Heat transfer control method for underground thermal storage system based on filling phase change material

Abstract

This application discloses a heat transfer control method for an underground thermal storage system based on phase change material (PCM) filling, relating to the field of heat transfer control technology. Based on the predicted analysis results of the actual heat supply and heat demand for each thermal cycle under different usage scenarios of the underground thermal storage system, the method predicts and analyzes the supply situation of each thermal cycle under different usage scenarios. It determines whether the underground thermal storage system meets future seasonal usage demands based on the predicted supply situation of each thermal cycle under different usage scenarios, uses historical data to calibrate theoretical trends, eliminates the deviation between theory and reality, achieves high-precision dynamic prediction of PCM performance degradation, and uses performance retention values ​​to correct the ideal heat supply prediction results. This allows for early prediction of whether the system can meet demand, establishing a forward-looking supply-demand balance early warning system. By providing early warning of potential energy supply gaps, the risk of system shutdown or user complaints due to insufficient heat supply is greatly reduced.

Description

Technical Field

[0001] This application belongs to the field of heat transfer control, specifically a heat transfer control method for underground thermal storage systems filled with phase change materials. Background Technology

[0002] Underground thermal storage systems, especially those incorporating phase change materials (PCMs), are key technologies for improving the efficiency of renewable energy utilization, achieving cross-seasonal energy storage, and smoothing out peak and off-peak energy demand. Their core principle is to store heat in underground storage bodies and PCMs during periods of low energy demand or surplus energy, releasing it during peak demand periods to meet heating or process needs. However, in practical engineering applications and long-term operation, this technology faces a series of complex challenges, leading to insufficient precision in its planning, operation, and maintenance. These challenges manifest in the following aspects:

[0003] 1. During long-term and repeated thermal cycling, the latent heat of phase change materials will inevitably decay. Existing technologies mostly rely on laboratory standard data provided by material suppliers or simple linear decay assumptions. There is a lack of a dynamic and accurate prediction method that integrates the material theoretical decay model with the actual historical data of the system. This leads to serious distortion in the assessment of the health status of the core heat storage medium of the system and fails to reflect its performance evolution under real complex working conditions.

[0004] 2. Traditional methods fail to dynamically couple the cumulative decay effect of phase change materials with the number of cycles into the heat transfer model when predicting the heat supply of a single or multiple future thermal cycles. As a result, the prediction results cannot accurately reflect the actual energy supply capacity of the system after long-term operation, causing a serious disconnect between the operation scheduling plan and the actual situation.

[0005] 3. Existing system operation and management are mostly based on real-time monitoring and short-term load forecasting, which is a passive response mode. There is a lack of a method to predict the degree of matching between the actual available heat supply and the expected demand before the start of the future heat cycle. This makes it impossible for operators to identify potential energy supply gaps in advance, and misses the valuable time window for formulating response strategies (such as starting backup heat sources and adjusting operation strategies), resulting in decreased system reliability and increased operational risks. In order to solve the problems proposed in the background technology, this application designs a heat transfer control method for underground thermal storage systems based on phase change materials. Summary of the Invention

[0006] To address the aforementioned technical shortcomings, this application proposes a heat transfer control method for underground thermal storage systems based on phase change materials.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: This application provides a heat transfer control method for an underground thermal storage system based on phase change material filling, which includes the following specific steps:

[0008] S1. Obtain data on the phase change material filling the underground thermal storage system under different usage scenarios, data on the underground thermal storage body, data on the predicted heat demand corresponding to each thermal cycle, and data on the actual cumulative decay ratio of the performance of the phase change material in each thermal cycle in history.

[0009] S2. Based on the data of phase change material filling in different usage scenarios of underground thermal storage system and the actual cumulative decay ratio of the performance of phase change material in each thermal cycle in history, predict and analyze the cumulative decay ratio of the thermal cycle performance of phase change material in different usage scenarios of underground thermal storage system.

[0010] S3. Based on the data of underground heat storage body under different usage scenarios and the prediction analysis results of the cumulative decay ratio of phase change material thermal cycle performance, predict and analyze the actual heat supply corresponding to each thermal cycle under different usage scenarios of underground heat storage system.

[0011] S4. Based on the predicted analysis results of the actual heat supply and the predicted heat demand data of each heat cycle under different usage scenarios of the underground heat storage system, predict and analyze the heat supply situation of each heat cycle under different usage scenarios of the underground heat storage system.

[0012] S5. Based on the prediction and analysis results of the heat supply situation of each heat cycle under different usage scenarios of the underground heat storage system, determine whether the underground heat storage system can meet the usage needs of future seasons.

[0013] It should be noted that, as a preferred technical solution for the heat transfer control method of an underground thermal storage system based on phase change material filling, the specific steps of S1 are as follows:

[0014] S11. By conducting thermal cycling attenuation experiments on the phase change materials filled in the underground thermal storage system under different usage scenarios, data on the phase change materials filled in the underground thermal storage system under different usage scenarios are obtained. Among them, the data on the phase change materials filled in the underground thermal storage system under different usage scenarios include the initial latent heat of phase change corresponding to the phase change material, the latent heat attenuation amplitude data, and the latent heat attenuation rate data.

[0015] S12. The actual cumulative decay ratio of the performance of the phase change material in each thermal cycle is obtained by dividing the historical decayed latent heat of phase change by the initial latent heat of phase change.

[0016] S13. Obtain data on the underground heat storage body under different usage scenarios of the underground heat storage system through temperature sensors and thermal response tests. The data on the underground heat storage body under different usage scenarios of the underground heat storage system includes the average temperature value of the underground heat storage body area, the temperature value of the circulating fluid, and the total thermal resistance data of heat transfer from the soil and the area filled with phase change material to the fluid.

[0017] S14. Obtain the predicted heat demand data for each heat cycle under different usage scenarios of the underground heat storage system by using historical heat demand data.

[0018] S15. Store the acquired data in the storage component for use in the analysis process.

[0019] It should be noted that, as a preferred technical solution for the heat transfer control method of an underground thermal storage system based on phase change material filling, step S2 includes the following specific steps:

[0020] S21. The theoretical cumulative attenuation ratio of phase change material performance in each thermal cycle under different usage scenarios of underground thermal storage system is obtained from the phase change latent heat attenuation amplitude data and phase change latent heat attenuation rate data of phase change material under different usage scenarios of underground thermal storage system.

[0021] S22. Based on the theoretical cumulative decay ratio analysis results of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system and the actual cumulative decay ratio data of the phase change material's thermal cycle performance under different usage scenarios, predict and analyze the cumulative decay ratio of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system.

[0022] It should be noted that, as a preferred technical solution for the heat transfer control method of an underground thermal storage system based on phase change material (PCM) filling, the specific step of S21 is as follows: Based on the initial latent heat of phase change (LCM) data, the LCM attenuation amplitude data, and the LCM attenuation rate data corresponding to the PCM under different usage scenarios of the underground thermal storage system, calculate the theoretical cumulative attenuation ratio of the PCM performance in each thermal cycle under different usage scenarios of the underground thermal storage system. The formula for calculating the theoretical cumulative attenuation ratio of the PCM performance in the i-th thermal cycle under different usage scenarios of the underground thermal storage system is:

[0023] Where i represents the number of thermal cycle performance degradation results of phase change material under different usage scenarios of underground thermal storage system, i is any one of 1 to N, N is the maximum number of thermal cycle performance degradation results of phase change material under different usage scenarios of underground thermal storage system, A is the phase change latent heat degradation amplitude data of phase change material under different usage scenarios of underground thermal storage system, R is the phase change latent heat degradation rate data of phase change material under different usage scenarios of underground thermal storage system, and H is the initial phase change latent heat data of phase change material under different usage scenarios of underground thermal storage system.

[0024] It should be noted that, as a preferred technical solution for the heat transfer control method of an underground thermal storage system based on phase change material, the specific steps of S22 are as follows: Based on the theoretical cumulative attenuation ratio analysis results of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system and the actual cumulative attenuation ratio data of the phase change material's thermal cycle performance under different historical usage scenarios, a prediction analysis of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios is performed. Specifically, the prediction analysis process for the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios is as follows: The theoretical cumulative attenuation ratio analysis results are subtracted from the actual cumulative attenuation ratio data of the phase change material's thermal cycle performance under different historical usage scenarios, and the average value is calculated to obtain the prediction deviation of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different historical usage scenarios. The prediction deviation of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different historical usage scenarios is then added to the theoretical cumulative attenuation ratio analysis results to obtain the prediction analysis results of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system.

[0025] It should be noted that, as a preferred technical solution for the heat transfer control method of an underground thermal storage system based on phase change material filling, the specific steps of S3 are as follows:

[0026] S31. The predicted and analyzed results of the heat supply of each heat cycle under different usage scenarios of the underground heat storage system are obtained from the average temperature data of the underground heat storage body area, the temperature data of the circulating fluid, and the total thermal resistance data of heat transfer from the soil and the filling phase change material area to the fluid.

[0027] S32. Based on the predicted analysis results of the heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system and the predicted analysis results of the cumulative decay ratio of the thermal cycle performance of the phase change material, the predicted analysis results of the actual heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system are obtained.

[0028] It should be noted that, as a preferred technical solution for the heat transfer control method of an underground thermal storage system based on phase change material filling, the specific steps of S31 are as follows: Based on the average temperature data of the underground thermal storage area, the temperature data of the circulating fluid, and the total thermal resistance data of heat transfer from the soil and the phase change material filling area to the fluid, a predictive analysis of the heat supply corresponding to each thermal cycle under different usage scenarios of the underground thermal storage system is performed. The formula for predicting the heat supply corresponding to the i-th thermal cycle under different usage scenarios of the underground thermal storage system is:

[0029] ,in, This refers to the k-th time point within the i-th thermal cycle under different usage scenarios of the underground thermal storage system, where k is the corresponding number of each time point, ranging from 1 to... Any one of them, Let i be the number of time steps corresponding to the i-th thermal cycle under different usage scenarios of the underground thermal storage system. To calculate the time interval, This represents the average temperature of the underground thermal storage area at the k-th time point of the i-th thermal cycle under different usage scenarios of the underground thermal storage system. This represents the temperature of the circulating fluid at the k-th time point of the i-th thermal cycle under different usage scenarios of the underground thermal storage system. The total thermal resistance of the underground thermal storage system during the i-th thermal cycle, from the soil and the area filled with phase change material to the fluid, under different application scenarios. This is a function indicating the operating status of the i-th thermal cycle under different usage scenarios of the underground thermal storage system.

[0030] It should be noted that, as a preferred technical solution for the heat transfer control method of an underground thermal storage system based on phase change material, the specific steps of S32 are as follows: Based on the predicted analysis results of the heat supply corresponding to each thermal cycle under different usage scenarios of the underground thermal storage system and the predicted analysis results of the cumulative attenuation ratio of the phase change material's performance under each thermal cycle, the actual heat supply corresponding to each thermal cycle under different usage scenarios of the underground thermal storage system is predicted and analyzed. Specifically, the process of predicting and analyzing the actual heat supply corresponding to each thermal cycle under different usage scenarios of the underground thermal storage system is as follows: the predicted analysis results of the cumulative attenuation ratio of the phase change material's performance under each thermal cycle are converted from a proportional form to a numerical form; the numerical form of the predicted analysis results of the cumulative attenuation ratio of the phase change material's performance under each thermal cycle is subtracted from the numerical form to obtain the predicted retention value of the phase change material's performance under each thermal cycle; and the predicted analysis results of the heat supply corresponding to each thermal cycle under different usage scenarios of the underground thermal storage system are multiplied by the predicted retention value of the phase change material's performance under each thermal cycle to obtain the predicted analysis results of the actual heat supply corresponding to each thermal cycle under different usage scenarios of the underground thermal storage system.

[0031] It should be noted that, as a preferred technical solution for the heat transfer control method of an underground thermal storage system based on phase change material filling, the specific steps of S4 are as follows: based on the predicted analysis results of the actual heat supply and the predicted heat demand data for each thermal cycle under different usage scenarios of the underground thermal storage system, the supply situation of each thermal cycle under different usage scenarios of the underground thermal storage system is predicted and analyzed. The process of predicting and analyzing the supply situation of each thermal cycle under different usage scenarios of the underground thermal storage system is as follows: dividing the predicted analysis results of the actual heat supply corresponding to each thermal cycle under different usage scenarios of the underground thermal storage system by the predicted heat demand data to obtain the predicted analysis results of the supply situation of each thermal cycle under different usage scenarios of the underground thermal storage system.

[0032] It should be noted that, as a preferred technical solution for the heat transfer control method of an underground thermal storage system based on phase change material filling, the specific steps of S5 are as follows: obtaining the prediction and analysis results of the supply situation of each heat cycle under different usage scenarios of the underground thermal storage system, comparing the prediction and analysis results of the supply situation of each heat cycle under different usage scenarios of the underground thermal storage system with the threshold range of the supply situation prediction and analysis results, if the prediction and analysis results of the supply situation of each heat cycle under different usage scenarios of the underground thermal storage system are within the threshold range of the supply situation prediction and analysis results, then it is determined that the underground thermal storage system can meet the future seasonal usage needs; otherwise, it is determined that the underground thermal storage system cannot meet the future seasonal usage needs, and the judgment result is pushed to relevant personnel for processing.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention obtains data on the phase change material filling of the underground thermal storage system under different usage scenarios, data on the underground thermal storage body, predicted values ​​of heat demand corresponding to each thermal cycle, and data on the actual cumulative attenuation ratio of the historical phase change material performance under each thermal cycle; based on the data on the phase change material filling of the underground thermal storage system under different usage scenarios and the data on the actual cumulative attenuation ratio of the historical phase change material performance under each thermal cycle, it performs prediction analysis on the cumulative attenuation ratio of the phase change material thermal cycle performance under different usage scenarios; based on the data on the underground thermal storage body and the prediction analysis results of the cumulative attenuation ratio of the phase change material thermal cycle performance under different usage scenarios, it performs prediction analysis on the actual heat supply corresponding to each thermal cycle under different usage scenarios; based on the prediction analysis results of the actual heat supply and the predicted values ​​of heat demand under different usage scenarios, it performs prediction analysis on the heat supply situation under different usage scenarios; and based on the underground thermal storage system's... The analysis of heat supply predictions for each heat cycle under the same usage scenario determines whether the underground thermal storage system can meet future seasonal demand. Historical data is used to calibrate theoretical trends, eliminating the discrepancy between theory and reality. This allows for a more accurate prediction of the performance retention rate of phase change materials (PCMs) in future heat cycles under different usage scenarios, achieving high-precision dynamic prediction of PCM performance degradation. The performance retention value is used to correct the ideal heat supply prediction results, thus obtaining the actual heat supply prediction results. This overcomes the shortcomings of traditional methods that ignore material aging, enabling the prediction results to truly reflect the actual output of the system at a specific life stage, significantly improving prediction accuracy and enhancing the accuracy of heat supply prediction under long-term operation. By comparing and analyzing the predicted actual heat supply with the predicted heat demand and setting clear thresholds for judgment, it is possible to predict in advance whether the system can meet the demand before the start of the next energy season, establishing a forward-looking supply and demand balance early warning system. By providing early warning of potential energy supply gaps, the risk of system shutdown or user complaints due to insufficient heat supply is greatly reduced. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the overall process of the heat transfer control method for an underground thermal storage system based on phase change material in this application.

[0035] Figure 2 This is a schematic diagram illustrating the process for obtaining the cumulative degradation ratio prediction analysis results of the heat transfer control method for underground thermal storage systems based on phase change materials in this application.

[0036] Figure 3 This is a schematic diagram illustrating the process of obtaining the actual heat supply prediction and analysis results for the heat transfer control method of the underground thermal storage system based on phase change material in this application.

[0037] Figure 4 This is a schematic diagram illustrating the process of obtaining the supply situation prediction and analysis results for the heat transfer control method of the underground thermal storage system based on phase change material filling, as described in this application. Detailed Implementation

[0038] To better understand this application, various aspects of this application will be described in more detail with reference to the accompanying drawings.

[0039] To address the technical problems raised in the background art, this application provides a preferred embodiment:

[0040] The specific content of this embodiment is as follows:

[0041] like Figure 1 As shown, the heat transfer control method for an underground thermal storage system based on phase change material includes the following specific steps:

[0042] S1. Obtain data on the phase change material filling the underground thermal storage system under different usage scenarios, data on the underground thermal storage body, data on the predicted heat demand corresponding to each thermal cycle, and data on the actual cumulative decay ratio of the performance of the phase change material in each thermal cycle in history.

[0043] In this embodiment, the specific steps of S1 are as follows:

[0044] S11. Data on the phase change material (PCM) filling in underground thermal storage systems under different usage scenarios includes initial latent heat of phase change (LCH) data, LCH attenuation amplitude data, and LCH attenuation rate data. This data is obtained by conducting thermal cycling experiments on the filled PCM under different usage scenarios of the underground thermal storage system. The specific experimental method is as follows: The PCM sample is sealed in a crucible specifically designed for differential scanning calorimetry (DSC). Under a high-purity nitrogen protective atmosphere, repeated thermal cycling experiments are performed on the sample. A fixed-program thermal cycle (e.g., constant heating and cooling rate between the upper and lower limits of the phase transition temperature range) is used. At each preset number of cycles (e.g., cycles 0, 1, 5, 10, 50, 100, etc.), the cycle is paused and a precise DSC scan is performed to acquire the raw experimental data for that node, resulting in a DSC curve (i.e., heat flow-temperature curve). The DSC curve is then integrated to obtain a sequence of measured latent heat of phase transition values ​​evolving with the number of cycles. Specifically, the fitting method involves importing the obtained cycle number i and the measured latent heat of phase transition value sequence dataset into MATLAB, and using the nonlinear least squares method, inputting the formula... To perform a fitting, j represents the number of thermal cycles, where j can be any value from 1 to m, and m is the maximum number of thermal cycles. The latent heat of phase change (LCH) of the phase change material after j thermal cycles is the measured value of the latent heat of phase change (i.e., the sequence data of the measured latent heat of phase change as the number of cycles changes). The initial latent heat of phase change corresponding to the phase change material (obtained through the first DSC test). The stable latent heat of phase change after an infinite number of thermal cycles is a theoretical asymptotic value, representing the value at which the latent heat of phase change eventually stabilizes when the number of thermal cycles is very large; k is the latent heat decay rate data of the corresponding latent heat of phase change material (obtained through fitting). Finally, the initial latent heat data, latent heat decay amplitude data, and latent heat decay rate data of the corresponding latent heat of phase change material are extracted.

[0045] S12. The actual cumulative decay ratio of the performance of the historical phase change material after each thermal cycle is obtained by dividing the historical decayed latent heat of phase change by the initial latent heat of phase change.

[0046] S13. Data on the underground thermal storage system under different application scenarios includes average temperature data of the underground thermal storage area, circulating fluid temperature data, and total thermal resistance data of heat transfer from the soil and phase change material filling area to the fluid. Circulating fluid temperature data is obtained by installing high-precision temperature sensors at the inlet of the heat exchanger pipes. Temperature sensor strings (thermocouples or thermistors) are buried at different locations and depths around the thermal storage body. Average temperature data of the underground thermal storage area is obtained through multi-point measurement and calculation. A constant heat flow (constant power heat injected into the underground circulation pipeline using electric heating) is injected into the underground circulating fluid through a thermal response test, and the fluid level is monitored. The change in average temperature (the average of the inlet and outlet temperatures) over time was analyzed. Using the infinite line source model formula (a commonly used theoretical model in ground source heat pumps, assuming an infinitely long line source continuously releasing heat in a homogeneous medium), the total thermal resistance data for heat transfer from the soil and phase change material filling area to the fluid was obtained through inversion. The inversion process involved: first, organizing the average fluid temperature data obtained from on-site thermal response tests into a two-dimensional array (time [seconds], temperature [°C]); then, defining the infinite line source model function in the numerical calculation software, with input variables being time t and four parameters to be inverted (initial ground temperature T0, overall thermal conductivity, etc.). Thermal diffusivity The total thermal resistance (Rb) inside the borehole is output as the corresponding theoretical fluid temperature; then, based on geological data, reasonable initial guess values ​​are set for these four parameters (e.g., T0 is taken as the average fluid temperature at the beginning of the test). Take 1.5-2.5 W / (m·K), Take 1.0-1.5×10 -6 m² / s, Rb is taken as 0.05-0.15m·K / W); then a nonlinear least squares fitting algorithm (such as the Levenberg-Marquardt algorithm) is called to target the measured temperature sequence, and the parameter values ​​are optimized iteratively to minimize the sum of squared residuals between the theoretically calculated temperature sequence and the measured data; after the fitting converges, the goodness of fit index (such as the coefficient of determination R² should be greater than 0.95) needs to be calculated and the residual distribution (should be close to random distribution, without significant systematic bias) needs to be analyzed to verify the reliability of the inversion results; finally, the total thermal resistance Rb extracted from the optimal solution is the total thermal resistance of heat transfer from the soil and the phase change material filling region to the fluid.

[0047] S14. The predicted heat demand data for each heat cycle under different usage scenarios of the underground heat storage system are obtained by predicting historical heat demand data.

[0048] S15. Store the acquired data in the storage component for use in the analysis process.

[0049] S2, such as Figure 2As shown, based on the data of phase change materials filled in different usage scenarios of underground thermal storage systems and the actual cumulative decay ratio data of the performance of phase change materials in each thermal cycle in history, the cumulative decay ratio of the thermal cycle performance of phase change materials in underground thermal storage systems under different usage scenarios is predicted and analyzed.

[0050] In this embodiment, S2 includes the following specific steps:

[0051] S21. The theoretical cumulative attenuation ratio of phase change material performance in each thermal cycle under different usage scenarios of underground thermal storage system is obtained from the phase change latent heat attenuation amplitude data and phase change latent heat attenuation rate data of phase change material under different usage scenarios of underground thermal storage system.

[0052] In this embodiment, S21 includes the following specific steps: Calculating the theoretical cumulative attenuation ratio of the phase change material's performance in each thermal cycle under different usage scenarios of the underground thermal storage system based on the initial latent heat of phase change data, latent heat attenuation amplitude data, and latent heat attenuation rate data corresponding to the phase change material under different usage scenarios of the underground thermal storage system. The formula for calculating the theoretical cumulative attenuation ratio of the phase change material's performance in the i-th thermal cycle under different usage scenarios of the underground thermal storage system is as follows:

[0053] Where i represents the number of thermal cycle performance degradation results of the phase change material under different usage scenarios of the underground thermal storage system, i can be any value from 1 to N, N is the maximum number of thermal cycle performance degradation results of the phase change material under different usage scenarios of the underground thermal storage system, A is the phase change latent heat degradation amplitude data of the phase change material under different usage scenarios of the underground thermal storage system, R is the phase change latent heat degradation rate data of the phase change material under different usage scenarios of the underground thermal storage system, and H is the initial phase change latent heat data of the phase change material under different usage scenarios of the underground thermal storage system. It should be noted that the phase change latent heat degradation amplitude data A represents the theoretical limit of the maximum possible degradation ratio that the phase change material may eventually reach, in percentage, which is determined by the intrinsic stability of the phase change material; the phase change latent heat degradation rate data R represents the rate at which the degradation approaches the limit, in cycles. -1 (That is, each thermal cycle, for example, if R = 0.002 times) -1When A = 15% and the number of thermal cycles is 1, it means that this thermal cycle will consume approximately two-thousandths of the total decay potential (i.e., 15%) of the filled phase change material, which contributes approximately 0.03% of the absolute decay. The larger the latent heat decay rate of the phase change material, the faster the phase change material decays and reaches stability. In the early thermal cycles of the phase change material, its defects are characterized by rapid generation and rapid interface deterioration. At this time, the phase change material decays relatively quickly. As the thermal cycle continues, the vulnerable part of the phase change material is exhausted (or the phase change material structure reaches a fatigue stable state). At this time, the decay rate of the phase change material slows down and asymptotically approaches a limit value A, which is the latent heat decay amplitude of the phase change material. In this formula, This represents the remaining percentage of decay potential as the number of cycles i increases. The proportion of decay potential consumed as the number of cycles i increases is represented by an exponential function because, according to Newton's law of cooling, this proportion decays exponentially. The asymptotic saturation process of thermal cycling decay of phase change materials is described when i is very small (i.e. when the phase change material first begins thermal cycling). ,at this time It can be simplified to That is, the thermal cycling decay of the phase change material is approximately linear in the initial stage, and the initial slope is the product of the phase change latent heat decay amplitude data and the phase change latent heat decay rate data; when i is very large (i.e. the vulnerable part of the phase change material is exhausted or the phase change material structure reaches the fatigue stability state). At this point, the thermal cycling attenuation of the phase change material is close to the attenuation amplitude data of the latent heat of phase change (i.e., the theoretical attenuation limit value A). This indicates the percentage decrease in the latent heat of phase change material after thermal cycling compared to its initial state without decrease; the cumulative decrease percentage is also indicated by the following data: latent heat of phase change amplitude data refers to the magnitude of decrease in the latent heat of phase change material (i.e., the heat absorbed or released per unit mass of material during the phase change process) as the number of cycles increases during thermal cycling; and the latent heat of phase change rate data refers to the rate at which the latent heat of phase change decreases, i.e., the rate at which the decrease amplitude changes with the number of cycles.

[0054] S22. Based on the theoretical cumulative decay ratio analysis results of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system and the actual cumulative decay ratio data of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system, predict and analyze the cumulative decay ratio of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system.

[0055] In this embodiment, the specific steps of S22 are as follows: Based on the theoretical cumulative attenuation ratio analysis results of the phase change material (PCM) performance under different usage scenarios of the underground thermal storage system and the actual cumulative attenuation ratio data of the PCM performance under different historical usage scenarios, a prediction analysis of the cumulative attenuation ratio of the PCM performance under different usage scenarios of the underground thermal storage system is performed. Specifically, the prediction analysis process for the cumulative attenuation ratio of the PCM performance under different usage scenarios of the underground thermal storage system is as follows: The theoretical cumulative attenuation ratio analysis results are subtracted from the actual cumulative attenuation ratio data of the PCM performance under different historical usage scenarios, and the average value is calculated to obtain the prediction deviation of the cumulative attenuation ratio of the PCM performance under different historical usage scenarios. The prediction deviation of the cumulative attenuation ratio of the PCM performance under different historical usage scenarios is then calculated. The cumulative decay ratio prediction deviation is added to the theoretical cumulative decay ratio analysis result to obtain the cumulative decay ratio prediction analysis result of the phase change material thermal cycling performance under different usage scenarios of the underground thermal storage system. It should be noted that by calculating the prediction deviation and using it to correct the theoretical value, the theoretical model is essentially calibrated and error compensated using historical data, so that the prediction results remain reasonable and accurate. As the underground thermal storage system is in operation, new thermal cycling data are constantly generated. The new data can be periodically (e.g., quarterly or annually) incorporated into the historical dataset to recalculate the average prediction deviation, so that the prediction can be dynamically updated to follow the aging trajectory of the underground thermal storage system itself, and achieve the adaptive capability of becoming more and more accurate over time.

[0056] S3, such as Figure 3 As shown, based on the data of underground thermal storage body under different usage scenarios and the prediction analysis results of the cumulative decay ratio of phase change material thermal cycle performance, the actual heat supply corresponding to each thermal cycle under different usage scenarios of underground thermal storage system is predicted and analyzed.

[0057] In this embodiment, the specific steps of S3 are as follows:

[0058] S31. The predicted and analyzed results of the heat supply of each heat cycle under different usage scenarios of the underground heat storage system are obtained from the average temperature data of the underground heat storage body area, the temperature data of the circulating fluid, and the total thermal resistance data of heat transfer from the soil and the filling phase change material area to the fluid.

[0059] In this embodiment, the specific step of S31 is as follows: based on the average temperature data of the underground thermal storage area, the temperature data of the circulating fluid, and the total thermal resistance data of heat transfer from the soil and the phase change material filling area to the fluid, the heat supply corresponding to each thermal cycle under different usage scenarios of the underground thermal storage system is predicted and analyzed. The formula for predicting and analyzing the heat supply corresponding to the i-th thermal cycle under different usage scenarios of the underground thermal storage system is:

[0060] ,in, This refers to the k-th time point within the i-th thermal cycle under different usage scenarios of the underground thermal storage system, where k is the corresponding number of each time point, ranging from 1 to... Any one of them, Let i be the number of time steps corresponding to the i-th thermal cycle under different usage scenarios of the underground thermal storage system. To calculate the time interval, This represents the average temperature of the underground thermal storage area at the k-th time point of the i-th thermal cycle under different usage scenarios of the underground thermal storage system. This represents the temperature of the circulating fluid at the k-th time point of the i-th thermal cycle under different usage scenarios of the underground thermal storage system. The total thermal resistance of the underground thermal storage system during the i-th thermal cycle, from the soil and the area filled with phase change material to the fluid, under different application scenarios. This is a function indicating the operating status of the i-th thermal cycle under different usage scenarios of the underground thermal storage system; it should be noted that in the calculation formula of this step, This is a switch function used to distinguish when the underground thermal storage system is in heating mode for the building. It takes a value of 1 or 0. A value of 1 indicates that the underground thermal storage system is in heating mode (i.e., during the winter heating season and the heat pump is running). A value of 0 indicates that the underground thermal storage system is in shutdown or reverse thermal storage mode (i.e., during the summer thermal storage season). The calculation time interval is... The response time needs to be less than or equal to one-tenth of the underground thermal storage system's response time to ensure accuracy; time steps The total duration of the i-th thermal cycle divided by the time interval ; The temperature difference between the heat storage medium and the fluid, measured in Kelvin. The unit is Kelvin per watt, and the time interval is... The unit is seconds, and the switching function is... Dimensionless; Discretize and sum the dynamic process of the temperature change of the heat storage body and the fluid temperature over time, and multiply by the switching function to make the formula distinguish different operating conditions, ensure the specificity of the prediction, avoid including invalid cycles in the heat supply, and improve the accuracy of heat supply prediction.

[0061] S32. Based on the predicted analysis results of the heat supply corresponding to each heat cycle under different usage scenarios of the underground heat storage system and the predicted analysis results of the cumulative decay ratio of the phase change material heat cycle performance, the predicted analysis results of the actual heat supply corresponding to each heat cycle under different usage scenarios of the underground heat storage system are obtained.

[0062] In this embodiment, the specific steps of S32 are as follows: Based on the predicted analysis results of the heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system and the predicted analysis results of the cumulative performance decay ratio of the phase change material under each heat cycle, the actual heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system is predicted and analyzed. Specifically, the process of predicting and analyzing the actual heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system is as follows: the predicted analysis results of the cumulative performance decay ratio of the phase change material under each heat cycle are converted from proportional form to numerical form; the numerical form of the predicted analysis results of the cumulative performance decay ratio of the phase change material under each heat cycle is subtracted from the numerical form to obtain the phase change material... The predicted heat supply value for each thermal cycle is obtained by multiplying the predicted heat supply value for each thermal cycle of the underground thermal storage system under different usage scenarios by the predicted heat supply value for each thermal cycle of the phase change material. It should be noted that the predicted heat supply value for each thermal cycle of the underground thermal storage system under different usage scenarios is based on the ideal heat supply under the current operating conditions. Multiplying the ideal heat supply under the current operating conditions by the predicted heat supply value yields the final predicted heat supply value. By predicting the actual heat supply value, a foundation is laid for accurately determining whether the underground thermal storage system meets future seasonal usage needs.

[0063] S4. Based on the predicted analysis results of the actual heat supply and the predicted heat demand data of each heat cycle under different usage scenarios of the underground heat storage system, predict and analyze the heat supply situation of each heat cycle under different usage scenarios of the underground heat storage system.

[0064] like Figure 4 As shown, in this embodiment, the specific steps of S4 are as follows: Based on the predicted analysis results of the actual heat supply and the predicted heat demand for each heat cycle under different usage scenarios of the underground thermal storage system, a predicted analysis of the supply situation for each heat cycle under different usage scenarios is performed. Specifically, the predicted analysis process for the supply situation for each heat cycle under different usage scenarios of the underground thermal storage system involves dividing the predicted analysis results of the actual heat supply for each heat cycle under different usage scenarios by the predicted heat demand data to obtain the predicted analysis results of the supply situation for each heat cycle under different usage scenarios. It should be noted that dividing the predicted results of the actual heat supply by the predicted results of the demand yields the predicted supply situation. The aforementioned data, such as the temperature and thermal resistance of the thermal storage body, are ultimately mapped onto the predicted supply situation analysis results, achieving a dynamic display of supply and demand matching.

[0065] S5. Based on the prediction and analysis results of the heat supply situation of each heat cycle under different usage scenarios of the underground heat storage system, determine whether the underground heat storage system can meet the usage needs of future seasons.

[0066] In this embodiment, the specific steps of S5 are as follows: Obtain the predicted and analyzed results of the supply situation of each heat cycle under different usage scenarios of the underground thermal storage system; compare the predicted and analyzed results of the supply situation of each heat cycle under different usage scenarios of the underground thermal storage system with the threshold range of the supply situation prediction and analysis results; if the predicted and analyzed results of the supply situation of each heat cycle under different usage scenarios of the underground thermal storage system are within the threshold range of the supply situation prediction and analysis results, then it is determined that the underground thermal storage system can meet the future seasonal usage needs; otherwise, it is determined that the underground thermal storage system cannot meet the future seasonal usage needs, and the judgment result is pushed to relevant personnel for processing; it should be noted that, through preset... The supply situation forecast analysis results threshold range (e.g., a threshold range of 0.9-1.1) automatically transforms continuous and complex forecast values ​​into discrete and clear binary decision signals, eliminating the subjectivity and ambiguity of human interpretation. Traditional alarms are often based on real-time monitoring (e.g., current water temperature is too low), which are post-event or in-event alarms. This method issues early warnings before the arrival of the next season, giving managers ample time to assess the severity of the problem, formulate and initiate remedial plans (e.g., repair equipment, purchase additional energy, activate backup systems, etc.), avoiding operational accidents or user complaints caused by insufficient energy supply, and upgrading the risk management model from passive emergency response to proactive defense.

[0067] Based on the above implementation details, this embodiment has the following advantages over the prior art: This embodiment acquires data on the phase change material filling in the underground thermal storage system under different usage scenarios, data on the underground thermal storage body, predicted data on the heat demand corresponding to each thermal cycle, and data on the actual cumulative attenuation ratio of the phase change material's performance in each historical thermal cycle; based on the data on the phase change material filling in the underground thermal storage system under different usage scenarios and the data on the actual cumulative attenuation ratio of the phase change material's performance in each historical thermal cycle, it performs a predictive analysis of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios; based on the data on the underground thermal storage body and the predictive analysis results of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios, it performs a predictive analysis of the actual heat supply corresponding to each thermal cycle under different usage scenarios; based on the predictive analysis results of the actual heat supply corresponding to each thermal cycle under different usage scenarios and the predicted heat demand data, it performs a predictive analysis of the heat supply situation under different usage scenarios; based on the underground thermal storage... The predictive analysis of the supply of heat in each heat cycle under different usage scenarios of the thermal system determines whether the underground thermal storage system can meet the usage demand of future seasons. Historical data is used to calibrate theoretical trends, eliminating the deviation between theory and reality. This allows for a more accurate prediction of the performance retention rate of phase change materials in future heat cycles under different usage scenarios, achieving high-precision dynamic prediction of phase change material performance degradation. The performance retention value is used to correct the ideal heat supply prediction results, thereby obtaining the actual heat supply prediction results. This overcomes the shortcomings of traditional methods that ignore material aging, enabling the prediction results to truly reflect the actual output of the system at a specific life stage, significantly improving prediction accuracy and enhancing the accuracy of heat supply prediction under long-term operation. By comparing and analyzing the predicted actual heat supply with the predicted heat demand and setting clear thresholds for judgment, it is possible to predict in advance whether the system can meet the demand before the start of the next energy season, establishing a forward-looking supply and demand balance early warning. By providing early warning of potential energy supply gaps, the risk of system shutdown or user complaints due to insufficient heat supply is greatly reduced.

[0068] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A heat transfer control method for an underground thermal storage system based on phase change material filling, characterized in that, include: S1. Obtain data on the phase change material filling the underground thermal storage system under different usage scenarios, data on the underground thermal storage body, data on the predicted heat demand corresponding to each thermal cycle, and data on the actual cumulative decay ratio of the performance of the phase change material in each thermal cycle in history. S2. Based on the data of phase change material filling in different usage scenarios of underground thermal storage system and the actual cumulative decay ratio of the performance of phase change material in each thermal cycle in history, predict and analyze the cumulative decay ratio of the thermal cycle performance of phase change material in different usage scenarios of underground thermal storage system. The specific steps include the following: S21. The theoretical cumulative attenuation ratio of phase change material performance in each thermal cycle under different usage scenarios of underground thermal storage system is obtained from the phase change latent heat attenuation amplitude data and phase change latent heat attenuation rate data of phase change material under different usage scenarios of underground thermal storage system. S22. Based on the theoretical cumulative decay ratio analysis results of phase change material performance under different usage scenarios of underground thermal storage system and the actual cumulative decay ratio data of phase change material performance under different usage scenarios of underground thermal storage system, predict and analyze the cumulative decay ratio of phase change material performance under different usage scenarios of underground thermal storage system. S3. Based on the data of underground heat storage body under different usage scenarios and the prediction analysis results of the cumulative decay ratio of phase change material thermal cycle performance, predict and analyze the actual heat supply corresponding to each thermal cycle under different usage scenarios of underground heat storage system. S4. Based on the predicted analysis results of the actual heat supply and the predicted heat demand data of each heat cycle under different usage scenarios of the underground heat storage system, predict and analyze the heat supply situation of each heat cycle under different usage scenarios of the underground heat storage system. S5. Determine whether the underground thermal storage system meets future seasonal usage needs based on the predicted and analyzed heat supply conditions for each heat cycle under different usage scenarios. The specific steps are as follows: Obtain the predicted and analyzed heat supply conditions for each heat cycle under different usage scenarios of the underground thermal storage system; compare these results with the threshold range of the supply condition prediction and analysis results; if the predicted and analyzed heat supply conditions for each heat cycle under different usage scenarios are within the threshold range, then the underground thermal storage system can meet future seasonal usage needs; otherwise, it is determined that the underground thermal storage system cannot meet future seasonal usage needs, and the judgment result is pushed to relevant personnel for processing.

2. The heat transfer control method for an underground thermal storage system based on a phase change material as described in claim 1, characterized in that, The specific steps of S21 are as follows: Calculate the theoretical cumulative attenuation ratio of the phase change material's performance in each thermal cycle under different usage scenarios of the underground thermal storage system based on the initial latent heat of phase change, latent heat attenuation amplitude, and latent heat attenuation rate data of the phase change material under different usage scenarios of the underground thermal storage system.

3. The heat transfer control method for an underground thermal storage system based on a phase change material as described in claim 2, characterized in that, The specific steps of S22 are as follows: Based on the theoretical cumulative attenuation ratio analysis results of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system and the actual cumulative attenuation ratio data of the phase change material's thermal cycle performance under different usage scenarios, a prediction analysis of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system is performed. The process of predicting the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system is as follows: Subtract the theoretical cumulative attenuation ratio analysis results from the actual cumulative attenuation ratio data of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system, and then calculate the average value to obtain the prediction deviation of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system. Add the prediction deviation of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system to the theoretical cumulative attenuation ratio analysis results to obtain the prediction analysis results of the cumulative attenuation ratio of the phase change material's thermal cycle performance under different usage scenarios of the underground thermal storage system.

4. The heat transfer control method for an underground thermal storage system based on a phase change material as described in claim 3, characterized in that, The specific steps of S3 are as follows: S31. The predicted and analyzed results of the heat supply of each heat cycle under different usage scenarios of the underground heat storage system are obtained from the average temperature data of the underground heat storage body area, the temperature data of the circulating fluid, and the total thermal resistance data of heat transfer from the soil and the filling phase change material area to the fluid. S32. Based on the predicted analysis results of the heat supply corresponding to each heat cycle under different usage scenarios of the underground heat storage system and the predicted analysis results of the cumulative decay ratio of the phase change material's heat cycle performance, the predicted analysis results of the actual heat supply corresponding to each heat cycle under different usage scenarios of the underground heat storage system are obtained.

5. The heat transfer control method for an underground thermal storage system based on a phase change material as described in claim 4, characterized in that, The specific steps of S31 are as follows: based on the average temperature data of the underground heat storage body area, the temperature data of the circulating fluid, and the total thermal resistance data of heat transfer from the soil and the area filled with phase change material to the fluid, predict and analyze the heat supply corresponding to each heat cycle under different usage scenarios of the underground heat storage system.

6. The heat transfer control method for an underground thermal storage system based on a phase change material as described in claim 5, characterized in that, The specific steps of S32 are as follows: Based on the predicted analysis results of the heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system and the predicted analysis results of the cumulative decay ratio of the phase change material's performance under different heat cycles, the actual heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system is predicted and analyzed. The process of predicting and analyzing the actual heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system is as follows: the predicted analysis results of the cumulative decay ratio of the phase change material's performance under different heat cycles are converted from proportional form to numerical form; the numerical form of the predicted analysis results of the cumulative decay ratio of the phase change material's performance under different heat cycles is subtracted from the numerical form to obtain the predicted retention value of the phase change material's performance under different heat cycles; the predicted analysis results of the heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system are multiplied by the predicted retention value of the phase change material's performance under different heat cycles to obtain the predicted analysis results of the actual heat supply corresponding to each heat cycle under different usage scenarios of the underground thermal storage system.

7. The heat transfer control method for an underground thermal storage system based on a phase change material as described in claim 6, characterized in that, The specific steps of S4 are as follows: Based on the predicted analysis results of the actual heat supply and the predicted heat demand for each heat cycle under different usage scenarios of the underground heat storage system, the predicted heat supply situation of each heat cycle under different usage scenarios of the underground heat storage system is predicted and analyzed. The process of predicting and analyzing the heat supply situation of each heat cycle under different usage scenarios of the underground heat storage system is as follows: Divide the predicted analysis results of the actual heat supply for each heat cycle under different usage scenarios of the underground heat storage system by the predicted heat demand data to obtain the predicted analysis results of the heat supply situation of each heat cycle under different usage scenarios of the underground heat storage system.

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