can be flexibly adjusted without affecting production, achieving the goal of "peak shifting and valley filling." This reduces the pressure on the power grid during high-load periods and enhances the grid's capacity to absorb renewable energy (such as wind and solar power). Compared with existing methods, this invention enhances the power regulation capability of steel plants through accurate regulation boundary estimation and effective utilization of by-product gas, providing the power grid with more stable and flexible peak-shaving resources. Attached Figure Description

[0016] Figure 1A flowchart for scheduling strategies for integrated steel enterprises with self-owned power plants to participate in grid peak shaving. Figure 2 A comparison of net load before and after applying the dispatch strategy for integrated steel enterprises with self-owned power plants to participate in grid peak shaving. Figure 3 A comparison of the power purchase load of steel mills before and after applying the dispatch strategy of allowing steel mills with self-owned power plants to participate in grid peak shaving. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0018] This embodiment presents a dispatching strategy that considers the participation of long-process steel enterprises with their own power plants in grid peak shaving. Figure 1 As shown, it includes the following steps: S1: As one implementation method of the example, a schedulable full-process production model is constructed that can reflect the relationship between process material coupling, by-product gas generation and inventory transfer, including output constraints and material inventory constraints.

[0019] Furthermore, based on the characteristics of the production cycle, the long-process steelmaking process is divided into intermittent output processes and continuous output processes, and output constraints and material inventory constraints are constructed for the two types of output processes respectively. 1.1: Intermittent output processes refer to processes with fixed and relatively long production cycles that require multiple scheduling periods before producing products, such as coking and blast furnace ironmaking. To facilitate the handling of intermittent output processes in the production model, the actual total output of the process at the end of each production cycle is equivalent to the output of each production period within that cycle, ensuring that the sum of the equivalent output of each period equals the actual total output of the cycle. The output constraint for intermittent output processes is: (1) (2) (3) (4) (5) Equation (1) indicates that the intermittent output process is only allowed to start once within the same production cycle to avoid repeated starts; Equation (2) indicates the equivalent output constraint for each time period to limit the output level within the cycle; Equation (3) indicates that the output of the intermittent output process is equal to the sum of the equivalent output of each time period within the cycle, and is only produced at the end of the production cycle; Equation (4) indicates that the process will not be started again when the end of the scheduling cycle is less than a complete production cycle to ensure production integrity; Equation (5) indicates the relationship between the electricity consumption of the process and the equivalent output in each time period.

[0020] In equations (1)-(5): Defined as the time slice index of the scheduling period; T is the number of scheduling periods; For process The duration of the operation cycle; This is a category for intermittent processes; for The equivalent output of process t during time period; For process Time period Product output; For process Upper and lower limits of equivalent yield; For process The initiation variable, when This indicates that the process is in A production cycle begins at the start of a time period; To control the production load of the process; The load per unit output of an intermittent production process.

[0021] 1.2: Continuous production processes refer to processes that can produce products within each scheduling period, such as sintering, pelletizing, converter steelmaking, refining, continuous casting, and rolling. The output constraint for continuous processes is as follows: (6) (7) Equation (6) represents the output limit for each time period of a continuous process; Equation (7) represents the relationship between the electricity consumption and output of the process during each scheduling period.

[0022] In equations (6)-(7): It is a continuous process category; For process output; For continuous processes Production limits; To control the production load of the process; The load per unit output of a continuous production process.

[0023] 1.3: Material Inventory Constraints (8) (9) Equation (8) represents the material transfer between processes, which is the relationship between inventory and product output and consumption; Equation (9) represents inventory constraints.

[0024] In equations (8)-(9), DC / CT is used to distinguish between continuous production processes and continuous output processes. An index used to distinguish between continuous output processes and continuous output processes; For product inventory; Consumed for products; For process The upper limit of inventory.

[0025] S2: As one implementation method of the example, a self-owned power plant output model is constructed. The output of the self-owned power plant is coupled with the long-process steelmaking process through by-product gas to establish the supply and demand balance constraints of by-product gas and the operation constraints of the self-owned power plant.

[0026] Compared to short-process steelmaking, long-process steelmaking generates coke oven gas, blast furnace gas, and converter gas, which serve as the main fuels for self-owned power plants. The scheduling of the gas system further enhances the power flexibility of steel enterprises. A self-owned power plant output model is constructed, coupling the power output of the self-owned power plant with the process through by-product gas. This includes supply and demand balance constraints for coke oven gas, blast furnace gas, and converter gas, as well as output constraints, start-up and shutdown constraints, and operating condition constraints for the gas-fired power generation unit.

[0027] Supply and demand balance constraints for coke oven gas, blast furnace gas, and converter gas: (10) (11) (12) (13) (14) Equation (10) represents the relationship between the generation of by-product gas and the output of the corresponding process products; Equation (11) represents the supply and demand balance constraint of various gases; Equation (12) represents the relationship between gas inventory and gas generation and consumption; Equation (13) represents the gas storage limit of the gas holder; Equation (14) represents the gas storage and release limit of the gas holder in a single scheduling period.

[0028] In equations (10)-(14), This is a group of gas types, including coke oven gas, blast furnace gas, and converter gas. For indexing; The unit gas yield coefficient represents the amount of gas (g) produced per ton of product. for Gas produced during the period The amount; for The period is for the gas consumed by the gas generator units of the self-owned power plant. The amount; for Gas released from the gas holder during the period quantity; for Gas stored in the gas holder during specific time periods quantity; for Gas consumed in each process during the period quantity; for Periodic gas The amount of emission; for Periodic gas The amount of gas stored in the gas holder; For the gas in the gas holder Minimum inventory requirements; For the gas in the gas holder Maximum inventory capacity; This represents the upper limit of the gas storage capacity of the g-gas holder within each time period. This represents the maximum amount of gas that can be released from gas holder g within each time period. It is a group of gas types, including coke oven gas, blast furnace gas, and converter gas.

[0029] The fuel for a self-owned power plant includes by-product gas generated during the production process and purchased natural gas. The output model of the self-owned power plant includes (15) the relationship between the power generation capacity of the self-owned power plant and the amount of gas consumed, and (16) the output limit of the self-owned power plant. The relationship between the start-up and shutdown variables of the self-owned power plant restricts the self-owned power plant from starting and stopping at the same time. (15) (16) (17) In equations (15)-(17), , , The coal consumption characteristic coefficient reflects the amount of coal gas consumed by the generator set to produce a certain power per unit time. for Power generation from steel companies' self-owned power plants during the specified period; The calorific value of g of coal gas; The calorific value of purchased natural gas; for Periodic gas The amount flowing into self-owned power plants; The amount of natural gas flowing into the self-owned power plant during time period t; The upper and lower limits of the unit's output; , , They are respectively Operating variables, start-up variables, and stop variables of the time-period unit.

[0030] S3: As one implementation method of the embodiment, based on the production continuity and product storage characteristics in the long-process steelmaking process, adjustable processes and non-adjustable processes are distinguished; the net load curve of the power grid is calculated to determine the peak-shaving period; by jointly adjusting the electrical load of the adjustable processes and the power generation output of the self-owned power plant, the adjustment boundary of the steel plant's peak-shaving period within the scheduling period without affecting the output is obtained.

[0031] Non-adjustable processes: In long-process steelmaking, the blast furnace ironmaking, converter steelmaking and refining processes are highly continuous, and intermediate products such as molten iron and molten steel are difficult to store for a long time. Their adjustment space is limited and they are considered non-adjustable processes.

[0032] Adjustable processes: Sintering, coking, and pelletizing products can be stored for a long time, and adjustments can be made by changing the production sequence while ensuring supply; the rolling process has adjustable cycle time, and can be dynamically adjusted on a scale of minutes to hours by adjusting the rolling rhythm and heating frequency.

[0033] The load regulation capacity of integrated steel mills stems from the coordinated changes in the production timing of adjustable processes and the output of their self-owned power plants. The specific boundary of their regulation potential is primarily constrained by production targets, peak-shaving periods, and the number of peak-shaving operations. Therefore, to accurately calculate this boundary, it is necessary to first clarify the peak-shaving period requirements of the power grid. Then, by jointly optimizing the electricity load of adjustable processes and the power generation output of self-owned power plants, while ensuring that total output remains unaffected, the maximum load regulation capacity that the steel mill can provide during a specific peak-shaving period can be determined.

[0034] Furthermore, existing methods for estimating the regulation boundary aim to maximize the total regulation capacity within the peak-shaving period, without distinguishing the differences in peak-shaving demand across different peak-shaving periods. Since the regulation boundary of integrated steel mills is constrained by production targets and the operating status of the gas system, there is a coupling relationship between the regulation capacity of each peak-shaving period; regulation in a single period will affect the adjustable space in other periods. This invention introduces a weighting coefficient reflecting the differences in peak-shaving demand during the regulation boundary solution process. Under the premise of satisfying the coupling constraints of production and gas, this allows periods with higher peak-shaving demand to obtain greater regulation capacity, thereby forming a regulation boundary that matches the peak-shaving demand of the power grid.

[0035] (18) (19) (20) (twenty one) (twenty two) (twenty three) Equation (18) represents the internal power balance relationship of the steel enterprise; Equation (19) represents that the power load of production consists of the power consumption of intermittent processes and continuous processes; Equation (20) is the objective function for estimating the adjustment boundary, with the goal of maximizing the total adjustable load during peak periods, and obtaining the minimum power consumption level for each peak period; Equation (21) is the calculation method for the weight coefficient; Equation (22) represents that the product inventory at the beginning and end of the period is equal to ensure the continuity of the scheduling process; Equation (23) represents the constraint that the output of the steel rolling process is not lower than the target output. Since the steel rolling process is the final production link, its output is used as the overall production target.

[0036] In a further implementation, the peak-shaving period is determined by the net load curve of the power grid. Specifically: (twenty four) In equations (18)-(24), The time slice index represents the scheduling period; for During certain periods, steel companies purchase electricity from the power grid; for Electricity consumption during production processes in a given time period; for Power generation from steel companies' self-owned power plants during the specified period; For periods when peak shaving is required; This is the scheduling period weighting coefficient, which measures the importance of the scheduling period within the peak shaving period. The baseline electricity purchase volume for steel enterprises during time period t is determined based on historical operating data. The baseline electricity purchase volume represents the electricity load level of steel enterprises without participating in grid peak shaving. The net load of the power grid during time period t; This represents the average net load of the power grid. The peak-shaving threshold correction coefficient is set to 0.7 in this invention; This represents the maximum net load of the power grid. Before scheduling begins Inventory of products produced during the process; End of scheduling cycle Inventory of products produced during the process; To manage the inventory of products from the rolling mill process at the end of the scheduling cycle, This refers to the steel rolling process; The target output.

[0037] S4: As one implementation method, a peak-shaving scheduling model is constructed to determine the power grid peak-shaving execution plan, satisfying the power purchase load constraints of steel enterprises, the adjustment boundary constraints, and the power grid operation constraints. The peak-shaving scheduling model comprehensively considers the power grid operation demand and energy-related factors, and determines the power purchase arrangement for steel enterprises to participate in peak shaving during the peak-shaving period. ① Construct an optimization objective function that simultaneously considers grid dispatch costs and steel companies' electricity purchase costs. Equation (25) takes minimizing the comprehensive cost as the optimization objective to achieve peak shaving. By leveraging the characteristic that the marginal cost of thermal power units increases rapidly with output, the system is guided to prioritize the use of load-side regulation and other flexible resources to share peak shaving demand during high-load periods. Equation (26) represents the operating cost of thermal power units, which consists of power generation costs and start-up and shutdown costs. Equation (27) represents the energy storage cost, which consists of operating costs and depreciation costs. Equation (28) represents the new energy cost, which consists of operating costs and power curtailment penalties. Equation (29) represents the compensation cost for steel companies participating in peak shaving. (25) (26) (27) (28) (29) In equations (25)-(29), The objective function of the scheduling optimization model represents the overall cost of system operation; , , , These represent the cost functions for conventional generating units, energy storage power stations, new energy generating units (including wind power and photovoltaic), and electricity purchases by steel companies, respectively. This is the number of conventional generating units; For the first The amount of electricity generated by each unit at time t; , , The first The power generation cost coefficient of the Taiwanese generator unit; For the first Start-up and shutdown cost coefficients for conventional generating units; For the first The start-up and shutdown status of a conventional generating unit at time t, where 1 indicates start-up and 0 indicates shutdown; This refers to the number of energy storage power stations; For energy storage power stations The force output at time t; The cost function of the energy storage power station; The depreciation cost factor per unit time representing the lifespan of energy storage; Representative energy storage station The start / stop status at time t; The number of generating units representing new energy sources; Representing the The output of a wind turbine at time t; The cost coefficient representing new energy generating units; Representing the The predicted output of each new energy unit at time t; This is the wind curtailment penalty coefficient, intended to promote the consumption of new energy sources. Price for peak-shaving compensation for steel companies.

[0038] ② Construct power balance constraints; (30) In the formula: This is a non-adjustable load.

[0039] ③ Construct power purchase load constraints for steel enterprises; (31) In the formula, This represents the minimum amount of electricity that steel companies purchase from the power grid during time period t.

[0040] ④ Set output constraints and regulation capacity limits for grid-side thermal power units, photovoltaic power plants, wind farms, and energy storage units; ⑤ Solve the joint optimization model to obtain a scheduling scheme that meets the peak shaving requirements.

[0041] As an example, under the premise of ensuring process continuity, the operational feasibility of self-owned power plants, and unchanged total output, a dispatching strategy is formulated that considers the participation of long-process steel enterprises with self-owned power plants in grid peak shaving: ①Based on the next day's power grid load forecast data and new energy output forecast data, the net load curves for each time period are calculated.

[0042] ② Based on the net load curve, identify the periods when the net load is high or fluctuates drastically, and determine the target periods when the power grid needs to perform peak shaving.

[0043] ③ During the peak-shaving target period, the load adjustment boundary of the steel plant is determined by combining the adjustable processes within the steel plant and the operating constraints of the self-owned power plant.

[0044] ④ The steel plant's regulation boundary is fed back to the power grid, which then determines the actual peak-shaving capacity to be used within the regulation boundary range and implements peak-shaving scheduling.

[0045] In one embodiment of this application, to verify the effectiveness of the constructed model and the feasibility of the scheduling strategy, a simulation system containing multiple types of power sources and typical industrial loads is constructed for example analysis. The system is configured with 9 conventional thermal power units, 1 wind farm, and 1 photovoltaic power station, as well as 4 energy storage power stations. The industrial side includes four large steel enterprises, all operating according to a long process structure, and each containing its own power plant, raw material preparation process, and steel rolling process, which can participate in system scheduling as regulating units with both source and load attributes.

[0046] Table 1 shows a comparison of the system operation results using the method of this invention with those not using the method of this invention. The results show that after applying the method of this invention, the system operating cost decreased from the original 11.712 million yuan to 9.552 million yuan, and the system scheduling cost was significantly reduced. Furthermore, by coordinating the adjustment of production load and the output of self-owned power plants, the electricity demand curve was smoothed, wind power output was fully absorbed, and wind curtailment decreased from 449.4 MW·h to zero, demonstrating the effectiveness of the method of this invention in improving the utilization rate of renewable energy. At the same time, the electricity purchase cost of steel enterprises decreased from 5.836 million yuan to 5.699 million yuan, indicating that, under the premise of ensuring unchanged output, economic optimization on the enterprise side can be achieved by reducing external electricity purchases during high-price periods and moderately increasing load during low-price periods.

[0047] Table 1 Comparison of System Operation Results

[0048] Furthermore, comparing the system net load curves with and without the scheduling strategy of this invention, the results are as follows: Figure 2 As shown, without the method of this invention, the system net load exhibits significant intraday fluctuations. With the method of this invention, industrial load reduces electricity consumption during high-load periods and moderately increases electricity consumption during low-load periods, thus achieving peak shaving and valley filling of the net load. Peak load decreases by 200–300 MW, and valley load increases by 100 MW, resulting in a smoother overall load curve. A comparison of the steel mill power purchase curves with and without this invention's dispatching strategy yields the following results: Figure 3 As shown, without scheduling, the industrial load power is basically stable, reflecting continuous production characteristics. After applying the method of this invention, the industrial load is reduced during some key periods and moderately increased during other periods, while the total electricity consumption remains unchanged, forming a typical peak-shifting and valley-filling operation characteristic. The load adjustment is phased, conforms to the process rhythm constraints, and has adjustable space that can be released on a short time scale.

[0049] As an embodiment of this application, a dispatching system for integrated steel enterprises to participate in power grid peak shaving is disclosed. The system, implemented using the dispatching method described above, includes: The output process modeling module is used to divide the long-process steelmaking process into intermittent output processes and continuous output processes according to the characteristics of the production cycle, and to build output constraints and material inventory constraints for the two types of output processes. Among them, in the output constraints of intermittent output processes, the actual total output at the end of each production cycle is equivalent to the output of each production period within that cycle. The energy coupling module is used to construct the output model of the self-owned power plant. It couples the output of the self-owned power plant with the long-process steelmaking process through by-product gas, and establishes the supply and demand balance constraints of coke oven gas, blast furnace gas and converter gas, as well as the operation constraints of the self-owned power plant. The steel plant-side adjustable capacity calculation module is used to calculate the steel plant-side adjustable capacity; based on the production continuity and product storage characteristics in the long-process steelmaking process, it distinguishes between adjustable and non-adjustable processes; it clarifies the peak-shaving period, and by jointly adjusting the electrical load of adjustable processes and the power generation output of self-owned power plants, it obtains the adjustment boundary of the steel plant's peak-shaving period within the scheduling period without affecting output. The grid-side collaborative scheduling module is used to establish a grid-side scheduling optimization model. It incorporates the peak-shaving cost of steel enterprises into the comprehensive cost calculation, uses the adjustment boundary of the peak-shaving period of steel plants as the constraint for calculation, and solves the optimal scheduling scheme with the goal of minimizing the comprehensive cost.

[0050] As an embodiment of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it employs the specific implementation described above for the scheduling method.

[0051] As an embodiment of this application, a computer-readable storage medium is provided, which stores a computer program, and the computer program, when executed by a processor, adopts the specific implementation described above for the scheduling method.

[0052] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0053] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0054] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0055] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0056] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for dispatching integrated steel enterprises to participate in power grid peak shaving, characterized in that, include: Based on the characteristics of the production cycle, the long-process steelmaking process is divided into intermittent output process and continuous output process, and output constraints and material inventory constraints are constructed for the two types of output processes. Among them, in the output constraint of the intermittent output process, the actual total output at the end of each production cycle is equivalent to the output of each production period within that cycle. Construct a power output model for self-owned power plants, couple the power output of self-owned power plants with long-process steelmaking through by-product gas, and establish supply and demand balance constraints for coke oven gas, blast furnace gas and converter gas, as well as operating constraints for self-owned power plants. Calculate the adjustable capacity of the steel mill; distinguish between adjustable and non-adjustable processes based on the production continuity and product storage characteristics in the long-process steelmaking process; calculate the net load curve of the power grid to identify peak-shaving periods; and determine the adjustment boundary without affecting output during the peak-shaving period of the steel mill by jointly adjusting the electrical load of adjustable processes and the power generation output of self-owned power plants. The process of determining the adjustment boundary includes: establishing an objective function, the objective of which is to minimize the total electricity purchased by the steel enterprise from the power grid during the peak-shaving period; the total electricity purchased is the difference between the electricity consumption of the production processes and the power generation of self-owned power plants during the peak-shaving period; the production processes during the peak-shaving period include intermittent and continuous production processes; introducing a weighting coefficient reflecting the demand difference during the peak-shaving period in the process of determining the objective function; taking the steel rolling process as the final production link, setting the constraint for solving the objective function to ensure that the output of the steel rolling process is not lower than the target output. A grid-side dispatch optimization model is established. This model incorporates the peak-shaving costs of steel enterprises into the comprehensive cost calculation, uses the adjustment boundary of the steel plant's peak-shaving period as a constraint for the calculation, and seeks the optimal dispatch scheme with the goal of minimizing the comprehensive cost. The comprehensive cost calculation includes the operating costs of conventional thermal power units, energy storage units, new energy units, and the compensation cost for steel enterprises participating in peak shaving. The compensation cost for steel enterprises participating in peak shaving is calculated by multiplying the difference between the steel enterprise's baseline load and the electricity purchased from the grid with the compensation price.

2. The dispatching method for integrated steel enterprises participating in power grid peak shaving according to claim 1, characterized in that, The supply and demand balance constraints include: definition For the time slice index of the scheduling period; the first By-product gas in The amount of gas produced during a given period is the product of the product output of the corresponding process during that period and the unit gas yield coefficient; the unit gas yield coefficient is the product of the output of the process during that period and the unit gas yield coefficient. The yield coefficient of by-product gas represents the amount of gas (g) produced per ton of product. The types include coke oven gas, blast furnace gas, and converter gas.

3. The dispatching method for integrated steel enterprises participating in power grid peak shaving according to claim 2, characterized in that, The supply and demand balance constraints of various by-product gases also include: The amount of gas produced, the amount released from the gas holder, the consumption in the process, the consumption for power plant generation, the storage capacity of the gas holder, and the amount released are balanced. Specifically: exist Time period, the The sum of the amount of gas produced by the by-product coal gas and the amount released from the gas holder is equal to the sum of the consumption in the process, the consumption in the power plant's power generation, the amount released, and the amount stored in the gas holder. Among them, the consumption of each process is the consumption of the first process. The amount of by-product gas is the power plant's power generation consumption, which is the first-order part of the consumption of the self-owned power plant's gas generator units. The amount of coal gas produced as a by-product.

4. The dispatching method for integrated steel enterprises participating in power grid peak shaving according to claim 1, characterized in that, In the power output model of the self-owned power plant: exist During the specified period, the relationship between the power generation of the self-owned power plant and the amount of coal gas consumed is represented by a coal consumption characteristic curve containing quadratic, linear, and constant terms; it can be described as an equation: The left side of the equation is a quadratic function related to the power generation and coal consumption characteristic coefficient of the self-owned power plant, and the right side of the equation is the total heat energy consumed; the total heat energy is the sum of the heat energy of by-product coal gas and the heat energy of purchased natural gas; the heat energy of by-product coal gas is the product of the power generation consumption of all types of by-product coal gas and the corresponding calorific value, and the heat energy of purchased natural gas is the product of the consumption of purchased natural gas used for power generation and the corresponding calorific value.

5. The dispatching method for integrated steel enterprises participating in power grid peak shaving according to claim 1, characterized in that, The calculation process of the weighting coefficients includes: For scheduling period The corresponding weighting coefficient is calculated as a fraction; the numerator is the scheduling period. The difference between the net load of enterprises and the average net load; the denominator is the sum of the differences between the net load of enterprises and the average net load for all scheduling periods within the peak-shaving period.

6. A dispatching system for integrated steel enterprises participating in power grid peak shaving, executing the dispatching method as described in any one of claims 1-5, characterized in that, The system includes: The output process modeling module is used to divide the long-process steelmaking process into intermittent output processes and continuous output processes according to the characteristics of the production cycle, and to build output constraints and material inventory constraints for the two types of output processes. Among them, in the output constraints of intermittent output processes, the actual total output at the end of each production cycle is equivalent to the output of each production period within that cycle. The energy coupling module is used to construct the output model of the self-owned power plant. It couples the output of the self-owned power plant with the long-process steelmaking process through by-product gas, and establishes the supply and demand balance constraints of coke oven gas, blast furnace gas and converter gas, as well as the operation constraints of the self-owned power plant. The steel plant-side adjustable capacity calculation module is used to calculate the adjustable capacity of the steel plant; based on the production continuity and product storage characteristics in the long-process steelmaking process, it distinguishes between adjustable and non-adjustable processes; it identifies the peak-shaving period, and by jointly adjusting the electrical load of adjustable processes and the power generation output of the self-owned power plant, it calculates the adjustment boundary without affecting the output during the peak-shaving period of the steel plant. The grid-side collaborative scheduling module is used to establish a grid-side scheduling optimization model. It incorporates the peak-shaving cost of steel enterprises into the comprehensive cost calculation, uses the adjustment boundary of the peak-shaving period of steel plants as the constraint for calculation, and solves the optimal scheduling scheme with the goal of minimizing the comprehensive cost.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the scheduling method according to any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the scheduling method according to any one of claims 1-5.

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