Equipment type selection method and system for large-scale distributed photovoltaic access power distribution network

Abstract

The invention relates to the field of power distribution network planning and design, in particular to an equipment type selection method and system for a large-scale distributed photovoltaic access power distribution network, and the method comprises the steps: collecting data, and building a multi-target optimization model; constructing a multi-objective optimization model constraint condition; an optimization algorithm is selected to solve the multi-objective optimization model, and an equipment type selection scheme is obtained; verifying the equipment type selection scheme; according to the method, the multi-target mathematical optimization model and the optimization algorithm are used for equipment type selection, so that the accuracy and rationality of equipment type selection are improved, and the subjectivity of artificial experience judgment is avoided; through simulation verification and scheme adjustment, the high efficiency and feasibility of the model selection scheme are ensured, the operation efficiency and economy of the distributed photovoltaic system and the safety and stability of the power distribution network are improved, and the scheme provided by the invention is suitable for distributed photovoltaic systems of different scales and different regions and has wide applicability.

Description

Technical Field

[0001] The present invention relates to the field of distribution network planning and design, and specifically to a method and system for equipment selection for large-scale distributed photovoltaic access to the distribution network. Background Art

[0002] In the process of distribution network planning, the selection of grid equipment plays a crucial role, which is directly related to the safety, reliability, and economy of the operation of the distribution network. With the large-scale application of distributed photovoltaics, their proportion in the distribution network is increasing continuously, and their impact on the operating characteristics of the distribution network is becoming more prominent. Unreasonable equipment selection will lead to problems such as low system efficiency and poor stability. To effectively ensure that distributed photovoltaics can be smoothly and reliably connected to the distribution network and maintain the overall safe and stable operation state of the power system, it is extremely urgent to carry out scientific and reasonable selection and configuration work for primary equipment.

[0003] Traditional methods for selecting primary grid equipment mainly follow national standards, established specifications and empirical judgments in the power industry. Based on the specific requirements for the insulation level of equipment under different grounding methods of the distribution network, while ensuring the safety and reliability of the equipment, fully combining the actual local load growth trend and the scale of distribution network construction and other actual situations, and adhering to the design concept of being moderately ahead to select equipment. However, from the current technical means, there are obvious deficiencies in the method for selecting primary equipment in the scenario of large-scale distributed photovoltaic access to the distribution network, and the energy conservation and environmental protection efficiency of the equipment are not fully considered during the selection process. This current situation causes large-scale distributed photovoltaics to be unable to give full play to their advantages after being connected to the distribution network, and even has a certain negative impact on the stability of the power system.

[0004] In view of this, it is urgent to construct an innovative method for selecting distribution network equipment that is specifically for large-scale distributed photovoltaic access and takes into account environmental protection characteristics. By applying this method, it is possible to effectively avoid the high dependence on the experience of designers in the traditional selection process, without the need for designers to manually perform selection operations one by one, thereby greatly reducing the input of human resources, significantly improving the overall efficiency of the selection work, and minimizing selection errors caused by human factors. In this way, it will provide great convenience for designers when carrying out distribution network design work, enabling them to complete the design task more efficiently and accurately, and strongly promoting the development of distribution network construction towards a more scientific, reasonable, and efficient direction. Summary of the Invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0006] To solve the above technical problems, the present invention provides the following technical solutions. A method for selecting equipment for large-scale distributed photovoltaic access to a distribution network includes: collecting data and establishing a multi-objective optimization model; constructing constraints for the multi-objective optimization model; selecting an optimization algorithm to solve the multi-objective optimization model to obtain an equipment selection plan; and verifying the equipment selection plan.

[0007] As a preferred solution of the method for selecting equipment for large-scale distributed photovoltaic access to a distribution network according to the present invention, wherein: the data collection includes collecting the total life cycle cost, total equipment energy consumption, total environmental protection indicators, and equipment reliability of the distributed photovoltaic power generation system.

[0008] As a preferred solution of the method for selecting equipment for large-scale distributed photovoltaic access to a distribution network according to the present invention, wherein: the establishment of the multi-objective optimization model includes, with the goal of minimizing the total life cycle cost of the candidate power grid equipment of the same model, constructing a first objective function, expressed as,

[0009]

[0010] C ij =C p,ij +C 0,ij +C d,ij

[0011] Wherein, F1 is the total life cycle cost of the candidate power grid equipment, C ij is the life cycle cost of the j-th model of the i-th type of candidate power grid equipment, C p,ij is the acquisition cost, C 0,ij is the operation and maintenance cost, C d,ij is the decommissioning and disposal cost, and x ij is the decision variable;

[0012] With the goal of minimizing the total equipment energy consumption of the power grid equipment, a second objective function is constructed, expressed as,

[0013]

[0014] Wherein, E ij is the equipment loss of the j-th model of the i-th type of equipment;

[0015] With the goal of minimizing the total environmental protection indicators of the power grid equipment, a third objective function is constructed, expressed as,

[0016]

[0017] P ij =P ce,ij +P nois,ij

[0018] Among them, P ij is the environmental protection index of the j-th model of the i-th type of device, and P ce,ij is the carbon emission, and P nois,ij is the noise intensity ratio;

[0019] Taking the maximum reliability of the power grid equipment as the goal, the fourth objective function is constructed, which is expressed as,

[0020]

[0021] Among them, R ij is the reliability index of the j-th model of the i-th type of device;

[0022] Integrating the above objective functions, a multi-objective optimization model for power grid equipment selection is obtained, which is expressed as,

[0023] minF{F1, F2, F3, F4}.

[0024] As a preferred solution of the device selection method for large-scale distributed photovoltaic access to the distribution network according to the present invention, wherein: the construction of the multi-objective optimization model constraint conditions includes constructing a first constraint condition for the number of candidate device types, which is expressed as,

[0025]

[0026] Among them, i = 1, 2,..., m, N i is the required quantity of the i-th type of device;

[0027] Construct a second constraint condition for the life cycle cost of the candidate device, which is expressed as,

[0028]

[0029] Among them, C up is the upper limit of the life cycle cost of the candidate device;

[0030] Construct a third constraint condition for the power grid energy consumption, which is expressed as,

[0031]

[0032] Among them, E max is the maximum energy consumption rate of the candidate device;

[0033] Construct a fourth constraint condition for the environmental protection of the power grid, which is expressed as,

[0034]

[0035] Among them, P max is the maximum carbon emission and noise intensity ratio of the candidate device;

[0036] Construct the fifth constraint condition for the reliability of the power grid, expressed as,

[0037]

[0038] where R min is the minimum reliability index of the equipment to be selected.

[0039] As a preferred solution of a method for selecting equipment for large-scale distributed photovoltaic access to the distribution network according to the present invention, wherein: the selection optimization algorithm solves the multi-objective optimization model including selecting an optimization algorithm according to the model characteristics, setting algorithm parameters, and using the optimization algorithm to generate an equipment selection solution that meets the constraint conditions.

[0040] As a preferred solution of a method for selecting equipment for large-scale distributed photovoltaic access to the distribution network according to the present invention, wherein: obtaining the equipment selection solution includes screening the generated equipment selection solution, constructing a decision matrix, calculating the efficiency score of the equipment selection solution using the data envelopment analysis (DEA) method, and eliminating invalid solutions; calculating the ideal solution of the indicators of the multi-objective model, and calculating the Euclidean distance from the equipment selection solution to the ideal solution of the indicators; calculating the relative closeness of the equipment selection solution to the ideal solution of the indicators according to the TOPSIS method, sorting the equipment selection solutions, and obtaining the optimal equipment selection solution.

[0041] As a preferred solution of a method for selecting equipment for large-scale distributed photovoltaic access to the distribution network according to the present invention, wherein: verifying the equipment selection solution includes using simulation software to establish a system model to simulate and verify the selection solution, evaluating the performance indicators of the system, and adjusting the equipment selection solution according to the simulation results.

[0042] To solve the above technical problems, the present invention provides the following technical solution: a system for selecting equipment for large-scale distributed photovoltaic access to the distribution network, characterized in that it includes: a model establishment module, a constraint condition construction module, a calculation module for equipment selection solutions, and a solution verification module;

[0043] The model establishment module collects data and establishes a multi-objective optimization model;

[0044] The constraint condition construction module constructs constraint conditions for the multi-objective optimization model;

[0045] The calculation module for equipment selection solutions selects an optimization algorithm to solve the multi-objective optimization model and obtains an equipment selection solution;

[0046] The solution verification module verifies the equipment selection solution.

[0047] A computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the method for selecting equipment for large-scale distributed photovoltaic access to the distribution network as described above are implemented.

[0048] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the method for selecting equipment for large-scale distributed photovoltaic access to the distribution network as described above are implemented.

[0049] Advantages of the present invention: Aiming at the problem of selecting typical primary equipment for distributed photovoltaic access to the distribution network, considering the full life cycle cost, energy conservation and environmental protection of the equipment, the present invention proposes a method for selecting equipment for distributed photovoltaic access to the distribution network. Through steps such as data collection, demand analysis, modeling optimization, and simulation verification, the systematicness and scientificity of equipment selection are ensured.

[0050] The present invention uses a multi-objective mathematical optimization model and an optimization algorithm for equipment selection, improving the accuracy and rationality of equipment selection and avoiding the subjectivity of manual experience judgment.

[0051] Through simulation verification and scheme adjustment, the present invention ensures the efficiency and feasibility of the selection scheme, improving the operation efficiency and economy of the distributed photovoltaic system and the safety and stability of the distribution network. The scheme proposed by the present invention is applicable to distributed photovoltaic systems of different scales and regions, with wide applicability. Description of the Drawings

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0053] Figure 1 It is a flowchart of a method for selecting equipment for large-scale distributed photovoltaic access to the distribution network provided by an embodiment of the present invention. Detailed Embodiments

[0054] To make the above objects, features and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0055] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0056] Embodiment 1

[0057] Referring to Figure 1 , an embodiment of the present invention, a method for selecting equipment for large-scale distributed photovoltaic access to the distribution network includes:

[0058] S1: Collect data and establish a multi-objective optimization model.

[0059] It should be noted that collecting data includes collecting the total life-cycle cost, total equipment energy consumption, total environmental protection indicators, and equipment reliability of the distributed photovoltaic power generation system.

[0060] Preprocess the collected data, including data cleaning, normalization, etc., to eliminate noise, outliers, and missing values in the data; according to the preprocessed data, analyze the requirements of the distribution network and the models, configurations, and costs of the grid equipment to be selected, and determine the calculation methods for the total life-cycle cost, energy-saving characteristic indicators, environmental protection characteristic indicators, and reliability indicators of various key equipment.

[0061] Furthermore, establishing a multi-objective optimization model includes taking the minimum total life-cycle cost of the grid equipment of the same model to be selected as the objective, constructing the first objective function, expressed as,

[0062]

[0063] C ij = C p,ij + C 0,ij + C d,ij

[0064] Wherein, F1 is the total life-cycle cost of the grid equipment to be selected, C ij is the life-cycle cost of the i-th type and j-th model of the grid equipment to be selected, C p,ij is the acquisition cost, C 0,ij is the operation and maintenance cost, C d,ij is the retirement and disposal cost, x ij is the decision variable;

[0065] Taking the minimum total equipment energy consumption of the grid equipment as the objective, constructing the second objective function, expressed as,

[0066]

[0067] Wherein, Eij The equipment loss of the j-th model of the i-th type of equipment;

[0068] Taking the minimum sum of the environmental protection indicators of grid equipment as the goal, construct the third objective function, expressed as

[0069]

[0070] P ij = P ce,ij + P nois,ij

[0071] Among them, P ij is the environmental protection indicator of the j-th model of the i-th type of equipment, P ce,ij is the carbon emission, P nois,ij is the noise intensity ratio;

[0072] Taking the maximum reliability of grid equipment as the goal, construct the fourth objective function, expressed as

[0073]

[0074] Among them, R ij is the reliability index of the j-th model of the i-th type of equipment;

[0075] Integrate the above objective functions to obtain a multi-objective optimization model for grid equipment selection, expressed as

[0076] minF{F1, F2, F3, F4}.

[0077] Consider factors such as equipment models, performance, life-cycle costs, reliability, energy efficiency, and environmental protection, establish a multi-objective optimization mathematical model for equipment selection, and determine the constraint conditions.

[0078] S2: Construct the constraint conditions of the multi-objective optimization model.

[0079] It should be noted that constructing the constraint conditions of the multi-objective optimization model includes constructing the first constraint condition for the quantity of equipment types to be selected, expressed as

[0080]

[0081] Among them, i = 1, 2,..., m, N i is the required quantity of the i-th type of equipment;

[0082] Construct the second constraint condition for the life-cycle cost of the equipment to be selected, expressed as

[0083]

[0084] Among them, C upis the upper limit of the life cycle cost of the equipment to be selected;

[0085] Construct the third constraint condition for the power grid energy consumption, expressed as

[0086]

[0087] where E max is the maximum energy consumption rate of the equipment to be selected;

[0088] Construct the fourth constraint condition for the power grid environmental protection, expressed as

[0089]

[0090] where P max is the ratio of the maximum carbon emission and noise intensity of the equipment to be selected;

[0091] Construct the fifth constraint condition for the power grid reliability, expressed as

[0092]

[0093] where R min is the minimum reliability index of the equipment to be selected.

[0094] Furthermore, the constraint conditions limit the feasible solution space of the optimization model. Any candidate solution must satisfy all the constraint conditions; otherwise, it will be regarded as infeasible. There may be conflicts among multiple objectives. For example, reducing costs may sacrifice reliability. Therefore, it is necessary to find a balance among these objectives.

[0095] S3: Select an optimization algorithm to solve the multi-objective optimization model and obtain the equipment selection scheme.

[0096] It should be noted that selecting an optimization algorithm to solve the multi-objective optimization model includes selecting an optimization algorithm according to the model characteristics, setting algorithm parameters, and using the optimization algorithm to generate an equipment selection scheme that meets the constraint conditions.

[0097] Determine the continuity / discreteness, convexity / non-convexity, constraint type, and search space size of the problem, evaluate the available computing resources, time resources, and accuracy requirements, select according to the search ability, convergence speed, complexity, and applicable scenarios of the algorithm, set algorithm parameters such as population size, number of iterations, crossover probability, mutation probability, etc. to balance the search ability and computing efficiency, compare the performance of different algorithms on the same problem through experiments, and select the algorithm with the best performance.

[0098] Furthermore, obtaining the equipment selection plan includes screening the generated equipment selection plans, constructing a decision matrix, calculating the efficiency scores of the equipment selection plans using the DEA method, and eliminating the invalid plans; calculating the ideal solutions of the indicators of the multi-objective model, and calculating the Euclidean distances from the equipment selection plans to the ideal solutions of the indicators; calculating the relative closeness degrees of the equipment selection plans with respect to the ideal solutions of the indicators according to the TOPSIS method, sorting the equipment selection plans, and obtaining the optimal equipment selection plan.

[0099] Construct an initial decision matrix based on the various indicators used to evaluate the equipment selection plans, perform normalization processing on the initial decision matrix to eliminate the dimensionality effects between different indicators. According to the normalized decision matrix, use the data envelopment analysis (DEA) method to calculate the efficiency scores of each plan, identify the effective and invalid plans, determine the weights of each indicator based on the DEA results, and merge the weighted decision matrix. Calculate the positive ideal solution and negative ideal solution of each indicator, calculate the Euclidean distances from each plan to the positive and negative ideal solutions, calculate the relative closeness degrees of each plan with respect to the ideal solution according to the TOPSIS method, and sort the various selection plans accordingly. The plan with the highest closeness degree is the optimal equipment selection plan.

[0100] S4: Verify the equipment selection plan.

[0101] It should be noted that verifying the equipment selection plan includes using simulation software to establish a system model to simulate and verify the selection plan, evaluating the performance indicators of the system, and adjusting the equipment selection plan according to the simulation results.

[0102] Furthermore, use simulation software to establish a system model, design simulation experiments, conduct result analysis, and combine experimental verification means such as expert review, theoretical analysis, economic analysis, historical data comparison, and small-scale pilot tests to verify the selection plan, evaluate the performance indicators of the system, such as reliability, stability, economy, etc., and make fine adjustments to the equipment selection plan according to the results until the design requirements are met, and output the final equipment selection plan.

[0103] Embodiment 2

[0104] This is an embodiment of the present invention, and the present invention provides the following technical solution: A system for equipment selection in a large-scale distributed photovoltaic access to the distribution network, including: a model establishment module, a constraint condition construction module, a calculation module for equipment selection plans, and a plan verification module;

[0105] The model establishment module collects data and establishes a multi-objective optimization model;

[0106] The constraint condition construction module constructs the constraint conditions of the multi-objective optimization model;

[0107] The computing device selection solution module selects an optimization algorithm to solve the multi-objective optimization model and obtains a device selection solution;

[0108] The solution verification module verifies the device selection solution.

[0109] This embodiment also provides a computing device, which is applicable to the situation of a device selection method for large-scale distributed photovoltaic access to the distribution network, including:

[0110] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a device selection method for large-scale distributed photovoltaic access to the distribution network as proposed in the above embodiment.

[0111] The storage medium proposed in this embodiment and the device selection method for large-scale distributed photovoltaic access to the distribution network proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0112] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0113] The logic and / or steps described in other ways herein, for example, can be considered as a fixed sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0114] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for equipment selection of large-scale distributed photovoltaic access to the distribution network, characterized in that: Including: Collect data and establish a multi-objective optimization model; Construct the constraint conditions of the multi-objective optimization model; Select an optimization algorithm to solve the multi-objective optimization model and obtain the equipment selection scheme; Verify the equipment selection scheme.

2. The equipment selection method for large-scale distributed photovoltaic access to the distribution network according to claim 1, wherein: The data collection includes collecting the total life cycle cost sum, total equipment energy consumption, total environmental protection indicators, and equipment reliability of the distributed photovoltaic power generation system.

3. The equipment selection method for large-scale distributed photovoltaic access to the distribution network according to claim 2, wherein: The establishment of the multi-objective optimization model includes taking the minimum total life cycle cost sum of the grid equipment to be selected of the same model as the goal, constructing the first objective function, expressed as C ij = C p,ij + C 0,ij + C d,ij Among them, F1 is the total life cycle cost of the power grid equipment to be selected, and C ij is the life cycle cost of the i-th type and j-th model of the power grid equipment to be selected, and C p,ij is the acquisition cost, and C 0,ij is the operation and maintenance cost, and C d,ij is the retirement and disposal cost, and x ij is the decision variable; Taking the minimum total equipment energy consumption of the grid equipment as the goal, constructing the second objective function, expressed as Among them, E ij is the equipment loss of the j-th model of the i-th type of equipment; Taking the minimum total environmental protection indicators of the grid equipment as the goal, constructing the third objective function, expressed as P ij = P ce,ij + P nois,ij Among them, P ij is the environmental protection index of the j-th model of the i-th type of device, P ce,ij is the carbon emission, and P nois,ij is the noise intensity ratio; Taking the maximum reliability of the grid equipment as the goal, constructing the fourth objective function, expressed as where, R ij is the reliability index of the j-th model of the i-th type of device; Integrate the objective functions to obtain the multi-objective optimization model for grid equipment selection, expressed as minF{F1, F2, F3, F4}.

4. The equipment selection method for large-scale distributed photovoltaic access to the distribution network according to claim 3, characterized in that: The construction of the constraint conditions of the multi-objective optimization model includes constructing the first constraint condition for the quantity of the equipment types to be selected, expressed as where \(i = 1, 2, \ldots, m, N\) i is the demand quantity of the \(i\)-th type of device; Constructing the second constraint condition for the total life cycle cost of the equipment to be selected, expressed as Among them, C up is the upper limit of the life cycle cost of the equipment to be selected; Constructing the third constraint condition for the grid energy consumption, expressed as Among them, E max is the maximum energy consumption rate of the device to be selected; Constructing the fourth constraint condition for the grid environmental protection, expressed as Among them, P max is the maximum carbon emission and noise intensity ratio of the device to be selected; Constructing the fifth constraint condition for the reliability of the grid, expressed as wherein, R min is the minimum reliability index of the device to be selected.

5. The equipment selection method for large-scale distributed photovoltaic power access to the distribution network according to claim 4, characterized in that: The selection of the optimization algorithm to solve the multi-objective optimization model includes selecting an optimization algorithm according to the model characteristics, setting the algorithm parameters, and using the optimization algorithm to generate an equipment selection scheme that meets the constraint conditions.

6. The equipment selection method for large-scale distributed photovoltaic access to the distribution network according to claim 5, characterized in that: The obtaining of the equipment selection scheme includes screening the generated equipment selection scheme, constructing a decision matrix, calculating the efficiency score of the equipment selection scheme using the data envelopment analysis method, and eliminating the invalid schemes; Calculating the ideal solution of the indicators of the multi-objective model, calculating the Euclidean distance from the equipment selection scheme to the ideal solution of the indicators; calculating the relative closeness of the equipment selection scheme to the ideal solution of the indicators according to the TOPSIS method, sorting the equipment selection schemes, and obtaining the optimal equipment selection scheme.

7. The equipment selection method for large-scale distributed PV access to the distribution network according to claim 6, characterized in that: The verification of the equipment selection scheme includes using simulation software to establish a system model to simulate and verify the selection scheme, evaluating the performance indicators of the system, and adjusting the equipment selection scheme according to the simulation results.

8. A system for equipment selection of large-scale distributed photovoltaic access to the distribution network according to any one of claims 1 to 7, characterized in that, Including: A model establishment module, a constraint condition construction module, a calculation module for equipment selection scheme, and a scheme verification module; The model establishment module collects data and establishes a multi-objective optimization model; The constraint condition construction module constructs the constraint conditions of the multi-objective optimization model; The calculation module for equipment selection scheme selects an optimization algorithm to solve the multi-objective optimization model and obtains the equipment selection scheme; The scheme verification module verifies the equipment selection scheme.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it realizes the steps of the equipment selection method for large-scale distributed photovoltaic access to the distribution network according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it realizes the steps of the equipment selection method for large-scale distributed photovoltaic access to the distribution network according to any one of claims 1 to 7.

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