Refrigerator optimization configuration method and system for large-capacity high-temperature superconducting energy storage magnet

Abstract

The present application relates to refrigeration optimization technical field, especially to a kind of large-capacity high-temperature superconducting energy storage magnet's refrigerator optimization configuration method and system, including according to superconducting energy storage magnet's magnetic field data and magnet current data, obtain superconducting magnet dynamic loss total amount;According to superconducting energy storage magnet's temperature data, surface emissivity and magnet material attribute, total thermal load of superconducting magnet is obtained using field-circuit coupling method;Based on superconducting magnet dynamic loss total amount and superconducting magnet total thermal load, construct the lumped thermal model of superconducting energy storage magnet;With refrigeration equipment total cost, magnet temperature rise and temperature recovery time as optimization goal, combined with lumped thermal model, refrigeration optimization configuration model is established using multi-objective optimization algorithm, and the optimal configuration scheme of refrigeration equipment is obtained.The optimal configuration scheme of refrigeration equipment is solved by lumped thermal model and multi-objective optimization algorithm etc., and the energy consumption and cost of refrigeration system are reduced, and the optimization precision and stability of system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigeration optimization, and in particular to a refrigerator optimization configuration method and system for a large-capacity high-temperature superconducting energy storage magnet. Background Art

[0002] The low-temperature design of high-temperature superconducting energy storage magnets is a key link to ensure their efficient and stable operation. Among them, the choice of refrigeration method is one of the core issues of low-temperature design. The existing refrigeration methods mainly include low-temperature medium immersion cooling and direct cooling by refrigerator. Compared with the traditional low-temperature medium immersion cooling method, direct cooling by refrigerator has the advantages of convenient operation and maintenance, long-term stable operation, compact structure and high safety, and has gradually become the mainstream choice for the low-temperature design of high-temperature superconducting energy storage magnets.

[0003] With the continuous breakthrough of small refrigerator technology, the direct cooling method of refrigerators is more and more widely used in high-temperature superconducting energy storage magnets. However, although the direct cooling method of refrigerators has many advantages, it still faces a series of challenges in the actual design of large-capacity high-temperature superconducting energy storage magnet low-temperature systems. For example, although some design schemes take into account the temperature gradient problem between the refrigerator cold head and the magnet to improve the cooling efficiency, they do not fully consider the magnet AC loss and eddy current loss of metal parts. These losses will have a significant impact on the overall performance of the system in actual operation, especially in large-capacity superconducting energy storage magnets. These losses will lead to a significant decrease in system efficiency. In addition, although the existing design schemes are evaluated The influence of steady-state heat leakage and joint resistance was estimated, but dynamic loss was ignored, which is also a factor that cannot be ignored in actual operation. Dynamic loss not only affects the operating efficiency of the system, but also causes unstable operation of the system and even causes safety hazards. Therefore, how to fully consider these loss factors in the design has become an important challenge in the current low-temperature design of high-temperature superconducting energy storage magnets. In terms of the choice of cooling method, although some design schemes use a combination of helium circulation and direct cooling, this method is costly and is mainly suitable for ultra-large-capacity superconducting energy storage systems. For small, medium or medium-capacity superconducting energy storage systems, this cooling method is obviously not economical and practical.

[0004] Therefore, most of the existing low-temperature designs of superconducting energy storage magnets adopt direct cooling with refrigerators. However, in actual configuration design, factors such as magnet refrigeration configuration and Dewar optimization are often ignored. In particular, during the operation of large-capacity superconducting energy storage magnets, factors such as radiant heat are often ignored, which leads to existing design schemes facing problems such as low operating efficiency in actual operation. Therefore, how to fully consider actual operating factors in the configuration of refrigerators for large-capacity, high-temperature superconducting energy storage magnets and fully consider the heat load during the operation of the superconducting energy storage system has become a problem that needs to be solved urgently. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a refrigerator optimization configuration method and system for a large-capacity high-temperature superconducting energy storage magnet.

[0006] In a first aspect, the present invention provides a method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet, the method comprising the following steps:

[0007] According to the magnetic field data and magnet current data of the superconducting energy storage magnet, the total dynamic loss of the superconducting magnet is obtained;

[0008] Based on the temperature data, surface emissivity and magnet material properties of the superconducting energy storage magnet, the total heat load of the superconducting magnet is calculated using the field-circuit coupling method.

[0009] Constructing a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet;

[0010] Taking the total cost of refrigeration equipment, magnet temperature rise and temperature recovery time as optimization objectives, combined with the lumped thermal model, a multi-objective optimization algorithm is used to establish a refrigeration optimization configuration model;

[0011] Solve the refrigeration optimization configuration model to obtain the optimal configuration solution for the refrigeration equipment.

[0012] In a further embodiment, the step of obtaining the total amount of dynamic loss of the superconducting magnet based on the magnetic field data and magnet current data of the superconducting energy storage magnet comprises:

[0013] According to the dynamic change characteristics of the current and magnetic field data of the superconducting energy storage magnet, the AC loss power generated by the superconducting energy storage magnet under the AC magnetic field is obtained;

[0014] According to the material properties and current data of the magnet cooling plate, the total eddy current loss power generated by the magnet cooling plate under the change of magnetic field is obtained;

[0015] The AC loss power and the total eddy current loss power are added together to obtain the total dynamic loss of the superconducting magnet.

[0016] In a further embodiment, the step of obtaining the AC power loss generated by the superconducting energy storage magnet under the AC magnetic field based on the current dynamic change characteristics and magnetic field data of the superconducting energy storage magnet includes:

[0017] According to the amplitude of the parallel component of the magnetic field of the superconducting energy storage magnet and the amplitude of the working current, the AC loss generated by the parallel magnetic field component is calculated;

[0018] The AC loss generated by the vertical magnetic field component is calculated based on the vertical component amplitude of the superconducting magnet and the width and thickness of the superconducting thin plate.

[0019] The total AC loss density of the superconducting magnet in a single cycle is obtained by adding the AC loss generated by the parallel magnetic field component and the AC loss generated by the perpendicular magnetic field component;

[0020] The AC loss power per unit volume of the superconducting energy storage magnet is calculated based on the total AC loss density of the superconducting magnet and the volume of the superconducting energy storage magnet.

[0021] In a further embodiment, the step of obtaining the total eddy current loss power generated by the magnet cooling plate under a changing magnetic field based on the material properties and current data of the magnet cooling plate comprises:

[0022] Based on the amplitude of the sinusoidal magnetic field in the superconducting energy storage magnet and the material properties of the magnet cooling plate, the eddy current loss power generated by a single magnet cooling plate in a sinusoidally varying magnetic field is calculated.

[0023] The eddy current loss power generated by each magnet cooling plate under the change of magnetic field is added together to obtain the total eddy current loss power of all magnet cooling plates in the magnet system.

[0024] In a further embodiment, the step of calculating the total heat load of the superconducting magnet using a field-circuit coupling method based on the temperature data, surface emissivity, and magnet material properties of the superconducting energy storage magnet comprises:

[0025] According to the temperature distribution and thermal conductivity of the superconducting energy storage magnet, the heat conduction load of the superconducting energy storage magnet is calculated using the heat conduction equation;

[0026] According to the surface emissivity, ambient temperature and geometric structure of the superconducting energy storage magnet, an equivalent radiation heat path model is constructed by finite element model and field-path coupling method.

[0027] Calculating the radiation heat load of the superconducting energy storage magnet according to the equivalent radiation heat circuit model;

[0028] The total heat load of the superconducting magnet is obtained by adding the conduction heat load and the radiation heat load of the superconducting energy storage magnet.

[0029] In a further embodiment, the step of constructing a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet comprises:

[0030] According to the geometric structure parameters and material property parameters of the superconducting energy storage magnet, a three-dimensional finite element model of the superconducting energy storage magnet is established using the finite element analysis method;

[0031] The total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet are used as heat source inputs, and a three-dimensional finite element model is used to simulate the heat conduction and radiation processes of the superconducting energy storage magnet under the action of the heat source to obtain the temperature distribution and heat flux density distribution of the superconducting energy storage magnet;

[0032] The temperature distribution and heat flux density distribution of the superconducting energy storage magnet are coupled with the equivalent radiation heat circuit model to construct a lumped thermal model of the superconducting energy storage magnet.

[0033] In a further embodiment, the step of establishing a refrigeration optimization configuration model using a multi-objective optimization algorithm with the total cost of the refrigeration equipment, the temperature rise of the magnet, and the temperature recovery time as optimization objectives in combination with the lumped thermal model includes:

[0034] According to the lumped thermal model of the superconducting energy storage magnet, the heat dissipation and temperature dynamic change of the superconducting energy storage magnet under different operating conditions are determined;

[0035] According to the heat dissipation and temperature dynamic change of superconducting energy storage magnets under different operating conditions, the optimization objective function is constructed with the total cost of refrigeration equipment, magnet temperature rise and temperature recovery time as optimization targets;

[0036] A mathematical model of a multi-objective optimization algorithm is constructed according to the optimization objective function and preset refrigerator operation constraints to obtain a refrigeration optimization configuration model.

[0037] In a further embodiment, the step of solving the refrigeration optimization configuration model to obtain the optimal configuration solution for the refrigeration equipment includes:

[0038] Determine the optimization variables of the multi-objective optimization algorithm, and input the optimization variables into the NSGA-II algorithm for population initialization and genetic iteration;

[0039] During the iteration process of the NSGA-II algorithm, the optimization objective function value of each individual is calculated using the cooling optimization configuration model to obtain the Pareto optimal solution set;

[0040] A gradient first-order algorithm is used to perform secondary optimization on each solution in the Pareto optimal solution set. In the secondary optimization process, the total cost of the refrigeration equipment is used as the optimization target, and the magnet temperature rise and temperature recovery time are converted into constraint conditions to obtain a secondary optimization solution set.

[0041] According to the secondary optimization solution set, an optimal configuration scheme for the refrigeration equipment is obtained.

[0042] In a further embodiment, the optimization variables include a superconducting energy storage magnet target temperature and an intermediate cold shield target temperature.

[0043] In a second aspect, the present invention provides a refrigerator optimization configuration system for a large-capacity high-temperature superconducting energy storage magnet, the system comprising:

[0044] A dynamic loss analysis module is used to obtain the total dynamic loss of the superconducting magnet based on the magnetic field data and magnet current data of the superconducting energy storage magnet;

[0045] The heat load analysis module is used to calculate the total heat load of the superconducting magnet using the field-circuit coupling method based on the temperature data, surface emissivity and magnet material properties of the superconducting energy storage magnet;

[0046] A heat source coupling construction module is used to construct a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet;

[0047] An optimization model building module is used to establish a refrigeration optimization configuration model using a multi-objective optimization algorithm in combination with the lumped thermal model, taking the total cost of the refrigeration equipment, the temperature rise of the magnet, and the temperature recovery time as optimization objectives;

[0048] The optimization model solving module is used to solve the refrigeration optimization configuration model and obtain the optimal configuration solution of the refrigeration equipment.

[0049] The present invention provides a method and system for optimizing the configuration of a refrigerator for a large-capacity, high-temperature superconducting energy storage magnet. The method calculates the total dynamic loss of the superconducting magnet based on the magnet magnetic field data and magnet current data of the superconducting magnet. The method also calculates the total heat load of the superconducting magnet using a field-circuit coupling method based on the magnet temperature data, surface emissivity, and magnet material properties. A lumped thermal model of the superconducting magnet is constructed based on the total dynamic loss and total heat load. A multi-objective optimization algorithm is used in conjunction with the lumped thermal model to establish a refrigerator optimization configuration model, using the total cost of the refrigerator, magnet temperature rise, and temperature recovery time as optimization objectives. The refrigerator optimization configuration model is then solved to obtain the optimal configuration of the refrigerator. Compared to existing technologies, this method, which uses a lumped thermal model and a multi-objective optimization algorithm to determine the optimal configuration of the refrigerator, not only reduces the energy consumption and cost of the refrigerator system, but also improves optimization accuracy and system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet provided by an embodiment of the present invention;

[0051] Figure 2 is a schematic diagram of an equivalent radiation heat path model provided by an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of a multi-layer insulating radiation screen model provided by an embodiment of the present invention;

[0053] Figure 4 Schematic diagram of a field-circuit coupling lumped model provided by an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of the cooling optimization configuration process provided by an embodiment of the present invention;

[0055] Figure 6 This is a block diagram of a refrigerator optimization configuration system for a large-capacity high-temperature superconducting energy storage magnet provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and are not to be construed as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of protection of the present invention. Many changes may be made to the present invention without departing from the spirit and scope of the present invention.

[0057] refer to Figure 1 The embodiment of the present invention provides a method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet, such as Figure 1 As shown, the method includes the following steps:

[0058] S1. Obtain the total dynamic loss of the superconducting magnet based on the magnetic field data and magnet current data of the superconducting energy storage magnet.

[0059] In this embodiment, the step of obtaining the total dynamic loss of the superconducting magnet based on the magnetic field data and magnet current data of the superconducting energy storage magnet includes:

[0060] According to the dynamic change characteristics of the current and magnetic field data of the superconducting energy storage magnet, the AC loss power generated by the superconducting energy storage magnet under the AC magnetic field is obtained;

[0061] According to the material properties and current data of the magnet cooling plate, the total eddy current loss power generated by the magnet cooling plate under the change of magnetic field is obtained;

[0062] The AC loss power and the total eddy current loss power are added together to obtain the total dynamic loss of the superconducting magnet.

[0063] The step of obtaining the AC power loss generated by the superconducting energy storage magnet under the AC magnetic field based on the current dynamic change characteristics and magnetic field data of the superconducting energy storage magnet comprises:

[0064] According to the amplitude of the parallel component of the magnetic field of the superconducting energy storage magnet and the amplitude of the working current, the AC loss generated by the parallel magnetic field component is calculated;

[0065] The AC loss generated by the vertical magnetic field component is calculated based on the vertical component amplitude of the superconducting magnet and the width and thickness of the superconducting thin plate.

[0066] The total AC loss density of the superconducting magnet in a single cycle is obtained by adding the AC loss generated by the parallel magnetic field component and the AC loss generated by the perpendicular magnetic field component;

[0067] The AC loss power per unit volume of the superconducting energy storage magnet is calculated based on the total AC loss density of the superconducting magnet and the volume of the superconducting energy storage magnet.

[0068] Specifically, the configuration of the superconducting magnet refrigerator must fully consider the heat load borne by the superconducting energy storage magnet. The heat load of the superconducting energy storage magnet is mainly composed of the radiation heat load P r , heat conduction power P c And the electromagnetic heat loss P generated under dynamic operating conditions em The electromagnetic heat loss is mainly caused by the eddy current loss in the magnet metal parts and the AC loss of the superconducting material itself under AC conditions. These losses are closely related to the design temperature of the magnet, the actual operating conditions and its power level. Specifically, the electromagnetic heat loss includes the AC loss power P of the superconducting coil. Q and the eddy current loss power P of the cooling structure w Two major sources.

[0069] Due to the presence of pinning centers in high-temperature superconducting materials, when a varying current is applied to the superconductor, the Lorentz force must overcome the pinning force to perform work, thereby generating heat energy, namely, AC loss. AC loss is a type of heat, which has an adverse effect on high-temperature superconductors that need to maintain a low-temperature operating environment. Therefore, this embodiment uses a method for calculating the energy density of AC losses generated by high-temperature superconducting tapes within a single magnetic field variation cycle to quantify the AC losses generated by the parallel magnetic field component and the perpendicular magnetic field component. For the AC losses generated by the parallel magnetic field component, this embodiment obtains magnetic field data (including the parallel magnetic field component B|| and the perpendicular magnetic field component B⊥) and magnet current data (including the operating current I, etc.) of the superconducting energy storage magnet through experimental measurement or numerical simulation. Based on the magnetic field data and magnet current data of the superconducting energy storage magnet, key electromagnetic parameters such as the amplitude of the parallel magnetic field component and the amplitude of the operating current of the superconducting energy storage magnet are obtained. Based on the key electromagnetic parameters such as the amplitude of the parallel magnetic field component and the amplitude of the operating current of the superconducting energy storage magnet, the AC losses generated by the parallel magnetic field component are calculated. The calculation formula for the AC losses generated by the parallel magnetic field component is:

[0070]

[0071] i=I / I c

[0072] β|| =B || / B p

[0073]

[0074] Where Q ∥ B is the AC loss caused by the parallel magnetic field component; ∥ is the amplitude of the parallel component of the magnetic field; μ0 is the magnetic permeability in vacuum; i is the transient current value; I is the operating current amplitude; I c is the critical current value, that is, the maximum current that the superconducting material can carry under specific conditions; B p is the penetration magnetic field, that is, the critical value of the magnetic field penetrating the superconducting material; J c is the critical current density, that is, the current per unit area of ​​the superconducting material in the critical state; d c is the thickness of the superconducting sheet.

[0075] Then, this embodiment calculates the AC loss generated by the vertical magnetic field component based on the amplitude of the vertical component of the superconducting magnet's magnetic field, the width of the superconducting thin plate, the thickness of the superconducting thin plate, and the characteristic magnetic field strength. The calculation formula for the AC loss generated by the vertical magnetic field component is:

[0076]

[0077] Where Q ⊥ B is the AC loss caused by the vertical magnetic field component; ⊥ is the amplitude of the vertical component of the magnetic field; w is the width of the superconducting sheet; d c is the thickness of the superconducting sheet; B d is the characteristic magnetic field intensity, that is, the specific magnetic field value that affects the AC loss of superconducting materials.

[0078] In this embodiment, the total AC loss density within a single cycle can be calculated using the AC loss generated by the parallel magnetic field component and the perpendicular magnetic field component, and the total AC loss power per unit volume can be further obtained. Specifically, in this embodiment, the AC loss generated by the parallel magnetic field component and the AC loss generated by the perpendicular magnetic field component are added together to obtain the total AC loss density of the superconducting magnet within a single cycle. Based on the total AC loss density within a single cycle and the volume of the superconducting magnet, the total AC loss power per unit volume is calculated. The calculation formula for the total AC loss power per unit volume is:

[0079] P=Q*V*f

[0080] QQ+Q

[0081] Where P is the total AC power loss per unit volume; Q is the total AC loss density of the superconducting magnet in a single cycle; V is the volume of the AC power loss calculation unit; and f is the frequency of the operating current.

[0082] At the same time, in this embodiment, the step of obtaining the total eddy current loss power generated by the magnet cooling plate under the change of the magnetic field according to the material properties and current data of the magnet cooling plate includes:

[0083] Based on the amplitude of the sinusoidal magnetic field in the superconducting energy storage magnet and the material properties of the magnet cooling plate, the eddy current loss power generated by a single magnet cooling plate in a sinusoidally varying magnetic field is calculated.

[0084] The eddy current loss power generated by each magnet cooling plate under the change of magnetic field is added together to obtain the total eddy current loss power of all magnet cooling plates in the magnet system.

[0085] Specifically, when the current in the magnet changes dynamically, a changing magnetic field will be generated around it. This changing magnetic field will induce eddy currents in the metal cooling parts, thereby causing eddy current losses. In order to cool more effectively, the magnet adopts conduction cooling, especially installing copper cooling plates on both sides of each unit coil in the magnet. Since these cooling plates are located in the area where the magnetic field is concentrated, they are most likely to be affected by eddy current losses. The existence of eddy current losses will not only cause the dynamic temperature rise of the magnet, but also reduce its charging and discharging efficiency. In severe cases, it may even threaten the safe and stable operation of the magnet. Therefore, it is very important to effectively limit the eddy current losses. The magnitude of the eddy current losses depends on the amplitude and frequency of the magnetic field and the metal Influenced by various factors such as the geometric dimensions, for a conductor disk in a sinusoidally varying magnetic field, this embodiment determines the material properties and geometric dimensions of the magnet cold plate, obtains the material properties (such as conductivity) and geometric dimensions (such as disk radius and thickness) of the magnet cold plate, and then calculates the eddy current loss power of a single conductor disk based on the data of the sinusoidally varying magnetic field in which the magnet cold plate is located (such as magnetic field amplitude and angular frequency). Finally, based on the number and layout of the cold plates in the magnet, the eddy current loss power of the copper cold plates on both sides of each unit coil is added to obtain the total eddy current loss power of the magnet cold plate. The axis of the disk is parallel to the direction of the magnetic field. The eddy current loss power calculation formula of the conductor disk in the sinusoidally varying magnetic field is:

[0086]

[0087] Where, P e is the eddy current loss power; σ is the conductivity of the disk; B m is the amplitude of the sinusoidal magnetic field; w is the angular frequency of the sinusoidal magnetic field; b is the radius of the disk; h is the thickness of the disk.

[0088] Finally, this embodiment adds the calculated AC power loss P and total eddy current power loss to obtain the total dynamic loss of the superconducting magnet. This total reflects the electromagnetic heat loss of the superconducting magnet under dynamic operating conditions and is of great significance for the configuration of the superconducting magnet refrigerator and the calculation of the heat load.

[0089] S2. Based on the temperature data, surface emissivity, and magnet material properties of the superconducting energy storage magnet, the total heat load of the superconducting magnet is calculated using the field-circuit coupling method.

[0090] In this embodiment, the step of calculating the total heat load of the superconducting magnet using the field-circuit coupling method based on the temperature data, surface emissivity, and magnet material properties of the superconducting energy storage magnet includes:

[0091] According to the temperature distribution and thermal conductivity of the superconducting energy storage magnet, the heat conduction load of the superconducting energy storage magnet is calculated using the heat conduction equation;

[0092] According to the surface emissivity, ambient temperature and geometric structure of the superconducting energy storage magnet, an equivalent radiation heat path model is constructed by finite element model and field-path coupling method.

[0093] Calculating the radiation heat load of the superconducting energy storage magnet according to the equivalent radiation heat circuit model;

[0094] The total heat load of the superconducting magnet is obtained by adding the conduction heat load and the radiation heat load of the superconducting energy storage magnet.

[0095] Specifically, in superconducting energy storage systems, the conductive heat load is mainly generated by mechanical supports and current leads. The magnitude of this heat load is closely related to the temperature of the magnet and the current intensity. For large-capacity magnets, conductive heat leakage dominates the total heat load. In order to effectively manage this heat load, the current leads of superconducting energy storage magnets usually adopt a binary structure design. This design consists of two main parts: the first section is a conventional metal lead, and the second section is a high-temperature superconducting current lead. Among them, the thermal characteristics of conventional metal leads are mainly determined by two factors: Joule heat and conductive heat. Joule heat is directly related to the resistivity of the metal, while conductive heat is related to the thermal conductivity of the metal. Therefore, the heat distribution of conventional metal leads is ultimately affected by the resistivity and thermal conductivity of the metal. The relationship between these two factors and temperature is:

[0096]

[0097] Among them, the heat distribution of conventional metal leads can be decomposed into two parts: one part is conduction heat, that is, heat is transferred through the thermal conductivity of the metal lead itself; the other part is Joule heat, that is, the resistance heat generated when the current passes through the metal lead. The sum of these two parts of heat is related to the cross-sectional area of ​​the lead. The larger the cross-sectional area, the more uniform the heat distribution. In contrast, the high-temperature superconducting current lead is in a superconducting state during operation, which means that no Joule heat is generated inside it. Therefore, the heat leakage of the high-temperature superconducting current lead is only composed of conduction heat. This feature gives the high-temperature superconducting current lead a significant advantage in superconducting energy storage systems because it can significantly reduce the thermal load of the system.

[0098] After determining the magnet temperature and current intensity, this embodiment accurately calculates the conduction heat leakage based on the design temperature, current intensity, structure, and material properties of the superconducting current lead. This calculation process is based on the thermal conductivity characteristics and current density distribution of the superconducting current lead. The relationship function between the magnet conduction heat leakage and the magnet temperature and current is:

[0099] P c =P c (T magnet ,I)

[0100] In summary, the conduction heat leakage of the magnet is mainly related to the temperature of the magnet and the design current. It is a function of these two parameters. In the design and optimization process of the superconducting energy storage system, fully considering these factors is crucial to improving the performance and stability of the system.

[0101] At the same time, in the thermal management of superconducting energy storage systems, the calculation of the radiation heat load of superconducting energy storage magnets is also crucial. The traditional calculation method is to directly apply the Stefan-Boltzmann law to realize the radiation heat load calculation of superconducting energy storage magnets. The Stefan-Boltzmann law states that the radiation power is proportional to the blackbody radiation constant, surface emissivity, surface area and the fourth variance of the surface temperature. However, this method is based on two idealized assumptions: one is that the radiation surface is regarded as an ideal surface that is protruding everywhere, and the radiation inside the surface is ignored; the other is that it is assumed that the area of ​​one surface is much larger than that of the other surface, so that it can be regarded as the environment; these assumptions are often not valid in the thermal radiation calculation of the Dewar room, and therefore will lead to large errors. In order to overcome this limitation, this embodiment adopts a combination of the equivalent radiation heat path model and the finite element method to calculate the radiation heat load of the superconducting magnet through field-path coupling. Among them, for the equivalent radiation heat path model of the multi-surface radiation system, such as Figure 2As shown, in the equivalent radiation heat circuit model, each surface is regarded as a node with equivalent internal potential (i.e., blackbody radiation power per unit area), internal resistance, and external voltage (i.e., total radiation power emitted per unit area). The connection between nodes is represented by equivalent resistances, which reflect the transfer of radiation heat between surfaces. In this way, this embodiment can simplify the complex radiation heat transfer process into a circuit problem, making it easier to calculate and analyze. Figure 2 In, E bi is the blackbody radiation power per unit area of ​​surface i, which is also the equivalent internal potential of surface i. The blackbody radiation power refers to the energy radiated per unit area of ​​an idealized blackbody surface at a certain temperature; r i is the equivalent internal resistance of surface i. In the equivalent radiation heat path model, the internal resistance is used to describe the surface’s own resistance to radiation heat; J i is the total radiation power emitted per unit area of ​​surface i, which is also the equivalent external voltage of surface i, representing the total energy radiated outward by surface i; R ij is the equivalent resistance between surface i and surface j, which reflects the transfer efficiency of radiant heat between surface i and surface j; P ri is the net total heat power radiated by surface i, which is also the output current of surface i. This is the net value of the total heat power radiated outward by surface i minus the heat power radiated from other surfaces; q ij is the net radiation heat power from surface i to surface j, which is also the current from node i to node j. This is the net radiation energy transferred from surface i to surface j. Its mathematical expression is:

[0102] E bi =σT i 4

[0103]

[0104]

[0105] Where, ε i 、T i 、A i are the surface emissivity, temperature, and area of ​​surface i respectively; X ij is the surface coefficient between surface i and surface j, that is, the proportion of radiation emitted from surface i that is input to surface j. It is only related to the geometric structure of surfaces i and j and has nothing to do with other factors. It should be noted that in the equivalent radiation heat circuit model, each surface is regarded as a node, and the connection between nodes is represented by an equivalent resistance. Therefore, when radiation heat is transferred between surfaces i and j, it is actually describing the current (that is, radiation heat power) transfer between nodes i and j.

[0106] like Figure 3 As shown, for the coverage of the multi-layer insulation radiation screen model, this embodiment further constructs an equivalent emissivity model of the multi-layer insulation radiation screen. In this model, this embodiment assumes that each radiation screen is completely wrapped, the surface coefficient is 1, and there is no conductive heat contact. In this way, the equivalent internal resistance of the surface becomes the sum of the series resistance of the multi-layer radiation screen, which makes it easier to calculate the radiation heat load under the multi-layer insulation structure. Its mathematical expression is:

[0107]

[0108] Where, ε r is the surface emissivity of the thermal insulation radiation screen; n is the number of thermal insulation radiation screen layers.

[0109] Although the equivalent radiation heat path model provides a simplified calculation method, it still has certain limitations when dealing with complex geometric structures and non-uniform surface coefficient distribution. In order to obtain more accurate results, this embodiment combines the finite element method. The finite element method simulates the transfer process of radiation heat in complex geometric structures by calculating the angular coefficient. However, this method requires a large amount of memory and computational complexity. In order to balance the calculation accuracy and efficiency, this embodiment adopts the field-path coupling method. This method first simplifies the radiation heat path model through star network transformation, and then uses the results of the finite element method to fit and identify the simplified model, thereby obtaining the equivalent lumped radiation thermal resistance R ∑ :

[0110]

[0111] This equivalent lumped radiation thermal resistance is only related to the geometric structure of the magnet and the cold shield, and is independent of the temperature and power of the refrigerator. Therefore, it can be reused under different cooling configurations, providing great convenience for the optimized design of superconducting energy storage systems. In summary, this embodiment combines the equivalent radiation heat path model with the finite element method and uses field-path coupling to efficiently and accurately calculate the radiation heat load of the superconducting energy storage magnet. This method not only overcomes the limitations of traditional methods, but also provides strong support for the thermal management of superconducting energy storage systems. Figure 4 A schematic diagram showing the field-circuit coupled lumped model.

[0112] S3. Constructing a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet.

[0113] In this embodiment, the step of constructing a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet includes:

[0114] According to the geometric structure parameters and material property parameters of the superconducting energy storage magnet, a three-dimensional finite element model of the superconducting energy storage magnet is established using the finite element analysis method;

[0115] The total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet are used as heat source inputs, and a three-dimensional finite element model is used to simulate the heat conduction and radiation processes of the superconducting energy storage magnet under the action of the heat source to obtain the temperature distribution and heat flux density distribution of the superconducting energy storage magnet;

[0116] The temperature distribution and heat flux density distribution of the superconducting energy storage magnet are coupled with the equivalent radiation heat circuit model to construct a lumped thermal model of the superconducting energy storage magnet.

[0117] S4. Taking the total cost of the refrigeration equipment, the temperature rise of the magnet, and the temperature recovery time as optimization objectives, combined with the lumped thermal model, a multi-objective optimization algorithm is used to establish a refrigeration optimization configuration model.

[0118] In this embodiment, the steps of establishing a refrigeration optimization configuration model using a multi-objective optimization algorithm with the total cost of the refrigeration equipment, the magnet temperature rise, and the temperature recovery time as optimization objectives and in combination with the lumped thermal model include:

[0119] According to the lumped thermal model of the superconducting energy storage magnet, the heat dissipation and temperature dynamic change of the superconducting energy storage magnet under different operating conditions are determined;

[0120] According to the heat dissipation and temperature dynamic change of superconducting energy storage magnets under different operating conditions, the optimization objective function is constructed with the total cost of refrigeration equipment, magnet temperature rise and temperature recovery time as optimization targets;

[0121] A mathematical model of a multi-objective optimization algorithm is constructed according to the optimization objective function and preset refrigerator operation constraints to obtain a refrigeration optimization configuration model.

[0122] Specifically, to ensure that the magnet can be cooled to the design temperature smoothly and remain stable, the total power of the refrigeration system must at least match the static total heat load of the magnet. This static total heat load consists of two parts: conduction heat and radiation heat, both of which change with the changes in the magnet temperature T1 and the intermediate cold screen temperature T2. Among them, the total power of the refrigerator P ∑ The function of magnet temperature T1 and intermediate cold screen temperature T2 is:

[0123] P ∑ (T1)≥P r (T1,T2)+P c (T1,T2)

[0124] During the actual dynamic operation of the magnet, due to the existence of electromagnetic loss, the magnet will experience a certain temperature rise. The maximum temperature rise Tmax in this dynamic process and the recovery time τ of the thermal system under the action of the refrigerator are closely related to the dynamic electromagnetic loss, refrigerator power and the design temperature of the magnet. These parameters are simulated by the finite element method and simplified into a lumped parameter model for simulation. Finally, their functional relationship as dynamic electromagnetic loss, refrigerator power and magnet design temperature is obtained. Among them, the dynamic electromagnetic loss includes eddy current loss and AC loss P. They are both functions of magnet temperature and current. They can be accurately solved by the magnet electromagnetic loss calculation method. The function of dynamic electromagnetic loss, refrigerator power and magnet design temperature is:

[0125] T max =T max (P em ,P ∑ ,T1,T2)

[0126] τ=τ(P em ,P ∑ ,T1,T2)

[0127] P em =P Q (T1,I)+P W (T1,I)

[0128] Combining the above process, such as Figure 5 As shown in the figure, the specific implementation process of cooling optimization configuration is as follows:

[0129] Determine the magnet structure, operating current, and power level based on the magnet's target design temperature T1 and the intermediate cooling screen's target design temperature T2;

[0130] Calculate the static radiative and conductive heat loads of the magnet based on its structure, operating current, and power level;

[0131] Calculate the electromagnetic loss of the magnet under dynamic operation, which includes eddy current loss and AC loss P;

[0132] The lumped thermal model of the heat transfer system is constructed by coupling the finite element model with the equivalent radiation heat path model.

[0133] According to the cooling power, heat load and electromagnetic loss of the magnet, the maximum dynamic temperature rise T of the magnet under dynamic operation is determined. max These two parameters, τ and temperature recovery time, are functions of refrigeration power and temperature, reflecting the thermal stability of the magnet in the dynamic process. Their mathematical expressions are:

[0134] T max =T max (P Σ,T1,T2)

[0135] τ=τ(P Σ ,T1,T2)

[0136] The total cost of refrigeration equipment is divided into one-time investment cost E1 and operating cost E2. When the refrigerator model is determined, the one-time investment cost is mainly determined by the cooling power and operating temperature. Binghuo calculates the total cost of the refrigeration equipment through the cooling power curve Pf(T1, T2) and the single unit investment cost E.

[0137] In the above steps, this embodiment establishes a model with operating temperatures T1, T2 and cooling power PΣ as optimization variables, and minimizing the total cost of the refrigeration equipment, the magnet temperature rise Tmax and the temperature recovery time τ as the optimization objective function. By adopting a multi-objective combined optimization algorithm, this embodiment can solve the optimal refrigeration configuration solution while meeting the thermal stability and economy of the magnet.

[0138] S5. Solve the refrigeration optimization configuration model to obtain the optimal configuration solution for the refrigeration equipment.

[0139] In this embodiment, the step of solving the refrigeration optimization configuration model to obtain the optimal configuration solution for the refrigeration equipment includes:

[0140] Determining optimization variables of a multi-objective optimization algorithm, wherein the optimization variables include a target temperature of a superconducting energy storage magnet and a target temperature of an intermediate cold shield;

[0141] The optimization variables are input into the NSGA-II algorithm for population initialization and genetic iteration, and during the iteration process of the NSGA-II algorithm, the optimization objective function value of each individual is calculated using the refrigeration optimization configuration model to obtain a Pareto optimal solution set;

[0142] A gradient first-order algorithm is used to perform secondary optimization on each solution in the Pareto optimal solution set. In the secondary optimization process, the total cost of the refrigeration equipment is used as the optimization target, and the magnet temperature rise and temperature recovery time are converted into constraint conditions to obtain a secondary optimization solution set.

[0143] According to the secondary optimization solution set, an optimal configuration scheme for the refrigeration equipment is obtained.

[0144] Specifically, in the application of superconducting magnets, as the operating temperature of the magnet decreases, the superconducting critical current will increase, while the volume of the magnet will decrease and the structure will become more compact. This change leads to a decrease in the radiation surface area of ​​the magnet, thereby reducing the radiation heat load. At the same time, the number of required structural supports is reduced, and the conductive heat load is also reduced. Therefore, the static heat load of the magnet will decrease as the operating temperature decreases. In addition, a larger critical current will also reduce AC losses, thereby reducing dynamic electromagnetic losses.

[0145] However, although lower operating temperatures can reduce the heat load of the magnet, it will also lead to a reduction in the economy and refrigeration efficiency of the refrigerator. Therefore, there is an economically optimal operating temperature point for superconducting magnets. When the operating temperature is higher than this point, the higher heat load will increase the refrigeration cost; and when the operating temperature is lower than this point, although the heat load is reduced, the refrigeration efficiency will also decrease, thereby reducing the economy.

[0146] In addition, there is a contradiction between the economic cost of the refrigerator and the thermal stability of the cryogenic system. Lower refrigeration power can reduce the cost of the cryogenic system, but it is more likely to cause magnet quenching or even burning under large dynamic heat loads. Conversely, higher refrigeration power can allow the magnet to operate under high current and high power conditions, reducing dynamic temperature rise and temperature recovery time, and improving the thermal stability of the system, but it also incurs greater costs. To address this issue, this embodiment adopts the concept of Pareto optimality to optimize the configuration of the cryogenic system in multiple dimensions. Pareto optimality allows finding the optimal trade-off solution between multiple objective functions, rather than simply pursuing the optimal solution of a single objective function. The specific implementation steps are as follows:

[0147] This embodiment defines multiple objective functions, including economy, refrigerator scale, volume, etc., and establishes corresponding constraints. These objective functions and constraints together constitute a mathematical model of the optimization problem. Then, the NSGA-II algorithm is used to solve the Pareto non-inferior set of the optimization problem. The NSGA-II algorithm is a commonly used multi-objective optimization algorithm. It uses a population method to find the global optimal solution through iteration and mutation mechanisms. During the solution process, this embodiment does not need to use the gradient or Hessian matrix of the objective function, which makes it easier for the algorithm to escape the local minimum. However, as a zero-order method, the NSGA-II algorithm has a slow calculation speed and low accuracy.

[0148] To overcome the shortcomings of the NSGA-II algorithm, this embodiment uses a gradient first-order algorithm for secondary optimization. This algorithm has the advantages of high precision and fast computational speed, and can complement the population method to obtain more accurate solutions within the local area. During the secondary optimization process, this embodiment uses each solution in the Pareto non-inferior set as the initial solution and uses the Zoutendijk feasible direction method to iteratively optimize within its domain. At the same time, this embodiment uses the total refrigeration cost as the most important optimization indicator and converts other indicators into constraints. After secondary optimization, this embodiment obtains a new set of solutions, namely Pareto set 2. In Pareto set 2, this embodiment comprehensively considers factors such as economy, refrigerator scale, and volume, and selects the optimal configuration scheme for the refrigeration equipment as the final optimization result. Through this method, this embodiment can find the optimal solution that balances the economy of the low-temperature system and the thermal stability of the magnet at the economically optimal operating temperature point, thereby meeting the needs of superconducting magnets in practical applications.

[0149] An embodiment of the present invention provides a method for optimizing the configuration of a refrigerator for a large-capacity, high-temperature superconducting energy storage magnet. The method obtains the total dynamic loss of the superconducting magnet based on the magnetic field data and magnet current data of the superconducting magnet. The method calculates the total heat load of the superconducting magnet using a field-circuit coupling method based on the temperature data, surface emissivity, and magnet material properties of the superconducting magnet. A lumped thermal model of the superconducting magnet is constructed based on the total dynamic loss and total heat load. A multi-objective optimization algorithm is used in conjunction with the lumped thermal model to establish a refrigerator configuration optimization model, taking the total cost of the refrigerator, the magnet temperature rise, and the temperature recovery time as optimization objectives. The refrigerator configuration optimization model is then solved to obtain the optimal configuration of the refrigerator. Compared with the prior art, this method, which uses a lumped thermal model and a multi-objective optimization algorithm to solve the optimal configuration of the refrigerator, not only improves the cooling efficiency of large-capacity, high-temperature superconducting energy storage magnets, reduces the energy consumption and operating cost of the refrigerator system, achieves rapid and uniform cooling of the magnet to the superconducting temperature, but also improves the response speed and stability of the system.

[0150] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of this application.

[0151] In one embodiment, Figure 6 As shown, an embodiment of the present invention provides a refrigerator optimization configuration system for a large-capacity high-temperature superconducting energy storage magnet, the system comprising:

[0152] The dynamic loss analysis module 101 is used to obtain the total dynamic loss of the superconducting magnet based on the magnetic field data and magnet current data of the superconducting energy storage magnet;

[0153] The heat load analysis module 102 is used to calculate the total heat load of the superconducting magnet using a field-circuit coupling method based on the temperature data, surface emissivity and magnet material properties of the superconducting energy storage magnet;

[0154] A heat source coupling construction module 103 is configured to construct a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet;

[0155] An optimization model building module 104 is configured to establish a refrigeration optimization configuration model using a multi-objective optimization algorithm in combination with the lumped thermal model, taking the total cost of the refrigeration equipment, the temperature rise of the magnet, and the temperature recovery time as optimization objectives;

[0156] The optimization model solving module 105 is used to solve the refrigeration optimization configuration model to obtain the optimal configuration solution of the refrigeration equipment.

[0157] For the specific definition of a refrigerator optimization configuration system for a large-capacity, high-temperature superconducting energy storage magnet, please refer to the above-mentioned definition of a refrigerator optimization configuration method for a large-capacity, high-temperature superconducting energy storage magnet, which will not be repeated here. A person of ordinary skill in the art will appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0158] An embodiment of the present invention provides a refrigerator optimization configuration system for a large-capacity, high-temperature superconducting energy storage magnet. The system's dynamic loss analysis module obtains the total dynamic loss of the superconducting magnet based on the magnetic field data and magnet current data of the superconducting energy storage magnet. The thermal load analysis module calculates the total thermal load of the superconducting magnet using a field-circuit coupling method based on the temperature data, surface emissivity, and magnet material properties of the superconducting energy storage magnet. The heat source coupling construction module constructs a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss and the total thermal load of the superconducting magnet. The optimization model construction module uses the total cost of the refrigeration equipment, the magnet temperature rise, and the temperature recovery time as optimization targets, and combines the lumped thermal model to establish a refrigeration optimization configuration model using a multi-objective optimization algorithm. The optimization model solving module solves the refrigeration optimization configuration model to obtain the optimal configuration solution for the refrigeration equipment. Compared with existing technologies, this system solves the optimal configuration plan for refrigeration equipment through methods such as lumped thermal models and multi-objective optimization algorithms. It not only improves the cooling efficiency of large-capacity high-temperature superconducting energy storage magnets, reduces the energy consumption and operating costs of the refrigeration system, achieves rapid and uniform cooling of the magnets to superconducting temperature, but also improves the response speed and stability of the system.

[0159] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet, characterized in that: The following steps are involved: According to the magnetic field data and magnet current data of the superconducting energy storage magnet, the total dynamic loss of the superconducting magnet is obtained; Based on the temperature data, surface emissivity and magnet material properties of the superconducting energy storage magnet, the total heat load of the superconducting magnet is calculated using the field-circuit coupling method. Constructing a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet; Taking the total cost of refrigeration equipment, magnet temperature rise and temperature recovery time as optimization objectives, combined with the lumped thermal model, a multi-objective optimization algorithm is used to establish a refrigeration optimization configuration model; Solving the refrigeration optimization configuration model to obtain the optimal configuration solution for the refrigeration equipment; The step of calculating the total heat load of the superconducting magnet using a field-circuit coupling method based on the temperature data, surface emissivity, and magnet material properties of the superconducting energy storage magnet comprises: According to the temperature distribution and thermal conductivity of the superconducting energy storage magnet, the heat conduction load of the superconducting energy storage magnet is calculated using the heat conduction equation; According to the surface emissivity, ambient temperature and geometric structure of the superconducting energy storage magnet, an equivalent radiation heat path model is constructed by finite element model and field-path coupling method. Calculating the radiation heat load of the superconducting energy storage magnet according to the equivalent radiation heat circuit model; Adding the conduction heat load and radiation heat load of the superconducting energy storage magnet to obtain the total heat load of the superconducting magnet; The step of constructing a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet comprises: According to the geometric structure parameters and material property parameters of the superconducting energy storage magnet, a three-dimensional finite element model of the superconducting energy storage magnet is established using the finite element analysis method; The total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet are used as heat source inputs, and a three-dimensional finite element model is used to simulate the heat conduction and radiation processes of the superconducting energy storage magnet under the action of the heat source to obtain the temperature distribution and heat flux density distribution of the superconducting energy storage magnet; The temperature distribution and heat flux density distribution of the superconducting energy storage magnet are coupled with the equivalent radiation heat circuit model to construct a lumped thermal model of the superconducting energy storage magnet.

2. The method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet according to claim 1, wherein: The step of obtaining the total amount of dynamic loss of the superconducting magnet based on the magnetic field data and magnet current data of the superconducting energy storage magnet comprises: According to the dynamic change characteristics of the current and magnetic field data of the superconducting energy storage magnet, the AC loss power generated by the superconducting energy storage magnet under the AC magnetic field is obtained; According to the material properties and current data of the magnet cooling plate, the total eddy current loss power generated by the magnet cooling plate under the change of magnetic field is obtained; The AC loss power and the total eddy current loss power are added together to obtain the total dynamic loss of the superconducting magnet.

3. The method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet according to claim 2, wherein: The step of obtaining the AC power loss generated by the superconducting energy storage magnet under the AC magnetic field according to the current dynamic change characteristics and magnetic field data of the superconducting energy storage magnet comprises: According to the amplitude of the parallel component of the magnetic field of the superconducting energy storage magnet and the amplitude of the working current, the AC loss generated by the parallel magnetic field component is calculated; The AC loss generated by the vertical magnetic field component is calculated based on the vertical component amplitude of the superconducting magnet and the width and thickness of the superconducting thin plate. The total AC loss density of the superconducting magnet in a single cycle is obtained by adding the AC loss generated by the parallel magnetic field component and the AC loss generated by the perpendicular magnetic field component; The AC loss power per unit volume of the superconducting energy storage magnet is calculated based on the total AC loss density of the superconducting magnet and the volume of the superconducting energy storage magnet.

4. The method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet according to claim 2, wherein: The step of obtaining the total eddy current loss power generated by the magnet cooling plate under a magnetic field change according to the material properties and current data of the magnet cooling plate comprises: Based on the amplitude of the sinusoidal magnetic field in the superconducting energy storage magnet and the material properties of the magnet cooling plate, the eddy current loss power generated by a single magnet cooling plate in a sinusoidally varying magnetic field is calculated. The eddy current loss power generated by each magnet cooling plate under the change of magnetic field is added together to obtain the total eddy current loss power of all magnet cooling plates in the magnet system.

5. The method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet according to claim 1, wherein: The steps of establishing a refrigeration optimization configuration model using a multi-objective optimization algorithm with the total cost of refrigeration equipment, magnet temperature rise, and temperature recovery time as optimization objectives and in combination with the lumped thermal model include: According to the lumped thermal model of the superconducting energy storage magnet, the heat dissipation and temperature dynamic change of the superconducting energy storage magnet under different operating conditions are determined; According to the heat dissipation and temperature dynamic change of superconducting energy storage magnets under different operating conditions, the optimization objective function is constructed with the total cost of refrigeration equipment, magnet temperature rise and temperature recovery time as optimization targets; A mathematical model of a multi-objective optimization algorithm is constructed according to the optimization objective function and preset refrigerator operation constraints to obtain a refrigeration optimization configuration model.

6. The method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet according to claim 5, wherein: The step of solving the refrigeration optimization configuration model to obtain the optimal configuration solution for the refrigeration equipment includes: Determine the optimization variables of the multi-objective optimization algorithm, and input the optimization variables into the NSGA-II algorithm for population initialization and genetic iteration; During the iteration process of the NSGA-II algorithm, the optimization objective function value of each individual is calculated using the cooling optimization configuration model to obtain the Pareto optimal solution set; A gradient first-order algorithm is used to perform secondary optimization on each solution in the Pareto optimal solution set. In the secondary optimization process, the total cost of the refrigeration equipment is used as the optimization target, and the magnet temperature rise and temperature recovery time are converted into constraint conditions to obtain a secondary optimization solution set. According to the secondary optimization solution set, an optimal configuration scheme for the refrigeration equipment is obtained.

7. The method for optimizing the configuration of a refrigerator for a large-capacity high-temperature superconducting energy storage magnet according to claim 6, wherein: The optimization variables include the target temperature of the superconducting energy storage magnet and the target temperature of the intermediate cold shield.

8. A refrigerator optimization configuration system for a large-capacity high-temperature superconducting energy storage magnet, characterized in that: The system comprises: A dynamic loss analysis module is used to obtain the total dynamic loss of the superconducting magnet based on the magnetic field data and magnet current data of the superconducting energy storage magnet; The heat load analysis module is used to calculate the total heat load of the superconducting magnet using the field-circuit coupling method based on the temperature data, surface emissivity and magnet material properties of the superconducting energy storage magnet; A heat source coupling construction module is used to construct a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet; An optimization model building module is used to establish a refrigeration optimization configuration model using a multi-objective optimization algorithm in combination with the lumped thermal model, taking the total cost of the refrigeration equipment, the temperature rise of the magnet, and the temperature recovery time as optimization objectives; An optimization model solving module is used to solve the refrigeration optimization configuration model to obtain the optimal configuration solution for the refrigeration equipment; The step of calculating the total heat load of the superconducting magnet using a field-circuit coupling method based on the temperature data, surface emissivity, and magnet material properties of the superconducting energy storage magnet comprises: According to the temperature distribution and thermal conductivity of the superconducting energy storage magnet, the heat conduction load of the superconducting energy storage magnet is calculated using the heat conduction equation; According to the surface emissivity, ambient temperature and geometric structure of the superconducting energy storage magnet, an equivalent radiation heat path model is constructed by finite element model and field-path coupling method. Calculating the radiation heat load of the superconducting energy storage magnet according to the equivalent radiation heat circuit model; Adding the conduction heat load and radiation heat load of the superconducting energy storage magnet to obtain the total heat load of the superconducting magnet; The step of constructing a lumped thermal model of the superconducting energy storage magnet based on the total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet comprises: According to the geometric structure parameters and material property parameters of the superconducting energy storage magnet, a three-dimensional finite element model of the superconducting energy storage magnet is established using the finite element analysis method; The total dynamic loss of the superconducting magnet and the total heat load of the superconducting magnet are used as heat source inputs, and a three-dimensional finite element model is used to simulate the heat conduction and radiation processes of the superconducting energy storage magnet under the action of the heat source to obtain the temperature distribution and heat flux density distribution of the superconducting energy storage magnet; The temperature distribution and heat flux density distribution of the superconducting energy storage magnet are coupled with the equivalent radiation heat circuit model to construct a lumped thermal model of the superconducting energy storage magnet.

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