Intelligent energy storage industrial application demand management method and system

Abstract

The invention discloses an intelligent energy storage industrial application demand management method and system, and relates to the technical field of power load management, and the method comprises the steps: obtaining the power data of an energy storage system, judging whether the demand of the energy storage system is normal or not based on the power data of the energy storage system, maintaining a current control mode if the demand is normal, and carrying out the next step if the demand is not normal; judging whether energy storage of the energy storage system is completely discharged or not based on the electric power data of the energy storage system, if not, automatically or manually cutting off one or more non-critical loads, and if yes, carrying out the next step; and carrying out demand exceeding economic analysis, judging whether the power utilization cost preset by the user is exceeded or not, if not, increasing the demand threshold value of a part of months, and if yes, judging whether demand capacity expansion can be carried out or not. According to the method, prediction can be carried out in combination with the actual demand, and the regulation threshold value is increased, so that the demand regulation capability of energy storage is exerted to the greatest extent under the condition that the investment of energy storage equipment is not additionally increased.

Description

Technical Field

[0001] The present invention relates to the technical field of power load management, and in particular to an intelligent energy storage industrial application demand management method and system. Background Art

[0002] As a large electricity consumer, industrial enterprises have a high demand for electricity. Their electricity consumption behavior has a significant impact on the grid load and energy consumption. However, most industrial enterprises still have the following problems in electricity consumption management: There is a lack of effective management of electricity consumption, and electricity consumption has not been properly arranged during peak and off-peak periods of grid load. This will increase the burden on the grid during peak periods of grid load, while also increasing the consumption of non-renewable energy and increasing electricity costs.

[0003] Manual management is inefficient. Some industrial enterprises manage power consumption manually, such as manually cutting off some excessive loads during peak load periods. Although this method has reduced the burden on the power grid and reduced electricity costs to a certain extent, it has obvious limitations. Using manual inspections to manually cut off some excessive loads during peak load periods has played a certain role in reducing the burden on the power grid during peak load periods and reducing electricity costs. However, manual inspections have certain limitations. Manual management requires a lot of manpower. At the same time, manual management lacks timeliness and accuracy, and cannot monitor and manage power loads in real time. Although some automation technologies have been applied to the field of power management, these technologies often fail to fully consider the actual needs of industrial enterprises, such as the optimal use of energy storage systems, accurate prediction of power loads, and real-time response to power grid loads. Therefore, the role of existing power management solutions in reducing power grid burdens, reducing electricity costs, and improving energy efficiency still needs to be improved. . Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an intelligent energy storage industrial application demand management method and system.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows: An intelligent energy storage industrial application demand management method, comprising: S1: Power data acquisition and demand determination.

[0006] Obtain the power data of the energy storage system, including load demand display value, current power measurement value, and current state of charge (SOC) of the energy storage system.

[0007] And based on the obtained power data of the energy storage system, determine whether the demand of the energy storage system is normal. If normal, maintain the current control mode; if abnormal, proceed to the next step; S2: Energy storage status judgment and load management.

[0008] Based on the power data of the energy storage system, it is determined whether the energy storage system has been fully discharged, and based on the current state of charge (SOC) of the energy storage system, it is determined whether its remaining capacity is less than the preset threshold. If the remaining capacity is less than the threshold, it means that the energy storage has been fully discharged; otherwise, it means that the energy storage has not been fully discharged.

[0009] If the energy storage is not fully discharged, one or more non-critical loads will be automatically or manually cut off. Automatic cut-off is to directly cut off the non-critical loads through the remote control switch. Manual cut-off is to preset the cut-off logic of non-critical loads, and send a confirmation message to the operator after judging that the energy storage is fully discharged, and execute the cut-off operation after the operator confirms.

[0010] If the stored energy has been fully discharged, proceed to the next step; S3: Excess demand economic analysis and threshold adjustment.

[0011] Conduct over-demand economic analysis to determine whether it exceeds the user's preset electricity costs. If not, increase the demand threshold for some months. If so, determine whether demand expansion is possible.

[0012] As a preferred solution of the intelligent energy storage industrial application demand management method described in the present invention, the power data includes the load demand display value, the current power measurement value, and the current charge state of the energy storage system.

[0013] As a preferred solution of the intelligent energy storage industrial application demand management method of the present invention, the method of judging whether the energy storage system has been fully discharged based on the power data of the energy storage system includes: According to the current state of charge of the energy storage system, it is determined whether the current remaining capacity of the energy storage system is less than a threshold value. If so, it means that the energy storage system has been fully discharged. If not, it means that the energy storage system has not been fully discharged.

[0014] As a preferred solution of the intelligent energy storage industrial application demand management method of the present invention, the automatic disconnection of single or multiple non-critical loads includes: Cut off single or multiple non-critical loads through remote control switches; Manually cutting off a single or multiple non-critical loads includes: The disconnection logic of non-critical loads is preset, and after judging that the energy storage system has been fully discharged, confirmation information is transmitted to the operator, and after the operator confirms, single or multiple non-critical loads are disconnected.

[0015] As a preferred solution of the intelligent energy storage industrial application demand management method of the present invention, the excess demand economic analysis includes: The maximum demand reduction is calculated by formula 1, which is: ,in, , is the maximum demand that the energy storage can provide after installation, is the total capacity of the energy storage system, η is the charging and discharging efficiency; The electricity cost C of the reduced demand is calculated by Formula 2, which is: ,in, The electricity price calculated based on the maximum demand; The optimized amount of electricity charges O is calculated by formula 3, which is: ,in, is the electricity price during peak hours, The electricity price during off-peak hours.

[0016] As a preferred solution of the intelligent energy storage industrial application demand management method of the present invention, the determination of whether demand expansion can be performed includes: According to the historical electricity consumption data, the number of months m in which the demand threshold is increased and the power difference of each month are obtained when only the energy storage system participates in demand control. ; The net present value F of the demand electricity charge over the entire life cycle is calculated by formula 4, which is: ; The expandable number N is calculated by Formula 5, which is: ,in, is the current power measurement value, is the demand threshold, is the power value that the energy storage system can release; when When the demand is increased, it is determined that the demand can be expanded, among which, Unit expansion cost.

[0017] The present invention also provides an intelligent energy storage industrial application demand management system, comprising: The first control module is used to obtain the power data of the energy storage system, and determine whether the demand of the energy storage system is normal based on the power data of the energy storage system. If it is normal, the current control mode is maintained; if it is abnormal, the second control module is executed; A second control module is used to determine whether the energy storage system has been fully discharged based on the power data of the energy storage system, and if not, automatically or manually cut off a single or multiple non-critical loads, and if so, execute a third control module; The third control module is used to perform over-demand economic analysis to determine whether the electricity cost exceeds the user's preset electricity cost. If not, the demand threshold of some months is increased. If so, it is determined whether the demand expansion can be performed.

[0018] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any of the above-mentioned intelligent energy storage industrial application demand management methods is implemented.

[0019] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any of the above-mentioned intelligent energy storage industrial application demand management methods is implemented.

[0020] The beneficial effects of the present invention are: (1) The present invention combines remote switches to perform intelligent demand management, and combines the automation level of the scene to perform a variety of demand control measures. When the production task is heavy or the heating and cooling demand in a specific month is high, it can be combined with the actual demand to make a prediction and raise the control threshold, thereby maximizing the demand regulation capability of energy storage without additional investment in energy storage equipment.

[0021] (2) In addition to carrying out demand control, the present invention conducts an economic analysis on the demand electricity fee to analyze whether the economic loss per unit power in the whole cycle caused by excess demand exceeds the benefits of investing in energy storage, which can be used to evaluate whether it is necessary to increase investment in energy storage equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0023] Figure 1 A schematic diagram of the flow chart of the intelligent energy storage industrial application demand management method provided by the present invention; Figure 2 Another schematic diagram of a flow chart of the intelligent energy storage industrial application demand management method provided by the present invention; Figure 3 A schematic diagram of the intelligent energy storage industrial application demand management system provided by the present invention; Figure 4 A schematic diagram of a computer device provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific implementation modes and in combination with the accompanying drawings.

[0025] Figure 1 A flow chart of an intelligent energy storage industrial application demand management method provided in an embodiment of the present application. The method specifically comprises the following steps: Step S101: Obtain power data of the energy storage system, and determine whether the demand of the energy storage system is normal based on the power data of the energy storage system. If normal, maintain the current control mode; if abnormal, proceed to step S102.

[0026] Specifically, the energy storage system collects power data by connecting to the smart meter or other measuring equipment on the low-voltage side of the main incoming transformer. The collected power data includes but is not limited to: load demand display value, current power measurement value, and current state of charge of the energy storage system. Among them, the current state of charge of the energy storage system is the SOC value of the current energy storage system.

[0027] After collecting the above power data, by judging whether the load demand display value is greater than the demand threshold, it can be judged whether the demand of the energy storage system is normal. If the load demand display value is less than or equal to the demand threshold, it is judged that the demand of the energy storage system is normal, and there is no need to change the existing control mode at this time, that is, maintain the current control mode; if the load demand display value is greater than the demand threshold, it is judged that the demand of the energy storage system is abnormal at this time.

[0028] Step S102: Based on the power data of the energy storage system, determine whether the energy storage system has been fully discharged. If not, automatically or manually cut off a single or multiple non-critical loads. If yes, proceed to step S103.

[0029] Specifically, after determining that the demand for the energy storage system is abnormal, it is necessary to determine whether the energy storage of the current energy storage system has been fully discharged based on the current operating status and charge status of the energy storage system.

[0030] After obtaining the current state of charge of the energy storage system, it is possible to determine whether the current remaining capacity of the energy storage system is less than the threshold value based on the current state of charge of the energy storage system. Specifically, it is to determine whether the SOC value of the current energy storage system is close to 0%. If so, it means that the energy storage system has been fully discharged. If not, it means that the energy storage system has not been fully discharged. At this time, the power mutation is too fast and the mode needs to be adjusted.

[0031] The adjustment modes include automatic adjustment and non-automatic adjustment. The automatic mode includes the use of a remote control switch. When the demand exceeds the limit and the energy storage power cannot make up for the difference between the current power measurement value and the demand threshold, one or more non-critical loads are cut off and a prompt is given. The non-automatic mode includes the preset cut-off logic of non-critical loads, and transmits confirmation information to the operator after judging that the energy storage system has been discharged, and cuts off one or more non-critical loads after the operator confirms. An app can be used to remind of excess demand.

[0032] It should be noted that when it is impossible to achieve that the sum of the power of energy storage discharge and non-critical load reduction exceeds the difference, the control target is to achieve the maximum reduction in demand.

[0033] In addition, once the demand control of the energy storage system is triggered, the energy storage system will adjust according to the preset control strategy and algorithm, including reducing the charging or discharging power of the energy storage system to reduce the load peak of the power grid.

[0034] Step S103: Perform an over-demand economic analysis to determine whether the electricity cost exceeds the user's preset electricity cost. If not, increase the demand threshold for some months. If so, determine whether demand expansion is possible.

[0035] Specifically, the over-demand economic analysis includes the calculation of the maximum demand reduction, the calculation of the electricity cost of the demand reduction part, and the calculation of the optimized electricity cost. The calculation method is as follows: The maximum demand reduction is calculated by formula 1, which is: ,in, , is the maximum demand that the energy storage can provide after installation, is the total capacity of the energy storage system, and η is the charging and discharging efficiency.

[0036] The electricity cost C of the reduced demand is calculated by formula 2, which is: ,in, The electricity price calculated based on the maximum demand; The optimized amount of electricity charges O is calculated by formula 3, which is: , is the electricity price during peak hours, The electricity price during off-peak hours.

[0037] Let's take an example to illustrate the above over-demand economic analysis: a household's current operating capacity is 400 kVA, the maximum demand is 300kW, and the total monthly electricity consumption is 100,000 kWh. A 200 kWh lithium battery energy storage system has been installed, with a charge and discharge efficiency of 0.9. Now the capacity is expanded to 260 kwh, and the calculation method is as follows: Calculate the original maximum demand: Pmax_old = 300 kW−200 kWh×0.9=300 kW−180 kW=120 kW.

[0038] Calculate the maximum demand after expansion: Pmax_new = Pmax - Es_new*η = 300 kW−260 kWh×0.9=300 kW−234 kW=66 kW.

[0039] Calculate the demand reduction electricity charge: Demand reduction electricity charge = (Pmax - Pmax_new) * Dprice.

[0040] Assuming the demand price is 100 yuan / kW, the demand reduction electricity fee = (120 kW − 66 kW) × 100 yuan / kW = 5,400 yuan.

[0041] Calculate the electricity price optimization: Assume that the electricity price during high electricity price period is 1.5 yuan / kWh, and the electricity price during low electricity price period is 0.5 yuan / kWh.

[0042] Daily electricity fee optimization = Es_new * (Ep_high - Ep_low) = 260 kWh × (1.5−0.5) yuan / kWh = 260 kWh × 1 yuan / kWh = 260.

[0043] If the calculation result is judged to be within the user's preset electricity cost, it is judged that the power is too fast, and the electricity cost exceeds the user's preset electricity cost, the demand threshold of certain months is increased, including adjusting the production plan, etc. If the calculation result is judged to exceed the user's preset electricity cost, it is judged whether the demand expansion can be carried out. The specific judgment method is as follows: According to the historical electricity consumption data, the number of months m in which the demand threshold is increased and the power difference of each month are obtained when only the energy storage system participates in demand control. .

[0044] The net present value F of the demand electricity charge over the entire life cycle (based on the expected energy storage life k years, such as generally 10 to 12 years) is calculated using Formula 4: .

[0045] The number of expandable units N is calculated by formula 5, which is: ,in, is the current power measurement value, is the demand threshold, is the power value that the energy storage system can release. It should be noted that N needs to be rounded up or down.

[0046] when If it is determined that capacity expansion is not possible, the demand threshold for certain months will be increased, including adjusting the production plan.

[0047] Figure 2 Another flow chart of the intelligent energy storage industrial application demand management method provided in this application.

[0048] In addition, the demand control function of the energy storage system can also be dynamically adjusted and optimized according to the real-time load of the power grid and the needs of users. For example, during the period of low power demand, the energy storage system can increase the charging capacity to release more power during the peak power demand period. At the same time, in coordination with other control functions, demand control usually works in coordination with other control functions of the energy storage system, such as planning curves, peak shaving and valley filling, and reverse flow prevention, to achieve the optimization of overall energy management.

[0049] The example of determining the demand during the peak-shaving and valley-filling period is as follows: The peak and valley periods throughout the year are divided into three periods of 8 hours each, namely, peak, flat and valley, according to the 24 hours per day. The specific periods are divided as follows: Peak: 11:00-14:00, 18:00-23:00 Flat: 7:00-11:00, 14:00-18:00 Valley: 23:00-7:00 the next day In January, July, August, September and December each year, seasonal peak electricity prices are implemented for industrial and commercial users who implement time-of-use electricity prices. The electricity price from 18:00 to 22:00 every day is 20% higher than the peak electricity price. According to the principles of valley charging and peak discharge (11:00-14:00), flat charging and peak discharge (18:00-22:00), and flat charging and peak discharge (18:00-23:00), two charges and two discharges are managed every day.

[0050] A factory calculates the basic electricity fee based on the maximum demand electricity price of the "two-part 1 to 10 kV" voltage level. The maximum demand electricity price in Jiangsu Province is 51.2 yuan / kW / month in June 2024. The power of the factory is 300 kW in most periods, and only 480 kW in some periods.

[0051] Before installing the energy storage system, the transformer demand electricity charge in June 2024 is: 480 kW*51.2 yuan / kW·month=24576 yuan; After installing a 215kW / 430kWh battery energy storage system, discharging during peak hours to maintain the transformer power within 300kW, the transformer demand charge is: 300 kW*51.2 yuan / kW·month=15360 yuan After installing the energy storage system, the basic electricity bill in June 2024 will be reduced by 9,216 yuan.

[0052] Therefore, the technical solution of the present application can make predictions based on actual demand requirements and raise the control threshold, thereby maximizing the demand regulation capability of energy storage without additional investment in energy storage equipment.

[0053] Figure 3 A schematic diagram of an intelligent energy storage industrial application demand management system provided in an embodiment of the present application, the system includes a first control module, a second control module and a third control module.

[0054] Specifically, the first control module is used to obtain power data of the energy storage system, and determine whether the demand of the energy storage system is normal based on the power data of the energy storage system. If normal, the current control mode is maintained; if abnormal, the second control module is executed.

[0055] The second control module is used to determine whether the energy storage system has been fully discharged based on the power data of the energy storage system. If not, a single or multiple non-critical loads are automatically or manually cut off. If yes, the third control module is executed.

[0056] The third control module is used to perform over-demand economic analysis to determine whether the electricity cost exceeds the user's preset electricity cost. If not, the demand threshold of some months is increased. If so, it is determined whether demand expansion can be performed.

[0057] See also Figure 4 This embodiment also provides a computer device, and the components of the computer device may include but are not limited to: one or more processors or processing units, a system memory, and a bus connecting different system components (including the system memory and the processing unit).

[0058] The term "bus" refers to one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0059] The computer system / server typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer system / server, including volatile and non-volatile media, removable and non-removable media.

[0060] The system memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The computer device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system may be used to read and write non-removable, non-volatile magnetic media. A disk drive for reading and writing removable non-volatile disks (such as "floppy disks") and an optical disk drive for reading and writing removable non-volatile optical disks (such as CD-ROMs, DVD-ROMs or other optical media) may be provided. In these cases, each drive may be connected to the bus via one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0061] A program / utility having a set (at least one) of program modules may be stored, for example, in a memory, such program modules including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules generally perform the functions and / or methods of the embodiments described herein.

[0062] The computer device may also communicate with one or more external devices such as a keyboard, a pointing device, a display, etc. Such communication may be performed through an input / output (I / O) interface. Furthermore, the computer device may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter.

[0063] The processing unit executes the functions and / or methods described in the embodiments of the present invention by running the programs stored in the system memory.

[0064] The above-mentioned computer program can be set in a computer storage medium, that is, the computer storage medium is encoded with a computer program, and when the program is executed by one or more computers, it enables one or more computers to execute the method flow and / or device operation shown in the above-mentioned embodiments of the present invention.

[0065] With the development of time and technology, the meaning of medium is becoming more and more extensive, and the propagation path of computer programs is no longer limited to tangible media, but can also be downloaded directly from the network, etc. Any combination of one or more computer-readable media can be used. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, - but not limited to - electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media (non-exhaustive list) include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing programs, which can be used by or in combination with instruction execution systems, devices or devices.

[0066] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0067] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0068] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0069] In addition to the above embodiments, the present invention may also have other implementation modes; any technical solutions formed by equivalent replacement or equivalent transformation shall fall within the protection scope required by the present invention.

Claims

1. An intelligent energy storage industrial application demand management method, characterized by: include: Obtain the power data of the energy storage system, and determine whether the demand of the energy storage system is normal based on the power data of the energy storage system. If normal, maintain the current control mode; if abnormal, proceed to the next step; Based on the power data of the energy storage system, determine whether the energy storage system has been fully discharged. If not, automatically or manually cut off one or more non-critical loads. If yes, proceed to the next step. Conduct over-demand economic analysis to determine whether it exceeds the user's preset electricity costs. If not, increase the demand threshold for some months. If so, determine whether demand expansion is possible.

2. The intelligent energy storage industrial application demand management method according to claim 1 is characterized by: The power data includes load demand display value, current power measurement value, and current charge state of the energy storage system.

3. The intelligent energy storage industrial application demand management method according to claim 2 is characterized in that: The method of judging whether the energy storage system has been fully discharged based on the power data of the energy storage system includes: According to the current state of charge of the energy storage system, it is determined whether the current remaining capacity of the energy storage system is less than a threshold value. If so, it means that the energy storage system has been fully discharged. If not, it means that the energy storage system has not been fully discharged.

4. The intelligent energy storage industrial application demand management method according to claim 1 is characterized by: The automatic disconnection of single or multiple non-critical loads includes: Cut off single or multiple non-critical loads through remote control switches; Manually cutting off a single or multiple non-critical loads includes: The disconnection logic of non-critical loads is preset, and after judging that the energy storage system has been discharged, confirmation information is transmitted to the operator, and after the operator confirms, single or multiple non-critical loads are disconnected.

5. The intelligent energy storage industrial application demand management method according to claim 1 is characterized by: The excess demand economic analysis includes: The maximum demand reduction is calculated by formula 1, which is: ,in, , is the maximum demand that the energy storage can provide after installation, is the total capacity of the energy storage system, η is the charging and discharging efficiency; The electricity cost C of the reduced demand is calculated by Formula 2, which is: ,in, The electricity price calculated based on the maximum demand; The optimized amount of electricity charges O is calculated by formula 3, which is: ,in, is the electricity price during peak hours, The electricity price during off-peak hours.

6. The intelligent energy storage industrial application demand management method according to claim 1 is characterized by: The determination of whether demand expansion can be performed includes: According to the historical electricity consumption data, the number of months m in which the demand threshold is increased and the power difference of each month are obtained when only the energy storage system participates in demand control. ; The net present value F of the demand electricity charge over the entire life cycle is calculated by formula 4, which is: ; The expandable number N is calculated by Formula 5, which is: ,in, is the current power measurement value, is the demand threshold, is the power value that the energy storage system can release; when When the demand is increased, it is determined that the demand can be expanded, among which, Unit expansion cost.

7. An intelligent energy storage industrial application demand management system, characterized by: include: The first control module is used to obtain the power data of the energy storage system, and determine whether the demand of the energy storage system is normal based on the power data of the energy storage system. If it is normal, the current control mode is maintained; if it is abnormal, the second control module is executed; A second control module is used to determine whether the energy storage system has been fully discharged based on the power data of the energy storage system, and if not, automatically or manually cut off a single or multiple non-critical loads, and if so, execute a third control module; The third control module is used to perform over-demand economic analysis to determine whether the electricity cost exceeds the user's preset electricity cost. If not, the demand threshold of some months is increased. If so, it is determined whether the demand expansion can be performed.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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