A power distribution network cluster division method and device, electronic equipment and storage medium

Abstract

The present application relates to power distribution network cluster division technical field, disclose a kind of power distribution network cluster division method, device, electronic equipment and storage medium, method includes: obtaining power distribution network operation data;According to operation data, the voltage risk weight of each electrical node is calculated, the support ability of resource node to each electrical node is quantified and support ability matrix is constructed;Combining operation data, voltage risk weight and support ability matrix generates multiple candidate cluster division mode;To each candidate cluster division mode is carried out node attribution score and iteration optimization, obtains optimal cluster division result under each mode;Through the construction of division effect evaluation index, optimal cluster division result is quantitatively evaluated, and the final cluster division result of power distribution network is determined by optimization.The present application can improve the risk adaptability of high proportion of new energy power distribution network cluster division, resource support matching precision and partition autonomy, meet the engineering management and control needs of new power distribution network hierarchical partition, local support, autonomous cooperation.

Description

Technical Field

[0001] This invention relates to the field of power distribution network cluster partitioning technology, specifically to a power distribution network cluster partitioning method, device, electronic equipment, and storage medium. Background Technology

[0002] With the increasing integration of distributed photovoltaic (PV), energy storage, and flexible loads into distribution networks, the operation of these networks is characterized by frequent changes in power flow direction, enhanced node voltage fluctuations, and a higher risk of local voltage exceedances. PV inverters, energy storage converters, and other renewable energy equipment possess rapid reactive power regulation capabilities and can serve as voltage support resources for distribution networks, participating in voltage control.

[0003] In power distribution networks where renewable energy sources are widely adopted, centralizing all network nodes and resources would increase the scope of communication and computational complexity. In practical engineering, it is typically necessary to divide the power distribution network into several control clusters, ensuring that each cluster possesses sufficient voltage support and communication coordination capabilities.

[0004] Existing distribution network zoning methods often use electrical topology, electrical distance, and voltage sensitivity to divide clusters. While these methods can generate zoning results for a specific type of topological relationship, in scenarios where renewable energy sources participate in voltage support, they can easily lead to one-sided cluster division criteria, ambiguous assessments of node support capabilities, and poor matching between the zoning results and the actual operation and communication characteristics of the power grid. This results in an inability to provide effective cluster-based protection for high-voltage risk nodes, potentially causing node voltage exceeding limits and power grid instability. Furthermore, these methods suffer from problems such as a limited zoning scenario, lack of iterative optimization mechanisms, and insufficient rationality in node affiliation. Summary of the Invention

[0005] This invention provides a distribution network cluster partitioning method, device, electronic equipment, and storage medium to solve the problems of existing distribution network cluster partitioning easily causing node voltage over-limit and grid instability, as well as the problems of limited partitioning scenarios, lack of iterative optimization mechanism, and insufficient rationality of node affiliation.

[0006] In a first aspect, the present invention provides a method for dividing a distribution network into clusters. The method includes: acquiring operational data of the distribution network; determining the voltage risk weight of each electrical node in the distribution network based on the operational data, and determining the support capability of resource nodes in each electrical node to each electrical node, thereby obtaining a corresponding support capability matrix; dividing the distribution network into clusters in multiple ways based on the operational data, voltage risk weights, and support capability matrix, thereby determining multiple candidate cluster division methods; sequentially using each candidate cluster division method as the target cluster division method, determining the affiliation score of electrical nodes to different candidate clusters under the target cluster division method, iteratively adjusting the node set of the candidate clusters based on the affiliation score, until the optimal cluster division result under the target cluster division method is obtained; after determining the optimal cluster division result under each candidate cluster division method, calculating the division effect evaluation index of each optimal cluster division result based on the support capability matrix, and determining the final cluster division result of the distribution network based on each division effect evaluation index.

[0007] The distribution network cluster partitioning method provided by this invention quantifies the voltage risk weight of each electrical node and the support capability of new energy resource nodes based on actual distribution network operation data, and constructs a standardized support capability matrix. This method overcomes the limitations of traditional single partitioning methods that rely solely on electrical topology and distance, and fully adapts to the dynamic reactive power support characteristics of new energy and the differentiated voltage risk features of nodes. By constructing a multi-dimensional evaluation system to generate multiple candidate cluster partitioning methods, iterative optimization of node affiliation is performed on each initial candidate partitioning result to obtain the optimal cluster partitioning result under each scenario. Finally, the globally optimal partitioning scheme is selected through comprehensive evaluation of multiple indicators. This method enables the construction of a closed-loop partitioning system that integrates "data quantification modeling - multi-scenario candidate partitioning - iterative optimization and correction - multi-dimensional selection," effectively reducing the communication interaction pressure and computational complexity of centralized management of the entire network. It significantly improves the risk adaptability, resource support matching accuracy, and autonomous partitioning capability of high-proportion new energy distribution network cluster partitioning, meeting the engineering management needs of hierarchical partitioning, local support, and autonomous collaboration in new distribution networks.

[0008] In one optional implementation, the operational data includes at least: the number of first nodes of the electrical nodes in the distribution network, the number of second nodes of resource nodes in the electrical nodes, the voltage amplitude, rated capacity, and current active power output of each electrical node, and the minimum communication cost between the electrical nodes and resource nodes. Based on the operational data, the voltage risk weight of each electrical node in the distribution network is determined, and the support capability of resource nodes in the electrical nodes for each electrical node is determined to obtain the corresponding support capability matrix. This includes: sequentially taking each electrical node as a target electrical node, calculating the voltage risk quantity of the target electrical node based on a preset voltage upper limit, a preset voltage lower limit, and voltage amplitude, and normalizing the voltage risk quantity to obtain the voltage risk weight of the target electrical node. The calculation formula is as follows:

[0009]

[0010] in, For voltage risk quantity, Voltage risk weight, Voltage amplitude, To preset the upper voltage limit, The preset lower voltage limit, The preset adjustment coefficient, This represents the maximum voltage risk at each electrical node. The preset positive number is used; each resource node is sequentially taken as the target resource node, and the remaining available reactive power capacity of the target resource node is calculated based on the corresponding rated capacity and current active power output. Then, the support sensitivity of the target resource node to each electrical node is calculated based on the remaining available reactive power capacity and voltage amplitude. The calculation formula is as follows:

[0011]

[0012] in, For rated capacity, To contribute one's efforts to the present, The remaining available reactive power capacity To support sensitivity, the communication reliability between the target resource node and each electrical node is calculated based on the minimum communication cost and a preset communication cost scaling factor. The calculation formula is as follows:

[0013] in, For communication reliability, To minimize communication costs, To predetermine the communication cost scaling factor, the remaining available reactive power capacity and support sensitivity are normalized. Based on the normalized remaining available reactive power capacity, normalized support sensitivity, communication reliability, and voltage risk weight, the support capacity of the target resource node for each electrical node is calculated. The calculation formula is shown below:

[0014] in, For resource nodes For electrical nodes Support capabilities This represents the remaining usable reactive power capacity after normalization. To provide the normalized support sensitivity, , and The preset calculation parameters are used. After determining the support capabilities of each resource node for each electrical node, a support capability matrix is ​​constructed based on these capabilities. The expression for the support capability matrix is ​​as follows:

[0015] in, To support the capability matrix, The number of the first node. The second number of nodes is represented by any row vector of the support capability matrix, which represents the support capability of the corresponding resource node for all electrical nodes. The column vector of the support capability matrix represents the support capability of all resource nodes for their corresponding electrical nodes.

[0016] This invention calculates the voltage risk of each electrical node in a hierarchical manner and normalizes it to obtain the voltage risk weight. At the same time, it combines the reactive power capacity, output characteristics, and communication costs of resource nodes to quantitatively calculate the support sensitivity, communication reliability, and comprehensive support capability of resource nodes. It can construct a support capability matrix with clear row and column definitions and clear physical meaning, realize refined quantitative modeling of distribution network node risk and resource support capability, and solve the problems of traditional technology having no quantitative basis, single parameter dimension, and vague support capability assessment. It provides an accurate data foundation for subsequent cluster division and node affiliation determination, and ensures that the subsequent partitioning results are consistent with the actual operating characteristics of the power grid.

[0017] In one optional implementation, the operational data further includes: the electrical impedance of electrical branches between different electrical nodes; the distribution network is divided into clusters in various ways based on the operational data, voltage risk weights, and support capability matrix; multiple candidate cluster division methods are determined, including: determining electrical branches and communication links between different electrical nodes based on the topology of the distribution network, and determining the support relationship between any two electrical nodes based on the electrical branch impedance, communication links, and support capability matrix; calculating the comprehensive support utility of each resource node based on the voltage risk weights and support capability matrix; setting multiple cluster numbers, sequentially using each cluster number as the target cluster number, and selecting the resource node corresponding to the highest comprehensive support utility as the first support source node of the support source node set; calculating the comprehensive difference in support capability distribution of each unselected resource node relative to the support source node set based on the support capability matrix, adding the unselected resource node corresponding to the largest comprehensive difference in support capability distribution to the support source node set until the number of nodes in the support source node set is the same as the target cluster number; obtaining the initial label of each support source node, and using a label propagation algorithm to divide the electrical nodes into clusters to obtain candidate clusters for the target cluster number, thus determining multiple candidate cluster division methods.

[0018] This invention combines the electrical topology of the power grid, communication links, and support capability matrix to quantify the support relationships between nodes. It selects a set of support sources based on the comprehensive support effectiveness of resource nodes, calculates the overall distribution difference between the unselected resource nodes and the set of support sources, and selects the resource nodes with the greatest differences to construct multiple sets of support sources. Combined with the label propagation algorithm, it generates candidate partitioning schemes corresponding to multiple cluster numbers. This ensures that the support sources of each cluster are differentiated and that the support characteristics are complementary. At the same time, it covers multiple cluster number scenarios, greatly improves the scenario adaptability of cluster partitioning, and avoids the defects of homogeneous support sources and single partitioning scenarios.

[0019] In one optional implementation, the support relationship between any two electrical nodes is determined based on the electrical branch impedance, communication link, and support capability matrix. This includes: if an electrical branch exists between any two electrical nodes, calculating the electrical connection attraction weight between the two electrical nodes based on the resistance and impedance corresponding to the electrical branch; otherwise, setting the electrical connection attraction weight to a preset weight value. The calculation formula is as follows:

[0020]

[0021] in, For electrical connections, attract weights. For electrical nodes With electrical nodes The resistance of the electrical branches between them For electrical nodes With electrical nodes Reactance of the electrical branches between them The impedance magnitude of the electrical branch. As a preset positive number, The preset weight value is used. If the communication link between any two electrical nodes is a bidirectional communication link, the communication connection weight between the two electrical nodes is calculated based on the bidirectional communication reliability between the two electrical nodes. If it is a unidirectional communication link, the communication connection weight is calculated based on the unidirectional communication reliability between the two electrical nodes. If there is no communication link, the communication connection weight is set to the preset weight value. The calculation formula is as follows:

[0022] in, For communication connection weights, For electrical nodes To electrical node The reliability of the first one-way communication For electrical nodes To electrical node The second one-way communication reliability, The preset reduction factor is used; any two column vectors in the support capacity matrix are selected, and the similarity of support capacity features between the corresponding two electrical nodes is calculated based on the two column vectors. The calculation formula is as follows:

[0023] in, To support capability feature similarity, For electrical nodes The corresponding column vector, For electrical nodes The corresponding column vector; calculate electrical nodes based on the similarity of electrical connection attraction weights, communication connection weights, and support capability features. and electrical nodes The support relationship between them is calculated using the following formula:

[0024] in, To support the relationship, , and Different preset weighting coefficients.

[0025] This invention calculates the attraction weight of electrical connections, the weight of communication connections, and the similarity of support features from three core dimensions: electrical coupling, communication transmission, and support feature similarity. The weighted fusion yields accurate support relationships between nodes. It comprehensively considers the electrical physical characteristics of the power grid, the differentiated scenarios of bidirectional / unidirectional / no-link communication links, and the matching degree of resource support features. This solves the one-sided problem of relying solely on topological distance to determine node association in traditional methods. It makes the determination of node association relationships more consistent with the actual electrical operation and communication transmission of the distribution network, and improves the physical rationality of cluster partitioning.

[0026] In one optional implementation, under the target cluster partitioning method, the affiliation score of electrical nodes to different candidate clusters is determined, and the node set of the candidate clusters is iteratively adjusted according to the affiliation score until the optimal cluster partitioning result under the target cluster partitioning method is obtained. This includes: sequentially taking each candidate cluster under the target cluster partitioning method as the target candidate cluster, and calculating the intra-cluster support relationship between any electrical node in the target candidate cluster and the other electrical nodes according to the support relationship. The calculation formula is as follows:

[0027] in, For support relationships within the group, Indicates electrical node The current cluster; based on the intra-cluster support relationship, voltage risk weight, and support capability matrix, the affiliation score of each electrical node to each candidate cluster is calculated, as shown in the following formula:

[0028] in, Indicates the target candidate cluster The set of resource nodes within; Indicates the target candidate cluster The supporting source node in , , and Different preset weight coefficients are used; based on the affiliation score of each electrical node to different candidate clusters, and with the supporting source nodes of the candidate clusters fixed, the electrical nodes are assigned to the candidate clusters corresponding to the highest affiliation score; after the assignment, the affiliation score of each electrical node to different candidate clusters is recalculated until the assignment reaches stability or the maximum number of calculations is reached, and then the optimal cluster assignment result under the target cluster assignment method is determined.

[0029] This invention calculates the support relationships within a cluster based on the support relationships between nodes, integrates voltage risk weights and support capability matrices to construct a multi-dimensional attribution scoring model, strictly constrains the cluster support source nodes to remain unchanged, and only iteratively optimizes the attribution of ordinary electrical nodes. Through iterative convergence or maximum number constraints, it achieves steady-state optimization of cluster partitioning, which can both preserve the stability of each cluster support core and avoid cluster core chaos and partition structure oscillation, and achieve optimal node attribution matching through refined attribution scoring. This solves the problems of traditional iterative partitioning without core constraints, strong randomness of node attribution, and unstable partition structure, and significantly improves the rationality and stability of the internal structure of the cluster.

[0030] In one optional implementation, the method further includes: calculating the partitioning effect evaluation index of each optimal cluster partitioning result based on the support capability matrix, and determining the final cluster partitioning result of the distribution network based on each partitioning effect evaluation index; for any optimal cluster partitioning result, calculating the intra-zone main support retention rate of each electrical node based on the support capability matrix, and calculating the risk-weighted intra-zone support strength of the optimal cluster partitioning result based on the intra-zone main support retention rate and voltage risk weight; for any load node under the optimal cluster partitioning result, determining the strongest support resource of the load node based on the support capability matrix, communication reliability, and the initial number of resource nodes, and calculating the optimal cluster partitioning result based on the strongest support resource, voltage risk weight, and intra-zone main support retention rate. The cross-cluster support dependency rate is calculated; the communication link set of each candidate cluster under the optimal cluster partitioning result is determined according to the communication reliability; and the communication reliability autonomous coordination index of the optimal cluster partitioning result is calculated based on the communication link set, communication reliability, and cross-cluster support dependency rate; the support strength, cross-cluster support dependency rate, and communication reliability autonomous coordination index within the risk-weighted area are normalized, and the cluster number comprehensive score of the optimal cluster partitioning result is calculated based on the normalized support strength, normalized cross-cluster support dependency rate, and normalized communication reliability autonomous coordination index; after obtaining the cluster number comprehensive score corresponding to each optimal cluster partitioning result, the optimal cluster partitioning result corresponding to the highest cluster number comprehensive score is selected as the final cluster partitioning result.

[0031] This invention constructs a three-dimensional evaluation system that includes voltage support, cross-regional dependence, and communication collaboration. It normalizes and integrates multi-dimensional indicators to obtain a comprehensive score, and selects the best cluster partitioning result. This breaks through the limitations of traditional technologies that lack quantitative evaluation and rely solely on experience to determine the partitioning effect. It quantifies the merits of partitioning from all aspects, including cluster self-sufficiency, cross-cluster dependence, and communication autonomy and collaboration performance, to achieve scientific selection of multiple candidate schemes and ensure that the final cluster partitioning result takes into account security, autonomy, and collaboration.

[0032] In one optional implementation, the intra-area major support retention rate of each electrical node is calculated based on the support capability matrix, and the risk-weighted intra-area support strength of the optimal cluster partitioning result is calculated based on the intra-area major support retention rate and voltage risk weight. This includes: taking each electrical node as the target electrical node, determining a first set of major support resources with a preset number of nodes from all resource nodes based on the support capability matrix, and determining a second set of major support resources with a preset number of nodes from the candidate clusters in which the target electrical node is located; and calculating the intra-area major support retention rate of the target electrical node based on the first set of major support resources and the second set of major support resources, using the following formula:

[0033] in, As the primary supporting resource set, This serves as the second primary support resource set. The risk-weighted support strength within the optimal cluster partitioning result is calculated based on the retention rate of the primary support within the zone and the voltage risk weight of each electrical node. The calculation formula is shown below:

[0034] in, The preset voltage risk weighting coefficient is used. This represents the number of the first node.

[0035] This invention filters the global and cluster-wide core support resource sets, calculates the main support retention rate within the node area, and constructs a risk-weighted support strength index within the risk area by combining voltage risk weights. This can accurately quantify the self-sufficiency support capability of high-risk nodes within the area, enabling targeted assessment of the voltage safety support performance within the cluster. It focuses on ensuring the support stability of high-voltage risk nodes, solves the problem that traditional evaluation cannot differentiate the support guarantee capability of risk nodes, and improves the targeting of cluster voltage safety management.

[0036] In one optional implementation, for any load node under the optimal cluster partitioning result, the strongest supporting resource of the load node is determined according to the support capability matrix, communication reliability, and the initial number of the resource node. The cross-cluster support dependency rate of the optimal cluster partitioning result is then calculated based on the strongest supporting resource, voltage risk weight, and the intra-area main support retention rate. This includes: for any load node under the optimal cluster partitioning result, obtaining the column vector corresponding to the load node in the support capability matrix and determining the maximum support capability in the column vector; if the number of maximum support capabilities is greater than or equal to two, the resource node corresponding to the highest communication reliability is selected as the strongest supporting resource; if the communication reliability is the same, the strongest supporting resource is determined according to a preset order of the initial numbers of the resource nodes; if the strongest supporting resource and the load node are not in the same candidate cluster, or the intra-area main support retention rate of the load node is less than a preset threshold, the cross-cluster support dependency flag of the load node is set to a first preset value; otherwise, it is set to a second preset value; the cross-cluster support dependency rate is calculated based on the cross-cluster support dependency flags and voltage risk weights of all load nodes, using the following formula:

[0037] in, To support dependency rates across clusters, This is a cross-cluster support dependency flag.

[0038] This invention combines multiple constraints, including support capability, communication reliability, and node number, to accurately determine the strongest support resources for load nodes. It quantifies the degree of cross-regional dependence of nodes by using cross-cluster support dependency flags and integrates voltage risk weights to obtain the global cross-cluster support dependency rate. This invention can accurately identify the external support dependency of high-risk load nodes, quantify the shortcomings of cluster autonomy, and solve the problems of traditional technologies that cannot accurately determine cross-regional dependence and ignore the risk differences of load nodes. It provides an accurate evaluation basis for optimizing cluster autonomy capabilities and reducing cross-regional scheduling pressure.

[0039] In one optional implementation, the communication link set of each candidate cluster under the optimal cluster partitioning result is determined based on communication reliability. Then, the communication reliability autonomous cooperation index of the optimal cluster partitioning result is calculated based on the communication link set, communication reliability, and cross-cluster support dependency rate. This includes: calculating the bidirectional communication reliability between any two resource nodes within a candidate cluster, and determining the communication link set of the candidate cluster, where the communication link set is the set of nodes connecting all resource nodes in the candidate cluster and having the largest sum of communication reliability; and calculating the cooperative communication backbone reliability of the candidate cluster based on the bidirectional communication reliability and the communication link set, using the following formula:

[0040] in, For the reliability of the collaborative communication backbone of candidate clusters, For two-way communication reliability, and , Represents resource nodes To resource nodes Communication reliability, Represents resource nodes To resource nodes The reliability of communication; This represents the set of communication links. The collaborative communication backbone reliability of the optimal cluster partitioning result is calculated based on the collaborative communication backbone reliability of each candidate cluster. The calculation formula is as follows:

[0041] The communication reliability autonomous collaboration index is calculated based on the reliability of the collaborative communication backbone and the cross-cluster support dependency rate. The calculation formula is as follows:

[0042] in, The reliability of the collaborative communication backbone based on the optimal cluster partitioning result. This is a reliable autonomous collaboration index for communication.

[0043] This invention constructs a cluster communication link set based on the principle of maximum communication reliability, calculates the reliability of the cluster collaborative communication backbone, and integrates cross-cluster support dependency rate to construct a communication reliability autonomous collaboration index. It can comprehensively consider the internal communication stability and regional autonomous capabilities of the cluster, and achieve accurate quantification of the cluster's communication collaboration and autonomous operation capabilities. It solves the defects of traditional cluster partitioning that ignores communication reliability and cannot evaluate the regional autonomous collaboration performance, ensuring that the partitioned cluster has a stable communication architecture and excellent autonomous collaboration capabilities, and adapts to the decentralized management and control requirements of new power distribution networks.

[0044] Secondly, the present invention provides a distribution network cluster partitioning device, comprising: an operation data acquisition module for acquiring operation data of the distribution network; a support capability determination module for determining the voltage risk weight of each electrical node in the distribution network based on the operation data, and determining the support capability of resource nodes in the electrical nodes for each electrical node, thereby obtaining a corresponding support capability matrix; a cluster initial partitioning module for performing cluster partitioning of the distribution network in multiple ways based on the operation data, voltage risk weight, and support capability matrix, thereby determining multiple candidate cluster partitioning methods; a cluster iterative optimization module for sequentially taking each candidate cluster partitioning method as the target cluster partitioning method, determining the affiliation score of electrical nodes to different candidate clusters under the target cluster partitioning method, iteratively adjusting the node set of the candidate clusters based on the affiliation score, until the optimal cluster partitioning result under the target cluster partitioning method is obtained; and a cluster partitioning determination module for calculating the partitioning effect evaluation index of each optimal cluster partitioning result based on the support capability matrix after determining the optimal cluster partitioning result under each candidate cluster partitioning method, and determining the final cluster partitioning result of the distribution network based on each partitioning effect evaluation index.

[0045] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the power distribution network cluster partitioning method of the first aspect or any corresponding embodiment described above.

[0046] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the distribution network cluster partitioning method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0047] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the distribution network cluster partitioning method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the distribution network topology according to the distribution network cluster partitioning method of the present invention; Figure 4 This is a schematic diagram of the second process of the distribution network cluster partitioning method according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the overall process of the distribution network cluster partitioning method according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the third process of the distribution network cluster partitioning method according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the final cluster partitioning result of the distribution network cluster partitioning method according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the sensitivity-only partitioning result of the distribution network cluster partitioning method according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the communication-only partitioning result of the distribution network cluster partitioning method according to an embodiment of the present invention; Figure 10 This is a schematic diagram of the electrical module partitioning result of the power distribution network cluster partitioning method according to an embodiment of the present invention; Figure 11 This is a structural block diagram of a power distribution network cluster partitioning device according to an embodiment of the present invention; Figure 12 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0051] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0052] As an optional application scenario of this invention, such as Figure 1 As shown, the constructed power distribution network cluster partitioning system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0053] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0054] This invention provides a method for dividing a power distribution network cluster. By quantifying the voltage risk of nodes and the support capability of new energy sources, and combining electrical, communication and support characteristics, a multi-scenario cluster division is carried out. Through iterative optimization of node affiliation and selection of multi-dimensional indicators, the optimal partitioning result is obtained, thereby achieving the technical effect of improving the accuracy, stability and autonomy of new energy power distribution network cluster division, and reducing the complexity of management and control.

[0055] According to an embodiment of the present invention, a method for dividing a power distribution network cluster 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.

[0056] This embodiment provides a method for dividing a power distribution network cluster, which can be used in the aforementioned terminal devices, such as computers. Figure 2 This is a flowchart of a distribution network cluster partitioning method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the operating data of the power distribution network.

[0057] Specifically, in this embodiment of the invention, static basic parameters and dynamic real-time operating data of the distribution network are comprehensively collected, covering four major categories of data: power grid topology, equipment parameters, operating status, and communication parameters. Among them, static basic parameters include the total number of electrical nodes in the distribution network. The number of resource nodes for new energy as a resource The data includes the overall network electrical branch topology, branch resistance, reactance, and impedance parameters, node reference voltage and capacity, node electrical type, and new energy equipment model and rated installed capacity; dynamic operation data includes real-time active power output of each new energy node and real-time voltage amplitude of each electrical node. The parameters include: real-time power consumption of flexible loads; communication parameters include minimum data communication cost between any two nodes, distribution of bidirectional / unidirectional communication links across the entire network, and baseline parameters for communication transmission delay and packet loss rate. Electrical nodes encompass all ordinary load nodes, common grid-connected nodes, and bus nodes in the distribution network; new energy resource nodes specifically refer to grid-connected nodes such as photovoltaic inverters and energy storage converters that possess rapid reactive power regulation capabilities and can actively participate in voltage support for the distribution network. New energy nodes without reactive power regulation capabilities are not included in the resource node category.

[0058] In this embodiment of the invention, the distribution network node set is assumed to be... ,in, Let be the total number of nodes in the distribution network; let the set of new energy nodes be . ,in, This represents the total number of new energy nodes. This represents a new energy node, which includes photovoltaic inverters, energy storage converters, or other nodes that can provide reactive power support. The electrical topology of the distribution network is denoted as... , For a set of electrical branches, the communication topology of the distribution network is denoted as . ,in, This is a set of communication links.

[0059] For example, the load level, renewable energy output level, voltage distribution, and communication conditions of an actual power distribution network system under a certain operating state are simulated to verify the feasibility of the method of the present invention and the effect of cluster partitioning: such as Figure 3The diagram shows the topology of the distribution network, which has 33 nodes and 32 electrical branches, including 13 renewable energy nodes. The system base voltage is 400V and the base capacity is 400kVA. The system electrical parameters are shown in Table 1.

[0060] Table 1 Branch Connections and Their Impedances Furthermore, the system's new energy node capacity and active power output data are shown in Table 2: Table 2 New Energy Node Data

[0061] This invention collects electrical, operational, communication, and equipment parameters of the power grid in a comprehensive and multi-dimensional manner, breaking through the limitations of traditional data that rely solely on topology parameters. This provides a comprehensive data foundation for subsequent refined quantitative modeling, differentiated risk assessment, and accurate support capability calculation, ensuring the authenticity and fit of cluster partitioning from the source.

[0062] Step S202: Determine the voltage risk weight of each electrical node in the distribution network based on the operating data, and determine the support capacity of resource nodes in each electrical node to obtain the corresponding support capacity matrix.

[0063] Specifically, in this embodiment of the invention, the traditional unified node evaluation model is abandoned, and a differentiated quantification mechanism is adopted to complete the two-layer modeling. First, based on the real-time voltage amplitude of each electrical node, combined with the preset upper and lower voltage thresholds for safe operation of the distribution network according to national standards, the risks of undervoltage exceeding the limit, overvoltage exceeding the limit, and the degree of voltage deviation from the rated value are calculated simultaneously. This comprehensively quantifies the real-time voltage risk of each node, accurately distinguishing the operational hazard levels of different nodes. Subsequently, the voltage risk of all nodes in the network is globally normalized to eliminate differences in numerical magnitude and interference from extreme value data, obtaining standardized node voltage risk weights within the 0-1 range, accurately representing the voltage operation hazard level and priority support requirements of each node.

[0064] Meanwhile, taking into account four core factors—the remaining reactive power adjustable capacity margin of new energy resource nodes, the voltage support sensitivity of load nodes, the reliability of inter-node communication transmission, and the voltage risk requirements of load nodes—the comprehensive voltage support capability of new energy resource nodes to each electrical node is quantified in multiple dimensions and weighted. Finally, a standardized support capability matrix is ​​constructed with rows corresponding to new energy resource nodes, columns corresponding to all electrical nodes in the region, clear physical meaning, and uniform dimensions, which fully restores the spatial support distribution characteristics and supply-demand matching relationship of new energy resources across the entire network.

[0065] The embodiments of the present invention can realize the differentiated quantification of node voltage risk and the refined modeling of new energy support capabilities, breaking away from the limitations of traditional assessments that rely solely on electrical topology distance. It fully adapts to the dynamic adjustment characteristics of reactive power of new energy and the differentiated risk characteristics of nodes, providing core quantitative basis for subsequent accurate cluster division and fallback protection of high-risk nodes.

[0066] Step S203: Based on the operating data, voltage risk weights, and support capability matrix, the distribution network is divided into clusters in various ways to determine multiple candidate cluster division methods.

[0067] Specifically, in this embodiment of the invention, the traditional distribution network cluster division is broken through by setting only a single cluster number and relying solely on topological coupling relationship. Instead, it combines the actual electrical coupling tightness of the distribution network, the bidirectional / unidirectional communication and coordination characteristics of nodes, the spatial distribution law of voltage risk of nodes across the entire domain, and the supply and demand matching characteristics of new energy resources to set multiple sets of gradient and differentiated candidate cluster numbers, covering the needs of small-scale, medium-scale, and large-scale multi-scenario partitioning.

[0068] For each target number of clusters, new energy core support source nodes with differentiated support characteristics and complementary coverage are selected based on the network support capability matrix. Relying on the multi-dimensional comprehensive support correlation between nodes in terms of electrical, communication and resources, a label propagation algorithm is introduced to complete adaptive cluster partitioning, generating multiple sets of initial candidate cluster partitioning schemes with independent structures, different adaptation scenarios and different support characteristics. A multi-scenario candidate sample set is constructed to completely solve the problems of traditional partitioning scenarios being single, poor adaptability and unable to adapt to different power grid operating conditions.

[0069] Therefore, the embodiments of the present invention construct multiple candidate partitioning schemes by using multiple gradient cluster numbers, which can cover multiple operating scenarios, avoid the limitations of traditional single partitioning results, provide sufficient samples for subsequent selection of the optimal scheme, and greatly improve the adaptability of cluster partitioning to complex new energy power grid scenarios.

[0070] Step S204: Each candidate cluster partitioning method is used as the target cluster partitioning method in turn. Under the target cluster partitioning method, the affiliation score of electrical nodes to different candidate clusters is determined. The node set of the candidate clusters is iteratively adjusted according to the affiliation score until the optimal cluster partitioning result under the target cluster partitioning method is obtained.

[0071] Specifically, in this embodiment of the invention, for each initial candidate cluster partitioning scheme, a multi-dimensional attribution scoring model is constructed that integrates the collaborative support strength within the cluster, the priority of node voltage risk protection, and the overall support and supply capacity of the cluster's new energy resources. This replaces the traditional single-distance attribution determination method, accurately quantifying the suitability and collaborative matching degree of each ordinary electrical node belonging to different candidate clusters. To ensure the stability of the cluster's core support architecture and avoid partition core oscillation failure, this embodiment of the invention strictly fixes the attribution of the core support source nodes selected for each cluster, only iteratively adjusting the cluster attribution relationship of ordinary electrical nodes based on the dynamic attribution score of each node. Through multiple rounds of iterative updates, score corrections, and node attribution optimization, until the attribution status of all cluster nodes in the network is completely stable or reaches the preset maximum number of iterations, the randomness, blindness, and irrationality of the initial label propagation partitioning are completely eliminated, obtaining the steady-state optimal partitioning result corresponding to each number of clusters, significantly improving the rationality of cluster node attribution and the tightness of internal collaboration within the cluster.

[0072] This invention effectively corrects the defects of the initial partitioning through a multi-dimensional attribution scoring and fixed core, iterative optimization mechanism, improves the resource coordination and node attribution accuracy within the cluster, ensures the stable output of the core support capabilities of each cluster, and avoids the problem of partition structure oscillation.

[0073] Step S205: After determining the optimal cluster partitioning result under each candidate cluster partitioning method, calculate the partitioning effect evaluation index of each optimal cluster partitioning result based on the support capability matrix, and determine the final cluster partitioning result of the distribution network based on each partitioning effect evaluation index.

[0074] Specifically, in this embodiment of the invention, a three-dimensional comprehensive evaluation index system is constructed, comprising risk-weighted zone support strength, cross-cluster support dependence rate, and communication reliability autonomous coordination index. This system comprehensively and meticulously evaluates all iteratively optimized optimal cluster partitioning results from three core dimensions: cluster local voltage support capability, autonomous and independent level of zone operation, and cluster communication coordination stability. This addresses the shortcomings of traditional methods that rely solely on experience and lack evaluation and selection of the best partitioning results. The three evaluation indicators with different dimensions are unified and normalized. A comprehensive performance score for each candidate scheme is obtained through scientific weighted fusion calculation. The overall advantages and disadvantages of all schemes are compared horizontally, and the optimal partitioning scheme with the best overall performance, strongest adaptability to power grid operation requirements, and best risk prevention and control capability is selected as the final cluster partitioning result for the distribution network, achieving scientific, precise, and optimal cluster partitioning.

[0075] As can be seen, the embodiments of the present invention achieve quantitative selection of zoning schemes through a three-dimensional index comprehensive evaluation system, taking into account voltage safety, zoning autonomy and communication stability, completely solving the problems of traditional zoning lacking a selection mechanism and having strong randomness in results, and ensuring that the final zoning results are adapted to the management and control requirements of high-proportion new energy distribution networks.

[0076] The distribution network cluster partitioning method provided by this invention quantifies the voltage risk weight of each electrical node and the support capability of new energy resource nodes based on actual distribution network operation data, and constructs a standardized support capability matrix. This method overcomes the limitations of traditional single partitioning methods that rely solely on electrical topology and distance, and fully adapts to the dynamic reactive power support characteristics of new energy and the differentiated voltage risk features of nodes. By constructing a multi-dimensional evaluation system to generate multiple candidate cluster partitioning methods, iterative optimization of node affiliation is performed on each initial candidate partitioning result to obtain the optimal cluster partitioning result under each scenario. Finally, the globally optimal partitioning scheme is selected through comprehensive evaluation of multiple indicators. This method enables the construction of a closed-loop partitioning system that integrates "data quantification modeling - multi-scenario candidate partitioning - iterative optimization and correction - multi-dimensional selection," effectively reducing the communication interaction pressure and computational complexity of centralized management of the entire network. It significantly improves the risk adaptability, resource support matching accuracy, and autonomous partitioning capability of high-proportion new energy distribution network cluster partitioning, meeting the engineering management needs of hierarchical partitioning, local support, and autonomous collaboration in new distribution networks.

[0077] This embodiment provides a method for dividing a power distribution network cluster, which can be used in the aforementioned terminal devices, such as computers. Figure 4 This is a flowchart of a distribution network cluster partitioning method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: Step S401: Obtain the operating data of the distribution network. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0078] Step S402: Determine the voltage risk weight of each electrical node in the distribution network based on the operating data, and determine the support capacity of resource nodes in each electrical node to obtain the corresponding support capacity matrix.

[0079] Specifically, step S402 includes: Step S4021: Sequentially select each electrical node as the target electrical node, calculate the voltage risk quantity of the target electrical node based on the preset upper voltage limit, preset lower voltage limit and voltage amplitude, and normalize the voltage risk quantity to obtain the voltage risk weight of the target electrical node.

[0080] Specifically, in this embodiment of the invention, the distribution network operation standards are strictly followed to set a lower safety limit for the distribution network voltage. and voltage safety limit Set the voltage deviation adjustment coefficient .like Figure 5 As shown, for each electrical node, combined with its real-time voltage amplitude... ,in The undervoltage risk, overvoltage risk, and voltage deviation are calculated separately and then combined to obtain the overall node voltage risk. The calculation formula is shown below:

[0081] in, For electrical nodes Voltage risk quantity, , indicating taking The larger value between 0 and 0. Voltage risk quantity. The first item represents overvoltage risk, the second represents undervoltage risk, and the third represents the degree to which the node voltage deviates from the rated value. This comprehensively covers various potential risks of abnormal voltage operation and avoids the problems of missed or misjudged risks caused by single deviation calculations.

[0082] Furthermore, to eliminate the differences in the magnitude of risk quantities at different nodes and avoid the interference of extreme value data with calculation accuracy, the voltage risk quantities of all electrical nodes in the entire network are globally normalized. At the same time, a very small positive number is introduced to prevent calculation anomalies caused by a zero denominator. The normalized formula is as follows:

[0083] Wherein, represents the maximum voltage risk in the system. It is a very small positive number, used to avoid the denominator being zero during calculation, thus reducing the number of nodes. The voltage risk is mapped to a standardized node voltage risk weight in the 0-1 range. The larger the weight value, the higher the voltage operation risk of the corresponding node and the more urgent the need for reactive power support from new energy sources.

[0084] The embodiments of the present invention can achieve comprehensive and thorough quantification of node voltage risk, taking into account overvoltage, undervoltage and voltage deviation risks. Through standardized processing, the risk assessment is guaranteed to be fair and accurate, providing a precise basis for subsequent priority support for high-risk nodes and differentiated cluster division.

[0085] Step S4022: Sequentially select each resource node as the target resource node, calculate the remaining available reactive power capacity of the target resource node based on the corresponding rated capacity and current active power output, and calculate the support sensitivity of the target resource node to each electrical node based on the remaining available reactive power capacity and voltage amplitude.

[0086] Specifically, in this embodiment of the invention, the reactive power regulation capability of the new energy node is constrained by the rated capacity and real-time active power output of the equipment. Based on the rated apparent capacity and real-time active power output of the equipment, the remaining available reactive power capacity of the new energy node is accurately calculated using a power constraint formula. For example, for a new energy node... Assume its rated capacity is Currently, those who have made contributions are Then its remaining usable reactive power capacity The calculation formula is as follows:

[0087] Remaining available reactive power capacity directly reflects the reactive power adjustment margin and voltage support potential that new energy equipment can currently utilize, and is a fundamental core parameter for measuring resource support capabilities.

[0088] Furthermore, the impact of new energy nodes on load nodes is calculated: Let... For new energy nodes For electrical nodes The voltage-reactive power support sensitivity is calculated using the following formula:

[0089] visible, Support sensitivity is a micro-increment response index, specifically referring to the influence coefficient of small changes in reactive power output of new energy nodes on voltage fluctuations of each load node. It can accurately characterize the response speed and sensitivity of different new energy nodes to voltage regulation of each load node, dynamically reflect the adaptability characteristics of new energy reactive power support, and is different from the traditional static capacity assessment method, which is more in line with the dynamic operation characteristics of new energy.

[0090] This invention assesses the potential of new energy support from two dimensions: static capacity margin and dynamic control sensitivity. It accurately restores the dynamic reactive power support characteristics of new energy and avoids the distortion of support capacity assessment caused by traditional assessment relying solely on rated capacity.

[0091] Step S4023: Calculate the communication reliability between the target resource node and each electrical node based on the minimum communication cost and the preset communication cost scaling factor.

[0092] Specifically, in this embodiment of the invention, the minimum communication cost between nodes is determined by a combination of factors including communication transmission distance, data interaction latency, link packet loss rate, and channel interference. A higher communication cost indicates longer data transmission delay, a higher probability of packet loss, and poorer communication link stability. This embodiment introduces a preset communication cost scaling factor to exponentially map and convert the non-linearly changing communication cost into a linearly standardized communication reliability within the 0-1 range.

[0093] For example, calculating electrical nodes With new energy nodes Communication reliability between them. Electrical nodes. To new energy nodes The minimum communication cost is The cost is determined by the communication between nodes; shorter communication distances and higher success rates result in lower link costs. This is defined as the connection between electrical nodes and new energy nodes. Communication reliability for:

[0094] in, This is a communication cost scaling factor. If the node... With nodes If there is no communication between them, set the minimum communication reliability. This effectively avoids problems such as computational failure, zero denominator, and data gaps in scenarios without communication, and accurately adapts to the complex heterogeneous communication architecture of the power distribution network, including bidirectional, unidirectional, and linkless communication.

[0095] The embodiments of the present invention can realize the quantitative standardization of communication performance, adapt to the complex communication topology of power distribution networks, fill the technical gap of traditional cluster partitioning that ignores communication reliability, and ensure that the partitioned clusters have a stable communication and coordination foundation.

[0096] Step S4024: Normalize the remaining available reactive power capacity and support sensitivity, and calculate the support capability of the target resource node for each electrical node based on the normalized remaining available reactive power capacity, normalized support sensitivity, communication reliability and voltage risk weight.

[0097] Specifically, in this embodiment of the invention, since the four types of parameters—remaining available reactive power capacity, support sensitivity, communication reliability, and voltage risk weight—have completely different dimensions and numerical ranges, direct weighting would lead to evaluation bias. Therefore, the remaining available reactive power capacity is first evaluated using a different approach. Support sensitivity The two types of dynamic operating parameters are globally normalized to eliminate dimensional differences and numerical deviations, thereby obtaining the following results. and And then, as Figure 5 As shown, constructing new energy nodes For electrical nodes Evaluation criteria for support capabilities:

[0098] in, , and The calculation parameters are preset. This implementation embodiment has undergone extensive simulation optimization beforehand, setting the core weight parameters of support capability to 0.45, 0.70, and 0.80, respectively, corresponding to the impact weights of remaining reactive power capacity, communication reliability, and node voltage risk. Through a multi-parameter weighted fusion algorithm, it comprehensively considers the adjustability margin of new energy resources, voltage regulation sensitivity, communication transmission stability, and the risk support requirements of load nodes, ultimately obtaining a standardized and horizontally comparable comprehensive support capability value, realizing a comprehensive, refined, and differentiated quantification of new energy support capability.

[0099] This invention achieves unified quantification of multi-dimensional indicators through multi-parameter normalization and weighted fusion, accurately aligning with the actual operation of the power grid. This allows the assessment of support capabilities to take into account multiple characteristics such as resources, communication, risk, and regulation, resulting in assessment results that are more in line with engineering practice.

[0100] Step S4025: After determining the support capabilities of each resource node for each electrical node, construct a support capability matrix based on each support capability.

[0101] Specifically, in this embodiment of the invention, a two-dimensional standardized support capability matrix is ​​constructed, with new energy resource nodes as the row dimension and all electrical nodes in the distribution network as the column dimension. Composition of support capability matrix:

[0102] Among them, matrix The rows correspond to new energy nodes, meaning each row vector represents the spatial distribution characteristics of the support capacity of a single new energy resource node for all electrical nodes in the entire network. This directly reflects the support coverage and output characteristics of that new energy node; the matrix The columns correspond to distribution network nodes, meaning each column vector represents the contribution ratio of all new energy resource nodes in the entire network to a single load node and their supply-demand matching relationship, allowing for precise location of the core support resources for a load node. Taking the row vector as an example, the support capability matrix... China and New Energy Nodes The corresponding row vector is denoted as:

[0103] in, Represents new energy nodes Distribution of support capabilities across all system nodes. Matrix elements. The larger the value, the more likely it is to be a resource node. For electrical nodes The higher the support capability, the better. All elements of the matrix have undergone standardization, resulting in a unified numerical range and clear physical meaning, providing core and accurate basic data support for subsequent node association analysis, support source screening, and cluster evaluation index calculation.

[0104] By constructing a standardized and structured support capability matrix, this invention enables the digital and visual representation of new energy support resources across the entire network, providing a regular and unified data foundation for subsequent cluster division, indicator calculation, and scheme optimization, thereby improving the stability and accuracy of the overall algorithm.

[0105] Step S403: Based on the operating data, voltage risk weights, and support capability matrix, the distribution network is divided into clusters in various ways to determine multiple candidate cluster division methods.

[0106] Specifically, step S403 includes: Step S4031: Determine the electrical branches and communication links between different electrical nodes based on the topology of the distribution network, and determine the support relationship between any two electrical nodes based on the impedance of the electrical branches, the communication links, and the support capability matrix.

[0107] Specifically, in this embodiment of the invention, the traditional approach of relying solely on the single correlation logic of electrical topology coupling is completely broken through. From the three core dimensions of electrical physical coupling, communication collaborative interaction, and resource support matching, the comprehensive support correlation between any two electrical nodes is quantified in a comprehensive and three-dimensional manner. This fully aligns with the actual operating characteristics of high-proportion new energy distribution networks, namely "electrical interconnection, communication collaboration, resource mutual assistance, and dynamic coupling," and effectively avoids the one-sidedness of traditional partitioning that only considers static topology coupling and ignores dynamic resource and communication characteristics.

[0108] In some optional implementations, step S4031 above includes: Step a1: If there is an electrical branch between any two electrical nodes, calculate the electrical connection attraction weight between the two electrical nodes based on the resistance and impedance corresponding to the electrical branch; otherwise, set the electrical connection attraction weight to a preset weight value.

[0109] Step a2: If the communication link between any two electrical nodes is a bidirectional communication link, calculate the communication connection weight between the two electrical nodes based on the bidirectional communication reliability between the two electrical nodes. If it is a unidirectional communication link, calculate the communication connection weight based on the unidirectional communication reliability between the two electrical nodes. If there is no communication link, set the communication connection weight to a preset weight value.

[0110] Step a3: Select any two column vectors in the support capability matrix, and calculate the similarity of support capability features between the two corresponding electrical nodes based on the two column vectors.

[0111] Step a4: Calculate the electrical nodes based on the electrical connection attraction weight, communication connection weight, and support capability feature similarity. and electrical nodes The supporting relationship between them.

[0112] Specifically, in this embodiment of the invention, the support relationship between nodes is examined based on the similarity of electrical connection relationships, communication connection relationships, and support capability characteristics. For example... Figure 5 As shown, electrical nodes With electrical nodes The support relationship between nodes is defined as follows:

[0113] in: Electrical connection weights are used to reflect the node's electrical connection weights. With nodes The electrical connection strength; Communication connection weights, used to reflect the node's connection weights. With nodes The reliability of communication between them; To support capability feature similarity, it is used to reflect the degree of similarity between two nodes supported by new energy resources; , and The weighting coefficient can be set according to the operation and control requirements of the distribution network. Different weight combinations can be compared through offline simulation to select the weight combination with better overall performance.

[0114] In some known implementations, if electrical nodes With electrical nodes If there are electrical branches, then the electrical connection attracts weight. Determine using the following formula:

[0115]

[0116] in, For electrical nodes With electrical nodes The resistance of the electrical branches between them For electrical nodes With electrical nodes Reactance of the electrical branches between them This is the impedance magnitude of the electrical branch. If the electrical node... With electrical nodes If there is no direct electrical branch between them, the preset weight value is used. ,For example .

[0117] In some alternative implementations, communication connection weights The formula is expressed as follows:

[0118] in, For electrical nodes To electrical node The reliability of the first one-way communication For electrical nodes To electrical node The second one-way communication reliability, The preset reduction factor is set to 0 in this embodiment of the invention.

[0119] It can be seen that if electrical nodes in the topology To electrical node If there is a direct two-way communication link, then If electrical nodes To electrical node If there is only a direct one-way communication link between them, then , This is the reduction factor; if If there is no direct communication link between them, then This method is precisely adapted to the complex heterogeneous communication architecture of power distribution networks, including unidirectional, bidirectional, and linkless communication, and accurately reproduces the communication and collaborative interaction capabilities between nodes.

[0120] In some alternative implementations, the similarity of support capability features Determine the support capability matrix as follows: The List as , This represents all new energy resource node-to-node pairs. The support capacity vector, where each element corresponds to a new energy node to a load node. The supporting capabilities. Similarly, the nodes The support capability vector is denoted as Then electrical node With electrical nodes Similarity of support capability features The calculation formula is as follows:

[0121] in, For electrical nodes The corresponding column vector, For electrical nodes The corresponding column vector. The larger the value, the higher the electrical node value. With electrical nodes The more similar the models supported by new energy resources, that is, for nodes... Resource nodes with high support capabilities, for nodes It also has high support capabilities. At this point, the node... and nodes Including them in the same cluster is beneficial for retaining similar key support resources within the cluster.

[0122] Furthermore, based on the set electrical connection weight coefficient, communication connection weight coefficient, and support feature similarity weight coefficient, the three types of core correlation factors are scientifically weighted and integrated to obtain the final comprehensive support relationship between nodes, comprehensively and meticulously depicting the multidimensional correlation characteristics of nodes, and ensuring that the correlation calculation results are scientific and reasonable.

[0123] Step S4032: Calculate the comprehensive support utility of each resource node based on the voltage risk weight and support capability matrix.

[0124] Specifically, in this embodiment of the invention, the comprehensive support utility is the core indicator for evaluating the global support value of new energy resource nodes. Using the voltage risk weight of all nodes in the network as the weighting coefficient, and combining the support contributions of resource nodes to each high-risk node in the support capability matrix, the global comprehensive support utility of a single new energy node is obtained by weighted summation. Specifically, for any new energy node According to the support capability matrix and node voltage risk weight Computing resource nodes Comprehensive support effect The calculation formula is as follows:

[0125] It is evident that comprehensive support effectiveness can achieve differentiated quantification of the support value of new energy nodes. The higher the effectiveness value, the stronger the new energy node's ability to provide backup support for high-voltage risk nodes across the entire network, the better the full-area coverage effect, and the more suitable it is as a core support source node for the cluster. This provides a quantitative basis for the precise selection of subsequent support sources, thereby ensuring that core support sources with high backup capabilities are selected first, ensuring that each cluster has high-quality and reliable voltage support cores from the source, and improving the overall voltage autonomy capability of the cluster.

[0126] Step S4033: Set multiple cluster numbers, take each cluster number as the target cluster number in turn, and select the resource node corresponding to the highest comprehensive support effectiveness as the first support source node of the support source node set.

[0127] Specifically, in this embodiment of the invention, a supporting source node refers to a new energy resource node that serves as the initial tag in the subsequent tag propagation cluster partitioning process. Each supporting source node corresponds to an initial cluster tag and serves as the starting resource node in the cluster formation process. Let the number of candidate clusters be... For each candidate From the set of new energy resource nodes Select Several new energy nodes serve as supporting source nodes, with the first supporting source node selected based on comprehensive supporting effectiveness. The highest level of new energy nodes.

[0128] For example, according to such Figure 3 For the distribution network scale shown, a gradient candidate cluster number range of 4-6 is set. The number of typical target clusters in groups of 4, 5, and 6 is traversed sequentially to cover the mainstream regional scenarios of small and medium-sized distribution networks. Under each candidate scenario, the new energy node with the highest overall network support effectiveness, the strongest back-up capability for high-risk nodes, and the widest coverage is selected as the first core support source to ensure that the initial support source has the best global support performance and lay the foundation for the construction of subsequent high-quality clusters.

[0129] Step S4034: Based on the support capability matrix, calculate the comprehensive difference in support capability distribution of each unselected resource node relative to the support source node set, and add the unselected resource node corresponding to the largest comprehensive difference in support capability distribution to the support source node set until the number of nodes in the support source node set is the same as the target cluster number.

[0130] Specifically, in this embodiment of the invention, to avoid the problems of homogenization of support features, overlapping support coverage, and waste of resources among multiple support source nodes, a differentiated screening mechanism is adopted. That is, by using the support capability distribution vector difference algorithm, the overall feature difference between the remaining unselected new energy nodes and the selected support source set is calculated. In each iteration, the new energy node with the largest difference and the strongest complementarity in support blind spots is selected and added to the support source node set until the selected node is selected. Given a set of supporting source nodes, we obtain the supporting source node set. ,in, Indicates the first Each cluster has a set of supporting source nodes, which complements the core supporting resource characteristics of each cluster, covers the entire power grid without blind spots, and obtains a supporting source set that is precisely matched with the number of each target cluster, has the best supporting performance, and is the most differentiated.

[0131] Step S4035: Obtain the initial labels of each supporting source node, and use the label propagation algorithm to divide the electrical nodes into clusters to obtain candidate clusters for the target number of clusters, and determine multiple candidate cluster division methods.

[0132] Specifically, in this embodiment of the invention, a unique initial cluster label is pre-assigned to each selected high-quality core support source node. Using the multi-dimensional comprehensive support relationship of all nodes as the propagation weight, the label propagation algorithm is used to achieve adaptive diffusion of cluster labels and classification of all nodes. Three initial candidate cluster partitioning schemes with 4, 5, and 6 clusters are automatically generated to achieve full coverage of candidate samples in multiple scenarios and structures, providing sufficient sample support for subsequent selection of the optimal scheme.

[0133] Step S404: Each candidate cluster partitioning method is sequentially adopted as the target cluster partitioning method. Under the target cluster partitioning method, the affiliation score of electrical nodes to different candidate clusters is determined. The node sets of the candidate clusters are iteratively adjusted based on the affiliation scores until the optimal cluster partitioning result under the target cluster partitioning method is obtained. For details, please refer to [link to details]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0134] Step S405: After determining the optimal cluster partitioning results under each candidate cluster partitioning method, calculate the partitioning effect evaluation index of each optimal cluster partitioning result based on the support capability matrix, and determine the final cluster partitioning result of the distribution network based on each partitioning effect evaluation index. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0135] The distribution network cluster partitioning method provided by this invention quantifies the voltage risk weight of each electrical node and the support capability of new energy resource nodes based on actual distribution network operation data, and constructs a standardized support capability matrix. This method overcomes the limitations of traditional single partitioning methods that rely solely on electrical topology and distance, and fully adapts to the dynamic reactive power support characteristics of new energy and the differentiated voltage risk features of nodes. By constructing a multi-dimensional evaluation system to generate multiple candidate cluster partitioning methods, iterative optimization of node affiliation is performed on each initial candidate partitioning result to obtain the optimal cluster partitioning result under each scenario. Finally, the globally optimal partitioning scheme is selected through comprehensive evaluation of multiple indicators. This method enables the construction of a closed-loop partitioning system that integrates "data quantification modeling - multi-scenario candidate partitioning - iterative optimization and correction - multi-dimensional selection," effectively reducing the communication interaction pressure and computational complexity of centralized management of the entire network. It significantly improves the risk adaptability, resource support matching accuracy, and autonomous partitioning capability of high-proportion new energy distribution network cluster partitioning, meeting the engineering management needs of hierarchical partitioning, local support, and autonomous collaboration in new distribution networks.

[0136] This embodiment provides a method for dividing a power distribution network cluster, which can be used in the aforementioned terminal devices, such as computers. Figure 6 This is a flowchart of a distribution network cluster partitioning method according to an embodiment of the present invention, such as... Figure 6 As shown, the process includes the following steps: Step S601: Obtain the operating data of the distribution network. For details, please refer to [link to relevant documentation]. Figure 4 Step S401 of the illustrated embodiment will not be described again here.

[0137] Step S602: Determine the voltage risk weight of each electrical node in the distribution network based on operational data, and determine the support capacity of resource nodes within each electrical node to obtain the corresponding support capacity matrix. For details, please refer to [link to relevant documentation]. Figure 4 Step S402 of the illustrated embodiment will not be described again here.

[0138] Step S603 involves performing various clustering methods on the distribution network based on operational data, voltage risk weights, and support capability matrices to determine multiple candidate clustering methods. For details, please refer to [link to relevant documentation]. Figure 4 Step S403 of the illustrated embodiment will not be described again here.

[0139] Step S604: Each candidate cluster partitioning method is used as the target cluster partitioning method in turn. Under the target cluster partitioning method, the affiliation score of electrical nodes to different candidate clusters is determined. The node set of the candidate clusters is iteratively adjusted according to the affiliation score until the optimal cluster partitioning result under the target cluster partitioning method is obtained.

[0140] Specifically, step S604 includes: Step S6041: Sequentially select each candidate cluster under the target cluster partitioning method as the target candidate cluster, and calculate the intra-group support relationship between any electrical node in the target candidate cluster and the other electrical nodes according to the support relationship.

[0141] Specifically, in this embodiment of the invention, based on the multi-dimensional comprehensive support relationship of all nodes, for each initial candidate cluster, the mutual support relationship strength between all electrical nodes within the cluster is calculated, and the complete intra-cluster support relationship is obtained. The calculation formula is as follows:

[0142] in, For support relationships within the group, Indicates electrical node The current cluster is identified, thereby accurately quantifying the degree of collaboration, resource adaptation, and integration adaptability of each node within the current cluster. This provides cluster-internal correlation data support for subsequent accurate calculation of node affiliation scores and optimization of node affiliation structure.

[0143] Step S6042: Calculate the affiliation score of each electrical node to each candidate cluster based on the intra-cluster support relationship, voltage risk weight, and support capability matrix.

[0144] Specifically, in this embodiment of the invention, for electrical nodes Define its affiliation to a cluster. The scoring function is:

[0145] in, Indicates the target candidate cluster The set of resource nodes within; Indicates the target candidate cluster The supporting source node in , , Different preset weighting coefficients can be determined by the system's requirements for support characteristics, and are used to adjust the support relationship between nodes, the maximum support capacity of new energy within the cluster, and the proportion of support requirements for high-risk nodes in the cluster partitioning process.

[0146] As can be seen, the embodiments of this invention construct a multi-dimensional fusion attribution scoring model, which no longer relies solely on distance or topological relationships, but comprehensively considers three core elements: first, the tightness of collaborative support among nodes within the cluster, representing the adaptability within the node group; second, the voltage risk weight of each node, prioritizing high-risk nodes to be matched with the optimal support cluster; and third, the overall new energy resource support and supply capacity of the cluster, representing the cluster's ability to provide voltage backup for the target node. Through a weighted fusion algorithm, an independent attribution score for each node to all candidate clusters is obtained. The higher the score, the higher the matching degree between the node and the corresponding cluster's support system, operating characteristics, and collaborative architecture, thereby accurately quantifying the node cluster adaptability and completely solving the problems of crude node attribution determination and poor adaptability of high-risk nodes in traditional methods.

[0147] Step S6043: Based on the affiliation score of each electrical node to different candidate clusters, and under the premise that the supporting source nodes of the candidate clusters are fixed, the electrical nodes are assigned to the candidate clusters corresponding to the highest affiliation score.

[0148] Specifically, in this embodiment of the invention, after calculating the scoring function, the node is updated. The cluster partitioning situation, the nodes Assign to a higher-scoring cluster , satisfy:

[0149] To avoid the problems caused by frequent changes in the core support source of the cluster during iterative optimization, which lead to oscillations in the partition architecture and disorder in the support system, this embodiment of the invention innovatively adopts an iterative strategy of "fixing the core and optimizing the periphery," that is, updating nodes... When partitioning clusters, the cluster affiliation of core support source nodes for each cluster is strictly locked. Only ordinary load nodes and electrical nodes without support capabilities are optimized for affiliation. In each iteration, electrical nodes are assigned to the candidate cluster with the highest affiliation score and best adaptability, gradually optimizing the cluster node structure and ensuring the continuous and stable output of core support capabilities. This iterative strategy of fixing core support sources achieves both peripheral node adaptation optimization and ensures the stability of the core support architecture, avoiding partition oscillations and support failures, and significantly improving the stability of the partition structure.

[0150] Step S6044: After partitioning, recalculate the affiliation score of each electrical node to different candidate clusters until the partitioning reaches stability or the maximum number of calculations is reached, and then determine the optimal cluster partitioning result under the target cluster partitioning method.

[0151] Specifically, in this embodiment of the invention, after each round of node affiliation adjustment, the internal structure of the cluster and the support relationship within the cluster are updated in real time, the node affiliation score of the entire domain is recalculated iteratively, and the optimization is repeatedly looped until the cluster affiliation of all electrical nodes in the entire network no longer changes, or the preset maximum number of iterations is reached, and the partition structure is determined to be completely converged and stable. Finally, three sets of steady-state optimal partition results for clusters 4, 5, and 6 are output, completing the fine optimization and correction of the initial partitions in multiple scenarios.

[0152] Taking a 4-cluster cluster as an example, such as Figure 7 As shown, nodes 1-12 and 23-25 ​​are considered as one candidate cluster, nodes 13 and 14 as another, nodes 15-18 as yet another, and nodes 19-22 and 26-22 as yet another.

[0153] Step S605: After determining the optimal cluster partitioning result under each candidate cluster partitioning method, calculate the partitioning effect evaluation index of each optimal cluster partitioning result based on the support capability matrix, and determine the final cluster partitioning result of the distribution network based on each partitioning effect evaluation index.

[0154] Specifically, step S605 includes: Step S6051: For any optimal cluster partitioning result, calculate the main support retention rate of each electrical node within the zone based on the support capability matrix, and calculate the risk-weighted support strength within the zone of the optimal cluster partitioning result based on the main support retention rate within the zone and the voltage risk weight.

[0155] Specifically, in embodiments of the present invention, such as Figure 5 As shown, before performing cluster partitioning, an evaluation index for the effectiveness of cluster partitioning based on support capabilities is established. The evaluation index includes the support strength within the risk-weighted zone. Cross-cluster support dependency rate And the communication reliability autonomous synergy index .in, It is a core indicator for evaluating the local autonomous support capability of a power grid cluster. Its primary purpose is to differentiate the local resource backup guarantee level of high-voltage risk nodes, effectively compensating for the shortcomings of traditional average evaluation methods that ignore node risk differences. A higher value indicates a higher concentration of core support resources within the cluster, a greater reliance of high-risk nodes on local cluster resources for voltage support, stronger local backup capability, and higher grid voltage operation safety.

[0156] In some optional implementations, step S6051 includes: Step b1: Take each electrical node as the target electrical node, determine the first set of primary supporting resources with a first preset number of nodes from all resource nodes according to the supporting capability matrix, and determine the second set of primary supporting resources with a first preset number of nodes from the candidate clusters in which they are located.

[0157] Step b2: Based on the first and second major support resource sets, calculate the retention rate of the major support within the target electrical node.

[0158] Step b3: Calculate the risk-weighted support strength within the optimal cluster partitioning result based on the main support retention rate and voltage risk weight of each electrical node within the zone.

[0159] Specifically, in the embodiments of the present invention, for ease of description, nodes are defined as follows: The cluster number is Set nodes The set of resource nodes within the cluster is .definition For the pair of nodes in set A The set consisting of the top m resource nodes with the greatest support capacity, then the support capacity of the nodes across the entire system... The first m main supporting resources are ,node Nodes within the cluster The first m main supporting resources are .

[0160] Further, define nodes The main support level within the zone is maintained at:

[0161] in, Represents a node The extent to which the main new energy support resources are retained within the cluster. The closer to 1, the stronger the node. The more concentrated the main supporting resources are within its own cluster.

[0162] Define the support strength within the risk-weighted zone as:

[0163] in, This is the voltage risk weighting factor. The larger the value, the better the cluster partitioning result can retain the main new energy support nodes of the load nodes with high voltage over-limit risk within the same cluster.

[0164] Step S6052: For any load node under the optimal cluster partitioning result, determine the strongest support resource of the load node based on the support capability matrix, communication reliability and the initial number of the resource node, and calculate the cross-cluster support dependency rate of the optimal cluster partitioning result based on the strongest support resource, voltage risk weight and the main support retention rate in the area.

[0165] Specifically, in this embodiment of the invention, cross-cluster support dependency rate This value is used to quantify the dependence of all network load nodes on external renewable energy support resources and is a key indicator for evaluating the autonomy and independence of cluster zones. The lower the value, the more the internal resources of the cluster can fully meet the local load voltage support requirements, the less cross-zone power and command interaction, the better the zone autonomy, and the lower the management complexity.

[0166] In some optional implementations, step S6052 above includes: Step c1: For any load node under the optimal cluster partitioning result, obtain the column vector corresponding to the load node in the support capacity matrix, and determine the maximum support capacity in the column vector.

[0167] Step c2: If the number of maximum support capabilities is greater than or equal to two, the resource node corresponding to the highest communication reliability is selected as the strongest support resource. If the communication reliability is the same, the strongest support resource is determined according to the preset order of the initial number of the resource nodes.

[0168] Step c3: If the strongest support resource and the load node are not in the same candidate cluster, or the main support retention rate of the load node within the region is less than a preset threshold, then set the cross-cluster support dependency flag of the load node to the first preset value; otherwise, set it to the second preset value.

[0169] Step c4: Calculate the cross-cluster support dependency rate based on the cross-cluster support dependency flag and voltage risk weight of all load nodes.

[0170] Specifically, in this embodiment of the invention, for load nodes According to the support capability matrix The Middle The column values ​​are compared across all new energy node-to-node pairs. The supporting capacity. This will have an impact on... The resource node with the greatest supporting capacity is denoted as node. The strongest supporting resource, and recorded as , Satisfy any ,have .

[0171] If there are two or more new energy node pairs If they have the same maximum support capacity, then the node with the same maximum support capacity should be selected first. Resource nodes with high communication reliability are used as If the communication reliability is still the same, the resource node with the larger remaining reactive power support capacity shall be selected first; if it is still the same, the node shall be selected according to the order of the new energy node numbers.

[0172] When the node's strongest supporting resources Not a node The set of resource nodes within the cluster , or node The main support level in the area Less than the threshold At that time, node It was flagged as having a cross-cluster support dependency, and the node was configured. The corresponding cross-cluster support dependency flag is:

[0173] Cross-cluster support dependency rate:

[0174] in, This indicates the degree to which a node depends on new energy resources outside its cluster. The smaller the value, the more likely the cluster partitioning result will enable the node to obtain the main new energy support resources within its cluster.

[0175] Step S6053: Determine the communication link set of each candidate cluster under the optimal cluster partitioning result based on the communication reliability, and calculate the communication reliability autonomous collaboration index of the optimal cluster partitioning result based on the communication link set, communication reliability and cross-cluster support dependency rate.

[0176] Specifically, in this embodiment of the invention, the communication reliability autonomous cooperation index It is a comprehensive evaluation index that integrates the stability of internal cluster communication, the rationality of the overall network communication architecture, and the autonomous capabilities of the regions. It takes into account both the reliability of communication operation and the performance of power grid regional operation. The higher the value, the more stable the cluster communication backbone architecture, the smoother the node collaborative interaction, and the better the overall performance of regional autonomous collaboration.

[0177] In some optional implementations, step S6053 includes: Step d1: Calculate the bidirectional communication reliability between any two resource nodes in the candidate cluster, and determine the communication link set of the candidate cluster. The communication link set is the set of nodes that connect all resource nodes in the candidate cluster and have the largest sum of communication reliability.

[0178] Step d2: Calculate the collaborative communication backbone reliability of the candidate cluster based on the two-way communication reliability and the communication link set.

[0179] Step d3: Calculate the collaborative communication backbone reliability of the optimal cluster partitioning result based on the collaborative communication backbone reliability of each candidate cluster.

[0180] Step d4: Calculate the communication reliability autonomous collaboration index based on the reliability of the collaborative communication backbone and the cross-cluster support dependency rate.

[0181] Specifically, in this embodiment of the invention, for clusters Its internal resource node set is For two resource nodes The reliability of its two-way communication is defined as:

[0182] in, Represents resource nodes To resource nodes Communication reliability, Represents resource nodes To resource nodes The communication reliability is determined by taking the smaller value, which represents the reliability of bidirectional collaborative information exchange between the two new energy nodes.

[0183] Select the connection The set of communication links with the highest sum of reliability among all new energy nodes is denoted as . This collection is called a cluster. The resource collaborative communication backbone. The new energy collaborative communication backbone represents a set of highly reliable communication relationships upon which new energy nodes within the cluster rely for basic collaborative information exchange.

[0184] Cluster The reliability of the new energy collaborative communication backbone is:

[0185] If there is only one resource node in the cluster, then the cluster does not require inter-resource coordination links, and its backbone reliability is recorded as 1.

[0186] The overall reliability of the new energy collaborative communication backbone is:

[0187] The Communication Reliability Autonomous Cooperation Index is defined as:

[0188] in, It reflects the reliability of collaborative communication among resource nodes within the cluster. The larger the value, the better the autonomous and collaborative conditions of the cluster partitioning result.

[0189] Step S6054: Normalize the support strength within the risk-weighted area, the cross-cluster support dependency rate, and the communication reliability autonomous collaboration index, and calculate the comprehensive score of the number of clusters in the optimal cluster partitioning result based on the normalized support strength within the risk-weighted area, the normalized cross-cluster support dependency rate, and the normalized communication reliability autonomous collaboration index.

[0190] Specifically, in this embodiment of the invention, the three types of evaluation indicators have different dimensions, numerical ranges, and optimization directions, and cannot be directly compared horizontally. Therefore, the three indicators are first uniformly normalized to map all indicators to a unified numerical range.

[0191] Number of each candidate cluster Based on the resulting candidate cluster partitioning scheme, the three types of support capability evaluation indicators mentioned above are calculated, and the comprehensive score for the number of clusters is calculated after normalization:

[0192] in, , , These are the normalized risk-weighted support strength within the region, cross-cluster support dependency rate, and communication reliability autonomous collaboration index, respectively. , , The weighting is used to represent the relative importance of three evaluation objectives—support strength within the risk-weighted zone, cross-cluster support dependence, and communication reliability and autonomous collaboration—in the final cluster scheme selection. The specific values ​​are adjusted according to the control objectives that are the focus of cluster division.

[0193] For example, the embodiments of the present invention have been verified through multi-scenario simulations. The optimal weights for the comprehensive score are set to 0.38, 0.30, and 0.32, which correspond to the support strength within the risk weighted zone, the cross-cluster support dependency rate, and the communication reliability autonomous collaboration index, respectively. Then, the weighted fusion is used to obtain a comprehensive score of the scheme that can be directly compared horizontally, so as to comprehensively, objectively, and scientifically evaluate the comprehensive operational performance of the schemes in each partition.

[0194] Step S6055: After obtaining the comprehensive score of the number of clusters corresponding to each optimal cluster partitioning result, select the optimal cluster partitioning result corresponding to the highest comprehensive score of the number of clusters as the final cluster partitioning result.

[0195] Specifically, in this embodiment of the invention, the comprehensive scores of the three optimal partitioning schemes of 4 clusters, 5 clusters, and 6 clusters are compared horizontally, and the optimal 4-cluster partitioning scheme with the highest comprehensive score, the best voltage support capability, the strongest autonomy, and the most stable communication is selected as the final cluster partitioning result of the distribution network, thereby achieving the global optimal selection of the partitioning scheme.

[0196] For example, the parameter settings in the embodiments of the present invention are shown in Table 3: Table 3 Implementation Parameters

[0197] According to the set parameters, follow as follows Figure 5 The evaluation results of candidate cluster partitioning schemes with different numbers of clusters obtained from the specific process shown are shown in Table 4: Table 4 Evaluation results of candidate cluster partitioning schemes

[0198] The partitioning scheme with the highest overall score was selected, which divides the system into 4 clusters. The partitioning results are shown in the attached figure. Figure 7 As shown.

[0199] Furthermore, in this embodiment of the invention, sensitivity-only partitioning, communication-only partitioning, and electrical modularity-only partitioning are used as comparison methods for cluster partitioning, and the results are as follows: Figure 8 , Figure 9 , Figure 10 As shown in Table 5, the cluster partitioning results indicators for different partitioning methods are as follows: Table 5 Cluster Partition Results Indicators

[0200] As can be seen, the embodiments of the present invention demonstrate good overall performance across the three evaluation indicators, and can provide a more reasonable basis for cluster division for new energy sources in distribution networks to participate in distributed voltage control.

[0201] The distribution network cluster partitioning method provided by this invention quantifies the voltage risk weight of each electrical node and the support capability of new energy resource nodes based on actual distribution network operation data, and constructs a standardized support capability matrix. This method overcomes the limitations of traditional single partitioning methods that rely solely on electrical topology and distance, and fully adapts to the dynamic reactive power support characteristics of new energy and the differentiated voltage risk features of nodes. By constructing a multi-dimensional evaluation system to generate multiple candidate cluster partitioning methods, iterative optimization of node affiliation is performed on each initial candidate partitioning result to obtain the optimal cluster partitioning result under each scenario. Finally, the globally optimal partitioning scheme is selected through comprehensive evaluation of multiple indicators. This method enables the construction of a closed-loop partitioning system that integrates "data quantification modeling - multi-scenario candidate partitioning - iterative optimization and correction - multi-dimensional selection," effectively reducing the communication interaction pressure and computational complexity of centralized management of the entire network. It significantly improves the risk adaptability, resource support matching accuracy, and autonomous partitioning capability of high-proportion new energy distribution network cluster partitioning, meeting the engineering management needs of hierarchical partitioning, local support, and autonomous collaboration in new distribution networks.

[0202] This embodiment also provides a distribution network cluster partitioning device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible and contemplated.

[0203] This embodiment provides a power distribution network cluster partitioning device, such as... Figure 11 As shown, it includes: The operation data acquisition module 1101 is used to acquire operation data of the power distribution network.

[0204] The support capability determination module 1102 is used to determine the voltage risk weight of each electrical node in the distribution network based on the operating data, and to determine the support capability of the resource nodes in the electrical nodes for each electrical node, so as to obtain the corresponding support capability matrix.

[0205] The initial cluster partitioning module 1103 is used to partition the distribution network into clusters in various ways based on operating data, voltage risk weights, and support capability matrices, and to determine multiple candidate cluster partitioning methods.

[0206] The cluster iterative optimization module 1104 is used to sequentially take each candidate cluster partitioning method as the target cluster partitioning method. Under the target cluster partitioning method, it determines the affiliation score of electrical nodes to different candidate clusters, and iteratively adjusts the node set of the candidate clusters according to the affiliation score until the optimal cluster partitioning result under the target cluster partitioning method is obtained.

[0207] The cluster partitioning determination module 1105 is used to calculate the partitioning effect evaluation index of each optimal cluster partitioning result based on the support capability matrix after determining the optimal cluster partitioning result under each candidate cluster partitioning method, and to determine the final cluster partitioning result of the distribution network based on each partitioning effect evaluation index.

[0208] The power distribution network cluster partitioning device provided in this embodiment of the invention can execute the power distribution network cluster partitioning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0209] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0210] The following is a detailed reference. Figure 12 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 1201, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1202 or a program loaded from memory 1208 into random access memory (RAM) 1203. The RAM 1203 also stores various programs and data required for the operation of the electronic device. The processor 1201, ROM 1202, and RAM 1203 are interconnected via a bus 1204. An input / output (I / O) interface 1205 is also connected to the bus 1204.

[0211] Typically, the following devices can be connected to I / O interface 1205: input devices 1206 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 1207 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 1208 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1209. Communication device 1209 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 12 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0212] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 1209, or installed from a memory 1208, or installed from a ROM 1202. When the computer program is executed by the processor 1201, it performs the functions defined in the distribution network cluster partitioning method of the embodiments of the present invention.

[0213] Figure 12 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0214] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the distribution network cluster partitioning method shown in the above embodiments is implemented.

[0215] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0216] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for dividing a power distribution network cluster, characterized in that, The method includes: Obtain operational data of the power distribution network; Based on the operational data, the voltage risk weight of each electrical node in the distribution network is determined, and the support capability of resource nodes in each electrical node is determined to obtain the corresponding support capability matrix. Based on the operational data, the voltage risk weight, and the support capability matrix, the distribution network is divided into clusters in various ways to determine multiple candidate cluster division methods; Each candidate cluster partitioning method is sequentially used as the target cluster partitioning method. Under the target cluster partitioning method, the affiliation score of the electrical node to different candidate clusters is determined. The node set of the candidate cluster is iteratively adjusted according to the affiliation score until the optimal cluster partitioning result under the target cluster partitioning method is obtained. After determining the optimal cluster partitioning result under each of the candidate cluster partitioning methods, the partitioning effect evaluation index of each optimal cluster partitioning result is calculated based on the support capability matrix, and the final cluster partitioning result of the distribution network is determined based on each partitioning effect evaluation index.

2. The method according to claim 1, characterized in that, The operational data includes at least: the number of first nodes of the electrical nodes in the distribution network, the number of second nodes of the resource nodes in the electrical nodes, the voltage amplitude, rated capacity and current active power output of each electrical node, and the minimum communication cost between the electrical nodes and the resource nodes; The step of determining the voltage risk weight of each electrical node in the distribution network based on the operational data, and determining the support capability of resource nodes in each electrical node to obtain the corresponding support capability matrix, includes: Each electrical node is sequentially selected as a target electrical node. The voltage risk quantity of each target electrical node is calculated based on a preset upper voltage limit, a preset lower voltage limit, and the voltage amplitude. The voltage risk quantity is then normalized to obtain the voltage risk weight of the target electrical node. The calculation formula is as follows: in, This refers to the voltage risk quantity. The voltage risk weight is... The voltage amplitude, The preset voltage upper limit, The preset lower voltage limit. The preset adjustment coefficient, This represents the maximum voltage risk at each of the aforementioned electrical nodes. Preset to positive numbers; Each resource node is sequentially designated as a target resource node. The remaining available reactive power capacity of each target resource node is calculated based on its corresponding rated capacity and current active power output. The support sensitivity of the target resource node to each of the electrical nodes is then calculated based on the remaining available reactive power capacity and the voltage amplitude. The calculation formula is as follows: in, For the rated capacity, For the current active power output, The remaining available reactive power capacity, The support sensitivity; The communication reliability between the target resource node and each electrical node is calculated based on the minimum communication cost and a preset communication cost scaling factor, using the following formula: in, For the aforementioned communication reliability, For the minimum communication cost, The preset communication cost scaling factor; The remaining available reactive power capacity and the support sensitivity are normalized, and the support capability of the target resource node for each of the electrical nodes is calculated based on the normalized remaining available reactive power capacity, the normalized support sensitivity, the communication reliability, and the voltage risk weight. The calculation formula is as follows: in, For resource nodes For electrical nodes Support capabilities The normalized remaining available reactive power capacity, The normalized support sensitivity, , and These are preset calculation parameters; Once the support capabilities of each resource node for each electrical node are determined, a support capability matrix is ​​constructed based on these capabilities. The expression for the support capability matrix is ​​as follows: in, The support capability matrix is... This is the number of the first node. Let be the second number of nodes. Any row vector of the support capability matrix represents the support capability of the corresponding resource node for all electrical nodes, and any column vector of the support capability matrix represents the support capability of all resource nodes for their corresponding electrical nodes.

3. The method according to claim 2, characterized in that, The operational data also includes: the resistivity of electrical branches between different electrical nodes. The distribution network is then divided into multiple clusters based on the operational data, the voltage risk weight, and the support capability matrix, determining multiple candidate cluster division methods, including: Based on the topology of the power distribution network, determine the electrical branches between different electrical nodes and the communication links of the electrical branches, and determine the support relationship between any two electrical nodes based on the impedance of the electrical branches, the communication links, and the support capability matrix; The comprehensive support utility of each resource node is calculated based on the voltage risk weight and the support capability matrix. Set multiple cluster numbers, use each cluster number as the target cluster number in turn, and select the resource node corresponding to the highest comprehensive support effectiveness as the first support source node of the support source node set; Based on the support capability matrix, calculate the comprehensive difference in support capability distribution of each unselected resource node relative to the support source node set, and add the unselected resource node corresponding to the largest comprehensive difference in support capability distribution to the support source node set until the number of nodes in the support source node set is the same as the target cluster number. The initial labels of each of the supporting source nodes are obtained, and the electrical nodes are divided into clusters using a label propagation algorithm to obtain candidate clusters for the target number of clusters and determine multiple candidate cluster division methods.

4. The method according to claim 3, characterized in that, Determining the support relationship between any two electrical nodes based on the electrical branch impedance, the communication link, and the support capability matrix includes: If an electrical branch exists between any two electrical nodes, the electrical connection attraction weight between the two electrical nodes is calculated based on the resistance and impedance corresponding to the electrical branch; otherwise, the electrical connection attraction weight is set to a preset weight value. The calculation formula is as follows: in, The electrical connection attracts weights. For electrical nodes With electrical nodes The resistance of the electrical branches between them For electrical nodes With electrical nodes Reactance of the electrical branches between them The impedance modulus of the electrical branch is given. As a preset positive number, The preset weight value; If the communication link between any two electrical nodes is a bidirectional communication link, the communication connection weight between the two electrical nodes is calculated based on the bidirectional communication reliability between the two electrical nodes. If it is a unidirectional communication link, the communication connection weight is calculated based on the unidirectional communication reliability between the two electrical nodes. If there is no communication link, the communication connection weight is set to the preset weight value. The calculation formula is as follows: in, The communication connection weight, For electrical nodes To electrical node The reliability of the first one-way communication For electrical nodes To electrical node The second one-way communication reliability, The preset reduction factor; Select any two column vectors from the support capability matrix, and calculate the similarity of support capability features between the corresponding two electrical nodes based on the two column vectors. The calculation formula is as follows: in, The similarity of the support capability features. For electrical nodes The corresponding column vector, For electrical nodes The corresponding column vector; Electrical nodes are calculated based on the electrical connection attraction weight, the communication connection weight, and the similarity of the support capability features. and electrical nodes The supporting relationship between them is calculated using the following formula: in, For the aforementioned support relationship, , and Different preset weighting coefficients.

5. The method according to any one of claims 3 or 4, characterized in that, Under the target cluster partitioning method, the process involves determining the affiliation score of the electrical node to different candidate clusters, and iteratively adjusting the node set of the candidate clusters based on the affiliation score until the optimal cluster partitioning result under the target cluster partitioning method is obtained, including: Each candidate cluster under the target cluster partitioning method is sequentially taken as the target candidate cluster, and the intra-cluster support relationship between any electrical node in the target candidate cluster and the other electrical nodes is calculated according to the support relationship. The calculation formula is as follows: in, This refers to the support relationship within the group. Indicates electrical node The current cluster; The affiliation score of each electrical node to each candidate cluster is calculated based on the intra-cluster support relationship, the voltage risk weight, and the support capability matrix, as shown in the following formula: in, Indicates the target candidate cluster The set of resource nodes within; Indicates the target candidate cluster The supporting source node in , , and Different preset weighting coefficients; Based on the affiliation score of each electrical node to different candidate clusters, and with the supporting source nodes of the candidate clusters fixed, the electrical nodes are assigned to the candidate clusters corresponding to the highest affiliation score. After partitioning, the affiliation score of each electrical node to different candidate clusters is recalculated until the partitioning reaches stability or the maximum number of calculations is reached, and then the optimal cluster partitioning result under the target cluster partitioning method is determined.

6. The method according to claim 5, characterized in that, The method further includes: calculating the partitioning effect evaluation index of each of the optimal cluster partitioning results based on the support capability matrix, and determining the final cluster partitioning result of the distribution network based on each of the partitioning effect evaluation indexes; For any optimal cluster partitioning result, the main support retention rate within the zone of each electrical node is calculated based on the support capability matrix, and the risk-weighted support strength within the zone of the optimal cluster partitioning result is calculated based on the main support retention rate within the zone and the voltage risk weight. For any load node under the optimal cluster partitioning result, the strongest support resource of the load node is determined according to the support capability matrix, the communication reliability and the initial number of the resource node, and the cross-cluster support dependency rate of the optimal cluster partitioning result is calculated according to the strongest support resource, the voltage risk weight and the main support retention rate in the area. The communication link set of each candidate cluster under the optimal cluster partitioning result is determined based on the communication reliability, and the communication reliability autonomous cooperation index of the optimal cluster partitioning result is calculated based on the communication link set, the communication reliability, and the cross-cluster support dependency rate. The support strength within the risk-weighted zone, the cross-cluster support dependency rate, and the communication reliability autonomous collaboration index are normalized, and the cluster number comprehensive score of the optimal cluster partitioning result is calculated based on the normalized support strength within the risk-weighted zone, the normalized cross-cluster support dependency rate, and the normalized communication reliability autonomous collaboration index. After obtaining the comprehensive score of the number of clusters corresponding to each optimal cluster partitioning result, the optimal cluster partitioning result corresponding to the highest comprehensive score of the number of clusters is selected as the final cluster partitioning result.

7. The method according to claim 6, characterized in that, The step of calculating the intra-regional main support retention rate of each electrical node based on the support capability matrix, and calculating the risk-weighted intra-regional support strength of the optimal cluster partitioning result based on the intra-regional main support retention rate and the voltage risk weight, includes: Each electrical node is taken as the target electrical node. Based on the support capability matrix, a first set of primary support resources with a first preset number of nodes is determined from all resource nodes, and a second set of primary support resources with a first preset number of nodes is determined from the candidate clusters in which they are located. Based on the first set of primary support resources and the second set of primary support resources, the retention rate of the primary support within the zone of the target electrical node is calculated using the following formula: in, This is the first primary set of supporting resources. This is the second main set of supporting resources; The risk-weighted support strength within the optimal cluster partitioning result is calculated based on the main support retention rate within the zone of each electrical node and the voltage risk weight. The calculation formula is as follows: in, The preset voltage risk weighting coefficient is used. The number of the first node.

8. The method according to claim 6, characterized in that, For any load node under the optimal cluster partitioning result, the strongest supporting resource of the load node is determined according to the supporting capability matrix, the communication reliability, and the initial number of the resource node. Then, the cross-cluster support dependency rate of the optimal cluster partitioning result is calculated based on the strongest supporting resource, the voltage risk weight, and the intra-area major support retention rate, including: For any load node under the optimal cluster partitioning result, obtain the column vector corresponding to the load node in the support capability matrix, and determine the maximum support capability in the column vector; If the number of maximum support capabilities is greater than or equal to two, the resource node corresponding to the highest communication reliability is selected as the strongest support resource. If the communication reliability is the same, the strongest support resource is determined according to the preset order of the initial number of the resource nodes. If the strongest support resource and the load node are not in the same candidate cluster, or the intra-regional main support retention rate of the load node is less than a preset threshold, then the cross-cluster support dependency flag of the load node is set to a first preset value; otherwise, it is set to a second preset value. The cross-cluster support dependency rate is calculated based on the cross-cluster support dependency flags of all load nodes and the voltage risk weights, using the following formula: in, For the cross-cluster support dependency rate, This is the cross-cluster support dependency flag.

9. The method according to claim 6, characterized in that, The step of determining the communication link set of each candidate cluster under the optimal cluster partitioning result based on communication reliability, and calculating the communication reliability autonomous cooperation index of the optimal cluster partitioning result based on the communication link set, the communication reliability, and the cross-cluster support dependency rate, includes: Calculate the bidirectional communication reliability between any two resource nodes in the candidate cluster, and determine the communication link set of the candidate cluster. The communication link set is the set of nodes that connect all resource nodes in the candidate cluster and have the largest sum of communication reliability. The collaborative communication backbone reliability of the candidate cluster is calculated based on the bidirectional communication reliability and the communication link set, using the following formula: in, The reliability of the collaborative communication backbone of the candidate cluster. The reliability of the bidirectional communication is, and , Represents resource nodes To resource nodes Communication reliability, Represents resource nodes To resource nodes Communication reliability; The set of communication links; The collaborative communication backbone reliability of the optimal cluster partitioning result is calculated based on the collaborative communication backbone reliability of each candidate cluster, using the following formula: The communication reliability autonomous collaboration index is calculated based on the reliability of the collaborative communication backbone and the cross-cluster support dependency rate, as shown in the following formula: in, The reliability of the collaborative communication backbone of the optimal cluster partitioning result. This refers to the communication reliability autonomous cooperation index.

10. A distribution network cluster partitioning device, characterized in that, The method includes: The operation data acquisition module is used to acquire operation data of the power distribution network; The support capability determination module is used to determine the voltage risk weight of each electrical node in the distribution network based on the operating data, and to determine the support capability of resource nodes in each electrical node for each electrical node, thereby obtaining the corresponding support capability matrix. The initial cluster partitioning module is used to partition the distribution network into clusters in various ways based on the operating data, the voltage risk weight, and the support capability matrix, and to determine multiple candidate cluster partitioning methods. The cluster iterative optimization module is used to sequentially take each candidate cluster partitioning method as the target cluster partitioning method, determine the affiliation score of the electrical node to different candidate clusters under the target cluster partitioning method, and iteratively adjust the node set of the candidate clusters according to the affiliation score until the optimal cluster partitioning result under the target cluster partitioning method is obtained. The cluster partitioning determination module is used to calculate the partitioning effect evaluation index of each optimal cluster partitioning result according to the support capability matrix after determining the optimal cluster partitioning result under each candidate cluster partitioning method, and determine the final cluster partitioning result of the distribution network according to each partitioning effect evaluation index.

11. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the power distribution network cluster partitioning method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the distribution network cluster partitioning method according to any one of claims 1 to 9.

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