Optimized scheduling system and method considering multiple self-contained units in iron and steel plant

Abstract

The invention provides an optimal scheduling system and method considering multiple self-contained units in a steel plant, and belongs to the technical field of load scheduling. The method specifically comprises the following steps: determining the operation load of the self-contained units according to the gas consumption of the self-contained units; determining an economic function of the self-contained unit according to the gas utilization efficiency of the self-contained unit under the gas consumption according to the operation load; determining an outsourcing economic function according to the electricity purchasing quantity and the unit price, constructing an economic function of the iron and steel plant through the economic function of the self-contained unit and the outsourcing economic function, constructing a comprehensive function by taking the economic function of the iron and steel plant and a safety function, and taking the maximum comprehensive function as a target. And a scheduling model is constructed to confirm the gas consumption and the electricity purchase quantity of the self-contained units, so that optimal scheduling of the multiple self-contained units of the iron and steel plant is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of load scheduling, and particularly relates to an optimization scheduling system and method considering multiple self-provided units in a steel plant. BACKGROUND

[0002] In order to meet the power demand of a steel plant, multiple operating units are often provided in a self-provided power plant in the steel plant, and the installed capacity and the coal gas utilization efficiency of different operating units are also different. Therefore, how to realize economic scheduling of different operating units on the basis of meeting the power demand of the steel plant becomes a technical problem to be solved.

[0003] In the prior art, an optimization scheduling model of a self-provided power plant in a steel plant is often constructed from the economic perspective of the steel plant, but the following technical problems exist.

[0004] 1. The difference between different operating units in the plant is not considered. The coal gas utilization efficiency and power generation efficiency of different operating units are different. Therefore, if multiple units are not considered, economic scheduling of the self-provided power plant of the steel plant cannot be realized.

[0005] 2. The safety state of the operation of different operating units is also not considered, and the distribution of the coal gas utilization amount between different operating units is not determined according to the safety state. Specifically, the safety states of different operating units are different. Therefore, if the safety state is not considered, the safety of the scheduling may not meet the requirements.

[0006] In view of the above technical problems, the present application provides an optimization scheduling system and method considering multiple self-provided units in a steel plant. SUMMARY

[0007] To achieve the object of the present application, the present application adopts the following technical solutions:

[0008] According to one aspect of the present application, an optimization scheduling method considering multiple self-provided units in a steel plant is provided.

[0009] An optimization scheduling method considering multiple self-provided units in a steel plant, characterized in that it specifically comprises:

[0010] Obtaining the operation life and historical failure data of the self-provided units in the steel plant to evaluate the safety state value of the self-provided units, and dividing the types of the self-provided units into safe units and other units according to the safety state value of the self-provided units;

[0011] The process gas consumption and gas generation of the steel plant are determined by the operating load of the steel plant, and a dynamic constraint model of the gas is constructed based on the gas generation, the process gas consumption, the gas storage capacity of the gas holder and the gas consumption of the self-provided unit.

[0012] The minimum load rate is determined by the type of the self-contained unit. The operating safety function of the self-contained unit is determined by the gas consumption, safety status value and minimum load rate of different self-contained units. The scheduling safety function of the steel plant is constructed according to the remaining gas quantity of different gas holders. The safety function of the steel plant is constructed by the operating safety function and the scheduling safety function.

[0013] The operating load of the self-owned generating unit is determined based on its gas consumption. Then, the economic function of the self-owned generating unit is determined based on its gas utilization efficiency under the given gas consumption, based on the operating load. The economic function of the externally purchased electricity is determined based on the purchased electricity volume and unit price. The economic function of the steel plant is constructed using the economic function of the self-owned generating unit and the externally purchased electricity. A comprehensive function is constructed using the economic function and safety function of the steel plant. With the goal of maximizing the comprehensive function, and with the gas consumption and purchased electricity volume of the self-owned generating unit as scheduling objectives, a scheduling model is constructed to confirm the gas consumption and purchased electricity volume of the self-owned generating unit.

[0014] By assessing the safety status of the self-owned generating units within the steel plant based on their operating years and historical fault data, dynamic evaluation of the safety and reliability of different self-owned generating units is achieved, ensuring the safety and reliability of the final scheduling model.

[0015] A dynamic constraint model for gas is constructed based on the gas generation, process gas consumption, gas storage in the gas holder, and gas consumption of the self-contained unit. This enables the construction of a dynamic constraint model for gas usage from multiple perspectives, ensuring the scheduling safety of the scheduling model.

[0016] The safety function of the steel plant is constructed by running the safety function and the scheduling safety function. This realizes the construction of the safety function from two perspectives: the safety of the operation of the self-owned units and the safety of the scheduling of the remaining gas volume. This not only ensures the safety of the operation of the self-owned units, but also ensures a certain scheduling margin.

[0017] By constructing a comprehensive function based on the economic and safety functions of the steel plant, and with the goal of maximizing the comprehensive function, a scheduling model is built to confirm the gas consumption and electricity purchase of the self-owned units. This not only ensures the economic efficiency of the steel plant's operation, but also guarantees the scheduling safety of the units and the operational safety of the self-owned units, thus ensuring the safe and economical operation of the steel plant.

[0018] A further technical solution is that the historical fault data includes, but is not limited to, the number of historical faults of different fault levels, the type of historical fault, and the number of historical non-stops.

[0019] A further technical solution is that the method for evaluating the safety status value of the self-provided generating unit is as follows:

[0020] S21 Obtain the operating years of the self-contained units inside the steel plant, and determine whether the safety status of the self-contained units meets the requirements based on the operating years of the self-contained units. If yes, proceed to step S22; otherwise, proceed to step S24.

[0021] S22 determines the number of historical non-stops of the self-contained unit by using the historical fault data of the self-contained unit inside the steel plant, and determines whether the safety status of the self-contained unit meets the requirements by using the number of historical non-stops of the self-contained unit. If yes, proceed to step S23; otherwise, proceed to step S24.

[0022] S23 determines the number of historical faults and the type of historical faults of the self-contained units at different fault levels by using the historical fault data of the self-contained units inside the steel plant, and determines the fault assessment value of the self-contained units by combining the historical non-stop count of the self-contained units, and determines whether the safety status of the self-contained units meets the requirements by using the fault assessment value of the self-contained units. If yes, the safety status value of the self-contained units is assessed by using the fault assessment value. If no, proceed to step S24.

[0023] S24 assesses the safety status of the self-contained generating units by combining the fault assessment values ​​and operating years of the self-contained generating units within the steel plant with the historical number of faults of different fault levels within the most recent preset time period.

[0024] A further technical solution is that the safety status value of the self-contained unit is between 0 and 1, wherein the larger the safety status value of the self-contained unit, the safer the operating status of the self-contained unit.

[0025] A further technical solution involves, before constructing the dynamic constraint model for the coal gas, screening the self-owned generating units based on the safety state value, the steel plant's minimum historical coal gas redundancy, and the historical power generation load of the self-owned power plant. The screened self-owned generating units are then used as scheduling objects to determine the safety function and scheduling model. The specific steps for screening the self-owned generating units are as follows:

[0026] S31 determines the predicted consumption and generation of process gas for the steel plant based on the operating load of the steel plant in the future preset time period, and determines the predicted gas redundancy based on the predicted gas generation and the predicted process gas consumption. The minimum gas consumption of the steel plant in the future preset time period is determined by the predicted gas redundancy and the gas storage capacity of the gas storage tank.

[0027] S32 determines whether the installed capacity of the self-owned unit meets the requirements based on the minimum gas consumption of the steel plant in the future preset time. If yes, proceed to step S33. If no, there is no need to screen the self-owned unit, and all self-owned units are used as scheduling objects to determine the safety function and scheduling model.

[0028] S33 obtains the historical gas redundancy of the steel plant within a preset time period, and determines the maximum value of the historical gas redundancy and the number of times the maximum value of the historical gas redundancy is obtained. Combined with the average value of the historical gas redundancy and the minimum gas consumption, the redundancy assessment value of the steel plant is determined. Based on the redundancy assessment value of the steel plant, it is determined whether the installed capacity of the self-owned unit meets the requirements. If yes, proceed to step S34. If no, there is no need to screen the self-owned unit. All self-owned units are used as scheduling objects to determine the safety function and scheduling model.

[0029] S34 uses the redundancy assessment value of the steel plant as a constraint, constructs a safety assessment function based on the safety status value of the self-contained units and the gas consumption of the self-contained units, and selects the self-contained units with the goal of maximizing the safety assessment function. The selected self-contained units are then used as scheduling objects to determine the safety function and scheduling model.

[0030] A further technical solution is that the preset time is determined based on the start-up time and installed capacity of the self-contained unit. The longer the start-up time and the smaller the installed capacity of the self-contained unit, the longer the preset time.

[0031] A further technical solution involves determining the specific steps for the operating safety function of the self-provided generator unit as follows:

[0032] S41 determines the minimum load rate of the self-contained unit based on its type and installed capacity, and constructs a safety function for the self-contained unit based on its gas consumption and the minimum load rate.

[0033] S42 constructs the modified safety function of the self-contained unit using the safety function of the self-contained unit and the safety state value of the self-contained unit;

[0034] S43 constructs the safety function of the self-contained unit by using the modified safety function of the self-contained unit, and by combining the number of self-contained units and the maximum value of the modified safety function of the self-contained unit.

[0035] A further technical solution is that the scheduling model is constructed based on an improved flying mouse algorithm, wherein the specific steps for constructing the scheduling model are as follows:

[0036] Step 1: Set the flying squirrel population size M, maximum number of iterations T, and predation probability P. dp Initialize the parameters of the flying mouse algorithm;

[0037] Step 2: Calculate the fitness value of all flying squirrel individuals, sort them according to their fitness values, and select the individual flying squirrels located at each food source;

[0038] Step 3: According to the greedy strategy, when T is greater than 2, some individuals of the optimal solutions selected in T and T-1 times are retained and the cycle continues to be repeated to supplement the initial population;

[0039] Step 4: Anneal the population updated by the greedy strategy, compare the fitness values ​​of individuals in the old and new populations, and obtain the new population positions after filtering. The probability of the new population position being accepted is calculated using the following formula:

[0040]

[0041] x n and x o The optimal solutions for both the old and new solutions; f(x) n (j)), f(x) o (j) represent individuals from the new and old populations, respectively, where j is the fitness value, t is the current temperature, and k is the Boltzmann constant;

[0042] Step 5: Assign individuals within the population located in oak trees and ordinary trees based on the control parameter P. dp and S c Choose to perform a sine / cosine search operation;

[0043] Step 6: Adjust the seasonal constant S cand S min Perform calculations to determine seasonal changes. If the seasonal changes are met, randomly assign positions to flying squirrels on ordinary trees; otherwise, proceed to step 2.

[0044] Step 7: Determine if the algorithm has completed T iterations. If it has, output the optimal fitness value and the position of the optimal solution. Otherwise, execute step 2 and continue the loop.

[0045] On the other hand, this application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed in a computer, it causes the computer to execute the above-mentioned optimized scheduling method considering multiple self-owned units in a steel plant.

[0046] On the other hand, this application provides a computer program product, characterized in that the computer program product stores instructions, which, when executed by a computer, cause the computer to implement the above-mentioned optimized scheduling method considering multiple self-owned units in a steel plant. Attached Figure Description

[0047] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0048] Figure 1 This is a flowchart of an optimized scheduling method for multiple self-owned units within a steel plant, based on Embodiment 1.

[0049] Figure 2 This is a flowchart illustrating the specific steps involved in screening the self-contained generator unit according to Embodiment 1.

[0050] Figure 3 This is a framework diagram of a computer-readable storage medium according to Embodiment 2. Detailed Implementation

[0051] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many ways and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0052] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that there may be other elements / components / etc. in addition to the listed elements / components / etc.

[0053] Example 1

[0054] To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, an optimized scheduling method considering multiple self-owned generating units within a steel plant is provided, characterized by specifically including:

[0055] The operating years and historical fault data of the self-contained units within the steel plant are obtained to assess the safety status value of the self-contained units, and the self-contained units are classified into safe units and other units based on their safety status values.

[0056] The process gas consumption and gas generation of the steel plant are determined by the operating load of the steel plant, and a dynamic constraint model of the gas is constructed based on the gas generation, the process gas consumption, the gas storage capacity of the gas holder and the gas consumption of the self-provided unit.

[0057] The minimum load rate is determined by the type of the self-contained unit. The operating safety function of the self-contained unit is determined by the gas consumption, safety status value and minimum load rate of different self-contained units. The scheduling safety function of the steel plant is constructed according to the remaining gas quantity of different gas holders. The safety function of the steel plant is constructed by the operating safety function and the scheduling safety function.

[0058] The operating load of the self-owned generating unit is determined based on its gas consumption. Then, the economic function of the self-owned generating unit is determined based on its gas utilization efficiency under the given gas consumption, based on the operating load. The economic function of the externally purchased electricity is determined based on the purchased electricity volume and unit price. The economic function of the steel plant is constructed using the economic function of the self-owned generating unit and the externally purchased electricity. A comprehensive function is constructed using the economic function and safety function of the steel plant. With the goal of maximizing the comprehensive function, and with the gas consumption and purchased electricity volume of the self-owned generating unit as scheduling objectives, a scheduling model is constructed to confirm the gas consumption and purchased electricity volume of the self-owned generating unit.

[0059] By assessing the safety status of the self-owned generating units within the steel plant based on their operating years and historical fault data, dynamic evaluation of the safety and reliability of different self-owned generating units is achieved, ensuring the safety and reliability of the final scheduling model.

[0060] A dynamic constraint model for gas is constructed based on the gas generation, process gas consumption, gas storage in the gas holder, and gas consumption of the self-contained unit. This enables the construction of a dynamic constraint model for gas usage from multiple perspectives, ensuring the scheduling safety of the scheduling model.

[0061] The safety function of the steel plant is constructed by running the safety function and the scheduling safety function. This realizes the construction of the safety function from two perspectives: the safety of the operation of the self-owned units and the safety of the scheduling of the remaining gas volume. This not only ensures the safety of the operation of the self-owned units, but also ensures a certain scheduling margin.

[0062] By constructing a comprehensive function based on the economic and safety functions of the steel plant, and with the goal of maximizing the comprehensive function, a scheduling model is built to confirm the gas consumption and electricity purchase of the self-owned units. This not only ensures the economic efficiency of the steel plant's operation, but also guarantees the scheduling safety of the units and the operational safety of the self-owned units, thus ensuring the safe and economical operation of the steel plant.

[0063] It should be noted that the historical fault data includes, but is not limited to, the number of historical faults at different fault levels, the types of historical faults, and the number of historical non-stops.

[0064] Specifically, the method for assessing the safety status value of the self-contained generating unit is as follows:

[0065] S21 Obtain the operating years of the self-contained units inside the steel plant, and determine whether the safety status of the self-contained units meets the requirements based on the operating years of the self-contained units. If yes, proceed to step S22; otherwise, proceed to step S24.

[0066] S22 determines the number of historical non-stops of the self-contained unit by using the historical fault data of the self-contained unit inside the steel plant, and determines whether the safety status of the self-contained unit meets the requirements by using the number of historical non-stops of the self-contained unit. If yes, proceed to step S23; otherwise, proceed to step S24.

[0067] S23 determines the number of historical faults and the type of historical faults of the self-contained units at different fault levels by using the historical fault data of the self-contained units inside the steel plant, and determines the fault assessment value of the self-contained units by combining the historical non-stop count of the self-contained units, and determines whether the safety status of the self-contained units meets the requirements by using the fault assessment value of the self-contained units. If yes, the safety status value of the self-contained units is assessed by using the fault assessment value. If no, proceed to step S24.

[0068] S24 assesses the safety status of the self-contained generating units by combining the fault assessment values ​​and operating years of the self-contained generating units within the steel plant with the historical number of faults of different fault levels within the most recent preset time period.

[0069] It is understood that the safety status value of the self-contained unit is between 0 and 1, and the larger the safety status value of the self-contained unit, the safer the operating status of the self-contained unit.

[0070] Further examples, such as Figure 2 As shown, before constructing the dynamic constraint model for the coal gas, it is necessary to screen the self-owned generating units based on the safety state value, the steel plant's minimum historical coal gas redundancy, and the historical power generation load of the self-owned power plant. The screened self-owned generating units will then be used as scheduling objects to determine the safety function and scheduling model. The specific steps for screening the self-owned generating units are as follows:

[0071] S31 determines the predicted consumption and generation of process gas for the steel plant based on the operating load of the steel plant in the future preset time period, and determines the predicted gas redundancy based on the predicted gas generation and the predicted process gas consumption. The minimum gas consumption of the steel plant in the future preset time period is determined by the predicted gas redundancy and the gas storage capacity of the gas storage tank.

[0072] S32 determines whether the installed capacity of the self-owned unit meets the requirements based on the minimum gas consumption of the steel plant in the future preset time. If yes, proceed to step S33. If no, there is no need to screen the self-owned unit, and all self-owned units are used as scheduling objects to determine the safety function and scheduling model.

[0073] S33 obtains the historical gas redundancy of the steel plant within a preset time period, and determines the maximum value of the historical gas redundancy and the number of times the maximum value of the historical gas redundancy is obtained. Combined with the average value of the historical gas redundancy and the minimum gas consumption, the redundancy assessment value of the steel plant is determined. Based on the redundancy assessment value of the steel plant, it is determined whether the installed capacity of the self-owned unit meets the requirements. If yes, proceed to step S34. If no, there is no need to screen the self-owned unit. All self-owned units are used as scheduling objects to determine the safety function and scheduling model.

[0074] S34 uses the redundancy assessment value of the steel plant as a constraint, constructs a safety assessment function based on the safety status value of the self-contained units and the gas consumption of the self-contained units, and selects the self-contained units with the goal of maximizing the safety assessment function. The selected self-contained units are then used as scheduling objects to determine the safety function and scheduling model.

[0075] It should be noted that the preset time is determined based on the start-up time and installed capacity of the self-contained unit. The longer the start-up time and the smaller the installed capacity of the self-contained unit, the longer the preset time will be.

[0076] Specifically, the steps for determining the operating safety function of the self-contained unit are as follows:

[0077] S41 determines the minimum load rate of the self-contained unit based on its type and installed capacity, and constructs a safety function for the self-contained unit based on its gas consumption and the minimum load rate.

[0078] S42 constructs the modified safety function of the self-contained unit using the safety function of the self-contained unit and the safety state value of the self-contained unit;

[0079] S43 constructs the safety function of the self-contained unit by using the modified safety function of the self-contained unit, and by combining the number of self-contained units and the maximum value of the modified safety function of the self-contained unit.

[0080] For example, the specific steps for confirming the gas consumption and electricity purchase of the self-provided unit are as follows:

[0081] S51 determines the gas utilization efficiency of the self-owned units by the gas consumption of different self-owned units, and determines the gas utilization efficiency under different gas consumption by the gas utilization efficiency of the different self-owned units, and constructs the economic function of the self-owned units under different gas consumption by the gas utilization efficiency and the gas consumption.

[0082] S52 constructs the external purchase economic function based on the purchased electricity volume and unit price, and constructs the steel plant's economic function based on the external purchase economic function and the economic function of the self-owned generating unit;

[0083] S53 constructs a comprehensive function using the economic and safety functions of the steel plant, and uses the maximum value of the comprehensive function as the objective to confirm the gas consumption and electricity purchase of the self-owned unit.

[0084] It is understood that the scheduling model is constructed based on an improved flying mouse algorithm, and the specific steps for constructing the scheduling model are as follows:

[0085] Step 1: Set the flying squirrel population size M, maximum number of iterations T, and predation probability P. dp Initialize the parameters of the flying mouse algorithm;

[0086] Step 2: Calculate the fitness value of all flying squirrel individuals, sort them according to their fitness values, and select the individual flying squirrels located at each food source;

[0087] Step 3: According to the greedy strategy, when T is greater than 2, some individuals of the optimal solutions selected in T and T-1 times are retained and the cycle continues to be repeated to supplement the initial population;

[0088] Step 4: Anneal the population updated by the greedy strategy, compare the fitness values ​​of individuals in the old and new populations, and obtain the new population positions after filtering. The probability of the new population position being accepted is calculated using the following formula:

[0089]

[0090] x n and x o The optimal solutions for both the old and new solutions; f(x) n (j)), f(x) o (j) represent individuals from the new and old populations, respectively, where j is the fitness value, t is the current temperature, and k is the Boltzmann constant;

[0091] Step 5: Assign individuals within the population located in oak trees and ordinary trees based on the control parameter P. dp and S c Choose to perform a sine / cosine search operation;

[0092] Step 6: Adjust the seasonal constant S c and S min Perform calculations to determine seasonal changes. If the seasonal changes are met, randomly assign positions to flying squirrels on ordinary trees; otherwise, proceed to step 2.

[0093] Step 7: Determine if the algorithm has completed T iterations. If it has, output the optimal fitness value and the position of the optimal solution. Otherwise, execute step 2 and continue the loop.

[0094] Example 2

[0095] like Figure 3 As shown, this application provides a computer-readable storage medium storing a computer program. When the computer program is executed in a computer, it causes the computer to execute the above-mentioned optimized scheduling method considering multiple self-owned units in a steel plant.

[0096] Example 3

[0097] This application provides a computer program product, characterized in that the computer program product stores instructions, which, when executed by a computer, cause the computer to implement the above-mentioned optimized scheduling method considering multiple self-owned units in a steel plant.

[0098] In this embodiment of the invention, the term "multiple" refers to two or more, unless otherwise explicitly defined. The terms "install," "connect," and "fix" should be interpreted broadly. For example, "connect" can mean a fixed connection, a detachable connection, or an integral connection. Those skilled in the art can understand the specific meaning of the above terms in this embodiment of the invention based on the specific circumstances.

[0099] In the description of the embodiments of the present invention, it should be understood that the terms "upper" and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or unit referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of the present invention.

[0100] In the description of this specification, the terms "an embodiment," "a preferred embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0101] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. For those skilled in the art, the embodiments of the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present invention should be included within the protection scope of the embodiments of the present invention.

Claims

1. An optimized scheduling method considering multiple self-owned generating units within a steel plant, characterized in that, Specifically, it includes: The operating years and historical fault data of the self-contained units within the steel plant are obtained to assess the safety status value of the self-contained units, and the self-contained units are classified into safe units and other units based on their safety status values. The process gas consumption and gas generation of the steel plant are determined by the operating load of the steel plant, and a dynamic constraint model of the gas is constructed based on the gas generation, the process gas consumption, the gas storage capacity of the gas holder and the gas consumption of the self-provided unit. The minimum load rate is determined by the type of the self-contained unit. The operating safety function of the self-contained unit is determined by the gas consumption, safety status value and minimum load rate of different self-contained units. The scheduling safety function of the steel plant is constructed according to the remaining gas quantity of different gas holders. The safety function of the steel plant is constructed by the operating safety function and the scheduling safety function. The operating load of the self-owned generating unit is determined based on its gas consumption. Then, the economic function of the self-owned generating unit is determined based on its gas utilization efficiency under the given gas consumption, based on the operating load. The economic function of the externally purchased electricity is determined based on the purchased electricity volume and unit price. The economic function of the steel plant is constructed using the economic function of the self-owned generating unit and the externally purchased electricity. A comprehensive function is constructed using the economic function and safety function of the steel plant. With the goal of maximizing the comprehensive function, and with the gas consumption and purchased electricity volume of the self-owned generating unit as scheduling objectives, a scheduling model is constructed to confirm the gas consumption and purchased electricity volume of the self-owned generating unit.

2. The optimized scheduling method considering multiple self-owned generating units within a steel plant as described in claim 1, characterized in that, The historical fault data includes, but is not limited to, the number of historical faults at different fault levels, the types of historical faults, and the number of historical non-stops.

3. The optimized scheduling method considering multiple self-owned generating units within a steel plant as described in claim 1, characterized in that, The method for assessing the safety status value of the self-contained generating unit is as follows: S21 Obtain the operating years of the self-contained units inside the steel plant, and determine whether the safety status of the self-contained units meets the requirements based on the operating years of the self-contained units. If yes, proceed to step S22; otherwise, proceed to step S24. S22 determines the number of historical non-stops of the self-contained unit by using the historical fault data of the self-contained unit inside the steel plant, and determines whether the safety status of the self-contained unit meets the requirements by using the number of historical non-stops of the self-contained unit. If yes, proceed to step S23; otherwise, proceed to step S24. S23 determines the number of historical faults and the type of historical faults of the self-contained units at different fault levels by using the historical fault data of the self-contained units inside the steel plant, and determines the fault assessment value of the self-contained units by combining the historical non-stop count of the self-contained units, and determines whether the safety status of the self-contained units meets the requirements by using the fault assessment value of the self-contained units. If yes, the safety status value of the self-contained units is assessed by using the fault assessment value. If no, proceed to step S24. S24 assesses the safety status of the self-contained generating units by combining the fault assessment values ​​and operating years of the self-contained generating units within the steel plant with the historical number of faults of different fault levels within the most recent preset time period.

4. The optimized scheduling method considering multiple self-owned units within a steel plant as described in claim 1, characterized in that, The safety status value of the self-contained generator set ranges from 0 to 1. The higher the safety status value of the self-contained generator set, the safer the operating status of the self-contained generator set.

5. The optimized scheduling method considering multiple self-owned generating units within a steel plant as described in claim 1, characterized in that, Before constructing the dynamic constraint model for the coal gas, it is necessary to screen the self-owned generating units based on the safety state value, the steel plant's minimum historical coal gas redundancy, and the historical power generation load of the self-owned power plant. The screened self-owned generating units will then be used as scheduling objects to determine the safety function and scheduling model. The specific steps for screening the self-owned generating units are as follows: Based on the operating load of the steel plant in the future preset time period, the predicted consumption and generation of process gas of the steel plant are determined, and the predicted gas redundancy is determined based on the predicted gas generation and the predicted process gas consumption. The minimum gas consumption of the steel plant in the future preset time period is determined by the predicted gas redundancy and the gas storage capacity of the gas storage tank. Based on the steel plant's minimum gas consumption within a preset future time period, determine whether the installed capacity of the self-owned generating unit meets the requirements. If yes, proceed to the next step; otherwise, there is no need to screen the self-owned generating unit, and all self-owned generating units are used as scheduling objects to determine the safety function and scheduling model. The historical gas redundancy of the steel plant within a preset time period is obtained. The maximum value of the historical gas redundancy and the number of times corresponding to the maximum value are determined based on the historical gas redundancy. The redundancy assessment value of the steel plant is determined by combining the average value of the historical gas redundancy and the minimum gas consumption. Based on the redundancy assessment value of the steel plant, it is determined whether the installed capacity of the self-owned units meets the requirements. If yes, proceed to the next step. If no, there is no need to screen the self-owned units. All self-owned units are used as scheduling objects to determine the safety function and scheduling model. The redundancy assessment value of the steel plant is used as a constraint, and a safety assessment function is constructed based on the safety status value of the self-owned units and the gas consumption of the self-owned units. With the goal of maximizing the safety assessment function, the self-owned units are screened to obtain the screened self-owned units. The screened self-owned units are then used as scheduling objects to determine the safety function and scheduling model.

6. The optimized scheduling method considering multiple self-owned units within a steel plant as described in claim 5, characterized in that, The preset time is determined based on the start-up time and installed capacity of the self-contained unit. The longer the start-up time and the smaller the installed capacity of the self-contained unit, the longer the preset time.

7. The optimized scheduling method considering multiple self-owned generating units within a steel plant as described in claim 1, characterized in that, The specific steps for determining the operating safety function of the self-provided generating unit are as follows: S41 determines the minimum load rate of the self-contained unit based on its type and installed capacity, and constructs a safety function for the self-contained unit based on its gas consumption and the minimum load rate. S42 constructs the modified safety function of the self-contained unit using the safety function of the self-contained unit and the safety state value of the self-contained unit; S43 constructs the safety function of the self-contained unit by using the modified safety function of the self-contained unit, and by combining the number of self-contained units and the maximum value of the modified safety function of the self-contained unit.

8. The optimized scheduling method considering multiple self-owned generating units within a steel plant as described in claim 1, characterized in that, The scheduling model is constructed using an improved flying mouse algorithm, and the specific steps for constructing the scheduling model are as follows: Step 1: Set the flying squirrel population size M, maximum number of iterations T, and predation probability P. dp Initialize the parameters of the flying mouse algorithm; Step 2: Calculate the fitness value of all flying squirrel individuals, sort them according to their fitness values, and select the individual flying squirrels located at each food source; Step 3: According to the greedy strategy, when T is greater than 2, some individuals of the optimal solutions selected in T and T-1 times are retained and the cycle continues to be repeated to supplement the initial population; Step 4: Anneal the population updated by the greedy strategy, compare the fitness values ​​of individuals in the old and new populations, and obtain the new population positions after filtering. The probability of the new population position being accepted is calculated using the following formula: x n and x o The optimal solutions for both the old and new solutions; f(x) n (j)), f(x) o (j) represent individuals from the new and old populations, respectively, where j is the fitness value, t is the current temperature, and k is the Boltzmann constant; Step 5: Assign individuals in the population located in oak trees and ordinary trees based on the control parameter P. dp and S c Choose to perform a sine / cosine search operation; Step 6: Adjust the seasonal constant S c and S min Perform calculations to determine seasonal changes. If the seasonal changes are met, randomly assign positions to flying squirrels on ordinary trees; otherwise, proceed to step 2. Step 7: Determine if the algorithm has completed T iterations. If it has, output the optimal fitness value and the position of the optimal solution. Otherwise, execute step 2 and continue the loop.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform an optimized scheduling method for multiple self-contained units in a steel plant, as described in any one of claims 1-8.

10. A computer program product, characterized in that, The computer program product stores instructions that, when executed by the computer, cause the computer to implement the optimized scheduling method for multiple self-owned units within a steel plant, as described in any one of claims 1-8.

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