CN112884242A - Short-process steel enterprise demand response potential analysis method and system - Google Patents

Short-process steel enterprise demand response potential analysis method and system Download PDF

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CN112884242A
CN112884242A CN202110272732.6A CN202110272732A CN112884242A CN 112884242 A CN112884242 A CN 112884242A CN 202110272732 A CN202110272732 A CN 202110272732A CN 112884242 A CN112884242 A CN 112884242A
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CN112884242B (en
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王沈征
张虓
张海静
鞠文杰
王为帅
王一
程思瑾
李心一
王海博
任艺婧
张利
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State Grid Corp of China SGCC
TaiAn Power Supply Co of State Grid Shandong Electric Power Co Ltd
Marketing Service Center of State Grid Shandong Electric Power Co Ltd
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Shandong University
TaiAn Power Supply Co of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention provides a method and a system for analyzing demand response potential of a short-process steel enterprise, which comprises the following steps: selecting typical adjustable and controllable equipment in the steel production process and establishing an electric property model of the equipment; giving out steel production constraint conditions by using a discrete time RTN model; adding the prolonging effect of the refining time on the heat allowable cooling time into the waiting time constraint and correcting the rest constraints; establishing a short-flow steel production scheduling model considering minimum electricity purchasing cost and maximum electricity utilization comfort level and converting the short-flow steel production scheduling model into a single-target model; and acquiring production information and electricity price information, inputting the model to solve to obtain the power consumption of the enterprise in each period, and comparing the power consumption with the power consumption before response to analyze the enterprise demand response potential. The method takes the relaxation effect of prolonging the refining time on the furnace waiting time limit into consideration so as to further release the translatable potential of the steel production load, and considers the influence of economy and power utilization comfort degree in the process of analyzing the response potential of an enterprise so as to enable the result to be more accurate and credible.

Description

Short-process steel enterprise demand response potential analysis method and system
Technical Field
The invention belongs to the technical field of power demand response analysis, and particularly relates to a method and a system for analyzing demand response potential of a short-process steel enterprise.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, with the continuous improvement of social and economic levels and the rapid increase of the scale of a renewable energy grid-connected installation, the capacity of a power grid for maintaining supply and demand balance is seriously challenged due to the fact that the difference between the peak and valley of the power load is enlarged and the fluctuation and the reverse peak regulation characteristics of wind power and photovoltaic are aggravated. The construction and development speed of the thermal power generating unit is hindered due to the requirements of increasing environmental protection pressure and reducing cost and improving efficiency of the power supply side, and the source side adjustability is difficult to meet the increasing power adjustment requirement. Based on the method, the demand response potential of the load side resource is fully excavated, the power utilization behavior is guided to be actively adjusted by using price signals and the like so as to meet the peak load shifting demand of the power grid, and the method becomes an important means for promoting the consumption of renewable energy and ensuring the safe and stable operation of the system.
Compared with other types of loads, the steel enterprise serving as a typical high-energy-carrying industrial user has the advantages of large adjustable capacity, high automation level, perfect information network and the like, and particularly for a short-flow steel enterprise with a high-power-consumption production load-electric arc furnace, the load fluctuation can be effectively stabilized by changing the service time of production equipment on the premise of ensuring that the order output is completed on time, so that the effects of peak clipping and valley filling are achieved. To the knowledge of the inventor, although the existing research literature reveals the demand response potential of short-flow steel enterprises to some extent, for such large industrial users with multiple production stages and multiple processing devices, the response willingness is not reflected in the production scheduling target of the enterprise, and the contribution of the enterprise participating in demand response to power grid load shifting may not be of practical significance only by using the controllable characteristic of the load of the electric arc furnace as a centralized representation of the demand response capability without considering the complete production flow.
For this problem, "Pan R, Li Z, Cao J, et al, electric local tracking scheduling of steel plants under time-of-use standards [ J ]. Computers & Industrial Engineering,2019,137: 106049", "Zhang X, Hug G, Kolter Z, et al, Industrial demand response by steel plants with driving stress [ C ]// North American Power System (NAPS), Octope 4-6,2015, Charotte, USA: 1-6' respectively adopts methods of continuous time modeling and Resource Task Network (RTN) based on discrete time representation to explore the coupling relation between short-flow steelmaking production arrangement and electric energy consumption, and the minimum electricity purchasing cost is taken as the target of an enterprise to make a production operation plan so as to express the profit-making behavior characteristics of the enterprise, and the iron and steel enterprise shifts the electricity load from the peak time period of high electricity price to the valley time period of low electricity price for pursuing the maximization of self interest under the promotion of the electricity price signal. Compared with other methods, the RTN has the advantages that the method is universal, the production scheduling problem of a large-scale, multi-stage and multi-parallel steel enterprise can be uniformly expressed based on the abstract modeling of the production flow, and the mathematical model of the RTN has higher solving efficiency under the discrete time representation. However, in the existing research, the time limit of the furnace waiting for processing outside the equipment is regarded as insurmountable rigid constraint when the process is modeled, and the time limit of the furnace waiting for processing outside the equipment is not considered, and the time limit of the furnace waiting can be prolonged to a certain extent by compensating the temperature loss caused by cooling steel water through adjusting the duration of the refining stage, so that the translatable capability of the production load of the steel enterprise can not be fully exerted under the condition that the adjustable range of the waiting time is limited. In addition, the above documents do not take into account the influence of changes in production arrangement on the electricity utilization comfort level before responding when designing the production scheduling targets of short-flow iron and steel enterprises, and the analysis of the demand response potential of the iron and steel enterprises made by the above documents cannot truly and accurately reflect the willingness of users to participate in demand response.
Disclosure of Invention
The invention aims to solve the problems and provides a method and a system for analyzing the demand response potential of a short-process steel enterprise, which take the effect of prolonging the refining time of a furnace number to widen the adjustable range of the waiting time into account in a short-process steel production RTN model based on discrete time expression, and construct a multi-target production scheduling model which takes into account the minimization of the electricity purchasing cost and the maximization of the electricity utilization comfort level so as to fully excavate the capacity of the production load of the model to participate in the peak clipping and valley filling of a power grid while reflecting the real response willingness of an enterprise, thereby realizing more accurate and credible analysis on the demand response potential of the short-process steel enterprise.
According to some embodiments, the invention adopts the following technical scheme:
the invention provides a method for analyzing demand response potential of a short-process steel enterprise, which comprises the following steps:
analyzing the production process of a short-process iron and steel enterprise, selecting main production equipment with strong power utilization regularity as a typical adjustable object and establishing a power utilization characteristic model of the main production equipment;
defining the selected production equipment and input and output heat and consumed power thereof as resources, defining equipment processing and heat transportation as tasks, describing the interactive relation between the resources and the tasks in the steel production process by using a discrete time RTN model, and obtaining production constraint conditions of short-flow steel enterprises;
adding the prolonging effect of refining time increase on the heat allowable cooling time into the waiting time constraint of the discrete time RTN model, and correcting the other production constraint conditions;
the method comprises the steps of establishing a short-flow steel production scheduling model reflecting the real desire of enterprises to participate in demand response by comprehensively considering two goals of minimum electricity purchasing cost and maximum electricity utilization comfort, and converting the production scheduling model into a single-target optimization model from a multi-target optimization model by adopting a normalization processing and linear weighted summation method;
and acquiring production information and electricity price information of the enterprise, inputting the converted single-target optimization model for solving to obtain the electricity consumption power of the enterprise in each period under a given weight coefficient, and comparing the electricity consumption power with the electricity consumption load condition of the enterprise when the electricity consumption power does not participate in demand response to determine the demand response potential of the short-flow steel enterprise.
As an alternative embodiment, the selection of the main production equipment with strong electrical regularity as a typical controllable object sequentially comprises the following steps according to the processing sequence of the product: an Electric Arc Furnace (EAF) for primarily refining a heat, an Argon Oxygen Decarburization (AOD) for decarburizing a heat, a Ladle refining Furnace (LF) for refining a heat, and a Continuous casting machine (Continuous casting, CC) for continuously casting a heat; the power consumption characteristic model of the typical controllable device is as follows:
Figure BDA0002975173700000031
in the formula:
Figure BDA0002975173700000032
actual operating power for device i at time t;
Figure BDA0002975173700000033
is the starting time of the device i;
Figure BDA0002975173700000034
is the fixed working duration of the device i;
Figure BDA0002975173700000035
is the rated power of the device i; and T is the scheduling end time.
As an alternative implementation, the specific steps of describing the interaction relationship between resources and tasks in the steel production process by using the discrete time RTN model to obtain the production constraint conditions of the short-flow steel enterprise are as follows:
refining the interactive relation between resources and tasks in the short-flow steel production flow, and establishing a graphical structure of the RTN model according to the interactive relation;
under the discrete time expression, a mathematical expression of the RTN model is given according to the graphical structure of the short-process steel production RTN model, namely the production constraint conditions of the short-process steel enterprises.
As an alternative embodiment, the production constraint conditions of the short-flow iron and steel enterprise include resource balance constraint, task execution constraint, waiting time constraint, resource quantity constraint, heat transportation constraint and completion time constraint, and the expressions are respectively:
resource balance constraint:
in discrete time, the result of the steel production schedule is to decide when each task starts at the beginning of the time segment, so the available amount of a certain resource in the time segment t is equal to the sum of the following two parts: one part is its available amount at time period t-1 (the item is fixed to 0 for power resources that cannot be transferred across time periods), and the other part is the amount of that resource that all tasks generate or consume at the beginning of time period t, i.e., the time period t
Figure BDA0002975173700000036
In the formula:
Figure BDA0002975173700000041
the available number of devices or heats m in time period t,
Figure BDA0002975173700000042
is the initial number of devices or heats m; k is a task set; tau iskFor the duration d of task kkThe number of the corresponding time periods,
Figure BDA00029751737000000411
δ is the period length; thetakThe relative time points with the starting time as the reference time point in the execution period of the task k have the value range of {0,1, …, tauk}; m is a set consisting of equipment and heat;
Figure BDA0002975173700000043
a variable 0-1 for judging whether the task k is started at the beginning of the time period t;
Figure BDA0002975173700000044
the average power consumption of the iron and steel enterprises in the time period t is calculated; t is the total scheduling time period number;
Figure BDA0002975173700000045
for task k at relative time θkThe number of generating/releasing equipment or the number of heats m (negative values represent consumption/occupation), and the value range is { -1,0,1 };
Figure BDA0002975173700000046
for task k at relative time θkThe consumed electric power has a value which is the average electric power consumed by the task k in the period starting from the time point.
Figure BDA0002975173700000047
And
Figure BDA0002975173700000048
interaction relation matrix mu of co-owned task k and resourcekThe element of (1), which is defined by the formula:
θk＝0 1 … τk
Figure BDA0002975173700000049
in the formula: mu.skIs s × (τ)k+1) dimension matrix, s is the number of resource classes required for task k execution, { r1,r2,…,rsThe j is a set of various resources required for task k execution.
And (3) task execution constraint:
in the short-flow steel production process, each task is executed once in a scheduling period, namely
Figure BDA00029751737000000410
In the formula: k ', K' are respectively a set of tasks whose duration is independent of and related to the execution device; u shapekA set of devices u that can execute task k.
And (3) latency constraint:
discrete time means that the next heat is only generated and consumed by the task at the beginning of the period, so the constraint on the out-of-facility waiting time of the heat can be translated into a limit on the number of 1 periods available for the heat waiting for processing within the scheduling time range, i.e. the number of 1 periods
Figure BDA0002975173700000051
Figure BDA0002975173700000052
In the formula:
Figure BDA0002975173700000053
respectively waiting for the heat h of AOD, LF and CC processing; TransferPDh、TransferDLh、TransferLChRespectively carrying out a transportation task of the heat h between the EAF and the AOD, a transportation task of the heat h between the AOD and the LF and a transportation task of the heat h between the LF and the CC; dkIs transported for the heat hThe allowable cooling time between the two production phases to which task k is connected.
And (3) resource quantity constraint:
the available number of all resources in any time period is not negative, i.e. the number of resources in any time period is not negative
Figure BDA0002975173700000054
Furnace transportation restraint:
the heat is transported immediately after being processed by the production equipment, i.e.
Figure BDA0002975173700000055
In the formula:
Figure BDA0002975173700000056
respectively, the heat h processed by EAF, AOD and LF.
Completion time constraint:
each production facility, particularly the CC, needs to be in an unoccupied state for a time period t to ensure that all tasks are completed at the end of the scheduling cycle, i.e.
Figure BDA0002975173700000057
In the formula:
Figure BDA0002975173700000058
for continuous casting machine CCnThe number of available times in time period T; q is the initial number of casters.
As an alternative embodiment, the adding of the effect of increasing the refining time on the prolongation of the allowed cooling time of the heat in the waiting time constraint refers to setting the allowed cooling time of the heat between the two production stages of decarburization and refining in the waiting time constraint as an adjustable physical quantity related to the temperature drop speed of the heat when waiting outside LF, the temperature rise speed of the heat during refining of the heat and the prolongation of the refining time, and the specific expression is as follows:
Figure BDA0002975173700000061
in the formula:
Figure BDA0002975173700000062
the value of the allowable cooling time of the heat h between two production stages connected with the transportation task k when no temperature compensation measure is taken can be given according to the outbound temperature of the production equipment of the heat h at the previous stage, the inbound temperature lower limit of the production equipment at the next stage and the temperature reduction speed of transportation and waiting between the two production stages;
Figure BDA0002975173700000063
LR refining task for Heat hhThe average temperature rise speed of the heat h is carried out;
Figure BDA0002975173700000064
the average temperature drop speed of the furnace h when waiting outside LF is obtained;
Figure BDA0002975173700000065
for extended heat h refining time;
Figure BDA0002975173700000066
in order to adopt extended refining time to compensate for heat h in transportation task transferDL during temperature drophMaximum allowable cooling time between the two production stages of decarburization and refining connected;
Figure BDA0002975173700000067
the refining time is prolonged to compensate the difference of the lower limit of the temperature of the furnace h at the LF arrival time before and after the temperature drop.
As an alternative embodiment, the specific step of modifying the remaining production constraints includes:
LR at refining taskhAnd the transfer LC of the heat transportation task from refining to continuous castinghMoment of interaction ofArray erasure and
Figure BDA0002975173700000068
the row vector corresponding to the power resource is transferred LC in the transportation taskhRather than the refinery task LRhIn the interaction relation matrix of (1) recording LF in the refining task LRhThe end time of (1), namely the transport task TransferLChIs released at the start of the time, i.e.
Figure BDA0002975173700000069
Figure BDA00029751737000000610
Figure BDA00029751737000000611
Figure BDA00029751737000000612
Order refining task LRhTransferLC from start time to transport taskhThe time difference at the starting moment is greater than the original refining time of the heat h to indicate that the former is performed before the latter in time, i.e. the
Figure BDA0002975173700000071
Introduction of characterization refining task LRhPerforming a 0-1 variable of state at each time interval
Figure BDA0002975173700000072
LR refining tasks of variable durationhAnd transport task transferLChJoined together in time, i.e.
Figure BDA0002975173700000073
In the formula:
Figure BDA0002975173700000074
indicating that heat h is being refined during time period t,
Figure BDA0002975173700000075
indicating that heat h has not been refined for time period t,
Figure BDA0002975173700000076
will refine the task LRhThe average electric power consumed during each period during execution is divided into the following two cases: one is the rated power of LF and one is LRhThe ratio of its actual electricity usage over the period of time when the end time is within the period of time, to the length of the period of time, the latter of which is only likely to occur at the LR under considerationhRespectively on the premise of end period of execution period
Figure BDA0002975173700000077
And
Figure BDA0002975173700000078
represents LRhAverage power consumption of other periods and last periods during execution and adding the average power consumption into resource balance constraints corresponding to power resources so as to accurately calculate the average power consumed by each production task of the short-flow steel enterprise in each period, namely
Figure BDA0002975173700000079
In the formula:
Figure BDA00029751737000000710
rated power for LF; ε is an infinitesimal positive number that tends to be 0.
As an alternative embodiment, the short-process steel production scheduling model reflecting the real willingness of enterprises to participate in demand response is established by comprehensively considering two objectives of minimum electricity purchasing cost and maximum electricity utilization comfort, and the multi-objective optimization model is converted into a single-objective optimization model by adopting a method of normalization processing and linear weighted summation, and the specific steps are as follows:
construction of Electricity Purchase cost F1Minimum and electricity comfort F2The maximum two objective functions are expressed as follows:
Figure BDA0002975173700000081
in the formula: lambda [ alpha ]tThe electricity purchase price of the iron and steel enterprises in the time period t is shown;
Figure BDA0002975173700000082
the self-generating power of the iron and steel enterprises in the time period t is obtained;
Figure BDA0002975173700000083
the electric power utilization in the time period t before the steel enterprises participate in the demand response.
Establishing a short-flow iron and steel enterprise production scheduling model by combining the corrected production constraint conditions and the two objective functions;
converting multiple targets into a single target by adopting a method of normalization processing and linear weighted summation, wherein the expression is as follows:
Figure BDA0002975173700000084
in the formula:
Figure BDA0002975173700000085
the minimum value and the maximum value of the electricity purchasing cost are respectively obtained by optimizing a scheduling model single target of maximizing the electricity purchasing cost and minimizing the electricity purchasing cost; and alpha is a weight coefficient.
As an alternative embodiment, the acquiring enterprise production information specifically includes: the number of the furnaces, the number of the pouring times, the corresponding relation between the furnaces and the pouring times, the number of various production devices and rated power thereof, the duration time of various production tasks and transportation tasks (including the original refining time of the furnaces), the allowable cooling time of the furnaces transported by the transportation tasks when temperature compensation measures are not taken, the average temperature rise speed during refining of the furnaces, the average temperature drop speed of the furnaces when waiting outside LF, the difference value of the lower limit of the temperature of the furnaces when the LF enters the station before compensation temperature drop is taken for prolonging the refining time, the self-generating power of enterprises in each period of time and the power consumption power of the enterprises in each period of time before participation in demand response.
In a second aspect of the present invention, a system for analyzing demand response potential of a short-process steel enterprise is provided, which includes:
the power utilization characteristic model establishing module is configured to select main production equipment with strong power utilization regularity of short-process iron and steel enterprises as a typical adjustable object and establish a power utilization characteristic model of the main production equipment;
the production constraint condition generation module is configured to define the selected production equipment, the input and output heat and the consumed power of the production equipment as resources, define equipment processing and heat transportation as tasks, and describe the interaction relation between the resources and the tasks in the steel production process by using a discrete time RTN model to obtain the production constraint conditions of the short-flow steel enterprise;
a production constraint condition correction module configured to take into account an effect of increasing refining time on extension of heat allowable cooling time in the waiting time constraint of the discrete time RTN model, and correct the remaining production constraint conditions;
the production scheduling model establishing module is configured to comprehensively consider two targets of minimum electricity purchasing cost and maximum electricity utilization comfort level to establish a short-flow steel production scheduling model reflecting the real willingness of enterprises to participate in demand response, and the production scheduling model is converted into a single-target optimization model from a multi-target optimization model by adopting a normalization processing and linear weighted summation method;
and the demand response potential analysis module is configured to acquire the production information and the electricity price information of the enterprise, input the converted single-target optimization model for solving, obtain the electricity power of the enterprise in each period under a given weight coefficient, compare the electricity power with the electricity load condition of the enterprise when the electricity power does not participate in demand response, and determine the demand response potential of the short-flow steel enterprise.
An electronic device comprises a memory, a processor and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, implement the steps of the method for analyzing the demand response potential of the short-flow steel enterprise.
A computer readable storage medium for storing computer instructions, which when executed by a processor, perform the steps of the above-mentioned method for analyzing demand response potential of a short-flow steel enterprise.
Compared with the prior art, the invention has the beneficial effects that:
(1) based on the production characteristics of short-flow steel enterprises, the invention improves the discrete time RTN model established in the short-flow steel production process, sets the allowable cooling time of the heat in the waiting time constraint between the two production stages of decarburization and refining into adjustable physical quantities related to the temperature drop speed of the heat when waiting outside LF, the temperature rise speed of the heat refining and the increased refining time, corrects other production constraint conditions, and greatly widens the translatable range of the production load by taking into account the compensation effect of prolonging the refining time on the temperature drop of the heat in the waiting process, thereby realizing the further excavation of the participation demand response potential of the short-flow steel enterprises.
(2) The invention comprehensively considers two targets of the minimum electricity purchasing cost and the maximum electricity utilization comfort level to carry out the production scheduling of the iron and steel enterprises, and considers the influence of the production arrangement adjustment on the electricity utilization comfort level while expressing the profit-making behavior characteristics of the enterprises, thereby more truly and accurately reflecting the willingness of the enterprises to participate in demand response, and the demand response potential analysis of the short-flow iron and steel enterprises made by the method has more practical significance.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a method according to the present embodiment;
FIG. 2 is a production flow chart of a short-flow steel enterprise according to the present embodiment;
FIG. 3 is a diagram of the RTN model for short run steel production according to the present embodiment;
FIG. 4 is a discrete-time representation as described in the present embodiment;
FIG. 5 shows the electric power consumption of the short-flow steel enterprise in each period before participating in the demand response;
fig. 6 shows the power consumption of the short-flow steel enterprise in each period after participating in the demand response in the case where the waiting time range is adjustable or not;
fig. 7 shows the electric power used in each time period when the short-flow steel enterprise schedules production with the minimum electricity purchasing cost according to the embodiment.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. 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 invention 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 exemplary embodiments according to the invention. 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.
Interpretation of terms:
(1) heating: molten steel contained in the same ladle;
(2) pouring for times: the set of heats that are continuously cast on a continuous caster is called a pour.
In one or more embodiments, as shown in fig. 1, a method for analyzing demand response potential of a short-process steel enterprise includes the following steps:
step 1, analyzing the production flow of a short-flow iron and steel enterprise, selecting main production equipment with strong electricity utilization regularity as a typical adjustable object and establishing an electricity utilization characteristic model of the main production equipment;
step 2, defining the selected production equipment and input and output heat and consumed power thereof as resources, defining equipment processing and heat transportation as tasks, describing the interactive relation between the resources and the tasks in the steel production process by using a discrete time RTN model, and obtaining production constraint conditions of short-flow steel enterprises;
step 3, adding the prolonging effect of increasing refining time on the heat allowable cooling time in the waiting time constraint of the discrete time RTN model, and correcting the other production constraint conditions;
step 4, comprehensively considering two targets of minimum electricity purchasing cost and maximum electricity utilization comfort level to establish a short-flow steel production scheduling model reflecting the real willingness of enterprises to participate in demand response, and converting the production scheduling model into a single-target optimization model from a multi-target optimization model by adopting a normalization processing and linear weighted summation method;
and 5, acquiring production information and electricity price information of the enterprise, inputting the converted single-target optimization model for solving to obtain the electricity power of the enterprise in each period under a given weight coefficient, and comparing the electricity power with the electricity load condition of the enterprise when the electricity power does not participate in demand response to determine the demand response potential of the short-flow steel enterprise.
The method adopts RTN to model the coupling relation of the heat, the electric power, the equipment and various production and transportation tasks in the short-flow steel enterprise production process under the discrete time expression, and incorporates the widening effect of temperature drop in the process of compensating the heat outside the equipment for the adjustable range of the waiting time, so that the translatable potential of the production load is further released on the premise that the limitation of the waiting time is relaxed, and meanwhile, the influence of production arrangement adjustment on the electricity utilization comfort level of the short-flow steel enterprise is established for expressing the electricity utilization behavior characteristics of the enterprise seeking the maximum benefit while considering the influence of the production arrangement adjustment on the electricity utilization comfort level of the enterprise, and the two targets of minimum electricity purchasing cost and maximum electricity utilization comfort level are comprehensively considered to establish a production scheduling model of the short-flow steel enterprise to reflect the real willingness of the short-flow steel enterprise to participate in demand response Therefore, more accurate and credible analysis on the demand response potential of short-flow iron and steel enterprises is realized.
The above steps are specifically described below.
The production flow of the short-flow steel enterprise in the step 1 is shown in figure 2, and the specific contents are as follows: charging metal waste materials such as waste steel and iron into EAF, electrifying to melt the metal waste materials into molten steel, pouring the molten steel into a ladle, conveying the molten steel into AOD through a crown block, a crane and other transportation equipment to perform decarburization operation, then the molten steel is conveyed to LF to carry out refining operations such as deoxidation, desulfurization, inclusion removal and the like, the refined molten steel is conveyed to the upper part of CC and injected into a tundish, casting times consisting of a plurality of similar furnace times of steel types are continuously cast into casting blanks meeting the specification through operations of cooling of a crystallizer, drawing of a withdrawal and straightening machine, cutting of a flame cutting machine and the like, the continuous casting process cannot be interrupted, so that the quality of products is prevented from being reduced and equipment is prevented from being damaged due to 'cutoff' of molten steel, however, enough time must be left between adjacent casting passes to replace the tundish and the crystallizer (referred to as casting replacement operation for short) so as to ensure the production safety and meet the requirements of order contracts on product specifications, and finally different types of rolling mills are selected according to the order requirements to roll the casting blank into steel meeting the standards.
It can be known from the analysis of the above processes that the main production equipment of a short-process steel enterprise includes EAF, AOD, LF, CC and rolling mill, and considering that the requirements of the order contract received by the steel enterprise on the shape and specification of the steel are usually different, and the working principles of different types of rolling mills, such as section rolling mill, wire rolling mill, plate rolling mill and pipe rolling mill, are obviously different, it is difficult to uniformly express the electrical laws thereof, so that the rolling process is not brought into the discussed category, and only EAF, AOD, LF and CC are selected as typical adjustable objects, and the processing tasks to be completed on the adjustable objects are respectively primary smelting task, decarburization task, refining task and continuous casting task. Since the power-on arcing and furnace-stopping processes of the EAF and the LF are usually completed within seconds, the start-stop time of the AOD and the CC using the motor-driven devices is also calculated in seconds, which is negligible compared with the working time, and the devices cannot be randomly interrupted once started, and the actual operating power of the devices randomly fluctuates around the rated power with a small amplitude, the power consumption characteristics of the devices can be expressed as:
Figure BDA0002975173700000121
in the formula:
Figure BDA0002975173700000122
actual operating power for device i at time t;
Figure BDA0002975173700000123
is the starting time of the device i;
Figure BDA0002975173700000124
is the fixed working duration of the device i;
Figure BDA0002975173700000125
is the rated power of the device i; and T is the scheduling end time.
As known from the power characteristics of the devices, the controllable characteristics refer to their ability to translate within a certain time range.
In the step 2, describing the interactive relationship between resources and tasks in the steel production process by using a discrete time RTN model, and obtaining the production constraint conditions of the short-flow steel enterprise specifically comprises the following steps:
2.1, abstracting the interactive relationship between resources and tasks in the short-process steel production process, and establishing a graphical structure of the RTN model according to the interactive relationship;
and 2.2, under the discrete time expression, giving out a mathematical expression of the RTN model according to the graphical structure of the short-process steel production model, namely the production constraint condition of the short-process steel enterprise.
In step 2.1, the interaction relationship between the resource and the task is specifically as follows:
the equipment or the heat is only occupied/consumed and released/generated at the starting and ending moments of the task, and the power is continuously consumed during the task execution period;
because the available quantity of the transportation equipment such as a crane is generally not limited, and the power consumption of the transportation equipment is negligible compared with the processing equipment such as an EAF (earth-insulated switchgear), the resource interacting with the transportation task is only the number of times of transportation;
in order to prevent the influence of too long molten steel cooling time on the quality of steel, the heat is usually transported immediately after the processing is finished, but at the moment, production equipment at the later stage may be in a busy state, so that a certain time is allowed to be reserved for waiting for the equipment to finish the processing task;
the power resources consumed by the production task during execution are the rated power of the corresponding equipment, the duration of the primary smelting, decarburization and refining tasks is mainly controlled by the input power of the equipment, when the rated power of the similar equipment is the same, the interactive relationship with specific individuals is not required to be established, the continuous casting process is greatly influenced by uncontrollable factors, and the same time for processing the same casting time cannot be ensured even if the input power of two continuous casting machines is the same, so the interactive relationship between the continuous casting tasks and the specific continuous casting machine equipment is required to be established.
According to the interaction relationship between the resources and the tasks, assuming that the rated powers of the similar devices are the same, a graphical structure of the short-process steel production RTN model can be constructed as shown in FIG. 3. RTN is a modeling tool integrating visual graphical representation and strict mathematical expression, and can comprehensively describe complex physicochemical processes based on a system method, particularly processes which are large-scale, multistage, multi-parallel equipment and have strict production constraints. In RTN, equipment, materials, and power are collectively referred to as resources, and process transport operations that generate or consume a particular set of resourcesAre abstracted to tasks, so that the equipment, power and heat in the diagram represent resources
Figure BDA0002975173700000131
Indicating that the primary smelting, the decarburization, the refining, the continuous casting and the heat transfer between the production stages are representative of the primary smelting
Figure BDA0002975173700000132
Represents; the interaction relationship between the resource and the task can be expressed in two forms: continuous interaction, meaning that a task needs to consume or generate some sort of resource continuously during execution, and discrete interaction, represented in the figure as power and task to represent continuous interaction
Figure BDA0002975173700000133
The latter refers to the interaction behavior of the task and the resource only occurring at the beginning and the end of the task, and is represented as the discrete interaction of the equipment, the heat and the task in the figure
Figure BDA0002975173700000134
Connecting; the resource interacted with the transportation task is only the number of times of transportation, so that the transportation task, the number of times of processing of the previous production task and the number of times of processing of the next production task in the graph represent discrete interaction
Figure BDA0002975173700000135
Connecting; the heat is transported immediately after the processing is completed, so that the production tasks represented in the figure as processing heat are linked together with the subsequent transport tasks on the time axis; a certain waiting time is left between the end of the transportation task and the start of the next production task, so that a certain time difference can exist between the end time of the transportation task and the start time of the next production task on a time axis in the drawing; the duration of the primary refining, decarbonizing and refining tasks is independent of the specific individual under the same rated power of the same type of equipment, so that the tasks are shown as representing discrete interactions
Figure BDA0002975173700000136
The connected equipment resources are all represented in the form of an aggregate; the continuous casting tasks have to be in interactive relation with specific continuous casting machine equipment, so that a plurality of continuous casting tasks are arranged for the same casting time in the drawing and are respectively corresponding to the continuous casting machine equipment to represent discrete interactive continuous casting tasks
Figure BDA0002975173700000137
Are connected.
In step 2.2, discrete time representation as shown in fig. 4, the discrete time description method divides the whole scheduling time range evenly into a finite number of slots, where δ represents the slot length, dkDenotes the duration of the task k, τkIndicating the number of periods corresponding to the duration of task k
Figure BDA0002975173700000138
θkEach relative time point with the start time as a reference time point in the execution period of the task k is shown, and T represents the total scheduling time period number.
The production constraint conditions of the short-flow iron and steel enterprise comprise resource balance constraint, task execution constraint, waiting time constraint, resource quantity constraint, heat transportation constraint and completion time constraint, and the expressions are respectively as follows:
(1) resource balancing constraints
In discrete time, the result of the steel production schedule is to decide when each task starts at the beginning of the time segment, so the available amount of a certain resource in the time segment t is equal to the sum of the following two parts: one part is its available amount at time period t-1 (the item is fixed to 0 for power resources that cannot be transferred across time periods), and the other part is the amount of that resource that all tasks generate or consume at the beginning of time period t, i.e., the amount of that resource that cannot be transferred across time periods
Figure BDA0002975173700000141
In the formula:
Figure BDA0002975173700000142
the available number of devices or heats m in time period t,
Figure BDA0002975173700000143
is the initial number of devices or heats m; k is a task set; m is a set consisting of equipment and heat;
Figure BDA0002975173700000144
a variable 0-1 for judging whether the task k is started at the beginning of the time period t;
Figure BDA0002975173700000145
the average power consumption of the iron and steel enterprises in the time period t is calculated;
Figure BDA0002975173700000146
for task k at relative time θkThe number of generating/releasing equipment or the number of heats m (negative values represent consumption/occupation), and the value range is { -1,0,1 };
Figure BDA0002975173700000147
for task k at relative time θkThe consumed electric power has a value which is the average electric power consumed by the task k in the period starting from the time point.
In the above two formulas, the amount of a resource generated or consumed by each task at the beginning of the time period t is expressed as the amount of each task K in the task set K during the time period t- τkSum of the amount of the resource consumed or generated at the beginning of the period t at start-up to each beginning of the period t, wherein for characterizing the interaction
Figure BDA0002975173700000148
And
Figure BDA0002975173700000149
interaction relation matrix mu taken from task k and resourcekIt is defined by the formula:
θk＝0 1 … τk
Figure BDA00029751737000001410
in the formula: mu.skIs s × (τ)k+1) dimension matrix, s is the number of resource classes required for task k execution, { r1,r2,…,rsThe j is a set of various resources required for task k execution.
(2) Task execution constraints
In the short-flow steel production process, each task is executed once in a scheduling period, namely
Figure BDA0002975173700000151
In the formula: k ', K' are respectively a set of tasks whose duration is independent of and related to the execution device; u shapekA set of devices u that can execute task k.
(3) Latency constraints
The purpose of setting the waiting time constraint is to prevent the quality of the casting blank from being damaged due to the fact that the heat stays outside the device for too long time. Since discrete-time representation the next heat is only generated and consumed by the task at the beginning of the period, the constraint on the off-plant latency of the heat may translate into a limitation on the number of periods of 1 available for the heat waiting for processing within the scheduling time horizon, i.e. a limit on the number of periods of 1 available for the heat waiting for processing
Figure BDA0002975173700000152
In the formula:
Figure BDA0002975173700000153
respectively waiting for the heat h of AOD, LF and CC processing; TransferPDh、TransferDLh、TransferLChRespectively carrying out a transportation task of the heat h between the EAF and the AOD, a transportation task of the heat h between the AOD and the LF and a transportation task of the heat h between the LF and the CC; dkTwo production stages connected to the transport task k for the heat hThe allowable cooling time.
(4) Resource quantity constraints
The available number of all resources in any time period is not negative, i.e. the number of resources in any time period is not negative
Figure BDA0002975173700000154
(5) Heat transport restraint
The heat is transported immediately after being processed by the production equipment, i.e.
Figure BDA0002975173700000155
In the formula:
Figure BDA0002975173700000156
respectively, the heat h processed by EAF, AOD and LF.
(6) Time of completion constraint
Each production facility, particularly the CC, needs to be in an unoccupied state for a time period t to ensure that all tasks are completed at the end of the scheduling cycle, i.e.
Figure BDA0002975173700000161
In the formula:
Figure BDA0002975173700000162
for continuous casting machine CCnThe number of available times in time period T; q is the initial number of casters.
Step 3, adding the effect of increasing the refining time on prolonging the allowable cooling time of the heat in the waiting time constraint, that is, setting the allowable cooling time of the heat in the waiting time constraint between two production stages of decarburization and refining to be adjustable physical quantities related to the temperature drop speed of the heat when waiting outside the LF, the temperature rise speed of the heat in the heat refining and the prolonged refining time, wherein the expression is as follows:
Figure BDA0002975173700000163
in the formula:
Figure BDA0002975173700000164
the value of the allowable cooling time of the heat h between two production stages connected with the transportation task k when no temperature compensation measure is taken can be given according to the outbound temperature of the production equipment of the heat h at the previous stage, the inbound temperature lower limit of the production equipment at the next stage and the temperature reduction speed of transportation and waiting between the two production stages;
Figure BDA0002975173700000165
LR refining task for Heat hhThe average temperature rise speed of the heat h is carried out;
Figure BDA0002975173700000166
the average temperature drop speed of the furnace h when waiting outside LF is obtained;
Figure BDA0002975173700000167
for extended heat h refining time;
Figure BDA0002975173700000168
in order to adopt extended refining time to compensate for heat h in transportation task transferDL during temperature drophMaximum allowable cooling time between the two production stages of decarburization and refining connected;
Figure BDA0002975173700000169
the refining time is prolonged to compensate the difference of the lower limit of the temperature of the furnace h at the LF arrival time before and after the temperature drop.
In the short-flow steel production process, the heat waits outside the equipment to produce temperature loss, if the waiting time is too long, the heating time needs to be prolonged to compensate so as to ensure that the temperature of the molten steel meets the process requirement, only two stages of primary smelting and refining in four production stages of primary smelting, decarburization, refining and continuous casting can utilize electric energy to heat the heat, the allowable cooling time of the heat between decarburization completion and refining start can be further prolonged by increasing the duration of the refining stage, and the adjustable range of the waiting time of the heat outside the LF is widened.
Because the refining time of the heat is changed into an adjustable physical quantity, other production constraints related to the refining task need to be corrected except the waiting time constraint, and the method comprises the following specific steps:
step 3.1, LR in refining taskhAnd the transfer LC of the heat transportation task from refining to continuous castinghIn the interaction relation matrix of
Figure BDA0002975173700000171
The row vector corresponding to the power resource is transferred LC in the transportation taskhRather than the refinery task LRhIn the interaction relation matrix of (1) recording LF in the refining task LRhThe end time of (1), namely the transport task TransferLChIs released at the start of the time, i.e.
Figure BDA0002975173700000172
Figure BDA0002975173700000173
Figure BDA0002975173700000174
Figure BDA0002975173700000175
Step 3.2, order refining task LRhTransferLC from start time to transport taskhThe time difference at the starting moment is greater than the original refining time of the heat h to indicate that the former is performed before the latter in time, i.e. the
Figure BDA0002975173700000176
Step 3.3, introducing a characterization refining task LRhPerforming a 0-1 variable of state at each time interval
Figure BDA0002975173700000177
LR refining tasks of variable durationhAnd transport task transferLChJoined together in time, i.e.
Figure BDA0002975173700000178
In the formula:
Figure BDA0002975173700000179
indicating that heat h is being refined during time period t,
Figure BDA00029751737000001710
indicating that heat h has not been refined for time period t,
Figure BDA00029751737000001711
step 3.4, LR refining taskhThe average electric power consumed during each period during execution is divided into the following two cases: one is the rated power of LF and one is LRhThe ratio of its actual electricity usage over the period of time when the end time is within the period of time, to the length of the period of time, the latter of which is only likely to occur at the LR under considerationhRespectively on the premise of end period of execution period
Figure BDA0002975173700000181
And
Figure BDA0002975173700000182
represents LRhAverage power consumption of other periods and last periods during execution and adding the average power consumption into resource balance constraints corresponding to power resources so as to consume production tasks of short-flow steel enterprises in each periodIs accurately calculated, i.e.
Figure BDA0002975173700000183
In the formula:
Figure BDA0002975173700000184
rated power for LF; ε is an infinitesimal positive number that tends to be 0.
Wherein, in step 3.1, the refining task LRhIs variable, so that its interaction with the partial resources at the execution and end times cannot be represented by the interaction matrix given before the scheduling, except only at the start time and LRhThe interaction taking place
Figure BDA0002975173700000185
Besides, the air conditioner is provided with a fan,
Figure BDA0002975173700000186
LF and Power and LRhThe interaction relationship needs to be described in other ways: for both the start and end times with LRhInteractive LF due to LRhThe end time of (1), namely the transport task TransferLChCan set LF at LRhThe event that the end time of (1) is released is recorded in the transferLChThe first column of the corresponding row vector in the interaction relation matrix; for being always LR in the process of executionhConsumed power resources due to LRhCan be changed by setting additional constraint conditions to represent LR after deleting the row vector corresponding to the power resource in the interaction relation matrixhElectrical power usage at each time interval; for LR and end time onlyhOf interaction
Figure BDA0002975173700000187
Since it is composed of LRhIs transferred LC immediately after generationhConsumption, LR can be deletedhAnd transferLChThe mutual relation matrix is added with a security after the row vector corresponding to the resourceTo prove the order of execution of two tasks and to replace the constraints linked in time
Figure BDA0002975173700000188
Corresponding resource balance constraints and heat transport constraints.
The function of the steps 3.2 and 3.3 is the newly added guaranteed LRhAnd transferLChOrder of execution and temporally connected constraints, the role of said step 3.4 being to set additional constraints as above to indicate LRhThe power usage at each time interval.
In step 3.4, LRhThe average power consumption in each period during execution is divided into the following two cases: one is the rated power of LF and one is LRhThe ratio of the actual power usage over the period of time when the ending time is within the period of time to the length of the period of time, since the latter may only occur at the LRhAt the end of the execution period, LR can be reducedhThe average power consumption of other time periods is separated from the average power consumption of the last time period in the execution process, the former can be expressed as the rated power of LF and LRhMultiplication of the execution states of periods on the time axis except the end period of its execution period, the latter using LRhTwo expression modes of electric energy consumption during execution are linked with the former and the prolonged furnace refining time, and finally, the two expression modes are added into a resource balance constraint corresponding to the original electric energy resource, so that the average electric power consumed by each production task of the short-flow steel enterprise in each period can be accurately calculated.
In step 4, a short-process steel production scheduling model reflecting the real willingness of enterprises to participate in demand response is established by comprehensively considering two targets of minimum electricity purchasing cost and maximum electricity utilization comfort, and the multi-objective optimization model is converted into a single-objective optimization model by adopting a normalization processing and linear weighted summation method, and the specific steps are as follows:
step 4.1, establishing the electricity purchasing cost F1Minimum and electricity comfort F2The maximum two objective functions are expressed as follows:
Figure BDA0002975173700000191
in the formula: lambda [ alpha ]tThe electricity purchase price of the iron and steel enterprises in the time period t is shown;
Figure BDA0002975173700000192
the self-generating power of the iron and steel enterprises in the time period t is obtained;
Figure BDA0002975173700000193
the electric power utilization in the time period t before the steel enterprises participate in the demand response.
Step 4.2, combining the corrected production constraint conditions and the two objective functions to establish a short-process steel enterprise production scheduling model;
4.3, converting multiple targets into a single target by adopting a normalization processing and linear weighted summation method, wherein the expression is as follows:
Figure BDA0002975173700000201
in the formula:
Figure BDA0002975173700000202
the minimum value and the maximum value of the electricity purchasing cost are respectively obtained by optimizing a scheduling model single target of maximizing the electricity purchasing cost and minimizing the electricity purchasing cost; and alpha is a weight coefficient.
In step 4.1, before participating in the demand response, the short-flow iron and steel enterprise usually performs power utilization arrangement according to a production mode (generally, minimizing completion time) most suitable for the enterprise, at the moment, the power utilization comfort level of the enterprise reaches a maximum value, when the enterprise is stimulated by a power price signal to adjust a production operation plan of the enterprise so as to pursue minimization of electricity purchasing cost, the power utilization comfort level is reduced, the reduction amplitude of the power utilization comfort level is in positive correlation with power adjustment quantity caused by power change of the enterprise at each time period before and after response, and therefore the power utilization comfort level is quantitatively represented by the ratio of the sum of the power change values of the available enterprise at each time period to the total power consumption.
And 4.2, forming a short-flow iron and steel enterprise production scheduling model by taking the formula (15) as an objective function and taking the formulas (2) to (14) as production constraint conditions.
In the step 4.3, because the two objective functions of the minimum power purchase cost and the maximum power utilization comfort level of the enterprise are mutually influenced or even mutually conflict, in order to realize the comprehensive optimization of the two objective functions, the multi-objective optimization model is converted into the single-objective optimization model by adopting a linear weighted summation method, and the power utilization comfort level and the power purchase cost with different dimensions are converted into per unit values through normalization processing.
For the very large target function F with larger value, better1Extremely small objective function F with smaller and better values2The normalization processing method respectively comprises the following steps:
Figure BDA0002975173700000203
Figure BDA0002975173700000204
in the formula:
Figure BDA0002975173700000205
are respectively F1Minimum and maximum values of;
Figure BDA0002975173700000206
are respectively F2Minimum and maximum values of;
Figure BDA0002975173700000207
respectively normalized object function F1And F2。
The minimum electricity purchasing cost belongs to a very small objective function, the maximum electricity using comfort degree belongs to a very large objective function, and the value range of the latter is [ -1,1], so that the objective function of the converted single-target optimization model is shown as a formula (16).
In step 5, obtaining enterprise production information specifically comprises: the number of the furnaces, the number of the pouring times, the corresponding relation between the furnaces and the pouring times, the number of various production devices and rated power thereof, the duration time of various production tasks and transportation tasks (including the original refining time of the furnaces), the allowable cooling time of the furnaces transported by the transportation tasks when temperature compensation measures are not taken, the average temperature rise speed during refining of the furnaces, the average temperature drop speed of the furnaces when waiting outside LF, the difference value of the lower limit of the temperature of the furnaces when the LF enters the station before compensation temperature drop is taken for prolonging the refining time, the self-generating power of enterprises in each period of time and the power consumption power of the enterprises in each period of time before participation in demand response.
The method for analyzing the demand response potential of the short-process steel enterprise is verified by adopting a specific example.
Taking daily production schedule of a certain short-flow steel enterprise as an example, the number of furnaces and the number of casting times in the obtained enterprise production information, the correspondence between the heat and the casting times is shown in table 1, the number and the rated power of main production equipment as a typical regulation object are shown in table 2, the duration of various production tasks and transportation tasks (including the original heat refining time) and the allowable cooling time of the heat transported by the transportation task when no temperature compensation measure is taken are shown in table 3, the average temperature rise speed during heat refining is 5 ℃/min, the average temperature drop speed during heat waiting outside the LF is 1 ℃/min, the difference between the lower limit of the heat entering the LF before and after refining time compensation temperature drop is extended is 20 ℃, the power consumption of each time period before the enterprise participates in demand response is shown in fig. 5, and no spontaneous power is generated within the scheduling time range. The time-of-use electricity price of the large industrial user shown in the table 4 is taken as the electricity purchase price of the enterprise in each period, the time-of-use electricity price and the production information are input into the constructed multi-target production scheduling model of the short-flow steel enterprise together, and a CPLEX solver is called by means of a YALMIP toolbox of an MATLAB platform to solve the time-of-use electricity price.
TABLE 1 number of heats, number of pours and their corresponding relations
Figure BDA0002975173700000211
TABLE 2 iron and steel enterprises' equipment parameters
Figure BDA0002975173700000212
TABLE 3 duration of various types of tasks (including the original heat refining time) and allowable cooling time (min) for the heat transported by the transportation task without temperature compensation
Figure BDA0002975173700000213
Figure BDA0002975173700000221
Note: continuous casting machine CC1The time of replacement and pouring is 70min, CC2The time of pouring is 50 min.
TABLE 4 time of use price of large industrial users
Figure BDA0002975173700000222
(1) Influence of adjustable waiting time range on demand response potential of short-flow steel enterprise
The weight coefficients of the two objective functions of the minimum electricity purchasing cost and the maximum electricity utilization comfort degree are respectively set to be 0.5, and the electricity utilization power of each time period after the short-flow steel enterprise participates in demand response under the two conditions of considering whether the waiting time range is adjustable or not is obtained by solving the multi-objective production scheduling model as shown in fig. 6. Comparing fig. 5 and fig. 6, it can be seen that the waiting time of the extended part of the heat outside the LF enables the enterprise to have the ability to shift the usage time of the EAF, which is a high power consumption device, from the peak time in the morning to the valley time in the morning, and on the premise that the total power consumption is increased by only 0.01% compared with that before the response, the power consumption in the peak time is reduced by 26.9%, the power consumption in the valley time is increased by 10.9%, and the power purchase cost of the enterprise is reduced by 7.2%; when an enterprise does not increase the temperature drop of the refining time compensation waiting process so that the time of furnace allows the cooling time to be fixed between the decarburization production stage and the refining production stage, the time of furnace waiting outside LF is limited in time range, which causes the translational capability of EAF to be greatly weakened compared with the former case, and in addition, the influence of electricity utilization comfort degree on the electricity utilization behavior of the enterprise is added, the electricity utilization power of the enterprise in each time period is basically not changed compared with that before response, and the demand response potential of the enterprise is not fully released under the condition that the waiting time range is not adjustable. Therefore, considering the relaxation effect of prolonging the refining time on the adjustable range of the waiting time, the further excavation of the demand response potential of the short-flow steel enterprises can be realized.
(2) Influence of weight coefficient on demand response potential of short-flow steel enterprise
In order to seek comprehensive optimization, each objective function in the multi-objective optimization generally takes the same weight, but when the subjective preference of an enterprise on a certain objective exceeds that of other objectives, the analysis of the demand response potential of the enterprise needs to be correspondingly adjusted. Table 5 shows the power change rate of the enterprise in the peak period and the peak period before and after the enterprise participates in the demand response and the power change rate of the power in the valley period when the weight coefficient is increased from 0.5 to 1 in steps of 0.1, it can be seen from the data in the table that as the weight occupied by the electricity purchasing cost is continuously increased, the enterprise pursuits the economy will gradually get rid of the obstruction caused by the reduction of the electricity utilization comfort level, the usage time of the high-power equipment such as EAF and the like can be more shifted from the peak period in the morning to the valley period in the morning and even to the valley period in the afternoon, when the weight coefficient reaches 1, that is, the enterprise only uses the minimum electricity purchasing cost as the production scheduling target, the electricity utilization and the electricity utilization comfort level in the peak period reach the minimum, but the release degree of the demand response potential and the contribution to the peak clipping and valley filling of the power grid reach the maximum. Therefore, in the process of analyzing the demand response potential of the short-flow steel enterprise, the paying attention degree of the enterprise to the electricity utilization comfort needs to be reasonably considered so as to enable the response capability assessment to be more accurate and credible.
TABLE 5 Peak time and Valley time Power Change Rate before and after Enterprise engagement with demand response when weight changes
Figure BDA0002975173700000231
Based on the same inventive concept, the embodiment provides a system for analyzing demand response potential of a short-process steel enterprise, which comprises:
the power utilization characteristic model establishing module is configured to select main production equipment with strong power utilization regularity of short-process iron and steel enterprises as a typical adjustable object and establish a power utilization characteristic model of the main production equipment;
the production constraint condition generation module is configured to define the selected production equipment, the input and output heat and the consumed power of the production equipment as resources, define equipment processing and heat transportation as tasks, and describe the interaction relation between the resources and the tasks in the steel production process by using a discrete time RTN model to obtain the production constraint conditions of the short-flow steel enterprise;
a production constraint condition correction module configured to take into account an effect of increasing refining time on extension of heat allowable cooling time in the waiting time constraint of the discrete time RTN model, and correct the remaining production constraint conditions;
the production scheduling model establishing module is configured to comprehensively consider two targets of minimum electricity purchasing cost and maximum electricity utilization comfort level to establish a short-flow steel production scheduling model reflecting the real willingness of enterprises to participate in demand response, and the production scheduling model is converted into a single-target optimization model from a multi-target optimization model by adopting a normalization processing and linear weighted summation method;
and the demand response potential analysis module is configured to acquire the production information and the electricity price information of the enterprise, input the converted single-target optimization model for solving, obtain the electricity power of the enterprise in each period under a given weight coefficient, compare the electricity power with the electricity load condition of the enterprise when the electricity power does not participate in demand response, and determine the demand response potential of the short-flow steel enterprise.
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.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A method for analyzing demand response potential of a short-process steel enterprise is characterized by comprising the following steps:
analyzing the production process of a short-process iron and steel enterprise, selecting main production equipment with strong power utilization regularity as a typical adjustable object and establishing a power utilization characteristic model of the main production equipment;
defining the selected production equipment and input and output heat and consumed power thereof as resources, defining equipment processing and heat transportation as tasks, describing the interactive relation between the resources and the tasks in the steel production process by using a discrete time RTN model, and obtaining production constraint conditions of short-flow steel enterprises;
adding the prolonging effect of refining time increase on the heat allowable cooling time into the waiting time constraint of the discrete time RTN model, and correcting the other production constraint conditions;
the method comprises the steps of establishing a short-flow steel production scheduling model reflecting the real desire of enterprises to participate in demand response by comprehensively considering two goals of minimum electricity purchasing cost and maximum electricity utilization comfort, and converting the production scheduling model into a single-target optimization model from a multi-target optimization model by adopting a normalization processing and linear weighted summation method;
and acquiring production information and electricity price information of the enterprise, inputting the converted single-target optimization model for solving to obtain the electricity consumption power of the enterprise in each period under a given weight coefficient, and comparing the electricity consumption power with the electricity consumption load condition of the enterprise when the electricity consumption power does not participate in demand response to determine the demand response potential of the short-flow steel enterprise.
2. The method for analyzing the demand response potential of the short-process steel enterprise as claimed in claim 1, wherein the main production equipment with strong electricity regularity is selected as a typical adjustable object, and the method sequentially comprises the following steps according to the product processing sequence: an electric arc furnace for primary refining of the heat, an argon-oxygen decarburization furnace for decarburization of the heat, a ladle refining furnace for refining the heat and a continuous casting machine for continuous casting of the heat; the power consumption characteristic model of the typical controllable device is as follows:
Figure FDA0002975173690000011
in the formula: pi tActual operating power for device i at time t;
Figure FDA0002975173690000012
is the starting time of the device i;
Figure FDA0002975173690000013
is the fixed working duration of the device i; pi ratedIs the rated power of the device i; and T is the scheduling end time.
3. The method for analyzing the demand response potential of the short-process steel enterprise as claimed in claim 1, wherein the specific steps of describing the interactive relationship between resources and tasks in the steel production process by using a discrete time RTN model to obtain the production constraint conditions of the short-process steel enterprise are as follows:
refining the interactive relation between resources and tasks in the short-flow steel production flow, and establishing a graphical structure of the RTN model according to the interactive relation;
under the discrete time expression, a mathematical expression of the RTN model is given according to the graphical structure of the short-process steel production RTN model, namely the production constraint conditions of the short-process steel enterprises.
4. The method for analyzing the demand response potential of the short-process steel enterprise as claimed in claim 1, wherein the production constraint conditions of the short-process steel enterprise comprise resource balance constraint, task execution constraint, waiting time constraint, resource quantity constraint, furnace transportation constraint and completion time constraint, and the expressions are respectively as follows:
resource balance constraint:
in discrete time, the result of the steel production schedule is to decide when each task starts at the beginning of the time segment, so the available amount of a certain resource in the time segment t is equal to the sum of the following two parts: one part is its available amount at time period t-1 and the other part is the amount of this resource that all tasks generate or consume at the beginning of time period t, i.e. the amount of this resource that is available at the beginning of time period t
Figure FDA0002975173690000021
In the formula:
Figure FDA0002975173690000022
the available number of devices or heats m in time period t,
Figure FDA0002975173690000023
is the initial number of devices or heats m; k is a task set; tau iskFor the duration d of task kkThe number of the corresponding time periods,
Figure FDA0002975173690000024
δ is the period length; thetakFor each relative time point within the execution period of task k with the start time as a reference time point,the value range is {0,1, …, tauk}; m is a set consisting of equipment and heat;
Figure FDA0002975173690000025
a variable 0-1 for judging whether the task k is started at the beginning of the time period t;
Figure FDA0002975173690000026
the average power consumption of the iron and steel enterprises in the time period t is calculated; t is the total scheduling time period number;
Figure FDA0002975173690000027
for task k at relative time θkThe number of generating/releasing equipment or the number of heats m (negative values represent consumption/occupation), and the value range is { -1,0,1 };
Figure FDA0002975173690000028
for task k at relative time θkThe consumed electric power has a value which is the average electric power consumed by the task k in the period starting from the time point.
Figure FDA0002975173690000029
And
Figure FDA00029751736900000210
interaction relation matrix mu of co-owned task k and resourcekThe element of (1), which is defined by the formula:
θk＝0 1 … τk
Figure FDA00029751736900000211
in the formula: mu.skIs s × (τ)k+1) dimension matrix, s is the number of resource classes required for task k execution, { r1,r2,…,rsThe resource is a set of various resources required by the execution of the task k;
and (3) task execution constraint:
in the short-flow steel production process, each task is executed once in a scheduling period, namely
Figure FDA0002975173690000031
In the formula: k ', K' are respectively a set of tasks whose duration is independent of and related to the execution device; u shapekA set of devices u that can execute task k;
and (3) latency constraint:
discrete time means that the next heat is only generated and consumed by the task at the beginning of the period, so the constraint on the out-of-facility waiting time of the heat can be translated into a limit on the number of 1 periods available for the heat waiting for processing within the scheduling time range, i.e. the number of 1 periods
Figure FDA0002975173690000032
Figure FDA0002975173690000033
In the formula:
Figure FDA0002975173690000034
respectively waiting for the heat h of AOD, LF and CC processing; TransferPDh、TransferDLh、TransferLChRespectively carrying out a transportation task of the heat h between the EAF and the AOD, a transportation task of the heat h between the AOD and the LF and a transportation task of the heat h between the LF and the CC; dkThe permissible cooling time between two production phases connected to the transport task k for the heat h;
and (3) resource quantity constraint:
the available number of all resources in any time period is not negative, i.e. the number of resources in any time period is not negative
Figure FDA0002975173690000035
Furnace transportation restraint:
the heat is transported immediately after being processed by the production equipment, i.e.
Figure FDA0002975173690000041
In the formula:
Figure FDA0002975173690000042
respectively is the heat h processed by EAF, AOD and LF;
completion time constraint:
each production facility, particularly the CC, needs to be in an unoccupied state for a time period t to ensure that all tasks are completed at the end of the scheduling cycle, i.e.
Figure FDA0002975173690000043
In the formula:
Figure FDA0002975173690000044
for continuous casting machine CCnThe number of available times in time period T; q is the initial number of casters.
5. The method for analyzing the demand response potential of the short-process steel enterprise as claimed in claim 1, wherein the step of adding the effect of increasing the refining time on the allowable cooling time of the heat in the waiting time constraint is to set the allowable cooling time of the heat in the waiting time constraint between the decarburization production stage and the refining production stage to be an adjustable physical quantity related to the temperature drop speed of the heat in the off-LF waiting process, the temperature rise speed of the heat in the refining process and the extended refining time, and the specific expression is as follows:
Figure FDA0002975173690000045
in the formula:
Figure FDA0002975173690000046
the value of the allowable cooling time of the heat h between two production stages connected with the transportation task k when no temperature compensation measure is taken can be given according to the outbound temperature of the production equipment of the heat h at the previous stage, the inbound temperature lower limit of the production equipment at the next stage and the temperature reduction speed of transportation and waiting between the two production stages;
Figure FDA0002975173690000047
LR refining task for Heat hhThe average temperature rise speed of the heat h is carried out;
Figure FDA0002975173690000048
the average temperature drop speed of the furnace h when waiting outside LF is obtained;
Figure FDA0002975173690000049
for extended heat h refining time;
Figure FDA00029751736900000410
in order to adopt extended refining time to compensate for heat h in transportation task transferDL during temperature drophMaximum allowable cooling time between the two production stages of decarburization and refining connected;
Figure FDA00029751736900000411
the refining time is prolonged to compensate the difference value of the lower limit of the temperature of the furnace h at the LF arrival time before and after the temperature drop;
the method for correcting the rest production constraint conditions comprises the following specific steps:
LR at refining taskhAnd transport task transferLChIn the interaction relation matrix of
Figure FDA0002975173690000051
The row vector corresponding to the power resource is transferred LC in the transportation taskhRather than the refinery task LRhIn the interaction relation matrix of (1) recording LF in the refining task LRhThe end time of (1), namely the transport task TransferLChIs released at the start of the time, i.e.
Figure FDA0002975173690000052
Figure FDA0002975173690000053
Figure FDA0002975173690000054
Figure FDA0002975173690000055
Order refining task LRhTransferLC from start time to transport taskhThe time difference at the starting moment is greater than the original refining time of the heat h to indicate that the former is performed before the latter in time, i.e. the
Figure FDA0002975173690000056
Introduction of characterization refining task LRhPerforming a 0-1 variable of state at each time interval
Figure FDA0002975173690000057
LR refining tasks of variable durationhAnd transport task transferLChJoined together in time, i.e.
Figure FDA0002975173690000058
In the formula:
Figure FDA0002975173690000059
indicating that heat h is being refined during time period t,
Figure FDA00029751736900000510
indicating that heat h has not been refined for time period t,
Figure FDA00029751736900000511
will refine the task LRhThe average electric power consumed during each period during execution is divided into the following two cases: one is the rated power of LF and one is LRhThe ratio of its actual electricity usage over the period of time when the end time is within the period of time, to the length of the period of time, the latter of which is only likely to occur at the LR under considerationhRespectively on the premise of end period of execution period
Figure FDA00029751736900000512
And
Figure FDA00029751736900000513
represents LRhAverage power consumption of other periods and last periods during execution and adding the average power consumption into resource balance constraints corresponding to power resources so as to accurately calculate the average power consumed by each production task of the short-flow steel enterprise in each period, namely
Figure FDA0002975173690000061
In the formula:
Figure FDA0002975173690000062
rated power for LF; ε is an infinitesimal positive number that tends to be 0.
6. The method for analyzing the demand response potential of the short-process steel enterprise as claimed in claim 1, wherein the method for establishing the short-process steel production scheduling model reflecting the real willingness of enterprises to participate in demand response by comprehensively considering two objectives of minimum electricity purchasing cost and maximum electricity utilization comfort degree, and the method for converting the multi-objective optimization model into the single-objective optimization model by adopting the normalization processing and the linear weighted summation comprises the following specific steps:
construction of Electricity Purchase cost F1Minimum and electricity comfort F2The maximum two objective functions are expressed as follows:
Figure FDA0002975173690000063
in the formula: lambda [ alpha ]tThe electricity purchase price of the iron and steel enterprises in the time period t is shown;
Figure FDA0002975173690000064
the self-generating power of the iron and steel enterprises in the time period t is obtained;
Figure FDA0002975173690000065
the electric power utilization in the time period t before the steel enterprises participate in the demand response.
Establishing a short-flow iron and steel enterprise production scheduling model by combining the corrected production constraint conditions and the two objective functions;
converting multiple targets into a single target by adopting a method of normalization processing and linear weighted summation, wherein the expression is as follows:
Figure FDA0002975173690000066
in the formula: f1 min、F1 maxThe minimum value and the maximum value of the electricity purchasing cost are respectively obtained by optimizing a scheduling model single target of maximizing the electricity purchasing cost and minimizing the electricity purchasing cost; alpha is alphaAre weight coefficients.
7. The method for analyzing the demand response potential of the short-process steel enterprise as claimed in claim 1, wherein the acquiring of the enterprise production information specifically comprises: the number of the furnaces, the number of the pouring times, the corresponding relation between the furnaces and the pouring times, the number of various production devices and rated power thereof, the duration time of various production tasks and transportation tasks (including the original refining time of the furnaces), the allowable cooling time of the furnaces transported by the transportation tasks when temperature compensation measures are not taken, the average temperature rise speed during refining of the furnaces, the average temperature drop speed of the furnaces when waiting outside LF, the difference value of the lower limit of the temperature of the furnaces when the LF enters the station before compensation temperature drop is taken for prolonging the refining time, the self-generating power of enterprises in each period of time and the power consumption power of the enterprises in each period of time before participation in demand response.
8. A short-process steel enterprise demand response potential analysis system is characterized by comprising:
the power utilization characteristic model establishing module is configured to select main production equipment with strong power utilization regularity of short-process iron and steel enterprises as a typical adjustable object and establish a power utilization characteristic model of the main production equipment;
the production constraint condition generation module is configured to define the selected production equipment, the input and output heat and the consumed power of the production equipment as resources, define equipment processing and heat transportation as tasks, and describe the interaction relation between the resources and the tasks in the steel production process by using a discrete time RTN model to obtain the production constraint conditions of the short-flow steel enterprise;
a production constraint condition correction module configured to take into account an effect of increasing refining time on extension of heat allowable cooling time in the waiting time constraint of the discrete time RTN model, and correct the remaining production constraint conditions;
the production scheduling model establishing module is configured to comprehensively consider two targets of minimum electricity purchasing cost and maximum electricity utilization comfort level to establish a short-flow steel production scheduling model reflecting the real willingness of enterprises to participate in demand response, and the production scheduling model is converted into a single-target optimization model from a multi-target optimization model by adopting a normalization processing and linear weighted summation method;
and the demand response potential analysis module is configured to acquire the production information and the electricity price information of the enterprise, input the converted single-target optimization model for solving, obtain the electricity power of the enterprise in each period under a given weight coefficient, compare the electricity power with the electricity load condition of the enterprise when the electricity power does not participate in demand response, and determine the demand response potential of the short-flow steel enterprise.
9. An electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the method for analyzing demand response potential of a short-run steel enterprise as claimed in any one of claims 1 to 7.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of analyzing demand response potential of a short-run steel enterprise as claimed in any one of claims 1 to 7.
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