Power system source-load coordinated optimization scheduling method considering flexibility of steel production process

Abstract

The invention provides an electric power system source-load coordinated optimization scheduling method considering the flexibility of a steel production process, and relates to the technical field of electric power system scheduling, comprising the following steps: carrying out operation characteristic analysis on the production load of a steel enterprise, and respectively establishing an electric arc furnace model, an air separation system model and a steel rolling line model; constructing an adjustable capacity model under the coupling of the power supply system and the production process; based on the operation cost of the iron and steel enterprise power system, constructing an iron and steel enterprise flexibility day-ahead scheduling model; the electric arc furnace model, the air separation system model, the steel rolling line model, the adjustable capacity model and the iron and steel enterprise flexibility day-ahead scheduling model are integrated into a mixed integer linear programming scheduling model, the mixed integer linear programming scheduling model is solved through a solving tool, and an optimal scheduling scheme of system source-load coordination is obtained. The flexibility of a power system can be remarkably improved, and the problem of supply and demand imbalance caused by wind power output fluctuation is effectively relieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system dispatching, in particular, to a power system source-load coordinated optimization dispatching method considering the flexibility of the steel production process. BACKGROUND

[0002] Under the strategic background of the in-depth promotion of green low-carbon development goals, the steel industry, as a pillar industry supporting the development of the national economy, is also a major energy consumer and carbon emitter in the industrial field. Its green and low-carbon transformation has become an inevitable trend of high-quality development of the industry. At present, steel production is accelerating the adjustment of process structure, and the traditional long-process blast furnace process with high energy consumption is gradually transforming into a short-process electric furnace process with low emissions. Steel enterprises have thus changed from traditional high-energy-consuming industrial subjects to important electricity load subjects with large electricity consumption and complex load characteristics in the power system.

[0003] At the same time, the installed capacity and power generation of renewable energy represented by wind power and photovoltaic in the power system continues to rise, but its output is significantly random, volatile and intermittent due to natural conditions, leading to a more prominent temporal and spatial mismatch between the source-side output and the load-side demand of the power system, posing serious challenges to the safe and stable operation and real-time balance of supply and demand of the power system.

[0004] In view of the problems in the related art, no effective solutions have been proposed so far. SUMMARY

[0005] Therefore, the present application provides a power system source-load coordinated optimization dispatching method considering the flexibility of the steel production process to solve the above-mentioned problems.

[0006] In order to solve the above problems, the specific technical scheme adopted by the present application is as follows:

[0007] The power system source-load coordinated optimization dispatching method considering the flexibility of the steel production process comprises the following steps:

[0008] S1, obtaining the operation data of each target device in the steel production process, analyzing the operation characteristics of the production load of the steel enterprise, and respectively establishing an electric arc furnace model, an air separation system model and a rolling line model according to the analysis results;

[0009] S2, constructing an adjustable capacity model under the coupling of the power supply system and the production process according to the power supply mode of the power supply system of the steel enterprise and combining the production process of the steel;

[0010] S3, constructing a steel enterprise flexibility day-ahead dispatching model with the minimum total cost of the power system as the objective function based on the operation cost of the power system of the steel enterprise;

[0011] S4, integrating the electric arc furnace model, the air separation system model, the rolling line model, the adjustable capacity model and the steel enterprise flexibility day-ahead scheduling model into a mixed integer linear programming scheduling model, and solving the mixed integer linear programming scheduling model through a solving tool to obtain an optimal scheduling scheme of system source-load coordination.

[0012] Preferably, the operation data of each target device in the steel production process is acquired, the operation characteristics of the steel enterprise production load are analyzed, and the electric arc furnace model, the air separation system model and the rolling line model are respectively established according to the analysis results, including the following steps:

[0013] S11, collecting operation data of the electric arc furnace in the steelmaking process, determining a smelting period of the electric arc furnace in the steelmaking process, and constructing an electric arc furnace model based on a power regulation mode of the electric arc furnace;

[0014] S12, collecting operation data of the air separation system in the steelmaking process, and constructing an air separation system model based on gas storage characteristics;

[0015] S13, collecting operation data of the steel rolling production line in the steelmaking process, and constructing a rolling line model based on operation characteristics of the steel rolling production line.

[0016] Preferably, the operation data of the electric arc furnace in the steelmaking process is collected, the smelting period of the electric arc furnace in the steelmaking process is determined, and the electric arc furnace model is constructed based on the power regulation mode of the electric arc furnace, including:

[0017] S111, constructing a power constraint of the electric arc furnace in a scheduling period according to the operation data of the electric arc furnace in the steelmaking process;

[0018] S112, determining regulation characteristics of power regulation of the electric arc furnace based on the power regulation mode of the electric arc furnace, and constructing a continuous regulation times constraint of the electric arc furnace in the scheduling period;

[0019] S113, respectively constructing a longest time constraint of smelting one heat of the electric arc furnace and a shortest time constraint of smelting one heat of the electric arc furnace based on production requirements of the electric arc furnace;

[0020] S114, constructing an electric power constraint required for a minimum output in the steelmaking process of the electric arc furnace according to production demands of product output of the steel enterprise before and after participating in scheduling;

[0021] S115, combining the power constraint, the continuous regulation times constraint, the longest time constraint, the shortest time constraint and the electric power constraint to obtain the electric arc furnace model.

[0022] Preferably, the operation data of the air separation system in the steelmaking process is collected, and the air separation system model is constructed based on gas storage characteristics, including:

[0023] S121, collect the operation data of the air separation system in the steelmaking process, and construct an air separation system gas production and power consumption proportional constraint based on the flexibility characteristics of the air separation system;

[0024] S122, based on the gas storage characteristics, construct a gas volume change constraint;

[0025] S123, combine the air separation system gas production and power consumption proportional constraint and the gas volume change constraint to obtain the air separation system model.

[0026] Preferably, the collection of the operation data of the steel rolling production line in the steelmaking process, and the construction of the steel rolling line model based on the operation characteristics of the steel rolling production line include:

[0027] S131, collect the operation data of the steel rolling production line in the steelmaking process, and determine the interruptible load characteristics of the steel rolling production line;

[0028] S132, according to the interruptible load characteristics, construct the steel rolling line model based on the interruptible characteristics.

[0029] Preferably, the power regulation mode of the electric arc furnace is transformer tap changer mode regulation.

[0030] Preferably, the construction of the adjustable capacity model under the coupling of the power supply system and the production process according to the power supply mode of the steel enterprise power supply system and in combination with the production process of the steel enterprise includes:

[0031] S21, according to the power supply mode of the steel enterprise power supply system, by analyzing the flexibility regulation capacity of the self-provided power plant power generation and power supply resource, determine the self-provided power plant output constraint;

[0032] S22, based on the production process of the steel, determine the power balance constraint, and establish the steel enterprise power supply system model and the adjustable production process model;

[0033] S23, coupling the steel enterprise power supply system model and the adjustable production process model to obtain the adjustable capacity model under the coupling of the power supply system and the production process.

[0034] Preferably, the construction of the steel enterprise flexibility day-ahead scheduling model with the minimum total cost of the power system as the objective function based on the operation cost of the steel enterprise power system includes:

[0035] S31, based on the production benefit orientation of the steel enterprise, the sum of the thermal power unit operation cost, the wind power curtailment penalty cost and the steel enterprise power cost is taken as the operation cost of the steel enterprise power system;

[0036] S32, constructing a steel enterprise flexibility day-ahead scheduling model with the minimum total cost of the power system as an objective function based on the constraint conditions of the power system.

[0037] Preferably, the constraint conditions of the power system include: power system power balance constraint, wind power output constraint and power system thermal power unit output constraint.

[0038] The power system thermal power unit output constraint includes: upper and lower limits of thermal power unit output constraint and thermal power unit climbing constraint.

[0039] The expressions of the power system power balance constraint, wind power output constraint, thermal power unit output upper and lower limit constraint and thermal power unit climbing constraint are respectively:

[0040]

[0041] P Gi,min ≤P Gi,t ≤P Gi,max ;

[0042] |P Gi,t -P Gi,t-1 |≤ΔP Gi,up ;

[0043] In the formula, P Gi,t represents the actual output of the thermal power unit u at the time period t, U represents the total number of thermal power units, P w,t represents the actual wind power consumption at the time period t, k represents the steel enterprise electricity purchase and sale symbol, P t net represents the interaction power of the steel enterprise with the power grid at the time period t, P L,t represents the conventional load forecast power at the time period t, represents the wind power day-ahead forecast power at the time period t, P Gi,min represents the lower limit of the thermal power unit output, P Gi,t represents the output of the power system thermal power unit i at the time period t, P Gi,max represents the upper limit of the thermal power unit output, P Gi,t-1 represents the output of the system thermal power unit i at the time period t-1, ΔP Gi,up represents the maximum climbing rate of the thermal power unit in adjacent time periods.

[0044] Preferably, the method further comprises the following steps of: integrating the electric arc furnace model, the air separation system model, the rolling line model, the adjustable capacity model and the steel enterprise flexibility day-ahead scheduling model into a mixed integer linear programming scheduling model, and solving the mixed integer linear programming scheduling model by a solving tool to obtain the optimal scheduling scheme of system source-load coordination.

[0045] S41. Based on the electric arc furnace model, air separation system model, rolling mill model, adjustable capacity model, and steel enterprise flexible day-ahead scheduling model, the decision variables are determined using the optimization modeling toolbox, and the objective function and constraints of the mixed integer linear programming scheduling model are constructed to realize the construction of the mixed integer linear programming scheduling model.

[0046] S42. Configure the solver parameters and call the solver to solve the constructed mixed integer linear programming scheduling model to obtain the solution results;

[0047] S43. Analyze the solution results and extract the optimized decision variable values ​​based on the analysis results to form an optimized scheduling scheme for system source-load coordination.

[0048] The beneficial effects of this invention are as follows: By constructing a power system source-load coordination optimization scheduling model that considers the flexibility of the steel production process, this invention deeply studies the flexibility potential of the steel industry as a high-energy-consuming industry in the power system and its promoting effect on wind power consumption. When steel enterprises adjust the load of production equipment such as electric arc furnaces, air separation systems, and rolling mills, combined with the joint control of self-owned power plants, the flexibility of the power system can be significantly improved, effectively alleviating the supply and demand imbalance caused by wind power output fluctuations. When steel enterprises participate in system scheduling under the joint control mode of self-owned power plants and electricity load, the system wind curtailment rate can be reduced from 16.29% to 1.6%, while significantly reducing the total system operating cost. This proves the effectiveness of this model in improving wind power consumption capacity and optimizing power system operation. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0050] Figure 1 This is a schematic diagram illustrating the principle of wind power absorption based on high energy-consuming loads in the power system source-load coordinated optimization scheduling method for power systems that considers the flexibility of the steel production process, according to an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the energy structure of steel enterprises in a power system source-load coordinated optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0052] Figure 3 This is a schematic diagram of the power variation during the electric arc furnace smelting cycle in a power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0053] Figure 4 This is the daily load curve of a steel enterprise in a power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention;

[0054] Figure 5 The wind power predicted output curve and the conventional load predicted curve are shown in the power system source-load coordinated optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0055] Figure 6 This is one of the schematic diagrams comparing the wind curtailment results of different schemes in the power system source-load coordinated optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention;

[0056] Figure 7 This is the second schematic diagram comparing the wind curtailment results of different schemes in the power system source-load coordinated optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0057] Figure 8 This is the third schematic diagram comparing the wind curtailment results of different schemes in the power system source-load coordinated optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0058] Figure 9 This is one of the power interaction diagrams between steel enterprises and the system under different schemes in the power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0059] Figure 10 This is the second schematic diagram of power interaction between steel enterprises and the system under different schemes in the power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0060] Figure 11 This is the third schematic diagram of power interaction between steel enterprises and the system under different schemes in the power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0061] Figure 12 This is one of the power balance diagrams of the system under different schemes in the power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to an embodiment of the present invention;

[0062] Figure 13 This is the second schematic diagram of power balance of the system under different schemes in the power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to an embodiment of the present invention.

[0063] Figure 14This is the third schematic diagram of power balance of the system under different schemes in the power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to an embodiment of the present invention.

[0064] Figure 15 This is one of the schematic diagrams illustrating the electric arc furnace gear adjustment under different schemes in the power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0065] Figure 16 This is the second schematic diagram of the electric arc furnace gear adjustment under different schemes in the power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0066] Figure 17 This is one of the schematic diagrams of load transfer in the space-separation system under different schemes in the power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention;

[0067] Figure 18 This is the second schematic diagram of the load transfer situation of the space-separation system under different schemes in the power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention.

[0068] Figure 19 This is one of the schematic diagrams of rolling mill line interruption under different schemes in the power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to an embodiment of the present invention;

[0069] Figure 20 This is the second intention of the rolling mill line interruption situation under different schemes in the power system source-load coordination optimization scheduling method that considers the flexibility of the steel production process according to the embodiment of the present invention;

[0070] Figure 21 This is a flowchart of a power system source-load coordinated optimization scheduling method that takes into account the flexibility of the steel production process, according to an embodiment of the present invention. Detailed Implementation

[0071] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0072] According to embodiments of the present invention, a power system source-load coordinated optimization scheduling method that takes into account the flexibility of the steel production process is provided.

[0073] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 21 As shown, the power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to an embodiment of the present invention includes the following steps:

[0074] S1. Obtain the operating data of each target equipment in the steel production process, analyze the operating characteristics of the steel enterprise's production load, and establish electric arc furnace model, air separation system model and rolling line model based on the analysis results.

[0075] As a preferred embodiment, the steps of acquiring the operating data of each target equipment in the steel production process, analyzing the operating characteristics of the steel enterprise's production load, and establishing electric arc furnace models, air separation system models, and rolling mill models based on the analysis results include the following steps:

[0076] S11. Collect the operating data of the electric arc furnace during the steelmaking process, determine the smelting cycle of the electric arc furnace during steelmaking, and construct an electric arc furnace model based on the power adjustment method of the electric arc furnace.

[0077] It should be noted that the modeling of core equipment in steel production aims to quantify the adjustable characteristics of electric arc furnaces, air separation systems, and rolling mills, providing a parameter basis for capacity coupling.

[0078] In a preferred embodiment, the step of collecting operating data of the electric arc furnace during the steelmaking process, determining the smelting cycle of the electric arc furnace during steelmaking, and constructing an electric arc furnace model based on the power adjustment method of the electric arc furnace includes:

[0079] S111. Based on the operating data of the electric arc furnace during the steelmaking process, construct the power constraint of the electric arc furnace within the scheduling cycle;

[0080] S112. Based on the power regulation method of the electric arc furnace, determine the regulation characteristics of the power regulation of the electric arc furnace, and construct a constraint on the number of continuous regulation times of the electric arc furnace within the scheduling cycle; the power regulation method of the electric arc furnace is transformer tap-fit ​​regulation.

[0081] S113. Based on the production requirements of the electric arc furnace, construct the longest time constraint and the shortest time constraint for one furnace smelting in the electric arc furnace, respectively.

[0082] S114. Based on the production demand of steel enterprises before and after participating in scheduling, construct the power constraint required for the minimum output of electric arc furnace in the steelmaking process.

[0083] S115. Combine the power constraint, continuous adjustment number constraint, maximum time constraint, minimum time constraint, and power constraint to obtain the electric arc furnace model.

[0084] S12. Collect operational data of the air separation system during the steelmaking process, and construct an air separation system model based on gas storage characteristics;

[0085] In a preferred embodiment, the collection of operational data from the air separation system during the steelmaking process, and the construction of an air separation system model based on gas storage characteristics, includes:

[0086] S121. Collect the operating data of the air separation system in the steelmaking process, and based on the flexibility characteristics of the air separation system, construct a constraint that the gas output of the air separation system is proportional to the power consumption.

[0087] S122. Based on the gas storage characteristics, construct gas volume change constraints;

[0088] S123. The constraints of proportionality between gas production and power consumption and gas volume change in the air separation system are combined to form an air separation system model.

[0089] S13. Collect the operating data of the steel rolling production line during the steelmaking process, and construct a steel rolling line model based on the operating characteristics of the steel rolling production line.

[0090] In a preferred embodiment, the step of collecting operational data of the steel rolling production line during the steelmaking process and constructing a rolling line model based on the operational characteristics of the steel rolling production line includes:

[0091] S131. Collect operating data of the steel rolling production line during the steelmaking process and determine the interruptible load characteristics of the steel rolling production line.

[0092] S132. Based on the interruptible load characteristics, construct a rolling mill model based on the interruptible characteristics.

[0093] Specifically, the first step is to analyze the principle of wind power consumption in steel production, such as... Figure 1 As shown, the load operation characteristics are analyzed, and models of the electric arc furnace, air separation system, and rolling mill are established. The electric arc furnace, as a major piece of equipment in the steelmaking process, accounts for approximately 40% of the total energy consumption of steel enterprises, making it a typical high-energy-consuming load. Figure 1 In the diagram, Region I represents the amount of air curtailed under the traditional scheduling mode, and Region II represents the amount of air curtailed under the source-load coordinated scheduling mode. The electric arc furnace production process exhibits significant flexibility. For example... Figure 3 As shown, Figure 3 In the middle, ΔP down Indicates the amount of power reduction in the electric arc furnace, ΔP up P indicates the amount of power increase for the electric arc furnace. max P min p represents the upper and lower limits of the electric arc furnace's operating power, respectively. i Representing time t i The power of the electric arc furnace, pi+1 Representing time t i+1 The electric arc furnace power is determined by the smelting cycle, which encompasses five stages: arc ignition, furnace penetration, melting, furnace shutdown, and tapping. The melting period accounts for approximately 70% of the entire smelting cycle. Optimizing enterprise production plans primarily involves adjusting the electric arc furnace power during these two periods. This invention employs a method of adjusting the transformer taps for power regulation. Compared to the former, which only adjusts by constraining upper and lower power limits, this method better reflects the actual operating conditions of the electric arc furnace. Therefore, the power constraint of the electric arc furnace can be expressed as:

[0094]

[0095] In the formula, This represents the actual power of electric arc furnace i during time period t within the entire scheduling cycle; u i,t This indicates the state of electric arc furnace i during time period t. A value of 1 indicates that the electric arc furnace is in operation, and a value of 0 indicates that the electric arc furnace is not in operation; c represents a constant, representing the percentage of rated power at the initial position; β i,t This indicates the gear position of electric arc furnace i during time period t. This represents the planned power of electric arc furnace i during time period t; U represents the number of electric arc furnace gears.

[0096] Because the power output of electric arc furnaces has a significant impact on the power grid, in actual operation, transformer tap changes cannot be made across different levels, and the number of consecutive adjustments to the electric arc furnace is limited.

[0097] |β i,t -β i,t-1 |≤1;

[0098]

[0099] In the formula, β i,t β represents the gear position of electric arc furnace i during time period t. i,t-1 This indicates the gear position of electric arc furnace i in time period t-1, and T represents the total number of time periods in a scheduling cycle; This represents the state variable of electric arc furnace power adjustment during time period t. When it is 1, it represents upward and downward adjustment respectively; when A value of 0 indicates that no adjustment is performed. This represents the state variable of electric arc furnace power adjustment during time period t-1, and M represents the maximum number of adjustments within a scheduling cycle.

[0100] In order to ensure production efficiency and product quality, the longest and shortest smelting time for a single furnace smelting operation should be constrained during the operation of the electric arc furnace.

[0101] The longest time for smelting a single batch is:

[0102]

[0103] The shortest time for smelting one batch is:

[0104] u i,t -u i,t-1 ≤u i,τ τ∈[t,min(T,t+T min -1)];

[0105] In the formula, T max T represents the maximum number of time periods in a single furnace cycle. min This represents the minimum number of time slots per furnace cycle, and T represents the total number of time slots in a scheduling cycle; u i,t U represents the state of electric arc furnace i during time period t. i,t-1 U represents the state of electric arc furnace i during time period t-1. i,τ The index variable in the constraint is used to express the minimum running time requirement, and ε represents the starting time of the electric arc furnace smelting.

[0106] In addition, to ensure that steel companies' product output is not affected before and after participating in the scheduling, the electricity required for the minimum production output needs to be constrained, as shown in the following formula:

[0107]

[0108] In the formula, The power adjustment of electric arc furnace i during time period t is represented by ΔT; ΔT represents the duration of a scheduling cycle; W pre This represents the minimum amount of electricity required to produce products within a scheduling cycle. This represents the planned power of electric arc furnace i during time period t; T represents the total number of time periods in a scheduling cycle.

[0109] Specifically, the air separation system, as an important auxiliary equipment in the steelmaking process, mainly provides the oxygen, nitrogen, and other gases required for production. Its power consumption accounts for approximately 15% of the total power consumption of steel enterprises. Different gases are equipped with corresponding gas holders. While ensuring gas storage safety, the air separation system possesses the flexibility of being transferable, with the following specific constraints:

[0110] The constraint that gas production in an air separation system is directly proportional to power consumption is as follows:

[0111] P j,t =p j V j,t ;

[0112] In the formula, P j,t p represents the amount of electricity consumed in producing gas j during time period t; j V represents the amount of electricity consumed to produce one unit volume of gas j. j,tThis represents the volume of gas j produced during time period t.

[0113] Meanwhile, considering factors such as gas storage safety and air separation system capacity, the gas volume change constraints within the gas holder are as follows:

[0114] V j,min ≤V j,t ≤V j,max ;

[0115] |V j,t -V j,t-1 |≤ΔV;

[0116] In the formula, V j,max V represents the upper limit of the gas holder capacity for storing gas j; j,min V represents the lower limit of the gas holder capacity for storing gas j. j,t ΔV represents the volume of gas j produced within time period t, and ΔV represents the maximum allowable change in gas volume between adjacent time periods.

[0117] Specifically, steel rolling is a crucial step in steel production, encompassing different processes such as hot rolling and cold rolling. Hot rolling involves heating steel billets and then rolling them through rolls to form the final product, while cold rolling further processes the steel at room temperature. From a technical perspective, with sufficient steel inventory, steel rolling production lines can be shut down with one hour's notice. Interruptions can occur once daily for one hour, and the production line can be scheduled one day in advance; therefore, the steel rolling load can be scheduled as an interruptible load. Its modeling based on interruptibility characteristics is as follows:

[0118] P t roll =x t P t roll,base ;

[0119]

[0120] In the formula, P t roll x represents the actual power of the rolling mill load during time period t. t x represents the start-up and shutdown variables of the rolling mill load during time period t. t =1 indicates that the rolling mill load is in operation; x t =0 indicates that the rolling load is stopped. P t roll,base T represents the planned power of the rolling mill load during time period t. shut This indicates the number of periods during which the steel rolling load is interrupted within the scheduling cycle T.

[0121] S2. Based on the power supply mode of the power supply system of steel enterprises and combined with the steel production process, construct an adjustable capacity model under the coupling of power supply system and production process.

[0122] As a preferred embodiment, the step of constructing an adjustable capacity model coupled with the production process of the power supply system based on the power supply mode of the steel enterprise's energy supply system and in conjunction with the steel production process includes:

[0123] S21. Based on the power supply mode of the steel enterprise's energy supply system, determine the output constraints of the self-owned power plant by analyzing the flexibility and adjustment capability of the power generation and consumption resources of the self-owned power plant.

[0124] S22. Based on the steel production process, determine the power balance constraints and establish a power supply system model and an adjustable production process model for steel enterprises.

[0125] S23. Couple the energy supply system model of the steel enterprise and the adjustable production process model to obtain the adjustable capacity model under the coupling of the power supply system and the production process.

[0126] It should be noted that when establishing a capacity regulation model that considers the integration of the energy supply system and the production process, the energy supply system of steel enterprises is mainly divided into two parts: self-generated electricity and electricity purchased from external sources. Figure 2 As shown, the output characteristics of the power generation units in new energy power plants do not match the input requirements of transformers in the power system, necessitating the conversion of electrical energy form through converters. Therefore, to study the flexible regulation capability of power generation and consumption resources in self-owned power plants, the generation and consumption sides need to be equivalently represented as a single load for a unified external response. The output constraints of self-owned power plants are as follows:

[0127] Assume a steel company's self-owned power plant has N generating units. To formulate a power generation plan for T equal-length periods, the output constraints are as follows:

[0128] (1) Upper and lower limits of output of self-provided generating units:

[0129] P g,min ≤P n,t ≤P g,max ;

[0130] In the formula, P g,min P g,max These represent the lower and upper limits of the output of thermal power units in the self-owned power plants of steel enterprises, respectively; P n,t This represents the output of the nth generator unit during time period t.

[0131] (2) Self-contained unit ramping constraints:

[0132] |P gn,t -P gn,t-1 |≤ΔP gn,up ;

[0133] In the formula, ΔP gn,up P represents the ramp rate of the nth generator unit.gn,t P represents the output of the self-contained generator unit n at time t. gn,t-1 This represents the output of the self-contained generator unit n during time period t-1.

[0134] (3) Constraints on the power generation and output of self-owned generating units:

[0135]

[0136] In the formula, P represents the amount of electricity generated by the nth self-provided generator unit during time period t; τ represents the time interval between adjacent time periods. gn,t This represents the output of the self-contained generator unit n during time period t.

[0137] The establishment of a coupling model between the power supply system and the power consumption system includes:

[0138] (1) The above energy supply system (power supply system) and adjustable production process modeling are coupled through power balance constraints:

[0139]

[0140] In the formula, N represents the number of self-owned power plant units; P t net This represents the interaction power between the steel company and the power grid at time t; k represents the steel company's electricity purchase and sale indicator, k=1 indicates purchasing electricity, k=-1 indicates selling electricity, and P gn,t P represents the output of the self-contained generator unit n at time t. j,t This represents the amount of electricity consumed by gas j during time period t. P represents the actual power of electric arc furnace i during time period t within the entire scheduling cycle. t roll This represents the actual power of the rolling mill load during time period t.

[0141] (2) Adjustable capacity model (i.e., capacity regulation model) under the coupling of power supply system and production process, the adjustable capacity consists of the adjustable capacity of self-provided units and the adjustable capacity of production load:

[0142]

[0143] In the formula, P adj,t This indicates that steel companies can adjust their power output. This indicates the planned output of self-contained unit n before adjustment within time period t; I represents the total number of electric arc furnaces. This indicates the original planned power output of the electric arc furnace before adjustment. P represents the planned power before the air separation load transfer; t roll,0 This indicates the planned power output before the steel rolling load was interrupted. P represents the actual power of electric arc furnace i during time period t within the entire scheduling cycle. gn,t This represents the output of the self-contained generator unit n at time t. This indicates the original planned power output of the electric arc furnace before adjustment. P represents the actual power of air separation system j during time period t. t roll This represents the interaction power between the steel company and the power grid at time t.

[0144] S3. Based on the operating cost of the power system of steel enterprises, construct a day-ahead flexible scheduling model for steel enterprises with the objective function of minimizing the total cost of the power system;

[0145] Specifically, by incorporating steel companies with flexibility potential into the power system dispatch model, the day-ahead dispatch mode is transformed from the traditional source-following-load dynamic to source-load interactive. This will effectively improve the flexibility of the power system and alleviate the power system supply and demand imbalance caused by the uncertainty of wind power output.

[0146] In the scheduling mode proposed in this invention, the objective function is the total system operating cost, including the operating cost of thermal power units and the system's wind curtailment penalty cost (here, "system" refers to the power system, and the power supply system and power consumption system refer to the internal power supply and consumption systems of the steel enterprise. The power supply system includes the steel enterprise's self-owned power plants and purchased electricity, and the power consumption system refers to the steel enterprise's production load, including electric arc furnaces, etc.). Since the steel enterprise is profit-oriented, considering the steel enterprise's enthusiasm for participating in system scheduling, the steel enterprise's electricity consumption cost should also be included in the objective function. Therefore, the overall objective function for the total system operating cost F is:

[0147] minF=F f +F w +F s ;

[0148] In the formula, F f This represents the operating cost of thermal power units in the power system; F w Indicates the penalty cost for wind curtailment in the power system; F s This indicates the electricity costs for steel companies.

[0149] The operating cost of thermal power units mainly consists of coal consumption costs. By referring to the coal consumption cost curve of thermal power units, its operating cost function can be obtained as follows:

[0150]

[0151] In the formula, F f This represents the operating cost of thermal power units in the power system, where U represents the total number of thermal power units, and a u b u c uP represents the coal consumption cost coefficient of unit u. Gu,t This represents the actual output of thermal power unit u during time period t, where T represents the total number of time periods in a dispatch cycle.

[0152] To promote the system's absorption of wind power, a wind curtailment penalty function is introduced here:

[0153]

[0154] In the formula, F w K represents the penalty cost for wind curtailment in the power system, T represents the total number of time periods in a dispatch cycle, and K represents the total number of time periods in a dispatch cycle. q This represents the wind curtailment penalty coefficient. P represents the day-ahead forecast power of wind power during time period t. w,t This represents the actual wind power absorbed during time period t.

[0155] In addition, the electricity costs for steel enterprises include three components: the total generation cost of their self-owned power plants, the cost of purchasing electricity when generation is insufficient in different time periods, and the revenue from selling electricity when generation is excessive in different time periods. The expression for the electricity costs of steel enterprises is as follows:

[0156]

[0157] In the formula, F s This represents the electricity cost for steel companies, where T represents the total number of time periods in a dispatch cycle, and a n b n c n P represents the coal consumption cost coefficient of the self-owned unit n; t buy P t sell B represents the electricity purchased and sold by the steel company during time period t, respectively; C represents the electricity purchase cost coefficient of the steel company, and P represents the electricity sales revenue coefficient of the steel company. gn,t This indicates the output of the self-contained generator unit n during time period t.

[0158] As a preferred implementation, the construction of a day-ahead flexible scheduling model for steel enterprises, based on the operating costs of the power system and with the objective function of minimizing the total cost of the power system, includes:

[0159] S31. Based on the production profit orientation of steel enterprises, the sum of the operating cost of thermal power units, the wind curtailment penalty cost of the power system, and the electricity consumption cost of steel enterprises shall be taken as the operating cost of the power system of steel enterprises.

[0160] S32. Based on the constraints of the power system, construct a day-ahead flexible scheduling model for steel enterprises with the objective function of minimizing the total cost of the power system.

[0161] As a preferred embodiment, the constraints of the power system include: power system power balance constraints, wind power output constraints, and power system thermal power unit output constraints.

[0162] The power system thermal power unit output constraints include: upper and lower limit constraints on thermal power unit output and thermal power unit ramping constraints.

[0163] The expressions for the power system power balance constraint, wind power output constraint, upper and lower limit constraints for thermal power unit output, and thermal power unit ramping constraint are as follows:

[0164]

[0165] P Gi,min ≤P Gi,t ≤P Gi,max ;

[0166] |P Gi,t -P Gi,t-1 |≤ΔP Gi,up ;

[0167] In the formula, P Gi,t P represents the actual output of thermal power unit u during time period t, where U represents the total number of thermal power units. w,t P represents the actual wind power absorbed during time period t, k represents the electricity purchase and sale quotation of steel enterprises, and P represents the actual wind power absorbed during time period t. t net P represents the interaction power between the steel company and the power grid at time t. L,t This represents the predicted power of the regular load during time period t. P represents the predicted wind power output for the day before time period t. Gi,min P represents the lower limit of the output of thermal power units. Gi,t P represents the output of thermal power unit i in the power system during time period t. Gi,max P represents the upper limit of the output of a thermal power unit. Gi,t-1 ΔP represents the output of thermal power unit i in the power system during time period t-1. Gi,up This indicates the maximum ramp rate of the thermal power unit in adjacent time periods.

[0168] S4. Integrate the electric arc furnace model, air separation system model, rolling mill model, adjustable capacity model, and steel enterprise flexible day-ahead scheduling model into a mixed integer linear programming scheduling model. Solve the mixed integer linear programming scheduling model using a solution tool to obtain an optimized scheduling scheme for system source-load coordination.

[0169] As a preferred embodiment, the integration of the electric arc furnace model, air separation system model, rolling mill model, adjustable capacity model, and steel enterprise flexible day-ahead scheduling model into a mixed-integer linear programming scheduling model, and the solution of the mixed-integer linear programming scheduling model using a solution tool to obtain an optimized scheduling scheme for system source-load coordination, includes the following steps:

[0170] S41. Based on the electric arc furnace model, air separation system model, rolling mill model, adjustable capacity model, and steel enterprise flexible day-ahead scheduling model, the decision variables are determined using the optimization modeling toolbox, and the objective function and constraints of the mixed integer linear programming scheduling model are constructed to realize the construction of the mixed integer linear programming scheduling model.

[0171] S42. Configure the solver parameters and call the solver to solve the constructed mixed integer linear programming scheduling model to obtain the solution results;

[0172] S43. Analyze the solution results and extract the optimized decision variable values ​​based on the analysis results to form an optimized scheduling scheme for system source-load coordination.

[0173] It should be noted that the power system source-load coordination optimization scheduling model considering the operational flexibility of steel enterprises proposed in this invention belongs to the mixed-integer linear programming (MILP) problem. It mainly consists of three elements: the objective function, decision variables, and constraints, and its standard form is as follows:

[0174]

[0175] In the formula, minF(x,y) represents the objective function, and x represents the range of x in the interval [x,y]. min x max The decision variables include, in this paper, the actual output of thermal power units, wind power output, output of steel enterprises' self-owned power plants, and the adjusted electricity consumption plan for steel load, x. min Let x represent the minimum value of the decision variable. max represents the maximum value of the decision variable; y represents the integer variable in the interval [-4, 4], including the power purchase and sale status of the steel enterprise in this paper, the electric arc furnace adjustment level, the transfer flag of the air separation system, and the interruption flag of the steel rolling production line. g i (x,y)=0 represents an equality constraint, including the system power balance constraint and the steel enterprise power balance constraint in this paper; h j (x,y)≤0 represents inequality constraints, including system thermal power unit output constraints, wind power unit output constraints, steel enterprise self-owned unit output constraints, and steel production load power adjustment constraints.

[0176] There are various methods for solving this type of problem. This invention uses the YALMIP toolbox to call the Gurobi solver for the solution (the steps for using the YALMIP toolbox to call the Gurobi solver are as follows:)

[0177] (1) Model building: Use YALMIP to define decision variables, objective function and constraints.

[0178] (2) Solver configuration: Set the parameters of the Gurobi solver.

[0179] (3) Model Solving: Call Gurobi to solve the model and process the results.

[0180] The optimized variable values ​​can then be obtained from the solution results. YALMIP allows for the construction of optimization models using a concise and intuitive syntax within the simulation platform, and can automatically convert model formats to adapt to the underlying solver. Gurobi features efficient solution algorithms that can quickly find optimal or near-optimal solutions when dealing with large-scale mixed-integer programming problems, exhibiting high stability and reliability. It also provides interfaces for various programming languages ​​for easy integration.

[0181] To verify the effectiveness and rationality of the proposed model for the participation of steel industry loads in power system dispatch, this paper takes a certain region as an example. This region includes a thermal power plant, a large steel enterprise, a wind farm, and conventional loads. The thermal power plant consists of 5 generating units with a total installed capacity of 1600MW. Specific parameters are shown in Table 1.

[0182] Table 1 Data on thermal power units in the power system

[0183]

[0184] The wind farm has a total installed capacity of 550MW. The steel company has 10 electric arc furnaces, 1 air separation system, 1 rolling mill line, and a self-owned power plant. Each electric arc furnace operates at a power output between 40MW and 60MW, and the furnace transformer has 9 power regulation settings, each setting regulating 5% of the furnace's rated power. The air separation system includes oxygen and nitrogen production systems, operating at a power output between 50MW and 60MW. The rolling mill line operates at a power output of 15MW. Figure 4 The figure shows the daily load curve of a steel company, with a sampling interval of 15 minutes. Figure 4 It can be seen that the daily load power of steel enterprises is between 652MW and 750MW, making them typical high-energy-consuming enterprises. They have many continuous production equipment and generally operate on a 24-hour continuous working system, with minimal load fluctuations throughout the day and no obvious peaks and troughs. To ensure continuous production, steel enterprises are equipped with their own power plants, which are centrally dispatched by the enterprise. The installed capacity of these power plants is 700MW; specific parameters are shown in Table 2. Figure 5It is evident that wind power output fluctuates significantly, exhibiting a clear anti-peak-shaving characteristic. Peak wind power output occurs during periods 1-20 and 73-94, coinciding with periods of low load demand. Wind power output is relatively low during periods 40-60, coinciding with peak load demand. This anti-peak-shaving characteristic of wind power output poses a severe challenge to the power system's supply-demand balance.

[0185] Table 2 Data on thermal power units in self-owned power plants

[0186]

[0187]

[0188] In addition, to verify the scheduling model proposed in this invention, three schemes are mainly set up for comparative analysis to verify the effectiveness of the model.

[0189] Option 1: Steel companies do not participate in system scheduling and produce according to the established production plan.

[0190] Option 2: Steel companies participate in system dispatch, but only regulate their own electricity load; self-owned power plants do not participate in system dispatch.

[0191] Option 3: Steel enterprises participate in system dispatching under the joint control of their own power plants and electricity load.

[0192] Figures 6-8 The results of wind curtailment under three day-ahead dispatch schemes are presented, with the dispatch period being the day-ahead 24 hours, divided into 96 time periods. Comparing the wind curtailment results of the three schemes, Scheme 1 shows wind curtailment in 45 time periods, with the maximum curtailed power in a single time period reaching 330MW, and a wind curtailment rate of 16.29% for the entire dispatch period. Scheme 2 shows wind curtailment in 40 time periods, with the maximum curtailed power in a single time period reaching 304MW, and a wind curtailment rate of 12.84% for the entire dispatch period, a reduction of 3.45% compared to Scheme 1. When Scheme 3 is adopted, the number of wind curtailment time periods is 13, a reduction of 32 compared to Scheme 1 and 27 compared to Scheme 3, with the maximum curtailed power in a single time period being 155MW, a significant reduction compared to Schemes 1 and 2, and a wind curtailment rate of 1.6% for the entire dispatch period. In conclusion, the participation of steel enterprises in system dispatch under the joint control mode of self-owned power plants and electricity load has a significant effect on absorbing obstructed wind power in the system, and can significantly reduce the system wind curtailment rate. This has a positive significance for the stable operation of the power system.

[0193] Figures 9-11 This represents the day-ahead dispatch results for power interaction between steel enterprises and the system. In Scheme 1, steel enterprises do not participate in system dispatch; they purchase electricity from the system as regular loads according to their predetermined production plans. As shown in the figure, under this scheme, the electricity purchased by steel enterprises at different times does not adjust with wind power fluctuations, resulting in a high wind curtailment rate. Figures 9-11In Scheme 2, Region I represents the wind curtailment under the traditional power system dispatch mode; Region II represents the wind curtailment after steel enterprises participate in the control; and Region III represents the power supply required by the loads in the power system. In Scheme 2, the steel enterprises' absorption of obstructed wind power is concentrated in periods 1-33, 54-74, and 93-96, which coincide with peak wind power output. Scheme 3's absorption periods for obstructed wind power are basically the same as Scheme 2. However, compared to Scheme 2, Scheme 3 demonstrates a stronger absorption capacity for obstructed wind power by steel enterprises. Specifically, in Scheme 2, the maximum absorption capacity of obstructed wind power by steel enterprises in a single period is 182.8MW, while in Scheme 3, the maximum absorption capacity is 200MW, representing a 9.3% increase. In conclusion, the steel enterprises' participation in system dispatch using the control mode in Scheme 3 can significantly improve the power system's peak-shaving capacity.

[0194] Depend on Figures 9-11 It can be seen that in Scheme 2, steel companies sell electricity to external users during periods 34-53 and 75-90. In Scheme 3, steel companies sell electricity to external users during periods 36, 37, 39, 40, 43, 44, 48, 51-53, 71, 75, 77-80, 82, 84, 87, and 89-90. Combining the wind power fluctuation curve and the conventional load consumption curve in the graph, it can be seen that these periods coincide with peak electricity consumption, while wind power output is at its lowest. Therefore, the sale of electricity by steel companies to external users can effectively alleviate the power supply pressure on the thermal power units of the system. Comparing the electricity sales data of Scheme 2 and Scheme 3, it can be seen that Scheme 2 has significantly more periods for electricity sales than Scheme 3, and the average electricity sales per period is also higher than that of Scheme 3. This is because under the control mode of Scheme 2, only the electricity load of steel companies participates in system dispatch, and steel companies adjust their production plans 24 hours in advance based on the information fed back by the power grid dispatch center. However, the output plans of the self-owned power plants remained unchanged, continuing to operate according to the original schedule. This resulted in overcapacity from these plants, with the excess electricity having to be sold through the grid. For the entire system, this reduced the risk of load shedding due to insufficient power supply. However, for steel companies, selling too much electricity externally reduced the ability of their self-owned power plants to guarantee steel production. In Scheme 3, the self-owned power plants and steel load are jointly regulated, with the output plans of the self-owned power plants adjusted according to the steel production plan, maintaining a certain reserve capacity and further improving the flexibility of steel companies' production operations. Under this scheme, steel companies not only improved the power system's peak shaving and valley filling capabilities but also ensured their own production.

[0195] Figures 12-14The diagram shows the total power balance of the system under three scenarios. In Scenario 1, the system relies solely on thermal power units to smooth out wind power fluctuations. As can be seen from the diagram, the output of thermal power units fluctuates significantly, which greatly increases the regulation pressure on the system's thermal power units. In Scenario 2, after incorporating the steel production load into the system dispatch, the output fluctuation of the system's thermal power units is significantly reduced, but the overall power generation is low, which is detrimental to the economic efficiency of the thermal power units. In Scenario 3, after jointly regulating the steel enterprises' self-owned power plants and the steel load and incorporating them into the system dispatch, as can be seen from the diagram, this scenario not only ensures the smoothness of the system's thermal power unit output and reduces the regulation pressure on the system's thermal power units, but also ensures the economic efficiency of the system's thermal power units.

[0196] Figures 15-16 , Figures 17-18 , Figures 19-20 These are the load operation status diagrams for electric arc furnaces, air separation systems, and rolling mills in steel enterprises. Figures 15-16 This is a diagram showing the power adjustment status of an electric arc furnace. Power level 0 represents the furnace's power level before any changes to the production plan, i.e., the rated power level. The diagram shows that in Scheme 2, the furnace concentrates on increasing the power level during periods 0-32 and 57-72, and decreases it during other periods. Compared to Scheme 2, Scheme 3 involves significantly fewer adjustments during the first 96 periods compared to Scheme 2. (Combined with...) Figures 6-8 From the perspective of the wind curtailment results of the medium system, Scheme 3 requires less adjustment to the production plan of steel enterprises, which is more beneficial to the production and operation of enterprises. At the same time, the wind curtailment rate of the system is also the smallest among the three schemes, achieving a win-win situation. Figures 17-18 , Figures 19-20 Based on the results of the air separation system transfer and the results of the middle section of the rolling mill, the load transfer amount of the air separation system in Scheme 3 is less than that in Scheme 2, which reduces the load adjustment pressure of the air separation system. The interruption period of the rolling mill is more dispersed than that in Scheme 2, which is more beneficial to the stability of the company's production.

[0197] Table 3 compares the costs of the three schemes. As shown in Table 3, compared to Scheme 1, Schemes 2 and 3 show significant reductions in total system operating costs, decreasing by 16.08% and 21.42% respectively. This is because steel companies participate in power system dispatch in Schemes 2 and 3, promptly absorbing blocked wind power during peak wind power output periods, alleviating system operational pressure. However, since only steel loads participate in dispatch in Scheme 2, the flexibility potential of steel companies is not fully utilized, resulting in limited capacity for absorbing blocked wind power. Compared to Scheme 1, the wind curtailment penalty cost in Scheme 2 decreased by 20.87%. In contrast, after adopting the joint dispatch mode of steel loads and self-owned power plants in Scheme 3, the flexibility potential of steel companies is fully utilized. The capacity of steel companies to absorb blocked wind power is significantly improved, with wind curtailment penalty costs decreasing by 72.31% compared to Scheme 1 and by 65.01% compared to Scheme 2. The electricity costs for steel companies decreased by 22.04% and 12.71% compared to Schemes 1 and 2, respectively. Increased flexibility for steel companies means they can optimize production plans to the maximum extent possible based on system peak and valley fluctuations, thereby alleviating system operational pressure and reducing electricity costs for the companies.

[0198] Table 3. Cost Comparison of the Three Options

[0199] Scheme System total operating cost / MWh Wind curtailment penalty cost / MWh Steel enterprise electricity cost / MWh 1 26866053 602100 4909875 2 22546881 476470 4385021 3 21112630 166725 3827798

[0200] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0201] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process, characterized in that, Includes the following steps: S1. Obtain the operating data of each target equipment in the steel production process, analyze the operating characteristics of the steel enterprise's production load, and establish electric arc furnace model, air separation system model and rolling line model based on the analysis results. S2. Based on the power supply mode of the power supply system of steel enterprises and combined with the steel production process, construct an adjustable capacity model under the coupling of power supply system and production process. S3. Based on the operating cost of the power system of steel enterprises, construct a day-ahead flexible scheduling model for steel enterprises with the objective function of minimizing the total cost of the power system; S4. Integrate the electric arc furnace model, air separation system model, rolling mill model, adjustable capacity model, and steel enterprise flexible day-ahead scheduling model into a mixed integer linear programming scheduling model. Solve the mixed integer linear programming scheduling model using a solution tool to obtain an optimized scheduling scheme for system source-load coordination.

2. The power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to claim 1, characterized in that, The process of acquiring operational data of various target equipment during steel production, analyzing the operational characteristics of steel enterprise production load, and establishing electric arc furnace models, air separation system models, and rolling mill models based on the analysis results includes the following steps: S11. Collect the operating data of the electric arc furnace during the steelmaking process, determine the smelting cycle of the electric arc furnace during steelmaking, and construct an electric arc furnace model based on the power adjustment method of the electric arc furnace. S12. Collect operational data of the air separation system during the steelmaking process, and construct an air separation system model based on gas storage characteristics; S13. Collect the operating data of the steel rolling production line during the steelmaking process, and construct a steel rolling line model based on the operating characteristics of the steel rolling production line.

3. The power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to claim 2, characterized in that, The process of collecting operational data of the electric arc furnace during steelmaking, determining the smelting cycle of the electric arc furnace during steelmaking, and constructing an electric arc furnace model based on the power adjustment method of the electric arc furnace includes: S111. Based on the operating data of the electric arc furnace during the steelmaking process, construct the power constraint of the electric arc furnace within the scheduling cycle; S112. Based on the power regulation method of the electric arc furnace, determine the regulation characteristics of the power regulation of the electric arc furnace, and construct the constraint on the number of continuous regulation times of the electric arc furnace within the scheduling cycle. S113. Based on the production requirements of the electric arc furnace, construct the maximum time constraint and the minimum time constraint for one furnace smelting in the electric arc furnace, respectively. S114. Based on the production demand of steel enterprises before and after participating in scheduling, construct the power constraint required for the minimum output of electric arc furnace in the steelmaking process. S115. Combine the power constraint, continuous adjustment number constraint, maximum time constraint, minimum time constraint and power constraint to obtain the electric arc furnace model.

4. The power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to claim 2, characterized in that, The collection of operational data from the air separation system during the steelmaking process, and the construction of an air separation system model based on gas storage characteristics, includes: S121. Collect the operating data of the air separation system in the steelmaking process, and based on the flexibility characteristics of the air separation system, construct a constraint that the gas output of the air separation system is proportional to the power consumption. S122. Based on the gas storage characteristics, construct gas volume change constraints; S123. The constraints of proportionality between gas production and power consumption and gas volume change in the air separation system are combined to form an air separation system model.

5. The power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to claim 2, characterized in that, The collection of operational data from the steel rolling production line during the steelmaking process, and the construction of a rolling line model based on the operational characteristics of the steel rolling production line, includes: S131. Collect operating data of the steel rolling production line during the steelmaking process and determine the interruptible load characteristics of the steel rolling production line. S132. Based on the interruptible load characteristics, construct a rolling mill model based on the interruptible characteristics.

6. The power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to claim 2, characterized in that, The power adjustment method of the electric arc furnace is a transformer tap changer.

7. The power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to claim 1, characterized in that, The construction of an adjustable capacity model coupling the power supply system and the production process, based on the power supply mode of the steel enterprise's energy supply system and in conjunction with the steel production process, includes: S21. Based on the power supply mode of the steel enterprise's energy supply system, determine the output constraints of the self-owned power plant by analyzing the flexibility and adjustment capability of the power generation and consumption resources of the self-owned power plant. S22. Based on the steel production process, determine the power balance constraints and establish a power supply system model and an adjustable production process model for steel enterprises. S23. Couple the energy supply system model of the steel enterprise and the adjustable production process model to obtain the adjustable capacity model under the coupling of the power supply system and the production process.

8. The power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to claim 1, characterized in that, The aforementioned day-ahead flexible scheduling model for steel enterprises, based on the operating costs of the power system and with the objective function of minimizing the total cost of the power system, includes: S31. Based on the production profit orientation of steel enterprises, the sum of the operating cost of thermal power units, the wind curtailment penalty cost of the power system, and the electricity consumption cost of steel enterprises shall be taken as the operating cost of the power system of steel enterprises. S32. Based on the constraints of the power system, construct a day-ahead flexible scheduling model for steel enterprises with the objective function of minimizing the total cost of the power system.

9. The power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to claim 8, characterized in that, The constraints of the power system include: power system power balance constraints, wind power output constraints, and power system thermal power unit output constraints. The power system thermal power unit output constraints include: upper and lower limit constraints on thermal power unit output and thermal power unit ramping constraints. The expressions for the power system power balance constraint, wind power output constraint, upper and lower limit constraints for thermal power unit output, and thermal power unit ramping constraint are as follows: P Gi,min ≤P Gi,t ≤P Gi,max ; |P Gi,t -P Gi,t-1 |≤ΔP Gi,up ; In the formula, P Gi,t P represents the actual output of thermal power unit u during time period t, where U represents the total number of thermal power units. w,t The actual wind power absorbed during time period t is represented by k, which represents the power purchase and sale quotation of the steel enterprise. t net P represents the interaction power between the steel company and the power grid at time t. L,t This represents the predicted power of the regular load during time period t. P represents the predicted wind power output for the day before time period t. Gi,min P represents the lower limit of the output of thermal power units. Gi,t P represents the output of thermal power unit i in the power system during time period t. Gi,max P represents the upper limit of the output of a thermal power unit. Gi,t-1 ΔP represents the output of thermal power unit i in the system during time period t-1. Gi,up This indicates the maximum ramp rate of the thermal power unit in adjacent time periods.

10. The power system source-load coordinated optimization scheduling method considering the flexibility of the steel production process according to claim 1, characterized in that, The process of integrating the electric arc furnace model, air separation system model, rolling mill model, adjustable capacity model, and steel enterprise flexible day-ahead scheduling model into a mixed-integer linear programming scheduling model, and then solving the mixed-integer linear programming scheduling model using a solution tool to obtain an optimized scheduling scheme for system source-load coordination includes the following steps: S41. Based on the electric arc furnace model, air separation system model, rolling mill model, adjustable capacity model, and steel enterprise flexible day-ahead scheduling model, the decision variables are determined using the optimization modeling toolbox, and the objective function and constraints of the mixed integer linear programming scheduling model are constructed to realize the construction of the mixed integer linear programming scheduling model. S42. Configure the solver parameters and call the solver to solve the constructed mixed integer linear programming scheduling model to obtain the solution results; S43. Analyze the solution results and extract the optimized decision variable values ​​based on the analysis results to form an optimized scheduling scheme for system source-load coordination.

Images