Method for dynamic partitioning of distribution network based on multi-objective ant colony algorithm

Abstract

This invention discloses a dynamic zoning method for distribution networks based on a multi-objective ant colony algorithm. The method includes: defining a binary modularity using a tracking algorithm to quantify the energy balance within a zoning interval; proposing a multi-objective ant colony algorithm suitable for distribution network zoning by combining a power reserve index; ensuring power balance in each zoning interval and strong control capability of the power source within the zoning interval; combining a probabilistic prediction scenario set with the zoning algorithm to obtain dynamic active power zoning, improving the zoning interval's ability to cope with the uncertainty of renewable energy and enhancing the robustness of the zoning scheme; obtaining a Pareto optimal solution set by solving the final power reserve and final binary modularity of the distribution network zoning using the multi-objective ant colony optimization algorithm; selecting the optimal zoning scheme; realizing dynamic zoning of the distribution network; improving zoning efficiency; and reducing the impact of subjective human selection.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and more specifically to a dynamic zoning method for power distribution networks based on a multi-objective ant colony algorithm. Background Technology

[0002] The widespread integration of distributed generation has transformed distribution networks from traditional unidirectional power supply networks into active distribution networks with bidirectional power flow. This transformation places higher demands on the state estimation, operation control, and regional division of distribution networks.

[0003] Regarding regional partitioning, some foreign scholars have proposed a dynamic partitioning method for distribution networks based on community discovery algorithms. This method identifies electrical coupling relationships within the power grid and enables adaptive partitioning and parallel computation of regions.

[0004] Traditional methods for zoning distribution networks, such as dynamic zoning based on community discovery algorithms, achieve adaptive partitioning and parallel computing of regions by identifying electrical coupling relationships in the power grid. However, these methods lack control over the power balance and power sources within each distribution network zone. Furthermore, since distributed power sources are mostly renewable energy sources such as wind or solar power, such zoning is poor at handling the uncertainties of renewable energy sources, resulting in relatively low zoning efficiency. Summary of the Invention

[0005] The present invention proposes a dynamic zoning method for power distribution networks based on a multi-objective ant colony algorithm to solve the technical problems mentioned in the background.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm of the present invention includes the following steps: S1. Obtain the core parameters, state transition rules, and pheromone update rules of the basic ant colony algorithm, and implement the initial zoning of the distribution network based on the core parameters, state transition rules, and pheromone update rules; S2. Modify the basic ant colony algorithm to form a multi-objective ant colony optimization algorithm; S3. By combining the Jacobian matrix with the heuristic information of the multi-objective ant colony optimization algorithm, the distribution network is re-partitioned and points with more compact physical structures are selected as the same partition. S4. Calculate the energy coupling degree between the distribution network sections using a tracking algorithm; S5. The Pareto optimal solution set is obtained by solving the final power reserve and final binary modularity of the distribution network partition using the multi-objective ant colony optimization algorithm, and the optimal partitioning scheme is selected to realize the dynamic partitioning of the distribution network.

[0007] Preferably, step S1 includes the following steps: S11. Obtain core parameters and state transition rules. Core parameters include heuristic information, pheromones, and population size. S12. Based on the state transition probability, use the roulette wheel method to determine the next path forward. After all the ants in the population have completed the path update, update the pheromone according to the pheromone update rule. S13. After completing the pheromone update, the ant colony updates the path until a certain number of updates are reached or all ants choose the same path. S14. The distribution network is initially divided into zones based on the path using trajectory coding.

[0008] Preferably, the steps of the multi-objective ant colony optimization algorithm are as follows: Given m ants, generate m sets of uniformly distributed and non-repeating weight vectors. Divide the ant colony into K groups based on the corresponding weights of the m ants, where K is the number of objective functions. Initialize heuristic information and K pheromone matrices. Ants in the same group share one pheromone matrix. Generate initial ant colonies and external files. Calculate the multi-objective solution set of each initial ant colony and save the non-dominated solutions in the external files. Update the corresponding pheromone matrix using the non-dominated solutions from the newly added external files; The next round of updates is performed using the pheromone matrix, heuristic information, and the current solution, until the stopping condition is met.

[0009] Preferably, step S3 includes the following steps: S31. Derive the path weight matrix A using the Jacobian matrix; S32. Based on the line weight matrix A, heuristic information is defined using Pearson similarity. S33. Guide the ant colony to select points with more compact physical structures as the same partition, and divide the power distribution network into partitions.

[0010] Preferably, the state transition rule is as follows:

[0011] in, This represents the state transition probability, which is the path chosen by the ant. - The probability, For heuristic information, and paths - Inversely proportional to length For point , Pheromones in the inter-path As an information heuristic factor, it determines the likelihood of the ant choosing to take a particular path; As a heuristic factor, The larger the ant is, the more likely it is to choose the shortest local path. For points A set of directly connected points; The pheromone update rules are as follows:

[0012] in, It is a pheromone volatile factor; For the ant colony on the path - Newly added pheromones.

[0013] Preferably, when calculating the multi-objective solution set, the MOPS is decomposed into a single-objective minimization problem, and the calculation formula is as follows:

[0014]

[0015] in, It is a single-objective function. For the weight vector, As decision variables, As a reference point, For decision-making space, It is a multi-objective function.

[0016] Preferably, the derivation process of the line weight matrix A is as follows:

[0017]

[0018]

[0019] in, , , , These are the active power increment, reactive power increment, node phase angle increment, and node voltage increment, respectively. It is a Jacobian matrix; , , , This is the sensitivity matrix; For nodes , The tightness of the physical structural connections, For nodes , Interaction power between them; Based on the line weight matrix, heuristic information is defined using Pearson similarity:

[0020] in, and The line weight matrix A is respectively The mean and standard deviation of the rows; Let A be the number of columns in matrix A; The degree of tightness of the physical structure connection. For Earson similarity, The first in the matrix i Line number s Column elements, The first in the matrix j Line number s Column elements, Similarly.

[0021] Preferably, the calculation steps for the final power reserve are as follows: Calculate the power reserve of the partition:

[0022] in, For partitioning i Reserve power, For partitioning i The maximum power that the internal power supply can provide is For partitioning i The load size, For reserve coefficient; Calculate the power reserve of all partitions in the entire network, and select the minimum value as the power reserve of the entire network. The maximum value is the partition optimization target:

[0023] Generate and compute the prediction scene set S E ={ , ,…, Power reserves for each prediction scenario, i.e., final power reserves:

[0024] in, Refers to the prediction scenario power reserve value .

[0025] Preferably, the final binary modularity calculation steps are as follows: Power flow tracing analysis algorithms are used to trace power flow in the distribution network.

[0026]

[0027] in, Assign a matrix to the downstream direction. , , Inject relevant quantities into the power. For the distribution coefficient matrix, For the elements of this matrix, For nodes i Upstream generator to load node Power allocation; The binary modularity is defined as follows:

[0028]

[0029]

[0030]

[0031]

[0032] in, For trend tracking matrix, for middle i OK The column element is : , These are the power supply and load collections for the entire network; For partition numbering, , Partitions Medium power source set, load set For the number of partitions, This refers to partitions. The degree of binary modularity, The expected association percentage for the partition. as well as To determine the correlation between regional power sources and loads within the entire distribution network, The weights are associated with the total global power. Obtain the prediction scene set S E ={ , ,…, The corresponding probability R = { , ,…, }, thus deriving the predicted scenario The power flow distribution or state estimation results are used to calculate the scenario. Corresponding binary modularity Finally, combining probability information, the following calculations were performed. ; .

[0033] Preferably, the steps of the multi-objective ant colony optimization algorithm to obtain the Pareto optimal solution set by solving the final power reserve and final binary modularity of the distribution network partition are as follows: The multi-objective ant colony optimization algorithm aims to minimize the final power reserve and the final binary modularity, and obtains the Pareto optimal solution set.

[0034]

[0035] in, , The minimum target; After obtaining the Pareto optimal solution set using the multi-objective ant colony algorithm, if the number of typical solutions in the Pareto optimal solution set is no greater than 1, then select... When the solution with the lowest value and the typical solution in the Pareto optimal solution set is greater than 1, choose... The solution with the lowest value The smallest solution;

[0036] in, , , , They are respectively , The minimum and maximum values ​​in the obtained Pareto optimal solution set.

[0037] As can be seen from the above technical solution, this invention provides a dynamic zoning method for distribution networks based on a multi-objective ant colony algorithm. Compared with the prior art, this invention has the following advantages: It utilizes a tracking algorithm to define the binary modularity to quantify the energy balance between zones, and proposes a multi-objective ant colony algorithm suitable for distribution network zoning by combining power reserve indicators. This ensures power balance in each zone of the distribution network and strong control capability of the power source within the zone. Combining the probabilistic prediction scenario set with the zoning algorithm yields dynamic active power zoning, improving the zoning's ability to cope with the uncertainty of renewable energy and enhancing the robustness of the zoning scheme. Furthermore, by using the multi-objective ant colony optimization algorithm to solve for the final power reserve and final binary modularity of the distribution network zoning, a Pareto optimal solution set is obtained, and the optimal zoning scheme is selected to achieve dynamic zoning of the distribution network, improving zoning efficiency and reducing the impact of subjective human selection. Attached Figure Description

[0038] Figure 1This is a flowchart illustrating the dynamic zoning method for power distribution networks based on the multi-objective ant colony algorithm of the present invention. Figure 2 This is a schematic diagram of the typical solution selection strategy for partitioning in this invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0040] like Figure 1 As shown, the dynamic zoning method for distribution networks based on the multi-objective ant colony algorithm in this embodiment includes the following steps: S1. Obtain the core parameters, state transition rules, and pheromone update rules of the basic ant colony algorithm, and implement the initial zoning of the distribution network based on the core parameters, state transition rules, and pheromone update rules; S2. Modify the basic ant colony algorithm to form a multi-objective ant colony optimization algorithm; S3. By combining the heuristic information of the Jacobian matrix and the multi-objective ant colony optimization algorithm, the distribution network is re-partitioned and points with more compact physical structures are selected as the same partition. S4. Calculate the energy coupling degree between distribution network sections using a tracking algorithm; S5. By using a multi-objective ant colony optimization algorithm to solve for the final power reserve and final binary modularity of the distribution network partition, the Pareto optimal solution set is obtained, the optimal partitioning scheme is selected, and dynamic partitioning of the distribution network is realized.

[0041] S1 includes the following steps: S11. Obtain core parameters and state transition rules. Core parameters include heuristic information, pheromones, and population size. S12. Based on the state transition probability, use the roulette wheel method to determine the next path forward. After all the ants in the population have completed the path update, update the pheromone according to the pheromone update rule. S13. After completing the pheromone update, the ant colony updates the path until a certain number of updates are reached or all ants choose the same path. S14. The distribution network is initially divided into zones based on the path using trajectory coding.

[0042] The steps of the multi-objective ant colony optimization algorithm are as follows: Set the number of ants to m, generate m sets of uniformly distributed and non-repeating weight vectors, and divide the ant colony into K groups according to the corresponding weights of the m ants, where K is the number of MOPS objective functions; Initialize heuristic information and K pheromone matrices. Ants in the same group share one pheromone matrix. Generate initial ant colonies and external files. Calculate the multi-objective solution set of each initial ant colony and save the non-dominated solutions in the external files. Update the corresponding pheromone matrix using the non-dominated solutions from the newly added external files; The next round of updates is performed using the pheromone matrix, heuristic information, and the current solution, until the stopping condition is met.

[0043] S3 includes the following steps: S31. Derive the path weight matrix A using the Jacobian matrix; S32. Based on the line weight matrix A, heuristic information is defined using Pearson similarity. S33. Guide the ant colony to select points with more compact physical structures as the same partition, and divide the power distribution network into partitions.

[0044] The state transition rules are as follows:

[0045] in, This represents the state transition probability, which is the path chosen by the ant. - The probability, For heuristic information, and paths - Inversely proportional to length For point , Pheromones in the inter-path As an information heuristic factor, it determines the likelihood of the ant choosing to take a particular path; As a heuristic factor, The larger the ant is, the more likely it is to choose the shortest local path. For points A set of directly connected points; The pheromone update rules are as follows:

[0046] in, It is a pheromone volatile factor; For the ant colony on the path - Newly added pheromones.

[0047] When calculating the multi-objective solution set, MOPS is decomposed into a single-objective minimization problem, and the calculation formula is as follows:

[0048]

[0049] in, It is a single-objective function. For the weight vector, As decision variables, As a reference point, For decision-making space, It is a multi-objective function.

[0050] The derivation of the line weight matrix A is as follows:

[0051]

[0052]

[0053] in, , , , These are the active power increment, reactive power increment, node phase angle increment, and node voltage increment, respectively. It is a Jacobian matrix; , , , This is the sensitivity matrix; For nodes , The tightness of the physical structural connections, For nodes , Interaction power between them; Based on the line weight matrix, heuristic information is defined using Pearson similarity:

[0054] in, and The line weight matrix A is respectively The mean and standard deviation of the rows; Let A be the number of columns in matrix A; The degree of tightness of the physical structure connection. For Earson similarity, The first in the matrix i Line number s Column elements, The first in the matrix j Line number s Column elements, Similarly.

[0055] The calculation steps for the final power reserve are as follows: Calculate the power reserve of the partition:

[0056] in, For partitioning i Reserve power, For partitioning i The maximum power that the internal power supply can provide is For partitioning i The load size, This is the reserve factor; the larger the value, the higher the requirement for power reserve. Calculate the power reserve of all partitions in the entire network, and select the minimum value as the power reserve of the entire network. The maximum value is the partition optimization target:

[0057] Generate and compute the prediction scene set S E ={ , ,…, Power reserves for each prediction scenario, i.e., final power reserves:

[0058] in, Refers to the prediction scenario power reserve value The larger this value, the larger the overall reserve power value of each zone of the distribution network, and the better it is at coping with the uncertainty of renewable energy.

[0059] Specifically, bipartite networks are an important manifestation of complex networks. These networks consist of two parts with nodes of different types, and nodes of the same type are not connected. Many real-world networks exhibit bipartite characteristics, such as scientist-paper networks, disease-gene networks, and peer-to-peer (P2P) networks. The energy flow network between sources and loads in a power distribution network also belongs to the bipartite network category. Partition mining in bipartite networks refers to grouping closely connected nodes into a single partition. Effectively dividing the energy flow network can reduce the degree of energy integration between partitions in the power distribution network.

[0060] The final steps for calculating the modularity of the binary subdivision are as follows: Power flow tracing analysis algorithms are used to trace power flow in the distribution network.

[0061]

[0062] in, Assign a matrix to the downstream direction. , , Inject relevant quantities into the power. For the distribution coefficient matrix, For the elements of this matrix, For nodes i Upstream generator to load node Power allocation; The binary modularity is defined as follows:

[0063]

[0064]

[0065]

[0066]

[0067] in, For trend tracking matrix, for middle i OK The column element is : , These are the power supply and load collections for the entire network; For partition numbering, , Partitions Medium power source set, load set For the number of partitions, This refers to partitions. The degree of binary modularity, The expected association percentage for the partition. as well as To determine the correlation between regional power sources and loads within the entire distribution network, Associating global total power with weights, calculate the weights for all partitions in the entire network. And sum them up to get the binary modularity. The larger the value, the lower the coupling between partitions; To improve the ability of the zoning scheme to cope with the uncertainties of renewable energy, a set of predicted scenarios S is obtained. E ={ , ,…, The corresponding probability R = { , ,…, }, thus deriving the predicted scenario The power flow distribution or state estimation results are used to calculate the scenario. Corresponding binary modularity Finally, combining probability information, the following calculations were performed. ; ; Energy binary modularity corresponding to the partitioning scheme The higher the value, the lower the degree of energy coupling between different regions under each prediction scenario, the more reasonable the distribution of renewable energy locations within the region, and the better able to cope with the impact of uncertainties in renewable energy.

[0068] The steps of the multi-objective ant colony optimization algorithm to obtain the Pareto optimal solution set by solving the final power reserve and final binary modularity of the distribution network partition are as follows: The multi-objective ant colony optimization algorithm aims to minimize the final power reserve and the final binary modularity, and obtains the Pareto optimal solution set.

[0069]

[0070] in, , The minimum target; like Figure 2 As shown, after obtaining the Pareto optimal solution set through the multi-objective ant colony algorithm, if the number of typical solutions in the Pareto optimal solution set is no greater than 1, then the solution is selected. When the solution with the lowest value and the typical solution in the Pareto optimal solution set is greater than 1, choose... The solution with the lowest value To find the minimum solution, develop corresponding typical solution selection strategies to improve efficiency and reduce the impact of subjective human selection, and delete... The solution with a power reserve greater than 0 is to avoid the partitioning scheme having a power reserve less than 0;

[0071] in, , , , They are respectively , In the obtained Pareto optimal solution set, the minimum and maximum values ​​are considered. However, choosing the solution with the largest power reserve may lead to strong energy coupling between different partitions. , The values ​​are contradictory; while ensuring the power reserves of each zone, the utilization... Figure 2 The compromise method shown is to select a partitioning scheme.

[0072] In summary, firstly, a binary modularity is defined using a power flow tracing algorithm to quantify the energy balance between zones, and a multi-objective ant colony algorithm suitable for distribution network zones is proposed by combining power reserve indicators. Secondly, to improve the ability of zones to cope with the uncertainties of renewable energy and enhance the robustness of the zoning scheme, a dynamic active power zoning is obtained by combining a probabilistic prediction scenario set with the zoning algorithm. Since the power of distributed renewable energy is constantly changing, the division of distribution network zones should be adjusted accordingly with changes in system operation to meet the requirements of strong coupling within zones, weak coupling between zones, and power reserve.

[0073] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0074] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0075] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0076] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic zoning method for distribution networks based on a multi-objective ant colony algorithm, characterized in that, Includes the following steps: S1. Obtain the core parameters, state transition rules, and pheromone update rules of the basic ant colony algorithm, and implement the initial zoning of the distribution network based on the core parameters, state transition rules, and pheromone update rules; S2. Modify the basic ant colony algorithm to form a multi-objective ant colony optimization algorithm; S3. By combining the Jacobian matrix with the heuristic information of the multi-objective ant colony optimization algorithm, the distribution network is re-partitioned and points with more compact physical structures are selected as the same partition. S4. Calculate the energy coupling degree between the distribution network sections using a tracking algorithm; S5. The Pareto optimal solution set is obtained by solving the final power reserve and final binary modularity of the distribution network partition using the multi-objective ant colony optimization algorithm, and the optimal partitioning scheme is selected to realize the dynamic partitioning of the distribution network.

2. The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm according to claim 1, characterized in that: S1 includes the following steps: S11. Obtain core parameters and state transition rules. Core parameters include heuristic information, pheromones, and population size. S12. Based on the state transition probability, use the roulette wheel method to determine the next path forward. After all the ants in the population have completed the path update, update the pheromone according to the pheromone update rule. S13. After completing the pheromone update, the ant colony updates the path until a certain number of updates are reached or all ants choose the same path. S14. The distribution network is initially divided into zones based on the path using trajectory coding.

3. The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm according to claim 2, characterized in that: The steps of the multi-objective ant colony optimization algorithm are as follows: Given m ants, generate m sets of uniformly distributed and non-repeating weight vectors. Divide the ant colony into K groups based on the corresponding weights of the m ants, where K is the number of objective functions. Initialize heuristic information and K pheromone matrices. Ants in the same group share one pheromone matrix. Generate initial ant colonies and external files. Calculate the multi-objective solution set of each initial ant colony and save the non-dominated solutions in the external files. Update the corresponding pheromone matrix using the non-dominated solutions from the newly added external files; The next round of updates is performed using the pheromone matrix, heuristic information, and the current solution, until the stopping condition is met.

4. The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm according to claim 3, characterized in that: S3 includes the following steps: S31. Derive the path weight matrix A using the Jacobian matrix; S32. Based on the line weight matrix A, heuristic information is defined using Pearson similarity. S33. Guide the ant colony to select points with more compact physical structures as the same partition, and divide the power distribution network into partitions.

5. The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm according to claim 4, characterized in that: The state transition rules are as follows: in, This represents the state transition probability, which is the path chosen by the ant. - The probability, For heuristic information, and paths - Inversely proportional to length For point , Pheromones in the inter-path As an information heuristic factor, it determines the likelihood of the ant choosing to take a particular path; As a heuristic factor, The larger the ant is, the more likely it is to choose the shortest local path. For points A set of directly connected points; The pheromone update rules are as follows: in, It is a pheromone volatile factor; For the ant colony on the path - Newly added pheromones.

6. The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm according to claim 5, characterized in that: When calculating the multi-objective solution set, MOPS is decomposed into a single-objective minimization problem, and the calculation formula is as follows: in, It is a single-objective function. For the weight vector, As decision variables, As a reference point, For decision-making space, It is a multi-objective function.

7. The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm according to claim 6, characterized in that: The derivation process of the line weight matrix A is as follows: in, , , , These are the active power increment, reactive power increment, node phase angle increment, and node voltage increment, respectively. It is a Jacobian matrix; , , , This is the sensitivity matrix; For nodes , The tightness of the physical structural connections, For nodes , Interaction power between them; Based on the line weight matrix, heuristic information is defined using Pearson similarity: in, and These are the line weight matrix A, respectively. The mean and standard deviation of the rows; Let be the number of columns in matrix A; The degree of tightness of the physical structure connection. For Earson similarity, The first in the matrix i Line number s Column elements, The first in the matrix j Line number s Column elements, Similarly.

8. The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm according to claim 7, characterized in that: The calculation steps for the final power reserve are as follows: Calculate the power reserve of the partition: in, For partitioning i Reserve power, For partitioning i The maximum power that the internal power supply can provide is For partitioning i The load size, For reserve coefficient; Calculate the power reserve of all partitions in the entire network, and select the minimum value as the power reserve of the entire network. The maximum value is the partition optimization target: Generate and compute the prediction scene set S E ={ , ,…, Power reserves for each prediction scenario, i.e., final power reserves: in, Refers to the prediction scenario power reserve value .

9. The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm according to claim 8, characterized in that: The final binary modularity calculation steps are as follows: Power flow tracing analysis algorithms are used to trace power flow in the distribution network. in, Assign a matrix to the downstream direction. , , Inject relevant quantities into the power. For the distribution coefficient matrix, For the elements of this matrix, For nodes i Upstream generator to load node Power allocation; The binary modularity is defined as follows: in, For trend tracking matrix, for middle i OK The column element is : , These are the power supply and load collections for the entire network; For partition numbering, , Partitions Medium power source set, load set For the number of partitions, This refers to partitions. The degree of binary modularity, The expected association percentage for the partition. as well as To determine the correlation between regional power sources and loads within the entire distribution network, The weights are associated with the total global power. Obtain the prediction scene set S E ={ , ,…, The corresponding probability R = { , ,…, }, thus deriving the predicted scenario The power flow distribution or state estimation results are used to calculate the scenario. Corresponding binary modularity Finally, combining probability information, the following calculations were performed. ; 。 10. The dynamic zoning method for distribution networks based on multi-objective ant colony algorithm according to claim 9, characterized in that: The steps of the multi-objective ant colony optimization algorithm to obtain the Pareto optimal solution set by solving for the final power reserve and final binary modularity of the distribution network partition are as follows: The multi-objective ant colony optimization algorithm aims to minimize the final power reserve and the final binary modularity, and obtains the Pareto optimal solution set. in, , The minimum target; After obtaining the Pareto optimal solution set using the multi-objective ant colony algorithm, if the number of typical solutions in the Pareto optimal solution set is no greater than 1, then select... When the solution with the lowest value and the typical solution in the Pareto optimal solution set is greater than 1, choose... The solution with the lowest value The smallest solution; in, , , , They are respectively , The minimum and maximum values ​​in the obtained Pareto optimal solution set.

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