Power distribution network voltage control area division method and device and electronic equipment

Abstract

The application discloses a power distribution network voltage control region division method and device and electronic equipment. The method comprises the following steps: obtaining power flow information of each node in a power distribution network, and determining a system admittance matrix of the power distribution network according to the power flow information; determining a cut set corresponding to each region in the power distribution network according to the system admittance matrix, the cut set being used for determining a preliminary partition result of the power distribution network; determining a voltage increment matrix corresponding to each node in the power distribution network, the voltage increment matrix being used for reflecting voltage change conditions of each node after a preset value of power change of any node in each node; taking the cut set as clustering prior information, clustering the voltage increment matrix to obtain a target partition result of the power distribution network, the target partition result comprising regions to which each node belongs; and performing voltage partition control on the power distribution network according to the target partition result. The application solves the technical problem of large partition control loss caused by the power distribution network static partition mode in the related art.

Description

Technical Field

[0001] This application relates to the field of electrical engineering, and more specifically, to a method, apparatus, and electronic equipment for dividing voltage control zones in a power distribution network. Background Technology

[0002] In related technologies, when performing zone control on distribution network systems, a static zoning followed by zone scheduling is often adopted. The problem with this approach is that the zoning results are not changed after zoning, while the operating state of the distribution network system is dynamically changing. This leads to the zone scheduling results sometimes not matching the actual situation, resulting in significant regulation losses.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for dividing voltage control areas in a power distribution network, in order to at least solve the technical problem of large zoning control losses caused by the use of static zoning in the power distribution network in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for dividing voltage control areas in a distribution network is provided, comprising: acquiring power flow information of each node in the distribution network and determining the system admittance matrix of the distribution network based on the power flow information, wherein the nodes include generator nodes and load nodes; determining cut sets corresponding to each region in the distribution network based on the system admittance matrix, wherein the cut sets are used to determine the preliminary partitioning result of the distribution network; determining the voltage increment matrix corresponding to each node in the distribution network, wherein the voltage increment matrix is ​​used to reflect the voltage change of each node after a preset value of power change of any node in each node; using the cut sets as clustering prior information, clustering the voltage increment matrix to obtain the target partitioning result of the distribution network, wherein the target partitioning result includes the region to which each node belongs; and performing voltage partitioning control on the distribution network based on the target partitioning result.

[0006] Optionally, determining the cut sets corresponding to each region in the distribution network based on the system admittance matrix includes: determining the inverse matrix of the system admittance matrix; performing a term-by-term product operation on the system admittance matrix and the inverse matrix to obtain the coupling matrix of the distribution network, wherein the elements in the coupling matrix are the coupling strength indices between two nodes in the distribution network; determining a preset coupling coefficient; comparing each element in the coupling matrix with the preset coupling coefficient and retaining the elements greater than the preset coupling coefficient; and determining the cut sets based on the retained elements.

[0007] Optionally, determining the cut set based on the retained elements includes: identifying the edge between the two nodes corresponding to the retained elements as the key edge; and determining the cut set based on the key edge.

[0008] Optionally, determining the voltage increment matrix corresponding to each node in the distribution network includes: determining the sensitivity matrix of each node in the distribution network, wherein the sensitivity matrix is ​​used to reflect the correlation between the voltage change of each node and the power change of the distribution network; sequentially changing the power of each node from a first power to a second power, and determining the voltage change of each node after the power change based on the sensitivity matrix, thereby obtaining the voltage increment matrix, wherein the power change of each node is the same, and when the power of any node in the distribution network is changed, the power of the nodes other than the changed node remains at the first power.

[0009] Optionally, clustering each node in the distribution network based on the cut set and voltage increment matrix includes: First, determining each row element in the voltage increment matrix as a sample, and determining each sample as a sample category; Second, determining the distance between each sample category; Third, merging the two closest sample categories into one sample category, and if the number of remaining sample categories is greater than the preset number of sample categories, jumping to the second step.

[0010] Optionally, determining the distance between each sample category includes: when there are multiple samples in a sample category, for each first sample in the first sample category, determining the distance between the first sample and each second sample in the second sample category, wherein the first sample category is any sample category in each sample category, and the second sample category is any sample category in each sample category other than the first sample category; the minimum value of the distance between the first sample and the second sample is the distance between the first sample category and the second sample category.

[0011] Optionally, determining the distance between each sample category includes: when there are multiple samples in a sample category, determining the average squared distance of all sample pairs between the first sample category and the second sample category as the distance between the first sample category and the second sample category, wherein the sample pair includes the first sample in the first sample category and the second sample in the second sample category, the first sample category is any sample category among all sample categories, and the second sample category is any sample category among all sample categories other than the first sample category.

[0012] Optionally, determining the distance between each sample category includes: determining the increment of the sum of squared deviations between any two sample categories, wherein the increment of the sum of squared deviations is obtained by subtracting the sum of squared deviations of the two sample categories before merging from the sum of squared deviations of the sample categories obtained after merging any two sample categories; and using the increment of the sum of squared deviations as the distance between any two sample categories.

[0013] According to another aspect of the embodiments of this application, a distribution network voltage control area division device is also provided, comprising: a first processing module, configured to acquire power flow information of each node in the distribution network and determine the system admittance matrix of the distribution network based on the power flow information, wherein the nodes include generator nodes and load nodes; a second processing module, configured to determine the cut sets corresponding to each region in the distribution network based on the system admittance matrix, wherein the cut sets are used to determine the preliminary partitioning result of the distribution network; a third processing module, configured to determine the voltage increment matrix corresponding to each node in the distribution network, wherein the voltage increment matrix is ​​used to reflect the voltage change of each node after any node power change by a preset value; a fourth processing module, configured to use the cut sets as clustering prior information to cluster the voltage increment matrix to obtain the target partitioning result of the distribution network, wherein the target partitioning result includes the region to which each node belongs; and a fifth processing module, configured to perform voltage partitioning control of the distribution network based on the target partitioning result.

[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, wherein a program is stored in the non-volatile storage medium, wherein the program controls the device where the non-volatile storage medium is located to execute a power distribution network voltage control area division method when it runs.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the program executes a distribution network voltage control area division method during runtime.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements a method for dividing a distribution network voltage control area.

[0017] In this embodiment, the power flow information of each node in the distribution network is obtained, and the system admittance matrix of the distribution network is determined based on the power flow information. The nodes include generator nodes and load nodes. The cut sets corresponding to each region in the distribution network are determined based on the system admittance matrix, and these cut sets are used to determine the preliminary partitioning results of the distribution network. The voltage increment matrix corresponding to each node in the distribution network is determined, and this voltage increment matrix reflects the voltage change of each node after a preset value of power change at any node. The cut sets are used as prior information for clustering, and the voltage increment matrix is ​​clustered to obtain the target partitioning results of the distribution network. The target partitioning results include the regions to which each node belongs. Voltage partitioning control of the distribution network is performed based on the target partitioning results. By using cut sets as prior information to cluster the voltage increment matrix to obtain the target partitioning results, the purpose of dynamic partitioning of the distribution network is achieved, thereby reducing the regulation losses of the distribution network and solving the technical problem of large partitioning control losses caused by the static partitioning method used in related technologies. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a schematic diagram of the structure of a computer terminal (mobile terminal) provided according to an embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating the distribution network voltage control area division method provided in the embodiments of this application;

[0021] Figure 3 This is a schematic diagram of a coupling node provided according to an embodiment of this application;

[0022] Figure 4 This is a schematic diagram of the correlation coefficients and coupling matrix provided according to embodiments of this application;

[0023] Figure 5 This is a flowchart illustrating the distribution network voltage dispatch control process provided according to an embodiment of this application;

[0024] Figure 6 This is a schematic diagram of the distribution network voltage control area division device provided according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] In scenarios with high penetration and decentralized distributed power source access in the distribution network, the distribution network becomes a complex network due to the large number of installed power generation devices and their non-centralized locations. Using a centralized optimization control method for all photovoltaic power generation systems can lead to a complex optimization process, excessive optimization time, and even the problem of the curse of dimensionality due to the large number of variables.

[0028] In recent years, voltage zoning control has provided a new approach to solving high-dimensional and complex voltage optimization control problems. "Zoning" refers to dividing the distribution network into several sub-regions according to certain rules. Nodes in different sub-regions exhibit weak coupling characteristics, while nodes within the same sub-region exhibit strong coupling characteristics. Therefore, the voltage in each sub-region can be controlled independently without being affected by other sub-regions. Currently, the main zoning methods for voltage zoning optimization control in domestic and international literature include: spectral clustering, K-means algorithm, immune algorithm, and complex network theory.

[0029] To address the challenges posed by the high proportion of renewable energy generation to the safe operation of distribution networks, a distributed optimization strategy based on cluster collaborative control is one solution. Leveraging the sparsity of the distribution network, it is divided into multiple sub-regions, achieving a control cluster with high cohesion of nodes within each region and low coupling between nodes in different regions. Then, a distributed control algorithm is used to collaboratively control the clusters formed by multiple sub-regions, achieving a distributed control pattern of intra-cluster autonomy and inter-cluster coordination. When partitioning the distribution network, it is necessary to consider not only geographical distribution and physical connections but also the degree of electrical coupling between nodes. Depending on the control objectives, different distribution network partitioning indices have been proposed. Some use node voltage and reactive power coupling as indices to construct electrical distances and employ clustering algorithms for cluster partitioning; others establish electrical distances based on the voltage-voltage coupling between nodes; still others conduct partitioning research based on voltage phase angle-active power sensitivity and branch impedance distance. Regarding partitioning methods, current research mainly focuses on clustering algorithms and community partitioning algorithms. In addition, some studies employ intelligent algorithms for partitioning.

[0030] However, most distribution network partitioning in related technologies is based on scheduling partitions, ensuring that the order of each sub-block is roughly equal to facilitate parallel processing by the computer. This partitioning method is called structural partitioning or static partitioning. It reflects the characteristics of the network structure. The power grid structure is static, while the parameters within the structure determine the network characteristics, which reflect dynamic behavior. However, existing methods do not address the problem from the perspective of the network's dynamic operating characteristics. Furthermore, they do not consider coordination between regions, leading to increased voltage regulation costs. Therefore, after partitioning the distribution network, it is necessary to achieve both parallel computing within each sub-region and coordinated optimization between regions, thereby truly realizing the partitioning of the distribution network and ensuring that the voltage of nodes in the divided voltage regions exhibits a concentrated distribution pattern.

[0031] To address the aforementioned issues, this application provides relevant solutions, which are detailed below.

[0032] According to an embodiment of this application, a method embodiment for dividing voltage control areas in a distribution network is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a method for dividing voltage control zones in a distribution network is shown. Figure 1As shown, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0034] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the power distribution network voltage control area division method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned power distribution network voltage control area division method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0037] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0038] Under the above operating environment, embodiments of this application provide a method for dividing the voltage control area of ​​a distribution network, such as... Figure 2 As shown, the method includes the following steps:

[0039] Step S202: Obtain the power flow information of each node in the distribution network, and determine the system admittance matrix of the distribution network based on the power flow information. The nodes include generator nodes and load nodes.

[0040] It should be noted that the power flow information of each node changes in real time, therefore the admittance matrix and partitioning results based on the power flow information also change in real time.

[0041] In some embodiments of this application, in order to systematically form a coupling matrix in an operational manner, the term-by-term product of two vectors is first defined, denoted by ⊙.

[0042] Let row vector a and column vector b be respectively:

[0043] a = [a1 a2 … a] n (1)

[0044] b = [b1 b2 … b] n ] T (2)

[0045] The term-by-term product of vectors a and b is defined as:

[0046] a☉b=[a1b1 a2b2 … a n b n (3)

[0047] In linear algebra, the scalar product (inner product) of two vectors is:

[0048] a·b=a1b1+a2b2+…+a n bn (4)

[0049] It expresses the degree of correlation between the two vectors as a whole.

[0050] If we take the normal form:

[0051] μ=(a·b) / |a||b| (5)

[0052] The correlation between two vectors can be quantitatively described by μ. When μ is close to 1, the two vectors a and b are said to be strongly linearly correlated; when μ is close to 0, the two vectors a and b are said to be weakly linearly correlated.

[0053] The term-by-term product further reveals the correlation between the corresponding elements of the two vectors. If, in terms of absolute value, a certain term accounts for a large proportion among the terms of a1b1, a2b2, ..., anbn, it indicates that the term plays a major role in the correlation between the two vectors a and b2.

[0054] The generation of the correlation matrix is ​​illustrated using the system admittance matrix as an example:

[0055] For the admittance matrix Y and its inverse matrix Y-1:

[0056]

[0057] In the formula, y1, y2…yn are row vectors, while y1-1, y2-1…an-1 are column vectors.

[0058] Then it exists:

[0059]

[0060]

[0061] Write the row vector as:

[0062]

[0063] As the i-th row of matrix G, and denote matrix G as:

[0064] G = Y☉Y -1 (9)

[0065] Define matrix G as the term-by-term product of Y and Y-1 matrices. Matrix G can be used as the coupling matrix between the admittances of each branch. Define G as the coupling matrix of Y.

[0066] Step S204: Determine the cut sets corresponding to each region in the distribution network based on the system admittance matrix, wherein the cut sets are used to determine the preliminary partitioning results of the distribution network;

[0067] The cut set described above can be seen as a partitioning result obtained based on the dynamic partitioning theory in this application, which partitions the network from the perspective of network parameters. Since a single partitioning method is constrained by real-time network structure changes and power flow factors in actual power grids, the voltage sensitivity method is combined with real-time power flow information to compensate for this shortcoming.

[0068] In the technical solution provided in step S204, the step of determining the cut sets corresponding to each region in the distribution network based on the system admittance matrix includes: determining the inverse matrix of the system admittance matrix; performing a term-by-term product operation on the system admittance matrix and the inverse matrix to obtain the coupling matrix of the distribution network, wherein the elements in the coupling matrix are the coupling strength index between two nodes in the distribution network; determining a preset coupling coefficient; comparing each element in the coupling matrix with the preset coupling coefficient, and retaining the elements that are greater than the preset coupling coefficient; and determining the cut sets based on the retained elements.

[0069] Specifically, the aforementioned preset coupling coefficient is used to determine the number of regions to be divided. The smaller the coefficient value, the more regions are divided; the larger the coefficient value, the fewer regions are divided. This coefficient is an empirically selected parameter, generally ranging from 0.001 to 0.005. Furthermore, the preset coupling coefficient can also be dynamically adjusted according to preset rules.

[0070] As an optional implementation, the step of determining the cut set based on the retained elements includes: determining the edge between the two nodes corresponding to the retained elements as the key edge; and determining the cut set based on the key edge.

[0071] Specifically, a matrix describing the static performance of a system (admittance matrix) or a matrix describing its dynamic behavior (state matrix and its inverse matrix) has corresponding elements, and their relationships usually express the relationship between certain physical or engineering quantities.

[0072] If Y -1 Array element uses y -1 It is indicated that the Y-element y ji With Y -1 Array element y ij -1 This is a pair of corresponding elements. To briefly illustrate the relationship between them, we will use flow graphs in graph theory for explanation. To better express the "causal" relationship between quantities, we will use directed edges to represent this relationship. According to flow graph convention, the direction of a directed edge always points from the second index to the first index.

[0073] First observe Y -1 The i-th element y of array j ij -1 The graph theory implication is that it equals (-1). i+j Δ ji / Δ, where Δ is the determinant of the Y matrix, and Δji It is Δ to remove row j (i j1 i j2 ,…,i jn ) and column i (i 1i i 2i ,…,i ni ) T The subdeterminant of the matrix has the following characteristics: in the corresponding network graph, node j has no incoming edges, only outgoing edges; node i has no outgoing edges, only incoming edges. Therefore, there is a directed edge from node j to node i with weight y. ij -1 ,See Figure 3 Left side.

[0074] Consider the Y-element y again ji In the corresponding network graph, this is a directed edge from node i to node j with weight y. ji , and the above y ij -1 The directed edge forms a first-order cycle, allowing the physical quantities at the two nodes to feedback to each other, thus coupling the two nodes together. Its first-order cycle product g ji =y ji y ij -1 This can be used to measure the degree of coupling between nodes i and j, and can be defined as the coupling coefficient between node i and node j, see [link to relevant documentation]. Figure 3 As shown on the right.

[0075] If we take g ji Place it in the j-th row and i-th column of matrix G, such as Figure 4 As shown, g is then assigned... ji The directionality from i to j. Let i,j = 1, 2, ..., n, and then the elements of the G matrix are formed one by one. The resulting G matrix is ​​defined as the incidence matrix of the Y matrix.

[0076] After obtaining the system admittance matrix Y, then Y is further calculated. -1 The array forms a coupled array G = Y ⊙ Y -1 Then, a critical coupling coefficient g is set. c In matrix G, discard all coupling coefficients whose absolute value is less than g. c The elements are discarded, meaning the corresponding coupling edges of the G matrix are removed, and consequently, the corresponding edges of the Y matrix are also removed, indicating that the edge has little impact on the static voltage characteristics of the system. The system is then divided into sub-blocks, and node connectivity is used to search for nodes connected to each sub-block, marking the nodes. Thus, after partitioning, nodes within the same sub-block are connected, while nodes at the boundaries of different sub-blocks can be considered disconnected.

[0077] Step S206: Determine the voltage increment matrix corresponding to each node in the distribution network. The voltage increment matrix is ​​used to reflect the voltage change of each node after the power of any node in each node changes by a preset value.

[0078] In the technical solution provided in step S206, the step of determining the voltage increment matrix corresponding to each node in the distribution network includes: determining the sensitivity matrix of each node in the distribution network, wherein the sensitivity matrix is ​​used to reflect the correlation between the voltage change of each node and the power change of the distribution network; sequentially changing the power of each node from a first power to a second power, and determining the voltage change of each node after the power change based on the sensitivity matrix, thereby obtaining the voltage increment matrix, wherein the power change of each node is the same, and when changing the power of any node in the distribution network, the power of the nodes other than the arbitrary node remains at the first power.

[0079] Specifically, any method for determining the voltage sensitivity matrix can be used, and this application is not limited in this regard. Furthermore, the voltage sensitivity matrix includes the sensitivity of each node within the distribution network. Sensitivity reflects how sensitive the voltage value of each node is to changes in power. A large change in the voltage value of a node indicates that this node is sensitive to changes in power; conversely, a small change in the voltage value of a node indicates that the voltage at that node is relatively stable and less affected by power changes at other nodes. It is also important to note that voltage sensitivity includes both active power voltage sensitivity and reactive power voltage sensitivity.

[0080] To more effectively regulate the voltage at each node of the distribution network, this application focuses on the impact of changes in the power system structure on each node. When a change in the system structure causes an increase in the power injected into a node, the resulting voltage change at other nodes is positive. When the power injected into a node decreases, the voltage change at other nodes will correspondingly decrease; in this case, the absolute value of the voltage change is used to describe it. To obtain the voltage increment matrix of the entire distribution network, the network structure is first changed. When the power injected into a node changes, it will cause a change in the voltage of other nodes in the system. Without considering the slack node, an n-1 dimensional voltage change vector will be generated. After changing the same power injection into n-1 nodes sequentially, a voltage change matrix of dimension (n-1)×(n-1) is generated; this matrix is ​​called the voltage increment matrix.

[0081] Step S208: Using the cut set as clustering prior information, cluster the voltage increment matrix to obtain the target partitioning result of the distribution network, wherein the target partitioning result includes the region to which each node belongs;

[0082] In the technical solution provided in step S208, the steps of clustering each node in the distribution network based on the cut set and voltage increment matrix include: First, determining each row element in the voltage increment matrix as a sample, and determining each sample as a sample category; Second, determining the distance between each sample category; Third, merging the two closest sample categories into one sample category, and if the number of remaining sample categories is greater than the preset number of sample categories, jumping to the second step.

[0083] As an optional implementation, the step of determining the distance between each sample category includes: when there are multiple samples in a sample category, for each first sample in a first sample category, determining the distance between the first sample and each second sample in a second sample category, wherein the first sample category is any sample category in each sample category, and the second sample category is any sample category in each sample category other than the first sample category; the minimum value of the distance between the first sample and the second sample is the distance between the first sample category and the second sample category.

[0084] In some embodiments of this application, the step of determining the distance between each sample category includes: when there are multiple samples in a sample category, determining the average squared distance of all sample pairs between the first sample category and the second sample category as the distance between the first sample category and the second sample category, wherein the sample pair includes a first sample in the first sample category and a second sample in the second sample category, the first sample category being any sample category among the sample categories, and the second sample category being any sample category other than the first sample category among the sample categories.

[0085] As an optional implementation, the step of determining the distance between each sample category includes: determining the increment of the sum of squared deviations between any two sample categories, wherein the increment of the sum of squared deviations is obtained by subtracting the sum of squared deviations of the two sample categories before merging from the sum of squared deviations of the sample categories obtained after merging any two sample categories; and using the increment of the sum of squared deviations as the distance between any two sample categories.

[0086] Specifically, in this embodiment, a hierarchical clustering algorithm can be used to cluster the voltage increment matrix. Hierarchical clustering, also known as hierarchical clustering, is a clustering method that continuously aggregates or splits samples to form a hierarchical tree structure. This algorithm divides based on the properties of the research objects, thus facilitating classification tailored to specific circumstances. Currently, commonly used hierarchical clustering methods include within-group linkage, between-group linkage, and Ward's sum of squared deviations method. Clustering statistics mainly include metrics such as Euclidean distance, Euclidean squared distance, Minkowski distance, Manhattan distance, and Person correlation coefficient. Taking agglomerative clustering as an example, the steps of the hierarchical clustering algorithm are as follows:

[0087] The first step is to calculate the distance between different categories and represent it using a distance matrix;

[0088] The second step is to compare the distances between different classes and merge the two closest classes into a new class.

[0089] The third step is to update the distance matrix, continue to compare the distances between different classes, and synthesize a new class.

[0090] Fourth, repeat steps two and three until the required number of termination classes are synthesized.

[0091] This application proposes to cluster the voltage increment matrix using three methods: intra-group linking, inter-group linking, and Ward's method (sum of squared deviations). Intra-group linking refers to representing the inter-class distance as the distance between points with the smallest mean squared distance between two classes. Inter-group linking uses the average of the squared distances between each pair of data points in two classes to represent the inter-class distance. The sum of squared deviations method is an extension of analysis of variance. When the system samples are properly classified, the sum of squared deviations between samples in each class should be small, while the sum of squared deviations between different classes should be relatively large. Therefore, this method can achieve small intra-group distances and large inter-group distances, thereby realizing voltage zoning of the distribution network. The specific idea of ​​the sum of squared deviations method is as follows:

[0092] The first step is to divide the n types of sample data into k classes: G1, G2, G3, ..., G... k Then the sum of squared deviations of the t-th class of samples is:

[0093]

[0094] In the formula, It is G t The center of gravity, X it (m-dimensional vector) represents G t The i-th sample, n t G represents t Number of samples.

[0095] The second step is when class G p and class G q Merge into a new class G r When, there are three types of sums of squared deviations S p S q and S r The increase in the sum of squared deviations is D. pq 2 =S r -S p -S q .

[0096] Third step, when G p and G q The two types are relatively close to each other. The value should be smaller; this classification is reasonable. When D... pq 2 When the value is large, it is classified as an unreasonable classification.

[0097] Fourth step: When the sum of squared deviations added by merging the two classes is regarded as the squared distance, the distance formula is given in equation (11), and the recursive formula is given in equation (12).

[0098]

[0099]

[0100] In the formula, For G q The center of gravity, X p For G p The sample in n p n q n r n k This represents the number of corresponding samples.

[0101] When calculating the distance between samples, Euclidean distance, Euclidean squared distance, Minkowski distance, and Manhattan distance can be used as classification statistics. For any two samples, the Euclidean distance is:

[0102]

[0103] For any two samples, their Euclidean squared distance is:

[0104]

[0105] For any two samples, their Minkowski distance is:

[0106]

[0107] In the formula, p is the power of the distance between samples, which is usually taken as 2.

[0108] For any two samples, their Manhattan distance is:

[0109]

[0110] In formulas (13) to (16), X in Let X represent the value of the nth variable in the i-th sample. jn This represents the nth vector value in the jth sample.

[0111] Step S210: Perform voltage zoning control on the distribution network based on the target zoning results.

[0112] In some embodiments of this application, the process for scheduling and controlling the voltage of the distribution network, as summarized above, is as follows: Figure 5 As shown, it is divided into two parts: dynamic segmentation based on network structure and auxiliary decision-making based on reactive power voltage sensitivity, including the following steps:

[0113] The first step is to record the power flow information of generator nodes and load nodes in the distribution network, and convert the power of the corresponding nodes into admittance and incorporate it into the system admittance matrix.

[0114] The second step is to calculate the coupling matrix G corresponding to the admittance matrix Y, and to give the coupling coefficients g. c .

[0115] The third step is to determine element y. ij Is it greater than g? c If y ij Greater than g c Determine the branches corresponding to the critical edges and form the cut sets corresponding to each region; if y ij Less than g c Then discard the irrelevant edges.

[0116] The fourth step involves making auxiliary decisions based on voltage sensitivity, obtaining the system Jacobian matrix and inverting it to obtain the sensitivity matrix [J]. aco ] -1 .

[0117] Fifth step, calculate the voltage increment ΔV i And obtain the voltage increment matrix.

[0118] The sixth step involves using a systematic clustering algorithm to partition the distribution network by voltage. Clustering methods employed include intra-group linkage, inter-group linkage, and Ward's sum of squares (MSS) clustering. Clustering methods used include Euclidean distance, Euclidean square distance, and Minkowski distance. The resulting partitions allow for precise voltage control of the distribution network.

[0119] By acquiring power flow information from each node in the distribution network and determining the system admittance matrix of the distribution network based on the power flow information (nodes include generator nodes and load nodes), determining cut sets corresponding to each region in the distribution network based on the system admittance matrix (cut sets are used to determine the preliminary partitioning results of the distribution network), determining the voltage increment matrix corresponding to each node in the distribution network (the voltage increment matrix is ​​used to reflect the voltage change of each node after a preset value of power change in any node), using the cut sets as prior information for clustering, the voltage increment matrix is ​​clustered to obtain the target partitioning results of the distribution network (the target partitioning results include the region to which each node belongs), and performing voltage partitioning control of the distribution network based on the target partitioning results, the dynamic partitioning of the distribution network is achieved by using cut sets as prior information to cluster the voltage increment matrix to obtain the target partitioning results. This achieves the technical effect of reducing the regulation loss of the distribution network and solves the technical problem of large partitioning control losses caused by the static partitioning method of the distribution network in related technologies.

[0120] Furthermore, this application also provides a dynamic partitioning theory applicable to distribution networks. The partitioning theory achieves a balance between global optimization and local control. Traditional voltage control methods often rely on centralized or decentralized control, making it difficult to balance global optimization and local control. The partitioning theory, by dividing network parameters, divides the distribution network into several relatively independent voltage regions, achieving both global optimization and improved local control response speed. Each region can independently adjust according to its own voltage requirements, thereby achieving voltage stability and optimization.

[0121] Furthermore, in this embodiment, auxiliary voltage partitioning of the entire distribution network can be performed by calculating the voltage increment matrix and conducting system clustering analysis. The clustering methods employed include the sum of squared deviations method, inter-group linkage method, and intra-group linkage method. The main clustering methods include Euclidean distance, Minkowski distance, and Manhattan distance. By observing the obtained dendrogram and icicle diagram, the distribution network is partitioned and classified. After obtaining the classification regions, the voltage characteristics of nodes in different regions are studied, and the affiliation of boundary nodes is further determined based on the magnitude of the voltage increment.

[0122] This application provides a distribution network voltage control zone division device. Figure 6 This is a schematic diagram of the device. From... Figure 6As can be seen from the diagram, the device includes: a first processing module 60, used to acquire power flow information of each node in the distribution network and determine the system admittance matrix of the distribution network based on the power flow information, wherein the nodes include generator nodes and load nodes; a second processing module 62, used to determine the cut sets corresponding to each region in the distribution network based on the system admittance matrix, wherein the cut sets are used to determine the preliminary partitioning results of the distribution network; a third processing module 64, used to determine the voltage increment matrix corresponding to each node in the distribution network, wherein the voltage increment matrix is ​​used to reflect the voltage change of each node after any node power change by a preset value; a fourth processing module 66, used to use the cut sets as clustering prior information to cluster the voltage increment matrix to obtain the target partitioning results of the distribution network, wherein the target partitioning results include the region to which each node belongs; and a fifth processing module 68, used to perform voltage partitioning control of the distribution network based on the target partitioning results.

[0123] In some embodiments of this application, the step of the second processing module 62 in determining the cut sets corresponding to each region in the distribution network based on the system admittance matrix includes: determining the inverse matrix of the system admittance matrix; performing a term-by-term product operation on the system admittance matrix and the inverse matrix to obtain the coupling matrix of the distribution network, wherein the elements in the coupling matrix are the coupling strength index between two nodes in the distribution network; determining a preset coupling coefficient; comparing each element in the coupling matrix with the preset coupling coefficient, and retaining the elements that are greater than the preset coupling coefficient; and determining the cut sets based on the retained elements.

[0124] In some embodiments of this application, the step of the second processing module 62 in determining the cut set based on the retained elements includes: determining the edge between the two nodes corresponding to the retained elements as the key edge; and determining the cut set based on the key edge.

[0125] In some embodiments of this application, the step of the third processing module 64 in determining the voltage increment matrix corresponding to each node in the distribution network includes: determining the sensitivity matrix of each node in the distribution network, wherein the sensitivity matrix is ​​used to reflect the correlation between the voltage change of each node and the power change of the distribution network; sequentially changing the power of each node from a first power to a second power, and determining the voltage change of each node after the power change based on the sensitivity matrix, thereby obtaining the voltage increment matrix, wherein the power change of each node is the same, and when the power of any node in the distribution network is changed, the power of the nodes other than the arbitrary node remains at the first power.

[0126] In some embodiments of this application, the fourth processing module 66 performs clustering of each node in the distribution network based on the cut set and the voltage increment matrix, including: first, determining each row element in the voltage increment matrix as a sample, and determining each sample as a sample category; second, determining the distance between each sample category; third, merging the two closest sample categories into one sample category, and if the number of remaining sample categories is greater than the preset number of sample categories, jumping to the second step.

[0127] In some embodiments of this application, the step of the fourth processing module 66 in determining the distance between each sample category includes: when there are multiple samples in a sample category, for each first sample in the first sample category, determining the distance between the first sample and each second sample in the second sample category, wherein the first sample category is any sample category in each sample category, and the second sample category is any sample category in each sample category other than the first sample category; and determining the minimum value of the distance between the first sample and the second sample as the distance between the first sample category and the second sample category.

[0128] In some embodiments of this application, the step of the fourth processing module 66 in determining the distance between each sample category includes: when there are multiple samples in a sample category, determining the average squared distance of all sample pairs between the first sample category and the second sample category as the distance between the first sample category and the second sample category, wherein the sample pair includes the first sample in the first sample category and the second sample in the second sample category, the first sample category is any sample category among the sample categories, and the second sample category is any sample category among the sample categories other than the first sample category.

[0129] In some embodiments of this application, the step of the fourth processing module 66 in determining the distance between each sample category includes: determining the increment of the sum of squared deviations between any two sample categories, wherein the increment of the sum of squared deviations is obtained by subtracting the sum of squared deviations of the two sample categories before merging from the sum of squared deviations of the sample categories obtained after merging any two sample categories; and using the increment of the sum of squared deviations as the distance between any two sample categories.

[0130] It should be noted that each module in the above-mentioned distribution network voltage control area division device can be a program module (for example, a set of program instructions to implement a certain function) or a hardware module. For the latter, it can be expressed in the following forms, but is not limited to them: each of the above modules is expressed as a processor, or the functions of each of the above modules are implemented by a processor.

[0131] According to an embodiment of this application, a non-volatile storage medium is also provided, which stores a program. During program execution, the device containing the non-volatile storage medium executes the following distribution network voltage control area division method: acquiring power flow information of each node in the distribution network and determining the system admittance matrix of the distribution network based on the power flow information, wherein the nodes include generator nodes and load nodes; determining cut sets corresponding to each region in the distribution network based on the system admittance matrix, wherein the cut sets are used to determine the preliminary partitioning results of the distribution network; determining the voltage increment matrix corresponding to each node in the distribution network, wherein the voltage increment matrix is ​​used to reflect the voltage change of each node after a preset value of power change in any node; using the cut sets as clustering prior information, clustering the voltage increment matrix to obtain the target partitioning result of the distribution network, wherein the target partitioning result includes the region to which each node belongs; and performing voltage partitioning control of the distribution network based on the target partitioning result.

[0132] According to an embodiment of this application, an electronic device is also provided, including: a memory and a processor. The processor is used to run a program stored in the memory, wherein the program executes the following distribution network voltage control area division method: acquiring power flow information of each node in the distribution network and determining the system admittance matrix of the distribution network based on the power flow information, wherein the nodes include generator nodes and load nodes; determining cut sets corresponding to each region in the distribution network based on the system admittance matrix, wherein the cut sets are used to determine the preliminary partitioning result of the distribution network; determining the voltage increment matrix corresponding to each node in the distribution network, wherein the voltage increment matrix is ​​used to reflect the voltage change of each node after a preset value of power change of any node in each node; using the cut sets as clustering prior information, clustering the voltage increment matrix to obtain the target partitioning result of the distribution network, wherein the target partitioning result includes the region to which each node belongs; and performing voltage partitioning control of the distribution network based on the target partitioning result.

[0133] According to an embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the following method for dividing a distribution network voltage control area: acquiring power flow information of each node in the distribution network and determining the system admittance matrix of the distribution network based on the power flow information, wherein the nodes include generator nodes and load nodes; determining cut sets corresponding to each region in the distribution network based on the system admittance matrix, wherein the cut sets are used to determine the preliminary partitioning result of the distribution network; determining the voltage increment matrix corresponding to each node in the distribution network, wherein the voltage increment matrix is ​​used to reflect the voltage change of each node after a preset value of power change of any node in each node; using the cut sets as clustering prior information, clustering the voltage increment matrix to obtain the target partitioning result of the distribution network, wherein the target partitioning result includes the region to which each node belongs; and performing voltage partitioning control of the distribution network based on the target partitioning result.

[0134] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0138] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0139] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for dividing voltage control zones in a power distribution network, characterized in that, include: The power flow information of each node in the distribution network is obtained, and the system admittance matrix of the distribution network is determined based on the power flow information, wherein the nodes include generator nodes and load nodes; Based on the system admittance matrix, cut sets corresponding to each region in the distribution network are determined, wherein the inverse matrix of the system admittance matrix is ​​determined; the system admittance matrix and the inverse matrix are multiplied term by term to obtain the coupling matrix of the distribution network, wherein the elements in the coupling matrix are coupling strength indices between two nodes in the distribution network; a preset coupling coefficient is determined; each element in the coupling matrix is ​​compared with the preset coupling coefficient, and elements greater than the preset coupling coefficient are retained; the cut sets are determined based on the retained elements; the cut sets are used to determine the preliminary partitioning results of the distribution network. Determine the voltage increment matrix corresponding to each node in the distribution network, wherein the voltage increment matrix is ​​used to reflect the voltage change of each node after a preset value of power change of any node in each node; Using the cut set as clustering prior information, the voltage increment matrix is ​​clustered to obtain the target partitioning result of the distribution network, wherein the target partitioning result includes the region to which each node belongs; Voltage zoning control is performed on the distribution network based on the target zoning results.

2. The method for dividing distribution network voltage control areas according to claim 1, characterized in that, Determining the cut set based on the retained elements includes: The edge between the two nodes corresponding to the elements to be retained is identified as the critical edge; The cut set is determined based on the key edges.

3. The method for dividing the voltage control area of ​​a distribution network according to claim 1, characterized in that, Determining the voltage increment matrix corresponding to each node in the distribution network includes: Determine the sensitivity matrix of each node in the distribution network, wherein the sensitivity matrix is ​​used to reflect the correlation between the voltage change of each node and the power change of the distribution network; The power of each node is changed from a first power to a second power in sequence, and the voltage change of each node after the power change is determined according to the sensitivity matrix, thereby obtaining the voltage increment matrix. The power change of each node is the same, and when the power of any node is changed, the power of the nodes other than the arbitrary node remains at the first power.

4. The method for dividing voltage control zones in a distribution network according to claim 1, characterized in that, Clustering of nodes in the distribution network based on the cut set and the voltage increment matrix includes: The first step is to determine that each row element in the voltage increment matrix is ​​a sample, and to determine each sample as a sample category; The second step is to determine the distance between each sample category; The third step is to merge the two closest sample categories into one sample category, and if the number of remaining sample categories is greater than the preset number of sample categories, then proceed to the second step.

5. The method for dividing the voltage control area of ​​a distribution network according to claim 4, characterized in that, Determining the distance between each sample category includes: When there are multiple samples in a sample category, for each first sample in the first sample category, the distance between the first sample and each second sample in the second sample category is determined, wherein the first sample category is any sample category among the various sample categories, and the second sample category is any sample category among the various sample categories other than the first sample category; The minimum distance between the first sample and the second sample is determined to be the distance between the first sample category and the second sample category.

6. The method for dividing voltage control zones in a distribution network according to claim 4, characterized in that, Determining the distance between each sample category includes: When there are multiple samples in a sample category, the average squared distance between all sample pairs between the first sample category and the second sample category is determined as the distance between the first sample category and the second sample category. The sample pairs include a first sample in the first sample category and a second sample in the second sample category. The first sample category is any sample category among the various sample categories, and the second sample category is any sample category among the various sample categories other than the first sample category.

7. The method for dividing voltage control zones in a distribution network according to claim 4, characterized in that, Determining the distance between each sample category includes: Determine the increment of the sum of squared deviations between any two sample categories in each sample category, wherein the increment of the sum of squared deviations is obtained by subtracting the sum of squared deviations of the two sample categories before merging from the sum of squared deviations of the sample categories obtained after merging the two sample categories; The sum of squared deviations increment is used as the distance between any two sample categories.

8. A power distribution network dispatching device, characterized in that, include: The first processing module is used to acquire power flow information of each node in the distribution network and determine the system admittance matrix of the distribution network based on the power flow information, wherein the nodes include generator nodes and load nodes; The second processing module is used to determine the cut sets corresponding to each region in the distribution network based on the system admittance matrix, wherein: the inverse matrix of the system admittance matrix is ​​determined; the system admittance matrix and the inverse matrix are multiplied term by term to obtain the coupling matrix of the distribution network, wherein the elements in the coupling matrix are coupling strength indices between two nodes in the distribution network; a preset coupling coefficient is determined; each element in the coupling matrix is ​​compared with the preset coupling coefficient, and elements greater than the preset coupling coefficient are retained; the cut sets are determined based on the retained elements; the cut sets are used to determine the preliminary partitioning results of the distribution network. The third processing module is used to determine the voltage increment matrix corresponding to each node in the distribution network, wherein the voltage increment matrix is ​​used to reflect the voltage change of each node after any node power change of a preset value. The fourth processing module is used to use the cut set as clustering prior information to cluster the voltage increment matrix to obtain the target partitioning result of the distribution network, wherein the target partitioning result includes the region to which each node belongs; The fifth processing module is used to perform voltage zoning control on the distribution network based on the target zoning results.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a program, wherein when the program is executed, it controls the device containing the non-volatile storage medium to execute the power distribution network voltage control area division method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the distribution network voltage control zone division method according to any one of claims 1 to 7.

11. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the distribution network voltage control zone division method according to any one of claims 1 to 7.

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