A method and device for improving power grid carrying capacity considering distributed photovoltaic power supply

Abstract

The application discloses a power grid carrying capacity improvement method and device considering a distributed photovoltaic power supply, and an abnormality identification method, and comprises the following steps: acquiring real-time data of equipment parameters, bus parameters and line parameters, and preprocessing the acquired real-time data to obtain preprocessed parameters; according to the preprocessed parameters, the operating parameters of the power grid after the distributed photovoltaic power supply is connected to the power grid are calculated, the calculated operating parameters are checked, and after the checking, preset evaluation indexes are used for assignment calculation to obtain scores for dividing the power grid carrying capacity level; if the checking fails, the power grid carrying capacity level is poor; according to the calculated operating parameters, a preset method is used for reactive power optimization to obtain reasonable configurations for suppressing voltage fluctuation, and the reasonable configurations for suppressing voltage fluctuation are used for improving the power grid carrying capacity. The application can evaluate the advantages and disadvantages of the power grid carrying capacity, and improve the power grid carrying capacity by reducing the network loss and voltage deviation.

Description

Technical Field

[0001] This invention relates to a method and apparatus for improving the grid carrying capacity of distributed photovoltaic power sources, belonging to the field of new energy grid technology. Background Technology

[0002] The inherent randomness and volatility of new energy output, represented by photovoltaic power generation, and the high proportion of new energy output bring high uncertainty, posing new challenges to power grid planning and operation. Due to the randomness, volatility, and unschedulable nature of distributed photovoltaic power output, it is prone to power quality problems such as voltage fluctuations and flicker, harmonic pollution, and voltage exceeding limits, causing economic losses to power users in the distribution network and jeopardizing the safe and stable operation of photovoltaic systems.

[0003] Inaccurate load capacity can lead to unnecessary delays in engineering analysis or hinder the installation of photovoltaic power generation equipment in suitable areas. At the same time, the quality of grid load capacity is greatly affected by voltage fluctuations, and voltage fluctuations that cause voltage over-limits can seriously affect the safety of users' lives and property.

[0004] Therefore, a carrying capacity assessment method is needed for photovoltaic power generation grid connection to calculate the carrying capacity of distributed photovoltaic grid connection and improve the grid carrying capacity by suppressing voltage over-limit caused by voltage fluctuations. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for improving grid carrying capacity considering distributed photovoltaic power generation. This method can assess the grid carrying capacity and improve it by reducing grid losses and voltage deviations. To achieve the above objective, this invention employs the following technical solution:

[0006] In a first aspect, the present invention provides a method for improving grid carrying capacity considering distributed photovoltaic power generation, comprising:

[0007] Acquire real-time data of equipment parameters, bus parameters, and line parameters, and preprocess the acquired real-time data to obtain preprocessed parameters;

[0008] Based on the preprocessed parameters, the operating parameters of the power grid after the distributed photovoltaic power source is connected to the grid are calculated. The calculated operating parameters are then checked. If the check is passed, the preset evaluation indicators are used to assign values ​​and calculate the scores used to classify the grid carrying capacity level. If the check is not passed, the grid carrying capacity level is poor.

[0009] Based on the calculated operating parameters, reactive power optimization is performed using a preset method to obtain a reasonable configuration for suppressing voltage fluctuations. This reasonable configuration for suppressing voltage fluctuations is used to improve the grid carrying capacity.

[0010] In conjunction with the first aspect, the preprocessing of the acquired real-time data further includes: abnormal data correction and data normalization.

[0011] In conjunction with the first aspect, the abnormal data correction is further as follows:

[0012] Calculate the mean and standard deviation of historical load data;

[0013] Based on the calculated mean and standard deviation, the deviation of historical data is calculated using the following formula:

[0014]

[0015] In equation (1), H represents the average historical load data for a certain period; H is the number of days in this period; x t (i) represents the data value at time t on day i; σ t λ is the standard deviation of historical load data over a certain period; λ is the deviation of historical data. When the real-time data at a certain point is greater than λ, the real-time data is considered abnormal.

[0016] When abnormal data is identified, it is removed. The removed abnormal data is then replaced by averaging the real-time data before and after the abnormal data, expressed by the following formula:

[0017]

[0018] In equation (2), This represents the average of the real-time data before and after the abnormal data.

[0019] In conjunction with the first aspect, the data normalization further refers to the following: after correcting abnormal data, a 0-1 normalization method is used to resolve the issue of inconsistent data dimensions, expressed by the following formula:

[0020]

[0021] In equation (3), xi' is the normalized value, x i The value before normalization, x min x is the minimum value in the sample data. max This represents the maximum value in the sample data.

[0022] In conjunction with the first aspect, further, the calculation of the grid operating parameters after the distributed photovoltaic power source is connected to the grid includes:

[0023] The voltage deviation is calculated using the following formula:

[0024]

[0025] In equation (4), ΔU is the voltage deviation, Ui U is the per-unit value of the node voltage. N This is the per-unit value of the rated voltage;

[0026] The voltage fluctuation state at distribution network nodes can be expressed by the following formula:

[0027]

[0028] In equation (5), W represents the voltage fluctuation state of the distribution network node, and N represents the number of distribution network nodes.

[0029] In conjunction with the first aspect, the verification based on the calculated operating parameters further includes: voltage fluctuation verification, short-circuit current verification, and harmonic verification.

[0030] In conjunction with the first aspect, the reactive power optimization using a preset method further includes:

[0031] Taking minimizing the grid voltage deviation as the first optimization objective and minimizing grid loss as the second optimization objective, the objective function is obtained as follows:

[0032]

[0033] In equation (6), w is the grid voltage offset, N is the number of nodes in the distribution network, and U i P represents the per-unit value of the node voltage; loss G represents the power grid loss value. ij Let V be the admittance from node i to node j. i Let V be the voltage at node i. j Let θ be the voltage at node j. ij Let be the angle between the phase angle differences of the voltages from node i to node j;

[0034] Normalizing the voltage offset and network loss yields the normalized objective function F:

[0035]

[0036] In equation (7), w init The initial value of the voltage offset before reactive power optimization; P loss(init) The initial value of the network loss before reactive power optimization; a1 and a2 are two coefficients greater than 0, and satisfy a1+a2=1;

[0037] The improved gray wolf algorithm is used to optimize the reactive power of equation (7) to obtain a reasonable configuration to suppress voltage fluctuations.

[0038] Secondly, the present invention provides a grid carrying capacity enhancement device considering distributed photovoltaic power generation, comprising:

[0039] Acquisition module: Used to acquire real-time data of equipment parameters, bus parameters, and line parameters, and to preprocess the acquired real-time data to obtain preprocessed parameters;

[0040] The grid carrying capacity assessment module is used to calculate the grid operating parameters after the distributed photovoltaic power source is connected to the grid based on the preprocessed parameters. The calculated operating parameters are then checked. If the check is passed, the preset assessment indicators are used to assign values ​​and calculate the score used to classify the grid carrying capacity level. If the check is not passed, the grid carrying capacity level is poor.

[0041] The optimization module is used to optimize reactive power using a preset method based on the calculated operating parameters, thereby obtaining a reasonable configuration to suppress voltage fluctuations. This reasonable configuration is used to improve the grid's carrying capacity.

[0042] Thirdly, the present invention provides an electronic device, comprising:

[0043] A memory, and one or more processors communicatively connected to the memory;

[0044] The memory stores instructions that can be executed by the one or more processors to cause the one or more processors to implement the method described in the first aspect.

[0045] Fourthly, the present invention provides a computer-readable storage medium, characterized in that the readable storage medium stores a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0046] Compared with the prior art, the beneficial effects achieved by the grid carrying capacity improvement method considering distributed photovoltaic power generation provided by the embodiments of the present invention include:

[0047] This invention acquires real-time data of equipment parameters, bus parameters, and line parameters, preprocesses the acquired real-time data to obtain preprocessed parameters, calculates the grid operation parameters after the distributed photovoltaic power source is connected to the grid based on the preprocessed parameters, and verifies the calculated operation parameters. This invention provides more comprehensive indicators to reflect the grid's carrying capacity after the distributed photovoltaic power source is connected.

[0048] This invention uses preset evaluation indicators to assign values ​​after verification, and obtains a score for classifying the power grid carrying capacity level. If the verification fails, the power grid carrying capacity level is poor. This invention uses the method of assigning values ​​to evaluate the indicators and uses scores to classify the quality of the power grid, providing users with a more intuitive and clear direction for evaluating the power grid carrying capacity in the future.

[0049] Based on the calculated operating parameters, this invention uses a preset method to optimize reactive power and obtain a reasonable configuration to suppress voltage fluctuations. This reasonable configuration to suppress voltage fluctuations is used to improve the grid carrying capacity. This invention improves the grid carrying capacity by reducing grid losses and voltage deviations.

[0050] This invention lays a foundation for assessing and optimizing the grid connection capacity of distributed photovoltaic power sources, and deepens the goal of ensuring the safe operation of distributed photovoltaic power systems and providing users with high-quality energy services. Attached Figure Description

[0051] Figure 1 This is a flowchart of a method for improving grid carrying capacity considering distributed photovoltaic power sources, provided in Embodiment 1 of the present invention;

[0052] Figure 2 This is a flowchart of the preprocessing of real-time data in a method for improving the grid carrying capacity of distributed photovoltaic power sources provided in Embodiment 1 of the present invention. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0054] Example 1:

[0055] like Figure 1 As shown, a method for improving grid carrying capacity considering distributed photovoltaic power generation includes:

[0056] Acquire real-time data of equipment parameters, bus parameters, and line parameters, and preprocess the acquired real-time data to obtain preprocessed parameters;

[0057] Based on the preprocessed parameters, the operating parameters of the power grid after the distributed photovoltaic power source is connected to the grid are calculated. The calculated operating parameters are then checked. If the check is passed, the preset evaluation indicators are used to assign values ​​and calculate the scores used to classify the grid carrying capacity level. If the check is not passed, the grid carrying capacity level is poor.

[0058] Based on the calculated operating parameters, reactive power optimization is performed using a preset method to obtain a reasonable configuration for suppressing voltage fluctuations. This reasonable configuration for suppressing voltage fluctuations is used to improve the grid carrying capacity.

[0059] The specific steps include:

[0060] Step 1: Obtain real-time data of equipment parameters, bus parameters, and line parameters, and preprocess the obtained real-time data to obtain preprocessed parameters.

[0061] like Figure 2As shown, the acquired real-time data is preprocessed, including: anomaly correction and data normalization.

[0062] Abnormal data correction:

[0063] Step 1.1: Calculate the mean and standard deviation of historical load data.

[0064] Step 1.2: Based on the calculated mean and standard deviation, calculate the deviation of the historical data using the following formula:

[0065]

[0066] In equation (1), H represents the average historical load data for a certain period; H is the number of days in this period; x t (i) represents the data value at time t on day i; σ t λ represents the standard deviation of historical load data over a certain period; λ is the deviation of historical data. When the real-time data at a certain point is greater than λ, the real-time data is considered abnormal.

[0067] Step 1.3: When abnormal data is identified, remove the abnormal data and replenish the removed abnormal data by averaging the real-time data before and after the abnormal data, expressed by the following formula:

[0068]

[0069] In equation (2), This represents the average of the real-time data before and after the abnormal data.

[0070] Step 1.4: Data normalization: After correcting outlier data, a 0-1 normalization method is used to resolve the issue of inconsistent data units, expressed by the following formula:

[0071]

[0072] In equation (3), xi' is the normalized value, x i The value before normalization, x min x is the minimum value in the sample data. max This represents the maximum value in the sample data.

[0073] Step 2: Based on the preprocessed parameters, calculate the grid operation parameters after the distributed photovoltaic power source is connected to the grid. Verify the calculated operation parameters. If the verification is successful, use the preset evaluation index to assign values ​​and calculate the score used to classify the grid carrying capacity level. If the verification fails, the grid carrying capacity level is poor.

[0074] Step 2.1: Calculate the grid operation parameters after the distributed photovoltaic power source is connected to the grid based on the preprocessed parameters.

[0075] Step 2.1.1: Calculate the voltage deviation.

[0076] Generally, the node voltage is 0.95U. N Up to 1.05U N Between, U N This refers to the rated voltage of the line. If the voltage exceeds a certain range, the reliability and efficiency of the power supply will decrease. Simultaneously, from the load side, the voltage received by electrical equipment may not be the rated voltage, potentially leading to equipment damage or operational instability. Therefore, maintaining node voltages as close as possible to their rated values ​​is a crucial task in distribution network planning and operation control. Voltage deviation is calculated using the following formula:

[0077]

[0078] In equation (4), ΔU is the voltage deviation, U i U is the per-unit value of the node voltage. N This is the per-unit value of the rated voltage.

[0079] Step 2.1.2: Express the voltage fluctuation state of the distribution network nodes using a formula, as follows:

[0080]

[0081] In equation (5), W represents the voltage fluctuation state of the distribution network node, and N represents the number of distribution network nodes.

[0082] Step 2.2: Verify the calculated operating parameters. If the verification fails, the power grid carrying capacity level is poor. This includes: voltage fluctuation verification, short-circuit current verification, and harmonic verification.

[0083] Step 2.2.1: Perform voltage fluctuation verification.

[0084] In the IEEE 33 power distribution system, the following formula is used for verification:

[0085] ΔW≤W m (6)

[0086] In equation (6), ΔW is the voltage fluctuation state value calculated in the IEEE 33 distribution system; W m The permissible voltage fluctuation limit.

[0087] When the input data does not satisfy equation (6), it indicates that the voltage fluctuation has exceeded the limit, and the data is judged to be unqualified.

[0088] Step 2.2.2: Perform short-circuit current verification.

[0089] The short-circuit current check should be based on the principle that the short-circuit current of each bus node in the system after the distributed power source is connected does not exceed the breaking current limit of the corresponding circuit breaker. Before the distributed photovoltaic system is connected, the check should be performed according to the following formula:

[0090] I XC <I m (7)

[0091] In equation (7), I XC For the system bus short-circuit current; I m The minimum short-circuit current limit should be selected from the corresponding circuit breaker breaking current limits of all equipment connected to the busbar.

[0092] When the input data does not satisfy equation (7), it indicates that the short-circuit current is higher than the limit, and the data is judged to be unqualified.

[0093] Step 2.2.3: Perform harmonic verification.

[0094] Harmonic verification is based on the principle that the harmonic current content at the nodes of distributed photovoltaic (PV) power grid connection in the system does not exceed the limit. The verification object includes all nodes that may be affected by the harmonic current provided by distributed PV. The harmonic current is verified using the following formula:

[0095] I XC,n <I n (8)

[0096] In equation (8), I XC,n I represents the measured value of the nth harmonic current at a power grid node. n This refers to the allowable value of the nth harmonic current injected into the grid nodes at each voltage level.

[0097] When the input data does not satisfy equation (8), it indicates that the harmonic current value has exceeded the limit, and the data is judged to be unqualified.

[0098] Step 2.3: After verification, the preset evaluation indicators are used to calculate the score used to classify the power grid carrying capacity level. If the verification fails, the power grid carrying capacity level is poor.

[0099] Assign weights δ to the voltage deviation index; γ to the short-circuit current index; and χ to the harmonic index. The scoring formula is as follows:

[0100] Q=δΔW+γΔI XC +χΔI XC,n (9)

[0101] In equation (9), Q is the score calculated by assignment.

[0102] Preset score threshold Q m If Q≤Q mIf the carrying capacity is good, then the power grid carrying capacity is good; otherwise, the power grid carrying capacity is poor.

[0103] Step 3: Based on the calculated operating parameters, reactive power optimization is performed using a preset method to obtain a reasonable configuration for suppressing voltage fluctuations. This reasonable configuration for suppressing voltage fluctuations is used to improve the grid carrying capacity.

[0104] Taking minimizing the grid voltage deviation as the first optimization objective and minimizing grid loss as the second optimization objective, the objective function is obtained as follows:

[0105]

[0106] In equation (10), w is the grid voltage offset, N is the number of nodes in the distribution network, and U i P represents the per-unit value of the node voltage; loss G represents the power grid loss value. ij Let V be the admittance from node i to node j. i Let V be the voltage at node i. j Let θ be the voltage at node j. ij Let be the angle between the phase angle differences of the voltages from node i to node j;

[0107] Normalizing the voltage offset and network loss yields the normalized objective function F:

[0108]

[0109] In equation (11), w init The initial value of the voltage offset before reactive power optimization; P loss(init) The initial value of the network loss before reactive power optimization; a1 and a2 are two coefficients greater than 0, and satisfy a1+a2=1;

[0110] The improved gray wolf algorithm is used to optimize the reactive power of equation (11) to obtain a reasonable configuration to suppress voltage fluctuations.

[0111] Specifically, gray wolf packs are divided into three groups: α, β, and δ. α is the alpha wolf, and the other wolves must obey its commands. β represents potential alpha candidates who assist the alpha in hunting, but may ultimately become the alpha. δ typically consists of pups and older wolves. The gray wolf algorithm involves steps such as alpha stratification, tracking, surrounding, and attacking prey. It can be expressed by the following formula:

[0112]

[0113] In equation (12), Indicates the current location of the wolf pack. Indicates the position for the next update. and The vector is randomly generated using the following formula:

[0114]

[0115] In equation (13), the iter flag indicates the number of the current iteration, and Max... iter This indicates the maximum number of iterations.

[0116] The original Grey Wolf algorithm was improved by dynamically updating C. α C β C δ Vectors enhance the balance between exploration and development processes. C α Supports randomized behavior, while C β and C δ Updated via exponential descent from 2 to 0. These vector updates are as follows:

[0117]

[0118] Assuming the photovoltaic inverter operates in constant power factor mode with a power factor of 0.98, the photovoltaic grid-connected nodes are considered as PQ nodes. The control variables are the active power output of the connected photovoltaic nodes, the OLTC tap level, and the parallel compensation capacitor tap level. The improved Grey Wolf algorithm described above is used to optimize the reactive power of the distribution network, reduce branch network losses, decrease voltage deviation, and improve the grid's carrying capacity.

[0119] Example 2:

[0120] This embodiment provides a grid carrying capacity enhancement device that takes into account distributed photovoltaic power generation, including:

[0121] Acquisition module: Used to acquire real-time data of equipment parameters, bus parameters, and line parameters, and to preprocess the acquired real-time data to obtain preprocessed parameters;

[0122] The grid carrying capacity assessment module is used to calculate the grid operating parameters after the distributed photovoltaic power source is connected to the grid based on the preprocessed parameters. The calculated operating parameters are then checked. If the check is passed, the preset assessment indicators are used to assign values ​​and calculate the score used to classify the grid carrying capacity level. If the check is not passed, the grid carrying capacity level is poor.

[0123] The optimization module is used to optimize reactive power using a preset method based on the calculated operating parameters, thereby obtaining a reasonable configuration to suppress voltage fluctuations. This reasonable configuration is used to improve the grid's carrying capacity.

[0124] Example 3:

[0125] This invention provides an electronic device, comprising:

[0126] A memory, and one or more processors communicatively connected to the memory;

[0127] The memory stores instructions that can be executed by the one or more processors to cause the one or more processors to implement the method as described in Embodiment 1.

[0128] Example 4:

[0129] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in Embodiment 1.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for improving grid carrying capacity considering distributed photovoltaic power generation, characterized in that, include: Real-time data of equipment parameters, bus parameters, and line parameters are acquired, and the acquired real-time data is preprocessed to obtain preprocessed parameters; wherein, the preprocessing of the acquired real-time data includes: abnormal data correction and data normalization. Based on the preprocessed parameters, the operating parameters of the power grid after the distributed photovoltaic power source is connected to the grid are calculated. The calculated operating parameters are then checked. If the check is passed, the preset evaluation indicators are used to assign values ​​and calculate the scores used to classify the grid carrying capacity level. If the check is not passed, the grid carrying capacity level is poor. Based on the calculated operating parameters, reactive power optimization is performed using a preset method to obtain a reasonable configuration for suppressing voltage fluctuations. This reasonable configuration for suppressing voltage fluctuations is used to improve the grid carrying capacity. The method for reactive power optimization includes: Taking minimizing the grid voltage deviation as the first optimization objective and minimizing grid loss as the second optimization objective, the objective function is obtained as follows: （6） In equation (6), w This is the grid voltage offset. N This represents the number of nodes in the distribution network. U i Per-unit value of node voltage; P loss This represents the power grid loss value. G ij Let be the admittance from node i to node j. V i Let be the voltage at node i. V j Let J be the voltage at node j. θ ij Let be the angle between the phase angle differences of the voltages from node i to node j; The voltage offset and network loss are normalized to obtain the normalized objective function. F : （7） In equation (7), w init The initial value of voltage offset before reactive power optimization; P loss(init) The initial value of network loss before reactive power optimization; a 1 and a 2 are two coefficients greater than 0, and satisfy... a 1+ a 2 = 1; The improved gray wolf algorithm is used to optimize the reactive power of equation (7) to obtain a reasonable configuration to suppress voltage fluctuations.

2. The method for improving grid carrying capacity considering distributed photovoltaic power sources according to claim 1, characterized in that, The abnormal data was corrected as follows: Calculate the mean and standard deviation of historical load data; Based on the calculated mean and standard deviation, the deviation of historical data is calculated using the following formula: （1） In equation (1), The average of historical load data over a certain period; H The number of days in this period; x t ( i ) is the first i sky t Data values ​​at any given time; σ t The standard deviation of historical load data over a certain period; λ This refers to the deviation of historical data; when the real-time data at a certain point is greater than... λ If so, the real-time data is abnormal. When abnormal data is identified, it is removed. The removed abnormal data is then replaced by averaging the real-time data before and after the abnormal data, expressed by the following formula: （2） In equation (2), This represents the average of the real-time data before and after the abnormal data.

3. The method for improving grid carrying capacity considering distributed photovoltaic power sources according to claim 1, characterized in that, The data normalization is as follows: after correcting abnormal data, a 0-1 normalization method is used to solve the problem of inconsistent data units, expressed by the following formula: （3） In equation (3), xi' The value is the normalized value. x i The values ​​before normalization. x min The minimum value in the sample data. x max This represents the maximum value in the sample data.

4. The method for improving grid carrying capacity considering distributed photovoltaic power generation according to claim 1, characterized in that, The calculation of the grid operating parameters after distributed photovoltaic power generation is connected to the grid includes: The voltage deviation is calculated using the following formula: （4） In equation (4), Δ U For voltage deviation, U i This represents the per-unit value of the node voltage. U N This is the per-unit value of the rated voltage; The voltage fluctuation state at distribution network nodes can be expressed by the following formula: （5） In equation (5), W This refers to the voltage fluctuation status at distribution network nodes. N This represents the number of nodes in the distribution network.

5. The method for improving grid carrying capacity considering distributed photovoltaic power sources according to claim 1, characterized in that, The verification based on the calculated operating parameters includes: voltage fluctuation verification, short-circuit current verification, and harmonic verification.

6. A grid carrying capacity enhancement device considering distributed photovoltaic power generation, characterized in that, include: Acquisition module: used to acquire real-time data of equipment parameters, bus parameters, and line parameters, and to preprocess the acquired real-time data to obtain preprocessed parameters; wherein, the preprocessing of the acquired real-time data includes: abnormal data correction and data normalization; The grid carrying capacity assessment module is used to calculate the grid operating parameters after the distributed photovoltaic power source is connected to the grid based on the preprocessed parameters. The calculated operating parameters are then checked. If the check is passed, the preset assessment indicators are used to assign values ​​and calculate the score used to classify the grid carrying capacity level. If the check is not passed, the grid carrying capacity level is poor. Optimization module: Used to optimize reactive power using a preset method based on the calculated operating parameters, so as to obtain a reasonable configuration for suppressing voltage fluctuations. The reasonable configuration for suppressing voltage fluctuations is used to improve the grid carrying capacity. The method for reactive power optimization includes: Taking minimizing the grid voltage deviation as the first optimization objective and minimizing grid loss as the second optimization objective, the objective function is obtained as follows: （6） In equation (6), w This is the grid voltage offset. N This represents the number of nodes in the distribution network. U i Per-unit value of node voltage; P loss This represents the power grid loss value. G ij For nodes i To the node j Admittance, V i Let be the voltage at node i. V j Let J be the voltage at node j. θ ij For nodes i To the node j The angle between the phase angles of the voltages; The voltage offset and network loss are normalized to obtain the normalized objective function. F : （7） In equation (7), w init The initial value of voltage offset before reactive power optimization; P loss(init) The initial value of network loss before reactive power optimization; a 1 and a 2 are two coefficients greater than 0, and satisfy... a 1+ a 2 = 1; The improved gray wolf algorithm is used to optimize the reactive power of equation (7) to obtain a reasonable configuration to suppress voltage fluctuations.

7. An electronic device, characterized in that, include: A memory, and one or more processors communicatively connected to the memory; The memory stores instructions that can be executed by the one or more processors to cause the one or more processors to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 5.

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