CN111769555B

CN111769555B - Short-flow iron and steel enterprise load optimization regulation and control method and system considering process limitation

Info

Publication number: CN111769555B
Application number: CN202010650973.5A
Authority: CN
Inventors: 张利; 王海博; 鉴庆之; 李雪亮; 孙东磊; 李文升; 赵龙
Original assignee: Shandong University; Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
Current assignee: Shandong University; Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
Priority date: 2020-07-08
Filing date: 2020-07-08
Publication date: 2021-04-30
Anticipated expiration: 2040-07-08
Also published as: CN111769555A

Abstract

The utility model provides a short-flow steel enterprise load optimization regulation and control method and a system considering process limitation, which comprises the following steps: acquiring enterprise equipment parameters, the number of heats, the number of casting times, the duration of a heat processing and transporting task and the heat composition in the casting times; reconstructing a discrete time RTN model based on the acquired data, and constructing production constraint conditions of short-flow steel enterprises; and setting an objective function for minimizing the electricity purchasing cost, and obtaining an optimized regulation and control strategy of the enterprise production load, namely the specific start-stop time of different individuals in various key electric equipment, through the objective function and a production constraint condition solving model. The method accurately combines the electric energy consumption and the production task together on the premise of ensuring that the process limitation of the iron and steel enterprise is strictly met, so that the load optimization regulation and control strategy which is established by taking the minimum electricity purchasing cost of the enterprise as the target has actual operability and improves the economic benefit of the enterprise.

Description

Short-flow iron and steel enterprise load optimization regulation and control method and system considering process limitation

Technical Field

The disclosure relates to the technical field of power demand response, in particular to a short-process steel enterprise load optimization regulation and control method and system considering process limitation.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

With the continuous increase of the renewable energy grid-connected proportion, the peak load regulation pressure of a power grid is further increased due to the increased fluctuation and uncertainty of wind power and photovoltaic output, in addition, the capacity of a new energy delivery channel is insufficient, the development speed of a conventional thermal power unit is hindered, and the traditional 'source follow-up load' operation mode cannot meet the flexibility requirement of high-capacity renewable energy. Under the background, the adjustable potential of the flexible resources on the load side is excavated, energy utilization optimization and power demand response are implemented, and the method becomes an important means for solving the contradiction between the supply and demand balance of the power grid and improving the optimal configuration level of the system resources.

Among various types of loads, steel enterprises, as loads with the energy value of more than 30% in product output value, have the characteristics of large adjustable capacity, high response speed, high automation level and the like, particularly short-process steel enterprises with high power consumption can receive electricity price signals to guide and change the service time of high-power equipment such as an electric arc furnace and the like on the premise of not influencing the output of orders, and the peak-load and difference-load regulation pressure of a power grid is reduced through measures such as peak load shifting and valley load filling. The document "Chen R, Sun H, Guo Q, et al, Process-cutting Energy-intensive feedstock technical in power system scheduling: Model and mechanism [ J ]. Applied Energy,2015,158(NOV.15): 263-274" uses an electric arc furnace as a typical representative of electric equipment for metallurgical enterprises such as short-flow steel enterprises and analyzes the start-stop state and start-stop time constraint conditions which should be met by the electric arc furnace as a discrete adjustable load so that users optimize the load distribution throughout the day to reduce the electric purchase cost of the enterprises. The method comprises the following steps of taking the energy utilization behavior analysis and power characteristic modeling of the steel industry load in consideration of process control [ J ] power system automation, 2018,42(2) 114 + 120' modeling the power characteristics of continuous impact load, intermittent impact load and stable load in an electric furnace steel-making enterprise, expanding the change rule of the power utilization power of the whole steel-making enterprise along with time by a power time domain expression of a single steel rolling production line and a single electric arc furnace, and adjusting the start-stop time sequence of the electric arc furnace by means of a power prediction model on the basis to reduce the impact of short-time large-amplitude power fluctuation on a power grid. Although the above studies reveal the electricity utilization characteristics of short-process steel enterprises to some extent, the models created by such industrial users with complex production processes are still rough because they do not accurately combine their power consumption with their production tasks.

Aiming at the problem, the document' Ding Y M, Hong S H, Li X H.A demand response energy management scheme for Industrial processes in smart grid [ J ]. IEEE Transactions on Industrial information, 2014,10(4): 2257-. The documents "Nolde K, Morari M.Electrical tracking scheduling of a Steel plant [ J ]. Computers & Chemical Engineering,2010,34(11): 1899-1903" and "Alain H, Christian A.On electric tracking scheduling for a Steel plant [ J ]. Computers & Chemical Engineering,2011,35(12): 3044-3047" propose a mathematical model based on a continuous time representation to precisely locate the start and stop times of the short-flow convertible Steel plant shifting load, considering that the production tasks do not necessarily start only at the end points of a discretized time axis. Both methods described above are desirable in process modeling, but when discussing parallel operation of similar devices, the model becomes abnormally complex, and even an unsolvable situation may occur.

To more clearly illustrate the influence of materials, equipment, energy, etc. on the steel production activities, documents "Zhang X, Hug G, Kolter Z, et al. Industrial demand response by steel plants with a spinning response [ C ]//2015North American Power Symposium (NAPS), October 4-6,2015, Charlotte, USA: 1-6" and "Zhang X, Hug G, Kolter Z, et al. computational application for impact procedure of steel plants as demand response Resource [ C ]// Power Systems Computing Conference (PSCC), June 20-24,2016, Genoa, Italy: 1-7" use discrete time Resource (C ]// Power Systems computing Network) to adjust the cost of steel production and minimize the cost of the steel production process for the purpose of obtaining a short time of electricity consumption and optimizing the process for the purpose of achieving the goal of the coupling of the electricity consumption of the steel production process and the cost of the steel production process by the Taenia, the method is mainly different from the former two methods in that equipment elements, materials and energy are brought into the resource range together, so that the risk that the number of processing tasks increases in proportion with the number of model equipment is avoided, but a fixed time grid in the method enables a scheduling result obtained when the ratio of the task duration to the time interval length is not an integer to have a certain rounding error, adjacent processes in a production flow are difficult to truly and seamlessly connect due to the influence of the rounding error, and particularly production tasks needing to be continuously performed in continuous casting and the like even can cause accidents such as equipment damage, product quality reduction and the like because front and back furnaces cannot be accurately handed over.

Therefore, although research on the problem of load optimization regulation and control of short-flow iron and steel enterprises has achieved a certain result, the prior art scheme still has insufficient accuracy and comprehensiveness in consideration of production constraint conditions existing in the production flow of the iron and steel enterprises, which results in that the formulated load optimization regulation and control scheme has no engineering practicability and practical feasibility, and cannot guide the enterprises to reduce electricity purchasing cost to the maximum extent by reasonably changing the service time of production equipment.

Disclosure of Invention

The invention aims to solve the problems and provides a short-process iron and steel enterprise load optimization regulation method and system considering process limitation, wherein a plurality of tasks meeting the linkage relation in time sequence are combined into a new production task by improving the existing discrete time RTN model, and meanwhile, relevant constraint conditions related to the tasks to be combined in the model are modified, so that the occurrence of events that the next process cannot be carried out in time after the heat processing is finished due to rounding errors caused by equipment operation time discretization is avoided, the accuracy and the engineering practicability of the model are greatly improved, the adjustment of the production load is more flexible due to the reduction of the rounding errors, a user can further reduce the electricity purchasing cost of an enterprise on the original basis, and the optimization regulation of the load in the short-process iron and steel enterprise is realized.

In order to achieve the purpose, the following technical scheme is adopted in the disclosure:

in a first aspect, one or more embodiments provide a short-flow steel enterprise load optimization regulation and control method considering process limitations, including the steps of:

acquiring enterprise equipment parameters, the number of heats, the number of casting times, the duration of a heat processing and transporting task and the heat composition in the casting times;

reconstructing a discrete time RTN model based on the acquired data, and constructing production constraint conditions of short-flow steel enterprises;

and setting an objective function for minimizing the electricity purchasing cost, and obtaining an optimized regulation and control strategy of the enterprise production load, namely the specific start-stop time of different individuals in various key electric equipment, through the objective function and a production constraint condition solving model.

According to the further technical scheme, before acquiring enterprise equipment parameters, the number of heats, the number of casting times, the duration time of a heat processing and transporting task and the heat composition in the casting times, firstly, key electric equipment and the types of production tasks completed on the key electric equipment are determined according to the production flow of a short-flow iron and steel enterprise, and the heats at the inlet and the outlet of the equipment are defined as material resources consumed and generated by corresponding tasks respectively.

In a further technical scheme, the key electric equipment and the types of production tasks completed on the key electric equipment are as follows: the method comprises the steps of carrying out primary smelting operation of a heat on an electric arc furnace, carrying out decarburization operation of the heat on an argon oxygen decarburization furnace, carrying out refining operation of the heat on a ladle refining furnace, carrying out casting replacement operation and continuous casting operation of the heat on a continuous casting machine, wherein the material resource is the heat of each equipment inlet and outlet position.

In a further technical scheme, the discrete time RTN model is reconstructed based on the acquired data, and the method specifically comprises the following steps:

and (3) combining enterprise production information, giving values of all elements in the interaction relation matrix of the tasks and the resources, and reconstructing resource balance constraint, task execution constraint, waiting time constraint and resource quantity constraint of the discrete time RTN model.

According to a further technical scheme, when the discrete time RTN model is reconstructed, a simplified power characteristic model is formed according to the operation characteristics of key electric equipment.

In a further technical scheme, the interaction relation matrix of the tasks and the resources comprises:

dividing all tasks into a continuous casting task and a non-continuous casting task, and establishing an interaction relation matrix of each task and corresponding material resources, power resources and equipment resources;

based on the connection relation between the production task of each heat in certain equipment in the non-continuous casting tasks and the subsequent transportation task, combining the production task and the subsequent transportation task into a new production task, and further constructing an interaction relation matrix of the new production task, materials, electric power and equipment resources;

combining the continuous casting task and the casting replacement task of each heat in the same casting time into a new task, and creating an interactive relation matrix of the new task, continuous casting machine equipment and power resources.

In a second aspect, one or more embodiments provide a short-run steel enterprise load optimization regulation system that accounts for process limitations, comprising:

a data acquisition module configured to: acquiring enterprise equipment parameters, the number of heats, the number of casting times, the duration of a heat processing and transporting task and the heat composition in the casting times;

a model building module configured to: reconstructing a discrete time RTN model based on the acquired data, constructing production constraint conditions of short-flow steel enterprises, and setting a target function for minimizing the electricity purchasing cost;

a solving module configured to: and solving the load optimization discrete time RTN model of the short-flow iron and steel enterprise to obtain the execution time of various production tasks, converting the execution time into a start-stop instruction of specific equipment according to the type and the number of the equipment to which the tasks belong, and then sending the start-stop instruction to a bottom workshop to control the production operation of key electric equipment.

The further technical scheme also comprises the following steps: an interaction relation matrix generation module: establishing an interactive relation matrix of each task and corresponding material resources, power resources and equipment resources according to data acquired by the data acquisition module;

and an interaction relation matrix merging module: combining an interactive relation matrix of a production task and a subsequent transportation task in the non-continuous casting task, and combining an interactive relation matrix of a casting task and a casting change task of each heat in the same casting time in the continuous casting task;

and reconstructing a discrete time RTN model according to the combined interaction relation matrix.

An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions, when executed by the processor, performing the steps of the above method.

A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the above method.

Through the technical scheme, the invention has the beneficial effects that:

(1) based on the production characteristics of short-flow iron and steel enterprises, the discrete time RTN model is improved, a heat processing task and a transportation task which are closely connected are combined into a new production task, the heat continuous casting task and a casting change task are uniformly processed in a casting mode, and simultaneously, the constraint conditions such as resource balance constraint, waiting time constraint and the like related to the tasks in the model are corrected and perfected, so that the uninterrupted operation of front and back connection processes can be really realized theoretically, and the influence of time scale selection on the task execution continuity is eliminated, thereby greatly improving the engineering practical value of the model.

(2) The invention ensures that the production process restriction of the iron and steel enterprise is strictly met, simultaneously accurately combines the electric energy consumption and the production task, and establishes the load optimization regulation and control strategy by taking the minimum electricity purchasing cost of the enterprise as the target, so that the economic benefit of the enterprise is really improved on the premise of having practical operability.

(3) The method further highlights the advantage of high solving speed of the discrete time RTN model, simplifies the resource and task interaction relation existing in the production flow through task combination, greatly reduces the number of 0-1 integer variables and constraint conditions in the model, and remarkably improves the making speed of the load optimization regulation strategy.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.

FIG. 1 is a flow chart of a method according to an embodiment of the present disclosure;

FIG. 2 is a graph of a power waveform of a key consumer according to an embodiment of the present disclosure;

FIG. 3 is a production flow diagram of a short-run steel enterprise according to an embodiment of the present disclosure;

FIG. 4 is a schematic block diagram of an RTN according to an embodiment of the present disclosure;

FIG. 5 is a uniform time grid according to an embodiment of the present disclosure;

FIG. 6 is a graphical representation of resource balancing constraints according to an embodiment of the present disclosure;

fig. 7 is an optimized scheduling result with the objective of minimizing the electricity purchasing cost according to the embodiment of the disclosure.

The specific implementation mode is as follows:

the present disclosure is further described with reference to the following drawings and examples.

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise. It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The embodiments will be described in detail below with reference to the accompanying drawings.

The noun explains:

(1) heating: molten steel smelted in the same electric furnace or molten steel in a steel ladle;

(2) pouring for times: and on the same continuous casting machine, the same tundish and the same continuous casting heat set of the crystallizer are used for continuous casting.

The technical scheme accurately combines the electric energy consumption and the production task together on the premise of ensuring that the process limitation of the iron and steel enterprise is strictly met, so that the load optimization regulation and control strategy which is set by taking the minimum electricity purchasing cost of the enterprise as a target has actual operability and improves the economic benefit of the enterprise; the method comprises the following steps: determining key electric equipment and types of production tasks completed on the key electric equipment according to the production process of a short-process iron and steel enterprise, and defining the times of the inlet and outlet positions of the equipment as material resources consumed and generated by corresponding tasks respectively; acquiring enterprise equipment parameters, the number of the heat and the casting times, the duration of heat processing and transportation and the composition of the heat in the casting times; improving a discrete time RTN model, and constructing production constraint conditions of a short-flow iron and steel enterprise; and setting an objective function for minimizing the electricity purchasing cost, and obtaining an optimized regulation and control strategy of the enterprise production load by solving the model.

In the technical solutions disclosed in one or more embodiments, as shown in fig. 1, a short-flow steel enterprise load optimization regulation and control method considering process limitation includes the following steps:

step 1, determining key electric equipment and types of production tasks completed on the key electric equipment according to a short-flow steel enterprise production flow, and defining the heat of an equipment inlet and an equipment outlet as material resources consumed and generated by corresponding tasks respectively;

step 2, acquiring enterprise equipment parameters, the number of heats, the number of casting times, the duration of a heat processing and transporting task and the heat composition in the casting times;

step 3, improving the existing discrete time RTN model, and constructing production constraint conditions of short-flow iron and steel enterprises;

and 4, setting an objective function for minimizing the electricity purchasing cost, and obtaining an optimized regulation and control strategy of the enterprise production load by solving the model, namely the specific start-stop time of different individuals in various key electric equipment.

The above steps are specifically described below.

The key electric equipment and the types of the production tasks completed on the key electric equipment in the step 1 are as follows:

the method comprises the steps of performing primary smelting operation of a heat on an electric arc furnace, performing decarburization operation of the heat on an argon oxygen decarburization furnace, performing refining operation of the heat on a ladle refining furnace, and performing casting replacement operation and continuous casting operation of the heat on a continuous casting machine.

Specifically, in step 1, the types of the key electric devices and the production tasks completed thereon are specifically: the primary refining operation of the heat is performed on an Electric Arc Furnace (EAF), the Decarburization operation of the heat is performed on an Argon Oxygen Decarburization (AOD), the refining operation of the heat is performed on a Ladle refining Furnace (LF), the casting replacement operation and the Continuous casting operation of the heat are performed on a Continuous casting machine (Continuous casting, CC). The material resource is the heat of the inlet and outlet positions of each device.

The production flow of a short-flow steel enterprise is shown in fig. 2, and the specific contents are as follows: EAF utilizes the high temperature energy of the electrode arc to melt metal waste materials such as scrap steel and the like into molten steel, the molten steel enters AOD after rough adjustment of components to generate oxidation reaction to reduce the content of carbon elements, then the operations of desulfurization, deoxidation and inclusion removal are completed in LF by means of argon blowing stirring, arc heating and the like, the refined molten steel is conveyed to the position above CC from a ladle and is continuously cast into a steel billet by depending on the steady flow effect of a tundish and the cooling effect of a crystallizer, enough time must be left between adjacent casting times to replace the tundish and the crystallizer, the former is used for preventing the occurrence of ladle-through accidents caused by long-time high-temperature erosion, the latter is used for meeting the requirement of an order contract on the size of a casting billet, and finally, a steel Rolling Mill (RM) with matched types is selected according to the requirement of products to roll the casting billet into steel meeting the.

Analyzing the above processes, it can be known that the electric equipment of short-process iron and steel enterprises mainly comprises EAF, AOD, LF, CC and RM, and considering that the working principles of various RMs in the iron and steel enterprises are obviously different and the electric laws of the RMs are difficult to express uniformly, and the operating process of the equipment has high requirements on temperature, speed and pressure, and the load is not suitable to be regulated and controlled, so that the rolling process can be regarded as an unscheduled task, only EAF, AOD, LF and CC are selected as key electric equipment, the finished production tasks are respectively summarized as a primary smelting task, a decarburization task, a refining task and a continuous casting task, correspondingly, the furnace number to be processed at the inlet position of the equipment and the furnace number processed at the outlet position are equal to the consumed and generated material resources of the corresponding tasks, and the raw materials such as scrap steel and the like can be supplied more sufficiently through reliable channels before production, and the scheduling arrangement of short-flow iron and steel enterprises is not influenced by the addition of material resources.

In the specific implementation example, in step 2, the information of the number of heats, the number of times of casting, the duration of the heat processing and transportation, and the heat composition in the times of casting is obtained by converting the factors of the number of products, the variety, the specification, and the like included in the daily production plan formulated by the enterprise according to the order of the downstream manufacturer through the design of the number of heats and the times of casting, and the process is usually completed by a Manufacturing Execution System (MES), so that the production data can be collected from the MES.

In a specific implementation example, step 3, the existing discrete time RTN model is improved, and the production constraint conditions of the short-flow steel enterprise are constructed, specifically implemented as follows:

step 3.1, forming a reasonable and simplified power characteristic model according to the operation characteristics of key electric equipment;

step 3.2, dividing all tasks into a continuous casting task and a non-continuous casting task, and establishing an interaction relation matrix of each task and corresponding material resources, power resources and equipment resources;

3.3, based on the connection relation between the production task of each heat in certain equipment in the non-continuous casting type task and the subsequent transportation task, combining the production task and the subsequent transportation task into a new production task, and further constructing an interaction relation matrix of the new production task, materials, electric power and equipment resources;

step 3.4, combining the continuous casting task and the casting change task of each heat in the same casting time into a new task, and creating an interactive relation matrix of the new task, continuous casting machine equipment and power resources;

and 3.5, combining the enterprise production information collected in the step 2, giving the numerical values of all elements in the interaction relation matrix, and reconstructing the resource balance constraint, the task execution constraint, the waiting time constraint and the resource quantity constraint of the discrete time RTN model.

In step 3.1, the power waveform curve of the key electric device is shown in fig. 3, and the power characteristic thereof can be modeled as:

wherein,

starting time for equipment i;

the time length required for the device i to reach the stable rated power moment from the starting moment;

the time length from the time when an operator orders the shutdown equipment i to the time when the power of the equipment i is 0;

the moment when the device i is completely powered off;

is the rated power of the device i; delta_i(t) is

To represent random power fluctuations of the device at steady state operation.

According to the operation characteristics of electric furnace equipment, the start-stop process of EAF and LF is usually completed within 5-10 s, the start-stop time of the equipment driven by motors is calculated according to seconds by adopting control systems such as AOD and CC, and the actual power of the two kinds of equipment in steady-state operation fluctuates randomly with the amplitude lower than 5% near the rated power, so that the power characteristic model of the key electric equipment can be simplified as follows:

equation (2) shows that the power resource consumed by the production task during execution is the rated power of the corresponding equipment, and compared with equation (1), the complexity of the model can be greatly reduced while the influence on the scheduling result is small.

In step 3.2, the non-continuous casting tasks comprise an initial smelting task, a decarburization task, a refining task and a transportation task between adjacent production ring sections, the continuous casting tasks comprise a continuous casting task and a casting change task, the continuous casting task comprises a batch processing of molten steel by taking a heat as a unit, and the continuous casting task comprises a casting time formed by a plurality of heats with similar steel types to realize continuous casting of a casting blank, the continuous casting process cannot be interrupted in order to ensure the product quality, but a tundish and a crystallizer of a continuous casting machine are required to be changed after the continuous casting process is completed. Therefore, the steel mill scheduling can be regarded as a set of large-scale batch tasks as a whole, and the interaction relation of each task with equipment, materials and power resources is shown in fig. 4, which is also an effective method for solving the problem of batch scheduling of the steel mill-a graphical representation of an RTN model. The models of the same type of equipment in the figure are assumed to be the same, and the meanings of the variables are as follows:

PM_his the primary smelting task of the heat h; decarb_hA decarburization task for heat h; TransferPD_hThe transportation task from primary refining to decarburization for the heat h; LR_hIs a refining task of the heat h; TransferDL_hThe transportation task from decarburization to refining for the heat h; cast _ G_g_CC_nIn a continuous casting machine CC for casting times_nThe continuous casting task of the above; cast_h_CC_nIn a continuous casting machine CC for heat h_nThe continuous casting task carried out above; TransferLC_hThe transportation task from refining to continuous casting for the heat h; ELEs are power resources;

the furnace time h after the primary smelting is finished;

the heat h for waiting decarburization;

the furnace time h after the decarburization is finished;

the number of times of refining is waiting for h;

the furnace number h after the refining is finished;

the furnace number h is the furnace number h of waiting for continuous casting; h_hA casting blank corresponding to the heat h; maintain is a pouring change task.

The continuous casting task duration is directly related to the withdrawal speed, the withdrawal speed of the continuous casting machine is influenced by factors such as the thickness of a billet shell at the outlet of a crystallizer, the secondary cooling strength and the like, and even if the equipment models are consistent, the same processing time of the same casting time is difficult to ensure, so that the continuous casting task needs to establish a one-to-one correspondence relationship with specific equipment. However, for a certain heat to be processed, the duration of the tasks such as primary smelting, decarburization and refining is basically unchanged under the condition that the input power of the equipment is the same, and only when the models of the equipment are different, the actual execution object of the tasks needs to be considered, namely, separate production tasks are set for different individuals of the same type of equipment. As for the heat transportation tasks between adjacent production ring sections, as the same type of process equipment is generally uniformly arranged in the same area of a factory, the obvious difference of the transportation distance cannot be caused by the operation of any equipment, and the transportation time length does not have an association relation with the equipment at the source and the destination, the transportation tasks are also distinguished only according to the heat number.

As can be seen from the observation of FIG. 4, the resource and task interaction relationships represented by the arrows are generally divided into two types: the former refers to that the equipment (materials) is only occupied (consumed) and released (generated) at the start and end time of the corresponding task, and the latter refers to that the task needs to continuously consume power resources in the execution process to maintain the normal operation of the equipment. For the discrete time RTN model, the time axis is represented as shown in FIG. 5, where δ is the length of the time interval and d is the length of the time interval_kIs the duration of task k, θ_kAnd establishing a relative time index for the execution period of the task k by taking the starting moment as a reference point, wherein the corresponding interaction relation matrix of the task and the resource is as follows:

in the formula: mu.s_s,kAn interaction relation matrix of the resource s and the task k is obtained; tau is_kThe number of time periods occupied by task k duration,

the operator is rounding up; a is₁₁、

a_n1The value of resource s is consumed for task k at a certain relative point in time. Primary smelting task PM with heat h_hFor example, when the processing time is 80min, the time interval is 15min, and the rated power of the EAF is 85MW, the interaction relationship matrix of the task and the resource is:

wherein,

for task PM_hAt relative time points

The electrical power consumed.

Step 3.3, in order to speed up the production progress and prevent the influence of the overlong molten steel cooling time on the product quality, the heat transportation task is usually executed immediately after the processing task is finished, and the requirement is reflected in the original RTN model

The value is always fixed to 0, but for a uniform gridding time axis, the rounding error caused by the rounding-up operation may cause the starting time of the transportation task to be later than the actual ending time of the production task, so that seamless connection of the two tasks cannot be really realized. Aiming at the problem, the invention provides an improved scheme for keeping each heat at a certain levelThe production task of the equipment and the transportation task connected with the equipment are combined into a new production task, the new production task is still named by the name of the original production task, and the combination method of the corresponding interaction relation matrix comprises the following steps:

in the formula: subscript k₁+k₂For task k₁And the subsequent task k₂New tasks generated after combination;

and combining operators for the customized interaction relation matrix. Due to the fact that

The above combination can significantly reduce rounding errors.

In the step 3.4, also for the purpose of reducing rounding errors, the continuous casting tasks and the casting change tasks of each heat in the same casting time can be combined into a new task named as the casting continuous casting tasks, but different from the non-continuous casting tasks, because material resources related to the continuous casting tasks of adjacent heats are not repeated, the purpose of reducing the total occupied time period of the casting continuous casting tasks cannot be achieved by simply combining the interaction relation matrix. Based on the above, the invention utilizes the formula (5) to establish an interactive relation matrix of the new generation task and continuous casting machine equipment and power resources, and the material resources related to the continuous casting task need to be separately and independently considered.

In the step 3.5, the numerical values of the elements in the interaction relation matrix are given by combining the enterprise production information collected in the step 2, so that the resource balance constraint, the task execution constraint, the waiting time constraint and the resource quantity constraint of the discrete time RTN model are reconstructed, specifically:

(1) resource balancing constraints

The resource balance equation manages the interaction process between each type of resource and the corresponding task on the time grid, and is shown in formulas (6) to (8):

wherein R is_s(t) is the available quantity of equipment or material resources s in time period t; k is a set of tasks after merging processing; s is a device or material resource set which is associated with the task set K in the interaction relation matrix; n is a radical of_k(t) is a binary variable for judging whether the task k is executed in the time period t; t is the total scheduling time period number; p_E(t) the power consumption consumed by the short-process iron and steel enterprise in the time period t; mu.s_e,k(θ_k) For task k at relative time θ_kConsumed electricity power; m is the number of continuous casting machines; h is₁Numbering the number of the furnace number cast firstly in the casting number g;

the operator is a round-down operator; h is the total number of times of the furnace; g is the total pouring times; and N is a natural number set.

The graphical depiction of equation (6) is shown in FIG. 6 when task k is at time t- θ_kStart up and mu_s,k(θ_k) When the time is not equal to 0, the available quantity of the material or equipment resources s can be changed in the time period t, and the electric power of an enterprise cannot be transferred in the time period, namely R in the formula (6)_sThe power resource balance constraint of the formula (7) can be obtained when the (t-1) is fixed to be 0, but for the material resource which has an interactive relation with the continuous casting task, the material resource is ensured as long as the material resource has an interactive relation with the continuous casting task

The available quantity of the continuous casting furnace is in a state of being not 0 in the casting starting time period of the furnace number h, and the continuous casting tasks of each furnace number in the same casting time can realize real continuous casting tasksSeamless connection, this requirement can be expressed in the form of equation (8) in mathematical language.

(2) Task execution constraints

According to the production process of the iron and steel enterprises, each procedure of the heat processing is only allowed to be executed once, and the expression is as follows:

in the formula: k' is a task with the duration being independent of the device, namely the different devices execute the task at the same time; k' is a task with duration related to the device, namely different time for executing the task by different devices; u shape_kA set of devices u that perform task k.

(3) Latency constraints

As mentioned above, the intermediate products obtained from each production task are immediately transported to the next area by the transportation equipment, but before the subsequent tasks are started, the intermediate products are allowed to stay outside the equipment for a short time to wait for the completion of the previous furnace processing, and considering that the quality of the casting blank and the steel product can be affected by the too long cooling time of the molten steel, the compensation through the expensive reheating process can cause the sudden increase of the production cost of the enterprise, so the waiting time also has the constraint condition limitation, as shown in formula (10). Wherein, after the model is improved

Once the molten steel is produced and can not be consumed any more, the additional arrangement of the casting starting time period of the heat h and the time period of the heat h reaching the continuous casting machine is needed to restrict the transportation task of the transferLC_hThe duration of time of (c).

Wherein D is_kThe maximum allowable time for the transportation task k;

the casting starting time period of the heat h;

is composed of

Yuda continuous casting machine CC_nA period of time of; h is₁The number of the first cast heat in casting g is given.

(4) Resource quantity constraints

The quantity of equipment resources and material resources is limited between 0 and the maximum available quantity, the power consumption of an enterprise in each time period cannot exceed the sum of the rated power of key equipment, and in addition, all the processing tasks of all the heats must be completed at the end time of a scheduling cycle, namely, the equipment continuous casting machine required by the last procedure is ensured to be in an unoccupied idle state in the time period T. The above constraints may be embodied as:

in the formula: EQ is a key electric equipment set; p_totalThe sum of the rated power of the key electric equipment;

the initial number of resources s, m, respectively.

In step 4, an objective function for minimizing the electricity purchasing cost is set, and an optimized regulation and control strategy of the enterprise production load, namely the specific start-stop time of different individuals in various key electric equipment, is obtained by solving the model. As a typical process industry, short-process steel enterprises have strict process limitations in their production process, the start time and running time of production equipment are determined by an operation plan, and once the operation cannot be stopped at will, the enterprises are not very motivated to respond to incentive demands affecting normal production, such as direct load control, and the like, and the participation enthusiasm is not high, and the load regulation and control objective is generally to adjust production schedule under a given price signal to reduce electricity consumption cost, that is:

in the formula, λ^tElectricity prices for time period t;

the self-generating power of short-process iron and steel enterprises in time period t comprises self-contained thermal power generating unit generating power and waste heat generating power generated by heating a boiler by using flue gas discharged by electric furnace steelmaking, and the expression is as follows:

in the formula,

the embodiment does not consider the generated power of the self-contained thermal power generating unit in the time period t;

generating power for the waste heat of short-process iron and steel enterprises in the time period t;

the power consumption of the EAF in the time period t; k is a radical of₁The proportion of the electric energy consumption in the total input energy during electric furnace smelting is determined as 60 percent; k is a radical of₂The proportion of high-temperature heat carried by the exhausted furnace gas to the total input energy is 20 percent; eta is the energy conversion efficiency of the waste heat power generation technology, and the model is set to be 65%.

And solving a load optimization regulation and control model to obtain execution time of various production tasks, and converting the execution time into specific start-stop time of different individuals in the key power utilization equipment according to the type and the number of the equipment to which the tasks belong, namely the formulated load optimization regulation and control strategy.

The short-process steel enterprise load optimization regulation and control method considering the process limitation is verified by adopting a specific calculation example.

Taking a daily production schedule of a certain short-flow steel enterprise as an example, information such as the number of furnaces, the number of casting times, the duration of a furnace processing and transportation task, the furnace composition in the casting times and the like is obtained through furnace and casting time design and is shown in tables 1 and 2, the rated Power of key electric equipment is shown in table 3, the division of industrial and commercial peak-valley level time periods and the purchase price of a large industrial user are shown in table 4, and a cpll solver is called by using a yalmap tool box of a MATLAB platform to respectively perform the following operations on documents of "Zhang X, Hug G, Kolter Z, et al. 1-6' the original discrete time RTN model and the improved discrete time RTN model are solved, and the optimization result with the minimum electricity cost as the objective function is shown in the table 5.

TABLE 1 Heat and casting times corresponding relationship

TABLE 2 duration (min) of Heat treatment transportation task

Note: continuous casting machine CC₁The time of replacement and pouring is 70min, CC₂The time of pouring is 50 min.

TABLE 3 iron and steel enterprises' equipment parameters

TABLE 4 division of peak-to-valley periods for industrial and commercial businesses and electricity purchase prices for large industrial users

TABLE 5 comparison of the optimization results with the aim of minimizing the cost of electricity (time interval selected as 15min)

As can be seen from Table 5, the load optimization regulation and control strategy obtained by improving the discrete time RTN obviously reduces the electricity purchasing cost of short-flow steel enterprises compared with the original discrete time RTN, taking the comparison of the 4-heat optimization results given in fig. 7 as an example, the production arrangement obtained by the method of the present disclosure not only enhances the tightness of the connection between the non-continuous casting type processing task and the transportation task, but also realizes the real continuous casting process of the adjacent heat in the same casting time due to the elimination of the rounding error, thereby shortening the total time length of actual occupation of the theoretically front and back connected working procedures while ensuring that the working procedure limitation is strictly met, greatly widening the movable range of the production task, therefore, the situation that an enterprise has to arrange part of loads to operate in a high electricity price period due to production time sequence limitation is avoided to the maximum extent, and the formulated load optimization regulation and control strategy can practically improve the economic benefit of the enterprise on the premise of having practical operability. In addition, compared with the original method, the improved discrete time RTN method provided by the disclosure reduces the number of decision variables and constraint conditions, and the solving speed of the model is obviously improved.

Based on the same inventive concept, the embodiment provides a short-process steel enterprise load optimization regulation and control system considering process limitation, which comprises:

a data acquisition module: acquiring enterprise equipment parameters, the number of heats, the number of casting times, the duration of a heat processing and transporting task and the heat composition in the casting times;

an interaction relation matrix generation module: establishing an interactive relation matrix of each task and corresponding material resources, power resources and equipment resources according to data stored in the data storage module;

a model building module: according to the combined interactive relation matrix, a discrete time RTN model is improved, resource balance constraint, task execution constraint, waiting time constraint and resource quantity constraint are constructed, and an objective function is determined by taking the minimum electricity purchasing cost of an enterprise as a target;

a solving module: and solving the short-flow iron and steel enterprise load optimization regulation and control model to obtain execution time of various production tasks, converting the execution time into start-stop instructions of specific equipment according to the types and the number of the equipment to which the tasks belong, and then sending the start-stop instructions to a bottom workshop to control the production operation of key electric equipment.

Based on the same inventive concept, the present embodiments provide an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the method of the embodiments.

Based on the same inventive concept, the present embodiments provide a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the method of the embodiments.

The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims

1. The short-process iron and steel enterprise load optimization regulation and control method considering the process limitation is characterized by comprising the following steps of:

reconstructing a discrete time RTN model based on the acquired data, which comprises the following specific steps:

combining enterprise production information, providing numerical values of all elements in an interaction relation matrix of tasks and resources, and reconstructing resource balance constraint, task execution constraint, waiting time constraint and resource quantity constraint of a discrete time RTN model;

the interaction relation matrix of the tasks and the resources comprises:

combining the continuous casting task and the casting replacement task of each heat in the same casting time into a new task, and creating an interactive relation matrix of the new task, continuous casting machine equipment and power resources;

2. The method as claimed in claim 1, wherein before obtaining the enterprise equipment parameters, the number of heats, the number of casting times, the duration of the heat processing and transporting tasks and the heat composition in the casting times, the method comprises determining key electric equipment and the types of the production tasks completed on the key electric equipment according to the short-flow steel enterprise production flow, and defining the heat of the inlet and outlet positions of the equipment as the material resources consumed and generated by the corresponding tasks.

3. The method for optimally regulating and controlling the load of the short-flow steel enterprise considering the process limitation as claimed in claim 2, wherein the key electric equipment and the types of the production tasks completed on the key electric equipment are as follows: the method comprises the steps of carrying out primary smelting operation of a heat on an electric arc furnace, carrying out decarburization operation of the heat on an argon oxygen decarburization furnace, carrying out refining operation of the heat on a ladle refining furnace, carrying out casting replacement operation and continuous casting operation of the heat on a continuous casting machine, wherein the material resource is the heat of each equipment inlet and outlet position.

4. The method as claimed in claim 1, wherein the method comprises forming a simplified power characteristic model according to the operation characteristics of the key electric devices when reconstructing the discrete time RTN model.

5. Consider load optimization regulation and control system of short flow steel enterprise of process restriction, characterized by includes:

an interaction relation matrix generation module: establishing an interactive relation matrix of each task and corresponding material resources, power resources and equipment resources according to data acquired by the data acquisition module;

reconstructing a discrete time RTN model according to the combined interaction relation matrix;

6. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executable on the processor, the computer instructions when executed by the processor performing the steps of the method of any of claims 1 to 4.

7. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of any one of claims 1 to 4.