A method for optimizing design of a U-shaped middle-deep ground heat pump system

Abstract

The application discloses a kind of U-shaped middle-deep buried pipe heat pump system optimization design methods, including carrying out thermal response test and obtaining rock-soil thermal physical property parameters, predict buried pipe dynamic heat extraction amount, carry out heat exchange coupling simulation and obtain prediction buried pipe heat pump system temperature dynamic distribution and determine heat pump machine capacity, according to buried pipe system parameter and the heat pump machine capacity design and build buried pipe heat pump system, run the buried pipe heat pump system and obtain buried pipe heat pump system operation data and fit and obtain heat pump machine performance function, according to the building heat load and buried pipe outlet water temperature of optimization period to be constructed flow optimization objective function and optimize the buried pipe heat pump system and obtain optimal flow parameter, according to the optimal flow parameter adjustment buried pipe heat pump system.This method can significantly reduce the energy consumption of ground source side circulating pump, and lay a theoretical foundation for the large-scale application of U-shaped middle-deep buried pipe heat pump system.

Description

Technical Field

[0001] This invention relates to the field of geothermal energy, and in particular to an optimized design method for a U-shaped medium-deep buried pipe heat pump system. Background Technology

[0002] With the continuous growth of global energy demand and increasing emphasis on environmental protection, medium-deep geothermal energy, as a renewable and clean energy source, plays a crucial role in optimizing the energy structure and achieving sustainable development. The medium-deep U-shaped buried pipe heat exchanger, as a new type of buried pipe heat exchanger, can effectively avoid thermal short-circuit effects, and the horizontal pipe at the bottom can increase the heat exchange area between the circulating fluid and the high-temperature rock and soil at the bottom, thereby improving the heat extraction capacity.

[0003] However, the design and calculation of U-shaped medium-deep buried pipe heat exchangers is a crucial part of the entire system design, construction, and cost calculation. Numerous influencing factors and parameter uncertainties pose significant challenges to the system design. Furthermore, in actual system operation, the real-time heat load of the terminal buildings is often lower than the design value in most cases, resulting in high energy consumption when using a constant flow rate. Therefore, there is an urgent need to design a variable flow rate system optimization method to reduce system energy consumption. This invention proposes an optimization design method for a U-shaped medium-deep buried pipe heat pump system. During the design phase, the characteristics of coupled heat exchange between the buried pipe system and the building user end are considered. The heat pump unit capacity is determined by combining the thermal properties of the soil and rock mass and the predicted dynamic load of the building, thereby designing the U-shaped medium-deep buried pipe heat pump system. The dynamic heat load of the terminal buildings and the dynamic heat exchange process on the ground source side are considered. Based on the goal of energy consumption optimization, the flow rate parameters are optimized to improve the U-shaped medium-deep buried pipe heat pump system. This technology breaks through the limitations of traditional design, significantly improves the scientific nature and adaptability of U-shaped medium-deep buried pipe heat pump system design and optimization, reduces the energy consumption of the ground source side circulation pump, and lays a theoretical foundation for the large-scale application of U-shaped medium-deep buried pipe heat pump system. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized design method for a U-shaped medium-deep buried pipe heat pump system.

[0005] To achieve the above objectives, the present invention is implemented according to the following technical solution:

[0006] This invention includes the following steps:

[0007] Thermal response tests are conducted to obtain thermal response data, and thermal physical parameters of the soil and rock mass are obtained by fitting the thermal response data; the thermal response tests include field engineering thermal response tests and thermal response simulations.

[0008] The predicted building dynamic heat load is obtained and the predicted dynamic heat extraction of the buried pipe is determined. Based on the predicted dynamic heat extraction of the buried pipe, the thermal properties of the soil and rock and the engineering parameters, a heat exchange coupling simulation is performed to obtain the predicted dynamic temperature distribution of the buried pipe heat pump system.

[0009] The heat pump capacity is determined based on the predicted dynamic temperature distribution of the buried pipe heat pump system. The buried pipe heat pump system is then designed and constructed based on the buried pipe system parameters and the heat pump capacity. The buried pipe system parameters include buried pipe structural parameters and buried pipe operating parameters.

[0010] Run the buried pipe heat pump system to obtain the buried pipe heat pump system operation data, and obtain the heat pump machine performance function by fitting the buried pipe heat pump system operation data;

[0011] A flow rate optimization objective function is constructed based on the building heat load and the outlet water temperature of the buried pipe during the period to be optimized. The buried pipe heat pump system is then optimized based on the flow rate optimization objective function to obtain the optimal flow rate parameters. The buried pipe heat pump system is then adjusted based on the optimal flow rate parameters.

[0012] Furthermore, the method for obtaining the thermal physical properties of the soil and rock mass includes:

[0013] A thermal response test dataset is obtained by conducting field engineering thermal response tests; the thermal response test dataset includes the inlet temperature, outlet temperature, and flow rate of the circulating fluid;

[0014] An initial thermal response model is constructed based on the engineering parameters and initial soil and rock thermal properties from the field engineering thermal response test to obtain a thermal response simulation dataset through numerical simulation. The engineering parameters from the field engineering thermal response test include the buried pipe structural parameters, buried pipe operating parameters, and geothermal gradient. The soil and rock thermal properties include the thermal conductivity, specific heat capacity, and thermal diffusivity of the soil and rock. The buried pipe structural parameters include the buried pipe dimensions, borehole structure, and parallel length. The buried pipe operating parameters include the circulating fluid velocity and circulating fluid properties.

[0015] The data deviation between the thermal response test dataset and the thermal response simulation dataset is calculated. Bayesian optimization is used to adjust the initial thermal property parameters of the soil and rock mass to obtain optimized thermal property parameters. The initial thermal response model is then adjusted based on the optimized thermal property parameters to obtain an optimized thermal response model. Numerical simulation is performed to obtain an optimized thermal response simulation dataset. The above Bayesian optimization operation is repeated until the data deviation between the optimized thermal response simulation dataset and the thermal response test dataset is minimized. Finally, the thermal property parameters of the soil and rock mass corresponding to the optimized thermal response model are output. The data deviation includes temperature deviation and heat flux density error.

[0016] Furthermore, the method for obtaining the predicted dynamic temperature distribution of a buried pipe heat pump system includes the following steps:

[0017] Historical building energy consumption data, building attributes, meteorological data, and building operation schedules are acquired. The data is preprocessed to obtain a building energy consumption dataset. A backpropagation (BP) neural network (BPNN) prediction model for building heat load is constructed using this dataset. The mean squared error loss function is used to evaluate the difference between the predicted and actual building heat load values. The Adam optimizer is used to optimize the parameters of the BPNN prediction model. The building energy consumption dataset is divided into a training set and a test set in a 6:3 ratio. The training set is used for the BPNN prediction model, and the test set is used to verify the performance of the BPNN prediction model.

[0018] The building attributes, building operation schedule, and meteorological data of the area to be constructed for the U-shaped medium-deep buried pipe heat pump system are input into a BP neural network prediction model to obtain the predicted dynamic heat load of the building. The hourly load integral method is used to calculate the standard heat pump unit's coefficient of performance (COP). sta The prediction of dynamic building heat load is transformed into the prediction of dynamic heat extraction through underground pipes.

[0019] A heat transfer model between the buried pipe and the soil / rock mass is constructed based on engineering parameters and standard operating parameters of the buried pipe. The predicted dynamic heat extraction of the buried pipe and the thermal properties of the soil / rock mass are used as boundary conditions for heat exchange coupling simulation to obtain the predicted dynamic temperature distribution of the buried pipe heat pump system and the actual dynamic heat extraction of the buried pipe. The dynamic temperature distribution of the buried pipe heat pump system includes the dynamic outlet water temperature and the dynamic return water temperature of the buried pipe.

[0020] Furthermore, the method for determining the capacity of the heat pump includes:

[0021] Based on the predicted dynamic temperature distribution of the buried pipe heat pump system, the actual dynamic heat extraction corresponding to the lowest dynamic outlet water temperature of the buried pipe during the heating season is determined. The design value of the heat pump's coefficient of performance is then calculated based on the actual dynamic heat extraction and the corresponding predicted building dynamic heat load. The expression is as follows:

[0022] COP design =P pre,build / (P pre,build -P act,BHE )

[0023] COP design Pp is the design value for the coefficient of performance of the heat pump. re,build To predict the dynamic heat load of a building, P act,BHE For dynamic heat extraction using actual buried pipes;

[0024] The capacity of the heat pump unit is matched according to the design value of the heat pump unit's coefficient of performance; the capacity of the heat pump unit corresponds uniquely to the rated coefficient of performance of the heat pump unit.

[0025] Furthermore, the method for constructing the traffic optimization objective function includes:

[0026] Acquire operating data of the buried pipe heat pump system; the operating data of the buried pipe heat pump system includes the outlet water temperature of the buried pipe, the building heating temperature, the building heat load, and the energy consumption of the heat pump unit;

[0027] The coefficient of performance (COP) of the heat pump is obtained by calculating the building heat load and the heat pump energy consumption. The heat pump performance function is obtained by fitting the COP and the outlet water temperature of the buried pipe under the same building heating temperature. Multiple temperature constants are obtained by fitting the heat pump performance function under different building heating temperatures, forming a heat pump performance function constant table. The expression for the heat pump performance function is as follows:

[0028] COP T =a1(T build )·T out +a2(T build )

[0029] COP T The building heating temperature T build The coefficient of performance of a heat pump is a1(T) build ), a2(T build T is a temperature constant, which takes different values ​​depending on the building's heating temperature. out The water outlet temperature of the buried pipe is (20+5n)℃, where n={0,1,2,3,4,5,6};

[0030] The building heat load, building heating temperature, and temperature distribution of the buried pipe heat pump system for the period to be optimized are obtained; the temperature distribution of the buried pipe heat pump system includes the return water temperature of the buried pipe and the inlet and outlet water temperatures of the buried pipe.

[0031] The heat pump performance function is retrieved from the heat pump performance function constant table to find the heat pump performance function corresponding to the building heating temperature during the period to be optimized. The heat pump performance coefficient (COP) for the period to be optimized is obtained by inputting the underground pipe outlet water temperature into the heat pump performance function. t ;

[0032] Determine the circulating water pump power P based on the parameters of the underground pipe system. cp The heat output P of the buried pipe heat exchanger is determined based on the parameters of the buried pipe system and the temperature distribution of the buried pipe heat pump system. BHE According to the coefficient of performance (COP) of the heat pump at the optimization time t The heat output P of the buried pipe heat exchanger BHE Determine the power P of the heat pump unit hp According to the power P of the heat pump unit hp and circulating water pump power P cp The objective function for traffic optimization is defined as follows:

[0033] W=∫[P hp (t)+P cp (t)]dt

[0034]

[0035] Where W is the objective function for flow optimization, representing the power consumption of the buried pipe heat pump system during the optimization period, and P... hp (t) represents the power of the heat pump unit at time t to be optimized, P cp (t) represents the circulating water pump power at time t to be optimized, and q f Let η be the flow rate of the circulating fluid per unit time, η be the efficiency of the circulating water pump, f be the Darcy friction coefficient, L be the borehole length, and D be the flow rate of the circulating fluid per unit time. h ρ is the hydraulic diameter of the pipe. f Let v be the density of the circulating fluid, v be the flow rate of the circulating fluid, and c be the flow rate of the circulating fluid. f For the specific heat capacity of the circulating liquid, m f T represents the mass of the circulating fluid per unit time. out (m f (T) represents the outlet water temperature of the buried pipe. out Regarding the mass m of the circulating fluid per unit time f The single-valued function, T, was determined by fitting the results of heat transfer coupling simulation. re This refers to the return water temperature of the buried pipe.

[0036] Furthermore, the method for obtaining the optimal flow parameters includes:

[0037] Based on the objective function value of flow optimization, a particle search algorithm is used to optimize the buried pipe heat pump system. The population size N and the maximum number of iterations K are initialized, and a chaotic mapping is applied to the search subpopulation, expressed as:

[0038]

[0039] Where x t Let x be the particle position after chaotic mapping. t0 Let q be the particle position before the chaotic mapping, q be a chaotic random number in the range [0,1], and p be 0.4.

[0040] Calculate the particle population fitness value, and then calculate the contraction-expansion coefficient based on the particle population fitness value and the number of iterations. The expression is as follows:

[0041]

[0042] in Let be the contraction-expansion coefficient for k iterations. The maximum contraction-expansion coefficient, The minimum contraction-expansion coefficient, K is the maximum number of iterations, k is the current number of iterations, α is the iteration decay weight, and Fitness i,t Let Fitness be the fitness of particle i at the position corresponding to the t-th iteration. g σ is the fitness of the position of the globally optimal particle, β is the population diversity weight, σ(Fitness) is the standard deviation of the particle population fitness, and max(Fitness) is the maximum particle population fitness value.

[0043] Update the individual optimal position p of the particle i,k+1 Global optimal position g k+1 and average optimal position m best Update particle velocity and position using the following expression:

[0044]

[0045] x i,k+1 =x i,k +v i,k+1 ·Δk

[0046] Where v i,k+1 For the velocity update of particle i in the (k+1)th iteration, v i,k This represents the velocity update of particle i in the k-th iteration, where ζ and z are random numbers in [0,1], and x... i,k+1 To update the position of particle i in the (k+1)th iteration, x i,k Let λ be the position of particle i in the k-th iteration, Cauchy(0,1) be the Cauchy perturbation, and λ be the perturbation intensity.

[0047] The expression for chaotic boundary mutation of out-of-bounds particles is:

[0048]

[0049] Where x' i,t+1 Let be the position of particle i after it goes out of bounds in the (k+1)th iteration and undergoes chaotic boundary mutation, N(0,0.1) represents a Gaussian distribution with a mean of 0 and a standard deviation of 0.1, and δ~U(-0.05,0.05) is a random perturbation that conforms to a uniform distribution U(-0.05,0.05);

[0050] The process iterates continuously until the objective function value for flow optimization is minimized or the maximum number of iterations is reached, at which point the iteration stops and the optimal flow parameters are output.

[0051] Secondly, a U-shaped medium-deep buried pipe heat pump system includes: a buried pipe system, a circulating water pump, a heat pump unit, and building terminal buried pipes; the buried pipe system is used to fully exchange heat with the soil and rock to heat the circulating fluid, and includes a U-shaped medium-deep buried pipe and an insulation layer; the U-shaped medium-deep buried pipe includes a downcomer, a horizontal pipe, and a riser; the insulation layer surrounds the top of the riser; the circulating water pump is connected to the U-shaped medium-deep buried pipe and is used to pump the heated circulating fluid from the outlet of the U-shaped medium-deep buried pipe to the heat pump unit, and to pump the heated circulating fluid to the U-shaped... The U-shaped medium-deep buried pipe inlet is located at the upper end of the riser pipe; the U-shaped medium-deep buried pipe inlet is located at the upper end of the fallr pipe; the heat pump is connected to the circulating water pump and the building terminal buried pipe, and is used to reheat the circulating fluid of the circulating water pump to the required heating temperature, and to transport the reheated circulating fluid to the building terminal buried pipe; the building terminal buried pipe is connected to the heat pump and the circulating water pump, and is used to receive the reheated circulating fluid to heat the building and to transport the heated circulating fluid to the circulating water pump.

[0052] The beneficial effects of this invention are:

[0053] This invention is an optimized design method for a U-shaped medium-deep buried pipe heat pump system. Compared with the prior art, this invention has the following technical advantages:

[0054] This invention proposes an optimized design method for U-shaped medium-deep geothermal buried pipe heat pump systems. During the design phase, the characteristics of coupled heat exchange between the buried pipe system and the building user end are considered. This method scientifically and rationally solves a series of problems, including the calculation of thermal properties of the soil and rock and the dynamic load of the building, the design of the medium-deep buried pipe heat exchanger system, and the selection of the heat pump unit. This lays a theoretical foundation for the large-scale application of U-shaped medium-deep buried pipe heat pump systems.

[0055] This invention further establishes a mathematical model for a medium-deep U-shaped buried pipe ground source heat pump system, comprehensively considering the dynamic heat load of the terminal building and the dynamic heat exchange process on the ground source side. It adopts a variable flow rate method to match the dynamic changes in the building's heat load, thereby reducing the energy consumption of the ground source side circulation pump and laying a theoretical foundation for the large-scale application of the U-shaped medium-deep buried pipe heat pump system. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the steps of an optimized design method for a U-shaped medium-deep buried pipe heat pump system according to the present invention.

[0057] Figure 2 This is a schematic diagram of a U-shaped medium-deep buried pipe heat pump system according to the present invention;

[0058] Figure 3 This is a schematic diagram of a U-shaped medium-deep underground pipe system according to the present invention;

[0059] In the diagram: Underground pipe system - A; Insulation layer - A1; U-shaped medium-deep underground pipe - A2; Downcomer - A2-1; Horizontal pipe - A2-2; Ascendant - A2-3; Circulating water pump - B; Heat pump - C; Building terminal buried pipe - D; Underground pipe depth - H; Parallel length - L. Detailed Implementation

[0060] The present invention will be further described below through specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.

[0061] The present invention provides an optimized design method for a U-shaped medium-deep buried pipe heat pump system, comprising the following steps:

[0062] like Figure 1 As shown, this embodiment includes the following steps:

[0063] Thermal response tests are conducted to obtain thermal response data, and thermal physical parameters of the soil and rock mass are obtained by fitting the thermal response data; the thermal response tests include field engineering thermal response tests and thermal response simulations.

[0064] The predicted building dynamic heat load is obtained and the predicted dynamic heat extraction of the buried pipe is determined. Based on the predicted dynamic heat extraction of the buried pipe, the thermal properties of the soil and rock and the engineering parameters, a heat exchange coupling simulation is performed to obtain the predicted dynamic temperature distribution of the buried pipe heat pump system.

[0065] The heat pump capacity is determined based on the predicted dynamic temperature distribution of the buried pipe heat pump system. The buried pipe heat pump system is then designed and constructed based on the buried pipe system parameters and the heat pump capacity. The buried pipe system parameters include buried pipe structural parameters and buried pipe operating parameters.

[0066] Run the buried pipe heat pump system to obtain the buried pipe heat pump system operation data, and obtain the heat pump machine performance function by fitting the buried pipe heat pump system operation data;

[0067] A flow rate optimization objective function is constructed based on the building heat load and the outlet water temperature of the buried pipe during the period to be optimized. The buried pipe heat pump system is then optimized based on the flow rate optimization objective function to obtain the optimal flow rate parameters. The buried pipe heat pump system is then adjusted based on the optimal flow rate parameters.

[0068] In this embodiment, the method for obtaining the thermal properties of soil and rock includes:

[0069] A thermal response test dataset is obtained by conducting field engineering thermal response tests; the thermal response test dataset includes the inlet temperature, outlet temperature, and flow rate of the circulating fluid;

[0070] An initial thermal response model is constructed based on the engineering parameters and initial soil and rock thermal properties from the field engineering thermal response test to obtain a thermal response simulation dataset through numerical simulation. The engineering parameters from the field engineering thermal response test include the buried pipe structural parameters, buried pipe operating parameters, and geothermal gradient. The soil and rock thermal properties include the thermal conductivity, specific heat capacity, and thermal diffusivity of the soil and rock. The buried pipe structural parameters include the buried pipe dimensions, borehole structure, and parallel length. The buried pipe operating parameters include the circulating fluid velocity and circulating fluid properties.

[0071] The data deviation between the thermal response test dataset and the thermal response simulation dataset is calculated. Bayesian optimization is used to adjust the initial thermal property parameters of the soil and rock mass to obtain optimized thermal property parameters. Based on these optimized parameters, the initial thermal response model is adjusted to obtain an optimized thermal response model. Numerical simulation is then performed to obtain an optimized thermal response simulation dataset. This Bayesian optimization process is repeated until the data deviation between the optimized thermal response simulation dataset and the thermal response test dataset is minimized. Finally, the corresponding thermal property parameters of the soil and rock mass for the optimized thermal response model are output. The data deviation includes temperature deviation and heat flux density error.

[0072] In the actual evaluation, a U-shaped medium-deep buried pipe (diameter 215.9 mm, parallel section length 210 m) was installed in a borehole (burial depth 2000 m) in the area where the U-shaped medium-deep buried pipe heat pump system was to be built. Circulating fluid (water) was injected into the pipe, and thermal excitation was applied to the soil and rock mass through a constant power heating or cooling device. The inlet temperature, outlet temperature, and flow rate of the circulating fluid were monitored in real time, and continuous data for at least 72 hours were recorded to form a thermal response test dataset. Based on engineering parameters (geothermal gradient 3℃ / 100m, circulation velocity 0.5m / s, borehole diameter 215.9 mm) and initial soil and rock parameters (thermal conductivity 2 W / m·K, specific heat capacity 1800 J / kg·K, thermal diffusivity 1.0 × 10⁻⁶), the system was tested. -6 m 2 A thermal response simulation dataset is obtained by constructing an initial thermal response model ( / s) and performing numerical simulation. The data deviations (temperature deviation, heat flux density error) between the thermal response test dataset and the thermal response simulation dataset are calculated. Based on the data deviations, the thermal properties of the soil and rock are repeatedly adjusted until the data deviations are minimized. Then, the thermal properties of the soil and rock are output (thermal conductivity 2.3 W / m·K, specific heat capacity 2000 J / kg·K, thermal diffusivity 1.2 × 10⁻⁶). -6 m 2 / s).

[0073] In this embodiment, the method for obtaining the predicted dynamic temperature distribution of a buried pipe heat pump system includes the following steps:

[0074] Historical building energy consumption data, building attributes, meteorological data, and building operation schedules are acquired. The data is preprocessed to obtain a building energy consumption dataset. A backpropagation (BP) neural network (BPNN) prediction model for building heat load is constructed using this dataset. The mean squared error loss function is used to evaluate the difference between the predicted and actual building heat load values. The Adam optimizer is used to optimize the parameters of the BPNN prediction model. The building energy consumption dataset is divided into a training set and a test set in a 6:3 ratio. The training set is used for the BPNN prediction model, and the test set is used to verify the performance of the BPNN prediction model.

[0075] The building attributes, building operation schedule, and meteorological data of the area to be constructed for the U-shaped medium-deep buried pipe heat pump system are input into a BP neural network prediction model to obtain the predicted dynamic heat load of the building. The hourly load integral method is used to calculate the standard heat pump unit's coefficient of performance (COP). sta The prediction of dynamic building heat load is transformed into the prediction of dynamic heat extraction through underground pipes.

[0076] A heat transfer model of the buried pipe-soil mass is constructed based on engineering parameters and standard operating parameters of the buried pipe. The predicted dynamic heat output of the buried pipe and the thermal properties of the soil mass are used as boundary conditions to perform heat exchange coupling simulation to obtain the predicted dynamic temperature distribution of the buried pipe heat pump system and the actual dynamic heat output of the buried pipe. The dynamic temperature distribution of the buried pipe heat pump system includes the dynamic outlet water temperature and the dynamic return water temperature of the buried pipe.

[0077] In the actual assessment, a BP neural network prediction model for building heat load was constructed using historical energy consumption data from the heating season, building attributes, meteorological data (temperature and humidity), and building operation schedules. The building attributes (area 5000㎡, office building), meteorological data (temperature and humidity), and operation schedule (8:00-22:00) of the area to be developed for the U-shaped deep underground pipe heat pump system were input into the model to obtain the predicted dynamic building heat load (peak 180kW, valley 60kW). The hourly load integration method was used to calculate the standard heat pump unit's coefficient of performance (COP). sta =3 The predicted building dynamic heat load is converted into the predicted buried pipe dynamic heat extraction (peak value 180 / 3 = 60kW, valley value 60 / 3 = 20kW). Combined with the above-mentioned geothermal thermal property parameters, heat exchange coupling simulation is performed to obtain the predicted temperature dynamic distribution of the buried pipe heat pump system (10℃ / peak load ~ 15℃ / valley load) and the actual buried pipe dynamic heat extraction.

[0078] In this embodiment, the method for determining the capacity of the heat pump includes:

[0079] Based on the predicted dynamic temperature distribution of the buried pipe heat pump system, the actual dynamic heat extraction corresponding to the lowest dynamic outlet water temperature of the buried pipe during the heating season is determined. The design value of the heat pump's coefficient of performance is then calculated based on the actual dynamic heat extraction and the corresponding predicted building dynamic heat load. The expression is as follows:

[0080] COP design =P pre,build / (P pre,build -P act,BHE )

[0081] COP design P is the design value for the coefficient of performance of the heat pump. pre,build To predict the dynamic heat load of a building, P act,BHE For dynamic heat extraction using actual buried pipes;

[0082] The heat pump unit capacity is matched according to the design value of the heat pump unit's coefficient of performance (COP); the heat pump unit capacity uniquely corresponds to the rated COP of the heat pump unit.

[0083] In actual assessments, the actual heat extraction P is determined based on the lowest outlet water temperature of 10℃ (corresponding to peak load). act,BHE =100kW, calculate the design value of the coefficient of performance (COP) of the heat pump. design =2.25. Based on the design value of 2.25 for the coefficient of performance of the heat pump, a heat pump with a rated power of 125kW is matched (the coefficient of performance of each heat pump is uniquely matched with the rated power / heat pump capacity).

[0084] In this embodiment, the method for constructing the traffic optimization objective function includes:

[0085] Acquire operating data of the buried pipe heat pump system; the operating data of the buried pipe heat pump system includes the outlet water temperature of the buried pipe, the building heating temperature, the building heat load, and the energy consumption of the heat pump unit;

[0086] The coefficient of performance (COP) of the heat pump is obtained by calculating the building heat load and the heat pump energy consumption. The heat pump performance function is obtained by fitting the COP and the outlet water temperature of the buried pipe under the same building heating temperature. Multiple temperature constants are obtained by fitting the heat pump performance function under different building heating temperatures, forming a heat pump performance function constant table. The expression for the heat pump performance function is as follows:

[0087] COP T =a1(T build )·T out +a2(T build )

[0088] COP T The building heating temperature T build The coefficient of performance of a heat pump is a1(T) build ), a2(Tbuild T is a temperature constant, which takes different values ​​depending on the building's heating temperature. out The water outlet temperature of the buried pipe is (20+5n)℃, where n={0,1,2,3,4,5,6};

[0089] The building heat load, building heating temperature, and temperature distribution of the buried pipe heat pump system for the period to be optimized are obtained; the temperature distribution of the buried pipe heat pump system includes the return water temperature of the buried pipe and the inlet and outlet water temperatures of the buried pipe.

[0090] The heat pump performance function is retrieved from the heat pump performance function constant table to find the heat pump performance function corresponding to the building heating temperature during the period to be optimized. The heat pump performance coefficient (COP) for the period to be optimized is obtained by inputting the underground pipe outlet water temperature into the heat pump performance function. t ;

[0091] Determine the circulating water pump power P based on the parameters of the underground pipe system. cp The heat output P of the buried pipe heat exchanger is determined based on the parameters of the buried pipe system and the temperature distribution of the buried pipe heat pump system. BHE According to the coefficient of performance (COP) of the heat pump at the optimization time t The heat output P of the buried pipe heat exchanger BHE Determine the power P of the heat pump unit hp According to the power P of the heat pump unit hp and circulating water pump power P cp The objective function for traffic optimization is defined as follows:

[0092]

[0093] Where W is the objective function for flow optimization, representing the power consumption of the buried pipe heat pump system during the optimization period, and P... hp (t) represents the power of the heat pump unit at time t to be optimized, P cp (t) represents the circulating water pump power at time t to be optimized, and q f Let η be the flow rate of the circulating fluid per unit time, η be the efficiency of the circulating water pump, f be the Darcy friction coefficient, L be the borehole length, and D be the flow rate of the circulating fluid per unit time. h ρ is the hydraulic diameter of the pipe. f Let v be the density of the circulating fluid, v be the flow rate of the circulating fluid, and c be the flow rate of the circulating fluid. f For the specific heat capacity of the circulating liquid, m f T represents the mass of the circulating fluid per unit time. out (m f (T) represents the outlet water temperature of the buried pipe. out Regarding the mass m of the circulating fluid per unit time f The single-valued function, T, was determined by fitting the results of heat transfer coupling simulation. re The return water temperature of the buried pipe;

[0094] In actual assessments, the operating data of the buried pipe heat pump system and the building heating temperature T are obtained. build =20℃, water outlet temperature T of buried pipe out =12℃, heat pump energy consumption 100kWh;

[0095] The performance function (COP) of a heat pump is obtained by fitting the coefficient of performance (COP) of the heat pump unit and the outlet water temperature of the buried pipe at the same building heating temperature. T =0.1·T out +1(when the building heating temperature is 20℃, a2(T) build =1), obtain the building heat load, building heating temperature, and temperature distribution of the buried pipe heat pump system (buried pipe outlet water temperature T) for the period to be optimized. out =12℃, underground pipe return water temperature T re =8℃), take the flow rate q of the circulating fluid per unit time. f =0.5m 3 / h, circulating water pump efficiency η=0.8, Darcy friction coefficient f=0.02, borehole length L=4210m, hydraulic diameter D h =0.2m, circulating fluid density ρ f =1000g / L, circulating liquid flow rate v = 0.5m / s, circulating liquid specific heat capacity c f =4200J / (kg·K), mass of circulating liquid per unit time m f =0.5g / s, water outlet temperature T of buried pipe out Regarding the mass m of the circulating fluid per unit time f single-valued function T out (m f =12, calculate the power P of the circulating water pump respectively. cp (t) = 15.625kW, heat pump unit power P hp (t) = 7kW, and the objective function is calculated to be 81.45kWh.

[0096] In this embodiment, the method for obtaining the optimal flow parameters includes:

[0097] Based on the objective function value of flow optimization, a particle search algorithm is used to optimize the buried pipe heat pump system. The population size N and the maximum number of iterations K are initialized, and a chaotic mapping is applied to the search subpopulation, expressed as:

[0098]

[0099] Where x t Let x be the particle position after chaotic mapping. t0 Let q be the particle position before the chaotic mapping, q be a chaotic random number in the range [0,1], and p be 0.4.

[0100] Calculate the particle population fitness value, and then calculate the contraction-expansion coefficient based on the particle population fitness value and the number of iterations. The expression is as follows:

[0101]

[0102] in Let be the contraction-expansion coefficient for k iterations. The maximum contraction-expansion coefficient, The minimum contraction-expansion coefficient, K is the maximum number of iterations, k is the current number of iterations, α is the iteration decay weight, and Fitness i,t Let Fitness be the fitness of particle i at the position corresponding to the t-th iteration. g σ is the fitness of the position of the globally optimal particle, β is the population diversity weight, σ(Fitness) is the standard deviation of the particle population fitness, and max(Fitness) is the maximum particle population fitness value.

[0103] Update the individual optimal position p of the particle i,k+1 Global optimal position g k+1 and average optimal position m best Update particle velocity and position using the following expression:

[0104]

[0105] x i,k+1 =x i,k +v i,k+1 ·Δk

[0106] Where v i,k+1 For the velocity update of particle i in the (k+1)th iteration, v i,k This represents the velocity update of particle i in the k-th iteration, where ζ and z are random numbers in [0,1], and x... i,k+1 To update the position of particle i in the (k+1)th iteration, x i,k Let be the position of particle i in the k-th iteration, Cauchy(0,1) be the Cauchy perturbation, and λ be the perturbation strength; perform chaotic boundary mutation on the out-of-bounds particles, expressed as:

[0107]

[0108] Where x ‘ i,t+1 Let be the position of particle i after it goes out of bounds in the (k+1)th iteration and undergoes chaotic boundary mutation, N(0,0.1) represents a Gaussian distribution with a mean of 0 and a standard deviation of 0.1, and δ~U(-0.05,0.05) is a random perturbation that conforms to a uniform distribution U(-0.05,0.05);

[0109] The process continues iterating until the objective function value for flow optimization is minimized or the maximum number of iterations is reached, at which point the iteration stops and the optimal flow parameters are output.

[0110] In the actual evaluation, the buried pipe heat pump system was optimized using a particle search algorithm based on the flow optimization objective function value. The initial population size N=20 and the maximum number of iterations K=50, and the maximum contraction-expansion coefficient was selected. Minimum contraction-expansion coefficient Iteration decay weight α = 0.8, population diversity weight β = 0.2, disturbance intensity λ = 0.15, particle position range (flow adjustment range, m) 3 / h)q f For the range [0.3, 0.7,], after 15 iterations of particle search, the minimum value of the flow optimization objective function is 75 kWh. At this point, the optimal flow parameter is output: the flow rate q of the circulating liquid per unit time. f =0.35m 3 / h, hydraulic diameter D h =0.18m, circulating fluid velocity ν =0.6m / s.

[0111] Secondly, a U-shaped medium-deep buried pipe heat pump system includes: a buried pipe system A, a circulating water pump B, a heat pump unit C, and a building terminal buried pipe D; the buried pipe system A is used to fully exchange heat with the soil and rock to heat the circulating fluid, and includes a U-shaped medium-deep buried pipe A2 and an insulation layer A1; the U-shaped medium-deep buried pipe A2 includes a downcomer A2-1, a horizontal pipe A2-2, and an upcomer A2-3; the insulation layer A1 surrounds the top of the upcomer A2-3; the circulating water pump B is connected to the U-shaped medium-deep buried pipe A and is used to pump the heated circulating fluid from the outlet of the U-shaped medium-deep buried pipe A2 to the heat pump unit C, and to heat the circulating fluid after heating. The liquid is pumped to the inlet of the U-shaped medium-deep buried pipe A2; the outlet of the U-shaped medium-deep buried pipe A2 is located at the upper end of the riser pipe A2-3; the inlet of the U-shaped medium-deep buried pipe A2 is located at the upper end of the downcomer pipe A2-1; the heat pump C is connected to the circulating water pump B and the building terminal buried pipe D, and is used to reheat the circulating liquid of the circulating water pump to the required heating temperature, and to transport the reheated circulating liquid to the building terminal buried pipe D; the building terminal buried pipe D is connected to the heat pump C and the circulating water pump B, and is used to receive the reheated circulating liquid to heat the building and to transport the heated circulating liquid to the circulating water pump B.

[0112] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the design of a U-shaped middle-deep ground heat pump system, characterized in that, The method comprises the following steps: S1, obtaining thermal response data by conducting a thermal response test, and fitting rock-soil thermal physical parameters according to the thermal response data; the thermal response test comprises an in-situ engineering thermal response test and a thermal response simulation; S2, obtaining predicted building dynamic heat load and predicted ground heat exchanger dynamic heat extraction amount, and performing heat exchange coupling simulation according to the predicted ground heat exchanger dynamic heat extraction amount, the rock-soil thermal physical parameters and engineering parameters to obtain predicted ground heat exchanger heat pump system temperature dynamic distribution; S3, determining heat pump machine capacity according to the predicted ground heat exchanger heat pump system temperature dynamic distribution, and designing and building a ground heat exchanger heat pump system according to ground heat exchanger system parameters and the heat pump machine capacity; the ground heat exchanger system parameters comprise ground heat exchanger structure parameters and ground heat exchanger operation parameters; S4, obtaining ground heat exchanger heat pump system operation data by operating the ground heat exchanger heat pump system, and fitting a heat pump machine performance function according to the ground heat exchanger heat pump system operation data; S5, constructing a flow optimization objective function according to building heat load and ground heat exchanger outlet water temperature in a to-be-optimized period, optimizing the ground heat exchanger heat pump system according to the flow optimization objective function to obtain optimal flow parameters, and adjusting the ground heat exchanger heat pump system according to the optimal flow parameters; The method for constructing the flow optimization objective function comprises: obtaining ground heat exchanger heat pump system operation data; the ground heat exchanger heat pump system operation data comprises ground heat exchanger outlet water temperature, building heating temperature, building heat load and heat pump machine energy consumption; calculating building heat load and heat pump machine energy consumption to obtain a heat pump machine coefficient of performance, fitting the heat pump machine coefficient of performance and ground heat exchanger outlet water temperature at the same building heating temperature to obtain a heat pump machine performance function, and fitting heat pump machine performance functions at different building heating temperatures to obtain a plurality of groups of temperature constants to form a heat pump machine performance constant table; the heat pump machine performance function is expressed as: Wherein The building heating temperature The lower heat pump coefficient of performance , The temperature constant, which is different at different building heating temperatures The buried pipe outlet water temperature; the building heating temperature is (20+5n)℃ ; obtaining building heat load, building heating temperature and ground heat exchanger heat pump system temperature distribution in a to-be-optimized period; the ground heat exchanger heat pump system temperature distribution comprises ground heat exchanger return water temperature and ground heat exchanger inlet and outlet water temperature; According to the heat pump machine performance function constant table, a heat pump machine performance function corresponding to a building heating temperature in a to-be-optimized period is queried, and a buried pipe outlet water temperature is input into the heat pump machine performance function to obtain a heat pump machine performance coefficient in the to-be-optimized period ; Determination of circulating water pump power according to buried pipe system parameters Determination of buried pipe heat exchanger heat extraction according to buried pipe system parameters and buried pipe heat pump system temperature distribution Determination of heat pump unit power according to heat pump unit performance coefficient at optimization time and buried pipe heat exchanger heat extraction Determination of heat pump unit power according to heat pump unit performance coefficient at optimization time and buried pipe heat exchanger heat extraction Determination of flow optimization target function according to heat pump unit power and circulating water pump power wherein is the flow optimization objective function, representing the power consumption of the ground heat pump system during the optimization period, is the time to be optimized is the corresponding heat pump unit power, is the time to be optimized is the corresponding circulating water pump power, is the flow of circulating liquid per unit time, is the efficiency of the circulating water pump, is the Darcy friction factor, is the borehole length, is the pipe hydraulic diameter, is the circulating liquid density, is the circulating liquid flow rate, is the specific heat capacity of the circulating liquid, is the mass of circulating liquid per unit time, is the ground heat pipe outlet water temperature is a single-valued function of the mass of circulating liquid per unit time is determined by fitting the heat exchange coupling simulation results, is the ground heat pipe return water temperature.

2. The method according to claim 1, wherein, The method for obtaining rock-soil thermal physical parameters comprises: conducting an in-situ engineering thermal response test to obtain a thermal response test data set; the thermal response test data set comprises inlet temperature, outlet temperature and flow rate of a circulating fluid; constructing an initial thermal response model according to engineering parameters of the in-situ engineering thermal response test and initial rock-soil thermal physical parameters to perform numerical simulation and obtain a thermal response simulation data set; the engineering parameters of the in-situ engineering thermal response test comprise ground heat exchanger structure parameters, ground heat exchanger operation parameters and ground temperature gradient; the rock-soil thermal physical parameters comprise thermal conductivity, specific heat capacity and thermal diffusivity of rock-soil; the ground heat exchanger structure parameters comprise ground heat exchanger size, borehole structure and parallel length; the ground heat exchanger operation parameters comprise circulating fluid flow rate and circulating fluid physical properties; The data deviation of the thermal response test data set and the thermal response simulation data set is calculated, the initial geotechnical thermal property parameters are adjusted by Bayesian optimization to obtain optimized geotechnical thermal property parameters, the initial thermal response model is adjusted according to the optimized geotechnical thermal property parameters to obtain an optimized thermal response model, numerical simulation is performed to obtain an optimized thermal response simulation data set, the above Bayesian optimization operation is repeated until the data deviation of the optimized thermal response simulation data set and the thermal response test data set is the minimum, and the geotechnical thermal property parameters corresponding to the optimized thermal response model are output; the data deviation includes temperature deviation and heat flow density error.

3. The method according to claim 2, wherein, The method for obtaining the predicted temperature dynamic distribution of the ground heat pump system comprises the following steps: Obtain building historical energy consumption data, building attributes, meteorological data, and building operation schedules, preprocess the data to obtain a building energy consumption data set, use the building energy consumption data set to construct a building heat load BP neural network prediction model, use the mean square error loss function to evaluate the difference between the predicted value and the true value of the building heat load, and use the Adam optimizer to optimize the parameters of the building heat load BP neural network prediction model; the building energy consumption data set is divided into a training set and a test set according to a ratio of 6:3; the training set is used for the building heat load BP neural network prediction model; and the test set is used to verify the performance of the building heat load BP neural network prediction model; The predicted building dynamic heat load is obtained by inputting the building properties, building operation schedule and meteorological data of the U-shaped middle-deep ground heat pump system to be built into a building heat load BP neural network prediction model, and the predicted ground heat pipe dynamic heat extraction amount is obtained by converting the predicted building dynamic heat load into the predicted ground heat pipe dynamic heat extraction amount. The predicted building dynamic heat load is obtained by inputting the building properties, building operation schedule and meteorological data of the U-shaped middle-deep ground heat pump system to be built into a building heat load BP neural network prediction model, and the predicted ground heat pipe dynamic heat extraction amount is obtained by converting the predicted building dynamic heat load into the predicted ground heat pipe dynamic heat extraction amount. According to the engineering parameters and the standard operation parameters of the ground heat exchanger, a ground heat exchanger-geotechnical heat transfer model is constructed, the predicted dynamic heat extraction amount of the ground heat exchanger and the geotechnical thermal property parameters are used as boundary conditions for heat exchange coupling simulation to obtain the predicted temperature dynamic distribution of the ground heat pump system and the actual dynamic heat extraction amount of the ground heat exchanger; the temperature dynamic distribution of the ground heat pump system includes the dynamic water outlet temperature of the ground heat exchanger and the dynamic return water temperature of the ground heat exchanger.

4. The method according to claim 1, wherein, The method for determining the capacity of the heat pump unit comprises the following steps: According to the predicted temperature dynamic distribution of the ground heat pump system, the actual dynamic heat extraction amount of the ground heat exchanger corresponding to the minimum dynamic water outlet temperature of the ground heat exchanger in the heating season is determined, the design value of the performance coefficient of the heat pump unit is calculated according to the actual dynamic heat extraction amount of the ground heat exchanger and the corresponding predicted dynamic heat load of the building, and the expression is: wherein is the design value of the coefficient of performance of the heat pump, is the predicted building dynamic thermal load, is the actual dynamic heat extraction of the ground loop. According to the design value of the performance coefficient of the heat pump unit, the capacity of the heat pump unit is matched; the capacity of the heat pump unit is uniquely corresponding to the rated performance coefficient of the heat pump unit.

5. The method according to claim 1, wherein, The method for obtaining the optimal flow parameter comprises: According to the flow optimization objective function value, a particle search algorithm is used to optimize the buried pipe heat pump system, and the population quantity is initialized and the maximum number of iterations The search sub-population is subjected to chaotic mapping, and the expression is: wherein is the particle position after the chaotic mapping, is the particle position before the chaotic mapping, is chaotic random number, takes the value 0.4; The fitness value of the particle population is calculated, and the contraction-expansion coefficient is calculated according to the fitness value of the particle population and the number of iterations, and the expression is: wherein is the contraction-expansion coefficient of the i-th iteration, is the maximum contraction-expansion coefficient, is the minimum contraction-expansion coefficient, is the maximum number of iterations, is the current number of iterations, is the iteration decay weight, is the particle the fitness of the corresponding position at the i-th iteration, is the fitness of the position where the global optimal particle is located, is the population diversity weight, is the standard deviation of the fitness of the particle population, is the maximum fitness value of the particle population; the individual best position of the update particle the global best position and the average best position the update particle velocity and position, expressed as: wherein is the velocity update of the particle at the th iteration, is the velocity update of the particle at the th iteration, , is a random number within [0, 1], is the position update of the particle at the th iteration, is the position of the particle at the th iteration, is a Cauchy perturbation, is a perturbation strength; The chaotic boundary mutation is performed on the out-of-boundary particles, and the expression is: wherein is a particle after the position of the particle is out of bounds after the position of the particle is varied by the chaotic boundary variation in the jth iteration, a position of the particle after the position of the particle is out of bounds after the position of the particle is varied by the chaotic boundary variation in the jth iteration, is a Gaussian distribution with a mean of 0 and a standard deviation of 0.1, is a random perturbation amount that conforms to a uniform distribution . The iteration is continuously performed until the minimum value of the flow optimization objective function or the maximum number of iterations is reached, and the optimal flow parameter is output.

6. A U-tube ground heat exchanger system for carrying out the method according to any one of claims 1 to 5, characterized in that, It comprises: The buried pipe system is used for heat exchange with rock and soil to heat circulating liquid, and comprises a U-shaped middle-deep buried pipe and an insulation layer; the U-shaped middle-deep buried pipe comprises a descending pipe, a horizontal pipe and an ascending pipe; the insulation layer is wrapped at the top of the ascending pipe; the circulating water pump is connected with the U-shaped middle-deep buried pipe, and is used for pumping the heated circulating liquid from the outlet of the U-shaped middle-deep buried pipe to the heat pump and pumping the heated circulating liquid to the inlet of the U-shaped middle-deep buried pipe; the outlet of the U-shaped middle-deep buried pipe is arranged at the upper end of the ascending pipe; the inlet of the U-shaped middle-deep buried pipe is arranged at the upper end of the descending pipe; the heat pump is connected with the circulating water pump and the building terminal buried pipe, and is used for heating the circulating liquid of the circulating water pump to a required heating temperature and delivering the heated circulating liquid to the building terminal buried pipe; the building terminal buried pipe is connected with the heat pump and the circulating water pump, and is used for receiving the heated circulating liquid to heat the building and delivering the heated circulating liquid to the circulating water pump.

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