Equipment energy efficiency management system and method based on cloud service

Abstract

The invention discloses an equipment energy efficiency management system and method based on cloud service, and relates to the technical field of equipment energy efficiency management. Real-time data and historical data of a power grid are acquired, and after the real-time data and the historical data are analyzed by using an intelligent analysis model, whether a superconducting magnetic energy storage system needs to be controlled to operate is judged; when it is judged that the superconducting magnetic energy storage system needs to be controlled for energy charging, after the health state of each superconducting magnet is analyzed, all the superconducting magnets are sorted based on the health state analysis result, a sorting table is generated, and the superconducting magnets of the corresponding number are selected for energy charging in combination with power grid load information and the sorting table. The management system dynamically optimizes the starting method of the superconducting magnetic energy storage system based on an intelligent analysis model, and performs health state analysis on all superconducting magnets before the superconducting magnetic energy storage system is charged, so that the superconducting magnet with a good health state is preferably selected for use, the management efficiency of the superconducting magnetic energy storage system is improved, and the energy utilization rate of the superconducting magnetic energy storage system is improved. And stable operation of the power grid is ensured.

Description

Technical Field

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

[0002] With the global emphasis on renewable energy (such as solar and wind energy), traditional power grids are facing load fluctuations and power quality problems. The intermittent and unpredictable nature of renewable energy requires the power grid to have higher flexibility and responsiveness to ensure the balance between supply and demand. The management system provides an important solution for achieving energy transformation, improving grid flexibility and integrating renewable energy, and helps build a smarter and more reliable modern power system.

[0003] The superconducting magnetic energy storage system (SMES) uses superconducting materials to store electrical energy, and has fast charging and discharging capabilities and high efficiency. When the grid load is low (such as low electricity consumption at night), the SMES obtains excess electrical energy from the grid for charging. In this process, the electrical energy is converted into magnetic energy and stored in superconducting magnets. 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 quickly increase the power supply capacity of the grid and avoid power outages or power quality problems caused by excessive load.

[0004] The prior art has the following defects:

[0005] 1. The management system usually sets two methods to determine whether the superconducting magnetic energy storage system needs to be operated. One is to set a power consumption threshold of the power grid, and control the superconducting magnetic energy storage system to charge or release energy based on the comparison result between the real-time power consumption of the power grid and the power consumption threshold. The other is to set a time period to control the superconducting magnetic energy storage system to charge or release energy. The first method may cause the management system to frequently control the start-up of the superconducting magnetic energy storage system, which will increase the operating burden of the superconducting magnetic energy storage system and cause power loss. The second method will cause the superconducting magnetic energy storage system to respond untimely and fail to effectively provide guarantee for the stable operation of the power grid.

[0006] 2. Since there are several groups of superconducting magnets in the superconducting magnetic energy storage system, the existing management system usually randomly selects superconducting magnets for energy storage when charging the superconducting magnetic energy storage system. This random selection easily leads to the selection of superconducting magnets in poor health status, thereby increasing power loss.

[0007] Based on this, the present invention proposes a cloud service-based equipment energy efficiency management system and method, which dynamically optimizes the startup method of the superconducting magnetic energy storage system based on an intelligent analysis model, and performs a health status analysis on all superconducting magnets before charging the superconducting magnetic energy storage system, so as to preferentially use superconducting magnets in 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. Summary of the invention

[0008] The purpose of the present invention is to provide a cloud service-based equipment energy efficiency management system and method to address the deficiencies in the background technology.

[0009] In order to achieve the above object, the present invention provides the following technical solution: a device energy efficiency management method based on cloud service, the management method comprising the following steps:

[0010] The management system obtains the number and information of superconducting magnets in the superconducting magnetic energy storage system, obtains the load status of the power grid at regular intervals, and performs an analysis based on the load status of the power grid;

[0011] When the result of the primary analysis indicates that a secondary analysis is required for the start-up conditions of the superconducting magnetic energy storage system, the management system obtains the real-time data and historical data of the power grid, and uses the intelligent analysis model to analyze the real-time data and historical data to determine whether the operation of the superconducting magnetic energy storage system needs to be controlled;

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

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

[0014] Obtaining real-time power consumption of the power grid, and comparing the real-time power consumption with a first power consumption threshold and a second power consumption threshold, the first power consumption threshold being used to determine whether the power grid is in a low-load state, and the second power consumption threshold being 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 the real-time power consumption is less than or equal to the second power consumption threshold, it is judged that the power grid is in a normal operating state, and there is no need to perform a secondary analysis on the start-up conditions of the superconducting magnetic energy storage system;

[0016] If the real-time power consumption is less than the first power consumption threshold, the power grid is judged to be in a low-load state. If the real-time power consumption is greater than the second power consumption threshold, the power grid is judged to be in a high-load state. When the power grid is judged to be 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 obtains real-time data and historical data of the power grid, and uses an intelligent analysis model to analyze the real-time data and historical data, and then determines whether it is necessary to control the operation of the superconducting magnetic energy storage system, including the following steps:

[0018] When it is judged that the power grid is in a low-load state, it is necessary to conduct a secondary analysis of the charging start conditions of the superconducting magnetic energy storage system to obtain real-time data and historical data of the power grid. The real-time data includes the duration of real-time power consumption changes, and the historical data includes discrete assignments of power consumption. The duration of real-time power consumption changes and the discrete assignments of power consumption are substituted into the intelligent analysis model. The intelligent analysis model outputs a charging control coefficient, and the charging control coefficient is compared 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 judged that the superconducting magnetic energy storage system needs to be controlled for charging. If the charging control coefficient is less than the charging control coefficient threshold, it is judged that the superconducting magnetic energy storage system does not need to be controlled for charging.

[0019] In a preferred embodiment, the management system obtains real-time data and historical data of the power grid, and uses an intelligent analysis model to analyze the real-time data and historical data to determine 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 judged that the power grid is in a high-load state, it is necessary to conduct a secondary analysis of the energy release start-up conditions of the superconducting magnetic energy storage system to obtain real-time data and historical data of the power grid. The real-time data includes the duration of real-time power consumption changes, and the historical data includes discrete assignments of power consumption. The duration of real-time power consumption changes and the discrete assignments of power consumption are substituted into the intelligent analysis model. The intelligent analysis model outputs a release energy control coefficient, and the release energy control coefficient is compared with a 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 the superconducting magnetic energy storage system needs to be controlled to release energy. If the release energy control coefficient is less than the release energy control coefficient threshold, it is judged that the superconducting magnetic energy storage system does not need to be controlled to release energy.

[0021] In a preferred embodiment, the processing logic of the intelligent analysis model is: after obtaining the duration of the power consumption change and the discrete assignment of power consumption, the duration of the power consumption change and the discrete assignment of power consumption are normalized, and the value range of the duration of the power consumption change and the discrete assignment of power consumption are mapped to [0,1] to obtain the normalized value of the duration of the power consumption change and the normalized value of the discrete assignment of power consumption. When the power grid is in a low load state, the normalized value of the duration of the power consumption change and the normalized value of the discrete assignment of power consumption are summed to obtain the charging control coefficient. When the power grid is in a high load state, the normalized value of the duration of the power consumption change and the normalized value of the discrete assignment of power consumption are summed 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 controlled to charge, 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 calculate the magnetic field strength deviation rate and energy storage level value comprehensively to obtain the magnet index, and 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 α and β are both greater than 0; the larger the magnet index, the better the health of the superconducting magnet.

[0025] In a preferred embodiment, selecting a corresponding number of superconducting magnets for charging in combination with grid load information and a ranking table includes the following steps:

[0026] All superconducting magnets are sorted from large to small according to the magnet index to generate a sorting table. When the power grid is in a low-load state, the superconducting magnet charging is started according to the positive sequence of the sorting table. After the current superconducting magnet is full, the superconducting magnet charging is controlled according to the positive sequence of the sorting table until the power grid is no longer in a low-load state.

[0027] Equipment energy efficiency management system based on cloud service, including primary analysis module, secondary analysis module and charging selection module;

[0028] Primary analysis module: obtains the number and information of superconducting magnets in the superconducting magnetic energy storage system, obtains the load status of the power grid at regular intervals, and performs a primary analysis based on the load status of the power grid;

[0029] Secondary analysis module: When the result of the primary analysis indicates that a secondary analysis is required for the start-up conditions of the superconducting magnetic energy storage system, the real-time data and historical data of the power grid are obtained, and after analyzing the real-time data and historical data using an intelligent analysis model, it is determined whether the operation of the superconducting magnetic energy storage system needs to be controlled;

[0030] Charging selection module: When it is determined that the superconducting magnetic energy storage system needs to be controlled for charging, the health status of each superconducting magnet is analyzed, and then all superconducting magnets are sorted based on the health status analysis results to generate a sorting table. The corresponding number of superconducting magnets are selected for charging in combination with the grid load information and the sorting table.

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

[0032] The present invention obtains real-time data and historical data of the power grid, and uses an intelligent analysis model to analyze the real-time data and historical data, and then determines whether it is necessary 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 controlled to charge, the health status of each superconducting magnet is analyzed, and then all superconducting magnets are sorted based on the health status analysis results to generate a sorting table, and a 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 analyzes the health status of all superconducting magnets before charging the superconducting magnetic energy storage system, so as to select superconducting magnets with good health status for use, which not only improves the management efficiency of the superconducting magnetic energy storage system, but also ensures the stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0034] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1: Please refer to Figure 1 As shown, the device energy efficiency management method based on cloud service 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, obtains the load status of the power grid at regular intervals, and performs an analysis based on the load status of the power grid. When the result of the first analysis indicates that a secondary analysis is required for the start-up conditions of the superconducting magnetic energy storage system, the management system obtains the real-time data and historical data of the power grid, and uses an intelligent analysis model to analyze the real-time data and historical data, and then determines whether it is necessary to control the operation of the superconducting magnetic energy storage system. When it is determined that the charging of the superconducting magnetic energy storage system needs to be controlled, the health status of each superconducting magnet is analyzed, and then all superconducting magnets are sorted based on the health status analysis results to generate a sorting table. In combination with the power grid load information and the sorting table, a corresponding number of superconducting magnets are selected for charging.

[0038] This application obtains the real-time data and historical data of the power grid, and uses the intelligent analysis model to analyze the real-time data and historical data to determine whether it is necessary 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 controlled to charge, the health status of each superconducting magnet is analyzed, and all superconducting magnets are sorted based on the health status analysis results to generate a sorting table. 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 analyzes the health status of all superconducting magnets before charging the superconducting magnetic energy storage system, so as to select superconducting magnets with good health status for use, 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, obtains the load status of the power grid at regular intervals, and performs an analysis based on the load status of the power grid, including the following steps:

[0040] Through sensors and monitoring systems, real-time information such as the number, status, temperature, charging status and energy storage level of superconducting magnets is obtained, the operating time, maintenance records and performance parameters of superconducting magnets are recorded, and load data is regularly obtained from the power grid monitoring system, including real-time load, historical load curves, peak and valley information. Smart meters and monitoring equipment are used to collect user-side electricity consumption data and analyze load distribution.

[0041] Obtaining real-time power consumption of the power grid, and comparing the real-time power consumption with a first power consumption threshold and a second power consumption threshold, the first power consumption threshold being used to determine whether the power grid is in a low-load state, and the second power consumption threshold being 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 the real-time power consumption is less than or equal to the second power consumption threshold, it is judged that the power grid is in a normal operating state, and there is no need to perform a secondary analysis on the start-up conditions of the superconducting magnetic energy storage system;

[0043] If the real-time power consumption is less than the first power consumption threshold, the power grid is judged to be in a low-load state. If the real-time power consumption is greater than the second power consumption threshold, the power grid is judged to be in a high-load state. When the power grid is judged to be 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.

[0044] When the result of the primary analysis indicates that a secondary analysis is required for the start-up conditions of the superconducting magnetic energy storage system, the management system obtains the real-time data and historical data of the power grid, and uses the intelligent analysis model to analyze the real-time data and historical data, and then determines whether it is necessary 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 of the charging start conditions of the superconducting magnetic energy storage system to obtain real-time data and historical data of the power grid. The real-time data includes the duration of real-time power consumption changes, and the historical data includes discrete assignments of power consumption. The duration of real-time power consumption changes and the discrete assignments of power consumption are substituted into the intelligent analysis model. The intelligent analysis model outputs a charging control coefficient, and the charging control coefficient is compared 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 judged that the superconducting magnetic energy storage system needs to be controlled for charging. If the charging control coefficient is less than the charging control coefficient threshold, it is judged that the superconducting magnetic energy storage system does not need to be controlled for charging.

[0046] When the power grid is judged to be in a high-load state, it is necessary to conduct a secondary analysis of the energy release start-up 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 of the energy release start-up conditions of the superconducting magnetic energy storage system to obtain real-time data and historical data of the power grid. The real-time data includes the duration of real-time power consumption changes, and the historical data includes discrete assignments of power consumption. The duration of real-time power consumption changes and the discrete assignments of power consumption are substituted into the intelligent analysis model. The intelligent analysis model outputs a release energy control coefficient, and the release energy control coefficient is compared with a 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 the superconducting magnetic energy storage system needs to be controlled to release energy. If the release energy control coefficient is less than the release energy control coefficient threshold, it is judged that the superconducting magnetic energy storage system does not need to be controlled to release energy.

[0048] The processing logic of the intelligent analysis model is as follows: after obtaining the duration of electricity consumption change and the discrete assignment of electricity consumption, the duration of electricity consumption change and the discrete assignment of electricity consumption are normalized, and the value range of the duration of electricity consumption change and the discrete assignment of electricity consumption are mapped to [0,1] to obtain the normalized value of the duration of electricity consumption change and the normalized value of the discrete assignment of electricity consumption. When the power grid is in a low load state, the normalized value of the duration of electricity consumption change and the normalized value of the discrete assignment of electricity consumption are summed to obtain the charging control coefficient. When the power grid is in a high load state, the normalized value of the duration of electricity consumption change and the normalized value of the discrete assignment of electricity consumption are summed to obtain the energy release control coefficient.

[0049] The calculation logic of the duration of power consumption change is as follows: when the power grid is in a low load state, the duration that the real-time power consumption is less than the first power consumption threshold is taken as the duration of the power consumption change; when the power grid is in a high load state, the duration that the real-time power consumption is greater than the second power consumption threshold is taken as the duration of the power consumption change. The longer the duration of the power consumption change, the longer the power grid is in a low load or high load state, and the more it needs to control the superconducting magnetic energy storage system to charge or release energy.

[0050] The calculation logic of discrete assignment of power consumption is as follows: obtain the power consumption of the power grid at multiple time points in the history of the distribution network, calculate the average power consumption and the standard deviation of power consumption based on the power consumption of the power grid at multiple time points, and calculate the standard deviation of power consumption by the general standard deviation calculation formula, which is expressed as follows: In the formula, bc is the standard deviation of electricity consumption, n is the number of time points, and D avg is the average power consumption, D i For the power consumption of the power grid at the i-th time point, the discrete value of power consumption is obtained 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 power standard deviation 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 in a period of history, and the power consumption discrete assignment is equal to 2.6; if the average power consumption is less than the first power consumption threshold, and the power standard deviation is greater than the standard deviation threshold, it is analyzed that the power grid is in a low-load state in a period of history, but the power consumption at some time points is greater than or equal to the first power consumption threshold, and the power consumption discrete assignment 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 in the power grid are not in a low-load state in a period of history, and the power consumption discrete assignment 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 power standard deviation 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 for a period of time in history, and the power consumption discrete assignment is equal to 2.6; if the average power consumption is greater than the second power consumption threshold, and the power standard deviation is greater than the standard deviation threshold, it is analyzed that the power grid is in a high-load state for a period of time in history, but the power consumption at some time points is less than or equal to the second power consumption threshold, and the power consumption discrete assignment 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 in the power grid are not in a high-load state for a period of time in history, and the power consumption discrete assignment is equal to 1.2;

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

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

[0055] Obtain the magnetic field strength deviation rate and energy storage level value of the superconducting magnet, and calculate the magnetic field strength deviation rate and energy storage level value comprehensively to obtain the magnet index, and 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 the magnetic field strength deviation rate, respectively, and α and β are both greater than 0; the larger the magnet index, the better the health of the superconducting magnet.

[0057] The calculation logic of the magnetic field strength deviation rate is as follows: 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, take the absolute value of the strength difference and divide it by the rated magnetic field strength to obtain the magnetic field strength deviation rate. The smaller the magnetic field strength deviation rate is, the more normal the current magnetic field strength of the superconducting magnet is, that is, the healthier the superconducting magnet is. Specifically:

[0058] Each superconducting magnet is designed with a specific rated magnetic field strength (B_nominal), which is the magnetic field strength it should reach when working stably. The rated magnetic field strength depends on the material, structure and application scenario of the magnet. When the magnet works at the rated magnetic field strength, it means that its physical and electrical characteristics are within the normal range and 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 there may be an abnormality in the system. The deviation of magnetic field strength may reflect the following problems:

[0060] Current Fluctuations: The strength of the magnetic field is proportional to the current flowing through the magnet. Current fluctuations or anomalies may cause changes in the magnetic field strength.

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

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

[0063] External disturbances: Mechanical stress or other external factors may cause instability in magnet performance, which in turn affects the magnetic field strength.

[0064] The smaller the deviation rate is, the closer the current magnetic field strength is to the rated value, indicating that the magnet is operating more stably. At this point, the superconducting magnet is in good working condition, which means that it is in good health and can continue to store or release energy effectively.

[0065] The larger the deviation rate, the farther the current magnetic field strength deviates from the rated value, which means that the magnet may have signs of abnormality or degradation. At this point, the magnet's health may be poor, with the risk of potential failure or performance degradation.

[0066] The performance of superconducting magnets is highly dependent on the stability of the magnetic field strength:

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

[0068] Stable superconducting state: Superconducting magnets need to maintain stable low temperatures and strong magnetic fields to maintain superconductivity. If the magnetic field strength fluctuates greatly, it may mean that superconductivity is beginning to be lost or the system is disturbed.

[0069] Extend service life: Magnets with large magnetic field strength deviation usually mean that the internal material or external environment has been disturbed or degraded. Long-term operation under non-rated conditions may accelerate the aging of the magnet and shorten its service life.

[0070] The calculation expression of energy storage level is: Where L is the inductance of the superconducting magnet, I is the current value, and the larger the energy storage level value, the better the health of the superconducting magnet, specifically:

[0071] The high energy storage level indicates that the superconducting magnet is able to effectively store a large amount of energy, which means that the duration of the system's inductance power consumption changes and the duration of the current power consumption changes are within the expected range. This shows that the superconducting magnet works normally within its design parameters and has efficient energy storage capabilities.

[0072] Superconducting properties: Superconducting magnets rely on their superconducting materials to maintain a zero-resistance state at low temperatures, thereby achieving efficient energy storage and rapid charging and discharging.

[0073] The significance of high energy storage levels: Higher energy storage levels mean 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 and there is no problem of loss of superconductivity (i.e., "quenching").

[0074] Energy efficiency: Under ideal conditions, the energy loss of superconducting magnets is very low and almost negligible. A higher energy storage level means that the system has less energy loss during charging and discharging and is highly efficient.

[0075] Reflection of health status: If the superconducting magnet has aging insulation materials, excessive mechanical stress or other defects, it may lead to increased energy loss, thereby reducing the energy storage level. Therefore, a higher energy storage level indirectly indicates that the various components of the system (such as insulation layer, cooling system, etc.) are in good condition.

[0076] Fast charging and discharging: The duration of SMES power consumption changes requires the system to complete energy charging and discharging within milliseconds to support instantaneous load regulation of the power grid. A high energy storage level indicates that the system has sufficient energy reserves and can respond quickly when needed.

[0077] Health status: If the system's responsiveness decreases, it may be due to the degradation of the superconducting magnets or the failure of the control system, resulting in the energy storage level not reaching the expected value. Therefore, high energy storage levels also reflect the system's fast response capability and overall health status.

[0078] Reduced stress: Efficient energy storage means that the current and magnetic field stresses to which the system is subjected during operation remain within reasonable limits, without premature aging of components due to excessive load.

[0079] Correlation of health status: Stable energy storage levels indicate that the superconducting magnets and related components are not overstressed or damaged, thereby extending the overall service life of the system.

[0080] All superconducting magnets are sorted based on the health status analysis results to generate a sorting table, and a corresponding number of superconducting magnets are selected for charging in combination with the grid load information and the sorting table, including the following steps:

[0081] All superconducting magnets are sorted from large to small according to the magnet index to generate a sorting table. The higher the superconducting magnet is in the sorting table, the better the health of the superconducting magnet is, and the more it should be used in priority;

[0082] When the power grid is in a low-load state, the superconducting magnet charging is started according to the positive sequence of the sequencing table. After the superconducting magnet is fully charged, the superconducting magnet charging is controlled according to the positive sequence of the sequencing table until the power grid is no longer in a low-load state.

[0083] Embodiment 3: The cloud service-based equipment energy efficiency management system of this embodiment includes a primary analysis module, a secondary analysis module, and a charging selection module;

[0084] Primary analysis module: obtains the number and information of superconducting magnets in the superconducting magnetic energy storage system, obtains the load status of the power grid at regular intervals, performs a primary analysis based on the load status of the power grid, and sends the primary analysis results to the secondary analysis module;

[0085] Secondary analysis module: When the primary analysis result indicates that a secondary analysis is required for the start-up conditions of the superconducting magnetic energy storage system, the real-time data and historical data of the power grid are obtained, and after analyzing the real-time data and historical data using the intelligent analysis model, it is determined whether the operation of the superconducting magnetic energy storage system needs to be controlled, and the judgment result is sent to the charging selection module;

[0086] Charging selection module: When it is determined that the superconducting magnetic energy storage system needs to be controlled for charging, the health status of each superconducting magnet is analyzed, and then all superconducting magnets are sorted based on the health status analysis results to generate a sorting table. The corresponding number of superconducting magnets are selected for charging in combination with the grid load information and the sorting table.

[0087] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0088] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does 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 explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well.

Claims

1. A cloud service-based equipment energy efficiency management method, characterized in that: The management method comprises the following steps: The management system obtains the number and information of superconducting magnets in the superconducting magnetic energy storage system, obtains the load status of the power grid at regular intervals, and performs an analysis based on the load status of the power grid; When the result of the primary analysis indicates that a secondary analysis is required for the start-up conditions of the superconducting magnetic energy storage system, the management system obtains the real-time data and historical data of the power grid, and uses the intelligent analysis model to analyze the real-time data and historical data to determine whether the operation of the superconducting magnetic energy storage system needs to be controlled; When it is determined that the superconducting magnetic energy storage system needs to be controlled to charge, the health status of each superconducting magnet is analyzed, and then all superconducting magnets are sorted based on the health status analysis results to generate a sorting table. The corresponding number of superconducting magnets are selected for charging in combination with the 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 number and information of superconducting magnets in the superconducting magnetic energy storage system, obtains the load status of the power grid at regular intervals, and performs an analysis based on the load status of the power grid, including the following steps: Obtaining real-time power consumption of the power grid, and comparing the real-time power consumption with a first power consumption threshold and a second power consumption threshold, the first power consumption threshold being used to determine whether the power grid is in a low-load state, and the second power consumption threshold being 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 the real-time power consumption is less than or equal to the second power consumption threshold, it is judged that the power grid is in a normal operating state, and there is no need to perform a secondary analysis on the start-up conditions of the superconducting magnetic energy storage system; If the real-time power consumption is less than the first power consumption threshold, the power grid is judged to be in a low-load state. If the real-time power consumption is greater than the second power consumption threshold, the power grid is judged to be in a high-load state. When the power grid is judged to be 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.

3. The device energy efficiency management method based on cloud service according to claim 2, characterized in that: The management system obtains the real-time data and historical data of the power grid, and uses the intelligent analysis model to analyze the real-time data and historical data, and then determines whether it is necessary to control the operation of the superconducting magnetic energy storage system, including the following steps: When it is judged that the power grid is in a low-load state, it is necessary to conduct a secondary analysis of the charging start conditions of the superconducting magnetic energy storage system to obtain real-time data and historical data of the power grid. The real-time data includes the duration of real-time power consumption changes, and the historical data includes discrete assignments of power consumption. The duration of real-time power consumption changes and the discrete assignments of power consumption are substituted into the intelligent analysis model. The intelligent analysis model outputs a charging control coefficient, and the charging control coefficient is compared 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 judged that the superconducting magnetic energy storage system needs to be controlled for charging. If the charging control coefficient is less than the charging control coefficient threshold, it is judged that the superconducting magnetic energy storage system does not need to be controlled for charging.

4. The cloud service-based device energy efficiency management method according to claim 2, characterized in that: The management system obtains the real-time data and historical data of the power grid, and uses the intelligent analysis model to analyze the real-time data and historical data, and then determines whether it is necessary to control the operation of the superconducting magnetic energy storage system, which 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 of the energy release start-up conditions of the superconducting magnetic energy storage system to obtain real-time data and historical data of the power grid. The real-time data includes the duration of real-time power consumption changes, and the historical data includes discrete assignments of power consumption. The duration of real-time power consumption changes and the discrete assignments of power consumption are substituted into the intelligent analysis model. The intelligent analysis model outputs a release energy control coefficient, and the release energy control coefficient is compared with a 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 the superconducting magnetic energy storage system needs to be controlled to release energy. If the release energy control coefficient is less than the release energy control coefficient threshold, it is judged that the superconducting magnetic energy storage system does not need to be controlled to release energy.

5. The device energy efficiency management method based on cloud service according to claim 4, characterized in that: 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, the duration of the change in power consumption and the discrete assignment of power consumption are normalized, and the value range of the duration of the change in power consumption and the discrete assignment of power consumption are mapped to [0,1] to 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, the normalized value of the duration of the change in power consumption and the normalized value of the discrete assignment of power consumption are summed to obtain the charging control coefficient; when the power grid is in a high load state, the normalized value of the duration of the change in power consumption and the normalized value of the discrete assignment of power consumption are summed to obtain the energy release control coefficient.

6. The device energy efficiency management method based on cloud service according to claim 5, characterized in that: When it is determined that the superconducting magnetic energy storage system needs to be controlled to charge, the health status of each superconducting magnet is analyzed, including the following steps: Obtain the magnetic field strength deviation rate and energy storage level value of the superconducting magnet, and calculate the magnetic field strength deviation rate and energy storage level value comprehensively to obtain the magnet index, and the expression is: 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 α and β are both greater than 0; the larger the magnet index, the better the health of the superconducting magnet.

7. The cloud service-based device energy efficiency management method according to claim 6, characterized in that: According to the grid load information and the sorting table, a corresponding number of superconducting magnets are selected for charging, including the following steps: All superconducting magnets are sorted from large to small according to the magnet index to generate a sorting table. When the power grid is in a low-load state, the superconducting magnet charging is started according to the positive sequence of the sorting table. After the current superconducting magnet is full, the superconducting magnet charging is controlled according to the positive sequence of the sorting table until the power grid is no longer in a low-load state.

8. A cloud service-based equipment energy efficiency management system, used to implement the management method according to any one of claims 1 to 7, characterized in that: It includes primary analysis module, secondary analysis module and charging selection module; Primary analysis module: obtains the number and information of superconducting magnets in the superconducting magnetic energy storage system, obtains the load status of the power grid at regular intervals, and performs a primary analysis based on the load status of the power grid; Secondary analysis module: When the result of the primary analysis indicates that a secondary analysis is required for the start-up conditions of the superconducting magnetic energy storage system, the real-time data and historical data of the power grid are obtained, and after analyzing the real-time data and historical data using an intelligent analysis model, it is determined whether the operation of the superconducting magnetic energy storage system needs to be controlled; Charging selection module: When it is determined that the superconducting magnetic energy storage system needs to be controlled for charging, the health status of each superconducting magnet is analyzed, and then all superconducting magnets are sorted based on the health status analysis results to generate a sorting table. The corresponding number of superconducting magnets are selected for charging in combination with the grid load information and the sorting table.

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