Novel energy storage operation monitoring analysis method based on multiple application scenes

Abstract

The invention relates to the technical field of energy storage operation monitoring analysis, and particularly discloses a novel energy storage operation monitoring analysis method based on multiple application scenarios, and the method comprises the steps: obtaining an energy storage operation state reference evaluation value and an energy storage operation state deviation evaluation value through comparison, carrying out the analysis of an energy storage operation power grid state, and obtaining a power grid state evaluation value, analyzing the energy storage operation battery state to obtain a battery state evaluation value, judging whether the energy storage operation state needs to be optimized or not, comparing to obtain an energy storage operation state initial optimization scheme corresponding to the energy storage operation state needing to be optimized, analyzing the user side evaluation state, judging whether the energy storage operation state needs to be subjected to secondary optimization or not, and judging whether the energy storage operation state needs to be subjected to secondary optimization or not. According to the method, the problems of information isolation, difficulty in realizing comprehensive analysis and decision support and insufficient consideration of user side data caused by incapability of effectively integrating data from different sources in a traditional method are solved, the overall performance of the system is favorably improved, and a guarantee is provided for safe and stable operation of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage operation monitoring and analysis, and specifically to a novel energy storage operation monitoring and analysis method based on multiple application scenarios. Background Art

[0002] With the rapid popularization of renewable energy such as solar energy and wind energy, the power system that relies on traditional fossil fuels is gradually transforming to low-carbon, clean renewable energy. The intermittent and volatile nature of these energy sources requires the power system to have stronger regulation capabilities. Energy storage systems are regarded as key technologies to solve the instability problem of renewable energy. They can store electricity when there is excess power generation and release it when needed, thereby improving the reliability and flexibility of the power system. The rapid development of technologies such as sensors, Internet of Things, cloud computing and big data analysis has made real-time monitoring and data analysis of energy storage systems more efficient and feasible. As competition in the electricity market intensifies, the needs of different users for energy storage services are becoming increasingly diversified, such as demand response and peak-valley electricity price arbitrage, which requires more flexible and sophisticated energy storage operation monitoring and analysis methods.

[0003] Nowadays, there are still some shortcomings in the research on new energy storage operation monitoring and analysis methods based on multiple application scenarios. Specifically, traditional methods are often unable to effectively integrate data from different sources, resulting in information isolation, difficulty in achieving comprehensive analysis and decision support, and insufficient consideration of user-side data. Many traditional monitoring and analysis models are designed based on a single application scenario and are difficult to adapt to the needs of multiple application scenarios, resulting in insufficient flexibility, resulting in the inability to form a complete global perspective for monitoring and analysis, and difficulty in identifying potential risks or optimization opportunities within the system. It is impossible to adjust to changing market demands and technological advances, which limits the flexibility of energy storage systems and affects the stability of power supply. It fails to fully consider user-side data, making it impossible for energy storage services to reflect the actual needs of users, reducing user satisfaction and system acceptance. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a novel energy storage operation monitoring and analysis method based on multiple application scenarios, which can effectively solve the problems involved in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a new energy storage operation monitoring and analysis method based on multiple application scenarios, comprising the following steps: analyzing the basic information of the application scenarios, and comparing to obtain a reference evaluation value of the energy storage operation status; analyzing the energy storage operation environment, and comparing to obtain a deviation evaluation value of the energy storage operation status; analyzing the energy storage operation grid status to obtain a grid status evaluation value; analyzing the energy storage operation battery status to obtain a battery status evaluation value; importing the grid status evaluation value, the battery status evaluation value and the energy storage operation status deviation evaluation value into the energy storage operation status evaluation model, combining the energy storage operation status reference evaluation value, judging whether the energy storage operation status needs to be optimized, and comparing the energy storage operation status initial optimization plan corresponding to the energy storage operation status that needs to be optimized; based on the energy storage operation status initial optimization plan, analyzing the user-side evaluation status to judge whether the energy storage operation status needs to be optimized for the second time.

[0006] As a further method, a reference evaluation value of the energy storage operation status is obtained by comparison. The specific analysis process is: obtaining a basic information data set of the application scenario, which specifically includes the maximum output power of the energy storage system, the grid load, and the output power of renewable energy; storing the maximum output power of the energy storage system, the grid load, and the output power of renewable energy as designated tags, and comparing the designated tag with the reference evaluation value of the energy storage operation status corresponding to each designated tag stored in the database to obtain the reference evaluation value of the energy storage operation status corresponding to the designated tag.

[0007] As a further method, the energy storage operation state deviation assessment value is obtained by comparison. The specific analysis process is: obtaining an energy storage operation environment data set, the energy storage operation environment data set specifically includes the energy storage operation environment temperature, the energy storage operation environment air pressure, and the energy storage operation environment electromagnetic field strength; storing the energy storage operation environment temperature, the energy storage operation environment air pressure, and the energy storage operation environment electromagnetic field strength as designated tags, and comparing the designated tag with the energy storage operation state deviation assessment value corresponding to each designated tag stored in the database to obtain the energy storage operation state deviation assessment value corresponding to the designated tag.

[0008] As a further method, the energy storage operation grid state is analyzed. The specific analysis process is: obtaining the energy storage operation grid state data set, the energy storage operation grid state data set specifically includes the absolute value of the difference between the grid frequency and the reference frequency, the grid power factor, and the number of abnormal load fluctuations per unit time; based on the obtained energy storage operation grid state data set, a comprehensive analysis is performed to obtain a grid state evaluation value, and the grid state evaluation value is used as an analysis basis for judging whether the energy storage operation state needs to be optimized.

[0009] As a further method, the grid state evaluation value, the specific analysis process is:

[0010]

[0011] Wherein, γ is the grid state assessment value, cz is the absolute value of the difference between the grid frequency and the reference frequency, ys is the grid power factor, bn is the number of abnormal load fluctuations per unit time, cz1 is the grid frequency, cz2 is the grid reference frequency, σ1 is the compensation factor of the set cz, σ2 is the compensation factor of the set ys, σ3 is the compensation factor of the set bn, and e is a natural constant.

[0012] As a further method, the energy storage operation battery status is analyzed, and the specific analysis process is: obtaining the energy storage operation battery status data set, the energy storage operation battery status data set specifically includes the battery maximum capacity, battery internal resistance, and battery energy efficiency; based on the obtained energy storage operation battery status data set, a comprehensive analysis is performed to obtain a battery status evaluation value, and the battery status evaluation value is used as an analysis basis for judging whether the energy storage operation status needs to be optimized.

[0013] As a further method, the battery status evaluation value, the specific analysis process is:

[0014]

[0015] Wherein, β is the battery state evaluation value, rl is the maximum capacity of the battery, nz is the internal resistance of the battery, xl is the battery energy efficiency, τ1 is the compensation factor of the set rl, τ2 is the compensation factor of the set nz, and τ3 is the compensation factor of the set xl.

[0016] As a further method, it is judged whether the energy storage operation state needs to be optimized. The specific analysis process is as follows: the grid state evaluation value, the battery state evaluation value and the energy storage operation state deviation evaluation value are introduced into the energy storage operation state evaluation model, and the energy storage operation state evaluation value is obtained through comprehensive analysis. The energy storage operation state evaluation value is used as the analysis basis for judging whether the energy storage operation state needs to be optimized; the energy storage operation state evaluation value is compared with the energy storage operation state reference evaluation value; if the energy storage operation state evaluation value is not lower than the energy storage operation state reference evaluation value, the energy storage operation state corresponding to the energy storage operation state evaluation value does not need to be optimized; if the energy storage operation state evaluation value is lower than the energy storage operation state reference evaluation value, the energy storage operation state corresponding to the energy storage operation state evaluation value needs to be optimized, and the difference between the energy storage operation state reference evaluation value and the energy storage operation state evaluation value is recorded as the energy storage operation state optimization characteristic value, and the energy storage operation state optimization characteristic value is compared with the energy storage operation state initial optimization scheme corresponding to each energy storage operation state optimization characteristic value stored in the database to obtain the energy storage operation state initial optimization scheme corresponding to the energy storage operation state optimization characteristic value.

[0017] As a further method, it is determined whether the energy storage operation state needs secondary optimization. The specific analysis process is as follows: based on the initial optimization plan for the energy storage operation state, a user-side evaluation state data set is obtained, and the user-side evaluation state data set specifically includes the proportion of favorable user-side evaluations, the proportion of favorable user-side evaluations of the use benefit, and the proportion of favorable user-side evaluations of the stability; based on the obtained user-side evaluation state data set, a user-side evaluation state characteristic value is obtained through comprehensive analysis, and the user-side evaluation state characteristic value is used as an analysis basis for determining whether the energy storage operation state needs secondary optimization; if the user-side evaluation state characteristic value is not lower than the user-side evaluation state reference characteristic value stored in the database, then the energy storage operation state corresponding to the user-side evaluation state characteristic value does not need secondary optimization; if the user-side evaluation state characteristic value is lower than the user-side evaluation state reference characteristic value stored in the database, then the energy storage operation state corresponding to the user-side evaluation state characteristic value needs secondary optimization, and the user-side evaluation state characteristic value is compared with the energy storage operation state secondary optimization plan corresponding to each user-side evaluation state characteristic value stored in the database to obtain the energy storage operation state secondary optimization plan corresponding to the user-side evaluation state characteristic value.

[0018] As a further method, the user side evaluates the state characteristic value, and the specific analysis process is as follows:

[0019]

[0020] Where ω is the characteristic value of the user-side evaluation state, zb is the proportion of positive user-side evaluations, xy is the proportion of positive user-side evaluations of usage benefits, wd is the proportion of positive user-side evaluations of stability, ε1 is the compensation factor of zb, ε2 is the compensation factor of xy, ε3 is the compensation factor of wd, and e is a natural constant.

[0021] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0022] (1) The present invention provides a new energy storage operation monitoring and analysis method based on multiple application scenarios. By analyzing multi-dimensional data such as basic information of application scenarios, grid status, and battery status, the present invention can provide more accurate and comprehensive decision support for energy storage operations and reduce the risk of decision-making errors. Continuous monitoring and deviation assessment of energy storage operation status can timely identify performance degradation, quickly take measures to optimize, and improve the overall performance of the system. In addition, a reliable evaluation model can reduce the risks caused by operational errors and provide guarantees for the safe and stable operation of the system.

[0023] (2) The present invention analyzes the basic information of the application scenario and obtains the reference evaluation value of the energy storage operation status by comparison. By analyzing the application scenario, the optimal performance indicators and goals under different operating conditions can be clarified, providing a quantifiable benchmark for subsequent actual operation. By analyzing different application scenarios, the optimal operation strategy can be identified to improve the responsiveness and efficiency of the energy storage system under different conditions. Accurate reference evaluation values ​​can help operators more reasonably allocate energy storage resources, avoid excessive or insufficient investment, and improve overall efficiency. By comparing the actual operation status with the reference value, comparative evaluation can timely discover problems and take corrective measures, forming an effective feedback mechanism.

[0024] (3) The present invention analyzes the user-side evaluation status based on the initial optimization plan of the energy storage operation status, determines whether the energy storage operation status needs secondary optimization, and attaches importance to user feedback and evaluation, so that the operator can adjust the operation strategy in time to better meet user expectations and enhance the service experience. Through secondary optimization, energy storage resources can be more reasonably allocated according to the real-time needs and usage patterns of users, avoiding inefficient use or waste of resources and achieving cost optimization. Secondary optimization enables the energy storage system to be finely managed based on the actual usage of users, thereby improving the overall performance of the system and enhancing system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0026] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0027] Figure 2 Flowchart of steps for determining whether the energy storage operation status needs to be optimized.

[0028] Figure 3 This is an image of the energy storage operation status assessment value changing with the battery status assessment value.

[0029] Figure 4 It is a three-dimensional image of the energy storage operation status assessment value changing with the grid status assessment value and the battery status assessment value.

[0030] Figure 5 This is an image of the user-side evaluation status feature value changing with the proportion of positive user-side evaluations. DETAILED DESCRIPTION

[0031] The technical solutions 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 only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0032] Reference Figure 1 As shown, the present invention provides a novel energy storage operation monitoring and analysis method based on multiple application scenarios, including: analyzing basic information of application scenarios and comparing to obtain reference evaluation values ​​of energy storage operation status.

[0033] The specific analysis process is as follows: obtaining a data set of basic information on application scenarios, which specifically includes the maximum output power of the energy storage system, grid load, and renewable energy output power. The maximum output power of the energy storage system refers to the maximum power that the energy storage system can release, which is obtained through the battery management system (BMS). The grid load refers to the total power required by all users in the grid, which is obtained based on, for example, an electric energy meter and a load monitoring system. The output power of renewable energy refers to the power output from a renewable energy source (such as solar energy, wind energy, etc.), which is obtained based on a solar inverter and a wind power generation monitoring system. The maximum output power of the energy storage system, the grid load, and the output power of renewable energy are stored as designated tags, and the designated tags are compared with the reference evaluation values ​​of the energy storage operation status corresponding to the designated tags stored in the database to obtain the reference evaluation values ​​of the energy storage operation status corresponding to the designated tags.

[0034] In a specific embodiment, the maximum power output of the energy storage system can be used to balance the grid load and the output power of renewable energy. The energy storage system can store excess renewable energy and release electricity when the grid load is peak to meet instantaneous demand. When the grid load is higher than the output power of renewable energy, the energy storage system can meet the load gap by releasing electricity. When the grid load is lower than the output power of renewable energy, excess power generation can be stored in the energy storage system for emergency use. Effective management of the energy storage system can promote the integration of renewable energy into the grid, help stabilize grid frequency and power quality, and enhance the flexibility and reliability of the entire power system.

[0035] It should be explained that the above comparison of the actual output power and grid load with the reference evaluation value can clearly evaluate the performance of the energy storage system in a specific application scenario, help identify the advantages and disadvantages of the system, and then optimize it. Real-time monitoring of these basic information and comparison with the evaluation value can effectively monitor the operating status of the energy storage system to ensure that it operates in the best operating state. According to the grid load and the output power of renewable energy, the energy storage system can be better dispatched to ensure that energy can be released in time during high-load periods of the grid, or energy can be stored when the output of renewable energy is high, thereby balancing the supply and demand of the grid. By comparing the actual output with the evaluation value, abnormal performance or potential faults of the system can be quickly discovered.

[0036] The energy storage operation environment is analyzed and compared to obtain the energy storage operation status deviation assessment value.

[0037] The specific analysis process is as follows: obtain the energy storage operation environment data set, which specifically includes the energy storage operation environment temperature, energy storage operation environment air pressure, and energy storage operation environment electromagnetic field strength. The energy storage operation environment temperature refers to the temperature of the environment where the energy storage system (such as batteries, energy storage devices, etc.) is located. The temperature has a significant impact on the performance, efficiency and life of the energy storage system. Too high a temperature may cause the battery to overheat, reducing its efficiency and safety; while too low a temperature may affect the battery's discharge capacity and charging rate, which is obtained through a temperature sensor. The energy storage operation environment air pressure refers to the air pressure of the environment where the energy storage system is located. Changes in air pressure may affect the efficiency of certain energy storage technologies, such as in certain types of pumped storage or compression. In the air energy storage system, the change of air pressure is directly related to the storage and release performance of energy, which is obtained through the air pressure sensor. The electromagnetic field strength of the energy storage operation environment refers to the electromagnetic field strength around the energy storage equipment. The strength and frequency of the electromagnetic field may have an impact on the energy storage system, especially near electronic equipment such as batteries and inverters. Excessive electromagnetic interference may cause equipment performance degradation or failure, which is obtained through a spectrum analyzer; the energy storage operation environment temperature, energy storage operation environment air pressure, and energy storage operation environment electromagnetic field strength are stored as designated tags, and the designated tag is compared with the energy storage operation state deviation assessment value corresponding to each designated tag stored in the database to obtain the energy storage operation state deviation assessment value corresponding to the designated tag.

[0038] It needs to be explained that the above-mentioned temperature changes may affect the density of the gas and thus the air pressure. Electromagnetic radiation may aggravate the heating of the equipment under certain temperature conditions. The battery performance (such as charging and discharging efficiency) and life of the energy storage system will be affected under different temperature and air pressure environments. Electromagnetic interference may have a negative impact on the control and monitoring of the system, leading to a higher failure rate.

[0039] It should be explained that the above comparison of environmental data with the energy storage operation status deviation assessment value can reveal how different environmental conditions affect the operating efficiency of the energy storage system, and can provide data support to help it make more scientific and reasonable decisions. The long-term collection and analysis of environmental data can provide a basis for the standardization of the energy storage industry, promote the healthy development of the industry, and deeply understand the impact of environmental factors on energy storage systems. It can provide direction for the research and development of new technologies and promote the innovation of energy storage technology. Monitoring these environmental data can also detect abnormal situations in a timely manner.

[0040] Analyze the state of the energy storage operation grid and obtain the grid state assessment value.

[0041] The specific analysis process is as follows: Obtain the energy storage operation grid status data set, which specifically includes the absolute value of the difference between the grid frequency and the reference frequency, the grid power factor, and the number of abnormal load fluctuations per unit time. The absolute value of the difference between the grid frequency and the reference frequency refers to the absolute difference between the current grid frequency and the standard frequency. Frequency is an important parameter for grid operation and reflects the balance of power supply and demand. Frequency deviation from the nominal value may cause unstable equipment operation or even failure. It is obtained based on the frequency monitor. The grid power factor refers to the ratio of active power to apparent power in the grid, usually expressed as a value between 0 and 1. The power factor reflects the effective utilization of the power system. The closer the power factor is to 1, the higher the efficiency of the power grid is. A low power factor may lead to increased power loss and equipment overload. It is obtained based on the power factor monitor. The number of abnormal load fluctuations per unit time refers to the number of abnormal load fluctuations in a specific time period (for example, 1 hour). Abnormal load fluctuations may be caused by equipment failure, changes in user electricity usage habits or other external factors. Frequent load fluctuations may affect the stability of the power grid and the safe operation of equipment. It is obtained based on the load monitoring system; based on the obtained energy storage operation grid status data set, a comprehensive analysis is performed to obtain the grid status evaluation value, which is used as the analysis basis for judging whether the energy storage operation status needs to be optimized.

[0042] It needs to be explained that there is a certain relationship between the above-mentioned grid frequency and power factor. When the grid load fluctuates greatly, it may cause changes in the grid frequency, thereby affecting the power factor. Frequency deviation from the nominal value may cause the power factor of the equipment to decrease, affecting the efficiency of the power system. When the number of abnormal load fluctuations per unit time increases, the grid frequency may fluctuate greatly, because the rapid change of load will lead to an imbalance between supply and demand, thus affecting the stability of the frequency.

[0043] It should be explained that the above-mentioned monitoring of grid frequency, power factor and load fluctuations can timely detect abnormal operation of the grid, quickly respond and make adjustments to ensure the optimal operation of the energy storage system. Changes in the grid status assessment value can serve as an early warning signal, prompting operators to take measures when the grid is in poor condition to avoid potential failures or accidents. Analysis of the grid status assessment value can optimize the charging and discharging strategy of the energy storage system to ensure that the energy storage system can effectively provide support and stabilize the grid during peak grid load or frequency fluctuations. When the grid load fluctuates abnormally, the energy storage system can help the grid balance the load and reduce the instability of the grid by adjusting the output. The grid status assessment value can help identify the best charging and discharging time, thereby improving the economic benefits of the energy storage system and maximizing the benefits. The grid status assessment value provides a quantitative basis for operation management, making decisions more scientific and reasonable, and corresponding optimization strategies and operation plans can be formulated based on the assessment value.

[0044] Furthermore, the specific analysis process of the power grid status evaluation value is as follows:

[0045]

[0046] Wherein, γ is the grid state assessment value, cz is the absolute value of the difference between the grid frequency and the reference frequency, ys is the grid power factor, bn is the number of abnormal load fluctuations per unit time, cz1 is the grid frequency, cz2 is the grid reference frequency, σ1 is the compensation factor of the set cz, σ2 is the compensation factor of the set ys, σ3 is the compensation factor of the set bn, and e is a natural constant.

[0047] It needs to be explained that the above-mentioned grid status evaluation value is calculated through the absolute value of the difference between the grid frequency and the reference frequency, the grid power factor, and the number of abnormal load fluctuations per unit time. Cz, ys, and bn are normalized, and the difference between the grid frequency and the reference frequency, the power factor, and the load fluctuation are monitored. Abnormal conditions in grid operation can be identified in real time, so that early warnings can be issued in time to prevent potential power failures or accidents. Analysis of the grid status evaluation value can optimize the grid operation strategy, such as adjusting the output of the generator set to ensure that the grid operates in the best condition, thereby improving the efficiency and reliability of power transmission. The difference between the frequency and the reference frequency can reflect the load balance of the grid. Timely adjustment of the balance between power generation and load will help maintain the stability of the grid frequency and reduce the impact of frequency fluctuations on equipment and users.

[0048] It should be explained that the compensation factors of cz, ys, and bn set above are obtained from the database. According to historical data, a mapping set of the absolute value of the difference between the historically measured grid frequency and the reference frequency, the grid power factor, the number of abnormal load fluctuations per unit time and the compensation factors of cz, ys, and bn is established to obtain the compensation factors of cz, ys, and bn corresponding to the current cz, ys, and bn.

[0049] It should be noted that τ1, τ2, τ3, ε1, ε2, and ε3 mentioned below are also obtained through a mapping set of historical data and compensation factors established in a database, that is, the corresponding compensation factors are obtained based on current data.

[0050] Analyze the status of the energy storage operation battery and obtain the battery status evaluation value.

[0051] The specific analysis process is as follows: obtain the energy storage operation battery status data set, which specifically includes the battery maximum capacity, battery internal resistance, and battery energy efficiency. The battery maximum capacity is the maximum electrical energy that the battery can store, usually expressed in kilowatt-hours (kWh) or ampere-hours (Ah), reflecting the total amount of electrical energy that the battery can store during the charging process. As the battery is used and aged, the maximum capacity will gradually decrease and is obtained through the battery management system. The battery internal resistance is the resistance inside the battery, usually expressed in milliohms (mΩ). The internal resistance will affect the energy loss generated by the battery during the charging and discharging process. An increase in internal resistance usually means that the battery Battery performance declines or ages. When the battery is charging or discharging, the smaller the internal resistance, the better the efficiency and output performance of the battery. It is obtained through the battery management system. Battery energy efficiency is the energy conversion efficiency of the battery during the charging and discharging process. It is usually expressed as a ratio (for example, discharge energy / charge energy) or a percentage. Energy efficiency reflects the amount of electricity lost by the battery during the process of storing and releasing energy. High energy efficiency means less energy loss. It is obtained through an energy meter. Based on the acquired energy storage operation battery status data set, a comprehensive analysis is performed to obtain the battery status evaluation value, which is used as an analytical basis for judging whether the energy storage operation status needs to be optimized.

[0052] It needs to be explained that when the maximum capacity of the battery decreases, the energy provided by the battery during the charging and discharging process will also decrease, which may lead to a decrease in energy efficiency. During the aging process of the battery, the reduction in capacity is usually accompanied by an increase in internal resistance, thereby affecting the energy efficiency. The internal resistance of the battery directly affects the energy efficiency. When the internal resistance increases, the energy loss increases, resulting in a decrease in the output of effective energy, thereby affecting the overall energy efficiency. If the internal resistance is low, energy can be transferred more efficiently during the charging and discharging process, thereby improving energy efficiency.

[0053] It needs to be explained that the above-mentioned regular monitoring and evaluation of the battery status can promptly detect the decline or failure of battery performance. When the evaluation value is lower than the set threshold, an early warning can be triggered and maintenance or replacement can be carried out in time. Continuous monitoring and evaluation can effectively prevent battery damage caused by excessive use or poor condition and extend the battery life. The battery status evaluation value can guide how to adjust the charging and discharging strategy to achieve optimal operating status and efficiency. The evaluation results can help to reasonably allocate energy storage resources. For example, in an environment with a large peak-to-valley gap, optimize the battery usage pattern. The decrease in the internal resistance and capacity of the battery may lead to overheating or other safety hazards. Monitoring the evaluation value can promptly detect potential safety risks and avoid accidents.

[0054] Furthermore, the battery status evaluation value, the specific analysis process is as follows:

[0055]

[0056] Wherein, β is the battery state evaluation value, rl is the maximum capacity of the battery, nz is the internal resistance of the battery, xl is the battery energy efficiency, τ1 is the compensation factor of the set rl, τ2 is the compensation factor of the set nz, and τ3 is the compensation factor of the set xl.

[0057] It should be explained that the above battery status evaluation value is calculated through the maximum capacity of the battery, the internal resistance of the battery, and the battery energy efficiency. rl, nz, and xl are normalized. Through the monitoring of the maximum capacity and internal resistance, the health status of the battery can be understood in real time, and the problems of battery aging or damage can be discovered in time to help take appropriate maintenance measures. The calculation of battery energy efficiency can evaluate the energy loss of the battery during the charging and discharging process, and help optimize the battery charging and discharging strategy to maximize the energy utilization of the battery. Regular monitoring of the battery status evaluation value can help identify potential faults and perform preventive maintenance, thereby reducing the frequency of unexpected downtime and battery replacement, and reducing overall operating costs. Through in-depth analysis of the battery status, the scheduling strategy of the energy storage system can be adjusted according to the actual performance of the battery to improve the operating efficiency and stability of the overall system.

[0058] The grid state assessment value, battery state assessment value and energy storage operation state deviation assessment value are imported into the energy storage operation state assessment model. Combined with the energy storage operation state reference assessment value, it is judged whether the energy storage operation state needs to be optimized, and the initial optimization plan of the energy storage operation state corresponding to the energy storage operation state that needs to be optimized is compared.

[0059] like Figure 2As shown, the specific analysis process is: importing the grid state evaluation value, the battery state evaluation value and the energy storage operation state deviation evaluation value into the energy storage operation state evaluation model, comprehensively analyzing to obtain the energy storage operation state evaluation value, and using the energy storage operation state evaluation value as the analysis basis for judging whether the energy storage operation state needs to be optimized; comparing the energy storage operation state evaluation value with the energy storage operation state reference evaluation value; if the energy storage operation state evaluation value is not lower than the energy storage operation state reference evaluation value, then the energy storage operation state corresponding to the energy storage operation state evaluation value does not need to be optimized; if the energy storage operation state evaluation value is lower than the energy storage operation state reference evaluation value, then the energy storage operation state corresponding to the energy storage operation state evaluation value needs to be optimized, and recording the difference between the energy storage operation state reference evaluation value and the energy storage operation state evaluation value as the energy storage operation state optimization characteristic value, comparing the energy storage operation state optimization characteristic value with the energy storage operation state initial optimization scheme corresponding to each energy storage operation state optimization characteristic value stored in the database, and obtaining the energy storage operation state initial optimization scheme corresponding to the energy storage operation state optimization characteristic value.

[0060] In a specific embodiment, by comprehensively analyzing multiple status evaluation values, the energy storage operation status evaluation value obtained can reflect the actual operation status of the system, thereby supporting scientific decision-making and avoiding blind optimization or unnecessary intervention. Comparison with the reference evaluation value can provide an objective basis for judgment, helping management to make data-based decisions and improve the accuracy of decisions. When it is identified that the energy storage operation status needs to be optimized, the operation strategy can be adjusted in a targeted manner, thereby improving energy utilization efficiency and reducing unnecessary waste of resources. By monitoring and evaluating the energy storage operation status, potential problems can be discovered and adjusted in a timely manner, thereby improving the overall reliability of the system and reducing downtime and losses caused by system failures. Regular optimization and adjustment of the energy storage system helps to reduce safety hazards and ensure that the system operates in a safe and controllable state.

[0061] It should be explained that the specific analysis process of the above energy storage operation status assessment model is as follows:

[0062]

[0063] Where θ is the energy storage operation status assessment value, γ is the grid status assessment value, β is the battery status assessment value, and e is a natural constant.

[0064] It should be explained that the above-mentioned energy storage operation status assessment value is calculated through the grid status assessment value and the battery status assessment value, which can provide an in-depth understanding of the overall performance of the energy storage system, help identify the mutual influence and coordination of different parts of the system, and ensure the efficient integration of the energy storage system under different operating conditions. Through comprehensive evaluation, it can be determined how to best use battery resources under specific grid conditions, thereby improving the overall resource utilization rate. Reasonable scheduling of energy storage systems can reduce operating costs and avoid waste of resources. The evaluation of grid and battery status can help identify potential risks in operation, such as battery failure, overload or grid fluctuations, and can take preventive measures to improve the safety and stability of the system. Comprehensive status evaluation can provide an accurate basis for power dispatching, such as quickly mobilizing energy storage resources to balance supply and demand during high-load periods or periods of power shortage, and enhance the system's responsiveness to fluctuations in the power market.

[0065] like Figure 3 , Figure 4 As shown, Figure 3 The graph of the energy storage operation status evaluation value as the battery status evaluation value changes, where the x-axis represents the battery status evaluation value and the y-axis represents the energy storage operation status evaluation value, can help intuitively understand how the battery status evaluation value affects the energy storage operation status evaluation value. The larger the battery status evaluation value, the larger the energy storage operation status evaluation value. As the battery status evaluation value increases, the impact of the battery status evaluation value on the energy storage operation status evaluation value gradually weakens. The grid status evaluation value is set to 2 and remains unchanged, and only the battery status evaluation value is changed. Example values ​​of the battery status evaluation value are as follows:

[0066] Table 1: Example values ​​of battery status evaluation values ​​in energy storage operation status evaluation values

[0067] n γ β θ 1 2 1 2.207 2 2 2 3.311 3 2 4 5.518

[0068] Based on the initial optimization plan for the energy storage operation status, the user-side evaluation status is analyzed to determine whether the energy storage operation status needs secondary optimization.

[0069] The specific analysis process is as follows: based on the initial optimization plan for the energy storage operation status, a user-side evaluation status data set is obtained, and the user-side evaluation status data set specifically includes the proportion of favorable user-side evaluations, the proportion of favorable user-side evaluations on the use benefit, and the proportion of favorable user-side evaluations on the stability; based on the obtained user-side evaluation status data set, a user-side evaluation status characteristic value is obtained through comprehensive analysis, and the user-side evaluation status characteristic value is used as an analysis basis for judging whether the energy storage operation status needs to be secondary optimized; if the user-side evaluation status characteristic value is not lower than the user-side evaluation status reference characteristic value stored in the database, then the energy storage operation status corresponding to the user-side evaluation status characteristic value does not need to be secondary optimized; if the user-side evaluation status characteristic value is lower than the user-side evaluation status reference characteristic value stored in the database, then the energy storage operation status corresponding to the user-side evaluation status characteristic value needs to be secondary optimized, and the user-side evaluation status characteristic value is compared with the energy storage operation status secondary optimization plan corresponding to each user-side evaluation status characteristic value stored in the database to obtain the energy storage operation status secondary optimization plan corresponding to the user-side evaluation status characteristic value.

[0070] It should be explained that the above-mentioned percentage of favorable user-side evaluations refers to the ratio of the number of favorable comments given by users after using the energy storage system to all user comments. A high percentage of favorable comments indicates that users are more satisfied with the overall usage experience. The percentage of favorable user-side evaluations on usage benefits specifically measures users' evaluation of the usage benefits of the energy storage system, and refers to the ratio of users' favorable comments on the system in terms of saving energy costs, improving energy utilization and other economic benefits to the total number of comments. The percentage of favorable user-side evaluations on stability measures the ratio of users' favorable comments on the operating stability of the energy storage system to the total number of comments. Users' favorable comments may be based on the reliability, long-term performance, failure rate and other aspects of the system. The percentage of favorable user-side evaluations, the percentage of favorable user-side evaluations on usage benefits and the percentage of favorable user-side evaluations on stability are obtained through user surveys, feedback systems or online evaluation platforms.

[0071] It should be explained that the above-mentioned users' overall satisfaction with the energy storage system (the percentage of positive reviews from the user side) is often significantly affected by their specific experience in terms of usage benefits and stability. The stability of the system will directly affect the user experience. If the system is stable and users can obtain the expected benefits during use, then their evaluation of the usage benefits will be relatively high, and their evaluation of the entire system will also increase accordingly.

[0072] It should be explained that by focusing on user-side evaluations, we can better understand user needs and expectations, thereby making targeted optimizations and improving user experience and satisfaction. The implementation of optimization plans can enhance the performance of the energy storage system, reduce problems encountered by users during use, and increase the number and proportion of positive reviews of the system. Based on the evaluation status of user feedback, we can dynamically adjust the operation strategy and optimization plan to ensure that the energy storage system operates efficiently under real needs and improve the flexibility and reliability of power supply. By analyzing the characteristic values ​​of the user-side evaluation status, we can provide data support for the secondary optimization plan, making the optimization measures more practical and reducing resource waste. The information obtained from the user side can help identify potential risks and problems, make preventive adjustments before problems affect users, and improve the resilience of the system.

[0073] Furthermore, the user side evaluates the state characteristic value, and the specific analysis process is as follows:

[0074]

[0075] Where ω is the characteristic value of the user-side evaluation state, zb is the proportion of positive user-side evaluations, xy is the proportion of positive user-side evaluations of usage benefits, wd is the proportion of positive user-side evaluations of stability, ε1 is the compensation factor of zb, ε2 is the compensation factor of xy, ε3 is the compensation factor of wd, and e is a natural constant.

[0076] It needs to be explained that Figure 5 The user-side evaluation status characteristic value shown increases with the increase in the proportion of favorable user-side evaluations. As the proportion of favorable user-side evaluations increases, the larger the user-side evaluation status characteristic value is, the better the user-side evaluation status is. The above-mentioned user-side evaluation status characteristic value is calculated by the proportion of favorable user-side evaluations, the proportion of favorable user-side evaluations of usage benefits, and the proportion of favorable user-side evaluations of stability. zb, xy, and wd are normalized. Through evaluation in multiple dimensions, we can fully understand the user's satisfaction, identify the user's specific views on usage benefits and stability, and provide more in-depth user experience feedback. The user-side evaluation status characteristic value can help companies identify which aspects are performing well and which aspects need improvement, so as to formulate targeted measures, optimize products and services, and improve user satisfaction. By understanding users' views on usage benefits and stability, companies can formulate more accurate market promotion and marketing strategies.

[0077] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A new energy storage operation monitoring and analysis method based on multiple application scenarios, characterized in that: The following steps are involved: Analyze the basic information of the application scenario and compare it to obtain the reference evaluation value of the energy storage operation status; Analyze the energy storage operation environment and compare to obtain the energy storage operation status deviation assessment value; Analyze the state of the energy storage operation grid and obtain the grid state assessment value; Analyze the status of the energy storage operation battery and obtain the battery status evaluation value; Import the grid state assessment value, battery state assessment value and energy storage operation state deviation assessment value into the energy storage operation state assessment model, combine the energy storage operation state reference assessment value, judge whether the energy storage operation state needs to be optimized, and compare the energy storage operation state initial optimization plan corresponding to the energy storage operation state that needs to be optimized; Based on the initial optimization plan for the energy storage operation status, the user-side evaluation status is analyzed to determine whether the energy storage operation status needs secondary optimization.

2. The novel energy storage operation monitoring and analysis method based on multiple application scenarios according to claim 1 is characterized in that: The comparison obtains the reference evaluation value of the energy storage operation status. The specific analysis process is as follows: Obtaining a basic information data set of the application scenario, wherein the basic information data set of the application scenario specifically includes the maximum output power of the energy storage system, the grid load, and the output power of renewable energy; The maximum output power of the energy storage system, the grid load, and the output power of renewable energy are stored as designated tags, and the designated tags are compared with the reference evaluation values ​​of the energy storage operation status corresponding to each designated tag stored in the database to obtain the reference evaluation value of the energy storage operation status corresponding to the designated tag.

3. The novel energy storage operation monitoring and analysis method based on multiple application scenarios according to claim 1 is characterized in that: The comparison obtains the energy storage operation state deviation assessment value, and the specific analysis process is as follows: Obtaining an energy storage operation environment data set, which specifically includes the energy storage operation environment temperature, the energy storage operation environment air pressure, and the energy storage operation environment electromagnetic field strength; The energy storage operating environment temperature, the energy storage operating environment air pressure, and the energy storage operating environment electromagnetic field strength are stored as designated tags, and the designated tags are compared with the energy storage operating state deviation assessment values ​​corresponding to the designated tags stored in the database to obtain the energy storage operating state deviation assessment values ​​corresponding to the designated tags.

4. The novel energy storage operation monitoring and analysis method based on multiple application scenarios according to claim 1 is characterized in that: The analysis of the energy storage operation grid state is specifically carried out as follows: Obtaining a data set of energy storage operation grid status, which specifically includes the absolute value of the difference between the grid frequency and the reference frequency, the grid power factor, and the number of abnormal load fluctuations per unit time; Based on the acquired energy storage operation grid status data set, a comprehensive analysis is performed to obtain a grid status assessment value, which is used as an analysis basis for determining whether the energy storage operation status needs to be optimized.

5. The novel energy storage operation monitoring and analysis method based on multiple application scenarios according to claim 4 is characterized in that: The specific analysis process of the power grid status evaluation value is as follows: Wherein, γ is the grid state assessment value, cz is the absolute value of the difference between the grid frequency and the reference frequency, ys is the grid power factor, bn is the number of abnormal load fluctuations per unit time, cz1 is the grid frequency, cz2 is the grid reference frequency, σ1 is the compensation factor of the set cz, σ2 is the compensation factor of the set ys, σ3 is the compensation factor of the set bn, and e is a natural constant.

6. The novel energy storage operation monitoring and analysis method based on multiple application scenarios according to claim 1 is characterized in that: The specific analysis process of analyzing the energy storage operation battery status is as follows: Obtaining a data set of energy storage operation battery status, which specifically includes the maximum capacity of the battery, the internal resistance of the battery, and the energy efficiency of the battery; Based on the acquired energy storage operation battery status data set, a comprehensive analysis is performed to obtain the battery status evaluation value, which is used as the analysis basis for judging whether the energy storage operation status needs to be optimized.

7. The novel energy storage operation monitoring and analysis method based on multiple application scenarios according to claim 6 is characterized in that: The battery status evaluation value, the specific analysis process is: Wherein, β is the battery state evaluation value, rl is the maximum capacity of the battery, nz is the internal resistance of the battery, xl is the battery energy efficiency, τ1 is the compensation factor of the set rl, τ2 is the compensation factor of the set nz, and τ3 is the compensation factor of the set xl.

8. The novel energy storage operation monitoring and analysis method based on multiple application scenarios according to claim 1 is characterized in that: The specific analysis process of judging whether the energy storage operation status needs to be optimized is as follows: The grid state assessment value, battery state assessment value and energy storage operation state deviation assessment value are imported into the energy storage operation state assessment model, and the energy storage operation state assessment value is obtained through comprehensive analysis. The energy storage operation state assessment value is used as the analysis basis for judging whether the energy storage operation state needs to be optimized. comparing the energy storage operation status assessment value with the energy storage operation status reference assessment value; If the energy storage operation status assessment value is not lower than the energy storage operation status reference assessment value, the energy storage operation status corresponding to the energy storage operation status assessment value does not need to be optimized; If the energy storage operation status assessment value is lower than the energy storage operation status reference assessment value, the energy storage operation status corresponding to the energy storage operation status assessment value needs to be optimized, and the difference between the energy storage operation status reference assessment value and the energy storage operation status assessment value is recorded as the energy storage operation status optimization characteristic value. The energy storage operation status optimization characteristic value is compared with the energy storage operation status initial optimization plan corresponding to each energy storage operation status optimization characteristic value stored in the database to obtain the energy storage operation status initial optimization plan corresponding to the energy storage operation status optimization characteristic value.

9. The novel energy storage operation monitoring and analysis method based on multiple application scenarios according to claim 1 is characterized in that: The specific analysis process for judging whether the energy storage operation status needs secondary optimization is as follows: Based on the initial optimization plan for the energy storage operation status, a user-side evaluation status data set is obtained. The user-side evaluation status data set specifically includes the proportion of favorable user-side evaluations, the proportion of favorable user-side evaluations of the use benefit, and the proportion of favorable user-side evaluations of the stability. Based on the acquired user-side evaluation status data set, a comprehensive analysis is performed to obtain the user-side evaluation status characteristic value, which is used as an analysis basis for judging whether the energy storage operation status needs secondary optimization. If the user-side evaluation state characteristic value is not lower than the user-side evaluation state reference characteristic value stored in the database, the energy storage operation state corresponding to the user-side evaluation state characteristic value does not need to be optimized twice; If the user-side evaluation state characteristic value is lower than the user-side evaluation state reference characteristic value stored in the database, the energy storage operation state corresponding to the user-side evaluation state characteristic value needs to be optimized secondary, and the user-side evaluation state characteristic value is compared with the energy storage operation state secondary optimization scheme corresponding to each user-side evaluation state characteristic value stored in the database to obtain the energy storage operation state secondary optimization scheme corresponding to the user-side evaluation state characteristic value.

10. The novel energy storage operation monitoring and analysis method based on multiple application scenarios according to claim 9 is characterized in that: The specific analysis process of the user-side evaluation status characteristic value is as follows: Where ω is the characteristic value of the user-side evaluation state, zb is the proportion of positive user-side evaluations, xy is the proportion of positive user-side evaluations of usage benefits, wd is the proportion of positive user-side evaluations of stability, ε1 is the compensation factor of zb, ε2 is the compensation factor of xy, ε3 is the compensation factor of wd, and e is a natural constant.

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