Access point selection method and access point selection device for low-voltage business expansion

Abstract

The invention provides an access point selection method and an access point selection device for low-voltage business expansion, and the method comprises the steps: sequentially adding new users to all alternative access points, carrying out the load flow calculation of a low-voltage power distribution network, and obtaining an optimal load flow calculation result corresponding to each alternative access point; according to the basic data, carrying out path distance measurement and calculation to obtain a shortest path and a path distance corresponding to each alternative access point from the newly added user; according to the optimal power flow calculation result, the shortest path and the path distance corresponding to all the alternative access points, a plurality of access costs are obtained through calculation; and determining the alternative access point corresponding to the lowest access cost as the target access point of the new user. According to the method, the problem that in the prior art, a low-voltage business expansion technical scheme rarely considers other factors of the power distribution network and only accesses one phase of three phases through simple calculation, so that the three-phase unbalance degree of a power distribution network system after access is excessively increased, and the access cost is increased is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network expansion, and in particular to an access point selection method, an access point selection device, a computer-readable storage medium and a computer program product for low-voltage power expansion. Background Art

[0002] Business expansion and installation is an important daily business in distribution network management. Reasonable selection of power supply points for business expansion and installation can reduce the complexity of distribution network, reduce line loss and improve grid reliability. In recent years, with the development of power informatization and business system informatization, the process of business expansion and installation has been gradually improved. However, the current selection of low-voltage user access system solutions in business expansion and installation is still relatively extensive and not sophisticated enough. The phenomenon of electric energy waste in my country is very serious, among which the loss of electric energy in low-voltage distribution network accounts for almost 65% of the total power grid loss.

[0003] Existing technical solutions: The existing technical solutions are access points based on the access path distance. One of the three phases is selected for access through simple calculation. The three-phase imbalance and safety margin of the system after access are not considered. A large amount of information cannot be correlated and analyzed, resulting in low quality of business expansion application. It may cause the crossover of the power supply range of the substation, the load rate of the connected line is already high, and there are lines with low load rates nearby, etc., which will bring a series of adverse effects to the low-voltage power grid. First of all, this method is only applicable to the scope of low-voltage business expansion application of the distribution network. When this method is applied to medium and high voltage business expansion, due to the change in the nature of the access point, this method is not suitable for use in medium and high voltage business expansion application. The main reason is that medium and high voltage business expansion is long-distance power transmission between regions and cities, and the business expansion access point is mainly a substation. Secondly, medium and high voltage business expansion is a large-scale power transmission between cities or regions. Therefore, medium and high voltage business expansion considers more factors than low voltage business expansion. For example, the area where the business expansion route passes is a mountainous area, a nature reserve, etc. Since low voltage business expansion is selected with the lowest access cost as the goal, it will mislead the selection of access points when used. Summary of the invention

[0004] The main purpose of the present application is to provide a method for selecting an access point for low-voltage business expansion, an access point selection device, a computer-readable storage medium and a computer program product, so as to at least solve the problem that the low-voltage business expansion installation technical solution in the prior art rarely considers other factors of the distribution network and only connects to one of the three phases through simple calculation, resulting in excessive increase in the three-phase imbalance of the distribution network system after connection, thereby increasing the access cost.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for selecting access points for low-voltage business expansion is provided, comprising: performing cluster analysis according to current daily load data and user types of newly added users to obtain newly added daily load data corresponding to the newly added users, wherein the user type is at least one of the following: residential users, commercial users and industrial users, and the current daily load data is the daily load data of users acquired in the low-voltage distribution network; adding the newly added users to all candidate access points in sequence, and performing power flow calculation on the low-voltage distribution network according to 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, wherein the optimal power flow calculation results include the active power of all lines in the low-voltage distribution network, the current of all nodes and the minimum line loss; Path distance is calculated according to basic data to obtain the shortest path and path distance corresponding to the newly added user from each candidate access point, wherein the basic data includes coordinate data of all the candidate access points, coordinate data of the newly added user, all wiring paths from all the candidate access points to the newly added user, and distances 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 an access point selection model to obtain the access cost of the newly added user accessing each candidate access point, wherein the access point selection model is a model with minimum access cost as an objective function and maximum three-phase unbalance constraint and safety margin constraint as constraint conditions; the candidate access point corresponding to the lowest access cost is determined as the target access point for the newly added 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, including: classifying the current daily load data according to the user type corresponding to the current daily load data to obtain multiple groups of daily load data, one group of daily load data corresponds to one user type; clustering analysis is performed on the first daily load data by using a K-means clustering algorithm according to the user type corresponding to the new user to obtain multiple groups of second daily load data, the first daily load data is the daily load data of the same user type corresponding to the new user; and selecting multiple daily load data that match the electricity consumption characteristics of the new user from the multiple groups of the second daily load data to obtain the new daily load data.

[0007] Optionally, a power flow calculation is performed on the low-voltage distribution network according to the current daily load data and the newly added daily load data in combination with line parameters to obtain an optimal power flow calculation result corresponding to each of the candidate access points, including: inputting the current daily load data, the newly added daily load data and the line parameters into an optimal power flow calculation model, so as to calculate the power flow information using the optimal power flow calculation model to obtain the optimal power flow calculation result corresponding to each of the candidate access points, wherein the optimal power flow calculation model takes minimizing the line loss of the low-voltage distribution network as an objective function, and takes the branch power flow balance constraint of the low-voltage distribution network and the stable operation constraint of the low-voltage distribution network as constraint conditions to construct a model.

[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 the power flow information using the optimal power flow calculation model to obtain the optimal power flow calculation results corresponding to each candidate access point, the method further includes: determining the objective function with the minimum line loss of the low-voltage distribution network as the optimization goal, and the expression of the objective function is: a, b are the node numbers of the low-voltage distribution network, φ represents the total number of nodes of the low-voltage distribution network, r s,ab represents the resistance of branch ab, I s,ab,t represents the current flowing through the branch ab during period t, P loss represents the line loss, s is the node number of the medium voltage distribution network to which the low voltage distribution network is connected; the power flow balance constraints are performed on all node voltages, all node currents, active power and reactive power of all branches in the low voltage distribution network to obtain the branch power flow balance constraints; the node voltages in the low voltage distribution network are constrained to obtain the stable operation constraints of the low voltage distribution network.

[0009] Optionally, performing path distance calculation according to the basic data to obtain the shortest path and path distance from the new user to all the candidate access points includes: taking the new user as the end point and all the candidate access points as the starting points, and performing calculation using the Dijkstra algorithm according to the basic data to obtain 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 paths and the path distances corresponding to all the candidate access points are input into an access point selection model to obtain the access cost of the new user accessing each of the candidate access points, including: performing a preliminary calculation of the optimal power flow calculation results through the access point selection model to obtain the safety margin of each section of the line 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 point; 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 wiring cost required for the new user to access each of the candidate access points through the shortest path and the path distance through the access point selection model; and adding the maintenance cost and the wiring cost corresponding to each of the candidate access points to obtain the access cost of the new user accessing each of the candidate access points.

[0011] Optionally, calculating the maintenance cost of the newly added user accessing each of the candidate access points according to 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), t is the power outage time, T is the maintenance time, λ is the power outage probability, C1 is the regional electricity price, C2 is the maintenance cost, K′ i represents the safety margin price coefficient of the ith line after the newly added user is connected; the maximum three-phase unbalance price coefficient is calculated according to the second formula, and the second formula is K1=P min loss ×C1×24×365,P min loss represents the minimum line loss; the maintenance cost is calculated according to the third formula, and the third formula is: f obj1 represents the maintenance cost, T represents the total number of lines accessing the area, M i is the safety margin of the ith line, U b Indicates the maximum three-phase imbalance.

[0012] According to another aspect of the present application, a low-voltage business expansion access point selection device is provided, the device comprising: a clustering unit, used to perform cluster analysis according to current daily load data and user types of newly added users, to obtain newly added daily load data corresponding to the newly added users, the user type being at least one of the following: residential users, commercial users and industrial users, the current daily load data being the daily load data of users acquired in the low-voltage distribution network; a first calculation unit, used to sequentially add the newly added users to all candidate access points, and perform power flow calculation on the low-voltage distribution network according to the current daily load data and the newly added daily load data combined with line parameters, to obtain an optimal power flow calculation result corresponding to each of the candidate access points, the optimal power flow calculation result comprising the active power of all lines in the low-voltage distribution network, the current of all nodes and the minimum line loss; a second calculation unit, used to The method comprises the following steps: performing path distance measurement according to basic data to obtain the shortest path and path distance corresponding to the newly added user to each of the candidate access points, wherein the basic data includes coordinate data of all the candidate access points, coordinate data of the newly added user, all wiring paths from all the candidate access points to the newly added user, and distances of each route segment; a 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 an access point selection model to obtain the access cost of the newly added user accessing each of the candidate access points, wherein the access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase unbalance constraint and the safety margin constraint as the constraint conditions; and a first determination unit is used to determine the candidate access point corresponding to the lowest access cost as the target access point of the newly added user.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0014] According to another aspect of the present application, a computer program product is provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the methods described above is implemented.

[0015] Applying the technical solution of the present application, in the access point selection method for low-voltage business expansion, first, a cluster analysis is performed based on the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the above-mentioned new user, and the above-mentioned user type is at least one of the following: residential users, commercial users and industrial users, and the above-mentioned current daily load data is the daily load data of users obtained in the low-voltage distribution network; then, the above-mentioned new users are added to all candidate access points in sequence, and the flow calculation of the above-mentioned low-voltage distribution network is performed based on the above-mentioned current daily load data and the above-mentioned newly added daily load data combined with line parameters to obtain the optimal flow calculation results corresponding to each of the above-mentioned candidate access points, and the above-mentioned optimal flow calculation results include the active power of all lines in the above-mentioned low-voltage distribution network, all node currents and minimum line loss; then, according to the basic data Path distance measurement is performed to obtain the shortest path and path distance corresponding to the above-mentioned new user to each of the above-mentioned candidate access points, the above-mentioned basic data includes the coordinate data of all the above-mentioned candidate access points, the coordinate data of the above-mentioned new user, all the wiring paths from the above-mentioned candidate access points to the above-mentioned new user, and the distances of each section of the route; then, the above-mentioned optimal power flow calculation results corresponding to all the above-mentioned candidate access points, the above-mentioned shortest path and the above-mentioned path distance are input 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, and the above-mentioned access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase unbalance constraint and the safety margin constraint as the constraint conditions; finally, the above-mentioned candidate access point corresponding to the lowest above-mentioned access cost is determined as the target access point of the above-mentioned new user. This application predicts the daily load data of new users through the daily load data of existing users and the types of new users, and then adds the new users to all alternative access points in turn, performs low-voltage distribution network flow calculations to obtain the voltage of each access point of the distribution network under the optimal flow and the active power of each line section, and obtains the shortest path and distance from the new user to all alternative access points through the shortest access path selection. Next, the access cost of each alternative access point is calculated by the access point selection model based on the optimal flow calculation results, the shortest path and the path distance, and the alternative access point with the lowest access cost is screened out and determined as the target access point. This application solves the problem that the low-voltage business expansion and installation technical solutions in the prior art rarely consider other factors of the distribution network and only connect to one of the three phases through simple calculations, resulting in an excessive increase in the three-phase imbalance of the distribution network system after access, thereby increasing the access cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for performing an access point selection method for low voltage business expansion provided in an embodiment of the present application is shown;

[0017] Figure 2A schematic diagram of a flow chart of a method for selecting an access point for low voltage business expansion provided according to an embodiment of the present application is shown;

[0018] Figure 3 A module function diagram of a low voltage business expansion access point selection system provided according to an embodiment of the present application is shown;

[0019] Figure 4 A flowchart of implementing a new user prediction module provided according to an embodiment of the present application is shown;

[0020] Figure 5 A schematic diagram of an IEEE 33-node low-voltage distribution network in an area provided according to an embodiment of the present application is shown;

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

[0022] Figure 7 A structural block diagram of an access point selection device for low voltage business expansion provided according to an embodiment of the present application is shown.

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

[0024] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0025] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] In order 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 in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] As introduced in the background technology, in the prior art, the access point of the access path distance selects one phase of the three phases for access through simple calculation, and does not consider the three-phase imbalance and safety margin of the system after access. It is impossible to correlate and analyze a large amount of information, resulting in low quality of business expansion reporting. In order to solve the problem that the low-voltage business expansion reporting technical solution in the prior art rarely considers other factors of the distribution network and only connects one phase of the three phases through simple calculation, resulting in excessive increase in three-phase imbalance in the distribution network system after access, thereby increasing the access cost, the embodiments of the present application provide an access point selection method, an access point selection device, a computer-readable storage medium and a computer program product for low-voltage business expansion.

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

[0030] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for selecting an access point for low voltage business expansion according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0031] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the access point selection method of the low voltage business expansion in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as 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] In this embodiment, a method for selecting an access point for low-voltage business expansion running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of 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 can be executed in an order different from that shown here.

[0033] Figure 2 FIG. 1 is a flow chart of a method for selecting an access point for low voltage business expansion according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

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

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

[0036] Step S202, the above-mentioned newly added users are added to all candidate access points in sequence, and the power flow calculation is performed on the above-mentioned low-voltage distribution network according to the above-mentioned current daily load data and the above-mentioned newly added daily load data combined with line parameters to obtain the optimal power flow calculation results corresponding to each of the above-mentioned candidate access points, and the above-mentioned optimal power flow calculation results include the active power of all lines in the above-mentioned low-voltage distribution network, all node currents and minimum line losses.

[0037] Specifically, a low-voltage distribution network power flow calculation model is designed, and the above-mentioned new users are added to all alternative access points in turn. Since there are already users at the access points of the low-voltage business expansion, the additional daily load data of the new users are added to the daily load data of the existing users in turn. The low-voltage distribution network power flow calculation model is used to obtain the current of each access point of the distribution network under the optimal power flow, the active power of each line section and the minimum line loss.

[0038] Step S203, performing path distance measurement based on basic data to obtain the shortest path and path distance corresponding to the above-mentioned new user to each of the above-mentioned candidate access points, wherein the above-mentioned basic data includes coordinate data of all the above-mentioned candidate access points, coordinate data of the above-mentioned new user, all wiring paths from all the above-mentioned candidate access points to the above-mentioned new user, and the distance of each route segment.

[0039] Specifically, the basic data of the newly added users and the candidate access points are obtained by using the GIS system, and the path distance is calculated by using the Dijkstra algorithm to obtain the shortest path and distance from the newly added users to all the candidate access points.

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

[0041] Specifically, an access point selection model is constructed with the lowest access cost as the goal and three-phase unbalance and safety margin as constraints. The access cost includes wiring cost and maintenance cost. Specifically, the maintenance cost generated after access is obtained by calculating the optimal power flow calculation result through the access point selection model, and then the wiring cost required for new users to access each alternative access point is calculated based on the shortest path and the path distance. The two costs are added together to obtain the final access cost.

[0042] Step S205: determining the candidate access point corresponding to the lowest access cost as the target access point for the newly added user.

[0043] Specifically, the candidate access point with the lowest access cost is screened out and determined as the target access point for the above-mentioned new user, thereby achieving the access point selection with the lowest access cost for the low-voltage business expansion candidate access point.

[0044] In this embodiment, first, a cluster analysis is performed based on the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the above-mentioned new user, and the above-mentioned user type is at least one of the following: residential users, commercial users and industrial users, and the above-mentioned current daily load data is the daily load data of users obtained in the low-voltage distribution network; then, the above-mentioned new users are added to all candidate access points in sequence, and the flow calculation of the above-mentioned low-voltage distribution network is performed based on the above-mentioned current daily load data and the above-mentioned newly added daily load data combined with line parameters to obtain the optimal flow calculation results corresponding to each of the above-mentioned candidate access points, and the above-mentioned optimal flow calculation results include the active power of all lines in the above-mentioned low-voltage distribution network, all node currents and minimum line loss; then, the path distance is calculated based on the basic data to obtain The shortest path and path distance corresponding to the above-mentioned new user to each of the above-mentioned alternative access points, the above-mentioned basic data include the coordinate data of all the above-mentioned alternative access points, the coordinate data of the above-mentioned new user, all the wiring paths and the distances of each section of the route from all the above-mentioned alternative access points to the above-mentioned new user; then, the above-mentioned optimal power flow calculation results corresponding to all the above-mentioned alternative access points, the above-mentioned shortest path and the above-mentioned path distance are input into the access point selection model to obtain the access cost of the above-mentioned new user to access each of the above-mentioned alternative access points, and the above-mentioned access point selection model is a model with the minimum access cost as the objective function, and the maximum three-phase unbalance constraint and the safety margin constraint as the constraint conditions; finally, the above-mentioned alternative access point corresponding to the lowest access cost is determined as the target access point of the above-mentioned new user. This application predicts the daily load data of the new user through the daily load data of the existing users and the type of the new user, and then adds the new user to all the alternative access points in turn, performs the low-voltage distribution network power flow calculation to obtain the voltage of each access point of the distribution network under the optimal power flow and the active power of each section of the line, and obtains the shortest path and its distance from the new user to all the alternative access points through the shortest access path selection. Next, the access point selection model is used to calculate the optimal power flow calculation results, the shortest path and the path distance to obtain the access cost of each candidate access point, and the candidate access point with the lowest access cost is selected as the target access point. This application solves the problem that the low-voltage business expansion and installation technical solutions in the prior art rarely consider other factors of the distribution network and only connect to one of the three phases through simple calculations, resulting in excessive increase in the three-phase imbalance of the distribution network system after access, thereby increasing the access cost.

[0045] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the access point selection method for low-voltage business expansion of the present application will be described in detail below in combination with specific embodiments.

[0046] In order to predict the daily load data of newly added users in advance to improve the accuracy of determining access points, in an optional implementation manner, the above step S201 includes:

[0047] Step S2011, classifying the current daily load data according to the user types corresponding to the current daily load data to obtain multiple groups of daily load data, one group of the daily load data corresponding to one of the user types;

[0048] Step S2012, performing cluster analysis on the first day's load data by using a K-means clustering algorithm according to the user type corresponding to the newly added user, to obtain multiple groups of second day's load data, wherein the first day's load data is the daily load data of the same user type as that corresponding to the newly added user;

[0049] Step S2013, selecting a plurality of daily load data that matches the electricity consumption characteristics of the newly added users from the plurality of groups of the second day load data to obtain the newly added daily load data.

[0050] In the above embodiment, Figure 3 FIG. 4 shows a module function diagram of a low voltage business expansion access point selection system provided according to an embodiment of the present 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 flow calculation module and a shortest path selection module. The new user prediction module aims to obtain the daily load data of the new user. Therefore, the obtained low-voltage distribution network daily load data (i.e., the above-mentioned current daily load data) is first classified to obtain the above-mentioned daily load data. Then, according to the user type of the new user, the K-means clustering algorithm is used to cluster the obtained user daily load data of the same user type. The number of cluster centers is the number of user types k. It can be understood that the user habits of the same type of users are different and the power consumption is different. Therefore, k kinds of representative user power consumption data should be found in the same type of users. Therefore, k kinds of new user daily load data can be obtained. Finally, the daily load data Ai that matches the new user is selected according to the power consumption characteristics of the new user on the obtained k kinds of new user daily load data to obtain the above-mentioned new daily load data. The above-mentioned power consumption characteristics are power consumption, peak power consumption and other power consumption characteristics, because different users have different power consumption equipment (power consumption) and peak power consumption.

[0051] In addition, the implementation process of the new user prediction module is as follows Figure 4As shown in the figure, according to the given parameter k, the distance between data objects is used as the similarity measure, and objects with high similarity are divided into the same cluster, thereby dividing all data objects into k clusters. K-means is an iterative process. Each time, the accuracy of the clustering results must be examined, and the cluster center is adjusted according to the results, and then the next operation is performed until all data are assigned to the correct cluster. The basic operation steps are as follows: 1) Suppose there are n load samples, and the sample set is: randomly select k samples as the initial cluster center M = {M1, M2, ..., Mk}; 2) Calculate the Euclidean distance between each sample point and the initial cluster center point, and the calculation formula is: Where D(x,Mi) is the residual load data x to the i-th cluster center M i Euclidean distance; 3) Classify the closest distances into the same category to form k clusters, and calculate the mean of the data points in each cluster respectively. The formula is: Where n i represents the number of data contained in the i-th cluster, and takes its mean as the new cluster center to make convergence judgment. Clustering ends when the convergence condition is met, otherwise it returns to step 2) and step 3). The convergence calculation formula is: In the formula, A i represents the collection of samples of the i-th category after the new clustering, x q is the sample object, a i For A i E represents the convergence criterion of K-means clustering, and its size determines the quality of clustering effect under different clustering conditions.

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

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

[0054] In the above embodiment, if Figure 3As shown, the low-voltage distribution network flow calculation module performs flow calculation, specifically by adding new users to all alternative access points in sequence, taking the above-mentioned current daily load data, the above-mentioned new daily load data and the above-mentioned line parameters as input data, and obtaining the current of each alternative access point of the distribution network and the active power of each line section under the optimal flow through the optimal flow calculation model in the low-voltage distribution network flow calculation module. The goal of the low-voltage distribution network flow calculation module is to perform optimal flow calculation on the distribution network after the new users are connected to all alternative access points in sequence, to ensure that the new users can meet the operating requirements of the distribution network after connecting to the distribution network, and to obtain the changes in the node current and power distribution of the distribution network while meeting the operating requirements. Through this low-voltage distribution network flow calculation module, with the goal of minimizing the line loss of the distribution network, the optimal flow calculation result of the distribution network after connecting to the alternative access point is obtained, and the active power and current of all lines of the distribution network in the optimal flow result and the minimum line loss obtained are calculated. Output to the access point selection module.

[0055] In order to ensure that the newly added user can meet the operation requirements of the low-voltage distribution network after being connected to the low-voltage distribution network, in an optional implementation manner, before the above step S202, the method further includes:

[0056] Step S301, the objective function is determined with the minimum line loss of the low voltage distribution network as the optimization target. The expression of the objective function is: a, b are the node numbers of the above low-voltage distribution network, φ represents the total number of nodes of the above low-voltage distribution network, r s,ab represents the resistance of branch ab, I s,ab,t represents the current flowing through the branch ab during period t, P loss represents the above line loss, s is the node number of the medium voltage distribution network to which the above low voltage distribution network is connected;

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

[0058] Step S303, constraining the node voltages in the low-voltage distribution network to obtain stable operation constraints for the low-voltage distribution network.

[0059] In the above embodiment, the low-voltage distribution network power flow calculation model 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 branch power flow of the distribution network needs to meet the balance constraint, constraint const1.1 is added. In order to ensure that the node voltage of the distribution network in each period operates within a reasonable range, constraint const1.2 is set. Formula (1) is: That is, the expression of 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 is the voltage value of node a in period t, U s,b,t is the voltage value of node b in period t, P s,ab,t is the active power flowing through the head end of branch ab during period t, r s,ab is the resistance of branch ab, x s,ab is the reactance of branch ab; Q s,ab,t is the reactive power flowing through the head end of branch ab during period t; I s,ab,t represents the current flowing through branch ab during period t; p s,b,t ,q s,b,t are the active power and reactive power injected into node b during period t; P s,bk,t , Q s,bk,t are respectively the active power and reactive power at the head end of branch bk at time t; k:b→k is represented by the set of all child nodes with b node as the parent node. The expression of the stable operation constraint of the above low-voltage distribution network is U′ min ≤U s,a,t ≤U′ max , U′ max , U′ min They are the upper and lower limits of the voltage at the nodes of the low-voltage distribution network respectively.

[0060] In order to fully consider the shortest path and the shortest distance from the newly added user to the candidate access point to reduce the subsequent wiring cost, in an optional implementation manner, the above step S203 includes:

[0061] Step S2031: Taking the newly added user as the end point and all the candidate access points as the start point, the Dijkstra algorithm is used to calculate according to the basic data to obtain the shortest path and path distance from the newly added user to all the candidate access points.

[0062] In the above embodiment, if Figure 3As shown, the shortest path selection module is used for calculation. The shortest path selection module is improved based on the Dijkstra algorithm. The goal is to obtain the shortest path from each candidate access point to the newly added user and the distance of the shortest path (i.e., path distance). First, the coordinate data of all candidate access points on the GIS system, the coordinate data of the newly added user, all wiring paths from all candidate access points to the newly added user and the distance of each section of the route are obtained as the data basis. Then, the newly added user is taken as the end point, and all the candidate access points are taken as the starting point in turn. Then, the Dijkstra algorithm is used to measure based on the data to obtain the shortest path and path distance from the above-mentioned new user to all the above-mentioned candidate access points. The following is the specific process of the Dijkstra algorithm: 1) First, determine the starting point and the end point of the shortest path, and create an array B to save 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, where set R is used to save the determined shortest path nodes; set U is used to save the undetermined shortest path nodes; 2) The initial set R only contains the source point S1, and the remaining nodes are stored in the set U. The initial path weight of source point S1 is 0, that is, B[1]=0. At the same time, the path weights of all other nodes that source point S1 cannot reach are set to infinity. Set Si, Sj, that is, the path weights between different nodes 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 by using nodes such as i in set R, and the judgment condition is such as: 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, and the update formula is such as: 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 end points is completed. In the obtained set R, the distances of each path are added in turn from the source point to the end point to obtain the shortest path distance L. Through the shortest path selection module, the set of the shortest path R and path distance L from the new user to each candidate access point is finally obtained, where the shortest path is represented by a vector R, and each value of the vector R represents the intersection of the path. Then the shortest path R and path distance L are output to the access point selection module.

[0063] In order to fully consider the cost of business expansion wiring and the maintenance cost caused by the change of three-phase imbalance and safety margin after access, so as to make the selection of access points more reasonable, in an optional implementation manner, the above step S204 includes:

[0064] Step S2041, performing a preliminary calculation on the optimal power flow calculation result through the access point selection model to obtain the safety margin of each line section of the access area and the maximum three-phase imbalance of the access area, where the access area is the area to which the newly added user accesses the candidate access point;

[0065] Step S2042, calculating the maintenance cost incurred by the newly added user accessing each of the candidate access points according to the safety margin and the maximum three-phase imbalance;

[0066] Step S2043, calculating the wiring cost required for the newly added user to access each of the candidate access points by using the access point selection model for the shortest path and the path distance;

[0067] Step S2044: Add the maintenance cost and the wiring cost corresponding to each of the candidate access points to obtain the access cost of the newly added user accessing each of the candidate access points.

[0068] In the above embodiment, the access point selection model first calculates the data obtained from the low-voltage distribution network power flow calculation model (i.e., the above-mentioned optimal power flow calculation result) to obtain the safety margin of each section of the line in each substation after accessing each candidate point and the maximum three-phase imbalance of the access substation, and calculates the maintenance cost generated by the access with the safety margin and the maximum three-phase imbalance, and then calculates the wiring cost required for the new user to access each candidate access point with the data obtained by the shortest access path selection module (i.e., the above-mentioned shortest path and the above-mentioned path distance). The two costs are added together to obtain the final access cost. The access point selection module takes the minimum access cost as the optimization goal, so the access point selection module takes the minimum access cost as the objective function: 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 substation line, and U is b is the maximum three-phase unbalance. K1, K2, K3, K4 are the price coefficients of the tower, wiring line, insulator and the maximum three-phase unbalance respectively. T is the number of lines in the substation area, K′ i is the safety margin price coefficient after the i-th line adds a new user, M i is the safety margin of the ith line, where In formula (3), N1 is the number of poles and towers on the path between intersections, N2 is the number of poles and towers on the intersections, the number of intersections is the number of all intersections passed 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 line, and α is the number of insulators on a pole and tower. The optimal selection of alternative access points is performed with the safety margin and three-phase imbalance as constraints.

[0069] It should also be noted that the above optimal power flow calculation results are preliminarily calculated by the above access point selection model to obtain the safety margin of each section of the access area and the maximum three-phase imbalance of the above access area, including: calculating the above maximum three-phase imbalance according to the third formula, the above third formula is I a is the effective value of phase A current, I b is the effective value of phase B current, I c is the effective value of phase C current, I avg is the average value of the three-phase current, U b is the maximum three-phase imbalance degree; the safety margin is calculated according to the fourth formula, and the fourth formula is M i =P max -P i , P max is the upper limit of the load that the line can bear, P i is the active power of the ith line, M i represents the above mentioned safety margin.

[0070] For example, the present invention selects 3 substations, each of which is an IEEE33 node low-voltage distribution network, as follows Figure 5 Shown: In Figure 2 1, 2, 3...99 in the table are user loads. The K-means clustering method is used to predict the load of new users through the existing user loads, and the number of cluster centers can be changed. The prediction results of new users are as follows: Figure 6 As shown. After the simulation experiment, the access point with the lowest access cost for the newly added access point is the 59th load point, and the minimum cost is 6968.3368 yuan. Compared with the traditional method of accessing the nearest access point, the experimental results are shown in Table 1:

[0071] Table 1 Cost comparison between the present invention and the traditional method

[0072]

[0073] In order to calculate the maintenance cost, in an optional implementation manner, the above step S2042 includes:

[0074] Step S20421, calculate the safety margin price coefficient according to the first formula, the first formula is K′ i =λ×P i ×(t+T)×(C1+C2), t is the power outage time, T is the maintenance time, λ is the power outage probability, C1 is the regional electricity price, C2 is the maintenance cost, K′ i represents the safety margin price coefficient of the ith line after the above-mentioned new user accesses;

[0075] Step S20422, calculating the maximum three-phase unbalance price coefficient according to the second formula, the second formula is K1 = P min loss ×C1×24×365,P min loss represents the above minimum line loss;

[0076] Step S20423, the maintenance cost is calculated according to the third formula, which is: f obj1 represents the above maintenance cost, T represents the total number of lines accessing the above area, M i is the safety margin of the ith line, U b Indicates the above maximum three-phase unbalance.

[0077] In the above embodiment, the maintenance cost caused by the change of three-phase imbalance and safety margin after access is also considered when calculating the access cost, so that the selection of access point is more reasonable. Therefore, it is necessary to calculate the safety margin price coefficient and the maximum three-phase imbalance price coefficient according to the above first formula and the above second formula respectively, and calculate the maintenance cost according to the third formula, so as to facilitate the subsequent calculation of the access cost.

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

[0079] The embodiment of the present application also provides an access point selection device for low-voltage business expansion. It should be noted that the access point selection device for low-voltage business expansion in the embodiment of the present application can be used to execute the access point selection method for low-voltage business expansion provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0080] The following introduces the access point selection device for low-voltage business expansion provided in an embodiment of the present application.

[0081] Figure 7 1 is a structural block diagram of an access point selection device for low voltage business expansion according to an embodiment of the present application. Figure 7 As shown, the device comprises:

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

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

[0084] The first calculation unit 20 is used to add the above-mentioned new users to all the candidate access points in sequence, and perform power flow calculation on the above-mentioned low-voltage distribution network according to the above-mentioned current daily load data and the above-mentioned new daily load data in combination with line parameters to obtain the optimal power flow calculation results corresponding to each of the above-mentioned candidate access points, and the above-mentioned optimal power flow calculation results include the active power of all lines in the above-mentioned low-voltage distribution network, all node currents and minimum line losses.

[0085] Specifically, a low-voltage distribution network power flow calculation model is designed, and the above-mentioned new users are added to all alternative access points in turn. Since there are already users at the access points of the low-voltage business expansion, the additional daily load data of the new users are added to the daily load data of the existing users in turn. The low-voltage distribution network power flow calculation model is used to obtain the current of each access point of the distribution network under the optimal power flow, the active power of each line section and the minimum line loss.

[0086] The second calculation unit 30 is used to perform path distance measurement according to the basic data to obtain the shortest path and path distance corresponding to the above-mentioned new user to each of the above-mentioned candidate access points, wherein the above-mentioned basic data includes the coordinate data of all the above-mentioned candidate access points, the coordinate data of the above-mentioned new users, all wiring paths from all the above-mentioned candidate access points to the above-mentioned new users, and the distance of each route segment.

[0087] Specifically, the basic data of the newly added users and the candidate access points are obtained by using the GIS system, and the path distance is calculated by using the Dijkstra algorithm to obtain the shortest path and distance from the newly added users to all the candidate access points.

[0088] The third calculation unit 40 is used to input the above-mentioned optimal power flow calculation results corresponding to all the above-mentioned candidate access points, the above-mentioned shortest path and the above-mentioned path distance 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 minimum access cost as the objective function and with maximum three-phase unbalance constraint and safety margin constraint as constraint conditions.

[0089] Specifically, an access point selection model is constructed with the lowest access cost as the goal and three-phase unbalance and safety margin as constraints. The access cost includes wiring cost and maintenance cost. Specifically, the maintenance cost generated after access is obtained by calculating the optimal power flow calculation result through the access point selection model, and then the wiring cost required for new users to access each alternative access point is calculated based on 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 candidate access point corresponding to the lowest access cost as the target access point for the newly added user.

[0091] Specifically, the candidate access point with the lowest access cost is screened out and determined as the target access point for the above-mentioned new user, thereby achieving the access point selection with the lowest access cost for the low-voltage business expansion candidate access point.

[0092] In this embodiment, the clustering unit is used to perform cluster analysis according to the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the above-mentioned new user, and the above-mentioned user type is at least one of the following: residential users, commercial users and industrial users, and the above-mentioned current daily load data is the daily load data of users obtained in the low-voltage distribution network; the first calculation unit is used to add the above-mentioned new users to all candidate access points in sequence, and perform power flow calculation on the above-mentioned low-voltage distribution network according to the above-mentioned current daily load data and the above-mentioned newly added daily load data combined with line parameters to obtain the optimal power flow calculation results corresponding to each of the above-mentioned candidate access points, and the above-mentioned optimal power flow calculation results include the active power of all lines in the above-mentioned low-voltage distribution network, all node voltages and minimum line loss; the second calculation unit is used to perform path distance measurement according to basic data to obtain the shortest path and the shortest distance corresponding to the newly added user to each of the above-mentioned candidate access points, the above-mentioned basic data including the coordinate data of all the above-mentioned candidate access points, the coordinate data of the above-mentioned new users, all the wiring paths and the distances of each section of the route from all the above-mentioned candidate access points to the above-mentioned new users; a third calculation unit, used to input the above-mentioned optimal power flow calculation results corresponding to all the above-mentioned candidate access points, the above-mentioned shortest path and the above-mentioned shortest distance into the access point selection optimization model to obtain the access cost of the above-mentioned new users accessing each of the above-mentioned candidate access points, the above-mentioned access point selection optimization model is a model with the minimum access cost as the objective function, and the maximum three-phase unbalance constraint and the safety margin constraint as the constraint conditions; a first determination unit, used to determine the above-mentioned candidate access point corresponding to the lowest above-mentioned access cost as the target access point of the above-mentioned new user. This application predicts the daily load data of new users through the daily load data of existing users and the types of new users, and then adds the new users to all alternative access points in turn, performs low-voltage distribution network flow calculations to obtain the voltage of each access point of the distribution network under the optimal flow and the active power of each line section, and obtains the shortest path and distance from the new user to all alternative access points through the shortest access path selection. Next, the access cost of each alternative access point is calculated by the access point selection model based on the optimal flow calculation results, the shortest path and the path distance, and the alternative access point with the lowest access cost is screened out and determined as the target access point. This application solves the problem that the low-voltage business expansion and installation technical solutions in the prior art rarely consider other factors of the distribution network and only connect to one of the three phases through simple calculations, resulting in an excessive increase in the three-phase imbalance of the distribution network system after access, thereby increasing the access cost.

[0093] In order to predict the daily load data of newly added users in advance to improve the accuracy of determining access points, in an optional implementation manner, the clustering unit includes:

[0094] A classification module, classifying the current daily load data according to the user types corresponding to the current daily load data, to obtain multiple groups of daily load data, wherein one group of the daily load data corresponds to one of the user types;

[0095] A clustering module, performing cluster analysis on the first day's load data by using a K-means clustering algorithm according to the user type corresponding to the newly added user, to obtain multiple groups of second day's load data, wherein the first day's load data is the daily load data of the same user type as that corresponding to the newly added user;

[0096] The matching module selects a plurality of daily load data that are consistent with the power consumption characteristics of the newly added users from the plurality of groups of the second day load data to obtain the newly added daily load data.

[0097] In the above embodiment, Figure 3 A technical solution diagram of a low voltage business expansion access point selection method provided according to an embodiment of the present application is shown, 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 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 the new user. Therefore, the obtained low-voltage distribution network daily load data (i.e., the above-mentioned current daily load data) is first classified to obtain the above-mentioned daily load data. Then, according to the user type of the new user, the K-means clustering algorithm is used to perform cluster analysis on the user daily load data of the same user type. The number of cluster centers is the number of user types k. It can be understood that the user habits of the same type of users are different and the power consumption is different. Therefore, k kinds of representative user power consumption data should be found in the same type of users. Therefore, k kinds of new user daily load data can be obtained. Finally, the daily load data Ai that matches the new user is selected according to the power consumption characteristics of the new user on the obtained k kinds of new user daily load data to obtain the above-mentioned new daily load data. The above-mentioned power consumption characteristics are power consumption, peak power consumption and other power consumption characteristics, because different users have different power consumption equipment (power consumption) and peak power consumption.

[0098] In addition, the implementation process of the new user prediction module is as follows Figure 4As shown in the figure, according to the given parameter k, the distance between data objects is used as the similarity measure, and objects with high similarity are divided into the same cluster, thereby dividing all data objects into k clusters. K-means is an iterative process. Each time, the accuracy of the clustering results must be examined, and the cluster center is adjusted according to the results, and then the next operation is performed until all data are assigned to the correct cluster. The basic operation steps are as follows: 1) Suppose there are n load samples, and the sample set is: randomly select k samples as the initial cluster center M = {M1, M2, ..., Mk}; 2) Calculate the Euclidean distance between each sample point and the initial cluster center point, and the calculation formula is: Where D(x,Mi) is the residual load data x to the i-th cluster center M i Euclidean distance; 3) Classify the closest distances into the same category to form k clusters, and calculate the mean of the data points in each cluster respectively. The formula is: Where n i represents the number of data contained in the i-th cluster, and takes its mean as the new cluster center to make convergence judgment. Clustering ends when the convergence condition is met, otherwise it returns to step 2) and step 3). The convergence calculation formula is: In the formula, A i represents the collection of samples of the i-th category after the new clustering, x q is the sample object, a i For A i E represents the convergence criterion of K-means clustering, and its size determines the quality of clustering effect under different clustering conditions.

[0099] In order to obtain the changes in the current and power distribution of the low-voltage distribution network while meeting the operation requirements, in an optional implementation, 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, so as to calculate the power flow information using the optimal power flow calculation model to obtain the optimal power flow calculation results corresponding to each of the above-mentioned alternative access points. The optimal power flow calculation model takes the minimum line loss of the above-mentioned low-voltage distribution network as the objective function, and is constructed with the branch power flow balance constraint of the above-mentioned low-voltage distribution network and the stable operation constraint of the low-voltage distribution network as the constraint conditions.

[0101] In the above embodiment, if Figure 3As shown, the low-voltage distribution network flow calculation module performs flow calculation, specifically by adding new users to all alternative access points in sequence, taking the above-mentioned current daily load data, the above-mentioned new daily load data and the above-mentioned line parameters as input data, and obtaining the current of each alternative access point of the distribution network and the active power of each line section under the optimal flow through the optimal flow calculation model in the low-voltage distribution network flow calculation module. The goal of the low-voltage distribution network flow calculation module is to perform optimal flow calculation on the distribution network after the new users are connected to all alternative access points in sequence, to ensure that the new users can meet the operating requirements of the distribution network after connecting to the distribution network, and to obtain the changes in the node current and power distribution of the distribution network while meeting the operating requirements. Through this low-voltage distribution network flow calculation module, with the goal of minimizing the line loss of the distribution network, the optimal flow calculation result of the distribution network after connecting to the alternative access point is obtained, and the active power and current of all lines of the distribution network in the optimal flow result and the minimum line loss obtained are calculated. Output to the access point selection module.

[0102] In order to ensure that the newly added users can meet the operation requirements of the low-voltage distribution network after being connected to the low-voltage distribution network, in an optional implementation manner, the device further includes:

[0103] The second determination unit is used to 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 to obtain the optimal power flow calculation results corresponding to each of the candidate access points, and determine the objective function with the minimum line loss of the low-voltage distribution network as the optimization goal. The expression of the objective function is: a, b are the node numbers of the above low-voltage distribution network, φ represents the total number of nodes of the above low-voltage distribution network, r s,ab represents the resistance of branch ab, I s,ab,t represents the current flowing through the branch ab during period t, P loss represents the above line loss, s is the node number of the medium voltage distribution network to which the above low voltage distribution network is connected;

[0104] The first constraint unit is used to perform power flow balance constraints on all node voltages, all node currents, and active power and reactive power of all branches in the above-mentioned low-voltage distribution network to obtain the above-mentioned branch power flow balance constraints;

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

[0106] In the above embodiment, the low-voltage distribution network power flow calculation model 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 branch power flow of the distribution network needs to meet the balance constraint, constraint const1.1 is added. In order to ensure that the node voltage of the distribution network in each period operates within a reasonable range, constraint const1.2 is set. Formula (1) is: That is, the expression of 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 is the voltage value of node a in period t, U s,b,t is the voltage value of node b in period t, P s,ab,t is the active power flowing through the head end of branch ab during period t, r s,ab is the resistance of branch ab, x s,ab is the reactance of branch ab; Q s,ab,t is the reactive power flowing through the head end of branch ab during period t; I s,ab,t represents the current flowing through branch ab during period t; p s,b,t ,q s,b,t are the active power and reactive power injected into node b during period t; P s,bk,t , Q s,bk,t are respectively the active power and reactive power at the head end of branch bk at time t; k:b→k is represented by the set of all child nodes with b node as the parent node. The expression of the stable operation constraint of the above low-voltage distribution network is U′ min ≤U s,a,t ≤U′ max , U′ max , U′ min They are the upper and lower limits of the voltage at the nodes of the low-voltage distribution network respectively.

[0107] In order to fully consider the shortest path and the shortest distance from the newly added user to the candidate access point to reduce the subsequent wiring cost, in an optional implementation manner, the second calculation unit includes:

[0108] The calculation module uses the newly added user as the end point and all the candidate access points as the starting point, and uses the Dijkstra algorithm to calculate according to the basic data to obtain the shortest path and path distance from the newly added user to all the candidate access points.

[0109] In the above embodiment, if Figure 3As shown, the shortest path selection module is used for calculation. The shortest path selection module is improved based on the Dijkstra algorithm. The goal is to obtain the shortest path from each candidate access point to the newly added user and the distance of the shortest path (i.e., path distance). First, the coordinate data of all candidate access points on the GIS system, the coordinate data of the newly added user, all wiring paths from all candidate access points to the newly added user and the distance of each section of the route are obtained as the data basis. Then, the newly added user is taken as the end point, and all the candidate access points are taken as the starting point in turn. Then, the Dijkstra algorithm is used to measure based on the data to obtain the shortest path and path distance from the above-mentioned new user to all the above-mentioned candidate access points. The following is the specific process of the Dijkstra algorithm: 1) First, determine the starting point and the end point of the shortest path, and create an array B to save 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, where set R is used to save the determined shortest path nodes; set U is used to save the undetermined shortest path nodes; 2) The initial set R only contains the source point S1, and the remaining nodes are stored in the set U. The initial path weight of source point S1 is 0, that is, B[1] = 0. At the same time, the path weights of all other nodes that source point S1 cannot reach are set to infinity. Set Si, Sj, that is, the path weights between different nodes 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 by using nodes such as i in set R, and the judgment condition is such as: 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, and the update formula is such as: 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 end points is completed. In the obtained set R, the distances of each path are added in turn from the source point to the end point to obtain the shortest path distance L. Through the shortest path selection module, the set of the shortest path R and path distance L from the new user to each candidate access point is finally obtained, where the shortest path is represented by a vector R, and each value of the vector R represents the intersection of the path. Then the shortest path R and path distance L are output to the access point selection module.

[0110] In order to fully consider the cost of business expansion wiring and the maintenance cost caused by the change of three-phase imbalance and safety margin after access, so that the selection of access points is more reasonable, in an optional implementation manner, the third calculation unit includes:

[0111] The first calculation module performs preliminary calculation on the optimal power flow calculation result through the access point selection model to obtain the safety margin of each line section of the access area and the maximum three-phase imbalance of the access area, where the access area is the area to which the newly added user accesses the candidate access point;

[0112] A second calculation module calculates the maintenance cost generated by the newly added user accessing each of the candidate access points according to the safety margin and the maximum three-phase imbalance;

[0113] A third calculation module calculates the wiring cost required for the new user to access each of the candidate access points based on the shortest path and the path distance through the access point selection model;

[0114] The fourth calculation module adds the maintenance cost and the wiring cost corresponding to each of the candidate access points to obtain the access cost of the newly added user accessing each of the candidate access points.

[0115] In the above embodiment, the access point selection model first calculates the data obtained from the low-voltage distribution network power flow calculation model (i.e., the above-mentioned optimal power flow calculation result) to obtain the safety margin of each section of the line in each substation after accessing each candidate point and the maximum three-phase imbalance of the access substation, and calculates the maintenance cost generated by the access with the safety margin and the maximum three-phase imbalance, and then calculates the wiring cost required for the new user to access each candidate access point with the data obtained by the shortest access path selection module (i.e., the above-mentioned shortest path and the above-mentioned path distance). The two costs are added together to obtain the final access cost. The access point selection module takes the minimum access cost as the optimization goal, so the access point selection module takes the minimum access cost as the objective function: 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 substation line, and U is b is the maximum three-phase unbalance. K1, K2, K3, K4 are the price coefficients of the tower, wiring line, insulator and the maximum three-phase unbalance respectively. T is the number of lines in the substation area, K′ i is the safety margin price coefficient after the i-th line adds a new user, M i is the safety margin of the ith line, where In formula (3), N1 is the number of poles and towers on the path between intersections, N2 is the number of poles and towers on the intersections, the number of intersections is the number of all intersections passed 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 line, and α is the number of insulators on a pole and tower. The optimal selection of alternative access points is performed with the safety margin and three-phase imbalance as constraints.

[0116] It should also be noted that the above optimal power flow calculation results are preliminarily calculated by the above access point selection model to obtain the safety margin of each section of the access area and the maximum three-phase imbalance of the above access area, including: calculating the above maximum three-phase imbalance according to the third formula, the above third formula is I a is the effective value of phase A current, I b is the effective value of phase B current, I c is the effective value of phase C current, I avg is the average value of the three-phase current, U b is the maximum three-phase imbalance degree; the safety margin is calculated according to the fourth formula, and the fourth formula is M i =P max -P i , P max is the upper limit of the load that the line can bear, P i is the active power of the ith line, M i represents the above mentioned safety margin.

[0117] For example, the present invention selects 3 substations, each of which is an IEEE33 node low-voltage distribution network, as follows Figure 5 Shown: In Figure 2 1, 2, 3...99 in the table are user loads. The K-means clustering method is used to predict the load of new users through the existing user loads, and the number of cluster centers can be changed. The prediction results of new users are as follows: Figure 6 As shown. After the simulation experiment, the access point with the lowest access cost for the newly added access point is the 59th load point, and the minimum cost is 6968.3368 yuan. Compared with the traditional method of accessing the nearest access point, the experimental results are shown in Table 1:

[0118] Table 1 Cost comparison between the present invention and the traditional method

[0119]

[0120] In order to calculate the maintenance cost, in an optional implementation manner, the above step S2042 includes:

[0121] Step S20421, calculate the safety margin price coefficient according to the first formula, the first formula is K′ i =λ×P i ×(t+T)×(C1+C2), t is the power outage time, T is the maintenance time, λ is the power outage probability, C1 is the regional electricity price, C2 is the maintenance cost, K′ i represents the safety margin price coefficient of the ith line after the above-mentioned new user accesses;

[0122] Step S20422, calculating the maximum three-phase unbalance price coefficient according to the second formula, the second formula is K1 = P min loss ×C1×24×365,P min loss represents the above minimum line loss;

[0123] Step S20423, the maintenance cost is calculated according to the third formula, which is: f obj1 represents the above maintenance cost, T represents the total number of lines accessing the above area, M i is the safety margin of the ith line, U b Indicates the above maximum three-phase unbalance.

[0124] In the above embodiment, the maintenance cost caused by the change of three-phase imbalance and safety margin after access is also considered when calculating the access cost, so that the selection of access point is more reasonable. Therefore, it is necessary to calculate the safety margin price coefficient and the maximum three-phase imbalance price coefficient according to the above first formula and the above second formula respectively, and calculate the maintenance cost according to the third formula, so as to facilitate the subsequent calculation of the access cost.

[0125] The access point selection device for low voltage business expansion includes a processor and a memory. The clustering unit, the first calculation unit, the second calculation unit, etc. are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0126] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to solve the problem that the low-voltage business expansion and installation technical solutions in the existing technology rarely consider other factors of the distribution network and only connect to one of the three phases through simple calculations, resulting in excessive increase in the three-phase imbalance of the distribution network system after connection, thereby increasing the connection cost.

[0127] The memory may include non-permanent memory in a computer-readable medium, 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] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the access point selection method for low voltage business expansion.

[0129] Specifically, the access point selection method for low voltage business expansion includes:

[0130] Step S201, performing cluster analysis based on the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the newly added 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 daily load data of the user obtained in the low-voltage distribution network;

[0131] Step S202, adding the newly added users to all candidate access points in sequence, and performing power flow calculation on the low-voltage distribution network according to 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, wherein the optimal power flow calculation results include the active power of all lines in the low-voltage distribution network, the current of all nodes and the minimum line loss;

[0132] Step S203, calculating the path distance according to the basic data to obtain the shortest path and path distance corresponding to the newly added user to each of the candidate access points, wherein the basic data includes the coordinate data of all the candidate access points, the coordinate data of the newly added user, all the wiring paths from all the candidate access points to the newly added user, and the distance of each route segment;

[0133] Step S204, inputting the above-mentioned optimal power flow calculation results corresponding to all the above-mentioned candidate access points, the above-mentioned shortest path and the above-mentioned path distance into the access point selection model, and obtaining 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 minimum access cost as the objective function and maximum three-phase unbalance constraint and safety margin constraint as constraint conditions;

[0134] Step S205: determining the candidate access point corresponding to the lowest access cost as the target access point for the newly added user.

[0135] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the access point selection method for low voltage business expansion is executed when the program is running.

[0136] Specifically, the access point selection method for low voltage business expansion includes:

[0137] Step S201, performing cluster analysis based on the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the newly added 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 daily load data of the user obtained in the low-voltage distribution network;

[0138] Step S202, adding the newly added users to all candidate access points in sequence, and performing power flow calculation on the low-voltage distribution network according to 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, wherein the optimal power flow calculation results include the active power of all lines in the low-voltage distribution network, the current of all nodes and the minimum line loss;

[0139] Step S203, calculating the path distance according to the basic data to obtain the shortest path and path distance corresponding to the newly added user to each of the candidate access points, wherein the basic data includes the coordinate data of all the candidate access points, the coordinate data of the newly added user, all the wiring paths from all the candidate access points to the newly added user, and the distance of each route segment;

[0140] Step S204, inputting the above-mentioned optimal power flow calculation results corresponding to all the above-mentioned candidate access points, the above-mentioned shortest path and the above-mentioned path distance into the access point selection model, and obtaining 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 minimum access cost as the objective function and maximum three-phase unbalance constraint and safety margin constraint as constraint conditions;

[0141] Step S205: determining the candidate access point corresponding to the lowest access cost as the target access point for the newly added user.

[0142] An embodiment of the present invention provides a low-voltage business expansion access point selection system, the low-voltage business expansion access point selection system includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0143] Step S201, performing cluster analysis based on the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the newly added 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 daily load data of the user obtained in the low-voltage distribution network;

[0144] Step S202, adding the newly added users to all candidate access points in sequence, and performing power flow calculation on the low-voltage distribution network according to 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, wherein the optimal power flow calculation results include the active power of all lines in the low-voltage distribution network, the current of all nodes and the minimum line loss;

[0145] Step S203, calculating the path distance according to the basic data to obtain the shortest path and path distance corresponding to the newly added user to each of the candidate access points, wherein the basic data includes the coordinate data of all the candidate access points, the coordinate data of the newly added user, all the wiring paths from all the candidate access points to the newly added user, and the distance of each route segment;

[0146] Step S204, inputting the above-mentioned optimal power flow calculation results corresponding to all the above-mentioned candidate access points, the above-mentioned shortest path and the above-mentioned path distance into the access point selection model, and obtaining 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 minimum access cost as the objective function and maximum three-phase unbalance constraint and safety margin constraint as constraint conditions;

[0147] Step S205: determining the candidate access point corresponding to the lowest access cost as the target access point for the newly added user.

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

[0149] Step S201, performing cluster analysis based on the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the newly added 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 daily load data of the user obtained in the low-voltage distribution network;

[0150] Step S202, adding the newly added users to all candidate access points in sequence, and performing power flow calculation on the low-voltage distribution network according to 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, wherein the optimal power flow calculation results include the active power of all lines in the low-voltage distribution network, the current of all nodes and the minimum line loss;

[0151] Step S203, calculating the path distance according to the basic data to obtain the shortest path and path distance corresponding to the newly added user to each of the candidate access points, wherein the basic data includes the coordinate data of all the candidate access points, the coordinate data of the newly added user, all the wiring paths from all the candidate access points to the newly added user, and the distance of each route segment;

[0152] Step S204, inputting the above-mentioned optimal power flow calculation results corresponding to all the above-mentioned candidate access points, the above-mentioned shortest path and the above-mentioned path distance into the access point selection model, and obtaining 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 minimum access cost as the objective function and maximum three-phase unbalance constraint and safety margin constraint as constraint conditions;

[0153] Step S205: determining the candidate access point corresponding to the lowest access cost as the target access point for the newly added user.

[0154] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0155] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0156] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified 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] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0161] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0162] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0163] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0164] 1) The access point selection method for low-voltage business expansion of the present application, first, performs cluster analysis based on the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the above-mentioned new user, the above-mentioned user type is at least one of the following: residential users, commercial users and industrial users, and the above-mentioned current daily load data is the daily load data of users obtained in the low-voltage distribution network; then, the above-mentioned new users are added to all candidate access points in sequence, and the flow calculation of the above-mentioned low-voltage distribution network is performed based on the above-mentioned current daily load data and the above-mentioned newly added daily load data combined with line parameters to obtain the optimal flow calculation results corresponding to each of the above-mentioned candidate access points, and the above-mentioned optimal flow calculation results include all line active powers, all node currents and minimum line losses in the above-mentioned low-voltage distribution network; then, the line is calculated based on the basic data. The path distance is measured to obtain the shortest path and path distance corresponding to the above-mentioned new user to each of the above-mentioned candidate access points, the above-mentioned basic data includes the coordinate data of all the above-mentioned candidate access points, the coordinate data of the above-mentioned new user, all the wiring paths from all the above-mentioned candidate access points to the above-mentioned new user and the distance of each route segment; then, the above-mentioned optimal power flow calculation results corresponding to all the above-mentioned candidate access points, the above-mentioned shortest path and the above-mentioned path distance are input 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, and the above-mentioned access point selection model is a model with the minimum access cost as the objective function and the maximum three-phase unbalance constraint and the safety margin constraint as the constraint conditions; finally, the above-mentioned candidate access point corresponding to the lowest above-mentioned access cost is determined as the target access point of the above-mentioned new user. This application predicts the daily load data of new users through the daily load data of existing users and the types of new users, and then adds the new users to all alternative access points in turn, performs low-voltage distribution network flow calculations to obtain the voltage of each access point of the distribution network under the optimal flow and the active power of each line section, and obtains the shortest path and distance from the new user to all alternative access points through the shortest access path selection. Next, the access cost of each alternative access point is calculated by the access point selection model based on the optimal flow calculation results, the shortest path and the path distance, and the alternative access point with the lowest access cost is screened out and determined as the target access point. This application solves the problem that the low-voltage business expansion and installation technical solutions in the prior art rarely consider other factors of the distribution network and only connect to one of the three phases through simple calculations, resulting in an excessive increase in the three-phase imbalance of the distribution network system after access, thereby increasing the access cost.

[0165] 2) The access point selection device for low-voltage business expansion of the present application, the clustering unit, is used to perform cluster analysis according to the current daily load data and the user type of the newly added user, and obtain the newly added daily load data corresponding to the above-mentioned new user, and the above-mentioned user type is at least one of the following: residential users, commercial users and industrial users, and the above-mentioned current daily load data is the daily load data of users obtained in the low-voltage distribution network; the first calculation unit is used to add the above-mentioned new users to all candidate access points in sequence, and perform power flow calculation on the above-mentioned low-voltage distribution network according to the above-mentioned current daily load data and the above-mentioned newly added daily load data combined with line parameters, and obtain the optimal power flow calculation result corresponding to each of the above-mentioned candidate access points, and the above-mentioned optimal power flow calculation result includes all line active power, all node voltages and minimum line loss in the above-mentioned low-voltage distribution network; the second calculation unit is used to perform line flow calculation according to basic data The basic data includes coordinate data of all the candidate access points, coordinate data of the new users, all wiring paths from all the candidate access points to the new users, and distances of each route segment; a third calculation unit is used to input the optimal power flow calculation results, the shortest paths, and the shortest distances corresponding to all the candidate access points into an access point selection optimization model to obtain access costs of the new users accessing the candidate access points, and the access point selection optimization model is a model with minimum access cost as the objective function and maximum three-phase unbalance constraint and safety margin constraint as constraint conditions; a 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. This application predicts the daily load data of new users through the daily load data of existing users and the types of new users, and then adds the new users to all alternative access points in turn, performs low-voltage distribution network flow calculations to obtain the voltage of each access point of the distribution network under the optimal flow and the active power of each line section, and obtains the shortest path and distance from the new user to all alternative access points through the shortest access path selection. Next, the access cost of each alternative access point is calculated by the access point selection model based on the optimal flow calculation results, the shortest path and the path distance, and the alternative access point with the lowest access cost is screened out and determined as the target access point. This application solves the problem that the low-voltage business expansion and installation technical solutions in the prior art rarely consider other factors of the distribution network and only connect to one of the three phases through simple calculations, resulting in an excessive increase in the three-phase imbalance of the distribution network system after access, thereby increasing the access cost.

[0166] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for selecting an access point for low voltage business expansion, characterized in that: include: Perform cluster analysis based on the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the newly added user, wherein the user type is at least one of the following: residential users, commercial users and industrial users, and the current daily load data is the daily load data of users obtained in the low-voltage distribution network; Add the newly added users to all candidate access points in sequence, and perform power flow calculation on the low-voltage distribution network according to the current daily load data and the newly added daily load data in combination with line parameters to obtain the optimal power flow calculation result corresponding to each of the candidate access points, wherein the optimal power flow calculation result includes the active power of all lines in the low-voltage distribution network, the current of all nodes, and the minimum line loss; Calculate the path distance according to the basic data to obtain the shortest path and path distance corresponding to the newly added user to each of the candidate access points, wherein the basic data includes the coordinate data of all the candidate access points, the coordinate data of the newly added user, all the wiring paths from the candidate access points to the newly added user, and the distance of each route segment; Inputting the optimal power flow calculation results, the shortest paths and the path distances corresponding to all the candidate access points into an access point selection model to obtain the access cost of the newly added user accessing each of the candidate access points, wherein the access point selection model is a model with the minimum access cost as the objective function and with the maximum three-phase unbalance constraint and the safety margin constraint as the constraint conditions; The candidate access point corresponding to the lowest access cost is determined as the target access point for the newly added user.

2. The method according to claim 1, characterized in that Perform cluster analysis based on the current daily load data and the user type of the newly added user to obtain the newly added daily load data corresponding to the newly added user, including: Classifying the current daily load data according to the user type corresponding to the current daily load data to obtain multiple groups of daily load data, wherein one group of the daily load data corresponds to one user type; Performing cluster analysis on the first day's load data by using a K-means clustering algorithm according to the user type corresponding to the newly added user, to obtain multiple groups of second day's load data, wherein the first day's load data is the daily load data of the same user type as the newly added user; A plurality of daily load data that are consistent with the power consumption characteristics of the newly added user are selected from the plurality of groups of the second daily load data to obtain the newly added daily load data.

3. The method according to claim 1, characterized in that Performing power flow calculation on the low-voltage distribution network according to the current daily load data and the newly added daily load data in combination with line parameters 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 an optimal power flow calculation model, so as to calculate power flow information using the optimal power flow calculation model to obtain the optimal power flow calculation results corresponding to each of the candidate access points. The optimal power flow calculation model is a model constructed with minimizing the line loss of the low-voltage distribution network as an objective function and with branch power flow balance constraints of the low-voltage distribution network and stable operation constraints of the low-voltage distribution network as 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 the power flow information using the optimal power flow calculation model to obtain the optimal power flow calculation results corresponding to each of the candidate access points, the method further includes: The objective function is determined by taking the minimum line loss of the low-voltage distribution network as the optimization goal. The expression of the objective function is: a, b are the node numbers of the low-voltage distribution network, φ represents the total number of nodes of the low-voltage distribution network, r s,ab represents the resistance of branch ab, I s,ab,t represents the current flowing through the branch ab during period t, P loss represents the line loss, s is the node number of the medium voltage distribution network to which the low voltage distribution network is connected; Performing power flow balance constraints on all node voltages, all node currents, and active power and reactive power of all branches in the low-voltage distribution network to obtain the branch power flow balance constraints; Constraining the node voltages in the low-voltage distribution network to obtain stable operation constraints for the low-voltage distribution network.

5. The method according to claim 1, characterized in that Calculating the path distance according to the basic data to obtain the shortest path and path distance from the newly added user to all the candidate access points includes: The newly added user is taken as the end point and all the candidate access points are taken as the start points, and the Dijkstra algorithm is used to perform calculations according to the basic data to obtain the shortest path and path distance from the newly added user to all the candidate access points.

6. The method according to claim 1, characterized in that Inputting the optimal power flow calculation results, the shortest paths, and the path distances corresponding to all the candidate access points into an access point selection model to obtain access costs for the newly added users to access each of the candidate access points, including: Preliminary calculation of the optimal power flow calculation result is performed through the access point selection model to obtain the safety margin of each line section of the access area and the maximum three-phase imbalance of the access area, where the access area is the area to which the newly added user belongs when accessing the candidate access point; Calculate the maintenance cost generated by the newly added user accessing each of the candidate access points according to the safety margin and the maximum three-phase imbalance; Calculating the shortest path and the path distance by using the access point selection model, the wiring cost required for the newly added user to access each of the candidate access points; The maintenance cost and the wiring cost corresponding to each of the candidate access points are added together to obtain the access cost of the newly added user accessing each of the candidate access points.

7. The method according to claim 6, characterized in that Calculating the maintenance cost generated by the newly added user accessing each of the candidate access points according to the safety margin and the maximum three-phase imbalance, including: The safety margin price coefficient is calculated according to the first formula, where K i '=λ×P i ×(t+T)×(C1+C2), t is the power outage time, T is the maintenance time, λ is the power outage probability, C1 is the regional electricity price, C2 is the maintenance cost, K i ' represents the safety margin price coefficient of the i-th line after the new user accesses; The maximum three-phase unbalance price coefficient is calculated according to the second formula, where the second formula is K1=P min loss ×C1×24×365,P min loss represents the minimum line loss; The maintenance cost is calculated according to the third formula, and the third formula is: f obj1 represents the maintenance cost, T represents the total number of lines accessing the area, M i is the safety margin of the ith line, U b Indicates the maximum three-phase imbalance.

8. A low voltage business expansion access point selection device, characterized in that: The device comprises: A clustering unit, configured to perform cluster analysis according to the current daily load data and the user type of the newly added user, to obtain the newly added daily load data corresponding to the newly added user, wherein the user type is at least one of the following: a residential user, a commercial user, and an industrial user, and the current daily load data is the daily load data of the user obtained in the low-voltage distribution network; A first calculation unit is used to add the newly added users to all candidate access points in sequence, and perform power flow calculation on the low-voltage distribution network according to the current daily load data and the newly added daily load data in combination with line parameters to obtain an optimal power flow calculation result corresponding to each of the candidate access points, wherein the optimal power flow calculation result includes active power of all lines in the low-voltage distribution network, current of all nodes, and minimum line loss; A second calculation unit is used to perform path distance measurement according to basic data to obtain the shortest path and path distance corresponding to the newly added user to each of the candidate access points, wherein the basic data includes coordinate data of all the candidate access points, coordinate data of the newly added user, all wiring paths from all the candidate access points to the newly added user, and distances of each route segment; a third calculation unit, configured to input the optimal power flow calculation results, the shortest paths, and the path distances corresponding to all the candidate access points into an access point selection model to obtain access costs of the newly added users accessing each of the candidate access points, wherein the access point selection model is a model that takes minimizing access costs as an objective function and takes a maximum three-phase unbalance constraint and a safety margin constraint as constraint conditions; The first determining unit is configured to determine the candidate access point corresponding to the lowest access cost as the target access point for the newly added 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, the device where the computer-readable storage medium is located is controlled to execute 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 a processor, the method according to any one of claims 1 to 7 is implemented.

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