A network adaptive dynamic allocation regulation method

Abstract

The application discloses a kind of follow net construction network adaptive dynamic allocation regulation and control method, belong to power system control technical field.The method includes: obtaining power system short-circuit ratio by impedance scanning method;The voltage deviation and frequency deviation of adjacent time grid are obtained, and voltage fluctuation rate and frequency fluctuation rate are obtained based on voltage deviation and frequency deviation;Voltage dominant criterion and frequency dominant criterion are obtained based on voltage deviation, frequency deviation, voltage fluctuation rate and frequency fluctuation rate;Optimized threshold value is obtained by using Snow Goose optimization algorithm to optimize initial threshold value;Comparison result is obtained according to the comparison of power system short-circuit ratio and optimized threshold value;Control mode is selected by comparing result and the range of voltage dominant criterion and frequency dominant criterion.The application can overcome the defects that hard switching strategy in prior art causes grid-connected current impact and threatens equipment safety.

Description

Technical Field

[0001] This invention relates to the field of power system control technology, and in particular to an adaptive dynamic allocation and control method for grid connection. Background Technology

[0002] With the surge in installed capacity of wind and solar power, the short-circuit capacity of the power grid has decreased, significantly reducing system strength and leading to risks such as voltage instability and frequency oscillations. In power grids with low short-circuit ratios, reliance on grid voltage phase makes them prone to phase-locked loop (PLL) synchronization failures, exacerbating frequency collapse. In power grids with high short-circuit ratios, the active voltage construction can easily cause overvoltage problems. In medium power system short-circuit ratio scenarios, a single control mode cannot simultaneously ensure voltage and frequency stability. For example, grid-following mode exacerbates voltage drops, while grid-building mode amplifies frequency fluctuations.

[0003] Existing control mode allocation methods rely solely on static power system short-circuit ratio thresholds for control mode switching, neglecting dynamic fluctuations in the power system short-circuit ratio and leading to frequent erroneous switching. In scenarios with medium power system short-circuit ratios, mainstream hybrid control schemes lack a quantitative correlation mechanism for voltage / frequency stability, making it impossible to dynamically allocate grid-connected weights. Hard switching strategies, due to the absence of transition algorithms, cause grid-connected current surges, threatening equipment safety. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive dynamic allocation and control method for grid connection. By considering the dynamic fluctuation of the power system short-circuit ratio and the factors of voltage / frequency stability quantification correlation mechanism, and selecting the appropriate control model, it can overcome the defects of the hard switching strategy in the prior art that causes grid current impact and threatens equipment safety.

[0005] This invention is achieved through the following technical solutions.

[0006] This invention provides a method for adaptive dynamic allocation and control of network structure, comprising:

[0007] The short-circuit ratio of the power system is obtained by impedance scanning.

[0008] Obtain the voltage deviation and frequency deviation of the power grid at adjacent time points, and obtain the voltage fluctuation rate and frequency fluctuation rate based on the voltage deviation and frequency deviation;

[0009] Based on the voltage deviation, frequency deviation, voltage fluctuation rate, and frequency fluctuation rate, voltage-dominant criteria and frequency-dominant criteria are obtained;

[0010] The initial threshold is optimized using the Snow Goose optimization algorithm to obtain the optimized threshold.

[0011] The comparison results are obtained by comparing the power system short-circuit ratio with the optimized threshold.

[0012] The control mode is selected based on the comparison results and the ranges of the voltage-dominant and frequency-dominant criteria.

[0013] In practical applications, existing control mode allocation methods only consider grid-following and grid-connecting modes, and these methods are limited to switching control modes based on the static power system short-circuit ratio threshold, neglecting dynamic fluctuations in the power system short-circuit ratio, leading to frequent erroneous switching. In scenarios with medium power system short-circuit ratios, mainstream hybrid control schemes lack a voltage / frequency stability quantification correlation mechanism, making it impossible to dynamically allocate grid-following / grid-connecting weights. This invention comprehensively considers both dynamic power system short-circuit ratio and the establishment of a voltage / frequency stability quantification correlation mechanism to select a control mode suitable for the actual application scenario. Furthermore, it adds a grid-connecting hybrid mode and a grid-following hybrid mode to overcome the problem that a single grid-following or grid-connecting mode cannot simultaneously address voltage and frequency dynamic performance, easily leading to multiple stability conflicts.

[0014] Optionally, the power system short-circuit ratio is calculated using the following formula:

[0015] ,

[0016] ,

[0017] In the formula, The short-circuit ratio of the power system. The voltage at the grid connection point. Impedance spectrum at grid connection point, For short-circuit capacity, This refers to the power at the grid connection point.

[0018] Optionally, the voltage-dominant criterion is calculated using the following formula:

[0019] ,

[0020] In the formula, Voltage is the dominant criterion. For the voltage deviation of the power grid, For frequency deviation, Voltage fluctuation rate;

[0021] The frequency-dominant criterion is calculated using the following formula:

[0022] ,

[0023] In the formula, Frequency-dominant criterion This refers to the frequency fluctuation rate.

[0024] Optionally, the optimized threshold includes a first threshold. Second threshold ;

[0025] The control mode is selected based on the comparison results and the ranges of the voltage-dominant and frequency-dominant criteria, including:

[0026] and or Select the network mode.

[0027] or but and Select the network configuration mode.

[0028] and or Select the hybrid network construction mode.

[0029] and or Select the network hybrid mode.

[0030] in, Voltage is the dominant criterion. Frequency is the dominant criterion.

[0031] The grid-connected hybrid mode is where grid-connected control is primarily based on grid-connected mode (accounting for 60%-80%), with the remaining percentage being based on grid-following mode; the grid-following hybrid mode is where grid-connected control is primarily based on grid-following mode (accounting for 60%-80%), with the remaining percentage being based on grid-connected mode.

[0032] Optionally, the Snow Goose optimization algorithm includes an initialization phase, a modeling phase, an exploration phase, and a development phase.

[0033] Optionally, the optimization of the initial threshold using the snow goose optimization algorithm to obtain the optimized threshold includes: randomly generating the position and speed of the population during the initialization phase using the snow goose optimization algorithm to simulate the initialization of the threshold; setting the heading angle of the snow goose migration process during the modeling phase to determine whether the snow goose enters the exploration phase or the development phase; when the heading angle is less than 180°, the snow goose enters the exploration phase and flies in a V-shape to encourage the group to conduct extensive searches in the search space to simulate the global optimal solution of the threshold; when the heading angle is greater than 180°, the snow goose enters the development phase and flies in a straight line to simulate the threshold jumping out of the local optimal solution.

[0034] Optionally, the initialization phase involves initializing the position and velocity of the population, calculated using the following formula:

[0035] ,

[0036] ,

[0037] In the formula, This is the position matrix of snow geese in the population. The first in the population Only Xueyan in the first The corresponding position under each optimization condition , , This represents the velocity matrix of snow geese within the population. The first in the population Only Xueyan in the first The speed corresponding to each optimization condition This represents the total number of snow geese in the population. The number of optimization variables in the optimization conditions of the optimization problem.

[0038] Optionally, the heading angle for setting the migration route of the snow geese is calculated using the following formula:

[0039] ,

[0040] In the formula, The navigation angle for the snow goose's migration route. The maximum number of iterations, This represents the number of iterations.

[0041] The modeling phase also includes velocity evolution and setting air resistance, which are calculated using the following formulas:

[0042] ,

[0043] ,

[0044] ,

[0045] In the formula, This is the serial number of the snow geese in the population. Indicates the first Xueyan is currently in the exploration phase. speed, Indicates the first Xueyan is in the next moment of the exploration phase. speed, Indicates the first The acceleration of the snow goose alone For Xueyan at the present moment The optimal position, For the first Only Xueyan at the present moment Location, is the transmission coefficient.

[0046] In this invention, speed evolution and air resistance settings are used to simulate current optimization conditions such as short-circuit ratio, voltage, and frequency to update the speed matrix.

[0047] Optionally, during the exploration phase, the position of the snow goose during its V-formation flight is updated using the following formula:

[0048] ,

[0049] ,

[0050] ,

[0051] ,

[0052] ,

[0053] In the formula, , and These represent the th individuals among the best individuals, weak individuals, and remaining individuals, respectively. The snow geese are flying in a V-formation. (Next moment) The updated positions, where the superior, weak, and remaining individuals are ranked according to fitness. Individuals are divided into the top 20% and bottom 20% from best to worst, with the remaining 60% being the surplus individuals. , and These represent the weight coefficients of the optimal position, the center position, and the lowest-rank position in the population, respectively. Indicates the current time The central position of the group Indicates the current time The lowest level position, It is a random number between 0 and 1.

[0054] This stage first divides the population into superior individuals, weak individuals, and other individuals based on their fitness. Superior individuals primarily consider the optimal position, which is the impact of the optimization objective, corresponding to the selection of the control mode; weak individuals primarily consider avoiding falling behind, which is the impact of satisfying constraints, corresponding to the impact of grid short-circuit ratio, voltage, and frequency, and then consider the optimization objective; other individuals normally consider the impact of the optimization objective and constraints.

[0055] Optionally, during the development phase, the snow goose updates its position using the following formula when flying in a straight line:

[0056] ,

[0057] In the formula, The first in the population The snow goose is flying in a straight line. (Next moment) The updated location It is a random number. To optimize variables Perform Brownian motion.

[0058] ① Collective guidance: If random numbers Xueyan followed her experienced and physically strong companions in a group search for the best destination.

[0059] ② Random behavior: when If trapped in a local solution, the snow goose exhibits random behavior similar to Brownian motion in order to escape the trap.

[0060] The above situation can be used for simulation: when the voltage and frequency of the power grid fluctuate drastically and have a significant impact on the selection of the control mode, it is necessary to escape the local optimum by flying in a straight line and taking into account the global conditions.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0062] The grid-connected adaptive dynamic allocation and control method introduced in this invention selects the appropriate control model by combining a dynamic power system short-circuit ratio and voltage / frequency stability quantification correlation mechanism, overcoming the shortcomings of existing technologies that only switch control modes based on static power system short-circuit ratio thresholds. This invention introduces a grid-connected hybrid mode and a grid-connected hybrid mode to overcome the shortcomings of existing technologies where a single control mode cannot simultaneously consider voltage and frequency stability. This invention introduces the Xueyan optimization algorithm to optimize the selection of control modes, making the results closer to actual application scenarios and satisfying constraints such as voltage and frequency. Attached Figure Description

[0063] Figure 1 The diagram shown is a schematic flowchart of a network adaptive dynamic allocation and control method in one embodiment of the present invention.

[0064] Figure 2 The diagram shown is a schematic flowchart of the Xueyan optimization algorithm in one embodiment of the present invention. Detailed Implementation

[0065] The following description, in conjunction with the accompanying drawings and specific embodiments, provides further details. In this description, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature.

[0066] Example 1

[0067] This embodiment 1 provides a network-based adaptive dynamic allocation and control method, including:

[0068] The short-circuit ratio of the power system is obtained by impedance scanning.

[0069] Obtain the voltage deviation and frequency deviation of the power grid at adjacent time points, and obtain the voltage fluctuation rate and frequency fluctuation rate based on the voltage deviation and frequency deviation;

[0070] Based on the voltage deviation, frequency deviation, voltage fluctuation rate, and frequency fluctuation rate, voltage-dominant criteria and frequency-dominant criteria are obtained;

[0071] The initial threshold is optimized using the Snow Goose optimization algorithm to obtain the optimized threshold.

[0072] The comparison results are obtained by comparing the power system short-circuit ratio with the optimized threshold.

[0073] The control mode is selected based on the comparison results and the ranges of the voltage-dominant and frequency-dominant criteria.

[0074] In practical applications, existing control mode allocation methods only consider grid-following and grid-connecting modes, and these methods are limited to switching control modes based on the static power system short-circuit ratio threshold, neglecting dynamic fluctuations in the power system short-circuit ratio, leading to frequent erroneous switching. In scenarios with medium power system short-circuit ratios, mainstream hybrid control schemes lack a voltage / frequency stability quantification correlation mechanism, making it impossible to dynamically allocate grid-following / grid-connecting weights. This invention comprehensively considers both dynamic power system short-circuit ratio and the establishment of a voltage / frequency stability quantification correlation mechanism to select a control mode suitable for the actual application scenario. Furthermore, it adds a grid-connecting hybrid mode and a grid-following hybrid mode to overcome the problem that a single grid-following or grid-connecting mode cannot simultaneously address voltage and frequency dynamic performance, easily leading to multiple stability conflicts.

[0075] This invention includes the following four control modes:

[0076] (1) Follow-up mode

[0077] Applicable scenarios: High short-circuit ratio in power systems ( (a strong power grid environment). The short-circuit ratio of the power system. This is the second threshold.

[0078] Control strategy: Current source control is adopted, and the phase-locked loop (PLL) tracks the grid voltage phase and frequency in real time, and prioritizes the adjustment of active / reactive power output according to grid dispatch instructions.

[0079] Core function: Using the power grid as a rigid support, suppressing high... In scenarios where overvoltage risks arise due to grid control, it is crucial to ensure low harmonic content in the grid-connected current and rapid dynamic response.

[0080] (2) Network construction mode

[0081] Applicable scenarios: Low short-circuit ratio in power systems ( In weak grid or isolated grid environments, This is the first threshold.

[0082] Control strategy: Voltage source control is adopted, and the external characteristics of the synchronous machine are simulated by the virtual synchronous machine (VSG) to autonomously construct the grid voltage and frequency, and provide inertia support and short-circuit capacity.

[0083] Core functions: Actively compensate for voltage drops in weak grids, suppress frequency fluctuations, enhance the system's anti-disturbance capability, and avoid the risk of grid disconnection caused by phase-locked loop synchronism loss in grid-following mode.

[0084] (3) Hybrid network construction mode

[0085] Applicable scenarios: Medium short-circuit ratio in power systems ( Furthermore, the power grid environment suffers from prominent voltage stability issues (such as large voltage deviations and frequent fluctuations).

[0086] Control strategy: The system integrates a dual control architecture of grid-following and grid-building, with grid-building mode as the main mode (accounting for 60%-80%) and the remaining proportion in grid-following mode. The output impedance characteristics are adjusted through virtual impedance; the grid-following mode is used to provide fast power point tracking capability.

[0087] Core functions: Prioritize the resolution of voltage instability issues, provide dynamic voltage support through grid control, and simultaneously use grid-following control to smooth power fluctuations and avoid voltage-frequency control conflicts in single mode.

[0088] (4) Network-based hybrid mode

[0089] Applicable scenarios: Medium short-circuit ratio in power systems ( Furthermore, the power grid environment suffers from prominent frequency stability issues (such as excessive frequency deviation and insufficient inertia).

[0090] Control strategy: Primarily based on grid-following mode (accounting for 60%-80%), with the remaining percentage in grid-building mode, to quickly respond to grid dispatch commands; embedding a virtual inertial control module in grid-building mode to provide short-term frequency support.

[0091] Core functions: Prioritize the suppression of frequency fluctuations, utilize the high-precision power tracking of the grid-following mode to ensure steady-state operating efficiency, and enhance system frequency stability through the inertial response of the grid-building module.

[0092] Example 2

[0093] Based on Example 1, Example 2 introduces the specific implementation process of a network-based adaptive dynamic allocation and control method, such as... Figure 1 As shown, it specifically includes the following:

[0094] In one specific embodiment of the present invention, the power system short-circuit ratio is calculated using the following formula:

[0095] (1),

[0096] (2),

[0097] In the formula, The short-circuit ratio of the power system. The voltage at the grid connection point. Impedance spectrum at grid connection point, For short-circuit capacity, This refers to the power at the grid connection point.

[0098] The voltage-dominant criterion is calculated using the following formula:

[0099] (3),

[0100] In the formula, Voltage is the dominant criterion. For the voltage deviation of the power grid, For frequency deviation, Voltage fluctuation rate;

[0101] The frequency-dominant criterion is calculated using the following formula:

[0102] (4),

[0103] In the formula, Frequency-dominant criterion This refers to the frequency fluctuation rate.

[0104] The optimized thresholds include the first threshold. Second threshold ;

[0105] The control mode is selected by comparing the results and the ranges of the voltage-dominant and frequency-dominant criteria, including:

[0106] and or Select the network mode.

[0107] or but and Select the network configuration mode.

[0108] and or Select the hybrid network construction mode.

[0109] and or Select the network hybrid mode.

[0110] in, Voltage is the dominant criterion. Frequency is the dominant criterion.

[0111] In one specific embodiment of the present invention, optimizing the selection of the control mode using the Xueyan optimization algorithm includes: optimizing the threshold using the Xueyan optimization algorithm, and then optimizing the selection of the control mode using the optimized threshold.

[0112] Among them, the Xueyan optimization algorithm is as follows: Figure 2 As shown, it includes the initialization phase, modeling phase, exploration phase, and development phase.

[0113] In one specific embodiment of the present invention, the optimization of the initial threshold using the Snow Goose Optimization Algorithm to obtain the optimized threshold includes: randomly generating the position and velocity of the population during the initialization phase using the Snow Goose Optimization Algorithm to simulate the initialization of the threshold; during the modeling phase, setting the heading angle of the Snow Goose migration process to determine whether the Snow Goose enters the exploration phase or the development phase; when the heading angle is less than 180°, the Snow Goose enters the exploration phase, performs V-shaped flight, and encourages the group to conduct extensive searches in the search space to simulate the global optimal solution of the threshold; when the heading angle is greater than 180°, the Snow Goose enters the development phase, performs straight flight, and simulates the threshold jumping out of the local optimum.

[0114] In one specific embodiment of the present invention, the initialization phase involves initializing the position and velocity of the population, which are calculated using the following formula:

[0115] (5),

[0116] (6),

[0117] In the formula, This is the position matrix of snow geese in the population. , , The first in the population Only Xueyan in the first The corresponding position under each optimization condition This represents the velocity matrix of snow geese within the population. The first in the population Only Xueyan in the first The speed corresponding to each optimization condition This represents the total number of snow geese in the population. This corresponds to the number of optimization variables in the optimization conditions of an optimization problem. For example, when optimizing a threshold, only the short-circuit ratio of the power system is considered. The impact at this time Set to 1. Used to simulate the initialization threshold. During the initialization phase, a set of random thresholds is generated, and a velocity matrix is ​​generated based on the number of variables in the optimization problem being considered.

[0118] In one specific embodiment of the present invention, the modeling stage involves setting the heading angle for the snow goose migration route, which is calculated using the following formula:

[0119] (7),

[0120] In the formula, The heading angles for the snow goose migration route indicate the transition of the snow goose population from the exploration stage to the development stage. The maximum number of iterations, Let be the number of iterations; as long as the executed iterations satisfy . The Snow Goose optimization algorithm will continue operating. Once the iteration count exceeds a set value, the Snow Goose optimization algorithm will stop executing. (Heading angle) This is used to determine whether a snow goose enters the exploration phase to conduct a detailed search for the optimal solution, or enters the development phase to escape a local optimum.

[0121] The modeling phase also includes velocity evolution and setting air resistance, which are calculated using the following formulas:

[0122] (8),

[0123] (9),

[0124] (10)

[0125] In the formula, This is the serial number of the snow geese in the population. For a moment, Indicates the first Xueyan is currently in the exploration phase. speed, Indicates the first Xueyan is in the next moment of the exploration phase. speed, Indicates the first The acceleration of the snow goose alone For Xueyan at the present moment The optimal position, For the first Only Xueyan at the present moment Location, For the transmission coefficient, 1.29 represents the maximum number of iterations and the air resistance constant.

[0126] In this invention, speed evolution and air resistance settings are used to simulate current optimization conditions such as short-circuit ratio, voltage, and frequency to update the speed matrix.

[0127] In one specific embodiment of the present invention, during the exploration phase, the position of the snow goose during its V-shaped flight is updated using the following formula:

[0128] (11),

[0129] (12)

[0130] (13)

[0131] (14)

[0132] (15)

[0133] In the formula, , and These represent the th individuals among the best individuals, weak individuals, and remaining individuals, respectively. The snow geese are flying in a V-formation. (Next moment) The updated positions, where the superior, weak, and remaining individuals are ranked according to fitness. Individuals are divided into the top 20% and bottom 20% from best to worst, with the remaining 60% being the surplus individuals. , and These represent the weight coefficients of the optimal position, the center position, and the lowest-rank position in the population, respectively. Indicates the current time The central position of the group For the current moment The lowest level position, It is a random number between 0 and 1.

[0134] This stage first divides the population into superior individuals, weak individuals, and other individuals based on their fitness. Superior individuals primarily consider the optimal position, which is the impact of the optimization objective, corresponding to the selection of the control mode; weak individuals primarily consider avoiding falling behind, which is the impact of satisfying constraints, corresponding to the impact of grid short-circuit ratio, voltage, and frequency, and then consider the optimization objective; other individuals normally consider the impact of the optimization objective and constraints.

[0135] In one specific embodiment of the present invention, during the development phase, the snow goose updates its position using the following formula when flying in a straight line:

[0136] (16)

[0137] In the formula, The first in the population The snow goose is flying in a straight line. (Next moment) The updated location It is a random number. To optimize variables Perform Brownian motion.

[0138] ① Collective guidance: If random numbers Xueyan followed her experienced and physically strong companions in a group search for the best destination.

[0139] ② Random behavior: when If trapped in a local solution, the snow goose exhibits random behavior similar to Brownian motion in order to escape the trap.

[0140] The above situation can be used for simulation: when the voltage and frequency of the power grid fluctuate drastically and have a significant impact on the selection of the control mode, it is necessary to escape the local optimum by flying in a straight line and taking into account the global conditions.

[0141] In addition, this embodiment also includes a smooth mode switching strategy and an emergency fault tolerance mechanism.

[0142] The smooth mode switching strategy includes weighted gradual transition and hysteresis comparator debouncing.

[0143] Gradual weight transition:

[0144] In hybrid mode, the weight ratio of network construction and network following. Adjust according to the ramp function:

[0145] (17)

[0146] The adjusted weights, For gradual rate change, The maximum switching time is set to 200ms. To switch time frequencies, The initial weights are used to determine the mode transition. When the proportion of network-type devices reaches 70%, the system enters a network-mixed mode; when the proportion of follow-network type devices reaches 70%, it enters a follow-network mixed mode. This gradual weight transition makes the mode switching smoother and avoids equipment damage or malfunctions caused by the control mode not being able to switch in time due to large changes in state variables.

[0147] Hysteresis comparator debouncing:

[0148] Set the power system short-circuit ratio Switch between hysteresis ranges (±0.2%), voltage hysteresis range (±1%), and frequency hysteresis range (±0.05Hz). Avoid frequent switching when the short-circuit ratio, grid voltage, and frequency are close to any of the listed hysteresis ranges.

[0149] Emergency fault tolerance mechanisms include the power system short-circuit ratio Mutation response and dual-mode interlocking.

[0150] Power system short-circuit ratio Sudden change response: If the power system short-circuit ratio If the value drops by more than 1.0 within 1 second, the system will force the system to enter network construction mode and issue an alarm.

[0151] Dual-mode interlock: The output stages of the network controller and the network structure controller are connected in series with an interlock circuit to ensure that only one mode dominates the output at any given time.

[0152] Power system short-circuit ratio Sudden Change Response: Under extremely weak grid conditions, grid-connected converters are prone to tripping due to overcurrent, overvoltage, or frequency exceeding limits, thus shutting down. Forced switching to grid-connected mode, through active control to stabilize the operating point, can avoid unnecessary grid disconnections and ensure the continuous grid-connected power generation capability of new energy power generation equipment.

[0153] The core of dual-mode interlocking is to ensure the continuity of output and avoid sudden jumps in output voltage or current caused by control command interruption during switching, thereby maintaining continuous support to the power grid.

[0154] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for adaptive dynamic allocation and control of network structure, characterized in that, include: The short-circuit ratio of the power system is obtained by impedance scanning. Obtain the voltage deviation and frequency deviation of the power grid at adjacent time points, and obtain the voltage fluctuation rate and frequency fluctuation rate based on the voltage deviation and frequency deviation; Based on the voltage deviation, frequency deviation, voltage fluctuation rate, and frequency fluctuation rate, voltage-dominant criteria and frequency-dominant criteria are obtained; The initial threshold is optimized using the Snow Goose optimization algorithm to obtain the optimized threshold. The comparison results are obtained by comparing the power system short-circuit ratio with the optimized threshold. The control mode is selected based on the comparison results and the ranges of the voltage-dominant criterion and the frequency-dominant criterion. The short-circuit ratio of the power system is calculated using the following formula: ， ， In the formula, The short-circuit ratio of the power system. The voltage at the grid connection point. Impedance spectrum at grid connection point, For short-circuit capacity, Power at the grid connection point; The voltage-dominant criterion is calculated using the following formula: ， In the formula, Voltage is the dominant criterion. For the voltage deviation of the power grid, For frequency deviation, Voltage fluctuation rate; The frequency-dominant criterion is calculated using the following formula: ， In the formula, Frequency-dominant criterion Frequency volatility; The optimized threshold includes the first threshold. Second threshold ; The control mode is selected based on the comparison results and the ranges of the voltage-dominant and frequency-dominant criteria, including: and or Select the network mode. or but and Select the network configuration mode. and or Select the hybrid network construction mode. and or Select the network hybrid mode. in, Voltage is the dominant criterion. Frequency is the dominant criterion.

2. The adaptive dynamic allocation and control method for network construction according to claim 1, characterized in that, The Xueyan optimization algorithm includes an initialization phase, a modeling phase, an exploration phase, and a development phase.

3. The adaptive dynamic allocation and control method for network construction according to claim 2, characterized in that, The process of optimizing the initial threshold using the Snow Goose Optimization Algorithm to obtain the optimized threshold includes: randomly generating the population's position and velocity during the initialization phase using the Snow Goose Optimization Algorithm to simulate the initialization of the threshold; setting the heading angle of the Snow Goose migration process during the modeling phase to determine whether the Snow Goose enters the exploration phase or the development phase; when the heading angle is less than 180°, the Snow Goose enters the exploration phase and flies in a V-shape, encouraging the group to conduct extensive searches in the search space to simulate the global optimal solution of the threshold; when the heading angle is greater than 180°, the Snow Goose enters the development phase and flies in a straight line to simulate the threshold jumping out of the local optimum.

4. The adaptive dynamic allocation and control method for network construction according to claim 3, characterized in that, The initialization phase involves initializing the position and velocity of the population, which are calculated using the following formula: ， ， In the formula, This is the position matrix of snow geese in the population. The first in the population Only Xueyan in the first The corresponding position under each optimization condition , , This represents the velocity matrix of snow geese within the population. The first in the population Only Xueyan in the first The speed corresponding to each optimization condition This represents the total number of snow geese in the population. The number of optimization variables in the optimization conditions of the optimization problem.

5. The adaptive dynamic allocation and control method for network construction according to claim 4, characterized in that, The heading angle used to determine the migration route of the snow geese is calculated using the following formula: ， In the formula, The navigation angle for the snow goose's migration route. The maximum number of iterations, This represents the number of iterations. The modeling phase also includes velocity evolution and setting air resistance, which are calculated using the following formulas: ， ， ， In the formula, This is the serial number of the snow geese in the population. Indicates the first Xueyan is currently in the exploration phase. speed, Indicates the first Xueyan is in the next moment of the exploration phase. speed, Indicates the first The acceleration of the snow goose alone For Xueyan at the present moment The optimal position, For the first Only Xueyan at the present moment Location, is the transmission coefficient.

6. The adaptive dynamic allocation and control method for network construction according to claim 5, characterized in that, in During the exploration phase, the position of the snow goose during its V-shaped flight is updated using the following formula: ， ， ， ， ， In the formula, , and These represent the th individuals among the best individuals, weak individuals, and remaining individuals, respectively. The snow geese are flying in a V-formation. (Next moment) The updated positions, where the superior, weak, and remaining individuals are ranked according to fitness. Individuals are divided into the top 20% and bottom 20% from best to worst, with the remaining 60% being the surplus individuals. , and These represent the weight coefficients of the optimal position, the center position, and the lowest-rank position in the population, respectively. Indicates the current time The central position of the group Indicates the current time The lowest level position, It is a random number between 0 and 1.

7. The adaptive dynamic allocation and control method for network construction according to claim 6, characterized in that, in During the development phase, the snow goose updates its position using the following formula when flying in a straight line: ， In the formula, The first in the population The snow goose is flying in a straight line. (Next moment) The updated location It is a random number. To optimize variables Perform Brownian motion.

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