Reactive partitioning method considering source-load correlation and regional coupling degree

Abstract

The invention discloses a reactive power partitioning method considering source load correlation and regional coupling degree. The reactive power partitioning method comprises the following steps: acquiring source load data; obtaining edge distribution; constructing a source-load correlation model based on a Copula function and generating a scene; reducing the scene; calculating a source load correlation index; calculating power flows in different scenes; obtaining a sensitivity matrix; calculating an electrical distance matrix and a correction coefficient; calculating a full-dimensional electrical distance matrix; establishing a corrected electrical distance matrix considering source-load correlation; clustering and partitioning by using a hierarchical clustering algorithm; calculating a regional coupling degree index; and outputting a partitioning result. The method is based on the source load correlation model of the Copula function, the source load correlation and the regional coupling degree are considered, the electrical distance is corrected by constructing the source load correlation index and the regional coupling degree index, reactive power partitioning is carried out on the basis, the purpose of reactive power optimization is achieved, and on-site balance of reactive power and accurate control over node voltage can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of reactive power zoning in the context of distributed renewable energy grid connection, and in particular to a reactive power zoning method that takes source-load correlation and regional coupling into consideration. Background Art

[0002] Reactive power zoning is to decompose the entire power grid online into several areas with strong internal coupling and close mutual decoupling. Each area needs to maintain reactive power balance and have sufficient reactive power reserves to cope with load disturbances. This is conducive to local reactive power balance and accurate control of node voltage, and is an important topic in automatic voltage control of grid layering and zoning.

[0003] At present, the method of reactive power partitioning mainly combines the electrical distance between system nodes and the requirements of partitioning indexes to calculate the partitioning using the corresponding optimization algorithm. The traditional static electrical distance calculation adopts the node mutual impedance method, the short-circuit impedance method, the B matrix method in the PQ decomposition method, etc. This kind of calculation method is simple and fast, and the partition obtained is fixed, which can be well applied to the traditional reactive power partition with stable operating flow. However, with the increasing scale of new energy grid connection and the improvement of the closeness of power system connection, the reactive power flow exchange between power grid areas can no longer be ignored. The original reactive weak coupling assumption between control partitions is difficult to guarantee, which easily leads to grid control oscillation. The electrical distance obtained is inaccurate and cannot reflect the changes in the system flow state.

[0004] Therefore, it is necessary to adopt a reactive power zoning method under the background of distributed renewable energy grid connection, and propose a reactive power zoning method that considers source-load correlation and regional coupling to achieve better reactive power zoning effect. Summary of the invention

[0005] The purpose of the present invention is to solve the above defects in the prior art and provide a reactive power zoning method taking into account source-load correlation and regional coupling degree.

[0006] The purpose of the present invention can be achieved by adopting the following technical solutions: A reactive power partitioning method considering source-load correlation and regional coupling degree, the reactive power partitioning method comprising the following steps: S1. Periodically obtain the output data of all grid-connected new energy power generation equipment and the power load data on the user side in the power grid to be partitioned. The above data is called source load data. The period for obtaining source load data is 1 hour. S2, fitting the source-charge data to obtain the marginal distribution of the source-charge data; S3. Based on the edge distribution of source-load data, a source-load correlation model based on Copula function is constructed; S4, calling the source-load correlation model based on the Copula function to generate basic scenarios, and using the K-means clustering algorithm to reduce the scenarios, reducing the total number of scenarios to K; S5. Calculate the source-load correlation index; S6, solving the network flow of the power grid, performing calculations and solving in the generated scenario, and obtaining K power grid flow operation states; S7, calculating the sensitivity between the nodes of the power grid; S8, calculating the electrical distance between grid nodes; S9, calculate the correction coefficient of the tidal current operation state; S10, calculating the full-dimensional electrical distance matrix; S11, establishing a modified electrical distance matrix considering source-charge correlation; S12, clustering using a hierarchical clustering algorithm according to the electrical distances between any two nodes in the power grid in the modified electrical distance matrix; S13. Calculate the regional coupling index and output the partition result.

[0007] Furthermore, the process of constructing the source-load correlation model based on the Copula function in step S3 is as follows: The multivariate Copula function has the following theorem: make For the marginal distributions , , The joint probability distribution function of ,satisfy: Taking the derivative of the above formula gives the corresponding joint probability density function: In the formula, is the probability density function of the Copula function, , , is the mth random variable The probability density function of m=1,2,3,…,M, where M is the number of variables for which the marginal distribution needs to be determined. At the same time, it represents the probability density function of the mth grid-connected renewable energy output device and the user-side load, Indicates continuous multiplication of f; Using nonparametric estimation and undetermined coefficients method, the marginal distribution of source load data is determined , , , The output data of M grid-connected new energy power generation equipment and the power load data on the user side in the source-load data; According to the correlation characteristics of random variables, a normal Copula function is selected to describe the correlation between random variables; based on the selected Copula function, the maximum likelihood estimation method is used to estimate the unknown parameters of the Copula probability density function.

[0008] Through the Copula function established above, given the power generation data of grid-connected new energy equipment and the user-side load data, this Copula function can be used to generate a sufficient number of original scenarios to determine the correction coefficient of the subsequent power flow operation state.

[0009] Furthermore, the calculation method of the source-load correlation index in step S5 is as follows: The output data of M grid-connected renewable energy power generation equipment and the power load data on the user side are obtained to form a joint distribution H, which is mathematically described as: in, is the output data of the first to the M-1th grid-connected renewable energy power generation equipment, P M is the power load data on the user side, They are all {q×T}-dimensional block matrices, T=24, representing the output of all grid-connected renewable energy power generation equipment and the changes in user-side load data within 24 hours; According to the joint distribution H, the source-load correlation index is obtained, and the mathematical description is as follows: in, , They are the power load data on the user side and the output data of the cth grid-connected renewable energy power generation equipment in the tth period, It is the power demand of the user side load minus the output of all new energy power generation equipment. , are the output change rate of the new energy source side and the change rate of the load side demand under the kth scenario respectively. and By normalizing the optimization index of the overall source-load correlation, is the probability of the kth scenario occurring, K is the total number of scenarios after reduction, J1 is an indicator representing the output ratio of the new energy source side, J2 is an indicator representing the time-varying correlation between the new energy source side and the user load side, and J is the overall evaluation index of source-load correlation. The closer its value is to 0, the stronger the time correlation between the output of the grid-connected new energy power generation equipment and the load on the user side, and the closer the source-load correlation characteristics are.

[0010] Through the above operation, the grid-connected new energy power generation equipment and the user-side load can be associated, and the impact of new energy grid connection on the grid partition can be well reflected in the subsequent partitioning.

[0011] Furthermore, the sensitivity is calculated in step S7 The process is as follows: Get the sensitivity matrix , mathematically described as: in, , , and To calculate the four sub-matrices of the Jacobian matrix of the power flow between PQ nodes in the power grid topology diagram, when the line impedance parameters and network topology remain unchanged, , , and It is only related to voltage; The sensitivity matrix Expanding to PV nodes, assuming that a power grid has a total of N nodes, from 1 to The node No. is a PQ node, Nodes from +1 to N-1 are PV nodes, the Nth node is called the zero point, and the Node +1 is a PQ node, called an observation node, and the other nodes remain unchanged to construct an augmented sensitivity matrix , mathematically described as: In which, let the matrix middle , represents the sensitivity of other PQ nodes in the power grid to the current observation node, that is, represents the sensitivity of the first node in the power grid to the current observation node, so It also means the same thing. Let the matrix middle , represents the sensitivity of the observation node to other PQ nodes in the power grid. Due to the symmetry of the matrix, and It has symmetry, It represents the sensitivity of the current observation node to itself, and the remaining elements represent the sensitivity of the PQ nodes in the power grid; Construct a full-dimensional augmented sensitivity matrix , mathematically described as: in, and In order to use the successive recursive method to list each power node as an observation node one by one, the augmented sensitivity matrix corresponding to different nodes is obtained respectively. , and then find the corresponding vector , The matrix formed by Construct a diagonal matrix , let the matrix The submatrix in ; Get the nodes in the power grid Voltage sensitivity between , mathematically described as: in, and The nodes in the power grid are The voltage at for The reactive power of the place; and Respectively represent nodes The sensitivity and Sensitivity to oneself, , .

[0012] The above sensitivity establishment process extends the original sensitivity between only PV nodes and PV nodes to all nodes, and determines a unique corresponding sensitivity for each node in the power grid to other nodes in the power grid.

[0013] Furthermore, the electrical distance between the grid nodes in step S8 is The calculation method is as follows: In the power grid, nodes Electrical distance between , which is mathematically described as: in, is the distance between the nth node and the The sensitivity between nodes, For the power grid The node and The sensitivity between nodes, The grid includes nodes , And remove any node of the balance node, .

[0014] Furthermore, the correction coefficient of the tidal current operation state in step S9 is The calculation method is as follows: The first Correction coefficient of the kth power flow operation state of the node , mathematically described as: in, is the statistical probability of the kth power flow operation state, For the The voltage of the kth node in the power flow operation state, , K is the number of scenarios after reduction. The flow operation state refers to connecting the output data of the grid-connected new energy power generation equipment and the user-side load data in each scenario to the nodes corresponding to the power grid to form different power grid operation states, and calculating the flow under this state to obtain the flow operation state.

[0015] The correction coefficient of the current operation state is established to quantify the impact of the volatility of new energy on electrical distance after the new energy power generation equipment is connected to the grid.

[0016] Furthermore, the mathematical description of calculating the full-dimensional electrical distance matrix D in step S10 is as follows: This full-dimensional electrical distance matrix only considers the volatility of grid-connected renewable energy power generation equipment, but does not consider the time correlation between grid-connected renewable energy power generation equipment and user-side load.

[0017] Furthermore, the calculation method of establishing the modified electrical distance matrix ED considering the source-charge correlation in step S11 is as follows: The modified electrical distance matrix ED considering the source-charge correlation is established, which is mathematically described as: in, , J is the total evaluation index of source-load correlation, k is the number of the flow operation state, and h is the node where the new energy power generation equipment is connected to the power grid, that is, in the power grid PV node with node number h, in addition to the original generator, there is also a new energy power generation equipment inputting electric energy to the node. The above-mentioned modified electrical distance matrix is ​​a distance matrix that can be partitioned, taking into account the volatility of the new energy power generation equipment and its time correlation with the load.

[0018] Furthermore, the hierarchical clustering operation in step S12 is as follows: The hierarchical clustering algorithm determines the similarity between each category's data points and all data points by calculating the distance between them. The smaller the distance, the higher the similarity. The two data points or categories with the closest distance are combined to generate a clustering tree.

[0019] According to the modified electrical distance matrix ED obtained in step S11, a bottom-up method is adopted, that is, each node in the power grid is first regarded as a different class, recorded as class 1, and the two class 1s with the smallest electrical distance in the electrical distance matrix are merged to form a new class, recorded as class 2. Class 2 has two unmerged class 1s, that is, two nodes; repeat the above operation until all nodes belong to one class, that is, class N.

[0020] Furthermore, the process of calculating the regional coupling index and outputting the partition result in step S13 is as follows: Define the coupling index between nodes and partitions : The smaller the value, the higher the coupling degree of nodes in the partition. l is the number of nodes in the lth partition, , L is the total number of partitions, , is the lth partition, ; Define coupling indicators between different partitions : It represents the weighted average of the electrical distances between the partitions bordering the lth partition. The larger the value, the higher the coupling degree between the lth partition and the adjacent partitions. R is the total number of partitions bordering the lth partition. is the rth adjacent partition, N r is the total number of nodes in the rth bordering partition; , , ; Define the regional coupling index P that takes into account the node-region coupling and region-region coupling: The larger the total evaluation index P of regional coupling degree is, the better the partitioning effect is; The process of selecting the number of partitions is as follows: After clustering is completed, the number of partitions is set starting from two partitions, that is, a merge distance that can divide all nodes of the power grid into two classes is selected in the merge distance of hierarchical clustering. The hierarchical clustering algorithm used in the present invention is bottom-up. When a larger merge distance is selected to divide the area, the individual nodes in the power grid will be automatically divided into the corresponding partition (class). At the same time, the total regional coupling index P corresponding to each number of partitions is calculated. When the total regional coupling index P changes by no more than 10% with the increase of the number of partitions, the number of partitions is the appropriate number of partitions considered by the present invention; if the requirement that the change of the total regional coupling index P does not exceed 10% with the increase of the number of partitions is not met, the number of partitions is increased until the previous stop condition is met.

[0021] The present invention subsequently reduces the modified electrical distance matrix ED to two dimensions, which represent the two directions with the largest variance in the (N-1)-dimensional vector space formed by the entire electrical distance matrix, and finally visualizes the partitioning result.

[0022] Compared with the prior art, the present invention has the following advantages and effects: (1) The present invention discloses a reactive power zoning method that takes into account source-load correlation and regional coupling. This method is a scientific and feasible multi-evaluation index reactive power zoning method in a power system. It has broad application prospects in the context of new energy grid connection.

[0023] (2) Compared with the existing methods, the present invention combines the power flow analysis of renewable energy grid connection, source-load correlation and regional coupling degree, which can reflect the changes in the system power flow state and solve for a more accurate electrical distance.

[0024] (3) The present invention proposes a new reactive power partitioning method that takes into account source-load correlation and regional coupling, which can obtain a more accurate number of reactive power partitions.

[0025] (4) The zoning method of the present invention takes into account the active power output fluctuation when the actual new energy is connected to the grid, and can better meet the reactive power zoning requirements for the power grid under the background of new energy being connected to the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 It is a flow chart of a reactive power partitioning method considering source-load correlation and regional coupling degree disclosed by the present invention; Figure 2It is an improved IEEE39 node system adopted in the embodiment of the present invention, and the numbers in the figure are the numbers of each node; Figure 3 is a clustering result diagram of the IEEE39 node system in an embodiment of the present invention; Figure 4 It is a cluster scatter plot after dimensionality reduction using principal component analysis in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Example like Figure 1 As shown, this embodiment discloses a reactive power zoning method that takes source-load correlation and regional coupling into consideration.

[0029] The reactive power partitioning method comprises the following steps: S1. Obtain the output data of all new energy power generation equipment in the power grid to be partitioned and the power load data on the user side with a period of 1 hour, which is called source load data; The embodiment of the present invention uses an improved IEEE39 node system for simulation. Figure 2 As shown in the figure, the numbers in the figure are the numbers of each node. The required data is generated by random numbers that meet a specific distribution.

[0030] S2. Fit the source-charge data to obtain their marginal distributions.

[0031] Under the assumption that the daily sunshine amplitude and wind speed distribution satisfy the β distribution and Weibull distribution respectively, the marginal probability distribution function of the original data is calculated using the undetermined coefficient method.

[0032] S3. Based on the marginal distribution, a source-load correlation model based on the Copula function is constructed; The Copula model was fitted by the marginal distribution in S2, and finally 200 original scenes were generated through the Copula model.

[0033] S4. Generate basic scenarios based on the above Copula model and use K-means clustering algorithm to reduce the scenarios.

[0034] The 200 original scenarios were reduced to 7 scenarios through the K-means clustering algorithm, and the probabilities of each scenario were 0.1809, 0.2261, 0.1156, 0.1457, 0.1307, 0.0754, and 0.1256.

[0035] S5. Calculate the source-load correlation index.

[0036] A photovoltaic power generation device that meets the above Copula model is connected to node 35 as a PV node, and a wind power generation device is connected to node 37 as a PV node. After the new energy power generation equipment is connected, the source-load correlation indicators of the entire node system in these seven scenarios are calculated.

[0037] S6. Solve the network power flow. Perform calculations and solve in the scenario generated by the example, and obtain 7 power flow operation states.

[0038] The data of photovoltaic power generation equipment, wind power generation equipment and user load at each sampling time determined in the seven scenarios are connected to the corresponding locations to calculate the power flow in this case.

[0039] S7, calculating the sensitivity between nodes in the node system; calculating the sensitivity between nodes in the node system according to the method of claim 4; Get the sensitivity matrix , mathematically described as: in, , , and To calculate the four sub-matrices of the Jacobian matrix of the power flow between PQ nodes in the power grid topology diagram, when the line impedance parameters and network topology remain unchanged, , , and It is only related to voltage; The sensitivity matrix Expanding to PV nodes, IEEE39 has a total of 39 nodes, of which nodes 1 to 29 are PQ nodes, nodes 30 to 38 are PV nodes, node 39 is called the zero point, and node 30 is made a PQ node, called an observation node, and the remaining nodes remain unchanged to construct the augmented sensitivity matrix , mathematically described as: In which, let the matrix middle , represents the sensitivity of other PQ nodes in the power grid to the current observation node, that is, represents the sensitivity of the first node in the power grid to the current observation node, so It also means the same thing. Let the matrix middle , represents the sensitivity of the observation node to other PQ nodes in the power grid. Due to the symmetry of the matrix, and It has symmetry, It represents the sensitivity of the current observation node to itself, and the remaining elements represent the sensitivity of the PQ nodes in the power grid; Construct a full-dimensional augmented sensitivity matrix , mathematically described as: in, and In order to use the successive recursive method to list each power node as an observation node one by one, the augmented sensitivity matrix corresponding to different nodes is obtained respectively. , and then find the corresponding vector , The matrix formed by Construct a diagonal matrix , let the matrix The submatrix in ; Get the nodes in the power grid Voltage sensitivity between , mathematically described as: in, and The nodes in the power grid are The voltage at for The reactive power of the place; and Respectively represent nodes The sensitivity and Sensitivity to oneself, , .

[0040] S8. Calculate the electrical distance between grid nodes , which is mathematically described as: in, is the distance between the nth node and the The sensitivity between nodes, For the power grid The node and The sensitivity between nodes, The grid includes nodes , And remove any node of the balance node, .

[0041] S9. Calculate the correction coefficient of the tidal current operation state, that is, the correction coefficient of the kth tidal current operation state of the nth node in the node system , mathematically described as: in, is the statistical probability of the kth power flow operation state, For the The voltage of the kth node in the power flow operation state, .

[0042] S10, calculate the full-dimensional electrical distance matrix D, which is mathematically described as: S11. Establish a modified electrical distance matrix ED considering the source-charge correlation, which is mathematically described as: in, , J is the overall evaluation index of source-load correlation, and k is the number of the tidal current operation state.

[0043] S12. Perform clustering using a hierarchical clustering algorithm.

[0044] According to the modified electrical distance matrix ED obtained in step S11, a bottom-up method is adopted, that is, each node in the power grid is first regarded as a different class, recorded as class 1, and the two classes 1 with the smallest electrical distance in the electrical distance matrix are merged to form a new class, recorded as class 2. Class 2 has two classes 1 before merging, that is, two nodes; repeat the above operation until all nodes belong to one class, that is, class N. The clustering results are shown in the figure. Figure 3 shown.

[0045] S13, calculating the regional coupling index and outputting the partition result; In order to select the appropriate number of partitions, after completing clustering, the number of partitions is set starting from two partitions, that is, a merge distance that can divide all nodes of the power grid into two classes is selected in the merge distance of hierarchical clustering. The hierarchical clustering algorithm used in the present invention is bottom-up. When a larger merge distance is selected to divide the area, the individual nodes in the power grid will be automatically divided into the corresponding partition (class). At the same time, the total regional coupling index P corresponding to each number of partitions is calculated. When the total regional coupling index P changes by no more than 10% with the increase in the number of partitions, the number of partitions is the appropriate number of partitions considered by the present invention; if the requirement that the change in the total regional coupling index P does not exceed 10% with the increase in the number of partitions is not met, the number of partitions is increased until the previous stop condition is met. The final number of partitions is 7.

[0046] The present invention subsequently reduces the modified electrical distance matrix ED to two dimensions, which represent the two directions with the largest variance in the (N-1)-dimensional vector space formed by the entire electrical distance matrix. The resulting partitioning results are visualized. Figure 4 shown.

[0047] The source-load correlation index and the regional coupling index together constitute the evaluation index of reactive power partitioning of this project. The calculation results are shown in Table 1. The results of the seven partitions are shown in Table 2.

[0048] Table 1. Evaluation index calculation results Table 2. Partition result table From the result data obtained in Table 2, it can be seen that the source-load correlation related index of the partition obtained by this method is in a smaller position, while the index within the region of the regional coupling index is smaller, indicating that the coupling degree between the nodes in the entire partition area is high, and the overall regional coupling result is satisfactory. Therefore, it can be shown that the partition result is more reasonable.

[0049] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A reactive power zoning method considering source-load correlation and regional coupling, characterized in that: The reactive power partitioning method comprises the following steps: S1. Periodically obtain the output data of all grid-connected new energy power generation equipment and the power load data on the user side in the power grid to be partitioned. The above data is called source load data. The period for obtaining source load data is 1 hour. S2, fitting the source-charge data to obtain the marginal distribution of the source-charge data; S3. Based on the edge distribution of source-load data, a source-load correlation model based on Copula function is constructed; S4, calling the source-load correlation model based on the Copula function to generate basic scenarios, and using the K-means clustering algorithm to reduce the scenarios, reducing the total number of scenarios to K; S5. Calculate the source-load correlation index; S6, solving the network flow of the power grid, performing calculations and solving in the generated scenario, and obtaining K power grid flow operation states; S7, calculating the sensitivity between the nodes of the power grid; S8, calculating the electrical distance between grid nodes; S9, calculate the correction coefficient of the tidal current operation state; S10, calculating the full-dimensional electrical distance matrix; S11, establishing a modified electrical distance matrix considering source-charge correlation; S12, clustering using a hierarchical clustering algorithm according to the electrical distances between any two nodes in the power grid in the modified electrical distance matrix; S13. Calculate the regional coupling index and output the partition result.

2. A reactive power zoning method considering source-load correlation and regional coupling according to claim 1, characterized in that: The process of constructing the source-load correlation model based on the Copula function in step S3 is as follows: The multivariate Copula function has the following theorem: make For the marginal distributions , , The joint probability distribution function of ,satisfy: Taking the derivative of the above formula gives the corresponding joint probability density function: In the formula, is the probability density function of the Copula function, , , is the mth random variable The probability density function of m=1,2,3,…,M, where M is the number of variables for which the marginal distribution needs to be determined. At the same time, it represents the probability density function of the mth grid-connected renewable energy output equipment and the user-side load, Indicates continuous multiplication of f; Using nonparametric estimation and undetermined coefficients method, the marginal distribution of source load data is determined , , , The output data of M grid-connected new energy power generation equipment and the power load data on the user side in the source-load data; According to the correlation characteristics of random variables, a normal Copula function is selected to describe the correlation between random variables; based on the selected Copula function, the maximum likelihood estimation method is used to estimate the unknown parameters of the Copula probability density function.

3. A reactive power zoning method considering source-load correlation and regional coupling according to claim 1, characterized in that: The calculation method of the source-load correlation index in step S5 is as follows: The output data of M grid-connected renewable energy power generation equipment and the power load data on the user side are obtained to form a joint distribution H, which is mathematically described as: in, is the output data of the first to the M-1th grid-connected renewable energy power generation equipment, P M is the power load data on the user side, They are all {q×T}-dimensional block matrices, T=24, representing the output of all grid-connected renewable energy power generation equipment and the changes in user-side load data within 24 hours; According to the joint distribution H, the source-load correlation index is obtained, and the mathematical description is as follows: in, , They are the power load data on the user side and the output data of the cth grid-connected renewable energy power generation equipment in the tth period, It is the power demand of the user side load minus the output of all new energy power generation equipment. , are the output change rate of the new energy source side and the change rate of the load side demand under the kth scenario respectively. and By normalizing the optimization index of the overall source-load correlation, is the probability of the kth scenario occurring, K is the total number of scenarios after reduction, J1 is an indicator representing the output ratio of the new energy source side, J2 is an indicator representing the time-varying correlation between the new energy source side and the user load side, and J is the overall evaluation index of source-load correlation. The closer its value is to 0, the stronger the time correlation between the output of the grid-connected new energy power generation equipment and the load on the user side, and the closer the source-load correlation characteristics are.

4. A reactive power zoning method considering source-load correlation and regional coupling according to claim 1, characterized in that: The sensitivity is calculated in step S7 The process is as follows: Get the sensitivity matrix , mathematically described as: in, , , and To calculate the four sub-matrices of the Jacobian matrix of the power flow between PQ nodes in the power grid topology diagram, when the line impedance parameters and network topology remain unchanged, , , and It is only related to voltage; The sensitivity matrix Expanding to PV nodes, assuming that a power grid has a total of N nodes, from 1 to The node No. is a PQ node, Nodes from +1 to N-1 are PV nodes, the Nth node is called the zero point, and the Node +1 is a PQ node, called an observation node, and the other nodes remain unchanged to construct an augmented sensitivity matrix , mathematically described as: In which, let the matrix middle , represents the sensitivity of other PQ nodes in the power grid to the current observation node, that is, represents the sensitivity of the first node in the power grid to the current observation node, so It also means the same thing. Let the matrix middle , represents the sensitivity of the observation node to other PQ nodes in the power grid. Due to the symmetry of the matrix, and It has symmetry, It represents the sensitivity of the current observation node to itself, and the remaining elements represent the sensitivity of the PQ nodes in the power grid; Construct a full-dimensional augmented sensitivity matrix , mathematically described as: in, and In order to use the successive recursive method to list each power node as an observation node one by one, the augmented sensitivity matrix corresponding to different nodes is obtained respectively. , and then find the corresponding vector , The matrix formed by Construct a diagonal matrix , let the matrix The submatrix in ; Get the nodes in the power grid Voltage sensitivity between , mathematically described as: in, and The nodes in the power grid are The voltage at for The reactive power of the place; and Respectively represent nodes The sensitivity and Sensitivity to oneself, , .

5. A reactive power zoning method considering source-load correlation and regional coupling according to claim 1, characterized in that: The electrical distance between the grid nodes in step S8 The calculation method is as follows: In the power grid, nodes Electrical distance between , which is mathematically described as: in, is the distance between the nth node and the The sensitivity between nodes, For the power grid The node and The sensitivity between nodes, The grid includes nodes , And remove any node of the balance node, .

6. A reactive power zoning method considering source-load correlation and regional coupling according to claim 5, characterized in that: The correction coefficient of the tidal current operation state in step S9 The calculation method is as follows: The first Correction coefficient of the kth power flow operation state of the node , mathematically described as: in, is the statistical probability of the kth power flow operation state, For the The voltage of the kth node in the power flow operation state, , K is the number of scenarios after reduction. The flow operation state refers to connecting the output data of the grid-connected new energy power generation equipment and the user-side load data in each scenario to the nodes corresponding to the power grid to form different power grid operation states, and calculating the flow under this state to obtain the flow operation state.

7. A reactive power zoning method considering source-load correlation and regional coupling according to claim 6, characterized in that: The mathematical description of calculating the full-dimensional electrical distance matrix D in step S10 is as follows: 。 8. A reactive power zoning method considering source-load correlation and regional coupling according to claim 1, characterized in that: The calculation method for establishing the modified electrical distance matrix ED considering the source-charge correlation in step S11 is as follows: The modified electrical distance matrix ED considering the source-charge correlation is established, which is mathematically described as: in, , J is the total evaluation index of source-load correlation, k is the number of the flow operation state, and h is the node where the new energy power generation equipment is connected to the power grid. That is, in the power grid PV node with node number h, in addition to the original generator, there is also new energy power generation equipment inputting electric energy to the node.

9. A reactive power zoning method considering source-load correlation and regional coupling according to claim 1, characterized in that: The hierarchical clustering operation in step S12 is as follows: According to the modified electrical distance matrix ED obtained in step S11, a bottom-up method is adopted, that is, each node in the power grid is first regarded as a different class, recorded as class 1, and the two class 1s with the smallest electrical distance in the electrical distance matrix are merged to form a new class, recorded as class 2. Class 2 has two unmerged class 1s, that is, two nodes; repeat the above operation until all nodes belong to one class, that is, class N.

10. A reactive power zoning method considering source-load correlation and regional coupling according to claim 1, characterized in that: The process of calculating the regional coupling index and outputting the partition result in step S13 is as follows: Define the coupling index between nodes and partitions : The smaller the value, the higher the coupling degree of nodes in the partition. l is the number of nodes in the lth partition, , L is the total number of partitions, , is the lth partition, ; Define coupling indicators between different partitions : It represents the weighted average of the electrical distances between the partitions bordering the lth partition. The larger the value, the higher the coupling degree between the lth partition and the adjacent partitions. R is the total number of partitions bordering the lth partition. is the rth adjacent partition, N r is the total number of nodes in the rth bordering partition; , , ; Define the regional coupling index P that takes into account the node-region coupling and region-region coupling: The larger the total evaluation index P of regional coupling degree is, the better the partitioning effect is.

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