Cryogenic liquefied air energy storage system utilizing low-temperature gaseous cold storage working medium

Abstract

The invention relates to the technical field of air energy storage, in particular to a cryogenic liquefied air energy storage system utilizing a low-temperature gaseous cold storage working medium, which comprises a temperature control and liquefaction regulation module, a power grid demand response module, an automatic control and feedback module and a performance monitoring and optimization module. According to the invention, by monitoring the environment change and the power grid load in real time, high-efficiency management of energy storage and release is realized, 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 remarkably improved, and the energy utilization rate is improved. An automatic feedback adjustment mechanism ensures that parameters of key equipment are always in an optimal state, the adaptability and response speed of the system to performance fluctuation are enhanced, performance monitoring and automatic optimization enable the system to effectively prevent and quickly solve abnormities in operation, continuous efficient operation is kept, and the service life of the system is prolonged. Therefore, the maintenance requirement and the operation cost are reduced, and more reliable energy support is provided for a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of air energy storage, and particularly to a cryogenic liquefied air energy storage system using a low-temperature gaseous cold storage working medium. Background Art

[0002] Air energy storage mainly involves using air as an energy storage medium to provide power 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, electricity is used to compress air during low grid load periods and stored in a sealed container. During peak grid periods, the compressed air is released to drive a steam turbine to generate electricity. Depending on the configuration, compressed air energy storage systems can be divided into traditional compressed air energy storage systems, compressed air energy storage systems with a heat storage device, and liquid-gas compression energy storage systems. Liquefied air energy storage involves cooling air below its liquefaction temperature for storage, and when released, heating causes the liquefied air to expand and drive a turbine to generate electricity.

[0003] Among them, the cryogenic liquefied air energy storage system using a 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 using a low-temperature gaseous cold storage working medium during the storage and release processes of cryogenic liquefied air, thereby significantly improving the overall energy conversion efficiency of the system. This system is mainly applied to peak shaving and valley filling of the power grid load, efficient storage and release of renewable energy, and providing stable power support under special working conditions. It is an advanced energy storage method for coping with energy fluctuations and improving power grid stability.

[0004] The prior art shows deficiencies in energy storage efficiency and rapid response. Especially when the grid demand changes rapidly, existing systems are difficult to accurately adjust the energy output, resulting in uneven energy utilization, power waste, and unstable power supply. The delayed response of existing systems when releasing energy during peak periods increases the operation risk of the power grid. 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 an effective real-time monitoring and automatic adjustment mechanism makes the low efficiency in the energy storage and release processes a prominent problem, which not only increases energy losses but also raises the maintenance and operation costs. Summary of the Invention

[0005] The object of the present invention is to solve the deficiencies existing in the prior art, and to propose a cryogenic liquefied air energy storage system using a low-temperature gaseous cold storage working medium.

[0006] To achieve the above object, the present invention adopts the following technical solution: A cryogenic liquefied air energy storage system using a low-temperature gaseous cold storage working medium, the system comprising: Based on the ambient temperature and compressor performance data, the temperature control and liquefaction regulation module monitors the real-time state of the cold storage working medium, analyzes the influence of the ambient temperature on the efficiency of the cold storage working medium, 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; Based on the compression parameter configuration, the grid demand response module analyzes the grid load and predicts the peak grid demand, 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 the power output and obtains the power rationing adjustment record; Based on the power rationing adjustment record, 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 the equipment parameter adjustment feedback data; Based on the equipment parameter adjustment feedback data, the performance monitoring and optimization module monitors the operating state and efficiency of the energy storage system, detects abnormal points or points with decreasing efficiency, identifies maintenance and upgrade requirements, and re-evaluates and optimizes its performance according to the data of the maintained energy storage system to obtain the energy storage performance optimization result.

[0007] The improvement of the present invention is that the step of analyzing the influence of the ambient temperature on the efficiency of the cold storage working medium is specifically as follows: Based on the ambient temperature and compressor performance data, monitor the real-time state of the cold storage working medium to obtain the ambient and performance data set; Based on the ambient and performance data set, conduct statistical analysis and use the formula: ; Calculate the influence of the ambient temperature on the efficiency of the cold storage working medium to obtain the adjusted efficiency value, where represents the efficiency of the cold storage working medium, represents the ambient temperature, represents the compressor performance parameter, is the regression coefficient associated with the ambient temperature is the regression coefficient associated with the compressor performance represents the baseline efficiency of the cold storage working medium when both the ambient temperature and the compressor performance are zero.

[0008] The improvement of the present invention is that the step of obtaining the compression parameter configuration is specifically as follows: According to the grid demand, analyze the current demand status and energy efficiency of the compressor to obtain the compression demand analysis record; Based on the compression demand analysis record, adjust the operating parameters of the compressor and use the formula: ; ​​Optimize the liquid air flow rate and pressure settings to obtain optimized compression parameters , where represents the original compression parameters, is the demand of the current power grid, is the current output of the compressor, is the adjustment coefficient; Based on the optimized compression parameters, monitor their impact on the liquid air production and quality, and then dynamically update the compressor settings according to the monitoring results to obtain the compression parameter configuration.

[0009] The improvement of the present invention is that the calculation steps of the liquid air release rate and amount are specifically as follows: Based on the compression parameter configuration, extract the real-time data of the power grid load and the storage status of the liquid air, classify and analyze the power grid data, determine the load demand during the peak period of the power grid, and obtain the predicted power grid peak demand information; Based on the predicted power grid peak demand information, calculate the optimal release rate and optimal release amount of the liquid air, using the formula: ; and ; Obtain the optimal release rate and release amount , where is the adjustment coefficient, is the predicted power grid peak period demand, is the current power grid demand, is the total storage amount of the liquid air, is the amount of used liquid air.

[0010] The improvement of the present invention is that the acquisition steps of the power rationing adjustment record are specifically as follows: Obtain the real-time monitoring log by continuously monitoring the real-time data of the power grid, including energy demand, power grid load fluctuation and the current release efficiency of the liquid air; Based on the real-time monitoring log, analyze the matching degree between the power grid demand and the liquid air release efficiency, calculate the liquid air release parameters that need to be adjusted, and obtain the release adjustment parameters; Based on the release adjustment parameters, update the liquid air release settings, synchronously adjust the power rationing process of the power grid, optimize the power output, and obtain the power rationing adjustment record.

[0011] The improvement of the present invention is that the analysis steps of the deviation from the standard performance threshold are specifically as follows: Based on the power rationing adjustment record, obtain the voltage, current and temperature data from the energy storage system through the Internet of Things technology, and perform data integration to obtain the real-time performance data set; Analyze the real-time performance dataset, compare it with the standard performance threshold, and use the formula: ; Perform deviation quantification calculation to evaluate whether the energy storage system meets the performance standard, and obtain the performance deviation analysis result. Among them, represents the total deviation between the performance of the energy storage system and the standard threshold, is the measured actual performance index, is the standard performance threshold.

[0012] The improvement of the present invention is that the specific steps for obtaining the device parameter adjustment feedback data are as follows: Automatically adjust the operating parameters of the key device, and collect the adjusted operating data to obtain the adjusted device parameters; Based on the adjusted device parameters, evaluate the impact of multiple parameter changes on the performance of the energy storage system, compare with the improvement target, determine the effect of device parameter adjustment, and obtain the device parameter adjustment feedback data.

[0013] The improvement of the present invention is that the specific steps for obtaining the energy storage performance optimization result are as follows: Based on the device parameter adjustment feedback data, perform real-time monitoring on the energy storage system, analyze the operating status and efficiency of the energy storage system, capture abnormal points or points with efficiency decline, and obtain real-time monitoring records; Based on the real-time monitoring records, identify the maintenance and upgrade requirements, perform maintenance or adjustment on the energy storage system, collect the operating data after maintenance, and then compare the performance differences before and after optimization. Use the formula: ; Calculate the performance improvement ratio , and obtain the energy storage performance optimization result. Among them, represents the performance index after maintenance or adjustment, represents the performance index before maintenance or adjustment.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by real-time monitoring the environmental changes and grid load, high-efficiency management of energy storage and release is achieved. The compression parameters and the liquefied air flow are adjusted to adapt to the real-time grid demand, the energy conversion process is optimized, the energy utilization rate is significantly improved, and the automated feedback adjustment mechanism ensures that the parameters of the key devices are always in the optimal state, enhancing the adaptability and response speed of the system to performance fluctuations. The performance monitoring and automatic optimization enable the system to effectively prevent and quickly solve the anomalies during operation, maintain continuous high-efficiency operation, thereby reducing maintenance requirements and operating costs, and providing more reliable energy support for the grid. Brief Description of the Drawings

[0015] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart for analyzing the influence of ambient temperature on the efficiency of the cold storage working medium in the present invention; Figure 3 is the flow chart for obtaining the compression parameter configuration in the present invention; Figure 4 is the flow chart for calculating the release rate and amount of liquefied air in the present invention; Figure 5 is the flow chart for obtaining the record of power ration adjustment in the present invention; Figure 6 is the flow chart for analyzing the deviation from the standard performance threshold in the present invention; Figure 7 is the flow chart for obtaining the equipment parameter adjustment feedback data in the present invention; Figure 8 is the flow chart for obtaining the optimization result of energy storage performance in the present invention. Detailed implementation manners

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Embodiment, please refer to Figure 1 , the present invention provides a technical solution: a cryogenic liquefied air energy storage system using a low-temperature gaseous cold storage working medium includes: The temperature control and liquefaction adjustment module monitors the real-time state of the cold storage working medium based on the ambient temperature and compressor performance data, analyzes the influence of the ambient temperature on the efficiency of the cold storage working medium, then adjusts the compression parameters according to the grid demand, dynamically updates the compressor settings, optimizes the liquefied air flow rate and pressure process, and obtains the compression parameter configuration; Based on the compression parameter configuration, the power grid demand response module analyzes the power grid load, predicts the power grid demand during peak periods, calculates the optimal liquid air release rate and quantity, and adjusts the liquid air release parameters by continuously monitoring the real-time data of the power grid to optimize the power output and obtain the power ration adjustment record; Based on the power ration adjustment record, the automatic control and feedback module exchanges data and control signals through Internet of Things technology, analyzes the deviation between the performance of the energy storage system and the standard performance threshold, automatically adjusts the operating parameters of key equipment, and obtains the equipment parameter adjustment feedback data; The data in the exchanged data and control signals includes the operating data of the compressor (including parameters such as the operating state, temperature, pressure, and energy consumption of the compressor), the storage data of liquid air (involving the quantity of liquid air, storage conditions, pressure, and temperature, etc.), the power output data (involving the amount of electricity obtained from the liquid air release process, the load response time, and the output efficiency), and the environmental monitoring data (including environmental temperature, humidity, etc., which is crucial for adjusting the compressor and the liquefaction process); Key equipment includes compressors, liquefaction equipment, and release equipment, etc., which are the main physical equipment used to achieve the compression, liquefaction, storage, and energy release of air during the operation of the energy storage system. Automatic adjustment refers to automatically modifying the operating parameters of the equipment according to the input data (such as energy demand, storage efficiency, environmental conditions, etc.); Based on the equipment parameter adjustment feedback data, the performance monitoring and optimization module monitors the operating state and effectiveness of the energy storage system, detects abnormal points or points of decreasing efficiency, identifies maintenance and upgrade requirements, and re-evaluates and optimizes its performance based on the data of the maintained energy storage system to obtain the energy storage performance optimization result.

[0019] The compression parameter configuration includes pressure standards, flow rate standards, and temperature control indicators. The power ration adjustment record specifically includes demand prediction values, power distribution times, and distribution accuracy. The equipment parameter adjustment feedback data includes operating efficiency, equipment stability, and maintenance cycles. The energy storage performance optimization result specifically includes performance increase information, stability optimization results, and response efficiency.

[0020] Please refer to Figure 2 for the specific steps of analyzing the influence of environmental temperature on the efficiency of the cold storage working medium: Based on the environmental temperature and compressor performance data, monitor the real-time state of the cold storage working medium to obtain the environmental and performance data set; The ambient temperature is obtained by multiple distributed temperature sensors, whose detection range covers the internal and external environment 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 through real-time monitoring devices, 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 subjected to format conversion and timestamp calibration, and it is stored as a structured data set. The data is used to match the synchronization and consistency of the ambient temperature and compressor performance parameters, and an effective data set for further analysis is selected, thus obtaining the ambient and performance data set.

[0021] Based on the ambient and performance data set, statistical analysis is carried out, using the formula: ; Calculate the influence of the ambient temperature on the efficiency of the cold storage working medium to obtain the adjusted efficiency value, where, 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 parameters, 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 the ambient temperature on the efficiency of the cold storage working medium, is the regression coefficient associated with the compressor performance indicating the average change in the efficiency of the cold storage working medium for each change in the compressor performance, represents the baseline efficiency of the cold storage working medium when both the ambient temperature and the compressor performance are zero; The actually measured ambient temperature is 30°C, and the compressor performance is 1. Let the regression coefficient be 0.01, be 0.1, be 0.65, and substitute the values into the formula: ; The result shows that under the current ambient temperature and compressor performance, the efficiency of the cold storage working medium is 1.05, that is, 105%, reflecting that the optimized system performance can achieve higher energy efficiency in practical applications.

[0022] Please refer to Figure 3 , and the specific steps for obtaining the compression parameter configuration are as follows: According to the power grid demand, analyze the current demand status and energy efficiency of the compressor to obtain a compression demand analysis record; By collecting power grid load data and energy distribution in real time, monitoring the current total power grid demand and the power consumption required by the compressor, calculating the current demand deviation using the historical operation data of the power grid management center, and retrieving the actual operation parameters of the compressor, including input power, rotational speed, and current output flow rate, etc., comparing them with the historical standard values, analyzing the energy efficiency performance of the compressor under the existing conditions, and at the same time combining the real-time monitoring data of ambient temperature and pressure, further adjusting the calculation of the reference energy efficiency value, finally forming a compression demand analysis record, which records the difference between the power grid demand and the compressor output, the current operating efficiency of the compressor, and suggestions for optimizing the demand.

[0023] Based on the compression demand analysis record, adjust the operating parameters of the compressor, using the formula: ; Optimize the liquid air flow rate and pressure settings to obtain optimized compression parameters , where represents the original compression parameters, reflecting the setting state of the compressor before adjustment, is the demand of the current power 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; The following data is collected, the current is 1.0 atm (atmospheric pressure), the power grid demand is 1.2 MW, the current output of the compressor is 1.0 MW, the adjustment coefficient is 0.5, substituting the values into the formula gives: ; ; ; The result shows that the adjusted pressure is 1.1 atm. By slightly adjusting the operating parameters of the compressor, the pressure can be slightly increased to meet the increased power grid demand and maintain the operating efficiency of the system.

[0024] Based on the optimized compression parameters, monitor their impact on the production and quality of liquid air, and then dynamically update the compressor settings according to the monitoring results to obtain the compression parameter configuration; Install real-time monitoring devices at the inlet and liquefied air outlet of the compressor. Collect the flow rate and pressure data of the liquefied air through sensors, and combine external environmental conditions such as temperature and humidity. Generate an evaluation report on the quality and output of the liquefied air by normalizing the data. Analyze the flow rate fluctuations and pressure deviations that occur during the air liquefaction process. Adjust the operating parameters of the compressor in real time for the deviation data, modify the opening degree of the inlet valve and the outlet pressure value, and reuse the optimized set parameters to generate a new compression parameter configuration.

[0025] Please refer to Figure 4 , and the calculation steps for the release rate and amount of liquefied air are specifically as follows: Based on the compression parameter configuration, extract the real-time data of the grid load and the storage status of the liquefied air. Classify and analyze the grid data to determine the load demand during the peak period of the grid, and obtain the predicted grid peak demand information. Extract the real-time data of the grid load and the storage status of the liquefied air, obtain the current load information of the grid, including key parameters such as load power, peak power, and load change rate, and obtain the current total storage amount and released amount of the liquefied air from the liquefied air storage management system. By integrating and classifying the parameters, group the grid load data according to the time series and associate the corresponding liquefied air storage parameters. Further analyze the data. By comparing the current grid load with the historical peak load records and combining the change trend of the liquefied air storage amount, calculate and mark the peak load intervals that will appear in the future, and finally obtain the predicted grid peak demand information.

[0026] Based on the predicted grid peak demand information, calculate the optimal release rate and optimal release amount of the liquefied air, using the formula: ; and ; Obtain the optimal release rate and release amount , where is an adjustment coefficient, used to adjust the sensitivity of the release rate and amount according to the actual demand of the grid and the storage status of the liquefied air, is the predicted grid peak demand, obtained by predicting based on historical data and trend analysis, and used to calculate the release demand of the liquefied air, is the current grid demand, obtained through real-time monitoring, reflecting the current load situation of the grid, is the total storage amount of the liquefied air, representing the total amount of liquefied air available in the storage facility, is the amount of liquefied air that has been used, representing the total amount of liquefied air that has been released from the storage so far; The following data is monitored, and the predicted grid peak demand is , the current power grid demand is , the total storage capacity of liquefied air is tons, and the amount of liquefied air already used is tons. Let , substitute the value into the formula: ; ; This result shows that the release rate is 10 tons per hour, and the release amount is 18.75 tons. The release rate and release amount of liquefied air obtained through calculation can effectively meet the power grid peak demand, ensure the stable operation of the power grid, and optimize the energy use efficiency.

[0027] Please refer to Figure 5 , and the specific steps for obtaining the power rationing adjustment record are as follows: By continuously monitoring the real-time data of the power grid, including energy demand, power grid load fluctuations, and the current release efficiency of liquefied air, obtain the real-time monitoring log; Collect the power grid load change data from sensors and monitoring devices in real time. The data needs to be initially cleaned to remove noise and invalid data. At the same time, extract the information of the liquefied air release rate and cumulative release amount. By classifying the real-time obtained data according to the time series, analyze the matching degree between the energy demand and the power grid load fluctuations, and record the dynamic change trend of the liquefied air release efficiency. Finally, obtain the real-time monitoring log, which contains the current power grid state and the distribution of liquefied air resources.

[0028] Based on the real-time monitoring log, analyze the matching degree between the power grid demand and the liquefied air release efficiency, calculate the liquefied air release parameters that need to be adjusted, and obtain the release adjustment parameters; Analyze the matching degree between the power grid demand and the liquefied air release efficiency. By matching the current release rate of liquefied air and the peak load demand of the power grid, calculate the difference value between the two, and evaluate the adjustment space in combination with the liquefied air storage capacity. Generate the release rate parameters that need to be adjusted according to the difference value between the power grid load and the release rate. At the same time, calculate the adjustment range of the liquefied air release amount according to the trend of the load demand, and integrate the results into the release adjustment parameters to obtain the applicable release adjustment parameters.

[0029] Based on the release adjustment parameters, update the liquefied air release settings, synchronously adjust the power rationing process of the power grid, optimize the power output, and obtain the power rationing adjustment record; Update the liquefied air release settings, synchronously adjust the power distribution process of the power grid, determine the operation instructions for the liquefied air release equipment according to the release adjustment parameters, dynamically adjust the real-time control data of the release rate and quantity, ensure that the equipment executes the adjusted parameters, confirm the matching of the release and the power grid distribution by monitoring the release process, and further correct the operation settings according to the fluctuations to complete the optimization operation of the power output of the power grid. Finally, record all the specific details of the release adjustment and the power grid distribution process to generate a power distribution adjustment record.

[0030] Please refer to Figure 6 , and the analysis steps for the deviation from the standard performance threshold are specifically as follows: Based on the power distribution adjustment record, obtain voltage, current, and temperature data from the energy storage system through Internet of Things technology, and perform data integration to obtain a real-time performance data set; The operating data extracted from the energy storage system through Internet of Things technology includes parameters such as voltage, current, and temperature. Obtain the corresponding values from the real-time sensors and control signal channels of the energy storage system in sequence. For voltage data, judge the voltage stability of each monitoring point, eliminate outliers and integrate valid data. For current data, compare different load distribution situations and record the imbalance phenomenon. For temperature data, extract the average value and peak value within the current time interval for statistics, and record the fluctuation range of each index during the equipment working cycle. Merge and organize the real-time data from different sources according to the time stamp and distribution information to form a complete real-time data set of the energy storage system performance.

[0031] Analyze the real-time performance data set, compare it with the standard performance threshold, and use the formula: ; Perform deviation quantification calculation to evaluate whether the energy storage system meets the performance standard, and obtain the performance deviation analysis result. Among them, represents the total deviation between the energy storage system performance and the standard threshold, is the measured actual performance index, and the data includes voltage, current, and temperature, etc., 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; Measured actual performance index , standard performance threshold , then substitute into the formula for calculation: ; ; ; This result shows that the performance deviation is 7.07, indicating that the system has obvious deviations in the two indicators of voltage and temperature.

[0032] Please refer to Figure 7 , and the steps for obtaining the device parameter adjustment feedback data are specifically as follows: Automatically adjust the operating parameters of key devices, where the key devices include compressors, liquefaction devices, release devices, etc., and collect the operating data after adjustment to obtain the adjusted device parameters; Regarding the operating conditions of compressors, liquefaction devices, and release devices, collect the initial operating parameter data of the devices. These data include basic operating information such as pressure, temperature, and flow rate. Based on the data, establish an analysis model for the device operation, set the adjustment range and step size of the device parameters, gradually adjust each parameter, and record the changes in the device operating data during the adjustment process, including the increase or decrease in pressure, the temperature change range, and the difference in operating power consumption, etc. By comparing and analyzing the device operating data, determine the optimal parameter configuration path for the device operation, and at the same time classify and organize the operating states before and after the parameter adjustment to ensure that the finally adjusted parameters can meet the stability and performance requirements for the long-term operation of the device.

[0033] Based on the adjusted device parameters, evaluate the impact of multiple parameter changes on the performance of the energy storage system, compare with the improvement target, determine the effect of the device parameter adjustment, and obtain the device parameter adjustment feedback data; Clarify the performance indicators that need to be evaluated. The indicators include energy storage efficiency, system output power, response time, etc. Then analyze the relationship between the adjusted device parameters and the energy storage system performance indicators, determine the degree of influence of the device parameter changes on the system performance, sort out the corresponding relationship between the device parameters and the performance changes one by one, extract the key parameters affecting the system performance, analyze the key parameters, set the influence priority, compare with the device historical operating data, further analyze the specific role of the adjusted parameters in improving the performance, and through the comparison of the performance indicators with the preset improvement target, identify whether the adjustment effect meets the optimization requirements, and summarize the results and data of the device parameter adjustment to obtain the feedback data of the device parameter adjustment.

[0034] Please refer to Figure 8 , and the steps for obtaining the energy storage performance optimization result are specifically as follows: Based on the device parameter adjustment feedback data, conduct real-time monitoring of the energy storage system, analyze the operating state and efficiency of the energy storage system, capture abnormal points or points of efficiency decline, and obtain the real-time monitoring record; Extract key operating parameters from the device parameter adjustment feedback data, including real-time indicators such as voltage, current, and temperature. After classifying the extracted multiple parameters, record them separately in a time series table. Conduct volatility analysis on each operating parameter to capture the changing trends and abnormal peaks. By setting dynamic thresholds calculated based on historical operating records, mark the points in the current data that exceed the threshold range, and further conduct a repeatability test on the marked points in combination with historical data to confirm whether there are abnormalities in the fluctuation patterns of the marked points. Subsequently, integrate all the marked points to generate a preliminary list of abnormal points. Further cross-compare the operating parameters of the abnormal points with the historical points of performance decline to identify the parameter fluctuation points related to efficiency decline. Record all the results to form the final real-time monitoring record.

[0035] Based on the real-time monitoring record, identify the maintenance and upgrade requirements, perform maintenance or adjustment on the energy storage system, and collect the operating data after maintenance. Then, compare the performance differences before and after optimization. Use the formula: ; Calculate the performance improvement ratio , and obtain the energy storage performance optimization result. Among them, represents the performance index after maintenance or adjustment, reflecting the new performance state of the energy storage system after necessary adjustments, represents the performance index before maintenance or adjustment, that is, the original performance level of the energy storage system before the implementation of the optimization measures; Assume that the performance index after maintenance or adjustment is , and the performance index before maintenance or adjustment is , substitute into the formula for calculation: ; ; ; The result shows that the performance of the energy storage system has increased by 20% after maintenance and adjustment, indicating that the maintenance measures have effectively improved its efficiency.

[0036] The above is only a preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A deep-cold liquefied air energy storage system using a low-temperature gaseous cold storage medium, characterized in that: The system comprises: The temperature control and liquefaction regulation module monitors the real-time status of the cold storage medium based on the ambient temperature and compressor performance data, analyzes the impact of the ambient temperature on the efficiency of the cold storage 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 amount, and adjusts the liquefied air release parameters and optimizes the power output by continuously monitoring the real-time data of the grid, thereby obtaining a power distribution adjustment record; The automatic control and feedback module exchanges data and control signals based on the power distribution adjustment record through the Internet of Things technology, analyzes the deviation between the performance of the energy storage system and the standard performance threshold, automatically adjusts the operating parameters of key equipment, and obtains equipment parameter adjustment feedback data; The performance monitoring and optimization module monitors the operating status 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 its performance based on the energy storage system data after maintenance to obtain energy storage performance optimization results.

2. The deep-cold liquefied air energy storage system using low-temperature gaseous cold storage medium according to claim 1 is characterized in that: The steps of analyzing the influence of ambient temperature on the efficiency of cold storage medium are specifically as follows: Based on the ambient temperature and compressor performance data, the real-time state of the cold storage medium is monitored to obtain the environmental and performance data sets; Based on the environment and performance data set, statistical analysis is performed using the formula: ； Calculate the effect of ambient temperature on the efficiency of the cold storage medium and obtain the adjusted efficiency value, where: Represents the efficiency of the cold storage medium, Indicates the ambient temperature, Indicates compressor performance parameters, Is related to the ambient temperature The associated regression coefficient, Is related to compressor performance The associated regression coefficient, Represents the baseline efficiency of the cold storage fluid when the ambient temperature and compressor performance are both zero.

3. The deep-cold liquefied air energy storage system using low-temperature gaseous cold storage medium according to claim 1 is characterized in that: The steps for obtaining the compression parameter configuration are specifically as follows: Analyze the current demand state and energy efficiency of the compressor according to the grid demand 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: ； Optimize the liquid air flow and pressure settings to obtain the optimal compression parameters ,in, Represents the original compression parameters, is the current grid demand, is the current output of the compressor, is the adjustment factor; Based on the optimized compression parameters, their impact on the liquefied air output and quality is monitored, and the compressor settings are dynamically updated according to the monitoring results to obtain the compression parameter configuration.

4. The deep-cold liquefied air energy storage system using low-temperature gaseous cold storage medium according to claim 1 is characterized in that: The calculation steps of the liquefied air release rate and amount are specifically as follows: Based on the compression parameter configuration, extract the 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 during the peak period, and obtain the predicted grid peak demand information; Based on the predicted peak demand information of the power grid, the optimal release rate and optimal release amount of liquefied air are calculated using the formula: ;and ; Get the best release rate and release amount ,in, is the adjustment factor, is the predicted peak demand on the grid, is the current grid demand, is the total storage capacity of liquefied air, is the amount of liquid air used.

5. The deep-cold liquefied air energy storage system using low-temperature gaseous cold storage medium according to claim 1 is characterized in that: The steps for obtaining the power distribution adjustment record are specifically as follows: By continuously monitoring the real-time data of the power grid, including energy demand, power grid load fluctuation and the current release efficiency of liquid air, a real-time monitoring log is obtained; Based on the real-time monitoring log, the matching degree between the power grid demand and the liquefied air release efficiency is analyzed, and the liquefied air release parameters that need to be adjusted are calculated to obtain the release adjustment parameters; Based on the release adjustment parameters, the liquefied air release settings are updated, the power distribution process of the power grid is synchronously adjusted, the power output is optimized, and the power distribution adjustment record is obtained.

6. The deep-cold liquefied air energy storage system using low-temperature gaseous cold storage medium according to claim 1 is characterized in that: The analysis steps of the deviation from the standard performance threshold are specifically as follows: Based on the power distribution adjustment record, voltage, current and temperature data are obtained from the energy storage system through the Internet of Things technology, and the data are integrated to obtain a real-time performance data set; The real-time performance data set is analyzed and compared with the standard performance threshold using the formula: ； Perform deviation quantification calculation to evaluate whether the energy storage system meets the performance standards and obtain performance deviation analysis results, where: Represents the total deviation between the energy storage system performance and the standard threshold, is the actual performance indicator measured, is the standard performance threshold.

7. The deep-cold liquefied air energy storage system using low-temperature gaseous cold storage medium according to claim 1 is characterized in that: The steps for obtaining the device parameter adjustment feedback data are specifically as follows: Automatically adjust the key equipment operating parameters, and collect the adjusted operating data to obtain the adjusted equipment parameters; Based on the adjusted equipment parameters, the impact of multiple parameter changes on the performance of the energy storage system is evaluated and compared with the improvement goals to determine the equipment parameter adjustment effect and obtain equipment parameter adjustment feedback data.

8. The deep-cold liquefied air energy storage system using low-temperature gaseous cold storage medium according to claim 1 is characterized in that: The steps for obtaining the energy storage performance optimization result are specifically as follows: Based on the device parameter adjustment feedback data, the energy storage system is monitored in real time, the operating status 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 requirements are identified, maintenance or adjustment is performed on the energy storage system, and operation data after maintenance is collected. The performance difference before and after optimization is compared using the formula: ； Computing performance improvement ratio , and obtain the energy storage performance optimization result, where Represents the performance index after maintenance or adjustment, Indicates the performance index before maintenance or adjustment.

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