Optimization methods, devices, terminals and media for low-voltage power distribution network equipment configuration

Abstract

This application discloses a method, device, terminal, and medium for optimizing the configuration scheme of low-voltage distribution network equipment, relating to the field of power distribution control technology. The technical solution provided by this application first divides the target distribution network area into several zones based on the geographical location and load conditions of the load nodes in the target distribution network area. Then, a closed-loop equipment configuration scheme optimization model is established. Taking into account the objective function of reliability benefits and economic costs, the number and location of closed-loop equipment in each zone are optimized and planned. Through the calculation of the closed-loop equipment configuration scheme optimization model, the closed-loop equipment configuration scheme of each zone is output so that staff can refer to the model to output the closed-loop equipment configuration scheme for closed-loop equipment configuration. This overcomes the limitation that the planning of closed-loop equipment configuration schemes in existing distribution networks is easily affected by the knowledge level of planners, and improves the stability of the effect of closed-loop equipment configuration schemes in distribution networks.

Description

Technical Field

[0001] This application relates to the field of power distribution control technology, and in particular to a method, device, terminal and medium for optimizing the configuration scheme of low-voltage power distribution network equipment. Background Technology

[0002] As the requirements for power supply reliability for users gradually increase during the operation of low-voltage distribution networks, closed-loop operation has become one of the most commonly used and effective methods to reduce the frequency of power outages and improve reliability.

[0003] Traditional closed-loop operation can lead to excessive voltage differences, resulting in increased closed-loop current and potential safety issues such as electrical equipment overload and relay protection failures. However, in recent years, with continuous technological advancements, the development and deployment of mobile closed-loop devices such as LCBs (Low Voltage Connection Boxes) and SCLTDs (Mobile Seamless Closed-Loop Devices) have provided new solutions for closed-loop operation in low-voltage distribution networks. To ensure the effectiveness of closed-loop operation, a reasonable configuration scheme for low-voltage distribution network closed-loop devices needs to be planned. However, current closed-loop device configuration schemes are derived from the professional knowledge of planners, making the quality of the configuration scheme susceptible to the influence of the planners' knowledge level. This can lead to technical problems such as distribution network instability during closed-loop operation. Summary of the Invention

[0004] This application provides a method, device, terminal, and medium for optimizing the configuration scheme of low-voltage distribution network equipment, which is used to solve the technical problem that the existing closed-loop equipment configuration scheme of distribution network is prone to poor stability due to the influence of the knowledge level of planners.

[0005] To address the aforementioned technical problems, the first aspect of this application provides a method for optimizing the configuration of low-voltage distribution network equipment, comprising:

[0006] Based on the set of load nodes in the target distribution network area, obtain the node operation data and node location information of each load node in the set of load nodes;

[0007] Based on the node location information, the geographical feature information of the load node is determined, and a geographical feature information matrix is ​​constructed based on the geographical feature information of each load node.

[0008] Based on the preset number of regional partitions and the geographic feature information matrix, the k-medoids algorithm is used to cluster each load node to obtain a number of cluster centers, wherein the number of cluster centers is the same as the number of regional partitions.

[0009] Calculate the distance between the load node and the cluster center point, and classify the load node into each partition based on the distance and the node number constraint of each partition;

[0010] Construct a closed-loop equipment configuration scheme optimization model, wherein the closed-loop equipment configuration scheme optimization model includes an objective function that maximizes the improvement of power outages in the distribution network and minimizes costs;

[0011] The node operation data is input into the closed-loop device configuration scheme optimization model. Taking the partition as the unit, the closed-loop device configuration scheme of each partition is output through the calculation of the closed-loop device configuration scheme optimization model.

[0012] Preferably, the closed-loop device configuration optimization model is as follows:

[0013] F = o1·F1 + o2·F2

[0014]

[0015]

[0016]

[0017] In the formula, F is the comprehensive index value; F1 is the annual power outage improvement index for the distribution network area, F2 is the annual cost index for the distribution network area, o1 and o2 are the weights of the annual power outage improvement index and cost index, respectively, and P before and P after C represents the annual cumulative power outage of the target distribution network area before and after the planning of the closed-loop equipment. max With C min These are the maximum and minimum values ​​in the cost sample, C. plan To plan the cost of the configuration scheme, num1 and num2 are the total number of the first closed-loop devices and the second closed-loop devices, respectively, and q1 and q2 are the annual maintenance costs of a single first closed-loop device and a single second closed-loop device, u r T represents the total transportation distance, x1 represents the transportation cost per unit distance, and T represents the total transportation distance. r,1 and T r,2 d1 and d2 are the number of times the first and second closed-loop devices are operated on day r, respectively, and the single operation cost of a single first closed-loop device and a single second closed-loop device are respectively. E r Z1 represents the cable cost incurred by using the first closed-loop device and the second closed-loop device. Z2 represents the investment cost of a single first closed-loop device and the investment cost of a single second closed-loop device.

[0018] Preferably, after outputting the closed-loop device configuration scheme for each partition, the method further includes:

[0019] According to the preset population optimization algorithm, multiple population objects are initialized, and each population object corresponds to a closed-loop device configuration scheme.

[0020] The closed-loop device configuration scheme optimization model calculates the comprehensive index value of each population object. Based on the comprehensive index value, the population optimization algorithm is used to iteratively optimize until the preset iteration termination condition is met. Then, the iteration stops and the closed-loop device configuration scheme corresponding to the current optimal population is output.

[0021] Preferably, the population optimization algorithm is the gannet optimization algorithm.

[0022] Preferably, calculating the distance between the load node and the cluster center specifically includes:

[0023] The weighted equivalent distance between the load node and the cluster center is calculated using a preset weighted equivalent distance calculation formula.

[0024] Preferably, the weighted equivalent distance calculation formula is as follows:

[0025] c(k,sp)=x1·a(k,sp)+x2·b(k,sp)

[0026]

[0027] b(k,sp)=Dist(s sp,3 ,λ k,3 )

[0028] In the formula, a(k,sp) represents the straight-line geographical distance between the sp-th node and the k-th cluster center; b(k,sp) represents the flow path length between the sp-th node and the k-th cluster center; c(k,sp) represents the weighted equivalent distance between the sp-th node and the k-th cluster center; and s SP,1 and s SP,2 s represents the first and second geographic features of the SP-th node, respectively. SP,3 λ represents the node index of the SP-th node. k,1 and λ k,2 λ represents the first and second geographical features of the k-th cluster center, respectively. k,3 The node number of the kth cluster center.

[0029] Preferably, the method for constructing the load node set specifically includes:

[0030] Traverse each load node in the target distribution network area, and add each load node to the node set corresponding to the load level according to the load level information of the load node to obtain several load node sets.

[0031] The second aspect of this application provides a device for optimizing the configuration of low-voltage distribution network equipment, comprising:

[0032] The node information acquisition unit is used to acquire the node operation data and node location information of each load node in the load node set in the target distribution network area.

[0033] The geographic feature matrix construction unit is used to determine the geographic feature information of the load node based on the node location information, and to construct a geographic feature information matrix based on the geographic feature information of each load node.

[0034] The node clustering unit is used to cluster each load node according to the preset number of regional partitions and the geographic feature information matrix using the k-medoids algorithm, so as to obtain a number of cluster centers based on the clustering results, wherein the number of cluster centers is the same as the number of regional partitions.

[0035] A node partitioning unit is used to calculate the distance between the load node and the cluster center point, so as to classify the load node into each partition according to the distance and the node number constraint of each partition.

[0036] The model building unit is used to build a closed-loop equipment configuration scheme optimization model, wherein the closed-loop equipment configuration scheme optimization model contains an objective function that maximizes the improvement of power outage in the distribution network and minimizes the cost.

[0037] The configuration scheme output unit is used to input the node operation data into the closed-loop device configuration scheme optimization model, and output the closed-loop device configuration scheme for each partition through the calculation of the closed-loop device configuration scheme optimization model.

[0038] A third aspect of this application provides a low-voltage distribution network equipment configuration optimization terminal, including: a memory and a processor;

[0039] The memory is used to store program code corresponding to the low-voltage power distribution network equipment configuration optimization method provided in the first aspect of this application;

[0040] The processor is used to execute the program code to implement a low-voltage distribution network equipment configuration optimization method as provided in the first aspect of this application.

[0041] The fourth aspect of this application provides a computer-readable storage medium storing program code corresponding to a low-voltage distribution network equipment configuration optimization method provided in the first aspect of this application.

[0042] As can be seen from the above technical solutions, this application has the following advantages:

[0043] The technical solution provided in this application first divides the target distribution network area into several zones based on the geographical location and load conditions of the load nodes within the target distribution network area. Then, it establishes a closed-loop equipment configuration optimization model, comprehensively considering the objective function of reliability benefits and economic costs, to optimize the planning of the quantity and location of closed-loop equipment in each zone. Through the calculation of the closed-loop equipment configuration optimization model, the closed-loop equipment configuration scheme for each zone is output, so that staff can refer to the model to output the closed-loop equipment configuration scheme for configuration. This overcomes the limitation that the planning of closed-loop equipment configuration schemes in existing distribution networks is easily affected by the knowledge level of planners, and improves the stability of the effect of closed-loop equipment configuration schemes in distribution networks. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating an embodiment of a method for optimizing the configuration of low-voltage power distribution network equipment provided in this application.

[0046] Figure 2 This is a flowchart illustrating a further embodiment of the low-voltage distribution network equipment configuration optimization method provided in this application.

[0047] Figure 3 This is a schematic diagram of an embodiment of a low-voltage power distribution network equipment configuration optimization device provided in this application.

[0048] Figure 4 This is a schematic diagram of the structure of an optimized terminal embodiment of a low-voltage distribution network equipment configuration scheme provided in this application. Detailed Implementation

[0049] This application provides a method, device, terminal, and medium for optimizing the configuration scheme of low-voltage distribution network equipment, which solves the technical problem that the existing closed-loop equipment configuration scheme of distribution network is prone to poor stability due to the influence of the knowledge level of planners.

[0050] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] Terminology Explanation:

[0052] A Low Voltage Connection Box (LCB) is a device used for power distribution and connection, typically used in temporary or mobile power distribution applications to facilitate flexible connection and disconnection of power sources. LCBs usually have multiple power input sockets for connecting to a mains power source, such as a generator or the power grid. These sockets are typically located on one side of the LCB and connected to the mains power supply. As a traditional closed-loop device, the LCB cannot eliminate closed-loop current, posing a risk of excessive current.

[0053] SCLTD (mobile seamless closed-loop device) can eliminate the effects of closed-loop current and the risks associated with excessive current. It can achieve closed-loop operation with a phase difference within the range of 0-30°, but it is relatively expensive.

[0054] Both types of equipment are mobile devices and need to be transported to the corresponding workstations before closed-loop operation.

[0055] The K-medoids algorithm is a centroid-based clustering algorithm that uses iterative computation to repeatedly optimize the clustering results. It selects a set of centroids from the dataset, then assigns the remaining objects to their closest centroids using a similarity metric. The algorithm then updates the centroids to be closer to these objects; this can be done by adjusting the centroids, and the changes can affect the clustering results. The K-medoids algorithm continues this process until it finds the optimal clustering solution.

[0056] First, this application provides a detailed description of an embodiment of a low-voltage distribution network equipment configuration optimization method.

[0057] Please see Figure 1 This embodiment provides a method for optimizing the configuration of low-voltage distribution network equipment, including:

[0058] Step 101: Based on the set of load nodes in the target distribution network area, obtain the node operation data and node location information of each load node in the set of load nodes.

[0059] It should be noted that, based on the target distribution network area that requires the configuration of closed-loop equipment, the node operation data and node location information of these load nodes are obtained from the load node set composed of load nodes in the target distribution network area. The node operation data includes: operation status information, electrical information, and operation cost information, etc., and the node location information includes: the location coordinates of the load node.

[0060] One example of generating load node sets is as follows: Traverse all load nodes within the target distribution network area, and add each load node to the corresponding load level node set based on its load level information to obtain several load node sets. For example, classify the loads belonging to SP nodes within the distribution network area into Level 1, Level 2, and Level 3 loads according to their importance, and assign them to three load sets N1, N2, and N3, with corresponding weights assigned as HI1, HI2, and HI3, respectively. To ensure a relatively even distribution of load nodes across different zones, set the maximum number of k-th level load nodes in each zone to G. k,max Correspondingly, the minimum number of load nodes at level k within each partition is defined as G. k,min .

[0061] Step 102: Determine the geographical features of the load nodes based on the node location information, and construct a geographical feature information matrix based on the geographical features of each load node.

[0062] It should be noted that, based on the node location information obtained in the previous step, the geographic feature information of each corresponding load node is calculated, which is then used to construct a geographic feature information matrix.

[0063] The expression for constructing the geographic feature information matrix can be:

[0064]

[0065] In the formula: s SP,1 and s SP,2 s represents the first and second geographic features of the SP node, respectively. SP,3 The node index representing the SP node is calculated using the following formula:

[0066] s SP,1 =x SP / R1,s SP,2 =y SP / R2,s SP,3 =sp

[0067] In the formula: x sp and y spThe geographic x-coordinate and y-coordinate positions of node SP. R1 and R2 are reference values ​​for the position.

[0068] Step 103: Based on the preset number of regional partitions and the geographic feature information matrix, cluster each load node using the k-medoids algorithm to obtain several cluster centers based on the clustering results.

[0069] The number of cluster centers is the same as the number of regional partitions.

[0070] It should be noted that, based on the preset number of regional partitions K, and following the basic principles of the k-medoids algorithm, the center point values ​​are randomly selected as the cluster centers of the k partitions.

[0071] [λ1,λ2,...,λ k ,...,λ K ](λ k =[λ k,1 ,λ k,2 ,λ k,3 ])

[0072] It is important to note that the center point values ​​of the k partition cluster centers should be selected from the existing load geographic feature information matrix, rather than being randomly assigned as in traditional K-means.

[0073] Step 104: Calculate the distance between the load node and the cluster center point, and classify the load node into each partition based on the distance and the node number constraint of each partition.

[0074] It should be noted that in step 104, based on the cluster center points determined in the previous step, the distance values ​​between these load nodes and the cluster center points are calculated using a preset distance algorithm. Based on the distance and the node number constraints of each partition, the load nodes are classified into each partition.

[0075] Step 105: Construct an optimization model for closed-loop device configuration scheme.

[0076] The closed-loop equipment configuration optimization model includes objective functions that maximize the improvement of power outage capacity in the distribution network and minimize costs.

[0077] More specifically, the closed-loop equipment configuration optimization model is as follows:

[0078] F = o1·F1 + o2·F2

[0079]

[0080]

[0081]

[0082] In the formula, F is the comprehensive index value; F1 is the annual power outage improvement index for the distribution network area, F2 is the annual cost index for the distribution network area, o1 and o2 are the weights of the annual power outage improvement index and cost index, respectively, where o1 + o2 = 1, P before and P after C represents the annual cumulative power outage of the target distribution network area before and after the planned closed-loop equipment. max With C min These are the maximum and minimum values ​​in the cost sample, C. plan To plan the cost of the configuration scheme, num1 and num2 are the total number of the first closed-loop devices and the second closed-loop devices, respectively, and q1 and q2 are the annual maintenance costs of a single first closed-loop device and a single second closed-loop device, u r T represents the total transportation distance, x1 represents the transportation cost per unit distance, and T represents the total transportation distance. r,1 and T r,2 d1 and d2 are the number of times the first and second closed-loop devices are operated on day r, respectively, and the single operation cost of a single first closed-loop device and a single second closed-loop device are respectively. E r Z1 represents the cable cost incurred by using the first closed-loop device and the second closed-loop device. Z2 represents the investment cost of a single first closed-loop device and the investment cost of a single second closed-loop device.

[0083] It is understood that the first closed-loop device and the second closed-loop device mentioned in this embodiment refer to LCB and SCLTD, respectively.

[0084] The formula for calculating the annual cumulative power outage amount P can be:

[0085]

[0086] In the formula, P represents the cumulative annual power outage in the region, and L... sp,r,t V is the load size of node sp on day r of the year at hour t. sp,r,t The state of node sp is the state of node sp on the t-th hour of the r-th day of the year, where 1 indicates a power outage and 0 indicates no power outage. sp,r,t SP represents the weight corresponding to the load importance level of a node on day r in the hour t of a year, where SP is the number of load nodes.

[0087] Step 106: Input the node operation data into the closed-loop device configuration scheme optimization model. Taking the partition as the unit, the closed-loop device configuration scheme of each partition is output through the calculation of the closed-loop device configuration scheme optimization model.

[0088] It should be noted that by constructing a closed-loop equipment configuration scheme optimization model, and then, based on the partitioning results of step 104, inputting the node operation data in each partition into the closed-loop equipment configuration scheme optimization model, and then, through the calculation of the closed-loop equipment configuration scheme optimization model, outputting the closed-loop equipment configuration scheme for each partition, staff can refer to the model to output the closed-loop equipment configuration scheme for closed-loop equipment configuration. This achieves automatic planning of closed-loop equipment configuration schemes, overcomes the limitation that existing distribution network closed-loop equipment configuration scheme planning is easily affected by the knowledge level of planners, and improves the stability of configuration scheme quality.

[0089] The above is a detailed description of a basic embodiment of the low-voltage distribution network equipment configuration optimization method provided in this application. The following is a detailed description of a further embodiment of the low-voltage distribution network equipment configuration optimization method provided in this application.

[0090] Please see Figure 2 Based on the above basic embodiments, the low-voltage distribution network equipment configuration optimization method provided in this embodiment may further include the following technical contents:

[0091] Furthermore, the specific steps involved in calculating the distance between the load node and the cluster center in step 104 include:

[0092] The weighted equivalent distance between the load node and the cluster center is calculated using a preset weighted equivalent distance calculation formula.

[0093] The specific formula for calculating the weighted equivalent distance is as follows:

[0094] c(k,sp)=x1·a(k,sp)+x2·b(k,sp)

[0095]

[0096] b(k,sp)=Dist(s sp,3 ,λ k,3 )

[0097] In the formula, a(k,sp) represents the straight-line geographical distance between the sp-th node and the k-th cluster center; b(k,sp) represents the traffic path length between the sp-th node and the k-th cluster center, which can be calculated directly using the Floyd algorithm; c(k,sp) represents the weighted equivalent distance between the sp-th node and the k-th cluster center; and s SP,1 and s SP,2 s represents the first and second geographic features of the SP-th node, respectively. SP,3 λ represents the node index of the SP-th node. k,1 and λk,2 λ represents the first and second geographical features of the k-th cluster center, respectively. k,3 The node number of the kth cluster center.

[0098] Calculate the weighted equivalent distance of each node relative to the cluster center, and classify each node into the partition M with the minimum weighted equivalent distance. k In the middle. If partition M k If the updated total number of nodes meets the requirements for the total number of nodes, the number of level 1 nodes, and the number of level 2 nodes, then the partition result should remain unchanged; if these requirements are not met, the nodes should be classified into the partition with the second smallest weighted equivalent distance, and this process should be repeated until the node number requirement is met.

[0099] In some implementations, if the load nodes have been divided into multiple sets according to different levels, in order to improve classification accuracy, the standard deviation of the distance between each node in sets N1, N2, and N3 and the cluster center can be further calculated based on the weighted equivalent distance calculation formula between nodes proposed above. This standard deviation value is used as an indicator reflecting the difference in distance to different cluster centers. The specific calculation formula is as follows:

[0100]

[0101] In the formula, ω Nm,n For set N m The standard deviation N of the weighted distance between the nth point and the kth cluster centers in the data is N. m The sets of load nodes representing different levels can include N1, N2, and N3. The loads belonging to the SP nodes within the distribution network area are classified into Level 1, Level 2, and Level 3 loads according to their importance, and then assigned to three load sets N1, N2, and N3. 'n' is the index of an individual node in the set. Based on this method, the standard deviation of the SP nodes is calculated, and the nodes in sets N1, N2, and N3 are reordered from largest to smallest, forming new node sets New1, New2, and New3.

[0102] Then, the cluster centers are updated by selecting the point that minimizes the sum of weighted distances from the point to all other points in the same region as the new cluster center.

[0103] Calculate the sum of weighted equivalent distances between nodes in each partition and the updated cluster centers. If this value is less than a given threshold δ or the maximum number of iterations J is reached, stop the iteration and obtain the distribution network area division result. Otherwise, repeat the steps of selecting partition cluster centers based on the k-medoids algorithm, updating the partition cluster centers, and selecting the point that minimizes the sum of weighted distances to all other points within the partition as the new cluster center, and carry out the next round of iterations until the requirements are met.

[0104] Furthermore, after outputting the closed-loop device configuration scheme for each partition in step 106, the following may also be included:

[0105] Step 107: Initialize multiple population objects according to the preset population optimization algorithm.

[0106] Each population object corresponds to a closed-loop device configuration scheme.

[0107] Step 108: Calculate the comprehensive index value of each population object through the closed-loop device configuration scheme optimization model. Based on the comprehensive index value, iterative optimization is performed in combination with the population optimization algorithm until the preset iteration termination condition is met. Then, stop the iteration and output the closed-loop device configuration scheme corresponding to the current optimal population.

[0108] More specifically, the population optimization algorithm mentioned in steps 107 and 108 is the gannet optimization algorithm.

[0109] It should be noted that, in order to solve the low-voltage distribution network closed-loop equipment configuration planning model constructed in the second step, the Gannet Optimization Algorithm (GOA) is used. GOA is a nature-inspired optimization algorithm, inspired by the foraging behavior of birds in ponds. This algorithm has a certain global search capability, which helps to find the global optimum of the problem and is less likely to get trapped in local optima. At the same time, its adaptability can be applied to different types of optimization problems. Furthermore, GOA typically has fewer parameters, which reduces the complexity of parameter tuning, making the algorithm easier to configure and use. The specific approach to applying it to solve the proposed planning model is as follows:

[0110] 1) Initialization: Randomly generate an initial population (configuration scheme), including the number and location of LCBs and SCLTDs in each distribution network section.

[0111] 2) Evaluation and Iteration: Calculate the objective function value F under the current configuration. Perform multiple iterations, each including the following steps: a. Update the population: Update the current population by applying the gannet algorithm search strategy. b. Calculate the objective function value F for each updated configuration. c. Selection: Select a subset of optimal solutions or configurations that are likely to become optimal solutions based on the objective function values.

[0112] 3) Stop iteration when the preset number of iterations is reached. Analyze the final configuration, including the number and location of LCBs and SCLTDs in each distribution network section, and the corresponding objective function value F.

[0113] The solution provided in this embodiment is based on the hierarchical and zonal planning concept. First, according to the geographical location and load conditions of the sites, the distribution network area is divided into appropriately sized and independent blocks to make the distribution network structure clear and facilitate equipment maintenance and management. Then, a planning model for the number and placement of closed-loop equipment is established. Taking into account both reliability benefits and economic costs, the number and location of LCB and SCLTD closed-loop equipment in each zone are optimized. The Goose Algorithm (GOA) is used to solve the planning problem to ensure the robustness and accuracy of the solution to the above optimization planning problem, so as to obtain a closed-loop equipment configuration scheme that comprehensively coordinates and balances the reliability level and economic investment of the low-voltage distribution network.

[0114] In addition, to more clearly demonstrate the technical effect of the technical solution in this embodiment, this embodiment selects a typical regional power distribution network in a certain area as a case study to verify the effectiveness of the proposed closed-loop equipment hierarchical and zonal optimization planning method.

[0115] The specific parameter settings in the example analysis are as follows:

[0116] Table 1. Load Weight Assignment to Distribution Network Nodes

[0117]

[0118] Table 2 shows the values ​​of other relevant parameters in the planning model.

[0119]

[0120] To demonstrate the hierarchical and zonal optimization configuration of low-voltage distribution network closed-loop equipment proposed in this paper, two comparison strategies were set up for comparative analysis. Comparison strategy 1 only considered the SCLTD in the lower-level planning; comparison strategy 2 only considered the LCB in the lower-level planning. The specific results are shown below:

[0121] Table 3 Comparison of Results for Different Planning Strategies

[0122] Comprehensive index F This application strategy Comparison Strategy 1 Comparison Strategy 2 Calculation results 0.735 0.721 0.704

[0123] As can be seen from the table above, compared with the other strategies mentioned above, the strategy proposed in this embodiment can achieve the best overall benefits and obtain the optimal planning scheme.

[0124] The above is a detailed description of a specific embodiment of a low-voltage distribution network equipment configuration optimization method provided in this application. The following is a detailed description of an embodiment of a low-voltage distribution network equipment configuration optimization device provided in this application.

[0125] Please see Figure 3 This embodiment provides a low-voltage distribution network equipment configuration optimization device, including:

[0126] The node information acquisition unit 201 is used to acquire the node operation data and node location information of each load node in the load node set according to the load node set in the target distribution network area.

[0127] The geographic feature matrix construction unit 202 is used to determine the geographic feature information of the load nodes based on the node location information, and to construct a geographic feature information matrix based on the geographic feature information of each load node.

[0128] The node clustering unit 203 is used to cluster each load node according to the preset number of regional partitions and the geographic feature information matrix, using the k-medoids algorithm, so as to obtain a number of cluster centers based on the clustering results. The number of cluster centers is the same as the number of regional partitions.

[0129] Node partitioning unit 204 is used to calculate the distance between the load node and the cluster center point, so as to classify the load node into each partition according to the distance and the node number constraint of each partition;

[0130] Model building unit 205 is used to build a closed-loop equipment configuration scheme optimization model, wherein the closed-loop equipment configuration scheme optimization model contains an objective function that maximizes the improvement of power outage in the distribution network and minimizes the cost.

[0131] The configuration scheme output unit 206 is used to input node operation data into the closed-loop device configuration scheme optimization model, and output the closed-loop device configuration scheme for each partition through the calculation of the closed-loop device configuration scheme optimization model.

[0132] In addition to the aforementioned embodiments of the low-voltage distribution network equipment configuration optimization device, this application also provides a detailed description of a low-voltage distribution network equipment configuration optimization terminal embodiment and a computer-readable storage medium embodiment.

[0133] Please see Figure 4 This embodiment provides a low-voltage distribution network equipment configuration scheme optimization terminal. The types of terminals include, but are not limited to, personal computers, industrial computers, server equipment and embedded smart terminals. The main components of the terminal include: memory 33 and processor 31, wherein memory 33 and processor 31 can be connected through communication bus 34.

[0134] The memory is used to store program code corresponding to the low-voltage distribution network equipment configuration optimization method provided in the previous embodiment;

[0135] The processor is used to execute program code to implement a low-voltage distribution network equipment configuration optimization method as provided in the previous embodiments.

[0136] The fourth aspect of this application provides a computer-readable storage medium storing program code corresponding to a low-voltage distribution network equipment configuration optimization method provided in the foregoing embodiments.

[0137] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the terminals, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0139] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0140] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 this application.

Claims

1. A method for optimizing the configuration of low-voltage distribution network equipment, characterized in that, include: Based on the set of load nodes in the target distribution network area, obtain the node operation data and node location information of each load node in the set of load nodes; Based on the node location information, the geographical feature information of the load node is determined, and a geographical feature information matrix is ​​constructed based on the geographical feature information of each load node. Based on the preset number of regional partitions and the geographic feature information matrix, the k-medoids algorithm is used to cluster each load node to obtain a number of cluster centers, wherein the number of cluster centers is the same as the number of regional partitions. Calculate the distance between the load node and the cluster center point, and classify the load node into each partition based on the distance and the node number constraint of each partition; Construct a closed-loop equipment configuration scheme optimization model, wherein the closed-loop equipment configuration scheme optimization model includes an objective function that maximizes the improvement of power outages in the distribution network and minimizes costs; The node operation data is input into the closed-loop device configuration scheme optimization model. Taking the partition as the unit, the closed-loop device configuration scheme of each partition is output through the calculation of the closed-loop device configuration scheme optimization model. The optimization model for the closed-loop device configuration scheme is as follows: ； ； ； ； In the formula, F is the comprehensive index value; F1 is the annual power outage improvement index for the distribution network area, F2 is the annual cost index for the distribution network area, o1 and o2 are the weights of the annual power outage improvement index and cost index, respectively, and P before and P after C represents the annual cumulative power outage of the target distribution network area before and after the planning of the closed-loop equipment. max With C min These are the maximum and minimum values ​​in the cost sample, C. plan To plan the cost of the configuration scheme, num1 and num2 are the total number of the first closed-loop devices and the second closed-loop devices, respectively, and q1 and q2 are the annual maintenance costs of a single first closed-loop device and a single second closed-loop device, u r x1 represents the total transportation distance and the transportation cost per unit distance, respectively, and T r,1 and T r,2 d1 and d2 are the number of times the first and second closed-loop devices are operated on day r, respectively, and the single operation cost of a single first closed-loop device and a single second closed-loop device are respectively. E r Z1 represents the cable cost incurred by using the first closed-loop device and the second closed-loop device. Z2 represents the investment cost of a single first closed-loop device and the investment cost of a single second closed-loop device.

2. The method for optimizing the configuration scheme of low-voltage distribution network equipment according to claim 1, characterized in that, The output of the closed-loop device configuration scheme for each partition also includes: According to the preset population optimization algorithm, multiple population objects are initialized, and each population object corresponds to a closed-loop device configuration scheme. The closed-loop device configuration scheme optimization model calculates the comprehensive index value of each population object. Based on the comprehensive index value, the population optimization algorithm is used to iteratively optimize until the preset iteration termination condition is met. Then, the iteration stops and the closed-loop device configuration scheme corresponding to the current optimal population is output.

3. The method for optimizing the configuration scheme of low-voltage distribution network equipment according to claim 2, characterized in that, The population optimization algorithm is the gannet optimization algorithm.

4. The method for optimizing the configuration scheme of low-voltage distribution network equipment according to claim 1, characterized in that, The calculation of the distance between the load node and the cluster center specifically includes: The weighted equivalent distance between the load node and the cluster center is calculated using a preset weighted equivalent distance calculation formula.

5. The method for optimizing the configuration scheme of low-voltage distribution network equipment according to claim 4, characterized in that, The weighted equivalent distance calculation formula is as follows: ； ； ； In the formula, a(k,sp) represents the straight-line geographical distance between the sp-th node and the k-th cluster center; b(k,sp) represents the flow path length between the sp-th node and the k-th cluster center; c(k,sp) represents the weighted equivalent distance between the sp-th node and the k-th cluster center; and s SP,1 and s SP,2 s represents the first and second geographic features of the SP-th node, respectively. SP,3 The node number representing the SP-th node. and These represent the first and second geographical features of the k-th cluster center, respectively. The node number of the kth cluster center.

6. The method for optimizing the configuration scheme of low-voltage distribution network equipment according to claim 1, characterized in that, The specific methods for constructing the load node set include: Traverse each load node in the target distribution network area, and add each load node to the node set corresponding to the load level according to the load level information of the load node to obtain several load node sets.

7. A device for optimizing the configuration scheme of low-voltage distribution network equipment, characterized in that, include: The node information acquisition unit is used to acquire the node operation data and node location information of each load node in the load node set in the target distribution network area. The geographic feature matrix construction unit is used to determine the geographic feature information of the load node based on the node location information, and to construct a geographic feature information matrix based on the geographic feature information of each load node. The node clustering unit is used to cluster each load node according to the preset number of regional partitions and the geographic feature information matrix using the k-medoids algorithm, so as to obtain a number of cluster centers based on the clustering results, wherein the number of cluster centers is the same as the number of regional partitions. A node partitioning unit is used to calculate the distance between the load node and the cluster center point, so as to classify the load node into each partition according to the distance and the node number constraint of each partition. The model building unit is used to build a closed-loop equipment configuration scheme optimization model, wherein the closed-loop equipment configuration scheme optimization model contains an objective function that maximizes the improvement of power outage in the distribution network and minimizes the cost. The configuration scheme output unit is used to input the node operation data into the closed-loop device configuration scheme optimization model, and output the closed-loop device configuration scheme for each partition through the calculation of the closed-loop device configuration scheme optimization model. The optimization model for the closed-loop device configuration scheme is as follows: ； ； ； ； In the formula, F is the comprehensive index value; F1 is the annual power outage improvement index for the distribution network area, F2 is the annual cost index for the distribution network area, o1 and o2 are the weights of the annual power outage improvement index and cost index, respectively, and P before and P after C represents the annual cumulative power outage of the target distribution network area before and after the planning of the closed-loop equipment. max With C min These are the maximum and minimum values ​​in the cost sample, C. plan To plan the cost of the configuration scheme, num1 and num2 are the total number of the first closed-loop devices and the second closed-loop devices, respectively, and q1 and q2 are the annual maintenance costs of a single first closed-loop device and a single second closed-loop device, u r x1 represents the total transportation distance and the transportation cost per unit distance, respectively, and T r,1 and T r,2 d1 and d2 are the number of times the first and second closed-loop devices are operated on day r, respectively, and the single operation cost of a single first closed-loop device and a single second closed-loop device are respectively. E r Z1 represents the cable cost incurred by using the first closed-loop device and the second closed-loop device. Z2 represents the investment cost of a single first closed-loop device and the investment cost of a single second closed-loop device.

8. A device for optimizing the configuration of low-voltage distribution network equipment, characterized in that, include: Memory and processor; The memory is used to store program code corresponding to the low-voltage distribution network equipment configuration optimization method as described in any one of claims 1 to 6; The processor is used to execute the program code to implement a low-voltage distribution network equipment configuration optimization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code corresponding to the method for optimizing the configuration scheme of low-voltage distribution network equipment as described in any one of claims 1 to 6.

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