A micro-grid aggregation method and device based on synergistic complementation, equipment and medium

Abstract

The present application relates to the technical field of micro-grid operation optimization, and particularly relates to a micro-grid aggregation method and device based on synergistic complementarity, equipment and medium, the method comprises the following steps: firstly, analyzing the fluctuation characteristics of the power generation of each micro-grid, and calculating the power generation state stability index; secondly, analyzing the complementarity characteristics between micro-grids in combination with the power generation and power generation load, and calculating the grid optimization complementarity factor; then, analyzing the similarity characteristics between micro-grids, and calculating the power generation characteristic similarity index; finally, calculating the similarity characteristic aggregation weight based on all power generation state stability indexes, combining the power generation characteristic similarity index and the grid optimization complementarity factor to calculate the synergistic complementation aggregation index, constructing a synergistic complementation matrix, and performing aggregation processing on all micro-grids to obtain a micro-grid cluster; through efficient aggregation of micro-grids, efficient control of micro-grids is realized, and the stability and safety of the power generation system are improved.

Description

Technical Field

[0001] This invention relates to the field of microgrid operation optimization technology, and in particular to a microgrid aggregation method, apparatus, equipment and medium based on synergistic complementarity. Background Technology

[0002] A microgrid is a small, independent power system based on distributed energy resources. It consists of multiple energy sources, such as solar, wind, and energy storage systems, as well as energy conversion equipment and user loads. It provides clean, reliable, and flexible power supply, can operate independently, and can be interconnected with the traditional power grid. Microgrids utilize renewable energy for power generation, significantly reducing greenhouse gas emissions and contributing to environmental protection and sustainable development. Microgrids can meet the electricity needs of remote islands and mountainous areas, and are gradually penetrating urban communities, driving the process of smart grid integration. Driven by demands for reliable, safe, efficient, and orderly electricity use, enterprises are increasingly willing to deploy microgrids.

[0003] Aggregating similar microgrids into a power generation cluster enables the sharing and optimized scheduling of power resources, thereby improving the operating efficiency of the entire power generation cluster and reducing grid redundancy and operation and maintenance costs.

[0004] However, since microgrids' energy resources include not only traditional energy sources such as natural gas and oil, but also renewable energy sources such as solar and wind power, and the size and nature of the electricity load also affect the stability of the microgrid, existing microgrid aggregation technologies are not very efficient at aggregating microgrids. When one microgrid in a power cluster experiences a fault or fluctuation in electricity demand, other microgrids may also experience faults or fluctuations in electricity demand simultaneously.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a microgrid aggregation method, apparatus, equipment, and medium based on synergistic complementarity, thereby effectively solving the problems in the background art.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a microgrid aggregation method based on synergistic complementarity, comprising the following steps:

[0008] S10: Record the location and scale of each microgrid, and collect the power generation load and power generation of each microgrid in each monitoring cycle;

[0009] S20: Based on the volatility characteristics of the power generation, calculate the power generation stability index of each microgrid in each monitoring cycle; based on the complementarity characteristics of the power generation stability index, calculate the fault support complementarity factor between the corresponding microgrids.

[0010] S30: Based on the matching degree between the power generation and the power generation load, construct the power load surplus / deficit sequence for each microgrid; based on the complementarity characteristics of the power load surplus / deficit sequence, calculate the surplus / deficit complementarity index between microgrids; combining the surplus / deficit complementarity index, the complementarity characteristics of the power generation and the power generation load, calculate the power support gap filling factor between microgrids.

[0011] S40: Based on the linear relationship between the power support gap filling factor and the fault support complementarity factor, calculate the grid optimization complementarity factor between microgrids; based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of their scales, calculate the generation characteristic similarity index between the two microgrids; based on the generation state stability index, calculate the similarity feature aggregation weight between the microgrids; combining the generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight, calculate the cooperative complementarity aggregation index between the microgrids.

[0012] S50: Based on the aforementioned synergistic and complementary aggregation index, construct a synergistic and complementary matrix. Based on the aforementioned synergistic and complementary matrix, use a multidimensional spectral clustering algorithm to aggregate all microgrids to obtain each microgrid cluster.

[0013] Further, in step S20, based on the volatility characteristics of the power generation, the power generation stability index of each microgrid in each monitoring cycle is calculated, including the following steps:

[0014] S211: Based on the power generation, construct a power generation time series for each microgrid in each monitoring period, calculate the average value of all elements in the power generation time series, and define it as the axial power generation of the microgrid in the corresponding monitoring period; calculate the absolute value of the difference between the power generation at each acquisition time and the axial power generation in the corresponding monitoring period, as the axial power difference of the microgrid at each acquisition time.

[0015] S212: Calculate the remainder between each acquisition time and the length of the monitoring cycle, which is defined as the relative time within the cycle corresponding to the acquisition time; calculate the average power generation of each microgrid at the relative time within the same cycle in all monitoring cycles, which is defined as the longitudinal power generation at the corresponding relative time within the cycle; calculate the absolute value of the difference between the power generation at each acquisition time and the longitudinal power generation at the corresponding relative time within the cycle, as the longitudinal power difference of each microgrid at each acquisition time.

[0016] S213: Based on the Pettt mutation point detection algorithm, mutation point detection is performed on each of the power generation time series, and the power mutation index of each mutation point is calculated, which is defined as the sum of the axial power difference and the longitudinal power difference of the corresponding mutation point; the average value of the power mutation index of all mutation points of each microgrid is calculated and multiplied by the total number of mutation points, which is defined as the power generation state runaway factor; the exponential function value is calculated with the natural constant as the base and the negative number of the power generation state runaway factor as the exponent, which is defined as the power generation state stability index of the microgrid.

[0017] Further, in step S20, based on the complementary characteristics of the power generation state stability index, the corresponding fault support complementarity factor between microgrids is calculated, including the following steps:

[0018] S221: Define the maximum value of the power generation stability index as the optimal power generation index; combine all microgrids in pairs, calculate the difference between the power generation stability indices of the two microgrids in each pair, and define it as the power generation stability difference index of the pair; calculate the average value of the power generation stability indices of the two microgrids in each pair, and define it as the average power generation stability index of the pair.

[0019] S222: Subtract the average power generation state index from the optimal power generation state index to define the optimal state difference factor for each combination; define the ratio of the power generation state difference index to the optimal state difference factor as the fault support complementarity factor for each combination.

[0020] Further, in step S30, based on the matching degree between the power generation and the power generation load, a power load surplus / deficit sequence for each microgrid is constructed; based on the complementarity characteristics of the power load surplus / deficit sequence, a surplus / deficit complementarity index between microgrids is calculated. The steps include:

[0021] S311: Define the difference between the power generation and the power generation load of each microgrid at each acquisition time as the power generation surplus of each microgrid at each acquisition time, and arrange the power generation surplus in ascending order according to the acquisition time to construct the power load surplus and deficit sequence of each microgrid.

[0022] S312: Calculate the Pearson correlation coefficient between the two power load surplus / deficit sequences, which is defined as the power correlation coefficient between the two microgrids; perform inverse proportional truncation on the power correlation coefficient to obtain the surplus / deficit complementarity index between the two microgrids.

[0023] Further, in step S30, combining the complementarity index, the power generation, and the complementarity characteristics of the power generation load, the power support gap filling factor between microgrids is calculated. This step includes:

[0024] S321: The average power generation of all microgrids at the same data collection time is defined as the average power generation at the data collection time; the difference between the power generation of each microgrid at each data collection time and the average power generation is calculated and defined as the individual power generation difference index of the microgrid; the maximum absolute value of the individual power generation difference index of two microgrids at each data collection time is calculated and defined as the individual power generation difference extreme factor of the microgrid combination at the corresponding data collection time; the average absolute value of the individual power generation difference index of two microgrids at all data collection times is calculated and defined as the complementary power generation difference index of the microgrid combination at the corresponding data collection time; the difference between the complementary power generation difference index and the individual power generation difference extreme factor is calculated and defined as the power generation complementarity factor of the microgrid combination at the corresponding data collection time; the average value of the power generation complementarity factors at all data collection times is defined as the power generation complementarity index between the two microgrids.

[0025] S322: The average power generation load of all microgrids at the same acquisition time is defined as the average power generation load at the acquisition time; the difference between the power generation load of each microgrid at each acquisition time and the average power generation load is defined as the individual load difference index of the microgrid; the maximum absolute value of the individual load difference index of two microgrids at each acquisition time is defined as the individual load difference extreme factor of the microgrid combination at the corresponding acquisition time; the average absolute value of the individual load difference index of two microgrids at all acquisition times is defined as the complementary load difference index of the microgrid combination at the corresponding acquisition time; the difference between the complementary load difference index and the individual load difference extreme factor is defined as the load complementarity factor of the microgrid combination at the corresponding acquisition time; the average value of the load complementarity factors at all acquisition times is defined as the load complementarity index between the two microgrids.

[0026] S323: Calculate the sum of the generation complementarity index and the load complementarity index between the two microgrids, and define it as the power complementarity factor between the two microgrids; calculate the product of the power complementarity factor and the profit and loss complementarity index, and define it as the power support gap filling factor between the two microgrids.

[0027] Further, in step S40, based on the Euclidean distance between the two microgrid locations and the similarity characteristics of their scales, a generation characteristic similarity index is calculated between the two microgrids; based on the generation state stability index, a similarity feature aggregation weight is calculated between the microgrids; and combining the generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight, a synergistic complementarity aggregation index between the microgrids is calculated, including:

[0028] S41: The product of the Euclidean distance between two microgrid locations and the absolute value of the difference in size between the two microgrids is defined as the power generation characteristic dissimilarity index between the two microgrids. The power generation characteristic dissimilarity index is processed to obtain the power generation characteristic similarity index between the two microgrids.

[0029] S42: The normalized value of the product of the power support gap filling factor and the corresponding fault support complementarity factor between the two microgrids is defined as the grid optimization complementarity factor between the two microgrids.

[0030] S43: Calculate the average value of the power generation state stability index for each microgrid across all monitoring periods, and define it as the power generation state stability mean for each microgrid; normalize the power generation state stability mean and define it as the similarity feature aggregation weight for each microgrid;

[0031] S44: Calculate the synergistic complementary aggregation index between microgrids by combining the power generation characteristic similarity index, the power grid optimization complementarity factor, and the similarity feature aggregation weight.

[0032] Further, in step S40, combining the power generation characteristic similarity index, the power grid optimization complementarity factor, and the similarity feature aggregation weight, the synergistic complementarity aggregation index between microgrids is calculated, and the synergistic complementarity aggregation index between the two microgrids is expressed as:

[0033] SH i，j =(1-w i，j )×HF i，j +w i，j ×Norm(FS i，j );

[0034]

[0035] In the formula, SH i，j FS is the synergistic and complementary aggregation index between the i-th microgrid and the j-th microgrid. i，j HF is the similarity index of power generation characteristics between the i-th microgrid and the j-th microgrid. i，j Let w be the grid optimization complementarity factor between the i-th microgrid and the j-th microgrid, and Norm() be the normalization function. i，j ws represents the collaborative aggregation weight between the i-th microgrid and the j-th microgrid. i For the similarity features aggregation weights of the i-th microgrid, ws j The similarity features are aggregated weights for the j-th microgrid.

[0036] Further, in step S50, a synergistic complementarity matrix is ​​constructed based on the synergistic complementarity aggregation index. Based on the synergistic complementarity matrix, a multidimensional spectral clustering algorithm is used to aggregate all microgrids to obtain each microgrid cluster. The steps include:

[0037] S51: Initialize a square matrix with all elements equal to 0, where the number of rows and columns of the matrix are equal to the number of microgrids; based on the synergistic complementarity aggregation index between two microgrids, assign it to the intersection of the corresponding rows and columns in the square matrix to obtain the synergistic complementarity matrix;

[0038] S52: Based on the aforementioned synergistic complementarity matrix, calculate the similarity relationship of each microgrid and construct a graph structure for cluster analysis; use a multidimensional spectral clustering algorithm to group all microgrids based on the feature vectors of the graph structure; output multiple microgrid clusters based on the clustering results, each microgrid cluster having synergistic complementarity characteristics.

[0039] The present invention also includes a microgrid aggregation device based on synergistic complementarity, using the method described above, comprising:

[0040] The data acquisition unit is used to record the location and scale of each microgrid, and to collect the power generation load and power generation of each microgrid in each monitoring cycle;

[0041] The stability analysis unit is used to calculate the power generation stability index of each microgrid in each monitoring period based on the volatility characteristics of the power generation; and to calculate the fault support complementarity factor between the corresponding microgrids based on the complementarity characteristics of the power generation stability index.

[0042] The profit and loss analysis unit is used to construct the power load profit and loss sequence of each microgrid based on the matching degree of the power generation and the power generation load; calculate the profit and loss complementarity index between microgrids based on the complementarity characteristics of the power load profit and loss sequence; and calculate the power support gap filling factor between microgrids by combining the profit and loss complementarity index, the power generation and the power generation load complementarity characteristics.

[0043] The collaborative complementarity calculation unit is used to calculate the grid optimization complementarity factor between microgrids based on the linear relationship between the power support gap filling factor and the fault support complementarity factor; calculate the generation characteristic similarity index between two microgrids based on the Euclidean distance between their locations and the similarity characteristics of their scales; calculate the similarity feature aggregation weight between microgrids based on the generation state stability index; and calculate the collaborative complementarity aggregation index between microgrids by combining the generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight.

[0044] The aggregation processing unit is used to construct a synergistic complementarity matrix based on the synergistic complementarity aggregation index, and to perform aggregation processing on all microgrids using a multidimensional spectral clustering algorithm based on the synergistic complementarity matrix to obtain each microgrid cluster.

[0045] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0046] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0047] The beneficial effects of this invention are as follows:

[0048] 1. This invention proposes a microgrid aggregation method and system based on synergistic complementarity. It records the location and scale of each microgrid and collects the power generation load and output of each microgrid in each monitoring period. First, it analyzes the volatility characteristics of the power generation of each microgrid and calculates the power generation state stability index of each microgrid in each monitoring period. Based on the complementarity characteristics of the power generation state stability indices between microgrids, it calculates the corresponding fault support complementarity factor between microgrids to evaluate the overall complementarity characteristics between microgrids. Then, it analyzes the matching degree of power generation and power generation load of each microgrid, constructs the power load surplus / deficit sequence of each microgrid, and based on... The complementarity characteristics of the power load surplus and deficit sequences between microgrids are used to calculate the surplus and deficit complementarity index between microgrids. Based on the surplus and deficit complementarity index, the complementarity characteristics of power generation and power load between microgrids, the power support gap filling factor between microgrids is calculated to evaluate the complementarity characteristics between microgrids at each data collection time. Taking into account the overall complementarity characteristics between microgrids and the complementarity characteristics between microgrids at each data collection time, the grid optimization complementarity factor between microgrids is calculated based on the linear relationship between the power support gap filling factor and the fault support complementarity factor, which improves the reliability of the evaluation of the complementarity characteristics between microgrids.

[0049] 2. Based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of their scale, the generation characteristic similarity index between the two microgrids is calculated. Based on the generation state stability index of the microgrids in all monitoring periods, the similarity characteristic aggregation weight between the microgrids is calculated. Based on the generation characteristic similarity index between the microgrids, the grid optimization complementarity factor, and the similarity characteristic aggregation weight of each microgrid, the synergistic complementarity aggregation index between the microgrids is calculated. The generation characteristic similarity index represents the similarity between the microgrids, and the grid optimization complementarity factor represents the complementarity between the microgrids. When aggregating the microgrids, the fluctuation characteristics of the microgrid generation are analyzed, and different weights are set for the similarity and complementarity between the two microgrids to evaluate the comprehensive improvement of system efficiency after aggregating the two microgrids, thereby improving the reliability of microgrid aggregation.

[0050] 3. A synergistic complementarity matrix is ​​constructed based on the synergistic complementarity aggregation index among all microgrids. Based on the synergistic complementarity matrix, all microgrids are aggregated to obtain individual microgrid clusters. By improving the similarity matrix of the multidimensional spectral clustering algorithm, it is possible not only to aggregate distributed power sources with similar power generation characteristics and regulation capabilities to achieve seamless connection and coordinated operation between microgrids and distribution networks, but also to enable other microgrids to provide timely power support when a microgrid fails, thereby effectively reducing the risk of power outages, improving the aggregation efficiency of microgrids, and enhancing the resilience of the entire system. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating a microgrid aggregation method based on synergistic complementarity.

[0053] Figure 2 This is a schematic diagram illustrating the acquisition of the complementary matrix.

[0054] Figure 3 This is a schematic diagram of a microgrid aggregation device based on synergistic complementarity.

[0055] Figure 4 This is a schematic diagram of the structure of a computer device. Detailed Implementation

[0056] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0057] like Figures 1 to 2 As shown: A microgrid aggregation method based on synergistic complementarity includes the following steps:

[0058] S10: Record the location and scale of each microgrid, collect the power generation load and power generation of each microgrid in each monitoring cycle; establish the basic operating data of each microgrid;

[0059] The location and scale of each microgrid are recorded. The power generation load and power generation of each microgrid are collected every t minutes in each monitoring cycle. It should be noted that the collection time interval t and the length of the monitoring cycle are preset values. As an embodiment of this application, the collection time interval t is 30 and the length of the monitoring cycle is 1 day.

[0060] S20: Based on the volatility characteristics of power generation, calculate the power generation stability index of each microgrid in each monitoring period; based on the complementarity characteristics of the power generation stability index, calculate the fault support complementarity factor between the corresponding microgrids.

[0061] It should be noted that using multidimensional spectral clustering algorithms to aggregate distributed power sources with similar power generation characteristics and regulation capabilities to form a virtual power cluster can achieve seamless connection and coordinated operation between microgrids and distribution networks, improving the efficiency and reliability of the entire power system. However, existing microgrid aggregation does not take into account the complementary characteristics between microgrids. When similar microgrids within the same power cluster fail simultaneously, the power cluster will be paralyzed, greatly reducing the security and stability of the power grid.

[0062] Therefore, by analyzing the complementarity characteristics between microgrids, complementary microgrids can be aggregated into a power cluster. When a fault occurs in one microgrid, other microgrids can provide timely power support, thereby effectively reducing the risk of power outages and enhancing the stability of the entire system.

[0063] S30: Based on the matching degree between power generation and power generation load, construct the power load surplus / deficit sequence for each microgrid; based on the complementarity characteristics of the power load surplus / deficit sequence, calculate the surplus / deficit complementarity index between microgrids; combining the surplus / deficit complementarity index, the complementarity characteristics of power generation and power generation load, calculate the power support gap filling factor between microgrids.

[0064] Specifically, power generation refers to the total amount of electrical energy generated by a microgrid within a certain period of time, while power generation load refers to the electricity demand of a microgrid at a certain moment or within a certain period of time. When power generation exceeds the load, it will lead to power surplus, which may cause problems such as increased grid voltage and increased line losses. When power generation is less than the load, it will lead to power shortage, which may cause serious consequences such as decreased grid voltage, equipment damage, or even power outages.

[0065] Therefore, when aggregating microgrids, efforts should be made to ensure that microgrids with large differences in power generation and load within the power cluster complement each other, microgrids with large differences in power generation at the same time complement each other, and microgrids with large differences in power generation load at the same time complement each other.

[0066] S40: Based on the linear relationship between the power support gap filling factor and the fault support complementarity factor, calculate the grid optimization complementarity factor between microgrids; based on the Euclidean distance between the locations of two microgrids and the similarity characteristics of their scales, calculate the generation characteristic similarity index between two microgrids; based on the generation state stability index, calculate the similarity feature aggregation weight between microgrids; combining the generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight, calculate the cooperative complementarity aggregation index between microgrids.

[0067] When aggregating microgrids, it is necessary to consider not only the similarity between microgrids, which can enable coordinated control of microgrids with high similarity to improve the efficiency of microgrid control, but also the complementarity between microgrids to improve the stability of the power system. In order to balance the influence of the two factors, different weights need to be set for the two factors according to the fluctuation characteristics of microgrid power generation.

[0068] S50: Based on the synergistic complementarity aggregation index, a synergistic complementarity matrix is ​​constructed. Based on the synergistic complementarity matrix, a multidimensional spectral clustering algorithm is used to aggregate all microgrids to obtain each microgrid cluster.

[0069] It should be noted that the multidimensional spectral clustering algorithm treats each sample in the dataset as a vertex in a graph, with vertices connected by edges. The weight of the edge represents the similarity between samples. Vertices in the same class have high similarity and large edge weights, while vertices in different classes have low similarity and small edge weights. The ultimate goal of spectral clustering is to find a method to cut the graph such that the weights within each subgraph after the cut are large, while the weights between subgraphs are small.

[0070] This invention proposes a microgrid aggregation method and system based on synergistic complementarity. It records the location and scale of each microgrid and collects the power generation load and output of each microgrid in each monitoring cycle. First, it analyzes the volatility characteristics of the power generation of each microgrid and calculates the power generation state stability index of each microgrid in each monitoring cycle. Based on the complementary characteristics of the power generation state stability indices between microgrids, it calculates the corresponding fault support complementarity factor between microgrids to evaluate the overall complementarity characteristics between microgrids. Then, it analyzes the matching degree of power generation and power generation load of each microgrid and constructs the power load surplus / deficit sequence for each microgrid. The complementarity index between microgrids is calculated based on the complementarity characteristics of the power load surplus and deficit sequences between microgrids. The power support gap filling factor between microgrids is then calculated based on the complementarity index, the complementarity characteristics of power generation, and the complementarity characteristics of power generation load to evaluate the complementarity characteristics between microgrids at various data collection points. Considering both the overall complementarity characteristics between microgrids and the complementarity characteristics between microgrids at various data collection points, the grid optimization complementarity factor between microgrids is calculated based on the linear relationship between the power support gap filling factor and the fault support complementarity factor, thus improving the reliability of the evaluation of the complementarity characteristics between microgrids.

[0071] Based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of their scale, a power generation characteristic similarity index is calculated between the two microgrids. Based on the power generation stability index of the microgrids across all monitoring periods, a similarity characteristic aggregation weight is calculated between the microgrids. Based on the power generation characteristic similarity index, the grid optimization complementarity factor, and the similarity characteristic aggregation weight of each microgrid, a synergistic complementarity aggregation index is calculated between the microgrids. The power generation characteristic similarity index represents the similarity between the microgrids, and the grid optimization complementarity factor represents the complementarity between the microgrids. When aggregating microgrids, by analyzing the volatility characteristics of the power generation of the microgrids, different weights are set for the similarity and complementarity between the two microgrids to evaluate the overall improvement of system efficiency after aggregating the two microgrids, thus improving the reliability of microgrid aggregation.

[0072] A synergistic complementarity matrix is ​​constructed based on the synergistic complementarity aggregation index among all microgrids. Based on the synergistic complementarity matrix, all microgrids are aggregated to obtain individual microgrid clusters. By improving the similarity matrix of the multidimensional spectral clustering algorithm, it is possible not only to aggregate distributed power sources with similar power generation characteristics and regulation capabilities to achieve seamless connection and coordinated operation between microgrids and distribution networks, but also to enable other microgrids to provide timely power support when a microgrid fails, thereby effectively reducing the risk of power outages, improving the aggregation efficiency of microgrids, and enhancing the resilience of the entire system.

[0073] As a preferred embodiment of the above, in step S20, based on the volatility characteristics of power generation, the power generation stability index of each microgrid in each monitoring cycle is calculated, and the steps include:

[0074] S211: Based on power generation, construct the power generation time series of each microgrid in each monitoring period, calculate the average value of all elements in the power generation time series, and define it as the axial power generation of the microgrid in the corresponding monitoring period; calculate the absolute value of the difference between the power generation at each acquisition time and the axial power generation in the corresponding monitoring period, as the axial power difference of the microgrid at each acquisition time.

[0075] S212: Calculate the remainder between each acquisition time and the length of the monitoring cycle, which is defined as the relative time within the cycle corresponding to the acquisition time; calculate the average power generation of each microgrid at the relative time within the same cycle in all monitoring cycles, which is defined as the longitudinal power generation at the corresponding relative time within the cycle; calculate the absolute value of the difference between the power generation at each acquisition time and the longitudinal power generation at the corresponding relative time within the cycle, which is taken as the longitudinal power difference of each microgrid at each acquisition time.

[0076] S213: Based on the Pettt mutation point detection algorithm, mutation points are detected for each power generation time series. The power mutation index of each mutation point is calculated, which is defined as the sum of the axial power difference and the longitudinal power difference of the corresponding mutation point. The average power mutation index of all mutation points in each microgrid is calculated and multiplied by the total number of mutation points, which is defined as the power generation state runaway factor. The exponential function value is calculated with the natural constant as the base and the negative of the power generation state runaway factor as the exponent, which is defined as the power generation state stability index of the microgrid.

[0077] By constructing a time series of power generation data and calculating axial and longitudinal power differences, the analysis of power generation volatility has been refined. In particular, the calculation of longitudinal differences based on periodic characteristics has supplemented the shortcomings of traditional unidirectional volatility analysis, enabling a more accurate description of the stability of microgrid power generation status and providing a scientific basis for subsequent evaluation of microgrid operating characteristics and optimized scheduling.

[0078] In this embodiment, in step S20, based on the complementary characteristics of the power generation state stability index, the corresponding fault support complementarity factor between microgrids is calculated. The steps include:

[0079] S221: Define the maximum value of the power generation stability index as the optimal power generation index; combine all microgrids in pairs and calculate the difference between the power generation stability indices of the two microgrids in each pair, which is defined as the power generation stability difference index of the pair; calculate the average value of the power generation stability indices of the two microgrids in each pair, which is defined as the average power generation stability index of the pair.

[0080] S222: Subtract the average power generation state index from the optimal power generation state index to define the optimal state difference factor for each combination; define the ratio of the power generation state difference index to the optimal state difference factor as the fault support complementarity factor for each combination.

[0081] It should be noted that the generation state difference index represents the difference in generation state when the two microgrids operate separately, while the optimal state difference factor represents the generation state after the two microgrids are aggregated. The greater the difference in generation state when the two microgrids operate separately, and the better the generation state after the two microgrids are aggregated, the more likely the other microgrid is to provide power support in a timely manner when one microgrid fails, thereby effectively reducing the risk of power outages and enhancing the stability of the entire system. The larger the fault support complementarity factor value of the two microgrids combined, the greater the difference in generation state.

[0082] In step S30, based on the matching degree between power generation and power generation load, a power load surplus / deficit sequence is constructed for each microgrid; based on the complementarity characteristics of the power load surplus / deficit sequence, a surplus / deficit complementarity index between microgrids is calculated. The steps include:

[0083] S311: Define the difference between the power generation and the power generation load of each microgrid at each acquisition time as the power generation surplus of each microgrid at each acquisition time. Arrange the power generation surplus in ascending order according to the acquisition time to construct the power load surplus and deficit sequence of each microgrid.

[0084] S312: Calculate the Pearson correlation coefficient between two power load surplus / deficit sequences, which is defined as the power correlation coefficient between the two microgrids; perform inverse proportional truncation on the power correlation coefficient to obtain the surplus / deficit complementarity index between the two microgrids.

[0085] The inverse proportional truncation process is designed as follows: the calculation result of the exponential function with the natural constant as the base and the inverse of the electricity correlation coefficient as the exponent is used as the corresponding electricity surplus and deficit complementarity factor; the electricity surplus and deficit complementarity factor minus the value of 1 is used as the corresponding surplus and deficit complementarity index, and all surplus and deficit complementarity indices less than 0 are set to 0.

[0086] It should be noted that the calculation of the Pearson correlation coefficient is a well-known technique. The Pearson correlation coefficient ranges from -1 to 1. When the Pearson correlation coefficient between the power load surplus and deficit sequences of two microgrids is 1, it indicates that there is a completely positive linear relationship between the power load surplus and deficit sequences of the two microgrids. That is, when the generation surplus of one microgrid increases, the generation surplus of the other microgrid also increases by a fixed proportion. When the Pearson correlation coefficient between the power load surplus and deficit sequences of two microgrids is -1, it indicates that there is a completely negative linear relationship between the power load surplus and deficit sequences of the two microgrids. That is, when the generation surplus of one microgrid increases, the generation surplus of the other microgrid decreases by a fixed proportion. When the Pearson correlation coefficient between the power load surplus and deficit sequences of two microgrids is 0, it indicates that there is no linear relationship between the power load surplus and deficit sequences of the two microgrids. That is, the change in the generation surplus of the two microgrids does not have a fixed proportional relationship.

[0087] When the power correlation coefficient between two microgrids is less than 0, it indicates that there is a complementary relationship between the power generation capacity of the two microgrids. The profit and loss complementarity index between the two microgrids is calculated by inverse proportional truncation processing, so that the profit and loss complementarity index between all microgrids is greater than or equal to 0. The larger the profit and loss complementarity index, the stronger the complementary relationship between the power generation capacity of the two microgrids. When aggregating microgrids, two microgrids should be aggregated into the same power cluster to enhance the stability of the power cluster.

[0088] As a preferred embodiment of the above, in step S30, the power support gap filling factor between microgrids is calculated by combining the complementarity index, the complementarity characteristics of power generation and power load. The steps include:

[0089] S321: The average power generation of all microgrids at the same data collection time is defined as the average power generation at the data collection time; the difference between the power generation of each microgrid at each data collection time and the average power generation is defined as the individual power generation difference index of the microgrid; the maximum absolute value of the individual power generation difference index of two microgrids at each data collection time is defined as the individual power generation difference extreme factor of the microgrid combination at the corresponding data collection time; the average absolute value of the individual power generation difference index of two microgrids at all data collection times is defined as the complementary power generation difference index of the microgrid combination at the corresponding data collection time; the difference between the complementary power generation difference index and the individual power generation difference extreme factor is defined as the power generation complementarity factor of the microgrid combination at the corresponding data collection time; the average value of the power generation complementarity factors at all data collection times is defined as the power generation complementarity index between the two microgrids.

[0090] S322: The average power generation load of all microgrids at the same data collection time is defined as the average power generation load at the data collection time; the difference between the power generation load of each microgrid at each data collection time and the average power generation load is defined as the individual load difference index of the microgrid; the maximum absolute value of the individual load difference index of two microgrids at each data collection time is defined as the individual load difference extreme factor of the microgrid combination at the corresponding data collection time; the average absolute value of the individual load difference index of two microgrids at all data collection times is defined as the complementary load difference index of the microgrid combination at the corresponding data collection time; the difference between the complementary load difference index and the individual load difference extreme factor is defined as the load complementarity factor of the microgrid combination at the corresponding data collection time; the average value of the load complementarity factors at all data collection times is defined as the load complementarity index between the two microgrids.

[0091] S323: Calculate the sum of the generation complementarity index and the load complementarity index between two microgrids, which is defined as the power complementarity factor between the two microgrids; calculate the product of the power complementarity factor and the profit and loss complementarity index, which is defined as the power support gap filling factor between the two microgrids.

[0092] By combining the power generation complementarity index, load complementarity index, and profit and loss complementarity index, a comprehensive analysis of the complementarity characteristics between microgrids can be conducted. This allows for a full quantification of the complementarity of microgrids in power generation and load, clarifies the synergistic effects between microgrids, and provides a scientific basis for optimizing aggregation decisions.

[0093] In this embodiment, in step S40, based on the Euclidean distance between the two microgrid locations and the similarity characteristics of the scale between the two microgrids, a generation characteristic similarity index between the two microgrids is calculated; based on the generation state stability index, a similarity feature aggregation weight between the microgrids is calculated; combining the generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight, a synergistic complementarity aggregation index between the microgrids is calculated, including:

[0094] S41: The product of the Euclidean distance between two microgrid locations and the absolute value of the difference in size between the two microgrids is defined as the generation characteristic dissimilarity index between the two microgrids. The generation characteristic dissimilarity index is processed to obtain the generation characteristic similarity index between the two microgrids.

[0095] S42: The normalized value of the product of the power support gap filling factor and the corresponding fault support complementarity factor between two microgrids is defined as the grid optimization complementarity factor between the two microgrids.

[0096] S43: Calculate the average value of the power generation state stability index of each microgrid across all monitoring periods, and define it as the power generation state stability mean of each microgrid; normalize the power generation state stability mean and define it as the similarity feature aggregation weight of each microgrid.

[0097] S44: Calculate the synergistic complementarity aggregation index between microgrids by combining the power generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight.

[0098] Taking into account the similarity of spatial location and scale (power generation characteristic similarity index), and integrating grid optimization capabilities (grid optimization complementary factors) and operational stability (similarity feature aggregation weights), a comprehensive and multi-dimensional quantitative indicator system is provided, which fully reflects the synergy and complementarity between microgrids.

[0099] It should be noted that the smaller the power generation stability index of the microgrid in all monitoring periods, the higher the fluctuation of the microgrid's power generation. When aggregating microgrids, the complementarity between microgrids should be taken into account to improve the stability and security of the system. The smaller the aggregation weight value of similar features, the better.

[0100] In step S40, the synergistic complementarity aggregation index between microgrids is calculated by combining the power generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight. The synergistic complementarity aggregation index between two microgrids is expressed as:

[0101] SH i，j =(1-w i，j )×HF i，j +w i，j ×Norm(FS i，j );

[0102]

[0103] In the formula, SH i，j FS is the synergistic and complementary aggregation index between the i-th microgrid and the j-th microgrid. i，j HF is the similarity index of power generation characteristics between the i-th microgrid and the j-th microgrid. i，j Let w be the grid optimization complementarity factor between the i-th microgrid and the j-th microgrid, and Norm() be the normalization function. i，j ws represents the collaborative aggregation weight between the i-th microgrid and the j-th microgrid. i For the similarity features aggregation weights of the i-th microgrid, ws j The similarity features are aggregated weights for the j-th microgrid.

[0104] It should be noted that the power generation characteristic similarity index represents the similarity between microgrids, while the grid optimization complementarity factor represents the complementarity between microgrids. When the aggregation weight of the similarity characteristics of microgrids is smaller, it indicates that the complementarity between microgrids should be considered more to improve the stability of the power system. When the aggregation weight of the similarity characteristics of microgrids is larger, it indicates that the similarity between microgrids should be considered more, and coordinated control can be carried out on microgrids with high similarity to improve the efficiency of microgrid control. When aggregating microgrids, different weights are set for the similarity and complementarity between two microgrids to evaluate the overall improvement in system efficiency after aggregating the two microgrids, thereby improving the reliability of microgrid aggregation.

[0105] As a preferred embodiment of the above, in step S50, a synergistic complementarity matrix is ​​constructed based on the synergistic complementarity aggregation index. Based on the synergistic complementarity matrix, a multidimensional spectral clustering algorithm is used to aggregate all microgrids to obtain each microgrid cluster. The steps include:

[0106] S51: Initialize a square matrix with all elements equal to 0, where the number of rows and columns is equal to the number of microgrids; based on the synergistic complementarity aggregation index between two microgrids, assign it to the intersection of the corresponding rows and columns in the square matrix to obtain the synergistic complementarity matrix;

[0107] S52: Based on the synergistic complementarity matrix, calculate the similarity relationship of each microgrid and construct a graph structure for cluster analysis; use a multidimensional spectral clustering algorithm to group all microgrids based on the feature vectors of the graph structure; output multiple microgrid clusters based on the clustering results, each microgrid cluster having synergistic complementarity characteristics.

[0108] By constructing a synergistic complementarity matrix through a synergistic complementarity aggregation index, the synergistic capabilities and complementary characteristics among microgrids are comprehensively quantified. The matrix structure intuitively expresses the relationships between microgrids, providing accurate and systematic basic data for subsequent clustering analysis. A multidimensional spectral clustering algorithm is adopted to cluster microgrids based on the feature vectors of the graph structure. The spectral clustering algorithm can efficiently handle complex nonlinear relationships, ensuring the scientific nature of the aggregation scheme. Each microgrid cluster has synergistic complementarity characteristics, which helps to achieve more accurate aggregation.

[0109] It should be noted that by improving the similarity matrix of the multidimensional spectral clustering algorithm, it is possible not only to aggregate distributed power sources with similar power generation characteristics and regulation capabilities to achieve seamless connection and coordinated operation between microgrids and distribution networks, but also to enable other microgrids to provide timely power support when a microgrid fails, thereby effectively reducing the risk of power outages, improving the aggregation efficiency of microgrids, and enhancing the resilience of the entire system.

[0110] The present invention also includes a microgrid aggregation device based on synergistic complementarity, using the method described above, comprising:

[0111] The data acquisition unit is used to record the location and scale of each microgrid, and to collect the power generation load and power generation of each microgrid in each monitoring cycle;

[0112] The stability analysis unit is used to calculate the power generation stability index of each microgrid in each monitoring period based on the volatility characteristics of power generation; and to calculate the fault support complementarity factor between the corresponding microgrids based on the complementarity characteristics of the power generation stability index.

[0113] The profit and loss analysis unit is used to construct the power load profit and loss sequence of each microgrid based on the matching degree of power generation and power load; calculate the profit and loss complementarity index between microgrids based on the complementarity characteristics of the power load profit and loss sequence; and calculate the power support gap filling factor between microgrids by combining the profit and loss complementarity index, the complementarity characteristics of power generation and power load.

[0114] The collaborative complementarity calculation unit is used to calculate the grid optimization complementarity factor between microgrids based on the linear relationship between the power support gap filling factor and the fault support complementarity factor; to calculate the generation characteristic similarity index between two microgrids based on the Euclidean distance between their locations and the similarity characteristics of their scales; to calculate the similarity feature aggregation weight between microgrids based on the generation state stability index; and to calculate the collaborative complementarity aggregation index between microgrids by combining the generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight.

[0115] The aggregation processing unit is used to construct a synergistic complementarity matrix based on the synergistic complementarity aggregation index, and then use a multidimensional spectral clustering algorithm to aggregate all microgrids based on the synergistic complementarity matrix to obtain each microgrid set.

[0116] Please see Figure 4 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0117] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0118] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0119] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0120] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0121] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0122] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0124] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0125] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0126] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A microgrid aggregation method based on synergistic complementarity, characterized in that, Includes the following steps: S10: Record the location and scale of each microgrid, and collect the power generation load and power generation of each microgrid in each monitoring cycle; S20: Based on the volatility characteristics of the power generation, calculate the power generation stability index of each microgrid in each monitoring cycle; Based on the complementary characteristics of the power generation stability index, the corresponding fault support complementarity factor between microgrids is calculated, including the following steps: S221: Define the maximum value of the power generation stability index as the optimal power generation index; combine all microgrids in pairs, calculate the difference between the power generation stability indices of the two microgrids in each pair, and define it as the power generation stability difference index of the pair; calculate the average value of the power generation stability indices of the two microgrids in each pair, and define it as the average power generation stability index of the pair. S222: Subtract the average power generation state index from the optimal power generation state index to define the optimal state difference factor for each combination; define the ratio of the power generation state difference index to the optimal state difference factor as the fault support complementarity factor for each combination. S30: Based on the matching degree between the power generation and the power generation load, construct the power load surplus / deficit sequence for each microgrid; based on the complementarity characteristics of the power load surplus / deficit sequence, calculate the surplus / deficit complementarity index between microgrids; combining the surplus / deficit complementarity index, the complementarity characteristics of the power generation and the power generation load, calculate the power support gap filling factor between microgrids. The steps include: S321: The average power generation of all microgrids at the same data collection time is defined as the average power generation at the data collection time; the difference between the power generation of each microgrid at each data collection time and the average power generation is calculated and defined as the individual power generation difference index of the microgrid; the maximum absolute value of the individual power generation difference index of two microgrids at each data collection time is calculated and defined as the individual power generation difference extreme factor of the microgrid combination at the corresponding data collection time; the average absolute value of the individual power generation difference index of two microgrids at all data collection times is calculated and defined as the complementary power generation difference index of the microgrid combination at the corresponding data collection time; the difference between the complementary power generation difference index and the individual power generation difference extreme factor is calculated and defined as the power generation complementarity factor of the microgrid combination at the corresponding data collection time; the average value of the power generation complementarity factors at all data collection times is defined as the power generation complementarity index between the two microgrids. S322: The average power generation load of all microgrids at the same acquisition time is defined as the average power generation load at the acquisition time; the difference between the power generation load of each microgrid at each acquisition time and the average power generation load is defined as the individual load difference index of the microgrid; the maximum absolute value of the individual load difference index of two microgrids at each acquisition time is defined as the individual load difference extreme factor of the microgrid combination at the corresponding acquisition time; the average absolute value of the individual load difference index of two microgrids at all acquisition times is defined as the complementary load difference index of the microgrid combination at the corresponding acquisition time; the difference between the complementary load difference index and the individual load difference extreme factor is defined as the load complementarity factor of the microgrid combination at the corresponding acquisition time; the average value of the load complementarity factors at all acquisition times is defined as the load complementarity index between the two microgrids. S323: Calculate the sum of the generation complementarity index and the load complementarity index between the two microgrids, and define it as the power complementarity factor between the two microgrids; calculate the product of the power complementarity factor and the profit and loss complementarity index, and define it as the power support gap filling factor between the two microgrids. S40: Based on the linear relationship between the power support gap filling factor and the fault support complementarity factor, calculate the grid optimization complementarity factor between microgrids; based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of their scales, calculate the generation characteristic similarity index between the two microgrids; based on the generation state stability index, calculate the similarity feature aggregation weight between the microgrids; combining the generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight, calculate the cooperative complementarity aggregation index between the microgrids. The steps include: S41: The product of the Euclidean distance between two microgrid locations and the absolute value of the difference in size between the two microgrids is defined as the power generation characteristic dissimilarity index between the two microgrids. The power generation characteristic dissimilarity index is processed to obtain the power generation characteristic similarity index between the two microgrids. S42: The normalized value of the product of the power support gap filling factor and the corresponding fault support complementarity factor between the two microgrids is defined as the grid optimization complementarity factor between the two microgrids. S43: Calculate the average value of the power generation state stability index for each microgrid across all monitoring periods, and define it as the power generation state stability mean for each microgrid; normalize the power generation state stability mean and define it as the similarity feature aggregation weight for each microgrid; S44: Calculate the synergistic complementarity aggregation index between microgrids by combining the power generation characteristic similarity index, the power grid optimization complementarity factor, and the similarity feature aggregation weight; S50: Based on the aforementioned synergistic and complementary aggregation index, construct a synergistic and complementary matrix. Based on the aforementioned synergistic and complementary matrix, use a multidimensional spectral clustering algorithm to aggregate all microgrids to obtain each microgrid cluster.

2. The microgrid aggregation method based on synergistic complementarity according to claim 1, characterized in that, In step S20, based on the volatility characteristics of the power generation, the power generation stability index of each microgrid in each monitoring cycle is calculated. The steps include: S211: Based on the power generation, construct a power generation time series for each microgrid in each monitoring period, calculate the average value of all elements in the power generation time series, and define it as the axial power generation of the microgrid in the corresponding monitoring period; calculate the absolute value of the difference between the power generation at each acquisition time and the axial power generation in the corresponding monitoring period, as the axial power difference of the microgrid at each acquisition time. S212: Calculate the remainder between each acquisition time and the length of the monitoring cycle, which is defined as the relative time within the cycle corresponding to the acquisition time; calculate the average power generation of each microgrid at the relative time within the same cycle in all monitoring cycles, which is defined as the longitudinal power generation at the corresponding relative time within the cycle; calculate the absolute value of the difference between the power generation at each acquisition time and the longitudinal power generation at the corresponding relative time within the cycle, as the longitudinal power difference of each microgrid at each acquisition time. S213: Based on the Pettt mutation point detection algorithm, mutation point detection is performed on each of the power generation time series, and the power mutation index of each mutation point is calculated, which is defined as the sum of the axial power difference and the longitudinal power difference of the corresponding mutation point; the average value of the power mutation index of all mutation points of each microgrid is calculated and multiplied by the total number of mutation points, which is defined as the power generation state runaway factor; the exponential function value is calculated with the natural constant as the base and the negative number of the power generation state runaway factor as the exponent, which is defined as the power generation state stability index of the microgrid.

3. The microgrid aggregation method based on synergistic complementarity according to claim 1, characterized in that, In step S30, based on the matching degree between the power generation and the power generation load, a power load surplus / deficit sequence for each microgrid is constructed; Based on the complementary characteristics of the power load surplus and deficit sequence, the surplus and deficit complementarity index between microgrids is calculated, including the following steps: S311: Define the difference between the power generation and the power generation load of each microgrid at each acquisition time as the power generation surplus of each microgrid at each acquisition time, and arrange the power generation surplus in ascending order according to the acquisition time to construct the power load surplus and deficit sequence of each microgrid. S312: Calculate the Pearson correlation coefficient between the two power load surplus / deficit sequences, which is defined as the power correlation coefficient between the two microgrids; The power correlation coefficient is truncated inversely to obtain the profit and loss complementarity index between the two microgrids.

4. The microgrid aggregation method based on synergistic complementarity according to claim 1, characterized in that, In step S40, combining the power generation characteristic similarity index, the power grid optimization complementarity factor, and the similarity feature aggregation weight, the synergistic complementarity aggregation index between microgrids is calculated, and the synergistic complementarity aggregation index between two microgrids is expressed as: ； ； In the formula, Let be the synergistic and complementary aggregation index between the i-th microgrid and the j-th microgrid. This is the similarity index of power generation characteristics between the i-th microgrid and the j-th microgrid. The grid optimization complementarity factor between the i-th microgrid and the j-th microgrid is given. For normalization function, The collaborative aggregation weights between the i-th microgrid and the j-th microgrid are... The similarity features of the i-th microgrid are aggregated as weights. The similarity features are aggregated weights for the j-th microgrid.

5. The microgrid aggregation method based on synergistic complementarity according to claim 1, characterized in that, In step S50, a synergistic complementarity matrix is ​​constructed based on the synergistic complementarity aggregation index. Based on the synergistic complementarity matrix, a multidimensional spectral clustering algorithm is used to aggregate all microgrids to obtain each microgrid cluster. The steps include: S51: Initialize a square matrix with all elements equal to 0, where the number of rows and columns of the matrix are equal to the number of microgrids; based on the synergistic complementarity aggregation index between two microgrids, assign it to the intersection of the corresponding rows and columns in the square matrix to obtain the synergistic complementarity matrix; S52: Based on the aforementioned synergistic complementarity matrix, calculate the similarity relationship of each microgrid and construct a graph structure for cluster analysis; use a multidimensional spectral clustering algorithm to group all microgrids based on the feature vectors of the graph structure; output multiple microgrid clusters based on the clustering results, each microgrid cluster having synergistic complementarity characteristics.

6. A microgrid aggregation device based on synergistic complementarity, characterized in that, Using the method as described in any one of claims 1 to 5, comprising: The data acquisition unit is used to record the location and scale of each microgrid, and to collect the power generation load and power generation of each microgrid in each monitoring cycle; The stability analysis unit is used to calculate the power generation stability index of each microgrid in each monitoring period based on the volatility characteristics of the power generation; and to calculate the fault support complementarity factor between the corresponding microgrids based on the complementarity characteristics of the power generation stability index. The profit and loss analysis unit is used to construct the power load profit and loss sequence of each microgrid based on the matching degree of the power generation and the power generation load; calculate the profit and loss complementarity index between microgrids based on the complementarity characteristics of the power load profit and loss sequence; and calculate the power support gap filling factor between microgrids by combining the profit and loss complementarity index, the power generation and the power generation load complementarity characteristics. The collaborative complementarity calculation unit is used to calculate the grid optimization complementarity factor between microgrids based on the linear relationship between the power support gap filling factor and the fault support complementarity factor; calculate the generation characteristic similarity index between two microgrids based on the Euclidean distance between their locations and the similarity characteristics of their scales; calculate the similarity feature aggregation weight between microgrids based on the generation state stability index; and calculate the collaborative complementarity aggregation index between microgrids by combining the generation characteristic similarity index, the grid optimization complementarity factor, and the similarity feature aggregation weight. The aggregation processing unit is used to construct a synergistic complementarity matrix based on the synergistic complementarity aggregation index, and to perform aggregation processing on all microgrids using a multidimensional spectral clustering algorithm based on the synergistic complementarity matrix to obtain each microgrid cluster.

7. A computer device, comprising 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 the method as described in any one of claims 1-5.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1-5.

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