Charging station optimal configuration method and equipment

Abstract

The invention belongs to the technical field of charging station planning, and discloses a charging station optimal configuration method and equipment, and the method comprises the following steps: constructing a target function with the minimization of total power loss and the minimization of total voltage deviation as targets; constructing a charging station locating and sizing model by taking a system power flow equality constraint and a node voltage inequality constraint as constraint conditions; identifying a nearest minimum power loss charging point through an arithmetic optimization algorithm; and predicting the electric vehicle charging demand of the charging station through a radial basis function neural network algorithm to obtain preliminary prediction of charging station site selection, and solving the charging station constant volume site selection model to obtain the optimal capacity and position of the charging station. According to the method, the arithmetic optimization algorithm and the radial basis function neural network are combined to optimize the layout configuration of the electric vehicle charging station, the power loss of a power distribution network system can be reduced to the maximum extent, the voltage stability is improved, the charging demand change can be dynamically reflected, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging station planning, and in particular relates to a charging station optimization configuration method and equipment. Background Art

[0002] Electric vehicles can effectively reduce carbon emissions, improve air quality, and play an increasingly important role in addressing global climate change and mitigating the greenhouse effect. At the same time, as the number of electric vehicles continues to grow, the demand for electric vehicle charging stations will also increase dramatically, which will inevitably have a significant impact on the existing power grid system. For cities, effectively deploying charging stations in densely populated areas with high charging demand to meet future transportation needs and promote the use of electric vehicles is particularly important. Currently, charging station configurations remain challenging, as do the challenges of optimizing station configuration and improving the adaptability of the power grid.

[0003] The Chinese patent application, CN119168277A, is titled "A hybrid algorithm-based method for electric vehicle charging station site selection and sizing." The patent application uses historical traffic flow information and Monte Carlo simulation to determine the charging demand for each node within the planned area. The objective function is to minimize the annualized total operating cost of the charging station and the annualized economic loss to users. The model is constructed using the total charging demand of electric vehicles, the number of charging stations, the service area, and the upper limit of the total power in the planned area as constraints. The model is solved using an improved whale optimization algorithm combined with a salp algorithm. The method in this patent application requires calculations for all candidate locations, which is computationally inefficient when dealing with large-scale and complex charging station sites. Summary of the Invention

[0004] In order to overcome the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a charging station optimization configuration method and equipment, which combines an arithmetic optimization algorithm with a radial basis function neural network to optimize the layout configuration of electric vehicle charging stations, can minimize the power loss of the distribution network system, improve voltage stability, and can dynamically respond to changes in charging demand, thereby improving computing efficiency.

[0005] To achieve the above object, the technical solution adopted by the present invention is: In a first aspect, the present invention provides a method for optimizing configuration of charging stations, comprising the following steps: Collect electric vehicle traffic flow information on traffic road nodes in the planned area and historical data on charging at stations in the area, and calculate the charging demand of electric vehicles in the current time period based on the collected data; Construct an objective function with the goal of minimizing total power loss and total voltage deviation; A charging station location and sizing model is constructed based on the system power flow equality constraint and node voltage inequality constraint. A hybrid algorithm of arithmetic optimization algorithm and radial basis function neural network is adopted to identify the nearest charging point with minimum power loss through the arithmetic optimization algorithm; the charging demand of electric vehicles at the charging station is predicted through the radial basis function neural network algorithm, and a preliminary prediction of the charging station site selection is obtained. The charging station fixed capacity site selection model is solved to obtain the optimal capacity and location of the charging station.

[0006] Optionally, the calculation formula for the charging demand of the electric vehicle is:

[0007] Where m represents the total number of road nodes in the area to be planned. represents the traffic flow at road node i, represents the proportion of electric vehicles in the traffic flow of road node i, represents the average charging power of the charging station, Indicates whether the user chooses to charge; when the user chooses to charge the electric vehicle, =1; when the user does not choose to charge the electric vehicle, =0.

[0008] Optionally, constructing the objective function includes the following steps: Calculate the total power loss of the distribution network system using the formula:

[0009] in, Indicates the total number of branches in the system, represents the power loss of the kth branch, Represents the line resistance, represents the line inductance, represents the active power of the load on the kth branch, represents the reactive power of the load on the kth branch, represents the branch voltage, Indicates the current flowing through the branch; Calculate the total voltage deviation of the distribution network system using the formula:

[0010] Where N represents the total number of nodes in the system. represents the actual voltage at node i, Then it represents the given voltage at node i; The objective function is:

[0011] in, 、 They represent the total power loss and total voltage deviation of the distribution network system, ζ and ψ represent the weighting factors of the total power system loss and total voltage deviation, respectively.

[0012] Optionally, the constraints include: power flow equation serving as equality constraint, power distribution system node voltage constraint, and distributed power source power limit.

[0013] Optionally, the calculation of the objective function includes the following steps: Road network data and current charging demand information are input into a radial basis function neural network, which provides a preliminary prediction of charging station placement based on current traffic network conditions. Set the initialization parameters of the arithmetic optimization algorithm; including the population size, dimension, maximum number of iterations, and randomly generated candidate solution sets; The objective function is used as the fitness function. The fitness of each individual is evaluated based on its ability to minimize power loss and voltage deviation rate. The optimal individual position is found and the individuals are sorted from small to large according to the fitness value. Determine whether to perform global search or local development by computing a mathematical optimizer acceleration function of an arithmetic optimization algorithm; Update the individual position, calculate the fitness value, and iterate until the iteration termination condition is met, and output the objective function value at this time as the optimal solution.

[0014] Optionally, the radial basis function neural network algorithm adopts a three-layer feedforward neural network, including: an input layer for receiving road network data and current charging demand data; a hidden layer, including several radial basis function nonlinear activation units, for performing nonlinear processing on the feature vector signal; an output layer, for performing linear summation on the vector data output by the hidden layer to obtain an output value.

[0015] In a second aspect, the present invention provides a charging station optimization configuration system, comprising: The data acquisition module is used to collect the electric vehicle traffic flow information on the traffic road nodes in the planned area and the historical data of charging at the station in the area, and calculate the charging demand of electric vehicles in the current time period based on the collected data; An objective function construction module is used to construct an objective function with the goal of minimizing total power loss and total voltage deviation; The charging station site selection and sizing model construction module is used to construct the charging station site selection and sizing model based on the system power flow equality constraint and node voltage inequality constraint; The calculation module is used to use an arithmetic optimization algorithm-radial basis function neural network hybrid algorithm to identify the nearest charging point with minimum power loss through the arithmetic optimization algorithm; predict the electric vehicle charging demand of the charging station through the radial basis function neural network algorithm, obtain a preliminary prediction of the charging station site selection, solve the charging station fixed capacity site selection model, and obtain the optimal capacity and location of the charging station.

[0016] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the charging station optimization configuration method when executing the computer program.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the charging station optimization configuration method is implemented.

[0018] In a fifth aspect, the present invention provides a computer program product comprising a computer-readable medium, wherein the computer-readable medium contains computer-readable program code, and the program code executes the charging station optimization configuration method.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a charging station optimization configuration method and device that combines an arithmetic optimization algorithm (AOA) with a radial basis function neural network (RBFNN) to optimize the search space. The RBFNN accurately predicts electric vehicle charging demand and potential optimal locations, reducing the likelihood of the arithmetic optimization algorithm falling into local minima and effectively improving convergence speed. The arithmetic optimization algorithm optimizes the distribution network by finding charging points that minimize power loss and improve voltage stability. This method effectively reduces power loss and improves voltage stability in the distribution network, while also lowering energy costs and increasing accuracy and computational efficiency.

[0020] Furthermore, the charging station optimization method of the present invention implements time prediction, dynamically reflecting changes in charging demand. If charging demand in certain areas is consistently low or decreasing, these charging points can be eliminated, reducing the computational effort of the subsequent AOA algorithm and improving computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.

[0022] In the attached figure: Figure 1 This is a flow chart of a charging station optimization configuration method according to an embodiment of the present invention.

[0023] Figure 2 FIG. 1 is a flow chart of configuring an electric vehicle control system (EVCS) considering a power distribution system according to an embodiment of the present invention.

[0024] Figure 3 This is a flow chart of position identification of an optimal electric vehicle control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts should fall within the scope of protection of the present invention.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0027] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined. The present invention will be described in detail below with reference to the accompanying drawings.

[0028] like Figure 1 As shown, a charging station optimization configuration method of the present invention includes the following steps: Collect electric vehicle traffic flow information on traffic road nodes in the planned area and historical data on charging at stations in the area, and calculate the charging demand of electric vehicles in the current time period based on the collected data; Construct an objective function with the goal of minimizing total power loss and total voltage deviation; A charging station location and sizing model is constructed based on the system power flow equality constraint and node voltage inequality constraint. A hybrid algorithm of arithmetic optimization algorithm and radial basis function neural network is adopted to identify the nearest charging point with minimum power loss through the arithmetic optimization algorithm; the charging demand of electric vehicles at the charging station is predicted through the radial basis function neural network algorithm, and a preliminary prediction of the charging station site selection is obtained. The charging station fixed capacity site selection model is solved to obtain the optimal capacity and location of the charging station.

[0029] The present invention provides a charging station optimization configuration method and device that combines an arithmetic optimization algorithm (AOA) with a radial basis function neural network (RBFNN) to optimize the search space. The RBFNN accurately predicts electric vehicle charging demand and potential optimal locations, reducing the likelihood of the arithmetic optimization algorithm falling into local minima and effectively improving convergence speed. The arithmetic optimization algorithm optimizes the distribution network by finding charging points that minimize power loss and improve voltage stability. This method effectively reduces power loss and improves voltage stability in the distribution network, while also lowering energy costs and increasing accuracy and computational efficiency.

[0030] Example 1 A charging station optimization configuration method according to this embodiment includes the following steps: Step 1: Collect the electric vehicle traffic flow information on the traffic road nodes in the planned area and the historical data of charging at the station in the area, and calculate the charging demand of electric vehicles in the current time period based on the collected data; Step 2: With minimizing total power loss and total voltage deviation as the objective function, and with system power flow equality constraints and node voltage inequality constraints as constraints, a charging station location and sizing model is constructed; Step 3: Use the AOA-RBFNN (arithmetic optimization algorithm-radial basis function neural network) hybrid algorithm to solve the charging station capacity and location selection model to determine the optimal capacity and location of the charging station.

[0031] In step 1, the charging demand of the electric vehicle is expressed as the total required power for charging the electric vehicle:

[0032] Where m represents the total number of road nodes in the area to be planned. represents the traffic flow at road node i, represents the proportion of electric vehicles in the traffic flow of road node i, represents the average charging power of the charging station, Indicates whether the user chooses to charge.

[0033] When users choose to charge their electric vehicles, =1; when the user does not choose to charge the electric vehicle, =0.

[0034] The specific steps of constructing the objective function in step 2 include: 1) Calculation of total power loss of distribution network system;

[0035] in, Indicates the total number of branches in the system, represents the power loss of the kth branch, Represents the line resistance, represents the line inductance, represents the active power of the load on the kth branch, represents the reactive power of the load on the kth branch, represents the branch voltage, Indicates the current flowing through the branch.

[0036] 2) Calculation of total voltage deviation of distribution network system;

[0037] Where N represents the total number of nodes in the system. represents the actual voltage at node i, Then represents a given voltage at node i.

[0038] 3) Express the objective function as:

[0039] in, 、 They represent the total power loss and total voltage deviation of the distribution network system, ζ and ψ represent the weighting factors of the total power system loss and total voltage deviation, respectively.

[0040] The constraints in step 2 include: The power flow equations act as equality constraints, identifying the voltage that satisfies the system requirements:

[0041] in, 、 represent the active power and reactive power of node i respectively, 、 Represent the voltage values of nodes i and j respectively, 、 denote the real and imaginary parts of the node admittance matrix respectively; θ is the voltage angle difference between nodes i and j.

[0042] The node voltage constraint of the distribution system is:

[0043] in, and Respectively represent the upper and lower limits of the voltage amplitude of the i-th node.

[0044] The power limits (active and reactive power) of distributed generation (DG) must be operated within the following limits:

[0045] Where, 、 They represent the upper and lower limits of the active power output of distributed energy resources respectively; 、 They represent the upper and lower limits of the reactive power output of distributed energy respectively.

[0046] The step 3 of solving the objective function by using the AOA-RBFNN hybrid algorithm specifically includes the following steps: 1) Inputting road network data, current charging demand, and other information into a radial basis function neural network, the RBFNN provides a preliminary prediction of charging station placement based on current traffic network conditions; 2) Set the initialization parameters of AOA (arithmetic optimization algorithm); including the population number n, dimension d and maximum number of iterations , randomly generated candidate solution set X;

[0047]

[0048] Among them, X(i, j) represents the jth position of the i-th solution, n represents the population size, d represents the dimension of the search domain, and rand represents a random number between 0 and 1. and They represent the upper and lower bounds of the j-th dimension in the search domain respectively.

[0049] 3) Taking the objective function as the fitness function, the fitness of each individual is evaluated based on its ability to minimize power loss and voltage offset rate, the optimal individual position is found, and the individuals are sorted from smallest to largest in terms of fitness value; 4) Determine whether to perform global search or local development by calculating the MOA value and MOP value of the mathematical optimizer acceleration function of AOA; 5) Based on the results, update the individual position and calculate the fitness value, and continue to iterate until the iteration termination condition is met, and output the optimal solution of the objective function at this time.

[0050] Furthermore, the radial basis function neural network is a three-layer feedforward neural network, specifically comprising: Input layer, used to receive road network data and current charging demand data; The hidden layer is composed of many radial basis function nonlinear activation units, which are used to perform nonlinear processing on the feature vector signal. The hidden layer calculation expression is as follows:

[0051] in, represents the output of the i-th node in the hidden layer, X represents the input vector, represents the center of the i-th node, represents the width of the i-th node, and both μ and σ parameters are learned in an unsupervised manner.

[0052] The output layer is used to linearly sum the vector data output by the hidden layer to obtain the output value. The calculation expression of the output layer is as follows:

[0053] Among them, y represents the output value, Represents the weight ratio of the numerical conversion between the hidden layer and the output layer, Represents the output of the i-th node in the hidden layer, and the preliminary prediction is finally completed based on the output value.

[0054] Furthermore, the mathematical optimizer acceleration function MOA value is defined as:

[0055] Among them, MOA(k) represents the acceleration function value of the mathematical optimizer at k iterations, 、 respectively represent the minimum and maximum values of the acceleration function, represents the maximum number of iterations, and k represents the current iteration.

[0056] The Mathematical Optimizer Probability (MOP) is defined as:

[0057] where α represents the sensitivity parameter. Let be a random number between 0 and 1. After updating MOA and MOP, the random number represents the transition between global exploration and local exploitation; When ≥MOA(t), AOA enters the global exploration stage:

[0058] When <MOA, AOA enters the local exploitation stage:

[0059] where and are random numbers between 0 and 1, μ is a control parameter, represents the individual position in the next iteration, represents the global optimal position in the current individual.

[0060] Embodiment 2 Based on the optimized configuration method of the charging station in Embodiment 1, a system is disclosed, including: A data acquisition module, configured to collect the electric vehicle traffic flow information on the traffic road nodes in the area to be planned and the historical data of the charging stations in the area, and calculate the charging demand of the electric vehicles in the current time period according to the collected data; A target function construction module, configured to construct a target function with the minimization of the total power loss and the total voltage deviation as the objectives; A charging station location and capacity determination model construction module, configured to construct a charging station location and capacity determination model with the system power flow equation constraint and the node voltage inequality constraint as the constraint conditions; A calculation module, configured to adopt an Arithmetic Optimization Algorithm - Radial Basis Function Neural Network hybrid algorithm, identify the nearest charging point with the minimum power loss through the arithmetic optimization algorithm; predict the electric vehicle charging demand of the charging station through the radial basis function neural network algorithm, obtain a preliminary prediction of the charging station location, and solve the charging station capacity and location determination model to obtain the optimal capacity and location of the charging station.

[0061] Embodiment 3 The purpose of this embodiment is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the charging station optimization configuration method when executing the computer program.

[0062] Example 4 The purpose of this embodiment is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the charging station optimization configuration method is implemented.

[0063] Example 5 The purpose of this embodiment is to provide a computer program product comprising a computer-readable medium, wherein the computer-readable medium contains computer-readable program code, and the program code executes the charging station optimization configuration method.

[0064] The steps involved in the devices of the above embodiments 2, 3, 4 and 5 correspond to those of the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of embodiment 1.

[0065] 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 take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. 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 block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0066] Unless otherwise specified, the working modes or control modes involved in the above embodiments are all conventional working modes or control modes in the art.

[0067] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application. Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limiting. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A charging station optimization configuration method, characterized in that: The following steps are involved: Collect electric vehicle traffic flow information on traffic road nodes in the planned area and historical data on charging at stations in the area, and calculate the charging demand of electric vehicles in the current time period based on the collected data; Construct an objective function with the goal of minimizing total power loss and total voltage deviation; A charging station location and sizing model is constructed based on the system power flow equality constraint and node voltage inequality constraint. A hybrid algorithm of arithmetic optimization algorithm and radial basis function neural network is adopted to identify the nearest charging point with minimum power loss through the arithmetic optimization algorithm; the charging demand of electric vehicles at the charging station is predicted through the radial basis function neural network algorithm, and a preliminary prediction of the charging station site selection is obtained. The charging station fixed capacity site selection model is solved to obtain the optimal capacity and location of the charging station.

2. A charging station optimization configuration method according to claim 1, characterized in that: The calculation formula for the charging demand of the electric vehicle is: Where m represents the total number of road nodes in the area to be planned. represents the traffic flow at road node i, represents the proportion of electric vehicles in the traffic flow of road node i, represents the average charging power of the charging station, Indicates whether the user chooses to charge; when the user chooses to charge the electric vehicle, =1; when the user does not choose to charge the electric vehicle, =0.

3. The method for optimizing the configuration of charging stations according to claim 1, wherein: Constructing the objective function involves the following steps: Calculate the total power loss of the distribution network system using the formula: in, Indicates the total number of branches in the system, represents the power loss of the kth branch, Represents the line resistance, represents the line inductance, represents the active power of the load on the kth branch, represents the reactive power of the load on the kth branch, represents the branch voltage, Indicates the current flowing through the branch; Calculate the total voltage deviation of the distribution network system using the formula: Where N represents the total number of nodes in the system. represents the actual voltage at node i, Then it represents the given voltage at node i; The objective function is: in, 、 They represent the total power loss and total voltage deviation of the distribution network system, ζ and ψ represent the weighting factors of the total power system loss and total voltage deviation, respectively.

4. The method for optimizing the configuration of charging stations according to claim 1, wherein: The constraints include: power flow equation serving as equality constraint, distribution system node voltage constraint and distributed generation power limit.

5. The method for optimizing the configuration of charging stations according to claim 1, characterized in that: The calculation of the objective function comprises the following steps: Road network data and current charging demand information are input into a radial basis function neural network, which provides a preliminary prediction of charging station placement based on current traffic network conditions. Set the initialization parameters of the arithmetic optimization algorithm; including the population size, dimension, maximum number of iterations, and randomly generated candidate solution sets; The objective function is used as the fitness function. The fitness of each individual is evaluated based on its ability to minimize power loss and voltage deviation rate. The optimal individual position is found and the individuals are sorted from small to large according to the fitness value. Determine whether to perform global search or local development by computing a mathematical optimizer acceleration function of an arithmetic optimization algorithm; Update the individual position, calculate the fitness value, and iterate until the iteration termination condition is met, and output the objective function value at this time as the optimal solution.

6. The method for optimizing the configuration of charging stations according to claim 1, characterized in that: The radial basis function neural network algorithm adopts a three-layer feedforward neural network, including: an input layer for receiving road network data and current charging demand data; a hidden layer, including several radial basis function nonlinear activation units, for performing nonlinear processing on the feature vector signal; and an output layer for linearly summing the vector data output by the hidden layer to obtain the output value.

7. A charging station optimization configuration system, characterized in that: include: The data acquisition module is used to collect the electric vehicle traffic flow information on the traffic road nodes in the planned area and the historical data of charging at the station in the area, and calculate the charging demand of electric vehicles in the current time period based on the collected data; An objective function construction module is used to construct an objective function with the goal of minimizing total power loss and total voltage deviation; The charging station site selection and sizing model construction module is used to construct the charging station site selection and sizing model based on the system power flow equality constraint and node voltage inequality constraint; The calculation module is used to use an arithmetic optimization algorithm-radial basis function neural network hybrid algorithm to identify the nearest charging point with minimum power loss through the arithmetic optimization algorithm; predict the electric vehicle charging demand of the charging station through the radial basis function neural network algorithm, obtain a preliminary prediction of the charging station site selection, solve the charging station fixed capacity site selection model, and obtain the optimal capacity and location of the charging station.

8. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the charging station optimization configuration method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the charging station optimization configuration method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer-readable medium, characterized in that The computer-readable medium contains computer-readable program code, and the program code executes the charging station optimization configuration method according to any one of claims 1 to 6.

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