Micro-grid aggregation method, device and equipment based on collaborative complementation and medium

Abstract

The invention relates to the technical field of micro-grid operation optimization, in particular to a micro-grid aggregation method, device and equipment based on collaborative complementation and a medium, and the method comprises the steps: firstly analyzing the fluctuation characteristics of the generating capacity of each micro-grid, and calculating the stable index of the generating state; secondly, the complementarity characteristics between the micro-grids are analyzed in combination with the power generation amount and the power generation load, and power grid optimization complementarity factors are calculated; secondly, analyzing similarity characteristics among the micro-grids, and calculating power generation characteristic similarity indexes; and finally, calculating a similar feature aggregation weight based on all power generation state stationary indexes, calculating a collaborative complementary aggregation index by combining a power generation characteristic similarity index and a power grid optimization complementary factor, constructing a collaborative complementary matrix, and performing aggregation processing on all micro-grids to obtain a micro-grid cluster. Through efficient aggregation of the micro-grid, efficient control of the micro-grid is realized, and stability and safety of a power generation system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid operation optimization, and in particular to a microgrid aggregation method, device, equipment and medium based on synergy and complementarity. Background Art

[0002] Microgrid is a small independent power system based on distributed energy resources. It is composed of multiple energy sources, such as solar energy, wind energy, energy storage systems, etc., as well as energy conversion equipment and user loads. It provides clean, reliable and flexible power supply, can operate independently and achieve interconnection with traditional power grids. Microgrid uses renewable energy to generate electricity, significantly reducing greenhouse gas emissions, and contributing to environmental protection and sustainable development. Microgrids can meet the power needs of remote islands, mountainous areas and other regions, and are gradually penetrating into urban communities to promote the process of intelligence. With the promotion of reliable power use, safe power use, efficient and economical power use, orderly power use and other power needs, enterprises are gradually increasing their willingness to configure microgrids.

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

[0004] However, since the energy resources of microgrids include not only traditional energy sources such as gas and oil, but also renewable energy sources such as solar energy and wind energy, and the size and nature of the power load will also affect the stability of the microgrid, the existing microgrid aggregation technology is not efficient in aggregating microgrids. When a microgrid in the power cluster fails or the power demand fluctuates, other microgrids may also fail or the power demand fluctuates at the same time.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a microgrid aggregation method, device, equipment and medium based on collaborative complementarity, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a microgrid aggregation method based on synergy and complementarity, comprising the following steps:

[0008] S10: record the location and size of each microgrid, and collect the power generation load and power generation of each microgrid in each monitoring period;

[0009] S20: Based on the fluctuation characteristics of the power generation, calculating the power generation state stability index of each microgrid in each monitoring period; based on the complementary characteristics of the power generation state stability index, calculating the fault support complementary factors between the corresponding microgrids;

[0010] S30: constructing a power load profit and loss sequence of each microgrid based on the matching degree of the power generation and the power generation load; calculating a profit and loss complementarity index between microgrids based on the complementary characteristics of the power load profit and loss sequence; and calculating a power support gap filling factor between microgrids based on the profit and loss complementarity index, the power generation and the complementary characteristics of the power generation load;

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

[0012] S50: Based on the synergistic and complementary aggregation index, a synergistic and complementary matrix is ​​constructed, and based on the synergistic and complementary matrix, a multi-dimensional spectral clustering algorithm is used to perform aggregation processing on all microgrids to obtain various microgrid clusters.

[0013] Further, in step S20, based on the fluctuation characteristics of the power generation, the power generation state stability index of each microgrid in each monitoring period is calculated, and the steps include:

[0014] S211: Based on the power generation, construct a 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 collection moment and the axial power generation of the corresponding monitoring period as the axial power difference of the microgrid at each collection moment;

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

[0016] S213: Based on the Pettitt 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 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 out-of-control factor; with the natural constant as the base and the opposite number of the power generation state out-of-control factor as the exponent, the exponential function value is calculated, 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 fault support complementary factors between the corresponding microgrids are calculated, and the steps include:

[0018] S221: defining the maximum value of the power generation state stability index as the optimal power generation state index; combining all microgrids in pairs, respectively calculating the difference between the power generation state stability indexes of the two microgrids in each combination, and defining it as the power generation state difference index of the combination; calculating the average value of the power generation state stability indexes of the two microgrids in each combination, and defining it as the power generation state average index of the combination;

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

[0020] Further, in step S30, based on the matching degree between the power generation and the power generation load, a power load profit and loss sequence of each microgrid is constructed; based on the complementary characteristics of the power load profit and loss sequence, a profit and loss complementary index between microgrids is calculated, and the steps include:

[0021] S311: defining the difference between the power generation and the power generation load of each microgrid at each collection time as the power generation margin of each microgrid at each collection time, arranging the power generation margin in ascending order according to the time sequence of acquisition, and constructing a power load profit and loss sequence of each microgrid;

[0022] S312: Calculate the Pearson correlation coefficient between the two power load profit and loss sequences, which is defined as the power correlation coefficient between the two microgrids; perform inverse proportional truncation processing on the power correlation coefficient to obtain a profit and loss complementary index between the two microgrids.

[0023] Further, in step S30, the power support gap filling factor between microgrids is calculated by combining the profit and loss complementarity index, the power generation and the complementary characteristics of the power generation load, and the steps include:

[0024] S321: define the average value of the power generation of all microgrids at the same collection time as the average power generation at the collection time; calculate the difference between the power generation of each microgrid at each collection time and the average power generation, and define it as the individual power generation difference index of the microgrid; calculate the maximum value of the absolute values ​​of the individual power generation difference indexes of the two microgrids at each collection time, and define it as the individual power generation difference extreme factor of the microgrid combination at the corresponding collection time; calculate the average value of the absolute values ​​of the individual power generation difference indexes of the two microgrids at all collection times, and define it as the complementary power generation difference index of the microgrid combination at the corresponding collection time; calculate the difference between the complementary power generation difference index and the individual power generation difference extreme factor, and define it as the power generation complementary factor of the microgrid combination at the corresponding collection time; define the average value of the power generation complementary factors at all the collection times as the power generation complementary index between the two microgrids;

[0025] S322: The average value of the power generation load of all microgrids at the same collection time is defined as the average power generation load at the collection time; the difference between the power generation load of each microgrid and the average power generation load at each collection time is calculated, which is defined as the individual load difference index of the microgrid; the maximum value of the absolute values ​​of the individual load difference indexes of the two microgrids at each collection time is calculated, which is defined as the individual load difference extreme factor of the microgrid combination at the corresponding collection time; the average value of the absolute values ​​of the individual load difference indexes of the two microgrids at all collection times is calculated, which is defined as the complementary load difference index of the microgrid combination at the corresponding collection time; the difference between the complementary load difference index and the individual load difference extreme factor is calculated, which is defined as the load complementary factor of the microgrid combination at the corresponding collection time; the average value of the load complementary factors at all the collection times is defined as the load complementary index between the two microgrids;

[0026] S323: Calculate the sum of the power generation complementarity index and the load complementarity index between the 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.

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

[0028] S41: defining the product of the Euclidean distance between the locations of the two microgrids and the absolute value of the difference in scale between the two microgrids as a power generation characteristic difference index between the two microgrids, and processing the power generation characteristic difference index to obtain a power generation characteristic similarity index between the two microgrids;

[0029] S42: defining a normalized value of the product of the power support gap filling factor between two microgrids and the corresponding fault support complementary factor as a grid optimization complementary factor between the two microgrids;

[0030] S43: Calculate the average value of the power generation state stability index of each microgrid in all monitoring cycles, and define it as the power generation state stability mean value of each microgrid; perform normalization processing on the power generation state stability mean value, and define it as the similar feature aggregation weight of each microgrid;

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

[0032] Further, in step S40, the synergistic complementary aggregation index between the microgrids is calculated by combining the power generation characteristic similarity index, the grid optimization complementary factor and the similar feature aggregation weight, and the synergistic complementary 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 is the synergistic and complementary aggregation index between the i-th microgrid and the j-th microgrid, FS i，j is the similarity index of power generation characteristics between the i-th microgrid and the j-th microgrid, HF i，j is the grid optimization complementary factor between the i-th microgrid and the j-th microgrid, Norm() is the normalization function, and w i，j is the collaborative aggregation weight between the i-th microgrid and the j-th microgrid, ws i is the similar feature aggregation weight of the i-th microgrid, ws j is the aggregation weight of similar features of the jth microgrid.

[0036] Further, in step S50, based on the synergistic and complementary aggregation index, a synergistic and complementary matrix is ​​constructed, and based on the synergistic and complementary matrix, a multi-dimensional spectral clustering algorithm is used to aggregate all microgrids to obtain each microgrid cluster, and the steps include:

[0037] S51: Initialize a square matrix in which all element values ​​are 0, and the number of rows and columns of the square matrix are equal to the number of microgrids; assign the synergistic and complementary aggregation index between the two microgrids to the intersection of the corresponding rows and columns in the square matrix to obtain a synergistic and complementary matrix;

[0038] S52: Based on the synergistic and complementary matrix, the similarity relationship of each microgrid is calculated to construct a graph structure for clustering analysis; a multidimensional spectral clustering algorithm is used to group all microgrids based on the characteristic vector of the graph structure; and according to the clustering results, multiple microgrid clusters are output, each microgrid cluster having synergistic and complementary characteristics.

[0039] The present invention also includes a microgrid aggregation device based on synergy and complementarity, using the method as described above, including:

[0040] A data acquisition unit is used to record the location and size of each microgrid and collect the power generation load and power generation of each microgrid in each monitoring period;

[0041] A stability analysis unit, configured to calculate a power generation state stability index of each microgrid in each monitoring period based on the fluctuation characteristics of the power generation; and calculate a fault support complementary factor between corresponding microgrids based on the complementary characteristics of the power generation state stability index;

[0042] A profit and loss analysis unit, configured to construct a power load profit and loss sequence of each microgrid based on the matching degree of the power generation and the power generation load; calculate a profit and loss complementarity index between microgrids based on the complementary characteristics of the power load profit and loss sequence; and calculate a power support gap filling factor between microgrids based on the profit and loss complementarity index, the power generation and the complementary characteristics of the power generation load;

[0043] A synergistic and complementary calculation unit is used to calculate the grid optimization complementary factor between the microgrids based on the linear relationship between the power support gap filling factor and the fault support complementary factor; calculate the power generation characteristic similarity index between the two microgrids based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of the scales between the two microgrids; calculate the similarity feature aggregation weight between the microgrids based on the power generation state stability index; calculate the synergistic and complementary aggregation index between the microgrids by combining the power generation characteristic similarity index, the grid optimization complementary 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 multi-dimensional spectral clustering algorithm based on the synergistic complementarity matrix to obtain various microgrid clusters.

[0045] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0046] The present invention also includes a storage medium on which a computer program is stored. Further, when the computer program is executed by a processor, the method as described above is implemented.

[0047] The beneficial effects of the present invention are:

[0048] 1. The present invention proposes a microgrid aggregation method and system based on collaborative complementarity, which records the location and scale of each microgrid, collects the power generation load and power generation of each microgrid in each monitoring period; firstly, the volatility characteristics of the power generation of each microgrid are analyzed, and the power generation state stability index of each microgrid in each monitoring period is calculated, and the corresponding fault support complementary factors between the microgrids are calculated based on the complementary characteristics of the power generation state stability index between the microgrids, so as to evaluate the complementary characteristics between the microgrids as a whole; then, the matching degree of the power generation and power generation load of each microgrid is analyzed, and the power load profit and loss sequence of each microgrid is constructed. The complementary characteristics of the profit and loss sequences of the electric quantity and load between the microgrids are used to calculate the profit and loss complementary index between the microgrids. The power support gap filling factor between the microgrids is calculated based on the profit and loss complementary index, the complementary characteristics of the power generation between the microgrids, and the complementary characteristics of the power generation load, so as to evaluate the complementary characteristics between the microgrids at each acquisition moment; the complementary characteristics between the microgrids as a whole and the complementary characteristics between the microgrids at each acquisition moment are comprehensively considered, and the grid optimization complementary factor between the microgrids is calculated based on the linear relationship between the power support gap filling factor and the fault support complementary factor, thereby improving the reliability of the evaluation of the complementary characteristics between the microgrids;

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

[0050] 3. A synergistic and complementary matrix is ​​constructed based on the synergistic and complementary aggregation index between all microgrids. All microgrids are aggregated based on the synergistic and complementary matrix to obtain various microgrid clusters. By improving the similarity matrix of the multidimensional spectral clustering algorithm, not only can distributed power sources with similar power generation characteristics and regulation capabilities be aggregated to achieve seamless connection and coordinated operation between microgrids and distribution networks, but also when a microgrid fails, other microgrids can provide power support in time, thereby effectively reducing the risk of power outages, improving the aggregation efficiency of microgrids, and enhancing the resilience of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a flowchart of the microgrid aggregation method based on synergy and complementarity;

[0053] Figure 2 Schematic diagram for obtaining the synergistic complementation matrix;

[0054] Figure 3 It is a structural schematic diagram of a microgrid aggregation device based on synergy and complementarity;

[0055] Figure 4 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

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

[0058] S10: Record the location and size of each microgrid, collect the power generation load and power generation of each microgrid in each monitoring period; establish basic operation data of each microgrid;

[0059] Among them, the location and scale of each microgrid are recorded, and 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 both artificially preset values. As an embodiment of the present application, the collection time interval t is 30, and the length of the monitoring cycle is 1 day;

[0060] S20: Based on the fluctuation characteristics of power generation, the power generation state stability index of each microgrid in each monitoring period is calculated; based on the complementary characteristics of the power generation state stability index, the fault support complementary factors between the corresponding microgrids are calculated;

[0061] It should be noted that the use of multi-dimensional spectral clustering algorithm 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, and improve the efficiency and reliability of the entire power system. However, the existing aggregation of microgrids does not take into account the complementary characteristics between microgrids. When similar microgrids in the same power cluster fail at the same time, the power cluster will be paralyzed, greatly reducing the safety and stability of the power grid.

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

[0063] S30: Based on the matching degree of power generation and power generation load, construct the power load profit and loss sequence of each microgrid; based on the complementary characteristics of the power load profit and loss sequence, calculate the profit and loss complementary index between microgrids; combine the profit and loss complementary index, power generation and power generation load complementary characteristics, calculate the power support gap filling factor between microgrids;

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

[0065] Therefore, when aggregating microgrids, we should try to make the microgrids with large differences in power generation and power generation load within the power cluster complement each other, the microgrids with large differences in power generation at the same time complement each other, and the 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 complementary factor, the grid optimization complementary factor between the microgrids is calculated; based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of the scales between the two microgrids, the power generation characteristic similarity index between the two microgrids is calculated; based on the power generation state stability index, the similarity feature aggregation weight between the microgrids is calculated; combined with the power generation characteristic similarity index, the grid optimization complementary factor and the similarity feature aggregation weight, the synergistic complementary aggregation index between the microgrids is calculated;

[0067] When aggregating microgrids, not only the similarity between microgrids should be considered, but also the complementarity between microgrids should be considered to improve the stability of the power system. In order to balance the influence of the two factors, different weights should be set for the two factors according to the volatility characteristics of the 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 multi-dimensional spectral clustering algorithm is used to aggregate all microgrids to obtain various microgrid clusters.

[0069] It should be noted that the multidimensional spectral clustering algorithm regards each sample in the data set as a vertex in the graph. The vertices are connected by edges. The weight of the edge represents the similarity between samples. The similarity between vertices of the same class is very high, and the weight of the edge is large; the similarity between vertices of different classes is very low, and the weight of the edge is small. The ultimate goal of spectral clustering is to find a method to cut the graph so that the weight within each subgraph after cutting is large, and the weight between subgraphs is small.

[0070] The present invention proposes a microgrid aggregation method and system based on collaborative complementarity, which records the location and scale of each microgrid, collects the power generation load and power generation of each microgrid in each monitoring period; firstly, the volatility characteristics of the power generation of each microgrid are analyzed, and the power generation state stability index of each microgrid in each monitoring period is calculated; based on the complementary characteristics of the power generation state stability index between the microgrids, the corresponding fault support complementary factors between the microgrids are calculated to evaluate the complementary characteristics between the microgrids as a whole; then, the matching degree of the power generation and power generation load of each microgrid is analyzed, and the power load profit and loss sequence of each microgrid is constructed. The profit and loss complementary index between microgrids is calculated based on the complementary characteristics of the profit and loss series of electricity and load between microgrids. The power support gap filling factor between microgrids is calculated based on the profit and loss complementary index, the complementary characteristics of power generation between microgrids, and the complementary characteristics of power generation load, so as to evaluate the complementary characteristics between microgrids at each collection moment; the complementary characteristics between microgrids as a whole and between microgrids at each collection moment are comprehensively considered, and the grid optimization complementary factor between microgrids is calculated based on the linear relationship between the power support gap filling factor and the fault support complementary factor, so as to improve the reliability of the evaluation of the complementary characteristics between microgrids;

[0071] Based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of the scales between the two microgrids, the similarity index of the power generation characteristics between the two microgrids is calculated. Based on the stable index of the power generation state of the microgrids in all monitoring periods, the aggregation weight of the similarity characteristics between the microgrids is calculated. Based on the similarity index of the power generation characteristics between the microgrids, the grid optimization complementary factor and the aggregation weight of the similarity characteristics of each microgrid, the synergistic complementary aggregation index between the microgrids is calculated. The similarity index of the power generation characteristics represents the similarity between the microgrids, and the grid optimization complementary factor represents the complementarity between the microgrids. When aggregating the microgrids, by analyzing the volatility characteristics of the microgrid power generation, different weights are set for the similarity and complementarity between the two microgrids to evaluate the comprehensive improvement of the system efficiency after aggregating the two microgrids, thereby improving the reliability of aggregating the microgrids.

[0072] Based on the synergistic and complementary aggregation index between all microgrids, a synergistic and complementary matrix is ​​constructed. Based on the synergistic and complementary matrix, all microgrids are aggregated to obtain various microgrid clusters. By improving the similarity matrix of the multidimensional spectral clustering algorithm, not only can the distributed power sources with similar power generation characteristics and regulation capabilities be aggregated to achieve seamless connection and coordinated operation between microgrids and distribution networks, but also when a microgrid fails, other microgrids can provide power support in time, 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 fluctuation characteristics of power generation, the power generation state stability index of each microgrid in each monitoring period is calculated, and the steps include:

[0074] S211: Based on the power generation, construct a 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 collection moment and the axial power generation of the corresponding monitoring period as the axial power difference of the microgrid at each collection moment;

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

[0076] S213: Based on the Pettitt mutation point detection algorithm, mutation point detection is performed on each 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 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 out-of-control factor; the exponential function value is calculated with the natural constant as the base and the opposite number of the power generation state out-of-control factor as the exponent, which is defined as the power generation state stability index of the microgrid.

[0077] By constructing the time series of power generation and calculating the axial and longitudinal power difference, the power generation volatility analysis is refined, especially the longitudinal difference calculation based on periodic characteristics, which supplements the shortcomings of traditional one-way fluctuation analysis and can more accurately describe the stability of the microgrid's power generation state, providing a scientific basis for the subsequent evaluation of the microgrid's 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 fault support complementary factors between the corresponding microgrids are calculated, and the steps include:

[0079] S221: defining the maximum value of the power generation state stability index as the optimal power generation state index; combining all microgrids in pairs, respectively calculating the difference between the power generation state stability indexes of the two microgrids in each combination, and defining it as the power generation state difference index of the combination; calculating the average value of the power generation state stability indexes of the two microgrids in each combination, and defining it as the power generation state average index of the combination;

[0080] S222: The optimal state difference factor of each combination is defined as the optimal state difference index minus the average power generation state index; and the ratio of the power generation state difference index to the optimal state difference factor is defined as the fault support complementary factor of each combination.

[0081] It should be noted that the power generation state difference index represents the difference in power generation state when the two microgrids act separately, and the optimal state difference factor represents the power generation state after the two microgrids are aggregated. The greater the difference in power generation state when the two microgrids act separately and the better the power generation state after the two microgrids are aggregated, it means that when one of the microgrids fails, the other microgrid is more likely to provide power support in time, thereby effectively reducing the risk of power outages and enhancing the stability of the entire system. The greater the fault support complementary factor value of the combination of the two microgrids.

[0082] Wherein, in step S30, based on the matching degree between power generation and power generation load, the power load profit and loss sequence of each microgrid is constructed; based on the complementary characteristics of the power load profit and loss sequence, the profit and loss complementary index between the microgrids is calculated, and the steps include:

[0083] S311: The difference between the power generation and the power generation load of each microgrid at each collection time is defined as the power generation surplus of each microgrid at each collection time, and the power generation surplus is arranged in ascending order according to the time sequence of acquisition, so as to construct the power load profit and loss sequence of each microgrid;

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

[0085] The inverse proportional truncation processing is designed as follows: the calculation result of the exponential function with the natural constant as the base and the opposite of the electricity correlation coefficient as the exponent is used as the corresponding electricity profit and loss complementary factor; the electricity profit and loss complementary factor minus the value 1 is used as the corresponding profit and loss complementary index, and all profit and loss complementary 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 technology, and the value range of the Pearson correlation coefficient is -1 to 1. When the Pearson correlation coefficient between the power load profit and loss sequences of two microgrids is 1, it means that there is a completely positive linear relationship between the power load profit and loss sequences of the two microgrids, that is, when the power generation surplus of one microgrid increases, the power generation surplus of the other microgrid also increases at a fixed ratio; when the Pearson correlation coefficient between the power load profit and loss sequences of the two microgrids is -1, it means that there is a completely negative linear relationship between the power load profit and loss sequences of the two microgrids, that is, when the power generation surplus of one microgrid increases, the power generation surplus of the other microgrid decreases at a fixed ratio; when the Pearson correlation coefficient between the power load profit and loss sequences of the two microgrids is 0, it means that there is no linear relationship between the power load profit and loss sequences of the two microgrids, that is, there is no fixed proportional relationship between the changes in the power generation surplus of the two microgrids.

[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 surplus of the two microgrids. The profit and loss complementary index between the two microgrids is calculated by inverse proportional truncation processing, so that the profit and loss complementary index between all microgrids is greater than or equal to 0. The larger the profit and loss complementary index, the stronger the complementary relationship between the power generation surplus of the two microgrids. When aggregating the microgrids, the 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 complementary characteristics of the profit and loss complementarity index, the power generation and the power generation load, and the steps include:

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

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

[0091] S323: Calculate the sum of the power generation complementarity index and the load complementarity index between the 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 complementary characteristics between microgrids can be conducted, which can comprehensively quantify the complementarity of microgrids in power generation and load, clarify the synergy between microgrids, and provide a scientific basis for optimizing aggregation decisions.

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

[0094] S41: defining the product of the Euclidean distance between the locations of the two microgrids and the absolute value of the difference in scale between the two microgrids as a power generation characteristic difference index between the two microgrids, and processing the power generation characteristic difference index to obtain a power generation characteristic similarity index between the two microgrids;

[0095] S42: defining a normalized value of the product of a power support gap filling factor and a corresponding fault support complementary factor between two microgrids as a grid optimization complementary factor between the two microgrids;

[0096] S43: Calculate the average value of the power generation state stability index of each microgrid in all monitoring periods, which is defined as the power generation state stability mean of each microgrid; perform normalization processing on the power generation state stability mean, which is defined as the similar feature aggregation weight of each microgrid;

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

[0098] Taking into account the similarity of spatial location and scale (generation characteristic similarity index), while integrating the grid optimization capability (grid optimization complementary factor) and operation stability (similar feature aggregation weight), 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 stability index of the microgrid's power generation status in all monitoring periods is, the higher the fluctuation of the microgrid's power generation is. When aggregating microgrids, the more the complementarity between microgrids should be considered to improve the stability and security of the system, and the smaller the aggregation weight value of similar features is.

[0100] In step S40, the synergistic and complementary aggregation index between the microgrids is calculated by combining the power generation characteristic similarity index, the grid optimization complementary factor and the similar feature aggregation weight, and the synergistic and complementary aggregation index between the 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 is the synergistic and complementary aggregation index between the i-th microgrid and the j-th microgrid, FS i，j is the similarity index of power generation characteristics between the i-th microgrid and the j-th microgrid, HF i，j is the grid optimization complementary factor between the i-th microgrid and the j-th microgrid, Norm() is the normalization function, and w i，j is the collaborative aggregation weight between the i-th microgrid and the j-th microgrid, ws i is the similar feature aggregation weight of the i-th microgrid, ws j is the aggregation weight of similar features of the jth microgrid.

[0104] It should be noted that the power generation characteristic similarity index represents the similarity between microgrids, and the grid optimization complementary factor represents the complementarity between microgrids. When the aggregation weight of similar characteristics of microgrids is smaller, it means that the complementarity between microgrids should be taken into account more to improve the stability of the power system; when the aggregation weight of similar characteristics of microgrids is larger, it means that the similarity between microgrids should be taken into account more, and microgrids with higher similarity can be coordinated and controlled to improve the efficiency of controlling the microgrids. When aggregating microgrids, different weights are set for the similarity and complementarity between two microgrids to evaluate the comprehensive improvement of system efficiency after aggregating the two microgrids, thereby improving the reliability of aggregating microgrids.

[0105] As a preferred embodiment of the above embodiment, in step S50, based on the synergistic complementary aggregation index, a synergistic complementary matrix is ​​constructed, and based on the synergistic complementary matrix, a multi-dimensional spectral clustering algorithm is used to aggregate all microgrids to obtain each microgrid cluster, and the steps include:

[0106] S51: Initialize a square matrix in which all element values ​​are 0, and the number of rows and columns of the square matrix are equal to the number of microgrids; according to the synergistic and complementary aggregation index between the two microgrids, assign it to the intersection position of the corresponding row and column in the square matrix to obtain a synergistic and complementary matrix;

[0107] S52: Based on the synergistic and complementary matrix, the similarity relationship of each microgrid is calculated, and a graph structure for clustering analysis is constructed; a multidimensional spectral clustering algorithm is used to group all microgrids based on the eigenvectors of the graph structure; based on the clustering results, multiple microgrid clusters are output, and each microgrid cluster has synergistic and complementary characteristics.

[0108] The synergistic and complementary matrix is ​​constructed by the synergistic and complementary aggregation index, which comprehensively quantifies the synergistic ability and complementary characteristics between microgrids. The matrix structure intuitively expresses the relationship between microgrids, providing accurate and systematic basic data for subsequent clustering analysis. A multidimensional spectral clustering algorithm is used to cluster microgrids based on the eigenvectors of the graph structure. The spectral clustering algorithm can efficiently handle complex nonlinear relationships and ensure the scientific nature of the aggregation scheme. Each microgrid cluster has synergistic and complementary 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, not only can distributed power sources with similar power generation characteristics and regulation capabilities be aggregated to achieve seamless connection and coordinated operation between microgrids and distribution networks, but also when a microgrid fails, other microgrids can provide power support in a timely manner, 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 synergy and complementarity, using the above method, including:

[0111] A data acquisition unit is used to record the location and size of each microgrid and collect the power generation load and power generation of each microgrid in each monitoring period;

[0112] The stability analysis unit is used to calculate the power generation state stability index of each microgrid in each monitoring period based on the fluctuation characteristics of the power generation; based on the complementary characteristics of the power generation state stability index, calculate the fault support complementary factors between the corresponding microgrids;

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

[0114] A synergistic and complementary calculation unit is used to calculate the grid optimization complementary factor between the microgrids based on the linear relationship between the power support gap filling factor and the fault support complementary factor; calculate the power generation characteristic similarity index between the two microgrids based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of the scales between the two microgrids; calculate the similarity feature aggregation weight between the microgrids based on the power generation state stability index; calculate the synergistic and complementary aggregation index between the microgrids by combining the power generation characteristic similarity index, the grid optimization complementary 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 to aggregate all microgrids using a multi-dimensional spectral clustering algorithm based on the synergistic complementarity matrix to obtain each microgrid set.

[0116] See also Figure 4 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0117] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0118] Among them, 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, disk or optical disk.

[0119] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0120] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0121] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations 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 one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0122] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

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

[0124] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0125] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0126] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A microgrid aggregation method based on synergy and complementarity, characterized in that: The steps include: S10: record the location and size of each microgrid, and collect the power generation load and power generation of each microgrid in each monitoring period; S20: Based on the fluctuation characteristics of the power generation, calculating the power generation state stability index of each microgrid in each monitoring period; based on the complementary characteristics of the power generation state stability index, calculating the fault support complementary factors between the corresponding microgrids; S30: constructing a power load profit and loss sequence of each microgrid based on the matching degree of the power generation and the power generation load; calculating a profit and loss complementarity index between microgrids based on the complementary characteristics of the power load profit and loss sequence; and calculating a power support gap filling factor between microgrids based on the profit and loss complementarity index, the power generation and the complementary characteristics of the power generation load; S40: Based on the linear relationship between the power support gap filling factor and the fault support complementary factor, the grid optimization complementary factor between the microgrids is calculated; based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of the scales between the two microgrids, the power generation characteristic similarity index between the two microgrids is calculated; based on the power generation state stability index, the similarity feature aggregation weight between the microgrids is calculated; Calculate the synergy and complementarity aggregation index between microgrids by combining the power generation characteristic similarity index, the grid optimization complementary factor and the similarity feature aggregation weight; S50: Based on the synergistic and complementary aggregation index, a synergistic and complementary matrix is ​​constructed, and based on the synergistic and complementary matrix, a multi-dimensional spectral clustering algorithm is used to perform aggregation processing on all microgrids to obtain various microgrid clusters.

2. The microgrid aggregation method based on synergy and complementarity according to claim 1 is characterized in that: In step S20, based on the fluctuation characteristics of the power generation, the power generation state stability index of each microgrid in each monitoring period is calculated, and the steps include: S211: Based on the power generation, construct a 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 collection moment and the axial power generation of the corresponding monitoring period as the axial power difference of the microgrid at each collection moment; S212: Calculate the remainder of each acquisition moment and the length of the monitoring cycle, which is defined as the relative moment in the cycle corresponding to the acquisition moment; calculate the average value of the power generation of each microgrid at the relative moment in the same cycle in all monitoring cycles, which is defined as the longitudinal power generation at the relative moment in the corresponding cycle; calculate the absolute value of the difference between the power generation at each acquisition moment and the longitudinal power generation at the relative moment in the corresponding cycle, as the longitudinal power difference of each microgrid at each acquisition moment; S213: Based on the Pettitt 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 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 out-of-control factor; with the natural constant as the base and the opposite number of the power generation state out-of-control factor as the exponent, the exponential function value is calculated, which is defined as the power generation state stability index of the microgrid.

3. The microgrid aggregation method based on synergy and complementarity according to claim 1 is characterized in that: In step S20, based on the complementary characteristics of the power generation state stability index, the fault support complementary factors between the corresponding microgrids are calculated, and the steps include: S221: defining the maximum value of the power generation state stability index as the optimal power generation state index; combining all microgrids in pairs, respectively calculating the difference between the power generation state stability indexes of the two microgrids in each combination, and defining it as the power generation state difference index of the combination; calculating the average value of the power generation state stability indexes of the two microgrids in each combination, and defining it as the power generation state average index of the combination; S222: Subtract the power generation state average index from the power generation state best index to define the optimal state difference factor of each combination; define the ratio of the power generation state difference index to the optimal state difference factor as the fault support complementary factor of each combination.

4. The microgrid aggregation method based on synergy and complementarity according to claim 1 is characterized in that: In step S30, based on the matching degree between the power generation and the power generation load, a power load profit and loss sequence of each microgrid is constructed; Based on the complementary characteristics of the power load profit and loss sequence, the profit and loss complementary index between the microgrids is calculated, and the steps include: S311: defining the difference between the power generation and the power generation load of each microgrid at each collection time as the power generation margin of each microgrid at each collection time, arranging the power generation margin in ascending order according to the time sequence of acquisition, and constructing a power load profit and loss sequence of each microgrid; S312: Calculate the Pearson correlation coefficient between the two power load profit and loss sequences, which is defined as the power correlation coefficient between the two microgrids; The power correlation coefficient is subjected to inverse proportional truncation processing to obtain a profit and loss complementary index between the two microgrids.

5. The microgrid aggregation method based on synergy and complementarity according to claim 1 is characterized in that: In step S30, the power support gap filling factor between microgrids is calculated by combining the profit and loss complementarity index, the power generation and the complementary characteristics of the power generation load, and the steps include: S321: define the average value of the power generation of all microgrids at the same collection time as the average power generation at the collection time; calculate the difference between the power generation of each microgrid at each collection time and the average power generation, and define it as the individual power generation difference index of the microgrid; calculate the maximum value of the absolute values ​​of the individual power generation difference indexes of the two microgrids at each collection time, and define it as the individual power generation difference extreme factor of the microgrid combination at the corresponding collection time; calculate the average value of the absolute values ​​of the individual power generation difference indexes of the two microgrids at all collection times, and define it as the complementary power generation difference index of the microgrid combination at the corresponding collection time; calculate the difference between the complementary power generation difference index and the individual power generation difference extreme factor, and define it as the power generation complementary factor of the microgrid combination at the corresponding collection time; define the average value of the power generation complementary factors at all the collection times as the power generation complementary index between the two microgrids; S322: The average value of the power generation load of all microgrids at the same collection time is defined as the average power generation load at the collection time; the difference between the power generation load of each microgrid and the average power generation load at each collection time is calculated, which is defined as the individual load difference index of the microgrid; the maximum value of the absolute values ​​of the individual load difference indexes of the two microgrids at each collection time is calculated, which is defined as the individual load difference extreme factor of the microgrid combination at the corresponding collection time; the average value of the absolute values ​​of the individual load difference indexes of the two microgrids at all collection times is calculated, which is defined as the complementary load difference index of the microgrid combination at the corresponding collection time; the difference between the complementary load difference index and the individual load difference extreme factor is calculated, which is defined as the load complementary factor of the microgrid combination at the corresponding collection time; the average value of the load complementary factors at all the collection times is defined as the load complementary index between the two microgrids; S323: Calculate the sum of the power generation complementarity index and the load complementarity index between the 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.

6. The microgrid aggregation method based on synergy and complementarity according to claim 1 is characterized in that: In step S40, based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of the scales between the two microgrids, the power generation characteristic similarity index between the two microgrids is calculated, and based on the power generation state stability index, the similarity characteristic aggregation weight between the microgrids is calculated; Combining the power generation characteristic similarity index, the grid optimization complementary factor and the similarity feature aggregation weight, calculating the synergy complementary aggregation index between microgrids, including: S41: defining the product of the Euclidean distance between the locations of the two microgrids and the absolute value of the difference in scale between the two microgrids as a power generation characteristic difference index between the two microgrids, and processing the power generation characteristic difference index to obtain a power generation characteristic similarity index between the two microgrids; S42: defining a normalized value of the product of the power support gap filling factor between two microgrids and the corresponding fault support complementary factor as a grid optimization complementary factor between the two microgrids; S43: Calculate the average value of the power generation state stability index of each microgrid in all monitoring cycles, and define it as the power generation state stability mean value of each microgrid; perform normalization processing on the power generation state stability mean value, and define it as the similar feature aggregation weight of each microgrid; S44: Calculate the synergistic and complementary aggregation index between microgrids by combining the power generation characteristic similarity index, the grid optimization complementary factor and the similar feature aggregation weight.

7. The microgrid aggregation method based on synergy and complementarity according to claim 1 is characterized in that: In step S40, the synergistic complementary aggregation index between the microgrids is calculated by combining the power generation characteristic similarity index, the grid optimization complementary factor and the similarity feature aggregation weight, and the synergistic complementary aggregation index between the two microgrids is expressed as: SH i，j =(1-w i，j )×HF i，j +w i，j ×NOrm(FS i，j ); In the formula, SH i，j is the synergistic and complementary aggregation index between the i-th microgrid and the j-th microgrid, FS i，j is the similarity index of power generation characteristics between the i-th microgrid and the j-th microgrid, HF i，j is the grid optimization complementary factor between the i-th microgrid and the j-th microgrid, Norm() is the normalization function, and w i，j is the collaborative aggregation weight between the i-th microgrid and the j-th microgrid, ws i is the similar feature aggregation weight of the i-th microgrid, ws j is the aggregation weight of similar features of the jth microgrid.

8. The microgrid aggregation method based on synergy and complementarity according to claim 1 is characterized in that: In step S50, based on the synergistic and complementary aggregation index, a synergistic and complementary matrix is ​​constructed, and based on the synergistic and complementary matrix, a multi-dimensional spectral clustering algorithm is used to aggregate all microgrids to obtain microgrid clusters. The steps include: S51: Initialize a square matrix in which all element values ​​are 0, and the number of rows and columns of the square matrix are equal to the number of microgrids; assign the synergistic and complementary aggregation index between the two microgrids to the intersection of the corresponding rows and columns in the square matrix to obtain a synergistic and complementary matrix; S52: Based on the synergistic and complementary matrix, the similarity relationship of each microgrid is calculated to construct a graph structure for clustering analysis; a multidimensional spectral clustering algorithm is used to group all microgrids based on the characteristic vector of the graph structure; and according to the clustering results, multiple microgrid clusters are output, each microgrid cluster having synergistic and complementary characteristics.

9. A microgrid aggregation device based on synergy and complementarity, characterized in that: Use of the method according to any one of claims 1 to 8, comprising: A data acquisition unit is used to record the location and size of each microgrid and collect the power generation load and power generation of each microgrid in each monitoring period; A stability analysis unit, configured to calculate a power generation state stability index of each microgrid in each monitoring period based on the fluctuation characteristics of the power generation; and calculate a fault support complementary factor between corresponding microgrids based on the complementary characteristics of the power generation state stability index; A profit and loss analysis unit, configured to construct a power load profit and loss sequence of each microgrid based on the matching degree of the power generation and the power generation load; calculate a profit and loss complementarity index between microgrids based on the complementary characteristics of the power load profit and loss sequence; and calculate a power support gap filling factor between microgrids based on the profit and loss complementarity index, the power generation and the complementary characteristics of the power generation load; A synergistic and complementary calculation unit is used to calculate the grid optimization complementary factor between the microgrids based on the linear relationship between the power support gap filling factor and the fault support complementary factor; calculate the power generation characteristic similarity index between the two microgrids based on the Euclidean distance between the locations of the two microgrids and the similarity characteristics of the scales between the two microgrids; calculate the similarity feature aggregation weight between the microgrids based on the power generation state stability index; calculate the synergistic and complementary aggregation index between the microgrids by combining the power generation characteristic similarity index, the grid optimization complementary 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 multi-dimensional spectral clustering algorithm based on the synergistic complementarity matrix to obtain various microgrid clusters.

10. 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, the method according to any one of claims 1 to 8 is implemented.

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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