A reactive power partitioning method considering source-load correlation and regional coupling degree

Abstract

The application discloses a reactive power partitioning method considering source-load correlation and regional coupling degree, and comprises the following steps: obtaining source-load data; obtaining edge distribution; constructing a source-load correlation model based on a Copula function and generating a scenario; reducing the scenario; calculating a source-load correlation index; calculating power flow under different scenarios; obtaining a sensitivity matrix; calculating an electrical distance matrix and a correction coefficient; calculating a full-dimensional electrical distance matrix; establishing a correction 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 source-load correlation model based on the Copula function considers source-load correlation and regional coupling degree, corrects the electrical distance by constructing a source-load correlation index and a regional coupling degree index, and carries out reactive power partitioning, so that the purpose of reactive power optimization is achieved, and the on-site balance of reactive power and the accurate control of node voltage are facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of reactive power partitioning in the context of distributed new energy grid connection, and in particular to a reactive power partitioning method considering source-load correlation and regional coupling degree. BACKGROUND

[0002] Reactive power partitioning is the process of decomposing the overall power grid into several regions that are strongly coupled internally and approximately decoupled from each other. Each region needs to maintain reactive power balance and have sufficient reactive power reserves to respond to load disturbances, which is beneficial for local reactive power balance and accurate control of node voltage. It is an important issue in hierarchical and partitioned automatic voltage control of power grids.

[0003] Current methods for reactive power partitioning mainly use optimization algorithms based on electrical distance between system nodes and partitioning index requirements. Traditional static electrical distance calculation methods include node mutual impedance method, short-circuit impedance method, and B matrix method in PQ decomposition method. These methods are simple and fast, and the obtained partitioning is fixed, which can be well applied to traditional reactive power partitioning with stable operation flow. However, with the increasing scale of new energy grid connection and the increasing tightness of power system connection, the exchange of reactive power between grid regions cannot be ignored. The original assumption of weak coupling between control partitions cannot be guaranteed, which may lead to grid control oscillation and inaccurate electrical distance calculation, making it difficult to reflect the changes in system flow state.

[0004] Therefore, there is a need for a reactive power partitioning method in the context of distributed new energy grid connection, which proposes a reactive power partitioning method considering source-load correlation and regional coupling degree to achieve better reactive power partitioning results. SUMMARY

[0005] The purpose of the present application is to solve the above-mentioned defects in the prior art and provide a reactive power partitioning method considering source-load correlation and regional coupling degree.

[0006] The purpose of the present application can be achieved by adopting the following technical solutions:

[0007] A reactive power partitioning method considering source-load correlation and regional coupling degree, comprising the following steps:

[0008] S1, periodically obtaining the output data of all grid-connected new energy power generation equipment and the user-side power consumption load data in the power grid to be partitioned, which are referred to as source-load data. The period for obtaining source-load data is 1 hour;

[0009] S2, fitting the source-load data to obtain the edge distribution of the source-load data;

[0010] S3, based on the edge distribution of the source-load data, constructing a source-load correlation model based on Copula function;

[0011] S4, calling a source-load correlation model based on a Copula function to generate a basic scenario, and using a K-means clustering algorithm to reduce the scenario, reducing the total number of scenarios to K;

[0012] S5, calculating a source-load correlation index;

[0013] S6, solving network power flow of the power grid, calculating and solving in the generated scenario, obtaining K power grid power flow operating states;

[0014] S7, calculating the sensitivity between nodes of the power grid;

[0015] S8, calculating the electrical distance between nodes of the power grid;

[0016] S9, calculating a correction coefficient of the power flow operating state;

[0017] S10, calculating a full-dimensional electrical distance matrix;

[0018] S11, establishing a corrected electrical distance matrix considering source-load correlation;

[0019] S12, using a hierarchical clustering algorithm to cluster according to the electrical distance between two nodes in the power grid in the corrected electrical distance matrix;

[0020] S13, calculating a regional coupling degree index, and outputting a partition result.

[0021] Further, the process of constructing a source-load correlation model based on a Copula function in step S3 is as follows:

[0022] The multivariate Copula function has the following theorem:

[0023] Let be a joint probability distribution function with marginal distributions , , , there must exist a Copula function satisfying:

[0024]

[0025] Taking the derivative of the above formula, the corresponding joint probability density function is obtained:

[0026]

[0027] In the formula, is the probability density function of the Copula function, , , is the mth random variable probability density function of the mth variable, m = 1, 2, 3, …, M, M is the number of variables whose marginal distribution needs to be determined, probability density function of the mth variable, m = 1, 2, 3, …, M, M is the number of variables whose marginal distribution needs to be determined, represents the continuous multiplication of f;

[0028] The non-parametric estimation and undetermined coefficient method are used to determine the marginal distribution of source-load data 、 、 , The output data of M grid-connected new energy power generation devices and the user-side power load data are obtained.

[0029] According to the correlation characteristics of random variables, a normal Copula function is selected to describe the correlation between random variables; according to the selected Copula function, the maximum likelihood estimation method is used to estimate the unknown parameters of the Copula probability density function.

[0030] Through the above established Copula function, under the given grid-connected new energy equipment power generation data and user-side load data, the Copula function can be used to generate a sufficient number of original scenes to determine the correction coefficient of the subsequent power flow operation state.

[0031] Further, the calculation method of the source-load correlation index in the step S5 is as follows:

[0032] The output data of M grid-connected new energy power generation devices and the user-side power load data are obtained.

[0033]

[0034] wherein, is the output data of the 1st to the M-1st grid-connected new energy power generation device, P M is the user-side power load data, are all block matrices of {q x T} dimensions, T = 24, representing the 24-hour change of the output of all grid-connected new energy power generation devices and the user-side load data;

[0035] According to the joint distribution H, the source-load correlation index is obtained, and the mathematical description is as follows:

[0036]

[0037]

[0038]

[0039]

[0040] wherein, , are the power consumption data of the user side and the output data of the cth grid-connected new energy power generation device in the tth period, respectively, is the demand power of the user side load minus the output of all new energy power generation devices, , are the output change rate of the new energy source side and the change rate of the load side demand in the kth scenario, respectively, and and are normalized to form an optimization index of the overall source-load correlation, is the probability of the kth scenario, K is the total number of reduced scenarios, J1 is an index representing the proportion of new energy source side output, J2 is an index representing the time-varying correlation between the new energy source side and the user load side, and J is the overall evaluation index of the source-load correlation. The closer the value is to 0, the stronger the time correlation between the grid-connected new energy power generation device output and the user side load, and the closer the source-load correlation characteristics.

[0041] Through the above operation, the grid-connected new energy power generation device and the user side load can be associated, and the impact of the new energy grid connection on the power grid partition can be well reflected in the subsequent partition.

[0042] Further, the sensitivity is calculated in step S7

[0043] The sensitivity matrix is obtained, and the mathematical description is as follows:

[0044]

[0045] wherein, , , and are four sub-matrices of the Jacobian matrix for calculating the power flow between PQ nodes in the power grid topology, when the line impedance parameters and the network topology structure are unchanged, , , and are only related to the voltage;

[0046] The sensitivity matrix is extended to the PV node. It is assumed that a power grid has N nodes in total, wherein the 1st to th nodes are PQ nodes, the +1th to N-1th nodes are PV nodes, the Nth node is called zero point, and the +1th node is a PQ node, called the observation node, and the remaining nodes remain unchanged, to construct an augmented sensitivity matrix , which is mathematically described as:

[0047]

[0048] wherein matrix is defined as , which represents the sensitivity of other PQ nodes in the power grid to the current observation node, i.e. represents the sensitivity of the first node in the power grid to the current observation node, so also represents the same meaning, and matrix is defined as , which represents the sensitivity of the observation node to other PQ nodes in the power grid, and since the matrix is symmetric, it is and have symmetry, 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;

[0049] The full-dimensional augmented sensitivity matrix is composed of

[0050]

[0051] wherein and are obtained by using the successive recursive method to sequentially list each power supply node as an observation node, respectively obtaining the augmented sensitivity matrix corresponding to different nodes , The matrix composed of each corresponding is a diagonal matrix , and the matrix is defined as the submatrix in the matrix

[0052] The voltage sensitivity between nodes in the power grid is obtained, which is mathematically described as:

[0053]

[0054] wherein and are the voltages at nodes in the power grid, respectively; is the reactive power at ; and represent the sensitivity of node and the sensitivity of to itself, respectively, , .​

[0055] The above sensitivity establishment process extends the original PV node and PV node sensitivity to all nodes, and determines the unique corresponding sensitivity for each node in the power grid to other nodes in the power grid.

[0056] Further, the electrical distance between the nodes in the power grid in the step S8 is calculated as follows:

[0057] In the power grid, the electrical distance between the nodes is calculated as follows: The mathematical description is as follows:

[0058]

[0059] Wherein, is the sensitivity between the nth node and the th node in the power grid, is the sensitivity between the th node and the th node in the power grid, is any node in the power grid including nodes , and excluding the balance node, .

[0060] Further, the calculation method of the correction coefficient of the power flow operating state in the step S9 is as follows:

[0061] The correction coefficient of the kth power flow operating state of the th node in the power grid is , and the mathematical description is as follows:

[0062]

[0063] Wherein, is the statistical probability of the kth power flow operating state, is the voltage of the kth power flow operating state of the th node, K is the reduced number of scenarios, and the power flow operating state refers to the different power grid operating states formed by connecting the output data of the grid-connected new energy power generation equipment and the user side load data into the corresponding nodes of the power grid, and calculating the power flow under the state to obtain the power flow operating state.

[0064] The establishment of the correction coefficient of the power flow operating state is to quantify the influence of the volatility of new energy power generation equipment on the electrical distance after the grid connection of the new energy power generation equipment.

[0065] Further, the mathematical description of calculating the full-dimensional electrical distance matrix D in step S10 is as follows:

[0066]

[0067] The full-dimensional electrical distance matrix only considers the volatility of the grid-connected new energy power generation equipment, and has not considered the time correlation between the grid-connected new energy power generation equipment and the user side load.

[0068] Further, the calculation method of the modified electrical distance matrix ED considering source-load correlation established in step S11 is as follows:

[0069] The modified electrical distance matrix ED considering source-load correlation is established, and the mathematical description is as follows:

[0070]

[0071] Wherein, J is the total evaluation index of source-load correlation, k is the number of power 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 PV node of the power grid with node number h, in addition to the original generator, the new energy power generation equipment also inputs power to the node. The above modified electrical distance matrix is the distance matrix that can be operated in partition, which considers the volatility of the new energy power generation equipment and the time correlation between the new energy power generation equipment and the load.

[0072] Further, the hierarchical clustering operation in step S12 is as follows:

[0073] The hierarchical clustering algorithm determines the similarity between them by calculating the distance between each class of data points and all data points, and the smaller the distance, the higher the similarity. The two data points or classes with the smallest distance are combined to generate a clustering tree.

[0074] 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 taken as a different class, denoted as the first class, and the two first classes with the smallest electrical distance in the electrical distance matrix are combined to form a new class, denoted as the second class, and the second class has two first classes before merging, that is, two nodes; Repeat the above operation until all nodes belong to one class, that is, the Nth class.

[0075] Further, the process of calculating the area coupling degree index and outputting the partition result in step S13 is as follows:

[0076] The coupling degree index between nodes and partitions is defined as :

[0077]

[0078] The smaller the value, the higher the coupling degree of the nodes in the partition, N l is the number of nodes in the lth partition, , L is the total number of partitions, , is the lth partition, ;

[0079] Define the coupling degree index between different partitions :

[0080]

[0081] which represents the weighted average of the electrical distance between the partitions adjacent to the lth partition, the larger the value, the higher the coupling degree between the lth partition and the adjacent partition, R is the total number of partitions adjacent to the lth partition, is the rth adjacent partition, N r is the total number of nodes in the rth adjacent partition; , , ;

[0082] Define the total regional coupling degree index P considering the node-region coupling degree and the region-region coupling degree:

[0083]

[0084] The larger the total regional coupling degree evaluation index P, the better the effect of the partition;

[0085] The selection process of the number of partitions is as follows:

[0086] After clustering, the number of partitions is set from two partitions, that is, a merging distance is selected in the merging distance of hierarchical clustering, which can divide all nodes of the power grid into two classes. The hierarchical clustering algorithm used in the present application is bottom-up. When a larger merging distance is selected to divide the region, the individual nodes in the power grid will be automatically divided into the corresponding partition (class). At the same time, the total regional coupling degree index P corresponding to each partition number is calculated. When the total regional coupling degree P changes by no more than 10% before and after the total regional coupling degree index P increases, the number of partitions is considered to be the appropriate number of partitions by the present application. If the requirement that the total regional coupling degree index P changes by no more than 10% before and after the number of partitions increases is not met, the number of partitions is increased until the above stopping condition is met.

[0087] Subsequently, the present application will reduce the 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 final partition result can be visualized.

[0088] The present application has the following advantages and effects relative to the prior art:

[0089] (1) The present application discloses a reactive power partitioning method considering source-load correlation and regional coupling degree, which is a scientific and easy-to-implement multi-evaluation-index reactive power partitioning method in a power system. Under the background of new energy grid connection, it has a wide application prospect.

[0090] (2) Relative to the existing method, the present application combines power flow analysis of new energy grid connection, source-load correlation and regional coupling degree, can reflect the change of system power flow state, and can solve more accurate electrical distance.

[0091] (3) The present application proposes a new reactive power partitioning method considering source-load correlation and regional coupling degree, which can obtain more accurate reactive power partitioning number.

[0092] (4) The partitioning method of the present application considers the active power output fluctuation in actual new energy grid connection, and can better meet the requirements of reactive power partitioning of the power grid under the background of new energy grid connection. BRIEF DESCRIPTION OF DRAWINGS

[0093] The accompanying drawings, which are included to provide a further understanding of the present application and are incorporated in and constitute a part of this application, illustrate embodiments of the present application and serve to explain the present application. Together with the general description of the present application given above, the drawings serve to explain the present application, but do not limit the present application. In the drawings:

[0094] Figure 1 is a flow chart of the reactive power partitioning method considering source-load correlation and regional coupling degree disclosed by the present application;

[0095] Figure 2 is an improved IEEE39 node system adopted by the embodiment of the present application, and the numbers in the figure are the numbers of each node;

[0096] Figure 3 is a clustering result graph of the IEEE39 node system in the embodiment of the present application;

[0097] Figure 4 is a clustering scatter plot after dimensionality reduction by principal component analysis in the embodiment of the present application. DETAILED DESCRIPTION

[0098] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0099] Embodiment

[0100] As Figure 1 shown, the embodiment discloses a reactive power partitioning method considering source-load correlation and regional coupling degree.

[0101] The reactive power partitioning method comprises the following steps:

[0102] S1, obtain the output data of all new energy power generation devices in the power grid to be partitioned and the user-side power consumption load data in a period of 1 hour, referred to as source-load data;

[0103] The embodiment of the application uses an improved IEEE39 node system for simulation, and the IEEE39 node system is as shown in the figure. Figure 2 The numbers in the figure are the numbers of the nodes. The required data is generated by satisfying a specific distribution of random numbers.

[0104] S2, fit the source-load data to obtain their edge distribution.

[0105] Under the assumption that the daily light amplitude and wind speed distribution satisfy the beta distribution and weibull distribution respectively, the edge probability distribution function of the original data is calculated using the undetermined coefficient method.

[0106] S3, based on the edge distribution, a source-load correlation model based on the Copula function is constructed;

[0107] The edge distribution in S2 is fitted to the Copula model, and finally 200 original scenarios are generated by the Copula model.

[0108] S4, generate the basic scenario based on the above Copula model, and use the K-means clustering algorithm to reduce the scenario.

[0109] The 200 original scenarios are reduced to 7 scenarios by the K-means clustering algorithm, and the probabilities of the respective scenarios are: 0.1809, 0.2261, 0.1156, 0.1457, 0.1307, 0.0754, 0.1256.

[0110] S5, calculate the source-load correlation index.

[0111] The photovoltaic power generation device satisfying the above Copula model is connected to node 35 through a PV node, and a wind power generation device is connected to node 37 through a PV node. The source-load correlation index of the entire node system under the 7 scenarios after connecting the new energy power generation device is calculated.

[0112] S6, solve the network power flow, and calculate and solve in the scenarios generated by the example, and 7 power flow operating states are obtained.

[0113] The data of photovoltaic power generation equipment and wind power generation equipment and user load at each sampling time determined in the seven scenes are accessed to the corresponding positions, and the power flow in this case is calculated.

[0114] S7, the sensitivity between nodes in the node system is calculated; according to the method of claim 4, the sensitivity between nodes in the node system is calculated;

[0115] Obtain the sensitivity matrix , the mathematical description is:

[0116]

[0117] Wherein, , , and are four sub-matrices of the Jacobian matrix for calculating the power flow between PQ nodes in the power grid topology, when the line impedance parameters and network topology are unchanged, , , and are only related to voltage;

[0118] The sensitivity matrix is extended to the PV node, IEEE39 has 39 nodes in total, wherein the first to the 29th node is a PQ node, the 30th to the 38th node is a PV node, the 39th node is called zero point, and the 30th node is a PQ node, called observation node, and the remaining nodes remain unchanged, to construct the augmented sensitivity matrix , the mathematical description is:

[0119]

[0120] Wherein, let the matrix in , which represents the sensitivity of other PQ nodes in the grid to the current observation node, that is , which represents the sensitivity of the first node in the grid to the current observation node, so also represents the same meaning, let the matrix in , which represents the sensitivity of the observation node to other PQ nodes in the grid, since the matrix is symmetric, and have symmetry, which represents the sensitivity of the current observation node to itself, and the remaining elements represent the sensitivity of the PQ node in the grid;

[0121] The full-dimensional augmented sensitivity matrix is constructed, and the mathematical description is:

[0122]

[0123] wherein, and are obtained by using the successive recursive method to list each power supply node as an observation node in succession, and different node pairs are obtained , and the corresponding vector , is obtained by using the successive recursive method to list each power supply node as an observation node in succession, and different node pairs are obtained , is obtained by using the successive recursive method to list each power supply node as an observation node in succession, and different node pairs are obtained , ;

[0124] The voltage sensitivity between nodes in the power grid is obtained, and the mathematical description is as follows:

[0125]

[0126] wherein, and are the voltages at nodes in the power grid; is the reactive power at ; and respectively represent the sensitivity of node and the sensitivity of to itself, , .

[0127] S8, the electrical distance between the nodes in the power grid is calculated , and the mathematical description is as follows:

[0128]

[0129] wherein, is the sensitivity between the nth node and the th node in the power grid, is the sensitivity between the th node and the th node in the power grid, is any node in the power grid including nodes , and excluding the balance node. .

[0130] S9, the correction coefficient of the power flow operation state, i.e., the correction coefficient of the kth power flow operation state of the nth node in the node system , is calculated, and the mathematical description is as follows: ​

[0131]

[0132] wherein, is the statistical probability of the kth power flow operating state, is the voltage of the kth power flow operating state of the jth node, .

[0133] S10, calculate the full-dimensional electrical distance matrix D, which is mathematically described as:

[0134]

[0135] S11, establish a modified electrical distance matrix ED considering source-load correlation, which is mathematically described as:

[0136]

[0137] wherein, J is the total evaluation index of source-load correlation, and k is the number of the power flow operating state.

[0138] S12, use the hierarchical clustering algorithm for clustering.

[0139] 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 taken as a different class, denoted 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, denoted as class 2, and the class 2 has two class 1s before merging, that is, two nodes; the above operation is repeated until all nodes belong to one class, that is, class N. The obtained clustering result is shown in Figure 3

[0140] S13, calculate the regional coupling degree index and output the partition result;

[0141] To select a suitable number of partitions, after completing the clustering, the number of partitions is set from two partitions, that is, a merging distance is selected in the merging distance of the hierarchical clustering to divide all nodes in the power grid into two classes. The hierarchical clustering algorithm used in the present application is from bottom to top. When a larger merging distance is selected to divide the regions, the single nodes in the power grid will be automatically divided into the corresponding partitions (classes). At the same time, the total regional coupling degree index P corresponding to each number of partitions is calculated. When the total regional coupling degree P changes by no more than 10% before and after the total regional coupling degree index P increases, the number of partitions is considered to be the suitable number of partitions in the present application. If the requirement that the total regional coupling degree P changes by no more than 10% before and after the total regional coupling degree index P increases is not met, the number of partitions is increased until the above stopping condition is met. The finally determined number of partitions is 7.​​

[0142] The application will correct the 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 electrical distance matrix. The final partition result can be visualized. The result is shown in Figure 4 .

[0143] The source-load correlation index and the area coupling index together constitute the evaluation index of the reactive power partition of the project. The calculation results are shown in Table 1. The results of the seven partitions are shown in Table 2.

[0144] Table 1. Evaluation index calculation result table

[0145]

[0146] Table 2. Partition result table

[0147]

[0148] According to the result data in Table 2, the source-load correlation index of the partition obtained by the method is in a small position, and the index in the area is small. The coupling degree between the nodes in the entire partition area is high, and the result of the total area coupling is satisfactory, so it can be concluded that the partition result is reasonable.

[0149] The above embodiment is a preferred embodiment of the application, but the embodiment of the application is not limited by the above embodiment. Any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the application are equivalent replacement methods and are included in the protection scope of the application.

Claims

1. A reactive power partitioning method considering source-load correlation and area coupling degree, characterized in that, The reactive power partitioning method comprises the following steps: S1, periodically acquiring output data of all grid-connected new energy power generation devices in the power grid to be partitioned and user-side power consumption load data, the above data being referred to as source-load data, and the period for acquiring the source-load data being 1 hour; S2, fitting the source-load data to obtain an edge distribution of the source-load data; S3, constructing a source-load correlation model based on a Copula function based on the edge distribution of the source-load data; S4, calling the source-load correlation model based on the Copula function to generate a basic scenario, and using a K-means clustering algorithm to reduce the scenario, so as to reduce the total number of scenarios to K; S5, calculating a source-load correlation index; S6, solving network power flow, and calculating and solving in the generated scenario to obtain K power flow running states; S7, calculating the sensitivity between nodes in the power grid; S8, calculating the electrical distance between nodes in the power grid; S9, calculating a correction coefficient of the power flow running state; S10, calculating a full-dimensional electrical distance matrix; S11, establishing a corrected electrical distance matrix considering source-load correlation; S12, using a hierarchical clustering algorithm to cluster according to the electrical distance between two nodes in the power grid in the corrected electrical distance matrix obtained in S11; S13, calculating a regional coupling degree index, and outputting a partitioning result, the process being as follows: Defining a coupling degree index X between nodes and partitions l : The smaller the value is, the higher the coupling degree of the nodes in the partition is, N l is the number of nodes in the lth partition, l = 1, 2, 3, …, L, L is the total number of partitions, x = 1, 2, 3, …, N l , y = 1, 2, 3, …, N l , Ω l is the lth partition, Definition of coupling degree index Y between different partitions l : represents the weighted average of the electrical distance between the partitions bordering the lth partition, the greater the value, the higher the coupling degree between the lth partition and the adjacent partitions, R is the total number of the partitions bordering the lth partition, Ω r is the rth bordering partition, N r is the total number of nodes in the rth bordering partition; w = 1, 2, 3, …, N r , e = 1, 2, 3, …, N r ; Defining a regional coupling degree total regional coupling degree index P considering the node-regional coupling degree and the regional-regional coupling degree: The greater the total evaluation index P of the regional coupling degree, the better the partitioning effect.

2. The reactive power partitioning method of claim 1, wherein, The process of constructing the source-load correlation model based on the Copula function in S3 is as follows: The multivariate Copula function has the following theorem: Let F(x1, x2, … x M ) be a joint probability distribution function with marginal distribution F1(x1), F2(x2), … F M (x M ) respectively, then there must exist a Copula function C[F1(x1), F2(x2), … F M (x M )] that satisfies: F(x1,x2,...,x M ) = C[F1(x1),F2(x2),...,F M (x M ) = C[u1,u2,...u M ] The corresponding joint probability density function can be obtained by derivation: In the formula, c(u) is the probability density function of the Copula function, u m =F m (x m ), u=(u1,u2,…,u m ), f(x) m Let x be the m-th random variable. m The probability density function, f(x), is given by m = 1, 2, 3, ..., M, where M is the number of variables whose marginal distributions need to be determined. m ) represents the probability density function of the m-th grid-connected renewable energy output device and the user-side load, and ∏f represents the product of f; By using non-parametric estimation and undetermined coefficient method, the marginal distribution of source-load data F1(x1), F2(x2), …, F M (x M ), x1, x2, …, x M are determined, which are the output data of M grid-connected new energy power generation devices and the user-side power consumption load data. According to the correlation characteristics of random variables, a normal Copula function is selected to describe the correlation between random variables; and unknown parameters of the Copula probability density function are estimated by using a maximum likelihood estimation method according to the selected Copula function.

3. The method of claim 1, wherein, The calculation method of the source-load correlation index in S5 is as follows: The output data of M grid-connected new energy power generation devices and the user-side power consumption load data are acquired to jointly constitute a joint distribution H, which is mathematically described as: wherein D1,…D M-1 are the output data of the 1st to the M-1st grid-connected new energy power generation devices, P M is the user-side power consumption load data, D1,…,D M-1 ,P M are all block matrices of dimension {q x T}, T = 24, representing the 24-hour changes in the output of all grid-connected new energy power generation devices and user-side load data; According to the joint distribution H, the source-load correlation index is obtained, which is mathematically described as: wherein P Load,t , P c,t are the user-side power consumption load data and the output data of the cth grid-connected new energy power generation device in the tth period, respectively, is the demand power of the user-side load minus the output of all new energy power generation devices, are the output change rate of the new energy source side and the change rate of the load side demand in the kth scenario, respectively, and and are the optimization indexes of the overall source-load correlation through normalization, p k is the probability of the kth scenario, K is the total number of reduced scenarios, J1 is an index representing the proportion of the output of the new energy source side, J2 is an index representing the time-varying correlation between the new energy source side and the user load side, and J is the overall evaluation index of the source-load correlation, and the closer the value is to 0, the stronger the time correlation between the output of the grid-connected new energy power generation device and the load on the user side, and the closer the source-load correlation characteristics.

4. The method of claim 1, wherein, The step S7 calculates the sensitivity a n,n′ The process is as follows: Obtaining sensitivity matrix S VQ Mathematically described as: where J QV , J Qθ , J Pθ and J PV are four sub-matrices of the Jacobian matrix for calculating the power flow between PQ nodes in the power grid topology, J QV , J Qθ , J Pθ and J PV are only related to the voltage when the line impedance parameters and the network topology are constant; The sensitivity matrix S VQ Extending to PV nodes, assuming that a power grid has N nodes in total, among which the 1st to Ith nodes are PQ nodes, the I+1th to N-1th nodes are PV nodes, the Nth node is called zero point, and the I+1th node is a PQ node, called observation node, and the rest of the nodes remain unchanged, the augmented sensitivity matrix S' is constructed, which is mathematically described as: Let matrix S′ contain S represents the sensitivity of other PQ nodes in the power grid to the currently observed node. 1,(I+1) S represents the sensitivity of the first node in the power grid to the currently observed node. (I+1),1 It also represents the same meaning, let matrix S′ contain This represents the sensitivity of the observed node to other PQ nodes in the power grid. Due to the symmetry of the matrix, therefore... and It has symmetry, S (I+1),(I+1) The first element represents the sensitivity of the current observation node to itself, while the other elements represent the sensitivity of the PQ nodes in the power grid. A full-dimensional augmented sensitivity matrix S is constituted, which is mathematically described as: wherein A I×(N-I-1) and B (N-I-1)×I are the respective power supply nodes are sequentially listed as observation nodes using a successive recursive method, and the augmented sensitivity matrix S' corresponding to different node pairs is obtained, and the corresponding vector is constructed, and the respective S (I+1),(I+1) is constructed into a diagonal matrix C (N-I-1)×(N-I-1) , and the sub-matrix The voltage sensitivity a between the node n and the node n' in the power grid is obtained n,n′ The mathematical description is as follows: where U n and U n′ are the voltages at node n and node n' in the power grid, respectively; Q n′ is the reactive power at node n'; and and represent the sensitivity of node n to node n' and the sensitivity of node n' to itself, respectively, n = 1, 2, 3,... N - 1, n' = 1, 2, 3,... N - 1.

5. The method of claim 1, wherein, The electrical distance d between the grid node and the node in the step S8 n,n′ The calculation method is as follows: In the power grid, the electrical distance d between node n and node n' n,n′ which is mathematically described as: wherein α n,n″ is the sensitivity between the nth node and the n"th node in the power grid, α n′,n″ is the sensitivity between then'th node and the n"th node in the power grid, n" is any node in the power grid including nodes n, n' and excluding the slack node, n" = 1, 2, 3,..., N-1.

6. The reactive power partitioning method of claim 5, wherein, The correction factor of the power flow operation state in the step S9 The calculation method is as follows: a correction factor of a kth power flow operating state of an nth node in a power grid The mathematical description is: wherein p (k) is the statistical probability of the kth power flow operating state, is the voltage of the nth node in the kth power flow operating state, k = 1, 2, 3, …, K, K is the number of reduced scenarios, the power flow operating state refers to different power grid operating states formed by connecting the output data of the grid-connected new energy power generation device and the user-side load data in the node corresponding to the connection of the power grid, and the power flow in the state is calculated to obtain the power flow operating state.

7. The method of claim 6, wherein, The mathematical description of the full-dimensional electrical distance matrix D calculated in S10 is as follows:

8. The reactive power partitioning method of claim 7, wherein, The calculation method of the corrected electrical distance matrix ED considering source-load correlation in S11 is as follows: The corrected electrical distance matrix ED considering source-load correlation is established, which is mathematically described as: wherein, J is the total evaluation index of source-load correlation, k is the number of power 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 PV node with node number h, in addition to the original generator, the new energy power generation equipment also inputs power to the node.

9. The method of claim 1, wherein, The hierarchical clustering operation in S12 is as follows: According to the corrected electrical distance matrix ED obtained in S11, a bottom-up method is adopted, that is, each node in the power grid is taken as a different class, which is referred to as the first class, the two first classes with the minimum electrical distance in the electrical distance matrix are merged to form a new class, which is referred to as the second class, and the second class has two first classes before merging, that is, two nodes; the above operation is repeated until all nodes belong to one class, that is, the Nth class.

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