Fine evaluation method for long-term operation performance of ground source heat pump system

Abstract

The application provides a long-term operation performance fine evaluation method of a ground source heat pump system, uses design parameter data groups for simulation calculation, establishes a buried pipe heat exchanger-heat pump-building coupled heat transfer model database, establishes a mapping of design parameters and operation results, forms a long-term operation performance fitting neural network model of the ground source heat pump system, inputs the design parameters, uses the neural network model to fit and predict the energy efficiency ratio, energy saving and emission reduction and the ground temperature of the buried pipe area, and finely evaluates the applicability, economy and environmental protection of the long-term operation of the system. The application is used for evaluating the energy efficiency ratio, energy saving and emission reduction and the ground temperature of the buried pipe area of the ground source heat pump system in a life cycle, and has the advantages of fast calculation speed, high calculation precision, a comprehensive evaluation system and the like.

Description

Technical Field

[0001] This invention relates to the field of predicting and evaluating the operational efficiency and economic and environmental performance of ground source heat pump systems, specifically to a refined evaluation and calculation method for the applicability, economy, and environmental friendliness of ground source heat pump systems in long-term operation. Background Technology

[0002] With the introduction of dual-carbon goals, the application of major energy-saving and carbon-reducing technologies has become a powerful support for promoting green development in urban and rural areas. Geothermal energy is a clean, low-carbon, abundant, widely distributed, promising, and potentially huge new renewable energy source. It is estimated that the total amount of geothermal energy is equivalent to 140 million times the heat energy released from all the coal buried within the Earth. In the energy consumption structure, every 1% increase in geothermal energy utilization is equivalent to replacing 37.5 million tons of standard coal, reducing carbon dioxide emissions by approximately 94 million tons, sulfur dioxide emissions by approximately 900,000 tons, and nitrogen oxide emissions by approximately 260,000 tons. Ground source heat pump technology utilizes geothermal energy in the shallow soil of the Earth's surface as an energy source to achieve efficient and environmentally friendly energy-saving technology for building heating in winter and cooling in summer.

[0003] Ground source heat pumps are the fastest-growing method of geothermal energy utilization and have been applied in numerous engineering projects. However, they have encountered the following technical problems during long-term operation: The understanding of shallow geothermal resources is unclear, making it impossible to accurately determine the suitability of the land, energy efficiency, and operating cost advantages before the construction and application of a ground source heat pump system; the performance of ground source heat pump systems deteriorates year by year, with uneven building heating and cooling loads leading to uneven energy exchange between the soil and the heat exchanger, resulting in uncontrolled soil heating and cooling balance and affecting heat exchange performance; currently, the design of buried pipe heat exchangers for ground source heat pump systems using national standards has a wide range of values, making it impossible to determine the efficiency, economic, and environmental performance of the buried pipe heat exchanger design scheme. Therefore, there is an urgent need to design a refined evaluation and calculation method for the long-term suitability, economy, and environmental friendliness of ground source heat pump systems. Summary of the Invention

[0004] Purpose of the invention: In order to evaluate the energy efficiency ratio, energy saving and emission reduction, and ground temperature in the buried pipe area of ​​a ground source heat pump system over its life cycle, this invention proposes a refined evaluation and calculation method for the long-term applicability, economy, and environmental protection of a ground source heat pump system.

[0005] Technical Solution: To solve the above-mentioned technical problems, the present invention provides a method for refined evaluation of the long-term operating performance of a ground source heat pump system, comprising:

[0006] Step 1: Use the design parameter data set for simulation calculations to establish a database of coupled heat transfer models of buried pipe heat exchanger-heat pump-building.

[0007] Step 2: Establish a mapping between design parameters and operating results to form a neural network model for fitting the long-term operating performance of the ground source heat pump system;

[0008] Step 3: Input design parameters, use a neural network model to fit and predict the energy efficiency ratio, energy saving and emission reduction, and ground temperature in the buried pipe area, and conduct a detailed evaluation of the system's long-term applicability, economy, and environmental friendliness.

[0009] The database of the buried pipe heat exchanger-heat pump-building coupled heat transfer model records the energy efficiency ratio, energy saving and emission reduction, and ground temperature operation results of the simulation model under design parameters such as the hourly characteristics of the annual load of different buildings, the performance parameters of the heat pump unit, and the thermophysical properties of the soil layer. The long-term operating performance fitting neural network model of the ground source heat pump system realizes the fitting mapping relationship between the design parameters and the operating results. The long-term operating performance prediction and evaluation algorithm of the ground source heat pump system can realize the prediction and refined evaluation of energy efficiency ratio, energy saving and emission reduction, and ground temperature of the buried pipe area under different parameter levels.

[0010] The database of coupled heat transfer models of ground source heat pump systems (GSPs) and buildings (BSPs) identifies key influencing factors on the long-term performance of GSP systems, including hourly characteristics of building annual load, performance parameters of heat pump units, and thermophysical properties of the soil. A full-scale, full-cycle numerical simulation model of vertical buried pipe heat exchangers and a coupled heat transfer model of GSPs and buildings (BSPs) are established. Energy efficiency ratio, energy saving and emission reduction, and ground temperature in the buried pipe area are calculated. An algorithm generates design parameter data sets for the corresponding levels of each influencing factor in the coupled model. Large-scale simulation calculations are then performed using the model, resulting in a database of influencing factors and operational results for the long-term performance of GSP systems.

[0011] The long-term operating performance fitting neural network model of the ground source heat pump system includes the energy efficiency ratio, energy saving and emission reduction, and ground temperature operation results of each model in the buried pipe area throughout the entire period of the large-scale simulation calculation. The design parameters and operating results of the large-scale calculation are used as inputs and outputs to train the neural network model, and the neural network model is used to establish a fitting relationship between the design parameters and the operating results.

[0012] When users calculate the long-term operational performance prediction and evaluation algorithm of a ground source heat pump system, they input relevant system design parameters. The algorithm then fits and predicts the energy efficiency ratio, energy saving and emission reduction, and corresponding ground temperature values ​​in the buried pipe area during the operating cycle, and conducts a refined evaluation of the applicability, economy, and environmental protection of the ground source heat pump system.

[0013] When in use, the refined evaluation method for the long-term operating performance of a ground source heat pump system is a predictive assessment method for the operating efficiency and economic and environmental benefits of the ground source heat pump system, including the following steps:

[0014] (1) Identify the key influencing factors of the long-term operating performance of the ground source heat pump system, including the hourly characteristics of the building's annual load, the performance parameters of the heat pump unit, and the thermophysical properties of the soil. Establish a full-size, full-cycle numerical simulation model of the ground source heat pump vertical buried pipe heat exchanger and verify the long-term operating reliability of the model.

[0015] (2) Establish a coupled heat transfer model of buried pipe heat exchanger-heat pump-building, calculate energy efficiency ratio, energy saving and emission reduction and ground temperature in buried pipe area, generate design parameter data group of each influencing factor of the coupled model, call the model to carry out large-scale simulation calculation, and form a database of long-term operating performance influencing factors and operating results of ground source heat pump system.

[0016] (3) Use the design parameters and running results of large-scale computation to form a database as input and output to train the neural network model, and use the neural network model to establish a fitting relationship between the design parameters and the running results;

[0017] (4) When users calculate the applicability, economy and environmental protection of the ground source heat pump system in the long term, they input the relevant design parameters of the system. The evaluation algorithm then fits and predicts the energy efficiency ratio, energy saving and emission reduction and the corresponding ground temperature in the buried pipe area during the operating cycle, and conducts a refined evaluation of the applicability, economy and environmental protection of the ground source heat pump system.

[0018] Invention Principle: The database of coupled heat transfer models of buried pipe heat exchangers, heat pumps, and buildings provides calculation results for the long-term operating energy efficiency ratio, energy saving and emission reduction, and ground temperature in the buried pipe area of ​​the ground source heat pump system under parameters such as hourly characteristics of annual building loads, heat pump unit performance parameters, and soil thermophysical properties. A neural network model for fitting the long-term operating performance of the ground source heat pump system establishes a fitting mapping relationship between the above design parameters and operating results. When users calculate the applicability, economy, and environmental friendliness of the ground source heat pump system in the long-term operation, the long-term operating performance prediction and evaluation algorithm of the ground source heat pump system, by inputting relevant system design parameters, will have the algorithm fit and predict the corresponding values ​​of energy efficiency ratio, energy saving and emission reduction, and ground temperature in the buried pipe area within the operating cycle, and conduct a refined evaluation of the applicability, economy, and environmental friendliness of the ground source heat pump system.

[0019] Beneficial effects: The evaluation method of this invention first identifies the key influencing factors of the long-term operating performance of the ground source heat pump system, establishes a full-size, full-cycle numerical simulation model of the ground source heat pump vertical buried pipe heat exchanger, and establishes a coupled heat transfer model of the buried pipe heat exchanger-heat pump-building to calculate the energy efficiency ratio, energy saving and emission reduction, and ground temperature in the buried pipe area. Then, the algorithm generates a set of design parameter data for each influencing factor of the coupled model at the corresponding level, calls the model to carry out large-scale simulation calculations, extracts the energy efficiency ratio, energy saving and emission reduction, and ground temperature in the buried pipe area of ​​each model for the entire time period in the large-scale simulation calculations, and uses a neural network model to establish a fitting relationship between the design parameters and the operating results.

[0020] Compared to traditional design estimation methods for ground source heat pump systems, this invention enables the evaluation and calculation of the energy efficiency ratio, energy saving and emission reduction, and ground temperature in the buried pipe area for long-term operation of the ground source heat pump system. It can calculate the system's long-term applicability, economy, and environmental friendliness, and boasts advantages such as fast calculation speed, high accuracy, and wide coverage. This method replaces the traditional annual estimation with precise hourly calculations, simplifying the specialized simulation modeling work into a simple method that only requires parameter input for rapid calculation. Simultaneously, it reduces the calculation time for the simulation model to calculate the dynamic operation results for the entire year from 8-12 hours to instant feedback within 2 seconds. Attached Figure Description

[0021] Figure 1 This is a flowchart of the evaluation calculation method according to an embodiment of the present invention;

[0022] Figure 2 This is a numerical simulation model of the coupled heat transfer of the buried pipe heat exchanger-heat pump-building according to an embodiment of the present invention.

[0023] Figure 3 The experimental and measured results in this embodiment of the invention are compared to verify the long-term operational reliability of the model;

[0024] Figure 4 This is a flowchart of the coupling process of underground pipe heat exchanger-heat pump-building;

[0025] Figure 5 These are the inlet and outlet water temperatures calculated by the simulation model;

[0026] Figure 6 These are the ground temperatures in different regions calculated by the simulation model;

[0027] Figure 7 It is the energy efficiency ratio calculated by the simulation model;

[0028] Figure 8 It is the power consumption, energy saving, and emission reduction calculated by the simulation model;

[0029] Figure 9 It is a performance parameter-run result database;

[0030] Figure 10 This is the training result of a neural network that uses energy as the output parameter.

[0031] Figure 11 It is the result of training the neural network with the central borehole as the output parameter;

[0032] Figure 12 This is the interface for the simulation model's prediction results. Detailed Implementation

[0033] Example:

[0034] This embodiment presents a refined evaluation and calculation method for the applicability, economy, and environmental friendliness of a ground source heat pump system in long-term operation. This includes a database of coupled heat transfer models of buried pipe heat exchangers, heat pumps, and buildings; a neural network model for fitting the long-term operating performance of the ground source heat pump system; and an algorithm for predicting and evaluating the long-term operating performance of the ground source heat pump system. The database of coupled heat transfer models of buried pipe heat exchangers, heat pumps, and buildings includes a database of hourly characteristics of annual building loads, a database of heat pump unit performance parameters, a database of soil thermophysical properties, a full-size, full-cycle numerical simulation model of the vertical buried pipe heat exchanger, a database of coupled heat transfer models of buried pipe heat exchangers, a database of energy efficiency ratio calculation results, a database of energy saving and emission reduction calculation results, and a database of ground temperature calculation results for the buried pipe area. The neural network model for predicting the long-term operating performance of the ground source heat pump system includes a neural network model of ground source heat pump system design parameters and operating results, as well as model configuration parameters. The algorithm for predicting and evaluating the long-term operating performance of the ground source heat pump system includes an interface for predicting and evaluating the operating performance of the ground source heat pump system, and methods for evaluating energy efficiency and energy saving and emission reduction.

[0035] The specific operating procedures are as follows: Figure 1 As shown, it includes the following steps:

[0036] (1) Through research, the key influencing factors of the long-term operating performance of the ground source heat pump system were identified. These mainly include the hourly characteristics of the annual load at the building end, which can be a complete hourly function or simplified to a sine function. Taking the sine function simplification as an example, the following parameters are included: ① maximum summer load, ② maximum winter load, ③ number of operating days in summer, ④ number of operating days in winter, and ⑤ daily operating time. The performance parameters of the heat pump unit at the equipment end are reflected in the fact that the energy efficiency ratio of different models of heat pump units in summer and winter exhibits a quadratic function relationship with the inlet water temperature, resulting in the following parameters: ⑥ coefficient of the quadratic term of the summer energy efficiency fitting function, ⑦ coefficient of the linear term of the summer energy efficiency fitting function, ⑧ coefficient of the constant term of the summer energy efficiency fitting function, ⑨ coefficient of the quadratic term of the winter energy efficiency fitting function, and ⑩ coefficient of the linear term of the winter energy efficiency fitting function. The coefficients of the winter energy efficiency fitting constant, as well as the thermophysical properties of the soil at the ground source end and the design scheme for the vertical buried pipe heat exchanger layout, include the following parameters. Overall thermal conductivity of soil Initial ground temperature, Pipe spacing, Pipe burial depth, Number of pipe rows Number of buried pipes Summer single-orifice flow rate Winter single-orifice flow rate.

[0037] Based on the above parameters, the numerical simulation model of coupled heat transfer between the ground source heat pump buried pipe heat exchanger, the heat pump, and the building is established as follows: Figure 2 The long-term operational reliability of the model was verified by comparing experimental and measured results. Figure 3 .

[0038] (2) The iterative calculation steps of the above-established buried pipe heat exchanger-heat pump-building coupled heat transfer model are as follows:

[0039] ① Assume that the initial outlet water temperature of the buried pipe in the heat transfer model of the buried pipe heat exchanger is the initial ground temperature.

[0040] ② Ignoring energy loss in the pipeline, the inlet water temperature of the heat pump unit is equal to the outlet water temperature of the buried pipe, Equation (1);

[0041] t in，unit =t out，pipe (1)

[0042] ③Calculate the energy efficiency ratio (EER) or COP based on the inlet water temperature of the heat pump unit using equations (2) and (3);

[0043]

[0044]

[0045] ④ The cooling / heating capacity of the heat pump unit is equal to the building's summer cooling load / winter heating load, as shown in equations (4) and (5);

[0046] Load sum =Q c (4)

[0047] Load win =Q h (5)

[0048] ⑤ Calculate the outlet water temperature of the heat pump unit after heat exchange according to equations (6) and (7);

[0049]

[0050]

[0051] ⑥ Ignoring energy loss in the pipeline, the inlet water temperature of the buried pipe is equal to the outlet water temperature of the heat pump unit, Equation (8);

[0052] t in，pipe =t out，unit (8)

[0053] ⑦ Repeat steps ②-⑥ until the calculation cycle ends.

[0054] In equations (1)-(8), t in，unit The inlet water temperature of the heat pump unit is ℃; t out，unit The outlet water temperature of the heat pump unit is ℃; t in，pipe The inlet water temperature of the buried pipe is ℃; t out，pipet represents the outlet water temperature of the buried pipe, in °C; EER represents the summer cooling energy efficiency ratio of the ground source heat pump unit, dimensionless; COP represents the summer cooling energy efficiency ratio of the ground source heat pump unit, dimensionless; in，unit ... sum For the building's summer cooling load, kW; Load win Building winter heating load, kW; Q c Q represents the cooling capacity of a heat pump unit, expressed in kW. h The heating capacity of the heat pump unit is expressed in kW; C. p is the specific heat of the circulating fluid, kJ / (kg·℃); M is the mass flow rate of the fluid, kg / s.

[0055] The coupling flow diagram of buried pipe heat exchanger-heat pump-building is as follows: Figure 4 As shown:

[0056] It can accurately calculate inlet and outlet water temperatures, energy efficiency ratios, ground temperatures in different areas, as well as power consumption, energy savings, and emission reductions throughout the entire operating cycle, such as... Figures 5-8 As shown.

[0057] The algorithm generates a set of design parameter data for each influencing factor in the coupled model, calls the model to conduct large-scale simulation calculations, and forms a database corresponding to the long-term operating performance influencing factors of the ground source heat pump system and the operating results. Figure 9 Displays partial information from the database;

[0058] (3) Figure 9 The design parameters and operational results of the large-scale computation database are used as inputs and outputs to train a neural network model. The neural network model is then used to establish a fitting relationship between the design parameters and the operational results. Figure 10 The results of neural network training, using energy savings and the temperature of the central borehole at the end of a year's operation as output parameters, are presented. The correlation coefficients between the training, test, and validation sets all exceed 0.99, indicating that the neural network achieves excellent fitting and accurate predictions.

[0059] (4) When users calculate the long-term applicability, economy, and environmental friendliness of a ground source heat pump system, they input relevant system design parameters. The evaluation algorithm then uses this information to predict the energy efficiency ratio, energy saving and emission reduction, and corresponding ground temperature values ​​in the buried pipe area within the operating cycle. This allows for a refined evaluation of the ground source heat pump system's applicability, economy, and environmental friendliness. In this example, the user inputs the borehole spacing, buried pipe depth, initial ground temperature, summer load, winter load, number of summer operating days, number of winter operating days, daily operating time, overall soil thermal conductivity, number of buried pipe rows, number of buried pipe columns, summer single-hole flow rate, and winter single-hole flow rate. The loaded neural network model then quickly predicts the annual electricity savings and the central borehole temperature after one year of operation. Figure 12 .

[0060] As can be seen from the above examples, this method replaces the annual estimation of the traditional method with accurate hourly calculation, simplifying the professional simulation modeling work into a simple way that only requires input parameters for rapid calculation. The simulation model can calculate the dynamic running results for the whole year and provide feedback in real time within 2 seconds.

Claims

1. A method for refined evaluation of the long-term operating performance of a ground source heat pump system, characterized in that... Includes the following steps: Step 1: Use the design parameter data set for simulation calculations to establish a database of coupled heat transfer models of buried pipe heat exchanger-heat pump-building. Step 2: Establish a mapping between design parameters and operating results to form a neural network model for fitting the long-term operating performance of the ground source heat pump system; Step 3: Input design parameters, use a neural network model to fit and predict the energy efficiency ratio, energy saving and emission reduction, and ground temperature in the buried pipe area, and conduct a detailed evaluation of the system's long-term applicability, economy, and environmental friendliness. In step 1, the database of the buried pipe heat exchanger-heat pump-building coupled heat transfer model records the hourly characteristics of the annual load of different buildings, the performance parameters of the heat pump unit, the energy efficiency ratio, energy saving and emission reduction, and the ground temperature operation results of the simulation model under the design parameters of the soil thermophysical properties. The algorithm generates a set of design parameter data for the corresponding levels of each influencing factor of the coupled model, calls the model to carry out large-scale simulation calculations, and forms a database of long-term operating performance influencing factors and operating results of the ground source heat pump system. In step 2, the long-term operating performance fitting neural network model of the ground source heat pump system includes the energy efficiency ratio, energy saving and emission reduction and ground temperature operation results of each model in the large-scale simulation calculation for all time periods. The design parameters and operating results of the large-scale calculation are used as input and output to train and form a neural network model. The neural network model is used to establish a fitting relationship between the design parameters and the operating results.

2. The method for refined evaluation of the long-term operating performance of a ground source heat pump system according to claim 1, characterized in that... include: (1) Identify the key influencing factors of the long-term operating performance of the ground source heat pump system, including the hourly characteristics of the building's annual load, the performance parameters of the heat pump unit, and the thermophysical properties of the soil. Establish a full-size, full-cycle numerical simulation model of the ground source heat pump vertical buried pipe heat exchanger and verify the long-term operating reliability of the model. (2) Establish a coupled heat transfer model of buried pipe heat exchanger-heat pump-building, calculate energy efficiency ratio, energy saving and emission reduction and ground temperature in buried pipe area, generate design parameter data group of corresponding levels of each influencing factor of the coupled model by algorithm, call the model to carry out large-scale simulation calculation, and form a database corresponding to the long-term operating performance influencing factors and operating results of ground source heat pump system. (3) Use the design parameters and running results of large-scale computation to form a database as input and output to train the neural network model, and use the neural network model to establish a fitting relationship between the design parameters and the running results; (4) When users calculate the applicability, economy and environmental protection of the ground source heat pump system in the long term, they input the relevant design parameters of the system. The evaluation algorithm will then fit and predict the energy efficiency ratio, energy saving and emission reduction and the corresponding ground temperature in the buried pipe area during the operating cycle, and conduct a refined evaluation of the applicability, economy and environmental protection of the ground source heat pump system.

3. The method for refined evaluation of long-term operating performance of a ground source heat pump system according to claim 1, characterized in that: The iterative calculation steps of the buried pipe heat exchanger-heat pump-building coupled heat transfer model are as follows: ① Assume that the initial outlet water temperature of the buried pipe in the heat transfer model of the buried pipe heat exchanger is the initial ground temperature. ② Ignoring energy loss in the pipeline, the inlet water temperature of the heat pump unit is equal to the outlet water temperature of the buried pipe, Equation (1); （1） ③Calculate the energy efficiency ratio using equations (2) and (3) based on the inlet water temperature of the heat pump unit. or ; （2） （3） ④ The cooling / heating capacity of the heat pump unit is equal to the building's summer cooling load / winter heating load, as shown in equations (4) and (5); （4） （5） ⑤ Calculate the outlet water temperature of the heat pump unit after heat exchange according to equations (6) and (7); （6） （7） ⑥ Ignoring energy loss in the pipeline, the inlet water temperature of the buried pipe is equal to the outlet water temperature of the heat pump unit, Equation (8); （8） ⑦ Repeat steps ②-⑥ until the calculation cycle ends; In the formula, The inlet water temperature of the heat pump unit is ℃; The outlet water temperature of the heat pump unit is ℃; The inlet water temperature of the buried pipe is ℃; , where is the outlet water temperature of the buried pipe, in °C; EER is the summer cooling energy efficiency ratio of the ground source heat pump unit, dimensionless; COP is the summer cooling energy efficiency ratio of the ground source heat pump unit, dimensionless. a1, b1, and c1 are the inlet water temperature of the ground source heat pump unit, in °C; a2, b2, and c2 are the cooling performance parameters, dimensionless; a2, b2, and c2 are the heating performance parameters, dimensionless. The building's summer cooling load is measured in kW. Building winter heating load, kW; The cooling capacity of the heat pump unit is expressed in kW. The heating capacity of the heat pump unit is expressed in kW. Specific heat of the circulating fluid, kJ / (kg·℃); denoted as mass flow rate of the fluid, in kg / s.

Images