Optimization design method for U-shaped middle-deep layer buried pipe heat pump system

Abstract

The invention discloses a U-shaped mid-deep buried pipe heat pump system optimization design method, which comprises the following steps: carrying out a thermal response test to obtain rock-soil body thermophysical parameters, predicting the dynamic heat extraction amount of a buried pipe, carrying out heat exchange coupling simulation to obtain and predict the dynamic temperature distribution of a buried pipe heat pump system, and determining the capacity of a heat pump machine. Designing and building a buried pipe heat pump system according to buried pipe system parameters and the heat pump capacity, operating the buried pipe heat pump system to obtain operating data of the buried pipe heat pump system, and fitting to obtain a heat pump performance function; and a flow optimization objective function is constructed according to the building heat load in the to-be-optimized period and the outlet water temperature of the buried pipe, the buried pipe heat pump system is optimized to obtain an optimal flow parameter, and the buried pipe heat pump system is adjusted according to the optimal flow parameter. According to the method, the energy consumption of the ground source side circulating pump can be remarkably reduced, and a theoretical foundation is laid for large-scale application of a U-shaped middle-deep layer buried pipe heat pump system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geothermal energy, in particular to a U-shaped middle-deep buried pipe heat pump system optimization design method. BACKGROUND

[0002] With the increasing global energy demand and the increasing emphasis on environmental protection, middle-deep geothermal energy as a renewable and clean energy, its efficient development and utilization has extremely key significance for realizing energy structure optimization and sustainable development. The middle-deep U-shaped buried pipe heat exchanger as a new type of buried pipe heat exchanger can effectively avoid the heat short circuit effect, and the horizontal pipe at the bottom can increase the heat exchange area of the circulating fluid and the bottom high-temperature rock-soil to improve the heat extraction capacity.

[0003] However, the design and calculation of the U-shaped middle-deep buried pipe heat exchanger is an important part of the whole system design, construction and cost calculation, and the uncertainty of the parameters brings great difficulty to the design of the system. At the same time, in the actual operation of the system, in most cases, the real-time heat load of the terminal building is often lower than the design value, and the energy consumption of the system is large by using the constant flow method, so it is urgent to design a variable flow system optimization method to reduce the energy consumption of the system. Therefore, the present application provides a U-shaped middle-deep buried pipe heat pump system optimization design method, which considers the characteristics of the buried pipe system and the building user end coupling heat exchange in the design stage, combines the rock-soil thermal physical parameters and the predicted building dynamic load to determine the heat pump unit capacity, thereby designing the U-shaped middle-deep buried pipe heat pump system, and considering the dynamic heat load of the terminal building and the dynamic heat exchange process of the ground source side, optimizing the flow parameters based on the energy consumption optimization target, and optimizing the U-shaped middle-deep buried pipe heat pump system. This technology breaks through the limitations of traditional design, significantly improves the scientificity and adaptability of the U-shaped middle-deep buried pipe heat pump system design and optimization, reduces the energy consumption of the ground source side circulating pump, and lays a theoretical foundation for the large-scale application of the U-shaped middle-deep buried pipe heat pump system. SUMMARY

[0004] The purpose of the present application is to provide a U-shaped middle-deep buried pipe heat pump system optimization design method.

[0005] In order to achieve the above purpose, the present application is implemented according to the following technical scheme:

[0006] The present application comprises the following steps:

[0007] Performing a thermal response test to obtain thermal response data, and fitting rock-soil thermal physical parameters according to the thermal response data; the thermal response test includes an in-situ engineering thermal response test and a thermal response simulation;

[0008] obtaining predicted building dynamic thermal load and determining predicted ground heat exchanger dynamic heat extraction, performing heat transfer coupling simulation according to the predicted ground heat exchanger dynamic heat extraction, the geotechnical thermal physical parameters and engineering parameters to obtain predicted ground heat pump system temperature dynamic distribution;

[0009] determining heat pump machine capacity according to the predicted ground heat pump system temperature dynamic distribution, designing and building ground heat pump system according to ground heat exchanger system parameters and the heat pump machine capacity; the ground heat exchanger system parameters include ground heat exchanger structure parameters and ground heat exchanger operation parameters;

[0010] obtaining ground heat pump system operation data by running the ground heat pump system, and fitting heat pump machine performance function according to the ground heat pump system operation data;

[0011] constructing flow optimization objective function according to building thermal load and ground heat exchanger outlet water temperature in the to-be-optimized period, optimizing the ground heat pump system to obtain optimal flow parameter according to the flow optimization objective function, and adjusting the ground heat pump system according to the optimal flow parameter.

[0012] Further, the method for obtaining geotechnical thermal physical parameters comprises:

[0013] obtaining heat response test data set by performing field engineering heat response test; the heat response test data set includes inlet temperature, outlet temperature and flow rate of circulating fluid;

[0014] constructing initial heat response model according to engineering parameters of field engineering heat response test and initial geotechnical thermal physical parameters to perform numerical simulation and obtain heat response simulation data set; the engineering parameters of field engineering heat response test include ground heat exchanger structure parameters, ground heat exchanger operation parameters and ground temperature gradient; the geotechnical thermal physical parameters include thermal conductivity, specific heat capacity and thermal diffusivity of geotechnical; the ground heat exchanger structure parameters include ground heat exchanger size, borehole structure and parallel length; the ground heat exchanger operation parameters include circulating fluid flow rate and circulating fluid physical property;

[0015] calculating data deviation of heat response test data set and heat response simulation data set, adjusting initial geotechnical thermal physical parameters to obtain optimized geotechnical thermal physical parameters by using Bayesian optimization, adjusting initial heat response model according to the optimized geotechnical thermal physical parameters to obtain optimized heat response model, performing numerical simulation to obtain optimized heat response simulation data set, repeating the above Bayesian optimization operation until the data deviation of the optimized heat response simulation data set and the heat response test data set is the minimum, and outputting geotechnical thermal physical parameters corresponding to the optimized heat response model; the data deviation includes temperature deviation and heat flux density error.

[0016] Further, the method for obtaining predicted ground heat pump system temperature dynamic distribution comprises the following steps:

[0017] The building historical energy consumption data, building attributes, meteorological data, and building operation schedule are acquired, the data is preprocessed to obtain a building energy consumption dataset, a building heat load BP neural network prediction model is constructed using the building energy consumption dataset, a mean square error loss function is used to evaluate the difference between the building heat load prediction value and the true value, and an Adam optimizer is used to optimize the building heat load BP neural network prediction model parameters; the building energy consumption dataset 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.

[0018] The building attributes, building operation schedule, and meteorological data of the to-be-built area of the U-shaped middle-deep ground buried pipe heat pump system are input into the building heat load BP neural network prediction model to obtain a predicted building dynamic heat load, and a time-by-time load integration method is used to obtain a predicted ground buried pipe dynamic heat extraction amount according to a standard heat pump machine performance coefficient COP sta The predicted building dynamic heat load is converted into a predicted ground buried pipe dynamic heat extraction amount.

[0019] A ground buried pipe-rock-soil heat transfer model is constructed according to engineering parameters and standard operation parameters of the ground buried pipe, the predicted ground buried pipe dynamic heat extraction amount and the rock-soil thermal physical property parameters are used as boundary conditions for heat exchange coupling simulation to obtain a predicted ground buried pipe heat pump system temperature dynamic distribution and an actual ground buried pipe dynamic heat extraction amount; the ground buried pipe heat pump system temperature dynamic distribution includes a ground buried pipe dynamic water outlet temperature and a ground buried pipe dynamic return water temperature.

[0020] Further, the method for determining the heat pump machine capacity comprises:

[0021] The actual ground buried pipe dynamic heat extraction amount corresponding to the minimum value of the ground buried pipe dynamic water outlet temperature in the heating season is determined according to the predicted ground buried pipe heat pump system temperature dynamic distribution, and the heat pump machine performance coefficient design value is calculated according to the actual ground buried pipe dynamic heat extraction amount and the corresponding predicted building dynamic heat load, and the expression is as follows:

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

[0023] Wherein COP design is the heat pump machine performance coefficient design value, Pp re,build is the predicted building dynamic heat load, and P act,BHE is the actual ground buried pipe dynamic heat extraction amount.

[0024] The heat pump machine capacity is matched according to the heat pump machine performance coefficient design value; and the heat pump machine capacity is uniquely corresponding to the rated heat pump machine performance coefficient.

[0025] Further, the method for constructing the flow optimization objective function comprises:

[0026] obtaining buried pipe heat pump system operation data; the buried pipe heat pump system operation data comprises buried pipe outlet water temperature, building heating temperature, building heat load and heat pump machine energy consumption;

[0027] calculating the building heat load and the heat pump machine energy consumption to obtain a heat pump machine coefficient of performance, fitting the heat pump machine coefficient of performance and the buried pipe outlet water temperature to obtain a heat pump machine performance function under the same building heating temperature, and fitting the heat pump machine performance function under different building heating temperatures to obtain a plurality of groups of temperature constants to form a heat pump machine performance function constant table; the heat pump machine performance function is expressed as:

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

[0029] wherein COP T is the heat pump machine coefficient of performance under the building heating temperature T build , a1(T build ) and a2(T build ) are temperature constants, which have different values under different building heating temperatures, and T out is the buried pipe outlet water temperature; the building heating temperature is 20+5n ℃, and n={0, 1, 2, 3, 4, 5, 6};

[0030] obtaining building heat load, building heating temperature and buried pipe heat pump system temperature distribution of a to-be-optimized period; the buried pipe heat pump system temperature distribution comprises buried pipe return water temperature and buried pipe inlet and outlet water temperature;

[0031] querying the heat pump machine performance function corresponding to the building heating temperature of the to-be-optimized period according to the heat pump machine performance function constant table, and inputting the buried pipe outlet water temperature into the heat pump machine performance function to obtain the heat pump machine coefficient of performance COP t of the to-be-optimized period;

[0032] determining circulating water pump power P cp according to buried pipe system parameters, determining buried pipe heat exchanger heat extraction amount P BHE according to the buried pipe system parameters and the buried pipe heat pump system temperature distribution, determining heat pump unit power P hp according to the heat pump machine coefficient of performance COP t of the to-be-optimized period and the buried pipe heat exchanger heat extraction amount P BHE , and determining the flow optimization objective function according to the heat pump unit power P hp and the circulating water pump power P cp , and the expression is:

[0033] W = ∫[P hp (t) + P cp (t)]dt

[0034]

[0035] wherein W is a flow optimization objective function, representing the power consumption of the ground heat pump system in the optimization period, P hp (t) is the heat pump unit power corresponding to the to-be-optimized time t, P cp (t) is the circulating water pump power corresponding to the to-be-optimized time t, q f is the flow of circulating liquid per unit time, η is the efficiency of the circulating water pump, f is the Darcy friction coefficient, L is the borehole length, D h is the pipe hydraulic diameter, ρ f is the circulating liquid density, v is the circulating liquid flow rate, c f is the specific heat capacity of the circulating liquid, m f is the mass of circulating liquid per unit time, T out (m f ) is the ground heat pipe outlet water temperature T out is a single-valued function of the mass m f of circulating liquid per unit time, T re is the ground heat pipe return water temperature.

[0036] Further, the method for obtaining the optimal flow parameter comprises:

[0037] According to the flow optimization objective function value, a particle search algorithm is used to optimize the ground heat pump system, the population number N and the maximum iteration number K are initialized, and the search sub-population is subjected to chaotic mapping, and the expression is:

[0038]

[0039] wherein x t is the particle position after chaotic mapping, x t0 is the particle position before chaotic mapping, q is a chaotic random number in [0, 1], and p is 0.4;

[0040] The particle population fitness value is calculated, and the contraction-expansion coefficient is calculated according to the particle population fitness value and the iteration number, and the expression is:

[0041]

[0042] wherein is the contraction-expansion coefficient of the kth iteration, is the maximum contraction-expansion coefficient, is the minimum shrinkage-expansion coefficient, K is the maximum iteration number, k is the current iteration number, a is the iteration attenuation weight, Fitness i,t is the fitness of particle i at the corresponding position at the tth iteration, Fitness g is the fitness of the global optimal particle position, β is the population diversity weight, σ(Fitness) is the standard deviation of the particle population fitness, max(Fitness) is the maximum particle population fitness value;

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

[0044]

[0045] x i,k+1 = x i,k + v i,k+1 · Δk

[0046] where v i,k+1 is the velocity update of particle i at the k+1th iteration, v i,k is the velocity update of particle i at the kth iteration, ζ, z are random numbers in [0,1], x i,k+1 is the position update of particle i at the k+1th iteration, x i,k is the position of particle i at the kth iteration, Cauchy(0,1) is the Cauchy disturbance, λ is the disturbance intensity;

[0047] Chaotic boundary mutation is performed on the out-of-boundary particles, the expression is:

[0048]

[0049] where x' i,t+1 is the position of particle i after chaotic boundary mutation after the k+1th iteration, N(0,0.1) is a Gaussian distribution with mean 0 and standard deviation 0.1, δ ~ U(-0.05,0.05) is a random disturbance quantity conforming to the uniform distribution U(-0.05,0.05);

[0050] Iterate continuously until the flow optimization objective function value is minimum or the maximum iteration number is reached, and output the optimal flow parameter.

[0051] In a second aspect, a U-shaped middle-deep ground pipe heat pump system comprises a ground pipe system, a circulating water pump, a heat pump machine and a building terminal buried pipe; the ground pipe system is used for heat exchange with rock soil to heat circulating liquid and comprises a U-shaped middle-deep ground pipe and an insulation layer; the U-shaped middle-deep ground 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 ground pipe and is used for pumping the heated circulating liquid from the outlet of the U-shaped middle-deep ground pipe to the heat pump machine and pumping the heated circulating liquid to the inlet of the U-shaped middle-deep ground pipe; the outlet of the U-shaped middle-deep ground pipe is arranged at the upper end of the ascending pipe; the inlet of the U-shaped middle-deep ground pipe is arranged at the upper end of the descending pipe; the heat pump machine 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 machine 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.

[0052] The present application has the following beneficial effects:

[0053] Compared with the prior art, the present application has the following technical effects:

[0054] The present application provides an optimization design method for a U-shaped middle-deep ground pipe heat pump system, which considers the coupling heat exchange characteristics of the ground pipe system and the building user end in the design stage, scientifically and reasonably solves a series of problems such as the calculation of rock-soil thermal physical parameters and building dynamic load, the design of the middle-deep ground pipe heat exchanger system and the selection of the heat pump unit, and lays a theoretical foundation for the large-scale application of the U-shaped middle-deep ground pipe heat pump system.

[0055] The present application further establishes a mathematical model of the middle-deep U-shaped ground pipe ground source heat pump system, comprehensively considers the dynamic heat load of the terminal building and the dynamic heat exchange process of the ground source side, adopts a variable flow mode to match the dynamic changes of the building heat load, reduces the energy consumption of the circulating pump of the ground source side, and lays a theoretical foundation for the large-scale application of the U-shaped middle-deep ground pipe heat pump system. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The present application provides an optimization design method for a U-shaped middle-deep ground pipe heat pump system, which considers the coupling heat exchange characteristics of the ground pipe system and the building user end in the design stage, scientifically and reasonably solves a series of problems such as the calculation of rock-soil thermal physical parameters and building dynamic load, the design of the middle-deep ground pipe heat exchanger system and the selection of the heat pump unit, and lays a theoretical foundation for the large-scale application of the U-shaped middle-deep ground pipe heat pump system.

[0057] Figure 2 The present application provides an optimization design method for a U-shaped middle-deep ground pipe heat pump system, which considers the coupling heat exchange characteristics of the ground pipe system and the building user end in the design stage, scientifically and reasonably solves a series of problems such as the calculation of rock-soil thermal physical parameters and building dynamic load, the design of the middle-deep ground pipe heat exchanger system and the selection of the heat pump unit, and lays a theoretical foundation for the large-scale application of the U-shaped middle-deep ground pipe heat pump system.

[0058] Figure 3 The present application provides an optimization design method for a U-shaped middle-deep ground pipe heat pump system, which considers the coupling heat exchange characteristics of the ground pipe system and the building user end in the design stage, scientifically and reasonably solves a series of problems such as the calculation of rock-soil thermal physical parameters and building dynamic load, the design of the middle-deep ground pipe heat exchanger system and the selection of the heat pump unit, and lays a theoretical foundation for the large-scale application of the U-shaped middle-deep ground pipe heat pump system.

[0059] In the figure: Ground heat exchanger system-A; Thermal insulation layer-A1; U-shaped middle-deep ground heat exchanger-A2; Downcomer-A2-1; Horizontal pipe-A2-2; Upriser-A2-3; Circulating water pump-B; Heat pump machine-C; Building terminal buried pipe-D; Ground heat exchanger depth-H; Parallel length-L. DETAILED DESCRIPTION

[0060] The present application will be further described by specific examples, the illustrative examples of the present application and the description used to explain the present application, but not as a limitation of the present application.

[0061] The U-shaped middle-deep ground heat exchanger heat pump system optimization design method of the present application comprises the following steps:

[0062] As shown in Figure 1 In the present embodiment, the following steps are included:

[0063] Performing a thermal response test to obtain thermal response data, and fitting to obtain rock-soil thermal physical property parameters according to the thermal response data; the thermal response test includes a field engineering thermal response test and a thermal response simulation;

[0064] Obtaining a predicted building dynamic heat load and determining a 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 property parameters and engineering parameters to obtain a predicted ground heat exchanger heat pump system temperature dynamic distribution;

[0065] Determining a 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 include ground heat exchanger structure parameters and ground heat exchanger operation parameters;

[0066] Running the ground heat exchanger heat pump system to obtain ground heat exchanger heat pump system running data, and fitting to obtain a heat pump machine performance function according to the ground heat exchanger heat pump system running data;

[0067] Constructing a flow optimization objective function according to a building heat load and a ground heat exchanger outlet water temperature in a to-be-optimized period, optimizing the ground heat exchanger heat pump system to obtain optimal flow parameters according to the flow optimization objective function, and adjusting the ground heat exchanger heat pump system according to the optimal flow parameters.

[0068] In the present embodiment, the method for obtaining rock-soil thermal physical property parameters comprises:

[0069] Performing a field engineering thermal response test to obtain a thermal response test data set; the thermal response test data set includes an inlet temperature, an outlet temperature and a flow rate of a circulating fluid;

[0070] The initial thermal response model is constructed according to the engineering parameters of the in-situ engineering thermal response test and the initial geotechnical thermal physical property parameters to obtain a thermal response simulation data set; the engineering parameters of the in-situ engineering thermal response test include buried pipe structure parameters, buried pipe operation parameters and a ground temperature gradient; the geotechnical thermal physical property parameters include the thermal conductivity, the specific heat capacity and the thermal diffusivity of the geotechnical body; the buried pipe structure parameters include the size of the buried pipe, the borehole structure and the parallel length; the buried pipe operation parameters include the circulating fluid flow rate and the physical property of the circulating fluid;

[0071] The data deviation of the thermal response test data set and the thermal response simulation data set is calculated, the initial geotechnical thermal physical property parameters are adjusted by using Bayesian optimization to obtain optimized geotechnical thermal physical property parameters, the initial thermal response model is adjusted according to the optimized geotechnical thermal physical property parameters to obtain an optimized thermal response model, the numerical simulation is performed to obtain an optimized thermal response simulation data set, and the above-mentioned 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 physical property parameters corresponding to the optimized thermal response model are output; the data deviation includes the temperature deviation and the heat flux error;

[0072] In the actual evaluation, a U-shaped middle-deep buried pipe (a hole diameter of 215.9 mm and a parallel segment length of 210 m) is installed in a borehole (a buried depth of 2000 m) in a region to be built of a U-shaped middle-deep buried pipe heat pump system, a circulating fluid (water) is injected into the pipe, a thermal excitation is applied to the geotechnological body by a constant-power heating or cooling device, the inlet temperature, the outlet temperature and the flow rate of the circulating fluid are monitored in real time, at least 72 hours of continuous data are recorded, and a thermal response test data set is formed; an initial thermal response model is constructed based on the engineering parameters (a ground temperature gradient of 3 ℃ / 100 m, a circulating flow rate of 0.5 m / s and a borehole structure diameter of 215.9 mm) and the initial geotechnical parameters (a thermal conductivity of 2 W / m·K, a specific heat capacity of 1800 J / kg·K and a thermal diffusivity of 1.0×10 -6 m 2 / s), numerical simulation is performed to obtain a thermal response simulation data set, the data deviation (the temperature deviation and the heat flux error) of the thermal response test data set and the thermal response simulation data set is calculated, the geotechnical thermal physical property parameters are repeatedly adjusted according to the data deviation until the data deviation is the minimum, and the geotechnical thermal physical property parameters (a thermal conductivity of 2.3 W / m·K, a specific heat capacity of 2000 J / kg·K and a thermal diffusivity of 1.2×10 -6 m 2 / s) are output.

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

[0074] The building historical energy consumption data, building attributes, meteorological data and building operation schedule are acquired, the data is preprocessed to obtain a building energy consumption dataset, a building heat load BP neural network prediction model is constructed using the building energy consumption dataset, a mean square error loss function is used to evaluate the difference between the building heat load prediction value and the true value, and an Adam optimizer is used to optimize the building heat load BP neural network prediction model parameters; the building energy consumption dataset 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.

[0075] The building attributes, building operation schedule and meteorological data of the U-shaped middle-deep ground buried pipe heat pump system to be built are input into the building heat load BP neural network prediction model to obtain a predicted building dynamic heat load, and a time-by-time load integration method is used to obtain a predicted ground buried pipe dynamic heat extraction amount according to a standard heat pump machine performance coefficient COP sta The predicted building dynamic heat load is converted into the predicted ground buried pipe dynamic heat extraction amount.

[0076] A ground buried pipe-rock-soil heat transfer model is constructed according to engineering parameters and ground buried pipe standard operation parameters, the predicted ground buried pipe dynamic heat extraction amount and rock-soil thermal physical parameters are used as boundary conditions for heat exchange coupling simulation to obtain a predicted ground buried pipe heat pump system temperature dynamic distribution and an actual ground buried pipe dynamic heat extraction amount; the ground buried pipe heat pump system temperature dynamic distribution includes a ground buried pipe dynamic water outlet temperature and a ground buried pipe dynamic return water temperature.

[0077] In actual evaluation, historical energy consumption data, building attributes, meteorological data (temperature, humidity), building operation schedule in the heating season are used to construct a building heat load BP neural network prediction model, building attributes (area 5000 square meters, office building), meteorological data (temperature, humidity), operation schedule (operation from 8:00 to 22:00) of the U-shaped middle-deep ground buried pipe heat pump system to be built are input into the model to obtain a predicted building dynamic heat load (peak value 180 kW, valley value 60 kW), and a time-by-time load integration method is used to obtain a predicted ground buried pipe dynamic heat extraction amount (peak value 180 / 3=60 kW, valley value 60 / 3=20 kW) according to a standard heat pump machine performance coefficient COP sta =3, and heat exchange coupling simulation is performed in combination with the above rock-soil thermal physical parameters to obtain a predicted ground buried pipe heat pump system temperature dynamic distribution (10℃ / peak load ~ 15℃ / valley load) and an actual ground buried pipe dynamic heat extraction amount.

[0078] In the embodiment, the method for determining the heat pump machine capacity comprises:

[0079] According to the predicted dynamic temperature distribution of the underground heat pump system, the actual underground heat intake corresponding to the lowest dynamic outlet water temperature of the underground pipe in the heating season is determined. The design value of the heat pump performance coefficient is calculated based on the actual underground heat intake and the corresponding predicted dynamic heat load of the building. The expression is:

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

[0081] Among them, COP design is the design value of the heat pump performance coefficient, P pre,build To predict the dynamic heat load of a building, P act,BHE Dynamically extract heat for actual buried pipes;

[0082] Matching the heat pump host capacity according to the heat pump performance coefficient design value; the heat pump host capacity uniquely corresponds to the rated heat pump performance coefficient;

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

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

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

[0086] The heat pump performance coefficient is obtained by calculating the building heat load and the heat pump energy consumption. The heat pump performance coefficient and the underground pipe outlet water temperature are fitted at the same building heating temperature to obtain the heat pump performance function. The heat pump performance function is fitted at different building heating temperatures to obtain multiple groups of temperature constants to form a heat pump performance function constant table. The heat pump performance function expression is:

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

[0088] Among them, COP T The building heating temperature T build The coefficient of performance of the heat pump is a1(T build )、a2(Tbuild ) is a temperature constant, which is different at different building heating temperatures, T out is the building heating temperature, which is 20+5n ℃, n={0, 1, 2, 3, 4, 5, 6};

[0089] obtain the building heat load, the building heating temperature and the ground heat pump system temperature distribution of the to-be-optimized period; the ground heat pump system temperature distribution includes the ground heat pump return water temperature and the ground heat pump inlet and outlet water temperature;

[0090] query the heat pump performance function corresponding to the building heating temperature of the to-be-optimized period according to the heat pump performance function constant table, and input the ground heat pump outlet water temperature into the heat pump performance function to obtain the heat pump performance coefficient COP t ;

[0091] determine the circulating water pump power P cp according to the ground heat pump system parameters, determine the ground heat pump heat exchanger heat extraction amount P BHE according to the ground heat pump system parameters and the ground heat pump system temperature distribution, determine the heat pump unit power P t according to the heat pump performance coefficient COP BHE of the to-be-optimized time and the heat extraction amount P hp of the ground heat pump heat exchanger, and determine the flow optimization objective function according to the heat pump unit power P hp and the circulating water pump power P cp , and the expression is:

[0092]

[0093] wherein W is the flow optimization objective function, representing the power consumption of the ground heat pump system in the to-be-optimized period, P hp (t) is the heat pump unit power corresponding to the to-be-optimized time t, P cp (t) is the circulating water pump power corresponding to the to-be-optimized time t, q f is the flow of circulating liquid per unit time, η is the efficiency of the circulating water pump, f is the Darcy friction coefficient, L is the borehole length, D h is the pipe hydraulic diameter, ρ f is the circulating liquid density, v is the circulating liquid flow rate, c f is the specific heat capacity of the circulating liquid, m f is the mass of the circulating liquid per unit time, T out (m f ) is the ground heat pump outlet water temperature T out , which is a single-valued function of the mass m f of the circulating liquid per unit time, T re is the ground heat pump return water temperature;

[0094] In the actual evaluation, the building heating temperature T build = 20℃, the ground heat exchanger outlet water temperature T out = 12℃, and the heat pump energy consumption is 100 kWh;

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

[0096] In the embodiment, the method for obtaining the optimal flow parameter comprises the following steps.

[0097] The ground heat exchanger heat pump system is optimized by using a particle search algorithm according to the flow optimization objective function value, the population number N and the maximum iteration number K are initialized, the search sub-population is subjected to chaotic mapping, and the expression is as follows:

[0098]

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

[0100] 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 iteration number, and the expression is:

[0101]

[0102] wherein is the contraction-expansion coefficient of the kth iteration, is the maximum contraction-expansion coefficient, is the minimum contraction-expansion coefficient, K is the maximum iteration number, k is the current iteration number, a is the iteration attenuation weight, Fitness i,t is the fitness of the corresponding position of particle i at the tth iteration, Fitness g is the fitness of the position of the global optimal particle, b is the population diversity weight, s(Fitness) is the standard deviation of the fitness of the particle population, and max(Fitness) is the maximum fitness of the particle population;

[0103] The individual optimal position p i,k+1 of the particle, the global optimal position g k+1 , and the average optimal position m best are updated, and the expression is:

[0104]

[0105] x i,k+1 = x i,k + v i,k+1 · Δk

[0106] wherein v i,k+1 is the velocity update of the ith particle at the k+1th iteration, v i,k is the velocity update of the ith particle at the kth iteration, ζ and z are random numbers in [0,1], x i,k+1 is the position update of the ith particle at the k+1th iteration, x i,k is the position of the ith particle at the kth iteration, Cauchy(0,1) is the Cauchy disturbance, and l is the disturbance intensity; the chaotic boundary variation is performed on the out-of-boundary particle, and the expression is:

[0107]

[0108] wherein x ‘ i,t+1 is the position of the ith particle after the chaotic boundary variation after the position of the ith particle at the k+1th iteration is out of bound, N(0,0.1) is a Gaussian distribution with a mean of 0 and a standard deviation of 0.1, and d ~ U(-0.05,0.05) is a random disturbance quantity conforming to the uniform distribution U(-0.05,0.05);

[0109] Iterate continuously until the flow optimization objective function value is minimized or the maximum number of iterations is reached, and then stop iterating and output the optimal flow parameters;

[0110] In the actual evaluation, the particle search algorithm is used to optimize the buried pipe heat pump system according to 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 is taken. Minimum shrinkage-expansion coefficient Iterative attenuation 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 ∈[0.3,0.7,], after 15 iterations of particle search, the minimum value of the flow optimization objective function is 75kWh. At this time, the optimal flow parameter is output: the flow rate of the circulating fluid per unit time q f =0.35m 3 / h, hydraulic diameter D h =0.18m, circulating liquid flow rate ν = 0.6m / s.

[0111] In the second aspect, a U-shaped medium-deep buried pipe heat pump system includes: a buried pipe system A, a circulating water pump B, a heat pump machine C and a building terminal buried pipe D; the buried pipe system A is used to fully exchange heat with the rock and soil to heat the circulating fluid, including 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 machine C, and ... 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 arranged at the upper end of the riser A2-3; the inlet of the U-shaped medium-deep buried pipe A2 is arranged at the upper end of the downcomer 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 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 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 in the scope of protection of the present invention.

Claims

1. A method for optimizing the design of a U-shaped medium-deep buried pipe heat pump system, characterized in that: The following steps are involved: S1. Conduct a thermal response test to obtain thermal response data, and obtain thermal physical property parameters of the rock and soil mass based on the thermal response data; the thermal response test includes a field engineering thermal response test and a thermal response simulation; S2. Obtain the predicted dynamic heat load of the building and determine the predicted dynamic heat intake of the buried pipes. Perform a heat exchange coupling simulation based on the predicted dynamic heat intake of the buried pipes, the thermal physical properties of the rock and soil, and the engineering parameters to obtain the predicted dynamic temperature distribution of the buried pipe heat pump system. S3. Determine the heat pump capacity based on the predicted dynamic temperature distribution of the underground heat pump system, and design and construct the underground heat pump system based on the underground heat pump system parameters and the heat pump capacity; the underground heat pump system parameters include underground pipe structural parameters and underground pipe operating parameters; S4. operating the underground heat pump system to obtain operating data of the underground heat pump system, and fitting a heat pump performance function based on the operating data of the underground heat pump system; S5. Construct a flow optimization objective function according to the building heat load and the underground pipe outlet water temperature during the optimization period, optimize the underground pipe heat pump system according to the flow optimization objective function to obtain optimal flow parameters, and adjust the underground pipe heat pump system according to the optimal flow parameters.

2. The optimization design method of a U-shaped medium-deep buried pipe heat pump system according to claim 1, characterized in that: The method for obtaining thermal physical property parameters of rock and soil comprises: Conducting a field engineering thermal response test to obtain a thermal response test data set; the thermal response test data set includes an inlet temperature, an outlet temperature, and a flow rate of a circulating fluid; An initial thermal response model is constructed based on the engineering parameters of the field engineering thermal response test and the initial geothermal physical property parameters, and numerical simulation is performed to obtain a thermal response simulation data set; the engineering parameters of the field engineering thermal response test include the buried pipe structural parameters, the buried pipe operating parameters, and the geothermal gradient; the geothermal physical property parameters include the thermal conductivity, specific heat capacity, and thermal diffusivity of the geothermal; the buried pipe structural parameters include the buried pipe size, drilling structure, and parallel length; and the buried pipe operating parameters include the circulating fluid flow rate and circulating fluid physical properties; The data deviation between the thermal response test data set and the thermal response simulation data set is calculated, and the initial geotechnical thermophysical parameters are adjusted by Bayesian optimization to obtain the optimized geotechnical thermophysical parameters. The initial thermal response model is adjusted according to the optimized geotechnical thermophysical parameters to obtain the optimized thermal response model. Numerical simulation is performed to obtain the optimized thermal response simulation data set. The above Bayesian optimization operation is repeated until the data deviation between the optimized thermal response simulation data set and the thermal response test data set is minimized, and the geotechnical thermophysical parameters corresponding to the optimized thermal response model are output; the data deviation includes temperature deviation and heat flux density error.

3. The optimization design method of a U-shaped medium-deep buried pipe heat pump system according to claim 2, characterized in that: The method for obtaining and predicting the dynamic temperature distribution of the buried pipe heat pump system comprises the following steps: Obtain historical building energy consumption data, building attributes, meteorological data, and building operation schedules, preprocess the data to obtain a building energy consumption dataset, use the building energy consumption dataset to construct a building heat load BP neural network prediction model, use a 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 dataset is divided into a training set and a test set in 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 building attributes, building operation schedule and meteorological data of the U-shaped medium-deep buried pipe heat pump system in the area to be built are input into the building heat load BP neural network prediction model to obtain the predicted building dynamic heat load. The hourly load integration method is used according to the standard heat pump performance coefficient COP. sta Convert the predicted building dynamic heat load into the predicted dynamic heat intake of underground pipes; A buried pipe-rock-soil heat transfer model is constructed based on the engineering parameters and the standard operating parameters of the buried pipe. The predicted dynamic heat intake of the buried pipe and the thermal physical properties of the rock and soil are used as boundary conditions to perform a heat exchange coupling simulation to obtain the predicted dynamic temperature distribution of the buried pipe heat pump system and the actual dynamic heat intake of the buried pipe; the dynamic temperature distribution of the buried pipe heat pump system includes the dynamic water outlet temperature of the buried pipe and the dynamic return water temperature of the buried pipe.

4. The optimization design method for a U-shaped medium-deep buried pipe heat pump system according to claim 1, characterized in that: The method for determining the capacity of a heat pump comprises the following steps: According to the predicted dynamic temperature distribution of the underground heat pump system, the actual underground heat intake corresponding to the lowest dynamic outlet water temperature of the underground pipe in the heating season is determined. The design value of the heat pump performance coefficient is calculated based on the actual underground heat intake and the corresponding predicted dynamic heat load of the building. The expression is: COP design =P pre,build / (P pre,build -P act,BHE ) Among them, COP design is the design value of the heat pump performance coefficient, P pre,build To predict the dynamic heat load of a building, P act,BHE Dynamically extract heat from actual buried pipes; The heat pump host capacity is matched according to the design value of the heat pump performance coefficient; the heat pump host capacity uniquely corresponds to the rated heat pump performance coefficient.

5. The optimization design method of a U-shaped medium-deep buried pipe heat pump system according to claim 1, characterized in that: The method for constructing a traffic optimization objective function includes: Obtaining operating data of the underground heat pump system; the operating data of the underground heat pump system includes the underground pipe water outlet temperature, the building heating temperature, the building heat load and the heat pump energy consumption; The heat pump performance coefficient is obtained by calculating the building heat load and the heat pump energy consumption. The heat pump performance coefficient and the underground pipe outlet water temperature are fitted at the same building heating temperature to obtain the heat pump performance function. The heat pump performance function is fitted at different building heating temperatures to obtain multiple groups of temperature constants to form a heat pump performance function constant table. The heat pump performance function expression is: COP T =a1(T build )·T out +a2(T build ) Among them, COP T The building heating temperature T build The coefficient of performance of the heat pump is a1(T build )、a2(T build ) is the temperature constant, which takes different values ​​at different building heating temperatures. out is the outlet water temperature of the buried pipe; the building heating temperature is 20+5n℃, n={0,1,2,3,4,5,6}; Obtaining the building heat load, building heating temperature, and underground heat pump system temperature distribution during the period to be optimized; the underground heat pump system temperature distribution includes the underground pipe return water temperature and the underground pipe inlet and outlet water temperatures; According to the heat pump performance function constant table, the heat pump performance function corresponding to the building heating temperature during the optimization period is queried, and the underground pipe outlet water temperature is input into the heat pump performance function to obtain the heat pump performance coefficient COP during the optimization period. t ; Determine the circulating water pump power P according to the buried pipe system parameters cp , determine the heat intake P of the buried pipe heat exchanger based on the buried pipe system parameters 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 and the heat intake P of the buried pipe heat exchanger BHE Determine the heat pump unit power P hp , according to the heat pump unit power P hp And circulating water pump power P cp Determine the traffic optimization objective function, the expression is: W=∫[P hp (t)+P cp (t)]dt Where W is the flow optimization objective function, represents the power consumption of the buried pipe heat pump system during the optimization period, P hp (t) is the power of the heat pump unit corresponding to the time t to be optimized, P cp (t) is the circulating water pump power corresponding to the time t to be optimized, q f is the flow rate of circulating fluid per unit time, η is the efficiency of circulating water pump, f is Darcy friction coefficient, L is the length of drilling hole, D h is the hydraulic diameter of the pipe, ρ f is the circulating fluid density, v is the circulating fluid flow rate, c f is the specific heat capacity of the circulating fluid, m f is the mass of the circulating fluid per unit time, T out (m f ) is the outlet water temperature of the buried pipe T out The mass m of the circulating fluid per unit time f The single-valued function is determined by fitting the heat transfer coupling simulation results, T re is the return water temperature of the buried pipe.

6. The optimization design method for a U-shaped medium-deep buried pipe heat pump system according to claim 1, characterized in that: The method for obtaining the optimal flow parameters comprises: According to the flow optimization objective function value, the 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 the search sub-population is subjected to chaotic mapping. The expression is: where x t is the particle position after chaos mapping, x t0 is the particle position before chaotic mapping, q is a chaotic random number in [0,1], and p is 0.4; Calculate the fitness value of the particle population, and calculate the contraction-expansion coefficient based on the fitness value of the particle population and the number of iterations. The expression is: in is the contraction-expansion coefficient for k iterations, is the maximum contraction-expansion coefficient, is the minimum contraction-expansion coefficient, K is the maximum number of iterations, k is the current number of iterations, α is the iteration attenuation weight, and Fitness i,t is the fitness of particle i at the corresponding position at the tth iteration, g is the fitness of the global 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; Update the individual optimal position p of the particle i,k+1 , global optimal position g k+1 and the average optimal position m best , update the particle speed and position, the expression is: x i,k+1 =x i,k +v i,k+1 ·Δk where v i,k+1 is the velocity update of particle i at the k+1th iteration, v i,k is the velocity update of particle i at the kth iteration, ζ and z are random numbers in [0,1], and x i,k+1 is the position update of particle i at the k+1th iteration, x i,k is the position of particle i at the kth iteration, Cauchy(0,1) is the Cauchy perturbation, and λ is the perturbation intensity; The chaotic boundary mutation is performed on the out-of-bounds particles, and the expression is: where x' i,t+1 is the position of particle i after the position of the particle crosses the boundary at the k+1th iteration after the 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 the uniform distribution U(-0.05,0.05); Iterate continuously until the flow optimization objective function value is minimized or the maximum number of iterations is reached, and then stop the iteration and output the optimal flow parameters.

7. A U-shaped medium-deep buried pipe heat pump system for executing the method according to any one of claims 1 to 6, characterized in that: include: An underground pipe system, a circulating water pump, a heat pump and a building terminal buried pipe; the buried pipe system is used to fully exchange heat with the rock and soil 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 an upcomer; the insulation layer surrounds the top of the upcomer; 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, and to pump the heated circulating fluid to the inlet of the U-shaped medium-deep buried pipe; The outlet of the U-shaped medium-deep buried pipe is arranged at the upper end of the riser; the inlet of the U-shaped medium-deep buried pipe is arranged at the upper end of the downcomer; 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 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 transport the heated circulating fluid to the circulating water pump.

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