Iron and steel industry power grid interactive full-process scheduling method based on mixed integer linear programming

Abstract

The invention discloses a mixed integer linear programming-based iron and steel industry power grid interactive full-process scheduling method. The method comprises the specific steps of S1, constructing a dynamic balance equation and an energy storage equipment dynamic management model; s2, establishing a multi-energy coupling collaborative optimization model; s3, setting a target function, an energy storage equipment constraint condition and a key equipment power constraint condition; s4, embedding a demand response load adjustment model; and S5, based on the model embedded in the step S4, solving by using a mixed integer linear programming algorithm to obtain an optimal scheduling scheme. According to the iron and steel industry power grid interactive full-process scheduling method based on mixed integer linear programming, by constructing an iron making-steel making-rolling full-process energy flow dynamic coupling model, secondary energy balance of coal gas, steam and the like and power grid demand response instructions are integrated, and collaborative optimization of energy and production between processes is achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of steel industry scheduling method, and particularly relates to a steel industry power grid interaction whole-process scheduling method based on mixed integer linear programming. BACKGROUND

[0002] Currently, a relatively mature optimization framework has emerged for scheduling production processes in the steel industry, with significant progress particularly in key processes such as sintering, hot rolling, and continuous casting. However, these methods generally focus on optimizing within-process time costs, equipment losses, or local energy consumption. Their optimization scope is limited to the static scenario of a single process, failing to plan scheduling for the entire steel industry production process. In the context of market-oriented energy pricing reform, time-of-use electricity pricing (TOU) mechanisms, through price signals, guide industrial users to participate in demand-side response and have become a key policy tool for addressing the spatiotemporal mismatch between electricity supply and demand. Existing methods demonstrate that deep coupling of TOU electricity pricing with industrial energy systems can effectively tap into the regulatory potential of flexible loads, confirming the leverage value of TOU electricity pricing in energy cost optimization. However, these approaches have not been fully extended to cross-process, multi-energy flow coordination scenarios, nor have they thoroughly quantified the dynamic game between electricity price fluctuations and production flexibility. Coordinated optimization of multi-energy coupled systems is a key approach to addressing the dual constraints of energy costs and carbon emissions. The key lies in deeply exploring the cross-scale interactions of multiple energy flows. Existing methods have proven that the flexibility and economy of the energy system can be significantly improved through the coordinated design of electricity-gas-heat multi-energy complementarity and flexible production regulation. However, there is a general lack of modeling methods for the dynamic coupling of energy flows in the entire process of ironmaking-steelmaking-rolling, and the real-time coordination mechanism of grid demand response instructions and production flexibility parameters has not been deeply integrated. In the field of grid demand response, many research results have laid the foundation for the construction of an interactive coordination mechanism between the steel industry and the grid. Although existing scheduling calculation methods have made significant progress in the interactive coordination mechanism between the steel industry and the grid, they have the following limitations: (1) Single process optimization limitations: Existing research mostly focuses on the static optimization of single processes such as sintering and steelmaking, and lacks coordinated scheduling of the entire process of ironmaking-steelmaking-rolling, resulting in limited overall energy efficiency and cost optimization. (2) Insufficient application of time-of-use electricity price mechanism: The coupling of time-of-use electricity price and production scheduling is limited to the local energy system, and has not been extended to the cross-process-multi-energy flow coordination scenario, and the dynamic game relationship between electricity price fluctuations and production flexibility has not been quantified. (3) Lack of dynamic coupling of energy flows: The existing multi-energy coupling model lacks the modeling of the dynamic interaction mechanism of energy flows (gas, steam, etc.) throughout the entire process, and cannot achieve accurate matching of energy supply and demand between processes. (4) Single dimension of grid interaction: The existing research on grid demand response has not constructed a multi-dimensional coupling model of "process-energy-grid", resulting in the steel industry's potential as an adjustable load not being fully tapped. (5) Inefficient processing of complex constraints: Traditional models have difficulty processing nonlinear coupling constraints such as gas generator start-up and shutdown, fuel input, etc., resulting in low solution efficiency and inability to support real-time scheduling needs. (6) Lack of real-time coordination mechanism: The existing methods do not integrate the minute-level coordinated optimization of grid demand response instructions and production flexibility parameters, making it difficult to dynamically respond to price fluctuations in the electricity spot market. Summary of the Invention

[0003] The application aims to provide a steel industry grid interaction full-process scheduling method based on mixed integer linear programming, realize the collaborative optimization of energy and production between processes by constructing an ironmaking-steelmaking-rolling full-process energy flow dynamic coupling model, and integrating secondary energy balance such as coal gas and steam and grid demand response instructions.

[0004] The technical scheme adopted by the application is a steel industry grid interaction full-process scheduling method based on mixed integer linear programming, and the specific steps are as follows: S1, constructing dynamic balance equations and dynamic management models of sintering bin, coal bin, billet storage, converter gas tank and waste heat steam storage energy equipment; S2, based on the model constructed in S1, establishing a multi-energy coupling collaborative optimization model composed of a blast furnace gas dynamic supply and demand balance model, a coal gas power generation output model, a waste heat power generation output model and an electric energy bus global balance model; S3, using the multi-energy coupling collaborative optimization model established in S2, setting an objective function containing material cost, electricity cost, purchased electricity cost and product revenue, and constraint conditions of energy storage equipment and power constraints of key equipment; S4, using the function and constraint of S3, embedding a demand response load adjustment model in the model constructed in S1 and S2; S5. Based on the embedded model of S4, the optimal scheduling scheme is obtained by using a mixed integer linear programming algorithm.

[0005] The application also has the characteristics that, The dynamic balance equation of the sintering bin is: (1) (2) (3) (4) In the formula, denotes the scheduling period the sinter output of the circular cooler, denotes the scheduling period the sinter output of the first sintering machine, denotes the scheduling period the sinter input of the sintering bin, denotes the scheduling period the sinter output of the circular cooler directly supplied to the blast furnace, denotes the scheduling period the sinter demand of the blast furnace, denotes the scheduling period the sinter output of the sintering bin, denotes the scheduling period sintering stock storage amount of the sintering bin, denotes the dispatching period sintering stock storage amount of the sintering bin in the previous period, denotes the material retention rate; The dynamic balance equation of the coal bunker is: (5) (6) (7) In the formula, denotes the dispatching period pulverized coal input amount of the coal bunker, denotes the dispatching period The pulverized coal output amount of the coal bunker, denotes the dispatching period pulverized coal amount directly supplied from the coal mill to the blast furnace, denotes the dispatching period pulverized coal demand amount of the blast furnace, denotes the dispatching period pulverized coal output amount of the coal bunker, denotes the dispatching period pulverized coal storage amount of the coal bunker, denotes the dispatching period pulverized coal storage amount of the coal bunker in the previous period; The dynamic balance equation of the billet storage is: (8) (9) (10) In the formula, denotes the dispatching period billet input amount of the billet storage, denotes the dispatching period billet output amount of the continuous casting system, denotes the dispatching period billet amount directly supplied from the continuous casting system to the rolling system, denotes the dispatching period billet demand amount of the rolling system, denotes the dispatching period billet output amount of the billet storage, denotes the dispatching period billet storage amount of the billet storage, denotes the dispatching period billet storage amount of the billet storage in the previous period.

[0006] The dynamic balance equation of the converter gas tank is: (11) (12) (13) Where, Indicates the scheduling period Converter gas input to converter gas tank, Indicates the scheduling period Converter gas production, Indicates the scheduling period No. The converter gas demand of Taiwan gas power generation units, Indicates the scheduling period Converter gas output of converter gas tank, Indicates the scheduling period No. The proportion of converter gas input to the total input of a gas-fired power generation unit, Indicates the scheduling period The converter gas storage capacity of the converter gas tank, Scheduling period The converter gas storage capacity of the converter gas tank in the previous period.

[0007] Before the molten steel produced in the converter steelmaking process is fed into the continuous casting machine, the ladle needs to be baked, and the use of blast furnace gas or converter gas is coordinated according to scheduling needs, expressed as: (14) Where, Indicates the scheduling period Blast furnace gas demand for ladle baking, Indicates the scheduling period converter gas demand for ladle baking; The dynamic balance equation of the waste heat steam bin is: (15) (16) (17) Where, Indicates the scheduling period The amount of waste heat steam input to the steam bunker, Indicates the scheduling period The waste heat steam production of the converter, Indicates the scheduling period Waste heat steam production from heating furnaces, Indicates the scheduling period The waste heat steam output of the steam bunker, Indicates the scheduling period The first The waste heat steam demand of the waste heat power generation unit of the sintering station, denotes the scheduling period The first The proportion of the waste heat steam input of the waste heat power generation unit of the sintering station to the output of the steam warehouse, denotes the scheduling period The waste heat steam storage of the steam warehouse, The scheduling period The waste heat steam storage of the steam warehouse in the previous period; Based on the dynamic balance equations of the sintering warehouse, the coal warehouse, the billet warehouse, the converter gas tank and the waste heat steam warehouse, a dynamic management model of the energy storage device is constructed.

[0008] The dynamic supply-demand balance model of the blast furnace gas is specifically: (18) In the formula, denotes the scheduling period The yield of the blast furnace gas, denotes the scheduling period The blast furnace gas demand of the sintering machine, denotes the scheduling period The blast furnace gas demand of the hot blast furnace in the blast furnace ironmaking system, denotes the scheduling period The blast furnace gas demand of the heating furnace, denotes the scheduling period The gas demand of the baking ladle, denotes the scheduling period The first The blast furnace gas demand of the gas power generation unit of the station.

[0009] The gas power generation output model is specifically: (19) In the formula, denotes the scheduling period The first The power generation of the gas power generation unit of the station. , respectively denote the calorific value of the blast furnace gas and the calorific value of the converter gas, , respectively denote the conversion efficiency of the blast furnace gas and the conversion efficiency of the converter gas; (20) In the formula, denotes the scheduling period The first The power generation of the gas power generation unit of the station, denotes the scheduling period step.

[0010] The waste heat power generation output model is specifically: (21) In the formula, denotes the scheduling period The power generation of the waste heat power generation unit, respectively denote the waste heat steam heat value, denotes the waste heat steam conversion efficiency; (22) In the formula, denotes the scheduling period The power generation of the waste heat power generation unit.

[0011] The global balance model of the power bus is specifically: (23) (24) In the formula, denotes the scheduling period total power consumption, denotes the scheduling period power consumption of the sintering system, denotes the scheduling period power consumption of the coal mill, denotes the scheduling period power consumption of the blast furnace ironmaking system, denotes the scheduling period power consumption of the converter steelmaking system, denotes the scheduling period power consumption of the refining furnace, denotes the scheduling period power consumption of the continuous casting system, denotes the scheduling period power consumption of the rolling system, denotes the scheduling period power consumption of the heating furnace, denotes the scheduling period power consumption of the oxygen production system. (25) In the formula, denotes the scheduling period total power generation, denotes the scheduling period photovoltaic power generation.

[0012] The objective function in S3 includes material cost, electricity cost, purchased electricity cost, and product revenue, and is specifically: (26) Where, Indicates the The unit price of the material, No. Material scheduling period The consumption, Indicates the scheduling period The purchased electricity price, Indicates the scheduling period of purchased electricity, represents the photovoltaic electricity price, Indicates the selling price of finished steel. Indicates the scheduling period The output of finished steel, Indicates the selling price of by-product steel slag, Indicates the scheduling period The output of by-product steel slag, Indicates the selling price of the by-product slag, Indicates the scheduling period The output of by-product water slag; The energy storage equipment constraints and key equipment power constraints in S3 are as follows: Storage capacity constraints: (27) Where, Indicates the scheduling period No. The storage capacity of the warehouse, 、 Respectively represent The lower and upper limits of the storage capacity of the Taiwan warehouse.

[0013] (28) Where, Indicates the scheduling period No. The input quantity of the warehouse, 、 Respectively represent The lower and upper limits of the input quantity of the Taiwan warehouse; (29) Where, Indicates the scheduling period No. The output of the warehouse, 、 Respectively represent The lower and upper limits of the output volume of the Taiwan warehouse; The constraints for gas power generation are: (30) , , respectively represent the start time and the end time, in minutes; The power lower limit and the power upper limit of the first coal gas generator set are: The power lower limit and the power upper limit of the first coal gas generator set are: The waste heat steam power generation constraint is: (31) , , respectively represent the start time and the end time, in minutes; The power lower limit and the power upper limit of the first waste heat generator set are: The power lower limit and the power upper limit of the first waste heat generator set are:

[0014] The embedded demand response load adjustment model is represented as: The time window for the steel industry to participate in the grid demand response is: (32) , (33) (34) , , respectively represent the start time and the end time, in minutes; The power to be cut by the steel industry in the time window is , and the adjusted load is: (35) , represents the purchased power after adjustment in response to the grid dispatching instruction in the dispatching period , and the target function is updated accordingly.

[0015] The mixed integer linear programming algorithm used in S5 is specifically: (36) , represents the start-up and shutdown state of the first sintering unit in the dispatching period represents the start-up and shutdown state of the first coal mill unit in the dispatching period represents the start-up and shutdown state of the first coal gas generator set in the dispatching period represents the start-up and shutdown state of the first waste heat generator set in the dispatching period represents the start-up and shutdown state of the first waste heat generator set in the dispatching period represents the start-up and shutdown state of the first waste heat generator set in the dispatching period ​​​​​​Representative dispatch period Whether the converter gas is used for the ladle baking.

[0016] Compared with the prior art, the present application has the beneficial effects that: (1) The steel industry grid interaction full-process scheduling method based on mixed integer linear programming provided by the present application innovatively constructs an ironmaking-steelmaking-rolling full-process energy flow dynamic coupling model, integrates secondary energy balance such as coal gas and steam and grid demand response instructions, realizes collaborative optimization of energy and production between processes, and proposes a double driving strategy of time-of-use electricity price and grid regulation demand, so that energy is stored in the electricity price valley period and released in the peak period through dynamic scheduling of multi-medium energy storage equipment, thereby simultaneously reducing electricity costs and providing peak shaving capacity.

[0017] (2) The steel industry grid interaction full-process scheduling method based on mixed integer linear programming provided by the present application implants grid interaction instructions (such as load transfer and standby capacity supply) into the full-process scheduling model, develops a grid sensitivity joint debugging mechanism of sinter direct supply, coal powder ratio optimization and slab storage regulation and storage, and realizes minute-level collaboration of "process-energy storage-grid".

[0018] (3) The steel industry grid interaction full-process scheduling method based on mixed integer linear programming provided by the present application uses the MILP framework to process complex constraints (such as coal gas generator set start-stop logic), linearizes the nonlinear terms through the big M method, significantly improves the solvability of the model, ensures the global optimality of the industrial problem, realizes the dual goals of economic benefits of the steel enterprise and low-carbon operation of the grid through electricity cost saving, auxiliary service income (peak shaving and valley filling) and multi-energy flow collaborative optimization, and meets the needs of the "double carbon" policy. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 It is a schematic diagram of the power generation efficiency of the photovoltaic power generation device in the embodiment 7 of the present application; Figure 2 It is a schematic diagram of the time-of-use electricity price in the embodiment 7 of the present application; Figure 3 It is a schematic diagram of the purchased electricity quantity in the steel industry full-process grid interaction collaborative scheduling in the embodiment 7 of the present application; Figure 4 It is a schematic diagram of the power generation quantity of each unit in the steel industry full-process grid interaction collaborative scheduling in the embodiment 7 of the present application; Figure 5 It is a schematic diagram of the power consumption of each unit in the steel industry full-process grid interaction collaborative scheduling in the embodiment 7 of the present application; Figure 6 It is a schematic diagram of the real-time state of the energy storage equipment in the steel industry full-process grid interaction collaborative scheduling in the embodiment 7 of the present application; Figure 7Figure 7 is a schematic diagram of the benefit-cost comparison of the whole-process power grid interaction collaborative scheduling in the steel industry in Embodiment 7 of the present application. DETAILED DESCRIPTION

[0020] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. The described embodiments are only part of the embodiments in the present application, not all.

[0021] Embodiment 1 The present application provides a steel industry power grid interaction whole-process scheduling method based on mixed integer linear programming, comprising the following steps: S1, constructing the dynamic balance equation and dynamic management model of the sintering bin, coal bin, billet storage, converter gas tank and waste heat steam storage energy equipment; S2, based on the model constructed in S1, establishing a multi-energy coupling collaborative optimization model composed of a blast furnace gas dynamic supply and demand balance model, a coal gas power generation output model, a waste heat power generation output model and an electric energy bus global balance model; S3. Using the multi-energy coupling collaborative optimization model established in S2, setting the objective function containing material cost, electricity cost, purchased electricity cost and product revenue, and the constraint conditions of the energy storage equipment and the power constraint conditions of the key equipment; S4, using the function and constraint of S3, embedding a demand response load adjustment model in the model constructed in S1 and S2; S5. Based on the model embedded in S4, using a mixed integer linear programming algorithm to solve and obtain an optimal scheduling scheme.

[0022] Embodiment 2 On the basis of Embodiment 1, S1 is specifically: In the whole process of the steel industry, the energy storage equipment is the key hub connecting the material flow and energy flow between processes, and its dynamic management directly affects the production continuity and energy utilization efficiency. Under the background of time-of-use electricity price response and photovoltaic power generation access, the charging and discharging strategy of the energy storage equipment needs to consider both economy and stability: on the one hand, through energy storage during off-peak periods and energy release during peak periods, the cost of purchased electricity is reduced; on the other hand, through buffering production fluctuations, the smooth running of high-energy-consumption processes is ensured. In the following, for the five typical energy storage equipment of sintering bin, coal bin, billet storage, converter gas tank and waste heat steam storage, the dynamic balance equation and capacity constraint model will be constructed to provide a physical basis for the subsequent optimization model.

[0023] (1 As a key buffer node between sintering and blast furnace ironmaking processes, the sintering bin ensures the stability of blast furnace raw material supply through dynamic storage capacity. The sinter produced by the sintering machine is distributed through two modes of direct supply and storage: part of it is directly supplied to the blast furnace for ironmaking, and the remaining part is stored to cope with production fluctuations. Its dynamic balance equation is as follows: (1) (2) (3) (4) In the formula, indicates the scheduling period sinter output of the circular cooler, indicates the scheduling period The sinter output of the No. 1 sintering machine, indicates the scheduling period sinter input of the sinter bin, indicates the scheduling period sinter amount of the circular cooler direct supply to the blast furnace, indicates the scheduling period sinter demand of the blast furnace, indicates the scheduling period sinter output of the sinter bin, indicates the scheduling period sinter storage of the sinter bin, indicates the scheduling period sinter storage of the sinter bin in the previous period, indicates the material retention rate.

[0024] (2) The coal bunker is the core hub of the blast furnace ironmaking pulverized coal supply, and its scheduling needs to balance the mill output and the real-time demand of the blast furnace. The model dynamically constrains the storage amount by the real-time production state of the mill, and the mill produces part of the pulverized coal directly supplied to the blast furnace, and the remaining is stored in the buffer, and its dynamic balance equation is as follows: (5) (6) (7) In the formula, indicates the scheduling period pulverized coal input of the coal bunker, indicates the scheduling period The pulverized coal output of the No. 1 mill, indicates the scheduling period pulverized coal amount of the mill direct supply to the blast furnace, indicates the scheduling period pulverized coal demand of the blast furnace, indicates the scheduling period pulverized coal output of the coal bunker, indicates the scheduling period pulverized coal storage of the coal bunker, indicates the scheduling period The coal powder storage amount of the previous period coal bunker.

[0025] (3) The billet storage is the intermediate storage unit between the continuous casting and rolling process, which needs to balance the direct supply and storage buffer of the cast billet. The cast billet produced by the continuous casting is directly rolled by the electric furnace or stored temporarily, and is sent to the rolling mill after preheating by the heating furnace when it is discharged. The dynamic balance equation is as follows: (8) (9) (10) In the formula, denotes the scheduling period the billet input amount of the billet storage, denotes the scheduling period the billet output amount of the continuous casting system, denotes the scheduling period the billet amount directly supplied from the continuous casting system to the rolling system, denotes the scheduling period the billet demand amount of the rolling system, denotes the scheduling period the billet output amount of the billet storage. denotes the scheduling period the billet storage amount of the billet storage, denotes the scheduling period the billet storage amount of the previous period billet storage.

[0026] (4) The converter gas tank is the dynamic buffer unit between the converter gas and the power generation and process gas, and its storage capacity can effectively suppress the fluctuation of gas supply and demand. The model is based on the gas injection amount and release amount, and constructs the recursive equation of the storage amount, and cooperates with the gas generator set to realize the optimization of energy cascade utilization, and the dynamic balance equation is as follows: (11) (12) (13) In the formula, denotes the scheduling period the converter gas input amount of the converter gas tank, denotes the scheduling period the production amount of the converter gas, denotes the scheduling period the converter gas demand amount of the first gas generator set, denotes the scheduling period the converter gas output amount of the converter gas tank, denotes the scheduling period No. The proportion of converter gas input to the total input of a gas-fired power generation unit is Indicates the scheduling period The converter gas storage capacity of the converter gas tank, Scheduling period The converter gas storage capacity of the converter gas tank in the previous period.

[0027] Before the molten steel produced in the converter steelmaking process is fed into the continuous casting machine, the ladle needs to be baked, and the use of blast furnace gas or converter gas is coordinated according to scheduling needs; it is expressed as: (14) Where, Indicates the scheduling period Blast furnace gas demand for ladle baking, Indicates the scheduling period Converter gas demand for baking ladles.

[0028] (5) The waste heat steam warehouse serves as a buffer hub between waste heat steam and power generation and process requirements. Its dynamic scheduling can improve waste heat utilization efficiency and reduce steam emission. The model constructs a recursive equation for storage capacity based on steam injection volume and power generation consumption, and coordinates with the waste heat power generation unit to achieve real-time matching of steam supply and demand and energy efficiency optimization. Its dynamic balance equation is as follows: (15) (16) (17) Where, Indicates the scheduling period The amount of waste heat steam input to the steam bunker, Indicates the scheduling period The waste heat steam production of the converter, Indicates the scheduling period Waste heat steam production from heating furnaces, Indicates the scheduling period The waste heat steam output of the steam bunker, Indicates the scheduling period No. The waste heat steam demand of the waste heat generator set, Indicates the scheduling period No. The ratio of the waste heat steam input of the waste heat generator set to the steam bunker output, Indicates the scheduling period The waste heat steam storage capacity of the steam bunker, Scheduling period The amount of waste heat steam stored in the steam warehouse in the previous period.

[0029] In summary, the embodiment constructs a dynamic management model of energy storage equipment through (1)-(5) above, providing a physical basis for subsequent optimization models.

[0030] Example 3 In this embodiment, a multi-energy coupling collaborative optimization model is further constructed based on Example 2, i.e., S2 is specifically: A. Blast furnace gas is the core secondary energy of the whole process of the steel industry, and its dynamic supply and demand balance is the key link of energy scheduling. The gas is produced from blast furnace ironmaking and is consumed by sintering process, blast furnace self-use, steel heating and gas power generation. In order to guarantee the energy utilization efficiency and system stability, the model needs to meet the real-time supply and demand balance: (18) In the formula, denotes the scheduling period the output of blast furnace gas, denotes the scheduling period the blast furnace gas demand of sintering machine, denotes the scheduling period the blast furnace gas demand of hot blast furnace in blast furnace ironmaking system, denotes the scheduling period the blast furnace gas demand of heating furnace, denotes the scheduling period the gas demand of baking ladle, denotes the scheduling period the blast furnace gas demand of the first set of gas power generation units.

[0031] B. Gas power generation is the core energy conversion node of multi-energy coupling system, which realizes the efficient conversion of clean energy through cascade utilization of blast furnace gas and converter gas. According to the time-of-use electricity price strategy, multiple sets of gas power generation units dynamically adjust the output: in the peak period of electricity price, they are preferentially operated at full load to replace high-priced purchased electricity, and in the valley period, they are coordinated with gas tank to realize gas storage and buffering. The output model is as follows: (19) In the formula, denotes the scheduling period the power generation of the first set of gas power generation units. , respectively represent the calorific value of blast furnace gas and the calorific value of converter gas, , respectively represent the conversion efficiency of blast furnace gas and the conversion efficiency of converter gas; (20) In the formula, denotes the scheduling period The The power generation of the coal-fired generator set, denotes the scheduling period step.

[0032] C. Waste heat power generation is a key link in the energy cascade utilization of the steel industry. By recovering waste heat steam from the converter steelmaking and rolling process, it is converted into electric energy to replace high-priced purchased electricity. The waste heat power generation set dynamically adjusts the output according to the real-time state of the steam bin and the time-of-use electricity price strategy: during the peak period of electricity price, it runs at full load to maximize the benefit, and during the valley period, it buffers the steam supply and demand fluctuations through the steam bin. Its output model is as follows: (21) In the formula, denotes the scheduling period The The power generation of the coal-fired generator set, respectively denote the waste heat steam heat value, denotes the waste heat steam conversion efficiency.

[0033] (22) In the formula, denotes the scheduling period The The power generation of the coal-fired generator set.

[0034] D. The power supply and demand balance of the whole process of the steel industry is the core of energy scheduling, and its dynamic coordination needs to integrate purchased electricity, self-generation and electric consumption of each process. Among all the equipment, it is particularly pointed out that the refining furnace decides whether to start according to the order demand. The global balance equation of the power bus is as follows: (23) (24) In the formula, denotes the scheduling period The total power consumption of the scheduling period, denotes the scheduling period The power consumption of the sintering system, denotes the scheduling period The power consumption of the coal mill, denotes the scheduling period The power consumption of the blast furnace ironmaking system, denotes the scheduling period The power consumption of the converter steelmaking system, denotes the scheduling period The power consumption of the refining furnace, denotes the scheduling period The power consumption of the continuous casting system, denotes the scheduling period Power consumption of steel rolling system, Indicates the scheduling period The power consumption of the heating furnace, Indicates the scheduling period Power consumption of the oxygen production system; (25) Where, Indicates the scheduling period The total power generation, Indicates the scheduling period Photovoltaic power generation.

[0035] Example 4 In this embodiment, based on the models in Examples 2 and 3, the objective function and constraints are set, that is, S3 is specifically: The core goal of full-process economic scheduling in the steel industry is to achieve coordinated optimization of minimizing total operating costs and maximizing overall benefits through multi-energy coupling and time-sharing response strategies. The objective function integrates material costs, basic electricity costs, purchased electricity costs, and product benefits to construct the following mathematical expression: (26) Where, Indicates the The unit price of the material, No. Material scheduling period The consumption, Indicates the scheduling period The purchased electricity price, Indicates the scheduling period of purchased electricity, represents the photovoltaic electricity price, Indicates the selling price of finished steel. Indicates the scheduling period The output of finished steel, Indicates the selling price of by-product steel slag, Indicates the scheduling period The output of by-product steel slag, Indicates the selling price of the by-product slag, Indicates the scheduling period The output of by-product water slag; b. The dynamic management of energy storage devices in the whole process of steel industry should strictly follow the capacity and flow constraints to ensure the continuity of production and the physical feasibility of energy scheduling. The storage capacity constraint defines the buffering capacity of the device to avoid overstocking or supply interruption caused by over-limit; the flow constraint ensures the conservation and timing of material flow through the dynamic balance equation. The model realizes the coordination of constraints through the following mechanisms: the constraint conditions of energy storage devices in S3 and the power constraint conditions of key devices, which are specifically: Storage capacity constraint: (27) wherein, denotes the scheduling period the input quantity of the first warehouse, , denotes the lower limit and the upper limit of the input quantity of the first warehouse, respectively.

[0036] (28) wherein, denotes the scheduling period the input quantity of the first warehouse, , denotes the lower limit and the upper limit of the input quantity of the first warehouse, respectively. (29) wherein, denotes the scheduling period the output quantity of the first warehouse, , denotes the lower limit and the upper limit of the output quantity of the first warehouse, respectively. Gas power generation constraint: (30) wherein, denotes the lower limit and the upper limit of the power of the first gas power generation unit during the scheduling period, respectively. The waste heat steam power generation constraint is represented as: (31) wherein, denotes the lower limit and the upper limit of the power of the first waste heat power generation unit during the scheduling period, respectively.

[0037] Example 5 ​​​​​​​​​​​​The embodiment is based on examples 1-4, and the embedded demand response load adjustment model, S4, is as follows: The steel industry participates in the supply and demand interaction of the power grid, and dynamically optimizes the operation time sequence of high-energy-consuming equipment through time-of-use price signals to reduce the cost of purchased electricity and improve the flexibility of power grid interaction. The model is driven by grid dispatching instructions, identifies the flexibility potential of adjustable load equipment, and builds a collaborative strategy for load transfer and power reduction.

[0038] Suppose the time window for the steel industry to participate in the demand response of the power grid is: (32) Wherein: (33) (34) In the formula, , respectively represent the start time and end time, in minutes.

[0039] Suppose that in this time window, the steel industry needs to reduce the power , and the adjusted load is: (35) In the formula, represents the purchased electricity quantity in the dispatching period in response to the grid dispatching instruction, and the objective function is updated accordingly.

[0040] Example 6 This embodiment is based on examples 1-5, and a mixed integer linear programming algorithm is used for solution, i.e. S5 is as follows: binary decision variables include 5 types of variables, i.e. start-stop of sintering unit, start-stop of coal mill unit, start-stop of coal gas generator, start-stop of waste heat generator, and whether to use converter gas for ladle baking, all of which are 0, 1 variables, i.e. (36) In the formula, represents the start-stop state of the th sintering unit in the dispatching period , represents the start-stop state of the th coal mill unit in the dispatching period , represents the start-stop state of the th coal gas generator in the dispatching period , represents the start-stop state of the th waste heat generator in the dispatching period , represents the start-stop state of the Whether the converter gas is used for the ladle baking.

[0041] The continuous decision variables include the categorical variables, i.e. the input quantity of the warehousing equipment , , , , ) and the output quantity , , , , ), the sinter quantity directly supplied by the circular cooler to the blast furnace ), the pulverized coal quantity directly supplied by the coal mill to the blast furnace ), the billet quantity directly supplied by the continuous casting unit to the rolling mill unit ), the gas intake quantity of the gas generator unit , ) and the gas intake quantity of the waste heat generator unit ).

[0042] The application constructs a mixed integer linear programming (MILP) model integrating equipment selection and operation scheduling to realize the collaborative optimization of the production system and the grid operation. The model introduces binary variables to represent the start-stop state of the equipment, continuous variables to represent the output level of the equipment, uses the large M linearization technology to process the nonlinear constraints, and solves by means of the branch and bound algorithm to ensure the global optimality of the model. The model is solved by calling the Gurobi solver for optimization calculation. The method effectively coordinates the start-stop and output scheduling of the equipment, and meets the flexibility and economic demand of the steel enterprise in participating in the supply-demand interaction of the grid.

[0043] Embodiment 7 In this embodiment, the industrial park of a certain steel group in Qujing, Yunnan, China is selected as the research object. The industrial park constructs an integrated energy-production coupling system, and the core equipment configuration includes two sintering units, three coal mill units, one group of sintering bins, coal bins, billet bins, converter gas tanks and waste heat steam bins, and the initial capacity and upper and lower limits are shown in Table 1. When the material is input and output, the retention rate reaches 99%. The energy conversion system is configured with two coal gas generator units and one waste heat generator unit, and the installed capacity of photovoltaic power generation reaches 22.7MW, the implementation power generation efficiency is shown in Table 2, and the time-of-use electricity price model is adopted, and the real-time electricity price is shown in Table 3. The system design maximum load reaches 220MW, forming a multi-energy complementary clean energy supply system. Figure 1 Figure 2 Table 1 Comparison table of initial capacity and operation parameter interval of each energy storage equipment

[0044] Table 1 Comparison table of initial capacity and operation parameter interval of each energy storage equipment

[0045] ​Taking the typical day power grid dispatching scenario in Qujing City, Yunnan Province as an example, during the period of 00:15 to 08:00, the dispatching center sends demand response instructions to the industrial users based on the time-of-use electricity price mechanism and the load peak-valley characteristics, requiring them to actively reduce the electricity purchase load by 20 MW.

[0046] Based on the analysis and verification of the conditions provided above, as shown in Figure 3 , after the response of the grid coordinated dispatching, the purchased power is reduced during the period of responding to the grid dispatching instructions, although it is in the low electricity price period. The average electricity purchase amount shows a phased increase, indicating that during the daytime and evening peak period, the steel plant actively responds to the grid demand, not only meeting its own high energy consumption production, but also providing emergency peak shaving or frequency modulation capacity to the grid, thereby significantly improving the dispatching flexibility and grid compatibility. Figure 4 Based on the comparative study of Figure 5 , the dynamic response characteristics of the power generation and consumption of the steel industry under the interactive coordinated dispatching of the power grid are revealed. Figure 4 By comparing Figure 5 , it can be seen that the power consumption and power consumption remain balanced, indicating that although the purchased power decreases on average after responding to the grid instructions, the power generation of the unit does not decrease significantly, but is compensated by internal units such as coal gas power generation and waste heat power generation, which shows that while reducing the purchased power, the internal units can quickly adjust the output, thereby avoiding the gap in power generation caused by the reduction of purchased power. It can be seen that after responding to the dispatching instructions, the steel plant suppresses the night purchased power through internal unit power generation and energy storage reverse output, and maintains stable power supply in each link, without causing fluctuations in the power demand of the production process, which means that the dynamic balance of internal power generation and consumption is not out of balance due to the sudden reduction of purchased power. Figure 6 It reveals the dynamic situation of various types of energy storage devices in the steel industry under the interactive coordinated dispatching of the power grid, and verifies that under the interactive coordinated dispatching mode of the power grid, by reasonably utilizing the adjustment effect of energy storage devices, the relationship between production and power load can be effectively balanced, and the active response to the dispatching instructions of the power grid can be realized. Figure 7 It reveals the characteristics of production benefit and cost control of the steel industry under the interactive coordinated dispatching of the power grid. Figure 7 It can be seen that the main product revenue shows significant dependence on the dispatching mode, and the stage jump in its revenue also corresponds to the change in the capacity of the billet, further verifying the feasibility of the invention.

[0047] The scheduling method provided by the application fully integrates the material flow and energy flow coupling characteristics of the whole process of the steel industry, and constructs a multi-energy collaborative optimization model covering multiple processes such as sintering, ironmaking, steelmaking and rolling. The model takes 'process collaboration and energy complementation' as the framework. Firstly, dynamic balance equations of key energy storage devices such as sintering bin, coal bin, billet storage, converter gas tank and waste heat steam bin are established. Through the time-of-use electricity price response mechanism, the low valley energy storage and peak energy release strategy are optimized, the cost of purchased electricity is reduced, and the energy buffer capacity is improved. Secondly, a dynamic supply and demand model of secondary energy such as blast furnace gas and waste heat steam is constructed. The output optimization of coal gas power generation and waste heat power generation is integrated to realize the collaboration of energy cascade utilization and power grid demand response. Then, a whole-process scheduling model based on mixed integer linear programming (MILP) is designed. Binary variables are introduced to represent the start-stop state of the equipment (such as sintering machine, coal mill and generator set). The nonlinear coupling constraints of gas input and power generation output are linearized by the big M method to ensure the solvability of the model. Next, the power grid interaction instructions (such as load transfer and peak regulation demand) are embedded in the constraint system to establish the linkage mechanism of sinter direct supply, coal powder ratio optimization and billet storage regulation and storage, and realize the minute-level collaborative scheduling of 'process-energy-power grid'. Finally, the Gurobi solver is called to efficiently solve the large-scale MILP model, and generate a globally optimal scheduling scheme within a minute time scale, and verify the comprehensive benefits in reducing operating costs (including material, electricity and by-product income), improving multi-energy collaborative efficiency and responding to power grid demand.

Claims

1. An interactive full-process scheduling method for the steel industry power grid based on mixed integer linear programming, characterized by: The specific steps are: S1: Construct the dynamic balance equation and dynamic management model of energy storage equipment for sintering bunker, coal bunker, billet warehouse, converter gas holder and waste heat steam bunker; S2, based on the model constructed in S1, establishes a multi-energy coupling collaborative optimization model consisting of a blast furnace gas dynamic supply and demand balance model, a gas power generation output model, a waste heat power generation output model, and a power bus global balance model; S3, using the multi-energy coupling collaborative optimization model established in S2, set the objective function including material cost, electricity cost, purchased electricity cost and product revenue, as well as energy storage equipment constraints and key equipment power constraints; S4, using the functions and constraints of S3, embeds the demand response load adjustment model into the model constructed by S1 and S2; S5. Based on the model embedded in S4, the mixed integer linear programming algorithm is used to solve the optimal scheduling solution.

2. The steel industry power grid interactive full-process scheduling method based on mixed integer linear programming according to claim 1 is characterized in that: The dynamic equilibrium equation of the sintering bin is: （1） （2） （3） （4） Where, Indicates the scheduling period Sinter output of the ring cooler, Indicates the scheduling period No. The sintering ore output of each sintering machine is Indicates the scheduling period Sinter ore input to the sintering bin, Indicates the scheduling period The amount of sintered ore directly supplied to the blast furnace by the ring cooler, Indicates the scheduling period The demand for sintered ore for blast furnaces, Indicates the scheduling period Sinter output from the sintering bin, Indicates the scheduling period Sinter storage capacity of sintering bin, Indicates the scheduling period The amount of sintered ore stored in the sintering bin in the previous period, Indicates material retention rate; The dynamic balance equation of the coal bunker is: （5） （6） （7） Where, Indicates the scheduling period Pulverized coal input to the coal bunker, Indicates the scheduling period No. Coal powder output of each coal mill, Indicates the scheduling period The amount of pulverized coal directly supplied to the blast furnace by the pulverizer, Indicates the scheduling period Pulverized coal demand for blast furnaces, Indicates the scheduling period Pulverized coal output from the coal bunker, Indicates the scheduling period The coal pulverized storage capacity of the coal bunker, Indicates the scheduling period The amount of pulverized coal stored in the coal bunker during the previous period; The dynamic balance equation of the billet warehouse is: （8） （9） （10） Where, Indicates the scheduling period Billet input quantity to the billet warehouse, Indicates the scheduling period Billet output of the continuous casting system, Indicates the scheduling period The amount of billets directly supplied from the continuous casting system to the rolling system, Indicates the scheduling period Billet demand of the rolling system, Indicates the scheduling period Billet output from the billet warehouse, Indicates the scheduling period The storage capacity of billets in the billet warehouse, Indicates the scheduling period The storage volume of steel billets in the steel billet warehouse in the previous period.

3. The steel industry power grid interactive full-process scheduling method based on mixed integer linear programming according to claim 1 is characterized in that: The dynamic balance equation of the converter gas tank is: （11） （12） （13） Where, Indicates the scheduling period Converter gas input to converter gas tank, Indicates the scheduling period Converter gas production, Indicates the scheduling period No. The converter gas demand of Taiwan gas power generation units, Indicates the scheduling period Converter gas output of converter gas tank, Indicates the scheduling period No. The proportion of converter gas input to the total input of a gas-fired power generation unit, Indicates the scheduling period The converter gas storage capacity of the converter gas tank, Scheduling period The converter gas storage capacity of the converter gas tank in the previous period; Before the molten steel produced in the converter steelmaking process is fed into the continuous casting machine, the ladle needs to be baked, and the use of blast furnace gas or converter gas is coordinated according to scheduling needs, expressed as: （14） Where, Indicates the scheduling period Blast furnace gas demand for ladle baking, Indicates the scheduling period converter gas demand for ladle baking; The dynamic balance equation of the waste heat steam bin is: （15） （16） （17） Where, Indicates the scheduling period The amount of waste heat steam input to the steam bunker, Indicates the scheduling period The waste heat steam production of the converter, Indicates the scheduling period Waste heat steam production from heating furnaces, Indicates the scheduling period The waste heat steam output of the steam bunker, Indicates the scheduling period No. The waste heat steam demand of the waste heat generator set, Indicates the scheduling period No. The ratio of the waste heat steam input of the waste heat generator set to the steam bunker output, Indicates the scheduling period The waste heat steam storage capacity of the steam bunker, Scheduling period The amount of waste heat steam stored in the steam bunker during the previous period; Based on the dynamic balance equations of the sintering bin, coal bin, billet warehouse, converter gas holder and waste heat steam bin, a dynamic management model for energy storage equipment is constructed.

4. The steel industry power grid interactive full-process scheduling method based on mixed integer linear programming according to claim 1 is characterized in that: The blast furnace gas dynamic supply and demand balance model is specifically as follows: （18） Where, Indicates the scheduling period Blast furnace gas production, Indicates the scheduling period Blast furnace gas demand of sintering machine, Indicates the scheduling period Blast furnace gas demand for hot blast furnaces in blast furnace ironmaking systems, Indicates the scheduling period Blast furnace gas demand for heating furnaces, Indicates the scheduling period Gas demand for ladle baking, Indicates the scheduling period No. The blast furnace gas demand of Taiwan's gas-fired power generation units.

5. The steel industry power grid interactive full-process scheduling method based on mixed integer linear programming according to claim 1 is characterized in that: The gas power generation output model is specifically as follows: （19） Where, Indicates the scheduling period No. The power generation of the gas generator set, 、 Respectively represent the calorific value of blast furnace gas and converter gas, 、 They represent the blast furnace gas conversion efficiency and converter gas conversion efficiency respectively; （20） Where, Indicates the scheduling period No. The power generation capacity of the gas generator set, Indicates the scheduling period step.

6. The steel industry power grid interactive full-process scheduling method based on mixed integer linear programming according to claim 1 is characterized in that: The waste heat power generation output model is specifically as follows: （21） Where, Indicates the scheduling period No. The power generation of the waste heat generator set is Respectively represent the calorific value of waste heat steam, Indicates the waste heat steam conversion efficiency; （22） Where, Indicates the scheduling period No. The power generation capacity of the waste heat generator set.

7. The method for interactive full-process scheduling of the steel industry power grid based on mixed integer linear programming according to claim 1 is characterized in that: The global balance model of the electric energy bus is specifically: （23） （24） Where, Indicates the scheduling period The total power consumption, Indicates the scheduling period Power consumption of sintering system, Indicates the scheduling period Power consumption of coal mill, Indicates the scheduling period Power consumption of blast furnace ironmaking system, Indicates the scheduling period Power consumption of converter steelmaking system, Indicates the scheduling period Power consumption of refining furnace, Indicates the scheduling period Power consumption of continuous casting system, Indicates the scheduling period Power consumption of steel rolling system, Indicates the scheduling period The power consumption of the heating furnace, Indicates the scheduling period Power consumption of the oxygen production system; （25） Where, Indicates the scheduling period The total power generation, Indicates the scheduling period Photovoltaic power generation.

8. The steel industry power grid interactive full-process scheduling method based on mixed integer linear programming according to claim 1 is characterized in that: The objective function described in S3, which includes material cost, electricity cost, purchased electricity cost and product revenue, is specifically: （26） Where, Indicates the The unit price of the material, No. Material scheduling period The consumption, Indicates the scheduling period The purchased electricity price, Indicates the scheduling period of purchased electricity, represents the photovoltaic electricity price, Indicates the selling price of finished steel. Indicates the scheduling period The output of finished steel, Indicates the selling price of by-product steel slag, Indicates the scheduling period The output of by-product steel slag, Indicates the selling price of the by-product slag, Indicates the scheduling period The output of by-product water slag; The energy storage equipment constraints and key equipment power constraints described in S3 are specifically: Storage capacity constraints: （27） Where, Indicates the scheduling period No. The storage capacity of the warehouse, 、 Respectively represent The lower and upper storage capacity limits of Taiwan warehouses; （28） Where, Indicates the scheduling period No. The input quantity of the warehouse, 、 Respectively represent The lower and upper limits of the input quantity of the Taiwan warehouse; （29） Where, Indicates the scheduling period No. The output of the warehouse, 、 Respectively represent The lower and upper limits of the output volume of the Taiwan warehouse; The constraints for gas power generation are: （30） Where, 、 Respectively represent the scheduling period No. The lower and upper power limits of the gas generator sets; The waste heat steam power generation constraints are: （31） Where, 、 Respectively represent the scheduling period No. The lower and upper power limits of the waste heat generator sets.

9. The steel industry power grid interactive full-process scheduling method based on mixed integer linear programming according to claim 1 is characterized in that: The embedded demand response load adjustment model is expressed as: Assume that the time window for the steel industry to participate in grid demand response is: （32） in: （33） （34） Where, 、 Respectively represent the start time and end time, in minutes; Assume that within this time window, the power that the steel industry needs to reduce is , the adjusted load is: (35) Where, Indicates that during the scheduling period The purchased electricity is adjusted in response to the grid dispatch instruction, and the objective function is updated accordingly.

10. The steel industry power grid interactive full-process scheduling method based on mixed integer linear programming according to claim 1, characterized in that: The mixed integer linear programming algorithm described in S5 is specifically solved as follows: （36） Where, Indicates the scheduling period No. The start and stop status of the sintering unit, Indicates the scheduling period No. The start and stop status of the coal mill unit, Indicates the scheduling period No. The start and stop status of the gas generator set, Indicates the scheduling period No. The start and stop status of the waste heat generator set, Indicates the scheduling period Whether converter gas is used for ladle baking.

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