A production scheduling method of short-process steelmaking-continuous casting considering shared energy storage

Abstract

The present application relates to the technical field of steelmaking-continuous casting, and particularly discloses a production scheduling method for short-process steelmaking-continuous casting considering shared energy storage, comprising the following steps: determining the total power consumption cost of the entire scheduling period based on the cost of purchasing electricity from an external power grid and the cost of purchasing electricity from shared energy storage; determining the total processing power variance of the entire scheduling period based on the total processing power of all processing machines at each time of the scheduling period; determining the difference degree of the external power supply power and the renewable energy power generation of the entire scheduling period based on the external power supply power and the renewable energy power generation at each time of the scheduling period; constructing a multi-objective production scheduling model for short-process steelmaking-continuous casting considering shared energy storage by taking the minimum total power consumption cost, total processing power variance and difference degree as targets, and solving the model to obtain the total scheduling decision of short-process steelmaking-continuous casting. The present application is conducive to realizing green-economical-balance production scheduling of short-process steelmaking-continuous casting.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steelmaking-continuous casting, and particularly relates to a production scheduling method of short-process steelmaking-continuous casting considering shared energy storage. BACKGROUND

[0002] Short-process steelmaking plays an important role in promoting the green and low-carbon transformation of the steel industry and promoting the renewable energy consumption of new power systems. China has clearly pointed out that the steel industry should increase the proportion of short-process electric furnace steelmaking, accelerate the transformation of blast furnace-converter long-process steelmaking, and promote the development and utilization of green microgrids, and strengthen the coupling of steel and electricity to improve efficiency.

[0003] However, with the increasing number of equipment in the short-process steelmaking-continuous casting production process, the workshop layout is becoming more and more complex, and the joint scheduling of steel production and electricity has become a key bottleneck. SUMMARY

[0004] Therefore, the present application provides a production scheduling method of short-process steelmaking-continuous casting considering shared energy storage, in an attempt to solve or at least alleviate the above problems.

[0005] According to one aspect of the present application, a production scheduling method of short-process steelmaking-continuous casting considering shared energy storage is provided, wherein the electricity of the short-process steelmaking-continuous casting production is jointly provided by an external power grid and shared energy storage, and each heat in the short-process steelmaking-continuous casting production is processed in turn by processing machines in each process. The method comprises: determining the total electricity cost of the entire scheduling period based on the cost of purchasing electricity from the external power grid and the cost of purchasing electricity from the shared energy storage within the scheduling period; determining the total processing power variance of the entire scheduling period based on the total processing power of all processing machines at each time within the scheduling period; determining the difference degree of the external power supply power and the renewable energy power of the entire scheduling period based on the external power supply power and the renewable energy power at each time within the scheduling period; constructing a multi-objective production scheduling model of short-process steelmaking-continuous casting considering shared energy storage by taking the minimum total electricity cost, total processing power variance, and difference degree as the electricity target function, production target function, and green electricity fitting target function, respectively; solving the multi-objective production scheduling model to obtain the total scheduling decision of the short-process steelmaking-continuous casting, wherein the total scheduling decision comprises a production scheduling decision and an electricity scheduling decision, and the production scheduling decision comprises the selected processing machines of each heat in each process within the scheduling period and the time when the processing starts and ends on the selected processing machines, and the electricity scheduling decision comprises the electricity purchased from the external power grid and the shared energy storage at each time within the scheduling period.

[0006] Optionally, in the production scheduling method of short-process steelmaking-continuous casting considering shared energy storage according to the present application, the production target function comprises:

[0007]

[0008] wherein, δ represents the total processing power variance of the entire scheduling period, P t represents the total processing power of all processing machines at time t, represents the mean of the total processing power of all processing machines at all times, and T represents the total number of times.

[0009] Optionally, in the production scheduling method for short-process steelmaking-continuous casting considering shared energy storage according to the present application, based on the external power grid power and renewable energy power at each time in the scheduling period, the difference degree of the external power grid power and the renewable energy power in the entire scheduling period is determined, comprising: normalizing the external power grid power and the renewable energy power at each time to obtain the normalized external power grid power and the normalized renewable energy power at each time; and obtaining the difference degree by calculating the cosine similarity of the normalized external power grid power and the normalized renewable energy power at each time.

[0010] Optionally, in the production scheduling method for short-process steelmaking-continuous casting considering shared energy storage according to the present application, the green electricity fitting target function comprises:

[0011]

[0012] wherein, fitness represents the difference degree of the external power grid power and the renewable energy power in the entire scheduling period, respectively represent the normalized green electricity power of the external power grid at time t and time (t-1), respectively represent the normalized external power grid power at time t and time (t-1), and T represents the total number of times.

[0013] Optionally, in the production scheduling method for short-process steelmaking-continuous casting considering shared energy storage according to the present application, based on the cost of purchasing electricity from the external power grid and the cost of purchasing electricity from the shared energy storage in the scheduling period, the total electricity cost in the entire scheduling period is determined, comprising: determining the cost of purchasing electricity from the external power grid in the scheduling period according to the production power provided by the power grid at each time and the time-of-use electricity price of the power grid at each time; determining the cost of purchasing electricity from the shared energy storage in the scheduling period according to the production power provided by the shared energy storage at each time and the electricity price of the shared energy storage; and obtaining the sum of the cost of purchasing electricity from the external power grid and the cost of purchasing electricity from the shared energy storage to obtain the total electricity cost in the entire scheduling period.

[0014] Optionally, in the production scheduling method of short-process steelmaking-continuous casting with shared energy storage according to the present application, the multi-objective production scheduling model further comprises production constraints on production, and the production constraints comprise continuity production constraints of heats, waiting time constraints of heats between adjacent processes, waiting time constraints of heats before continuous casting, processing time constraints of adjacent heats, constraints of heats on selection of processing machines in processes, and constraints between heats and casting.

[0015] Optionally, in the production scheduling method of short-process steelmaking-continuous casting with shared energy storage according to the present application, the multi-objective production scheduling model further comprises shared energy storage constraints on shared energy storage, and the shared energy storage constraints comprise constraints between shared energy storage power supply power and machine processing power, external power grid power supply power, shared energy storage power supply power and discharge power, power upper and lower limits and state constraints of shared energy storage charging and discharging, total shared energy storage power supply amount constraint, and total shared energy storage charging and discharging amount constraint.

[0016] Optionally, in the production scheduling method of short-process steelmaking-continuous casting with shared energy storage according to the present application, the continuity production constraints of heats comprise:

[0017] c jlm = s jlm + t jlm · x jlmt

[0018] wherein c jlm , s jlm respectively represent the processing end time and the processing start time of the heat j on the machine m of the process l, t jlm represents the processing time of the heat j on the machine m of the process l, and x jlmt is a 0-1 variable, wherein if the heat j selects the machine m for processing at the time t in the production stage l, x jlmt = 1, and if the heat j does not select the machine m for processing at the time t in the production stage l, x jlmt = 0.

[0019] According to still another aspect of the present application, there is provided a computing device comprising at least one processor and a memory storing program instructions configured to be executed by the at least one processor, the program instructions comprising instructions for executing the production scheduling method of short-process steelmaking-continuous casting with shared energy storage according to the present application.

[0020] According to still another aspect of the present application, there is provided a readable storage medium storing program instructions, which, when read and executed by a computing device, cause the computing device to execute the production scheduling method of short-process steelmaking-continuous casting with shared energy storage according to the present application.

[0021] According to the production scheduling method of the short-process steelmaking-continuous casting considering shared energy storage provided in the application, a joint power supply mode of shared energy storage and the power grid is proposed, which can not only bring more flexible and efficient power resource scheduling scheme for steel production, but also reduce the electricity cost of steel production and improve the renewable energy consumption capacity of steel production. In addition, the application constructs a multi-objective production scheduling model with the optimization objectives of improving green electricity consumption, reducing total processing power variance and electricity cost, which is beneficial to realize green-economical-balance production scheduling of short-process steelmaking-continuous casting. BRIEF DESCRIPTION OF DRAWINGS

[0022] To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings. These aspects are indicative of various ways in which the principles disclosed herein can be practiced and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The foregoing and other objects, features, and advantages of the disclosure will be apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

[0023] Figure 1 A structural block diagram of a computing device 100 according to an embodiment of the application is shown;

[0024] Figure 2 A flowchart of a production scheduling method 200 of short-process steelmaking-continuous casting considering shared energy storage according to an embodiment of the application is shown;

[0025] Figure 3 A schematic diagram of a production framework of short-process steelmaking-continuous casting considering shared energy storage according to an embodiment of the application is shown;

[0026] Figure 4 A schematic diagram of time-of-use electricity price of the power grid according to an embodiment of the application is shown;

[0027] Figure 5 A schematic diagram of solving the multi-objective production scheduling model by using NSGA-II algorithm according to an embodiment of the application is shown;

[0028] Figure 6 A schematic diagram of a wind power curve graph of the power grid in a certain typical day according to an embodiment of the application is shown;

[0029] Figure 7 A schematic diagram of a target value comparison graph under different scenarios according to an embodiment of the application is shown;

[0030] Figure 8 A schematic diagram of the fitting situation of the power grid and wind power output under different scenarios according to an embodiment of the application is shown;

[0031] Figure 9 Fig. 2 shows a schematic diagram of objective fitness function convergence according to an embodiment of the present application;

[0032] Figure 10 Fig. 3 shows a schematic diagram of a Pareto solution set according to an embodiment of the present application;

[0033] Figure 11 Fig. 4 shows a schematic diagram of an optimal production scheduling scheme Gantt chart and device power change diagram according to an embodiment of the present application;

[0034] Figure 12 Fig. 5 shows a schematic diagram of joint power supply at each time point according to an embodiment of the present application;

[0035] Figure 13 Fig. 6 shows a schematic diagram of shared energy storage daily charging and discharging strategy according to an embodiment of the present application;

[0036] Figure 14 Fig. 7 shows a schematic diagram of grid power supply power and wind power output fitting according to an embodiment of the present application;

[0037] Figure 15 Fig. 8 shows a schematic diagram of each target value under different time-of-use price peak-valley price difference according to an embodiment of the present application;

[0038] Figure 16 Fig. 9 shows a schematic diagram of each target value under different waiting time between different processes according to an embodiment of the present application;

[0039] Figure 17 Fig. 10 shows a schematic diagram of target values under different shared energy storage contract capacities according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] Exemplary embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings; however, they are not limited to the embodiments set forth herein but can be implemented in various forms. The present disclosure will be described herein with reference to individual embodiments, but combinations of these embodiments can also be used. The present disclosure should not be construed as being limited to the embodiments set forth herein; various changes in form and details can be made therein without departing from the spirit and scope of the present disclosure, which can be defined only by the appended claims.

[0041] Short-process steelmaking is an important direction of green transformation of the steel industry and a powerful support for promoting renewable energy consumption in new power systems. Among them, the production scheduling optimization of short-process steelmaking-continuous casting is a key link, and how to effectively balance the production process, power load and green electricity friendly consumption is an important bottleneck. Therefore, the present application provides a production scheduling method for short-process steelmaking-continuous casting considering shared energy storage.

[0042] The production scheduling method of short process steelmaking-continuous casting considering shared energy storage of the present application can be executed in a computing device. Figure 1 A block diagram of physical components (i.e., hardware) of a computing device 100 is shown. In a basic configuration, computing device 100 includes at least one processing unit 102 and system memory 104. According to an aspect, depending on the configuration and type of computing device, processing unit 102 can be implemented as a processor. System memory 104 includes, but is not limited to, volatile (e.g., random access memory (RAM)), non-volatile (e.g., read-only memory (ROM)), flash memory, or any combination thereof. According to an aspect, system memory 104 includes operating system 105 and program module 106, which includes production scheduling module 120 configured to execute the production scheduling method 200 of short process steelmaking-continuous casting considering shared energy storage of the present application.

[0043] According to an aspect, operating system 105 is suitable for controlling the operation of computing device 100. Furthermore, examples are practiced in conjunction with a graphics library, other operating systems, or any other application program, and are not limited to any particular application or system. In Figure 1 This basic configuration is illustrated in FIG. 1 by those components within dashed line 108. According to an aspect, computing device 100 has additional features or functionality. For example, according to an aspect, computing device 100 includes additional data storage devices (removable and / or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 1 by removable storage 109 and non-removable storage 110. Figure 1 According to an aspect, removable storage 109 includes a computer-readable storage medium having stored thereon computer executable instructions (e.g., software) that, when executed by computing device 100, cause the computing device to perform desired functions. According to an aspect, non-removable storage 110 includes a computer-readable storage medium having stored thereon computer executable instructions (e.g., software) that, when executed by computing device 100, cause the computing device to perform desired functions.

[0044] According to an aspect, program module 106 can include one or more applications. The present application is not limited by the type of application, and examples can include, for example, email and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application, web browser applications, etc.

[0045] According to an aspect, examples can be practiced with one or more computer program products 113 having computer readable instructions (e.g., software) embodied thereon that when executed by one or more processing units 102, program the processing unit(s) to implement examples. According to an aspect, examples can be implemented using wired and / or wireless media, as desired. Figure 1Each or many of the components illustrated in FIG. 1 can be practiced on a system-on-a-chip (SOC) integrated on a single integrated circuit in accordance with examples. According to one aspect, such an SOC device can include one or more processing units, graphics units, communications units, system virtualization units, and various application functionality all of which are integrated (or "burned") onto the chip substrate according to an embodiment. When operating via an SOC, the functionality described herein can be operated via specialized logic integrated with other components of the computing device 100 on the single integrated circuit (chip). Embodiments of the application can also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the application can be practiced within a general computer device, or in any other circuit or system.

[0046] According to one aspect, the computing device 100 can also have one or more input device(s) 112 such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output device(s) 114 such as a display, speakers, a printer, etc. can also be included. The aforementioned devices are examples and others can be used. The computing device 100 can include one or more communication connections 116 allowing communications with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver, and / or transceiver circuitry; universal serial bus (USB), parallel, and / or serial ports.

[0047] The term computer readable media as used herein includes computer storage media. Computer storage media can include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, or program modules. The system memory 104, the removable storage 109, and the non-removable storage 110 are all computer storage media examples (i.e., memory storage.) Computer storage media can include Random Access Memory (RAM), Read Only Memory (ROM), Electronically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 100. According to one aspect, any such computer storage media can be part of the computing device 100. Computer storage media does not include a modulated data signal or other propagated data signal.

[0048] According to an aspect, communication media are embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and include any information delivery media. According to an aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0049] Figure 2 A flowchart illustrating a production scheduling method 200 of a short-process steelmaking-continuous casting taking into account shared energy storage according to an embodiment of the present application is shown, the method 200 being suitable for being executed in a computing device (for example Figure 1 the computing device 100 shown).

[0050] In the method 200, a production framework of a short-process steelmaking-continuous casting taking into account shared energy storage can be first constructed, and then the steps starting with 210 are executed. Next, the production framework of a short-process steelmaking-continuous casting taking into account shared energy storage constructed in the present embodiment is described.

[0051] At present, there are mainly two sources of production power supply for steel enterprises: one is to sign a power contract with the power grid and purchase power from the power grid; the other is to provide the required power through the self-provided power plant of the steel enterprise. However, most self-provided power plants have problems of high emission, low energy efficiency, high cost, etc., which are contrary to the double carbon target requirements. Compared with self-provided power plants, shared energy storage is more flexible, can be adjusted in time according to the power demand of steel production, and can store excess power during the peak period of renewable energy generation, thereby alleviating the intermittency of renewable energy generation and the problems of abandoned wind and light. Based on this, in order to better promote the green and low-carbon development of the steel industry, the present application proposes that the relatively flexible power supply can be provided by shared energy storage instead of self-provided power plants, and further, the shared energy storage and the power grid can be jointly used for power supply. By jointly supporting the power supply for steel production through shared energy storage and the power grid, the stability of the power supply for steel production can be improved.

[0052] Based on this, in the embodiment, the power source of the steel enterprise (i.e. the power consumed by the short-process steelmaking-continuous casting process) can have the following two channels: 1) the power grid provides production power support. Among them, the power grid enterprise implements time-of-use electricity price policy to power consumers. As a price-based demand response means, time-of-use electricity price can guide users to reduce power consumption during peak period and increase power consumption during valley period, which not only effectively balances the power grid load and reduces the power supply pressure during peak period, but also provides a way for power users to reduce power consumption costs. 2) provided by the shared energy storage system participated by the steel enterprise. Among them, the steel enterprise signs a power contract with the shared energy storage supplier, agrees on the capacity and price of the shared energy storage that can be provided, and then purchases power from the shared energy storage operator at the contracted contract price.

[0053] It can be seen that in the production framework of the short-process steelmaking-continuous casting process considering shared energy storage constructed in the embodiment, the power of the short-process steelmaking-continuous casting production is jointly provided by the external power grid and the shared energy storage, and the selling price of the external power grid adopts time-of-use electricity price. Among them, the external power grid includes renewable energy power generation and traditional energy power generation, and the renewable energy power generation can include photovoltaic power generation, wind power generation, etc., and the traditional energy power generation can include thermal power generation, hydroelectric power generation, nuclear power generation, etc. As for the shared energy storage, it can include electrochemical energy storage, mechanical energy storage, etc., which is not limited by the present application. Regarding the above-mentioned production framework of the short-process steelmaking-continuous casting process considering shared energy storage, it can be seen that Figure 3 .

[0054] Figure 4 A schematic diagram of a power grid time-of-use electricity price is also shown. It can be seen that under the time-of-use electricity price, the electricity price in different time periods is different (for example, the electricity prices in the three time periods of 0 to T1, T1 to T2, and T2 to T3 are p1, p2, and p3 respectively, which is only an example and the specific value is set by the power grid according to the actual situation). Generally speaking, the electricity price during the peak demand period is usually higher, while the electricity price during the low demand period is relatively lower. By setting different electricity prices in different time periods, the steel enterprise can be guided to increase production during the low demand period and reduce production during the peak demand period, which not only reduces the electricity cost, but also helps the safe and stable operation of the power grid. Based on this, when arranging production tasks, the electricity price factor needs to be fully considered, and the high energy consumption process is arranged in the low electricity price period as much as possible to reduce production cost and improve economic benefit of production.

[0055] In addition, regarding the production process of steelmaking, the following is explained. In the steel production process, the basic unit of steelmaking is a heat, i.e., the process of smelting simultaneously in the same electric arc furnace or converter within the same time period. In the short process steelmaking-continuous casting process, each heat will go through three main production stages (or three processes) of steelmaking, refining and continuous casting in turn, which are cooperatively completed in the specific production environment and limiting conditions of the electric arc furnace, the refining furnace and the continuous casting machine.

[0056] The role of the steelmaking stage is to melt scrap steel and remove non-metallic impurities and excessive carbon, oxygen and phosphorus elements. The refining stage is to remove oxygen, sulfur and non-metallic inclusions in the molten steel to ensure that the proportion of each component meets the established requirements. The continuous casting stage is to convert the high-temperature molten steel into a predetermined specification billet. Among them, in the continuous casting link, one or more heats can be divided into a continuous casting group, and the heat set divided into a group is called a casting heat. Thus, it can be seen that each heat in the short process steelmaking-continuous casting production is processed in turn by the processing machines in each process (for the sake of description, the processing machines are referred to as machines in the following part of the description).

[0057] Next, the short process steelmaking-continuous casting production scheduling method of the present application is specifically explained, and the established assumptions and the involved parameters are first explained.

[0058] The established assumptions are as follows: 1) all heats sequentially pass through the electric arc furnace, the refining furnace and the continuous casting machine, and the parallel equipment processing capacity of each process is the same and the assignment is unrestricted, and the processing equipment will not be stopped in the middle; 2) the processing starts by default from 0 point; 3) the heat can only select one parallel machine for processing in the same process stage; 4) the inter-process transportation equipment and power are ignored. The involved parameters can be seen in Table 1 below.

[0059] Table 1

[0060]

[0061]

[0062] Next, the short process steelmaking-continuous casting production scheduling method of the present application is specifically explained. As shown in Figure 2 The short process steelmaking-continuous casting production scheduling method 200 of the present application starts at 210.

[0063] In 210, the total electricity cost of the entire scheduling period is determined based on the cost of purchasing electricity from the external power grid and the cost of purchasing electricity from the shared energy storage within the scheduling period. According to an embodiment of the present application, the total electricity cost of the entire scheduling period can be determined in the following manner.

[0064] First, the cost of purchasing electricity from the external power grid in the dispatch period is determined according to the production supply power provided by the power grid at each time and the time-of-use electricity price at each time. Specifically, in some embodiments, it can be expressed as the following formula.

[0065]

[0066] In the formula, Q out represents the cost of purchasing electricity from the external power grid in the production process in the dispatch period, represents the production supply power provided by the power grid at time t, and Δt represents the length of a time period, represents the time-of-use electricity price at time t.

[0067] wherein the production supply power provided by the power grid at time t According to one embodiment of the present application, it can be obtained by the following formula.

[0068]

[0069] In the formula, represents the total power supplied by the power grid and the shared energy storage when the furnace j selects the machine m for processing at time t in the production stage l, represents the production supply power provided by the shared energy storage at time t, L represents the total number of production stages, J represents the total number of furnaces, and M l represents the total number of machines in the production stage l.

[0070] Further, the total power supplied by the power grid and the shared energy storage when the furnace j selects the machine m for processing at time t in the production stage l can be obtained by the following formula.

[0071]

[0072] In the formula, x jlmt is a 0-1 variable, wherein if the furnace j selects the machine m for processing at time t in the production stage l, x jlmt = 1, if the furnace j does not select the machine m for processing at time t in the production stage l, x jlmt = 0, and P jlm represents the processing power of the machine m on the furnace j in the production stage l.

[0073] Then, the cost of purchasing electricity from the shared energy storage in the dispatch period is determined according to the production supply power provided by the shared energy storage at each time and the electricity price of the shared energy storage. Specifically, in some embodiments, it can be expressed as the following formula.

[0074]

[0075] In the formula, Q sesQ represents the cost of purchasing electricity from the external power grid during the production process in the scheduling period, Q represents the production power supplied by the shared energy storage at time t, and Δt represents the length of a time period, Q represents the shared energy storage price at time t, wherein the shared energy storage price at each time is the same.

[0076] Finally, the sum of the cost of purchasing electricity from the external power grid and the cost of purchasing electricity from the shared energy storage is obtained, and the total electricity cost in the entire scheduling period is obtained. Specifically, in some embodiments, it can be represented as follows.

[0077] Q = Q out + Q ses

[0078] In the formula, Q represents the total electricity cost in the entire scheduling period, Q out represents the cost of purchasing electricity from the external power grid during the production process in the scheduling period, and Q ses represents the cost of purchasing electricity from the shared energy storage during the production process in the scheduling period.

[0079] In 220, based on the total processing power of all processing machines at each time in the scheduling period, the total processing power variance in the entire scheduling period is determined. Specifically, in some embodiments, it can be represented as follows.

[0080]

[0081] In the formula, δ represents the total processing power variance in the short-process steelmaking-continuous casting production process in the entire scheduling period, P t represents the total processing power of all machines at time t, represents the mean of the total processing power of all machines at all times, and T represents the total number of times.

[0082] Regarding and P t According to one embodiment of the present application, they can be obtained by the following formulas, respectively.

[0083]

[0084] In the formula, represents the total power supplied by the power grid and the shared energy storage when the furnace j is selected to process in the production stage l at time t.

[0085] In 230, based on the external power grid power and the renewable energy power at each time in the scheduling period, the difference degree of the external power grid power and the renewable energy power in the entire scheduling period is determined.

[0086] According to one embodiment of the present invention, the difference between the power supplied by the external power grid and the power generated by renewable energy during the entire dispatch cycle (also referred to as the green electricity fitting parameter) can be calculated from the cosine similarity between the power supplied by the external power grid and the power generated by renewable energy at each time point after standardization (i.e., normalization). The closer the cosine similarity of the two power curves after standardization is to 0, the higher the fitting degree between them, and the stronger the ability of short-process steelmaking-continuous casting to absorb green electricity.

[0087] Specifically, in some embodiments, the difference between the power supplied by the external power grid and the power generated by renewable energy during the entire dispatch cycle can be obtained in the following manner.

[0088] First, the power supplied by the external power grid and the power generated by renewable energy at each time point are normalized to obtain the normalized power supplied by the external power grid and the power generated by renewable energy at each time point. In some embodiments, the power supplied by the external power grid and the power generated by renewable energy at each time point can be normalized using the following formula.

[0089]

[0090] In the formula, This represents the grid power supply at time t after normalization. This represents the power supplied by the power grid at time t. These represent the minimum and maximum power supply of the power grid during the dispatching period, respectively. This represents the normalized green electricity power (i.e., renewable energy generation power) connected to the grid at time t. Represents the green power output at time t. These represent the minimum and maximum values ​​of green power output within the dispatch cycle, respectively.

[0091] Then, by calculating the cosine similarity between the external grid power supply and renewable energy generation at each time point after normalization, the difference between the external grid power supply and renewable energy generation over the entire scheduling cycle is obtained. Specifically, in some embodiments, this can be obtained using the following formula.

[0092]

[0093] In the formula, "fitness" represents the difference between the power supplied by the external power grid and the power generated by renewable energy throughout the entire dispatch cycle (i.e., the green electricity fitting parameter). Let represent the green electricity power connected to the grid at time t and (t-1) after standardization, respectively. Let T represent the grid power supply at time t and (t-1) after standardization, respectively, and T represent the total number of times.

[0094] At this point, the green electricity fitting parameters, total processing rate variance, and total electricity cost are obtained. In order to improve the green electricity consumption level, reduce the amount of curtailed wind and light, and maximize the utilization of renewable energy, the steel enterprise needs to fully combine the green electricity output situation in production scheduling, reasonably arrange the production tasks and the use of machines, and arrange the production as much as possible when the renewable energy generation is abundant. Based on the above description, the green electricity fitting parameter quantifies the fitting degree of the power supply load of the power grid and the output load of green power (such as wind power, photovoltaic power, etc.). Therefore, in some embodiments, in order to maximize the consumption of green power, the green electricity fitting parameter can be minimized.

[0095] The total processing power variance is related to the continuity of production and the balance of resource supply in the steel scheduling period, and is a key factor to ensure the stability of steel production and supply. In the production scheduling, on the basis of ensuring the quality of steel products, the total processing power variance should be minimized as much as possible to improve the production stability and deliver the products on time. Based on this, in some embodiments, in order to maximize the production stability, the total processing rate variance can be minimized. In addition, regarding the total electricity cost, in order to reduce the electricity cost as much as possible to increase the benefit, it should be minimized.

[0096] Therefore, in order to maximize the consumption of renewable energy (i.e., the maximum green electricity consumption capacity) in the short process steelmaking-continuous casting production, maximize the production stability, and minimize the electricity cost, so as to realize the green-economic-balance production of the short process steelmaking-continuous casting, according to one embodiment of the present application, the green electricity fitting parameter can be minimized, the total processing power variance can be minimized, and the total electricity cost can be minimized, which will be described in detail below.

[0097] In 240, the total electricity cost, the total processing power variance, and the minimum difference are used as the electricity objective function, the production objective function, and the green electricity fitting objective function, respectively, to construct a multi-objective production scheduling model of the short process steelmaking-continuous casting considering shared energy storage.

[0098] The electricity objective function can be specifically as follows:

[0099] minQ = Q out + Q ses

[0100] The production objective function can be specifically as follows:

[0101]

[0102] The green electricity fitting objective function can be specifically as follows:

[0103]

[0104] Further, the multi-objective production scheduling model further comprises constraint conditions, specifically comprising constraints on production (referred to as production constraints) and constraints on shared energy storage (referred to as shared energy storage constraints). The production constraints comprise continuity production constraints of heats, waiting time constraints of heats between adjacent processes, waiting time constraints of heats before continuous casting, processing time constraints of adjacent heats (or processing time constraints of two adjacent heats), constraints of heats on selection of processing machines in processes, and constraints between heats and casting. The shared energy storage constraints comprise constraints between shared energy storage power supply power, machine processing power and external power grid power supply power, constraints between shared energy storage power supply power and discharge power, power upper and lower limits and state constraints of shared energy storage charging and discharging, total power supply constraints of shared energy storage, and total charging and discharging constraints of shared energy storage. The constraint conditions are described one by one as follows.

[0105] Continuity production constraints of heats: processing of heats in the steelmaking-continuous casting stage should be closely connected. Once a heat starts processing on a process, the continuity of production should be maintained, and interruption is not allowed. If the production of a heat is interrupted unexpectedly on a process, the normal production rhythm will be seriously disturbed, and the production efficiency will be reduced. Therefore, the continuity production of heats should be ensured. The continuity production constraints of heats can be specifically represented by the following formula.

[0106] c jlm s jlm +t jlm ·x jlmt

[0107] In the formula, c jlm , s jlm represent the end time and start time of processing of heat j on machine m in production stage l, t jlm represents the processing time of heat j on machine m in production stage l, and x jlmt is a 0-1 variable, wherein x jlmt = 1 if heat j selects machine m for processing at time t in production stage l, and x jlmt = 0 if heat j does not select machine m for processing at time t in production stage l.

[0108] Waiting time constraints of heats between adjacent processes: also referred to as transportation time constraints between adjacent processes. In the steelmaking-continuous casting production process, after a heat completes processing in a stage, the heat needs to be transported to a machine in the next production stage by a ladle. Therefore, there will be a waiting time between adjacent production stages for the same heat, and the time interval should be greater than or equal to the transportation time between adjacent production stages. Specifically, the constraint can be represented by the following formula.

[0109] t lj≥t wt

[0110] where t lj denotes the waiting time of the heat j between the production phase l and the adjacent production phase of the production phase l (in short, the waiting time between adjacent processes), t wt denotes the transportation time of the heat between adjacent processes.

[0111] The waiting time constraint of the heat before continuous casting: the temperature of the molten steel has strict restrictions in the continuous casting phase. Due to the influence of transportation and processing sequence, there is a certain waiting time for the heat from the refining to the continuous casting phase, and different degrees of temperature drop will occur according to different ladle materials. In order to ensure that the temperature of the molten steel in the continuous casting phase is within the standard range, the waiting time of the heat from the refining to the continuous casting phase should not exceed the maximum waiting time limit (i.e., there is a maximum waiting limit between the refining and continuous casting processes). The constraint can be expressed as follows.

[0112] s j,y -c j,y-1 ≤t dl

[0113] where s j,y denotes the start processing time of the heat j in the continuous casting phase, c j,yj-1 denotes the end processing time of the heat j in the refining phase, t dl denotes the maximum waiting time of the heat before continuous casting.

[0114] The processing time constraint of adjacent heats: on the same machine, the processing of the next heat can only start after the processing of the previous heat is completed. This can ensure the orderly processing of steel production, avoid the conflict and interference of heat processing time, and ensure the smooth progress of production processing. Specifically, the constraint can be expressed as follows.

[0115] s j+1,lm -c jlm ≥0

[0116] where s j+1,lm denotes the start processing time of the heat (j+1) on the machine m in the production phase l, c jlm denotes the end processing time of the heat j on the machine m in the production phase l.

[0117] The constraint of the heat selecting processing machine in each process: in any processing phase, the same heat can only be processed on one machine, which can be specifically expressed as follows.

[0118]

[0119] where x jlmtis a 0-1 variable, where x jlmt = 1 if the heat j chooses the machine m to process at time t in the production stage l, and x jlmt = 0 if the heat j does not choose the machine m to process at time t in the production stage l, M l represents the total number of machines in the production stage l.

[0120] The constraint between heats and casting lots: the same heat can only belong to one casting lot, but one casting lot can contain multiple heats, which can be specifically expressed as follows.

[0121]

[0122] y1∪y2∪…∪y n = Y

[0123] In the formula, y1, y2, y n represent casting lot 1, casting lot 2, and casting lot n respectively, and Y represents the set of casting lots.

[0124] In addition, the continuous casting process is a process of continuously casting molten steel into solid steel billets. This process needs to maintain continuity, and once interrupted, it will seriously affect the quality of steel products and also cause equipment damage. In addition, the start-stop cost of the continuous casting machine and the replacement cost of auxiliary equipment such as tundish and crystallization package are high. In order to ensure production quality and effectively reduce operating costs, it is necessary to ensure continuous production of the same casting lot. Therefore, in some embodiments, the production constraint also includes the casting constraint of adjacent heats in the same casting lot, specifically, after the casting of the previous heat is completed on the same machine, the casting of the next heat starts processing, which can be expressed as follows.

[0125] s j+1,y -c j,y = 0, j, (j+1) ∈ y n

[0126] In the formula, s j+1,y represents the start processing time of the heat (j+1) in the casting stage in the casting lot n, c j,y represents the end processing time of the heat j in the casting stage in the casting lot n.

[0127] The constraint between shared energy supply power, machine processing power, and external grid supply power: the processing power at the same time is equal to the sum of the shared energy supply power and the grid supply power, which can be specifically expressed as follows.

[0128]

[0129] In the formula, represents the production supply power provided by the shared energy at time t, represents the grid supply power at time t, Pt Ptotal(t) represents the total processing power of all machines at time t.

[0130] The constraint between the shared energy storage power supply and discharge power: the shared energy storage power supply at the same time should be equal to the shared energy storage discharge power at the same time, which can be specifically expressed as the following formula:

[0131]

[0132] In the formula, Pshared(t) represents the production power supply provided by the shared energy storage at time t, Pshared(t) represents the discharge power of the shared energy storage at time t.

[0133] The upper and lower limits of the power of the shared energy storage charging and discharging and the state constraint: that is, the upper and lower limits of the power of the shared energy storage charging and discharging and the state constraint of the shared energy storage charging and discharging, which can be specifically expressed as the following formula.

[0134]

[0135] In the formula, Pshared(t) represents the charging power and discharge power of the shared energy storage at time t, Pshared(t) represents the maximum charging power and maximum discharge power of the shared energy storage, is a 0-1 variable representing the charging state of the shared energy storage, if the shared energy storage is in the charging state at time t, if the shared energy storage is in the discharging state at time t, is a 0-1 variable representing the discharging state of the shared energy storage, if the shared energy storage is in the discharging state at time t, if the shared energy storage is in the charging state at time t,

[0136] The total power supply constraint of the shared energy storage: the total amount of power provided by the shared energy storage within the scheduling period cannot exceed the contract capacity, which can be specifically expressed as the following formula.

[0137]

[0138] In the formula, C ses Ptotal represents the total power provided by the shared energy storage within the scheduling period, C represents the contract capacity signed by the shared energy storage, Pshared(t) represents the discharge power of the shared energy storage at time t, and Δt represents the length of a time period.

[0139] The total charging and discharging amount constraint of the shared energy storage: the total charging and discharging amount of the shared energy storage within the scheduling period is equal, which can be specifically expressed as the following formula.

[0140]

[0141] So far, the construction of the green-economy-balance multi-objective production scheduling model of the short-process steelmaking-continuous casting considering shared energy storage is completed. Next, 250 is entered, the constructed multi-objective production scheduling model is solved, and the total scheduling decision of the short-process steelmaking-continuous casting is obtained. The total scheduling decision includes a production scheduling decision and an electricity scheduling decision, the production scheduling decision includes the processing machines selected by each heat in each process in the scheduling period, and the start time (i.e., the processing start time) and the end time (the processing end time) of the processing of each heat on the processing machines selected by each heat in each process, and the electricity scheduling decision includes the electricity purchased from the external power grid at each time and the electricity purchased from the shared energy storage at each time.

[0142] According to one embodiment of the present application, the short-process steelmaking-continuous casting production scheduling problem considering shared energy storage can be regarded as a hybrid flow shop scheduling problem, there are multiple workpieces in a manufacturing system, each workpiece needs to go through a specific process, and there are multiple parallel machines in each process, and the selection of a machine for processing by each workpiece in each process needs to be decided. In addition, the shared energy storage and the power grid jointly supply power for steel production, and the charging and discharging state and the electricity of the shared energy storage at each time period also need to be decided. For this problem, in some embodiments, the NSGA-II algorithm combined with the GUROBI solver can be used to solve the constructed multi-objective production scheduling model, which will be described below in combination with Figure 5 .

[0143] (1) Initialization setting: set the population size N, the crossover probability, the mutation probability, the number of iterations, etc. The initial population P0 is generated by using the Monte Carlo method;

[0144] (2) GUROBI solving: for each individual, extract the processing selection matrix (i.e., which machine is selected by each workpiece in each process for processing), use the GUROBI solver to optimize the processing sequence of the workpieces on each machine with the objective of minimizing the total processing power variance, solve the processing sequence matrix, and then calculate the three fitness values of each individual;

[0145] (3) Non-dominated sorting: non-dominated sorting of the individuals in the population P0 is performed to generate non-dominated solutions at different levels;

[0146] (4) Calculate the crowding distance: for the individuals at each level, the crowding distance is calculated;

[0147] (5) Selection: the tournament method is used, and the individuals at a lower level are given priority, and if the levels are the same, the crowding degree is compared, and the individual with a larger crowding distance is selected;

[0148] (6) Crossover and mutation: the selected individuals are crossed and mutated to generate new individuals and form a new population Q0;

[0149] (7) Merge populations: merge populations P0 and Q0 into a new population R0;

[0150] (8) Perform GUROBI solving, non-dominated sorting and crowded distance calculation on the new population R0, and select N individuals to form a new population P1;

[0151] (9) Stop output: repeatedly repeat the above steps, and stop and output if the set number of iterations or other termination conditions are met.

[0152] Regarding the above solving process, NSGA-II algorithm is relatively mature, and specific reference can be made to its related description.

[0153] Among them, the scheduling period and the time point will be explained here. In some embodiments, every 15 minutes can be taken as a time point, and 96 time points in a day can be taken as a production scheduling period. Of course, this is only an example, and the present application is not limited thereto. In specific embodiments, those skilled in the art can set it according to actual needs.

[0154] The above is the production scheduling method of the short process steelmaking-continuous casting considering shared energy storage of the present application. Next, taking the daily order production process of a short process steelmaking-continuous casting of a steel enterprise in a city as an example, the specific process is as follows.

[0155] 1. Parameter setting

[0156] 1) Steel production related parameters

[0157] The pipeline of the enterprise has 2 arc furnaces, 2 refining furnaces, and 2 continuous casting machines. Taking 20 furnace times of daily orders as the scheduling object, every 15 minutes as a research period, and the processing time and electric power of the arc furnace, the refining furnace and the continuous casting machine are shown in Table 2.

[0158] Table 2

[0159]

[0160] The maximum waiting time between the refining and continuous casting processes is affected by many factors such as ladle material, processing technology, and workshop environment, and there is no general numerical value. The production workshop of the daily order adopts polyurethane integral foaming insulation material, and the insulation time is about 300 minutes, so 300 minutes is taken as the maximum waiting time between the refining and continuous casting processes, and 15 minutes is taken as the transportation time between the processes.

[0161] 2) Power grid related parameters

[0162] To verify the effectiveness of the model, the non-summer time-of-use electricity price of large industrial electricity in the city is selected as the input parameter of the power grid time-of-use electricity price, as shown in Table 3.

[0163] Table 3

[0164]

[0165] The wind power output power of the steel plant connected to the power grid in the city from 0:00 to 24:00 on a typical day is selected as green power for the experiment, and the production load provided by the power grid is optimized. The wind power output power of the city on a typical day in April is shown in Figure 6

[0166] 3) Shared energy storage related parameters

[0167] The related parameters of the shared energy storage system in which the steel plant participates can be seen in Table 4 below.

[0168] Table 4

[0169]

[0170]

[0171] 2, Scene setting

[0172] This example takes a steel enterprise in a city as the research object. In order to verify the short process steelmaking-continuous casting production scheduling optimization model considering shared energy storage proposed by the present application, the following three target scenes are set for comparison and verification to prove the effectiveness of the optimization results. Table 5 below shows the settings of different optimization objectives under each scene.

[0173] Table 5

[0174]

[0175] According to the above different scenes, the short process steelmaking-continuous casting production scheduling scheme is solved and analyzed and compared to verify the effectiveness of the model of the present application.

[0176] 3, Result analysis

[0177] 1) Scene comparison

[0178] The total processing power variance, electricity economic cost and green electricity fitting parameters obtained under the four scenes are shown in Figure 7

[0179] ​​Since the four scenarios all optimize the total processing power variance, this target has no significant difference in different scenarios. For the electricity economic cost, scenario 1 fully utilizes the time-of-use electricity price and the difference between the grid price and the energy storage price in the same period, flexibly arranges the production scheduling and electricity strategy, and has the lowest electricity cost of 71449 yuan under the premise of ensuring production efficiency, thereby obtaining the highest economic benefit. Since scenario 2 does not consider the economic cost, the influence of the time-of-use electricity price and the shared energy storage price is ignored, and in the period with high wind power output, the production is concentrated and the grid power supply is selected as the main power source, which inevitably leads to an increase in the electricity economic cost. The electricity economic cost in scenario 2 is 80564 yuan. Scenario 3 is only powered by the grid, and compared with the shared energy storage and grid combined power supply mode, the power regulation effect of the grid alone is poor. The electricity economic cost in scenario 3 is 77179 yuan. Scenario 4 is the scenario studied in the present application, which optimizes the electricity economic cost and green electricity fitting as the optimization target while ensuring the minimum total processing power variance. Through reasonable scheduling, the electricity economic cost in scenario 4 is 77210 yuan, which obtains higher economic benefit than scenario 2. Although the economic cost of scenario 4 is higher than that of scenario 1, scenario 4 takes into account the economy and greenness of production.

[0180] For the green electricity fitting level, the fitting of the grid power supply power curve and the wind power curve connected to the grid in different scenarios is as follows Figure 8The scenario 1 comprehensively considers the change of time-of-use electricity price and the difference between time-of-use electricity price and shared energy storage price in the same period, arranges production and formulates power purchase strategy. In the period of low grid electricity price, the production is arranged centrally and the power supply is more inclined to choose the grid. Due to the neglect of the influence of green electricity fitting parameter, the grid power supply load will not be actively scheduled according to the change of green power output, thereby limiting the consumption level of renewable energy generation by steel production, and the green electricity fitting parameter is 0.35. The scenario 2 ignores the economic cost as an optimization target, so the production scheduling arrangement and power purchase decision of the steel plant are no longer affected by the time-of-use electricity price and the shared energy storage price, but are adjusted according to the fluctuation of green power connected to the grid. As can be seen from the figure, the grid power supply power of scenario 2 is closest to the trend of green power connected to the grid, and the green electricity fitting parameter is 0.04. The scenario 3 cannot be used as a flexible regulation power supply to improve the green electricity fitting effect because the shared energy storage no longer participates in the production and power supply. Therefore, the green electricity fitting effect is poor, and the green electricity fitting parameter is 0.14. The scenario 4 adopts the shared energy storage and grid combined power supply mode, and considers the total processing power variance, electricity cost and green electricity fitting. Therefore, the production scheduling arrangement and power purchase strategy of the steel plant not only respond to the electricity price, but also are affected by the wind power generation. The green electricity fitting parameter in this scenario is 0.13, and the grid power supply load generally tends to shift to the non-peak period of electricity price and the period of sufficient wind power. It not only comprehensively utilizes the price difference of different power supply sources, but also effectively consumes the green power resources connected to the grid, improves the green production, and also considers the economic benefits.

[0181] 2) Solution results

[0182] This example studies the optimization of a short-process steelmaking-continuous casting production scheduling model under the shared energy storage and grid combined power supply mode, with the objectives of minimizing the total processing power variance, minimizing the electricity cost and optimizing the green electricity fitting. This example uses the NSGA-II algorithm combined with the GUROBI solver for solving. The initial parameter values of the algorithm are set as follows: the crossover probability is set to 0.5, the mutation probability is set to 0.1, the population size is 30, and the iteration number is 50.

[0183] Figure 9 The figure shows the convergence of the fitness function of each target value under the change of iteration number. As can be seen from the figure, the total processing power variance value gradually stabilizes at 187882 after 9 iterations; the grid power supply quantity and the wind power processing fitting value gradually stabilize at 0.04 after 7 iterations; and the total electricity economic cost value gradually stabilizes at 68832 yuan after 41 iterations.

[0184] Figure 10 The figure shows the Pareto optimal solution set obtained by the scheduling method and the three-objective Pareto front surface fitted by the solution set.

[0185] The daily order furnace smelting route is: electric arc furnace-refining furnace-continuous casting machine. Figure 11 To optimize the total processing power variance, the economic cost of electricity, and the green electricity fitting of a production scheduling scheme Gantt chart and the power change graph of each device. All the furnace numbers are numbered and distinguished by different colors in the Gantt chart. The Gantt chart clearly shows the selection of each furnace in each production stage, as well as the start time, processing time and end time of the processing. The power value of each device in the corresponding period is shown below the Gantt chart, which well reflects the power change.

[0186] Under this scheduling scheme, due to the influence of wind power output and time-of-use electricity price, production is concentrated in the 6:00-8:00 stage, during which the wind power output reaches its peak and the grid price is in the normal period, which is relatively low. Therefore, the most processing furnace is arranged in this period, and more grid power is used, making full use of the advantages of grid price and wind power in this period. Due to the influence of the minimum completion time, more processing is arranged in the 8:00-11:00 period to ensure production efficiency, but the grid price in this period is in the peak period, which is the highest, and the wind power output is also reduced. Therefore, more shared energy storage is selected as the power source in this period, as shown in Figure 12 .

[0187] The total processing power variance of this scheme is 292677. While ensuring the stability of production, the economic cost of electricity is 77210 yuan, and the green electricity fitting value is 0.13, which takes into account the economy and greenness. Figure 13 The daily charging and discharging strategy of the shared energy storage contract capacity signed by the steel enterprise. Due to the constraint of the balance of shared energy storage charging and discharging in the scheduling period, the total amount of shared energy storage daily charging and discharging is equal.

[0188] After normalizing the power of the grid power supply for each period of short process steelmaking-continuous casting production and the wind power connected to the grid on a typical day, the curve comparison chart is shown in Figure 14 . From the figure, it can be seen that the grid power supply load curve is similar to the wind power curve in most periods. When the wind power output is sufficient, the grid power supply power used by steel production is also relatively high; when the wind power output is low, the grid power supply power is also relatively low. The grid power supply load power basically changes with the trend of green electricity output, effectively absorbing the volatility of green electricity, reducing the adjustment pressure of the grid on green electricity access, and improving the safety and stability of the grid operation. At the same time, it also alleviates the problem of green electricity abandonment, realizing green and low-carbon production.

[0189] 4、Sensitivity analysis

[0190] 1) Sensitivity analysis of time-of-use electricity price peak-valley price difference

[0191] As an effective price-based demand response mechanism, time-of-use price (TOU) sets different electricity prices in different time periods to guide users to reduce electricity consumption in peak periods and increase electricity consumption in off-peak periods, so as to smooth the peak-valley difference of electricity demand, ensure the balance and stable operation of the power system, and improve the utilization efficiency of power resources. Under the influence of TOU, steel enterprises formulate production scheduling plans and power purchase strategies according to the difference between the grid price in each period and the shared energy storage price.

[0192] In order to further explore the influence of TOU on the scheduling results of short-process steelmaking-continuous casting production, the fluctuation range of the peak-valley price difference is set to be between 2.5 and 4.5 under the condition of keeping other parameters unchanged, and the change of the target value is as shown in Figure 15 It can be seen from the figure that as the peak-valley electricity price difference gradually increases, the total processing power variance of short-process steelmaking changes little, and the overall trend is downward, while the production electricity cost shows a significant downward trend. The main reason for this phenomenon is that when the peak-valley electricity price difference is large, the steel plant will choose to concentrate production in the period with lower electricity price, and at the same time, it will prefer to choose grid power purchase as the main power source in this period. The processing and production in the peak period of electricity price are relatively reduced, and more production in this period depends on shared energy storage power supply. The greater the peak-valley price difference, the stronger the willingness of steel enterprises to participate in grid demand response. However, since the electricity price and the green electricity power have a common peak period, the increase of the peak-valley price difference also causes the green electricity fitting effect to be weakened.

[0193] 2) Sensitivity analysis of inter-process transportation time

[0194] After the heat is completed in the previous process, it needs to be transported to the next process for further processing in the form of a ladle, so the inter-process waiting time will affect the flexibility and production efficiency of production scheduling.

[0195] In order to further explore the influence of inter-process transportation time on short-process production scheduling, the fluctuation range of the inter-process transportation time is set to be between 5 and 25 min under the condition of keeping other parameters unchanged, and the change of the target value is as shown in Figure 16The impact of inter-process transportation time on the three objectives is shown in FIG. 6. As can be seen from the figure, the inter-process transportation time has a relatively low impact on the electricity cost and green electricity fitting parameters. However, as the inter-process transportation time increases, the total processing power variance tends to increase. This is because in the steel production process, after the completion of the previous process, the heat is transported to the next process for further processing. If the transportation time is too long, the machine equipment of the subsequent process will be idle and cannot immediately produce because it is waiting for the completion of the previous process. As the transportation time increases, the idle time of the machine equipment in the production process will increase, and the processing power during the idle period of the equipment will be significantly lower than that during the normal processing period, thus increasing the total processing power variance.

[0196] 3) Sensitivity analysis of shared energy storage capacity

[0197] The shared energy storage capacity also plays a key role in the short process steelmaking-continuous casting production scheduling. The capacity signed by the steel enterprise and the shared energy storage operator directly affects the power supply and electricity purchase decision in the production process, and thus affects the electricity cost and green electricity fitting.

[0198] In order to further explore the impact of shared energy storage contract capacity on short process production scheduling, the fluctuation range of the shared energy storage contract capacity is set to be between 5 and 25 MWh in the example, and the change of the target value is shown in FIG. 7. Figure 17 As can be seen from the figure, the shared energy storage capacity has different degrees of impact on the three target values. As the shared energy storage capacity increases, the green electricity fitting value decreases slightly, and the total processing power variance and electricity cost decrease significantly. This is because the increase of the shared energy storage capacity improves the flexibility of the power supply in the production process. The increase of the shared energy storage capacity means that the steel enterprise can better respond to energy demand fluctuations and adjust the electricity purchase decision, and fully utilize the difference between the grid price and the shared energy storage price at different times to make flexible adjustments to the short process steelmaking-continuous casting production plan. In the period with low wind power output and relatively high grid price, the shared energy storage is mainly used as power support, which effectively reduces the electricity cost and improves the level of renewable energy consumption. However, it should be noted that as the shared energy storage contract capacity increases, the trend of reduction of the electricity cost and the green electricity fitting parameter gradually slows down. This is because the shared energy storage capacity gradually approaches the production requirement. At the same time, the increase of the scheduling potential caused by the increase of the shared energy storage capacity further promotes the balanced production of the steel. Thus, the balance between economy, greenness and stability in the steel production is ensured, which provides a boost for the sustainable development of the steel industry.

[0199] In summary, for the short process steelmaking-continuous casting production, the application proposes a joint power supply mode of shared energy storage and power grid, which can provide stable power support for steel production, and this mode not only can bring more flexible and efficient power resource allocation scheme for steel production, but also can reduce the electricity cost of steel production and improve the renewable energy consumption capacity of steel production. In addition, according to the processing task, processing flow, equipment production capacity and energy supply, the application takes the total processing power variance, electricity economic cost and green electricity fitting as the optimization target, and from the perspective of production process and power supply, a multi-objective production scheduling model of short process steelmaking-continuous casting is constructed, which takes into account the production stability, the lowest electricity cost and the maximum renewable energy consumption, which is beneficial to realize the green-economical-balance production scheduling of short process steelmaking-continuous casting.

[0200] The various techniques described herein can be implemented in connection with hardware or software or, where appropriate, with a combination of both. Thus, the methods and apparatus of the application, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embodied in tangible media, such as removable hard disks, USB flash drives, floppy diskettes, CD-ROMs, or any other machine-readable storage medium wherein, when the program code is loaded into an internal memory of the machine such as a computer, the machine becomes an apparatus for practicing the application.

[0201] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been described in detail in order to not obscure aspects of the present application.

[0202] It is to be understood that the embodiments of the application herein described are merely illustrative of the many applications of the principles of the application. Numerous modifications can be made to the embodiments without departing from the scope of the application. It is not intended to limit the application to the exact construction and instrumentalities shown and described, but rather, the application is intended to cover all modifications and equivalents consistent with the spirit and scope of the application as defined by the appended claims.

[0203] Furthermore, unless otherwise indicated, the use of the ordinal adjectives such as "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

[0204] While the application has been described in accordance with the various embodiments shown and described, it is to be understood that the application is not limited to those precise embodiments, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the present application. It is intended that the scope of the application should only be limited as recited in the appended claims.

Claims

1. A production scheduling method for short-process steelmaking-continuous casting that takes into account shared energy storage, wherein, The electricity generated in the short-process steelmaking-continuous casting production is jointly provided by an external power grid and shared energy storage, and each heat in the short-process steelmaking-continuous casting production is sequentially processed by processing machines in each process step, the method including: Based on the cost of purchasing electricity from the external power grid and the cost of purchasing electricity from shared energy storage during the scheduling cycle, the total electricity cost for the entire scheduling cycle is determined. Based on the total processing power of all processing machines at each moment within the scheduling cycle, the variance of the total processing power for the entire scheduling cycle is determined. Based on the power supply from the external power grid and the power generation from renewable energy at each moment within the scheduling cycle, the degree of difference between the power supply from the external power grid and the power generation from renewable energy throughout the entire scheduling cycle is determined. Using the total electricity cost, the variance of total processing power, and the minimum difference as the objective functions for electricity consumption, production, and green electricity fitting, respectively, a multi-objective production scheduling model for short-process steelmaking-continuous casting, taking into account shared energy storage, is constructed. Solve the multi-objective production scheduling model to obtain the overall scheduling decision for short-process steelmaking-continuous casting. The overall scheduling decision includes production scheduling decision and power consumption scheduling decision. The production scheduling decision includes the processing machine selected for each furnace in each process within the scheduling cycle, as well as the start and end times of processing on the selected processing machine. The power consumption scheduling decision includes the electricity purchased from the external power grid and shared energy storage at each time within the scheduling cycle. The production objective function includes: Where δ represents the variance of the total processing power throughout the entire scheduling cycle, P t This represents the total processing power of all processing machines at time t. This represents the average total processing power of all processing machines at all times, and T represents the total number of times. The determination of the difference between the external grid power supply and renewable energy power generation throughout the entire scheduling period, based on the external grid power supply and renewable energy power generation at each moment within the scheduling cycle, includes: The power supplied by the external power grid and the power generated by renewable energy at each time point are normalized to obtain the normalized power supplied by the external power grid and the power generated by renewable energy at each time point. The difference is obtained by calculating the cosine similarity between the power supply from the external power grid and the power generation from renewable energy sources at each time point after normalization. The objective function for green electricity fitting includes: Here, "fitness" represents the degree of difference between the power supplied by the external power grid and the power generated by renewable energy throughout the entire dispatch cycle. These represent the green electricity power connected to the grid at time t and time t-1, after normalization. Let represent the external power supply at time t and time t-1 after normalization, respectively, and T represent the total number of times.

2. The method as described in claim 1, wherein, The total electricity cost for the entire scheduling cycle is determined based on the costs of purchasing electricity from the external power grid and the costs of purchasing electricity from shared energy storage within the scheduling cycle, including: The cost of purchasing electricity from the external power grid during the dispatch cycle is determined based on the power supply provided by the power grid at each time and the time-of-use electricity price of the power grid at each time. The cost of purchasing electricity from shared energy storage during the dispatch cycle is determined based on the power output provided by shared energy storage at each time point and the electricity price of shared energy storage. The total electricity cost for the entire dispatch cycle is obtained by summing the cost of purchasing electricity from the external power grid and the cost of purchasing electricity from shared energy storage.

3. The method as described in claim 1 or 2, wherein, The multi-objective production scheduling model also includes production constraints, which include continuous production constraints for furnace runs, waiting time constraints between adjacent processes for furnace runs, waiting time constraints before continuous casting for furnace runs, processing time constraints between adjacent furnace runs, constraints on the selection of processing machines for furnace runs in each process, and constraints between furnace runs and casting runs.

4. The method of claim 3, wherein, The multi-objective production scheduling model also includes shared energy storage constraints, which include constraints between the power supply of shared energy storage and the processing power of the machine, the power supply of the external power grid, the power supply and discharge power of shared energy storage, the upper and lower limits of the power and state constraints of the charging and discharging of shared energy storage, the total power supply of shared energy storage, and the total charging and discharging of shared energy storage.

5. The method of claim 3, wherein, The continuous production constraints for the aforementioned batches include: c jlm =s jlm +t jlm ·x jlmt Among them, c jlm s jlm These represent the end time and start time of furnace j on machine m in process l, respectively. jlm This indicates the processing time of furnace j on machine m in process l, x jlmt Let x be a 0-1 variable, where if furnace j is processed by machine m in process l at time t, then x jlmt =1, if furnace j does not select machine m for processing in process l at time t, then x jlmt =0.

6. A computing device, comprising: At least one processor; as well as A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing the method as described in any one of claims 1-5.

7. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method as described in any one of claims 1-5.

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