Cluster division and scheduling method and device based on honeycomb power distribution network

Abstract

The invention discloses a cluster division and scheduling method and device based on a honeycomb power distribution network, and the method comprises the steps: constructing a honeycomb power distribution network topological structure which comprises micro-grid nodes and commutation nodes, and the commutation nodes are connected with each other through a current converter to form a polygon structure, the micro-grid node is connected to a line between two adjacent current conversion nodes; constructing a comprehensive index influencing cluster division; establishing a first objective function of cluster division according to the comprehensive index; performing cluster division on the distributed power supply in the microgrid node based on a community discovery algorithm according to the first objective function to obtain a cluster division result; and monitoring each cluster, and when the cluster has a fault, realizing electric energy scheduling between the clusters through the commutation node. According to the method, the reliability of distributed power supply cluster division can be improved.

Description

Technical Field

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

[0002] With the large-scale distributed generation (DG) represented by photovoltaic and wind power connected to the distribution network, the number of flexible power electronic devices in the distribution network is increasing, which poses new challenges to the traditional distribution network. Massive distributed power sources are difficult to regulate due to the randomness and uncertainty of their output. Therefore, how to uniformly manage and regulate massive distributed power sources has become an urgent problem to be solved. The traditional solution is to install reactive power compensation equipment in the distribution network. However, this solution has the risk of over-compensation, which causes voltage instability. In addition, the equipment's response lag under rapid load changes, space occupation problems, and increased system complexity may affect its overall effectiveness.

[0003] Patent text CN116404642A discloses a distributed power cluster partitioning method, device, electronic device and storage medium, belonging to the field of distributed power technology. The method includes: constructing a cluster partitioning comprehensive index function; calculating the adjacency matrix of the network topology of the cluster 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 gene encoding and decoding. The parameters need to be set based on experience, and the reliability is not high and the convergence speed is slow, and power scheduling between clusters cannot be achieved. 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 dispatching method based on a cellular distribution network, comprising:

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

[0008] Constructing comprehensive indicators that influence cluster division;

[0009] According to the comprehensive index, a first objective function of cluster division is established;

[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] Further, 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 also 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 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 electrical distances between 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 power generation of distributed generation and the load power demand in the microgrid node;

[0020] The comprehensive index is obtained by calculation according to 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 It represents 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 Represents the reactive power balance coefficient index, μ P It represents the active balance coefficient index, R represents the system redundancy, when distributed generation i and distributed generation j are in the same cluster, σ(i, j) = 1, when distributed generation i and distributed generation 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 node are clustered based on a community discovery algorithm to obtain a clustering result, 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. When the difference is greater than the preset threshold, the current allocation result is retained; when the difference is less than or equal to the preset threshold, the allocation result obtained in the previous round is retained;

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

[0030] Furthermore, the power dispatching between clusters is realized by the commutation node, including:

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

[0032] A second objective function is established according to the active load in the fault cluster, the active power of the normal power supply cluster, the active power loss of the commutation node, and the electrical distance between the fault 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 dispatching 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 converter 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 dispatching plan, including:

[0038] The power dispatching scheme is used as a sparrow, the sum of the active load in the fault 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, and the electrical distance between the fault cluster and the normal power supply cluster is used as the Y coordinate to initialize the sparrow population;

[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 stop condition is met to obtain an optimal power scheduling solution.

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

[0041] A topology building module, used to build 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 a line between two adjacent commutation nodes;

[0042] Indicator building module, used to build comprehensive indicators that affect cluster division;

[0043] An objective function establishment module, used for establishing a first objective function for cluster division according to the comprehensive index;

[0044] A cluster division module, 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 node.

[0046] Further, 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 also 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 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 electrical distances between 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 power generation of distributed generation and the load power demand in the microgrid node;

[0054] The comprehensive index is obtained by calculation according to 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 It represents 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 Represents the reactive power balance coefficient index, μ P It represents the active balance coefficient index, R represents the system redundancy, when distributed generation i and distributed generation j are in the same cluster, σ(i, j) = 1, when distributed generation i and distributed generation 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 node 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. When the difference is greater than the preset threshold, the current allocation result is retained; when the difference is less than or equal to the preset threshold, the allocation result obtained in the previous round is retained;

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

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

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

[0066] A second objective function is established according to the active load in the fault cluster, the active power of the normal power supply cluster, the active power loss of the commutation node, and the electrical distance between the fault 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 dispatching 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 converter 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 dispatching scheme is used as a sparrow, the sum of the active load in the fault 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, and the electrical distance between the fault cluster and the normal power supply cluster is used as the Y coordinate to initialize the sparrow population;

[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 stop condition is met to obtain an optimal power scheduling solution.

[0074] An electronic device comprises 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 honeycomb distribution network provided by the present invention have at least the following beneficial effects:

[0076] (1) The 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 result is 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 plan, which can realize information interaction and power dispatching between microgrid nodes, better utilize distributed power sources, and achieve optimal allocation of distribution station 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 schematic structural diagram of an embodiment of a topological structure of a cellular distribution network in a cluster division and scheduling method based on a 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 cellular 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 dispatching 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 in conjunction with 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. 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;

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

[0089] S3. Establishing a first objective function for cluster division according to the comprehensive index;

[0090] S4. According to the first objective function, cluster the distributed power sources in the microgrid node based on a community discovery algorithm to obtain a cluster division result;

[0091] S5. Monitor each cluster, and 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 also 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 converter node A, the three-port converter includes a first converter 1, a second converter 2 and a third converter 3, which 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 ports or connection ends of adjacent converter nodes, and the third port c is used to connect to an 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 power grid.

[0097] The microgrid node B is connected to the line between two adjacent converter 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 interaction 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, using the Newton-Raphson method to calculate the voltage sensitivity between the distributed power sources in the microgrid node;

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

[0102] S23, establishing reactive power balance coefficient index and active power balance coefficient index of distributed power generation cluster;

[0103] S24, calculating system redundancy according to the power generation of the distributed power source and the load power demand in the microgrid node;

[0104] S25. Calculate and obtain the comprehensive index according to 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 source clusters in the cellular distribution network, it is first necessary to calculate the distance between each distributed power source according to the geographical location. The electrical distance between the distributed power sources is determined by their respective voltage sensitivities. The Newton-Raphson method can be used to calculate the voltage sensitivity between these power sources.

[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 of 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 power 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] Further, 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 generation i and distributed generation j, is the element corresponding to distributed generation i and distributed generation j in the active sensitivity matrix, is the element corresponding to distributed generation i and distributed generation j in the reactive sensitivity matrix

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

[0113] Specifically, the reactive power balance coefficient index is calculated by 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 power 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 ability of the system to maintain power supply when a power supply or device failure occurs. Assuming that there is a cluster, due to the failure of a distributed power supply, part of the power fails. Can the system quickly adjust and compensate for the loss of this part of the power? Define system redundancy as the additional power capacity of the cluster, that is, the difference between the total power that the power supply inside the cluster can provide and the actual demand of the system, and calculate it by the following formula:

[0121]

[0122] Among them, P c,i is the power generation of the ith distributed generation, 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 by 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 It represents 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 Represents the reactive power balance coefficient index, μ P It represents the active balance coefficient index, R represents the system redundancy, when distributed generation i and distributed generation j are in the same cluster, σ(i, j) = 1, when distributed generation i and distributed generation 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 Represents 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 node are clustered based on the 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 value. When the difference is greater than the preset threshold value, the current allocation result is retained; when the difference is less than or equal to the preset threshold value, the allocation result obtained in the previous round is retained;

[0133] S44. When the difference is 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 dispatch 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, establishing a second objective function according to 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 converter 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 inverter.

[0145] Further, 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] Wherein, 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 converter node respectively, A represents the set of fault clusters, B represents the set of normal power supply clusters, 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 faulty cluster and the normal power supply cluster is calculated based on the distributed power source at the center of the cluster.

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

[0151] The power dispatching scheme is used as a sparrow, the sum of the active load in the fault 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, and the electrical distance between the fault cluster and the normal power supply cluster is used as the Y coordinate to initialize the sparrow population;

[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 stop condition is met to obtain 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] In view of the above problems, the goal of optimization is to minimize the fitness function fit. In each round of optimization, the position of the finder is updated:

[0155]

[0156] Among them, K represents the current number of iterations, It means that when k iterations are completed, the value of the jth dimension in the i-th row of the population's fitness matrix, iter max represents the maximum number of iterations allowed, Q represents a random number that obeys 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 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 follows a 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 building module 201 is used to build 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 hexagonal structure, and the microgrid nodes are connected to the line between two adjacent commutation nodes;

[0162] An indicator construction module 202, used to construct a comprehensive indicator affecting cluster division;

[0163] An objective function establishment module 203 is used to establish a first objective function for cluster division according to the comprehensive index;

[0164] A cluster division module 204 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;

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

[0166] Further, 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 also 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 electrical distances between distributed power sources based on the voltage sensitivity;

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

[0173] Calculate system redundancy based on the power generation of distributed generation and the load power demand in the microgrid node;

[0174] The comprehensive index is obtained by calculation according to 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 Represents the reactive power balance coefficient index, μ P It represents the active balance coefficient index, R represents the system redundancy, when distributed generation i and distributed generation j are in the same cluster, σ(i, j) = 1, when distributed generation i and distributed generation 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 node based on the community discovery algorithm according to the objective function to obtain a cluster division result, 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. When the difference is greater than the preset threshold, the current allocation result is retained; when the difference is less than or equal to the preset threshold, the allocation result obtained in the previous round is retained;

[0183] When the difference is 0, the calculation is stopped, 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] A second objective function is established according to the active load in the fault cluster, the active power of the normal power supply cluster, the active power loss of the commutation node, and the electrical distance between the fault 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 dispatching 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 converter 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 dispatching scheme is used as a sparrow, the sum of the active load in the fault 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, and the electrical distance between the fault cluster and the normal power supply cluster is used as the Y coordinate to initialize the sparrow population;

[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 stop condition is met to obtain 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 used to read the instructions and execute the above method.

[0195] The method and device provided in the above embodiment have at least the following beneficial effects:

[0196] (1) The 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 result is 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 plan, which can realize information interaction and power dispatching between microgrid nodes, better utilize distributed power sources, and achieve optimal allocation of distribution station area resources.

[0199] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. 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 present 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 present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A cluster division and scheduling method based on a cellular distribution network, characterized in that: include: Constructing a cellular distribution network topology structure, wherein the cellular distribution network topology structure includes a microgrid node and a commutation node, wherein the commutation nodes are interconnected through a converter to form a polygonal structure, and the microgrid node is connected to a line between two adjacent commutation nodes; Constructing comprehensive indicators that influence cluster division; According to the comprehensive index, a first objective function of cluster division is established; 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; Each cluster is monitored, and when a cluster fails, power scheduling between clusters is achieved through the commutation node.

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 also 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 Construct comprehensive indicators that affect cluster division, including: The Newton-Raphson method is used to calculate the voltage sensitivity between the distributed power sources in the microgrid node; Calculating electrical distances between distributed power sources based on the voltage sensitivity; Establish reactive power balance coefficient index and active power balance coefficient index of distributed power generation cluster; Calculate system redundancy based on the power generation of distributed generation and the load power demand in the microgrid node; The comprehensive index is obtained by calculation according to the electrical distance, the reactive power balance coefficient index, the active power balance coefficient index and the system redundancy.

4. The method according to claim 3, characterized in that The comprehensive index is calculated by 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, ∑ ij D ij Represents the sum of the electrical distances of all distributed power sources, D i It represents 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 Represents the reactive power balance coefficient index, μ P It represents the active balance coefficient index, R represents the system redundancy, when distributed generation i and distributed generation j are in the same cluster, σ(i, j) = 1, when distributed generation i and distributed generation j are not in the same cluster, σ(i, j) = 0.

5. The method according to claim 1 or 4, characterized in that: The first objective function is to maximize the comprehensive index; According to the objective function, the distributed power sources in the microgrid node are clustered based on a community discovery algorithm to obtain a clustering result, 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. When the difference is greater than the preset threshold, the current allocation result is retained; when the difference is less than or equal to the preset threshold, the allocation result obtained in the previous round is retained; When the difference is 0, the calculation is stopped, and each community obtained is used as the cluster division result.

6. The method according to claim 1, characterized in that The power dispatching between clusters is realized by the commutation node, including: Constructing power constraints and capacity constraints of the commutation node; A second objective function is established according to the active load in the fault cluster, the active power of the normal power supply cluster, the active power loss of the commutation node, and the electrical distance between the fault 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 dispatching plan.

7. The method according to claim 6, 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 converter node is less than or equal to the capacity of the converter.

8. The method according to claim 7, characterized in that The sparrow algorithm is used to solve and determine the power dispatching plan, including: The power dispatching scheme is used as a sparrow, the sum of the active load in the fault 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, and the electrical distance between the fault cluster and the normal power supply cluster is used as the Y coordinate to initialize the sparrow population; 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 stop condition is met to obtain an optimal power scheduling solution.

9. A cluster division and dispatching device based on a cellular distribution network, characterized in that: include: A topology building module, used to build 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 a line between two adjacent commutation nodes; Indicator building module, used to build comprehensive indicators that affect cluster division; An objective function establishment module, used for establishing a first objective function for cluster division according to the comprehensive index; A cluster division module, 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; The scheduling module is used to monitor each cluster and, when a cluster fails, to implement power scheduling between clusters through the commutation node.

10. 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 used to read the instructions and execute the method according to any one of claims 1 to 8.

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