Device operation control method and device, cloud server, and storage medium

Abstract

The application discloses a device operation control method and device, a cloud server and a storage medium, and relates to the technical field of industrial energy management, in particular to a device operation control method and device, a cloud server and a storage medium. The method comprises the following steps: a cloud server obtains a first power consumption in a target period and a first available power prediction value in the target period from a target industrial user end; the first available power prediction value and the first power consumption are input into a scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve and a third pre-scheduling curve of the target industrial user end in the target period; the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve are respectively used for controlling the operation of a metallurgical device, a power generation device and an energy storage device of the target industrial user end. Through implementation of the method, the actual power consumption demand of the metallurgical device is met according to the first power consumption sent by the target industrial user end, and the problem of power gap generated by a factory with special demands under an energy management system is solved.

Description

Technical Field

[0001] This invention relates to the field of general data processing technology in energy management systems, and more particularly to a method, apparatus, cloud server, and storage medium for equipment operation control. Background Technology

[0002] An energy management system is a comprehensive system integrating software and hardware used to monitor, control, and optimize the operation of energy systems. It manages numerous electrical devices, power generation equipment, and energy storage devices. During off-peak hours, the system stores electrical energy, and during peak hours, it releases this stored energy to address power shortages and unstable power supply. However, simply storing energy during off-peak hours and discharging it during peak hours cannot meet the actual power demands of specific industrial scenarios. This can lead to power shortages for some factories with special needs under the energy management system. Summary of the Invention

[0003] To address the aforementioned issues, this application provides a method, apparatus, cloud server, and storage medium for equipment operation control. The cloud server acquires a first predicted available power and a first power consumption. The first predicted available power and the first power consumption are then input into a scheduling cost optimization model to obtain a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve. This controls the operation of metallurgical equipment, power generation equipment, and energy storage equipment managed by industrial users, meeting the actual power demand of the metallurgical equipment and solving the problem of power shortages in factories with special needs under their energy management systems.

[0004] To achieve the above objectives, in a first aspect, embodiments of this application provide a device operation control method applied to a cloud server of an energy management system. The energy management system further includes multiple industrial user terminals, each of which manages metallurgical equipment, power generation equipment, and energy storage equipment. The method includes: the cloud server obtaining a first electricity consumption during a target time period from the target industrial user terminal; the first electricity consumption includes the electricity consumption required by the metallurgical equipment managed by the target industrial user terminal and the electricity consumption of the power generation equipment managed by the target industrial user terminal during the target time period; the cloud server obtaining a first predicted available electricity value during the target time period; the cloud server inputting the first predicted available electricity value and the first electricity consumption into a scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period; the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period are respectively used by the target industrial user terminal to control the operation of the metallurgical equipment, the power generation equipment, and the energy storage equipment during the target time period.

[0005] It can be seen that by obtaining the predicted values ​​of the first electricity consumption and the first available electricity within the target time period from the target industrial user through the cloud server, and inputting the predicted values ​​of the first electricity consumption and the first available electricity into the scheduling cost optimization model, the scheduling cost optimization model allocates electricity to the target industrial user based on the predicted value of the first available electricity, so as to meet the actual electricity demand of metallurgical equipment according to the first electricity consumption sent by the target industrial user, thus solving the problem of power shortage in factories with special needs under the energy management system.

[0006] In conjunction with the first aspect, the energy management system includes a first power supply network composed of power generation equipment managed by multiple industrial users. Energy storage devices and metallurgical equipment managed by multiple industrial users are connected to the first power supply network. The cloud server obtains a first predicted value of available electricity during a target period, including: the cloud server obtaining the utilization rate of key metallurgical equipment from the target industrial users during the target period, and determining whether the metallurgical equipment managed by the target industrial users is in peak production period based on the utilization rate of key metallurgical equipment (key metallurgical equipment is metallurgical equipment that needs to operate continuously during the target period); if the metallurgical equipment managed by the target industrial users is not in peak production period and the first power supply network is in a low-consumption period, the cloud server obtains the amount of electricity that all energy storage devices in the energy management system need to obtain from the first power supply network during the target period; the cloud server obtains the first predicted value of available electricity based on the amount of electricity needed to be obtained from the first power supply network during the target period and the amount of electricity that the energy storage devices managed by the target industrial users can still store during the target period.

[0007] It can be seen that the cloud server first determines whether it is in a peak production period based on the utilization rate of key metallurgical equipment managed by the industrial user. If it is in a peak production period, and the primary power supply network is in a low-demand period, the first available power prediction value is adjusted based on the amount of electricity that the energy storage devices managed by the target industrial user can still store during the target period, resulting in a second available power prediction value, thus improving the accuracy of the power prediction value. During the low-demand period, the energy storage devices are controlled to store excess electricity from the power generation equipment, so that the energy storage devices can release electricity during the peak demand period to meet the power needs of the metallurgical equipment.

[0008] In conjunction with the first aspect, in one possible embodiment, if the metallurgical equipment managed by the target industrial user is in peak production and the first power supply network is in a low-consumption period, the cloud server obtains the amount of electricity that all energy storage devices in the energy management system need to obtain from the second power supply network during the target period; the second power supply network is the power supply network composed of power generation devices in the first power supply network other than the power generation equipment managed by the target industrial user; the cloud server obtains a first available power prediction value based on the amount of electricity that needs to be obtained from the second power supply network during the target period and the amount of electricity that the energy storage devices managed by the target industrial user can still store during the target period.

[0009] It can be seen that when the metallurgical equipment managed by the target industrial user is in peak production, the cloud server obtains the difference between the amount of electricity that all energy storage devices in the energy management system need to obtain from the second power supply network during the target period and the amount of electricity that the energy storage devices managed by the target industrial user can still store during the target period, to obtain the third available power prediction value. The electricity from the energy storage and power generation devices managed by the target industrial user is dedicated to the metallurgical equipment managed by the target industrial user, reducing the problem of power shortages that occur when the second power supply network is in a low-demand period while the metallurgical equipment is in peak production. The corrected third available power prediction value improves the accuracy of the power prediction value. When the metallurgical equipment managed by the target industrial user is in peak production, all the electricity that the power generation and energy storage devices managed by the target industrial user can provide is used for the metallurgical equipment managed by the target industrial user, solving the problem that the power supply network may not be able to meet the demand of the metallurgical equipment in peak production.

[0010] In conjunction with the first aspect, in one possible embodiment, the cloud server obtains the electricity consumption of the target industrial user cluster during the target time period, and determines a second available power prediction value for the target industrial user cluster based on the electricity consumption of the target industrial user cluster during the target time period and the power required to be obtained from the first power supply network; the electricity consumption of the target industrial user cluster during the target time period includes the power consumption required by all metallurgical equipment managed by all industrial users in the target industrial user cluster during the target time period and the power that all power generation equipment managed by all industrial users can provide during the target time period; the target industrial user cluster includes the target industrial user and industrial user terminals whose distance from the target industrial user is less than a preset distance; the cloud server inputs the first available power prediction value and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: the cloud server inputs the second available power prediction value, the first available power prediction value, and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period.

[0011] As can be seen, by merging the target industrial user terminal and multiple industrial user terminals at a preset distance into a target industrial user terminal cluster, and determining the second available power prediction value of the target industrial user terminal cluster, the second available power prediction value, the first available power prediction value, and the first power consumption are input into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period. The power allocation of the target industrial user terminal cluster is first determined based on the second available power prediction value and the first power consumption. Then, the first, second, and third pre-scheduling curves for the target industrial user terminal during the target time period are determined based on the second available power prediction value. This reduces the computational difficulty and improves the computational accuracy.

[0012] In conjunction with the first aspect, in one possible embodiment, the cloud server obtains the electricity consumption of the target industrial user cluster during the target time period, and determines a third available power prediction value for the target industrial user cluster based on the electricity consumption of the target industrial user cluster during the target time period and the power required to be obtained from the second power supply network; the electricity consumption of the target industrial user cluster during the target time period includes the power consumption required by all metallurgical equipment managed by all industrial users in the target industrial user cluster during the target time period and the power that all power generation equipment managed by all industrial users can provide during the target time period; the target industrial user cluster includes the target industrial user and industrial user terminals whose distance from the target industrial user is less than a preset distance; the cloud server inputs the first available power prediction value and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: the cloud server inputs the third available power prediction value, the first available power prediction value, and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period.

[0013] As can be seen, by merging the target industrial user terminal and multiple industrial user terminals within a preset distance into a target industrial user terminal cluster, and determining the third available power prediction value of the target industrial user terminal cluster, the third available power prediction value, the first available power prediction value, and the first power consumption are input into the scheduling cost optimization model to generate the first, second, and third pre-scheduling curves for the target time period. The power allocation of the target industrial user terminal cluster is first determined based on the second available power prediction value and the first power consumption, and then the first, second, and third pre-scheduling curves for the target industrial user terminal during the target time period are determined based on the third available power prediction value. This reduces the computational difficulty and improves the computational accuracy.

[0014] In conjunction with the first aspect, in one possible embodiment, the cloud server obtains the total electricity consumption of all metallurgical equipment in the energy management system during a target time period; if the first available electricity prediction value is less than the total electricity consumption of all metallurgical equipment, the cloud server determines the electricity priority of the target industrial user, wherein the higher the utilization rate of the key metallurgical equipment corresponding to the industrial user, the higher the electricity priority; the cloud server inputs the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: the cloud server inputs the electricity priority, the first available electricity prediction value, and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period.

[0015] It can be seen that when the first predicted available power is less than the total power consumption of all metallurgical equipment, the cloud server prioritizes the power consumption of metallurgical equipment managed by industrial users with higher power consumption priority by prioritizing the power consumption of multiple industrial users. This reduces the losses caused by the inability of many critical metallurgical equipment to operate normally when the available power is low.

[0016] In conjunction with the first aspect, in one possible embodiment, determining whether the metallurgical equipment managed by the industrial user terminal is in peak production period based on the utilization rate of key metallurgical equipment includes: the cloud server obtaining the equipment type of the metallurgical equipment managed by the industrial user terminal from the industrial user terminal, and identifying metallurgical equipment with the equipment type of continuous heating equipment or emission equipment as key metallurgical equipment; the cloud server obtaining the number of key metallurgical equipment and the number of key metallurgical equipment that the target industrial user terminal needs to operate during the target period from the target industrial user terminal; the cloud server determining the ratio of the number of key metallurgical equipment that needs to be operated to the total number of key metallurgical equipment as the key metallurgical equipment utilization rate; if the key metallurgical equipment utilization rate is less than a preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is not in peak production period; if the key metallurgical equipment utilization rate is not less than the preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is in peak production period.

[0017] It can be seen that by measuring the utilization rate of key equipment at the target industrial user end during the target period, the cloud server can determine whether it is a peak production period, thereby enabling the cloud server to correct the target available power prediction value and improve the accuracy of the available power prediction value of the cloud server input scheduling cost optimization model.

[0018] Secondly, embodiments of this application provide an equipment operation control device for executing an equipment operation control method, applied to an energy management system. The energy management system further includes multiple industrial user terminals, each of which manages metallurgical equipment, power generation equipment, and energy storage equipment. The equipment operation control device includes:

[0019] Acquisition Unit: Used by the cloud server to acquire the first electricity consumption in the first time period from the target industrial user terminal. The first electricity consumption includes the electricity consumption required by the metallurgical equipment managed by the target industrial user terminal and the power generation equipment managed by the target industrial user terminal in the first time period.

[0020] Acquisition Unit: Also used for cloud servers to obtain the first predicted available power level for the target time period;

[0021] The generation unit is used by the cloud server to input the first available power prediction value and the first power consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period; the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period are respectively used by the target industrial user to control the operation of metallurgical equipment, power generation equipment and energy storage equipment during the target time period.

[0022] Thirdly, embodiments of this application provide a cloud server, including a processor, a memory, a communication interface, and one or more programs, the one or more programs being stored in the memory and configured to be executed by the processor, and one or more instructions being adapted to be loaded by the processor and executed in part or in whole the method of the first aspect.

[0023] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform part or all of the methods as described in the first aspect. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario of a device operation control method provided in an embodiment of this application.

[0026] Figure 2 A flowchart illustrating a device operation control method provided in an embodiment of this application;

[0027] Figure 3 A schematic diagram of a first pre-scheduling curve provided for an embodiment of this application;

[0028] Figure 4 A schematic diagram illustrating the partitioning of a target industrial user terminal cluster, provided for an embodiment of this application;

[0029] Figure 5 This is a schematic diagram of the structure of a device operation control device provided in an embodiment of this application;

[0030] Figure 6 This is a schematic diagram of the structure of a cloud server provided in an embodiment of this application. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0032] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0033] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0034] The embodiments of this application will now be described with reference to the accompanying drawings.

[0035] Please see Figure 1 , Figure 1This is a schematic diagram illustrating an application scenario of a device operation control method provided in this application embodiment. Application scenario 100 includes a cloud server 101, an industrial user terminal 102, metallurgical equipment 1021, power generation equipment 1022, and energy storage equipment 1023. The cloud server 101 obtains the first electricity consumption in a first time period from the industrial user terminal 102, and predicts the first available electricity value of the energy management system based on historical available electricity prediction values. The cloud server then inputs the first electricity consumption and the first available electricity prediction value into a scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the industrial user terminal. The industrial user terminal 102 manages various devices in the factory and controls the operation of the metallurgical equipment 1021, power generation equipment 1022, and energy storage equipment 1023 according to the first, second, and third pre-scheduling curves, respectively. The industrial user terminal here can be a factory with periodic massive electricity demand, such as a smelting plant, chemical plant, metal processing plant, and chip factory. In this application embodiment, a metallurgical plant is used as an example.

[0036] Application scenario 100 may include multiple industrial user terminals, each managing different metallurgical equipment, power generation equipment, and energy storage equipment. This example only illustrates a scenario where a single industrial user terminal 102 exists and should not be construed as a limitation of any of the embodiments described in this application. The equipment managed by multiple industrial user terminals includes at least one metallurgical device 1021 and may include any number of power generation devices 1022 and energy storage devices 1023.

[0037] It can be seen that by obtaining the predicted values ​​of the first electricity consumption and the first available electricity in the first time period from the target industrial user through the cloud server, and inputting the predicted values ​​of the first electricity consumption and the first available electricity into the scheduling cost optimization model, the scheduling cost optimization model generates the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the industrial user based on the predicted value of the first available electricity, so as to indicate the metallurgical equipment, power generation equipment and energy storage equipment controlled by the target industrial user, thus meeting the actual electricity demand of the metallurgical equipment and solving the problem of power shortage in factories with special needs under the energy management system.

[0038] Please see Figure 2 , Figure 2 This is a flowchart illustrating a device operation control method provided in an embodiment of this application, which can be based on... Figure 1 The application scenarios shown are implemented as follows: Figure 2 As shown, steps S201-S203 are included:

[0039] S201: The cloud server obtains the first electricity consumption during the target period from the target industrial user terminal. The first electricity consumption includes the electricity consumption required by the metallurgical equipment managed by the target industrial user terminal and the power generation equipment managed by the target industrial user terminal during the target period.

[0040] Specifically, the first electricity consumption here refers to the electricity consumption or available electricity of the equipment in the factory managed by the target industrial user during a first time period. The first time period is the target time period, which can be a period of 8:00 am to 12:00 pm, 12:00 am to 2:00 pm, etc. In this embodiment, the target industrial user is a metallurgical plant within the energy management system. The first electricity consumption can include the electricity consumption of the metallurgical equipment, the power generation of the generator, and the discharge or storage capacity of the energy storage device. Alternatively, the electricity consumption of the metallurgical equipment, the power generation of the generator, and the discharge or storage capacity of the energy storage device can be summed to obtain a specific required electricity consumption or available electricity consumption. If the equipment managed by the target industrial user does not include generator or energy storage devices, the first electricity consumption can include only the electricity consumption required by the metallurgical equipment, or it can include the power generation of the generator and the electricity released by the energy storage device. If the energy storage device needs charging during the first time period, the first electricity consumption also includes the electricity consumption required by the energy storage device.

[0041] S202: The cloud server obtains the first predicted available power level for the target time period.

[0042] Specifically, the first available power forecast value refers to the power that the cloud server can obtain and mobilize in the first time period. The first available power forecast value comes from the power generation of the power generation equipment in the first time period or the power released by the energy storage equipment in the first time period.

[0043] Optionally, the cloud server can predict the available power for today's 8:00am-10:00am period based on the available power in the first historical time period, such as the available power for the previous day's 8:00am-10:00am period.

[0044] S203: The cloud server inputs the first available power prediction value and the first power consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period; the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period are respectively used by the target industrial user to control the operation of metallurgical equipment, power generation equipment and energy storage equipment during the target time period.

[0045] Specifically, the scheduling cost optimization model here generates a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target industrial user terminal in the first time period using the first available power prediction value and the first available power. For example, an energy management system may include more than one industrial user terminal, including industrial user terminals that manage a large number of metallurgical equipment requiring power reception, and industrial user terminals that manage a large number of power generation and energy storage devices capable of providing power. Therefore, it is necessary to consider how to schedule power in scenarios such as factories managed by multiple industrial user terminals. The scheduling cost optimization model can obtain the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for each industrial user terminal with the lowest scheduling cost using the first available power prediction value and the first available power of each industrial user terminal. This scheduling cost optimization model can be implemented using the particle swarm optimization algorithm. The cloud server instructs the industrial user terminal to control the operation of metallurgical equipment, power generation equipment, and energy storage equipment based on the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of a first pre-scheduling curve provided in an embodiment of this application, where the horizontal axis represents a first time period and the vertical axis represents the operating power of the metallurgical equipment. The scheduling cost optimization model can obtain multiple first pre-scheduling curves representing different first time periods, which are then merged to obtain the curve shown below. Figure 3 The first pre-scheduling curve shown includes multiple time periods and multiple operating power levels, in which the equipment operates normally at 100% power in time periods 1 and 2, and stops operating in time period 3.

[0046] In one possible embodiment, the energy management system includes a first power supply network composed of multiple power generation devices managed by multiple industrial users. Energy storage devices and metallurgical equipment managed by multiple industrial users are connected to the first power supply network. A cloud server obtains a first predicted value of available electricity during a target time period, including: the cloud server obtaining the utilization rate of key metallurgical equipment from the target industrial users during the target time period, and determining whether the metallurgical equipment managed by the target industrial users is in a peak production period based on the utilization rate of the key metallurgical equipment (key metallurgical equipment is metallurgical equipment that needs to operate continuously during the target time period); if the metallurgical equipment managed by the target industrial users is not in a peak production period and the first power supply network is in a low-consumption period, the cloud server obtains the amount of electricity that all energy storage devices in the energy management system need to obtain from the first power supply network during the target time period; the cloud server obtains the first predicted value of available electricity based on the amount of electricity needed to be obtained from the first power supply network during the target time period and the amount of electricity that the energy storage devices managed by the target industrial users can still store during the target time period.

[0047] Specifically, the first power supply network here includes multiple power generation devices managed by industrial users. Further, the energy management system may also include independent power generation devices not managed by industrial users. The critical metallurgical equipment here refers to metallurgical equipment that needs to operate continuously during the second time period, which also refers to the target time period, specifically 8:00 am-12:00 am, 12:00 am-2:00 pm, etc. The first and second time periods can be the same or different periods. For example, metallurgical equipment such as smelting furnaces, high-temperature furnaces, and kilns primarily use electricity to generate high temperatures to heat raw materials to high temperatures, causing them to melt and separate, as well as to calcine, roast, or reduce metallurgical raw materials. These devices require electricity to provide heating energy to maintain the high-temperature environment. Reducing or canceling the power supply to these devices during the second time period would lead to unnecessary economic losses and even production accidents. Therefore, the utilization rate of critical metallurgical equipment can determine whether the factory managed by industrial users is in its peak production period.

[0048] Off-peak electricity demand refers to the period when the demand for electricity by metallurgical equipment within the energy management system is relatively low. Metallurgical equipment often cannot fully utilize the electricity generated by power generation equipment. Therefore, the cloud server instructs energy storage devices to charge and store the excess power generated by the power generation equipment, releasing it during peak electricity demand periods to compensate for the higher demand of metallurgical equipment. Thus, during off-peak electricity demand periods, the cloud server controls energy storage devices to store energy. A corrected first available power prediction value is obtained by calculating the difference between the electricity obtained from the first power supply network and the amount of electricity that the energy storage devices managed by the target industrial user can still store in the second time period. This corrected first available power prediction value and the first power demand required by the metallurgical equipment managed by the target industrial user in the second time period are input into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target industrial user in the second time period. This allows the target industrial user to control the metallurgical equipment, power generation equipment, and energy storage devices according to these pre-scheduling curves.

[0049] It can be seen that the cloud server first determines whether it is in a peak production period based on the utilization rate of key metallurgical equipment managed by the industrial user. If it is in a peak production period, and the primary power supply network is in a low-demand period, the cloud server adjusts the first available power prediction value based on the amount of electricity that the energy storage devices managed by the target industrial user can still store in the second time period, thus obtaining a second available power prediction value, improving the accuracy of the power prediction. During the low-demand period, the cloud server controls the energy storage devices to store excess electricity from the power generation equipment, so that the energy storage devices can release electricity during the peak demand period to meet the power needs of the metallurgical equipment.

[0050] In one possible embodiment, if the metallurgical equipment managed by the target industrial user is in peak production and the first power supply network is in a low-consumption period, the cloud server obtains the amount of electricity that all energy storage devices in the energy management system need to obtain from the second power supply network during the target period; the second power supply network is the power supply network composed of power generation devices in the first power supply network other than the power generation equipment managed by the target industrial user; the cloud server obtains a first available power prediction value based on the amount of electricity that needs to be obtained from the second power supply network during the target period and the amount of electricity that the energy storage devices managed by the target industrial user can still store during the target period.

[0051] Specifically, the second power supply network here includes multiple energy storage devices and metallurgical equipment managed by industrial users. Furthermore, the energy management system may also include independent power generation and energy storage devices not managed by the industrial users. If the factory controlled by the target industrial user is in peak production, the electricity provided by the power generation and energy storage devices managed by the target industrial user needs to be supplied to the metallurgical equipment managed by the target industrial user in a limited manner. Therefore, the cloud server will subtract the amount of electricity that the energy storage devices managed by the target industrial user can still store in the second time period from the electricity obtained from the power supply network to obtain a first predicted value of available electricity. The corrected first predicted value of available electricity and the first electricity consumption required by the metallurgical equipment managed by the target industrial user in the second time period are input into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target industrial user in the second time period. This allows the target industrial user to control the metallurgical equipment, power generation equipment, and energy storage devices according to the first, second, and third pre-scheduling curves.

[0052] It can be seen that when the metallurgical equipment managed by the target industrial user is in peak production, the cloud server obtains the difference between the amount of electricity that all energy storage devices in the energy management system need to obtain from the second power supply network in the second time period and the amount of electricity that the energy storage devices managed by the target industrial user can still store in the second time period, to obtain the third available power prediction value. The electricity from the energy storage and power generation devices managed by the target industrial user is dedicated to the metallurgical equipment managed by the target industrial user, reducing the problem of power shortages caused by the metallurgical equipment being in peak production while the second power supply network is in a low-demand period. The corrected third available power prediction value improves the accuracy of the power prediction value. When the metallurgical equipment managed by the target industrial user is in peak production, all the electricity that the power generation and energy storage devices managed by the target industrial user can provide is used for the metallurgical equipment managed by the target industrial user, solving the problem that the power supply network may not be able to meet the demand of the metallurgical equipment in peak production.

[0053] In one possible embodiment, the cloud server obtains the electricity consumption of the target industrial user cluster during the target time period, and determines a second available power prediction value for the target industrial user cluster based on the electricity consumption of the target industrial user cluster during the target time period and the power required to be obtained from the first power supply network. The electricity consumption of the target industrial user cluster during the target time period includes the power consumption required by all metallurgical equipment managed by all industrial users in the target industrial user cluster during the target time period and the power that all power generation equipment managed by all industrial users can provide during the target time period. The target industrial user cluster includes the target industrial user and industrial user terminals whose distance from the target industrial user is less than a preset distance. The cloud server inputs the first available power prediction value and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: the cloud server inputs the second available power prediction value, the first available power prediction value, and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period.

[0054] Specifically, before the cloud server calculates the second available power prediction value from the difference between the power obtained from the first power supply network and the remaining power that the energy storage devices managed by the target industrial user terminal can store in the second time period, the cloud server can merge the target industrial user terminal and multiple industrial user terminals within a preset distance from the target industrial user terminal into a target industrial user terminal cluster. Since the actual locations of the factories managed by the industrial user terminals are usually relatively concentrated, such as industrial parks, the cloud server merges multiple industrial user terminals within a fixed area into a target industrial user terminal cluster, thus considering power scheduling only among multiple user terminal clusters and reducing computational complexity. Please see [link to relevant documentation]. Figure 4 , Figure 4 This application provides a schematic diagram of the partitioning of a target industrial user terminal cluster, as shown in the embodiments of the present application. Figure 4 As shown, the distances between industrial user terminal 1, industrial user terminal 2 and the target industrial user terminal are less than a preset distance, while the distance between industrial user terminal 3 and the target industrial user terminal is greater than a preset distance. Therefore, the target industrial user terminal, industrial user terminal 1, and industrial user terminal 2 are merged into a target industrial user terminal cluster.

[0055] The second available power forecast value is determined based on the sum of the power required by the target industrial user cluster from the first power supply network and the power generation of the power generation equipment managed by the target industrial user cluster, minus the sum of the power consumption of the metallurgical equipment managed by the target industrial user cluster and the remaining power that the energy storage equipment can store in the second time period. When the target industrial user cluster is in a low-power consumption period and the factory controlled by the target industrial user is not in a peak production period, the second available power forecast value, the first available power forecast value, and the first power consumption are input into the scheduling cost optimization model. Based on the first available power forecast value and the first power consumption, the power allocation of the target industrial user cluster is first determined. Then, based on the second available power forecast value, the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve of the target industrial user are determined, so that the target industrial user can control the metallurgical equipment, power generation equipment, and energy storage equipment according to the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve.

[0056] As can be seen, by merging the target industrial user terminal and multiple industrial user terminals within a preset distance into a target industrial user terminal cluster, and determining the second available power prediction value of the target industrial user terminal cluster, the second available power prediction value, the first available power prediction value, and the first power consumption are input into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the second time period. The power allocation of the target industrial user terminal cluster is first determined based on the first available power prediction value and the first power consumption, and then the first, second, and third pre-scheduling curves for the target industrial user terminal in the second time period are determined based on the second available power prediction value. This reduces the computational difficulty and improves the computational accuracy.

[0057] In one possible embodiment, the cloud server obtains the electricity consumption of the target industrial user cluster during the target time period, and determines a third available power prediction value for the target industrial user cluster based on the electricity consumption of the target industrial user cluster during the target time period and the power required to be obtained from the second power supply network. The electricity consumption of the target industrial user cluster during the target time period includes the power consumption required by all metallurgical equipment managed by all industrial users in the target industrial user cluster during the target time period and the power that all power generation equipment managed by all industrial users can provide during the target time period. The target industrial user cluster includes the target industrial user and industrial user terminals whose distance from the target industrial user is less than a preset distance. The cloud server inputs the first available power prediction value and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: the cloud server inputs the third available power prediction value, the first available power prediction value, and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period.

[0058] Specifically, before the difference between the electricity obtained by the cloud server from the power supply network and the electricity that the energy storage devices managed by the target industrial user can still store in the second period is used to obtain the third available electricity prediction value, when the factory managed by the target industrial user is in peak production period, regardless of whether the power supply network is in peak or off-peak electricity consumption period, the power generation and energy storage devices managed by the target industrial user will prioritize supplying local metallurgical equipment.

[0059] During off-peak electricity demand periods, the third predicted available electricity value is determined by subtracting the sum of the electricity the target industrial user cluster needs to obtain from the second power supply network and the power generation of the power generation equipment managed by the target industrial user cluster, from the sum of the electricity consumption of the metallurgical equipment managed by the target industrial user cluster and the electricity that the energy storage equipment can still store during the target period. Here, the second power supply network is the first power supply network excluding the power supply network for the power generation equipment managed by the target industrial user, so that the power generation equipment managed by the target industrial user can use the electricity for the metallurgical equipment managed by the target industrial user.

[0060] When the target industrial user is in a period of low electricity consumption and the factory controlled by the target industrial user is not in a period of peak production, the third available power prediction value, the first available power prediction value, and the first power consumption are input into the scheduling cost optimization model. Based on the first available power prediction value and the first power consumption, the power allocation of the target industrial user cluster is first determined. Then, based on the third available power prediction value, the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve of the target industrial user are determined. The target industrial user controls the metallurgical equipment, power generation equipment, and energy storage equipment according to the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve.

[0061] As can be seen, by merging the target industrial user terminal and multiple industrial user terminals at a preset distance into a target industrial user terminal cluster, and determining the third available power prediction value of the target industrial user terminal cluster, the third available power prediction value, the first available power prediction value, and the first power consumption are input into the scheduling cost optimization model to generate the first, second, and third pre-scheduling curves for the second time period. The power allocation of the target industrial user terminal cluster is first determined based on the first available power prediction value and the first power consumption, and then the first, second, and third pre-scheduling curves for the target industrial user terminal in the second time period are determined based on the third available power prediction value. This reduces the computational difficulty and improves the computational accuracy.

[0062] In one possible embodiment, the cloud server obtains the total electricity consumption of all metallurgical equipment in the energy management system during a target time period; if the first available electricity prediction value is less than the total electricity consumption of all metallurgical equipment, the cloud server determines the electricity priority of the target industrial user, wherein the higher the utilization rate of the key metallurgical equipment corresponding to the industrial user, the higher the electricity priority; the cloud server inputs the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: the cloud server inputs the electricity priority, the first available electricity prediction value, and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period.

[0063] Specifically, before the cloud server inputs the third available power prediction and the first power consumption into the scheduling cost optimization model, if the third available power prediction is less than the total power consumption of all metallurgical equipment, it proves that the power supply network cannot fully meet the power demand of all metallurgical equipment during that period. Therefore, the cloud server needs to determine the industrial user terminal to which power should be supplied first based on the priority of multiple industrial user terminals. This priority can be determined based on the utilization rate of key metallurgical equipment in the industrial user terminal; the higher the utilization rate of the key metallurgical equipment corresponding to the industrial user terminal, the higher the power priority of the industrial user terminal. For example, if the key metallurgical equipment in the target industrial user terminal includes a smelting furnace, a high-temperature furnace, and ventilation equipment, the priority of these three key metallurgical devices needing to operate in the second period is higher than the priority of any two devices needing to operate in the second period. The cloud server prioritizes supplying power to the higher-priority industrial user terminal. When two industrial user terminals with different priorities need to obtain power simultaneously, the metallurgical equipment of the higher-priority industrial user terminal is prioritized to operate at maximum power, while the lower-priority industrial user terminal operates at lower power with the remaining available power.

[0064] Alternatively, the utilization rate of key metallurgical equipment here can also be determined based on the number of key metallurgical equipment that the target industrial user needs to operate in the second time period.

[0065] Optionally, the power consumption priority of industrial users can also include multiple levels. When the utilization rate of key metallurgical equipment is 100%, the power consumption priority of industrial users is the first priority; when the utilization rate of key metallurgical equipment is above 60%, the power consumption priority of industrial users is the second priority; when the utilization rate of key metallurgical equipment is above 30%, the power consumption priority of industrial users is the third priority. Metallurgical equipment managed by different industrial users operates at different power levels. The metallurgical equipment managed by the first-priority cloud server with the first power consumption priority operates at maximum operating power; the metallurgical equipment managed by the second-priority cloud server with the first power consumption priority operates at 50% operating power; and the metallurgical equipment managed by the third-priority cloud server with the first power consumption priority operates at 30% operating power.

[0066] It can be seen that when the first predicted available power is less than the total power consumption of all metallurgical equipment, the cloud server prioritizes the power consumption of metallurgical equipment managed by industrial users with higher power consumption priority by prioritizing the power consumption of multiple industrial users. This reduces the losses caused by the inability of many critical metallurgical equipment to operate normally when the available power is low.

[0067] In one possible embodiment, determining whether the metallurgical equipment managed by the industrial user terminal is in peak production period based on the utilization rate of key metallurgical equipment includes: the cloud server obtaining the equipment type of the metallurgical equipment managed by the industrial user terminal from the industrial user terminal, and identifying metallurgical equipment of the equipment type as continuous heating equipment or emission equipment as key metallurgical equipment; the cloud server obtaining the number of key metallurgical equipment and the number of key metallurgical equipment that the target industrial user terminal needs to operate during the target period from the target industrial user terminal; the cloud server determining the ratio of the number of key metallurgical equipment that needs to be operated to the total number of key metallurgical equipment as the key metallurgical equipment utilization rate; if the key metallurgical equipment utilization rate is less than a preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is not in peak production period; if the key metallurgical equipment utilization rate is not less than the preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is in peak production period.

[0068] Specifically, the target industrial users include a wide variety of industrial equipment. In this embodiment, a metallurgical plant is used as an example. Here, metallurgical equipment refers to all industrial equipment in a metallurgical plant, which may include resistance heating furnaces, electric arc furnaces, induction furnaces, jaw crushers, cone crushers, impact crushers, ventilation and purification equipment, etc. Resistance heating furnaces, electric arc furnaces, and induction furnaces are continuous heating equipment, ventilation and purification equipment is emission equipment, and jaw crushers, cone crushers, and impact crushers are other types of equipment. Because continuous heating equipment needs to maintain a high temperature for a long time, it generates a large amount of electricity. If a sudden power loss causes the continuous heating equipment to stop operating, compared to other equipment such as jaw crushers, restoring the continuous heating equipment to its previous temperature level would incur significant unnecessary costs. Therefore, continuous heating equipment is a critical piece of equipment in a metallurgical plant that needs to operate continuously. Emission equipment is used to treat and purify the flue gas and waste gas generated during production. If these flue gas and waste gas are not treated and discharged to designated locations in a timely manner, it will cause serious losses or even safety accidents. Therefore, it is necessary to ensure the continuous operation of emission equipment.

[0069] The peak production period for the target industrial user can be determined based on the utilization rate of key metallurgical equipment during the second time period. For example, if the key metallurgical equipment managed by the target industrial user includes smelting furnaces, high-temperature furnaces, and ventilation equipment, and the preset utilization rate is 30%, then if the only equipment requiring continuous operation by the target industrial user during the second time period is ventilation equipment, then it is not a peak production period. However, if the key metallurgical equipment requiring continuous operation by the target industrial user during the second time period includes smelting furnaces, high-temperature furnaces, and ventilation equipment, then the plant managed by the target industrial user is in a peak electricity consumption period.

[0070] It can be seen that by measuring the utilization rate of key equipment at the target industrial user end in the second period, the cloud server can determine whether it is a peak production period, thereby enabling the cloud server to correct the target available power prediction value and improve the accuracy of the available power prediction value of the cloud server input scheduling cost optimization model.

[0071] By implementing the method in the above-mentioned embodiments, it can be seen that the cloud server obtains the predicted values ​​of the first power consumption and the first available power during the second time period from the target industrial user terminal, and inputs these values ​​into the scheduling cost optimization model. The scheduling cost optimization model allocates power to the target industrial user terminal based on the predicted value of the first available power, thereby meeting the actual power demand of metallurgical equipment according to the first power consumption sent by the target industrial user terminal, and solving the problem of power shortages in factories with special needs under the energy management system. By judging whether the factory managed by the target industrial user terminal is in its peak production period and whether the power supply network is in its peak power consumption period, the predicted value of available power is corrected, improving the accuracy of the predicted value of available power. Dividing multiple industrial user terminals into user terminal clusters, first determining the power allocation of the target industrial user terminal cluster, and then determining the first, second, and third pre-scheduling curves for the target industrial user terminals, reduces the computational difficulty and improves the computational accuracy. When the predicted value of the third available power is less than the total power consumption of all metallurgical equipment, the power demand of the metallurgical equipment managed by the industrial user terminal with higher power priority is given priority, thereby reducing the losses caused by the inability of many key metallurgical equipment to operate normally when the available power is low.

[0072] Based on the description of the above configuration method embodiments, this application also provides a device operation control device 500, which may be a computer program (including program code) running on a terminal. This device operation control device 500 can be applied to... Figure 1 The application scenarios shown are executed. Figure 2 The method shown. Please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic diagram of the structure of a device for controlling equipment operation according to an embodiment of this application. The device for controlling equipment operation includes:

[0073] Acquisition unit 501: used by the cloud server to acquire the first electricity consumption in the first time period from the target industrial user terminal. The first electricity consumption includes the electricity consumption required by the metallurgical equipment managed by the target industrial user terminal and the power generation equipment managed by the target industrial user terminal in the first time period.

[0074] Acquisition unit 501: is also used for the cloud server to acquire the first predicted value of available power during the target time period;

[0075] Generation unit 502: is used by the cloud server to input the first available power prediction value and the first power consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period; the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period are respectively used by the target industrial user to control the operation of metallurgical equipment, power generation equipment and energy storage equipment during the target time period.

[0076] In one possible embodiment, the energy management system includes multiple industrial user-managed power generation devices forming a first power supply network. Multiple industrial user-managed energy storage devices and metallurgical equipment are connected to the first power supply network. Regarding the cloud server obtaining the first available power prediction value for the target time period, the acquisition unit 501 is further specifically used for: the cloud server obtaining the utilization rate of key metallurgical equipment from the target industrial user during the target time period, and determining whether the metallurgical equipment managed by the target industrial user is in a peak production period based on the key metallurgical equipment utilization rate. Key metallurgical equipment refers to metallurgical equipment that needs to operate continuously during the target time period. If the metallurgical equipment managed by the target industrial user is not in a peak production period, and the first power supply network is in a low-consumption period, the cloud server obtains the amount of electricity that all energy storage devices in the energy management system need to obtain from the first power supply network during the target time period. The cloud server obtains the first available power prediction value based on the amount of electricity needed to be obtained from the first power supply network during the target time period and the amount of electricity that the energy storage devices managed by the target industrial user can still store during the target time period.

[0077] In one possible embodiment, if it is during a peak production period, the acquisition unit 501 is further specifically used to: if the metallurgical equipment managed by the target industrial user is in a peak production period and the first power supply network is in a low power consumption period, the cloud server acquires the amount of electricity that all energy storage devices in the energy management system need to acquire from the second power supply network during the target period; the second power supply network is the power supply network composed of power generation devices in the first power supply network other than the power generation devices managed by the target industrial user; the cloud server obtains a first available power prediction value based on the amount of electricity that needs to be acquired from the second power supply network during the target period and the amount of electricity that the energy storage devices managed by the target industrial user can still store during the target period.

[0078] In one possible embodiment, the generation unit 502 is further specifically configured to: obtain the electricity consumption of the target industrial user terminal cluster during the target time period, and determine the second available electricity prediction value of the target industrial user terminal cluster based on the electricity consumption of the target industrial user terminal cluster during the target time period and the electricity to be obtained from the first power supply network; the electricity consumption of the target industrial user terminal cluster during the target time period includes the electricity consumption required by all metallurgical equipment managed by all industrial users in the target industrial user terminal cluster during the target time period and the electricity that all power generation equipment managed by all industrial users can provide during the target time period; the target industrial user terminal cluster includes the target industrial user terminal and industrial user terminals whose distance from the target industrial user terminal is less than a preset distance; the cloud server inputs the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period, including: the cloud server inputs the second available electricity prediction value, the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period.

[0079] In one possible embodiment, the generation unit 502 is further specifically configured to: obtain the electricity consumption of the target industrial user terminal cluster during the target time period, and determine the third available electricity prediction value of the target industrial user terminal cluster based on the electricity consumption of the target industrial user terminal cluster during the target time period and the electricity to be obtained from the second power supply network; the electricity consumption of the target industrial user terminal cluster during the target time period includes the electricity consumption required by all metallurgical equipment managed by all industrial users in the target industrial user terminal cluster during the target time period and the electricity that all power generation equipment managed by all industrial users can provide during the target time period; the target industrial user terminal cluster includes the target industrial user terminal and industrial user terminals whose distance from the target industrial user terminal is less than a preset distance; the cloud server inputs the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period, including: the cloud server inputs the third available electricity prediction value, the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period.

[0080] In one possible embodiment, the acquisition unit 501 is further specifically configured to: acquire the total electricity consumption of all metallurgical equipment in the energy management system during the target time period; if the first available electricity prediction value is less than the total electricity consumption of all metallurgical equipment, the cloud server determines the electricity priority of the target industrial user, wherein the higher the utilization rate of the key metallurgical equipment corresponding to the industrial user, the higher the electricity priority; the cloud server inputs the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period, including: the cloud server inputs the electricity priority, the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period.

[0081] In one possible embodiment, in determining whether the metallurgical equipment managed by the industrial user terminal is in peak production period based on the utilization rate of key metallurgical equipment, the acquisition unit 501 is further specifically configured to: obtain the equipment type of the metallurgical equipment managed by the industrial user terminal from the industrial user terminal, and identify the metallurgical equipment with the equipment type of continuous heating equipment or emission equipment as key metallurgical equipment; obtain the number of key metallurgical equipment and the number of key metallurgical equipment that the target industrial user terminal needs to operate during the target period from the target industrial user terminal; determine the ratio of the number of key metallurgical equipment that needs to be operated to the number of key metallurgical equipment as the key metallurgical equipment utilization rate; if the key metallurgical equipment utilization rate is less than the preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is not in peak production period; if the key metallurgical equipment utilization rate is not less than the preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is in peak production period.

[0082] Based on the description of the above method and apparatus embodiments, please refer to... Figure 6 , Figure 6 This is a schematic diagram of the structure of a cloud server 600 provided in an embodiment of this application, as shown below. Figure 6 As shown, the cloud server 600 described in this embodiment includes a processor 601, a memory 602, a communication interface 603, and one or more programs. These programs are stored in the memory in the form of application code and are configured to be executed by the processor. In this embodiment, the programs include instructions for performing the following steps:

[0083] The cloud server obtains the first electricity consumption during the target period from the target industrial user terminal. The first electricity consumption includes the electricity consumption required by the metallurgical equipment managed by the target industrial user terminal and the power generation equipment managed by the target industrial user terminal during the target period. The cloud server obtains the first available electricity prediction value during the target period. The cloud server inputs the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target period. The first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target period are used by the target industrial user terminal to control the operation of the metallurgical equipment, the power generation equipment and the energy storage equipment during the target period.

[0084] In one possible embodiment, the energy management system includes a first power supply network composed of multiple power generation devices managed by multiple industrial users. Energy storage devices and metallurgical equipment managed by multiple industrial users are connected to the first power supply network. A cloud server obtains a first predicted value of available electricity during a target time period, including: the cloud server obtaining the utilization rate of key metallurgical equipment from the target industrial users during the target time period, and determining whether the metallurgical equipment managed by the target industrial users is in a peak production period based on the utilization rate of the key metallurgical equipment (key metallurgical equipment is metallurgical equipment that needs to operate continuously during the target time period); if the metallurgical equipment managed by the target industrial users is not in a peak production period and the first power supply network is in a low-consumption period, the cloud server obtains the amount of electricity that all energy storage devices in the energy management system need to obtain from the first power supply network during the target time period; the cloud server obtains the first predicted value of available electricity based on the amount of electricity needed to be obtained from the first power supply network during the target time period and the amount of electricity that the energy storage devices managed by the target industrial users can still store during the target time period.

[0085] In one possible embodiment, if the metallurgical equipment managed by the target industrial user is in peak production and the first power supply network is in a low-consumption period, the cloud server obtains the amount of electricity that all energy storage devices in the energy management system need to obtain from the second power supply network during the target period; the second power supply network is the power supply network composed of power generation devices in the first power supply network other than the power generation equipment managed by the target industrial user; the cloud server obtains a first available power prediction value based on the amount of electricity that needs to be obtained from the second power supply network during the target period and the amount of electricity that the energy storage devices managed by the target industrial user can still store during the target period.

[0086] In one possible embodiment, the cloud server obtains the electricity consumption of the target industrial user cluster during the target time period, and determines a second available power prediction value for the target industrial user cluster based on the electricity consumption of the target industrial user cluster during the target time period and the power required to be obtained from the first power supply network. The electricity consumption of the target industrial user cluster during the target time period includes the power consumption required by all metallurgical equipment managed by all industrial users in the target industrial user cluster during the target time period and the power that all power generation equipment managed by all industrial users can provide during the target time period. The target industrial user cluster includes the target industrial user and industrial user terminals whose distance from the target industrial user is less than a preset distance. The cloud server inputs the first available power prediction value and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: the cloud server inputs the second available power prediction value, the first available power prediction value, and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period.

[0087] In one possible embodiment, the cloud server obtains the electricity consumption of the target industrial user cluster during the target time period, and determines a third available power prediction value for the target industrial user cluster based on the electricity consumption of the target industrial user cluster during the target time period and the power required to be obtained from the second power supply network. The electricity consumption of the target industrial user cluster during the target time period includes the power consumption required by all metallurgical equipment managed by all industrial users in the target industrial user cluster during the target time period and the power that all power generation equipment managed by all industrial users can provide during the target time period. The target industrial user cluster includes the target industrial user and industrial user terminals whose distance from the target industrial user is less than a preset distance. The cloud server inputs the first available power prediction value and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: the cloud server inputs the third available power prediction value, the first available power prediction value, and the first electricity consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period.

[0088] In one possible embodiment, the cloud server obtains the total electricity consumption of all metallurgical equipment in the energy management system during a target time period; if the first available electricity prediction value is less than the total electricity consumption of all metallurgical equipment, the cloud server determines the electricity priority of the target industrial user, wherein the higher the utilization rate of the key metallurgical equipment corresponding to the industrial user, the higher the electricity priority; the cloud server inputs the first available electricity prediction value and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: the cloud server inputs the electricity priority, the first available electricity prediction value, and the first electricity consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period.

[0089] In one possible embodiment, determining whether the metallurgical equipment managed by the industrial user terminal is in peak production period based on the utilization rate of key metallurgical equipment includes: the cloud server obtaining the equipment type of the metallurgical equipment managed by the industrial user terminal from the industrial user terminal, and identifying metallurgical equipment of the equipment type as continuous heating equipment or emission equipment as key metallurgical equipment; the cloud server obtaining the number of key metallurgical equipment and the number of key metallurgical equipment that the target industrial user terminal needs to operate during the target period from the target industrial user terminal; the cloud server determining the ratio of the number of key metallurgical equipment that needs to be operated to the total number of key metallurgical equipment as the key metallurgical equipment utilization rate; if the key metallurgical equipment utilization rate is less than a preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is not in peak production period; if the key metallurgical equipment utilization rate is not less than the preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is in peak production period.

[0090] For example, the cloud server described above may include, but is not limited to, a processor, memory, a communication interface, and one or more programs, and may also include memory, power supply, application client modules, etc. Those skilled in the art will understand that the schematic diagram is merely an example of a cloud server and does not constitute a limitation on the cloud server; it may include more or fewer components than illustrated, or combine certain components, or use different components.

[0091] This application also provides a computer storage medium (memory), which is a memory device in an information processing device, information transmitting device, or information receiving device, used to store programs and data. It is understood that the computer storage medium here can include the built-in storage medium in a terminal, or it can include an extended storage medium supported by the terminal. The computer storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer storage medium here can be high-speed RAM, or non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer storage medium located remotely from the aforementioned processor. In one embodiment, the processor can load and execute one or more instructions stored in the computer storage medium to implement the corresponding steps of the above-described device operation control method.

[0092] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for controlling equipment operation, characterized in that, A cloud server is used in an energy management system, which further includes multiple industrial user terminals. Each industrial user terminal manages metallurgical equipment, power generation equipment, and energy storage equipment. The power generation equipment managed by the multiple industrial user terminals forms a first power supply network. The energy storage equipment and the metallurgical equipment managed by the multiple industrial user terminals are connected to the first power supply network. The method includes: The cloud server obtains the first electricity consumption during the target period from the target industrial user terminal. The first electricity consumption includes the electricity consumption required by the metallurgical equipment managed by the target industrial user terminal and the power generation equipment managed by the target industrial user terminal during the target period. The cloud server obtains the first predicted value of available power during the target period, specifically including: the cloud server obtains the utilization rate of key metallurgical equipment during the target period from the target industrial user terminal, and determines whether the metallurgical equipment managed by the target industrial user terminal is in a peak production period based on the utilization rate of key metallurgical equipment, wherein the key metallurgical equipment is metallurgical equipment that needs to operate continuously during the target period; if the metallurgical equipment managed by the target industrial user terminal is not in the peak production period, and the first power supply network is in a low power consumption period, the cloud server obtains the amount of electricity that all the energy storage devices in the energy management system need to obtain from the first power supply network during the target period; the cloud server obtains the first predicted value of available power based on the amount of electricity that needs to be obtained from the first power supply network during the target period and the amount of electricity that the energy storage devices managed by the target industrial user terminal can still store during the target period. The cloud server inputs the first available power prediction value and the first power consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period. The first, second, and third pre-scheduling curves for the target time period are respectively used by the target industrial user terminal to control the operation of the metallurgical equipment, the power generation equipment, and the energy storage equipment during the target time period.

2. The method according to claim 1, characterized in that, The method further includes: If the metallurgical equipment managed by the target industrial user is in the peak production period and the first power supply network is in the off-peak period, the cloud server obtains the amount of electricity that all the energy storage devices in the energy management system need to obtain from the second power supply network during the target period; the second power supply network is a power supply network composed of power generation devices in the first power supply network other than the power generation devices managed by the target industrial user. The cloud server obtains the first predicted value of available power based on the amount of power that needs to be obtained from the second power supply network during the target time period and the amount of power that the energy storage device managed by the target industrial user can still store during the target time period.

3. The method according to claim 1, characterized in that, The method further includes: The cloud server acquires the electricity consumption of the target industrial user cluster during the target time period, and determines a second predicted value of available electricity for the target industrial user cluster based on the electricity consumption of the target industrial user cluster during the target time period and the electricity required to be obtained from the first power supply network; the electricity consumption of the target industrial user cluster during the target time period includes the electricity required by all metallurgical equipment managed by all industrial users in the target industrial user cluster during the target time period and the electricity that the power generation equipment managed by all industrial users can provide during the target time period. The target industrial user terminal cluster includes the target industrial user terminal and industrial user terminals whose distance from the target industrial user terminal is less than a preset distance; The cloud server inputs the first available power prediction value and the first power consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: The cloud server inputs the second available power prediction value, the first available power prediction value, and the first power consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period.

4. The method according to claim 2, characterized in that, The method further includes: The cloud server acquires the electricity consumption of the target industrial user cluster during the target time period, and determines a third predicted value of available electricity for the target industrial user cluster based on the electricity consumption of the target industrial user cluster during the target time period and the electricity to be obtained from the second power supply network; the electricity consumption of the target industrial user cluster during the target time period includes the electricity consumption required by all metallurgical equipment managed by all industrial users in the target industrial user cluster during the target time period and the electricity that the power generation equipment managed by all industrial users can provide during the target time period. The target industrial user terminal cluster includes the target industrial user terminal and industrial user terminals whose distance from the target industrial user terminal is less than a preset distance; The cloud server inputs the first available power prediction value and the first power consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: The cloud server inputs the third available power prediction value, the first available power prediction value, and the first power consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period.

5. The method according to claim 2, characterized in that, The method further includes: The cloud server obtains the total electricity consumption of all metallurgical equipment in the energy management system during the target time period; If the first predicted available power consumption is less than the total power consumption of all the metallurgical equipment, the cloud server determines the power consumption priority of the target industrial user terminal, wherein the higher the utilization rate of the key metallurgical equipment corresponding to the industrial user terminal, the higher the power consumption priority. The cloud server inputs the first available power prediction value and the first power consumption into the scheduling cost optimization model to generate a first pre-scheduling curve, a second pre-scheduling curve, and a third pre-scheduling curve for the target time period, including: The cloud server inputs the electricity priority, the first available power prediction value, and the first power consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve, and the third pre-scheduling curve for the target time period.

6. The method according to any one of claims 1-5, characterized in that, The step of determining whether the metallurgical equipment managed by the industrial user is in its peak production period based on the utilization rate of the key metallurgical equipment includes: The cloud server obtains the equipment type of the metallurgical equipment managed by the industrial user terminal from the industrial user terminal, and identifies the metallurgical equipment whose equipment type is continuous heating equipment or emission equipment as the key metallurgical equipment. The cloud server obtains the number of key metallurgical equipment and the number of key metallurgical equipment that the target industrial user needs to operate during the target time period from the target industrial user terminal. The cloud server determines the key metallurgical equipment utilization rate by the ratio of the number of key metallurgical equipment that needs to be operated to the total number of key metallurgical equipment. If the utilization rate of the key metallurgical equipment is less than the preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is not in the peak production period; If the utilization rate of the key metallurgical equipment is not less than the preset utilization rate, the cloud server determines that the metallurgical equipment managed by the target industrial user terminal is in the peak production period.

7. A device for controlling the operation of equipment, characterized in that, A method for executing equipment operation control is applied to an energy management system, which further includes multiple industrial user terminals. Each of the multiple industrial user terminals manages metallurgical equipment, power generation equipment, and energy storage equipment. The power generation equipment managed by the multiple industrial user terminals in the energy management system forms a first power supply network. The energy storage equipment and the metallurgical equipment managed by the multiple industrial user terminals are connected to the first power supply network. The equipment operation control device includes: Acquisition Unit: Used for cloud servers to acquire the first electricity consumption during a target time period from the target industrial user terminal. The first electricity consumption includes the electricity consumption required by the metallurgical equipment managed by the target industrial user terminal and the power generation equipment managed by the target industrial user terminal during the target time period. The acquisition unit is further configured to have the cloud server acquire a first predicted value of available power during the target time period. Specifically, this includes: the cloud server acquiring the utilization rate of key metallurgical equipment during the target time period from the target industrial user terminal, and determining whether the metallurgical equipment managed by the target industrial user terminal is in a peak production period based on the utilization rate of the key metallurgical equipment, wherein the key metallurgical equipment is metallurgical equipment that needs to operate continuously during the target time period; if the metallurgical equipment managed by the target industrial user terminal is not in the peak production period, and the first power supply network is in a low-consumption period, the cloud server acquiring the amount of electricity that all the energy storage devices in the energy management system need to acquire from the first power supply network during the target time period; and the cloud server obtaining the first predicted value of available power based on the amount of electricity that needs to be acquired from the first power supply network during the target time period and the amount of electricity that the energy storage devices managed by the target industrial user terminal can still store during the target time period. Generation unit: The cloud server inputs the first available power prediction value and the first power consumption into the scheduling cost optimization model to generate the first pre-scheduling curve, the second pre-scheduling curve and the third pre-scheduling curve for the target time period; The first, second, and third pre-scheduling curves for the target time period are respectively used by the target industrial user terminal to control the operation of the metallurgical equipment, the power generation equipment, and the energy storage equipment during the target time period.

8. A cloud server, characterized in that, The method includes a processor, a memory, a communication interface, and one or more programs, said programs being stored in the memory and configured to be executed by the processor, said programs including instructions for performing the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform the method as described in any one of claims 1-6.

Images