Method for evaluating photovoltaic carrying capacity of multi-level distribution network based on dichotomy

Abstract

The application is based on a multi-level distribution network photovoltaic carrying capacity evaluation method based on dichotomy, including S1, topology parameter collection and multi-level division; S2, multi-level operation constraint verification model construction; S3, improved dichotomy search mechanism design; S4, multi-level photovoltaic carrying capacity iterative evaluation; S5, evaluation result and access scheme output; the application builds a multi-level topology coupling model, adopts a top-down layered iteration strategy, realizes effective transmission of upper and lower level power grid constraints, solves the evaluation deviation problem caused by ignoring level coupling in the traditional method; optimizes the search mechanism of dichotomy, introduces a variable step convergence strategy, which can improve the iteration convergence speed; integrates the operation constraint verification link suitable for the multi-level power grid, and provides a complete theoretical formula, which can guarantee that the evaluation result is consistent with the actual operation condition of the distribution network.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic distribution network technology, and specifically to a method for assessing the photovoltaic carrying capacity of multi-level distribution networks based on the dichotomy method. Background Technology

[0002] As the national "dual carbon" goals are being promoted, the scale and penetration rate of distributed photovoltaic (PV) grids in distribution networks continue to increase. Distribution networks exhibit multi-level topological coupling characteristics of main distribution network, regional distribution network, and transformer area distribution network. PV carrying capacity assessment has become a core technical link to ensure the safe and stable operation of the power grid and guide the orderly grid connection of PV.

[0003] Traditional photovoltaic (PV) capacity assessment methods often employ the conventional dichotomy approach, narrowing the PV access capacity search range with a fixed step size to complete the assessment. This approach has the following technical drawbacks:

[0004] 1. The topological coupling relationship of multi-level distribution networks is not considered. The evaluation is conducted independently for a single power grid level, which is prone to problems such as the upper-level power grid operating constraints being met, the lower-level power grid voltage exceeding the limit, and line overload. The evaluation results deviate greatly from the actual power grid operating conditions.

[0005] 2. The conventional binary search method uses a fixed search step size, which results in slow convergence speed in the early stages of iteration and insufficient accuracy when approaching the limit value, making it impossible to balance evaluation efficiency and accuracy.

[0006] 3. Constraint verification was not carried out in conjunction with random operating scenarios, and the evaluation results cannot adapt to the randomness of photovoltaic power output and load fluctuations. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method for assessing the photovoltaic carrying capacity of multi-level distribution networks based on the bisection method, thus solving the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A method for assessing the photovoltaic carrying capacity of multi-level distribution networks based on the dichotomy approach includes:

[0010] S1. Topology parameter acquisition and multi-level partitioning:

[0011] The topology, electrical parameters, load parameters, existing photovoltaic parameters, and operating constraint thresholds of the multi-level distribution network are collected, and the distribution network is divided into a three-level structure: main distribution network layer, regional distribution network layer, and transformer area distribution network layer.

[0012] S2. Construction of a multi-level runtime constraint verification model:

[0013] Establish a four-layer constraint verification model that includes voltage constraints, line current carrying capacity constraints, network loss constraints, and power flow constraints, and form a standard for judging the compliance of photovoltaic access.

[0014] S3. Improved design of binary search mechanism:

[0015] The initial search interval is determined by taking the theoretical maximum photovoltaic access capacity of each level of the power grid as the upper bound and the existing photovoltaic installed capacity as the lower bound; a top-down hierarchical iteration strategy is adopted, and the evaluation results of the upper level are used as the upper limit of the access capacity of the lower level to pass on the constraints.

[0016] In the early stages of iteration, a classic binary search with a large step size is used. When the constraint satisfaction rate is not up to standard, it is switched to a fine-grained search with a fixed small step size to complete the variable step size convergence optimization.

[0017] S4. Multi-level photovoltaic load-bearing capacity iterative evaluation:

[0018] Based on the improved bisection method combined with Monte Carlo simulation, the photovoltaic carrying capacity of each level of the power grid is obtained by iterative calculation in the order of main distribution network layer - regional distribution network layer - transformer area distribution network layer, and the constraint satisfaction rate is used to determine the effectiveness of the iteration.

[0019] S5. Evaluation results and access scheme output.

[0020] Furthermore, in S1,

[0021] The electrical parameters include line resistance. Reactance Rated current carrying capacity Transformer rated capacity Transformer turns ratio ;

[0022] The load parameters include load type and peak / valley load values. The existing photovoltaic parameters include the capacity of photovoltaic systems already connected to the grid. With output characteristics;

[0023] The operational constraint thresholds include the allowable voltage fluctuation range, the upper limit of line current carrying capacity, the upper limit of network loss rate, and the power factor constraint of photovoltaic grid connection point.

[0024] Furthermore, in S2, the voltage constraint model is: for any node in the power grid at each level After photovoltaic (PV) grid connection, the node voltage must meet the fluctuation range constraint:

[0025]

[0026] In the formula, , These represent the maximum and minimum rated voltages of the power grid at different voltage levels, with subscripts indicating the values. This indicates distribution network systems with different voltage levels; For nodes The magnitude of the node voltage.

[0027] Furthermore, in S2, the line current carrying capacity constraint model is as follows:

[0028]

[0029] In the formula, The active power transmitted by the line (kW); This refers to the line power factor. The actual line current must meet the rated upper limit constraint.

[0030]

[0031] In the formula, This is the rated current carrying capacity.

[0032] Furthermore, in S2, the network loss constraint model is:

[0033]

[0034] The comprehensive network loss rate of multi-level distribution networks is:

[0035]

[0036] In the formula, The total active power input to the upstream power grid is kW; Total output of all photovoltaic power sources, kW;

[0037] The overall network loss rate must meet the upper limit constraint:

[0038] .

[0039] Furthermore, in S2, the power flow constraint is modeled as follows:

[0040]

[0041] In the formula, A set of nodes; Inject active power into node i; Inject reactive power into node i; and These represent the active power of the photovoltaic system and the load at node i, respectively. and These represent the reactive power of the photovoltaic system and the load at node i, respectively. , , where i and j are the node voltages, respectively; and Let be the impedance and admittance of branch ij, respectively; Let be the phase difference between the first and last nodes of branch ij.

[0042] Furthermore, in S3,

[0043] The specific process for determining the initial search interval is as follows: the theoretical maximum photovoltaic grid connection capacity at each level of the power grid is used as the upper bound. Using the existing installed capacity as the lower limit Initialize the search interval The formula for calculating the capacity of the initial interval midpoint is:

[0044]

[0045] Based on the limits of line and transformer capacity;

[0046] The top-down hierarchical iteration strategy is as follows: First, assess the photovoltaic carrying capacity of the main distribution network layer, and use the assessment results of the main distribution network layer as the upper limit of the access capacity of the regional distribution network layer; then, use the assessment results of the regional distribution network layer as the upper limit of the access capacity of the distribution network layer of the transformer area, so as to realize the transmission of constraints between levels.

[0047] Furthermore, in S3, the specific steps for convergence optimization are as follows:

[0048] In the initial iteration, the classic binary search method is used, with a relatively large step size of 1 / 2 of the interval length, for a coarse search. The formula for calculating the new midpoint in the coarse search stage is:

[0049]

[0050] When the constraint satisfaction rate is less than the constraint verification rate when using Monte Carlo simulation, the system switches to a fixed small step size for fine-grained search. The access capacity for each step in the fine-grained search phase becomes:

[0051]

[0052]

[0053] In the formula, This represents the change in photovoltaic installed capacity, and is a fixed value. , These represent the maximum and minimum access capacity ranges corresponding to the last use of the classic binary search method.

[0054] Furthermore, S4 specifically refers to:

[0055] S4.1 For the main distribution network layer, take the midpoint of the initial interval. ,Will The photovoltaic access capacity to be evaluated is the main distribution network layer; for all distribution network layers, N sets of random photovoltaic access scenarios under this access capacity are generated based on Monte Carlo simulation.

[0056] S4.2. Combining the power grid operation constraint verification model of S2, verify whether the distribution network layer constraints are satisfied under each scenario through power flow calculation;

[0057] If the distribution network layer constraints are satisfied in all scenarios, then the lower bound is updated to X. min =X mid ;

[0058] If the distribution network layer constraints are not satisfied in all scenarios, then the upper bound will be updated to X. max =X mid ;

[0059] If the constraint satisfaction rate is less than the set value in all scenarios, then the set value will be updated to X. new Repeat the iteration until the constraint satisfaction rate meets the set value, then determine that the evaluation result is valid and obtain the upper limit C1 of the photovoltaic carrying capacity of the main distribution network layer;

[0060] The constraint satisfaction rate is defined as:

[0061]

[0062] In the formula, The number of scenarios that satisfy all constraints; This represents the total number of simulation scenarios.

[0063] S4.3. Using C1 as the upper limit of the photovoltaic access capacity of the regional distribution network layer, repeat the iterative process of steps S4.1 and S4.2 to obtain the upper limit of the photovoltaic carrying capacity C2 of the regional distribution network layer.

[0064] S4.4. Using C2 as the upper limit of the photovoltaic access capacity of the distribution network layer, repeat the iterative process of steps S4.1 and S4.2 to obtain the upper limit of the photovoltaic carrying capacity C3 of the distribution network layer.

[0065] Furthermore, S5 specifically outputs the photovoltaic carrying capacity limits C1, C2, and C3 of each level of the distribution network and the corresponding optimal access scheme, including photovoltaic access nodes and access capacity allocation.

[0066] This invention provides a method for assessing the photovoltaic carrying capacity of multi-level distribution networks based on the bisection method. Compared with existing technologies, it has the following advantages:

[0067] This invention constructs a multi-level topological coupling model and adopts a top-down hierarchical iteration strategy to achieve effective transmission of constraints between upper and lower level power grids, solving the evaluation bias problem caused by neglecting hierarchical coupling in traditional methods. It optimizes the search mechanism of the bisection method and introduces a variable step size convergence strategy to improve the iteration convergence speed. This method integrates an operational constraint verification link adapted to multi-level power grids and is equipped with complete theoretical formulas to ensure that the evaluation results are consistent with the actual operating conditions of the distribution network. Attached Figure Description

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 A schematic diagram of the process for evaluating the photovoltaic carrying capacity of a multi-level distribution network based on the dichotomy method of the present invention is shown. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] Example

[0072] To address the technical issues in the background section, the following photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method is presented:

[0073] Combination Figure 1 As shown, the present invention provides a method for assessing the photovoltaic carrying capacity of multi-level distribution networks based on the bisection method.

[0074] Step 1: Multi-level Distribution Network Topology and Parameter Acquisition: The purpose of Step 1 is to obtain the complete topological connections and equipment parameters (line impedance, current carrying capacity, transformer capacity, load characteristics, etc.) of the main distribution network layer, regional distribution network layer, and transformer substation distribution network layer, providing data support for subsequent power flow calculations and constraint verification. This involves collecting constraint thresholds such as voltage fluctuation range, upper limit of line current carrying capacity, upper limit of network loss rate, and power factor for each level of the power grid, ensuring that the evaluation results conform to actual operating procedures. It also involves understanding the existing photovoltaic grid connection capacity and output characteristics to avoid redundant evaluations or omissions of grid-connected capacity.

[0075] (1) Construct a multi-level distribution network topology, dividing the distribution network into three levels: main distribution network layer, regional distribution network layer, and transformer area distribution network layer, and determine the node connection relationship and line parameters (resistance) of each level of the power grid. Reactance Rated current carrying capacity Transformer parameters (rated capacity) Transformation ratio Load parameters (load type, peak and valley load values) and the capacity of photovoltaic systems already connected With output characteristics;

[0076] (2) Collect the operating constraint thresholds of each level of the power grid, including the allowable voltage fluctuation range, the upper limit of line current carrying capacity, the upper limit of network loss rate and the power factor constraint of photovoltaic grid connection point.

[0077] Step 2: Construction of a multi-level power grid operation constraint verification model:

[0078] The purpose of setting up step two is as follows: Voltage constraint model: Ensures that the voltage of each node does not exceed the limit after photovoltaic access, preventing damage to user equipment due to excessive voltage or degradation of power quality due to excessively low voltage. Line current carrying capacity constraint model: Prevents line overload and overheating, ensuring equipment lifespan and grid safety. Network loss constraint model: Controls the increase in network losses caused by photovoltaic access, avoiding a decline in economic efficiency. Power flow constraint model: Based on the power balance equation, accurately describes the physical operating state of the power grid and is the mathematical foundation for all constraint verifications.

[0079] (1) Establishing a voltage constraint model: for any node in the power grid at all levels After photovoltaic (PV) grid connection, the node voltage must meet the fluctuation range constraint:

[0080]

[0081] In the formula, , These represent the maximum and minimum rated voltages of the power grid at different voltage levels, with subscripts indicating the values. This indicates distribution network systems with different voltage levels; For nodes The magnitude of the node voltage.

[0082] (2) Establishing a line current carrying capacity constraint model: The formula for calculating the effective value of the actual line current is:

[0083]

[0084] In the formula, The active power transmitted by the line (kW); This refers to the line power factor. The actual line current must meet the rated upper limit constraint.

[0085]

[0086] In the formula, This is the rated current carrying capacity.

[0087] (3) Establishing a network loss constraint model: The formula for calculating the active power loss of a single line is as follows:

[0088]

[0089] The comprehensive network loss rate of multi-level distribution networks is:

[0090]

[0091] In the formula, The total active power input to the upstream power grid is kW; Total output of all photovoltaic power sources, in kW.

[0092] The overall network loss rate must meet the upper limit constraint:

[0093]

[0094] (4) Power flow constraint model:

[0095]

[0096] In the formula, A set of nodes; Inject active power into node i; Inject reactive power into node i; and These represent the active power of the photovoltaic system and the load at node i, respectively. and These represent the reactive power of the photovoltaic system and the load at node i, respectively. , , where i and j are the node voltages, respectively; and Let be the impedance and admittance of branch ij, respectively; Let be the phase difference between the first and last nodes of branch ij.

[0097] Step 3: Improve the design of the binary search mechanism:

[0098] The purpose of step three is to: determine the initial search interval: using the theoretical maximum capacity as the upper bound and the existing installed capacity as the lower bound, ensuring that the search range covers all possible carrying capacities without omission. A hierarchical iterative search mechanism: propagating evaluation results from top to bottom (main distribution network → regional distribution network → transformer area distribution network), achieving coupled transmission of constraints between levels, avoiding lower-level exceeding limits caused by traditional single-layer evaluation. Variable step-size convergence optimization: initially using a large step size to quickly narrow the interval, and later using a fixed small step size to finely approximate the boundary, solving the problem of slow convergence or missing boundaries caused by the fixed step size in the conventional bisection method.

[0099] (1) Determine the initial search interval: The theoretical maximum photovoltaic access capacity of each level of the power grid (based on the limit value of line and transformer capacity) is used as the upper bound. Using the existing installed capacity as the lower limit Initialize the search interval The formula for calculating the capacity of the initial interval midpoint is:

[0100]

[0101] (2) Layered iterative search mechanism: A top-down layered iterative strategy is adopted. First, the photovoltaic carrying capacity of the main distribution network layer is evaluated, and the evaluation result of the main distribution network layer is used as the upper limit of the access capacity of the regional distribution network layer. Then, the evaluation result of the regional distribution network layer is used as the upper limit of the access capacity of the distribution network layer of the transformer area, so as to realize the transmission of constraints between layers.

[0102] (3) Variable step size convergence optimization: In the early stage of iteration, the classic binary search method is used. At this time, the step size is relatively large, which is 1 / 2 of the interval length, to perform a coarse search. The formula for calculating the new midpoint in the coarse search stage is:

[0103]

[0104] When the constraint satisfaction rate is less than the constraint verification rate when using Monte Carlo simulation, the system switches to a fixed small step size for fine-grained search. The access capacity for each step in the fine-grained search phase becomes:

[0105]

[0106]

[0107] In the formula, This represents the change in photovoltaic installed capacity, and is a fixed value. , These represent the maximum and minimum access capacity ranges corresponding to the last use of the classic binary search method.

[0108] Step 4: Iterative evaluation of multi-level photovoltaic carrying capacity based on the improved bisection method:

[0109] The purpose of step four is to search for the maximum photovoltaic (PV) grid connection capacity that satisfies all constraints at each level using an improved bisection method (large step size + small step size). Monte Carlo simulation is introduced to handle the randomness of PV grid connection locations and output, ensuring the evaluation results cover various distribution scenarios that may occur in actual operation. The constraint satisfaction rate (number of scenarios satisfying all constraints / total number of scenarios) is used as the convergence criterion, rather than a simple "completely satisfied" or "completely unsatisfied," thus giving the evaluation statistical reliability. The evaluation results from the upper level are used as the upper bound of the capacity of the lower level, and iterative execution is performed to obtain the upper limits of the carrying capacity C1, C2, and C3 for all levels. The optimal grid connection scheme (grid nodes and capacity allocation) is output to guide actual engineering projects.

[0110] (1) For the main distribution network layer, take the midpoint of the initial interval. ,Will The photovoltaic (PV) grid connection capacity to be evaluated is used as the main distribution network layer. For all distribution network layers, N sets of random PV grid connection scenarios under this connection capacity are generated based on Monte Carlo simulation. Combined with the grid operation constraint verification model in step 2, power flow calculations are used to verify whether the constraints are met.

[0111] (2) If the distribution network layer constraints are satisfied in all scenarios, then the lower bound is updated to X. min =X mid If the distribution network layer constraints are not satisfied in all scenarios, then the upper bound will be updated to X. max =X mid If the constraint satisfaction rate is less than the set value in all scenarios, then the set value will be updated to X. new Repeat the iteration until the constraint satisfaction rate meets the set value, then determine that the evaluation result is valid and obtain the upper limit C1 of the photovoltaic carrying capacity of the main distribution network layer;

[0112] The constraint satisfaction rate is defined as:

[0113]

[0114] In the formula, The number of scenarios that satisfy all constraints; This represents the total number of simulation scenarios.

[0115] (3) Take C1 as the upper limit of the photovoltaic access capacity of the regional distribution network layer, repeat the iterative process of steps (1) and (2) to obtain the upper limit of the photovoltaic carrying capacity of the regional distribution network layer, C2.

[0116] (4) C2 is taken as the upper limit of the photovoltaic access capacity of the distribution network layer of the distribution area, and similarly the upper limit of the photovoltaic carrying capacity of the distribution network layer of the distribution area is obtained as C3.

[0117] (5) Output the upper limit of photovoltaic carrying capacity C1, C2, C3 of each level of distribution network and the corresponding optimal access scheme, including photovoltaic access nodes and access capacity allocation.

[0118] This method is fully feasible and can be widely applied in actual power distribution network planning.

[0119] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0120] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic carrying capacity assessment method for multi-level distribution networks based on the dichotomy method, characterized in that: S1. Topology parameter acquisition and multi-level partitioning: The topology, electrical parameters, load parameters, existing photovoltaic parameters, and operating constraint thresholds of the multi-level distribution network are collected, and the distribution network is divided into a three-level structure: main distribution network layer, regional distribution network layer, and transformer area distribution network layer. S2. Construction of a multi-level runtime constraint verification model: Establish a four-layer constraint verification model that includes voltage constraints, line current carrying capacity constraints, network loss constraints, and power flow constraints, and form a standard for judging the compliance of photovoltaic access. S3. Improved design of binary search mechanism: The initial search interval is determined by using the theoretical maximum photovoltaic access capacity of each level of the power grid as the upper bound and the existing photovoltaic installed capacity as the lower bound. A top-down hierarchical iterative strategy is adopted, using the evaluation results of the higher level as the upper limit of the access capacity of the lower level to pass on constraints; In the early stages of iteration, a classic binary search with a large step size is used. When the constraint satisfaction rate is not up to standard, it is switched to a fine-grained search with a fixed small step size to complete the variable step size convergence optimization. S4. Multi-level photovoltaic load-bearing capacity iterative evaluation: Based on the improved bisection method combined with Monte Carlo simulation, the photovoltaic carrying capacity of each level of the power grid is obtained by iterative calculation in the order of main distribution network layer - regional distribution network layer - transformer area distribution network layer, and the constraint satisfaction rate is used to determine the effectiveness of the iteration. S5. Evaluation results and access scheme output.

2. The photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method according to claim 1, characterized in that: In S1, The electrical parameters include line resistance. Reactance Rated current carrying capacity Transformer rated capacity Transformer turns ratio ; The load parameters include load type and peak / valley load values. The existing photovoltaic parameters include the capacity of photovoltaic systems already connected to the grid. With output characteristics; The operational constraint thresholds include the allowable voltage fluctuation range, the upper limit of line current carrying capacity, the upper limit of network loss rate, and the power factor constraint of photovoltaic grid connection point.

3. The photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method according to claim 1, characterized in that: In S2, the voltage constraint model is: for any node in the power grid at each level After photovoltaic (PV) grid connection, the node voltage must meet the fluctuation range constraint: In the formula, , These represent the maximum and minimum rated voltages of the power grid at different voltage levels, with subscripts indicating the values. This indicates distribution network systems with different voltage levels; For nodes The magnitude of the node voltage.

4. The photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method according to claim 1, characterized in that: In S2, the line current carrying capacity constraint model is as follows: In the formula, The active power transmitted by the line (kW); This refers to the line power factor. The actual line current must meet the rated upper limit constraint. In the formula, This is the rated current carrying capacity.

5. The photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method according to claim 1, characterized in that: In S2, the network loss constraint model is: The comprehensive network loss rate of multi-level distribution networks is: In the formula, The total active power input to the upstream power grid is kW; Total output of all photovoltaic power sources, kW; The overall network loss rate must meet the upper limit constraint: 。 6. The photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method according to claim 1, characterized in that: In S2, the power flow constraint is modeled as follows: In the formula, A set of nodes; Inject active power into node i; Inject reactive power into node i; and These represent the active power of the photovoltaic system and the load at node i, respectively. and These represent the reactive power of the photovoltaic system and the load at node i, respectively. , , where i and j are the node voltages, respectively; and Let be the impedance and admittance of branch ij, respectively; Let be the phase difference between the first and last nodes of branch ij.

7. The photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method according to claim 1, characterized in that: In S3, The specific process for determining the initial search interval is as follows: the theoretical maximum photovoltaic grid connection capacity at each level of the power grid is used as the upper bound. Using the existing installed capacity as the lower limit Initialize the search interval The formula for calculating the capacity of the initial interval midpoint is: Based on the limits of line and transformer capacity; The top-down hierarchical iteration strategy is as follows: First, assess the photovoltaic carrying capacity of the main distribution network layer, and use the assessment results of the main distribution network layer as the upper limit of the access capacity of the regional distribution network layer; then, use the assessment results of the regional distribution network layer as the upper limit of the access capacity of the distribution network layer of the transformer area, so as to realize the transmission of constraints between levels.

8. The photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method according to claim 7, characterized in that: In S3, the specific steps for convergence optimization are as follows: In the initial iteration, the classic binary search method is used, with a relatively large step size of 1 / 2 of the interval length, for a coarse search. The formula for calculating the new midpoint in the coarse search stage is: When the constraint satisfaction rate is less than the constraint verification rate when using Monte Carlo simulation, the system switches to a fixed small step size for fine-grained search. The access capacity for each step in the fine-grained search phase becomes: In the formula, This represents the change in photovoltaic installed capacity, and is a fixed value. , These represent the maximum and minimum access capacity ranges corresponding to the last use of the classic binary search method.

9. The photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method according to claim 1, characterized in that: S4 specifically refers to: S4.1 For the main distribution network layer, take the midpoint of the initial interval. ,Will The photovoltaic access capacity to be evaluated is the main distribution network layer; for all distribution network layers, N sets of random photovoltaic access scenarios under this access capacity are generated based on Monte Carlo simulation. S4.

2. Combining the power grid operation constraint verification model of S2, verify whether the distribution network layer constraints are satisfied under each scenario through power flow calculation; If the distribution network layer constraints are satisfied in all scenarios, then the lower bound is updated to X. min =X mid ; If the distribution network layer constraints are not satisfied in all scenarios, then the upper bound will be updated to X. max =X mid ; If the constraint satisfaction rate is less than the set value in all scenarios, then the set value will be updated to X. new Repeat the iteration until the constraint satisfaction rate meets the set value, then determine that the evaluation result is valid and obtain the upper limit C1 of the photovoltaic carrying capacity of the main distribution network layer; The constraint satisfaction rate is defined as: In the formula, The number of scenarios that satisfy all constraints; This represents the total number of simulation scenarios. S4.

3. Using C1 as the upper limit of the photovoltaic access capacity of the regional distribution network layer, repeat the iterative process of steps S4.1 and S4.2 to obtain the upper limit of the photovoltaic carrying capacity C2 of the regional distribution network layer. S4.

4. Using C2 as the upper limit of the photovoltaic access capacity of the distribution network layer, repeat the iterative process of steps S4.1 and S4.2 to obtain the upper limit of the photovoltaic carrying capacity C3 of the distribution network layer.

10. The photovoltaic carrying capacity assessment method for multi-level distribution networks based on the bisection method according to claim 1, characterized in that: S5 specifically outputs the photovoltaic carrying capacity limits C1, C2, and C3 of each level of the distribution network and the corresponding optimal access scheme, including photovoltaic access nodes and access capacity allocation.

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