Heat storage collaborative optimization method for cold and heat source system

Abstract

The invention discloses a heat storage collaborative optimization method for a cold and heat source system, and relates to the technical field of ground source heat pump energy utilization. S2, load calculation and parameter range calculation are carried out; s3, calculating the heat absorption and release of the soil; s4, optimizing the position of the heat storage body; s5, the optimal drilling depth and the optimal heat storage body volume are determined; in the optimization design process, the minimum energy consumption and the minimum cost are achieved by determining the volume of the heat storage body and the drilling depth of the buried pipe. The system dynamically adjusts the variables according to the cooling and heating load requirements of a user until an optimal combination is found, and the efficient and intelligent ground source heat pump system optimization design method is provided by solving the problems of high energy consumption, soil heat unbalance and unstable energy supply in the prior art; and the long-term stability and the operation efficiency of the system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ground source heat pump energy utilization, and in particular to a method for collaborative optimization of heat storage in a cold and hot source system. Background Art

[0002] Ground source heat pump technology is a renewable energy utilization technology that has received widespread attention in recent years. Its working principle is to use the constant temperature of the underground as a cold and heat source to provide cooling and heating for buildings. Compared with traditional air conditioning and heating systems, ground source heat pumps have higher energy efficiency and lower operating costs because they do not directly generate heat, but transfer heat from the underground through a heat pump cycle. However, over time, the operating efficiency of such a system may be affected, resulting in performance degradation. One of the key issues is that during continuous operation, the temperature change of the underground soil causes thermal imbalance, which affects the efficiency of the heat pump. For example, in cold areas, since the heat load is often higher than the cold load, long-term operation may cause the soil temperature to gradually decrease. In addition, energy cost is also a key factor to be considered when designing and operating a ground source heat pump system. In order to solve this problem, many studies and practices have tried to introduce heat storage technology in combination with ground source heat pump systems. Heat storage technology allows thermal energy to be stored when demand is low and energy prices are low, and then released when demand is high and energy prices are high. This can not only improve the economic benefits of the system, but also balance the temperature changes underground, further improving the operating stability and efficiency of the ground source heat pump system.

[0003] The prior art has the following deficiencies: The invention with the existing publication number CN206669942U "A kind of energy storage ground source heat pump system" proposes an energy storage ground source heat pump system, which solves the problem that the existing ground source heat pump system consumes a lot of energy and has no energy storage effect. It includes a water storage tank, a heat exchanger, a vertical buried pipe, a heat pump room, a heat recovery heat exchanger and a semi-annular clip, one end of the water storage tank is connected to the heat recovery heat exchanger through a hot water pipe, one end of the heat recovery heat exchanger is connected to a compressor, one end of the compressor is connected to a condenser through a conduit, the other end of the condenser is connected to an evaporator through a conduit, the other end of the heat recovery heat exchanger is connected to one end of the heat exchanger through a cold water pipe, one end of the heat exchanger is connected to a hot water tank, one end of the condenser is connected to a heat pump room through a water outlet pipe, the other end of the heat pump room is connected to a vertical buried pipe through a conduit, the inner layer of the vertical buried pipe is coated with a buried pipe cage, the inner side wall of the vertical buried pipe is provided with a floating barrier, the bottom end of the vertical buried pipe is connected with a vertical column, the inside of the floating barrier is provided with a semi-annular clip, and the outer layer of the semi-annular clip is coated with a protective layer. The invention can store heat well and improve the functionality of the ground source heat pump system.

[0004] However, the invention only studies the structure, and does not design and select the geothermal heat pump system with energy storage from the perspective of system operation performance and economy. Existing geothermal heat pump systems and heat storage technologies are usually designed and installed independently. This means that their operation may not be fully coordinated, resulting in unnecessary energy and economic waste and unstable performance of the system. In addition, commercial parks have multi-load characteristics, and the current design of geothermal heat pump systems does not comprehensively consider the impact of user load change characteristics in application scenarios on system optimization design and capacity matching results.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method for collaborative optimization of heat storage in cold and hot source systems to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for collaborative optimization of heat storage in cold and hot source systems, S1. Collect measurement data; S2. Carry out load calculation and parameter range calculation; S3. Calculate the heat absorption and release of the soil; S4, optimizing the position of the heat storage body; S5. Determine the optimal drilling depth and the optimal heat storage volume.

[0008] Preferably, in S1, the specific steps of collecting data are as follows: Collect soil properties: soil type, thermal conductivity, thermal diffusivity, density and specific heat capacity; used to measure soil type, thermal conductivity, thermal diffusivity, density and specific heat capacity, the equipment includes sensors, data loggers and analysis modules, used to calculate the physical parameters of the heat storage body based on soil characteristics. The expression is: ,in, The heat stored and released by the heat storage body. is the thermal conductivity of the soil, is the heat transfer area, is the temperature difference, is the thickness of the heat storage body, For time; Calculation of the physical properties of the heat storage body; The thermal conductivity measurement of the soil property measurement device is based on the following formula: ,in, For the heat transferred, is the heat transfer area, is the temperature difference, The thermal conductivity of the soil is determined by measuring the amount of heat transferred under known conditions. Collect historical data on heating and cooling used by users of different business formats in the park; the equipment for collecting heating and cooling data includes flow meters and temperature sensors. The flow meters are used to measure the volume of fluid passing through the heat exchanger, and the temperature sensors are used to measure the temperature difference between the inlet and outlet of the fluid. The heating and cooling consumption is calculated based on the following formula: ,in, For the user's calorie consumption, is the fluid mass flow rate, is the specific heat capacity of the fluid, is the temperature difference between the inlet and outlet of the fluid; Collect system efficiency and energy consumption data at different heat storage positions; the system energy consumption analysis unit calculates the total energy consumption based on the power consumption data measured at different heat storage positions using the following formula: , is the total energy consumption, is the system power, For the operation time, the layout of the heat storage bodies is optimized by combining the energy consumption data at each heat storage body location; Collect meteorological data, holiday information, park activities and events; meteorological data collection equipment records temperature, humidity, wind speed and sunshine intensity in real time through meteorological sensors. The data is used to predict the fluctuation of the park's cooling and heating demand and estimate the heat load required by the system according to the following formula; , is the system load, is the heat transfer coefficient of the building envelope, is the area of ​​the enclosure structure, is the temperature difference between indoor and outdoor, For time.

[0009] Preferably, in S2, the specific steps of load calculation and parameter range calculation are as follows: based on the data collected in the initial design part, considering that the heat data has seasonality and trend, the time series model ARIMA is selected to predict the load and generate a representative load curve: Data preprocessing: cleaning data, deleting and repairing missing, abnormal and erroneous data points; data normalization; Data set division: use 70% of the data as the training set and 30% of the data as the test set; Model training: Apply the ARIMA model to train the training set, adjust the learning rate, number of iterations, and regularization parameter model parameters; Model verification: Use the test set to verify the trained ARIMA model, and calculate the square error: MSE, root mean square error: RMSE, and determination coefficient: R2; Output: The best model is retrained using the entire data set to obtain the final load curve; According to the obtained load curve, the hourly heat load is divided into m intervals according to the value, and the hourly cooling load is divided into n intervals according to the value. The load change rate of each interval does not exceed 200kJ / h2; the maximum value of the hourly cooling and heating load in each interval is used as the heat load of the interval. and cooling load , and count the cooling and heating loads of each interval , the corresponding time frequency and , where i = 1, 2, ..., m; j = 1, 2, ..., n; the maximum heat load of the end user is the heat load in m heat load intervals The maximum value among; the maximum cooling load of the end user is the cooling load in n cooling load intervals The maximum value in .

[0010] Preferably, in S3, the specific steps of calculating the heat absorption and release of the soil are as follows: according to the physical properties of the heat storage material collected in the initial design part, the thermal conductivity characteristics of the soil, and the load curve, the value range of the drilling depth L of the buried pipe heat exchanger [Hmin, Hmax] and the value range of the heat storage body capacity V [0, Vmax] are calculated; the heat storage cooperative ground source heat pump system is a parallel system, and the value range of the drilling depth L is [Hmin, Hmax]: ; The value range of heat storage capacity V is [0, Vmax]:

[0011] ; In the formula, is the maximum heat load of the end user; is the maximum cooling load of the end user; is the initial temperature of the rock and soil in the buried pipe area; is the designed average temperature of the heat transfer medium in the ground heat exchanger; is the wall thermal resistance of the U-tube; is the convection heat transfer thermal resistance between the heat transfer medium and the inner wall of the U-shaped tube; Thermal resistance of the drilling grouting backfill material; is the soil thermal resistance; Additional thermal resistance caused by short-term continuous pulse load; The share of heating operation; is the cooling operation share; COP is the heating performance coefficient of the heat pump unit; EER is the cooling performance coefficient of the heat pump unit; a is the safety factor of the heat storage unit operation; is the density of the heat storage medium (kg / m³); is the maximum volume of the heat storage body (m³); is the specific heat capacity of the heat storage medium (J / kg·°C); is the temperature change (°C); the amount of heat absorbed by the ground heat exchanger to the soil throughout the year and heat release The calculation formula is: ; In the formula, The underground heat exchanger absorbs heat from the soil in winter. The heat released to the soil by the underground heat exchanger in summer is designed. is the total heat absorbed by the ground heat exchanger from the soil throughout the year, It is the total heat released to the soil by the ground heat exchanger throughout the year; is the maximum heat load of the end user; is the maximum cooling load of the end user; Designed heat absorption per unit time of underground heat exchanger from soil in winter The designed heat release per unit time of the buried pipe heat exchanger to the soil in summer The formula is: .

[0012] Preferably, in S4, the specific steps are as follows: extracting the relative positions of the heat storage body and the ground and the heat storage body and the heat pump as input features, respectively expressed as the distance between the heat storage body and the ground The relative distance between the heat storage body and the heat pump ; These characteristics directly affect the energy consumption and heat transfer efficiency of the heat storage body, and are subsequently used as input variables in the optimization algorithm. and heat transfer efficiency Determine the optimization goal: .

[0013] Preferably, in S5, the specific steps are as follows: determine the optimal drilling depth H and the optimal heat storage volume V, select The minimum and lowest energy cost are used as the optimization objective function, as follows:

[0014] ; Where W is the total power consumption of the heat storage and ground source heat pump system; p is the unit electricity price, and the optimization variables are the drilling depth H and the heat storage volume V; The constraints are: ; The above objective function is converted into a specific solvable optimization equation, which is solved using the whale algorithm. The final optimal solution is the optimal underground pipe drilling depth H and heat storage capacity V of the ground source heat pump system considering heat storage synergy under the premise of meeting system reliability. The specific solution implementation steps are as follows: Use binary coding to randomly generate an initial whale group, which contains N whales, and each whale is characterized by a set optimization variable; define the fitness function: based on the objective function in step 6, that is, Minimize and minimize energy cost, construct fitness function; substitute each whale into fitness function to calculate its fitness, and judge whether it meets the optimization criteria; determine the leading whale: in the current whale group, select the whale with the highest fitness as the leading whale; update the whale position: simulate the search and pursuit behavior of whales, simulate the whale tracking the leading whale and prey hunting behavior: calculate the relative distance between each whale and the leading whale, and update the position of the solution replacement and elimination: if the whale in the new position has a higher fitness, replace the whale in the original position; if the fitness is lower, keep the original position; judge the stopping condition: if the preset number of iterations is reached and the fitness change is less than the threshold, stop the algorithm; through several rounds of iterations, extract the whale with the highest fitness, that is, obtain the optimal heat storage volume V and drilling depth H under the premise of meeting the system reliability.

[0015] In the above technical scheme, the technical effects and advantages provided by the present invention are as follows: by integrating the vehicle-mounted high-definition camera, GPS measuring instrument, gyroscope, speed sensor and altitude measuring instrument equipment, the system can collect high-frequency road images, location information and vehicle posture data from multiple angles, and ensure data synchronization through time tag matching technology. The system also removes redundant information through data cleaning, and accurately locates the geographical location of the image data through pile number calibration to ensure the accuracy of the inspection information. In terms of hidden danger inspection, the present invention systematically analyzes the types of hidden dangers that may exist in traffic signs, markings, scientific and technological facilities, safety facilities, and lighting facilities based on the guarding mode of railway crossings (manned and unmanned), and provides hidden danger identification standards, location descriptions and rectification suggestions. This not only improves the comprehensiveness and accuracy of hidden danger inspection, but also significantly improves the efficiency of safety management and reduces omissions in the manual inspection process.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: By comprehensively considering the changing characteristics of user loads, soil thermal balance and system energy consumption, the problem of insufficient coordination between heat storage technology and ground source heat pump systems in the prior art is effectively solved. In the optimization design of the prior art, the changes in user loads in different application scenarios are often ignored, resulting in the lack of coordination between the heat storage body and the ground source heat pump system during operation, which in turn causes energy waste and unstable system performance. The present invention overcomes these shortcomings by optimizing the location and capacity design of the heat storage body and the coordinated operation of the heat storage body and the ground source heat pump system.

[0017] First, the present invention ensures the thermal balance of the soil through precise calculation and optimization. In traditional systems, due to the lack of full consideration of the thermal balance of the soil, long-term operation can easily lead to overheating and overcooling of the soil temperature, affecting the stability of the system. The present invention avoids such extreme situations by rationally designing the heat exchange method between the heat storage body and the ground source heat pump, ensuring the stability of the soil temperature during the operation of the system and improving the long-term operation effect of the system.

[0018] Secondly, the present invention significantly reduces energy costs. By optimizing the layout and capacity design of the heat storage body and combining the intelligent model of the random forest algorithm, the system can automatically adjust the energy distribution between the heat storage body and the heat pump according to the dynamic changes of the user's load, avoiding unnecessary energy consumption. The traditional system fails to match the load changes in time, resulting in increased energy consumption. The present invention maximizes energy efficiency and greatly reduces operating costs by coordinating the collaboration between the heat storage body and the ground source heat pump.

[0019] Finally, the present invention ensures a stable supply of energy through the coordinated work of the heat storage body and the ground source heat pump system. As the core component of the system, the heat storage body can store excess heat when the load is low and release the stored heat when the load is high, ensuring that the system can continue to supply energy stably during peak demand. This design improves the responsiveness of the ground source heat pump system, avoids the problems of insufficient supply and performance fluctuations that may occur in traditional systems during peak loads, and thus improves the reliability and stability of the entire system.

[0020] In general, the present invention provides an efficient and intelligent ground source heat pump system optimization design method by solving the problems of high energy consumption, soil thermal imbalance and unstable energy supply in the prior art, which can not only reduce energy costs but also improve the long-term stability and operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application and the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0022] Figure 1 The following is a flow chart of the system of the invention.

[0023] Figure 2 Optimization flow chart for the heat storage volume V and the ground heat exchanger length L.

[0024] Figure 3 Schematic diagram of the heat storage-coordinated ground-source heat pump system. DETAILED DESCRIPTION

[0025] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0026] The present invention provides Figures 1 to 3 The heat storage collaborative optimization method for cold and hot source systems is shown in FIG. S1. Collect measurement data; S2. Carry out load calculation and parameter range calculation; S3. Calculate the heat absorption and release of the soil; S4, optimizing the position of the heat storage body; S5. Determine the optimal drilling depth and the optimal heat storage volume.

[0027] In S1, the specific steps for collecting data are as follows: Collect soil properties: soil type, thermal conductivity, thermal diffusivity, density and specific heat capacity; used to measure soil type, thermal conductivity, thermal diffusivity, density and specific heat capacity, the equipment includes sensors, data loggers and analysis modules, used to calculate the physical parameters of the heat storage body based on soil characteristics. The expression is: ,in, The heat stored and released by the heat storage body. is the thermal conductivity of the soil, is the heat transfer area, is the temperature difference, is the thickness of the heat storage body, For time; Calculation of the physical properties of the heat storage body; The thermal conductivity measurement of the soil property measurement device is based on the following formula: ,in, For the heat transferred, is the heat transfer area, is the temperature difference, The thermal conductivity of the soil is determined by measuring the amount of heat transferred under known conditions. Collect historical data on heating and cooling used by users of different business formats in the park; the equipment for collecting heating and cooling data includes flow meters and temperature sensors. The flow meters are used to measure the volume of fluid passing through the heat exchanger, and the temperature sensors are used to measure the temperature difference between the inlet and outlet of the fluid. The heating and cooling consumption is calculated based on the following formula: ,in, For the user's calorie consumption, is the fluid mass flow rate, is the specific heat capacity of the fluid, is the temperature difference between the inlet and outlet of the fluid; Collect system efficiency and energy consumption data at different heat storage positions; the system energy consumption analysis unit calculates the total energy consumption based on the power consumption data measured at different heat storage positions using the following formula: , is the total energy consumption, is the system power, For the operation time, the layout of the heat storage bodies is optimized by combining the energy consumption data at each heat storage body location; Collect meteorological data, holiday information, park activities and events; meteorological data collection equipment records temperature, humidity, wind speed and sunshine intensity in real time through meteorological sensors. The data is used to predict the fluctuation of the park's cooling and heating demand and estimate the heat load required by the system according to the following formula; , is the system load, is the heat transfer coefficient of the building envelope, is the area of ​​the enclosure structure, is the temperature difference between indoor and outdoor, First, the soil characteristics are collected, including soil type, thermal conductivity, thermal diffusivity, density and specific heat capacity. In order to accurately measure the soil characteristics, a professional measurement device is used, which includes sensors, data loggers and analysis modules to obtain data based on the physical characteristics of the soil. These characteristic data are used to calculate the physical properties of the thermal storage body to ensure that the heat transfer process between the thermal storage body and the surrounding soil environment can be optimized.

[0028] Secondly, collect historical cooling and heating data of users of different business formats in the park. Different users have obvious differences in cooling and heating needs, so the system must record and analyze historical data of each user's specific cooling and heating needs. To this end, the present invention is equipped with special measuring equipment, including flow meters and temperature sensors. The flow meter is used to measure the volume of the user's fluid during the cooling and heating exchange process, and the temperature sensor is used to detect the temperature difference between the inlet and outlet of the fluid. Through these devices, the actual cooling and heating consumption of each user can be accurately calculated in real time.

[0029] Next, the system uses the collected data to analyze the impact of the heat storage body in different positions on the system efficiency and energy consumption. In this step, the position of the heat storage body plays an important role in energy efficiency. To this end, the present invention monitors the energy efficiency performance of the heat storage body by arranging sensors at different positions. Combined with the energy consumption analysis unit of the system, relevant energy consumption data and heat transfer efficiency data can be collected according to the different positions of the heat storage body, and the layout of the heat storage body can be further optimized.

[0030] In addition, meteorological data, holiday information, and park activities and event information are also collected. Meteorological data is an important external factor that affects the system's cooling and heating loads. The present invention obtains temperature, humidity, wind speed, and sunshine intensity data through a meteorological data acquisition device, and predicts future fluctuations in the system's cooling and heating demand based on these data. In addition, the system also takes into account holidays and large-scale activities and events in the park. These special circumstances will cause significant fluctuations in the park's overall cooling and heating demand. By collecting and analyzing these data in advance, it can ensure that the system can still stably and efficiently supply energy during peak demand periods.

[0031] In the process of realizing the coordinated work of the heat storage body and the ground source heat pump system, the present invention focuses on the optimal utilization of energy. The heat storage body realizes the load regulation function by storing excess heat at low load and releasing the stored energy at high load. In this way, even during the peak period of cooling and heating demand, the system can still maintain stable operation, avoiding the instability and energy waste caused by load fluctuations in traditional systems.

[0032] In summary, the present invention collects and analyzes multi-dimensional information on soil characteristics, user heating and cooling requirements, heat storage location, and meteorological data, and combines the coordinated work of the ground source heat pump system and the heat storage body to ultimately achieve energy efficiency optimization of the system. This optimized design can effectively reduce energy costs, ensure soil thermal balance, and ensure stable operation of the system.

[0033] In S2, the specific steps of load calculation and parameter range calculation are as follows: Based on the data collected in the initial design part, considering the seasonality and trend of heat consumption data, the time series model ARIMA is selected to predict the load and generate a representative load curve: Data preprocessing: cleaning data, deleting and repairing missing, abnormal and erroneous data points; data normalization; Data set division: use 70% of the data as the training set and 30% of the data as the test set; Model training: Apply the ARIMA model to train the training set, adjust the learning rate, number of iterations, and regularization parameter model parameters; Model verification: Use the test set to verify the trained ARIMA model, and calculate the square error: MSE, root mean square error: RMSE, and determination coefficient: R2; Output: The best model is retrained using the entire data set to obtain the final load curve; According to the obtained load curve, the hourly heat load is divided into m intervals according to the value, and the hourly cooling load is divided into n intervals according to the value. The load change rate of each interval does not exceed 200kJ / h2; the maximum value of the hourly cooling and heating load in each interval is used as the heat load of the interval. and cooling load , and count the cooling and heating loads of each interval , the corresponding time frequency and , where i = 1, 2, ..., m; j = 1, 2, ..., n; the maximum heat load of the end user is the heat load in m heat load intervals The maximum value among; the maximum cooling load of the end user is the cooling load in n cooling load intervals The maximum value in ; In this step, the collected historical data is first analyzed, mainly including the historical data of heating and cooling demand of users in the park. These data usually have the characteristics of seasonal and trend changes. For this reason, the time series model ARIMA is used for load forecasting. The representative load curve generated by the ARIMA model can effectively predict the system load in the future. In order to ensure the accuracy of the data and the stability of the model, data preprocessing includes cleaning data, deleting and repairing missing values, and excluding abnormal and erroneous data. The data is then normalized to improve the convergence speed and accuracy of the model; in order to ensure the generalization ability of the model, the collected historical data is divided into a training set and a test set, of which 70% of the data is used for model training and 30% of the data is used for model verification. During the training process, the learning rate, number of iterations and regularization coefficient parameters are adjusted to improve the fitting effect of the model. By optimizing the key parameters of the ARIMA model, such as the autoregressive order, the difference order, and the moving average order, the prediction accuracy of the model is further improved; the trained ARIMA model is verified using a test set, and commonly used evaluation indicators include mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R²). These indicators can effectively measure the accuracy and stability of the model to ensure its suitability for actual load forecasting. According to the test results, the best model parameters are selected to further improve the performance of the model; after completing the preliminary model training and verification, the final ARIMA model is retrained using the entire data set, and a load forecast curve is generated. The load forecast results provide data support for further optimization of the system design, ensuring that the heat storage body and the cold and heat source system can adjust the working mode according to the actual load changes, avoiding system over-design and energy waste.

[0034] In S3, the specific steps for calculating the heat absorption and release of the soil are as follows: According to the physical properties of the heat storage material collected in the initial design part and the thermal conductivity characteristics of the soil, the load curve calculates the value range of the drilling depth L of the buried pipe heat exchanger [Hmin, Hmax] and the value range of the heat storage body capacity V [0, Vmax]; the heat storage cooperative ground source heat pump system is a parallel system, and the value range of the drilling depth L is [Hmin, Hmax]: ; The value range of heat storage capacity V is [0, Vmax]:

[0035] ; In the formula, is the maximum heat load of the end user; is the maximum cooling load of the end user; is the initial temperature of the rock and soil in the buried pipe area; is the designed average temperature of the heat transfer medium in the ground heat exchanger; is the wall thermal resistance of the U-tube; is the convection heat transfer thermal resistance between the heat transfer medium and the inner wall of the U-shaped tube; Thermal resistance of the drilling grouting backfill material; is the soil thermal resistance; Additional thermal resistance caused by short-term continuous pulse load; The share of heating operation; is the cooling operation share; COP is the heating performance coefficient of the heat pump unit; EER is the cooling performance coefficient of the heat pump unit; a is the safety factor of the heat storage unit operation; is the density of the heat storage medium (kg / m³); is the maximum volume of the heat storage body (m³); is the specific heat capacity of the heat storage medium (J / kg·°C); is the temperature change (°C); the amount of heat absorbed by the ground heat exchanger to the soil throughout the year and heat release The calculation formula is: ; In the formula, The underground heat exchanger absorbs heat from the soil in winter. The heat released to the soil by the underground heat exchanger in summer is designed. is the total heat absorbed by the ground heat exchanger from the soil throughout the year, It is the total heat released to the soil by the ground heat exchanger throughout the year; is the maximum heat load of the end user; is the maximum cooling load of the end user; Designed heat absorption per unit time of underground heat exchanger from soil in winter The designed heat release per unit time of the buried pipe heat exchanger to the soil in summer The formula is: ; First, collect the physical parameters related to the heat storage material, such as the density, thermal conductivity and specific heat capacity of the heat storage medium. These parameters are crucial for calculating the heat absorption and release of the heat storage body, and can accurately describe the heat transfer capacity of the heat storage body under different working conditions. At the same time, the thermal conductivity and heat absorption characteristics of the soil will also affect the overall heat transfer effect of the ground pipe heat exchanger, so these parameters need to be fully measured and analyzed; then, combined with the heat absorption and heat release capacity required by the heat storage body, calculate its optimal volume range. The volume of the heat storage body and the drilling depth of the ground pipe will be dynamically optimized according to the user's cold and hot load requirements. In order to ensure the effective operation of the system, the volume of the heat storage body needs to be set within a reasonable range to ensure that it can store and release enough heat. At the same time, the drilling depth of the ground pipe heat exchanger must also be within the set range to ensure the heat exchange effect; through the analysis of the user's cold and hot loads, the collaborative work of the heat storage body and the ground source heat pump system is designed to achieve the load balance of the system. The heat exchanger absorbs heat from the soil and stores it in the heat storage body. When the demand for heat and cold fluctuates, the system can reasonably adjust the working state of the heat exchanger according to the capacity of the heat storage body and the temperature change of the soil to ensure the stability of the heat supply and avoid the decrease in system efficiency caused by fluctuations in heat and cold loads. In the design of the heat storage body's heat absorption and release process, it is necessary to consider the operating efficiency of the heat pump, the heat exchange efficiency of the buried pipe, and the changes in user load. By optimizing the system design parameters, the energy efficiency of the heat storage body can be improved and the energy consumption of the system can be reduced. At the same time, the dynamic adjustment mechanism of the heat storage body and the buried pipe heat exchanger enables the system to adapt to the load requirements under different working conditions, ensuring the stability and economy of long-term operation.

[0036] In S4, the specific steps are as follows: extract the relative positions of the heat storage body and the ground and the heat storage body and the heat pump as input features, which are represented as the distance between the heat storage body and the ground The relative distance between the heat storage body and the heat pump ; These characteristics directly affect the energy consumption and heat transfer efficiency of the heat storage body, and are subsequently used as input variables in the optimization algorithm. and heat transfer efficiency Determine the optimization goal: ; First, in the process of system optimization design, it is necessary to extract the distance between the heat storage body and the ground and the relative distance between the heat storage body and the heat pump as input features. The distance between the heat storage body and the ground reflects the heat conduction effect between the heat storage body and the soil, while the distance between the heat storage body and the heat pump affects the energy efficiency of the system. These features directly affect the energy consumption and heat transfer efficiency of the heat storage body, and will be used as input variables in the optimization algorithm later; after these features are extracted, they are input into the optimization model of the system as key parameters affecting the thermal performance of the heat storage body. The goal of system optimization is to reduce energy loss and improve heat transfer efficiency by reasonably setting the relative position of the heat storage body. The system comprehensively analyzes energy consumption and heat transfer efficiency to determine the heat storage body layout plan that makes the system energy consumption the lowest and the heat transfer efficiency the highest; the optimization goal of the system is to reduce total energy consumption and improve heat transfer efficiency at the same time. The focus of the optimization design process is to ensure that the energy consumption and heat transfer efficiency of the heat storage body at different positions are in a balanced state. By continuously adjusting the relative position of the heat storage body, the ground and the heat pump, the best position combination that can reduce the total energy consumption of the system and improve the heat transfer efficiency is found. After the optimization is completed, the location of the heat storage body can effectively meet the changes in the demand for heat and cold in different application scenarios; by optimizing the relative distance between the heat storage body and various parts of the system, it can not only reduce the energy loss in the heat and cold transfer, but also improve the working efficiency of the heat storage body, thereby ensuring that the energy consumption is reduced while improving the overall performance of the system. Ultimately, the optimized design ensures that the heat storage body and the ground source heat pump system can work together efficiently.

[0037] In S5, the specific steps are as follows: determine the optimal drilling depth H and the optimal heat storage volume V, select The minimum and lowest energy cost are used as the optimization objective function, as follows:

[0038] ; Where W is the total power consumption of the heat storage and ground source heat pump system; p is the unit electricity price, and the optimization variables are the drilling depth H and the heat storage volume V; The constraints are: ; The above objective function is converted into a specific solvable optimization equation, which is solved using the whale algorithm. The final optimal solution is the optimal underground pipe drilling depth H and heat storage capacity V of the ground source heat pump system considering heat storage synergy under the premise of meeting system reliability. The specific solution implementation steps are as follows: Use binary coding to randomly generate an initial whale group, which contains N whales, and each whale is characterized by a set optimization variable; define the fitness function: based on the objective function in step 6, that is, Minimize and minimize energy cost, construct fitness function; substitute each whale into fitness function to calculate its fitness, and judge whether it meets the optimization criteria; determine the leading whale: in the current whale group, select the whale with the highest fitness as the leading whale; update the position of the whale: simulate the search and pursuit behavior of the whale, simulate the whale tracking the leading whale and prey hunting behavior: calculate the relative distance between each whale and the leading whale, and update the position of the solution replacement and elimination: if the whale in the new position has a higher fitness, replace the whale in the original position; if the fitness is low, keep the original position; judge the stopping condition: if the preset number of iterations is reached and the fitness change is less than the threshold, stop the algorithm; through several rounds of iterations, extract the whale with the highest fitness, that is, obtain the optimal heat storage volume V and drilling depth H under the premise of meeting the system reliability; in the optimization design, first determine the optimization goal of the system, that is, by adjusting the capacity of the heat storage body and the drilling depth of the buried pipe, to minimize energy consumption and system operation cost. To achieve this goal, the system will automatically adjust the volume of the heat storage body and the range of drilling depth according to the user's demand for cold and hot loads and energy costs, so that it is within the optimal value range; during the optimization process, the system sets the value range of the heat storage body capacity and drilling depth. The volume of the heat storage body needs to be kept between the specified minimum and maximum values, and the drilling depth is dynamically adjusted according to the heat exchange demand of the heat storage body to ensure that the system maximizes energy efficiency and minimizes costs while meeting the load demand; in the specific optimization process, the present invention adopts an intelligent algorithm-whale algorithm. The algorithm simulates the behavior of whale groups to find the optimal solution. Initially, a group of whale individuals are randomly generated, and each whale represents a potential combination of heat storage body volume and drilling depth. The fitness of each whale will be evaluated based on the energy consumption and operating cost of the system; the system judges its performance under the current design scheme based on the fitness of each whale individual. If the new whale position shows a higher fitness, it will replace the whale in the original position. As the number of iterations increases, the fitness of the whale gradually approaches the optimal value. If the set convergence condition is reached, the iteration stops and the optimal combination of heat storage capacity and drilling depth is output; when the fitness change of individuals in the whale group is less than the predetermined threshold, or the maximum number of iterations is reached, the algorithm stops running and finally outputs the optimal combination of heat storage volume and drilling depth. At this point, the system has achieved the optimization effect of the lowest energy consumption and the lowest economic cost, meeting the user's cooling and heating load requirements and ensuring the stable operation of the system under various load conditions. The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for collaborative optimization of heat storage in cold and hot source systems, characterized in that: The following steps are involved: Collect measurement data; Carry out load calculation and parameter range calculation; Calculate the heat absorption and release of soil; Optimize the location of the heat storage body; Determine the optimal drilling depth and optimal thermal storage volume.

2. The method for collaborative optimization of heat storage in cold and hot source systems according to claim 1, characterized in that: The specific steps for collecting data are as follows: Collect soil properties; soil type, thermal conductivity, thermal diffusivity, density and specific heat capacity; used to measure soil type, thermal conductivity, thermal diffusivity, density and specific heat capacity, the equipment includes sensors, data loggers and analysis modules, used to calculate the physical parameters of the heat storage body based on soil characteristics. The expression is: ,in, The heat stored and released by the heat storage body. is the thermal conductivity of the soil, is the heat transfer area, is the temperature difference, is the thickness of the heat storage body, For time; Calculation of the physical properties of the heat storage body; The thermal conductivity measurement of the soil property measurement device is based on the following formula: ,in, For the heat transferred, is the heat transfer area, is the temperature difference, The thermal conductivity of the soil is determined by measuring the amount of heat transferred under known conditions. Collect historical data on heating and cooling used by users of different business formats in the park; the equipment for collecting heating and cooling data includes flow meters and temperature sensors. The flow meters are used to measure the volume of fluid passing through the heat exchanger, and the temperature sensors are used to measure the temperature difference between the inlet and outlet of the fluid. The heating and cooling consumption is calculated based on the following formula: ,in, For the user's calorie consumption, is the fluid mass flow rate, is the specific heat capacity of the fluid, is the temperature difference between the inlet and outlet of the fluid; Collect system efficiency and energy consumption data at different heat storage positions; the system energy consumption analysis unit calculates the total energy consumption based on the power consumption data measured at different heat storage positions using the following formula: , is the total energy consumption, is the system power, For the operation time, the layout of the heat storage bodies is optimized by combining the energy consumption data at each heat storage body location; Collect meteorological data, holiday information, park activities and events; meteorological data collection equipment records temperature, humidity, wind speed and sunshine intensity in real time through meteorological sensors. The data is used to predict the fluctuation of the park's cooling and heating demand and estimate the heat load required by the system according to the following formula; , is the system load, is the heat transfer coefficient of the building envelope, is the area of ​​the enclosure structure, is the temperature difference between indoor and outdoor, For time.

3. The method for collaborative optimization of heat storage in cold and hot source systems according to claim 1, characterized in that: The specific steps of load calculation and parameter range calculation are as follows: Based on the data collected in the initial design part, considering the seasonality and trend of heat consumption data, the time series model ARIMA is selected to predict the load and generate a representative load curve: Clean the data to remove and repair missing, abnormal and erroneous data points; Data normalization; Use 70% of the data as the training set and 30% of the data as the test set; model training: Apply the ARIMA model to train the training set, adjust the learning rate, number of iterations, and regularization parameter model parameters; Model verification: Use the test set to verify the trained ARIMA model, and calculate the square error: MSE, root mean square error: RMSE, and determination coefficient: R2; The best model is retrained using the entire dataset to obtain the final load curve; According to the obtained load curve, the hourly heat load is divided into m intervals according to the value, and the hourly cooling load is divided into n intervals according to the value. The load change rate of each interval does not exceed 200kJ / h2; the maximum value of the hourly cooling and heating load in each interval is used as the heat load of the interval. and cooling load , and count the cooling and heating loads of each interval , the corresponding time frequency and , where i = 1, 2, ..., m; j = 1, 2, ..., n; the maximum heat load of the end user is the heat load in m heat load intervals The maximum value among; the maximum cooling load of the end user is the cooling load in n cooling load intervals The maximum value in .

4. The method for collaborative optimization of heat storage in cold and hot source systems according to claim 1, characterized in that: The specific steps for calculating the heat absorption and release of soil are as follows: According to the physical properties of the heat storage material collected in the initial design part and the thermal conductivity characteristics of the soil, the load curve calculates the value range of the drilling depth L of the buried pipe heat exchanger [Hmin, Hmax] and the value range of the heat storage body capacity V [0, Vmax]; the heat storage cooperative ground source heat pump system is a parallel system, and the value range of the drilling depth L is [Hmin, Hmax]: ； The value range of heat storage capacity V is [0, Vmax]: ; In the formula, is the maximum heat load of the end user; is the maximum cooling load of the end user; is the initial temperature of the rock and soil in the buried pipe area; is the designed average temperature of the heat transfer medium in the ground heat exchanger; is the wall thermal resistance of the U-tube; is the convection heat transfer thermal resistance between the heat transfer medium and the inner wall of the U-shaped tube; Thermal resistance of the drilling grouting backfill material; is the soil thermal resistance; Additional thermal resistance caused by short-term continuous pulse load; The share of heating operation; is the cooling operation share; COP is the heating performance coefficient of the heat pump unit; EER is the cooling performance coefficient of the heat pump unit; a is the safety factor of the heat storage unit operation; is the density of the heat storage medium (kg / m³); is the maximum volume of the heat storage body (m³); is the specific heat capacity of the heat storage medium (J / kg·°C); is the temperature change (°C); the amount of heat absorbed by the ground heat exchanger to the soil throughout the year and heat release The calculation formula is: ； In the formula, The underground heat exchanger absorbs heat from the soil in winter. The heat released to the soil by the underground heat exchanger in summer is designed. is the total heat absorbed by the ground heat exchanger from the soil throughout the year, It is the total heat released to the soil by the ground heat exchanger throughout the year; is the maximum heat load of the end user; is the maximum cooling load of the end user; Designed heat absorption per unit time of underground heat exchanger from soil in winter The designed heat release per unit time of the buried pipe heat exchanger to the soil in summer The formula is: .

5. The method for collaborative optimization of heat storage in cold and hot source systems according to claim 1, characterized in that: The specific steps are as follows: Extract the relative positions of the heat storage body and the ground and the heat storage body and the heat pump as input features, which are represented as the distance between the heat storage body and the ground The relative distance between the heat storage body and the heat pump ; These characteristics directly affect the energy consumption and heat transfer efficiency of the heat storage body, and are subsequently used as input variables in the optimization algorithm. and heat transfer efficiency Determine the optimization goal: .

6. The method for collaborative optimization of heat storage in cold and hot source systems according to claim 1, characterized in that: The specific steps are as follows: determine the optimal drilling depth H and the optimal heat storage volume V, select The minimum and lowest energy cost are used as the optimization objective function, as follows: ； Where W is the total power consumption of the heat storage and ground source heat pump system; p is the unit electricity price, and the optimization variables are the drilling depth H and the heat storage volume V; The constraints are: ； The above objective function is converted into a specific solvable optimization equation, which is solved using the whale algorithm. The final optimal solution is the optimal underground pipe drilling depth H and heat storage capacity V of the ground source heat pump system considering heat storage synergy under the premise of meeting system reliability. The specific solution implementation steps are as follows: An initial whale group is randomly generated using binary coding. The group contains N whales, and each whale is characterized by a set optimization variable. Define the fitness function: Based on the objective function in step 6, that is Minimize and minimize energy cost, construct fitness function; Substitute each whale into the fitness function to calculate its fitness and determine whether it meets the optimization criteria; determine the leading whale: in the current whale group, select the whale with the highest fitness as the leading whale; update the whale position: simulate the whale's search and pursuit behavior, simulate the whale's tracking of the leading whale and prey predation behavior: calculate the relative distance between each whale and the leading whale, and update the solution position replacement and elimination: if the whale in the new position has a higher fitness, replace the whale in the original position; If the fitness is low, keep the original position; judge the stopping condition: if the preset number of iterations is reached and the fitness change is less than the threshold, stop the algorithm; after several rounds of iterations, extract the whale with the highest fitness, that is, obtain the optimal heat storage volume V and drilling depth H under the premise of meeting the system reliability.

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