Low-voltage power expansion access point selection method and access point selection device

Abstract

This application provides a method and device for selecting access points for low-voltage business expansion. The method includes: sequentially adding new users to all candidate access points and performing power flow calculations on the low-voltage distribution network to obtain the optimal power flow calculation results for each candidate access point; calculating path distances based on basic data to obtain the shortest path and path distance from the new user to each candidate access point; calculating multiple access costs based on the optimal power flow calculation results, shortest paths, and path distances for all candidate access points; and determining the candidate access point with the lowest access cost as the target access point for the new user. This method solves the problem that existing low-voltage business expansion application schemes rarely consider other factors of the distribution network and simply connect one phase of the three-phase network based on simple calculations, leading to an excessive increase in three-phase imbalance in the distribution network system after connection, thus increasing access costs.

Description

Technical Field

[0001] This invention relates to the field of power distribution network expansion technology, and more specifically, to a method for selecting access points, an access point selection device, a computer-readable storage medium, and a computer program product for low-voltage power distribution expansion. Background Technology

[0002] Business expansion applications are a crucial daily operation in distribution network management. Appropriate power source selection for these applications can reduce the complexity of the distribution network, decrease line losses, and improve grid reliability. In recent years, with the development of power information technology and business system informatization, the application process for business expansion has been gradually improved. However, the selection of low-voltage user access system solutions in current business expansion applications remains relatively crude and lacks precision. Energy waste is a serious problem in my country, with energy losses in low-voltage distribution networks accounting for almost 65% of the total grid losses in power system protection and control.

[0003] Existing technical solutions: Current solutions involve selecting one phase of the three-phase power grid for connection based on the access point distance along the connection path. This approach neglects to consider issues such as three-phase imbalance and safety margins after connection, and fails to correlate and analyze large amounts of information. This results in low-quality business expansion applications, potentially leading to issues like overlapping substation power supply areas, high load rates on the connected lines while nearby lines have low load rates. This can have a series of adverse effects on the low-voltage power grid. First, this method is only applicable to low-voltage business expansion applications in distribution networks. When applied to medium- and high-voltage business expansion, the nature of the access point changes, making this method unsuitable. The main reason is that medium- and high-voltage business expansion involves long-distance power transmission between regions and cities, with substations being the primary access point. Second, medium- and high-voltage business expansion involves large-scale power transmission between cities or regions, requiring consideration of more factors than low-voltage business expansion. For example, the expansion route may pass through mountainous areas or nature reserves. Since low-voltage business expansion prioritizes the lowest access cost, this method can mislead the selection of access points. Summary of the Invention

[0004] The main objective of this application is to provide a method, device, computer-readable storage medium, and computer program product for selecting access points for low-voltage business expansion, so as to at least solve the problem that existing low-voltage business expansion application solutions rarely consider other factors of the distribution network and simply connect one phase of the three-phase system through simple calculations, resulting in an excessive increase in the three-phase imbalance of the distribution network system after connection, thereby increasing the connection cost.

[0005] To achieve the above objectives, according to one aspect of this application, a method for selecting access points for low-voltage business expansion is provided, comprising: performing cluster analysis based on current daily load data and the user type of the new user to obtain new daily load data corresponding to the new user, wherein the user type is at least one of the following: residential user, commercial user, and industrial user, and the current daily load data is user daily load data already acquired in the low-voltage distribution network; sequentially adding the new user to all candidate access points, and performing power flow calculation on the low-voltage distribution network based on the current daily load data and the new daily load data combined with line parameters to obtain the optimal power flow calculation result corresponding to each candidate access point, wherein the optimal power flow calculation result includes the active power of all lines, all node currents, and minimum line loss in the low-voltage distribution network; Based on the basic data, path distance calculations are performed to obtain the shortest path and path distance from the new user to each of the candidate access points. The basic data includes the coordinate data of all candidate access points, the coordinate data of the new user, all cabling paths from the candidate access points to the new user, and the distance of each route segment. The optimal power flow calculation results, the shortest path, and the path distance corresponding to all candidate access points are input into the access point selection model to obtain the access cost of the new user accessing each of the candidate access points. The access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as constraints. The candidate access point corresponding to the lowest access cost is determined as the target access point for the new user.

[0006] Optionally, cluster analysis is performed based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. This includes: classifying the current daily load data according to the user type corresponding to the current daily load data to obtain multiple sets of daily load data, where each set of daily load data corresponds to one user type; performing cluster analysis on the first daily load data using the K-means clustering algorithm according to the user type corresponding to the new user to obtain multiple sets of second daily load data, where the first daily load data is the same as the daily load data corresponding to the user type of the new user; and selecting multiple daily load data sets that match the electricity consumption characteristics of the new user from the multiple sets of second daily load data to obtain the new daily load data.

[0007] Optionally, power flow calculation is performed on the low-voltage distribution network based on the current daily load data and the newly added daily load data, combined with line parameters, to obtain the optimal power flow calculation results corresponding to each of the candidate access points. This includes: inputting the current daily load data, the newly added daily load data, and the line parameters into the optimal power flow calculation model, and using the optimal power flow calculation model to calculate power flow information to obtain the optimal power flow calculation results corresponding to each of the candidate access points. The optimal power flow calculation model is constructed with the minimum line loss of the low-voltage distribution network as the objective function and the branch power flow balance constraints and stable operation constraints of the low-voltage distribution network as constraints.

[0008] Optionally, before inputting the current daily load data, the newly added daily load data, and the line parameters into the optimal power flow calculation model to calculate power flow information using the optimal power flow calculation model and obtain the optimal power flow calculation results corresponding to each of the candidate access points, the method further includes: determining the objective function with the minimum line loss of the low-voltage distribution network as the optimization objective, wherein the expression of the objective function is: a and b are both node numbers of the low-voltage distribution network, φ represents the total number of nodes in the low-voltage distribution network, and r s,ab I represents the resistance of branch ab. s,ab,t P represents the current flowing through branch ab during time period t. loss The line loss is represented by s, which is the node number of the medium-voltage distribution network to which the low-voltage distribution network is connected. Power flow balance constraints are applied to the voltage of all nodes, the current of all nodes, and the active and reactive power of all branches in the low-voltage distribution network to obtain the power flow balance constraints of the branches. Constraints are applied to the voltage of the nodes in the low-voltage distribution network to obtain the stable operation constraints of the low-voltage distribution network.

[0009] Optionally, the path distance is calculated based on the basic data to obtain the shortest path and path distance from the new user to all the candidate access points, including: taking the new user as the end point and all the candidate access points as the starting point, and using the Dijkstra algorithm based on the basic data to calculate the shortest path and path distance from the new user to all the candidate access points.

[0010] Optionally, the optimal power flow calculation results, the shortest path, and the path distance corresponding to all the candidate access points are input into the access point selection model to obtain the access cost of the new user accessing each of the candidate access points. This includes: performing preliminary calculations on the optimal power flow calculation results using the access point selection model to obtain the safety margin of each line segment in the access area and the maximum three-phase imbalance of the access area, where the access area is the area to which the new user belongs when accessing the candidate access points; calculating the maintenance cost incurred by the new user accessing each of the candidate access points based on the safety margin and the maximum three-phase imbalance; calculating the cabling cost required for the new user to access each of the candidate access points using the access point selection model based on the shortest path and the path distance; and adding the maintenance cost and the cabling cost corresponding to each candidate access point to obtain the access cost of the new user accessing each of the candidate access points.

[0011] Optionally, calculating the maintenance cost incurred by the new user accessing each of the alternative access points based on the safety margin and the maximum three-phase imbalance includes: calculating the safety margin price coefficient according to a first formula, where the first formula is K′. i =λ×P i ×(t+T)×(C1+C2), where t is the power outage time, T is the repair time, λ is the power outage probability, C1 is the local electricity price, C2 is the repair cost, and K′ i This represents the safety margin price coefficient for the i-th line after the new user access; the maximum three-phase imbalance price coefficient is calculated according to the second formula, which is K1 = P. min loss ×C1×24×365,P min loss This represents the minimum line loss; the maintenance cost is calculated according to the third formula, which is: f obj1 The maintenance cost is represented by T, the total number of lines in the access area is represented by M. i U is the safety margin of the i-th line. b This indicates the maximum three-phase imbalance.

[0012] According to another aspect of this application, a low-voltage distribution network access point selection device is provided. The device includes: a clustering unit, used to perform cluster analysis based on current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user, wherein the user type is at least one of the following: residential user, commercial user, and industrial user, and the current daily load data is the user daily load data already acquired in the low-voltage distribution network; a first calculation unit, used to add the new user sequentially to all candidate access points, and perform power flow calculation on the low-voltage distribution network based on the current daily load data and the new daily load data combined with line parameters to obtain the optimal power flow calculation result corresponding to each candidate access point, wherein the optimal power flow calculation result includes the active power of all lines, all node currents, and minimum line loss in the low-voltage distribution network; and a second calculation unit, used for... The system calculates path distances based on basic data to obtain the shortest path and path distance from the new user to each of the candidate access points. The basic data includes the coordinate data of all candidate access points, the coordinate data of the new user, all cabling paths from the candidate access points to the new user, and the distance of each route segment. The third calculation unit is used to input the optimal power flow calculation results, the shortest path, and the path distance corresponding to all candidate access points into the access point selection model to obtain the access cost of the new user accessing each of the candidate access points. The access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as constraints. The first determination unit is used to determine the candidate access point corresponding to the lowest access cost as the target access point for the new user.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0014] According to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement any of the methods described.

[0015] Applying the technical solution of this application, in the method for selecting access points for low-voltage business expansion, firstly, cluster analysis is performed based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already acquired in the low-voltage distribution network. Then, the new user is added to all candidate access points in sequence, and power flow calculation is performed on the low-voltage distribution network based on the current daily load data and the new daily load data combined with line parameters to obtain the optimal power flow calculation results corresponding to each candidate access point. The optimal power flow calculation results include the active power of all lines, all node currents, and minimum line loss in the low-voltage distribution network. Afterwards, based on the basic data... Path distance calculations are performed to obtain the shortest paths and path distances from the new user to each of the aforementioned candidate access points. The basic data includes the coordinates of all candidate access points, the coordinates of the new user, all cabling paths from the candidate access points to the new user, and the distances of each route segment. Then, the optimal power flow calculation results, the shortest paths, and the path distances corresponding to all candidate access points are input into the access point selection model to obtain the access cost for the new user to access each of the aforementioned candidate access points. The access point selection model is a model with the minimum access cost as the objective function, and the maximum three-phase imbalance constraint and safety margin constraint as constraints. Finally, the candidate access point corresponding to the lowest access cost is determined as the target access point for the new user. This application predicts the daily load data of new users based on existing users' daily load data and the types of new users. Then, the new users are sequentially added to all candidate access points. Low-voltage distribution network power flow calculations are performed to obtain the voltage at each access point and the active power of each line segment under optimal power flow. Shortest access path selection is used to obtain the shortest path and distance from the new user to all candidate access points. Next, the access point selection model calculates the access cost of each candidate access point based on the optimal power flow calculation results, the shortest path, and the path distance. The candidate access point with the lowest access cost is selected as the target access point. This application addresses the problem in existing low-voltage business expansion application technologies that rarely consider other factors of the distribution network and simply connect one phase of the three-phase system based on simple calculations. This leads to an excessive increase in three-phase imbalance in the distribution network system after access, thus increasing access costs. Attached Figure Description

[0016] Figure 1 A hardware structure block diagram of a mobile terminal performing an access point selection method for low-voltage ISP provided in an embodiment of this application is shown.

[0017] Figure 2A flowchart illustrating a method for selecting an access point for a low-voltage expansion device according to an embodiment of this application is shown.

[0018] Figure 3 A modular functional diagram of a low-voltage business expansion access point selection system according to an embodiment of this application is shown.

[0019] Figure 4 A flowchart illustrating the implementation of a new user prediction module according to an embodiment of this application is shown.

[0020] Figure 5 A schematic diagram of a low-voltage distribution network of IEEE 33 nodes in a distribution area according to an embodiment of this application is shown;

[0021] Figure 6 A specific new user predicted load curve is shown according to an embodiment of this application;

[0022] Figure 7 A structural block diagram of a low-voltage ISP access point selection device according to an embodiment of this application is shown.

[0023] The above figures include the following reference numerals:

[0024] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, 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 for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover 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.

[0028] As described in the background section, existing technologies select one phase of the three-phase network for connection based on the access path distance through simple calculations. This approach does not consider issues such as three-phase imbalance and safety margin in the system after connection, and fails to correlate and analyze a large amount of information, resulting in low-quality business expansion applications. To address the problem that existing low-voltage business expansion application solutions rarely consider other factors of the distribution network and only connect one phase of the three-phase network through simple calculations, leading to an excessive increase in three-phase imbalance in the distribution network system after connection and thus increasing access costs, embodiments of this application provide a method for selecting access points for low-voltage business expansion, an access point selection device, a computer-readable storage medium, and a computer program product.

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a low-voltage ISP access point selection method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the low-voltage business expansion access point selection method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] This embodiment provides a method for selecting access points for low-voltage commercial expansion devices that operate on mobile terminals, computer terminals, or similar computing devices. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] Figure 2 This is a flowchart of a low-voltage ISP access point selection method according to an embodiment of this application. For example... Figure 2 As shown, the method includes the following steps:

[0034] Step S201: Perform cluster analysis based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already obtained in the low-voltage distribution network.

[0035] Specifically, in order to determine the target access point for new users, the daily load data of new users (i.e., the current daily load data mentioned above) is first predicted using the daily load data of existing users (i.e., the current daily load data mentioned above) and the user type of new users, so that the changes in power and current generated after the new users are connected to the low-voltage distribution network can be analyzed based on the new daily load data.

[0036] Step S202: Add the newly added users to all alternative access points in sequence, and perform power flow calculation on the low-voltage distribution network based on the current daily load data and the newly added daily load data combined with the line parameters to obtain the optimal power flow calculation results corresponding to each alternative access point. The optimal power flow calculation results include the active power of all lines, the current of all nodes and the minimum line loss in the low-voltage distribution network.

[0037] Specifically, a low-voltage distribution network power flow calculation model is designed, and the newly added users are added to all the alternative access points in sequence. Since the access points of the low-voltage business expansion already have users, the new daily load data of the new users are added to the daily load data of the existing users in sequence. The current of each access point of the distribution network, the active power of each line segment and the minimum line loss under the optimal power flow are obtained through the low-voltage distribution network power flow calculation model.

[0038] Step S203: Calculate the path distance based on the basic data to obtain the shortest path and path distance from the newly added user to each of the above-mentioned alternative access points. The basic data includes the coordinate data of all the above-mentioned alternative access points, the coordinate data of the newly added user, all the cabling paths from the above-mentioned alternative access points to the newly added user, and the distance of each segment of the route.

[0039] Specifically, the basic data of new users and alternative access points are obtained using a GIS system, and the shortest path and distance from the new user to all alternative access points are calculated using the Dijkstra algorithm.

[0040] Step S204: Input the above-mentioned optimal power flow calculation results, the above-mentioned shortest path and the above-mentioned path distance corresponding to all the above-mentioned candidate access points into the access point selection model to obtain the access cost of the above-mentioned new user accessing each of the above-mentioned candidate access points. The above-mentioned access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as the constraint conditions.

[0041] Specifically, with the goal of minimizing access costs, and with three-phase imbalance and safety margin as constraints, an access point selection model is constructed. The access costs include cabling costs and maintenance costs. Specifically, the maintenance costs generated after access are calculated by using the optimal power flow calculation results through the access point selection model. Then, the cabling costs required for new users to access each alternative access point are calculated by using the shortest path and the path distance. The two costs are added together to obtain the final access cost.

[0042] Step S205: The alternative access point corresponding to the lowest access cost is determined as the target access point for the new user.

[0043] Specifically, the candidate access point with the lowest access cost was selected and determined as the target access point for the aforementioned new users, thus achieving the selection of the access point with the lowest access cost among the candidate access points for low-voltage business expansion.

[0044] In this embodiment, firstly, cluster analysis is performed based on the current daily load data and the user types of the newly added users to obtain the new daily load data corresponding to the newly added users. The user types are at least one of the following: residential users, commercial users, and industrial users. The current daily load data is the user daily load data already acquired in the low-voltage distribution network. Then, the newly added users are added to all candidate access points in sequence, and power flow calculation is performed on the low-voltage distribution network based on the current daily load data and the newly added daily load data combined with line parameters to obtain the optimal power flow calculation results corresponding to each candidate access point. The optimal power flow calculation results include the active power of all lines, the current of all nodes, and the minimum line loss in the low-voltage distribution network. Afterwards, path distance is calculated based on the basic data to obtain... The shortest path and path distance from the newly added user to each of the aforementioned candidate access points are determined. The basic data includes the coordinates of all candidate access points, the coordinates of the newly added user, and all wiring paths and distances from each candidate access point to the newly added user. Then, the optimal power flow calculation results, the shortest path, and the path distance for each candidate access point are input into the access point selection model to obtain the access cost for the newly added user to each of the aforementioned candidate access points. The access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as constraints. Finally, the candidate access point corresponding to the lowest access cost is determined as the target access point for the newly added user. This application predicts the daily load data of the newly added user based on the daily load data of existing users and the type of the newly added user. Then, the newly added user is added sequentially to all candidate access points, and low-voltage distribution network power flow calculations are performed to obtain the voltage of each access point and the active power of each line segment under optimal power flow. The shortest path and distance from the newly added user to all candidate access points are obtained through shortest access path selection. Next, the access cost of each candidate access point is calculated using the optimal power flow calculation results, shortest path, and path distance through the access point selection model. The candidate access point with the lowest access cost is then selected as the target access point. This application addresses the problem in existing low-voltage business expansion application technologies that rarely consider other factors of the distribution network and simply connect one phase of the three-phase system based on simple calculations. This leads to an excessive increase in three-phase imbalance in the distribution network system after access, thereby increasing access costs.

[0045] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the access point selection method for low-voltage business expansion of this application will be described in detail below with reference to specific embodiments.

[0046] In order to predict the daily load data of new users in advance to improve the accuracy of determining access points, in one optional implementation, step S201 includes:

[0047] Step S2011: Classify the current daily load data according to the user type corresponding to the current daily load data to obtain multiple sets of daily load data, and each set of the above daily load data corresponds to one of the above user types;

[0048] Step S2012: Based on the user type corresponding to the newly added user, perform cluster analysis on the first day load data using the K-means clustering algorithm to obtain multiple sets of second day load data. The first day load data is the same as the daily load data corresponding to the user type of the newly added user.

[0049] Step S2013: Select multiple daily load data that match the electricity consumption characteristics of the newly added user from multiple sets of the above-mentioned second-day load data to obtain the above-mentioned new daily load data.

[0050] In the above embodiments, Figure 3 This diagram illustrates a module functional diagram of a low-voltage business expansion access point selection system according to an embodiment of this application, such as... Figure 3 As shown, the low-voltage business expansion access point selection system includes a new user prediction module, an access point selection module, a low-voltage distribution network power flow calculation module, and a shortest path selection module. The goal of the new user prediction module is to obtain the daily load data of new users. Therefore, the existing low-voltage distribution network daily load data (i.e., the current daily load data mentioned above) is first classified to obtain the aforementioned daily load data. Then, based on the user type of the new users, the K-means clustering algorithm is used to perform cluster analysis on the existing daily load data of users of the same user type. The number of cluster centers is k, which can be understood as the user habits and electricity consumption of the same type of users being different. Therefore, k representative user electricity consumption data need to be found within the same user type. Thus, k types of new user daily load data can be obtained. Finally, based on the existing k types of new user daily load data, daily load data Ai that matches the electricity consumption characteristics of the new users is selected to obtain the aforementioned new daily load data. The aforementioned electricity consumption characteristics include electricity consumption, peak electricity consumption, etc., because different users have different electrical equipment (electricity consumption) and peak electricity consumption.

[0051] In addition, the implementation process of the newly added user prediction module is as follows: Figure 4As shown, based on the given parameter k, the distance between data objects is used as a similarity measure to group objects with high similarity into the same cluster, thus dividing all data objects into k clusters. K-means is an iterative process that requires examining the accuracy of the clustering results each time and adjusting the cluster centers accordingly before proceeding to the next calculation, until all data are assigned to the correct clusters. The basic calculation steps are as follows: 1) Given n loading samples, the sample set is: randomly select k samples as initial cluster centers M = {M1, M2, ..., Mk}; 2) Calculate the Euclidean distance between each sample point and the initial cluster center point, using the following formula: In the formula, D(x,Mi) represents the remaining load data x to the i-th cluster center M. i 3) Group the closest data points into the same cluster, forming k clusters, and calculate the mean of the data points in each cluster using the following formula: In the formula, n i This represents the number of data points in the i-th cluster. The mean of this cluster is used as the new cluster center. A convergence check is performed until the convergence condition is met; otherwise, the process returns to steps 2) and 3). The convergence calculation formula is: In the formula, A i Let x represent the set of samples from the i-th class after the new clustering. q For the sample object, a i For the Ath i Cluster centroids. E represents the convergence criterion of K-means clustering, and its magnitude determines the number of clusters and the quality of clustering under different clustering methods.

[0052] In order to obtain the changes in current and power distribution of the low-voltage distribution network while meeting operational requirements, in an optional implementation, step S202 above includes:

[0053] Step S2021: Input the current daily load data, the newly added daily load data, and the line parameters into the optimal power flow calculation model to calculate the power flow information using the optimal power flow calculation model, and obtain the optimal power flow calculation results corresponding to each of the above-mentioned candidate access points. The optimal power flow calculation model is constructed with the minimum line loss of the low-voltage distribution network as the objective function and the branch power flow balance constraints and the stable operation constraints of the low-voltage distribution network as the constraint conditions.

[0054] In the above embodiments, such as Figure 3As shown, the power flow calculation module of the low-voltage distribution network performs power flow calculations. Specifically, it adds new users sequentially to all alternative access points. Using the current daily load data, the newly added daily load data, and the line parameters mentioned above as input data, the optimal power flow calculation model in the low-voltage distribution network power flow calculation module obtains the current of each alternative access point and the active power of each line segment under the optimal power flow. The goal of the low-voltage distribution network power flow calculation module is to perform optimal power flow calculations on the distribution network after new users are sequentially connected to all alternative access points, ensuring that the new users meet the operating requirements of the distribution network after connection, and obtaining the changes in node current and power distribution of the distribution network while meeting the operating requirements. Through this low-voltage distribution network power flow calculation module, with the goal of minimizing distribution network line losses, the optimal power flow calculation results of the distribution network after connecting to alternative access points are obtained. The optimal power flow results include the active power and current of all lines in the distribution network, as well as the obtained minimum line losses. Output to the access point selection module.

[0055] To ensure that new users meet the operational requirements of the low-voltage distribution network after being connected, in one optional embodiment, before step S202 above, the method further includes:

[0056] Step S301: Determine the objective function with the goal of minimizing line losses in the low-voltage distribution network. The expression for the objective function is as follows: a and b are both node numbers of the aforementioned low-voltage distribution network, φ represents the total number of nodes in the aforementioned low-voltage distribution network, and r s,ab I represents the resistance of branch ab. s,ab,t P represents the current flowing through branch ab in the above circuit during time period t. loss The above-mentioned line loss is represented by s, which is the node number of the medium-voltage distribution network to which the low-voltage distribution network is connected.

[0057] Step S302: Apply power flow balance constraints to all node voltages, all node currents, and active and reactive power of all branches in the low-voltage distribution network to obtain the power flow balance constraints of the branches.

[0058] Step S303: Constrain the voltage of the nodes in the low-voltage distribution network to obtain the stability operation constraints of the low-voltage distribution network.

[0059] In the above embodiments, the power flow calculation model of the low-voltage distribution network is shown in formula (1). The optimal power flow calculation model is used to calculate the power flow information of the distribution network. The objective function is to minimize the line loss of the distribution network. The distribution network needs to ensure safe and stable operation. Considering that the power flow of the distribution network branches needs to meet the balance constraint, constraint const1.1 is added. In order to ensure that the voltage of the distribution network nodes in each time period is within a reasonable range, constraint const1.2 is set. Formula (1) is: That is, the expression for the above branch power flow balance constraint is: The subscript 's' indicates that this parameter is a low-voltage distribution network parameter belonging to the medium-voltage distribution network node 's'. s,a,t Let U be the voltage value of node a during time period t. s,b,t Let P be the voltage value of node b during time period t. s,ab,t Let r be the active power flowing through the first end of branch ab during time period t. s,ab Let x be the resistance of branch ab. s,ab Q is the reactance of branch ab; s,ab,t I represents the reactive power flowing through the first end of branch ab during time period t; s,ab,t p represents the current flowing through branch ab during time period t; s,b,t q s,b,t P represents the active power and reactive power injected by node b during time period t; s,bk,t Q s,bk,t Let be the active power and reactive power at the beginning of branch bk at time t, respectively; k:b→k represents the set of all child nodes with node b as the parent node. The expression for the stability operation constraint of the above low-voltage distribution network is U′. min ≤U s,a,t ≤U′ max ,U′ max 、U′ min These are the upper and lower limits of the voltage at low-voltage distribution network nodes, respectively.

[0060] To fully consider the shortest path and shortest distance from new users to alternative access points in order to reduce subsequent cabling costs, in one optional implementation, step S203 includes:

[0061] Step S2031: Using the newly added user as the endpoint and all the candidate access points as the starting points, the Dijkstra algorithm is used to calculate the shortest path and path distance from the newly added user to all the candidate access points based on the basic data.

[0062] In the above embodiments, such as Figure 3As shown, the shortest path selection module performs the calculations. This module is an improvement on Dijkstra's algorithm, aiming to obtain the shortest path and distance (i.e., path distance) from each candidate access point to the new user. First, the coordinate data of all candidate access points in the GIS system, the coordinate data of the new user, all wiring paths from all candidate access points to the new user, and the distances of each segment of these paths are obtained as the data foundation. Then, the new user is used as the endpoint, and all candidate access points are used as starting points in sequence. Based on this data, Dijkstra's algorithm is used to calculate the shortest path and path distance from the new user to all the aforementioned candidate access points. The specific process of Dijkstra's algorithm is as follows: 1) First, determine the starting and ending points of the shortest path and create an array B to store the shortest distance from the source point (i.e., the current candidate access point) to all nodes (i.e., the remaining candidate access points). Create two sets: set R stores the determined shortest path nodes; set U stores the undetermined shortest path nodes. 2) Initially, set R contains only the source point S1, and the remaining nodes are stored in set U. The initial path weight of the source point S1 is 0, that is, B[1] = 0. At the same time, the path weights of all other nodes that cannot be reached by the source point S1 are set to infinity. The path weights between different nodes Si and Sj are set to B[i,j]; 3) Select the minimum value in array B. The node corresponding to this value is the shortest path to the source point. Delete this node from set U and add it to set R; 4) Update array B with nodes such as i in set R. The judgment condition is as follows: B[j] > B[i] + B[i,j], where B[j] represents the shortest distance when there are j nodes on the shortest path. If the judgment condition is met, the value of array B is updated. The update formula is as follows: B[j] = B[i] + B[i,j]; Repeat steps 3) and 4) until all nodes in set U are added to set R and the calculation of the shortest path from the source point to all destination points is completed. In the obtained set R, the distances of each path segment are added in turn by the method from the source point to the destination point to obtain the shortest path distance L. The shortest path selection module ultimately obtains a set of shortest paths R and path distances L from new users to each candidate access point. The shortest path is represented by a vector R, where each value of vector R represents an intersection of the path. The shortest path R and path distance L are then output to the access point selection module.

[0063] To fully consider the costs of expansion cabling and the maintenance costs arising from changes in three-phase imbalance and safety margin after access, and to make the selection of access points more reasonable, in an optional implementation, step S204 above includes:

[0064] Step S2041: The safety margin of each line segment in the access area and the maximum three-phase imbalance of the access area are obtained by performing preliminary calculations on the above optimal power flow calculation results through the above access point selection model. The above access area is the area to which the above new user belongs when accessing the above alternative access points.

[0065] Step S2042: Calculate the maintenance costs incurred by the new user accessing each of the above-mentioned alternative access points based on the above-mentioned safety margin and the above-mentioned maximum three-phase imbalance.

[0066] Step S2043: Calculate the cabling cost required for the new user to access each of the above-mentioned candidate access points using the access point selection model based on the shortest path and the path distance.

[0067] Step S2044: Add the maintenance cost and cabling cost corresponding to each of the above-mentioned alternative access points to obtain the access cost of the new user accessing each of the above-mentioned alternative access points.

[0068] In the above embodiment, the access point selection model first calculates the safety margin of each line segment in each transformer substation and the maximum three-phase imbalance of the substation after connecting to each candidate point using the data obtained from the low-voltage distribution network power flow calculation model (i.e., the optimal power flow calculation results mentioned above). Then, it calculates the maintenance cost incurred by the connection based on the safety margin and the maximum three-phase imbalance. Next, it calculates the cabling cost required for new users to connect to each candidate access point using the data obtained from the shortest access path selection module (i.e., the shortest path and the path distance mentioned above). The two costs are then added together to obtain the final access cost. The access point selection module optimizes for minimizing access cost; therefore, its objective function is to minimize access cost. In formula (2), N is the number of towers, S is the wiring distance, V is the number of insulators, M is the safety margin of the access point to the transformer substation line, and U b K1 represents the maximum three-phase imbalance. K1, K2, K3, and K4 are the price coefficients for poles, wiring, insulators, and the maximum three-phase imbalance, respectively. T is the number of lines in the transformer substation, and K′ is the maximum three-phase imbalance. i M is the safety margin price coefficient after a new user is added to the i-th line. i It is the safety margin of the i-th line, where, In formula (3), N1 is the number of poles on the path between intersections, N2 is the number of poles at intersections, the number of intersections is the number of intersections traversed by the shortest path R obtained by the shortest path selection module, L is the path distance obtained by the shortest path selection module, β represents the increase coefficient caused by the bending of the wiring, and α is the number of insulators on a pole. The optimal selection of alternative access points is made with safety margin and three-phase imbalance as constraints.

[0069] It should also be noted that, through the aforementioned access point selection model, preliminary calculations are performed on the optimal power flow calculation results to obtain the safety margin of each line segment in the access area and the maximum three-phase imbalance of the access area. This includes: calculating the maximum three-phase imbalance according to the third formula, where the third formula is... I a I is the effective value of phase A current. b I is the effective value of phase B current. c I is the effective value of the C-phase current. avg U is the average value of the three-phase current. b The maximum three-phase imbalance is given above; the safety margin is calculated according to the fourth formula, where M is the maximum three-phase imbalance. i =P max -P i P max P is the upper limit of the load that the line can withstand. i M represents the active power of the i-th line. i This indicates the aforementioned safety margin.

[0070] For example, this invention selects three distribution areas, each of which is an IEEE 33-node low-voltage distribution network, as follows: Figure 5 As shown: In Figure 2 The numbers 1, 2, 3...99 represent user load. For distribution network lines, the K-means clustering method is first used to predict the load of new users based on the existing user load. The number of cluster centers can be changed. The prediction results for new users are as follows: Figure 6 As shown in the figure. After simulation experiments, the access point with the lowest access cost for adding new access points was identified as the 59th load point, with a minimum cost of 6968.3368 yuan. The experimental results, compared with the traditional method of accessing the nearest access point, are shown in Table 1.

[0071] Table 1. Cost Comparison Between the Invention and Traditional Methods

[0072]

[0073] In order to calculate maintenance costs, in one optional implementation, step S2042 above includes:

[0074] Step S20421: Calculate the safety margin price coefficient according to the first formula, where the first formula is K′. i =λ×P i ×(t+T)×(C1+C2), where t is the power outage time, T is the repair time, λ is the power outage probability, C1 is the local electricity price, C2 is the repair cost, and K′ i This represents the safety margin price coefficient for the i-th line after the aforementioned new user access;

[0075] Step S20422: Calculate the price coefficient of the maximum three-phase imbalance according to the second formula, which is K1 = P. min loss ×C1×24×365,P min loss This indicates the minimum line loss mentioned above;

[0076] Step S20423: The maintenance cost is calculated according to the third formula, which is as follows: f obj1 The above maintenance cost is represented by T, which represents the total number of lines in the above access area, and M represents the total number of lines in the access area. i U is the safety margin of the i-th line. b This indicates the maximum three-phase imbalance mentioned above.

[0077] In the above embodiments, the maintenance costs caused by changes in three-phase imbalance and safety margin after access are also considered when calculating access costs, making the selection of access points more reasonable. Therefore, the safety margin price coefficient and the maximum three-phase imbalance price coefficient need to be calculated according to the first formula and the second formula, respectively, and the maintenance cost needs to be calculated according to the third formula, so as to facilitate the subsequent calculation of access costs.

[0078] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0079] This application also provides an access point selection device for low-voltage expansion. It should be noted that this access point selection device can be used to execute the access point selection method for low-voltage expansion provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0080] The following describes the access point selection device for low-voltage business expansion provided in the embodiments of this application.

[0081] Figure 7 This is a structural block diagram of the access point selection device for low-voltage servo expansion according to an embodiment of this application. For example... Figure 7 As shown, the device includes:

[0082] Clustering unit 10 is used to perform cluster analysis based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already obtained in the low-voltage distribution network.

[0083] Specifically, in order to determine the target access point for new users, the daily load data of new users (i.e., the current daily load data mentioned above) is first predicted using the daily load data of existing users (i.e., the current daily load data mentioned above) and the user type of new users, so that the changes in power and current generated after the new users are connected to the low-voltage distribution network can be analyzed based on the new daily load data.

[0084] The first calculation unit 20 is used to add the newly added users to all the alternative access points in sequence, and to perform power flow calculation on the low-voltage distribution network based on the current daily load data and the newly added daily load data combined with the line parameters, so as to obtain the optimal power flow calculation results corresponding to each of the alternative access points. The optimal power flow calculation results include the active power of all lines, the current of all nodes and the minimum line loss in the low-voltage distribution network.

[0085] Specifically, a low-voltage distribution network power flow calculation model is designed, and the newly added users are added to all the alternative access points in sequence. Since the access points of the low-voltage business expansion already have users, the new daily load data of the new users are added to the daily load data of the existing users in sequence. The current of each access point of the distribution network, the active power of each line segment and the minimum line loss under the optimal power flow are obtained through the low-voltage distribution network power flow calculation model.

[0086] The second calculation unit 30 is used to calculate the path distance based on the basic data to obtain the shortest path and path distance from the newly added user to each of the above-mentioned alternative access points. The basic data includes the coordinate data of all the above-mentioned alternative access points, the coordinate data of the newly added user, all the cabling paths from all the above-mentioned alternative access points to the newly added user, and the distance of each segment of the route.

[0087] Specifically, the basic data of new users and alternative access points are obtained using a GIS system, and the shortest path and distance from the new user to all alternative access points are calculated using the Dijkstra algorithm.

[0088] The third calculation unit 40 is used to input the above-mentioned optimal power flow calculation results, the above-mentioned shortest path and the above-mentioned path distance corresponding to all the above-mentioned candidate access points into the access point selection model to obtain the access cost of the above-mentioned new user accessing each of the above-mentioned candidate access points. The above-mentioned access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as the constraint conditions.

[0089] Specifically, with the goal of minimizing access costs, and with three-phase imbalance and safety margin as constraints, an access point selection model is constructed. The access costs include cabling costs and maintenance costs. Specifically, the maintenance costs generated after access are calculated by using the optimal power flow calculation results through the access point selection model. Then, the cabling costs required for new users to access each alternative access point are calculated by using the shortest path and the path distance. The two costs are added together to obtain the final access cost.

[0090] The first determining unit 50 determines the alternative access point corresponding to the lowest access cost as the target access point for the new user.

[0091] Specifically, the candidate access point with the lowest access cost was selected and determined as the target access point for the aforementioned new users, thus achieving the selection of the access point with the lowest access cost among the candidate access points for low-voltage business expansion.

[0092] In this embodiment, the clustering unit is used to perform cluster analysis based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already acquired in the low-voltage distribution network. The first calculation unit is used to add the new user to all candidate access points in sequence, and perform power flow calculation on the low-voltage distribution network based on the current daily load data and the new daily load data combined with line parameters to obtain the optimal power flow calculation result corresponding to each candidate access point. The optimal power flow calculation result includes the active power of all lines, the voltage of all nodes, and the minimum line loss in the low-voltage distribution network. The second calculation unit is used to calculate the path distance based on the basic data to obtain... The basic data includes the coordinates of all the candidate access points, the coordinates of the new user, and all cabling paths and distances from the candidate access points to the new user. The third calculation unit inputs the optimal power flow calculation results, the shortest paths, and the shortest distances corresponding to all the candidate access points into the access point selection optimization model to obtain the access cost for the new user to access each candidate access point. The access point selection optimization model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as constraints. The first determination unit determines the candidate access point corresponding to the lowest access cost as the target access point for the new user. This application predicts the daily load data of new users based on existing users' daily load data and the types of new users. Then, the new users are sequentially added to all candidate access points. Low-voltage distribution network power flow calculations are performed to obtain the voltage at each access point and the active power of each line segment under optimal power flow. Shortest access path selection is used to obtain the shortest path and distance from the new user to all candidate access points. Next, the access point selection model calculates the access cost of each candidate access point based on the optimal power flow calculation results, the shortest path, and the path distance. The candidate access point with the lowest access cost is selected as the target access point. This application addresses the problem in existing low-voltage business expansion application technologies that rarely consider other factors of the distribution network and simply connect one phase of the three-phase system based on simple calculations. This leads to an excessive increase in three-phase imbalance in the distribution network system after access, thus increasing access costs.

[0093] To improve the accuracy of access point determination by predicting the daily load data of new users in advance, in one optional implementation, the clustering unit includes:

[0094] The classification module classifies the current daily load data according to the user type corresponding to the current daily load data, resulting in multiple sets of daily load data, with each set of the above daily load data corresponding to one of the above user types;

[0095] The clustering module performs cluster analysis on the first day's load data using the K-means clustering algorithm based on the user type corresponding to the newly added users, and obtains multiple sets of second day's load data. The first day's load data is the same as the daily load data corresponding to the user type of the newly added users.

[0096] The matching module selects multiple daily load data sets from the above-mentioned second-day load data sets that match the electricity consumption characteristics of the newly added users, thus obtaining the above-mentioned new daily load data.

[0097] In the above embodiments, Figure 3 The diagram illustrates a technical solution for a low-voltage utilities expansion access point selection method according to an embodiment of this application. Figure 3 As shown, the low-voltage business expansion access point selection system includes a new user prediction module, an access point selection module, a low-voltage distribution network power flow calculation module, and a shortest path selection module. The goal of the new user prediction module is to obtain the daily load data of new users. Therefore, the existing low-voltage distribution network daily load data (i.e., the current daily load data mentioned above) is first classified to obtain the aforementioned daily load data. Then, based on the user type of the new users, the K-means clustering algorithm is used to perform cluster analysis on the existing daily load data of users of the same user type. The number of cluster centers is k, which can be understood as the user habits and electricity consumption of the same type of user being different. Therefore, k representative user electricity consumption data types need to be identified within the same user type. Thus, k types of new user daily load data can be obtained. Finally, based on the existing k types of new user daily load data, daily load data Ai matching the electricity consumption characteristics of the new users is selected to obtain the aforementioned new daily load data. These electricity consumption characteristics include electricity consumption, peak electricity consumption, etc., because different users have different electrical equipment (electricity consumption) and peak electricity consumption.

[0098] In addition, the implementation process of the newly added user prediction module is as follows: Figure 4As shown, based on the given parameter k, the distance between data objects is used as a similarity measure to group objects with high similarity into the same cluster, thus dividing all data objects into k clusters. K-means is an iterative process that requires examining the accuracy of the clustering results each time and adjusting the cluster centers accordingly before proceeding to the next calculation, until all data are assigned to the correct clusters. The basic calculation steps are as follows: 1) Given n loading samples, the sample set is: randomly select k samples as initial cluster centers M = {M1, M2, ..., Mk}; 2) Calculate the Euclidean distance between each sample point and the initial cluster center point, using the following formula: In the formula, D(x,Mi) represents the remaining load data x to the i-th cluster center M. i 3) Group the closest data points into the same cluster, forming k clusters, and calculate the mean of the data points in each cluster using the following formula: In the formula, n i This represents the number of data points in the i-th cluster. The mean of this cluster is used as the new cluster center. A convergence check is performed until the convergence condition is met; otherwise, the process returns to steps 2) and 3). The convergence calculation formula is: In the formula, A i Let x represent the set of samples from the i-th class after the new clustering. q For the sample object, a i For the Ath i Cluster centroids. E represents the convergence criterion of K-means clustering, and its magnitude determines the number of clusters and the quality of clustering under different clustering methods.

[0099] In order to obtain the changes in current and power distribution in the low-voltage distribution network while meeting operational requirements, in one optional embodiment, the first calculation unit includes:

[0100] The input module inputs the current daily load data, the newly added daily load data, and the line parameters into the optimal power flow calculation model to calculate the power flow information and obtain the optimal power flow calculation results corresponding to each of the above-mentioned candidate access points. The optimal power flow calculation model is constructed with the minimum line loss of the low-voltage distribution network as the objective function and the branch power flow balance constraints and the stable operation constraints of the low-voltage distribution network as the constraint conditions.

[0101] In the above embodiments, such as Figure 3As shown, the power flow calculation module of the low-voltage distribution network performs power flow calculations. Specifically, it adds new users sequentially to all alternative access points. Using the current daily load data, the newly added daily load data, and the line parameters mentioned above as input data, the optimal power flow calculation model in the low-voltage distribution network power flow calculation module obtains the current of each alternative access point and the active power of each line segment under the optimal power flow. The goal of the low-voltage distribution network power flow calculation module is to perform optimal power flow calculations on the distribution network after new users are sequentially connected to all alternative access points, ensuring that the new users meet the operating requirements of the distribution network after connection, and obtaining the changes in node current and power distribution of the distribution network while meeting the operating requirements. Through this low-voltage distribution network power flow calculation module, with the goal of minimizing distribution network line losses, the optimal power flow calculation results of the distribution network after connecting to alternative access points are obtained. The optimal power flow results include the active power and current of all lines in the distribution network, as well as the obtained minimum line losses. Output to the access point selection module.

[0102] To ensure that new users can meet the operational requirements of the low-voltage distribution network after being connected, in one optional embodiment, the device further includes:

[0103] The second determining unit is used to determine the objective function with the minimum line loss of the low-voltage distribution network as the optimization objective before inputting the current daily load data, the newly added daily load data, and the line parameters into the optimal power flow calculation model to calculate the power flow information using the optimal power flow calculation model and obtain the optimal power flow calculation results corresponding to each of the above-mentioned candidate access points. The expression of the objective function is as follows: a and b are both node numbers of the aforementioned low-voltage distribution network, φ represents the total number of nodes in the aforementioned low-voltage distribution network, and r s,ab I represents the resistance of branch ab. s,ab,t P represents the current flowing through branch ab in the above circuit during time period t. loss The above-mentioned line loss is represented by s, which is the node number of the medium-voltage distribution network to which the low-voltage distribution network is connected.

[0104] The first constraint unit is used to perform power flow balance constraints on the voltage of all nodes, the current of all nodes, the active power and reactive power of all branches in the above-mentioned low-voltage distribution network, so as to obtain the power flow balance constraints of the above-mentioned branches.

[0105] The second constraint unit is used to constrain the voltage of the nodes in the low-voltage distribution network to obtain the constraints for stable operation of the low-voltage distribution network.

[0106] In the above embodiments, the power flow calculation model of the low-voltage distribution network is shown in formula (1). The optimal power flow calculation model is used to calculate the power flow information of the distribution network. The objective function is to minimize the line loss of the distribution network. The distribution network needs to ensure safe and stable operation. Considering that the power flow of the distribution network branches needs to meet the balance constraint, constraint const1.1 is added. In order to ensure that the voltage of the distribution network nodes in each time period is within a reasonable range, constraint const1.2 is set. Formula (1) is: That is, the expression for the above branch power flow balance constraint is: The subscript 's' indicates that this parameter is a low-voltage distribution network parameter belonging to the medium-voltage distribution network node 's'. s,a,t Let U be the voltage value of node a during time period t. s,b,t Let P be the voltage value of node b during time period t. s,ab,t Let r be the active power flowing through the first end of branch ab during time period t. s,ab Let x be the resistance of branch ab. s,ab Q is the reactance of branch ab; s,ab,t I represents the reactive power flowing through the first end of branch ab during time period t; s,ab,t p represents the current flowing through branch ab during time period t; s,b,t q s,b,t P represents the active power and reactive power injected by node b during time period t; s,bk,t Q s,bk,t Let be the active power and reactive power at the beginning of branch bk at time t, respectively; k:b→k represents the set of all child nodes with node b as the parent node. The expression for the stability operation constraint of the above low-voltage distribution network is U′. min ≤U s,a,t ≤U′ max ,U′ max 、U′ min These are the upper and lower limits of the voltage at low-voltage distribution network nodes, respectively.

[0107] To fully consider the shortest path and shortest distance from new users to alternative access points in order to reduce subsequent cabling costs, in one optional implementation, the second calculation unit includes:

[0108] The calculation module takes the newly added user as the endpoint and all the above-mentioned alternative access points as the starting point, and uses Dijkstra's algorithm to calculate the shortest path and path distance from the newly added user to all the above-mentioned alternative access points based on the above-mentioned basic data.

[0109] In the above embodiments, such as Figure 3As shown, the shortest path selection module performs the calculations. This module is an improvement on Dijkstra's algorithm, aiming to obtain the shortest path and distance (i.e., path distance) from each candidate access point to the new user. First, the coordinate data of all candidate access points in the GIS system, the coordinate data of the new user, all wiring paths from all candidate access points to the new user, and the distances of each segment of these paths are obtained as the data foundation. Then, the new user is used as the endpoint, and all candidate access points are used as starting points in sequence. Based on this data, Dijkstra's algorithm is used to calculate the shortest path and path distance from the new user to all the aforementioned candidate access points. The specific process of Dijkstra's algorithm is as follows: 1) First, determine the starting and ending points of the shortest path and create an array B to store the shortest distance from the source point (i.e., the current candidate access point) to all nodes (i.e., the remaining candidate access points). Create two sets: set R stores the determined shortest path nodes; set U stores the undetermined shortest path nodes. 2) Initially, set R contains only the source point S1, and the remaining nodes are stored in set U. The initial path weight of the source point S1 is 0, i.e., B[1] = 0. At the same time, the path weights of all other nodes that cannot be reached by the source point S1 are set to infinity. The path weights between different nodes Si and Sj are set to B[i,j]; 3) Select the minimum value in array B. The node corresponding to this value is the shortest path to the source point. Delete this node from set U and add it to set R; 4) Update array B with nodes such as i in set R. The judgment condition is as follows: B[j] > B[i] + B[i,j], where B[j] represents the shortest distance when there are j nodes on the shortest path. If the judgment condition is met, the value of array B is updated. The update formula is as follows: B[j] = B[i] + B[i,j]; Repeat steps 3) and 4) until all nodes in set U are added to set R and the calculation of the shortest path from the source point to all destination points is completed. In the obtained set R, the distances of each path segment are added in turn by the method from the source point to the destination point to obtain the shortest path distance L. The shortest path selection module ultimately obtains a set of shortest paths R and path distances L from new users to each candidate access point. The shortest path is represented by a vector R, where each value of vector R represents an intersection of the path. The shortest path R and path distance L are then output to the access point selection module.

[0110] To fully consider the costs of expansion cabling and the maintenance costs arising from changes in three-phase imbalance and safety margin after access, and to make the selection of access points more reasonable, in one optional implementation, the aforementioned third calculation unit includes:

[0111] The first calculation module performs preliminary calculations on the optimal power flow calculation results using the access point selection model to obtain the safety margin of each line segment in the access area and the maximum three-phase imbalance of the access area. The access area is the area to which the new user belongs when accessing the alternative access point.

[0112] The second calculation module calculates the maintenance costs incurred by the new user accessing each of the above-mentioned alternative access points based on the above-mentioned safety margin and the above-mentioned maximum three-phase imbalance.

[0113] The third calculation module calculates the cabling cost required for the new user to access each of the above-mentioned alternative access points based on the above-mentioned access point selection model, the shortest path and the path distance.

[0114] The fourth calculation module adds the maintenance cost and cabling cost corresponding to each of the above-mentioned alternative access points to obtain the access cost for the new user to access each of the above-mentioned alternative access points.

[0115] In the above embodiment, the access point selection model first calculates the safety margin of each line segment in each transformer substation and the maximum three-phase imbalance of the substation after connecting to each candidate point using the data obtained from the low-voltage distribution network power flow calculation model (i.e., the optimal power flow calculation results mentioned above). Then, it calculates the maintenance cost incurred by the connection based on the safety margin and the maximum three-phase imbalance. Next, it calculates the cabling cost required for new users to connect to each candidate access point using the data obtained from the shortest access path selection module (i.e., the shortest path and the path distance mentioned above). The two costs are then added together to obtain the final access cost. The access point selection module optimizes for minimizing access cost; therefore, its objective function is to minimize access cost. In formula (2), N is the number of towers, S is the wiring distance, V is the number of insulators, M is the safety margin of the access point to the transformer substation line, and U b K1 represents the maximum three-phase imbalance. K1, K2, K3, and K4 are the price coefficients for poles, wiring, insulators, and the maximum three-phase imbalance, respectively. T is the number of lines in the transformer substation, and K′ is the maximum three-phase imbalance. i M is the safety margin price coefficient after a new user is added to the i-th line. i It is the safety margin of the i-th line, where, In formula (3), N1 is the number of poles on the path between intersections, N2 is the number of poles at intersections, the number of intersections is the number of intersections traversed by the shortest path R obtained by the shortest path selection module, L is the path distance obtained by the shortest path selection module, β represents the increase coefficient caused by the bending of the wiring, and α is the number of insulators on a pole. The optimal selection of alternative access points is made with safety margin and three-phase imbalance as constraints.

[0116] It should also be noted that, through the aforementioned access point selection model, preliminary calculations are performed on the optimal power flow calculation results to obtain the safety margin of each line segment in the access area and the maximum three-phase imbalance of the access area. This includes: calculating the maximum three-phase imbalance according to the third formula, where the third formula is... I a I is the effective value of phase A current. b I is the effective value of phase B current. c I is the effective value of the C-phase current. avg U is the average value of the three-phase current. b The maximum three-phase imbalance is given above; the safety margin is calculated according to the fourth formula, where M is the maximum three-phase imbalance. i =P max -P i P max P is the upper limit of the load that the line can withstand. i M represents the active power of the i-th line. i This indicates the aforementioned safety margin.

[0117] For example, this invention selects three distribution areas, each of which is an IEEE 33-node low-voltage distribution network, as follows: Figure 5 As shown: In Figure 2 The numbers 1, 2, 3...99 represent user load. For distribution network lines, the K-means clustering method is first used to predict the load of new users based on the existing user load. The number of cluster centers can be changed. The prediction results for new users are as follows: Figure 6 As shown in the figure. After simulation experiments, the access point with the lowest access cost for adding new access points was identified as the 59th load point, with a minimum cost of 6968.3368 yuan. The experimental results, compared with the traditional method of accessing the nearest access point, are shown in Table 1.

[0118] Table 1. Cost Comparison Between the Invention and Traditional Methods

[0119]

[0120] In order to calculate maintenance costs, in one optional implementation, step S2042 above includes:

[0121] Step S20421: Calculate the safety margin price coefficient according to the first formula, where the first formula is K′. i =λ×P i ×(t+T)×(C1+C2), where t is the power outage time, T is the repair time, λ is the power outage probability, C1 is the local electricity price, C2 is the repair cost, and K′ i This represents the safety margin price coefficient for the i-th line after the aforementioned new user access;

[0122] Step S20422: Calculate the price coefficient of the maximum three-phase imbalance according to the second formula, which is K1 = P. min loss ×C1×24×365,P min loss This indicates the minimum line loss mentioned above;

[0123] Step S20423: The maintenance cost is calculated according to the third formula, which is as follows: f obj1 The above maintenance cost is represented by T, which represents the total number of lines in the above access area, and M represents the total number of lines in the access area. i U is the safety margin of the i-th line. b This indicates the maximum three-phase imbalance mentioned above.

[0124] In the above embodiments, the maintenance costs caused by changes in three-phase imbalance and safety margin after access are also considered when calculating access costs, making the selection of access points more reasonable. Therefore, the safety margin price coefficient and the maximum three-phase imbalance price coefficient need to be calculated according to the first formula and the second formula, respectively, and the maintenance cost needs to be calculated according to the third formula, so as to facilitate the subsequent calculation of access costs.

[0125] The aforementioned low-voltage service expansion access point selection device includes a processor and a memory. The clustering unit, the first calculation unit, and the second calculation unit are all stored as program units in the memory. The processor executes these program units stored in the memory to implement the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0126] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured. By adjusting kernel parameters, the problem can be addressed. In existing low-voltage business expansion application technologies, few consider other factors of the distribution network; they often simply connect one phase of the three-phase network based on simple calculations. This leads to excessive three-phase imbalance in the distribution network system after connection, thus increasing connection costs.

[0127] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0128] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the access point selection method for low-voltage expansion.

[0129] Specifically, the methods for selecting access points for low-voltage expansion include:

[0130] Step S201: Perform cluster analysis based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already obtained in the low-voltage distribution network.

[0131] Step S202: Add the newly added users to all alternative access points in sequence, and perform power flow calculation on the low-voltage distribution network based on the current daily load data and the newly added daily load data combined with the line parameters to obtain the optimal power flow calculation results corresponding to each alternative access point. The optimal power flow calculation results include the active power of all lines, the current of all nodes and the minimum line loss in the low-voltage distribution network.

[0132] Step S203: Calculate the path distance based on the basic data to obtain the shortest path and path distance from the newly added user to each of the above-mentioned alternative access points. The basic data includes the coordinate data of all the above-mentioned alternative access points, the coordinate data of the newly added user, all the cabling paths from all the above-mentioned alternative access points to the newly added user, and the distance of each route segment.

[0133] Step S204: Input the above-mentioned optimal power flow calculation results, the above-mentioned shortest path and the above-mentioned path distance corresponding to all the above-mentioned candidate access points into the access point selection model to obtain the access cost of the above-mentioned new user accessing each of the above-mentioned candidate access points. The above-mentioned access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as the constraint conditions.

[0134] Step S205: The alternative access point corresponding to the lowest access cost is determined as the target access point for the new user.

[0135] This invention provides a processor for running a program, wherein the program executes the access point selection method for low-voltage expansion during runtime.

[0136] Specifically, the methods for selecting access points for low-voltage expansion include:

[0137] Step S201: Perform cluster analysis based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already obtained in the low-voltage distribution network.

[0138] Step S202: Add the newly added users to all alternative access points in sequence, and perform power flow calculation on the low-voltage distribution network based on the current daily load data and the newly added daily load data combined with the line parameters to obtain the optimal power flow calculation results corresponding to each alternative access point. The optimal power flow calculation results include the active power of all lines, the current of all nodes and the minimum line loss in the low-voltage distribution network.

[0139] Step S203: Calculate the path distance based on the basic data to obtain the shortest path and path distance from the newly added user to each of the above-mentioned alternative access points. The basic data includes the coordinate data of all the above-mentioned alternative access points, the coordinate data of the newly added user, all the cabling paths from all the above-mentioned alternative access points to the newly added user, and the distance of each route segment.

[0140] Step S204: Input the above-mentioned optimal power flow calculation results, the above-mentioned shortest path and the above-mentioned path distance corresponding to all the above-mentioned candidate access points into the access point selection model to obtain the access cost of the above-mentioned new user accessing each of the above-mentioned candidate access points. The above-mentioned access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as the constraint conditions.

[0141] Step S205: The alternative access point corresponding to the lowest access cost is determined as the target access point for the new user.

[0142] This invention provides a low-voltage business expansion access point selection system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0143] Step S201: Perform cluster analysis based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already obtained in the low-voltage distribution network.

[0144] Step S202: Add the newly added users to all alternative access points in sequence, and perform power flow calculation on the low-voltage distribution network based on the current daily load data and the newly added daily load data combined with the line parameters to obtain the optimal power flow calculation results corresponding to each alternative access point. The optimal power flow calculation results include the active power of all lines, the current of all nodes and the minimum line loss in the low-voltage distribution network.

[0145] Step S203: Calculate the path distance based on the basic data to obtain the shortest path and path distance from the newly added user to each of the above-mentioned alternative access points. The basic data includes the coordinate data of all the above-mentioned alternative access points, the coordinate data of the newly added user, all the cabling paths from all the above-mentioned alternative access points to the newly added user, and the distance of each route segment.

[0146] Step S204: Input the above-mentioned optimal power flow calculation results, the above-mentioned shortest path and the above-mentioned path distance corresponding to all the above-mentioned candidate access points into the access point selection model to obtain the access cost of the above-mentioned new user accessing each of the above-mentioned candidate access points. The above-mentioned access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as the constraint conditions.

[0147] Step S205: The alternative access point corresponding to the lowest access cost is determined as the target access point for the new user.

[0148] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0149] Step S201: Perform cluster analysis based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already obtained in the low-voltage distribution network.

[0150] Step S202: Add the newly added users to all alternative access points in sequence, and perform power flow calculation on the low-voltage distribution network based on the current daily load data and the newly added daily load data combined with the line parameters to obtain the optimal power flow calculation results corresponding to each alternative access point. The optimal power flow calculation results include the active power of all lines, the current of all nodes and the minimum line loss in the low-voltage distribution network.

[0151] Step S203: Calculate the path distance based on the basic data to obtain the shortest path and path distance from the newly added user to each of the above-mentioned alternative access points. The basic data includes the coordinate data of all the above-mentioned alternative access points, the coordinate data of the newly added user, all the cabling paths from all the above-mentioned alternative access points to the newly added user, and the distance of each route segment.

[0152] Step S204: Input the above-mentioned optimal power flow calculation results, the above-mentioned shortest path and the above-mentioned path distance corresponding to all the above-mentioned candidate access points into the access point selection model to obtain the access cost of the above-mentioned new user accessing each of the above-mentioned candidate access points. The above-mentioned access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as the constraint conditions.

[0153] Step S205: The alternative access point corresponding to the lowest access cost is determined as the target access point for the new user.

[0154] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0155] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0157] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0159] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0160] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0161] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0162] It should also be noted that 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.

[0163] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0164] 1) The low-voltage business expansion access point selection method of this application firstly involves cluster analysis based on the current daily load data and the user types of the new users to obtain the new daily load data corresponding to the new users. The user types are at least one of the following: residential users, commercial users, and industrial users. The current daily load data refers to the user daily load data already acquired in the low-voltage distribution network. Then, the new users are added to all candidate access points in sequence, and power flow calculations are performed on the low-voltage distribution network based on the current daily load data and the new daily load data combined with line parameters to obtain the optimal power flow calculation results corresponding to each candidate access point. The optimal power flow calculation results include the active power of all lines, the current of all nodes, and the minimum line loss in the low-voltage distribution network. Afterward, based on the basic data, the power flow is further optimized. The path distance is calculated to obtain the shortest path and path distance from the new user to each of the aforementioned candidate access points. The basic data includes the coordinate data of all the aforementioned candidate access points, the coordinate data of the new user, all cabling paths from the aforementioned candidate access points to the new user, and the distance of each segment of the route. Then, the optimal power flow calculation results, the shortest path, and the path distance corresponding to all the aforementioned candidate access points are input into the access point selection model to obtain the access cost of the new user accessing each of the aforementioned candidate access points. The access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as constraints. Finally, the candidate access point corresponding to the lowest access cost is determined as the target access point for the new user. This application predicts the daily load data of new users based on existing users' daily load data and the types of new users. Then, the new users are sequentially added to all candidate access points. Low-voltage distribution network power flow calculations are performed to obtain the voltage at each access point and the active power of each line segment under optimal power flow. Shortest access path selection is used to obtain the shortest path and distance from the new user to all candidate access points. Next, the access point selection model calculates the access cost of each candidate access point based on the optimal power flow calculation results, the shortest path, and the path distance. The candidate access point with the lowest access cost is selected as the target access point. This application addresses the problem in existing low-voltage business expansion application technologies that rarely consider other factors of the distribution network and simply connect one phase of the three-phase system based on simple calculations. This leads to an excessive increase in three-phase imbalance in the distribution network system after access, thus increasing access costs.

[0165] 2) The low-voltage distribution network access point selection device of this application includes a clustering unit, used to perform cluster analysis based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already acquired in the low-voltage distribution network. A first calculation unit is used to add the new user sequentially to all candidate access points, and to perform power flow calculation on the low-voltage distribution network based on the current daily load data and the new daily load data combined with line parameters to obtain the optimal power flow calculation result corresponding to each candidate access point. The optimal power flow calculation result includes the active power of all lines, the voltage of all nodes, and the minimum line loss in the low-voltage distribution network. A second calculation unit is used to perform line... The system calculates the shortest path and shortest distance from the new user to each of the candidate access points. The basic data includes the coordinates of all candidate access points, the coordinates of the new user, all cabling paths from the candidate access points to the new user, and the distance of each route segment. The third calculation unit inputs the optimal power flow calculation results, the shortest path, and the shortest distance corresponding to all candidate access points into the access point selection optimization model to obtain the access cost for the new user to access each of the candidate access points. The access point selection optimization model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as constraints. The first determination unit determines the candidate access point corresponding to the lowest access cost as the target access point for the new user. This application predicts the daily load data of new users based on existing users' daily load data and the types of new users. Then, the new users are sequentially added to all candidate access points. Low-voltage distribution network power flow calculations are performed to obtain the voltage at each access point and the active power of each line segment under optimal power flow. Shortest access path selection is used to obtain the shortest path and distance from the new user to all candidate access points. Next, the access point selection model calculates the access cost of each candidate access point based on the optimal power flow calculation results, the shortest path, and the path distance. The candidate access point with the lowest access cost is selected as the target access point. This application addresses the problem in existing low-voltage business expansion application technologies that rarely consider other factors of the distribution network and simply connect one phase of the three-phase system based on simple calculations. This leads to an excessive increase in three-phase imbalance in the distribution network system after access, thus increasing access costs.

[0166] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for selecting the access point for low-voltage business expansion, characterized in that, include: Cluster analysis is performed based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user, and industrial user. The current daily load data is the user daily load data already obtained in the low-voltage distribution network. The new users are added to all candidate access points in sequence, and the power flow calculation of the low-voltage distribution network is performed based on the current daily load data and the new daily load data combined with the line parameters to obtain the optimal power flow calculation result corresponding to each candidate access point. The optimal power flow calculation result includes the active power of all lines, the current of all nodes and the minimum line loss in the low-voltage distribution network. Based on the basic data, the path distance is calculated to obtain the shortest path and path distance from the new user to each of the candidate access points. The basic data includes the coordinate data of all the candidate access points, the coordinate data of the new user, all the cabling paths from the candidate access points to the new user, and the distance of each route segment. The optimal power flow calculation results, the shortest path, and the path distance corresponding to all the candidate access points are input into the access point selection model to obtain the access cost of the new user accessing each of the candidate access points. The access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as the constraints. The candidate access point corresponding to the lowest access cost is determined as the target access point for the new user.

2. The method according to claim 1, characterized in that, Cluster analysis is performed based on the current daily load data and the user type of the new users to obtain the new daily load data corresponding to the new users, including: The current daily load data is classified according to the user type corresponding to the current daily load data to obtain multiple sets of daily load data, and each set of daily load data corresponds to one user type; Based on the user type corresponding to the new user, the load data of the first day is clustered using the K-means clustering algorithm to obtain multiple sets of load data for the second day. The load data of the first day is the daily load data that is the same as the user type corresponding to the new user. The newly added daily load data is obtained by selecting multiple sets of second-day load data that match the electricity consumption characteristics of the new user.

3. The method according to claim 1, characterized in that, Based on the current daily load data and the newly added daily load data, combined with line parameters, power flow calculations are performed on the low-voltage distribution network to obtain the optimal power flow calculation results corresponding to each of the candidate access points, including: The current daily load data, the newly added daily load data, and the line parameters are input into the optimal power flow calculation model to calculate power flow information using the optimal power flow calculation model, thereby obtaining the optimal power flow calculation results corresponding to each of the candidate access points. The optimal power flow calculation model is constructed with the minimum line loss of the low-voltage distribution network as the objective function and the branch power flow balance constraints and stable operation constraints of the low-voltage distribution network as the constraint conditions.

4. The method according to claim 3, characterized in that, Before inputting the current daily load data, the newly added daily load data, and the line parameters into the optimal power flow calculation model to calculate power flow information using the optimal power flow calculation model and obtain the optimal power flow calculation results corresponding to each of the candidate access points, the method further includes: The objective function is determined with the goal of minimizing the line loss of the low-voltage distribution network. The expression of the objective function is as follows: a and b are both node numbers of the low-voltage distribution network, φ represents the total number of nodes in the low-voltage distribution network, and r s,ab I represents the resistance of branch ab. s,ab,t P represents the current flowing through branch ab during time period t. loss The line loss is represented by s, where s is the node number of the medium-voltage distribution network to which the low-voltage distribution network is connected. Power flow balance constraints are applied to all node voltages, all node currents, and active and reactive power of all branches in the low-voltage distribution network to obtain the power flow balance constraints of the branches. By constraining the node voltages in the low-voltage distribution network, the stable operation constraints of the low-voltage distribution network are obtained.

5. The method according to claim 1, characterized in that, Based on the aforementioned basic data, path distance calculations are performed to obtain the shortest path and path distance from the new user to all the candidate access points, including: Using the newly added user as the endpoint and all the candidate access points as the starting points, the shortest path and path distance from the newly added user to all the candidate access points are calculated using the Dijkstra algorithm based on the basic data.

6. The method according to claim 1, characterized in that, The optimal power flow calculation results, the shortest path, and the path distance corresponding to all the candidate access points are input into the access point selection model to obtain the access cost for the new user to access each of the candidate access points, including: The access point selection model is used to perform preliminary calculations on the optimal power flow calculation results to obtain the safety margin of each line segment in the access area and the maximum three-phase imbalance of the access area. The access area is the area to which the new user belongs when accessing the candidate access point. Calculate the maintenance cost incurred by the new user accessing each of the alternative access points based on the safety margin and the maximum three-phase imbalance. The access point selection model is used to calculate the cabling cost required for a new user to access each of the candidate access points based on the shortest path and the path distance. The maintenance cost and cabling cost corresponding to each of the candidate access points are added together to obtain the access cost for the new user to access each of the candidate access points.

7. The method according to claim 6, characterized in that, The maintenance costs incurred by the new user accessing each of the alternative access points are calculated based on the safety margin and the maximum three-phase imbalance, including: The safety margin price coefficient is calculated according to the first formula, where K is the first formula. i '=λ×P i ×(t+T)×(C1+C2), where t is the power outage time, T is the repair time, λ is the power outage probability, C1 is the local electricity price, C2 is the repair cost, and K... i ' represents the safety margin price coefficient for the i-th line after the new user access; The price coefficient for the maximum three-phase imbalance is calculated using the second formula, which is K1 = P. min loss ×C1×24×365,P min loss This indicates the minimum line loss; The maintenance cost is calculated according to the third formula, which is: f obj1 The maintenance cost is represented by T, the total number of lines in the access area is represented by M. i U is the safety margin of the i-th line. b This indicates the maximum three-phase imbalance.

8. A low-voltage service expansion access point selection device, characterized in that, The device includes: Clustering unit is used to perform clustering analysis based on the current daily load data and the user type of the new user to obtain the new daily load data corresponding to the new user. The user type is at least one of the following: residential user, commercial user and industrial user. The current daily load data is the user daily load data already obtained in the low-voltage distribution network. The first calculation unit is used to add the new user to all the alternative access points in sequence, and to perform power flow calculation on the low-voltage distribution network based on the current daily load data and the new daily load data combined with the line parameters, so as to obtain the optimal power flow calculation result corresponding to each of the alternative access points. The optimal power flow calculation result includes the active power of all lines, the current of all nodes and the minimum line loss in the low-voltage distribution network. The second calculation unit is used to calculate the path distance based on the basic data to obtain the shortest path and path distance from the new user to each of the candidate access points. The basic data includes the coordinate data of all the candidate access points, the coordinate data of the new user, all the cabling paths from all the candidate access points to the new user, and the distance of each route segment. The third calculation unit is used to input the optimal power flow calculation results, the shortest path and the path distance corresponding to all the candidate access points into the access point selection model to obtain the access cost of the new user accessing each of the candidate access points. The access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase imbalance constraint and safety margin constraint as the constraint conditions. The first determining unit is used to determine the candidate access point corresponding to the lowest access cost as the target access point for the new user.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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