A cluster division and scheduling method and device based on honeycomb distribution network

Abstract

The present invention discloses a clustering and scheduling method and device based on a cellular distribution network. The method includes: constructing a cellular distribution network topology structure, the cellular distribution network topology including microgrid nodes and commutation nodes, the commutation nodes interconnected via converters to form a polygonal structure, and the microgrid nodes connected to the lines between two adjacent commutation nodes; constructing a comprehensive index that affects clustering; establishing a first objective function for clustering based on the comprehensive index; clustering the distributed power sources in the microgrid nodes based on the first objective function based on a community discovery algorithm to obtain clustering results; monitoring each cluster, and when a cluster fails, implementing power scheduling between clusters through the commutation nodes. This method can improve the reliability of distributed power source clustering.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular to a cluster division and scheduling method and device based on a honeycomb distribution network. Background Art

[0002] With the integration of large-scale distributed generation (DG) such as photovoltaic and wind power into distribution networks, the number of flexible power electronic devices in these networks is increasing, posing new challenges to traditional distribution networks. Due to the randomness and uncertainty of the output of these massive DGs, they are difficult to regulate. Therefore, the unified management and regulation of these massive DGs has become an urgent issue. The traditional solution is to install reactive power compensation equipment in the distribution network. However, this approach carries the risk of overcompensation, which can cause voltage instability. Furthermore, the equipment's response lag under rapid load changes, space requirements, and increased system complexity can all affect its overall effectiveness.

[0003] Patent text CN116404642A discloses a distributed power cluster partitioning method, device, electronic device and storage medium, which belongs to the field of distributed power supply technology. The method includes: constructing a cluster partitioning comprehensive index function; calculating the adjacency matrix of the cluster network topology to be divided; according to the adjacency matrix, using a genetic algorithm to solve the cluster partitioning comprehensive index function to obtain the optimal result of distributed power cluster partitioning; wherein, the cluster partitioning comprehensive index function is constructed and obtained based on the modularity index, power balance index and inertia support flexibility comprehensive index, combined with the weights corresponding to the modularity index, power balance index and inertia support flexibility comprehensive index.

[0004] The above method uses genetic algorithm for cluster division, which has complex programming and involves genetic encoding and decoding. The parameters need to be set based on experience, which has low reliability and slow convergence speed, and cannot achieve power scheduling between clusters. Summary of the Invention

[0005] The present invention provides a cluster division and scheduling method and device based on a honeycomb distribution network, which can uniformly manage and regulate massive distributed power sources.

[0006] A cluster division and scheduling method based on a honeycomb distribution network, comprising:

[0007] Constructing a cellular distribution network topology structure, the cellular distribution network topology structure includes microgrid nodes and commutation nodes, the commutation nodes are interconnected through converters to form a polygonal structure, and the microgrid nodes are connected to the line between two adjacent commutation nodes;

[0008] Constructing comprehensive indicators that influence cluster division;

[0009] Establishing a first objective function for cluster division based on the comprehensive indicator;

[0010] According to the first objective function, clustering the distributed power sources in the microgrid nodes based on a community discovery algorithm to obtain a clustering result;

[0011] Each cluster is monitored, and when a cluster fails, power scheduling between clusters is achieved through the commutation node.

[0012] Furthermore, the commutation node is a three-port converter formed by connecting three converters, or a two-port converter formed by connecting two converters, and the two-port converter further includes a connection end;

[0013] Each port of the three-port converter is used to connect to an adjacent commutation node or a superior power grid;

[0014] The two ports of the dual-port converter and the connection end are used to connect to adjacent commutation nodes or a superior power grid.

[0015] Furthermore, comprehensive indicators that affect cluster division are constructed, including:

[0016] The Newton-Raphson method is used to calculate the voltage sensitivity between the distributed power sources in the microgrid node;

[0017] calculating the electrical distance between the distributed power sources based on the voltage sensitivity;

[0018] Establish reactive power balance coefficient index and active power balance coefficient index of distributed power cluster;

[0019] Calculate system redundancy based on the generated power of distributed generation and the load power requirements within the microgrid node;

[0020] The comprehensive index is obtained by calculation based on the electrical distance, the reactive power balance coefficient index, the active power balance coefficient index and the system redundancy.

[0021] Furthermore, the comprehensive index is calculated by the following formula:

[0022]

[0023] Among them, K ch represents the comprehensive index, D ij represents the electrical distance between the i-th distributed power source and the j-th distributed power source, ∑ ij D ij Represents the sum of the electrical distances of all distributed power sources, D i The sum of the electrical distances between distributed power source i and other distributed power sources, Dj represents the sum of the electrical distances between distributed power source j and other distributed power sources, μ Q Indicates the reactive balance coefficient index, μ P It represents the active power balance coefficient index, R represents the system redundancy, when distributed power i and distributed power j are in the same cluster, σ(i, j) = 1, when distributed power i and distributed power j are not in the same cluster, σ(i, j) = 0.

[0024] Furthermore, the first objective function is to maximize the comprehensive index;

[0025] According to the objective function, the distributed power sources in the microgrid nodes are clustered based on a community discovery algorithm to obtain clustering results, including:

[0026] Treat each distributed power source in the microgrid node as a community and calculate the current first objective function value;

[0027] For any community, randomly assign it to a neighboring community and calculate the first objective function value after assignment;

[0028] In each round of calculation, the difference between the first objective function value after allocation and the first objective function value obtained in the previous round of calculation is calculated, and the difference is compared with a preset threshold. If the difference is greater than the preset threshold, the current allocation result is retained; if the difference is less than or equal to the preset threshold, the allocation result obtained in the previous round is retained;

[0029] The calculation is stopped when the difference reaches 0, and each community obtained is used as the cluster division result.

[0030] Furthermore, the power scheduling between clusters is realized through the commutation node, including:

[0031] Constructing power constraints and capacity constraints of the commutation node;

[0032] Establishing a second objective function based on the active load in the faulty cluster, the active power of the normal power supply cluster, the active power loss of the commutation node, and the electrical distance between the faulty cluster and the normal power supply cluster;

[0033] Based on the power constraint condition, the capacity constraint condition and the second objective function, a sparrow algorithm is used to solve and determine an electric energy scheduling plan.

[0034] Furthermore, the second objective function includes minimizing the sum of the active load of the fault cluster, the active power of the normal power supply cluster, and the active power loss of the commutation node, and minimizing the electrical distance between the fault cluster and the normal power supply cluster;

[0035] The power constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the product of the active power loss of the converter and the active power loss coefficient;

[0036] The capacity constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the capacity of the converter.

[0037] Furthermore, the sparrow algorithm is used to solve and determine the power dispatch plan, including:

[0038] The power scheduling plan is used as a sparrow. The sum of the active load in the faulty cluster, the active power of the normal power supply cluster, and the active loss of the commutation node in the second objective function is used as the X coordinate of the sparrow position. The electrical distance between the faulty cluster and the normal power supply cluster is used as the Y coordinate. The sparrow population is initialized.

[0039] A sparrow with the best fitness is selected from the initialized sparrow population as a discoverer, and the remaining sparrows are selected as followers. The discoverers and followers are iteratively updated until a stopping condition is met, thereby obtaining an optimal power scheduling solution.

[0040] A cluster division and dispatching device based on a honeycomb distribution network, comprising:

[0041] A topology construction module is used to construct a cellular distribution network topology structure, wherein the cellular distribution network topology structure includes microgrid nodes and commutation nodes, wherein the commutation nodes are interconnected through converters to form a polygonal structure, and the microgrid nodes are connected to the line between two adjacent commutation nodes;

[0042] Indicator construction module, used to construct comprehensive indicators that affect cluster division;

[0043] An objective function establishment module, configured to establish a first objective function for cluster division based on the comprehensive index;

[0044] A cluster division module is used to cluster the distributed power sources in the microgrid node based on the community discovery algorithm according to the first objective function to obtain a cluster division result;

[0045] The scheduling module is used to monitor each cluster and, when a cluster fails, to implement power scheduling between clusters through the commutation nodes.

[0046] Furthermore, the commutation node is a three-port converter formed by connecting three converters, or a two-port converter formed by connecting two converters, and the two-port converter further includes a connection end;

[0047] Each port of the three-port converter is used to connect to an adjacent commutation node or a superior power grid;

[0048] The two ports of the dual-port converter and the connection end are used to connect to adjacent commutation nodes or a superior power grid.

[0049] Furthermore, the indicator construction module constructs comprehensive indicators that affect cluster division, including:

[0050] The Newton-Raphson method is used to calculate the voltage sensitivity between the distributed power sources in the microgrid node;

[0051] calculating the electrical distance between the distributed power sources based on the voltage sensitivity;

[0052] Establish reactive power balance coefficient index and active power balance coefficient index of distributed power cluster;

[0053] Calculate system redundancy based on the generated power of distributed generation and the load power requirements within the microgrid node;

[0054] The comprehensive index is obtained by calculation based on the electrical distance, the reactive power balance coefficient index, the active power balance coefficient index and the system redundancy.

[0055] Furthermore, the comprehensive index is calculated by the following formula:

[0056]

[0057] Among them, K ch represents the comprehensive index, D ij represents the electrical distance between the i-th distributed power source and the j-th distributed power source, ∑ ij D ij Represents the sum of the electrical distances of all distributed power sources, D i The sum of the electrical distances between distributed power source i and other distributed power sources, D j represents the sum of the electrical distances between distributed power source j and other distributed power sources, μ Q Indicates the reactive balance coefficient index, μ P It represents the active power balance coefficient index, R represents the system redundancy, when distributed power i and distributed power j are in the same cluster, σ(i, j) = 1, when distributed power i and distributed power j are not in the same cluster, σ(i, j) = 0.

[0058] Furthermore, the first objective function is to maximize the comprehensive index;

[0059] The cluster division module performs cluster division on the distributed power sources in the microgrid nodes based on the community discovery algorithm according to the objective function to obtain a cluster division result, including:

[0060] Treat each distributed power source in the microgrid node as a community and calculate the current first objective function value;

[0061] For any community, randomly assign it to a neighboring community and calculate the first objective function value after assignment;

[0062] In each round of calculation, the difference between the first objective function value after allocation and the first objective function value obtained in the previous round of calculation is calculated, and the difference is compared with a preset threshold. If the difference is greater than the preset threshold, the current allocation result is retained; if the difference is less than or equal to the preset threshold, the allocation result obtained in the previous round is retained;

[0063] The calculation is stopped when the difference reaches 0, and each community obtained is used as the cluster division result.

[0064] Furthermore, the scheduling module implements power scheduling between clusters through the commutation nodes, including:

[0065] Constructing power constraints and capacity constraints of the commutation node;

[0066] Establishing a second objective function based on the active load in the faulty cluster, the active power of the normal power supply cluster, the active power loss of the commutation node, and the electrical distance between the faulty cluster and the normal power supply cluster;

[0067] Based on the power constraint condition, the capacity constraint condition and the second objective function, a sparrow algorithm is used to solve and determine an electric energy scheduling plan.

[0068] Furthermore, the second objective function includes minimizing the sum of the active load of the fault cluster, the active power of the normal power supply cluster, and the active power loss of the commutation node, and minimizing the electrical distance between the fault cluster and the normal power supply cluster;

[0069] The power constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the product of the active power loss of the converter and the active power loss coefficient;

[0070] The capacity constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the capacity of the converter.

[0071] Furthermore, the scheduling module adopts the sparrow algorithm to solve and determine the power scheduling plan, including:

[0072] The power scheduling plan is used as a sparrow. The sum of the active load in the faulty cluster, the active power of the normal power supply cluster, and the active loss of the commutation node in the second objective function is used as the X coordinate of the sparrow position. The electrical distance between the faulty cluster and the normal power supply cluster is used as the Y coordinate. The sparrow population is initialized.

[0073] A sparrow with the best fitness is selected from the initialized sparrow population as a discoverer, and the remaining sparrows are selected as followers. The discoverers and followers are iteratively updated until a stopping condition is met, thereby obtaining an optimal power scheduling solution.

[0074] An electronic device includes a processor and a storage device, wherein the storage device stores a plurality of instructions, and the processor is used to read the instructions and execute the above method.

[0075] The cluster division and scheduling method and device based on the cellular distribution network provided by the present invention have at least the following beneficial effects:

[0076] (1) A community discovery algorithm is used to cluster the distributed power sources of the microgrid nodes of the honeycomb distribution network. The cluster allocation results are judged by the threshold to avoid premature merging of clusters during the division process, which affects the quality of the division. The convergence speed is fast and the reliability is strong.

[0077] (2) Based on the comprehensive index consisting of electrical distance, reactive power balance coefficient index, active power balance coefficient index, and system redundancy as the basis for cluster division, the cluster division results are more accurate and reliable;

[0078] (3) The established honeycomb topology of the distribution network is combined with the sparrow algorithm to determine the power dispatching scheme, which can realize information interaction and power dispatching between microgrid nodes, better utilize distributed power sources, and achieve optimal configuration of distribution area resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 The present invention provides a flow chart of an embodiment of a cluster division and scheduling method based on a cellular distribution network.

[0080] Figure 2 A structural schematic diagram of an embodiment of a cellular distribution network topology structure in the cluster division and scheduling method based on the cellular distribution network provided by the present invention.

[0081] Figure 3 The present invention provides a flowchart of an embodiment of establishing comprehensive indicators in the cluster division and scheduling method based on the honeycomb distribution network.

[0082] Figure 4 The present invention provides a flow chart of an embodiment of cluster division in the cluster division and scheduling method based on the cellular distribution network.

[0083] Figure 5 The present invention provides a flow chart of an embodiment of electric energy scheduling in the cluster division and scheduling method based on the cellular distribution network.

[0084] Figure 6The present invention provides a flow chart of an embodiment of a cluster division and scheduling device based on a cellular distribution network. DETAILED DESCRIPTION

[0085] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0086] refer to Figure 1 In some embodiments, a cluster division and scheduling method based on a cellular distribution network is provided, comprising:

[0087] S1. Constructing a honeycomb distribution network topology structure, wherein the honeycomb distribution network topology structure includes microgrid nodes and commutation nodes, wherein the commutation nodes are interconnected through converters to form a polygonal structure, and the microgrid nodes are connected to the line between two adjacent commutation nodes;

[0088] S2, construct comprehensive indicators that affect cluster division;

[0089] S3. Establishing a first objective function for cluster division based on the comprehensive indicator;

[0090] S4. Clustering the distributed power sources in the microgrid nodes based on a community discovery algorithm according to the first objective function to obtain a clustering result;

[0091] S5. Monitor each cluster. When a cluster fails, implement power scheduling between clusters through the commutation node.

[0092] Further, refer to Figure 2 In step S1, the commutation node is a three-port converter formed by connecting three converters, or a two-port converter formed by connecting two converters, and the two-port converter further includes a connection end;

[0093] Each port of the three-port converter is used to connect to an adjacent commutation node or a superior power grid;

[0094] The two ports of the dual-port converter and the connection end are used to connect to adjacent commutation nodes or a superior power grid.

[0095] Specifically, Figure 2 In a commutation node A, the three-port converter includes a first converter 1, a second converter 2 and a third converter 3. The first converter 1, the second converter 2 and the third converter 3 are interconnected to form a first port a, a second port b and a third port c, wherein the first port a and the second port b are respectively connected to the ports or connection ends of adjacent commutation nodes, and the third port c is used to connect to the upper-level power grid C.

[0096] The dual-port converter includes a fourth converter 4 and a fifth converter 5. The fourth converter 4 and the fifth converter 5 are interconnected to form a fourth port d, a fifth port e and a connection end f, wherein the fourth port d and the connection end f are respectively connected to the ports of adjacent converter nodes, and the fifth port e is used to connect to the upper-level power grid.

[0097] Microgrid node B is connected to the line between two adjacent commutation nodes A;

[0098] Multiple commutation nodes are interconnected to form a polygonal honeycomb structure, which can be a triangle, quadrilateral, pentagon, hexagon, etc. The commutation nodes are used to realize information exchange and power scheduling between various microgrid nodes.

[0099] Further, refer to Figure 3 ,In step S2, a comprehensive index affecting cluster division is constructed, including:

[0100] S21. Calculate the voltage sensitivity between distributed power sources in the microgrid node using the Newton-Raphson method;

[0101] S22. Calculating the electrical distance between distributed power sources based on the voltage sensitivity;

[0102] S23. Establish reactive power balance coefficient index and active power balance coefficient index of distributed power cluster;

[0103] S24. Calculate system redundancy based on the generated power of the distributed power source and the load power demand within the microgrid node;

[0104] S25. Calculate and obtain the comprehensive index based on the electrical distance, the reactive power balance coefficient index, the active power balance coefficient index, and the system redundancy.

[0105] Specifically, in step S21, when considering the classification of distributed power generation clusters in a cellular distribution network, the distance between each distributed power generation needs to be calculated based on its geographic location. The electrical distance between distributed power generation is determined by their respective voltage sensitivities. The Newton-Raphson method can be used to calculate the voltage sensitivity between these power generation units.

[0106]

[0107] ΔV=S PV ΔP+S QV ·ΔQ; (2)

[0108] Among them, △θ and △V represent the changes in the phase angle and amplitude of the distributed power supply voltage respectively, H, N, M, and L are the elements in the Jacobian matrix in the power flow calculation; S PV Represents the elements in the active sensitivity matrix of distributed generation; S QVRepresents the elements in the reactive sensitivity matrix of distributed power sources, △P is the difference in active power between distributed power sources, △Q is the difference in reactive power between distributed power sources, S Pδ is the active phase angle sensitivity, S Qδ is the reactive phase angle sensitivity.

[0109] Furthermore, in step S22, the electrical distance is calculated according to the following formula:

[0110]

[0111] Among them, D ij represents the electrical distance between distributed power sources i and j, is the element corresponding to distributed generation i and distributed generation j in the active sensitivity matrix, is the element in the reactive sensitivity matrix corresponding to distributed power source i and distributed power source j

[0112] Furthermore, in step S23, a power balance coefficient index for distributed power cluster division is introduced to solve the problems of power self-balancing within the cluster and power complementarity between clusters. The reactive power balance coefficient can be applied to the distributed power cluster voltage regulation scenario, and the active power balance coefficient can be applied to the distributed power cluster frequency regulation scenario.

[0113] Specifically, the reactive balance coefficient index is calculated using the following formula:

[0114]

[0115] Among them, Q i is the reactive balance degree of cluster i, represents the maximum reactive power that can be supplied within cluster i, is the reactive power required within cluster i; N is the number of clusters; μ Q It is the reactive balance coefficient indicator.

[0116] The constructed reactive balance coefficient index has strong randomness and volatility for massive distributed power sources in cluster j high-cellular distribution network, and shows strong time correlation.

[0117] Furthermore, the active power balance coefficient index is calculated by the following formula:

[0118]

[0119] Among them, P (i)j and P (i)i are the net power characteristics of cluster j and cluster i at time t, max(P (i)) is the maximum net power characteristic of cluster i at time t, μ P is the active power balance coefficient index; N is the number of clusters; T is the distribution network time scale.

[0120] Furthermore, in step S24, fault tolerance evaluates the system's ability to maintain power supply in the event of a power supply or device failure. Consider a cluster where a distributed power supply (DG) failure causes partial power loss. The system's redundancy is defined as the cluster's excess power capacity—the difference between the total power available from the cluster's internal power supplies and the system's actual demand. This is calculated using the following formula:

[0121]

[0122] Among them, P c,i is the power generation of the i-th distributed power source, P load is the power required by the system load, and R is the system redundancy.

[0123] Furthermore, in step S25, the comprehensive index is calculated using the following formula:

[0124]

[0125] Among them, K ch represents the comprehensive index, D ij represents the electrical distance between the i-th distributed power source and the j-th distributed power source, ∑ ij D ij Represents the sum of the electrical distances of all distributed power sources, D i The sum of the electrical distances between distributed power source i and other distributed power sources, D j represents the sum of the electrical distances between distributed power source j and other distributed power sources, μ Q Indicates the reactive balance coefficient index, μ P It represents the active power balance coefficient index, R represents the system redundancy, when distributed power i and distributed power j are in the same cluster, σ(i, j) = 1, when distributed power i and distributed power j are not in the same cluster, σ(i, j) = 0.

[0126] Furthermore, in step S3, the first objective function is to maximize the comprehensive index, that is:

[0127] F1=maxK ch ;(10)

[0128] Among them, F1 represents the first objective function, K ch Indicates a comprehensive indicator.

[0129] Further, refer to Figure 4 In step S4, according to the first objective function, the distributed power sources in the microgrid nodes are clustered based on a community discovery algorithm to obtain a clustering result, including:

[0130] S41, treating each distributed power source in the microgrid node as a community and calculating a current first objective function value;

[0131] S42. For any community, randomly assign it to a neighboring community and calculate the first objective function value after the assignment;

[0132] S43. In each round of calculation, the difference between the first objective function value after allocation and the first objective function value obtained in the previous round of calculation is calculated, and the difference is compared with a preset threshold. If the difference is greater than the preset threshold, the current allocation result is retained; if the difference is less than or equal to the preset threshold, the allocation result obtained in the previous round is retained.

[0133] S44. When the difference reaches 0, the calculation is stopped, and each community obtained is used as the cluster division result.

[0134] In this embodiment, when performing distributed power cluster division, in order to cope with complex, sparse or unclearly structured networks and avoid premature merging of clusters during the division process, which affects the quality of the division, the community allocation result is determined by comparing preset thresholds, thereby improving the quality of cluster division.

[0135] Further, refer to Figure 5 In step S5, the power scheduling between clusters is realized through the commutation node, including:

[0136] S51, constructing power constraint conditions and capacity constraint conditions of the commutation node;

[0137] S52: Establish a second objective function based on the active load in the faulty cluster, the active power of the normal power supply cluster, the active power loss of the commutation node, and the electrical distance between the faulty cluster and the normal power supply cluster;

[0138] S53. Based on the power constraint condition, the capacity constraint condition and the second objective function, a sparrow algorithm is used to solve and determine an electric energy dispatching plan.

[0139] Specifically, in step S51, the power constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the product of the active power loss of the converter and the active power loss coefficient, which is specifically expressed as follows:

[0140]

[0141] Among them, Psop,k represents the active power injected by the converter into the kth cluster, Q sop,k represents the reactive power injected by the converter into the kth cluster, represents the apparent power injected by the converter into the kth cluster, H sop represents the active power loss coefficient of the converter, Indicates the active power loss of the converter.

[0142] The capacity constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the capacity of the converter, which is specifically expressed as follows:

[0143]

[0144] Among them, P sop,k represents the active power injected by the converter into the kth cluster, Q sop,k represents the reactive power injected by the converter into the kth cluster, represents the apparent power injected by the converter into the kth cluster, S sop Indicates the capacity of the converter.

[0145] Furthermore, in step S52, the second objective function includes minimizing the sum of the active load of the faulty cluster, the active power of the normal power supply cluster, and the active power loss of the commutation node, and minimizing the electrical distance between the faulty cluster and the normal power supply cluster.

[0146] The second objective function is:

[0147]

[0148] Where f1 and f2 represent the second objective function, α1, α2, and α3 represent the active load weight of the fault cluster, the active power weight of the normal power supply cluster, and the active loss weight of the commutation node, respectively. A represents the set of fault clusters, B represents the set of normal power supply clusters, and C represents the set of converters. represents the active load of the mth fault cluster, Indicates the active power of the nth normal power supply cluster, represents the active power loss of the kth converter, D mn Indicates the electrical distance between the mth faulty cluster and the nth normal power supply cluster.

[0149] The electrical distance between the fault cluster and the normal power supply cluster is calculated based on the distributed power supply at the center of the cluster.

[0150] Furthermore, in step S53, the sparrow algorithm is used to solve and determine the power dispatching plan, including:

[0151] The power scheduling plan is used as a sparrow. The sum of the active load in the faulty cluster, the active power of the normal power supply cluster, and the active loss of the commutation node in the second objective function is used as the X coordinate of the sparrow position. The electrical distance between the faulty cluster and the normal power supply cluster is used as the Y coordinate. The sparrow population is initialized.

[0152] A sparrow with the best fitness is selected from the initialized sparrow population as a discoverer, and the remaining sparrows are selected as followers. The discoverers and followers are iteratively updated until a stopping condition is met, thereby obtaining an optimal power scheduling solution.

[0153] Specifically, each sparrow is regarded as a point on a two-dimensional plane with coordinates (x, y). In this embodiment, the calculated value of f1 in the second objective function is used as the horizontal coordinate, and the calculated value of f2 is used as the vertical coordinate. The quality of the current position is measured by the fitness function: fit = x 2 +y 2 To evaluate, where x represents.

[0154] To solve the above problem, the goal of optimization is to minimize the fitness function fit. In each round of optimization, the position of the discoverer is updated:

[0155]

[0156] Among them, K represents the current number of iterations, It indicates the value of the jth dimension in the i-th row of the fitness matrix of the population when k iterations are completed. max represents the maximum number of iterations allowed, Q represents a random number that follows a normal distribution, L represents a 1×d-dimensional matrix whose elements are all 1, R represents the alarm value, and ST represents the safety threshold.

[0157] The scout's position is updated as follows:

[0158]

[0159] Among them, X best represents the optimal position in the current population, β represents a random number that obeys the normal distribution, with a mean of 0 and a variance of 1, and K represents the control factor; f j Indicates the current sparrow's fitness value, f g 、f w They represent the current global optimal fitness value and the global worst fitness value respectively, and ε represents a very small number.

[0160] In some embodiments, referring to FIG. N , a cluster division and scheduling device based on a cellular distribution network is provided, including:

[0161] A topology construction module 201 is configured to construct a cellular distribution network topology structure, wherein the cellular distribution network topology structure includes microgrid nodes and commutation nodes, wherein the commutation nodes are interconnected via converters to form a hexagonal structure, and the microgrid nodes are connected to the lines between two adjacent commutation nodes;

[0162] An indicator construction module 202 is used to construct a comprehensive indicator that affects cluster division;

[0163] An objective function establishing module 203 is configured to establish a first objective function for cluster division based on the comprehensive index;

[0164] A cluster division module 204 is configured to perform cluster division on the distributed power sources in the microgrid nodes based on the community discovery algorithm according to the first objective function to obtain a cluster division result;

[0165] The scheduling module 205 is used to monitor each cluster and, when a cluster fails, to implement power scheduling between clusters through the commutation nodes.

[0166] Furthermore, the commutation node is a three-port converter formed by connecting three converters, or a two-port converter formed by connecting two converters, and the two-port converter further includes a connection end;

[0167] Each port of the three-port converter is used to connect to an adjacent commutation node or a superior power grid;

[0168] The two ports of the dual-port converter and the connection end are used to connect to adjacent commutation nodes or a superior power grid.

[0169] Furthermore, the indicator construction module 202 constructs comprehensive indicators that affect cluster division, including:

[0170] The Newton-Raphson method is used to calculate the voltage sensitivity between the distributed power sources in the microgrid node;

[0171] calculating the electrical distance between the distributed power sources based on the voltage sensitivity;

[0172] Establish reactive power balance coefficient index and active power balance coefficient index of distributed power cluster;

[0173] Calculate system redundancy based on the generated power of distributed generation and the load power requirements within the microgrid node;

[0174] The comprehensive index is obtained by calculation based on the electrical distance, the reactive power balance coefficient index, the active power balance coefficient index and the system redundancy.

[0175] Furthermore, the comprehensive index is calculated by the following formula:

[0176]

[0177] Among them, K ch represents the comprehensive index, D ij represents the electrical distance between the i-th distributed power source and the j-th distributed power source, ∑ ij D ij Represents the sum of the electrical distances of all distributed power sources, P i P represents the sum of the electrical distances between distributed power source i and other distributed power sources. j represents the sum of the electrical distances between distributed power source j and other distributed power sources, μ Q Indicates the reactive balance coefficient index, μ P It represents the active power balance coefficient index, R represents the system redundancy, when distributed power i and distributed power j are in the same cluster, σ(i, j) = 1, when distributed power i and distributed power j are not in the same cluster, σ(i, j) = 0.

[0178] Furthermore, the first objective function is to maximize the comprehensive index;

[0179] The cluster division module 204 performs cluster division on the distributed power sources in the microgrid nodes based on the community discovery algorithm according to the objective function, and obtains cluster division results, including:

[0180] Treat each distributed power source in the microgrid node as a community and calculate the current first objective function value;

[0181] For any community, randomly assign it to a neighboring community and calculate the first objective function value after assignment;

[0182] In each round of calculation, the difference between the first objective function value after allocation and the first objective function value obtained in the previous round of calculation is calculated, and the difference is compared with a preset threshold. If the difference is greater than the preset threshold, the current allocation result is retained; if the difference is less than or equal to the preset threshold, the allocation result obtained in the previous round is retained;

[0183] The calculation is stopped when the difference reaches 0, and each community obtained is used as the cluster division result.

[0184] Furthermore, the scheduling module 205 implements power scheduling between clusters through the commutation nodes, including:

[0185] Constructing power constraints and capacity constraints of the commutation node;

[0186] Establishing a second objective function based on the active load in the faulty cluster, the active power of the normal power supply cluster, the active power loss of the commutation node, and the electrical distance between the faulty cluster and the normal power supply cluster;

[0187] Based on the power constraint condition, the capacity constraint condition and the second objective function, a sparrow algorithm is used to solve and determine an electric energy scheduling plan.

[0188] Furthermore, the second objective function includes minimizing the sum of the active load of the fault cluster, the active power of the normal power supply cluster, and the active power loss of the commutation node, and minimizing the electrical distance between the fault cluster and the normal power supply cluster;

[0189] The power constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the product of the active power loss of the converter and the active power loss coefficient;

[0190] The capacity constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the capacity of the converter.

[0191] Furthermore, the scheduling module 205 uses the sparrow algorithm to solve and determine the power scheduling plan, including:

[0192] The power scheduling plan is used as a sparrow. The sum of the active load in the faulty cluster, the active power of the normal power supply cluster, and the active loss of the commutation node in the second objective function is used as the X coordinate of the sparrow position. The electrical distance between the faulty cluster and the normal power supply cluster is used as the Y coordinate. The sparrow population is initialized.

[0193] A sparrow with the best fitness is selected from the initialized sparrow population as a discoverer, and the remaining sparrows are selected as followers. The discoverers and followers are iteratively updated until a stopping condition is met, thereby obtaining an optimal power scheduling solution.

[0194] In some embodiments, an electronic device is also provided, including a processor and a storage device, wherein the storage device stores a plurality of instructions, and the processor is configured to read the instructions and execute the above method.

[0195] The methods and devices provided in the above embodiments have at least the following beneficial effects:

[0196] (1) A community discovery algorithm is used to cluster the distributed power sources of the microgrid nodes of the honeycomb distribution network. The cluster allocation results are judged by the threshold to avoid premature merging of clusters during the division process, which affects the quality of the division. The convergence speed is fast and the reliability is strong.

[0197] (2) Based on the comprehensive index consisting of electrical distance, reactive power balance coefficient index, active power balance coefficient index, and system redundancy as the basis for cluster division, the cluster division results are more accurate and reliable;

[0198] (3) The established honeycomb topology of the distribution network is combined with the sparrow algorithm to determine the power dispatching scheme, which can realize information interaction and power dispatching between microgrid nodes, better utilize distributed power sources, and achieve optimal configuration of distribution area resources.

[0199] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A cluster division and scheduling method based on a honeycomb distribution network, characterized in that: include: Constructing a cellular distribution network topology structure, the cellular distribution network topology structure includes microgrid nodes and commutation nodes, the commutation nodes are interconnected through converters to form a polygonal structure, and the microgrid nodes are connected to the line between two adjacent commutation nodes; Constructing a comprehensive index affecting cluster division: using the Newton-Raphson method to calculate the voltage sensitivity between the distributed power sources in the microgrid node; calculating the electrical distance between the distributed power sources based on the voltage sensitivity; establishing a reactive power balance coefficient index and an active power balance coefficient index for the distributed power source cluster; calculating the system redundancy based on the generated power of the distributed power sources and the load power demand in the microgrid node; and calculating the comprehensive index based on the electrical distance, the reactive power balance coefficient index, the active power balance coefficient index, and the system redundancy; Establishing a first objective function for cluster division based on the comprehensive indicator; According to the first objective function, clustering the distributed power sources in the microgrid nodes based on a community discovery algorithm to obtain a clustering result; Monitor each cluster, and when a cluster fails, implement power scheduling between clusters through the commutation node; The comprehensive index is calculated using the following formula: ; Among them, K ch represents the comprehensive index, D ij represents the electrical distance between the i-th distributed power source and the j-th distributed power source, , Represents the sum of the electrical distances of all distributed power sources, D i The sum of the electrical distances between distributed power source i and other distributed power sources, D j represents the sum of the electrical distances between distributed power source j and other distributed power sources, μ Q Indicates the reactive balance coefficient index, μ P Represents the active power balance coefficient index, R represents the system redundancy, when distributed power i and distributed power j are in the same cluster, ,When distributed power i and distributed power j are not in the same cluster, ;The first objective function is to maximize the comprehensive index.

2. The method according to claim 1, characterized in that The commutation node is a three-port converter formed by connecting three converters, or a two-port converter formed by connecting two converters, and the two-port converter further includes a connection end; Each port of the three-port converter is used to connect to an adjacent commutation node or a superior power grid; The two ports of the dual-port converter and the connection end are used to connect to adjacent commutation nodes or a superior power grid.

3. The method according to claim 1, characterized in that According to the objective function, the distributed power sources in the microgrid nodes are clustered based on a community discovery algorithm to obtain clustering results, including: Treat each distributed power source in the microgrid node as a community and calculate the current first objective function value; For any community, randomly assign it to a neighboring community and calculate the first objective function value after assignment; In each round of calculation, the difference between the first objective function value after allocation and the first objective function value obtained in the previous round of calculation is calculated, and the difference is compared with a preset threshold. If the difference is greater than the preset threshold, the current allocation result is retained; if the difference is less than or equal to the preset threshold, the allocation result obtained in the previous round is retained; The calculation is stopped when the difference reaches 0, and each community obtained is used as the cluster division result.

4. The method according to claim 1, wherein The power dispatching between clusters is realized by the commutation node, including: Constructing power constraints and capacity constraints of the commutation node; Establishing a second objective function based on the active load in the faulty cluster, the active power of the normal power supply cluster, the active power loss of the commutation node, and the electrical distance between the faulty cluster and the normal power supply cluster; Based on the power constraint condition, the capacity constraint condition and the second objective function, a sparrow algorithm is used to solve and determine an electric energy scheduling plan.

5. The method according to claim 4, characterized in that The second objective function includes minimizing the sum of the active load of the fault cluster, the active power of the normal power supply cluster, and the active power loss of the commutation node, and minimizing the electrical distance between the fault cluster and the normal power supply cluster; The power constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the product of the active power loss of the converter and the active power loss coefficient; The capacity constraint condition is that the apparent power of the converter in the commutation node is less than or equal to the capacity of the converter.

6. The method according to claim 5, characterized in that The sparrow algorithm is used to solve and determine the power dispatch plan, including: The power scheduling plan is used as a sparrow. The sum of the active load in the faulty cluster, the active power of the normal power supply cluster, and the active loss of the commutation node in the second objective function is used as the X coordinate of the sparrow position. The electrical distance between the faulty cluster and the normal power supply cluster is used as the Y coordinate. The sparrow population is initialized. A sparrow with the best fitness is selected from the initialized sparrow population as a discoverer, and the remaining sparrows are selected as followers. The discoverers and followers are iteratively updated until a stopping condition is met, thereby obtaining an optimal power scheduling solution.

7. A cluster division and dispatching device based on a honeycomb distribution network, characterized in that: include: A topology construction module is used to construct a cellular distribution network topology structure, wherein the cellular distribution network topology structure includes microgrid nodes and commutation nodes, wherein the commutation nodes are interconnected through converters to form a polygonal structure, and the microgrid nodes are connected to the line between two adjacent commutation nodes; An indicator construction module is used to construct a comprehensive indicator that affects cluster division: using the Newton-Raphson method to calculate the voltage sensitivity between the distributed power sources in the microgrid node; calculating the electrical distance between the distributed power sources based on the voltage sensitivity; establishing a reactive power balance coefficient indicator and an active power balance coefficient indicator for the distributed power source cluster; calculating system redundancy based on the generated power of the distributed power sources and the load power demand in the microgrid node; and calculating the comprehensive indicator based on the electrical distance, the reactive power balance coefficient indicator, the active power balance coefficient indicator, and the system redundancy; An objective function establishment module, configured to establish a first objective function for cluster division based on the comprehensive index; A cluster division module is used to cluster the distributed power sources in the microgrid node based on the community discovery algorithm according to the first objective function to obtain a cluster division result; A scheduling module is used to monitor each cluster and, when a cluster fails, to implement power scheduling between clusters through the commutation nodes; The comprehensive index is calculated using the following formula: ; Among them, K ch represents the comprehensive index, D ij represents the electrical distance between the i-th distributed power source and the j-th distributed power source, , Represents the sum of the electrical distances of all distributed power sources, D i The sum of the electrical distances between distributed power source i and other distributed power sources, D j represents the sum of the electrical distances between distributed power source j and other distributed power sources, μ Q Indicates the reactive balance coefficient index, μ P Represents the active power balance coefficient index, R represents the system redundancy, when distributed power i and distributed power j are in the same cluster, , when distributed power sources i and j are not in the same cluster, ;The first objective function is to maximize the comprehensive index.

8. An electronic device, characterized in that: The method comprises a processor and a storage device, wherein the storage device stores a plurality of instructions, and the processor is configured to read the instructions and execute the method according to any one of claims 1 to 6.

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