A main-distribution micro-grid dynamic division and adaptive networking method and system based on real-time supply-demand analysis

Abstract

The application provides a main-distribution micro-grid dynamic division and adaptive networking method and system based on real-time supply and demand analysis, comprising the following steps: S1, collecting real-time source and load supply and demand data in a three-level architecture of a main grid, a distribution grid and a micro-grid, and calculating multi-dimensional dynamic division indexes based on the real-time supply and demand data; S2, taking maximization of source and load self-balancing rate in a region and minimization of power exchange amount between regions as optimization objectives, and establishing a dynamic division optimization model of the main-distribution micro-grid; S3, solving the optimization model by using a clustering algorithm based on eigenvalue decomposition of a graph Laplacian matrix, automatically determining an optimal number of partitions through eigenvalue gap analysis, and obtaining a grid division scheme; and S4, designing an adaptive networking control mechanism, including a division triggering condition based on an index deviation degree, a power gradual and progressive smoothing switching strategy and a frequent switching prevention mechanism, and realizing online updating and smooth switching of the grid division result.

Description

Technical Field

[0001] This invention proposes a method and system for dynamic partitioning and adaptive networking of main and distribution microgrids based on real-time supply and demand analysis, which relates to the field of power system technology. Background Technology

[0002] With the large-scale integration of distributed energy resources, the operation mode of modern distribution networks is shifting from the traditional one-way power supply mode to a two-way interactive mode. Against this backdrop, the coordinated operation of the three-tier architecture (main grid to distribution grid to microgrid) has become an important direction for power grid development. In this three-tier architecture, reasonable grid partitioning is the foundation for achieving efficient coordinated operation, determining the boundary range, internal resource allocation, and power exchange relationship between each microgrid and the external power grid.

[0003] However, with the rapid growth of distributed power generation and load (PGD) integration, such as distributed photovoltaic (PV) systems and electric vehicles, the power generation and load distribution of the distribution network exhibits significant time-varying characteristics. The net power of the power generation and load varies drastically throughout the day. During peak PV output periods in the daytime, some areas experience power surplus; during peak load periods in the evening, these areas become power deficit areas. This dynamic change in power generation and load distribution necessitates that the grid partitioning scheme also be dynamically adjusted accordingly.

[0004] The existing methods for dividing main and distribution microgrids have the following main shortcomings:

[0005] First, the grid division method is fixed and rigid. Traditional division methods typically determine the grid boundaries during the planning phase and do not adjust them during the operational phase. A fixed division scheme may lead to a significant increase in cross-regional power exchange during the "most unfavorable period" of source-load distribution, reducing regional autonomy.

[0006] Second, the categorization criteria are too simplistic. Existing methods typically only consider single factors such as network topology or electrical distance, failing to comprehensively consider multi-dimensional indicators such as source-load matching, power supply reliability, and communication connectivity.

[0007] Third, it lacks an adaptive networking mechanism. There are no clear triggering conditions, smooth transition measures, or mechanisms to prevent frequent handovers.

[0008] Fourth, the solution efficiency is insufficient. Traditional graph theory partitioning algorithms have high computational complexity when dealing with large-scale distribution networks. For example, the minimum cut algorithm has a complexity of O(n), which is difficult to meet the timeliness requirements of online dynamic partitioning. Summary of the Invention

[0009] In view of this, in order to fill the gaps and deficiencies in the existing technology, this invention proposes a method and system for dynamic partitioning and adaptive networking of main and distribution microgrids based on real-time supply and demand analysis.

[0010] This invention proposes a method and system for dynamic partitioning and adaptive networking of primary and secondary microgrids based on real-time supply and demand analysis, including the following:

[0011] According to a first aspect of the present invention, the present invention proposes a method for dynamic partitioning and adaptive networking of primary and secondary microgrids based on real-time supply and demand analysis, characterized in that it includes the following:

[0012] Step S1: Collect real-time source-load supply and demand data in the three-level architecture of main grid, distribution network and microgrid, and calculate multi-dimensional dynamic partitioning indicators including source-load matching degree index, electrical distance index and power supply reliability index based on the real-time supply and demand data;

[0013] Step S2: With the optimization objectives of maximizing the self-balancing rate of source loads within the region and minimizing the power exchange between regions, and with network connectivity constraints and capacity constraints as conditions, establish a dynamic partitioning optimization model for the main and distribution microgrids.

[0014] Step S3: The optimization model is solved by a clustering algorithm based on the eigenvalue decomposition of the graph Laplacian matrix. The optimal number of partitions is automatically determined by eigenvalue gap analysis to obtain the grid partitioning scheme.

[0015] Step S4: Design an adaptive network control mechanism, including partitioning trigger conditions based on index deviation, a power progressive smooth switching strategy, and an anti-frequent switching mechanism, to achieve online updating and smooth switching of the grid partitioning results.

[0016] Furthermore, step S1 includes the following:

[0017] In step S1, the source-load matching degree index reflects the real-time balance between power generation and load power in the region. The matching degree is at its maximum when the power generation and load power are equal, and the matching degree is zero when either is zero.

[0018] The electrical distance index is calculated based on the node impedance matrix and reflects the tightness of electrical coupling between nodes;

[0019] The power supply reliability index is evaluated based on the relationship between the number of available power sources in the area and the minimum number of power sources required to meet the N-1 criterion.

[0020] The formula for calculating the source-load matching index is the ratio of the smaller value to the larger value of the total power generation and total load power in the region.

[0021] Furthermore, step S2 includes the following:

[0022] In step S2, the objective function of the optimization model is a weighted multi-objective function, including:

[0023] The inter-regional power exchange index is defined as the sum of the absolute values ​​of the power transmitted through all tie lines;

[0024] The sum of source-load mismatch in each region;

[0025] The power supply reliability loss index is defined as the number of areas that do not meet the N-1 check.

[0026] The sum of the weighting coefficients of each indicator is 1.

[0027] Furthermore, step S3 includes the following:

[0028] In step S3, a weighted adjacency matrix is ​​constructed based on the topology and electrical parameters of the distribution network, wherein the edge weights comprehensively consider the electrical distance between nodes and the contribution of source-load matching degree;

[0029] Calculate the normalized Laplacian matrix and perform eigenvalue decomposition;

[0030] The optimal number of partitions is determined based on the eigenvalue gaps. The eigenvectors corresponding to the smallest eigenvalue of the corresponding number of partitions are then clustered to obtain the initial partitioning scheme.

[0031] The boundary nodes of the initial scheme are adjusted and optimized based on connectivity constraints and capacity constraints;

[0032] The clustering described herein uses the K-means algorithm to cluster the normalized row vectors of the feature vector matrix.

[0033] Furthermore, step S4 includes the following:

[0034] In step S4, the partitioning triggering condition includes:

[0035] A re-partition is triggered when the source load matching degree of any partition deviates from the value at the time of the last partition by more than a preset deviation threshold.

[0036] A re-partitioning is triggered when the network topology changes or a device failure event occurs.

[0037] Furthermore, step S4 also includes the following:

[0038] In step S4, the smooth switching strategy includes:

[0039] During the transition period between the old and new classification schemes, the tie line power transitions from the old value to the new value by linear interpolation, and the power change rate does not exceed the preset change rate limit.

[0040] The mechanism to prevent frequent switching includes:

[0041] Set a minimum partitioning hold time, during which re-partitioning will not be performed even if the triggering condition is met within the hold time;

[0042] Set a matching degree deviation dead zone; when the matching degree deviation is less than the dead zone range, re-division will not be triggered.

[0043] The network connectivity constraints include: requiring all nodes in each partition to be interconnected through network lines; and the capacity constraints require that each partition contain at least one controllable power source, and the total power source capacity is not less than a preset proportion of the load peak.

[0044] Furthermore, step S4 also includes the following:

[0045] Step S4 also includes establishing an evaluation system for the grid partitioning results. The evaluation indicators include the source-load self-balancing rate of each partition, the power exchange rate between regions, and the total network loss of the system. Based on the evaluation results, the trigger condition threshold and the weight coefficient of the optimization model are adaptively adjusted.

[0046] According to a second aspect of the present invention, the present invention proposes a dynamic partitioning and adaptive networking system for a primary and secondary microgrid based on real-time supply and demand analysis, for executing a dynamic partitioning and adaptive networking method for a primary and secondary microgrid based on real-time supply and demand analysis as described in any one of the present invention, characterized in that it includes:

[0047] The data acquisition module is used to collect source load supply and demand data of each node in the master-slave-micro three-level architecture in real time;

[0048] The indicator calculation module is communicatively connected to the data acquisition module and is used to calculate multi-dimensional classification indicators such as source-load matching degree, electrical distance and power supply reliability based on real-time supply and demand data.

[0049] The optimization solution module is communicatively connected to the index calculation module and is used to establish a dynamic partitioning optimization model and use a clustering algorithm to solve for the optimal partitioning scheme.

[0050] The adaptive control module is communicatively connected to the index calculation module and the optimization solution module, respectively, and is used to monitor the partitioning trigger conditions and perform smooth switching of the partitioning scheme.

[0051] According to a third aspect of the present invention, the present invention proposes a dynamic partitioning and adaptive networking system for a primary and secondary microgrid based on real-time supply and demand analysis, comprising an electronic device, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements a dynamic partitioning and adaptive networking method for a primary and secondary microgrid based on real-time supply and demand analysis as described in any one of the present invention.

[0052] According to a fourth aspect of the present invention, the present invention proposes a dynamic partitioning and adaptive networking system for a primary and secondary microgrid based on real-time supply and demand analysis, comprising a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements a dynamic partitioning and adaptive networking method for a primary and secondary microgrid based on real-time supply and demand analysis as described in any one of the present invention.

[0053] The present invention has the following advantages:

[0054] This invention introduces source-load matching degree as the core driving indicator for dynamic partitioning. When the matching degree deviates to a threshold, it triggers the re-optimization of the grid boundary, enabling each partition to maintain a high source-load self-balancing rate for most of the runtime. Simultaneously, a fast solution is achieved through a spectral clustering algorithm based on the graph Laplacian matrix, and a mechanism to prevent frequent switching, including hold-time and dead zones, is designed to achieve a balance between dynamism and stability. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the steps of the present invention.

[0056] Figure 2 This is a schematic diagram of the daily variation curve of the source-load matching degree of each partition in this invention.

[0057] Figure 3 This is a schematic diagram illustrating the distribution of eigenvalues ​​and the optimal number of partitions of the Laplace matrix according to the present invention.

[0058] Figure 4 This is a bar chart comparing the self-balancing rates of source loads in each partition under dynamic and fixed partitioning according to the present invention.

[0059] Figure 5 This is a schematic diagram of the smooth switching process curve of the tie line power according to the present invention.

[0060] Figure 6 This is a bar chart comparing the key operational indicators of dynamic and fixed partitioning in this invention. Detailed Implementation

[0061] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0062] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0063] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0064] like Figures 1 to 6 As shown, this invention proposes a method and system for dynamic partitioning and adaptive networking of primary and secondary microgrids based on real-time supply and demand analysis, characterized by the following:

[0065] like Figure 1 As shown, according to a first aspect of the present invention, the present invention proposes a method for dynamic partitioning and adaptive networking of primary and secondary microgrids based on real-time supply and demand analysis, characterized in that it includes the following:

[0066] Step S1: Collect real-time source-load supply and demand data in the three-level architecture of main grid, distribution network and microgrid, and calculate multi-dimensional dynamic partitioning indicators including source-load matching degree index, electrical distance index and power supply reliability index based on the real-time supply and demand data;

[0067] Step S2: With the optimization objectives of maximizing the self-balancing rate of source loads within the region and minimizing the power exchange between regions, and with network connectivity constraints and capacity constraints as conditions, establish a dynamic partitioning optimization model for the main and distribution microgrids.

[0068] Step S3: The optimization model is solved by a clustering algorithm based on the eigenvalue decomposition of the graph Laplacian matrix. The optimal number of partitions is automatically determined by eigenvalue gap analysis to obtain the grid partitioning scheme.

[0069] Step S4: Design an adaptive network control mechanism, including partitioning trigger conditions based on index deviation, a power progressive smooth switching strategy, and an anti-frequent switching mechanism, to achieve online updating and smooth switching of the grid partitioning results.

[0070] In one embodiment of the present invention, step S1 includes the following:

[0071] In step S1, the source-load matching degree index reflects the real-time balance between power generation and load power in the region. The matching degree is at its maximum when the power generation and load power are equal, and the matching degree is zero when either is zero.

[0072] The electrical distance index is calculated based on the node impedance matrix and reflects the tightness of electrical coupling between nodes;

[0073] The power supply reliability index is evaluated based on the relationship between the number of available power sources in the area and the minimum number of power sources required to meet the N-1 criterion.

[0074] The formula for calculating the source-load matching index is the ratio of the smaller value to the larger value of the total power generation and total load power in the region.

[0075] In one embodiment of the present invention, step S2 includes the following:

[0076] In step S2, the objective function of the optimization model is a weighted multi-objective function, including:

[0077] The inter-regional power exchange index is defined as the sum of the absolute values ​​of the power transmitted through all tie lines;

[0078] The sum of source-load mismatch in each region;

[0079] The power supply reliability loss index is defined as the number of areas that do not meet the N-1 check.

[0080] The sum of the weighting coefficients of each indicator is 1.

[0081] In one embodiment of the present invention, step S3 includes the following:

[0082] In step S3, a weighted adjacency matrix is ​​constructed based on the topology and electrical parameters of the distribution network, wherein the edge weights comprehensively consider the electrical distance between nodes and the contribution of source-load matching degree;

[0083] Calculate the normalized Laplacian matrix and perform eigenvalue decomposition;

[0084] The optimal number of partitions is determined based on the eigenvalue gaps. The eigenvectors corresponding to the smallest eigenvalue of the corresponding number of partitions are then clustered to obtain the initial partitioning scheme.

[0085] The boundary nodes of the initial scheme are adjusted and optimized based on connectivity constraints and capacity constraints;

[0086] The clustering described herein uses the K-means algorithm to cluster the normalized row vectors of the feature vector matrix.

[0087] In one embodiment of the present invention, step S4 includes the following:

[0088] In step S4, the partitioning triggering condition includes:

[0089] A re-partition is triggered when the source load matching degree of any partition deviates from the value at the time of the last partition by more than a preset deviation threshold.

[0090] A re-partitioning is triggered when the network topology changes or a device failure event occurs.

[0091] In one embodiment of the present invention, step S4 further includes the following:

[0092] In step S4, the smooth switching strategy includes:

[0093] During the transition period between the old and new classification schemes, the tie line power transitions from the old value to the new value by linear interpolation, and the power change rate does not exceed the preset change rate limit.

[0094] The mechanism to prevent frequent switching includes:

[0095] Set a minimum partitioning hold time, during which re-partitioning will not be performed even if the triggering condition is met within the hold time;

[0096] Set a matching degree deviation dead zone; when the matching degree deviation is less than the dead zone range, re-division will not be triggered.

[0097] The network connectivity constraints include: requiring all nodes in each partition to be interconnected through network lines; and the capacity constraints require that each partition contain at least one controllable power source, and the total power source capacity is not less than a preset proportion of the load peak.

[0098] In one embodiment of the present invention, step S4 further includes the following:

[0099] Step S4 also includes establishing an evaluation system for the grid partitioning results. The evaluation indicators include the source-load self-balancing rate of each partition, the power exchange rate between regions, and the total network loss of the system. Based on the evaluation results, the trigger condition threshold and the weight coefficient of the optimization model are adaptively adjusted.

[0100] According to a second aspect of the present invention, the present invention proposes a dynamic partitioning and adaptive networking system for a primary and secondary microgrid based on real-time supply and demand analysis, for executing a dynamic partitioning and adaptive networking method for a primary and secondary microgrid based on real-time supply and demand analysis as described in any one of the present invention, characterized in that it includes:

[0101] The data acquisition module is used to collect source load supply and demand data of each node in the master-slave-micro three-level architecture in real time;

[0102] The indicator calculation module is communicatively connected to the data acquisition module and is used to calculate multi-dimensional classification indicators such as source-load matching degree, electrical distance and power supply reliability based on real-time supply and demand data.

[0103] The optimization solution module is communicatively connected to the index calculation module and is used to establish a dynamic partitioning optimization model and use a clustering algorithm to solve for the optimal partitioning scheme.

[0104] The adaptive control module is communicatively connected to the index calculation module and the optimization solution module, respectively, and is used to monitor the partitioning trigger conditions and perform smooth switching of the partitioning scheme.

[0105] According to a third aspect of the present invention, the present invention proposes a dynamic partitioning and adaptive networking system for a primary and secondary microgrid based on real-time supply and demand analysis, comprising an electronic device, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements a dynamic partitioning and adaptive networking method for a primary and secondary microgrid based on real-time supply and demand analysis as described in any one of the present invention.

[0106] According to a fourth aspect of the present invention, the present invention proposes a dynamic partitioning and adaptive networking system for a primary and secondary microgrid based on real-time supply and demand analysis, comprising a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements a dynamic partitioning and adaptive networking method for a primary and secondary microgrid based on real-time supply and demand analysis as described in any one of the present invention.

[0107] Furthermore, in one embodiment of the present invention, the method and system proposed by the present invention further include the following:

[0108] In a three-tiered distribution-microgrid architecture, reasonable grid partitioning is fundamental to achieving efficient collaborative operation. It determines the boundary range, internal resource allocation, and power exchange relationship between each microgrid and the external grid. The grid partitioning problem can be formally formulated as a graph partitioning problem: given a distribution network topology... , set of nodes Divided into Disjoint subsets:

[0109] (in , ) ;

[0110] To minimize a certain optimization objective.

[0111] However, with the rapid growth of distributed power sources such as distributed photovoltaics and electric vehicles, the distribution of power sources in the distribution network exhibits significant time-varying characteristics.

[0112] Net power of source load The output of solar power varies dramatically throughout the day: peak solar power output during the daytime. In some areas, there is a power surplus;

[0113] Evening peak load period These regions become power deficit states. This dynamic change in source-load distribution requires that the mesh generation scheme also be dynamically adjusted accordingly.

[0114] Furthermore, in one embodiment of the present invention, the relevant steps include the following:

[0115] Step 1: Collect real-time source and load supply and demand data. Through the Supervisory Control and Data Acquisition (SCADA) system, the energy management system (EMS) of the microgrid, and smart terminals, the power generation of each node in the three-tier architecture of the main grid, distribution network, and microgrid is collected in real time. Load power Energy storage state of charge and line current Data collection period is no more than 1 minute.

[0116] Step 2: Establish a dynamic classification indicator system.

[0117] (1) Source-load matching index: for each candidate region ,calculate:

[0118] ;

[0119] when hour When one side is zero When both power generation and load power are zero (i.e., Gk(t)=0 and Lk(t)=0), Mk(t)=1 is defined, which means that there is no source or load in the region during this period, there is no supply-demand imbalance, and it is considered to be perfectly matched.

[0120] (2) Electrical distance index: based on node impedance matrix calculate Electrical distance can be understood as a node. When injecting unit current at the node Caused voltage change A smaller electrical distance indicates a tighter electrical coupling between nodes. For radial distribution networks, the electrical distance between adjacent nodes on the same feeder is small, while the electrical distance between nodes on different feeders is large.

[0121] (3) Power supply reliability indicators: ,in For the region Number of available power sources (including upstream interconnection lines, distributed power sources, and energy storage). The minimum number of power sources required to satisfy the N-1 criterion. This indicates that the reliability requirements are met.

[0122] Step 3: Establish a dynamic partitioning optimization model.

[0123] The objective function is a weighted multi-objective:

[0124] ;

[0125] The weights satisfy w1 reflects the importance attached to regional autonomy, w2 reflects the importance attached to source-load balance, and w3 reflects the importance attached to power supply reliability. In practical applications, adjustments can be made according to the operational needs of the distribution network. For example, w3 can be appropriately increased in important load areas with high power supply reliability requirements, and w1 and w2 can be appropriately increased in areas with high distributed photovoltaic penetration.

[0126] in This represents the power exchange between regions (the sum of the absolute values ​​of tie-line power), reflecting the region's autonomy.

[0127] This represents the sum of source-load mismatches for each region.

[0128] The number of areas that do not meet reliability requirements.

[0129] Constraints include: connectivity constraints (for each partition) (for connected subgraphs)

[0130] Capacity constraints (per partition) ),

[0131] Safety constraints ( , ).

[0132] Step 4: Solve using an improved spectral clustering algorithm.

[0133] The mathematical foundation of spectral clustering algorithms is the Laplace matrix theory of graphs. For distribution network graphs... Construct a weighted adjacency matrix ,

[0134] weight ;

[0135] Degree matrix ;

[0136] Normalized Laplace matrix:

[0137] ;

[0138] It is a positive semi-definite matrix, and its eigenvalues ​​are... The number of eigenvalues ​​with a value of 0 is equal to the number of connected components in the graph. For a connected graph, and (Algebraic connectivity). A larger value indicates stronger connectivity in the graph. Optimal number of partitions. Determined by the eigengap:

[0139] ,

[0140] Intuitively understood as before There is a significant jump between the small (approximately 0) eigenvalues ​​and subsequent eigenvalues.

[0141] Take before eigenvectors ,

[0142] Normalization of row vectors Then, K-means clustering is performed. The objective function of K-means is:

[0143] ;

[0144] in For the first There are several cluster centers. The clustering result is the node partitioning scheme. The computational complexity of the above spectral clustering algorithm mainly depends on the eigenvalue decomposition step. For a distribution network with n nodes, the sparse eigenvalue decomposition complexity of the normalized Laplace matrix is ​​approximately O(n×m), where m is the number of non-zero elements (m≈n for a radial distribution network); the complexity of K-means clustering is O(n×k×iter), where k is the number of partitions and iter is the number of iterations. The total computational complexity is much lower than the traditional minimum cut algorithm's O(n³), which can meet the timeliness requirements of online dynamic partitioning.

[0145] Finally, the initial scheme is constrained and modified: traverse the boundary nodes (nodes adjacent to other partition nodes), check connectivity and capacity constraints, and if they are not satisfied, adjust the node to the adjacent partition that maximizes the improvement of the objective function.

[0146] Step 5: Design an adaptive networking control mechanism.

[0147] (1) Defining trigger conditions: when Triggered by time; or triggered by topology change / failure event.

[0148] (2) Smooth transition: transition period Internal, linear interpolation of tie line power:

[0149] ;

[0150] Power change rate:

[0151] ;

[0152] (3) Prevention of frequent switching: minimum hold time No repartitioning within; matching deviation in dead zone. No triggering occurs within this condition. The mathematical expression for the triggering condition is:

[0153] ;

[0154] Correspondingly, Figure 2 The curves showing the daily variation of the source-load matching degree M_k(t) for each partition demonstrate the change in matching degree of the four partitions over time and the triggering time of the repartitioning event.

[0155] Figure 3 A graph showing the distribution of eigenvalues ​​of the Laplacian matrix and the determination of the optimal number of partitions illustrates the results of eigenvalue gap analysis.

[0156] Figure 4 A bar chart comparing the source load self-balance rate of each zone under dynamic and fixed division;

[0157] Figure 5 The curve for the smooth switching process of tie line power shows the linear gradual change of power during the transition period;

[0158] Figure 6 A bar chart comparing key operating indicators for dynamic and fixed partitioning, including a comparison of power exchange, system losses, and self-balancing rate.

[0159] In addition to the above, the present invention also has related embodiments, including the following:

[0160] The power distribution network of an industrial park was used as the verification object. This power distribution network contains 5 10kV feeders, 220 nodes, and is connected to 18MW of distributed photovoltaic power, 4MW / 8MWh of energy storage, and 200 electric vehicle charging piles. The peak load is approximately 30MW.

[0161] 1) Data collection and indicator calculation:

[0162] Taking the operational data of a certain workday as an example. 10:00 AM: Feeder 1 area MW, MW, (Highly matched); Feeder 3 area MW, MW, (Severe mismatch). 12:30 PM (lunch break): Feeder 1 Down to ; Feeder 3 Rise to .

[0163] Electrical distance calculation: based on a 220×220 node impedance matrix Average electrical distance between internal nodes of feeder 1 PU, between feeder 1 and 2 boundary nodes PU, feeder lines 1-5 pu.

[0164] 2) Optimization model establishment and solution:

[0165] Set weights , , Construct a weighted adjacency matrix Weights:

[0166] .

[0167] Spectral clustering solution: Calculate the 220×220 cluster size. Then perform eigenvalue decomposition. The first 20 smallest eigenvalues ​​are... Eigenvalue gap:

[0168] ;

[0169] Maximum, therefore Take the first 4 feature vectors. K-means clustering was performed. Three boundary nodes that did not satisfy the connectivity constraints were adjusted to adjacent partitions. The entire solution took approximately 2.3 seconds.

[0170] 3) Dynamic partitioning effect:

[0171] There were 6 allocation adjustments across 96 time slots throughout the day. Compared to a fixed allocation: average source-load self-balancing rate. The percentage increased from 68.5% to 85.3% (+16.8%); the power exchange rate between different zones decreased from 4,250 kWh to 2,180 kWh (-48.7%).

[0172] Total system losses:

[0173] ;

[0174] It decreased from 892 kWh to 756 kWh (-15.2%).

[0175] 4) Adaptive networking control:

[0176] set up , minute, minute, Taking the switch from 12:00 to 12:30 as an example: at 12:00... 12:15 It dropped to 0.68, deviating from... This triggered a re-allocation. The new tie-line power decreased linearly from 1.2MW to 0.6MW within 5 minutes. Transitional period system voltage fluctuations .

[0177] The anti-frequent switching mechanism suppressed four unnecessary re-partitions (matching deviation within the dead zone). Within the scope of the plan, the actual division and adjustment are controlled at a reasonable level.

[0178] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for dynamic partitioning and adaptive networking of primary and distribution microgrids based on real-time supply and demand analysis, characterized in that, Includes the following: Step S1: Collect real-time source-load supply and demand data in the three-level architecture of main grid, distribution network and microgrid, and calculate multi-dimensional dynamic partitioning indicators including source-load matching degree index, electrical distance index and power supply reliability index based on the real-time supply and demand data; Step S2: With the optimization objectives of maximizing the self-balancing rate of source loads within the region and minimizing the power exchange between regions, and with network connectivity constraints and capacity constraints as conditions, establish a dynamic partitioning optimization model for the main and distribution microgrids. Step S3: The optimization model is solved by a clustering algorithm based on the eigenvalue decomposition of the graph Laplacian matrix. The optimal number of partitions is automatically determined by eigenvalue gap analysis to obtain the grid partitioning scheme. Step S4: Design an adaptive network control mechanism, including partitioning trigger conditions based on index deviation, a power progressive smooth switching strategy, and an anti-frequent switching mechanism, to achieve online updating and smooth switching of the grid partitioning results.

2. The method for dynamic partitioning and adaptive networking of main and distribution microgrids based on real-time supply and demand analysis according to claim 1, characterized in that, Step S1 includes the following: In step S1, the source-load matching degree index reflects the real-time balance between power generation and load power in the region. The matching degree is at its maximum when the power generation and load power are equal, and the matching degree is zero when either is zero. The electrical distance index is calculated based on the node impedance matrix and reflects the tightness of electrical coupling between nodes; The power supply reliability index is evaluated based on the relationship between the number of available power sources in the area and the minimum number of power sources required to meet the N-1 criterion. The formula for calculating the source-load matching index is the ratio of the smaller value to the larger value of the total power generation and total load power in the region.

3. The method for dynamic partitioning and adaptive networking of main and distribution microgrids based on real-time supply and demand analysis according to claim 1, characterized in that, Step S2 includes the following: In step S2, the objective function of the optimization model is a weighted multi-objective function, including: The inter-regional power exchange index is defined as the sum of the absolute values ​​of the power transmitted through all tie lines; The sum of source-load mismatch in each region; The power supply reliability loss index is defined as the number of areas that do not meet the N-1 check. The sum of the weighting coefficients of each indicator is 1.

4. The method for dynamic partitioning and adaptive networking of main and distribution microgrids based on real-time supply and demand analysis according to claim 1, characterized in that, Step S3 includes the following: In step S3, a weighted adjacency matrix is ​​constructed based on the topology and electrical parameters of the distribution network, wherein the edge weights comprehensively consider the electrical distance between nodes and the contribution of source-load matching degree; Calculate the normalized Laplacian matrix and perform eigenvalue decomposition; The optimal number of partitions is determined based on the eigenvalue gaps. The eigenvectors corresponding to the smallest eigenvalue of the corresponding number of partitions are then clustered to obtain the initial partitioning scheme. The boundary nodes of the initial scheme are adjusted and optimized based on connectivity constraints and capacity constraints; The clustering described herein uses the K-means algorithm to cluster the normalized row vectors of the feature vector matrix.

5. The method for dynamic partitioning and adaptive networking of main and distribution microgrids based on real-time supply and demand analysis according to claim 1, characterized in that, Step S4 includes the following: In step S4, the partitioning triggering condition includes: A re-partition is triggered when the source load matching degree of any partition deviates from the value at the time of the last partition by more than a preset deviation threshold. A re-partitioning is triggered when the network topology changes or a device failure event occurs.

6. The method for dynamic partitioning and adaptive networking of primary and secondary microgrids based on real-time supply and demand analysis according to claim 1, characterized in that, Step S4 also includes the following: In step S4, the smooth switching strategy includes: During the transition period between the old and new classification schemes, the tie line power transitions from the old value to the new value by linear interpolation, and the power change rate does not exceed the preset change rate limit. The mechanism to prevent frequent switching includes: Set a minimum partitioning hold time, during which re-partitioning will not be performed even if the triggering condition is met within the hold time; Set a matching degree deviation dead zone; when the matching degree deviation is less than the dead zone range, re-division will not be triggered. The network connectivity constraints include: requiring all nodes in each partition to be interconnected through network lines; and the capacity constraints require that each partition contain at least one controllable power source, and the total power source capacity is not less than a preset proportion of the load peak.

7. The method for dynamic partitioning and adaptive networking of main and distribution microgrids based on real-time supply and demand analysis according to claim 1, characterized in that, Step S4 also includes the following: Step S4 also includes establishing an evaluation system for the grid partitioning results. The evaluation indicators include the source-load self-balancing rate of each partition, the power exchange rate between regions, and the total network loss of the system. Based on the evaluation results, the trigger condition threshold and the weight coefficient of the optimization model are adaptively adjusted.

8. A dynamic partitioning and adaptive networking system for main and distribution microgrids based on real-time supply and demand analysis, used to execute the dynamic partitioning and adaptive networking method for main and distribution microgrids based on real-time supply and demand analysis as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to collect source load supply and demand data of each node in the master-slave-micro three-level architecture in real time; The indicator calculation module is communicatively connected to the data acquisition module and is used to calculate multi-dimensional classification indicators such as source-load matching degree, electrical distance and power supply reliability based on real-time supply and demand data. The optimization solution module is communicatively connected to the index calculation module and is used to establish a dynamic partitioning optimization model and use a clustering algorithm to solve for the optimal partitioning scheme. The adaptive control module is communicatively connected to the index calculation module and the optimization solution module, respectively, and is used to monitor the partitioning trigger conditions and perform smooth switching of the partitioning scheme.

9. A dynamic partitioning and adaptive networking system for a primary and secondary microgrid based on real-time supply and demand analysis, comprising an electronic device, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a method for dynamic partitioning and adaptive networking of main and distribution microgrids based on real-time supply and demand analysis as described in any one of claims 1 to 7.

10. A dynamic partitioning and adaptive networking system for a primary and secondary microgrid based on real-time supply and demand analysis, comprising a computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for dynamic partitioning and adaptive networking of main and distribution microgrids based on real-time supply and demand analysis as described in any one of claims 1 to 7.

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