Power distribution network cluster division method and system, medium and program product

Abstract

The invention discloses a power distribution network cluster division method and system, a medium and a program product in the technical field of cluster division of an active power distribution network, and the method comprises the steps: carrying out the quantification and normalization processing of the capability characteristics of nodes based on the load characteristics of the nodes and the control parameters of a distributed photovoltaic system, and obtaining the similarity between the nodes through weighting calculation; according to the electrical relevance between the nodes, the coupling degree between the nodes is quantified and normalized, and the similarity between the nodes and the coupling degree are synthesized to form an adjacent matrix; and taking the adjacent matrix as input, iteratively optimizing a modularity value through a Fast-Unfolding algorithm, and gradually combining nodes based on a modularity increment maximization mode, thereby realizing cluster division of the power distribution network. According to the power distribution network cluster division method provided by the invention, the similarity and the electrical coupling degree between the nodes are comprehensively considered, the actual relevance of each node in the power distribution network is comprehensively reflected, and more accurate cluster division is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of cluster division of active power distribution networks, and in particular relates to a distribution network cluster division method, system, medium and program product. Background Art

[0002] In recent years, the construction of new power systems based on distributed photovoltaic, wind power and other new energy sources has been accelerated, and its installed capacity has continued to grow. However, due to the characteristics of small distributed photovoltaic units, large numbers and wide distribution, the workload of each node is large, and it is difficult to use traditional centralized control methods. It is urgent to consider the use of cluster-based control mode to better solve the regulation problem of distribution networks with high photovoltaic penetration rates.

[0003] With the large-scale access of new energy sources such as distributed photovoltaics, the operation mode and load characteristics of the distribution network have become more complex. The distribution network clustering technology can effectively divide the power grid area, facilitate hierarchical management and flexible control, and thus improve the operation stability of the power grid. The traditional distribution network clustering method has certain limitations in dealing with this change: first, the single factor considered makes it difficult for the division results to fully reflect the multi-dimensional correlation characteristics between nodes; second, it is sensitive to the initial data, and the number of divisions needs to be given manually, resulting in strong subjectivity in the division results; third, the existing heuristic algorithm has poor local search ability, is easy to fall into the local optimum, and has a slow convergence speed and high computational complexity, resulting in inaccurate division results. Therefore, it is urgent to solve the above problems to improve the accuracy and rationality of cluster division. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a distribution network cluster partitioning method, system, medium and program product, which can effectively improve the control accuracy of the distribution network and meet the needs of accurate and flexible zoning management in complex power environments.

[0005] To achieve the above objectives, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a method for dividing distribution network clusters, characterized by comprising:

[0007] Based on the load characteristics of the nodes and the control parameters of the distributed photovoltaic system, the capacity characteristics of the nodes are quantified and normalized, and the similarity between the nodes is obtained by weighted calculation;

[0008] According to the electrical correlation between the nodes, the coupling degree between the nodes is quantified and normalized, and the similarity and coupling degree between the nodes are combined to form an adjacency matrix;

[0009] The adjacency matrix is ​​used as input, the value of modularity is iteratively optimized through the Fast-Unfolding algorithm, and nodes are gradually merged based on the method of maximizing the modularity increment until the modularity increment no longer increases, thereby realizing the cluster division of the distribution network.

[0010] In combination with the first aspect, further, based on the load characteristics of the node and the control parameters of the distributed photovoltaic system, the capability characteristics of the processing node are quantified and normalized, including:

[0011] According to the net load of the source-load node under multiple sections and the control parameters of the distributed photovoltaic system, the absorption capacity and regulation capacity of the node are quantified and normalized. The control parameters include the proportional coefficient of the power outer loop controller of the photovoltaic inverter at the node. and the integral coefficient ;

[0012] The net load of the source load node under the multi-section is calculated by the following formula:

[0013] ;

[0014] In the formula, Indicates the node under multiple sections The net load; express Time Node The power of the load; express Time Node The power of photovoltaic power; Represents a time period;

[0015] The normalization process is calculated by the following formula:

[0016] ;

[0017] In the formula, represents the node i The original value of the indicator, represents the normalized node i The value of an indicator.

[0018] In combination with the first aspect, further, weighted calculation is used to obtain the similarity between nodes, including:

[0019] The entropy weight method is used to weight the characteristic values ​​of different nodes to obtain the net load of the source-load node under multiple sections and the proportional coefficient of the power outer loop controller of the photovoltaic inverter. and the integral coefficient The weights corresponding to these three indicators;

[0020] The calculation steps of the entropy weight method include data normalization, calculation of index entropy value, and determination of weight. The calculation formulas of each step are as follows:

[0021] Normalization:

[0022] ;

[0023] In the formula, Indicates The node in The proportion of each indicator; Indicates the total number of nodes;

[0024] Calculate information entropy:

[0025] ;

[0026] In the formula, Indicates The entropy value of each indicator; is a constant used to ensure The value of is in the range [0,1];

[0027] Calculate the coefficient of variation:

[0028] ;

[0029] In the formula, the coefficient of difference Indicates The importance of the weight of each indicator;

[0030] Calculate weights:

[0031] ;

[0032] In the formula, Indicates The weight of each indicator;

[0033] After obtaining the weights of each indicator, the similarity between nodes is obtained through the following formula:

[0034] ;

[0035] ;

[0036] In the formula, Representation Node With Node The similarity between represents the normalized node j The value of an indicator; R represents the maximum value of the weighted product of any two node indicators.

[0037] In combination with the first aspect, further, according to the electrical correlation between the nodes, the coupling degree between the nodes is quantified and normalized, including:

[0038] The power flow equation is linearized based on the net load of the source load node under multiple sections:

[0039] ;

[0040] ;

[0041] In the formula, Indicates the change in active power injected by the node; Indicates the change in reactive power injected by the node; Indicates the change in the node voltage phase angle; Indicates the change in node voltage amplitude; represents the Jacobian matrix; It represents the relationship between the active power fluctuation injected into the node and the phase angle change of the node voltage; It represents the relationship between the active power fluctuation injected into the node and the change of the node voltage amplitude; It represents the relationship between the reactive power fluctuation injected into the node and the phase angle change of the node voltage; It represents the relationship between the reactive power fluctuation injected into the node and the node voltage amplitude and phase angle change;

[0042] Multiply both sides of the linearized power flow equation by ,get:

[0043] ;

[0044] ;

[0045] In the formula, represents the inverse matrix of the Jacobian matrix; They represent the change of node voltage phase angle when unit active power and reactive power are injected into the grid node, represents the active sensitivity matrix, , which indicates the sensitivity of node voltage in the power grid to the change of active power; represents the reactive sensitivity matrix, , which indicates the sensitivity of node voltage in the power grid to the change of reactive power;

[0046] Based on the active sensitivity matrix and reactive sensitivity matrix, the relative impact of the power change of node j on its own voltage change and the voltage change of node i is calculated:

[0047] ;

[0048] In the formula, Representation Node When reactive power and active power change, its own voltage change value and node The sum of the ratios of voltage change values; Respectively represent nodes Its own reactive and active node voltage sensitivity; Respectively represent nodes and nodes Reactive and active node voltage sensitivity between them;

[0049] Based on the relative influence between the above nodes, considering the correlation between the node relationship and its own characteristics and other nodes in the network, the electrical distance between node i and node j is defined as for:

[0050] ;

[0051] right After normalization, we get:

[0052] ;

[0053] In the formula, Represents the normalized node With Node The electrical distance between nodes represents the degree of coupling between nodes in the power grid.

[0054] In combination with the first aspect, further, the similarity and coupling degree between the nodes are comprehensively formed into an adjacency matrix, including:

[0055] The adjacency matrix is ​​obtained by weighted calculation of the similarity and coupling degree between nodes. The elements of the adjacency matrix are calculated by the following formula:

[0056] ;

[0057] In the formula, Represents a connected node With Node The weight of the edge between them; Represent the weights of similarity and electrical distance respectively.

[0058] In combination with the first aspect, further, the adjacency matrix is ​​used as input, the value of modularity is iteratively optimized through the Fast-Unfolding algorithm, and nodes are gradually merged based on the method of maximizing the modularity increment until the modularity increment no longer increases, thereby realizing the cluster division of the distribution network, including:

[0059] Consider each node in the distribution network as a separate cluster and calculate the initial modularity value;

[0060] In each iteration, starting from each node i in the distribution network, the modularity increment after moving it to other nodes is examined in turn. ;

[0061] like > 0, then the cluster to which node i belongs is merged with the cluster to which the corresponding adjacent node belongs to form a new cluster;

[0062] After each merger, update the modularity of the distribution network and parameters, and calculate the modularity increment based on the updated parameters , the parameters include edge weight ratios between clusters and degree ratios within clusters;

[0063] The iterative merging process continues until there is no further modularity increment;

[0064] When the modularity of the entire distribution network When no more additions are possible, the algorithm terminates and outputs the final optimal clustering result;

[0065] The calculation formula of modularity Q is as follows:

[0066] ;

[0067] ;

[0068]

[0069] ;

[0070] In the formula, represents modularity; Representation and Node The sum of the weights of all connected edges; represents the sum of the weights of all edges connected to node j; Represents the sum of the weights between all nodes in the network; It is a 0-1 variable, indicating the relationship between node i and node j. If they are in the same cluster, it is 1, otherwise it is 0;

[0071] Modularity increment The calculation formula is as follows:

[0072] ;

[0073] ;

[0074] ;

[0075] In the formula, Represents the edge weight ratio between clusters, by calculating the cluster With cluster The ratio of the connected edge weight to the weight of all edges in the network is obtained; Represents the degree ratio within the cluster, by calculating the cluster The ratio of the sum of the degrees of the internal nodes to the sum of the degrees of all nodes is obtained; Represents the degree ratio within the cluster, by calculating the cluster The ratio of the sum of the degrees of the internal nodes to the sum of the degrees of all nodes is obtained.

[0076] In a second aspect, the present invention provides a distribution network cluster division system, comprising:

[0077] The similarity analysis module is configured to: quantify and normalize the capacity characteristics of the processing nodes based on the load characteristics of the nodes and the control parameters of the distributed photovoltaic system, and obtain the similarity between the nodes by weighted calculation;

[0078] The coupling degree analysis module is configured to: quantify and normalize the coupling degree between nodes according to the electrical correlation between the nodes;

[0079] The weighted fusion module is configured to: comprehensively analyze the similarity and coupling degree between the nodes to form an adjacency matrix;

[0080] The cluster partitioning module is configured to: take the adjacency matrix as input, iteratively optimize the modularity value through the Fast-Unfolding algorithm, gradually merge nodes based on the modularity increment maximization method until the modularity increment no longer increases, thereby realizing the cluster partitioning of the distribution network.

[0081] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps of the above-mentioned distribution network cluster division method are implemented.

[0082] In a fourth aspect, the present invention provides a computer program product, comprising a computer program / instruction, characterized in that when the computer program / instruction is executed by a processor, the steps of the above-mentioned distribution network cluster division method are implemented.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] 1. The distribution network cluster division method provided by the present invention can more comprehensively reflect the actual correlation of each node in the distribution network and achieve more accurate cluster division by comprehensively considering the similarity and electrical coupling between nodes. In the present invention, the node's load characteristics and the control parameters of the distributed photovoltaic system are used to quantify and normalize the node's absorption and regulation capabilities, thereby obtaining the similarity between each node. At the same time, the electrical coupling is introduced to evaluate the physical connection relationship and electrical influence between nodes through electrical correlation. Combining these two dimensions, the generated adjacency matrix can fully express the multi-dimensional characteristics between each node.

[0085] Based on similarity and coupling, an adjacency matrix is ​​constructed and input into the Fast-Unfolding algorithm. Dynamic merging of nodes is achieved by maximizing the modularity increment, avoiding falling into the local optimal solution and improving the effect and accuracy of power grid cluster division. Compared with the division method based on a single feature, the cluster division scheme of the present invention improves the flexibility and accuracy of the distribution network, significantly improves the adaptability of distributed photovoltaic access and the power coordination performance between nodes.

[0086] 2. The distribution network cluster division method provided by the present invention quantifies and normalizes the capacity characteristics of each node based on the load characteristics of the node and the control parameters of the distributed photovoltaic system, so that the cluster division is more comprehensive and accurate. The net load of the source-load node under multiple sections and the control parameters of the distributed photovoltaic system are selected as indicators, which not only considers the net load characteristics of the node under different working conditions, but also combines the proportional coefficient and integral coefficient of the distributed photovoltaic inverter to accurately reflect the node's absorption and regulation capabilities. This multi-dimensional and multi-condition indicator selection makes the node's capacity characteristic evaluation not only limited to a specific state, but also takes into account a variety of operating conditions, thereby providing a more representative and adaptive basis for cluster division.

[0087] Through the fine quantification and normalization of node characteristics, the solution provided by the present invention effectively improves the accuracy of cluster division, enables the distribution network to better achieve load balancing and power regulation in complex application environments, and helps to enhance the access capacity of distributed photovoltaics and the operating stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 It is a flow chart of the power grid cluster division method provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0089] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0090] The terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this disclosure / application, unless otherwise specified, "plurality" means two or more.

[0091] Embodiment 1:

[0092] Figure 1 This is a flow chart of a distribution network cluster division method in the first embodiment of the present invention. This flow chart only shows the logical sequence of the method described in this embodiment. In other possible embodiments of the present invention, different Figure 1 The steps shown or described are accomplished in the order shown.

[0093] See also Figure 1 The method of this embodiment specifically includes the following steps:

[0094] Based on the load characteristics of the nodes and the control parameters of the distributed photovoltaic system, the capacity characteristics of the nodes are quantified and normalized, and the similarity between the nodes is obtained by weighted calculation;

[0095] According to the electrical correlation between the nodes, the coupling degree between the nodes is quantified and normalized, and the similarity and coupling degree between the nodes are combined to form an adjacency matrix;

[0096] The adjacency matrix is ​​used as input, the value of modularity is iteratively optimized through the Fast-Unfolding algorithm, and nodes are gradually merged based on the method of maximizing the modularity increment until the modularity increment no longer increases, thereby realizing the cluster division of the distribution network.

[0097] Based on the load characteristics of the node and the control parameters of the distributed photovoltaic system, the net load of the source-load node under multiple sections and the proportional coefficient of the power outer loop controller of the photovoltaic inverter are selected. and the integral coefficient As an indicator of cluster division in the power grid.

[0098] A source-load node refers to a node that can both generate electricity and consume electricity, that is, a node that can both serve as a power source and generate load. For example, a company can consume electricity and also generate electricity through an installed photovoltaic system. Net load refers to the "net" electricity demand of a node. The net load of a node can be obtained by subtracting the power consumption from the power generation. If this value is positive, it means that the node needs to draw electricity from the grid; if it is negative, it means that the node can "supply" electricity to the grid.

[0099] Multi-section refers to the load situation of the node in different time periods or under different operating conditions. For example, different light conditions in the morning, noon, and evening will cause changes in photovoltaic power generation, and the net load situation will also change accordingly. Therefore, examining the net load of the node under multiple "sections" can enable a more comprehensive assessment.

[0100] An inverter is a device that converts the direct current generated by solar panels into alternating current. Distributed photovoltaic inverters refer to inverters distributed in various photovoltaic power generation devices. There are multiple levels of inverter control, divided into "inner loop" and "outer loop". "Outer loop control" is usually used to adjust the overall output of the inverter, such as controlling the output voltage or frequency, so as to better work with the power grid. These outer loop control parameters can help the system remain stable at different photovoltaic power generation levels and avoid excessive fluctuations that affect the power grid. "Proportional coefficient" and "integral coefficient" are two important parameters in the inverter control system, especially in the control of the inverter, which are used to adjust the response speed and stability of the system.

[0101] The proportional coefficient Kp controls the speed of the system response. When the system detects a deviation, that is, the gap between the target value and the actual value, the controller will adjust the output according to this deviation multiplied by the proportional coefficient Kp. In a photovoltaic inverter, the proportional coefficient Kp can help regulate the power output of the photovoltaic system so that it responds more quickly to changes in photovoltaic power generation.

[0102] The integral coefficient Ki is used to eliminate the "steady-state error". Even if the deviation value is small, the integral controller will accumulate the deviation and multiply it by Ki, gradually reducing the error until the target value is reached. In a photovoltaic inverter, the integral coefficient can help the system eliminate the long-term deviation between the photovoltaic output power and the expected power, and improve the steady-state performance of the system.

[0103] The net load of the source-load node under multiple sections and the control parameters of the distributed photovoltaic system are selected as indicators. Not only the net load characteristics of the node under different working conditions are considered, but also the proportional coefficient and integral coefficient of the distributed photovoltaic inverter are combined to accurately reflect the node's absorption and regulation capabilities. This multi-dimensional and multi-working condition indicator selection makes the node's capacity characteristic evaluation not only limited to a specific state, but also takes into account a variety of operating conditions, thus providing a more representative and adaptable basis for cluster division.

[0104] The net load of the source load node under multiple sections is calculated by the following formula:

[0105] ;

[0106] In the formula, Indicates the node under multiple sections The net load; express Time Node The power of the load; express Time Node The power of photovoltaic power; Represents a time period.

[0107] After obtaining the net load value, proportional coefficient Kp and integral coefficient Ki of each node in the power grid, they are normalized by the following formula:

[0108] ;

[0109] In the formula, represents the node i The original value of the indicator, represents the normalized node i The value of an indicator.

[0110] The entropy weight method is used to weight the characteristic values ​​of different nodes to obtain the net load of the source-load node under multiple sections and the proportional coefficient of the power outer loop controller of the photovoltaic inverter. and the integral coefficient The weights corresponding to these three indicators;

[0111] The calculation steps of the entropy weight method include data normalization, calculation of index entropy value, and determination of weight. The calculation formulas of each step are as follows:

[0112] Normalization:

[0113] ;

[0114] In the formula, Indicates The node in The proportion of each indicator; Indicates the total number of nodes;

[0115] Calculate information entropy:

[0116] ;

[0117] In the formula, Indicates The entropy value of each indicator; is a constant used to ensure The value of is in the range [0,1];

[0118] Calculate the coefficient of variation:

[0119] ;

[0120] In the formula, the coefficient of difference Indicates The importance of the weight of each indicator;

[0121] Calculate weights:

[0122] ;

[0123] In the formula, Indicates The weight of each indicator;

[0124] After obtaining the weights of each indicator, the similarity between nodes is obtained through the following formula:

[0125] ;

[0126] ;

[0127] In the formula, Representation Node With Node The similarity between represents the normalized node j The value of an indicator; R is the maximum value of the weighted product of any two node indicators.

[0128] After obtaining the similarity between nodes, the coupling degree between nodes is further quantified and normalized according to the electrical correlation between nodes.

[0129] First, the power flow equation is linearized based on the net load of the source and load nodes under multiple sections:

[0130] ;

[0131] ;

[0132] In the formula, Indicates the change in active power injected by the node; Indicates the change in reactive power injected by the node; Indicates the change in the node voltage phase angle; Indicates the change in node voltage amplitude; represents the Jacobian matrix; It represents the relationship between the active power fluctuation injected into the node and the phase angle change of the node voltage; It represents the relationship between the active power fluctuation injected into the node and the change of the node voltage amplitude; It represents the relationship between the reactive power fluctuation injected into the node and the phase angle change of the node voltage; It represents the relationship between the reactive power fluctuation injected into the node and the node voltage amplitude and phase angle change;

[0133] Active power is the part of the power system that actually does work and is used to drive motors, lighting equipment, etc. Active power directly affects the energy transmission and consumption of the power system.

[0134] Reactive power is the part of the power system that does not do actual work. It is used to establish and maintain electric and magnetic fields. Reactive power mainly affects the voltage level and the stability of the power system. The sensitivity matrix is ​​used to describe how changes in certain variables in the system affect other variables. In this embodiment, the active sensitivity matrix and the reactive sensitivity matrix are mainly used to describe how changes in active power and reactive power affect voltage. In the distribution network, the sensitivity matrix helps engineers understand and predict the system's response to different operations, thereby optimizing the control strategy.

[0135] Multiply both sides of the linearized power flow equation by , thus obtaining the active sensitivity matrix and reactive sensitivity matrix:

[0136] ;

[0137] ;

[0138] In the formula, represents the inverse matrix of the Jacobian matrix; They represent the change of node voltage phase angle when unit active power and reactive power are injected into the grid node, represents the active sensitivity matrix, , which indicates the sensitivity of node voltage in the power grid to the change of active power; represents the reactive sensitivity matrix, , which indicates the sensitivity of node voltage in the power grid to the change of reactive power;

[0139] Based on the active sensitivity matrix and reactive sensitivity matrix, the relative impact of the power change of node j on its own voltage change and the voltage change of node i is calculated:

[0140] ;

[0141] In the formula, Representation Node When reactive power and active power change, its own voltage change value and node The sum of the ratios of voltage change values; Respectively represent nodes Its own reactive and active node voltage sensitivity; Respectively represent nodes and nodes Reactive and active node voltage sensitivity between them;

[0142] Based on the relative influence between the above nodes, considering the correlation between the node relationship and its own characteristics and other nodes in the network, the electrical distance between node i and node j is defined as for:

[0143] ;

[0144] right After normalization, we get:

[0145] ;

[0146] In the formula, Represents the normalized node With Node The electrical distance between nodes represents the degree of coupling between nodes in the power grid.

[0147] Based on the similarities and coupling degrees between nodes obtained above, different weights are assigned to the similarities and coupling degrees between nodes and then added to obtain an adjacency matrix. The elements of the adjacency matrix are calculated using the following formula:

[0148] ;

[0149] In the formula, Represents a connected node With Node The weight of the edge between them; Represent the weights of similarity and electrical distance respectively.

[0150] In this embodiment, the node's load characteristics and the control parameters of the distributed photovoltaic system are used to quantify and normalize the node's absorption and regulation capabilities, thereby obtaining the similarity between the nodes. At the same time, the electrical coupling degree is introduced to evaluate the physical connection relationship and electrical influence between nodes through electrical correlation. Combining these two dimensions, the generated adjacency matrix fully expresses the multi-dimensional characteristics between the nodes, making the subsequent cluster division more comprehensive and accurate.

[0151] The obtained adjacency matrix is ​​used as input, and the value of modularity is iteratively optimized through the Fast-Unfolding algorithm. Nodes are gradually merged based on the maximum modularity increment until the modularity increment no longer increases, thereby realizing the cluster division of the distribution network, including:

[0152] Consider each node in the distribution network as a separate cluster and calculate the initial modularity value;

[0153] In each iteration, starting from each node i in the distribution network, the modularity increment after moving it to the cluster to which other adjacent nodes belong is examined in turn. ;

[0154] like > 0, then the cluster to which node i belongs is merged with the cluster to which the corresponding adjacent node belongs to form a new cluster;

[0155] After each merger, update the modularity of the distribution network and parameters, calculating the modularity increment according to the updated parameters, wherein the parameters include edge weight ratios between clusters and degree ratios within clusters;

[0156] The iterative merging process continues until there is no further modularity increment;

[0157] When the modularity of the entire network When no more additions are possible, the algorithm terminates and outputs the final optimal clustering result;

[0158] The calculation formula of modularity Q is as follows:

[0159] ;

[0160] ;

[0161]

[0162] ;

[0163] In the formula, represents modularity; Representation and Node The sum of the weights of all connected edges; represents the sum of the weights of all edges connected to node j; Represents the sum of the weights between all nodes in the network; It is a 0-1 variable, indicating the relationship between node i and node j. If they are in the same cluster, it is 1, otherwise it is 0;

[0164] Modularity increment The calculation formula is as follows:

[0165] ;

[0166] ;

[0167] ;

[0168] In the formula, Represents the edge weight ratio between clusters, by calculating the cluster With cluster The ratio of the connected edge weight to the weight of all edges in the network is obtained; Represents the degree ratio within the cluster, by calculating the cluster The ratio of the sum of the degrees of the internal nodes to the sum of the degrees of all nodes is obtained; Represents the degree ratio within the cluster, by calculating the cluster The ratio of the sum of the degrees of the internal nodes to the sum of the degrees of all nodes is obtained.

[0169] The Fast-Unfolding algorithm iteratively optimizes cluster partitioning in the direction of maximizing the modularity increment. By gradually optimizing the modularity increment, the algorithm can more smoothly find the global optimal or closer to the optimal partitioning, avoiding falling into the local optimal in the early stage. At each update, the modularity increment is used to determine whether to continue merging, which reduces unnecessary merging operations and improves the stability of the partitioning results. And the continuously updated edge weights and ratios ensure the real-time accuracy of the modularity. This update mechanism is more in line with the real community structure of the network than the static calculation of edge weights, making the partitioning results more accurate.

[0170] In summary, the Fast-Unfolding algorithm uses modularity increment as the optimization target and dynamically updates the edge weight ratio, making the division of power grid clusters more stable and effectively avoiding local optimal problems, thereby obtaining a division result that is closer to the global optimal, making the division result not only accurate but also with good generalization and applicability.

[0171] Embodiment 2:

[0172] Based on the distribution network cluster division method described in Embodiment 1, this embodiment provides a distribution network cluster division system, including:

[0173] The similarity analysis module is configured to: quantify and normalize the capacity characteristics of the processing nodes based on the load characteristics of the nodes and the control parameters of the distributed photovoltaic system, and obtain the similarity between the nodes by weighted calculation;

[0174] The coupling degree analysis module is configured to: quantify and normalize the coupling degree between nodes according to the electrical correlation between the nodes;

[0175] The weighted fusion module is configured to: comprehensively analyze the similarity and coupling degree between the nodes to form an adjacency matrix;

[0176] The cluster partitioning module is configured to: take the adjacency matrix as input, iteratively optimize the modularity value through the Fast-Unfolding algorithm, gradually merge nodes based on the modularity increment maximization method until the modularity increment no longer increases, thereby realizing the cluster partitioning of the distribution network.

[0177] Embodiment three:

[0178] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the distribution network cluster division method described in the first embodiment are first implemented.

[0179] The computer-readable storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0180] Embodiment 4:

[0181] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for dividing distribution network clusters described in Embodiment 1.

[0182] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0183] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0184] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0186] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A distribution network cluster division method, characterized in that: include: Based on the load characteristics of the nodes and the control parameters of the distributed photovoltaic system, the capacity characteristics of the nodes are quantified and normalized, and the similarity between the nodes is obtained by weighted calculation; According to the electrical correlation between the nodes, the coupling degree between the nodes is quantified and normalized, and the similarity and coupling degree between the nodes are combined to form an adjacency matrix; The adjacency matrix is ​​used as input, the value of modularity is iteratively optimized through the Fast-Unfolding algorithm, and nodes are gradually merged based on the method of maximizing the modularity increment until the modularity increment no longer increases, thereby realizing the cluster division of the distribution network.

2. The method for dividing distribution network clusters according to claim 1, characterized in that: Based on the load characteristics of the nodes and the control parameters of the distributed photovoltaic system, the capacity characteristics of the nodes are quantified and normalized, including: According to the net load of the source-load node under multiple sections and the control parameters of the distributed photovoltaic system, the absorption capacity and regulation capacity of the node are quantified and normalized. The control parameters include the proportional coefficient of the power outer loop controller of the photovoltaic inverter at the node. and the integral coefficient ; The net load of the source load node under the multi-section is calculated by the following formula: ； In the formula, Indicates the node under multiple sections The net load; express Time Node The power of the load; express Time Node The power of photovoltaic power; Represents a time period; The normalization process is calculated by the following formula: ； In the formula, represents the node i The original value of the indicator, represents the normalized node i The value of an indicator.

3. The method for dividing distribution network clusters according to claim 2, characterized in that: The similarity between nodes is obtained by weighted calculation, including: The entropy weight method is used to weight the characteristic values ​​of different nodes to obtain the net load of the source-load node under multiple sections and the proportional coefficient of the power outer loop controller of the photovoltaic inverter. and the integral coefficient The weights corresponding to these three indicators; The calculation steps of the entropy weight method include data normalization, calculation of index entropy value, and determination of weight. The calculation formulas of each step are as follows: Normalization: ； In the formula, Indicates that node i is in The proportion of each indicator; Indicates the total number of nodes; Calculate information entropy: ； In the formula, Indicates The entropy value of each indicator; is a constant used to ensure The value of is in the range [0,1]; Calculate the coefficient of variation: ； In the formula, the coefficient of difference Indicates The importance of the weight of each indicator; Calculate weights: ； In the formula, Indicates The weight of each indicator; After obtaining the weights of each indicator, the similarity between nodes is obtained through the following formula: ； ； In the formula, Representation Node With Node The similarity between represents the normalized node j The value of an indicator; R represents the maximum value of the weighted product of any two node indicators.

4. The method for dividing distribution network clusters according to claim 3, characterized in that: Quantify and normalize the degree of coupling between nodes based on the electrical correlation between nodes, including: The power flow equation is linearized based on the net load of the source load node under multiple sections: ； ； In the formula, Indicates the change in active power injected by the node; Indicates the change in reactive power injected by the node; Indicates the change in the node voltage phase angle; Indicates the change in node voltage amplitude; represents the Jacobian matrix; It represents the relationship between the active power fluctuation injected into the node and the phase angle change of the node voltage; It represents the relationship between the active power fluctuation injected into the node and the change of the node voltage amplitude; It represents the relationship between the reactive power fluctuation injected into the node and the phase angle change of the node voltage; It represents the relationship between the reactive power fluctuation injected into the node and the node voltage amplitude and phase angle change; Multiply both sides of the linearized power flow equation by ,get: ； ； In the formula, represents the inverse matrix of the Jacobian matrix; They represent the change of node voltage phase angle when unit active power and reactive power are injected into the grid node, represents the active sensitivity matrix, , which indicates the sensitivity of node voltage in the power grid to the change of active power; represents the reactive sensitivity matrix, , which indicates the sensitivity of node voltage in the power grid to the change of reactive power; Based on the active sensitivity matrix and reactive sensitivity matrix, the relative impact of the power change of node j on its own voltage change and on the voltage change of node i is calculated: ； In the formula, Representation Node When reactive power and active power change, its own voltage change value and node The sum of the ratios of voltage change values; Respectively represent nodes Its own reactive and active node voltage sensitivity; Respectively represent nodes and nodes Reactive and active node voltage sensitivity between them; Based on the relative influence between the above nodes, considering the correlation between the node relationship and its own characteristics and other nodes in the network, the electrical distance between node i and node j is defined as for: ； right After normalization, we get: ； In the formula, Represents the normalized node With Node The electrical distance between nodes represents the degree of coupling between nodes in the power grid.

5. The method for dividing distribution network clusters according to claim 4, characterized in that: The similarity and coupling degree between the nodes are combined to form an adjacency matrix, including: The adjacency matrix is ​​obtained by weighted calculation of the similarity and coupling degree between nodes. The elements of the adjacency matrix are calculated by the following formula: ； In the formula, Represents a connected node With Node The weight of the edge between them; Represent the weights of similarity and electrical distance respectively.

6. The method for dividing distribution network clusters according to claim 5, characterized in that: The adjacency matrix is ​​used as input, and the value of modularity is iteratively optimized through the Fast-Unfolding algorithm. Nodes are gradually merged based on the method of maximizing the modularity increment until the modularity increment no longer increases, thereby realizing the cluster division of the distribution network, including: Consider each node in the distribution network as a separate cluster and calculate the initial modularity value; In each iteration, starting from each node i in the distribution network, the modularity increment after moving it to the cluster to which other adjacent nodes belong is examined in turn. ; like > 0, then the cluster to which node i belongs is merged with the cluster to which the corresponding adjacent node belongs to form a new cluster; After each merger, update the modularity of the distribution network and parameters, and calculate the modularity increment based on the updated parameters , the parameters include edge weight ratios between clusters and degree ratios within clusters; The iterative merging process continues until there is no further modularity increment; When the modularity of the entire distribution network When no more additions are possible, the algorithm terminates and outputs the final optimal clustering result; The calculation formula of modularity Q is as follows: ； ； ； ； In the formula, represents modularity; Representation and Node The sum of the weights of all connected edges; represents the sum of the weights of all edges connected to node j; Represents the sum of the weights between all nodes in the network; It is a 0-1 variable, indicating the relationship between node i and node j. If they are in the same cluster, it is 1, otherwise it is 0; Modularity increment The calculation formula is as follows: ； ； ； In the formula, Represents the edge weight ratio between clusters, by calculating the cluster With cluster The ratio of the connected edge weight to the weight of all edges in the network is obtained; Represents the degree ratio within the cluster, by calculating the cluster The ratio of the sum of the degrees of the internal nodes to the sum of the degrees of all nodes is obtained; Represents the degree ratio within the cluster, by calculating the cluster The ratio of the sum of the degrees of the internal nodes to the sum of the degrees of all nodes is obtained.

7. A distribution network cluster division system, characterized in that: include: The similarity analysis module is configured to: quantify and normalize the capacity characteristics of the processing nodes based on the load characteristics of the nodes and the control parameters of the distributed photovoltaic system, and obtain the similarity between the nodes by weighted calculation; The coupling degree analysis module is configured to: quantify and normalize the coupling degree between nodes according to the electrical correlation between the nodes; The weighted fusion module is configured to: comprehensively analyze the similarity and coupling degree between the nodes to form an adjacency matrix; The cluster partitioning module is configured to: take the adjacency matrix as input, iteratively optimize the modularity value through the Fast-Unfolding algorithm, gradually merge nodes based on the modularity increment maximization method until the modularity increment no longer increases, thereby realizing the cluster partitioning of the distribution network.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the distribution network cluster division method described in any one of claims 1 to 6 are implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the distribution network cluster division method described in any one of claims 1 to 6 are implemented.

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