A cryogenic liquefied air energy storage system using a low temperature gaseous regenerator

Abstract

The present application relates to the technical field of air energy storage, specifically a cryogenic liquefied air energy storage system using low-temperature gaseous cold storage working medium, the system comprising a temperature control and liquefaction adjustment module, a grid demand response module, an automatic control and feedback module, and a performance monitoring and optimization module. Through real-time monitoring of environmental changes and grid load, the present application achieves efficient management of energy storage and release, adjusts compression parameters and liquefied air flow to adapt to real-time grid demand, optimizes the energy conversion process, significantly improves energy utilization, and an automated feedback adjustment mechanism ensures that the parameters of key equipment are always in an optimal state, enhancing the system's adaptability and response speed to performance fluctuations. Performance monitoring and automatic optimization enable the system to effectively prevent and quickly solve abnormalities during operation, maintaining continuous and efficient operation, thereby reducing maintenance requirements and operating costs, and providing more reliable energy support for the grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air energy storage, and in particular to a deep cooling liquefied air energy storage system using low-temperature gaseous cold storage working medium. BACKGROUND

[0002] Air energy storage mainly involves using air as an energy storage medium to provide electric energy regulation and storage solutions. This technology can be divided into compressed air energy storage (CAES) and liquefied air energy storage (LAES). In a compressed air energy storage system, during the off-peak period of the power grid, electricity is used to compress air and store it in a sealed container. During the peak period of the power grid, compressed air is released to drive a turbine to generate electricity. Depending on the configuration, the compressed air energy storage system can be divided into traditional compressed air energy storage systems, compressed air energy storage systems with heat storage devices, and liquid-gas compressed energy storage systems. Liquefied air energy storage involves storing air at a temperature below the liquefaction temperature, and when released, the liquefied air is expanded by heating to drive a turbine to generate electricity.

[0003] Among them, the deep cooling liquefied air energy storage system using low-temperature gaseous cold storage working medium is a form of liquefied air energy storage technology. Its core is to recover and utilize cold energy by using low-temperature gaseous cold storage working medium during the storage and release of deep cooling liquefied air, thereby significantly improving the overall energy conversion efficiency of the system. This system is mainly used for peak load shifting of the power grid, efficient storage and release of renewable energy, and stable power support under special working conditions. It is an advanced energy storage method for addressing energy fluctuations and improving the stability of the power grid.

[0004] The existing technology is deficient in energy storage efficiency and rapid response, especially when the demand of the power grid changes rapidly. The existing system cannot accurately regulate energy output, leading to uneven energy utilization, power waste, and unstable power supply. The delayed response of the existing system when releasing energy during peak periods increases the risk of power grid operation. Especially when the demand prediction is inaccurate or extreme weather events occur, the stability and reliability of the power grid are affected. The lack of effective real-time monitoring and automatic adjustment mechanisms makes the low efficiency in the energy storage and release process a prominent problem, which not only increases energy loss but also increases maintenance and operation costs. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art, and a deep cooling liquefied air energy storage system using low-temperature gaseous cold storage working medium is proposed.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: a deep cooling liquefied air energy storage system using low-temperature gaseous cold storage working medium, the system comprises:

[0007] The temperature control and liquefaction regulation module monitors the real-time status of the cold storage medium based on ambient temperature and compressor performance data, analyzes the impact of ambient temperature on the efficiency of the cold storage medium, adjusts the compression parameters according to the power grid demand, dynamically updates the compressor settings, optimizes the liquefied air flow and pressure process, and obtains the compression parameter configuration.

[0008] Based on the compression parameter configuration, the power grid demand response module analyzes the power grid load and predicts the power grid demand during peak periods, calculates the optimal liquefied air release rate and quantity, and adjusts the liquefied air release parameters by continuously monitoring real-time power grid data to optimize power output and obtain power distribution adjustment records.

[0009] Based on the power distribution adjustment records, the automatic control and feedback module exchanges data and control signals through Internet of Things technology, analyzes the deviation between the energy storage system performance and the standard performance threshold, automatically adjusts the operating parameters of key equipment, and obtains equipment parameter adjustment feedback data.

[0010] Based on the device parameter adjustment feedback data, the performance monitoring and optimization module monitors the operating status and efficiency of the energy storage system, detects abnormal points or efficiency degradation points, identifies maintenance and upgrade needs, and re-evaluates and optimizes the performance of the energy storage system based on the data after maintenance, thereby obtaining the energy storage performance optimization results.

[0011] The present invention is improved in that the step of analyzing the effect of ambient temperature on the efficiency of the cold storage working fluid is specifically as follows:

[0012] Based on ambient temperature and compressor performance data, the real-time status of the cold storage medium is monitored to obtain an environmental and performance dataset.

[0013] Based on the aforementioned environment and performance dataset, statistical analysis was performed using the following formula:

[0014] ;

[0015] The effect of ambient temperature on the efficiency of the cold storage medium was calculated, and the adjusted efficiency value was obtained. This indicates the efficiency of the cold storage medium. Indicates ambient temperature. Indicates compressor performance parameters, It is related to the ambient temperature The correlation regression coefficients, It is related to compressor performance The correlation regression coefficients, This represents the baseline efficiency of the cryogenic storage medium when both the ambient temperature and compressor performance are zero.

[0016] The present invention is improved in that the step of obtaining the compression parameter configuration is specifically as follows:

[0017] According to the power grid demand, the current demand state and energy efficiency of the compressor are analyzed to obtain a compression demand analysis record;

[0018] Based on the compression demand analysis record, the operation parameters of the compressor are adjusted, and a formula is used:

[0019] ;

[0020] Optimizing the liquefied air flow and pressure settings to obtain optimized compression parameters , wherein, represents the original compression parameters, is the demand amount of the current power grid, is the current output amount of the compressor, is an adjustment coefficient;

[0021] Based on the optimized compression parameters, the influence on the liquefied air yield and quality is monitored, and the compressor settings are dynamically updated according to the monitoring results to obtain the compression parameter configuration.

[0022] The liquefied air release rate and amount calculation step of the present application is improved as follows:

[0023] Based on the compression parameter configuration, real-time data of the power grid load and storage status of the liquefied air are extracted, the power grid data is classified and analyzed, the load demand of the power grid peak period is determined, and the predicted power grid peak demand information is obtained;

[0024] Based on the predicted power grid peak demand information, the optimal release rate and optimal release amount of the liquefied air are calculated, and a formula is used:

[0025] ; and ;

[0026] to obtain the optimal release rate and release amount , wherein, is an adjustment coefficient, is the predicted power grid peak demand, is the current power grid demand, is the total storage amount of the liquefied air, is the used liquefied air amount.

[0027] The power distribution adjustment record acquisition step of the present application is improved as follows:

[0028] Through the continuous monitoring of the real-time data of the power grid, including energy demand, power grid load fluctuation and current release efficiency of the liquefied air, a real-time monitoring log is obtained;

[0029] Based on the real-time monitoring log, the matching degree between power grid demand and liquefied air release efficiency is analyzed, the liquefied air release parameter needing to be adjusted is calculated, and a release adjustment parameter is obtained.

[0030] Based on the release adjustment parameter, the liquefied air release setting is updated, the power distribution process of the power grid is synchronously adjusted, the power output is optimized, and a power distribution adjustment record is obtained.

[0031] The application improves that the analysis step of the deviation from the standard performance threshold is specifically:

[0032] Based on the power distribution adjustment record, voltage, current and temperature data are obtained from the energy storage system through Internet of Things technology, and data integration is performed to obtain a real-time performance data set;

[0033] The real-time performance data set is analyzed and compared with the standard performance threshold, and the formula is:

[0034] ;

[0035] The deviation is quantified, and it is evaluated whether the energy storage system meets the performance standard, and a performance deviation analysis result is obtained, wherein, represents the total deviation between the energy storage system performance and the standard threshold, is the measured actual performance index, is the standard performance threshold.

[0036] The application improves that the device parameter adjustment feedback data acquisition step is specifically:

[0037] The key device operating parameters are automatically adjusted, and the adjusted operating data are collected to obtain adjusted device parameters;

[0038] Based on the adjusted device parameters, the influence of multiple parameter changes on the performance of the energy storage system is evaluated and compared with the improvement target to determine the device parameter adjustment effect, and device parameter adjustment feedback data are obtained.

[0039] The application improves that the energy storage performance optimization result acquisition step is specifically:

[0040] Based on the device parameter adjustment feedback data, the energy storage system is monitored in real time, the operating state and efficiency of the energy storage system are analyzed, abnormal points or efficiency reduction points are captured, and real-time monitoring records are obtained;

[0041] Based on the real-time monitoring records, maintenance and upgrading needs are identified, maintenance or adjustment is performed on the energy storage system, operating data after maintenance are collected, and performance differences before and after optimization are compared, and the formula is:

[0042] ;

[0043] Performance improvement ratio , obtaining energy storage performance optimization results, wherein, represents the performance index after maintenance or adjustment, represents the performance index before maintenance or adjustment.

[0044] Compared with the prior art, the advantages and positive effects of the present application are that:

[0045] In the present application, by monitoring the environmental changes and power grid load in real time, efficient management of energy storage and release is realized, the compression parameters and liquefied air flow are adjusted to adapt to the real-time power grid demand, the energy conversion process is optimized, the energy utilization rate is significantly improved, the automatic feedback adjustment mechanism ensures that the parameters of the key equipment are always in the optimal state, the adaptability and response speed of the system to performance fluctuations are enhanced, and the performance monitoring and automatic optimization enable the system to effectively prevent and quickly solve the abnormalities in operation, maintain continuous and efficient operation, thereby reducing the maintenance requirements and operating costs, and providing more reliable energy support for the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a system flowchart of the present application;

[0047] Figure 2 is a flowchart of analyzing the influence of environmental temperature on the efficiency of the cold storage working medium in the present application;

[0048] Figure 3 is a flowchart of obtaining the compression parameter configuration in the present application;

[0049] Figure 4 is a flowchart of calculating the release rate and amount of liquefied air in the present application;

[0050] Figure 5 is a flowchart of obtaining the power distribution adjustment record in the present application;

[0051] Figure 6 is a flowchart of analyzing the deviation from the standard performance threshold in the present application;

[0052] Figure 7 is a flowchart of obtaining the device parameter adjustment feedback data in the present application;

[0053] Figure 8 is a flowchart of obtaining the energy storage performance optimization results in the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0055] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0056] Embodiment, please refer to Figure 1 The present application provides a technical solution: a cryogenic liquefied air energy storage system using low-temperature gaseous regenerative working medium includes:

[0057] The temperature control and liquefaction regulation module monitors the real-time state of the regenerative working medium based on the environmental temperature and compressor performance data, analyzes the influence of environmental temperature on the efficiency of the regenerative working medium, and then adjusts the compression parameters according to the grid demand, dynamically updates the compressor settings, optimizes the liquefied air flow and pressure process, and obtains the compression parameter configuration;

[0058] The grid demand response module analyzes the grid load and predicts the peak grid demand based on the compression parameter configuration, calculates the optimal liquefied air release rate and quantity, and adjusts the liquefied air release parameters through continuous monitoring of real-time grid data to optimize power output, and obtains power allocation adjustment records;

[0059] The automatic control and feedback module exchanges data and control signals through Internet of Things technology based on the power allocation adjustment records, analyzes the deviation of the energy storage system performance from the standard performance threshold, automatically adjusts the operating parameters of the key equipment, and obtains equipment parameter adjustment feedback data. The data exchanged in the data and control signals include the operating data of the compressor (including the operating state, temperature, pressure and energy consumption of the compressor), the storage data of the liquefied air (related to the amount, storage conditions, pressure and temperature of the liquefied air), the power output data (related to the amount of power obtained from the liquefied air release process, load response time and output efficiency) and environmental monitoring data (including environmental temperature, humidity, etc., which is crucial for adjusting the compressor and liquefaction process); The key equipment includes compressors, liquefaction equipment and release equipment, etc., which are the main physical equipment for realizing the compression, liquefaction, storage and energy release of air in the operation of the energy storage system, and the automatic adjustment means automatically modifying the operating parameters of the equipment according to the input data (such as energy demand, storage efficiency, environmental conditions, etc.);

[0060] The performance monitoring and optimization module monitors the running state and efficiency of the energy storage system based on the equipment parameter adjustment feedback data, detects abnormal points or efficiency reduction points, identifies maintenance and upgrade needs, and re-evaluates and optimizes the performance of the energy storage system according to the data of the energy storage system after maintenance to obtain energy storage performance optimization results.

[0061] The compression parameter configuration includes pressure standards, flow rate standards, and temperature control indicators, the power allocation adjustment record specifically includes demand prediction values, power allocation time, and allocation accuracy, the equipment parameter adjustment feedback data includes running efficiency, equipment stability, and maintenance cycle, and the energy storage performance optimization result specifically includes performance increase information, stability optimization result, and response efficiency.

[0062] Please refer to Figure 2 , the steps of analyzing the influence of ambient temperature on the efficiency of the cold storage working medium are as follows:

[0063] Based on the ambient temperature and compressor performance data, the real-time state of the cold storage working medium is monitored to obtain the ambient and performance data set;

[0064] The ambient temperature is obtained by a plurality of distributed temperature sensors, which cover the internal and external environmental areas of the cold storage system. The sensors continuously collect temperature values at a set sampling frequency and upload them. The compressor performance data is obtained by real-time monitoring equipment, including performance indicators such as the output pressure, flow rate, and operating power of the compressor. The parameters are integrated, the collected multi-parameter data is format-converted and time-stamped, and it is stored as a structured data set. The data is used to match the synchronicity and consistency of ambient temperature and compressor performance parameters, and the effective data set for further analysis is selected, so as to obtain the ambient and performance data set.

[0065] Based on the ambient and performance data set, statistical analysis is performed using the formula:

[0066] ;

[0067] The influence of ambient temperature on the efficiency of the cold storage working medium is calculated to obtain the adjusted efficiency value, wherein, represents the efficiency of the cold storage working medium, represents the ambient temperature, reflecting how the temperature conditions of the surrounding environment affect the efficiency of the cold storage working medium, represents the compressor performance parameter, including the working state and output characteristics of the compressor, reflecting the influence of the compressor performance on the efficiency of the cold storage working medium, is the regression coefficient associated with the ambient temperature , quantifying the average influence of each change in ambient temperature on the efficiency of the cold storage working medium, is the regression coefficient associated with the compressor performance , representing the average change in the efficiency of the cold storage working medium when the compressor performance changes once, represents the baseline efficiency of the regenerative working fluid at ambient temperature and zero compressor performance;

[0068] the actual measured ambient temperature is 30℃, the compressor performance is 1, and the regression coefficient is 0.01, is 0.1, is 0.65, the values are substituted into the formula:

[0069] ;

[0070] The results show that the efficiency of the regenerative working fluid under the current ambient temperature and compressor performance is 1.05, i.e. 105%, reflecting that the optimized system performance can achieve higher energy efficiency in practical application.

[0071] Please refer to Figure 3 , the acquisition steps of the compression parameter configuration are as follows:

[0072] According to the grid demand, analyze the current demand state and energy efficiency of the compressor to obtain a compression demand analysis record;

[0073] By real-time collection of grid load data and energy distribution, the current total demand of the grid and the power consumption required by the compressor are monitored, the current demand deviation is calculated using the historical operation data of the grid management center, the actual operating parameters of the compressor are retrieved, including input power, speed and current output flow, etc., which are compared with the historical standard values, the energy efficiency performance of the compressor under the existing conditions is analyzed, and the real-time monitoring data of the ambient temperature and pressure are further adjusted to calculate the baseline energy efficiency value, and finally the compression demand analysis record is formed, which records the difference between the grid demand and the compressor output, the current operating efficiency of the compressor, and the optimization demand suggestion.

[0074] Based on the compression demand analysis record, the operating parameters of the compressor are adjusted, and the formula is:

[0075] ;

[0076] Optimize the liquefied air flow and pressure settings to obtain optimized compression parameters , wherein represents the original compression parameters, reflecting the setting state of the compressor before adjustment, is the demand of the current grid, which is the power demand level that the compressor needs to respond to, is the current output of the compressor, which is the actual performance of the compressor under the existing parameters, is the adjustment coefficient, which is used to dynamically adjust the compression parameters according to the difference between the demand and the actual output;

[0077] The following data is collected, currently The grid demand is 1.0 atm (atmospheric pressure), the compressor current output is 1.2 MW, the adjustment factor is 1.0 MW, the value is substituted into the formula to obtain:

[0078] ;

[0079] ;

[0080] ;

[0081] The results show that the adjusted pressure is 1.1 atm, and by slightly adjusting the working parameters of the compressor, the pressure can be slightly increased to meet the increased grid demand, and the operation efficiency of the system is maintained.

[0082] Based on the optimized compression parameters, monitor their impact on liquefied air yield and quality, and dynamically update the compressor settings according to the monitoring results to obtain the compression parameter configuration;

[0083] Install real-time monitoring devices at the inlet of the compressor and the outlet of the liquefied air, collect the flow and pressure data of the liquefied air through sensors, and combine external environmental conditions such as temperature and humidity, normalize the data, generate an evaluation report of the liquefied air quality and yield, analyze the flow fluctuations and pressure deviations that occur during the air liquefaction process, and adjust the operating parameters of the compressor in real time according to the deviation data, modify the opening of the inlet valve and the outlet pressure value, and use the optimized settings parameters again to generate new compression parameter configuration.

[0084] Please refer to Figure 4 , the calculation steps of the release rate and amount of liquefied air are as follows:

[0085] Based on the compression parameter configuration, extract real-time data of the grid load and the storage status of the liquefied air, classify and analyze the grid data, determine the load demand of the grid peak period, and obtain the predicted grid peak demand information;

[0086] ​Real-time data of power grid load and storage status of liquefied air are extracted, current load information of the power grid is obtained, including key parameters such as load power, peak power, load change rate, and current total storage amount and released amount of liquefied air are obtained from the liquefied air storage management system, by integrating and classifying the parameters, the power grid load data is grouped according to time sequence and associated with the corresponding liquefied air storage parameters, further analysis is performed on the data, by comparing the current power grid load with the historical peak load record and combining the change trend of the liquefied air storage amount, the peak period load interval appearing in the future is calculated and marked, and finally the predicted peak demand information of the power grid is obtained.

[0087] Based on the predicted peak demand information of the power grid, the optimal release rate and the optimal release amount of liquefied air are calculated, using the formula:

[0088] ; and ;

[0089] The optimal release rate and the release amount , wherein, is an adjustment coefficient, used to adjust the sensitivity of the release rate and amount according to the actual demand of the power grid and the storage status of liquefied air, is the predicted peak demand of the power grid, predicted according to historical data and trend analysis, used to calculate the release demand of liquefied air, is the current demand of the power grid, obtained by real-time monitoring, reflecting the load situation of the current power grid, is the total storage amount of liquefied air, representing the total amount of liquefied air available in the storage facility, is the used amount of liquefied air, representing the total amount of liquefied air released from the storage so far;

[0090] The following data is monitored, the predicted peak demand of the power grid is , the current demand of the power grid is , the total storage amount of liquefied air is tons, the used amount of liquefied air is tons, and , the values are substituted into the formula:

[0091] ;

[0092] ;

[0093] The result shows that the release rate is 10 tons per hour, and the release amount The liquefied air release rate and release amount calculated can effectively meet the peak demand of the power grid, ensure the stable operation of the power grid, and optimize the energy use efficiency.

[0094] Please refer to Figure 5 The obtaining step of the power distribution adjustment record is specifically:

[0095] By continuously monitoring real-time data of the power grid, including energy demand, power grid load fluctuation, and current release efficiency of liquefied air, a real-time monitoring log is obtained.

[0096] Real-time power grid load change data is collected from sensors and monitoring devices, and the data needs to be preliminarily cleaned to eliminate noise and invalid data, while the information of liquefied air release rate and cumulative release amount is extracted. By classifying the real-time obtained data according to time sequence, the matching degree of energy demand and power grid load fluctuation is analyzed, and the dynamic change trend of liquefied air release efficiency is recorded. Finally, the real-time monitoring log is obtained, which contains the current state of the power grid and the distribution of liquefied air resources.

[0097] Based on the real-time monitoring log, the matching degree between power grid demand and liquefied air release efficiency is analyzed, and the liquefied air release adjustment parameter is calculated.

[0098] The matching degree between power grid demand and liquefied air release efficiency is analyzed, the difference value between the current release rate of liquefied air and the peak load demand of the power grid is calculated, and the adjustment space is evaluated in combination with the liquefied air storage amount. According to the difference value of the load and the release rate, the release rate parameter that needs to be adjusted is generated, and the release amount adjustment range of liquefied air is calculated according to the trend of load demand. The results are integrated into the release adjustment parameter to obtain the applicable release adjustment parameter.

[0099] Based on the release adjustment parameter, the liquefied air release setting is updated, the power distribution process of the power grid is adjusted synchronously, and the power output is optimized to obtain the power distribution adjustment record.

[0100] The liquefied air release setting is updated, the power distribution process of the power grid is adjusted synchronously, and the operation instruction of the liquefied air release device is determined according to the release adjustment parameter. The real-time control data of release rate and amount are dynamically adjusted to ensure that the device executes the adjusted parameters. The matching condition of release and power distribution is confirmed by monitoring the release process, and the operation setting is further corrected according to the fluctuation. The power output optimization operation of the power grid is completed, and finally the specific details of all release adjustments and power distribution processes are recorded to generate the power distribution adjustment record.

[0101] Please refer to Figure 6 The analysis step of the deviation from the standard performance threshold is specifically:

[0102] Based on the power allocation adjustment record, voltage, current and temperature data are obtained from the energy storage system through Internet of Things technology, and data integration is performed to obtain a real-time performance data set;

[0103] The operating data extracted from the energy storage system through Internet of Things technology includes parameters such as voltage, current and temperature, which are obtained from the real-time sensors and control signal channels of the energy storage system in turn. For voltage data, the voltage stability of each monitoring point is judged, and abnormal values are eliminated and effective data are integrated. For current data, different load distribution situations are compared and unbalanced phenomena are recorded. For temperature data, the average value and peak value in the current time interval are extracted for statistics, and the fluctuation range of each index in the equipment working cycle is recorded. The real-time data from different sources are merged and sorted according to the time stamp and distribution information to form a complete real-time data set of the performance of the energy storage system.

[0104] The real-time performance data set is analyzed and compared with the standard performance threshold, using the formula:

[0105] ;

[0106] The deviation is quantified and calculated to evaluate whether the energy storage system meets the performance standard, and the performance deviation analysis result is obtained, wherein, represents the total deviation between the performance of the energy storage system and the standard threshold, is the measured actual performance index, including voltage, current and temperature, reflecting the actual working state of the energy storage system in real-time operation, is the standard performance threshold, which is the target performance index set according to safety and efficiency requirements;

[0107] measured actual performance index , standard performance threshold , then substitute into the formula to calculate:

[0108] ;

[0109] ;

[0110] ;

[0111] The result shows that the performance deviation is 7.07, indicating that the system deviates significantly in voltage and temperature.

[0112] Please refer to Figure 7 , the steps of obtaining device parameter adjustment feedback data are as follows:

[0113] The operating parameters of the key equipment, including compressors, liquefaction equipment and release equipment, are automatically adjusted, and the adjusted operating data are collected to obtain the adjusted equipment parameters.

[0114] According to the operating conditions of the compressor, liquefaction equipment and release equipment, initial operating parameter data of the equipment is collected, which includes basic operating information such as pressure, temperature and flow rate, an analysis model of equipment operation is established according to the data, the adjustment range and step size of the equipment parameters are set, each parameter is adjusted step by step, and the operating data changes of the equipment during the adjustment process are recorded, including pressure increase and decrease, temperature change range, and operating power consumption difference, etc. Through comparison and analysis of the operating data of the equipment, the optimal parameter configuration path of the equipment operation is determined, and the operating states before and after parameter adjustment are classified and sorted to ensure that the finally adjusted parameters can meet the stability and performance requirements of long-term operation of the equipment.

[0115] Based on the adjusted equipment parameters, the influence of changes in multiple parameters on the performance of the energy storage system is evaluated and compared with the improvement target to determine the effect of equipment parameter adjustment, and feedback data of equipment parameter adjustment is obtained;

[0116] The performance indicators to be evaluated are determined, including energy storage efficiency, system output power and response time, etc., then the relationship between the adjusted equipment parameters and the performance indicators of the energy storage system is analyzed to determine the influence of changes in equipment parameters on system performance, the corresponding relationship between equipment parameters and performance changes is sorted one by one, the key parameters affecting system performance are extracted, the key parameters are analyzed and the influence priority is set, the historical operating data of the equipment is compared to further analyze the specific effect of the adjusted parameters on performance improvement, through comparison of performance indicators and preset improvement targets, it is identified whether the adjustment effect meets the optimization requirements, and the results and data of equipment parameter adjustment are summarized to obtain feedback data of equipment parameter adjustment.

[0117] Please refer to Figure 8 , the obtaining steps of the energy storage performance optimization result are specifically:

[0118] Based on the feedback data of equipment parameter adjustment, the energy storage system is monitored in real time, the operating state and efficiency of the energy storage system are analyzed, abnormal points or efficiency decline points are captured, and real-time monitoring records are obtained;

[0119] From the device parameter adjustment feedback data, key operating parameters are extracted, including voltage, current, temperature and other real-time indicators. After classifying the extracted multiple parameters, they are recorded in a time series table. The volatility of each operating parameter is analyzed to capture the trend and abnormal peak value. By setting a dynamic threshold calculated based on historical operation records, points in the current data that exceed the threshold range are marked. Further, the marked points are subjected to repetitive testing in combination with historical data to determine whether the fluctuation pattern of the marked points is abnormal. Subsequently, a preliminary list of abnormal points is generated by integrating all the marked points. The operating parameters of the abnormal points are further cross-compared with historical efficiency decline points to identify parameter fluctuation points related to efficiency decline. All the results are recorded to form the final real-time monitoring record.

[0120] Based on the real-time monitoring record, maintenance and upgrade needs are identified, and maintenance or adjustment is performed on the energy storage system. The post-maintenance operation data is collected, and the performance difference before and after optimization is compared. The performance improvement ratio is calculated using the formula:

[0121] ;

[0122] The performance improvement ratio is calculated using the formula: , and the energy storage performance optimization result is obtained, wherein, represents the performance index after maintenance or adjustment, reflecting the new performance state of the energy storage system after necessary adjustment, represents the performance index before maintenance or adjustment, i.e. the original performance level of the energy storage system before the implementation of optimization measures;

[0123] Let the performance index after maintenance or adjustment be , and the performance index before maintenance or adjustment be . Substitute the formula for calculation:

[0124] ;

[0125] ;

[0126] ;

[0127] The result shows that the performance of the energy storage system is improved by 20% after maintenance and adjustment, indicating that the maintenance measures effectively improve its efficiency.

[0128] The above is only a preferred embodiment of the present application, and does not limit the form of the present application. Any skilled person in the art can modify or change the above disclosed technical content to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, in accordance with the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A cryogenic liquid air energy storage system utilizing a cryogenic gaseous regenerator working fluid, characterized in that, The system comprises: The temperature control and liquefaction adjustment module monitors the real-time state of the cold storage working medium based on environmental temperature and compressor performance data, analyzes the influence of environmental temperature on the efficiency of the cold storage working medium, adjusts the compression parameters according to the grid demand, dynamically updates the compressor settings, optimizes the liquefied air flow and pressure process, and obtains the compression parameter configuration; The grid demand response module analyzes the grid load and predicts the peak grid demand based on the compression parameter configuration, calculates the optimal liquefied air release rate and quantity, and adjusts the liquefied air release parameters by continuously monitoring the real-time grid data to optimize power output and obtain power allocation adjustment records; The automatic control and feedback module exchanges data and control signals through Internet of Things technology based on the power allocation adjustment records, analyzes the deviation of the energy storage system performance from the standard performance threshold, automatically adjusts the operating parameters of the key equipment, and obtains equipment parameter adjustment feedback data; The performance monitoring and optimization module monitors the operating state and efficiency of the energy storage system based on the equipment parameter adjustment feedback data, detects abnormal points or efficiency reduction points, identifies maintenance and upgrade needs, and reevaluates and optimizes the performance of the energy storage system after maintenance to obtain energy storage performance optimization results.

2. The cryogenic liquid air energy storage system utilizing a cryogenic gaseous regenerator working fluid of claim 1, wherein, The step of analyzing the influence of environmental temperature on the efficiency of the cold storage working medium is specifically: Based on environmental temperature and compressor performance data, the real-time state of the cold storage working medium is monitored to obtain environmental and performance data sets; Based on the environmental and performance data sets, statistical analysis is performed using the formula: ； The effect of ambient temperature on the efficiency of the cold storage medium was calculated, and the adjusted efficiency value was obtained. This indicates the efficiency of the cold storage medium. Indicates ambient temperature. Indicates compressor performance parameters, It is related to the ambient temperature The correlation regression coefficients, It is related to compressor performance The correlation regression coefficients, This represents the baseline efficiency of the cryogenic storage medium when both the ambient temperature and compressor performance are zero.

3. The cryogenic liquid air energy storage system utilizing a cryogenic gaseous regenerator working fluid of claim 1, wherein, The step of obtaining the compression parameter configuration is specifically: Based on the grid demand, the current demand state and energy efficiency of the compressor are analyzed to obtain a compression demand analysis record; Based on the compression demand analysis record, the operating parameters of the compressor are adjusted using the formula: ； Optimizing the liquefied air flow and pressure settings to obtain optimized compression parameters wherein, represents the original compression parameters, is the current demand of the power grid, is the current output of the compressor, is an adjustment factor; Based on the optimized compression parameters, the influence on the liquefied air yield and quality is monitored, and the compressor settings are dynamically updated based on the monitoring results to obtain the compression parameter configuration.

4. The cryogenic liquid air energy storage system utilizing a cryogenic gaseous regenerator working fluid of claim 1, wherein, The step of calculating the liquefied air release rate and quantity is specifically: Based on the compression parameter configuration, real-time grid load data and liquefied air storage conditions are extracted, and grid data is classified and analyzed to determine the load demand during the peak grid period to obtain predicted peak grid demand information; Based on the predicted peak grid demand information, the optimal release rate and optimal release quantity of liquefied air are calculated using the formula: ; and ; to obtain an optimal release rate and release amount wherein, is an adjustment factor, is a predicted grid peak demand, is a current grid demand, is a total storage of liquefied air, is an amount of liquefied air used.

5. The cryogenic liquid air energy storage system utilizing a cryogenic gaseous regenerator working fluid of claim 1, wherein, The step of obtaining the power allocation adjustment record is specifically: Through the continuous monitoring of real-time grid data, including energy demand, grid load fluctuation, and current release efficiency of liquefied air, real-time monitoring logs are obtained; Based on the real-time monitoring logs, the matching degree between grid demand and liquefied air release efficiency is analyzed, the liquefied air release parameters that need to be adjusted are calculated, and release adjustment parameters are obtained; Based on the release adjustment parameters, the liquefied air release settings are updated, the power allocation process of the grid is adjusted synchronously, the power output is optimized, and the power allocation adjustment record is obtained.

6. The cryogenic liquid air energy storage system utilizing a cryogenic gaseous regenerator working fluid of claim 1, wherein, The step of analyzing the deviation from the standard performance threshold is specifically: Based on the power allocation adjustment record, voltage, current, and temperature data are obtained from the energy storage system through Internet of Things technology, and data integration is performed to obtain a real-time performance data set; The real-time performance data set is analyzed, compared with a standard performance threshold, and a formula is used: ； The deviation quantification calculation is performed to evaluate whether the energy storage system meets the performance standard, and a performance deviation analysis result is obtained, wherein, represents the total deviation between the performance of the energy storage system and the standard threshold, is a measured actual performance index, is a standard performance threshold.

7. The cryogenic liquid air energy storage system utilizing a cryogenic gaseous regenerator working fluid of claim 1, wherein, The device parameter adjustment feedback data acquisition step is specifically: The key device operating parameters are automatically adjusted, and the adjusted operating data is collected to obtain the adjusted device parameters; Based on the adjusted device parameters, the influence of multiple parameter changes on the performance of the energy storage system is evaluated and compared with the improvement target to determine the device parameter adjustment effect, and device parameter adjustment feedback data is obtained.

8. The cryogenic liquid air energy storage system utilizing a cryogenic gaseous regenerator working fluid of claim 1, wherein, The energy storage performance optimization result acquisition step is specifically: Based on the device parameter adjustment feedback data, the energy storage system is monitored in real time, the operating state and efficiency of the energy storage system are analyzed, abnormal points or efficiency reduction points are captured, and real-time monitoring records are obtained; Based on the real-time monitoring records, maintenance and upgrade needs are identified, maintenance or adjustment is performed on the energy storage system, post-maintenance operating data is collected, and the performance difference before and after optimization is compared, and a formula is used: ； Computing performance improvement ratio , obtaining energy storage performance optimization results, wherein, represents the performance index after maintenance or adjustment, represents the performance index before maintenance or adjustment.

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