Point distribution method and device of power distribution terminal, electronic equipment and storage medium

Abstract

The invention provides a point distribution method and device of a power distribution terminal, electronic equipment and a storage medium. The method comprises the following steps: dividing a power distribution system into a plurality of target areas; key nodes having important influences on the stability, reliability and safety of the power distribution system are identified from the target area; and on the premise that constraint conditions are met, taking the minimum number of the power distribution terminals as a first objective function, taking the minimum point distribution cost as a second objective function, establishing a mathematical model, and solving the mathematical model through a target optimization algorithm to obtain point distribution data of the power distribution terminals. In the mode, the mathematical model is solved through the target optimization algorithm, so that on the premise that various constraint conditions are met, the minimum number of power distribution terminals are installed at the minimum point distribution cost, global observability, measurability and controllability of the power system are achieved, the problem of data acquisition redundancy is avoided, and the system construction and operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, device, electronic equipment and storage medium for distributing power distribution terminals. Background Art

[0002] With the rapid development of photovoltaic and charging network construction, the distribution network has undergone significant changes, urgently requiring advanced sensing, measurement, and control technologies. However, traditional terminal deployment methods often lead to redundant data collection, increasing system construction and operation and maintenance costs. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, device, electronic device and storage medium for the deployment of distribution terminals. Under the premise of meeting various constraints, the minimum number of distribution terminals can be installed with the minimum deployment cost to achieve full-area observability, measurability and control, so as to avoid the problem of data collection redundancy and reduce system construction and operation and maintenance costs.

[0004] In a first aspect, an embodiment of the present invention provides a method for deploying power distribution terminals, the method comprising:

[0005] Dividing the power distribution system into multiple target areas at different levels based on the power distribution characteristics of the power distribution system; wherein the power distribution characteristics include geographical distribution, load density, line type, electrical characteristics, and load requirements; the difference between the regional characteristics of each target area satisfies a preset condition, wherein the regional characteristics include the electrical characteristics and the load requirements;

[0006] By analyzing the topology of the power distribution system, key nodes connecting multiple important load points or carrying large current transmission are identified in the target area; the key nodes are used to install power distribution terminals; the key nodes include: substations, switch stations, ring network cabinets, box-type substations, feeder switches, industrial dedicated distribution stations, and distribution boxes in civil buildings;

[0007] On the premise of satisfying the constraints on the distribution terminals in the key nodes, with the minimum number of the distribution terminals as the first objective function and the minimum cost of the distribution terminals as the second objective function, a distribution mathematical model is established, and the distribution mathematical model is solved by the target optimization algorithm to obtain the distribution data of the distribution terminals; wherein, the constraints include: the installation location of the distribution terminal meets the preset installation conditions, the electrical reliability index of the distribution system meets the preset index, and the terminal fee of the distribution terminal meets the preset fee.

[0008] Furthermore, the method of solving the distribution mathematical model by a target optimization algorithm to obtain the distribution data of the power distribution terminals includes:

[0009] Step 1: Read the topological data of the power distribution system, determine component reliability parameters, load point parameters, fault handling time, and cost data, and number the key nodes to correspond to the binary codes in the genetic algorithm; wherein the component reliability parameters include the failure rate of each target area in a specified time period, the load point parameters include the average load of each key node in the specified time period, the fault handling time includes the time when a fault occurs in each target area, and the cost data includes the cost of the distribution terminal and the cost loss per unit of power outage caused by installing the distribution terminal at each key node;

[0010] Step 2: determining a fitness function according to the first objective function, the second objective function and the constraint conditions;

[0011] Step 3: Initialize the simulated annealing algorithm parameters to control the temperature T, initialize the population X through the genetic algorithm, determine the chromosome length G according to the number of key nodes, and randomly generate a population of appropriate size. The population includes k chromosomes, and each chromosome (i.e., a row of X) corresponds to a type of distribution terminal layout data;

[0012] ;

[0013] in, express Distribution terminal data, Indicates the In the distribution terminal layout data, the jth key node in the i-th target area is not equipped with a remote distribution terminal. Indicates the The second remote distribution terminal is installed at the jth key node in the i-th target area in the distribution terminal layout data. Indicates the In the distribution terminal layout data, the jth key node in the i-th target area is not installed with the three-remote distribution terminal. Indicates the The three remote distribution terminals are installed at the jth key node in the ith target area in the distribution terminal layout data, where G represents the number of the key nodes;

[0014] Step 4: for each chromosome in the population, based on the distribution terminal layout data corresponding to the chromosome, calculate the reliability index and preset cost of the distribution system;

[0015] Step 5, calculating the fitness value of the chromosome according to the fitness function;

[0016] Step 6: Determine whether the convergence condition is met; if not, execute steps 7 and 8; if yes, execute step 9;

[0017] Step 7: selecting a target chromosome that meets the constraint conditions from a plurality of chromosomes based on the reliability index of the power distribution system and the preset cost; performing a mutation operation on the target chromosome according to the mutation rate to obtain a new chromosome population;

[0018] Step 8: Based on the first objective function and the second objective function, determine whether the new chromosome population meets the Metropolis criterion in the simulated annealing algorithm; if not, continue to step 7; if yes, reduce T and continue to step 6;

[0019] Step 9, taking the chromosome with the best fitness value in the chromosome population as the layout data of the power distribution terminal;

[0020] Wherein, the simulated annealing algorithm is used to jump out of the local optimal solution and realize global search when the genetic algorithm falls into the local optimal solution.

[0021] Furthermore, the convergence condition is any one of the following:

[0022] The number of iterations meets the preset number;

[0023] The fitness value remains unchanged;

[0024] The average fitness value meets the preset optimal fitness value.

[0025] Furthermore, the first objective function is:

[0026] ;

[0027] The second objective function is: ;

[0028] Wherein, L is the number of the power distribution terminals, Indicates that the second remote power distribution terminal is not installed at the jth key node in the ith target area. Indicates that two remote power distribution terminals are installed at the jth key node in the i-th target area. Indicates that the jth key node in the i-th target area is not equipped with a three-remote distribution terminal. It means that three remote distribution terminals are installed at the jth key node in the i-th target area; is the deployment cost, For purchase and installation costs, For maintenance costs, For loss expenses.

[0029] Furthermore, it is characterized in that

[0030] ;

[0031] ;

[0032] ;

[0033] in, is the cost of the second remote distribution terminal, The cost of the three remote power distribution terminals;

[0034] DR is the discount rate, The proportion of maintenance, is the preset time;

[0035] is the failure rate of the i-th target area in year t, is the average load of the jth key node in the i-th target area in the t-th year, is the cost loss caused by the unit power outage when the two remote distribution terminals are installed at the jth key node in the i-th target area in the t-th year, It is the cost loss caused by unit power outage when the three remote distribution terminals are installed at the jth key node in the i-th target area in the t-th year.

[0036] Furthermore, the constraints include:

[0037] The first constraint is: ;

[0038] Second constraint: ;

[0039] The third constraint: ;

[0040] in, Indicates that the second remote power distribution terminal and the third remote power distribution terminal cannot be installed at the same key node at the same time; K is the reliability index value, is the preset indicator value, is the power outage time of the load when the two remote distribution terminals are installed at the jth key node when a fault occurs in the i-th area, is the power outage time of the load when the three remote power distribution terminals are installed at the jth key node when a fault occurs in the i-th area;

[0041] is the number of the second remote distribution terminals, is the number of three remote distribution terminals, is the cost of the second remote distribution terminal, is the cost of the three remote distribution terminals, and F is the preset fee.

[0042] Furthermore, the method further comprises:

[0043] Determining the installation location and terminal type of the power distribution terminal according to the layout data of the power distribution terminal;

[0044] Determining a monitoring range of the power distribution system according to the installation location and the terminal type, and judging whether the monitoring range of the power distribution terminal meets a preset monitoring range of the power distribution system;

[0045] If not, optimizing the layout data of the power distribution terminals;

[0046] If yes, verifying the accuracy of the power data collected by the power distribution terminal and the communication stability of the power distribution system;

[0047] If the verification is passed or not, the layout data of the power distribution terminal is optimized.

[0048] In a second aspect, an embodiment of the present disclosure provides a device for distributing power distribution terminals, including:

[0049] A device for distributing power distribution terminals, characterized in that the device comprises:

[0050] a region division module, configured to divide the power distribution system into a plurality of target regions at different levels based on the power distribution characteristics of the power distribution system; wherein the power distribution characteristics include geographical distribution, load density, line type, electrical characteristics, and load requirements; and wherein the difference between the regional characteristics of each target region satisfies a preset condition, wherein the regional characteristics include the electrical characteristics and the load requirements;

[0051] A key node identification module is used to identify key nodes in the target area that connect multiple important load points or transmit high currents by analyzing the topology of the power distribution system; the key nodes are used to install power distribution terminals; the key nodes include: substations, switch stations, ring network cabinets, box-type substations, feeder switches, industrial dedicated distribution stations, and distribution boxes in civil buildings;

[0052] The distribution point solving module is used to establish a distribution point mathematical model with the minimum number of the distribution terminals as the first objective function and the minimum distribution point cost of the distribution terminals as the second objective function, under the premise of satisfying the constraint conditions, and solve the distribution point mathematical model through the target optimization algorithm to obtain the distribution point data of the distribution terminals; wherein the constraint conditions include: the installation location of the distribution terminal meets the preset installation conditions, the electrical reliability index of the distribution system meets the preset index, and the terminal fee of the distribution terminal meets the preset fee.

[0053] In a third aspect, an embodiment of the present disclosure provides an electronic device comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the method for deploying distribution terminals of any one of the first aspects.

[0054] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method for deploying distribution terminals of any one of the first aspects.

[0055] The embodiments of the present invention have the following beneficial effects:

[0056] The present invention solves the mathematical model of distribution through the target optimization algorithm, and realizes the installation of the minimum number of distribution terminals at the lowest distribution cost under the premise of satisfying various constraints, thereby realizing the observability, measurability and controllability of the entire power system, avoiding the problem of data collection redundancy, and reducing system construction and operation and maintenance costs.

[0057] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 A flow chart of a method for deploying power distribution terminals provided by an embodiment of the present invention;

[0061] Figure 2 A flowchart of another method for deploying power distribution terminals provided by an embodiment of the present invention;

[0062] Figure 3 A schematic structural diagram of a distribution terminal arrangement device provided in an embodiment of the present invention;

[0063] Figure 4A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0065] With the rapid development of photovoltaic and charging network construction, the distribution network has undergone significant changes, urgently requiring advanced sensing, measurement, and control technologies. However, traditional terminal deployment methods often result in redundant data collection when generating distribution terminal deployment data, increasing system construction and operation and maintenance costs. Therefore, embodiments of the present invention provide a method, device, and electronic device for deploying distribution terminals. This technology can be applied to devices equipped with a distribution system.

[0066] To facilitate understanding of this embodiment, a method for distributing power distribution terminals disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method includes the following steps:

[0067] Step S101: Divide the power distribution system into multiple target areas at different levels based on the power distribution characteristics of the power distribution system; wherein the power distribution characteristics include geographical distribution, load density, line type, electrical characteristics, and load requirements; the difference between the regional characteristics of each target area meets a preset condition, and the regional characteristics include electrical characteristics and load requirements;

[0068] The above-mentioned preset condition may be less than a preset threshold. In one possible implementation, an optimization model may be established with the goal of the difference between the regional characteristics of each target area meeting the preset condition, and the power distribution system may be regionalized using an optimization algorithm. First, the power distribution system is randomly regionalized, and the difference between the regional characteristics of each target area is calculated to see whether it meets the preset condition. If not, the parameters and regional division are further adjusted until the difference between the regional characteristics of each target area meets the preset condition.

[0069] Another possible implementation involves initially dividing the power distribution system into initial regions based on geographic distribution. Regional characteristics for each initial region are then calculated. Based on pre-defined regional characteristics, the initial regions are adjusted so that the difference between the final regional characteristics of each target region and the pre-defined regional characteristics meets pre-defined conditions. Finally, the target regions are visualized using a Geographic Information System (GIS). The pre-defined regional characteristics are pre-set based on the power distribution system. Adjustments to the initial regions can include more detailed divisions or merging of initial regions.

[0070] The aforementioned load density refers to the average power consumption per square kilometer, typically measured in MW / km². It is a quantitative parameter that characterizes the density of load distribution and reflects the power load per unit area. Load density is crucial in power planning and design. It helps power companies or planning departments assess power demand within their supply areas, enabling them to rationally plan grid construction and power distribution. The aforementioned line types primarily include transmission lines and distribution lines. Lines within power systems can also be categorized by structure as overhead lines or power cables.

[0071] The above-mentioned electrical characteristics refer to the electrical performance and parameters of the equipment, mainly including voltage, current, power factor, frequency, etc. These characteristics directly affect the operating efficiency and stability of the equipment. The above-mentioned load requirements refer to the requirements of the equipment on the load during operation, including load stability, fluctuation range, peak handling capacity, etc. The load characteristics of the power system can be divided into comprehensive power load, power supply load and power generation load. The comprehensive power load is the sum of the power consumption of various industrial sectors, agriculture, transportation and municipal life; the power supply load is the comprehensive power load plus the power loss in the network; the power generation load is the power supply load plus the power consumption of each power plant itself.

[0072] Step S102: By analyzing the topology of the power distribution system, key nodes that connect multiple important load points or transmit high currents are identified in the target area; key nodes are used to install power distribution terminals; key nodes include: substations, switch stations, ring network cabinets, box-type substations, feeder switches, industrial dedicated distribution stations, and distribution boxes in civil buildings;

[0073] Optionally, substations, switch stations, ring network cabinets, box-type substations, feeder switches, places that need to be equipped with stable power systems, etc. are identified from the target area, where places that need to be equipped with stable power systems usually include industrial plants, shopping malls, buildings, etc.

[0074] Step S103, under the premise of satisfying the constraints on the distribution terminals in the key nodes, with the minimum number of distribution terminals as the first objective function and the minimum cost of distribution terminals as the second objective function, a distribution mathematical model is established, and the distribution mathematical model is solved by the target optimization algorithm to obtain the distribution data of the distribution terminals; wherein the constraints include: the installation location of the distribution terminal meets the preset installation conditions, the electrical reliability index of the distribution system meets the preset index, and the terminal fee of the distribution terminal meets the preset fee.

[0075] The aforementioned constraints may also include: the monitoring range of the distribution terminal meeting a preset monitoring range; wherein, the monitoring range of the distribution terminal meeting the preset monitoring range is required to achieve observable, measurable, and controllable functions across the entire power system. The aforementioned deployment costs include the terminal costs of the distribution terminal (including purchase and installation costs), maintenance costs, and loss costs. The aforementioned target optimization algorithm can be any of the following: simulated annealing algorithm, genetic algorithm, ant colony algorithm, particle swarm optimization algorithm, etc. It can also be a multi-objective grey wolf optimization algorithm, multi-objective particle swarm optimization algorithm, non-dominated sorting genetic algorithm, multi-objective artificial hummingbird algorithm, etc. The aforementioned target optimization algorithm can also be a combination of multiple algorithms, such as a combination of a genetic algorithm and an ant colony algorithm, or a combination of a simulated annealing algorithm and a genetic algorithm.

[0076] In terms of power supply reliability, the secondary remote terminal can provide some basic information to help operations and maintenance personnel make a preliminary assessment of the grid's operating status. For example, when telemetry data indicates an abnormal drop in voltage on a certain line, operations and maintenance personnel can infer the possibility of a line fault or a sudden increase in load. However, it cannot directly isolate the fault and restore power. In terms of fault location, since it can only obtain status and simple electrical quantity information, its positioning accuracy is relatively low. For some complex power grid structures, it may be necessary to combine data from multiple secondary remote terminals for comprehensive analysis, which will increase the time required to locate the fault and, to a certain extent, affect the speed of power restoration. Moreover, when dealing with some transient faults, because the switch operation cannot be remotely controlled, manual on-site inspection and restoration may be required, which also reduces power supply reliability.

[0077] In terms of power supply reliability, the three-remote terminals have a better performance. When a fault occurs, after quickly and accurately locating the fault location through telesignaling and telemetry functions, the three-remote terminals can use the remote control function to promptly isolate the fault area. For example, when a permanent fault occurs on a distribution line, the master station system can quickly control the switches on both sides of the fault point based on the fault information fed back by the three-remote terminals, isolate the fault area, and restore power to non-fault areas, greatly shortening the power outage time. For transient faults, the three-remote terminals can also quickly reclose the circuit breakers, reducing users' perception of power outages. Moreover, during routine operation and maintenance or load adjustments of the power grid, the remote control function can easily implement changes to the grid topology, making the power supply more flexible and reliable, and improving the power supply reliability rate.

[0078] The above-mentioned method for distributing distribution terminals divides the distribution system into multiple target areas based on the distribution characteristics of the distribution system. The distribution characteristics include geographical distribution, load density, line type, electrical characteristics, and load requirements. The difference between the regional characteristics of each target area meets preset conditions, and the regional characteristics include electrical characteristics and load requirements. Key nodes that have a significant impact on the stability, reliability, and safety of the distribution system are identified from the target areas. Key nodes are used to install distribution terminals. Key nodes include: substations, switch stations, ring network cabinets, box-type substations, feeder switches, and places requiring a stable power system. Under the premise of meeting constraints, a distribution mathematical model is established with the minimum number of distribution terminals as the first objective function and the minimum distribution cost as the second objective function. The distribution mathematical model is solved using a target optimization algorithm to obtain distribution terminal distribution data. The constraints include: the installation location of the distribution terminal meets preset installation conditions, the electrical reliability index of the distribution system meets preset indicators, and the terminal fee of the distribution terminal meets preset fees. The monitoring range of the distribution terminal meets the preset monitoring range to achieve observable, measurable, and controllable functions across the entire power system. In this method, the mathematical model of distribution is solved by the target optimization algorithm, which enables the installation of the minimum number of distribution terminals at the lowest distribution cost while satisfying various constraints. This makes the power system observable, measurable and controllable across the entire area, avoids the problem of redundant data collection, reduces system construction and operation and maintenance costs, and ensures the stable operation and efficient management of the power system.

[0079] This embodiment provides another method for distributing power distribution terminals, such as Figure 2 As shown,

[0080] Step S201: Divide the power distribution system into multiple target areas based on the power distribution characteristics of the power distribution system; wherein the power distribution characteristics include geographical distribution, load density, line type, electrical characteristics, and load requirements; the difference between the regional characteristics of each target area meets a preset condition, and the regional characteristics include electrical characteristics and load requirements;

[0081] Step S202: Identify key nodes in the target area that have a significant impact on the stability, reliability, and safety of the power distribution system; key nodes are used to install power distribution terminals; key nodes include: substations, switch stations, ring network cabinets, box-type substations, feeder switches, and places that require a stable power system.

[0082] Step S203: Under the premise of satisfying the constraints, a mathematical model for distribution terminal location is established with the minimum number of distribution terminals as the first objective function and the minimum distribution terminal cost as the second objective function. The constraints include: the monitoring range of the distribution terminal satisfies the preset monitoring range, the installation location of the distribution terminal satisfies the preset installation conditions, the electrical reliability index of the distribution system satisfies the preset index, and the terminal fee of the distribution terminal satisfies the preset fee. The monitoring range of the distribution terminal satisfies the preset monitoring range to achieve observable, measurable, and controllable functions of the entire power system.

[0083] Step S204: Generate a series of random solutions as an initial population through a genetic algorithm, and calculate the fitness value of each individual in the initial population through a preset function;

[0084] Step S205, determining whether the fitness value meets the convergence condition;

[0085] Step S206: If not, perform a mutation operation based on the mutation rate using a simulated annealing algorithm to obtain a mutation result. If the mutation result satisfies the Metropolis criterion, reduce the control temperature and continue to perform the step of determining whether the fitness value satisfies the convergence condition. If the mutation result does not satisfy the Metropolis criterion, continue to perform a mutation operation based on the mutation rate to obtain a mutation result. The control temperature is a parameter in the simulated annealing algorithm.

[0086] Step S207: If yes, take the chromosome with the best fitness value in the chromosome population as the layout data of the distribution terminal; wherein the simulated annealing algorithm is used to: when the genetic algorithm falls into a local optimal solution, jump out of the local optimal solution and realize global search.

[0087] The above convergence condition is any one of the following: the number of iterations meets the preset number; the fitness value remains unchanged; the average fitness value meets the preset optimal fitness value.

[0088] The preset number of times can be set in advance as needed, and the preset most suitable value can also be set in advance as needed.

[0089] Optionally, a crossover operation may be performed to obtain a crossover result. If the crossover result satisfies the Metropolis criterion of the simulated annealing algorithm, the control temperature is lowered.

[0090] The Metropolis criterion is the core principle of the simulated annealing (SA) algorithm, which determines the probability of a particle transitioning from one state to another at temperature T. The Metropolis criterion enables the SA algorithm to effectively avoid local minima and ultimately reach a global optimal solution.

[0091] In fact, the genetic algorithm has a strong global search capability and is applicable to a variety of problems, including optimization problems, combinatorial problems, and constraint problems. By simulating processes such as natural selection, crossover, and mutation, it can perform global search and optimization on complex problems. However, it may fall into a local optimal solution, resulting in the inability to obtain a global optimal solution. Although the simulated annealing algorithm has a weaker global search capability, it has a strong local search capability. By accepting inferior solutions with a lower probability, it can jump out of the local optimal solution and achieve global search. This gives it an advantage in solving complex problems and can find better solutions. Through the improved genetic algorithm, the genetic algorithm and the simulated annealing algorithm can be combined to have a strong global search capability and a strong local search capability, and finally obtain the optimal distribution terminal layout data.

[0092] In the above method, the idea of ​​simulated annealing algorithm is applied to the improvement of genetic algorithm. First, the initial population is selected by genetic algorithm, and then the initial population is improved by simulated annealing algorithm, and then the corresponding mutation operation is performed. The improved genetic algorithm can accept bad solutions to a certain extent, avoiding the problem that the genetic algorithm converges prematurely and can only find the optimal solution in a local range.

[0093] A possible implementation of the step of solving the distribution mathematical model by using a target optimization algorithm to obtain the distribution data of the power distribution terminals is as follows:

[0094] Step 1: Read the topological structure data of the power distribution system, determine component reliability parameters, load point parameters, fault handling time, and cost data, and number the key nodes to correspond to the binary codes in the genetic algorithm; wherein the component reliability parameters include the failure rate of each target area in a specified time period, the load point parameters include the average load of each key node in the specified time period, the fault handling time includes the time when a fault occurs in each target area, and the cost data includes the cost of the distribution terminal and the cost loss per unit of power outage caused by installing the distribution terminal at each key node;

[0095] Step 2: determining a fitness function according to the first objective function, the second objective function and the constraint conditions;

[0096] Step 3: Initialize the simulated annealing algorithm parameters to control the temperature T, initialize the population X through the genetic algorithm, determine the chromosome length G according to the number of key nodes, and randomly generate a population of appropriate size. The population includes k chromosomes, and each chromosome (i.e., a row of X) corresponds to a type of distribution terminal layout data;

[0097] ;

[0098] in, express Distribution terminal data, Indicates the In the distribution terminal layout data, the jth key node in the i-th target area is not equipped with a remote distribution terminal. Indicates the The second remote distribution terminal is installed at the jth key node in the i-th target area in the distribution terminal layout data. Indicates the In the distribution terminal layout data, the jth key node in the i-th target area is not installed with the three-remote distribution terminal. Indicates the The three remote distribution terminals are installed at the jth key node in the ith target area in the distribution terminal layout data, where G represents the number of the key nodes;

[0099] Step 4: for each chromosome in the population, based on the distribution terminal layout data corresponding to the chromosome, calculate the reliability index and preset cost of the distribution system;

[0100] Step 5, calculating the fitness value of the chromosome according to the fitness function;

[0101] Step 6: Determine whether the convergence condition is met; if not, execute steps 7 and 8; if yes, execute step 9;

[0102] Step 7: selecting a target chromosome that meets the constraint conditions from a plurality of chromosomes based on the reliability index of the power distribution system and the preset cost; performing a mutation operation on the target chromosome according to the mutation rate to obtain a new chromosome population;

[0103] Step 8: Based on the first objective function and the second objective function, determine whether the new chromosome population meets the Metropolis criterion in the simulated annealing algorithm; if not, continue to step 7; if yes, reduce the control temperature and continue to step 6; wherein the control temperature is a parameter in the simulated annealing algorithm;

[0104] Step 9, taking the chromosome with the best fitness value in the chromosome population as the layout data of the power distribution terminal;

[0105] The simulated annealing algorithm is used to: when the genetic algorithm falls into a local optimal solution, jump out of the local optimal solution and realize global search.

[0106] In step 8 above, the Metropolis criterion is based on the Monte Carlo method. Its core idea is to accept not only new solutions that reduce the objective function value, but also new solutions that increase the objective function value with a certain probability during the search process, so that the algorithm has the opportunity to jump out of the local optimal solution and finally reach the global optimal solution. Specifically, under the control temperature T, for the transition from the current state (i.e.) to the new state (the new chromosome population ), calculate the acceptance probability of the new chromosome population :

[0107] ;

[0108] in, , and New chromosome populations and new chromosome populations The objective function value or fitness value of , k is the Boltzmann constant. In the practical application of the simulated annealing algorithm, k is usually taken as 1.

[0109] by and New chromosome populations and new chromosome populations The objective function value of is used as an example to illustrate the difference between the objective function of the new chromosome population and the current chromosome population. , according to the Metropolis criterion, determine whether to accept the new chromosome population ,if Then determine that the new chromosome population meets the Metropolis criterion and directly accept the new solution as the current solution; if , then generate a random number between [0,1] ,like , then the new chromosome population is determined to meet the Metropolis criterion and the new solution is directly accepted as the current solution; otherwise, the new chromosome population is determined not to meet the Metropolis criterion.

[0110] The Metropolis criterion, used above, allows the algorithm to accept inferior solutions under certain conditions. This is the key to the simulated annealing algorithm's ability to escape local optima. This approach allows the algorithm to explore a wider solution space, potentially finding the global optimal solution, rather than simply stopping search due to the current local optimum.

[0111] In the early stages of the algorithm, the temperature is high, and the probability of accepting inferior solutions is high. At this stage, the algorithm primarily conducts a broad search, moving rapidly through the solution space, seeking areas where more optimal solutions may exist. As the algorithm progresses, the temperature gradually decreases, and the probability of accepting inferior solutions gradually decreases. The algorithm then begins to focus on the area near the currently found optimal solution, conducting a local, refined search, and gradually converging to a better solution. In this way, the Metropolis criterion effectively balances the breadth and depth of the search, enabling the algorithm to search globally while also converging to a better solution later in the process.

[0112] In the above method, the genetic algorithm and the simulated annealing algorithm are combined to obtain an improved genetic algorithm. The simulated annealing algorithm can help the genetic algorithm avoid premature convergence during the evolution process. After the crossover and mutation operations of the genetic algorithm, the simulated annealing algorithm is used to further optimize and adjust the newly generated individuals, so that the individuals in the population can better explore the solution space, thereby enhancing the global search capability.

[0113] The genetic algorithm's swarm search advantage can be used to quickly locate an area likely to contain the optimal solution, and then the simulated annealing algorithm can be used to conduct a detailed search within this area. For example, when optimizing the weights of a neural network, the genetic algorithm can initially screen out a set of optimal weight combinations, and then the simulated annealing algorithm can fine-tune within this range, thereby accelerating convergence and improving the accuracy of the solution.

[0114] First, the versatility of the genetic algorithm is used to preliminarily explore the solution space, and then the simulated annealing algorithm is used to adapt to the special structure of the problem, such as processing function optimization problems with complex distribution of local extreme points or special constraints in combinatorial optimization problems, thereby enhancing the adaptability to different types of problems.

[0115] The first objective function mentioned above is: ; The second objective function is: ; Wherein, L is the number of the distribution terminals, Indicates that the second remote power distribution terminal is not installed at the jth key node in the ith target area. Indicates that two remote power distribution terminals are installed at the jth key node in the i-th target area. Indicates that the jth key node in the i-th target area is not equipped with a three-remote distribution terminal. It means that three remote distribution terminals are installed at the jth key node in the i-th target area; is the deployment cost, For purchase and installation costs, For maintenance costs, For loss expenses.

[0116] above ;

[0117] ;

[0118] ;in, is the cost of the second remote distribution terminal, The cost of the three remote power distribution terminals;

[0119] DR is the discount rate, The proportion of maintenance, is the preset time;

[0120] is the failure rate of the i-th target area in year t, is the average load of the jth key node in the i-th target area in the t-th year, is the cost loss caused by the unit power outage when the two remote distribution terminals are installed at the jth key node in the i-th target area in the t-th year, It is the cost loss caused by unit power outage when the three remote distribution terminals are installed at the jth key node in the i-th target area in the t-th year.

[0121] The above constraints include:

[0122] The first constraint is: ;

[0123] in, This means that the second remote power distribution terminal and the third remote power distribution terminal cannot be installed at the same key node at the same time;

[0124] Second constraint: ;

[0125] represents the monitoring range of the second remote power distribution terminal installed at the jth key node in the i-th target area, represents the monitoring range of the three remote distribution terminals installed at the jth key node in the i-th target area, S is the monitoring range of all distribution terminals, The monitoring range is the preset monitoring range; the monitoring range of the power distribution terminal installed in the target area includes the target equipment, protection devices and environmental parameters in the target area. The target equipment includes: high-voltage equipment, transformer equipment, power distribution equipment, communication and network equipment;

[0126] The third constraint:

[0127] ;

[0128] K is the reliability index value, is the preset indicator value, is the power outage time of the load when the two remote distribution terminals are installed at the jth key node when a fault occurs in the i-th area, is the power outage time of the load when the three remote power distribution terminals are installed at the jth key node when a fault occurs in the i-th area;

[0129] The above constraints include the fourth constraint: ;

[0130] is the number of the second remote distribution terminals, is the number of three remote distribution terminals, and F is the preset fee.

[0131] The above content describes the placement data for installing distribution terminals at key nodes connecting multiple important load points or carrying high currents. Since the objective function is to minimize the number of terminals and the cost of distributing them, the resulting distribution terminal placement data is based on installing distribution terminals at key nodes. Installing distribution terminals at other, non-critical nodes generally fails to meet the objective function.

[0132] Therefore, for non-critical nodes, the topological structure of the distribution system can be analyzed to identify non-critical nodes in the target area that are connected to less than a specified number of important load points or carry less than a specified current value. Under the premise of satisfying the specified constraints for installing two remote distribution terminals in non-critical nodes, a distribution mathematical model is established with the minimum investment cost of installing two remote distribution terminals as the objective function. The distribution mathematical model is solved by the target optimization algorithm to obtain the distribution data of the two remote distribution terminals. The specified constraints include: the electrical reliability index of the distribution system meets the preset index.

[0133] After obtaining preliminary distribution terminal location data, the preliminarily determined terminal location plan is verified to ensure that it meets the actual system requirements. Verification includes terminal coverage, data accuracy, communication stability, and other aspects. If necessary, the plan is adjusted until the optimal solution is achieved. The preliminarily determined terminal location plan is verified, including through simulation and field testing, to ensure that it meets the actual system requirements. Based on the verification results, the plan is adjusted and optimized until the optimal solution is achieved. At the same time, a monitoring and feedback mechanism is established to continuously track and evaluate the implementation effectiveness of the plan.

[0134] The above method also includes: determining the installation location and terminal type of the distribution terminal based on the distribution terminal's distribution data; determining the monitoring range of the distribution system based on the installation location and terminal type, and judging whether the monitoring range of the distribution terminal meets the preset monitoring range of the distribution system; if not, optimizing the distribution terminal's distribution data; if yes, verifying the accuracy of the power data collected by the distribution terminal and the communication stability of the distribution system; if passing or failing the verification, optimizing the distribution terminal's distribution data.

[0135] The above-mentioned terminal types include two-remote distribution terminals and three-remote distribution terminals. Different types of distribution terminals have different monitoring functions and monitoring ranges, and the monitoring ranges will be different when installed in different locations. Therefore, according to the installation location and terminal type, the monitoring range of each distribution terminal is determined, and then the monitoring range of the distribution system is calculated, and then it is determined whether the monitoring range of the distribution terminal meets the preset monitoring range of the distribution system. If the monitoring range of the distribution terminal meets the preset monitoring range of the distribution system, continue to verify the accuracy of the power data collected by the distribution terminal and the communication stability of the distribution system. Specifically, the accuracy of the power data and the communication stability of the distribution system can be verified by simulation. After the verification, the distribution terminal layout data can be determined. If the verification fails, the distribution terminal layout data can be re-optimized. If the monitoring range of the distribution terminal does not meet the preset monitoring range of the distribution system, continue to optimize the distribution terminal.

[0136] Finally, terminal equipment is installed and debugged according to the optimized deployment data plan to ensure normal operation of the equipment and smooth communication. A comprehensive operation and maintenance management system is established to conduct regular inspections and maintenance of terminal equipment to promptly detect and resolve faults.

[0137] The method of the present invention can significantly reduce the number of distribution terminals, reduce data collection redundancy, improve data collection efficiency, and reduce system construction and operation and maintenance costs. Furthermore, the method can improve the intelligence level of the distribution network and provide strong support for the construction of new power systems.

[0138] Corresponding to the above method embodiment, the present disclosure provides a device for distributing power distribution terminals, such as Figure 3 As shown, the device includes:

[0139] The regional division module 301 is configured to divide the power distribution system into multiple target regions at different levels based on the power distribution characteristics of the power distribution system, wherein the power distribution characteristics include geographical distribution, load density, line type, electrical characteristics, and load requirements. The difference between the regional characteristics of each target region satisfies a preset condition, and the regional characteristics include electrical characteristics and load requirements.

[0140] The key node identification module 302 is used to identify key nodes in the target area that connect multiple important load points or transmit high currents by analyzing the topology of the power distribution system. Key nodes are used to install power distribution terminals. Key nodes include: substations, switch stations, ring network cabinets, box-type substations, feeder switches, industrial distribution stations, and distribution boxes in civil buildings.

[0141] The distribution point solving module 303 is used to establish a distribution point mathematical model with the minimum number of distribution terminals as the first objective function and the minimum distribution point cost as the second objective function, under the premise of satisfying the constraints on the distribution terminals in the key nodes, and solve the distribution point mathematical model through the target optimization algorithm to obtain the distribution point data of the distribution terminals; wherein the constraints include: the installation location of the distribution terminal meets the preset installation conditions, the electrical reliability index of the distribution system meets the preset index, and the terminal fee of the distribution terminal meets the preset fee.

[0142] The above-mentioned distribution terminal layout device divides the distribution system into multiple target areas according to the distribution characteristics of the distribution system; wherein the distribution characteristics include geographical distribution, load density, line type, electrical characteristics and load requirements; the difference between the regional characteristics of each target area meets the preset conditions, and the regional characteristics include electrical characteristics and load requirements; key nodes that have a significant impact on the stability, reliability and safety of the distribution system are identified from the target area; key nodes are used to install distribution terminals; key nodes include: substations, switch stations, ring network cabinets, box substations, feeder switches, places that need to be equipped with a stable power system Under the premise of satisfying the constraints, taking the minimum number of distribution terminals as the first objective function and the minimum distribution cost as the second objective function, a distribution mathematical model is established, and the distribution mathematical model is solved by the target optimization algorithm to obtain the distribution data of the distribution terminals; wherein, the constraints include: the monitoring range of the distribution terminal meets the preset monitoring range, the installation location of the distribution terminal meets the preset installation conditions, the electrical reliability index of the distribution system meets the preset index, and the terminal fee of the distribution terminal meets the preset fee; wherein, the monitoring range of the distribution terminal meets the preset monitoring range to achieve the observable, measurable and controllable function of the entire power system. In this method, the distribution mathematical model is solved by the target optimization algorithm to achieve the installation of the minimum number of distribution terminals at the minimum distribution cost under the premise of satisfying various constraints, and the observable, measurable and controllable power system is achieved in the entire area, avoiding the problem of data collection redundancy, reducing the system construction and operation and maintenance costs, and ensuring the stable operation and efficient management of the power system.

[0143] The above-mentioned distribution point solving module 303 solves the distribution point mathematical model through the target optimization algorithm to obtain the distribution point data of the distribution terminal, which specifically includes:

[0144] Step 1: Read the topological structure data of the power distribution system, determine component reliability parameters, load point parameters, fault handling time, and cost data, and number the key nodes to correspond to the binary codes in the genetic algorithm; wherein the component reliability parameters include the failure rate of each target area in a specified time period, the load point parameters include the average load of each key node in the specified time period, the fault handling time includes the time when a fault occurs in each target area, and the cost data includes the cost of the distribution terminal and the cost loss per unit of power outage caused by installing the distribution terminal at each key node;

[0145] Step 2: determining a fitness function according to the first objective function, the second objective function and the constraint conditions;

[0146] Step 3: Initialize the simulated annealing algorithm parameters to control the temperature T. Initialize the population X through the genetic algorithm. Determine the chromosome length G according to the number of key nodes. Randomly generate a population of appropriate size. The population includes k chromosomes. Each chromosome (i.e., a row of X) corresponds to a

[0147] Distribution terminal layout data;

[0148] ;

[0149] in, express Distribution terminal data, Indicates the In the distribution terminal layout data, the jth key node in the i-th target area is not equipped with a remote distribution terminal. Indicates the The second remote distribution terminal is installed at the jth key node in the i-th target area in the distribution terminal layout data. Indicates the In the distribution terminal layout data, the jth key node in the i-th target area is not installed with the three-remote distribution terminal. Indicates the The three remote distribution terminals are installed at the jth key node in the ith target area in the distribution terminal layout data, where G represents the number of the key nodes;

[0150] Step 4: for each chromosome in the population, based on the distribution terminal layout data corresponding to the chromosome, calculate the reliability index and preset cost of the distribution system;

[0151] Step 5, calculating the fitness value of the chromosome according to the fitness function;

[0152] Step 6: Determine whether the convergence condition is met; if not, execute steps 7 and 8; if yes, execute step 9;

[0153] Step 7: selecting a target chromosome that meets the constraint conditions from a plurality of chromosomes based on the reliability index of the power distribution system and the preset cost; performing a mutation operation on the target chromosome according to the mutation rate to obtain a new chromosome population;

[0154] Step 8: Based on the first objective function and the second objective function, determine whether the new chromosome population meets the Metropolis criterion in the simulated annealing algorithm; if not, continue to step 7; if yes, reduce the control temperature T and continue to step 6; wherein the control temperature is a parameter in the simulated annealing algorithm;

[0155] Step 9: Take the chromosome with the best fitness value in the chromosome population as the layout data of the distribution terminal; wherein the simulated annealing algorithm is used to: when the genetic algorithm falls into a local optimal solution, jump out of the local optimal solution and realize global search.

[0156] The above convergence condition is any one of the following: the number of iterations meets the preset number; the fitness value remains unchanged; the average fitness value meets the preset optimal fitness value.

[0157] The first objective function mentioned above is: ; The second objective function is: ; Wherein, L is the number of the distribution terminals, Indicates that the second remote power distribution terminal is not installed at the jth key node in the ith target area. Indicates that two remote power distribution terminals are installed at the jth key node in the i-th target area. Indicates that the jth key node in the i-th target area is not equipped with a three-remote distribution terminal. It means that three remote distribution terminals are installed at the jth key node in the i-th target area; is the deployment cost, For purchase and installation costs, For maintenance costs, For loss expenses.

[0158] above ; ; ;in, is the cost of the second remote distribution terminal, The cost of the three remote power distribution terminals;

[0159] DR is the discount rate, The proportion of maintenance, is the preset time; is the failure rate of the i-th target area in year t, is the average load of the jth key node in the i-th target area in the t-th year, is the cost loss caused by the unit power outage when the two remote distribution terminals are installed at the jth key node in the i-th target area in the t-th year, It is the cost loss caused by unit power outage when the three remote distribution terminals are installed at the jth key node in the i-th target area in the t-th year.

[0160] The above constraints include:

[0161] The first constraint is: ;

[0162] Second constraint: ;

[0163] The third constraint: ;

[0164] in, Indicates that the second remote power distribution terminal and the third remote power distribution terminal cannot be installed at the same key node at the same time; K is the reliability index value, is the preset indicator value, is the power outage time of the load when the two remote distribution terminals are installed at the jth key node when a fault occurs in the i-th area, is the power outage time of the load when the three remote power distribution terminals are installed at the jth key node when a fault occurs in the i-th area; is the number of the second remote distribution terminals, is the number of three remote distribution terminals, is the cost of the second remote distribution terminal, is the cost of the three remote distribution terminals, and F is the preset fee.

[0165] The above-mentioned device also includes a verification module, which is used to: determine the installation location and terminal type of the distribution terminal based on the distribution terminal's distribution data; determine the monitoring range of the distribution system based on the installation location and terminal type, and judge whether the monitoring range of the distribution terminal meets the preset monitoring range of the distribution system; if not, optimize the distribution terminal's distribution data; if yes, verify the accuracy of the power data collected by the distribution terminal and the communication stability of the distribution system; if the verification is passed or not, optimize the distribution terminal's distribution data.

[0166] The distribution terminal deployment device provided in the embodiment of the present invention has the same technical features as the distribution terminal deployment method provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0167] This embodiment further provides an electronic device, including a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor executes the machine-executable instructions to implement the above-mentioned method for deploying power distribution terminals. The electronic device can be a server or a terminal device.

[0168] See also Figure 4 As shown, the electronic device includes a processor 100 and a memory 101. The memory 101 stores machine executable instructions that can be executed by the processor 100. The processor 100 executes the machine executable instructions to implement the above-mentioned method for deploying power distribution terminals.

[0169] Further, Figure 4 The electronic device shown further includes a bus 102 and a communication interface 103 , and the processor 100 , the communication interface 103 and the memory 101 are connected via the bus 102 .

[0170] The memory 101 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is achieved through at least one communication interface 103 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 102 may be an ISA bus, a PCI bus, or an EISA bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0171] The processor 100 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 100 or software instructions. The above processor 100 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as a random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or register. The storage medium is located in the memory 101. The processor 100 reads the information in the memory 101 and, in conjunction with its hardware, completes the steps of the method of the aforementioned embodiment.

[0172] This embodiment also provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the above-mentioned method for deploying distribution terminals.

[0173] The computer program product of the distribution terminal deployment method, device and system provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be found in the method embodiments and will not be repeated here.

[0174] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and devices can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0175] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0176] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0177] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0178] Finally, it should be noted that the above embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for distributing power distribution terminals, characterized in that: The steps include: Dividing the power distribution system into multiple target areas at different levels based on the power distribution characteristics of the power distribution system; wherein the power distribution characteristics include geographical distribution, load density, line type, electrical characteristics, and load requirements; the difference between the regional characteristics of each target area satisfies a preset condition, wherein the regional characteristics include the electrical characteristics and the load requirements; By analyzing the topology of the power distribution system, key nodes connecting multiple important load points or carrying large current transmission are identified in the target area; the key nodes are used to install power distribution terminals; the key nodes include: substations, switch stations, ring network cabinets, box-type substations, feeder switches, industrial dedicated distribution stations, and distribution boxes in civil buildings; On the premise of satisfying the constraints on the distribution terminals in the key nodes, with the minimum number of the distribution terminals as the first objective function and the minimum cost of the distribution terminals as the second objective function, a distribution mathematical model is established, and the distribution mathematical model is solved by the target optimization algorithm to obtain the distribution data of the distribution terminals; wherein, the constraints include: the installation location of the distribution terminal meets the preset installation conditions, the electrical reliability index of the distribution system meets the preset index, and the terminal fee of the distribution terminal meets the preset fee.

2. The method according to claim 1, characterized in that The step of solving the distribution mathematical model by a target optimization algorithm to obtain the distribution data of the power distribution terminals includes: Step 1: Read the topological data of the power distribution system, determine component reliability parameters, load point parameters, fault handling time, and cost data, and number the key nodes to correspond to the binary codes in the genetic algorithm; wherein the component reliability parameters include the failure rate of each target area in a specified time period, the load point parameters include the average load of each key node in the specified time period, the fault handling time includes the time when a fault occurs in each target area, and the cost data includes the cost of the distribution terminal and the cost loss per unit of power outage caused by installing the distribution terminal at each key node; Step 2: determining a fitness function according to the first objective function, the second objective function and the constraint conditions; Step 3: Initialize the simulated annealing algorithm parameters to control the temperature T, initialize the population X through the genetic algorithm, determine the chromosome length G according to the number of key nodes, and randomly generate a population of appropriate size. The population includes k chromosomes, and each chromosome (i.e., a row of X) corresponds to a type of distribution terminal layout data; ； in, express Distribution terminal data, Indicates the In the distribution terminal layout data, the jth key node in the i-th target area is not equipped with a remote distribution terminal. Indicates the The second remote distribution terminal is installed at the jth key node in the i-th target area in the distribution terminal layout data. Indicates the In the distribution terminal layout data, the jth key node in the i-th target area is not installed with the three-remote distribution terminal. Indicates the The three remote distribution terminals are installed at the jth key node in the ith target area in the distribution terminal layout data, where G represents the number of the key nodes; Step 4: for each chromosome in the population, based on the distribution terminal layout data corresponding to the chromosome, calculate the reliability index and preset cost of the distribution system; Step 5, calculating the fitness value of the chromosome according to the fitness function; Step 6: Determine whether the convergence condition is met; if not, execute steps 7 and 8; if yes, execute step 9; Step 7: selecting a target chromosome that meets the constraint conditions from a plurality of chromosomes based on the reliability index of the power distribution system and the preset cost; performing a mutation operation on the target chromosome according to the mutation rate to obtain a new chromosome population; Step 8: Based on the first objective function and the second objective function, determine whether the new chromosome population meets the Metropolis criterion in the simulated annealing algorithm; if not, continue to step 7; if yes, reduce T and continue to step 6; Step 9, taking the chromosome with the best fitness value in the chromosome population as the layout data of the power distribution terminal; Wherein, the simulated annealing algorithm is used to jump out of the local optimal solution and realize global search when the genetic algorithm falls into the local optimal solution.

3. The method according to claim 2, characterized in that The convergence condition is any of the following: The number of iterations meets the preset number; The fitness value remains unchanged; The average fitness value meets the preset optimal fitness value.

4. The method according to claim 1, wherein The first objective function is: ； The second objective function is: ; Wherein, L is the number of the power distribution terminals, Indicates that the second remote power distribution terminal is not installed at the jth key node in the ith target area. Indicates that two remote power distribution terminals are installed at the jth key node in the i-th target area. Indicates that the jth key node in the i-th target area is not equipped with a three-remote distribution terminal. It means that three remote distribution terminals are installed at the jth key node in the i-th target area; is the deployment cost, For purchase and installation costs, For maintenance costs, For loss expenses.

5. The method according to claim 4, characterized in that ; ； ； in, is the cost of the second remote distribution terminal, The cost of the three remote power distribution terminals; DR is the discount rate, The proportion of maintenance, is the preset time; is the failure rate of the i-th target area in year t, is the average load of the jth key node in the i-th target area in the t-th year, is the cost loss caused by the unit power outage when the two remote distribution terminals are installed at the jth key node in the i-th target area in the t-th year, It is the cost loss caused by unit power outage when the three remote distribution terminals are installed at the jth key node in the i-th target area in the t-th year.

6. The method according to claim 1, characterized in that The constraints include: The first constraint is: ; Second constraint: ; The third constraint: ; in, Indicates that the second remote power distribution terminal and the third remote power distribution terminal cannot be installed at the same key node at the same time; K is the reliability index value, is the preset indicator value, is the power outage time of the load when the two remote distribution terminals are installed at the jth key node when a fault occurs in the i-th area, is the power outage time of the load when the three remote power distribution terminals are installed at the jth key node when a fault occurs in the i-th area; is the number of the second remote distribution terminals, is the number of three remote distribution terminals, is the cost of the second remote distribution terminal, is the cost of the three remote distribution terminals, and F is the preset fee.

7. The method according to claim 1, characterized in that The method further comprises: Determining the installation location and terminal type of the power distribution terminal according to the layout data of the power distribution terminal; Determining a monitoring range of the power distribution system according to the installation location and the terminal type, and judging whether the monitoring range of the power distribution terminal meets a preset monitoring range of the power distribution system; If not, optimizing the layout data of the power distribution terminals; If yes, verifying the accuracy of the power data collected by the power distribution terminal and the communication stability of the power distribution system; If the verification is passed or not, the layout data of the power distribution terminal is optimized.

8. A distribution terminal layout device, characterized in that: The device comprises: a region division module, configured to divide the power distribution system into a plurality of target regions at different levels based on the power distribution characteristics of the power distribution system; wherein the power distribution characteristics include geographical distribution, load density, line type, electrical characteristics, and load requirements; and wherein the difference between the regional characteristics of each target region satisfies a preset condition, wherein the regional characteristics include the electrical characteristics and the load requirements; A key node identification module is used to identify key nodes in the target area that connect multiple important load points or transmit high currents by analyzing the topology of the power distribution system; the key nodes are used to install power distribution terminals; the key nodes include: substations, switch stations, ring network cabinets, box-type substations, feeder switches, industrial dedicated distribution stations, and distribution boxes in civil buildings; The distribution point solving module is used to establish a distribution point mathematical model with the minimum number of the distribution terminals as the first objective function and the minimum distribution point cost of the distribution terminals as the second objective function, under the premise of satisfying the constraint conditions, and solve the distribution point mathematical model through the target optimization algorithm to obtain the distribution point data of the distribution terminals; wherein the constraint conditions include: the installation location of the distribution terminal meets the preset installation conditions, the electrical reliability index of the distribution system meets the preset index, and the terminal fee of the distribution terminal meets the preset fee.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the method for distributing distribution terminals according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method for deploying distribution terminals according to any one of claims 1 to 7.

Images