Electric vehicle charging station layout optimization configuration method and system

Abstract

The invention discloses an electric vehicle charging station layout optimization configuration method and system, and the method comprises the following steps: obtaining the traffic network information and power distribution network information of a planning target area, and determining the charging demand number of electric vehicles and the charging load demand distribution condition; according to the electric vehicle charging load demand distribution condition, determining the service range of each charging station, and constructing a proposed charging station capacity determination model; an upper-layer charging station site selection model is constructed, and the optimal site of each charging station is determined; establishing a lower-layer mixed integer programming model, and determining the configuration number of various charging piles of each charging station; solving the double-layer locating and sizing planning configuration model by using a hybrid genetic simulated annealing algorithm and an analytic hierarchy process to obtain an optimal locating and sizing scheme of the charging station; according to the method, the attraction of the charging station to the user and different types of charging pile configuration schemes are comprehensively considered, a bilevel planning configuration model is established, an optimal solution set is solved by using a hybrid genetic simulated annealing algorithm, and finally an optimal scheme is determined through an analytic hierarchy process.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging station planning, and in particular to an electric vehicle charging station layout optimization configuration method and system. Background Art

[0002] As global awareness of sustainable transportation and environmental protection increases, the market demand for electric vehicles (EVs) is growing rapidly, and the construction of charging infrastructure has become a key factor in supporting this trend. However, the reasonable layout and optimal configuration of charging stations still face challenges, including the diversity of user charging needs, the flexibility of charging locations, and the cost-effectiveness of charging facility investment. Therefore, developing an effective layout optimization configuration method can not only improve the efficiency and service quality of charging stations, but also provide electric vehicle users with a more convenient charging experience, thereby promoting the widespread popularization of electric vehicles and the realization of green travel.

[0003] In terms of planning methods, current research focuses on the constraints of traffic road flow or distribution systems, or uses queuing theory to determine the capacity of charging stations through their locations. However, existing methods usually prioritize minimizing investment costs and fail to fully evaluate distance factors, especially in terms of carbon emissions. This results in the overall economic efficiency of charging stations not being optimal. In addition, unreasonable planning and configuration design may cause electric vehicles to travel too long to charging stations, or cause long queuing times, resulting in higher energy costs and increased carbon emissions. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for optimizing the layout of electric vehicle charging stations, which is used to solve the technical problems of mismatch between electric vehicle demand and charging station layout and low economic benefits caused by unreasonable planning and configuration.

[0005] In order to achieve the above object, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for optimizing the layout of electric vehicle charging stations, the configuration method comprising the following steps: Obtain the transportation network information and distribution network information of the planning target area, and determine the number of electric vehicle charging requirements and the distribution of charging load requirements; According to the distribution of electric vehicle charging load demand, determine the service scope of each charging station and build a capacity determination model for the proposed charging station; Construct an upper-level charging station site selection model to determine the optimal site for each charging station; Establish a lower-level mixed integer programming model to determine the number of charging piles of each type at each charging station; The upper charging station site selection model and the lower mixed integer programming model constitute a double-layer site selection and sizing planning configuration model. The hybrid genetic simulated annealing algorithm and the hierarchical analysis method are used to solve the double-layer site selection and sizing planning configuration model, and the optimal site selection and sizing planning configuration scheme for charging stations is obtained.

[0006] Furthermore, the acquisition of the traffic network information and the distribution network information of the planning target area and the determination of the number of electric vehicle charging requirements and the distribution of charging load requirements include the following steps: Obtain the traffic network information and distribution network information of the planning target area. The traffic network information includes road node coordinates, road length, and road traffic volume. The distribution network information includes transformer node coordinates, line impedance, and transformer node load. Traffic network information is represented as: , in, is a collection of transportation network data; is the horizontal coordinate of the kth road node; is the ordinate of the kth road node; is the length of the jth road; is the traffic volume of the jth road; The distribution network information is expressed as: , in, is the distribution network data set; is the transformer node coordinate set; is the line impedance set; is the transformer node load set; Introducing the electric vehicle scaling factor ; Electric vehicle charging probability ; Then the number of electric vehicle charging requirements on the jth road is It is expressed as: , Charging load instantaneous demand for: , in, is the proportion of private cars; is the taxi ratio; The proportion of public vehicles; It is the slow charging power; is the supercharge power; Fast charging power.

[0007] Furthermore, the method of determining the service range of each charging station according to the distribution of electric vehicle charging load demand and constructing a capacity determination model for the proposed charging station includes the following steps: According to the land properties and road distribution of the target area, the number of charging stations to be built, n, is determined, and the service range of each charging station is determined using the Voronoi diagram; The distance limits for each charging station are: , in, is the service range of the i-th charging station; is the distance between the i-th charging station and the i+1-th charging station; The number of charging stations n satisfies the following constraints: , in, is the rounding function; is the average daily operating time of the charging station; The upper limit of the number of supercharging piles at the charging station; The lower limit of the number of slow charging piles at a charging station; The power of the super charging pile; The power of the slow charging pile; charging thresholds for electric vehicles; It is the charging start state; is the average capacity of electric vehicles; is the total number of roads; is the total number of electric vehicles on the jth road; The proportion of electric vehicles charged daily; According to the node charging load demand, the maximum load demand of the nodes adjacent in time and space is superimposed, and the charging station capacity is: , in, is the capacity of the nth charging station; Charging electric vehicles with random coefficients; is the node set within the service range of the charging station. It is the maximum daily load demand of the node adjacent to time and space.

[0008] Furthermore, the construction of the upper-level charging station site selection model to determine the optimal site for each charging station includes the following steps: The objective function 1 is to maximize the total attractiveness of the charging station to users: , in, is the distance of the electric vehicle from the xth charging demand point to the i-th charging station; is the number of charging demand points; is the weight of the attractiveness coefficient of the charging station to users; is the characteristic attractiveness coefficient of different charging stations; is the distance attraction utility coefficient function, which is specifically expressed as: , in, is the minimum service radius of the i-th charging station; is the maximum service radius of the i-th charging station;

[0009] The second objective function is to minimize the time loss of users driving to the charging station within the service range: , Among them, B is the time loss cost coefficient; Charging time for the year; is the average speed of electric vehicles; is the smoothness index of the jth road; The objective function three is to minimize the carbon emission cost of users driving to charging stations within the service range: , , , in, The energy cost of driving to a charging station; is the carbon cost coefficient; is the carbon emission factor of the i-th charging station; For power efficiency; The longitudinal traction of electric vehicles; Energy consumption for additional loads; is the air density; is the air resistance coefficient; is the front projection area; is the rolling resistance coefficient; For the quality of electric vehicles; is the acceleration due to gravity; is the road slope angle; is the equivalent mass of the electric vehicle when inertia is considered; Acceleration for electric vehicles; The final objective function is: , in, and They are The minimum and maximum values ​​of and They are The minimum and maximum values ​​of and They are The minimum and maximum values ​​of .

[0010] Furthermore, the establishment of the lower layer mixed integer programming model to determine the number of various types of charging piles to be configured at each charging station includes the following steps: On the premise of meeting the target capacity of the charging station planning, the objective function is to minimize the total cost of configuring and constructing various types of charging piles, and its expression is: , in, Configure the total cost of charging piles for charging stations; is the number of slow charging piles; is the annualized rate of investment; The construction cost of slow charging piles; Maintenance cost for slow charging piles; The number of fast charging piles; The cost of building fast charging piles; Maintenance costs for fast charging piles; is the number of super charging piles; Cost of super charging pile construction; Maintenance costs for super charging piles; The capacity constraints are: , in, The power of the slow charging pile; The power of the fast charging pile; The power of the super charging pile; To allow for configuration capacity differences; The quantity constraints are: , , , in, and They are the upper and lower limits of the number of slow charging piles at charging stations; and They are the upper and lower limits of the number of fast charging piles at charging stations; and They are the upper and lower limits of the number of supercharging piles at charging stations.

[0011] Furthermore, the hybrid genetic simulated annealing algorithm and the analytic hierarchy process are used to solve the double-layer site selection and capacity planning configuration model to obtain the optimal site selection and capacity planning configuration scheme for the charging station, including the following steps: The hybrid genetic simulated annealing algorithm is used to solve the two-layer location and capacity planning configuration model and calculate the Pareto optimal solution set. The analytic hierarchy process is used to combine subjective and objective indicators to calculate the scores of each solution set, and the solution with the highest score is taken as the optimal site selection and capacity planning configuration solution.

[0012] In a second aspect, the present invention provides an electric vehicle charging station layout optimization configuration system, including an acquisition module, a charging station capacity determination module, a model building module, and a planning configuration module; Acquisition module: used to obtain the traffic network information and distribution network information of the planning target area, and determine the number of electric vehicle charging requirements and the distribution of charging load requirements; Charging station capacity determination module: used to determine the service scope of each charging station according to the distribution of electric vehicle charging load demand and build a capacity determination model for the proposed charging station; Model construction module: used to construct a double-layer site selection and capacity planning configuration model consisting of an upper-layer charging station site selection model and a lower-layer mixed integer programming model. The upper-layer charging station site selection model is used to determine the optimal site for each charging station, and the lower-layer mixed integer programming model is used to determine the number of various types of charging piles for each charging station. Planning and configuration module: used to solve the two-layer site selection and sizing planning and configuration model using a hybrid genetic simulated annealing algorithm and a hierarchical analysis method to obtain the optimal site selection and sizing planning and configuration scheme for charging stations.

[0013] In a third aspect, the present invention provides an electronic device comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the above-mentioned electric vehicle charging station layout optimization configuration method.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for optimizing the layout configuration of electric vehicle charging stations.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are: The electric vehicle charging station layout optimization configuration method and system of the present application obtains the traffic network information and distribution network information of the planning target area, determines the number of electric vehicle charging demands and the distribution of charging load demands; determines the service scope of each charging station according to the distribution of electric vehicle charging load demands, and constructs a capacity determination model for the proposed charging station; constructs an upper-level charging station site selection model to determine the optimal site for each charging station; establishes a lower-level mixed integer programming model to determine the number of various types of charging piles configured for each charging station; uses a hybrid genetic simulated annealing algorithm and a hierarchical analysis method to solve the double-layer site selection and capacity planning configuration model to obtain a charging station planning configuration plan. The present invention comprehensively considers the attractiveness of the charging station to users and the configuration plans of different types of charging piles, establishes a double-layer planning configuration model, uses a hybrid genetic simulated annealing algorithm to solve the optimal solution set, and finally determines the optimal plan through a hierarchical analysis method, which improves the solution accuracy and speed, can reduce costs, and improve the utilization rate of charging facilities, thereby achieving carbon reduction and efficiency improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of a method for optimizing the layout of electric vehicle charging stations provided by one embodiment of the present invention; Figure 2 A flow chart of a hybrid genetic simulated annealing algorithm provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, an embodiment of the present invention provides a method for optimizing the layout of electric vehicle charging stations, the configuration method comprising the following steps: Step S101, obtaining the traffic network information and distribution network information of the planning target area, and determining the number of electric vehicle charging requirements and the distribution of charging load requirements; In this step, the traffic network information of the target area is obtained through the map software. The traffic network information includes road node coordinates, road length, and road traffic volume. The distribution network information of the target area is obtained through the distribution network system. The distribution network information includes transformer node coordinates, line impedance, and transformer node load.

[0020] Traffic network information is represented as: , in, is a collection of transportation network data; is the horizontal coordinate of the kth road node; is the ordinate of the kth road node; is the length of the jth road; is the traffic volume of the jth road; The distribution network information is expressed as: , in, is the distribution network data set; is the transformer node coordinate set; is the line impedance set; is the transformer node load set; Introducing the electric vehicle scaling factor ; Electric vehicle charging probability ; Then the number of electric vehicle charging requirements on the jth road is It can be expressed as: , At this time, the instantaneous demand for charging load for: , in, is the proportion of private cars; is the taxi ratio; The proportion of public vehicles; It is the slow charging power; is the supercharge power; Fast charging power.

[0021] Step S102, determining the service range of each charging station according to the distribution of electric vehicle charging load demand, and constructing a capacity determination model for the proposed charging station; According to the land properties and road distribution of the target area, the number of charging stations to be built, n, is determined, and the service range of each charging station is determined using the Voronoi diagram; The distance limits for each charging station are: , in, is the service range of the i-th charging station; is the distance between the i-th charging station and the i+1-th charging station; The number of charging stations n satisfies the following constraints: , in, is the rounding function; is the average daily operating time of the charging station; The upper limit of the number of supercharging piles at the charging station; The lower limit of the number of slow charging piles at a charging station; The power of the super charging pile; The power of the slow charging pile; charging thresholds for electric vehicles; It is the charging start state; is the average capacity of electric vehicles; is the total number of roads; is the total number of electric vehicles on the jth road; The proportion of electric vehicles charged daily; According to the node charging load demand, the maximum load demand of the nodes adjacent in time and space is superimposed, and the charging station capacity is: , in, is the capacity of the nth charging station; Charging electric vehicles with random coefficients; A set of nodes within the service range of the charging station; It is the maximum daily load demand of the node adjacent to time and space.

[0022] Step S103, constructing an upper-level charging station site selection model to determine the optimal site for each charging station; The objective function 1 is to maximize the total attractiveness of the charging station to users: , in, is the distance of the electric vehicle from the xth charging demand point to the i-th charging station; is the number of charging demand points; is the weight of the attractiveness coefficient of the charging station to users; is the characteristic attractiveness coefficient of different charging stations; is the distance attraction utility coefficient function, which is specifically expressed as: , in, is the minimum service radius of the i-th charging station; is the maximum service radius of the i-th charging station;

[0023] The second objective function is to minimize the time loss of users driving to the charging station within the service range: , Among them, B is the time loss cost coefficient; Charging time for the year; is the average speed of electric vehicles; is the smoothness index of the jth road; The objective function three is to minimize the carbon emission cost of users driving to charging stations within the service range: , , , in, The energy cost of driving to a charging station; is the carbon cost coefficient; is the carbon emission factor of the i-th charging station; For power efficiency; The longitudinal traction of electric vehicles; Energy consumption for additional loads; is the air density; is the air resistance coefficient; is the front projection area; is the rolling resistance coefficient; For the quality of electric vehicles; is the acceleration due to gravity; is the road slope angle; is the equivalent mass of the electric vehicle when inertia is considered; Acceleration for electric vehicles; Normalizing the above objective function, the final objective function is: , in, and They are The minimum and maximum values ​​of and They are The minimum and maximum values ​​of and They are The minimum and maximum values ​​of .

[0024] Step S104, establishing a lower-level mixed integer programming model to determine the number of various types of charging piles to be configured at each charging station; On the premise of meeting the target capacity of the charging station planning, the objective function is to minimize the total cost of configuring and constructing various types of charging piles, and its expression is: , in, Configure the total cost of charging piles for charging stations; is the number of slow charging piles; is the annualized rate of investment; The construction cost of slow charging piles; Maintenance cost for slow charging piles; The number of fast charging piles; The cost of building fast charging piles; Maintenance costs for fast charging piles; is the number of super charging piles; Cost of super charging pile construction; Maintenance costs for super charging piles; The capacity constraints are: , in, The power of the slow charging pile; The power of the fast charging pile; The power of the super charging pile; To allow for configuration capacity differences; The quantity constraints are: , , , in, and They are the upper and lower limits of the number of slow charging piles at charging stations; and They are the upper and lower limits of the number of fast charging piles at charging stations; and They are the upper and lower limits of the number of supercharging piles at charging stations.

[0025] Step S105, using a hybrid genetic simulated annealing algorithm and a hierarchical analysis method to solve a two-layer site selection and sizing planning configuration model, and obtain an optimal site selection and sizing planning configuration scheme for the charging station; The hybrid genetic simulated annealing algorithm is used to solve the two-layer location and capacity planning configuration model and calculate the Pareto optimal solution set. The analytic hierarchy process is used to combine subjective and objective indicators to calculate the scores of each solution set, and the solution with the highest score is taken as the optimal site selection and capacity planning configuration solution.

[0026] See also Figure 2 , which shows a flow chart of the hybrid genetic simulated annealing algorithm, comprising the following steps: Generate an initial population; Create new populations through selection, crossover, mutation, and evaluation processes; These processes are repeated until a predetermined termination requirement is met, such as reaching a maximum number of generations; Set the starting temperature and use the existing solution; Adding perturbations to an existing solution can produce a neighboring solution; Calculate the difference between the current solution and the adjacent solution; Use the Metropolis criterion and temperature to decide whether to accept or reject neighboring solutions; Adjust the temperature according to the annealing schedule; When certain requirements are met, such as reaching the minimum temperature, the simulated annealing algorithm (SA) phase ends; A predetermined number of genetic algorithm (GA) generations or a predetermined situation may be used to determine the junction point of the GA phase and the SA phase; Determine the requirements to end the entire hybrid genetic simulated annealing algorithm by taking into account various factors such as the number of GA generations, the number of SA iterations, or reaching a predetermined convergence criterion; By observing the final results of the hybrid genetic simulated annealing algorithm, the degree of performance improvement compared with using GA or SA alone is evaluated, and finally the optimal result is obtained.

[0027] In summary, the method of the present application obtains the transportation network information and distribution network information of the planning target area, determines the number of electric vehicle charging demands and the distribution of charging load demands; determines the service scope of each charging station according to the distribution of electric vehicle charging load demands, and constructs a capacity determination model for the proposed charging station; constructs an upper-level charging station site selection model to determine the optimal site for each charging station; establishes a lower-level mixed integer programming model to determine the number of various types of charging piles configured for each charging station; uses a hybrid genetic simulated annealing algorithm and a hierarchical analysis method to solve the double-layer site selection and capacity planning configuration model to obtain a charging station planning configuration plan. The present invention comprehensively considers the attractiveness of the charging station to users and the configuration plans of different types of charging piles, establishes a double-layer planning configuration model, uses a hybrid genetic simulated annealing algorithm to solve the optimal solution set, and finally determines the optimal plan through a hierarchical analysis method, which improves the solution accuracy and speed, can reduce costs, and improve the utilization rate of charging facilities, thereby achieving carbon reduction and efficiency improvement.

[0028] Another embodiment of the present invention provides an electric vehicle charging station layout optimization configuration system, including an acquisition module, a charging station capacity determination module, a model building module, and a planning configuration module; Acquisition module: used to obtain the traffic network information and distribution network information of the planning target area, and determine the number of electric vehicle charging requirements and the distribution of charging load requirements; Charging station capacity determination module: used to determine the service scope of each charging station according to the distribution of electric vehicle charging load demand and build a capacity determination model for the proposed charging station; Model construction module: used to construct a double-layer site selection and capacity planning configuration model consisting of an upper-layer charging station site selection model and a lower-layer mixed integer programming model. The upper-layer charging station site selection model is used to determine the optimal site for each charging station, and the lower-layer mixed integer programming model is used to determine the number of various types of charging piles for each charging station. Planning and configuration module: used to solve the two-layer site selection and sizing planning and configuration model using a hybrid genetic simulated annealing algorithm and a hierarchical analysis method to obtain the optimal site selection and sizing planning and configuration scheme for charging stations.

[0029] It should be understood that the modules recorded in the electric vehicle charging station layout optimization configuration system correspond to the various steps in the above method embodiment. Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in the electric vehicle charging station layout optimization configuration system, and will not be repeated here.

[0030] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the processor is caused to execute the electric vehicle charging station layout optimization configuration method in the above method embodiment; As an embodiment, the computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the electric vehicle charging station layout optimization configuration system, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the electric vehicle charging station layout optimization configuration system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0031] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3The example of the bus connection is taken. The memory 320 is the computer-readable storage medium mentioned above. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the electric vehicle charging station layout optimization configuration method of the above method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the electric vehicle charging station layout optimization configuration system. The output device 340 may include display devices such as display screens.

[0032] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0033] As an embodiment, the above-mentioned electronic device is applied to an electric vehicle charging station layout optimization configuration system, for a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the instructions stored in a computer-readable storage medium.

[0034] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the layout of electric vehicle charging stations, characterized in that: The configuration method comprises the following steps: Obtain the transportation network information and distribution network information of the planning target area, and determine the number of electric vehicle charging requirements and the distribution of charging load requirements; According to the distribution of electric vehicle charging load demand, determine the service scope of each charging station and build a capacity determination model for the proposed charging station; Construct an upper-level charging station site selection model to determine the optimal site for each charging station; Establish a lower-level mixed integer programming model to determine the number of charging piles of each type at each charging station; The upper charging station site selection model and the lower mixed integer programming model constitute a double-layer site selection and sizing planning configuration model. The double-layer site selection and sizing planning configuration model is solved by using a hybrid genetic simulated annealing algorithm and a hierarchical analysis method to obtain the optimal site selection and sizing planning configuration scheme for charging stations. The step of obtaining the traffic network information and the distribution network information of the planning target area and determining the number of electric vehicle charging requirements and the distribution of charging load requirements includes the following steps: Obtain the traffic network information and distribution network information of the planning target area. The traffic network information includes road node coordinates, road length, and road traffic volume. The distribution network information includes transformer node coordinates, line impedance, and transformer node load. Traffic network information is represented as: ， in, is a collection of transportation network data; is the horizontal coordinate of the kth road node; is the ordinate of the kth road node; is the length of the jth road; is the traffic volume of the jth road; The distribution network information is expressed as: ， in, is the distribution network data set; is the transformer node coordinate set; is the line impedance set; is the transformer node load set; Introducing the electric vehicle scaling factor ; Electric vehicle charging probability ; Then the number of electric vehicle charging requirements on the jth road is It is expressed as: ， Charging load instantaneous demand for: ， in, is the proportion of private cars; is the taxi ratio; The proportion of public vehicles; It is the slow charging power; is the supercharge power; Fast charging power.

2. The method for optimizing the layout of electric vehicle charging stations according to claim 1, characterized in that: The method of determining the service range of each charging station according to the distribution of electric vehicle charging load demand and constructing a capacity determination model for the proposed charging station includes the following steps: According to the land properties and road distribution of the target area, the number of charging stations to be built, n, is determined, and the service range of each charging station is determined using the Voronoi diagram; The distance limits for each charging station are: ， in, is the service range of the i-th charging station; is the distance between the i-th charging station and the i+1-th charging station; The number of charging stations n satisfies the following constraints: ， in, is the rounding function; is the average daily operating time of the charging station; The upper limit of the number of supercharging piles at the charging station; The lower limit of the number of slow charging piles at a charging station; The power of the super charging pile; The power of the slow charging pile; charging thresholds for electric vehicles; It is the charging start state; is the average capacity of electric vehicles; is the total number of roads; is the total number of electric vehicles on the jth road; The proportion of electric vehicles charged daily; According to the node charging load demand, the maximum load demand of the nodes adjacent in time and space is superimposed, and the charging station capacity is: ， in, is the capacity of the nth charging station; Charging electric vehicles with random coefficients; is the node set within the service range of the charging station. It is the maximum load demand of the node near the time and space.

3. The method for optimizing the layout of electric vehicle charging stations according to claim 2, characterized in that: The construction of the upper-level charging station site selection model to determine the optimal site of each charging station includes the following steps: The objective function 1 is to maximize the total attractiveness of the charging station to users: ， in, is the distance of the electric vehicle from the xth charging demand point to the i-th charging station; is the number of charging demand points; is the weight of the attractiveness coefficient of the charging station to users; is the characteristic attractiveness coefficient of different charging stations; is the distance attraction utility coefficient function, which is specifically expressed as: ， in, is the minimum service radius of the i-th charging station; is the maximum service radius of the i-th charging station; The second objective function is to minimize the time loss of users driving to the charging station within the service range: ， Among them, B is the time loss cost coefficient; Charging time for the year; is the average speed of electric vehicles; is the smoothness index of the jth road; The objective function three is to minimize the carbon emission cost of users driving to charging stations within the service range: ， ， ， in, The energy cost of driving to a charging station; is the carbon cost coefficient; is the carbon emission factor of the i-th charging station; For power efficiency; The longitudinal traction of electric vehicles; Energy consumption for additional loads; is the air density; It is the air resistance system; is the front projection area; is the rolling resistance coefficient; For the quality of electric vehicles; is the acceleration due to gravity; is the road slope angle; is the equivalent mass of the electric vehicle when inertia is considered; Acceleration for electric vehicles; The final objective function is: ， in, and They are The minimum and maximum values ​​of and They are The minimum and maximum values ​​of and They are The minimum and maximum values ​​of .

4. The method for optimizing the layout of electric vehicle charging stations according to claim 3, characterized in that: The establishment of the lower layer mixed integer programming model to determine the number of various types of charging piles configured at each charging station includes the following steps: On the premise of meeting the target capacity of the charging station planning, the objective function is to minimize the total cost of configuring and constructing various types of charging piles, and its expression is: ， in, Configure the total cost of charging piles for charging stations; is the number of slow charging piles; is the annualized rate of investment; The construction cost of slow charging piles; Maintenance cost for slow charging piles; is the number of fast charging piles; The cost of building fast charging piles; Maintenance costs for fast charging piles; is the number of super charging piles; Cost of super charging pile construction; Maintenance costs for super charging piles; The capacity constraints are: ， in, The power of the slow charging pile; The power of the fast charging pile; The power of the super charging pile; To allow for configuration capacity differences; The quantity constraints are: ， ， ， in, and They are the upper and lower limits of the number of slow charging piles at charging stations; and They are the upper and lower limits of the number of fast charging piles at charging stations; and They are the upper and lower limits of the number of supercharging piles at charging stations.

5. The method for optimizing the layout of electric vehicle charging stations according to claim 4, characterized in that: The method of solving the double-layer location and capacity planning configuration model by using the hybrid genetic simulated annealing algorithm and the hierarchical analysis method to obtain the optimal location and capacity planning configuration scheme for the charging station includes the following steps: The hybrid genetic simulated annealing algorithm is used to solve the two-layer location and capacity planning configuration model and calculate the Pareto optimal solution set. The analytic hierarchy process is used in combination with subjective and objective indicators to calculate the scores of each solution set, and the solution with the highest score is taken as the optimal site selection and capacity planning configuration solution.

6. An electric vehicle charging station layout optimization configuration system, used to implement the configuration method according to any one of claims 1 to 5, characterized in that: It includes acquisition module, charging station capacity determination module, model building module and planning configuration module; Acquisition module: used to obtain the transportation network information and distribution network information of the planning target area, and determine the number of electric vehicle charging requirements and the distribution of charging load requirements; Charging station capacity determination module: used to determine the service scope of each charging station according to the distribution of electric vehicle charging load demand and build a capacity determination model for the proposed charging station; Model construction module: used to construct a double-layer site selection and capacity planning configuration model consisting of an upper-layer charging station site selection model and a lower-layer mixed integer programming model. The upper-layer charging station site selection model is used to determine the optimal site for each charging station, and the lower-layer mixed integer programming model is used to determine the number of various types of charging piles for each charging station. Planning and configuration module: used to solve the two-layer site selection and sizing planning and configuration model using a hybrid genetic simulated annealing algorithm and a hierarchical analysis method to obtain the optimal site selection and sizing planning and configuration scheme for charging stations.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the configuration method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the configuration method according to any one of claims 1 to 5 are implemented.

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