Improved particle swarm optimization algorithm optimization method for soil cross-season heat and cold storage buried pipe system oriented to yield maximization

Abstract

The invention discloses an improved particle swarm optimization algorithm optimization method for a soil cross-season heat and cold storage buried pipe system oriented to yield maximization, and belongs to the technical field of heating, ventilation and air conditioning and renewable energy utilization. Comprising the step of building a cross-season heat and cold storage buried pipe system model. Optimization variables are set from multiple dimensions such as the configuration scheme and the arrangement mode of the buried pipe heat exchange well. And an optimization objective function oriented to internal yield maximization is established. And setting constraint conditions, solving an optimization problem by using an improved particle swarm optimization (PSO) algorithm in optimization algorithms, and obtaining an optimal configuration scheme. According to the method, the operation benefit of the soil cross-season heat and cold storage buried pipe system can be effectively improved, and energy conservation and emission reduction in the system operation process are promoted.

Description

Technical Field

[0001] This invention belongs to the field of HVAC and renewable energy utilization technology, and relates to an improved particle swarm optimization strategy for soil cross-seasonal thermal and cold storage buried pipe systems aimed at maximizing returns. Specifically, it relates to a strategy and method that uses an improved particle swarm optimization algorithm to optimize the annual internal rate of return of cross-seasonal thermal and cold storage buried pipe systems from multiple dimensions such as the configuration scheme and arrangement of buried pipe heat exchange wells. Background Technology

[0002] The transition of energy systems towards low-carbon and sustainable development has become a global consensus, and energy storage technology, as a core means to address the mismatch between the volatility of renewable energy and the seasonality of energy demand, has received widespread attention. Among them, soil cross-seasonal thermal and cold storage technology based on buried pipe heat exchange wells has become a research hotspot in fields such as building heating, agricultural facility temperature control, and district heating due to its environmentally friendly, efficient, and economical characteristics.

[0003] The working principle of the soil cross-seasonal heat and cold storage buried pipe system is as follows: During the cooling season, the heat exchange well side of the buried pipe acts as the condenser of a heat pump unit, cooling the medium in the buried pipe and storing the condensation heat of the heat pump unit in the soil heat storage body, thus raising the soil temperature. At the same time, solar photovoltaic thermal energy and industrial waste heat are used to supplement the soil heating. During the heating season, the heat exchange well side of the buried pipe acts as the evaporator of a heat pump unit, heating the medium in the buried pipe, thus realizing heat extraction from the soil for heating and storing the cold energy in the soil. The soil temperature decreases and basically recovers to the temperature level before the cooling season, so as to start the heat and cold storage cycle for the next year.

[0004] Current optimization research on soil-based inter-seasonal thermal and cold storage buried pipe systems mainly focuses on control strategies, lacking systematic exploration of the configuration optimization of soil thermal storage bodies. Existing optimization methods generally rely on historical data, engineering experience, or energy consumption simulation software, making it difficult to accurately adapt to different geological conditions and project requirements. Meanwhile, traditional optimization methods are limited by computational complexity and parameter dimensionality, making it difficult to balance long-term thermal balance and system economics. With the rapid development of artificial intelligence and intelligent optimization algorithms, multi-objective optimization methods based on particle swarm optimization (PSO) and genetic algorithm (GA) provide new ideas for thermal storage body configuration optimization. Under complex constraints, through mathematical modeling and evolutionary optimization, they can optimize soil thermal and cold storage efficiency and heat loss, thereby optimizing the system's life-cycle cost and promoting the development of soil-based inter-seasonal thermal and cold storage buried pipe system technology towards intelligence and customization. Summary of the Invention

[0005] To maximize the profitability of soil-based cross-seasonal thermal and cold storage buried pipe systems, this invention proposes an improved particle swarm optimization strategy for such systems. This strategy optimizes the configuration parameters of the buried pipes in multiple dimensions using the improved particle swarm algorithm, thereby improving the system's operational efficiency and profitability.

[0006] The technical solution of this invention:

[0007] An improved particle swarm optimization method for soil-based transseasonal thermal and cold storage buried pipe systems, aimed at maximizing yield, includes the following steps:

[0008] S1. Construct a model of a cross-seasonal thermal and cold storage underground pipe system;

[0009] S2. Set optimization variables;

[0010] S3. Establish an objective function for maximizing returns;

[0011] S4. Set constraints;

[0012] S5. Solve the optimization problem using the improved particle swarm optimization algorithm.

[0013] Furthermore, the model for the cross-seasonal thermal and cold storage underground pipe system in step 1 includes the following steps:

[0014] S1.1. Based on the law of conservation of energy, a thermal balance model of the soil heat storage body is constructed, and the governing equations are as follows:

[0015]

[0016] The boundary conditions are:

[0017]

[0018] In the formula, ρ is the density of the soil heat storage body; c is the heat capacity of the soil heat storage body; k is the thermal conductivity of the soil heat storage body; T0 is the initial temperature of the soil; T S R is the soil temperature; r0 is the radius of the heat exchange well; R1 is the thermal resistance between the outer pipe and the soil; T W is the average temperature of the heat transfer fluid in the heat storage pipeline; r1 is the distance between heat exchange wells; r is the radius; t is time.

[0019] S1.2. Based on the law of conservation of energy, a heat balance model of the coaxial casing buried pipe heat exchange well is constructed. The governing equations are as follows:

[0020]

[0021] The boundary conditions are:

[0022]

[0023] In the formula, T1(L) and T2(L) are the temperatures of the fluids along the flow path in the outer and inner pipes, respectively; R2 is the thermal resistance between the outer and inner pipes; q m T is the mass flow rate of the fluid. in L represents the inlet temperature of the fluid in the buried pipe heat exchanger; L represents the length of the flow path; and H represents the depth of the buried pipe.

[0024] S1.3 Build a COP model for a heat pump operating under varying conditions;

[0025] S1.4 Build a pump model operating under varying conditions.

[0026] Furthermore, the optimization variables set in step 2 are selected from the source-side parameters of the cross-seasonal thermal and cold storage buried pipe system model built in step 1. These variables include key parameters of the buried pipe heat exchange wells and the soil heat storage body, namely the number of buried pipe heat exchange wells, the spacing between buried pipe heat exchange wells, the depth of buried pipe heat exchange wells, the inner and outer pipe diameters of the buried pipe heat exchange wells, the arrangement of the buried pipe heat exchange wells, the shape of the soil heat storage body, and the temperature change range of the soil heat storage body during system operation.

[0027] Furthermore, the steps described in step 3

[0028] An objective function for maximizing the rate of return is established. Using the cross-seasonal thermal and cold storage underground pipe system model built in step 1, the project configuration scheme and operating energy consumption are derived, and then the project's net present value and cash flow are calculated. The rate of return can be evaluated using the system's annual internal rate of return (IRR), calculated as follows:

[0029] NPV=CF0+CF1(1+IRR)+CF2(1+IRR) 2 +...+CF n (1+IRR) n =0

[0030] In the formula, NPV is the net present value of the project; CF i Let be the cash flow of the project in year i.

[0031] The cost includes the initial investment in drilling the underground pipe well and the electricity cost generated by hourly energy consumption. The total cost is calculated using the following formula:

[0032] W = 0.001·C·e + W0;

[0033] In the formula, e is the local average electricity price; W0 is the cost of arranging the buried pipe heat exchange well; C is the hourly cumulative energy consumption of the heat pump unit and the source-side heat pump, calculated as follows:

[0034] C=∑(P c (t)+Ph (t)+P wp (t));

[0035] In the formula, P c P h These represent the hourly energy consumption of the heat pump unit during the refrigerant and heating seasons, respectively; P wp The hourly energy consumption of the water pump is calculated using the following formula:

[0036]

[0037] In the formula, Q1 is the hourly cooling load, Q2 is the hourly heating load, and COPc and COPh are the performance coefficients of the heat pump unit in the cooling and heating seasons, respectively; H f η is the pump head; g is the acceleration due to gravity; η is the pump efficiency.

[0038] Furthermore, the setting of constraints in step 4 includes the following three points:

[0039] The temperature of the soil thermal storage body should change by less than 0.5℃ throughout the year after the system has been operating year-round using the buried pipe system for heat and cold storage and supplemented by solar photovoltaic thermal and industrial waste heat utilization devices.

[0040] The configured well drilling area should not exceed the actual available well drilling area of ​​the project; that is, the product of the number of wells and the area occupied by a single well should be less than the available well drilling area. The spacing between double U-shaped thermal storage wells is 2-5m, and the spacing between casing-type heat exchange wells is 5-10m.

[0041] During system operation under varying conditions, the flow rates of the evaporator, condenser, and water pump of the heat pump unit should not exceed or fall below the flow rate limits.

[0042] Furthermore, step 5 involves solving the optimization process using the improved particle swarm optimization algorithm. Specifically, this involves finding the optimal configuration scheme by optimizing the optimization variables determined in step 2 within the range of constraints determined in step 4, with the goal of maximizing the rate of return established in step 3. The improved particle swarm optimization algorithm includes the following steps:

[0043] S5.1 Initialize the particle position and velocity;

[0044] S5.2 Initialize the particle's flight history optimal position and the global optimal position in the swarm;

[0045] S5.3 Calculate the inertia factor and acceleration constant based on the number of iterations;

[0046] S5.4 updates the particle's position and velocity;

[0047] S5.5 finds the globally optimal particle;

[0048] S5.6 reaches the maximum number of iterations and outputs the result.

[0049] The beneficial effects of this invention are as follows: This invention proposes an improved particle swarm optimization method for soil-based transseasonal thermal and cold storage buried pipe systems aimed at maximizing the internal rate of return (IRR). Based on the improved particle swarm optimization method, the configuration of the soil-based transseasonal thermal and cold storage buried pipe system is optimized to maximize the IRR. This invention improves the convergence and stability of the algorithm by improving the particle update strategy and adaptive parameter adjustment; it establishes a mathematical model of the system, enabling efficient optimization of the source-side configuration scheme under different application scenarios; and it uses maximizing the system's operating rate of return as the optimization objective, which can significantly improve the energy efficiency and operational benefits of the soil-based transseasonal thermal and cold storage buried pipe system, achieving long-term efficient operation and maximizing benefits, and has significant economic advantages and engineering application value. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the steps of the improved particle swarm optimization strategy for a soil cross-seasonal thermal and cold storage buried pipe system aimed at maximizing yield in this invention.

[0051] Figure 2 This is a logic diagram of the soil cross-seasonal heat and cold storage buried pipe system of the present invention;

[0052] Figure 3 This is the logic diagram of the improved particle swarm algorithm used in this invention;

[0053] Figure 4 This invention relates to an optimized arrangement of buried pipe heat exchange wells. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1 This invention provides an improved particle swarm optimization strategy for soil cross-seasonal thermal and cold storage buried pipe systems aimed at maximizing returns. The improved particle swarm optimization algorithm optimizes the configuration parameters of the soil cross-seasonal thermal and cold storage buried pipe system in multiple dimensions, so that the system achieves the highest annual internal rate of return (IRR) under the given constraints. The strategy includes the following steps.

[0056] S1. Construct a model of a cross-seasonal thermal and cold storage underground pipe system;

[0057] S2. Set optimization variables;

[0058] S3. Establish an objective function for maximizing returns;

[0059] S4. Set constraints;

[0060] S5. Solve the optimization problem using the improved particle swarm optimization algorithm;

[0061] Please see Figure 2 The operating conditions of the cross-seasonal combined cooling and heating storage system include the following three types:

[0062] During the transitional season, solar photovoltaic and solar thermal modules, industrial waste heat utilization modules, and other waste heat utilization modules collect waste heat and store it in the soil heat storage body through buried pipe heat exchange wells to increase the soil temperature for use in winter heating.

[0063] In summer, all the condensing heat from the air conditioner and the compressor unit will be recovered and stored in the soil through the buried pipe heat exchanger. At the same time, the waste heat recovery equipment heats the inlet water temperature of the buried pipe through a plate heat exchanger, storing this heat in the soil for use in winter heating.

[0064] In winter, the extra waste heat will serve as a low-grade heat source for the heating heat pump unit to meet the heating needs of users; when the outlet water temperature of the waste heat utilization module is lower than the outlet water temperature of the buried pipe side, the heating will be entirely provided by the soil as a low-grade heat source.

[0065] Please see Figure 3 The optimization process of the improved particle swarm optimization algorithm is as follows:

[0066] The initial population of the particle swarm optimization algorithm is randomly generated, with each particle acting as a carrier of potential solutions and exhibiting a uniform distribution in the solution space. Each particle has two attributes: a position vector and a velocity vector. The position vector, in real coordinate form, determines the particle's specific orientation in the search space, while the velocity vector characterizes the particle's motion state and guides its subsequent movement.

[0067] The particle's velocity vector is updated by adjusting the particle's velocity based on its own best position in its flight history (pbest) and its global best position in the swarm (gbest), as calculated by the following formula:

[0068]

[0069] In the formula, w is the inertia factor; c1 and c2 are acceleration constants; r1 and r2 are randomly generated numbers in the interval [0,1]; pbest i This represents the best position in the flight history of each particle i;

[0070] The particle updates its position vector based on its current velocity and position vectors, using the following formula;

[0071]

[0072] The inertia factor and acceleration coefficient jointly control the update characteristics of particle velocity, thus affecting the balance between the algorithm's global and local search capabilities in the solution space. In traditional PSO implementations, both are usually set to fixed values. This configuration can easily lead to premature convergence when dealing with high-dimensional and complex optimization problems.

[0073] The linear parameter decay strategy, as a typical dynamic adjustment method, follows a time-varying decreasing law in its mathematical expression. Therefore, a higher parameter value is assigned in the early stages of iteration, enabling the particle to maintain strong global exploration capabilities to cover a broad solution space. As the number of iterations increases, the parameter value gradually decays at a predetermined slope, and the algorithm transitions to a refined local optimization mode in the later stages. The formula for parameter update is as follows:

[0074]

[0075] In the formula, t is the current iteration number; T is the set total number of iterations;

[0076] During the iteration process, the fitness of each particle's position is calculated and recorded, with the fitness function determined by the optimization objective. This process aims to find the globally optimal particle.

[0077] After reaching the preset number of iterations, the iteration stops and the final global optimal solution is output.

[0078] Example:

[0079] This study takes a public medical center project as the subject of research. The total construction area of ​​the project is 111,000 square meters. 2 The heating area is 85,037 m². 2 The air-conditioned area is 85037m² 2 A simulation of its dynamic annual load yielded an air conditioning cooling index of 126 W / m². 2 The heating thermal index is 100W / m² 2 ;

[0080] Soil thermal response experiments were conducted at the project site, and the soil thermal conductivity was found to be 2.07 W / (m·℃), and the soil volumetric heat capacity was 2970 kJ / (m·℃).

[0081] The project utilizes solar photovoltaic and photothermal modules as waste heat recovery equipment to improve the operational efficiency of the inter-seasonal combined cooling and heating soil thermal storage system. The construction of the inter-seasonal combined cooling and heating soil thermal storage system includes the following steps:

[0082] A thermal balance model for the soil heat storage body is constructed based on the law of conservation of energy, and the governing equations are as follows:

[0083]

[0084] The boundary conditions are:

[0085]

[0086] In the formula, ρ is the density of the soil thermal storage body; c is the heat capacity of the soil thermal storage body; T S t is the temperature of the soil heat storage body; k is the thermal conductivity of the soil heat storage body; T0 is the initial temperature of the soil; T S (r,t) represents the soil temperature; r0 represents the radius of the heat exchange well; R1 represents the thermal resistance between the outer pipe and the soil.

[0087] A heat balance model for a coaxial casing buried pipe heat exchange well is constructed based on the law of conservation of energy. The governing equations are as follows:

[0088]

[0089] The boundary conditions are:

[0090]

[0091] In the formula, T1(L) and T2(L) are the temperatures of the fluids along the flow path in the outer and inner pipes, respectively; R2 is the thermal resistance between the outer and inner pipes; q m T is the mass flow rate of the fluid. in The inlet temperature of the fluid in the buried pipe heat exchanger is denoted by L; L is the length coordinate along the flow path.

[0092] Build a COP model for a heat pump operating under varying conditions;

[0093] Build a model of a water pump operating under varying conditions;

[0094] Set optimization variables, including key parameters of buried pipe heat exchange wells and soil heat storage bodies, namely the number of buried pipe heat exchange wells, the spacing between buried pipe heat exchange wells, the depth of buried pipe heat exchange wells, the inner and outer pipe diameters of buried pipe heat exchange wells, the arrangement of buried pipe heat exchange wells, the shape of soil heat storage bodies, and the temperature change range of soil heat storage bodies during system operation.

[0095] An objective function for maximizing the rate of return is established. The rate of return can be evaluated by the internal rate of return (IRR) of the system throughout the year. The calculation formula is as follows:

[0096] NPV=CF0+CF1(1+IRR)+CF2(1+IRR) 2 +...+CF n (1+IRR) n =0;

[0097] In the formula, NPV is the net present value of the project; CF i This represents the cash flow of the project in year i.

[0098] The cost includes the initial investment in drilling the underground pipe well and the electricity cost generated by hourly energy consumption. The total cost is calculated using the following formula:

[0099] W = 0.001·C·e + W0;

[0100] In the formula, e is the local average electricity price; W0 is the cost of arranging the buried pipe heat exchange well; C is the hourly cumulative energy consumption of the heat pump unit and the source-side heat pump, calculated as follows:

[0101] C=∑(P c (t)+P h (t)+P wp (t));

[0102] In the formula, P c P h These are the hourly energy consumption of the heat pump unit in terms of refrigerant and heating season, respectively.

[0103] P wp The hourly energy consumption of the water pump is calculated using the following formula:

[0104]

[0105] In the formula, COPc and COPh are the performance coefficients of the heat pump unit in the cooling and heating seasons, respectively; H f Let g be the pump head; g is the acceleration due to gravity, taken as 9.8 m / s². 2 η is the efficiency of the water pump, taken as 0.8;

[0106] Setting constraints for optimization problems includes the following three points:

[0107] The temperature of the soil thermal storage body should change by less than 0.5℃ throughout the year after the system has been operating year-round using the buried pipe system for heat and cold storage and the waste heat recovery device for supplemental heating.

[0108] The configured well drilling area should not exceed the actual available well drilling area of ​​the project; that is, the product of the number of wells and the area occupied by a single well should be less than the available well drilling area. The spacing between double U-shaped thermal storage wells is 2-5m, and the spacing between casing-type heat exchange wells is 5-10m.

[0109] During the operation of the system under varying operating conditions, the flow rates of the evaporator, condenser, and water pump of the heat pump unit should not exceed or fall below the flow rate limits.

[0110] An improved particle swarm optimization algorithm was used to optimize a soil-based cross-seasonal thermal and cold storage buried pipe system with the objective of maximizing yield. The optimized buried pipe configuration is as follows: 153 heat exchange wells, a well depth of 130m, a well spacing of 7m, an outer pipe diameter of 120mm, and an inner pipe diameter of 40mm. The soil thermal storage temperature rises from 12℃ to 22.23℃ during the cooling and transitional seasons, and drops back to 11.87℃ during the heating season.

[0111] Please see Figure 4 The optimized arrangement of the soil thermal storage wells is hexagonal, making the entire soil thermal storage body approximately cylindrical.

[0112] The optimized system internal rate of return is 14%, an increase of 4% compared to the original solution;

[0113] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An improved particle swarm optimization method for soil-based trans-seasonal thermal and cold storage buried pipe systems aimed at maximizing yield, characterized in that, Includes the following steps: S1. Construct a model of a cross-seasonal thermal and cold storage underground pipe system; S2. Set optimization variables; S3. Establish an objective function for maximizing returns; S4. Set constraints; S5. Solve the optimization problem using the improved particle swarm optimization algorithm.

2. The improved particle swarm optimization method for a soil-based trans-seasonal thermal and cold storage buried pipe system aimed at maximizing yield, as described in claim 1, is characterized in that... Step 1 involves building a model of a cross-seasonal thermal and cold storage underground pipe system, which includes the following steps: S1.

1. Based on the law of conservation of energy, a thermal balance model of the soil heat storage body is constructed, and the governing equations are as follows: The boundary conditions are: In the formula, ρ is the density of the soil heat storage body; c is the heat capacity of the soil heat storage body; k is the thermal conductivity of the soil heat storage body; T0 is the initial temperature of the soil; T S R is the soil temperature; r0 is the radius of the heat exchange well; R1 is the thermal resistance between the outer pipe and the soil; T W r1 is the average temperature of the heat transfer fluid in the heat storage pipeline; r1 is the distance between heat exchange wells; r is the radius; t is time; S1.

2. Based on the law of conservation of energy, a heat balance model for a coaxial casing buried pipe heat exchange well is constructed; the governing equations are as follows: The boundary conditions are: In the formula, T1(L) and T2(L) are the temperatures of the fluids along the flow path in the outer and inner pipes, respectively; R2 is the thermal resistance between the outer and inner pipes; q m T is the mass flow rate of the fluid. in The inlet temperature of the fluid in the buried pipe heat exchanger is given by L; the length of the flow path is given by H; and the depth of the buried pipe is given by H. S1.3 Build a COP model for a heat pump operating under varying conditions; S1.4 Build a pump model operating under varying conditions.

3. The improved particle swarm optimization method for a soil-based trans-seasonal thermal and cold storage buried pipe system aimed at maximizing yield, as described in claim 1, is characterized in that... The optimization variables set in step 2 are selected from the source-side parameters of the cross-seasonal thermal and cold storage buried pipe system model built in step 1. These variables include key parameters of the buried pipe heat exchange wells and the soil heat storage body, namely the number of buried pipe heat exchange wells, the spacing between buried pipe heat exchange wells, the depth of buried pipe heat exchange wells, the inner and outer pipe diameters of buried pipe heat exchange wells, the arrangement of buried pipe heat exchange wells, the shape of the soil heat storage body, and the temperature change range of the soil heat storage body during system operation.

4. The improved particle swarm optimization method for a soil-based trans-seasonal thermal and cold storage buried pipe system aimed at maximizing yield, as described in claim 1, is characterized in that... Step 3 involves establishing an objective function to maximize the rate of return. Using the cross-seasonal thermal and cold storage underground pipe system model built in Step 1, the project configuration scheme and operating energy consumption are derived, and then the project's net present value and cash flow are calculated. The rate of return can be evaluated using the system's annual internal rate of return (IRR), calculated as follows: NPV=CF0+CF1(1+IRR)+CF2(1+IRR) 2 +...+CF n (1+IRR) n =0 In the formula, NPV is the net present value of the project; CF i This represents the cash flow of the project in year i. The cost includes the initial investment in drilling the underground pipe well and the electricity cost generated by hourly energy consumption. The total cost is calculated using the following formula: W = 0.001·C·e + W0; In the formula, e is the local average electricity price; W0 is the cost of arranging the buried pipe heat exchange well; C is the hourly cumulative energy consumption of the heat pump unit and the source-side heat pump, calculated as follows: C=∑(P c (t)+P h (t)+P wp (t)); In the formula, P c P h These represent the hourly energy consumption of the heat pump unit during the refrigerant and heating seasons, respectively; P wp The hourly energy consumption of the water pump is calculated using the following formula: In the formula, Q1 is the hourly cooling load, Q2 is the hourly heating load, and COPc and COPh are the performance coefficients of the heat pump unit in the cooling and heating seasons, respectively; H f η is the pump head; g is the acceleration due to gravity; η is the pump efficiency.

5. An improved particle swarm optimization method for a soil-based trans-seasonal thermal and cold storage buried pipe system aimed at maximizing yield, as described in claim 1, is characterized in that... The setting of constraints in step 4 includes the following three points: The temperature of the soil thermal storage body should change by less than 0.5℃ throughout the year after the system has been operating year-round using the buried pipe system for heat and cold storage and supplemented by solar photovoltaic thermal and industrial waste heat utilization devices. The configured well drilling area should not exceed the actual available well drilling area of ​​the project, that is, the product of the number of wells and the area occupied by a single well is less than the available well drilling area; the well spacing of double U-shaped heat storage wells is 2-5m, and the spacing of casing heat exchange wells is 5-10m; During system operation under varying conditions, the flow rates of the evaporator, condenser, and water pump of the heat pump unit should not exceed or fall below the flow rate limits.

6. An improved particle swarm optimization method for a soil-based trans-seasonal thermal and cold storage buried pipe system aimed at maximizing yield, as described in claim 1, is characterized in that... Step 5 describes solving the optimization process using the improved particle swarm optimization algorithm. Specifically, it involves finding the optimal configuration scheme by optimizing the optimization variables determined in Step 2 within the range of constraints determined in Step 4, with the goal of maximizing the rate of return established in Step 3. The improved particle swarm optimization algorithm includes the following steps: S5.1 Initialize the particle position and velocity; S5.2 Initialize the particle's flight history optimal position and the global optimal position in the swarm; S5.3 Calculate the inertia factor and acceleration constant based on the number of iterations; S5.4 updates the particle's position and velocity; S5.5 finds the globally optimal particle; S5.6 reaches the maximum number of iterations and outputs the result.

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