Device Energy Efficiency Management System and Method Based on Cloud Services

Abstract

The present invention discloses an equipment energy efficiency management system and method based on cloud services, which relates to the technical field of equipment energy efficiency management. By acquiring real-time data and historical data of the power grid, and using an intelligent analysis model to analyze the real-time data and historical data, it is determined whether it is necessary to control the operation of the superconducting magnetic energy storage system. When it is determined that it is necessary to control the superconducting magnetic energy storage system to charge, after analyzing the health status of each superconducting magnet, all superconducting magnets are sorted based on the health status analysis results to generate a sorting table, and the corresponding number of superconducting magnets is selected for charging in combination with the power grid load information and the sorting table. The management system dynamically optimizes the startup method of the superconducting magnetic energy storage system based on the intelligent analysis model, and analyzes the health status of all superconducting magnets before the superconducting magnetic energy storage system charges, so as to preferably use superconducting magnets with good health status, which not only improves the management efficiency of the superconducting magnetic energy storage system, but also ensures the stable operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment energy efficiency management, and particularly to an equipment energy efficiency management system and method based on cloud services. Background Art

[0002] With the global emphasis on renewable energy sources (such as solar and wind energy), traditional power grids are facing load fluctuations and power quality issues. The intermittency and unpredictability of renewable energy require power grids to have higher flexibility and response capabilities to ensure the balance between supply and demand. Management systems provide important solutions for achieving energy transformation, enhancing grid flexibility, and integrating renewable energy, contributing to the construction of a more intelligent and reliable modern power system;

[0003] A superconducting magnetic energy storage system (SMES) uses superconducting materials to store electrical energy, featuring fast charge and discharge capabilities and high efficiency. When the grid load is low (such as during low electricity consumption at night), the SMES obtains excess electrical energy from the grid for charging. During this process, the electrical energy is converted into magnetic energy and stored in the superconducting magnet. When the grid load is high, the SMES can quickly release the stored energy to support the grid and balance the load demand. This discharge can rapidly improve the power supply capacity of the grid and avoid power outages or power quality problems caused by excessive loads.

[0004] The existing technologies have the following defects:

[0005] 1. Management systems usually set two methods to determine whether to operate the superconducting magnetic energy storage system. One is to set a power consumption threshold for the grid and control the charging or discharging of the superconducting magnetic energy storage system based on the comparison result between the real-time power consumption of the grid and the power consumption threshold. The other is to set a time period to control the charging or discharging of the superconducting magnetic energy storage system. The first method may cause the management system to frequently control the startup and operation of the superconducting magnetic energy storage system, increasing the operation burden of the superconducting magnetic energy storage system and causing power loss at the same time. The second method will result in the superconducting magnetic energy storage system not responding in a timely manner and being unable to effectively guarantee the stable operation of the grid;

[0006] 2. Since there are several groups of superconducting magnets in the superconducting magnetic energy storage system, the existing management systems usually randomly select superconducting magnets for energy storage when the superconducting magnetic energy storage system is charging. This random selection is likely to lead to the use of superconducting magnets with relatively poor health states, thereby increasing power loss.

[0007] Based on this, the present invention proposes an equipment energy efficiency management system and method based on cloud services, dynamically optimizing the startup method of the superconducting magnetic energy storage system based on an intelligent analysis model, and analyzing the health states of all superconducting magnets before the superconducting magnetic energy storage system is charged, so as to preferentially use superconducting magnets with good health states, not only improving the management efficiency of the superconducting magnetic energy storage system, but also guaranteeing the stable operation of the grid. Summary of the Invention

[0008] The objective of the present invention is to provide a device energy efficiency management system and method based on cloud services to solve the deficiencies in the background art.

[0009] To achieve the above objective, the present invention provides the following technical solution: A device energy efficiency management method based on cloud services, the management method comprising the following steps:

[0010] The management system acquires the number and information of superconducting magnets in the superconducting magnetic energy storage system, regularly acquires the load status of the power grid, and performs a primary analysis based on the load status of the power grid;

[0011] When the result of the primary analysis is that a secondary analysis of the starting conditions of the superconducting magnetic energy storage system is required, the management system acquires the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using an intelligent analysis model, determines whether to control the operation of the superconducting magnetic energy storage system;

[0012] When it is determined that the superconducting magnetic energy storage system needs to be charged, after analyzing the health status of each superconducting magnet, based on the analysis result of the health status, all superconducting magnets are sorted to generate a sorting table, and the corresponding number of superconducting magnets is selected for charging in combination with the power grid load information and the sorting table.

[0013] In a preferred embodiment, the management system acquires the number and information of superconducting magnets in the superconducting magnetic energy storage system, regularly acquires the load status of the power grid, and performs a primary analysis based on the load status of the power grid, including the following steps:

[0014] Acquire the real-time power consumption of the power grid, and compare the real-time power consumption with a first power consumption threshold and a second power consumption threshold. The first power consumption threshold is used to determine whether the power grid is in a low load state, and the second power consumption threshold is used to determine whether the power grid is in a high load state;

[0015] If the real-time power consumption is greater than or equal to the first power consumption threshold and less than or equal to the second power consumption threshold, it is determined that the power grid is in a normal operation state and a secondary analysis of the starting conditions of the superconducting magnetic energy storage system is not required;

[0016] If the real-time power consumption is less than the first power consumption threshold, it is determined that the power grid is in a low load state. If the real-time power consumption is greater than the second power consumption threshold, it is determined that the power grid is in a high load state. When it is determined that the power grid is in a low load state or a high load state, a secondary analysis of the starting conditions of the superconducting magnetic energy storage system is required.

[0017] In a preferred embodiment, the management system acquires the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using the intelligent analysis model, determines whether it is necessary to control the operation of the superconducting magnetic energy storage system, including the following steps:

[0018] When it is determined that the power grid is in a low-load state, it is necessary to perform a secondary analysis on the charging start condition of the superconducting magnetic energy storage system, acquire the real-time data and historical data of the power grid. The real-time data includes the duration of continuous change in real-time power consumption, and the historical data includes the discrete assignment of power consumption. Substitute the duration of continuous change in real-time power consumption and the discrete assignment of power consumption into the intelligent analysis model. The intelligent analysis model outputs a charging control coefficient, and compares the charging control coefficient with a preset charging control coefficient threshold. If the charging control coefficient is greater than or equal to the charging control coefficient threshold, it is determined that it is necessary to control the superconducting magnetic energy storage system to charge. If the charging control coefficient is less than the charging control coefficient threshold, it is determined that it is not necessary to control the superconducting magnetic energy storage system to charge.

[0019] In a preferred embodiment, the management system acquires the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using the intelligent analysis model, determines whether it is necessary to control the operation of the superconducting magnetic energy storage system, and further includes the following steps:

[0020] When it is determined that the power grid is in a high-load state, it is necessary to perform a secondary analysis on the energy release start condition of the superconducting magnetic energy storage system, acquire the real-time data and historical data of the power grid. The real-time data includes the duration of continuous change in real-time power consumption, and the historical data includes the discrete assignment of power consumption. Substitute the duration of continuous change in real-time power consumption and the discrete assignment of power consumption into the intelligent analysis model. The intelligent analysis model outputs an energy release control coefficient, and compares the energy release control coefficient with a preset energy release control coefficient threshold. If the energy release control coefficient is greater than or equal to the energy release control coefficient threshold, it is determined that it is necessary to control the superconducting magnetic energy storage system to release energy. If the energy release control coefficient is less than the energy release control coefficient threshold, it is determined that it is not necessary to control the superconducting magnetic energy storage system to release energy.

[0021] In a preferred embodiment, the processing logic of the intelligent analysis model is as follows: After acquiring the duration of change in power consumption and the discrete assignment of power consumption, perform a normalization process on the duration of change in power consumption and the discrete assignment of power consumption, map the value ranges of the duration of change in power consumption and the discrete assignment of power consumption to between [0, 1], obtain the normalized value of the duration of change in power consumption and the normalized value of the discrete assignment of power consumption. When the power grid is in a low-load state, sum the normalized value of the duration of change in power consumption and the normalized value of the discrete assignment of power consumption to obtain the charging control coefficient. When the power grid is in a high-load state, sum the normalized value of the duration of change in power consumption and the normalized value of the discrete assignment of power consumption to obtain the energy release control coefficient.

[0022] In a preferred embodiment, when it is determined that the superconducting magnetic energy storage system needs to be charged, the health status of each superconducting magnet is analyzed, including the following steps:

[0023] Obtain the magnetic field strength deviation rate and energy storage level value of the superconducting magnet, and comprehensively calculate the magnet index by combining the magnetic field strength deviation rate and the energy storage level value. The expression is:

[0024] magnet = α·μ - β·δ, where magnet is the magnet index, μ is the energy storage level value, δ is the magnetic field strength deviation rate, α and β are the adjustment coefficients of the energy storage level value and the magnetic field strength deviation rate respectively, and both α and β are greater than 0; the larger the magnet index, the better the health status of the superconducting magnet.

[0025] In a preferred embodiment, select the corresponding number of superconducting magnets for charging by combining the grid load information and the sorting table, including the following steps:

[0026] Sort all superconducting magnets in descending order according to the magnet index to generate a sorting table. When the grid is in the low-load state stage, start charging the superconducting magnets in ascending order according to the sorting table. After the current superconducting magnet is fully charged, control the charging of the superconducting magnets in ascending order according to the sorting table until the grid is not in the low-load state.

[0027] The device energy efficiency management system based on cloud service includes a primary analysis module, a secondary analysis module, and a charging selection module;

[0028] Primary analysis module: Obtain the number and information of superconducting magnets in the superconducting magnetic energy storage system, regularly obtain the load status of the grid, and perform primary analysis based on the load status of the grid;

[0029] Secondary analysis module: When the result of the primary analysis is that the start-up conditions of the superconducting magnetic energy storage system need to be analyzed secondarily, obtain the real-time data and historical data of the grid, and judge whether it is necessary to control the operation of the superconducting magnetic energy storage system after analyzing the real-time data and historical data using the intelligent analysis model;

[0030] Charging selection module: When it is determined that the superconducting magnetic energy storage system needs to be charged, after analyzing the health status of each superconducting magnet, sort all superconducting magnets based on the health status analysis result to generate a sorting table, and select the corresponding number of superconducting magnets for charging by combining the grid load information and the sorting table.

[0031] In the above technical solution, the technical effects and advantages provided by the present invention:

[0032] The present invention obtains real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using an intelligent analysis model, determines whether it is necessary to control the operation of the superconducting magnetic energy storage system. When it is determined that it is necessary to control the charging of the superconducting magnetic energy storage system, after analyzing the health status of each superconducting magnet, all superconducting magnets are sorted based on the analysis result of the health status to generate a sorting table, and the corresponding number of superconducting magnets are selected for charging in combination with the power grid load information and the sorting table. The management system dynamically optimizes the startup method of the superconducting magnetic energy storage system based on the intelligent analysis model, and before the superconducting magnetic energy storage system is charged, analyzes the health status of all superconducting magnets, so as to preferably use superconducting magnets with good health status, which not only improves the management efficiency of the superconducting magnetic energy storage system, but also ensures the stable operation of the power grid. Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0034] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0036] Embodiment 1: Please refer to Figure 1 As shown, the device energy efficiency management method based on cloud services in this embodiment includes the following steps:

[0037] The management system obtains the number and information of superconducting magnets in the superconducting magnetic energy storage system, regularly obtains the load status of the power grid, and conducts a primary analysis based on the load status of the power grid. When the result of the primary analysis is that a secondary analysis of the startup conditions of the superconducting magnetic energy storage system is required, the management system obtains the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using an intelligent analysis model, determines whether it is necessary to control the operation of the superconducting magnetic energy storage system. When it is determined that it is necessary to control the charging of the superconducting magnetic energy storage system, after analyzing the health status of each superconducting magnet, all superconducting magnets are sorted based on the analysis result of the health status to generate a sorting table, and the corresponding number of superconducting magnets are selected for charging in combination with the power grid load information and the sorting table.

[0038] This application obtains the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using an intelligent analysis model, it determines whether to control the operation of the superconducting magnetic energy storage system. When it is determined that the superconducting magnetic energy storage system needs to be charged, after analyzing the health status of each superconducting magnet, all superconducting magnets are sorted based on the analysis result of the health status to generate a sorting table, and the corresponding number of superconducting magnets is selected for charging in combination with the power grid load information and the sorting table. The management system dynamically optimizes the startup method of the superconducting magnetic energy storage system based on the intelligent analysis model, and before the superconducting magnetic energy storage system is charged, the health status of all superconducting magnets is analyzed, so as to preferentially use superconducting magnets with good health status, which not only improves the management efficiency of the superconducting magnetic energy storage system, but also ensures the stable operation of the power grid.

[0039] Embodiment 2: The management system obtains the number and information of superconducting magnets in the superconducting magnetic energy storage system, regularly obtains the load status of the power grid, and conducts a primary analysis based on the load status of the power grid, including the following steps:

[0040] Through sensors and monitoring systems, the number, status, temperature, charging status, energy storage level, etc. of superconducting magnets are obtained in real time, the operation time, maintenance records, and performance parameters of superconducting magnets are recorded, load data including real-time load, historical load curves, peak and valley values are regularly obtained from the power grid monitoring system, and the power consumption data of the user side is collected using smart meters and monitoring devices to analyze the load distribution.

[0041] Obtain the real-time power consumption of the power grid, and compare the real-time power consumption with a first power consumption threshold and a second power consumption threshold. The first power consumption threshold is used to determine whether the power grid is in a low-load state, and the second power consumption threshold is used to determine whether the power grid is in a high-load state;

[0042] If the real-time power consumption is greater than or equal to the first power consumption threshold and less than or equal to the second power consumption threshold, it is determined that the power grid is in a normal operation state and there is no need to conduct a secondary analysis on the startup conditions of the superconducting magnetic energy storage system;

[0043] If the real-time power consumption is less than the first power consumption threshold, it is determined that the power grid is in a low-load state. If the real-time power consumption is greater than the second power consumption threshold, it is determined that the power grid is in a high-load state. When it is determined that the power grid is in a low-load state or a high-load state, a secondary analysis on the startup conditions of the superconducting magnetic energy storage system is required.

[0044] When the result of the primary analysis is that a secondary analysis on the startup conditions of the superconducting magnetic energy storage system is required, the management system obtains the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using an intelligent analysis model, it determines whether to control the operation of the superconducting magnetic energy storage system, including the following steps:

[0045] When it is judged that the power grid is in a low-load state, it is necessary to conduct a secondary analysis on the charging start conditions of the superconducting magnetic energy storage system, obtain the real-time data and historical data of the power grid. The real-time data includes the duration of the continuous change in real-time power consumption, and the historical data includes the discrete assignment of power consumption. Substitute the duration of the continuous change in real-time power consumption and the discrete assignment of power consumption into the intelligent analysis model. The intelligent analysis model outputs the charging control coefficient, and compare the charging control coefficient with the preset charging control coefficient threshold. If the charging control coefficient is greater than or equal to the charging control coefficient threshold, it is judged that it is necessary to control the superconducting magnetic energy storage system to charge. If the charging control coefficient is less than the charging control coefficient threshold, it is judged that it is not necessary to control the superconducting magnetic energy storage system to charge.

[0046] When it is judged that the power grid is in a high-load state, it is necessary to conduct a secondary analysis on the energy release start conditions of the superconducting magnetic energy storage system;

[0047] When it is judged that the power grid is in a high-load state, it is necessary to conduct a secondary analysis on the energy release start conditions of the superconducting magnetic energy storage system, obtain the real-time data and historical data of the power grid. The real-time data includes the duration of the continuous change in real-time power consumption, and the historical data includes the discrete assignment of power consumption. Substitute the duration of the continuous change in real-time power consumption and the discrete assignment of power consumption into the intelligent analysis model. The intelligent analysis model outputs the energy release control coefficient, and compare the energy release control coefficient with the preset energy release control coefficient threshold. If the energy release control coefficient is greater than or equal to the energy release control coefficient threshold, it is judged that it is necessary to control the superconducting magnetic energy storage system to release energy. If the energy release control coefficient is less than the energy release control coefficient threshold, it is judged that it is not necessary to control the superconducting magnetic energy storage system to release energy.

[0048] The processing logic of the intelligent analysis model is as follows: After obtaining the duration of the change in power consumption and the discrete assignment of power consumption, perform normalization processing on the duration of the change in power consumption and the discrete assignment of power consumption, map the value ranges of the duration of the change in power consumption and the discrete assignment of power consumption to between [0, 1], obtain the normalized value of the duration of the change in power consumption and the normalized value of the discrete assignment of power consumption. When the power grid is in a low-load state, sum the normalized value of the duration of the change in power consumption and the normalized value of the discrete assignment of power consumption to obtain the charging control coefficient. When the power grid is in a high-load state, sum the normalized value of the duration of the change in power consumption and the normalized value of the discrete assignment of power consumption to obtain the energy release control coefficient.

[0049] The calculation logic for the duration of power consumption change is as follows: When the power grid is in a low-load state, the duration during which the real-time power consumption is less than the first power consumption threshold is taken as the duration of power consumption change. When the power grid is in a high-load state, the duration during which the real-time power consumption is greater than the second power consumption threshold is taken as the duration of power consumption change. The greater the duration of power consumption change, the longer the power grid is in a low-load or high-load state, and the more necessary it is to control the charging or discharging of the superconducting magnetic energy storage system.

[0050] The calculation logic for discrete assignment of power consumption is as follows: Obtain the power consumption of the power grid at multiple time points within a certain period of the distribution network history. Calculate the average power consumption and the standard deviation of power consumption based on the power consumption of multiple time points. The standard deviation of power consumption is calculated through the general standard deviation calculation formula, and the expression is: In the formula, bc is the standard deviation of power consumption, n is the number of time points, D avg is the average power consumption, D i is the power consumption of the power grid at the i-th time point. Obtain the discrete assignment of power consumption based on the average power consumption and the standard deviation of power consumption:

[0051] When the power grid is in a low-load state, if the average power consumption is less than the first power consumption threshold and the standard deviation of power consumption is less than or equal to the standard deviation threshold, it is analyzed that the power grid is in a low-load state as a whole within a certain period of the distribution network history, and the discrete assignment of power consumption is equal to 2.6; if the average power consumption is less than the first power consumption threshold and the standard deviation of power consumption is greater than the standard deviation threshold, it is analyzed that the power grid is in a low-load state within a certain period of the distribution network history, but there are some time points when the power consumption is greater than or equal to the first power consumption threshold, and the discrete assignment of power consumption is equal to 2.2; if the average power consumption is greater than or equal to the first power consumption threshold, it is analyzed that most time points within a certain period of the distribution network history are not in a low-load state, and the discrete assignment of power consumption is equal to 1.2;

[0052] When the power grid is in a high-load state, if the average power consumption is greater than the second power consumption threshold and the standard deviation of power consumption is less than or equal to the standard deviation threshold, it is analyzed that the power grid is in a high-load state as a whole within a certain period of the distribution network history, and the discrete assignment of power consumption is equal to 2.6; if the average power consumption is greater than the second power consumption threshold and the standard deviation of power consumption is greater than the standard deviation threshold, it is analyzed that the power grid is in a high-load state within a certain period of the distribution network history, but there are some time points when the power consumption is less than or equal to the second power consumption threshold, and the discrete assignment of power consumption is equal to 2.2; if the average power consumption is less than or equal to the second power consumption threshold, it is analyzed that most time points within a certain period of the distribution network history are not in a high-load state, and the discrete assignment of power consumption is equal to 1.2;

[0053] In summary, the greater the discrete assignment of power consumption, the more necessary it is to control the charging or discharging of the superconducting magnetic energy storage system.

[0054] When it is determined that the superconducting magnetic energy storage system needs to be charged, analyze the health status of each superconducting magnet, including the following steps:

[0055] Obtain the magnetic field strength deviation rate and energy storage level value of the superconducting magnet, and comprehensively calculate the magnet index by combining the magnetic field strength deviation rate and energy storage level value. The expression is:

[0056] magnet = α·μ - β·δ, where magnet is the magnet index, μ is the energy storage level value, δ is the magnetic field strength deviation rate, α and β are the adjustment coefficients of the energy storage level value and magnetic field strength deviation rate respectively, and both α and β are greater than 0; the larger the magnet index, the better the health status of the superconducting magnet.

[0057] The calculation logic of the magnetic field strength deviation rate is: obtain the current magnetic field strength and rated magnetic field strength of the superconducting magnet, subtract the rated magnetic field strength from the current magnetic field strength to obtain the strength difference, and divide the absolute value of the strength difference by the rated magnetic field strength to obtain the magnetic field strength deviation rate. The smaller the magnetic field strength deviation rate, the more normal the current magnetic field strength of the superconducting magnet, that is, the better the health status of the superconducting magnet. Specifically:

[0058] Each superconducting magnet has a specific rated magnetic field strength (B_nominal) during design, which is the magnetic field strength that it should reach when operating stably. The rated magnetic field strength depends on the material, structure and application scenario of the magnet. When the magnet operates at the rated magnetic field strength, it means that its physical and electrical characteristics are within the normal range and it can operate efficiently and reliably;

[0059] When the current magnetic field strength (B_current) of the superconducting magnet deviates from the rated magnetic field strength, it means that the system may have abnormalities. The deviation of the magnetic field strength may reflect the following problems:

[0060] Current fluctuation: The magnetic field strength is proportional to the current flowing through the magnet. Current fluctuation or abnormality may cause changes in the magnetic field strength.

[0061] Material performance degradation: The material properties of superconducting magnets may degrade over time, which will affect the stability of the magnetic field strength.

[0062] Loss of superconductivity: Superconducting magnets rely on low temperature to maintain their superconductivity. If the temperature or external environmental factors change, the superconducting magnet may enter the normal conducting state, resulting in the magnetic field strength not reaching the rated value.

[0063] External disturbance: Mechanical stress or other external factors may cause the magnet performance to be unstable, thereby affecting the magnetic field strength.

[0064] The smaller the deviation rate: the closer the current magnetic field strength is to the rated value, indicating that the operating state of the magnet is more stable. At this time, the superconducting magnet is in good working condition, meaning its health state is better and it can continue to effectively store or release energy.

[0065] The larger the deviation rate: it indicates that the current magnetic field strength deviates far from the rated value, meaning there may be signs of abnormality or degradation in the magnet. At this time, the health state of the magnet may be poor, with a risk of potential failure or performance decline.

[0066] The performance of the superconducting magnet highly depends on the stability of the magnetic field strength:

[0067] Energy storage: The magnetic field strength is a key factor for the energy storage and release of the superconducting magnet. The more stable the magnetic field strength, the more efficiently the magnet can store or release energy. If the magnetic field strength is unstable, the energy storage capacity of the magnet will be affected.

[0068] Stable superconducting state: The superconducting magnet needs to maintain a stable low temperature and strong magnetic field to maintain superconductivity. If the magnetic field strength fluctuates greatly, it may mean that superconductivity begins to be lost or the system is disturbed.

[0069] Extended service life: A magnet with a large deviation in magnetic field strength usually means that the internal materials or the external environment are disturbed or degraded. Working at a non-rated state for a long time may accelerate the aging of the magnet and shorten its service life.

[0070] The calculation expression for the energy storage level value is: In the formula, L is the inductance value of the superconducting magnet, I is the current value. The larger the energy storage level value, the better the health state of the superconducting magnet, specifically:

[0071] A higher energy storage level indicates that the superconducting magnet can effectively store a large amount of energy, which means that both the duration of the change in the system's inductance power consumption and the duration of the change in the current power consumption are within the expected range. This indicates that the superconducting magnet is operating normally within its design parameters and has an efficient energy storage capacity.

[0072] Superconducting characteristics: The superconducting magnet relies on its superconducting material to maintain a zero-resistance state at low temperature to achieve efficient energy storage and rapid charge and discharge.

[0073] The significance of a high energy storage level: A higher energy storage level means that the superconducting magnet has successfully maintained its superconducting state, avoiding energy loss and heat accumulation. This indicates that the cooling system and the magnet itself are working properly without the problem of losing superconductivity (i.e., "quenching").

[0074] Energy efficiency: In an ideal situation, the energy loss of a superconducting magnet is extremely low and can be almost negligible. A higher energy storage level indicates that the system has less energy loss during the charge and discharge processes and is highly efficient.

[0075] Reflection of the health state: If there is aging of insulating materials, excessive mechanical stress, or other defects in the superconducting magnet, it may lead to an increase in energy loss and thus a decrease in the energy storage level. Therefore, a higher energy storage level indirectly indicates that all components of the system (such as the insulation layer, cooling system, etc.) are in good condition.

[0076] Fast charge and discharge: The SMES power consumption change duration system needs to complete the charge and discharge of energy within the millisecond level to support the instantaneous load regulation of the power grid. A higher energy storage level indicates that the system has sufficient energy reserves and can respond quickly when needed.

[0077] Embodiment of the health state: If the response ability of the system decreases, it may be due to the performance degradation of the superconducting magnet or the failure of the control system, resulting in the energy storage level not reaching the expected value. Therefore, a high energy storage level also reflects the fast response ability and overall health state of the system.

[0078] Reduce stress: High-efficiency energy storage means that the current and magnetic field stresses borne by the system during operation are kept within a reasonable range and will not cause premature aging of components due to overloading.

[0079] Association with the health state: A stable energy storage level indicates that the superconducting magnet and related components are not subject to excessive stress or damage, thereby extending the overall service life of the system.

[0080] Based on the health state analysis results, sort all superconducting magnets to generate a sorting table, and select the corresponding number of superconducting magnets for charging in combination with the power grid load information and the sorting table, including the following steps:

[0081] Sort all superconducting magnets in descending order according to the magnet index to generate a sorting table. The higher the ranking of a superconducting magnet in the sorting table, the better its health state and the more it should be preferentially selected for use;

[0082] During the low-load state of the power grid, start charging the superconducting magnets in ascending order according to the sorting table. After the current superconducting magnet is fully charged, control the charging of the superconducting magnets in ascending order according to the sorting table until the power grid is no longer in the low-load state.

[0083] Embodiment 3: The device energy efficiency management system based on cloud services in this embodiment includes a primary analysis module, a secondary analysis module, and a charging selection module;

[0084] Primary analysis module: Obtain the number and information of superconducting magnets in the superconducting magnetic energy storage system, regularly obtain the load status of the power grid, conduct primary analysis based on the load status of the power grid, and send the primary analysis results to the secondary analysis module;

[0085] Secondary analysis module: When the primary analysis result is that the start-up conditions of the superconducting magnetic energy storage system need to be analyzed secondarily, obtain the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using the intelligent analysis model, determine whether it is necessary to control the operation of the superconducting magnetic energy storage system, and send the determination result to the charging selection module;

[0086] Charging selection module: When it is determined that the superconducting magnetic energy storage system needs to be charged, after analyzing the health status of each superconducting magnet, sort all superconducting magnets based on the analysis results of the health status, generate a sorting table, and select the corresponding number of superconducting magnets for charging in combination with the power grid load information and the sorting table.

[0087] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0088] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0089] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical fields can well understand and utilize the present invention.

Claims

1. A method for device energy efficiency management based on cloud services, characterized in that: The management method includes the following steps: The management system obtains the number and information of superconducting magnets in the superconducting magnetic energy storage system, regularly obtains the load status of the power grid, and conducts a primary analysis based on the load status of the power grid; Obtain the real-time power consumption of the power grid, and compare the real-time power consumption with the first power consumption threshold and the second power consumption threshold. The first power consumption threshold is used to determine whether the power grid is in a low-load state, and the second power consumption threshold is used to determine whether the power grid is in a high-load state; If the real-time power consumption is greater than or equal to the first power consumption threshold and less than or equal to the second power consumption threshold, it is determined that the power grid is in a normal operation state, and there is no need to conduct a secondary analysis on the startup conditions of the superconducting magnetic energy storage system; If the real-time power consumption is less than the first power consumption threshold, it is determined that the power grid is in a low-load state. If the real-time power consumption is greater than the second power consumption threshold, it is determined that the power grid is in a high-load state. When it is determined that the power grid is in a low-load state or a high-load state, a secondary analysis on the startup conditions of the superconducting magnetic energy storage system is required; When the result of the primary analysis is that a secondary analysis on the startup conditions of the superconducting magnetic energy storage system is required, the management system obtains the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using the intelligent analysis model, determines whether to control the operation of the superconducting magnetic energy storage system; When it is determined that the superconducting magnetic energy storage system needs to be charged, analyze the health status of each superconducting magnet; Obtain the magnetic field strength deviation rate and energy storage level value of the superconducting magnet, and comprehensively calculate the magnetic field strength deviation rate and energy storage level value to obtain the magnet index. The expression is: , where is the magnet index, is the energy storage level value, is the magnetic field strength deviation rate, , are the adjustment coefficients of the energy storage level value and the magnetic field strength deviation rate respectively, and , are both greater than 0; the larger the magnet index, the better the health state of the superconducting magnet; Based on the health status analysis results, sort all superconducting magnets to generate a sorting table, and select the corresponding number of superconducting magnets for charging in combination with the power grid load information and the sorting table.

2. The device energy efficiency management method based on cloud service according to claim 1, characterized in that: The management system obtains the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using the intelligent analysis model, determines whether to control the operation of the superconducting magnetic energy storage system, including the following steps: When it is determined that the power grid is in a low-load state, a secondary analysis on the charging startup conditions of the superconducting magnetic energy storage system is required. Obtain the real-time data and historical data of the power grid. The real-time data includes the duration of continuous change in real-time power consumption, and the historical data includes the discrete assignment of power consumption. Substitute the duration of continuous change in real-time power consumption and the discrete assignment of power consumption into the intelligent analysis model. The intelligent analysis model outputs the charging control coefficient, and compare the charging control coefficient with the preset charging control coefficient threshold. If the charging control coefficient is greater than or equal to the charging control coefficient threshold, it is determined that the superconducting magnetic energy storage system needs to be charged. If the charging control coefficient is less than the charging control coefficient threshold, it is determined that the superconducting magnetic energy storage system does not need to be charged.

3. The device energy efficiency management method based on cloud service according to claim 2, wherein: The management system obtains the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using the intelligent analysis model, determines whether to control the operation of the superconducting magnetic energy storage system, and also includes the following steps: When it is judged that the power grid is in a high-load state, it is necessary to conduct a secondary analysis on the release energy startup conditions of the superconducting magnetic energy storage system, obtain the real-time data and historical data of the power grid. The real-time data includes the duration of continuous change in real-time power consumption, and the historical data includes the discrete assignment of power consumption. Substitute the duration of continuous change in real-time power consumption and the discrete assignment of power consumption into the intelligent analysis model. The intelligent analysis model outputs the release energy control coefficient, and compare the release energy control coefficient with the preset release energy control coefficient threshold. If the release energy control coefficient is greater than or equal to the release energy control coefficient threshold, it is judged that it is necessary to control the superconducting magnetic energy storage system to release energy. If the release energy control coefficient is less than the release energy control coefficient threshold, it is judged that there is no need to control the superconducting magnetic energy storage system to release energy.

4. The method for device energy efficiency management based on cloud service according to claim 3, wherein: The processing logic of the intelligent analysis model is as follows: After obtaining the duration of power consumption change and the discrete assignment of power consumption, perform normalization processing on the duration of power consumption change and the discrete assignment of power consumption, map the value ranges of the duration of power consumption change and the discrete assignment of power consumption to between [0, 1], obtain the normalized value of the duration of power consumption change and the normalized value of the discrete assignment of power consumption. When the power grid is in a low-load state, sum the normalized value of the duration of power consumption change and the normalized value of the discrete assignment of power consumption to obtain the charging control coefficient. When the power grid is in a high-load state, sum the normalized value of the duration of power consumption change and the normalized value of the discrete assignment of power consumption to obtain the release energy control coefficient.

5. The method for device energy efficiency management based on cloud services according to claim 4, characterized in that: Select the corresponding number of superconducting magnets for charging in combination with the power grid load information and the sorting table, including the following steps: Sort all the superconducting magnets in descending order according to the magnet index to generate a sorting table. During the low-load state stage of the power grid, start the charging of the superconducting magnets in ascending order according to the sorting table. After the current superconducting magnet is fully charged, control the charging of the superconducting magnets in ascending order according to the sorting table until the power grid is no longer in the low-load state.

6. A device energy efficiency management system based on cloud services, for implementing the management method according to any one of claims 1-5, characterized in that: It includes a primary analysis module, a secondary analysis module, and a charging selection module; Primary analysis module: Obtain the number and information of superconducting magnets in the superconducting magnetic energy storage system, regularly obtain the load state of the power grid, and conduct a primary analysis based on the load state of the power grid; Secondary analysis module: When the primary analysis result is that a secondary analysis of the startup conditions of the superconducting magnetic energy storage system is required, obtain the real-time data and historical data of the power grid, and after analyzing the real-time data and historical data using the intelligent analysis model, judge whether it is necessary to control the operation of the superconducting magnetic energy storage system; Charging selection module: When it is judged that it is necessary to control the charging of the superconducting magnetic energy storage system, after analyzing the health status of each superconducting magnet, sort all the superconducting magnets based on the analysis result of the health status to generate a sorting table, and select the corresponding number of superconducting magnets for charging in combination with the power grid load information and the sorting table.

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